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Upadhya S, Klein JA, Nathanson A, Holton KM, Barrett LE. Single-cell analyses reveal increased gene expression variability in human neurodevelopmental conditions. Am J Hum Genet 2025; 112:876-891. [PMID: 40056913 DOI: 10.1016/j.ajhg.2025.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2024] [Revised: 02/10/2025] [Accepted: 02/10/2025] [Indexed: 03/14/2025] Open
Abstract
Interindividual variation in phenotypic penetrance and severity is found in many neurodevelopmental conditions, although the underlying mechanisms remain largely unresolved. Within individuals, homogeneous cell types (i.e., genetically identical and in similar environments) can differ in molecule abundance. Here, we investigate the hypothesis that neurodevelopmental conditions can drive increased variability in gene expression, not just differential gene expression. Leveraging independent single-cell and single-nucleus RNA sequencing datasets derived from human brain-relevant cell and tissue types, we identify a significant increase in gene expression variability driven by the autosomal aneuploidy trisomy 21 (T21) as well as autism-associated chromodomain helicase DNA binding protein 8 (CHD8) haploinsufficiency. Our analyses are consistent with a global and, in part, stochastic increase in variability, which is uncoupled from changes in transcript abundance. Highly variable genes tend to be cell-type specific with modest enrichment for repressive H3K27me3, while least variable genes are more likely to be constrained and associated with active histone marks. Our results indicate that human neurodevelopmental conditions can drive increased gene expression variability in brain cell types, with the potential to contribute to diverse phenotypic outcomes. These findings also provide a scaffold for understanding variability in disease, essential for deeper insights into genotype-phenotype relationships.
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Affiliation(s)
- Suraj Upadhya
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Jenny A Klein
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Anna Nathanson
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Kristina M Holton
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Lindy E Barrett
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA.
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2
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Crotta Asis A, Asaro A, D'Angelo G. Single cell lipid biology. Trends Cell Biol 2025:S0962-8924(24)00255-1. [PMID: 39814618 DOI: 10.1016/j.tcb.2024.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2024] [Revised: 12/05/2024] [Accepted: 12/10/2024] [Indexed: 01/18/2025]
Abstract
Lipids are major cell constituents endowed with astonishing structural diversity. The pathways responsible for the assembly and disposal of different lipid species are energetically demanding, and genes encoding lipid metabolic factors and lipid-related proteins comprise a sizable fraction of our coding genome. Despite the importance of lipids, the biological significance of lipid structural diversity remains largely obscure. Recent technological developments have enabled extensive lipid analysis at the single cell level, revealing unexpected cell-cell variability in lipid composition. This new evidence suggests that lipid diversity is exploited in multicellularity and that lipids have a role in the establishment and maintenance of cell identity. In this review, we highlight the emerging concepts and technologies in single cell lipid analysis and the implications of this research for future studies.
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Affiliation(s)
- Agostina Crotta Asis
- Institute of Bioengineering (IBI) and Global Health Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Antonino Asaro
- Institute of Bioengineering (IBI) and Global Health Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Giovanni D'Angelo
- Institute of Bioengineering (IBI) and Global Health Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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3
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Gao X, Zhang F, Guo X, Yao M, Wang X, Chen D, Zhang G, Wang X, Lai L. Attention-based deep learning for accurate cell image analysis. Sci Rep 2025; 15:1265. [PMID: 39779905 PMCID: PMC11711278 DOI: 10.1038/s41598-025-85608-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2024] [Accepted: 01/03/2025] [Indexed: 01/11/2025] Open
Abstract
High-content analysis (HCA) holds enormous potential for drug discovery and research, but widely used methods can be cumbersome and yield inaccurate results. Noisy and redundant signals in cell images impede accurate deep learning-based image analysis. To address these issues, we introduce X-Profiler, a novel HCA method that combines cellular experiments, image processing, and deep learning modeling. X-Profiler combines the convolutional neural network and Transformer to encode high-content images, effectively filtering out noisy signals and precisely characterizing cell phenotypes. In comparative tests on drug-induced cardiotoxicity, mitochondrial toxicity classification, and compound classification, X-Profiler outperformed both DeepProfiler and CellProfiler, as two highly recognized and representative methods in this field. Our results demonstrate the utility and versatility of X-Profiler, and we anticipate its wide application in HCA for advancing drug development and disease research.
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Affiliation(s)
- Xiangrui Gao
- XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China
| | - Fan Zhang
- XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China
| | - Xueyu Guo
- XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China
| | - Mengcheng Yao
- XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China
| | - Xiaoxiao Wang
- XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China
| | - Dong Chen
- XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China
| | - Genwei Zhang
- XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China
| | - Xiaodong Wang
- XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China.
| | - Lipeng Lai
- XtalPi Innovation Center, 706 Block B, Dongsheng Building, Haidian District, Beijing, China.
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4
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Shaik R, Malik MS, Basavaraju S, Qurban J, Al-Subhi FMM, Badampudi S, Peddapaka J, Shaik A, Abd-El-Aziz A, Moussa Z, Ahmed SA. Cellular and molecular aspects of drug resistance in cancers. Daru 2024; 33:4. [PMID: 39652186 PMCID: PMC11628481 DOI: 10.1007/s40199-024-00545-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Accepted: 10/09/2024] [Indexed: 12/12/2024] Open
Abstract
OBJECTIVES Cancer drug resistance is a multifaceted phenomenon. The present review article aims to comprehensively analyze the cellular and molecular aspects of drug resistance in cancer and the strategies employed to overcome it. EVIDENCE ACQUISITION A systematic search of relevant literature was conducted using electronic databases such as PubMed, Scopus, and Web of Science using appropriate key words. Original research articles and secondary literature were taken into consideration in reviewing the development in the field. RESULTS AND CONCLUSIONS Cancer drug resistance is a pervasive challenge that causes many treatments to fail therapeutically. Despite notable advances in cancer treatment, resistance to traditional chemotherapeutic agents and novel targeted medications remains a formidable hurdle in cancer therapy leading to cancer relapse and mortality. Indeed, a majority of patients with metastatic cancer experience are compromised on treatment efficacy because of drug resistance. The multifaceted nature of drug resistance encompasses various factors, such as tumor heterogeneity, growth kinetics, immune system, microenvironment, physical barriers, and the emergence of undruggable cancer drivers. Additionally, alterations in drug influx/efflux transporters, DNA repair mechanisms, and apoptotic pathways further contribute to resistance, which may manifest as either innate or acquired traits, occurring prior to or following therapeutic intervention. Several strategies such as combination therapy, targeted therapy, development of P-gp inhibitors, PROTACs and epigenetic modulators are developed to overcome cancer drug resistance. The management of drug resistance is compounded by the patient and tumor heterogeneity coupled with cancer's ability to evade treatment. Gaining further insight into the mechanisms underlying medication resistance is imperative for the development of effective therapeutic interventions and improved patient outcomes.
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Affiliation(s)
- Rahaman Shaik
- Department of Pharmacology, School of Pharmaceutical Education & Research, Jamia Hamdard, New Delhi, India
| | - M Shaheer Malik
- Department of Chemistry, Faculty of Science, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.
| | | | - Jihan Qurban
- Department of Chemistry, Faculty of Science, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
| | - Fatimah M M Al-Subhi
- Department of Environmental and Occupational Health, College of Public Health and Health Informatics, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
| | - Sathvika Badampudi
- Department of Pharmacology, St.Pauls College of Pharmacy, Turkayamjal, Hyderabad, India
| | - Jagruthi Peddapaka
- Department of Pharmaceutical Chemistry, St.Paul's College of Pharmacy, Turkayamjal, Hyderabad, India
| | - Azeeza Shaik
- Research&Development Department, KVB Asta Life sciences, Hyderabad, India
| | - Ahmad Abd-El-Aziz
- Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao, 266400, China
| | - Ziad Moussa
- Department of Chemistry, College of Science, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates
| | - Saleh A Ahmed
- Department of Chemistry, Faculty of Science, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.
- Department of Chemistry, Faculty of Science, Assiut University, Assiut, 71516, Egypt.
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5
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Ghanegolmohammadi F, Eslami M, Ohya Y. Systematic data analysis pipeline for quantitative morphological cell phenotyping. Comput Struct Biotechnol J 2024; 23:2949-2962. [PMID: 39104709 PMCID: PMC11298594 DOI: 10.1016/j.csbj.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/07/2024] Open
Abstract
Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.
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Affiliation(s)
- Farzan Ghanegolmohammadi
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab, Schepen’s Eye Research Institute of Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, USA
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
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6
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Sistemich L, Ebbinghaus S. Heat application in live cell imaging. FEBS Open Bio 2024; 14:1940-1954. [PMID: 39489617 PMCID: PMC11609584 DOI: 10.1002/2211-5463.13912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 08/29/2024] [Accepted: 10/01/2024] [Indexed: 11/05/2024] Open
Abstract
Thermal heating of biological samples allows to reversibly manipulate cellular processes with high temporal and spatial resolution. Manifold heating techniques in combination with live-cell imaging were developed, commonly tailored to customized applications. They include Peltier elements and microfluidics for homogenous sample heating as well as infrared lasers and radiation absorption by nanostructures for spot heating. A prerequisite of all techniques is that the induced temperature changes are measured precisely which can be the main challenge considering subcellular structures or multicellular organisms as target regions. This article discusses heating and temperature sensing techniques for live-cell imaging, leading to future applications in cell biology.
