Evidence mapPaperPMID 41674713Full record

ReviewQuantitative biology (Beijing, China)2025

Cell lineage tracing: Methods, applications, and challenges.

Shanjun Mao, Chenyang Zhang, Runjiu Chen, Shan Tang, Xiaodan Fan, Jie Hu

Abstract readReview
In one paragraph

Review in Quantitative biology (Beijing, China), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
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  5. Review
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

6 authors.

Shanjun MaoDepartment of Statistics Hunan University Changsha China.
Chenyang ZhangDepartment of Statistics Hunan University Changsha China.
Runjiu ChenDepartment of Statistics Hunan University Changsha China.
Shan TangDepartment of Statistics Hunan University Changsha China.
Xiaodan FanDepartment of Statistics The Chinese University of Hong Kong Hong Kong China.
Jie HuSchool of Mathematical Science Xiamen University Xiamen China.ORCID https://orcid.org/0000-0002-0597-4176

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell lineage tracing is a crucial technique for understanding cell fate and lineage relationships, with wide applications in developmental biology, tissue regeneration, and disease progression studies. Over the years, experimental cell lineage tracing methods have advanced from early labeling techniques to modern genetic tools such as CRISPR-Cas9-based barcoding, whereas computational methods have emerged to analyze high-dimensional data from single-cell sequencing and other omics technologies. This paper reviews both experimental and computational methods, highlighting their respective strengths, limitations, and synergies. Experimental techniques focus on labeling and tracking cells, whereas computational approaches reconstruct lineage relationships and model cellular dynamics. Despite significant progress, challenges remain, including issues with accuracy, resolution, multi-omics integration, and scalability. Future directions involve improvements in experimental techniques and the development of computational methods enhanced by advancements in artificial intelligence. These innovations are expected to drive the field forward, offering potential applications in uncovering the mysteries of life.

Indexed as

cell lineage tracingCRISPR‐Cas9RNA velocitysingle‐cell sequencing

Identifiers

PMID41674713
PMCPMC12806085

What Socratic 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 Socratic graph.