ABSTRACT Recent technological development in spatial transcriptomics allows researchers to measure gene expression of cells and their spatial locations at the almost single-cell level, which generates detailed biological insight into biological processes. However, specialized spatial transcriptomics databases are rare. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a user-friendly database with multifunctions including search of relevant publications and tools, public dataset visualization, customized specialized databases, new data archive, and online analysis. The current version of STOmicsDB consists of 141 curated spatial transcript datasets covering 12 species, and includes 5,618 spatial multi-omics publications and 674 tools. STOmicsDB is freely accessible at https://db.cngb.org/stomics/ .
Abstract Advances in spatial transcriptomics technologies has enabled gene expression profiling of tissues while retaining the spatial context. To effectively exploit the data, spatially informed analysis tools are required. Here, we present DeepST, a versatile graph self-supervised contrastive learning framework that incorporates spatial location information and gene expression profiles to accomplish three key tasks, spatial clustering, spatial transcriptomics (ST) data integration, and single-cell RNA-seq (scRNA-seq) data transfer onto ST. DeepST combines graph neural networks (GNNs) with self-supervised contrastive learning to learn spot representations in the ST data, and an auto-encoder to extract informative features in the scRNA-seq data. Spatial self-supervised contrastive learning enables the learned spatial spot representation to be more informative and discriminative by minimizing the embedding distance between spatially adjacent spots and vice versa. With DeepST, we found biologically consistent clusters with higher accuracy than competing methods. We next demonstrated DeepST’s ability to jointly analyze multiple tissue slices in both vertical and horizontal integration while correcting for batch effects. Lastly, we used DeepST to deconvolute cell types present in ST with scRNA-seq data, showing better performance than cell2location. We also demonstrated DeepST’s accurate cell type mapping to recover immune cell distribution in the different regions of breast tumor tissue. DeepST is a user-friendly and computationally efficient tool for capturing and dissecting the heterogeneity within ST data, enabling biologists to gain insights into the cellular states within tissues.
The transition from peri-implantation to gastrulation in mammals entails the specification and organization of the lineage progenitors into a body plan. Technical and ethical challenges have limited understanding of the cellular and molecular mechanisms that underlie this transition. We established a culture system that enabled the development of cynomolgus monkey embryos in vitro for up to 20 days. Cultured embryos underwent key primate developmental stages, including lineage segregation, bilaminar disc formation, amniotic and yolk sac cavitation, and primordial germ cell-like cell (PGCLC) differentiation. Single-cell RNA-sequencing analysis revealed development trajectories of primitive endoderm, trophectoderm, epiblast lineages, and PGCLCs. Analysis of single-cell chromatin accessibility identified transcription factors specifying each cell type. Our results reveal critical developmental events and complex molecular mechanisms underlying nonhuman primate embryogenesis in the early postimplantation period, with possible relevance to human development.
Adaptive immunity, mediated by T and B cell responses, is essential for defending against infections and cancers while also being implicated in autoimmune diseases. Tracking T and B cell repertoires in situ at single-cell resolution is essential for understanding adaptive immune responses. To address the lack of tools for in situ single-cell T/BCR (XCR) sequencing, we developed Stereo-XCR-seq, an efficient strategy for retrieving and sequencing TCR and BCR from Stereo-seq cDNA libraries at subcellular resolution. Stereo-XCR-seq provides unbiased full-length XCR reads alongside spatial transcriptomics, enabling the identification of heterogeneous lymphoid aggregates with distinct clonal activities in cancers and inflammatory bowel disease (IBD). We identified plasma cell aggregates that differ from tertiary lymphoid structures (TLSs) in both transcriptomic profiles and clonal activities, with spatial positioning potentially mediating unique immune responses. Collectively, Stereo-XCR-seq enables in situ single-cell profiling of T and B cell clonal activities within tissue microenvironments, providing insights into lymphocyte adaption to environmental stimuli. This technology provides potential for advancing our understanding of tissue immunity and the development of therapeutic strategies for immune disorders.
Abstract As of early May 2021, the ongoing pandemic COVID-19 has caused over 160 million of infections and over 3 million deaths worldwide. Many risk factors, such as age, gender, and comorbidities, have been studied to explain the variable symptoms of infected patients. However, these effects may not fully account for the diversity in disease severity. Here, we present a comprehensive analysis of a broad range of patients’ laboratory and clinical assessments to investigate the genetic contributions to COVID-19 severity. By performing GWAS analysis, we discovered several concrete associations for laboratory features. Based on these findings, we performed Mendelian randomization (MR) analysis to investigate the causality of laboratory traits on disease severity. From the MR study, we identified two causal traits, cholesterol levels and WBC counts. The functional gene related to cholesterol levels is ApoE and people with particular ApoE genotype are more likely to have higher cholesterol levels, facilitating the process that SARS-CoV-2 binds on its receptor ACE2 and aggravating COVID-19 disease. The functional gene related to WBC counts is MHC system that plays a central role in the immune system. The host immune response to the SARS-CoV-2 infection greatly affects the patients’ severity status and clinical outcome. Additionally, our gene-based and GSEA analysis revealed interferon pathways, including type I interferon receptor binding, regulation of IFNA signaling, and SARS coronavirus and innate immunity. We hope that our work will make a contribution in studying the genetic mechanisms of disease illness and serve as useful reference for the clinical diagnosis and treatment of COVID-19.
Abstract Summary The FASTQ+ format is designed for single-cell experiments. It extends various optional tags, including cell barcodes and unique molecular identifiers, to the sequence identifier and is fully compatible with the FASTQ format. In addition, PISA implements various utilities for processing sequences in the FASTQ format and alignments in the SAM/BAM/CRAM format from single-cell experiments, such as converting FASTQ format to FASTQ+, annotating alignments, PCR deduplication, feature counting and barcodes correction. The software is open-source and written in C language. Availability and implementation https://doi.org/10.5281/zenodo.7007056 or https://github.com/shiquan/PISA Supplementary information Supplementary data are available at Bioinformatics online.
Abstract Global profile of gene expression at single-cell resolution remains to be determined for primates. Using a recently developed technology (“Stereo-seq”), we have obtained a comprehensive single-cell spatial transcriptome map at the whole-brain level for cynomolgus monkeys, with ∼600 genes per cell for 10 μm-thick coronal sections (up to 15 cm 2 in size). Large-scale single-nucleus RNA-seq analysis for ∼1 million cells helped to identify cell types corresponding to Stereo-seq gene expression profiles, providing a 3-D cell type atlas of the monkey brain. Quantitative analysis of Stereo-seq data revealed molecular fingerprints that mark distinct neocortical layers and subregions, as well as domains within subcortical structures including hippocampus, thalamus, striatum, cerebellum, hypothalamus and claustrum. Striking whole-brain topography and coordinated patterns were found in the expression of genes encoding receptors and transporters for neurotransmitters and neuromodulators. These results pave the way for cellular and molecular understanding of organizing principles of the primate brain.
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