Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction

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Abstract

We present a scalable, integrated strategy for coupled protein and RNA detection from single cells. Our approach leverages the DNA polymerase activity of reverse transcriptase to simultaneously perform proximity extension assays and complementary DNA synthesis in the same reaction. Using the Fluidigm C1™ system, we profile the transcriptomic and proteomic response of a human breast adenocarcinoma cell line to a chemical perturbation, benchmarking against in situ hybridizations and immunofluorescence staining, as well as recombinant proteins, ERCC Spike-Ins, and population lysate dilutions. Through supervised and unsupervised analyses, we demonstrate synergies enabled by simultaneous measurement of single-cell protein and RNA abundances. Collectively, our generalizable approach highlights the potential for molecular metadata to inform highly-multiplexed single-cell analyses.

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The online version of this article (doi:10.1186/s13059-016-1045-6) contains supplementary material, which is available to authorized users.

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Most cited references 53

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Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Da Huang, Brad T. Sherman, Richard A. Lempicki (2009)

DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

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Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

Evan Z. Macosko, Anindita Basu, Rahul Satija … (2015)

Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.

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Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.

Anoop Patel, Itay Tirosh, John J. Trombetta … (2014)

Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy. Copyright © 2014, American Association for the Advancement of Science.

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Author and article information

Contributors

Alex S Genshaft: genshaft@mit.edu

Shuqiang Li: shuqiang@broadinstitute.org

Caroline J. Gallant: caroline.gallant@igp.uu.se

Spyros Darmanis: spyros@stanford.edu

Sanjay M. Prakadan: sanju@mit.edu

Carly G. K. Ziegler: cziegler@mit.edu

Martin Lundberg: Martin.Lundberg@olink.com

Simon Fredriksson: fredriksson.simon@gmail.com

Joyce Hong: joyceh@mit.edu

Aviv Regev: aregev@broadinstitute.org

Kenneth J. Livak: ken.livak@fluidigm.com

Ulf Landegren: ulf.landegren@igp.uu.se

Alex K. Shalek: shalek@mit.edu

Journal

Journal ID (nlm-ta): Genome Biol

Journal ID (iso-abbrev): Genome Biol

Title: Genome Biology

Publisher: BioMed Central (London )

ISSN (Print): 1474-7596

ISSN (Electronic): 1474-760X

Publication date (Electronic): 19 September 2016

Publication date PMC-release: 19 September 2016

Publication date Collection: 2016

Volume: 17

Electronic Location Identifier: 188

Affiliations

[1 ]Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA USA

[2 ]Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA USA

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

[4 ]Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Cambridge, MA USA

[5 ]Fluidigm Corporation, South San Francisco, CA USA

[6 ]Department of Immunology, Genetics & Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden

[7 ]Departments of Bioengineering and Applied Physics, Stanford University and Howard Hughes Medical Institute, Stanford, CA USA

[8 ]Division of Health Sciences & Technology, Harvard University and Massachusetts Institute of Technology, Cambridge, MA USA

[9 ]Olink Proteomics, Uppsala, Sweden

[10 ]Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA USA

[11 ]Department of Biology and Koch Institute, MIT, Boston, MA 02142 USA

[12 ]Howard Hughes Medical Institute, Chevy Chase, MD 20815 USA

Article

Publisher ID: 1045

DOI: 10.1186/s13059-016-1045-6

PMC ID: 5027636

PubMed ID: 27640647

SO-VID: 5a03a851-a83a-449c-803e-b1fbfd5eb32d

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 4 July 2016

Date accepted : 11 August 2016

Funding

Funded by: Searle Scholars Program

Funded by: Arnold and Mabel Beckman Foundation (US)

Award ID: Beckman Young Investigator

Award Recipient : Alex K. Shalek

Funded by: National Institutes of Health (US)

Award ID: P50HG006193

Award Recipient : Alex K. Shalek

Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;

Award ID: P50HG006193

Award Recipient : Aviv Regev

Funded by: Klarman Cell Observatory

Funded by: European Community

Award ID: 7th Framework Program

Award Recipient : Caroline J. Gallant Spyros Darmanis

Funded by: Howard Hughes Medical Institute (US)

Funded by: National Institutes of Health (US)

Award ID: T32GM007753

Award Recipient : Carly G. K. Ziegler

Funded by: National Institutes of Health (US)

Award ID: U24AI11862

Award Recipient : Alex K. Shalek

Funded by: National Institutes of Health (US)

Award ID: U24AI11862

Award Recipient : Aviv Regev

Funded by: National Institutes of Health (US)

Award ID: DP2OD020839

Award Recipient : Alex K. Shalek

Funded by: European Community's 7th Framework Program

Award ID: 259796

Award Recipient : Ulf Landegren

Funded by: European Research Council (BE)

Award ID: 294409

Award Recipient : Ulf Landegren

Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;

Funded by: FundRef http://dx.doi.org/10.13039/501100003849, IngaBritt och Arne Lundbergs Forskningsstiftelse;

Funded by: FundRef http://dx.doi.org/10.13039/501100003748, Svenska Sällskapet för Medicinsk Forskning;

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: single-cell transcriptomics,single-cell proteomics,single-cell multi-omics,proximity extension assay,metadata

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ScienceOpen disciplines: Genetics

Keywords: single-cell transcriptomics, single-cell proteomics, single-cell multi-omics, proximity extension assay, metadata

Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction

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Evolutionary Cell Biology

Most cited references 53

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.

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