Integration of pathomics and single-cell omics in pancreatic ductal adenocarcinoma: Applications and clinical translation prospects

Table 1 Types of quality defects and detection methods

Defect type	Causes	Impact on analysis	Detection and evaluation methods
Out-of-focus blur	Autofocus failure or uneven tissue section thickness	Loss of cell boundaries and nuclear details, distortion of texture features	Traditional methods: Feature-based. Quantify blurriness by calculating image edge sharpness (e.g., Sobel gradient magnitude, Laplacian variance). Frequency-domain analysis. Detect the attenuation of high-frequency components using Fourier transform or wavelet transform[28]; deep learning methods: Train a binary convolutional neural network (e.g., ResNet, Inception) to intelligently classify the sharpness of image patches[29]
Tissue folding/tissue tearing	Improper sectioning or mounting	Introduces artificial textures and edges, interfering with segmentation	Traditional methods: Edge and morphological analysis. Perform edge detection on low-resolution overview images to identify abnormal edges (e.g., excessively long, overly straight, or sharply curved). Morphological operations are then applied to locate elongated “ridges” (folds) or “gaps” (tears). Texture consistency analysis: Calculate texture features of local image patches (e.g., gray-level co-occurrence matrix contrast, energy) and identify regions that significantly deviate from normal tissue through statistical testing; deep learning methods (mainstream): Segmentation models (e.g., U-Net): Input image patches and directly output pixel-level masks of defect areas[30]. Classification or anomaly detection models: Perform classification or detect anomalous regions[31,32]
Hyper-staining/hypo-staining	Improper staining time or concentration	Loss of contrast, bias in feature extraction	Staining quality assessment methods: Color distribution analysis. Calculate intensity histograms in specific color channels of hematoxylin and eosin-stained images (e.g., the blue channel corresponding to hematoxylin) to detect abnormal distributions that are excessively concentrated near extremes[32]. Contrast measurement. Evaluate the clarity of staining layers by calculating local or global contrast metrics (e.g., root mean square contrast, Weber contrast)
Air bubbles/blade marks	Mounting or sectioning defects	Misidentified as tissue structures	Traditional methods: Morphological and texture analysis can be combined for preliminary screening (similar to fold detection). Deep learning methods (mainstream): Segmentation or classification models. Identify defect regions through segmentation or classification[31,33]. Anomaly detection models: Detect defects as unseen anomalous structures[32]
Uneven illumination	Scanner light source issues	Intensity gradients within the same tissue	Traditional methods: Background analysis. Fit a brightness plane over pure background areas and detect significant brightness gradients by evaluating the fitting residuals. Tissue region analysis. Calculate the spatial distribution of brightness within uniform tissue regions to detect systematic gradients; deep learning methods: Classification or segmentation models can be used to directly learn and identify regions with abnormal brightness distribution[31]

Table 2 Key advances in pancreatic ductal adenocarcinoma research using single-cell RNA sequencing in recent years

Research direction	Key findings	Key techniques	Ref.
Precursor lesions and early detection	High transcriptional similarity between pancreatic intraepithelial neoplasia and pancreatic ductal adenocarcinoma; NKX6-2 drives gastric-like differentiation in intraductal papillary mucinous neoplasm	Spatial transcriptomics	[7,13]
Tumor heterogeneity	Identification of distinct ductal cell subtypes (classical, basal-like, normal-like, cycling); basal-like subtype proportion ≥ 22% is associated with poor prognosis	Consensus non-negative matrix factorization, copy number variation analysis, pseudotime trajectory inference	[66-68]
Tumor microenvironment	Discovery of CAF subtypes (myofibroblastic CAFs, inflammatory CAFs, antigen-presenting CAFs); characterization of immunosuppressive roles of Tregs and M0/M2 macrophages	Cell-cell communication analysis (CellPhoneDB), spatial transcriptomic integration	[69-72]
Metastasis and evolution	Increased clonal homogeneity in liver metastases, accompanied by KRAS/ETV1 amplification and SMAD2/MAP2K4 deletion; identification of metastasis-related transcriptional modules	Single-cell copy number variation analysis, non-negative matrix factorization-based module detection	[66,73,74]
Therapeutic resistance	Upregulation of calcium signaling in gemcitabineresistant cells; HIF-1α-mediated angiogenesis in hypoxic niches; TAM polarization associated with immunotherapy resistance	Differential gene expression analysis, pathway enrichment, validation in animal models	[63,75,76]

CAF: Cancer-associated fibroblast; HIF-1α: Hypoxia-inducible factor-1α; TAM: Tumor-associated macrophage; MAP2K4: Mitogen-activated protein kinase kinase 4.