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BioTuring
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.
BioTuring
Expanded CRISPR-compatible CITE-seq (ECCITE-seq) which is built upon pooled CRISPR screens, allows to simultaneously measure transcriptomes, surface protein levels, and single-guide RNA (sgRNA) sequences at single-cell resolution. The technique enables multimodal characterization of each perturbation and effect exploration. However, it also encounters heterogeneity and complexity which can cause substantial noise into downstream analyses.
Mixscape (Papalexi, Efthymia, et al., 2021) is a computational framework proposed to substantially improve the signal-to-noise ratio in single-cell perturbation screens by identifying and removing confounding sources of variation.
In this notebooks, we demonstrate Mixscape's features using pertpy - a Python package offering a range of tools for perturbation analysis. The original pipeline of Mixscape implemented in R can be found here.
BioTuring
Doublets are a characteristic error source in droplet-based single-cell sequencing data where two cells are encapsulated in the same oil emulsion and are tagged with the same cell barcode. Across type doublets manifest as fictitious phenotypes that can be incorrectly interpreted as novel cell types. DoubletDetection present a novel, fast, unsupervised classifier to detect across-type doublets in single-cell RNA-sequencing data that operates on a count matrix and imposes no experimental constraints.
This classifier leverages the creation of in silico synthetic doublets to determine which cells in the
input count matrix have gene expression that is best explained by the combination of distinct cell
types in the matrix.
In this notebook, we will illustrate an example workflow for detecting doublets in single-cell RNA-seq count matrices.
BioTuring
Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information.
Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images.
**Tangram** can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.
Trends
BioTuring
Knowledge of cell type composition in disease relevant tissues is an important step towards the identification of cellular targets of disease. MuSiC is a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues.
By appropriate weighting of genes showing cross-subject and cross-cell consistency, MuSiC enables the transfer of cell type-specific gene expression information from one dataset to another.
MuSiC enables the characterization of cellular heterogeneity of complex tissues for understanding of disease mechanisms. As bulk tissue data are more easily accessible than single-cell RNA-seq, MuSiC allows the utilization of the vast amounts of disease relevant bulk tissue RNA-seq data for elucidating cell type contributions in disease.
This notebook provides a walk through tutorial on how to use MuSiC to estimate cell type proportions from bulk sequencing data based on multi-subject single cell data by reproducing the analysis in MuSiC paper, now is published on Nature Communications.