Evidence map›Paper›PMID 35061661›Full record

ArticlePLoS genetics2022

Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation.

Reem Elorbany, Joshua M Popp, Katherine Rhodes, Benjamin J Strober, Kenneth Barr, Guanghao Qi, Yoav Gilad, Alexis Battle

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 1 pooled it
7.6field-weighted citation impact, top 2% 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

55 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.

  1. Pooled it
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  18. Characterizing the Cellular Constituents of Proximal Airway Disease in Granulomatosis With Polyangiitis.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2025
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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

8 authors at 2 institutions in 1 country.

Reem ElorbanyInterdisciplinary Scientist Training Program, University of Chicago, Chicago, Illinois, United States of America.ORCID 0000-0002-2059-6767
Joshua M PoppDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID 0000-0001-8522-5732
Katherine RhodesDepartment of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.ORCID 0000-0002-0631-3994
Benjamin J StroberDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID 0000-0003-2969-2808
Kenneth BarrDepartment of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.ORCID 0000-0002-0769-7053
Guanghao QiDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
Yoav GiladDepartment of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.ORCID 0000-0001-8284-8926
Alexis BattleDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID 0000-0002-5287-627X
Johns Hopkins University · USUniversity of Chicago · US

Funding

MEDICAL SCIENTIST TRAININGT32GM007281 · NIGMS · UNIVERSITY OF CHICAGO · PI CLARK, MARCUS RAMSAY · 1985 to 2022
$32.1M
Utilizing Technology and AI Approaches to Facilitate Independence andResilience in Older AdultsP30AG073104 · NIA · JOHNS HOPKINS UNIVERSITY · PI Rama Chellappa · 2021 to 2026
$31.2M
BIOMEDICAL ENGINEERING TRAINING PROGRAMT32GM007057 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI KANOLD, PATRICK O, KARCHIN, RACHEL · 1985 to 2023
$10.4M
Integrating genomics and gene expression analyses to map CVD-associated lociR01HL092206 · NHLBI · UNIVERSITY OF CHICAGO · PI GILAD, YOAV · 2009 to 2021
$7.3M
Characterizing and Understanding Variation in Gene Regulatory Mechanisms Within and Between Species'R35GM131726 · NIGMS · UNIVERSITY OF CHICAGO · PI Yoav Gilad · 2019 to 2026
$3.7M
Modeling the dynamicimpact of rare and common genetic variation on gene expression anddiseaseR35GM139580 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI BATTLE, ALEXIS · 2021 to 2025
$3.1M
Methods for analysis of regulatory variation in cellular differentiationR01GM120167 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI BATTLE, ALEXIS · 2016 to 2019
$1.9M
Pre-Doctoral Training Program in Computational MedicineT32GM119998 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI MAC GABHANN, FEILIM C · 2017 to 2021
$1.5M
Mapping Functional Variation in Gene Regulatory Dynamics during Human Cardiomyocyte DifferentiationF31HL146171 · NHLBI · UNIVERSITY OF CHICAGO · PI RHODES, KATHERINE LOUISE · 2019 to 2020
$90k
NHLBI NIH HHS F31 HL146171NHLBI NIH HHS R01 HL092206NIA NIH HHS P30 AG073104NIGMS NIH HHS R01 GM120167NIGMS NIH HHS R35 GM131726NIGMS NIH HHS R35 GM139580NIGMS NIH HHS T32 GM007057NIGMS NIH HHS T32 GM007281NIGMS NIH HHS T32 GM119998
6 · The paper itself

Abstract

Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.

Indexed as

Cell Culture TechniquesCell DifferentiationCell LineCell LineageGene Expression ProfilingGene Expression RegulationHumansInduced Pluripotent Stem CellsMyocytes, CardiacRNA-SeqSingle-Cell Analysis

Identifiers

PMID35061661
PMCPMC8809621
OpenAlexW4225695255

What OpenQuestion holds

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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 OpenQuestion graph.