• DNA
• fluorescence
• molecular detection
• optofluidic laser
• silica microsphere

• Silicon Dioxide/chemistry
• Microspheres
• DNA/chemistry
• DNA/analysis
• Lasers
• Biosensing Techniques/methods
• Biosensing Techniques/instrumentation
• Fluorescent Dyes/chemistry

• 2E33221 Korea Institute of Science and Technology

• 5-Fluorouracil
• AOM
• Biomarkers
• Colorectal Cancer
• DSS
• Diagnosis
• Exercise
• Palliative care
• Sparassis latifolia

• Microarrays
• Precision medicine
• RNA sequencing
• Recurrent hepatocellular carcinoma
• Single-cell RNA sequencing
• Tumor heterogeneity
• Tumor microenvironment

• Humans
• Carcinoma, Hepatocellular/pathology
• Liver Neoplasms/pathology
• Precision Medicine
• Gene Expression Profiling
• Transcriptome
• Tumor Microenvironment

• diuron
• metabolism fate
• phytohormones
• rice
• transcriptome

• Diuron/metabolism
• Oryza/genetics
• Oryza/metabolism
• Herbicides/pharmacology
• Herbicides/metabolism
• Plant Growth Regulators/pharmacology
• Plant Growth Regulators/metabolism
• Oxidative Stress
• Crops, Agricultural/genetics
• Crops, Agricultural/metabolism
• Gene Expression Regulation, Plant

• DEGs
• DESeq2
• Differentially expressed genes
• EBSeq
• EdgeR
• NOISeq
• RNA-Seq data
• Shiny web application
• Web-based application

A monolayer Caco-2 cell model was established to explore the effects of sea bass sausage digestive juice containing phosphate on calcium ion transport. Differential proteins of Caco-2 cells treated with fish sausage juice were detected and analyzed by gene ontology (GO) functional annotation and kyoto encyclopedia of genes and genomes (KEGG) pathway analyses. Results revealed that after treatment with 0.23 mg/mL digestive juice of perch sausage in vitro, Caco-2 cell viability was the highest at 72 h (99.84%). Additionally, 0.23 mg/mL digestive juice of perch sausage in vitro significantly increased calcium ion transport. The transfer volume was 1.396 μg/well. Fish sausages containing phosphate significantly affected the protein expression levels of Caco-2 cells. Two hundred one differential proteins were detected, including 114 up-regulated and 87 down-regulated proteins. The main differential proteins included P02795, Q9P0W0, Q96PU5, Q9GZT9 and Q5EBL8. The adjustment ratios of the fish sausage group were 0.7485, 1.373, 1.2535, 0.6775, and 0.809, respectively. The pathway analysis showed that phosphate affected calcium ion absorption and transport through the P02795 enrichment pathway. The fish sausage group showed that the immune-related functions of cells were affected. This study expounds the effects of water-retaining agents on the nutritional quality of aquatic products and provides theoretical support for the research and application of surimi products.

Purpose: Septic cardiomyopathy (SCM) is an important world public health problem with high morbidity and mortality. It is necessary to identify SCM biomarkers at the genetic level to identify new therapeutic targets and strategies.

Method: DEGs in SCM were identified by comprehensive bioinformatics analysis of microarray datasets (GSE53007 and GSE79962) downloaded from the GEO database. Subsequently, bioinformatics analysis was used to conduct an in-depth exploration of DEGs, including GO and KEGG pathway enrichment analysis, PPI network construction, and key gene identification. The top ten Hub genes were identified, and then the SCM model was constructed by treating HL-1 cells and AC16 cells with LPS, and these top ten Hub genes were examined using qPCR.

Result: STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP were significantly elevated in the established SCM cells model.

Conclusion: After bioinformatics analysis and experimental verification, it was demonstrated that STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP might play important roles in SCM.

