Evidence map›Paper›PMID 38530829›Full record

ArticlePloS one2024

MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles.

Hiba Kobeissi, Javiera Jilberto, M Çağatay Karakan, Xining Gao, Samuel J DePalma, Shoshana L Das, Lani Quach, Jonathan Urquia, Brendon M Baker, Christopher S Chen and 2 more

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

12 authors.

Hiba KobeissiDepartment of Mechanical Engineering, Boston University, Boston, MA, United States of America.
Javiera JilbertoDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.
M Çağatay KarakanDepartment of Mechanical Engineering, Boston University, Boston, MA, United States of America.ORCID 0000-0002-7587-8913
Xining GaoDepartment of Biomedical Engineering, Boston University, Boston, MA, United States of America.
Samuel J DePalmaDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.ORCID 0000-0001-6708-5019
Shoshana L DasDepartment of Biomedical Engineering, Boston University, Boston, MA, United States of America.
Lani QuachDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.
Jonathan UrquiaDepartment of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, United States of America.
Brendon M BakerDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.
Christopher S ChenDepartment of Biomedical Engineering, Boston University, Boston, MA, United States of America.
David NordslettenDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.
Emma LejeuneDepartment of Mechanical Engineering, Boston University, Boston, MA, United States of America.ORCID 0000-0001-8099-3468

Funding

Tissue Engineering and RegenerationT32DE007057 · NIDCR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DAVID H. KOHN · 1985 to 2026
$17.3M
Training Program in Translational Cardiovascular Research and EntrepreneurshipT32HL125242 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MICHELE, DANIEL E · 2015 to 2024
$1.9M
NHLBI NIH HHS T32 HL125242NIDCR NIH HHS T32 DE007057
6 · The paper itself

Abstract

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute," a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, runs without user intervention, requires minimal input parameter setting selection, and is computationally inexpensive. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community.

Indexed as

Induced Pluripotent Stem CellsCell Culture TechniquesCell DifferentiationHumansMyocytes, Cardiac

Identifiers

PMID38530829
PMCPMC10965069

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.