Spatial transcriptomics (ST) enables the comprehensive analysis of gene expression while preserving the spatial context of tissues. The histological images accompanying ST data provide spatially cohesive information that is often challenging to capture through gene expression alone. However, analyzing such images is challenging due to the presence of fiducial markers and background regions, which can obscure important features and complicate downstream analysis.
View Article and Find Full Text PDFCell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images.
View Article and Find Full Text PDFBackground: Direct comparison of tumor microenvironment of matched lung cancer biopsies and pleural effusions (PE) from the same patients is critical in understanding tumor biology but has not been performed. This is the first study to compare the lung cancer and PE microenvironment by single-cell RNA sequencing (scRNA-seq).
Methods: Matched lung cancer biopsies and PE were obtained prospectively from ten patients.
Background: Pathologic antibody mediated rejection (pAMR) remains a major driver of graft failure in cardiac transplant patients. The endomyocardial biopsy remains the primary diagnostic tool but presents with challenges, particularly in distinguishing the histologic component (pAMR-H) defined by 1) intravascular macrophage accumulation in capillaries and 2) activated endothelial cells that expand the cytoplasm to narrow or occlude the vascular lumen. Frequently, pAMR-H is difficult to distinguish from acute cellular rejection (ACR) and healing injury.
View Article and Find Full Text PDFSifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods.
View Article and Find Full Text PDFModeling temporal and spatial gene expression patterns in large-scale single-cell and spatial transcriptomics data is a computationally intensive task. We present PreTSA, a method that offers computational efficiency in modeling these patterns and is applicable to single-cell and spatial transcriptomics data comprising millions of cells. PreTSA consistently matches the results of state-of-the-art methods while significantly reducing computational time.
View Article and Find Full Text PDFHere we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation.
View Article and Find Full Text PDFCell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images.
View Article and Find Full Text PDFImage classification plays a pivotal role in analyzing biomedical images, serving as a cornerstone for both biological research and clinical diagnostics. We demonstrate that large multimodal models (LMMs), like GPT-4, excel in one-shot learning, generalization, interpretability, and text-driven image classification across diverse biomedical tasks. These tasks include the classification of tissues, cell types, cellular states, and disease status.
View Article and Find Full Text PDFT cells exhibit high heterogeneity in both their gene expression profiles and antigen specificities. We analyzed fifteen single-cell immune profiling datasets to systematically investigate the association between T-cell receptor (TCR) sequences and the gene expression profiles of T cells. Our findings reveal that T cells sharing identical or similar TCR sequences tend to have highly similar gene expression profiles.
View Article and Find Full Text PDFPseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here, we introduce Lamian, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis.
View Article and Find Full Text PDFWhen analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels.
View Article and Find Full Text PDFChronic pain is a significant public health issue that is often refractory to existing therapies. Here we use a multiomic approach to identify cis-regulatory elements that show differential chromatin accessibility and reveal transcription factor (TF) binding motifs with functional regulation in the rat dorsal root ganglion (DRG), which contain cell bodies of primary sensory neurons, after nerve injury. We integrated RNA-seq to understand how differential chromatin accessibility after nerve injury may influence gene expression.
View Article and Find Full Text PDFThe phosphatase and tensin homologue deleted on chromosome 10 (PTEN) tumor suppressor governs a variety of biological processes, including metabolism, by acting on distinct molecular targets in different subcellular compartments. In the cytosol, inactive PTEN can be recruited to the plasma membrane where it dimerizes and functions as a lipid phosphatase to regulate metabolic processes mediated by the phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin complex 1 (mTORC1) pathway. However, the metabolic regulation of PTEN in the nucleus remains undefined.
View Article and Find Full Text PDFSifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods.
View Article and Find Full Text PDFQuality-related process monitoring as a supervised technology has increasingly attracted attention in complex industries. Various approaches have been studied to cope with this issue. Nevertheless, these methods cannot reasonably decompose the process variable space, resulting in deficiencies in monitoring quality-related faults.
View Article and Find Full Text PDFCell type annotation is an essential step in single-cell RNA-seq analysis. However, it is a time-consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high-quality reference datasets and the development of additional pipelines.
View Article and Find Full Text PDFCell type annotation is an essential step in single-cell RNA-seq analysis. However, it is a time-consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high-quality reference datasets and the development of additional pipelines.
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