• Human hepatocytes
• Lipids
• PFAS
• Perfluorinated compounds

• Alkanesulfonic Acids/toxicity
• Fluorocarbons/toxicity
• Hepatocytes
• Humans
• Lipid Metabolism
• Lipogenesis
• Transcriptome

• P20 GM103430 NIGMS NIH HHS
• P30 ES005022 NIEHS NIH HHS
• R35 ES031709 NIEHS NIH HHS
• R15 ES023148 NIEHS NIH HHS
• R01 ES031080 NIEHS NIH HHS
• P42 ES027706 NIEHS NIH HHS

Ablative fractional laser treatment is considered the gold standard for skin rejuvenation. In order to understand how fractional laser works to rejuvenate skin, we performed microarray profiling on skin biopsies to identify temporal and dose-response changes in gene expression following fractional laser treatment. The backs of 14 women were treated with ablative fractional laser (Fraxel®) and 4 mm punch biopsies were collected from an untreated site and at the treated sites 1, 3, 7, 14, 21 and 28 days after the single treatment. In addition, in order to understand the effect that multiple fractional laser treatments have on skin rejuvenation, several sites were treated sequentially with either 1, 2, 3, or 4 treatments (with 28 days between treatments) followed by the collection of 4 mm punch biopsies. RNA was extracted from the biopsies, analyzed using Affymetrix U219 chips and gene expression was compared between untreated and treated sites. We observed dramatic changes in gene expression as early as 1 day after fractional laser treatment with changes remaining elevated even after 1 month. Analysis of individual genes demonstrated significant and time related changes in inflammatory, epidermal, and dermal genes, with dermal genes linked to extracellular matrix formation changing at later time points following fractional laser treatment. When comparing the age-related changes in skin gene expression to those induced by fractional laser, it was observed that fractional laser treatment reverses many of the changes in the aging gene expression. Finally, multiple fractional laser treatments, which cover different regions of a treatment area, resulted in a sustained or increased dermal remodeling response, with many genes either differentially regulated or continuously upregulated, supporting previous observations that maximal skin rejuvenation requires multiple fractional laser treatments. In conclusion, fractional laser treatment of human skin activates a number of biological processes involved in wound healing and tissue regeneration.

• Circulating Tumor DNA
• DNA Methylation
• Genomics
• High-Throughput Nucleotide Sequencing/standards
• Humans
• Neoplasms/genetics
• Precision Medicine/standards
• Quality Control
• Sequence Analysis, RNA
• Single-Cell Analysis

• R01 MH117406 NIMH NIH HHS
• R01 AI151059 NIAID NIH HHS
• P01 CA214274 NCI NIH HHS

• Drug Delivery Systems/methods
• Humans
• Nanostructures/chemistry
• Nanotechnology/methods
• Animals

• MC_PC_16048 Medical Research Council
• NC/R001006/1 National Centre for the Replacement, Refinement and Reduction of Animals in Research

• DNA analysis
• Gene expression studies
• Immunodetection
• Post-translational protein modifications
• Proteomic

To explore synergistic effects related to skin regeneration, peptides with distinct biological mechanisms of action were evaluated in combination with different skin cell lines in the presence or absence of niacinamide (Nam). Furthermore, the synergistic responses of peptide combinations on global gene expression were compared with the changes that occur with fractional laser resurfacing treatment, a gold standard approach for skin rejuvenation, to further define optimal peptide combinations.

Microarray profiling was used to characterize the biological responses of peptide combinations (+/− Nam) relative to the individual components in epidermal keratinocyte and dermal fibroblast cell lines. Cellular functional assays were utilized to confirm the synergistic effects of peptide combinations. Bioinformatics approaches were used to link the synergistic effects of peptide combinations on gene expression to the transcriptomics of the skin rejuvenation response from fractional laser treatment.

