Evidence map›Paper›PMID 35108499›Full record

ArticleCell2022

Mapping transcriptomic vector fields of single cells.

Xiaojie Qiu, Yan Zhang, Jorge D Martin-Rufino, Chen Weng, Shayan Hosseinzadeh, Dian Yang, Angela N Pogson, Marco Y Hein, Kyung Hoi Joseph Min, Li Wang and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 274 papers.

0numbers the graph read from it
0cells of the map it votes in
274citing 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

274 citing papers in PubMed.

  1. Article
  2. Review
  3. Arteriosclerosis, thrombosis, and vascular biology · 2026
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  4. Preclinical models of COPD: a state of the art review.The European respiratory journal · 2026
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  10. Review
  11. Article
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  13. Prenatal PMNature communications · 2026
    Article
  14. Review
  15. Article
  16. Article
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  18. Single-cell analysis of microglia and monocyte dynamics uncovers distinct TNF-driven neuroimmune signatures in humans after intracerebral hemorrhage.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026
    Article
  19. Article
  20. Article

214 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

22 authors.

Xiaojie QiuWhitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: xqiu@wi.mit.edu.
Yan ZhangDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Joint CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Jorge D Martin-RufinoBroad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Chen WengWhitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Shayan HosseinzadehDepartment of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
Dian YangWhitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
Angela N PogsonWhitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
Marco Y HeinChan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158, USA.
Kyung Hoi Joseph MinWhitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Li WangDepartment of Mathematics, University of Texas at Arlington, Arlington, TX, USA.
Emanuelle I GrodyBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Matthew J ShurtleffLycia Therapeutics, South San Francisco, San Francisco, CA, USA.
Ruoshi YuanCalifornia Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA.
Song XuMicrosoft, Redmond, WA, USA.
Yian MaHalıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA, USA.
Joseph M ReplogleWhitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Medical Scientist Training Program, University of California, San Francisco, CA, USA.
Eric S LanderBroad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Systems Biology Harvard Medical School, Boston, MA 02125, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Spyros DarmanisGenentech Inc., South San Francisco, CA, USA.
Ivet BaharDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Joint CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA.
Vijay G SankaranBroad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Jianhua XingDepartment of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA; Joint CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA, USA; UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA; Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: xing1@pitt.edu.
Jonathan S WeissmanWhitehead Institute for Biomedical Research, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA; Koch Institute For Integrative Cancer Research at MIT, MIT, Cambridge, MA, USA. Electronic address: weissman@wi.mit.edu.

Funding

Center for Genomic Editing and Recording: Development and Application of Next-Generation Genome and Epigenome Editing Methods to Advance the Study and Treatment of Human DiseaseRM1HG009490 · NHGRI · WHITEHEAD INSTITUTE FOR BIOMEDICAL RES · PI Brittany S. Adamson, Martin Joseph Ankrah Aryee · 2017 to 2026
$22.7M
TrainingP41GM103712 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FAEDER, JAMES · 2012 to 2021
$16.3M
Systematic Genetic Dissection of Human ErythropoiesisR01DK103794 · NIDDK · BOSTON CHILDREN'S HOSPITAL · PI Vijay Ganesh Sankaran · 2014 to 2026
$5.9M
Three-dimensional organoid models to study breast cancer progressionR37CA232209 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SANT, SHILPA · 2018 to 2024
$3.0M
Role of the Snail1-Twist-p21 axis on cell cycle arrest and renal fibrosis developmentR01DK119232 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI XING, JIANHUA · 2019 to 2022
$1.4M
Howard Hughes Medical InstituteNCI NIH HHS R37 CA232209NHGRI NIH HHS RM1 HG009490NIDDK NIH HHS R01 DK103794NIDDK NIH HHS R01 DK119232NIGMS NIH HHS P41 GM103712
6 · The paper itself

Abstract

Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo's power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Indexed as

Single-Cell AnalysisAlgorithmsFemaleGene Expression RegulationHematopoiesisHematopoietic Stem CellsHL-60 CellsHumansKineticsModels, BiologicalRNA, MessengerStaining and LabelingTranscriptomeRNA, Messengercell-fate transitionsdifferential geometry analysisdynamical systems theorydynamohematopoiesisin silico perturbationleast action pathRNA JacobianRNA metabolic labelingvector field reconstruction

Identifiers

PMID35108499
PMCPMC9332140

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.