E-spatial

Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop **CellChat**, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying **CellChat** to mouse and human skin datasets shows its ability to extract complex signaling patterns.

The recent development of single-cell RNA-sequencing (scRNA-seq) technology has enabled us to infer cell-type-specific co-expression networks, enhancing our understanding of cell-type-specific biological functions. However, existing methods proposed for this task still face challenges due to unique characteristics in scRNA-seq data, such as high sequencing depth variations across cells and measurement errors. CS-CORE (Su, C., Xu, Z., Shan, X. et al., 2023), an R package for cell-type-specific co-expression inference, explicitly models sequencing depth variations and measurement errors in scRNA-seq data. In this notebook, we will illustrate an example workflow of CS-CORE using a dataset of Peripheral Blood Mononuclear Cells (PBMC) from COVID patients and healthy controls (Wilk et al., 2020). The notebook content is inspired by CS-CORE's vignette and modified to demonstrate how the tool works on BioTuring's platform.

Single-cell RNA sequencing (scRNA-seq) data often encountered technical artifacts called "doublets" which are two cells that are sequenced under the same cellular barcode. Doublets formed from different cell types or states are called heterotypic and homotypic otherwise. These factors constrain cell throughput and may result in misleading biological interpretations. DoubletFinder (McGinnis, Murrow, and Gartner 2019) is one of the methods proposed for doublet detection. In this notebook, we will illustrate an example workflow of DoubletFinder. We use a 10x Genomics dataset which captures peripheral blood mononuclear cells (PBMCs) from a healthy donor stained with a panel of 31 TotalSeq™-B antibodies (BioLegend).

Spatially resolved gene expression profiles are key to understand tissue organization and function. However, spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). Simulating varying reference quantities and qualities, we confirmed high prediction accuracy also with shallowly sequenced or small-sized scRNA-seq reference datasets. SPOTlight deconvolution of the mouse brain correctly mapped subtle neuronal cell states of the cortical layers and the defined architecture of the hippocampus. In human pancreatic cancer, we successfully segmented patient sections and further fine-mapped normal and neoplastic cell states. Trained on an external single-cell pancreatic tumor references, we further charted the localization of clinical-relevant and tumor-specific immune cell states, an illustrative example of its flexible application spectrum and future potential in digital pathology.