Microarray analysis of skin cells treated with peptide combinations revealed synergistic changes in gene expression compared with individual peptide controls. Bioinformatic analysis of synergy genes in keratinocytes revealed the activation of NRF2‐mediated oxidative stress responses by a combination of Ac‐PPYL, Pal‐KTTKS and Nam. Additional analysis revealed direct downstream transcriptional targets of NRF2/ARE exhibiting synergistic regulation by this combination of materials, which was corroborated by a cellular reporter assay. NRF2‐mediated oxidative stress response pathways were also found to be activated in the transcriptomics of the early skin rejuvenation response to fractional laser treatment, suggesting the importance of this biology in the early stages of tissue repair. Additionally, the second combination of peptides (pal‐KT and Ac‐PPYL) was found to synergistically restore cellular ATP levels that had been depleted due to the presence of ROS, indicating an additional mechanism, whereby peptide synergies may accelerate skin repair.

Through combinatorial synergy studies, we have identified additional in vitro skin repair mechanisms beyond the previously described functions of individual peptides and correlated these to the transcriptomics of the skin rejuvenation response of fractional laser treatment. These findings suggest that specific peptides can act together, via complementary and synergistic mechanisms, to holistically enhance the regenerative capacity of in vitro skin cells.

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.

• Animals
• Atlases as Topic
• Brain/anatomy & histology
• Brain/metabolism
• Datasets as Topic
• Gene Expression Profiling
• Mice
• Reproducibility of Results

• U19 MH114821 NIMH NIH HHS
• R01 MH113005 NIMH NIH HHS
• R01 NS073129 NINDS NIH HHS
• R01 DA036913 NIDA NIH HHS
• R01 LM012736 NLM NIH HHS
• U01 MH109113 NIMH NIH HHS
• RF1 MH114132 NIMH NIH HHS
• Edward and Martha Gerry Fellowship
• National Institutes of Health
• Swedish Research Council
• Brain Research Foundation
• Intelligence Advanced Research Projects Activity
• Paul G. Allen Family Foundation
• Chan Zuckerberg Initiative
• Robert Lourie

• cartilage
• channelome
• chondrocyte
• chondroprogenitor
• mesenchymal stem cell
• osteoarthritis
• transcriptomics

• molecular biology
• protein folding
• chaperones
• diseases
• huntington's disease

• Age Factors
• Animals
• Brain/drug effects
• Brain/physiopathology
• Disease Models, Animal
• Female
• Gene Expression Profiling/methods
• Heat Shock Transcription Factors/genetics
• Heat Shock Transcription Factors/metabolism
• Heat-Shock Response/drug effects
• Heat-Shock Response/genetics
• Heat-Shock Response/physiology
• Huntingtin Protein/genetics
• Huntington Disease/genetics
• Huntington Disease/physiopathology
• Male
• Mice, Inbred C57BL
• Mice, Inbred CBA
• Mice, Knockout
• Pyridones/pharmacology
• Pyrimidines/pharmacology
• Reproducibility of Results
• Mice

• G0801314 Medical Research Council
• CHDI Foundation
• UK Dementia Research Institute

Retinal bipolar cells survive even in the later stages of inherited retinal degenerations (IRDs) and so are attractive targets for optogenetic approaches to vision restoration. However, it is not known to what extent the remodelling that these cells undergo during degeneration affects their function. Specifically, it is unclear if they are free from metabolic stress, receptive to adeno-associated viral vectors, suitable for opsin-based optogenetic tools and able to propagate signals by releasing neurotransmitter.

Fluorescence activated cell sorting (FACS) was performed to isolate labelled bipolar cells from dissociated retinae of litter-mates with or without the IRD mutation Pde6b^rd1/rd1 selectively expressing an enhanced yellow fluorescent protein (EYFP) as a marker in ON-bipolar cells. Subsequent mRNA extraction allowed Illumina® microarray comparison of gene expression in bipolar cells from degenerate to those of wild type retinae. Changes in four candidate genes were further investigated at the protein level using retinal immunohistochemistry over the course of degeneration.