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Affiliation(s)
- Linda Sistemich
- Chair of Biophysical ChemistryRuhr‐University BochumGermany
- Research Center Chemical Sciences and Sustainability, Research Alliance RuhrBochumGermany
| | - Simon Ebbinghaus
- Chair of Biophysical ChemistryRuhr‐University BochumGermany
- Research Center Chemical Sciences and Sustainability, Research Alliance RuhrBochumGermany
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7
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Petkidis A, Suomalainen M, Andriasyan V, Singh A, Greber UF. Preexisting cell state rather than stochastic noise confers high or low infection susceptibility of human lung epithelial cells to adenovirus. mSphere 2024; 9:e0045424. [PMID: 39315811 PMCID: PMC11542551 DOI: 10.1128/msphere.00454-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Accepted: 08/20/2024] [Indexed: 09/25/2024] Open
Abstract
Viruses display large variability across all stages of their life cycle, including entry, gene expression, replication, assembly, and egress. We previously reported that the immediate early adenovirus (AdV) E1A transcripts accumulate in human lung epithelial A549 cancer cells with high variability, mostly independent of the number of incoming viral genomes, but somewhat correlated to the cell cycle state at the time of inoculation. Here, we leveraged the classical Luria-Delbrück fluctuation analysis to address whether infection variability primarily arises from the cell state or stochastic noise. The E1A expression was measured by the expression of green fluorescent protein (GFP) from the endogenous E1A promoter in AdV-C5_E1A-FS2A-GFP and found to be highly correlated with the viral plaque formation, indicating reliability of the reporter virus. As an ensemble, randomly picked clonal A549 cell isolates displayed significantly higher coefficients of variation in the E1A expression than technical noise, indicating a phenotypic variability larger than noise. The underlying cell state determining infection variability was maintained for at least 9 weeks of cell cultivation. Our results indicate that preexisting cell states tune adenovirus infection in favor of the cell or the virus. These findings have implications for antiviral strategies and gene therapy applications.IMPORTANCEViral infections are known for their variability. Underlying mechanisms are still incompletely understood but have been associated with particular cell states, for example, the eukaryotic cell division cycle in DNA virus infections. A cell state is the collective of biochemical, morphological, and contextual features owing to particular conditions or at random. It affects how intrinsic or extrinsic cues trigger a response, such as cell division or anti-viral state. Here, we provide evidence that cell states with a built-in memory confer high or low susceptibility of clonal human epithelial cells to adenovirus infection. Results are reminiscent of the Luria-Delbrück fluctuation test with bacteriophage infections back in 1943, which demonstrated that mutations, in the absence of selective pressure prior to infection, cause infection resistance rather than being a consequence of infection. Our findings of dynamic cell states conferring adenovirus infection susceptibility uncover new challenges for the prediction and treatment of viral infections.
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Affiliation(s)
- Anthony Petkidis
- Department of
Molecular Life Sciences, Universitat
Zurich, Zurich,
Switzerland
| | - Maarit Suomalainen
- Department of
Molecular Life Sciences, Universitat
Zurich, Zurich,
Switzerland
| | - Vardan Andriasyan
- Department of
Molecular Life Sciences, Universitat
Zurich, Zurich,
Switzerland
| | - Abhyudai Singh
- Department of
Electrical and Computer Engineering, University of
Delaware, Newark,
Delaware, USA
| | - Urs F. Greber
- Department of
Molecular Life Sciences, Universitat
Zurich, Zurich,
Switzerland
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8
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Hao K, Barrett M, Samadi Z, Zarezadeh A, McGrath Y, Askary A. Reconstructing signaling history of single cells with imaging-based molecular recording. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.11.617908. [PMID: 39416000 PMCID: PMC11482953 DOI: 10.1101/2024.10.11.617908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
The intensity and duration of biological signals encode information that allows a few pathways to regulate a wide array of cellular behaviors. Despite the central importance of signaling in biomedical research, our ability to quantify it in individual cells over time remains limited. Here, we introduce INSCRIBE, an approach for reconstructing signaling history in single cells using endpoint fluorescence images. By regulating a CRISPR base editor, INSCRIBE generates mutations in genomic target sequences, at a rate proportional to signaling activity. The number of edits is then recovered through a novel ratiometric readout strategy, from images of two fluorescence channels. We engineered human cell lines for recording WNT and BMP pathway activity, and demonstrated that INSCRIBE faithfully recovers both the intensity and duration of signaling. Further, we used INSCRIBE to study the variability of cellular response to WNT and BMP stimulation, and test whether the magnitude of response is a stable, heritable trait. We found a persistent memory in the BMP pathway. Progeny of cells with higher BMP response levels are likely to respond more strongly to a second BMP stimulation, up to 3 weeks later. Together, our results establish a scalable platform for genetic recording and in situ readout of signaling history in single cells, advancing quantitative analysis of cell-cell communication during development and disease.
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Affiliation(s)
- Kai Hao
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Mykel Barrett
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Zainalabedin Samadi
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Amirhossein Zarezadeh
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Yuka McGrath
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA
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Jena SG, Verma A, Engelhardt BE. Answering open questions in biology using spatial genomics and structured methods. BMC Bioinformatics 2024; 25:291. [PMID: 39232666 PMCID: PMC11375982 DOI: 10.1186/s12859-024-05912-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 08/22/2024] [Indexed: 09/06/2024] Open
Abstract
Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present a framework to respond to this new avenue of research: four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each open question that may yield specific insights, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological understanding.
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Affiliation(s)
- Siddhartha G Jena
- Department of Stem Cell and Regenerative Biology, Harvard, 7 Divinity Ave, Cambridge, MA, USA
| | - Archit Verma
- Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA
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10
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Le Cunff Y, Chesneau L, Pastezeur S, Pinson X, Soler N, Fairbrass D, Mercat B, Rodriguez-Garcia R, Alayan Z, Abdouni A, de Neidhardt G, Costes V, Anjubault M, Bouvrais H, Héligon C, Pécréaux J. Unveiling inter-embryo variability in spindle length over time: Towards quantitative phenotype analysis. PLoS Comput Biol 2024; 20:e1012330. [PMID: 39236069 PMCID: PMC11376571 DOI: 10.1371/journal.pcbi.1012330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 07/15/2024] [Indexed: 09/07/2024] Open
Abstract
How can inter-individual variability be quantified? Measuring many features per experiment raises the question of choosing them to recapitulate high-dimensional data. Tackling this challenge on spindle elongation phenotypes, we showed that only three typical elongation patterns describe spindle elongation in C. elegans one-cell embryo. These archetypes, automatically extracted from the experimental data using principal component analysis (PCA), accounted for more than 95% of inter-individual variability of more than 1600 experiments across more than 100 different conditions. The two first archetypes were related to spindle average length and anaphasic elongation rate. The third archetype, accounting for 6% of the variability, was novel and corresponded to a transient spindle shortening in late metaphase, reminiscent of kinetochore function-defect phenotypes. Importantly, these three archetypes were robust to the choice of the dataset and were found even considering only non-treated conditions. Thus, the inter-individual differences between genetically perturbed embryos have the same underlying nature as natural inter-individual differences between wild-type embryos, independently of the temperatures. We thus propose that beyond the apparent complexity of the spindle, only three independent mechanisms account for spindle elongation, weighted differently in the various conditions. Interestingly, the spindle-length archetypes covered both metaphase and anaphase, suggesting that spindle elongation in late metaphase is sufficient to predict the late anaphase length. We validated this idea using a machine-learning approach. Finally, given amounts of these three archetypes could represent a quantitative phenotype. To take advantage of this, we set out to predict interacting genes from a seed based on the PCA coefficients. We exemplified this firstly on the role of tpxl-1 whose homolog tpx2 is involved in spindle microtubule branching, secondly the mechanism regulating metaphase length, and thirdly the central spindle players which set the length at anaphase. We found novel interactors not in public databases but supported by recent experimental publications.
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Affiliation(s)
- Yann Le Cunff
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Laurent Chesneau
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Sylvain Pastezeur
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Xavier Pinson
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Nina Soler
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Danielle Fairbrass
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Benjamin Mercat
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Ruddi Rodriguez-Garcia
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Zahraa Alayan
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Ahmed Abdouni
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Gary de Neidhardt
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Valentin Costes
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Mélodie Anjubault
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Hélène Bouvrais
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Christophe Héligon
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
| | - Jacques Pécréaux
- CNRS, Univ Rennes, IGDR (Institut Genetics and Development of Rennes) - UMR 6290, Rennes, France
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11
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Zhong Y, Cui S, Yang Y, Cai JJ. Controlled Noise: Evidence of epigenetic regulation of Single-Cell expression variability. Bioinformatics 2024; 40:btae457. [PMID: 39018178 PMCID: PMC11283284 DOI: 10.1093/bioinformatics/btae457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 06/24/2024] [Accepted: 07/16/2024] [Indexed: 07/19/2024] Open
Abstract
MOTIVATION Understanding single-cell expression variability (scEV) or gene expression noise among cells of the same type and state is crucial for delineating population-level cellular function. While epigenetic mechanisms are widely implicated in gene expression regulation, a definitive link between chromatin accessibility and scEV remains elusive. Recent advances in single-cell techniques enable the study of single-cell multiomics data that include the simultaneous measurement of scATAC-seq and scRNA-seq within individual cells, presenting an unprecedented opportunity to address this gap. RESULTS This paper introduces an innovative testing pipeline to investigate the association between chromatin accessibility and scEV. With single-cell multiomics data of scATAC-seq and scRNA-seq, the pipeline hinges on comparing the prediction performance of scATAC-seq data on gene expression levels between highly variable genes (HVGs) and non-highly variable genes (non-HVGs). Applying this pipeline to paired scATAC-seq and scRNA-seq data from human hematopoietic stem and progenitor cells, we observed a significantly superior prediction performance of scATAC-seq data for HVGs compared to non-HVGs. Notably, there was substantial overlap between well-predicted genes and HVGs. The gene pathways enriched from well-predicted genes are highly pertinent to cell type-specific functions. Our findings support the notion that scEV largely stems from cell-to-cell variability in chromatin accessibility, providing compelling evidence for the epigenetic regulation of scEV and offering promising avenues for investigating gene regulation mechanisms at the single-cell level. AVAILABILITY The source code and data used in this paper can be found at https://github.com/SiweiCui/EpigeneticControlOfSingle-CellExpressionVariability. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yan Zhong
- School of Statistics, KLATASDS-MOE, East China Normal University, Shanghai, 200062, China
| | - Siwei Cui
- School of Statistics, KLATASDS-MOE, East China Normal University, Shanghai, 200062, China
| | - Yongjian Yang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
| | - James J Cai
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, United States
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, United States
- Interdisciplinary Program of Genetics, Texas A&M University, College Station, TX 77843, United States
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12
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Hale BD, Severin Y, Graebnitz F, Stark D, Guignard D, Mena J, Festl Y, Lee S, Hanimann J, Zangger NS, Meier M, Goslings D, Lamprecht O, Frey BM, Oxenius A, Snijder B. Cellular architecture shapes the naïve T cell response. Science 2024; 384:eadh8697. [PMID: 38843327 DOI: 10.1126/science.adh8967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 04/16/2024] [Indexed: 06/15/2024]
Abstract
After antigen stimulation, naïve T cells display reproducible population-level responses, which arise from individual T cells pursuing specific differentiation trajectories. However, cell-intrinsic predeterminants controlling these single-cell decisions remain enigmatic. We found that the subcellular architectures of naïve CD8 T cells, defined by the presence (TØ) or absence (TO) of nuclear envelope invaginations, changed with maturation, activation, and differentiation. Upon T cell receptor (TCR) stimulation, naïve TØ cells displayed increased expression of the early-response gene Nr4a1, dependent upon heightened calcium entry. Subsequently, in vitro differentiation revealed that TØ cells generated effector-like cells more so compared with TO cells, which proliferated less and preferentially adopted a memory-precursor phenotype. These data suggest that cellular architecture may be a predeterminant of naïve CD8 T cell fate.
