Evidence map›Paper›PMID 41792497›Full record

ReviewNature protocols2026

A scalable, low-cost, sample hashing workflow for multiomic single-cell analysis using the Seq-Well S

Daniela D Russo, Sarah L Quinn, Olha Kholod, Michal A Elovitz, Douglas A Lauffenburger, Pardis Sabeti, Boris Julg, Andrea G Edlow, Brittany A Goods, Alex K Shalek and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nature protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Daniela D RussoDepartment of Immunology, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-2244-1080
Sarah L QuinnRagon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
Olha KholodThayer School of Engineering, Program in Quantitative Biomedical Sciences, and Department of Molecular and Systems, Dartmouth College, Hanover, NH, USA.
Michal A ElovitzNuttall Women's Health, New York, NY, USA.
Douglas A LauffenburgerDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-0050-989X
Pardis SabetiBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Boris JulgRagon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-4687-9626
Andrea G EdlowDepartment of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA, USA.
Brittany A GoodsThayer School of Engineering, Program in Quantitative Biomedical Sciences, and Department of Molecular and Systems, Dartmouth College, Hanover, NH, USA.
Alex K Shalek *Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-5670-8778
Sergio Triana *Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. strianas@broadinstitute.org.

Funding

Zhao - Proj 2P20GM130454 · NIGMS · DARTMOUTH COLLEGE · PI Shannon Soucy · 2019 to 2026
$27.2M
Research Project 2 The pregnancy AdaptOMEU19AI167899 · NIAID · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI DOUGLAS A LAUFFENBURGER · 2022 to 2026
$14.7M
Defining the impact of drug use on immune function and fitness against HIV-1DP1DA053731 · NIDA · MASSACHUSETTS GENERAL HOSPITAL · PI SHALEK, ALEX K · 2021 to 2025
$5.9M
NIAID NIH HHS U19 AI167899NIDA NIH HHS DP1 DA053731NIGMS NIH HHS P20 GM130454U.S. Department of Health & Human Services | National Institutes of Health (NIH) 1DP1DA053731U.S. Department of Health & Human Services | National Institutes of Health (NIH) 5U19AI167899U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) P20GM130454
6 · The paper itself

Abstract

In-depth analyses of clinical samples have the potential to provide unparalleled insights into the cellular mechanisms that underlie both health and disease, as well as therapeutic and prophylactic responses. However, these specimens are often paucicellular, necessitating the use of workflows that maximize the amount of information that can be learned. Here we provide a detailed protocol for generating and analyzing single-cell multiomic data from low-input samples with the Seq-Well S

Indexed as

High-Throughput Nucleotide SequencingSingle-Cell AnalysisHumansMultiomicsSequence Analysis, RNASingle-Cell Gene Expression AnalysisWorkflow

Identifiers

PMID41792497
PMCPMC13573644

What OpenQuestion holds

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Registered trials

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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.