Evidence map›Paper›PMID 34381231›Full record

ReviewNature2021

Exploring tissue architecture using spatial transcriptomics.

Anjali Rao, Dalia Barkley, Gustavo S França, Itai Yanai

2 registry-linked trialsOpen access · greenAbstract readReview
In one paragraph

Review in Nature, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 1,014 papers.

0numbers the graph read from it
0cells of the map it votes in
1,014citing papers in PubMed
89.9field-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.

NCT05986461 unknown statusnot on this mapstarted 2023, after this paper: background citation

Spatially Resolved Transcriptomics Reveals Phenotype-specific Molecular Signatures in Complex Regional Pain Syndrome

Typeobservational_patient_registrySponsorSeoul National UniversityRan2023 to 2024Enrolled10ConditionsComplex Regional Pain Syndromes
NCT06625203 recruitingnot on this mapstarted 2023, after this paper: background citation

Multi Omics and Spatial Atlas In Cancer

TypeobservationalSponsorOWKINRan2023 to 2028Enrolled7,000ConditionsDiffuse Large B Cell Lymphoma, Solid Tumor Cancer
3 · Its place in the literature

Who cites it

1,014 citing papers in PubMed, 1,597 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
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  7. Article
  8. Review
  9. Insights Into Spatial Transcriptomics: Exploring Recent Technical Developments and Their Diverse Applications.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Review
  10. Mapping Embryonic Mouse Lung Development Using Enhanced Spatial Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  11. A meta-interaction basis for cell-cell communication in tissues.bioRxiv : the preprint server for biology · 2026
    Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

954 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

4 authors at 1 institution in 1 country.

Anjali Rao *Institute for Computational Medicine, NYU Langone Health, New York, NY, USA.ORCID http://orcid.org/0000-0001-8815-9865
Dalia Barkley *Institute for Computational Medicine, NYU Langone Health, New York, NY, USA.
Gustavo S FrançaInstitute for Computational Medicine, NYU Langone Health, New York, NY, USA.
Itai YanaiInstitute for Computational Medicine, NYU Langone Health, New York, NY, USA. itai.yanai@nyulangone.org.ORCID http://orcid.org/0000-0002-8438-2741
NYU Langone Health · US

Funding

Spatiotemporal analysis of gene regulatory programs associated with GBS infection during pregnancyR01AI143290 · NIAID · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI RATNER, ADAM JONATHAN, YANAI, ITAI · 2019 to 2023
$3.8M
Computational approaches for the systematic detection of cell-cell interactions by spatial transcriptomics - Resubmission - 1R01LM013522 · NLM · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI YANAI, ITAI · 2021 to 2024
$1.4M
Cancer cell state interactions with immune populations of the tumor microenvironmentF30CA257400 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BARKLEY, DALIA · 2021 to 2023
$135k
NCI NIH HHS F30 CA257400NIAID NIH HHS R01 AI143290NLM NIH HHS R01 LM013522
6 · The paper itself

Abstract

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

Indexed as

TranscriptomeAnimalsData AnalysisDiseaseGene Expression ProfilingHumansOrgan SpecificityTranscription, Genetic

Identifiers

PMID34381231
PMCPMC8475179
OpenAlexW3190202966

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

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.