Comprehensive Integration of Single-Cell Data

Cell, Journal Year: 2019, Volume and Issue: 177(7), P. 1888 - 1902.e21

Published: June 1, 2019

Language: Английский

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Integrated analysis of multimodal single-cell data

Cell, Journal Year: 2021, Volume and Issue: 184(13), P. 3573 - 3587.e29

Published: May 31, 2021

The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, unsupervised framework to learn the relative utility each data type in cell, enabling integrative analysis modalities. We apply our procedure a CITE-seq dataset 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending 228 antibodies construct reference atlas circulating immune system. Multimodal substantially improves ability resolve cell states, allowing us identify validate previously unreported lymphoid subpopulations. Moreover, demonstrate how leverage this rapidly map new datasets interpret responses vaccination coronavirus disease 2019 (COVID-19). Our approach broadly applicable strategy analyze look beyond transcriptome toward unified definition identity.

Language: Английский

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The single-cell transcriptional landscape of mammalian organogenesis

Nature, Journal Year: 2019, Volume and Issue: 566(7745), P. 496 - 502

Published: Feb. 20, 2019

Language: Английский

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DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

Cell Systems, Journal Year: 2019, Volume and Issue: 8(4), P. 329 - 337.e4

Published: April 1, 2019

Language: Английский

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Current best practices in single‐cell RNA‐seq analysis: a tutorial

Molecular Systems Biology, Journal Year: 2019, Volume and Issue: 15(6)

Published: June 1, 2019

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more tools are becoming available, it increasingly difficult navigate landscape and produce up-to-date workflow analyse one's data. Here, we detail the steps typical analysis, including pre-processing (quality control, normalization, data correction, feature selection, dimensionality reduction) cell- gene-level downstream analysis. We formulate current best-practice recommendations these based on independent comparison studies. have integrated into workflow, which apply public dataset further illustrate how work in practice. Our documented case study can found https://www.github.com/theislab/single-cell-tutorial This review will serve as tutorial new entrants field, help established users update their pipelines.

Language: Английский

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Cells of the adult human heart

Nature, Journal Year: 2020, Volume and Issue: 588(7838), P. 466 - 472

Published: Sept. 24, 2020

Abstract Cardiovascular disease is the leading cause of death worldwide. Advanced insights into mechanisms and therapeutic strategies require a deeper understanding molecular processes involved in healthy heart. Knowledge full repertoire cardiac cells their gene expression profiles fundamental first step this endeavour. Here, using state-of-the-art analyses large-scale single-cell single-nucleus transcriptomes, we characterize six anatomical adult heart regions. Our results highlight cellular heterogeneity cardiomyocytes, pericytes fibroblasts, reveal distinct atrial ventricular subsets with diverse developmental origins specialized properties. We define complexity vasculature its changes along arterio-venous axis. In immune compartment, identify cardiac-resident macrophages inflammatory protective transcriptional signatures. Furthermore, cell-to-cell interactions different networks macrophages, fibroblasts cardiomyocytes between atria ventricles that are from those skeletal muscle. human cell atlas improves our provides valuable reference for future studies.

Language: Английский

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Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species

Immunity, Journal Year: 2019, Volume and Issue: 50(5), P. 1317 - 1334.e10

Published: April 9, 2019

Language: Английский

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Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia

Science Advances, Journal Year: 2020, Volume and Issue: 6(31)

Published: July 24, 2020

Abstract: Altered olfactory function is a common symptom of COVID-19, but its etiology unknown. A key question whether SARS-CoV-2 (CoV-2) – the causal agent in COVID-19 affects olfaction directly, by infecting sensory neurons or their targets bulb, indirectly, through perturbation supporting cells. Here we identify cell types epithelium and bulb that express entry molecules. Bulk sequencing demonstrated mouse, non-human primate human mucosa expresses two genes involved CoV-2 entry, ACE2 TMPRSS2. However, single revealed expressed support cells, stem perivascular rather than neurons. Immunostaining confirmed these results pervasive expression protein dorsally-located epithelial sustentacular cells pericytes mouse. These findings suggest infection non-neuronal leads to anosmia related disturbances odor perception patients.

Language: Английский

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Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer

Cell, Journal Year: 2020, Volume and Issue: 181(2), P. 442 - 459.e29

Published: April 1, 2020

Language: Английский

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SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data

GigaScience, Journal Year: 2020, Volume and Issue: 9(12)

Published: Dec. 1, 2020

Abstract Background Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free contained within the input solution also captured by these assays. This sequencing of constitutes a background contamination confounds biological interpretation transcriptomic data. Results We demonstrate from this "soup" is ubiquitous, with experiment-specific variations in composition and magnitude. present method, SoupX, for quantifying extent estimating "background-corrected" cell expression profiles seamlessly integrate existing downstream analysis tools. Applying method several datasets using multiple droplet technologies, we its application improves otherwise misleading data, as well improving quality control metrics. Conclusions tool removing ambient droplet-based experiments. has broad applicability, can improve utility future datasets.

Language: Английский

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