Evidence map›Paper›PMID 39169166›Full record

ReviewNature reviews. Molecular cell biology2025

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.

Gunsagar S Gulati, Jeremy Philip D'Silva, Yunhe Liu, Linghua Wang, Aaron M Newman

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Molecular cell biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 142 papers.

0numbers the graph read from it
0cells of the map it votes in
142citing 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

142 citing papers in PubMed.

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82 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

5 authors.

Gunsagar S Gulati *Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0003-2798-6220
Jeremy Philip D'Silva *Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0009-0005-0053-7726
Yunhe LiuDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.ORCID http://orcid.org/0000-0002-2120-952X
Linghua WangDepartment of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Aaron M NewmanDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA. amnewman@stanford.edu.ORCID http://orcid.org/0000-0002-1857-8172

Funding

Delineating developmental programs driving tumorigenesis in triple-negative breast cancerR01CA255450 · NCI · STANFORD UNIVERSITY · PI NEWMAN, AARON M · 2021 to 2025
$2.5M
NCI NIH HHS R01 CA255450
6 · The paper itself

Abstract

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisTranscriptomeAnimalsComputational BiologyHumans

Identifiers

PMID39169166

What Socratic holds

Textmetadata
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 Socratic graph.