A total of sixty differentially expressed transcripts reached statistical significance: these did not include any genes directly associated with native primary bipolar cell signalling, nor changes consistent with metabolic stress. Four significantly altered genes (Srm2, Slf2, Anxa7 & Cntn1), implicated in synaptic remodelling, neurotransmitter release and viral vector entry had immunohistochemical staining colocalising with ON-bipolar cell markers and varying over the course of degeneration.

Our findings suggest relatively few gene expression changes in the context of degeneration: that despite remodelling, bipolar cells are likely to remain viable targets for optogenetic vision restoration. In addition, several genes where changes were seen could provide a basis for investigations to enhance the efficacy of optogenetic therapies.

•

Bipolar cells are attractive targets for therapeutic optogenetics in IRDs.

• Bipolar cell
• Gene expression
• Inherited retinal degeneration
• Optogenetics

• Acidithiobacillus caldus
• RT-qPCR normalization
• reference gene
• sigma factor
• stress response

• Glancing angle deposition
• Metal-enhanced fluorescence
• Microarray
• Nanorods
• Signal-to-background noise ratio

• Nextflow pipeline
• RNA-Seq
• differential gene expression analysis
• workflow

• Animals
• Humans
• Mice
• RNA-Seq
• Reverse Transcriptase Polymerase Chain Reaction

• Apitoxin
• Bee venom
• Chemotherapy
• Cisplatin
• Head and neck squamous cell carcinoma

• Antineoplastic Agents/pharmacology
• Antineoplastic Agents/therapeutic use
• Cell Line, Tumor
• Humans
• Prognosis
• Squamous Cell Carcinoma of Head and Neck/drug therapy

RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.

• A multigene expression assay
• Chemotherapy
• FOLFOX regimen
• Gastric cancer
• Gene prognostic signature
• Overall survival

• Antineoplastic Combined Chemotherapy Protocols/adverse effects
• Antineoplastic Combined Chemotherapy Protocols/therapeutic use
• Biomarkers, Tumor/genetics
• Databases, Genetic
• Female
• Gene Expression Profiling/methods
• Gene Expression Regulation, Neoplastic
• Genetic Testing/methods
• Genetic Testing/standards
• Humans
• Kaplan-Meier Estimate
• Male
• Neoplasm Grading
• Neoplasm Metastasis
• Neoplasm Staging
• Prognosis
• Proportional Hazards Models
• Stomach Neoplasms/diagnosis
• Stomach Neoplasms/drug therapy
• Stomach Neoplasms/genetics
• Transcriptome
• Treatment Outcome

Precision medicine for breast cancer relies on biomarkers to select therapies. However, the reliability of biomarkers drawn from gene expression arrays has been questioned and calls for reassessment, in particular for large datasets. We revisit widely used data-normalization procedures and evaluate differences in outcome in order to pinpoint the most reliable reprocessing methods biomarkers can be based upon. We generated a database of 3753 breast cancer patients out of 38 studies by downloading and curating patient samples from NCBI-GEO. As gene-expression biomarkers, we select the assessment of receptor status and breast cancer subtype classification. Each normalization procedure is applied separately, and biomarkers are then evaluated for each patient. Differences between normalization pipelines are quantified as percentages of patients having outcomes different for each pipeline. Some normalization procedures lead to quite consistent biomarkers, differing only in 1-2% of patients. Other normalization procedures—some of them have been used in many clinical studies—end up with distrusting discrepancies (10% and more). A good deal of doubt regarding the reliability of microarrays may root in the haphazard application of inadequate preprocessing pipelines. Several modes of batch corrections are evaluated regarding a possible improvement of receptor prediction from gene expression versus the golden standard of immunohistochemistry. Finally, we nominate those normalization methods yielding consistent and trustable results. Adequate bioinformatics data preprocessing is key and crucial for any subsequent statistics to arrive at trustable results. We conclude with a suggestion for future bioinformatics development to further increase the reliability of cancer biomarkers.

