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

---

The protein expression profile of ACE2 in human tissues

Molecular Systems Biology, Journal Year: 2020, Volume and Issue: 16(7)

Published: July 1, 2020

Language: Английский

---

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

---

Single‐cell RNA sequencing technologies and applications: A brief overview

Clinical and Translational Medicine, Journal Year: 2022, Volume and Issue: 12(3)

Published: March 1, 2022

Language: Английский

---

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

Nature Methods, Journal Year: 2021, Volume and Issue: 18(11), P. 1352 - 1362

Published: Oct. 28, 2021

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and relate such cellular features anatomical scale. Single-cell single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for measurements, at lower resolution with limited sensitivity. Targeted in situ technologies solve both issues, are gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data various forms of collected from same region, including MERFISH, STARmap, smFISH, Transcriptomics (Visium) histological images. Tangram map any type data, multimodal as those SHARE-seq, which used reveal patterns chromatin accessibility. We demonstrate on healthy mouse brain tissue, by reconstructing genome-wide anatomically integrated visual somatomotor areas. is versatile tool aligning resolved using deep learning.

Language: Английский

---

The Spatial and Cell-Type Distribution of SARS-CoV-2 Receptor ACE2 in the Human and Mouse Brains

Frontiers in Neurology, Journal Year: 2021, Volume and Issue: 11

Published: Jan. 20, 2021

By engaging angiotensin-converting enzyme 2 (ACE2 or Ace2), the novel pathogenic severe acute respiratory syndrome coronavirus (SARS-CoV-2) invades host cells and affects many organs, including brain. However, distribution of ACE2 in brain is still obscure. Here, we investigated expression by analyzing data from publicly available transcriptome databases. According to our spatial analysis, was relatively highly expressed some locations, such as choroid plexus paraventricular nuclei thalamus. cell-type nuclear found neurons (both excitatory inhibitory neurons) non-neuron (mainly astrocytes, oligodendrocytes, endothelial cells) human middle temporal gyrus posterior cingulate cortex. A few ACE2-expressing were a hippocampal dataset, none detected prefrontal Except for additional high Ace2 olfactory bulb areas well pericytes distribution, mouse similar that Thus, results reveal an outline ACE2/Ace2 brains, which indicates infection SARS-CoV-2 may be capable inducing central nervous system symptoms disease 2019 (COVID-19) patients. Potential species differences should considered when using models study neurological effects infection.

Language: Английский

---

Statistics or biology: the zero-inflation controversy about scRNA-seq data

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Jan. 21, 2022

Researchers view vast zeros in single-cell RNA-seq data differently: some regard as biological signals representing no or low gene expression, while others missing to be corrected. To help address the controversy, here we discuss sources of and non-biological zeros; introduce five mechanisms adding computational benchmarking; evaluate impacts on analysis; benchmark three input types: observed counts, imputed binarized counts; open questions regarding advocate importance transparent analysis.

Language: Английский

---

Comparison and evaluation of statistical error models for scRNA-seq

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Jan. 18, 2022

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation cellular state as well technical introduced during experimental processing. Deconvolving these effects a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models scRNA-seq analysis, but there lack consensus on which statistical distributions parameter settings are appropriate.

Language: Английский

---

Applications of single-cell sequencing in cancer research: progress and perspectives

Journal of Hematology & Oncology, Journal Year: 2021, Volume and Issue: 14(1)

Published: June 9, 2021

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics is a powerful tool to decipher the cellular molecular landscape at single-cell resolution, unlike bulk which provides averaged data. The use of sequencing in cancer research has revolutionized our understanding biological characteristics dynamics within lesions. In this review, we summarize emerging technologies recent progress obtained by information related landscapes malignant cells immune cells, tumor heterogeneity, circulating underlying mechanisms behaviors. Overall, prospects facilitating diagnosis, targeted therapy prognostic prediction among spectrum tumors are bright. near future, advances will undoubtedly improve highlight potential precise therapeutic targets for patients.

Language: Английский

---

Modular, efficient and constant-memory single-cell RNA-seq preprocessing

Nature Biotechnology, Journal Year: 2021, Volume and Issue: 39(7), P. 813 - 818

Published: April 1, 2021

Language: Английский

---