Evidence map›Paper›PMID 31101904›Full record

ArticleNature protocols2019

MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing.

Hadas Keren-Shaul, Ephraim Kenigsberg, Diego Adhemar Jaitin, Eyal David, Franziska Paul, Amos Tanay, Ido Amit

Abstract read
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In one paragraph

Article in Nature protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 160 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
160citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

160 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026
    Pooled it
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  12. Lysosome-related organelles orchestrate guanine crystal formation in pigment cells.Proceedings of the National Academy of Sciences of the United States of America · 2026
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100 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hadas Keren-Shaul *Department of Immunology, Weizmann Institute of Science, Rehovot, Israel.
Ephraim Kenigsberg *Precision Immunology Institute, Icahn Institute for Data Science and Genomic Technology, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Diego Adhemar JaitinDepartment of Immunology, Weizmann Institute of Science, Rehovot, Israel.
Eyal DavidDepartment of Immunology, Weizmann Institute of Science, Rehovot, Israel.
Franziska PaulDepartment of Immunology, Weizmann Institute of Science, Rehovot, Israel.
Amos TanayDepartment of Computer Science and Applied Mathematics and Department of Biological Regulation, Weizmann Institute, Rehovot, Israel. amos.tanay@weizmann.ac.il.ORCID http://orcid.org/0000-0001-9419-3824
Ido AmitDepartment of Immunology, Weizmann Institute of Science, Rehovot, Israel. ido.amit@weizmann.ac.il.ORCID http://orcid.org/0000-0003-2968-877X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions and that vary in abundance by 4-9 orders of magnitude. Relying solely on unbiased sampling to characterize cellular niches becomes infeasible, as the marginal utility of collecting more cells diminishes quickly. Furthermore, in many clinical samples, the relevant cell types are scarce and efficient processing is critical. We developed an integrated pipeline for index sorting and massively parallel single-cell RNA sequencing (MARS-seq2.0) that builds on our previously published MARS-seq approach. MARS-seq2.0 is based on >1 million cells sequenced with this pipeline and allows identification of unique cell types across different tissues and diseases, as well as unique model systems and organisms. Here, we present a detailed step-by-step procedure for applying the method. In the improved procedure, we combine sub-microliter reaction volumes, optimization of enzymatic mixtures and an enhanced analytical pipeline to substantially lower the cost, improve reproducibility and reduce well-to-well contamination. Data analysis combines multiple layers of quality assessment and error detection and correction, graphically presenting key statistics for library complexity, noise distribution and sequencing saturation. Importantly, our combined FACS and single-cell RNA sequencing (scRNA-seq) workflow enables intuitive approaches for depletion or enrichment of cell populations in a data-driven manner that is essential to efficient sampling of complex tissues. The experimental protocol, from cell sorting to a ready-to-sequence library, takes 2-3 d. Sequencing and processing the data through the analytical pipeline take another 1-2 d.

Indexed as

SoftwareAnimalsCell LineFlow CytometryGene Expression ProfilingGene LibraryHigh-Throughput Nucleotide SequencingHumansMiceReproducibility of ResultsRNASingle-Cell AnalysisWorkflowRNA

Identifiers

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.