Glycans are primarily generated by “glycogenes,” which consist of more than 200 genes for glycosynthesis, including sugar-nucleotide synthases, sugar-nucleotide transporters, and glycosyltransferases. Measuring the expression level of glycogenes is one of the approaches to analyze the glycomes of particular biological and clinical samples. To develop an effective strategy for identifying the glycosylated biomarkers, we performed transcriptome analyses using quantitative real-time polymerase chain reaction (qRT-PCR) arrays and RNA sequencing (RNA-Seq). First, we measured and analyzed the transcriptome from the primary culture of human liver cells and hepatocarcinoma cells using RNA-Seq. This analysis revealed similar but distinctive expression profiles of glycogenes among hepatic cells as indicated by the qRT-PCR arrays, which determined a copy number of 186 glycogenes. Both data sets indicated that altered expression of glycosyltransferases affect the glycosylation of particular glycoproteins, which is consistent with the mass analysis data. Moreover, RNA-Seq analysis can uncover mutations in glycogenes and search differently expressed genes out of more than 50,000 distinct human gene transcripts including candidate biomarkers that were previously reported for hepatocarcinoma cells. Identification of candidate glyco-biomarkers from the expression profile of the glycogenes and proteins from liver cancer tissues available from public database emphasized the possibility that even though the expression level of biomarkers might not be altered, the expression of the glycogenes modifying biomarkers, generating glyco-biomarkers, might be different. Pathway analysis revealed that ~20% of the glycogenes exhibited different expression levels in normal and cancer cells. Thus, transcriptome analyses using both qRT-PCR array and RNA-Seq in combination with glycome and glycoproteome analyses can be advantageous to identify “glyco-biomarkers” by reinforcing information at the expression levels of both glycogenes and proteins.

• biomarker
• glycosyltransferase
• next-generation sequencing
• quantitative real-time-PCR
• transcriptome

Osteoporosis is the most common bone metabolic disease, characterized by bone mass loss and bone microstructure changes due to unbalanced bone conversion, which increases bone fragility and fracture risk. Glucocorticoids are clinically used to treat a variety of diseases, including inflammation, cancer and autoimmune diseases. However, excess glucocorticoids can cause osteoporosis. Herein we performed an integrated analysis of two glucocorticoid-related microarray datasets. The WGCNA analysis identified 3 and 4 glucocorticoid-related gene modules, respectively. Differential expression analysis revealed 1047 and 844 differentially expressed genes in the two datasets. After integrating differentially expressed glucocorticoid-related genes, we found that most of the robust differentially expressed genes were up-regulated. Through protein-protein interaction analysis, we obtained 158 glucocorticoid-related candidate genes. Enrichment analysis showed that these genes are significantly enriched in the osteoporosis related pathways. Our results provided new insights into glucocorticoid-induced osteoporosis and potential candidate markers of osteoporosis.

• enrichment analysis
• glucocorticoid
• microarray
• osteoporosis
• protein-protein interaction

• neuroscience
• diseases of the nervous system
• huntington's disease

• 3' Untranslated Regions/genetics
• Animals
• Brain/metabolism
• Branched DNA Signal Amplification Assay/methods
• Disease Models, Animal
• Exons/genetics
• High-Throughput Screening Assays/methods
• Huntingtin Protein/biosynthesis
• Huntingtin Protein/genetics
• Introns/genetics
• Mice
• Mice, Inbred C57BL
• Mice, Neurologic Mutants
• Nerve Tissue Proteins/biosynthesis
• Nerve Tissue Proteins/genetics
• Organ Specificity
• Protein Isoforms/biosynthesis
• Protein Isoforms/genetics
• RNA Splicing
• RNA, Messenger/biosynthesis
• RNA, Messenger/genetics
• Real-Time Polymerase Chain Reaction

• MR/L003627/1 Medical Research Council
• MR/L003627/2 Medical Research Council
• CHDI Foundation (CHDI Foundation, Inc.)

