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Author

Tim Stuart

Other affiliations: New York University
Bio: Tim Stuart is a academic researcher from University of Western Australia. The author has contributed to research in topic(s): DNA methylation & Epigenomics. The author has an hindex of 12, co-authored 23 publication(s) receiving 5940 citation(s). Previous affiliations of Tim Stuart include New York University.

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Topics: DNA methylation, Epigenomics, Genomics ...read more
Papers
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Open accessJournal ArticleDOI: 10.1016/J.CELL.2019.05.031
13 Jun 2019-Cell
Abstract: Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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3,853 Citations


Journal ArticleDOI: 10.1038/S41576-019-0093-7
Tim Stuart, Rahul Satija1Institutions (1)
Abstract: The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.

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528 Citations


Open accessPosted ContentDOI: 10.1101/460147
02 Nov 2018-bioRxiv
Abstract: Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

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522 Citations


Open accessJournal ArticleDOI: 10.1016/J.CELL.2021.04.048
Yuhan Hao1, Stephanie Hao2, Erica Andersen-Nissen3, William M. Mauck1  +21 moreInstitutions (4)
24 Jun 2021-Cell
Abstract: 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, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19) Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity

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238 Citations


Open accessJournal ArticleDOI: 10.1038/S41586-019-1629-X
09 Oct 2019-Nature
Abstract: Author(s): Snyder, Michael P; Lin, Shin; Posgai, Amanda; Atkinson, Mark; Regev, Aviv; Rood, Jennifer; Rozenblatt-Rosen, Orit; Gaffney, Leslie; Hupalowska, Anna; Satija, Rahul; Gehlenborg, Nils; Shendure, Jay; Laskin, Julia; Harbury, Pehr; Nystrom, Nicholas A; Silverstein, Jonathan C; Bar-Joseph, Ziv; Zhang, Kun; Borner, Katy; Lin, Yiing; Conroy, Richard; Procaccini, Dena; Roy, Ananda L; Pillai, Ajay; Brown, Marishka; Galis, Zorina S; Cai, Long; Shendure, Jay; Trapnell, Cole; Lin, Shin; Jackson, Dana; Snyder, Michael P; Nolan, Garry; Greenleaf, William James; Lin, Yiing; Plevritis, Sylvia; Ahadi, Sara; Nevins, Stephanie A; Lee, Hayan; Schuerch, Christian Martijn; Black, Sarah; Venkataraaman, Vishal Gautham; Esplin, Ed; Horning, Aaron; Bahmani, Amir; Zhang, Kun; Sun, Xin; Jain, Sanjay; Hagood, James; Pryhuber, Gloria; Kharchenko, Peter; Atkinson, Mark; Bodenmiller, Bernd; Brusko, Todd; Clare-Salzler, Michael; Nick, Harry; Otto, Kevin; Posgai, Amanda; Wasserfall, Clive; Jorgensen, Marda; Brusko, Maigan; Maffioletti, Sergio; Caprioli, Richard M; Spraggins, Jeffrey M; Gutierrez, Danielle; Patterson, Nathan Heath; Neumann, Elizabeth K; Harris, Raymond; deCaestecker, Mark; Fogo, Agnes B; van de Plas, Raf; Lau, Ken; Cai, Long; Yuan, Guo-Cheng; Zhu, Qian; Dries, Ruben; Yin, Peng; Saka, Sinem K; Kishi, Jocelyn Y; Wang, Yu; Goldaracena, Isabel; Laskin, Julia; Ye, DongHye; Burnum-Johnson, Kristin E; Piehowski, Paul D | Abstract: Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible 3D molecular and cellular atlas of the human body, in health and various disease settings.

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163 Citations


Cited by
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Open accessJournal ArticleDOI: 10.1016/J.CELL.2019.05.031
13 Jun 2019-Cell
Abstract: Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

...read more

3,853 Citations


Open accessJournal ArticleDOI: 10.1016/J.CELL.2020.04.004
14 May 2020-Cell
Abstract: We have previously provided the first genetic evidence that angiotensin converting enzyme 2 (ACE2) is the critical receptor for severe acute respiratory syndrome coronavirus (SARS-CoV), and ACE2 protects the lung from injury, providing a molecular explanation for the severe lung failure and death due to SARS-CoV infections. ACE2 has now also been identified as a key receptor for SARS-CoV-2 infections, and it has been proposed that inhibiting this interaction might be used in treating patients with COVID-19. However, it is not known whether human recombinant soluble ACE2 (hrsACE2) blocks growth of SARS-CoV-2. Here, we show that clinical grade hrsACE2 reduced SARS-CoV-2 recovery from Vero cells by a factor of 1,000-5,000. An equivalent mouse rsACE2 had no effect. We also show that SARS-CoV-2 can directly infect engineered human blood vessel organoids and human kidney organoids, which can be inhibited by hrsACE2. These data demonstrate that hrsACE2 can significantly block early stages of SARS-CoV-2 infections.

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Topics: Vero cell (51%)

1,234 Citations


Open accessJournal ArticleDOI: 10.1038/S41591-020-0901-9
Mingfeng Liao, Yang Liu, Jing Yuan, Yanling Wen  +10 moreInstitutions (3)
12 May 2020-Nature Medicine
Abstract: Respiratory immune characteristics associated with Coronavirus Disease 2019 (COVID-19) severity are currently unclear. We characterized bronchoalveolar lavage fluid immune cells from patients with varying severity of COVID-19 and from healthy people by using single-cell RNA sequencing. Proinflammatory monocyte-derived macrophages were abundant in the bronchoalveolar lavage fluid from patients with severe COVID-9. Moderate cases were characterized by the presence of highly clonally expanded CD8+ T cells. This atlas of the bronchoalveolar immune microenvironment suggests potential mechanisms underlying pathogenesis and recovery in COVID-19.

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Topics: Bronchoalveolar lavage (65%), Immune system (57%)

1,090 Citations


Open accessJournal ArticleDOI: 10.1186/S13059-019-1874-1
Christoph Hafemeister, Rahul Satija1Institutions (1)
23 Dec 2019-Genome Biology
Abstract: Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.

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Topics: Count data (60%), Negative binomial distribution (55%), Covariate (53%) ...read more

817 Citations


Open access
01 Apr 2013-
Topics: Spliceosome (72%), Germline mutation (58%)

750 Citations


Performance
Metrics

Author's H-index: 12

No. of papers from the Author in previous years
YearPapers
20214
20203
20197
20182
20167

Top Attributes

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Author's top 5 most impactful journals

bioRxiv

9 papers, 755 citations

Cell

3 papers, 4.1K citations

eLife

2 papers, 148 citations

Nature Methods

1 papers, 1 citations

Nature

1 papers, 163 citations

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