Evidence map›Paper›PMID 30712874›Full record

ArticleCell2019

Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.

Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, Brian Cleary, Vidya Subramanian, Aryeh Solomon, Joshua Gould, Siyan Liu, Stacie Lin, Peter Berube and 8 more

Erratum issuedOpen access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 375 papers.

0numbers the graph read from it
0cells of the map it votes in
375citing papers in PubMed
31.5field-weighted citation impact, top 1% of its field
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

375 citing papers in PubMed, 813 citations in OpenAlex.

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315 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors at 7 institutions in 1 country.

Geoffrey SchiebingerKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; MIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Jian ShuKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA. Electronic address: jianshu@broadinstitute.org.
Marcin TabakaKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Brian ClearyKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA.
Vidya SubramanianKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Aryeh SolomonKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Joshua GouldKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Siyan LiuKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Biochemistry Program, Wellesley College, Wellesley, MA 02481, USA.
Stacie LinKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Peter BerubeKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Lia LeeKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Jenny ChenKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA.
Justin BrumbaughCancer Center, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA; Harvard Medical School, Boston, MA 02115, USA.
Philippe RigolletMIT Center for Statistics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Konrad HochedlingerDepartment of Molecular Biology, Center for Regenerative Medicine and Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
Rudolf JaenischWhitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Computational and Systems Biology Program, MIT, Cambridge, MA 02142, USA.
Aviv RegevKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA. Electronic address: aregev@broadinstitute.org.
Eric S LanderKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Systems Biology Harvard Medical School, Boston, MA 02125, USA. Electronic address: lander@broadinstitute.org.
Broad Institute · USMassachusetts Institute of Technology · USCenter for Systems Biology · USHarvard–MIT Division of Health Sciences and Technology · USHarvard Stem Cell Institute · USHarvard University · USWhitehead Institute for Biomedical Research · US

Funding

Generation of novel cell type specific mouse genetic toolsU19MH114830 · NIMH · ALLEN INSTITUTE · PI ZENG, HONGKUI · 2017 to 2021
$64.7M
In Vitro Reprogramming of Somatic Cells into Pluripotent ES-like CellsR37HD045022 · NICHD · WHITEHEAD INSTITUTE FOR BIOMEDICAL RES · PI JAENISCH, RUDOLF · 2009 to 2017
$7.5M
Transcriptional condensates, epigenetic editing and Rett SyndromeR01MH104610 · NIMH · WHITEHEAD INSTITUTE FOR BIOMEDICAL RES · PI JAENISCH, RUDOLF, YOUNG, RICHARD · 2014 to 2023
$7.4M
An iPSC based platform for functionally assessing genetic and environmental riskR01NS088538 · NINDS · WHITEHEAD INSTITUTE FOR BIOMEDICAL RES · PI JAENISCH, RUDOLF, MYERS, RICHARD H · 2014 to 2018
$5.0M
Nuclear Cloning and the Reprogramming of the GenomeR01HD045022 · NICHD · WHITEHEAD INSTITUTE FOR BIOMEDICAL RES · PI JAENISCH, RUDOLF · 2003 to 2008
$4.7M
In situ transcriptional analysis of brain circuits at single cell resolutionR01MH111502 · NIMH · HARVARD UNIVERSITY · PI DULAC, CATHERINE, REGEV, AVIV · 2016 to 2018
$4.6M
Graduate Training in Computational and Systems BiologyT32GM087237 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BURGE, CHRISTOPHER B · 2009 to 2023
$4.6M
Transcription Factor Induced ReprogrammingR01HD058013 · NICHD · MASSACHUSETTS GENERAL HOSPITAL · PI HOCHEDLINGER, KONRAD · 2010 to 2019
$4.4M
An integrated multiplexed genomic assay for low input clinical samples1U24AI118672 · NIAID · BROAD INSTITUTE, INC. · PI SHALEK, ALEX K · 2015 to 2019
$2.0M
Dissecting and reconstructing the molecular roadmaps of cellular reprogramming to iPSCs in single-cell resolutionK99HD096049 · NICHD · BROAD INSTITUTE, INC. · PI SHU, JIAN · 2018 to 2019
$265k
Howard Hughes Medical InstituteNIAID NIH HHS U24 AI118672NICHD NIH HHS K99 HD096049NICHD NIH HHS R01 HD045022NICHD NIH HHS R01 HD058013NICHD NIH HHS R37 HD045022NIGMS NIH HHS T32 GM087237NIMH NIH HHS R01 MH104610NIMH NIH HHS R01 MH111502NIMH NIH HHS U19 MH114830NINDS NIH HHS R01 NS088538
6 · The paper itself

Abstract

Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.

Indexed as

AnimalsCell DifferentiationCells, CulturedCellular ReprogrammingEmbryonic Stem CellsFibroblastsGene ExpressionGene Expression ProfilingGene Expression Regulation, DevelopmentalInduced Pluripotent Stem CellsMiceSequence Analysis, RNASingle-Cell AnalysisTranscription FactorsTranscription FactorsancestorsdescendantsdevelopmentiPSCsoptimal-transportparacrine interactionsregulationreprogrammingscRNA-seqtrajectories

Identifiers

PMID30712874
PMCPMC6402800
OpenAlexW2911279170

What OpenQuestion holds

Textmetadata
LicenceTDM
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.