• Limit of detection
• Microarray
• Performance evaluation
• Qualitative analysis
• Reference material
• Reliability

• RNA sequencing
• diabetic nephropathy
• long noncoding RNA
• network biology

• biomarkers

• Algorithms
• Biomarkers/analysis
• Datasets as Topic
• Gene Expression Profiling/methods
• High-Throughput Nucleotide Sequencing/methods
• High-Throughput Screening Assays/methods
• Humans
• Microarray Analysis/methods
• Real-Time Polymerase Chain Reaction/methods
• Sepsis/diagnosis
• Sepsis/genetics
• Signal Transduction/genetics

• R01 DA040395 NIDA NIH HHS
• U01 CA213330 NCI NIH HHS
• U01 HL126496 NHLBI NIH HHS
• R56 HL133887 NHLBI NIH HHS
• United States Department of Defense | Defense Threat Reduction Agency (DTRA)
• U.S. Department of Defense (United States Department of Defense)
• U.S. Department of Health & Human Services | National Institutes of Health (NIH)

Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices.

We here introduce a new tool for estimating differential expression in noisy real-life data. It employs a novel normalization procedure (qtotal), which takes account of the overall distribution of read counts for data standardization enhancing reliable identification of differential gene expression, especially in case of asymmetrical distributions of up- and downregulated genes. The tool then introduces a polynomial algorithm (aFold) to model the uncertainty of read counts across treatments and genes. We extensively benchmark aFold on a variety of simulated and validated real-life data sets (e.g. ABRF, SEQC and MAQC-II) and show a higher ability to correctly identify differentially expressed genes under most tested conditions. aFold infers fold change values that are comparable across experiments, thereby facilitating data clustering, visualization, and other downstream applications.

We here present a new transcriptomics analysis tool that includes both a data normalization method and a differential expression analysis approach. The new tool is shown to enhance reliable identification of significant differential expression across distinct data distributions. It outcompetes alternative procedures in case of asymmetrical distributions of up- versus down-regulated genes and also the presence of outliers, all common to real data sets.

The online version of this article (10.1186/s12864-019-5686-1) contains supplementary material, which is available to authorized users.

• ABSSeq
• DESeq2
• Differential expression analysis
• Normalization
• RNASeq
• Transcriptomics
• Voom
• baySeq
• edgeR

• E prostanoid receptor agonist
• horse
• inflammation
• leucocyte
• pharmacokinetics
• tumour necrosis factor-α

• Abortifacient Agents, Nonsteroidal/administration & dosage
• Abortifacient Agents, Nonsteroidal/pharmacokinetics
• Administration, Oral
• Animals
• Area Under Curve
• Cells, Cultured
• Horse Diseases/drug therapy
• Horse Diseases/metabolism
• Horses/blood
• Horses/metabolism
• Inflammation/drug therapy
• Inflammation/metabolism
• Inflammation/veterinary
• Leukocytes/drug effects
• Leukocytes/metabolism
• Misoprostol/administration & dosage
• Misoprostol/pharmacokinetics

• D15EQ018 Morris Animal Foundation

• Alternative splicing
• Degradation normalization
• Non-negative matrix factorization
• Normalization
• RNA degradation
• RNA-seq

• Algorithms
• Cell Line
• Humans
• RNA Stability
• Sequence Analysis, RNA
• Software

• R01 GM107177 NIGMS NIH HHS
• R01GM107177 National Institute of Health

• IL-6
• correlation
• inflammatory factors
• p53
• ulcerative colitis

We propose eight data transformations (r, r2, rv, rv2, l, l2, lv, and lv2) for RNA-seq data analysis aiming to make the transformed sample mean to be representative of the distribution center since it is not always possible to transform count data to satisfy the normality assumption. Simulation studies showed that for data sets with small (e.g., nCases = nControls = 3) or large sample size (e.g., nCases = nControls = 100) limma based on data from the l, l2, and r2 transformations performed better than limma based on data from the voom transformation in term of accuracy, FDR, and FNR. For datasets with moderate sample size (e.g., nCases = nControls = 30 or 50), limma with the rv and rv2 transformations performed similarly to limma with the voom transformation. Real data analysis results are consistent with simulation analysis results: limma with the r, l, r2, and l2 transformation performed better than limma with the voom transformation when sample sizes are small or large; limma with the rv and rv2 transformations performed similarly to limma with the voom transformation when sample sizes are moderate. We also observed from our data analyses that for datasets with large sample size, the gene-selection via the Wilcoxon rank sum test (a non-parametric two sample test method) based on the raw data outperformed limma based on the transformed data.

