Evidence map›Paper›PMID 41446238›Full record

ArticlebioRxiv : the preprint server for biology2025

Cell Barcoding Reveals Lineage-dependent Outcomes in hiPSC Cardiac Differentiation.

Sogu Sohn, Daylin Morgan, Cody Callahan, Katelyn Dockery, Amy Brock, Janet Zoldan

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Sogu SohnDepartment of Biomedical Engineering, The University of Texas at Austin.ORCID 0000-0001-5467-0235
Daylin MorganDepartment of Biomedical Engineering, The University of Texas at Austin.
Cody CallahanDepartment of Biomedical Engineering, The University of Texas at Austin.
Katelyn DockeryDepartment of Biomedical Engineering, The University of Texas at Austin.
Amy BrockDepartment of Biomedical Engineering, The University of Texas at Austin.
Janet ZoldanDepartment of Biomedical Engineering, The University of Texas at Austin.

Funding

Elucidation and improved control of human induced pluripotent stem cell cardiac differentiation by using single-guide RNA-based cellular barcoding to track and manipulate lineagesF31HL170717 · NHLBI · UNIVERSITY OF TEXAS AT AUSTIN · PI SOHN, SOGU · 2023 to 2025
$100k
NHLBI NIH HHS F31 HL170717
6 · The paper itself

Abstract

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have potential applications in treating cardiovascular disease but are currently limited in their clinical translation. A primary limitation is the poor clinical scalability of hiPSC-CMs, with the heterogeneity of hiPSC cardiac differentiation significantly contributing to this limitation. We hypothesize that clinical scalability can be improved by tracking and controlling hiPSC clonal heterogeneity, a variable often overlooked in current differentiation approaches. "Fate priming", wherein clonal lineage identity determines differentiation fate, has been demonstrated in other stem cell differentiation pathways. We investigated fate priming in hiPSC cardiac differentiation using the ClonMapper cell barcoding platform to label, track, and isolate distinct hiPSC lineages from the same cell line. We show that certain hiPSC lineages preferentially differentiate into hiPSC-CMs or non-CMs. After isolating lineages with apparent fate priming, we found significant differences in cardiac differentiation outcomes between these single-clone populations and heterogeneous, multi-clone hiPSC populations. These findings indicate that lineage identity influences hiPSC cardiac differentiation outcomes.

Indexed as

Cell DifferentiationCell LineageInduced Pluripotent Stem CellsTissue Engineering

Identifiers

PMID41446238
PMCPMC12724671

What OpenQuestion holds

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Registered trials

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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.