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MESH Headings
- Animals
- Mice
- Calcium/metabolism
- CD8-Positive T-Lymphocytes/immunology
- CD8-Positive T-Lymphocytes/ultrastructure
- Cell Differentiation
- Immunologic Memory
- Lymphocyte Activation
- Mice, Inbred C57BL
- Nuclear Envelope/metabolism
- Nuclear Receptor Subfamily 4, Group A, Member 1/genetics
- Nuclear Receptor Subfamily 4, Group A, Member 1/metabolism
- Receptors, Antigen, T-Cell/immunology
- Receptors, Antigen, T-Cell/metabolism
- Microscopy, Fluorescence
- Fluorescent Antibody Technique
- Humans
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Affiliation(s)
- Benjamin D Hale
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Yannik Severin
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Fabienne Graebnitz
- Institute of Microbiology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Dominique Stark
- Institute of Microbiology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Daniel Guignard
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Julien Mena
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Yasmin Festl
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Sohyon Lee
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Jacob Hanimann
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Nathan S Zangger
- Institute of Microbiology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Michelle Meier
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - David Goslings
- Blood Transfusion Service Zürich, Swiss Red Cross (SRC), Schlieren, Switzerland
| | - Olga Lamprecht
- Blood Transfusion Service Zürich, Swiss Red Cross (SRC), Schlieren, Switzerland
| | - Beat M Frey
- Blood Transfusion Service Zürich, Swiss Red Cross (SRC), Schlieren, Switzerland
| | - Annette Oxenius
- Institute of Microbiology, Department of Biology, ETH Zürich, Zürich, Switzerland
| | - Berend Snijder
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, Zürich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Comprehensive Cancer Center Zurich (CCCZ), Zürich, Switzerland
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13
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Li Y, Deng D, Höfer CT, Kim J, Do Heo W, Xu Q, Liu X, Zi Z. Liebig's law of the minimum in the TGF-β/SMAD pathway. PLoS Comput Biol 2024; 20:e1012072. [PMID: 38753874 PMCID: PMC11135686 DOI: 10.1371/journal.pcbi.1012072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/29/2024] [Accepted: 04/11/2024] [Indexed: 05/18/2024] Open
Abstract
Cells use signaling pathways to sense and respond to their environments. The transforming growth factor-β (TGF-β) pathway produces context-specific responses. Here, we combined modeling and experimental analysis to study the dependence of the output of the TGF-β pathway on the abundance of signaling molecules in the pathway. We showed that the TGF-β pathway processes the variation of TGF-β receptor abundance using Liebig's law of the minimum, meaning that the output-modifying factor is the signaling protein that is most limited, to determine signaling responses across cell types and in single cells. We found that the abundance of either the type I (TGFBR1) or type II (TGFBR2) TGF-β receptor determined the responses of cancer cell lines, such that the receptor with relatively low abundance dictates the response. Furthermore, nuclear SMAD2 signaling correlated with the abundance of TGF-β receptor in single cells depending on the relative expression levels of TGFBR1 and TGFBR2. A similar control principle could govern the heterogeneity of signaling responses in other signaling pathways.
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Affiliation(s)
- Yuchao Li
- Max Planck Institute for Molecular Genetics, Otto Warburg Laboratory, Berlin, Germany
| | - Difan Deng
- German Federal Institute for Risk Assessment, Department of Experimental Toxicology and ZEBET, Berlin, Germany
| | - Chris Tina Höfer
- German Federal Institute for Risk Assessment, Department of Experimental Toxicology and ZEBET, Berlin, Germany
| | - Jihye Kim
- Department of Biological Sciences, KAIST Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Won Do Heo
- Department of Biological Sciences, KAIST Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Quanbin Xu
- Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Xuedong Liu
- Department of Biochemistry, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Zhike Zi
- Max Planck Institute for Molecular Genetics, Otto Warburg Laboratory, Berlin, Germany
- German Federal Institute for Risk Assessment, Department of Experimental Toxicology and ZEBET, Berlin, Germany
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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14
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Ramirez Flores RO, Schäfer PSL, Küchenhoff L, Saez-Rodriguez J. Complementing Cell Taxonomies with a Multicellular Analysis of Tissues. Physiology (Bethesda) 2024; 39:0. [PMID: 38319138 DOI: 10.1152/physiol.00001.2024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024] Open
Abstract
The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Sven Lars Schäfer
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Leonie Küchenhoff
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
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15
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Brückner DB, Broedersz CP. Learning dynamical models of single and collective cell migration: a review. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2024; 87:056601. [PMID: 38518358 DOI: 10.1088/1361-6633/ad36d2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 03/22/2024] [Indexed: 03/24/2024]
Abstract
Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.
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Affiliation(s)
- David B Brückner
- Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria
| | - Chase P Broedersz
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig-Maximilian-University Munich, Theresienstr. 37, D-80333 Munich, Germany
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16
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Müller JA, Schäffler N, Kellerer T, Schwake G, Ligon TS, Rädler JO. Kinetics of RNA-LNP delivery and protein expression. Eur J Pharm Biopharm 2024; 197:114222. [PMID: 38387850 DOI: 10.1016/j.ejpb.2024.114222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/23/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024]
Abstract
Lipid nanoparticles (LNPs) employing ionizable lipids are the most advanced technology for delivery of RNA, most notably mRNA, to cells. LNPs represent well-defined core-shell particles with efficient nucleic acid encapsulation, low immunogenicity and enhanced efficacy. While much is known about the structure and activity of LNPs, less attention is given to the timing of LNP uptake, cytosolic transfer and protein expression. However, LNP kinetics is a key factor determining delivery efficiency. Hence quantitative insight into the multi-cascaded pathway of LNPs is of interest to elucidate the mechanism of delivery. Here, we review experiments as well as theoretical modeling of the timing of LNP uptake, mRNA-release and protein expression. We describe LNP delivery as a sequence of stochastic transfer processes and review a mathematical model of subsequent protein translation from mRNA. We compile probabilities and numbers obtained from time resolved microscopy. Specifically, live-cell imaging on single cell arrays (LISCA) allows for high-throughput acquisition of thousands of individual GFP reporter expression time courses. The traces yield the distribution of mRNA life-times, expression rates and expression onset. Correlation analysis reveals an inverse dependence of gene expression efficiency and transfection onset-times. Finally, we discuss why timing of mRNA release is critical in the context of codelivery of multiple nucleic acid species as in the case of mRNA co-expression or CRISPR/Cas gene editing.
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Affiliation(s)
- Judith A Müller
- Faculty of Physics and Center for NanoScience, Ludwig Maximilians-University, Munich, Germany
| | - Nathalie Schäffler
- Faculty of Physics and Center for NanoScience, Ludwig Maximilians-University, Munich, Germany
| | - Thomas Kellerer
- Multiphoton Imaging Lab, Munich University of Applied Sciences, Munich, Germany
| | - Gerlinde Schwake
- Faculty of Physics and Center for NanoScience, Ludwig Maximilians-University, Munich, Germany
| | | | - Joachim O Rädler
- Faculty of Physics and Center for NanoScience, Ludwig Maximilians-University, Munich, Germany.
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17
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Zhang T, Wu Z, Li L, Ren J, Zhang Z, Wang G. CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data. Brief Bioinform 2024; 25:bbae198. [PMID: 38678387 PMCID: PMC11056015 DOI: 10.1093/bib/bbae198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/08/2024] [Accepted: 04/10/2024] [Indexed: 04/30/2024] Open
Abstract
In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.
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Affiliation(s)
- Tianjiao Zhang
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Zhenao Wu
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Liangyu Li
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Jixiang Ren
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Ziheng Zhang
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
- Faculty of Computing, Harbin Institute of Technology Harbin, 150001, China
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18
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Metz-Zumaran C, Uckeley ZM, Doldan P, Muraca F, Keser Y, Lukas P, Kuropka B, Küchenhoff L, Rastgou Talemi S, Höfer T, Freund C, Cavalcanti-Adam EA, Graw F, Stanifer M, Boulant S. The population context is a driver of the heterogeneous response of epithelial cells to interferons. Mol Syst Biol 2024; 20:242-275. [PMID: 38273161 PMCID: PMC10912784 DOI: 10.1038/s44320-024-00011-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 12/31/2023] [Accepted: 01/04/2024] [Indexed: 01/27/2024] Open
Abstract
Isogenic cells respond in a heterogeneous manner to interferon. Using a micropatterning approach combined with high-content imaging and spatial analyses, we characterized how the population context (position of a cell with respect to neighboring cells) of epithelial cells affects their response to interferons. We identified that cells at the edge of cellular colonies are more responsive than cells embedded within colonies. We determined that this spatial heterogeneity in interferon response resulted from the polarized basolateral interferon receptor distribution, making cells located in the center of cellular colonies less responsive to ectopic interferon stimulation. This was conserved across cell lines and primary cells originating from epithelial tissues. Importantly, cells embedded within cellular colonies were not protected from viral infection by apical interferon treatment, demonstrating that the population context-driven heterogeneous response to interferon influences the outcome of viral infection. Our data highlights that the behavior of isolated cells does not directly translate to their behavior in a population, placing the population context as one important factor influencing heterogeneity during interferon response in epithelial cells.
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Affiliation(s)
- Camila Metz-Zumaran
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA
- Department of Infectious Disease, Virology, University Hospital Heidelberg, Im Neuenheimer Feld 344, 60120, Heidelberg, Germany
| | - Zina M Uckeley
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA
| | - Patricio Doldan
- Department of Infectious Disease, Virology, University Hospital Heidelberg, Im Neuenheimer Feld 344, 60120, Heidelberg, Germany
| | - Francesco Muraca
- Department of Infectious Disease, Virology, University Hospital Heidelberg, Im Neuenheimer Feld 344, 60120, Heidelberg, Germany
| | - Yagmur Keser
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA
| | - Pascal Lukas
- BioQuant-Center for Quantitative Biology, Heidelberg University, 60120, Heidelberg, Germany
| | - Benno Kuropka
- Institute of Chemistry and Biochemistry, Protein Biochemistry, Freie Universität Berlin, Thielallee 63, 14195, Berlin, Germany
| | - Leonie Küchenhoff
- BioQuant-Center for Quantitative Biology, Heidelberg University, 60120, Heidelberg, Germany
| | - Soheil Rastgou Talemi
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christian Freund
- Institute of Chemistry and Biochemistry, Protein Biochemistry, Freie Universität Berlin, Thielallee 63, 14195, Berlin, Germany
| | - Elisabetta Ada Cavalcanti-Adam
- Max Planck Institute for Medical Research, Heidelberg, Germany
- Cellular Biomechanics, University of Bayreuth, Bayreuth, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
| | - Frederik Graw
- BioQuant-Center for Quantitative Biology, Heidelberg University, 60120, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Department of Medicine 5, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Megan Stanifer
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA.
| | - Steeve Boulant
- Department of Molecular Genetics and Microbiology, University of Florida, College of Medicine, 1200 Newell Drive, 32610, Gainesville, FL, USA.
