Evidence map›Paper›PMID 41509909›Full record

ArticleiScience2026

Constructing the spatiotemporal atlas of single-cell lineage trajectories in stereotypic biological structures.

Ran Wang, Xianfa Yang, Chengfei Lian, Jianjie Wang, Jiehui Chen, Yun Qian, Yaochen Xu, Liantang Wang, John C Marioni, Patrick P L Tam and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Ran WangKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
Xianfa YangGuangzhou National Laboratory, Guangzhou 510005, China.
Chengfei LianKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
Jianjie WangKey Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
Jiehui ChenState Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China.
Yun QianState Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China.
Yaochen XuState Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China.
Liantang WangSchool of Mathematics, Northwest University, Xi'an 710127, China.
John C MarioniCancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
Patrick P L TamEmbryology Research Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia.
Naihe JingState Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spatial transcriptomics technologies have emerged as instrumental tools for elucidating cellular heterogeneity and molecular regulation within the complex tissue microenvironment, but are constrained by insufficient gene recovery or an inability to achieve intact single-cell resolution. By integrating spatial transcriptomics and single-cell RNA sequencing technologies, we develop a mathematical method of single-cell resolved spatiotemporal (SCST) mapping that comprises tiered algorithms for constructing the spatial molecular atlas of the biospecimen at single-cell resolution across a timeline of development. The embedded spatial-smoothing algorithm in SCST significantly enhances the spatial mapping accuracy of single cells, thereby improving the fidelity of the annotation of cell identity to the equivalent

Indexed as

Computational bioinformaticsDevelopmental biologyTranscriptomics

Identifiers

PMID41509909
PMCPMC12774768

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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 OpenQuestion graph.