Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer&apos;s disease.

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Abstract

The extent of microglial heterogeneity in humans remains a central yet poorly explored question in light of the development of therapies targeting this cell type. Here, we investigate the population structure of live microglia purified from human cerebral cortex samples obtained at autopsy and during neurosurgical procedures. Using single cell RNA sequencing, we find that some subsets are enriched for disease-related genes and RNA signatures. We confirm the presence of four of these microglial subpopulations histologically and illustrate the utility of our data by characterizing further microglial cluster 7, enriched for genes depleted in the cortex of individuals with Alzheimer's disease (AD). Histologically, these cluster 7 microglia are reduced in frequency in AD tissue, and we validate this observation in an independent set of single nucleus data. Thus, our live human microglia identify a range of subtypes, and we prioritize one of these as being altered in AD.

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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

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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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Author and article information

Journal

Journal ID (iso-abbrev): Nat Commun

Title: Nature communications

Publisher: Springer Science and Business Media LLC

ISSN (Electronic): 2041-1723

ISSN (Print): 2041-1723

Publication date (Electronic): November 30 2020

Volume: 11

Issue: 1

Affiliations

[1 ] Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA.

[2 ] Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA.

[3 ] Department of Neurology, Columbia University Medical Center, New York, NY, USA.

[4 ] Cell Circuits Program, Broad Institute, Cambridge, MA, USA.

[5 ] Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.

[6 ] Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA.

[7 ] Institute of Neuropathology, Medical Faculty, University of Freiburg, Freiburg, Germany.

[8 ] Berta-Ottenstein-Programme for Clinician Scientists, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

[9 ] Max-Planck-Institute of Immunobiology and Epigenetics, Freiburg, Germany.

[10 ] Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.

[11 ] Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA.

[12 ] Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.

[13 ] Signaling Research Centers BIOSS and CIBSS, University of Freiburg, Freiburg, Germany.

[14 ] Center for NeuroModulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

[15 ] Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.

[16 ] Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.

[17 ] Howard Hughes Medical Institute, Department of Biology, MIT, Cambridge, MA, 02140, USA.

[18 ] Genentech, 1 DNA Way, South San Francisco, CA, 94080, USA.

[19 ] Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA. pld2115@cumc.columbia.edu.

[20 ] Taub Institute for Research on Alzheimer's Disease and Aging Brain, Columbia University Medical Center, New York, NY, USA. pld2115@cumc.columbia.edu.

[21 ] Department of Neurology, Columbia University Medical Center, New York, NY, USA. pld2115@cumc.columbia.edu.

[22 ] Cell Circuits Program, Broad Institute, Cambridge, MA, USA. pld2115@cumc.columbia.edu.

Article

Publisher Item ID: 10.1038/s41467-020-19737-2

DOI: 10.1038/s41467-020-19737-2

PMC ID: 7704703

PubMed ID: 33257666

SO-VID: 1a0558ee-a385-41e4-a1e4-da0cc3f7cc04

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Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer's disease.

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Most cited references 39

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Comprehensive Integration of Single-Cell Data

Integrating single-cell transcriptomic data across different conditions, technologies, and species

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