TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

Nucleic Acids Research, Journal Year: 2020, Volume and Issue: 49(D1), P. D1420 - D1430

Published: Oct. 16, 2020

Abstract Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only minority patients respond to treatment due the stochastic heterogeneity tumor microenvironment (TME). Recent advances single-cell RNA-seq technologies enabled comprehensive characterization immune system tumors but posed computational challenges on integrating and utilizing massive published datasets inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), large-scale curated database that integrates transcriptomic profiles nearly 2 million cells from 76 high-quality across 27 All data were uniformly processed with standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis functional enrichment analysis. TISCH provides interactive gene visualization multiple at level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, response groups, even cancer-types. In summary, user-friendly interface for systematically visualizing, searching downloading atlas TME types, enabling fast, flexible exploration TME.

Language: Английский

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Computational principles and challenges in single-cell data integration

Nature Biotechnology, Journal Year: 2021, Volume and Issue: 39(10), P. 1202 - 1215

Published: May 3, 2021

Language: Английский

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TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment

Nucleic Acids Research, Journal Year: 2022, Volume and Issue: 51(D1), P. D1425 - D1431

Published: Nov. 2, 2022

Abstract The Tumor Immune Single Cell Hub 2 (TISCH2) is a resource of single-cell RNA-seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive characterization gene expression in the tumor microenvironment (TME) across multiple cancer types. As an increasing number datasets are generated public domain, this update, TISCH2 has included 190 scRNA-seq covering 6 million cells 50 types, with 110 newly collected almost tripling compared previous release. Furthermore, includes several new functions that allow users to better utilize large-scale datasets. First, Dataset module, provides cell–cell communication results each dataset, facilitating analyses interacted cell types discovery significant ligand–receptor pairs between also transcription factor for dataset visualization top enriched factors type. Second, Gene adds identifying correlated genes providing survival information input genes. In summary, user-friendly, up-to-date well-maintained TME. freely available at http://tisch.comp-genomics.org/.

Language: Английский

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CellCall: integrating paired ligand–receptor and transcription factor activities for cell–cell communication

Nucleic Acids Research, Journal Year: 2021, Volume and Issue: 49(15), P. 8520 - 8534

Published: July 16, 2021

With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, systematic decoding cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most them lack ability to predict pathways connecting insides and outsides cells. Here, we developed CellCall, a toolkit infer inter- intracellular by integrating paired ligand-receptor transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method identify significantly activated involved in intercellular crosstalk between certain cell types. Additionally, offers rich suite visualization options (Circos plot, Sankey bubble ridge etc.) present results. Case studies on scRNA-seq datasets human testicular cells tumor immune microenvironment demonstrated reliable unique functionality internal TF exploration, which were further validated experimentally. Comparative other tools indicated that was more accurate offered functions. In summary, provides sophisticated practical tool allowing researchers decipher related regulatory signals based data. is freely available at https://github.com/ShellyCoder/cellcall.

Language: Английский

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Integrative analyses of single-cell transcriptome and regulome using MAESTRO

Genome biology, Journal Year: 2020, Volume and Issue: 21(1)

Published: Aug. 7, 2020

Abstract We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses single-cell RNA-seq (scRNA-seq) ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions pre-processing, alignment, quality control, expression chromatin accessibility quantification, clustering, differential analysis, annotation. By modeling gene regulatory potential accessibilities at level, outperforms existing methods integrating cell clusters between scRNA-seq scATAC-seq. Furthermore, supports automatic cell-type annotation using predefined type marker genes identifies driver regulators scATAC-seq peaks.

Language: Английский

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Multi-omics integration in the age of million single-cell data

Nature Reviews Nephrology, Journal Year: 2021, Volume and Issue: 17(11), P. 710 - 724

Published: Aug. 20, 2021

Language: Английский

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Functional inference of gene regulation using single-cell multi-omics

Cell Genomics, Journal Year: 2022, Volume and Issue: 2(9), P. 100166 - 100166

Published: Aug. 4, 2022

Language: Английский

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In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target

Cell, Journal Year: 2021, Volume and Issue: 184(21), P. 5357 - 5374.e22

Published: Sept. 27, 2021

Language: Английский

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Chromatin accessibility profiling methods

Nature Reviews Methods Primers, Journal Year: 2021, Volume and Issue: 1(1)

Published: Jan. 21, 2021

Language: Английский

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Fast alignment and preprocessing of chromatin profiles with Chromap

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: Nov. 12, 2021

Abstract As sequencing depth of chromatin studies continually grows deeper for sensitive profiling regulatory elements or spatial structures, aligning and preprocessing these data have become the bottleneck analysis. Here we present Chromap, an ultrafast method high throughput profiles. Chromap is comparable to BWA-MEM Bowtie2 in alignment accuracy over 10 times faster than traditional workflows on bulk ChIP-seq/Hi-C profiles 10x Genomics’ CellRanger v2.0.0 pipeline single-cell ATAC-seq

Language: Английский

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