• cancer
• degradation
• stability
• transcript

• Female
• Humans
• Cold Ischemia/methods
• Colonic Neoplasms/genetics
• Colonic Neoplasms/metabolism
• Colonic Neoplasms/pathology
• Fixatives/chemistry
• Formaldehyde/chemistry
• Kidney Neoplasms/genetics
• Kidney Neoplasms/metabolism
• Kidney Neoplasms/pathology
• Neoplasm Proteins/genetics
• Neoplasm Proteins/metabolism
• Ovarian Neoplasms/genetics
• Ovarian Neoplasms/metabolism
• Ovarian Neoplasms/pathology
• Paraffin Embedding/methods
• Reverse Transcriptase Polymerase Chain Reaction/methods
• RNA, Messenger/genetics
• RNA, Messenger/metabolism
• Time Factors
• Tissue Fixation/methods
• Transcriptome

• annatto delta-tocotrienol
• murine preimplantation embryos
• nicotine
• reproductive toxicant
• tocopherols
• vitamin E

• Animals
• Bixaceae/metabolism
• Cyclin D1/genetics
• Cyclin D1/metabolism
• Dietary Supplements
• Embryo, Mammalian/drug effects
• Embryo, Mammalian/metabolism
• Embryonic Development/drug effects
• Female
• Gene Expression Regulation, Developmental/drug effects
• Mice
• Mice, Inbred BALB C
• Nicotine/pharmacology
• Phosphatidylinositol 3-Kinases/genetics
• Phosphatidylinositol 3-Kinases/metabolism
• Proto-Oncogene Proteins c-akt/genetics
• Proto-Oncogene Proteins c-akt/metabolism
• Signal Transduction/drug effects
• Glycine max/metabolism
• Superovulation/drug effects
• Tocotrienols/pharmacology
• Vitamin E/analogs & derivatives
• Vitamin E/pharmacology

• Calcium
• Cartilage
• Chondrocytes
• Ion channels
• Pectus carinatum
• Pectus excavatum

A simple and cost-effective fabrication method for plasmonic nanolens arrays (PNA) with a narrow gap has been proposed for fabricating enhanced fluorescence substrates, in which the fluorophores interacting with the enhanced electromagnetic field generated by localized surface plasmons provide a higher fluorescence signal. The PNA was fabricated by the sequential depositions of the SiO₂ and Ag layers on a UV-nanoimprinted nanodot array with a pitch of 500 nm, a diameter of 250 nm, and a height of 100 nm. During the deposition processes, the shape of the nanodots changed to that of nanolenses, and the gap between the nanolenses was decreased via sidewall deposition. To examine the feasibility of the fabricated PNA for enhanced fluorescence application, a streptavidin-Cy5 (SA-Cy5) conjugate dissolved in a saline buffer solution was spotted on the PNA, and the fluorescence signals of the SA-Cy5 were measured and compared with those on a bare glass substrate. The enhancement factor was affected by the gap between the nanolenses, and the maximum enhancement factor of ~128 was obtained from the PNA with a SiO₂ layer thickness of 150 nm and an Ag layer thickness of 100 nm. Finally, an electromagnetic field analysis was used to examine the fluorescence signal enhancement, and was conducted using rigorous coupled wave analysis.

• fluorescence
• polymer nanoimprinting
• surface plasmon resonance

• Cell secretion
• Cytokines
• Inflammatory response
• Mouse models
• Secretome
• cytokines
• exercise
• skeletal muscle