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19
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Liu Y, Huang K, Chen W. Resolving cellular dynamics using single-cell temporal transcriptomics. Curr Opin Biotechnol 2024; 85:103060. [PMID: 38194753 DOI: 10.1016/j.copbio.2023.103060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 01/11/2024]
Abstract
Cellular dynamics, the transition of a cell from one state to another, is central to understanding developmental processes and disease progression. Single-cell transcriptomics has been pushing the frontiers of cellular dynamics studies into a genome-wide and single-cell level. While most single-cell RNA sequencing approaches are disruptive and only provide a snapshot of cell states, the dynamics of a cell could be reconstructed by either exploiting temporal information hiding in the transcriptomics data or integrating additional information. In this review, we describe these approaches, highlighting their underlying principles, key assumptions, and the rationality to interpret the results as models. We also discuss the recently emerging nondisruptive live-cell transcriptomics methods, which are highly complementary to the computational models for their assumption-free nature.
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Affiliation(s)
- Yifei Liu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Kai Huang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wanze Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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20
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Caetano A, Sharpe P. Redefining Mucosal Inflammation with Spatial Genomics. J Dent Res 2024; 103:129-137. [PMID: 38166489 PMCID: PMC10845836 DOI: 10.1177/00220345231216114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2024] Open
Abstract
The human oral mucosa contains one of the most complex cellular systems that are essential for normal physiology and defense against a wide variety of local pathogens. Evolving techniques and experimental systems have helped refine our understanding of this complex cellular network. Current single-cell RNA sequencing methods can resolve subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the oral mucosa in health and disease. However, it requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss the contribution of spatial technologies in shaping our understanding of this complex system. We consider the impact on identifying disease cellular neighborhoods and how space defines cell state. We also discuss the limitations and future directions of spatial sequencing technologies with recent advances in machine learning. Finally, we offer a perspective on open questions about mucosal homeostasis that these technologies are well placed to address.
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Affiliation(s)
- A.J. Caetano
- Centre for Oral Immunobiology and Regenerative Medicine, Barts Centre for Squamous Cancer, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK
| | - P.T. Sharpe
- Centre for Craniofacial and Regenerative Biology, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London, UK
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21
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Zhao Q, Shen Y, Li X, Li Y, Tian F, Yu X, Liu Z, Tong R, Park H, Yobas L, Huang P. Nanobead-based single-molecule pulldown for single cells. Heliyon 2023; 9:e22306. [PMID: 38027957 PMCID: PMC10679481 DOI: 10.1016/j.heliyon.2023.e22306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 12/01/2023] Open
Abstract
Investigation of cell-to-cell variability holds critical physiological and clinical implications. Thus, numerous new techniques have been developed for studying cell-to-cell variability, and these single-cell techniques can also be used to investigate rare cells. Moreover, for studying protein-protein interactions (PPIs) in single cells, several techniques have been developed based on the principle of the single-molecule pulldown (SiMPull) assay. However, the applicability of these single-cell SiMPull (sc-SiMPull) techniques is limited because of their high technical barrier and special requirements for target cells and molecules. Here, we report a highly innovative nanobead-based approach for sc-SiMPull that is based on our recently developed microbead-based, improved version of SiMPull for cell populations. In our sc-SiMPull method, single cells are captured in microwells and lysed in situ, after which commercially available, pre-surface-functionalized magnetic nanobeads are placed in the microwells to specifically capture proteins of interest together with their binding partners from cell extracts; subsequently, the PPIs are examined under a microscope at the single-molecule level. Relative to previously published methods, nanobead-based sc-SiMPull is considerably faster, easier to use, more reproducible, and more versatile for distinct cell types and protein molecules, and yet provides similar sensitivity and signal-to-background ratio. These crucial features should enable universal application of our method to the study of PPIs in single cells.
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Affiliation(s)
- Qirui Zhao
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
| | - Yusheng Shen
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | - Xiaofen Li
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
| | - Yulin Li
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
| | - Fang Tian
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China
| | - Xiaojie Yu
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
| | - Zhengzhao Liu
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
| | - Rongbiao Tong
- Department of Chemistry, Hong Kong University of Science and Technology, Hong Kong, China
| | - Hyokeun Park
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China
| | - Levent Yobas
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
- Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China
| | - Pingbo Huang
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
- Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong, China
- State Key Laboratory of Molecular Neuroscience, Hong Kong University of Science and Technology, Hong Kong, China
- HKUST Shenzhen Research Institute, Hong Kong University of Science and Technology, Hong Kong, China
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
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22
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Küchenhoff L, Lukas P, Metz-Zumaran C, Rothhaar P, Ruggieri A, Lohmann V, Höfer T, Stanifer ML, Boulant S, Talemi SR, Graw F. Extended methods for spatial cell classification with DBSCAN-CellX. Sci Rep 2023; 13:18868. [PMID: 37914751 PMCID: PMC10620226 DOI: 10.1038/s41598-023-45190-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023] Open
Abstract
Local cell densities and positioning within cellular monolayers and stratified epithelia have important implications for cell interactions and the functionality of various biological processes. To analyze the relationship between cell localization and tissue physiology, density-based clustering algorithms, such as DBSCAN, allow for a detailed characterization of the spatial distribution and positioning of individual cells. However, these methods rely on predefined parameters that influence the outcome of the analysis. With varying cell densities in cell cultures or tissues impacting cell sizes and, thus, cellular proximities, these parameters need to be carefully chosen. In addition, standard DBSCAN approaches generally come short in appropriately identifying individual cell positions. We therefore developed three extensions to the standard DBSCAN-algorithm that provide: (i) an automated parameter identification to reliably identify cell clusters, (ii) an improved identification of cluster edges; and (iii) an improved characterization of the relative positioning of cells within clusters. We apply our novel methods, which are provided as a user-friendly OpenSource-software package (DBSCAN-CellX), to cellular monolayers of different cell lines. Thereby, we show the importance of the developed extensions for the appropriate analysis of cell culture experiments to determine the relationship between cell localization and tissue physiology.
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Affiliation(s)
- Leonie Küchenhoff
- BioQuant-Center for Quantitative Biology, Heidelberg University, 69120, Heidelberg, Germany
| | - Pascal Lukas
- BioQuant-Center for Quantitative Biology, Heidelberg University, 69120, Heidelberg, Germany
| | - Camila Metz-Zumaran
- Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Paul Rothhaar
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Alessia Ruggieri
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Volker Lohmann
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Megan L Stanifer
- Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Steeve Boulant
- Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Soheil Rastgou Talemi
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik Graw
- BioQuant-Center for Quantitative Biology, Heidelberg University, 69120, Heidelberg, Germany.
- Interdisciplinary Center for Scientific Computing, Heidelberg University, 69120, Heidelberg, Germany.
- Department of Medicine 5, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 12, 91054, Erlangen, Germany.
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23
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Hall D. MIL-CELL: a tool for multi-scale simulation of yeast replication and prion transmission. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2023; 52:673-704. [PMID: 37670150 PMCID: PMC10682183 DOI: 10.1007/s00249-023-01679-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/08/2023] [Accepted: 08/14/2023] [Indexed: 09/07/2023]
Abstract
The single-celled baker's yeast, Saccharomyces cerevisiae, can sustain a number of amyloid-based prions, the three most prominent examples being [URE3], [PSI+], and [PIN+]. In the laboratory, haploid S. cerevisiae cells of a single mating type can acquire an amyloid prion in one of two ways (i) spontaneous nucleation of the prion within the yeast cell, and (ii) receipt via mother-to-daughter transmission during the cell division cycle. Similarly, prions can be lost due to (i) dissolution of the prion amyloid by its breakage into non-amyloid monomeric units, or (ii) preferential donation/retention of prions between the mother and daughter during cell division. Here we present a computational tool (Monitoring Induction and Loss of prions in Cells; MIL-CELL) for modelling these four general processes using a multiscale approach describing both spatial and kinetic aspects of the yeast life cycle and the amyloid-prion behavior. We describe the workings of the model, assumptions upon which it is based and some interesting simulation results pertaining to the wave-like spread of the epigenetic prion elements through the yeast population. MIL-CELL is provided as a stand-alone GUI executable program for free download with the paper. MIL-CELL is equipped with a relational database allowing all simulated properties to be searched, collated and graphed. Its ability to incorporate variation in heritable properties means MIL-CELL is also capable of simulating loss of the isogenic nature of a cell population over time. The capability to monitor both chronological and reproductive age also makes MIL-CELL potentially useful in studies of cell aging.
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Affiliation(s)
- Damien Hall
- WPI Nano Life Science Institute, Kanazawa University, Kakumamachi, Kanazawa, Ishikawa, 920-1164, Japan.
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24
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Gao X, Zhou P, Li F. The multiple activations in budding yeast S-phase checkpoint are Poisson processes. PNAS NEXUS 2023; 2:pgad342. [PMID: 37941810 PMCID: PMC10629469 DOI: 10.1093/pnasnexus/pgad342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 10/11/2023] [Indexed: 11/10/2023]
Abstract
Eukaryotic cells activate the S-phase checkpoint signal transduction pathway in response to DNA replication stress. Affected by the noise in biochemical reactions, such activation process demonstrates cell-to-cell variability. Here, through the analysis of microfluidics-integrated time-lapse imaging, we found multiple S-phase checkpoint activations in a certain budding yeast cell cycle. Yeast cells not only varied in their activation moments but also differed in the number of activations within the cell cycle, resulting in a stochastic multiple activation process. By investigating dynamics at the single-cell level, we showed that stochastic waiting times between consecutive activations are exponentially distributed and independent from each other. Finite DNA replication time provides a robust upper time limit to the duration of multiple activations. The mathematical model, together with further experimental evidence from the mutant strain, revealed that the number of activations under different levels of replication stress agreed well with Poisson distribution. Therefore, the activation events of S-phase checkpoint meet the criterion of Poisson process during DNA replication. In sum, the observed Poisson activation process may provide new insights into the complex stochastic dynamics of signal transduction pathways.
