Seq-Well: A Portable, Low-Cost Platform for High-Throughput Single-Cell RNA-Seq of Low-Input Samples

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Abstract

Single-cell RNA-Seq can precisely resolve cellular states but application to sparse samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively-parallel single-cell RNA-Seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semi-permeable membrane, enabling efficient cell lysis and transcript capture. We characterize Seq-Well using species-mixing experiments and PBMCs, and profile thousands of primary human macrophages exposed to tuberculosis.

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Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Alex Shalek, Rahul Satija, Xian Adiconis … (2013)

Recent molecular studies have revealed that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels, and phenotypic output 1–5 , with important functional consequences 4,5 . Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs 1,2 or proteins 5,6 simultaneously because genomic profiling methods 3 could not be applied to single cells until very recently 7–10 . Here, we use single-cell RNA-Seq to investigate heterogeneity in the response of bone marrow derived dendritic cells (BMDCs) to lipopolysaccharide (LPS). We find extensive, and previously unobserved, bimodal variation in mRNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization (RNA-FISH) for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.

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Single cell RNA Seq reveals dynamic paracrine control of cellular variation

Alex Shalek, Rahul Satija, Joe Shuga … (2014)

High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis, and function of gene expression variation between seemingly identical cells. Here, we sequence single-cell RNA-Seq libraries prepared from over 1,700 primary mouse bone marrow derived dendritic cells (DCs) spanning several experimental conditions. We find substantial variation between identically stimulated DCs, in both the fraction of cells detectably expressing a given mRNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a “core” module of antiviral genes is expressed very early by a few “precocious” cells, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analyzing DCs from knockout mice, and modulating secretion and extracellular signaling, we show that this response is coordinated via interferon-mediated paracrine signaling. Surprisingly, preventing cell-to-cell communication also substantially reduces variability in the expression of an early-induced “peaked” inflammatory module, suggesting that paracrine signaling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations use to establish complex dynamic responses.

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Classification of low quality cells from single-cell RNA-seq data

Tomislav Ilicic, Jong Kim, Aleksandra A. Kolodziejczyk … (2016)

Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0888-1) contains supplementary material, which is available to authorized users.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101215604

Journal ID (pubmed-jr-id): 32338

Journal ID (nlm-ta): Nat Methods

Journal ID (iso-abbrev): Nat. Methods

Title: Nature methods

ISSN (Print): 1548-7091

ISSN (Electronic): 1548-7105

Publication date Nihms-submitted: 14 February 2017

Publication date (Electronic): 13 February 2017

Publication date (Print): April 2017

Publication date PMC-release: 13 August 2017

Volume: 14

Issue: 4

Pages: 395-398

Affiliations

[1 ]Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts, USA

[2 ]Institute for Medical Engineering & Science (IMES) and Department of Chemistry, MIT, Cambridge, Massachusetts, USA

[3 ]Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

[4 ]Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA

[5 ]Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA

[6 ]Center for Genomics and Systems Biology, Department of Biology, New York University, New York City, New York, USA

[7 ]New York Genome Center, New York City, New York, USA

Author notes

[* ]To whom correspondence should be addressed: shalek@ 123456mit.edu (AKS), clove@ 123456mit.edu (JCL)

[#]

These authors contributed equally to this work

[*]

These senior authors contributed equally to this work

Article

Manuscript ID: NIHMS843531

DOI: 10.1038/nmeth.4179

PMC ID: 5376227

PubMed ID: 28192419

SO-VID: 3c59ff9c-0f14-489e-aa21-59ec196caf34

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Seq-Well: A Portable, Low-Cost Platform for High-Throughput Single-Cell RNA-Seq of Low-Input Samples

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Most cited references 10

Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Single cell RNA Seq reveals dynamic paracrine control of cellular variation

Classification of low quality cells from single-cell RNA-seq data

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