Evidence map›Paper›PMID 30700234›Full record

ArticleCirculation research2019

SarcTrack.

Christopher N Toepfer, Arun Sharma, Marcelo Cicconet, Amanda C Garfinkel, Michael Mücke, Meraj Neyazi, Jon A L Willcox, Radhika Agarwal, Manuel Schmid, Jyoti Rao and 6 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Circulation research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed
6.1field-weighted citation impact, top 3% of its field
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

74 citing papers in PubMed, 119 citations in OpenAlex.

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14 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 5 institutions in 3 countries.

Christopher N ToepferFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Arun SharmaFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Marcelo CicconetImage and Data Analysis Core (M.C.), Harvard Medical School, Boston, MA.
Amanda C GarfinkelFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Michael MückeCardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine, Berlin, Germany (M.M.).
Meraj NeyaziFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Jon A L WillcoxFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Radhika AgarwalFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Manuel SchmidFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Jyoti RaoFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Jourdan EwoldtBiomedical Engineering, Boston University, MA (J.E., A.C., C.S.C.).
Olivier PourquiéFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Anant ChopraBiomedical Engineering, Boston University, MA (J.E., A.C., C.S.C.).
Christopher S ChenBiomedical Engineering, Boston University, MA (J.E., A.C., C.S.C.).
Jonathan G SeidmanFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Christine E SeidmanFrom the Department of Genetics (C.N.T., A.S., A.C.G., M.N., J.A.L.W., R.A., M.S., J.R., O.P., J.G.S., C.E.S.), Harvard Medical School, Boston, MA.
Harvard University · USBoston University · USHarvard Stem Cell Institute · USCharité - Universitätsmedizin Berlin · DEMedizinische Hochschule Hannover · DE

Funding

Mapping Transcriptional Networks in Cardiac DevelopmentU01HL098166 · NHLBI · HARVARD MEDICAL SCHOOL · PI SEIDMAN, JONATHAN G · 2009 to 2014
$9.5M
Defining Pathways from Gene Mutation to Heart FailureR01HL080494 · NHLBI · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI CHEN, CHRISTOPHER S, SEIDMAN, CHRISTINE E · 2005 to 2024
$9.4M
Role of Energy Metabolism in Patterning the Vertebrate Musculo-Skeletal AxisR01HD085121 · NICHD · BRIGHAM AND WOMEN'S HOSPITAL · PI OLIVIER POURQUIE · 2016 to 2026
$6.8M
Molecular Signaling in Hypertrophic CardiomyopathyR01HL084553 · NHLBI · HARVARD MEDICAL SCHOOL · PI SEIDMAN, JONATHAN G · 2007 to 2022
$5.9M
Howard Hughes Medical InstituteNHLBI NIH HHS R01 HL084553NHLBI NIH HHS U01 HL098166NICHD NIH HHS R01 HD085121Wellcome Trust 206466/Z/17/Z
6 · The paper itself

Abstract

rationaleHuman induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in combination with CRISPR/Cas9 genome editing provide unparalleled opportunities to study cardiac biology and disease. However, sarcomeres, the fundamental units of myocyte contraction, are immature and nonlinear in hiPSC-CMs, which technically challenge accurate functional interrogation of contractile parameters in beating cells. Furthermore, existing analysis methods are relatively low-throughput, indirectly assess contractility, or only assess well-aligned sarcomeres found in mature cardiac tissues.

objectiveWe aimed to develop an analysis platform that directly, rapidly, and automatically tracks sarcomeres in beating cardiomyocytes. The platform should assess sarcomere content, contraction and relaxation parameters, and beat rate. METHODS AND

resultsWe developed SarcTrack, a MatLab software that monitors fluorescently tagged sarcomeres in hiPSC-CMs. The algorithm determines sarcomere content, sarcomere length, and returns rates of sarcomere contraction and relaxation. By rapid measurement of hundreds of sarcomeres in each hiPSC-CM, SarcTrack provides large data sets for robust statistical analyses of multiple contractile parameters. We validated SarcTrack by analyzing drug-treated hiPSC-CMs, confirming the contractility effects of compounds that directly activate (CK-1827452) or inhibit (MYK-461) myosin molecules or indirectly alter contractility (verapamil and propranolol). SarcTrack analysis of hiPSC-CMs carrying a heterozygous truncation variant in the myosin-binding protein C ( MYBPC3) gene, which causes hypertrophic cardiomyopathy, recapitulated seminal disease phenotypes including cardiac hypercontractility and diminished relaxation, abnormalities that normalized with MYK-461 treatment.

conclusionsSarcTrack provides a direct and efficient method to quantitatively assess sarcomere function. By improving existing contractility analysis methods and overcoming technical challenges associated with functional evaluation of hiPSC-CMs, SarcTrack enhances translational prospects for sarcomere-regulating therapeutics and accelerates interrogation of human cardiac genetic variants.

Indexed as

AlgorithmsSoftwareBenzylaminesCardiovascular AgentsCarrier ProteinsClustered Regularly Interspaced Short Palindromic RepeatsComputer-Aided DesignFluorescenceHumansInduced Pluripotent Stem CellsMicroscopy, Atomic ForceMyocardial ContractionMyocytes, CardiacMyosin Binding Protein CMyosinsPropranololBenzylaminesCardiovascular AgentsCarrier ProteinsMYK-461Myosin Binding Protein CMyosinsomecamtiv mecarbilPropranololUracilUreaVerapamilcardiomyocyte contractilitycell imaginghypertrophic cardiomyopathyinduced pluripotent stem cellsMYK-461myosin binding protein-Csarcomeres

Identifiers

PMID30700234
PMCPMC6485312
OpenAlexW2913765509

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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.