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Affiliation(s)
- Xin Gao
- School of Physics, Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Peijie Zhou
- Department of Mathematics, University of California, Irvine, CA 92697, USA
| | - Fangting Li
- School of Physics, Center for Quantitative Biology, Peking University, Beijing 100871, China
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25
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Zheng H, Vijg J, Fard AT, Mar JC. Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and applications to B cell aging. Genome Biol 2023; 24:238. [PMID: 37864221 PMCID: PMC10588274 DOI: 10.1186/s13059-023-03036-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 08/11/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Single-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently facilitate the study of cell-to-cell variability at the cell type level. Although different methods have been proposed to quantify cell-to-cell variability, it is unclear what the optimal statistical approach is, especially in light of challenging data structures that are unique to scRNA-seq data like zero inflation. RESULTS We systematically evaluate the performance of 14 different variability metrics that are commonly applied to transcriptomic data for measuring cell-to-cell variability. Leveraging simulations and real datasets, we benchmark the metric performance based on data-specific features, sparsity and sequencing platform, biological properties, and the ability to recapitulate true levels of biological variability based on known gene sets. Next, we use scran, the metric with the strongest all-round performance, to investigate changes in cell-to-cell variability that occur during B cell differentiation and the aging processes. The analysis of primary cell types from hematopoietic stem cells (HSCs) and B lymphopoiesis reveals unique gene signatures with consistent patterns of variable and stable expression profiles during B cell differentiation which highlights the significance of these methods. Identifying differentially variable genes between young and old cells elucidates the regulatory changes that may be overlooked by solely focusing on mean expression changes and we investigate this in the context of regulatory networks. CONCLUSIONS We highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and aging and emphasize the value of these findings at the level of individual cell types.
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Affiliation(s)
- Huiwen Zheng
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Jan Vijg
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
- Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Atefeh Taherian Fard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia.
| | - Jessica Cara Mar
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia.
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26
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Ahmadian M, Rickert C, Minic A, Wrobel J, Bitler BG, Xing F, Angelo M, Hsieh EWY, Ghosh D, Jordan KR. A platform-independent framework for phenotyping of multiplex tissue imaging data. PLoS Comput Biol 2023; 19:e1011432. [PMID: 37733781 PMCID: PMC10547204 DOI: 10.1371/journal.pcbi.1011432] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 10/03/2023] [Accepted: 08/14/2023] [Indexed: 09/23/2023] Open
Abstract
Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-scale representations of the input data, but exhibit significantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.
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Affiliation(s)
- Mansooreh Ahmadian
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Christian Rickert
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Angela Minic
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Julia Wrobel
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Benjamin G. Bitler
- Division of Reproductive Sciences, Department of OB/GYN, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Fuyong Xing
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, California, United States of America
| | - Elena W. Y. Hsieh
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- Pediatrics, Section of Allergy and Immunology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Kimberly R. Jordan
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
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27
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Repina NA, Johnson HJ, Bao X, Zimmermann JA, Joy DA, Bi SZ, Kane RS, Schaffer DV. Optogenetic control of Wnt signaling models cell-intrinsic embryogenic patterning using 2D human pluripotent stem cell culture. Development 2023; 150:dev201386. [PMID: 37401411 PMCID: PMC10399980 DOI: 10.1242/dev.201386] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 06/21/2023] [Indexed: 07/05/2023]
Abstract
In embryonic stem cell (ESC) models for early development, spatially and temporally varying patterns of signaling and cell types emerge spontaneously. However, mechanistic insight into this dynamic self-organization is limited by a lack of methods for spatiotemporal control of signaling, and the relevance of signal dynamics and cell-to-cell variability to pattern emergence remains unknown. Here, we combine optogenetic stimulation, imaging and transcriptomic approaches to study self-organization of human ESCs (hESC) in two-dimensional (2D) culture. Morphogen dynamics were controlled via optogenetic activation of canonical Wnt/β-catenin signaling (optoWnt), which drove broad transcriptional changes and mesendoderm differentiation at high efficiency (>99% cells). When activated within cell subpopulations, optoWnt induced cell self-organization into distinct epithelial and mesenchymal domains, mediated by changes in cell migration, an epithelial to mesenchymal-like transition and TGFβ signaling. Furthermore, we demonstrate that such optogenetic control of cell subpopulations can be used to uncover signaling feedback mechanisms between neighboring cell types. These findings reveal that cell-to-cell variability in Wnt signaling is sufficient to generate tissue-scale patterning and establish a hESC model system for investigating feedback mechanisms relevant to early human embryogenesis.
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Affiliation(s)
- Nicole A. Repina
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
- Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, CA 94720, USA
| | - Hunter J. Johnson
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
- Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, CA 94720, USA
| | - Xiaoping Bao
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA
| | - Joshua A. Zimmermann
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA
| | - David A. Joy
- Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, CA 94720, USA
- Gladstone Institute of Cardiovascular Disease, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Shirley Z. Bi
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
| | - Ravi S. Kane
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - David V. Schaffer
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA
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28
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Reffsin S, Miller J, Ayyanathan K, Dunagin MC, Jain N, Schultz DC, Cherry S, Raj A. Single cell susceptibility to SARS-CoV-2 infection is driven by variable cell states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.06.547955. [PMID: 37461472 PMCID: PMC10350037 DOI: 10.1101/2023.07.06.547955] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
The ability of a virus to infect a cell type is at least in part determined by the presence of host factors required for the viral life cycle. However, even within cell types that express known factors needed for infection, not every cell is equally susceptible, suggesting that our knowledge of the full spectrum of factors that promote infection is incomplete. Profiling the most susceptible subsets of cells within a population may reveal additional factors that promote infection. However, because viral infection dramatically alters the state of the cell, new approaches are needed to reveal the state of these cells prior to infection with virus. Here, we used single-cell clone tracing to retrospectively identify and characterize lung epithelial cells that are highly susceptible to infection with SARS-CoV-2. The transcriptional state of these highly susceptible cells includes markers of retinoic acid signaling and epithelial differentiation. Loss of candidate factors identified by our approach revealed that many of these factors play roles in viral entry. Moreover, a subset of these factors exert control over the infectable cell state itself, regulating the expression of key factors associated with viral infection and entry. Analysis of patient samples revealed the heterogeneous expression of these factors across both cells and patients in vivo. Further, the expression of these factors is upregulated in particular inflammatory pathologies. Altogether, our results show that the variable expression of intrinsic cell states is a major determinant of whether a cell can be infected by SARS-CoV-2.
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Affiliation(s)
- Sam Reffsin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Jesse Miller
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kasirajan Ayyanathan
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Margaret C. Dunagin
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Naveen Jain
- Genetics and Epigenetics, Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David C. Schultz
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, USA
| | - Sara Cherry
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Microbiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Arjun Raj
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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29
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Choudhary D, Lagage V, Foster KR, Uphoff S. Phenotypic heterogeneity in the bacterial oxidative stress response is driven by cell-cell interactions. Cell Rep 2023; 42:112168. [PMID: 36848288 PMCID: PMC10935545 DOI: 10.1016/j.celrep.2023.112168] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/14/2022] [Accepted: 02/09/2023] [Indexed: 02/27/2023] Open
Abstract
Genetically identical bacterial cells commonly display different phenotypes. This phenotypic heterogeneity is well known for stress responses, where it is often explained as bet hedging against unpredictable environmental threats. Here, we explore phenotypic heterogeneity in a major stress response of Escherichia coli and find it has a fundamentally different basis. We characterize the response of cells exposed to hydrogen peroxide (H2O2) stress in a microfluidic device under constant growth conditions. A machine-learning model reveals that phenotypic heterogeneity arises from a precise and rapid feedback between each cell and its immediate environment. Moreover, we find that the heterogeneity rests upon cell-cell interaction, whereby cells shield each other from H2O2 via their individual stress responses. Our work shows how phenotypic heterogeneity in bacterial stress responses can emerge from short-range cell-cell interactions and result in a collective phenotype that protects a large proportion of the population.
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Affiliation(s)
- Divya Choudhary
- Department of Biochemistry, University of Oxford, Oxford, UK
| | | | - Kevin R Foster
- Department of Biochemistry, University of Oxford, Oxford, UK; Department of Biology, University of Oxford, Oxford, UK
| | - Stephan Uphoff
- Department of Biochemistry, University of Oxford, Oxford, UK.
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30
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Ho KKY, Srivastava S, Kinnunen PC, Garikipati K, Luker GD, Luker KE. Oscillatory ERK Signaling and Morphology Determine Heterogeneity of Breast Cancer Cell Chemotaxis via MEK-ERK and p38-MAPK Signaling Pathways. Bioengineering (Basel) 2023; 10:bioengineering10020269. [PMID: 36829763 PMCID: PMC9952091 DOI: 10.3390/bioengineering10020269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/24/2023] [Accepted: 02/12/2023] [Indexed: 02/22/2023] Open
Abstract
Chemotaxis, regulated by oscillatory signals, drives critical processes in cancer metastasis. Crucial chemoattractant molecules in breast cancer, CXCL12 and EGF, drive the activation of ERK and Akt. Regulated by feedback and crosstalk mechanisms, oscillatory signals in ERK and Akt control resultant changes in cell morphology and chemotaxis. While commonly studied at the population scale, metastasis arises from small numbers of cells that successfully disseminate, underscoring the need to analyze processes that cancer cells use to connect oscillatory signaling to chemotaxis at single-cell resolution. Furthermore, little is known about how to successfully target fast-migrating cells to block metastasis. We investigated to what extent oscillatory networks in single cells associate with heterogeneous chemotactic responses and how targeted inhibitors block signaling processes in chemotaxis. We integrated live, single-cell imaging with time-dependent data processing to discover oscillatory signal processes defining heterogeneous chemotactic responses. We identified that short ERK and Akt waves, regulated by MEK-ERK and p38-MAPK signaling pathways, determine the heterogeneous random migration of cancer cells. By comparison, long ERK waves and the morphological changes regulated by MEK-ERK signaling, determine heterogeneous directed motion. This study indicates that treatments against chemotaxis in consider must interrupt oscillatory signaling.
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Affiliation(s)
- Kenneth K. Y. Ho
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Siddhartha Srivastava
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Patrick C. Kinnunen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Krishna Garikipati
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Computational Discovery & Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gary D. Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA
- Correspondence: (G.D.L.); (K.E.L.)
| | - Kathryn E. Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
- Correspondence: (G.D.L.); (K.E.L.)
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31
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Sarma U, Ripka L, Anyaegbunam UA, Legewie S. Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway. Methods Mol Biol 2023; 2634:215-251. [PMID: 37074581 DOI: 10.1007/978-1-0716-3008-2_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.
