Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion

Satpathy, Ansuman T.; Granja, Jeffrey M.; Yost, Kathryn E.; Qi, Yanyan; Meschi, Francesca; McDermott, Geoffrey P.; Olsen, Brett N.; Mumbach, Maxwell R.; Pierce, Sarah E.; Corces, M. Ryan; Shah, Preyas; Bell, Jason C.; Jhutty, Darisha; Nemec, Corey M.; Wang, Jean; Wang, Li; Yin, Yifeng; Giresi, Paul G.; Chang, Anne Lynn S.; Zheng, Grace; Greenleaf, William J.; Chang, Howard Y.

doi:10.1101/610550

Cited by 183 publications

(304 citation statements)

References 75 publications

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“…We reasoned that an ideal method for cell type prioritization would make no assumptions about the distributions of features provided as input 29 , and more broadly, would be agnostic to the particular molecular features provided as input: that is, it would readily incorporate single-cell RNA-seq [30][31][32][33] , proteomics 34,35 , epigenomics 11,[36][37][38] , and imaging transcriptomics 15,17,39 datasets, among other modalities. Accordingly, Augur uses a random forest 40 classifier to predict sample labels for each cell type.…”

Section: Methodsmentioning

confidence: 99%

Cell type prioritization in single-cell data

Skinnider¹,

Squair²,

Kathe³

et al. 2019

Preprint

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jordan.squair@epfl.ch a, Schematic overview of Augur. b, AUCs of Augur and a naive random forest classifier without subsampling in simulated scRNA-seq datasets containing increasing numbers of cells. Cell type prioritizations are confounded by training dataset size for the naive classifier, but Augur abolishes this confounding factor. The mean and standard deviation of ten simulation replicates are shown. c, Pearson correlations between the AUC of each cell type, and the number of cells of that type sequenced, across a compendium of 22 scRNA-seq datasets, for Augur and a naive random forest classifier without subsampling. d, Augur AUCs scale monotonically with both the proportion of DE genes and the magnitude of DE in simulated cell populations. e, Relationship between number of DE genes detected by a representative test for single-cell differential gene expression (Wilcoxon rank-sum test), and the proportion of differentially expressed genes simulated between the two populations, for simulated populations of between 100 and 1,000 cells. f, Augur cell type prioritizations track with duration of LPS exposure in a cross-species scRNA-seq experiment 7 . Grey points show AUCs with sample labels randomly permuted. g-h, Cell type prioritization in matched single-cell 4 and bulk 8 transcriptomic profiles of PBMCs after interferon stimulation. g, Left, Augur cell type prioritizations mirror the number of DE genes in a microarray dataset of FACS-purified cells. Right, the number of DE genes detected in the scRNA-seq dataset by a Wilcoxon rank-sum test is uncorrelated with the FACS gold standard. h, Correlation coefficients between cell type prioritizations (AUC or number of DE genes) in the scRNA-seq dataset and the FACS gold standard. i-j, Cell type prioritization in the mouse ventromedial hypothalamus reflects induction of IEG transcription. i, Correlation between AUC and the difference in the first principal component of IEG expression (∆IEG eigengene) engaging in aggressive behavior. j, Pearson correlation coefficients between cell type-specific AUC and ∆IEG eigengene values for eleven behavioral stimuli 9 . k, Reproducibility of cell type prioritization in two independent scRNA-seq studies of Alzheimer's disease 5,10 . l, Augur cell type prioritizations in a scATAC-seq dataset 11 track with the number of DE genes in an RNA-seq dataset of FACS-purified cells.

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Section: Methodsmentioning

confidence: 99%

Cell type prioritization in single-cell data

Skinnider¹,

Squair²,

Kathe³

et al. 2019

Preprint

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“…Filtering cells by TSS enrichment and unique fragments: The method for calculating enrichment at TSS was adapted from a previously described method 67 . TSS positions were obtained from the GENCODE database (RRID:SCR_014966).…”

Section: Chromatin Accessibility (Snatac-seq) Data Pre-processing (Ucsd)mentioning

confidence: 99%

An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Yao

Liu

Xie

et al. 2020

Preprint

138

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Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp.Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis. IntroductionNeural circuits are characterized by extraordinary diversity of their cellular components 1,2 . Single-cell molecular assays, especially transcriptomic measurements by RNA-Seq, have accelerated the discovery and characterization of cell types across brain regions and in diverse species. Recent advances include single-cell transcriptome datasets with >10 5 individual cells, identifying hundreds of neuronal and non-neuronal cell types across the mouse nervous system 3-5 . As the number of profiled cells grows into the millions, a key question is whether these data will converge toward a comprehensive and coherent taxonomy of cell types with broad utility for organizing knowledge of brain cells and their function. Data from different modalities, including transcriptomic and epigenomic data, must be cross-referenced and integrated to establish robust and consistent cell type classifications.Although a comprehensive atlas should incorporate anatomical and physiological information, the high throughput of single cell sequencing assays makes integration of molecular data a particularly urgent challenge. A rigorous and reproducible consensus molecular atlas of brain cell types would drive progress across modalities, including obtaining functional information.Single cell sequencing technologies can measure multiple molecular signatures of cell identity. The core molecular identity of a cell is largely established during development and maintained by a combination of gene regulatory proteins...

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“…When cell type annotations or cell type marker genes for some of the analyzed cells are available, we can include semi-supervised learning to annotate cell types (Xu et al, 2019;Zhang et al, 2019). Given the rapid development of spatial transcriptomics (Rodriques et al, 2019;Vickovic et al, 2019), single-cell ATAC-seq (Lareau et al, 2019;Satpathy et al, 2019) and other complementary measurements, scPhere can be extended for integrative analysis of multi-modality data. We can also learn discrete hierarchical trees for better interpreting developmental trajectories.…”

Section: Discussionmentioning

confidence: 99%

Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces

Ding

Regev

2019

Preprint

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Single-cell RNA-Seq (scRNA-seq) has become an invaluable tool for studying biological systems in health and diseases. While dimensionality reduction is a crucial step in interpreting the relation between cells based on scRNA-seq, current methods often are hampered by "crowding" of cells in the center of the latent space, biased by batch effects, or inadequately capture developmental relationships. Here, we introduced scPhere, a scalable deep generative model to embed cells into low-dimensional hyperspherical or hyperbolic spaces, as a more accurate representation of the data. ScPhere resolves cell crowding, corrects multiple, complex batch factors, facilitates interactive visualization of large datasets, and gracefully uncovers pseudotemporal trajectories.We demonstrate scPhere on six large datasets in complex tissue from human patients or animal development, demonstrating how it controls for both technical and biological factors and highlights complex cellular relations and biological insights.

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Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion

Cited by 183 publications

References 75 publications

Cell type prioritization in single-cell data

Cell type prioritization in single-cell data

An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces

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