Evidence mapPaperPMID 39187683Full record

ArticleNature methods2024

Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics.

Axel A Almet, Yuan-Chen Tsai, Momoko Watanabe, Qing Nie

Abstract read
In one paragraph

Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

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

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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

4 authors.

Axel A AlmetDepartment of Mathematics, University of California, Irvine, Irvine, CA, USA.ORCID http://orcid.org/0000-0001-9173-8278
Yuan-Chen TsaiDepartment of Anatomy & Neurobiology, University of California, Irvine, Irvine, CA, USA.ORCID http://orcid.org/0000-0001-5250-0646
Momoko WatanabeDepartment of Anatomy & Neurobiology, University of California, Irvine, Irvine, CA, USA.ORCID http://orcid.org/0000-0001-5014-2849
Qing NieDepartment of Mathematics, University of California, Irvine, Irvine, CA, USA. qnie@uci.edu.ORCID http://orcid.org/0000-0002-8804-3368

Funding

Tissue Size and Precision Control in Growing Hair FolliclesR01AR079150 · NIAMS · UNIVERSITY OF CALIFORNIA-IRVINE · 2023 to 2025
$1.6M
Dissecting single cell dynamics that coordinate neural crest migration and diversificationR01DE030565 · UNIVERSITY OF CALIFORNIA-IRVINE · 2025 to 2025
$552k
Development of tools for analyzing cell-cell communication using spatial transcriptomic dataR01GM152494 · UNIVERSITY OF CALIFORNIA-IRVINE · 2025 to 2025
$342k
FRAXA Research Foundation (FRAXA Research Foundation, Inc.) Postdoctoral FellowshipNational Science Foundation (NSF) CBET2134916National Science Foundation (NSF) DMS1763272National Science Foundation (NSF) MCB2028424NIAMS NIH HHS R01 AR079150NICHD NIH HHS R00 HD096105NIDCR NIH HHS R01 DE030565NIGMS NIH HHS R01 GM152494NSF | ENG/OAD | Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET) RECODE2225624Simons Foundation 594598U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) R00HD096105U.S. Department of Health & Human Services | NIH | National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) R01AR079150U.S. Department of Health & Human Services | NIH | National Institute of Dental and Craniofacial Research (NIDCR) R01DE030565
6 · The paper itself

Abstract

From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synthetic data generated from mathematical modeling. We demonstrate FlowSig's utility by applying it to various studies, showing that FlowSig can capture stimulation-induced changes to paracrine signaling in pancreatic islets, demonstrate shifts in intercellular flows due to increasing COVID-19 severity and reconstruct morphogen-driven activator-inhibitor patterns in mouse embryogenesis.

Indexed as

COVID-19Single-Cell AnalysisTranscriptomeAnimalsCell CommunicationEmbryonic DevelopmentGene Expression ProfilingHumansIslets of LangerhansMiceSARS-CoV-2Sequence Analysis, RNA

Identifiers

PMID39187683
PMCPMC11466815

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.