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Affiliation(s)
- Uddipan Sarma
- Institute of Molecular Biology (IMB), Mainz, Germany
| | - Lorenz Ripka
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Uchenna Alex Anyaegbunam
- Institute of Molecular Biology (IMB), Mainz, Germany
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany
| | - Stefan Legewie
- Institute of Molecular Biology (IMB), Mainz, Germany.
- Department of Systems Biology, Institute for Biomedical Genetics, University of Stuttgart, Stuttgart, Germany.
- Stuttgart Research Center for Systems Biology, University of Stuttgart, Stuttgart, Germany.
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32
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Severin Y, Hale BD, Mena J, Goslings D, Frey BM, Snijder B. Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes. SCIENCE ADVANCES 2022; 8:eabn5631. [PMID: 36322666 PMCID: PMC9629716 DOI: 10.1126/sciadv.abn5631] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of eight distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T cell morphology to transcriptional state based on their joint donor variability and validate an inflammation-associated polarized T cell morphology and an age-associated loss of mitochondria in CD4+ T cells. Together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping of individual people in health and disease.
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Affiliation(s)
- Yannik Severin
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
| | - Benjamin D. Hale
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
| | - Julien Mena
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
| | - David Goslings
- Blood Transfusion Service Zürich, SRC, 8952 Schlieren, Switzerland
| | - Beat M. Frey
- Blood Transfusion Service Zürich, SRC, 8952 Schlieren, Switzerland
| | - Berend Snijder
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
- Corresponding author.
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33
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Tsuchiya T, Hori H, Ozaki H. CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells. Bioinformatics 2022; 38:4868-4877. [PMID: 36063454 PMCID: PMC9620831 DOI: 10.1093/bioinformatics/btac599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/17/2022] [Accepted: 09/04/2022] [Indexed: 11/15/2022] Open
Abstract
MOTIVATION Cell-cell communications regulate internal cellular states, e.g. gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation of cell-to-cell expression variability of HVGs via cell-cell communications is still largely unexplored. The recent advent of spatial transcriptome methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels influenced by neighboring cell types. However, limitations remain in the quantitativeness and interpretability: they neither focus on HVGs nor consider the effects of multiple neighboring cell types. RESULTS Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated the effects of multiple neighboring cell types on HVGs. Furthermore, applications to the two real datasets demonstrate that CCPLS can extract biologically interpretable insights from the inferred cell-cell communications. AVAILABILITY AND IMPLEMENTATION The R package is available at https://github.com/bioinfo-tsukuba/CCPLS. The data are available at https://github.com/bioinfo-tsukuba/CCPLS_paper. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Takaho Tsuchiya
- Bioinformatics Laboratory, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
- Center for Artificial Intelligence Research, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
| | - Hiroki Hori
- Bioinformatics Laboratory, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
- Doctoral Program in Medical Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
| | - Haruka Ozaki
- Bioinformatics Laboratory, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
- Center for Artificial Intelligence Research, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
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34
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Lu M, Zhang Y, Yang F, Mai J, Gao Q, Xu X, Kang H, Hou L, Shang Y, Qain Q, Liu J, Jiang M, Zhang H, Bu C, Wang J, Zhang Z, Zhang Z, Zeng J, Li J, Xiao J. TWAS Atlas: a curated knowledgebase of transcriptome-wide association studies. Nucleic Acids Res 2022; 51:D1179-D1187. [PMID: 36243959 PMCID: PMC9825460 DOI: 10.1093/nar/gkac821] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/08/2022] [Accepted: 09/14/2022] [Indexed: 01/30/2023] Open
Abstract
Transcriptome-wide association studies (TWASs), as a practical and prevalent approach for detecting the associations between genetically regulated genes and traits, are now leading to a better understanding of the complex mechanisms of genetic variants in regulating various diseases and traits. Despite the ever-increasing TWAS outputs, there is still a lack of databases curating massive public TWAS information and knowledge. To fill this gap, here we present TWAS Atlas (https://ngdc.cncb.ac.cn/twas/), an integrated knowledgebase of TWAS findings manually curated from extensive literature. In the current implementation, TWAS Atlas collects 401,266 high-quality human gene-trait associations from 200 publications, covering 22,247 genes and 257 traits across 135 tissue types. In particular, an interactive knowledge graph of the collected gene-trait associations is constructed together with single nucleotide polymorphism (SNP)-gene associations to build up comprehensive regulatory networks at multi-omics levels. In addition, TWAS Atlas, as a user-friendly web interface, efficiently enables users to browse, search and download all association information, relevant research metadata and annotation information of interest. Taken together, TWAS Atlas is of great value for promoting the utility and availability of TWAS results in explaining the complex genetic basis as well as providing new insights for human health and disease research.
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Affiliation(s)
| | | | | | | | - Qianwen Gao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaowei Xu
- Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
| | - Hongyu Kang
- Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
| | - Li Hou
- Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
| | - Yunfei Shang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiheng Qain
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Liu
- North China University of Science and Technology Affiliated Hospital, Tangshan 063000, China
| | - Meiye Jiang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Congfan Bu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jinyue Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhewen Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zaichao Zhang
- Department of Biology, The University of Western Ontario, London, OntarioN6A 5B7, Canada
| | - Jingyao Zeng
- Correspondence may also be addressed to Jingyao Zeng.
| | - Jiao Li
- Correspondence may also be addressed to Jiao Li.
| | - Jingfa Xiao
- To whom correspondence should be addressed. Tel: +86 10 8409 7443; Fax: +86 10 8409 7720;
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35
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Gehling K, Parekh S, Schneider F, Kirchner M, Kondylis V, Nikopoulou C, Tessarz P. RNA-sequencing of single cholangiocyte-derived organoids reveals high organoid-to organoid variability. Life Sci Alliance 2022; 5:e202101340. [PMID: 35914813 PMCID: PMC9348635 DOI: 10.26508/lsa.202101340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 12/13/2022] Open
Abstract
Over the last decades, organoids have been established from most of the tissue-resident stem and iPS cells. They hold great promise for our understanding of mammalian organ development, but also for the study of disease or even personalised medicine. In recent years, several reports hinted at intraculture organoid variability, but a systematic analysis of such heterogeneity has not been performed before. Here, we used RNA-seq of individual intrahepatic cholangiocyte organoids to address this question. We find that batch-to-batch variation is very low, whereas passage number has a profound impact on gene expression profiles. On the other hand, there is organoid-to-organoid variability within a culture. Using differential gene expression, we did not identify specific pathways that drive this variability, pointing towards possible effects of the microenvironment within the culture condition. Taken together, our study provides a framework for organoid researchers to properly consider experimental design.
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Affiliation(s)
- Kristin Gehling
- Max Planck Research Group "Chromatin and Ageing," Max Planck Institute for Biology of Ageing, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-associated Diseases (CECAD), Cologne, Germany
| | - Swati Parekh
- Max Planck Research Group "Chromatin and Ageing," Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Farina Schneider
- Institute for Pathology, University Hospital Cologne, Cologne, Germany
| | - Marcel Kirchner
- FACS and Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Vangelis Kondylis
- Institute for Pathology, University Hospital Cologne, Cologne, Germany
| | - Chrysa Nikopoulou
- Max Planck Research Group "Chromatin and Ageing," Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Peter Tessarz
- Max Planck Research Group "Chromatin and Ageing," Max Planck Institute for Biology of Ageing, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-associated Diseases (CECAD), Cologne, Germany
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36
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Cavanagh R, Shubber S, Vllasaliu D, Stolnik S. Enhanced permeation by amphiphilic surfactant is spatially heterogenous at membrane and cell level. J Control Release 2022; 345:734-743. [PMID: 35367276 DOI: 10.1016/j.jconrel.2022.03.053] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 03/23/2022] [Accepted: 03/27/2022] [Indexed: 11/25/2022]
Abstract
In the context of increased interest in permeability enhancement technologies to achieve mucosal delivery of drugs and biologics, we report our study on effects of the amphiphilic surfactant at cell membrane and cell population levels. Our results show that modulation in membrane order and fluidity initially occurs on insertion of individual surfactant molecules into the outer leaflet of membrane lipid bilayer; a process occurring at concentrations below surfactant's critical micellar concentration. The surfactant insertion, and consequent increase in membrane fluidity, are observed to be spatially heterogenous, i.e. manifested as 'patches' of increased membrane fluidity. At the cell population level, spatially heterogeneous activity of surfactant is also manifested, with certain cells displaying high permeability amongst a 'background' population. We propose that this heterogeneity is further manifested in a broad profile of intracellular and nuclear exposure levels to a model drug (doxorubicin) observed in cell population. The study points to heterogeneous nature of surfactant effects at cell membrane and cells in population levels.
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Affiliation(s)
- Robert Cavanagh
- School of Pharmacy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Saif Shubber
- School of Pharmacy, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Driton Vllasaliu
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
| | - Snjezana Stolnik
- School of Pharmacy, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
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37
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Chen HY, Palendira U, Feng CG. Navigating the cellular landscape in tissue: Recent advances in defining the pathogenesis of human disease. Comput Struct Biotechnol J 2022; 20:5256-5263. [PMID: 36212528 PMCID: PMC9519395 DOI: 10.1016/j.csbj.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/04/2022] [Accepted: 09/04/2022] [Indexed: 11/19/2022] Open
Affiliation(s)
- Helen Y. Chen
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
| | - Umaimainthan Palendira
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
| | - Carl G. Feng
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
- Corresponding author at: Level 5 (East) The Charles Perkins Centre (D17), The University of Sydney, NSW, 2006, Australia
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38
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Li B, Ritchie MD. From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries. Front Genet 2021; 12:713230. [PMID: 34659337 PMCID: PMC8515949 DOI: 10.3389/fgene.2021.713230] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/27/2021] [Indexed: 12/12/2022] Open
Abstract
Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.
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Affiliation(s)
- Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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39
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Cortez MJ, Hong H, Choi B, Kim JK, Josić K. Hierarchical Bayesian models of transcriptional and translational regulation processes with delays. Bioinformatics 2021; 38:187-195. [PMID: 34450624 PMCID: PMC8696106 DOI: 10.1093/bioinformatics/btab618] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/19/2021] [Accepted: 08/25/2021] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION Simultaneous recordings of gene network dynamics across large populations have revealed that cell characteristics vary considerably even in clonal lines. Inferring the variability of parameters that determine gene dynamics is key to understanding cellular behavior. However, this is complicated by the fact that the outcomes and effects of many reactions are not observable directly. Unobserved reactions can be replaced with time delays to reduce model dimensionality and simplify inference. However, the resulting models are non-Markovian, and require the development of new inference techniques. RESULTS We propose a non-Markovian, hierarchical Bayesian inference framework for quantifying the variability of cellular processes within and across cells in a population. We illustrate our approach using a delayed birth-death process. In general, a distributed delay model, rather than a popular fixed delay model, is needed for inference, even if only mean reaction delays are of interest. Using in silico and experimental data we show that the proposed hierarchical framework is robust and leads to improved estimates compared to its non-hierarchical counterpart. We apply our method to data obtained using time-lapse microscopy and infer the parameters that describe the dynamics of protein production at the single cell and population level. The mean delays in protein production are larger than previously reported, have a coefficient of variation of around 0.2 across the population, and are not strongly correlated with protein production or growth rates. AVAILABILITY AND IMPLEMENTATION Accompanying code in Python is available at https://github.com/mvcortez/Bayesian-Inference. CONTACT kresimir.josic@gmail.com or jaekkim@kaist.ac.kr or cbskust@korea.ac.kr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mark Jayson Cortez
- Department of Mathematics, University of Houston, Houston, TX 77204, USA,Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, Laguna 4031, Philippines
| | - Hyukpyo Hong
- Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea,Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, Korea
| | - Boseung Choi
- To whom correspondence should be addressed. or or
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40
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Sotoudegan MS, Arnold JJ, Cameron CE. Single-cell analysis for the study of viral inhibitors. Enzymes 2021; 49:195-213. [PMID: 34696832 DOI: 10.1016/bs.enz.2021.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Stochastic outcomes of viral infections are attributed in large part to multiple layers of intrinsic and extrinsic heterogeneity that exist within a population of cells and viruses. Traditional methods in virology often lack the ability to demonstrate cell-to-cell variability in response to the invasion of viruses, and to decipher the sources of heterogeneities that are reflected in the variable infection dynamics. To overcome this challenge, the field of single-cell virology emerged less than a decade ago, enabling researchers to reveal the behavior of single, isolated, infected cells that has been masked in population-based assays. The use of microfluidics in single-cell virology, in particular, has resulted in the development of high-throughput devices that are capable of capturing, isolating, and monitoring single infected cells over the duration of an infection. Results from the studies of viral infection dynamics presented in this chapter indicate how single-cell data provide a more accurate prediction of the start time, replication rate, duration, and yield of infection when compared to population-based data. Additionally, single-cell analysis reveals striking differences between genetically distinct viruses that are almost indistinguishable in population methods. Importantly, both the efficacy and distinct mechanisms of action of antiviral compounds can be elucidated by using single-cell analysis.
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Affiliation(s)
- Mohamad S Sotoudegan
- Department of Microbiology and Immunology, The University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC, United States
| | - Jamie J Arnold
- Department of Microbiology and Immunology, The University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC, United States
| | - Craig E Cameron
- Department of Microbiology and Immunology, The University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC, United States.
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41
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Zhang MM, Yang KL, Cui YC, Zhou YS, Zhang HR, Wang Q, Ye YJ, Wang S, Jiang KW. Current Trends and Research Topics Regarding Intestinal Organoids: An Overview Based on Bibliometrics. Front Cell Dev Biol 2021; 9:609452. [PMID: 34414174 PMCID: PMC8369504 DOI: 10.3389/fcell.2021.609452] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 06/08/2021] [Indexed: 01/10/2023] Open
Abstract
Currently, research on intestinal diseases is mainly based on animal models and cell lines in monolayers. However, these models have drawbacks that limit scientific advances in this field. Three-dimensional (3D) culture systems named organoids are emerging as a reliable research tool for recapitulating the human intestinal epithelium and represent a unique platform for patient-specific drug testing. Intestinal organoids (IOs) are crypt–villus structures that can be derived from adult intestinal stem cells (ISCs), embryonic stem cells (ESCs), or induced pluripotent stem cells (iPSCs) and have the potential to serve as a platform for individualized medicine and research. However, this emerging field has not been bibliometric summarized to date. Here, we performed a bibliometric analysis of the Web of Science Core Collection (WoSCC) database to evaluate 5,379 publications concerning the use of organoids; the studies were divided into four clusters associated with the current situation and future directions for the application of IOs. Based on the results of our bibliometric analysis of IO applications, we systematically summarized the latest advances and analyzed the limitations and prospects.
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Affiliation(s)
- Meng-Meng Zhang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Ke-Lu Yang
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Yan-Cheng Cui
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China
| | - Yu-Shi Zhou
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China
| | - Hao-Ran Zhang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China
| | - Quan Wang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Ying-Jiang Ye
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Shan Wang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Ke-Wei Jiang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
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42
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Mo N, Zhang X, Shi W, Yu G, Chen X, Yang JR. Bidirectional Genetic Control of Phenotypic Heterogeneity and Its Implication for Cancer Drug Resistance. Mol Biol Evol 2021; 38:1874-1887. [PMID: 33355660 PMCID: PMC8097262 DOI: 10.1093/molbev/msaa332] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Negative genetic regulators of phenotypic heterogeneity, or phenotypic capacitors/stabilizers, elevate population average fitness by limiting deviation from the optimal phenotype and increase the efficacy of natural selection by enhancing the phenotypic differences among genotypes. Stabilizers can presumably be switched off to release phenotypic heterogeneity in the face of extreme or fluctuating environments to ensure population survival. This task could, however, also be achieved by positive genetic regulators of phenotypic heterogeneity, or "phenotypic diversifiers," as shown by recently reported evidence that a bacterial divisome factor enhances antibiotic resistance. We hypothesized that such active creation of phenotypic heterogeneity by diversifiers, which is functionally independent of stabilizers, is more common than previously recognized. Using morphological phenotypic data from 4,718 single-gene knockout strains of Saccharomyces cerevisiae, we systematically identified 324 stabilizers and 160 diversifiers and constructed a bipartite network between these genes and the morphological traits they control. Further analyses showed that, compared with stabilizers, diversifiers tended to be weaker and more promiscuous (regulating more traits) regulators targeting traits unrelated to fitness. Moreover, there is a general division of labor between stabilizers and diversifiers. Finally, by incorporating NCI-60 human cancer cell line anticancer drug screening data, we found that human one-to-one orthologs of yeast diversifiers/stabilizers likely regulate the anticancer drug resistance of human cancer cell lines, suggesting that these orthologs are potential targets for auxiliary treatments. Our study therefore highlights stabilizers and diversifiers as the genetic regulators for the bidirectional control of phenotypic heterogeneity as well as their distinct evolutionary roles and functional independence.
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Affiliation(s)
- Ning Mo
- Department of Medical Genetics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyu Zhang
- Department of Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Wenjun Shi
- Department of Medical Genetics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Gongwang Yu
- Department of Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xiaoshu Chen
- Department of Medical Genetics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Corresponding authors: E-mails: ;
| | - Jian-Rong Yang
- Department of Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, China
- RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Corresponding authors: E-mails: ;
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43
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Hagemann C, Tyzack GE, Taha DM, Devine H, Greensmith L, Newcombe J, Patani R, Serio A, Luisier R. Automated and unbiased discrimination of ALS from control tissue at single cell resolution. Brain Pathol 2021; 31:e12937. [PMID: 33576079 PMCID: PMC8412073 DOI: 10.1111/bpa.12937] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/21/2020] [Accepted: 01/07/2021] [Indexed: 12/27/2022] Open
Abstract
Histopathological analysis of tissue sections is invaluable in neurodegeneration research. However, cell-to-cell variation in both the presence and severity of a given phenotype is a key limitation of this approach, reducing the signal to noise ratio and leaving unresolved the potential of single-cell scoring for a given disease attribute. Here, we tested different machine learning methods to analyse high-content microscopy measurements of hundreds of motor neurons (MNs) from amyotrophic lateral sclerosis (ALS) post-mortem tissue sections. Furthermore, we automated the identification of phenotypically distinct MN subpopulations in VCP- and SOD1-mutant transgenic mice, revealing common morphological cellular phenotypes. Additionally we established scoring metrics to rank cells and tissue samples for both disease probability and severity. By adapting this paradigm to human post-mortem tissue, we validated our core finding that morphological descriptors robustly discriminate ALS from control healthy tissue at single cell resolution. Determining disease presence, severity and unbiased phenotypes at single cell resolution might prove transformational in our understanding of ALS and neurodegeneration more broadly.
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Affiliation(s)
- Cathleen Hagemann
- The Francis Crick InstituteLondonUK
- Centre for Craniofacial & Regenerative BiologyKing's College LondonLondonUK
| | - Giulia E. Tyzack
- The Francis Crick InstituteLondonUK
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Doaa M. Taha
- The Francis Crick InstituteLondonUK
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Helen Devine
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Linda Greensmith
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Jia Newcombe
- NeuroResourceDepartment of NeuroinflammationUCL Queen Square Institute of NeurologyLondonUK
| | - Rickie Patani
- The Francis Crick InstituteLondonUK
- Department of Neuromuscular DiseasesUCL Queen Square Institute of NeurologyLondonUK
| | - Andrea Serio
- The Francis Crick InstituteLondonUK
- Centre for Craniofacial & Regenerative BiologyKing's College LondonLondonUK
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Christopher JA, Stadler C, Martin CE, Morgenstern M, Pan Y, Betsinger CN, Rattray DG, Mahdessian D, Gingras AC, Warscheid B, Lehtiö J, Cristea IM, Foster LJ, Emili A, Lilley KS. Subcellular proteomics. NATURE REVIEWS. METHODS PRIMERS 2021; 1:32. [PMID: 34549195 PMCID: PMC8451152 DOI: 10.1038/s43586-021-00029-y] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/15/2021] [Indexed: 12/11/2022]
Abstract
The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.
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Affiliation(s)
- Josie A. Christopher
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
| | - Charlotte Stadler
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Claire E. Martin
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Marcel Morgenstern
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Yanbo Pan
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Cora N. Betsinger
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - David G. Rattray
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Diana Mahdessian
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Bettina Warscheid
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
- BIOSS and CIBSS Signaling Research Centers, University of Freiburg, Freiburg, Germany
| | - Janne Lehtiö
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Ileana M. Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Leonard J. Foster
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrew Emili
- Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Kathryn S. Lilley
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
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45
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Benary M, Bohn S, Lüthen M, Nolis IK, Blüthgen N, Loewer A. Disentangling Pro-mitotic Signaling during Cell Cycle Progression using Time-Resolved Single-Cell Imaging. Cell Rep 2021; 31:107514. [PMID: 32294432 DOI: 10.1016/j.celrep.2020.03.078] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 02/19/2020] [Accepted: 03/23/2020] [Indexed: 11/26/2022] Open
Abstract
Cells rely on input from extracellular growth factors to control their proliferation during development and adult homeostasis. Such mitogenic inputs are transmitted through multiple signaling pathways that synergize to precisely regulate cell cycle entry and progression. Although the architecture of these signaling networks has been characterized in molecular detail, their relative contribution, especially at later cell cycle stages, remains largely unexplored. By combining quantitative time-resolved measurements of fluorescent reporters in untransformed human cells with targeted pharmacological inhibitors and statistical analysis, we quantify epidermal growth factor (EGF)-induced signal processing in individual cells over time and dissect the dynamic contribution of downstream pathways. We define signaling features that encode information about extracellular ligand concentrations and critical time windows for inducing cell cycle transitions. We show that both extracellular signal-regulated kinase (ERK) and phosphatidylinositol 3-kinase (PI3K) activity are necessary for initial cell cycle entry, whereas only PI3K affects the duration of S phase at later stages of mitogenic signaling.
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Affiliation(s)
- Manuela Benary
- Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; Institute for Theoretical Biology, Charité-Universitätsmedizin Berlin, 10115 Berlin, Germany; Integrative Research Institute Life Sciences, Humboldt University Berlin, 10115 Berlin, Germany
| | - Stefan Bohn
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany
| | - Mareen Lüthen
- Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Ilias K Nolis
- Berlin Institute for Medical Systems Biology, Max Delbrueck Center in the Helmholtz Association, 13125 Berlin, Germany
| | - Nils Blüthgen
- Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany; Institute for Theoretical Biology, Charité-Universitätsmedizin Berlin, 10115 Berlin, Germany; Integrative Research Institute Life Sciences, Humboldt University Berlin, 10115 Berlin, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
| | - Alexander Loewer
- Department of Biology, Technische Universität Darmstadt, 64287 Darmstadt, Germany; Berlin Institute for Medical Systems Biology, Max Delbrueck Center in the Helmholtz Association, 13125 Berlin, Germany.
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46
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Jacques M, Dobrzyński M, Gagliardi PA, Sznitman R, Pertz O. CODEX, a neural network approach to explore signaling dynamics landscapes. Mol Syst Biol 2021; 17:e10026. [PMID: 33835701 PMCID: PMC8034356 DOI: 10.15252/msb.202010026] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 03/01/2021] [Accepted: 03/03/2021] [Indexed: 12/19/2022] Open
Abstract
Current studies of cell signaling dynamics that use live cell fluorescent biosensors routinely yield thousands of single-cell, heterogeneous, multi-dimensional trajectories. Typically, the extraction of relevant information from time series data relies on predefined, human-interpretable features. Without a priori knowledge of the system, the predefined features may fail to cover the entire spectrum of dynamics. Here we present CODEX, a data-driven approach based on convolutional neural networks (CNNs) that identifies patterns in time series. It does not require a priori information about the biological system and the insights into the data are built through explanations of the CNNs' predictions. CODEX provides several views of the data: visualization of all the single-cell trajectories in a low-dimensional space, identification of prototypic trajectories, and extraction of distinctive motifs. We demonstrate how CODEX can provide new insights into ERK and Akt signaling in response to various growth factors, and we recapitulate findings in p53 and TGFβ-SMAD2 signaling.
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Affiliation(s)
| | | | | | - Raphael Sznitman
- ARTORG Center for Biomedical Engineering ResearchUniversity of BernBernSwitzerland
| | - Olivier Pertz
- Institute of Cell BiologyUniversity of BernBernSwitzerland
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47
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Messelink JJB, van Teeseling MCF, Janssen J, Thanbichler M, Broedersz CP. Learning the distribution of single-cell chromosome conformations in bacteria reveals emergent order across genomic scales. Nat Commun 2021; 12:1963. [PMID: 33785756 PMCID: PMC8010069 DOI: 10.1038/s41467-021-22189-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 02/15/2021] [Indexed: 02/01/2023] Open
Abstract
The order and variability of bacterial chromosome organization, contained within the distribution of chromosome conformations, are unclear. Here, we develop a fully data-driven maximum entropy approach to extract single-cell 3D chromosome conformations from Hi-C experiments on the model organism Caulobacter crescentus. The predictive power of our model is validated by independent experiments. We find that on large genomic scales, organizational features are predominantly present along the long cell axis: chromosomal loci exhibit striking long-ranged two-point axial correlations, indicating emergent order. This organization is associated with large genomic clusters we term Super Domains (SuDs), whose existence we support with super-resolution microscopy. On smaller genomic scales, our model reveals chromosome extensions that correlate with transcriptional and loop extrusion activity. Finally, we quantify the information contained in chromosome organization that may guide cellular processes. Our approach can be extended to other species, providing a general strategy to resolve variability in single-cell chromosomal organization.
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Affiliation(s)
- Joris J B Messelink
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig Maximilian University Munich, Munich, Germany
| | - Muriel C F van Teeseling
- Department of Biology, University of Marburg, Marburg, Germany
- Prokaryotic Cell Biology Group, Department of Microbial Interactions, Institute for Microbiology, Friedrich Schiller University Jena, Jena, Germany
| | - Jacqueline Janssen
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig Maximilian University Munich, Munich, Germany
| | - Martin Thanbichler
- Department of Biology, University of Marburg, Marburg, Germany
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
| | - Chase P Broedersz
- Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Department of Physics, Ludwig Maximilian University Munich, Munich, Germany.
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
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48
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Spinosa PC, Kinnunen PC, Humphries BA, Luker GD, Luker KE, Linderman JJ. Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling. Cell Mol Bioeng 2021; 14:49-64. [PMID: 33643466 PMCID: PMC7878609 DOI: 10.1007/s12195-020-00640-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 07/22/2020] [Indexed: 10/23/2022] Open
Abstract
INTRODUCTION CXCR4 and epidermal growth factor receptor (EGFR) represent two major families of receptors, G-protein coupled receptors and receptor tyrosine kinases, with central functions in cancer. While utilizing different upstream signaling molecules, both CXCR4 and EGFR activate kinases ERK and Akt, although single-cell activation of these kinases is markedly heterogeneous. One hypothesis regarding the origin of signaling heterogeneity proposes that intercellular variations arise from differences in pre-existing intracellular states set by extrinsic noise. While pre-existing cell states vary among cells, each pre-existing state defines deterministic signaling outputs to downstream effectors. Understanding causes of signaling heterogeneity will inform treatment of cancers with drugs targeting drivers of oncogenic signaling. METHODS We built a single-cell computational model to predict Akt and ERK responses to CXCR4- and EGFR-mediated stimulation. We investigated signaling heterogeneity through these receptors and tested model predictions using quantitative, live-cell time-lapse imaging. RESULTS We show that the pre-existing cell state predicts single-cell signaling through both CXCR4 and EGFR. Computational modeling reveals that the same set of pre-existing cell states explains signaling heterogeneity through both EGFR and CXCR4 at multiple doses of ligands and in two different breast cancer cell lines. The model also predicts how phosphatidylinositol-3-kinase (PI3K) targeted therapies potentiate ERK signaling in certain breast cancer cells and that low level, combined inhibition of MEK and PI3K ablates potentiated ERK signaling. CONCLUSIONS Our data demonstrate that a conserved motif exists for EGFR and CXCR4 signaling and suggest potential clinical utility of the computational model to optimize therapy.
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Affiliation(s)
- Phillip C. Spinosa
- Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109-2800 USA
| | - Patrick C. Kinnunen
- Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109-2800 USA
| | - Brock A. Humphries
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Gary D. Luker
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI USA 48109
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI USA 48109
| | - Kathryn E. Luker
- Department of Radiology Center for Molecular Imaging, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Radiology, Center for Molecular Imaging, University of Michigan, 109 Zina Pitcher Place, A526 BSRB, Ann Arbor, MI 48109-2200 USA
| | - Jennifer J. Linderman
- Department of Chemical Engineering, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109-2800 USA
- Department of Biomedical Engineering, University of Michigan Medical School, Ann Arbor, MI USA 48109
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49
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Muñoz-Rojas AR, Kelsey I, Pappalardo JL, Chen M, Miller-Jensen K. Co-stimulation with opposing macrophage polarization cues leads to orthogonal secretion programs in individual cells. Nat Commun 2021; 12:301. [PMID: 33436596 PMCID: PMC7804107 DOI: 10.1038/s41467-020-20540-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 12/07/2020] [Indexed: 12/13/2022] Open
Abstract
Macrophages are innate immune cells that contribute to fighting infections, tissue repair, and maintaining tissue homeostasis. To enable such functional diversity, macrophages resolve potentially conflicting cues in the microenvironment via mechanisms that are unclear. Here, we use single-cell RNA sequencing to explore how individual macrophages respond when co-stimulated with inflammatory stimuli LPS and IFN-γ and the resolving cytokine IL-4. These co-stimulated macrophages display a distinct global transcriptional program. However, variable negative cross-regulation between some LPS + IFN-γ-specific and IL-4-specific genes results in cell-to-cell heterogeneity in transcription. Interestingly, negative cross-regulation leads to mutually exclusive expression of the T-cell-polarizing cytokine genes Il6 and Il12b versus the IL-4-associated factors Arg1 and Chil3 in single co-stimulated macrophages, and single-cell secretion measurements show that these specialized functions are maintained for at least 48 h. This study suggests that increasing functional diversity in the population is one strategy macrophages use to respond to conflicting environmental cues.
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Affiliation(s)
- Andrés R Muñoz-Rojas
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Department of Immunology, Harvard Medical School, Boston, MA, USA
| | - Ilana Kelsey
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Jenna L Pappalardo
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Meibin Chen
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Kathryn Miller-Jensen
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA.
- Systems Biology Institute, Yale University, New Haven, CT, USA.
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50
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Gnann C, Cesnik AJ, Lundberg E. Illuminating Non-genetic Cellular Heterogeneity with Imaging-Based Spatial Proteomics. Trends Cancer 2021; 7:278-282. [PMID: 33436349 DOI: 10.1016/j.trecan.2020.12.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 10/22/2022]
Abstract
Cellular heterogeneity is an important biological phenomenon observed across space and time in human tissues. Imaging-based spatial proteomic technologies can provide fruitful new readouts of phenotypic states for individual cells at subcellular resolution, which may help unravel the roles of non-genetic cellular heterogeneity in tumorigenesis and drug resistance.
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Affiliation(s)
- Christian Gnann
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 17121, Sweden
| | - Anthony J Cesnik
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 17121, Sweden
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 17121, Sweden.
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