Evidence map›Paper›PMID 37486064›Full record

ArticleAmerican journal of physiology. Cell physiology2023

A single-cell CRISPRi platform for characterizing candidate genes relevant to metabolic disorders in human adipocytes.

Ewa Bielczyk-Maczynska, Disha Sharma, Montgomery Blencowe, Peter Saliba Gustafsson, Michael J Gloudemans, Xia Yang, Ivan Carcamo-Orive, Martin Wabitsch, Katrin J Svensson, Chong Y Park and 3 more

Open access · greenAbstract read
In one paragraph

Article in American journal of physiology. Cell physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. Colocalization and functional analyses identifyAmerican journal of physiology. Endocrinology and metabolism · 2026
    Article
  4. Article
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

13 authors at 4 institutions in 2 countries.

Ewa Bielczyk-MaczynskaDivision of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.ORCID 0000-0002-0558-1188
Disha SharmaDivision of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
Montgomery BlencoweDepartment of Integrative Biology and Physiology, University of California, Los Angeles, California, United States.
Peter Saliba GustafssonDivision of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
Michael J GloudemansDepartment of Pathology, Stanford University School of Medicine, Stanford, California, United States.
Xia YangDepartment of Integrative Biology and Physiology, University of California, Los Angeles, California, United States.
Ivan Carcamo-OriveDivision of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
Martin WabitschDepartment of Pediatrics and Adolescent Medicine, Center for Rare Endocrine Diseases, Division of Pediatric Endocrinology and Diabetes, Ulm University Medical Centre, Ulm, Germany.ORCID 0000-0001-6795-8430
Katrin J SvenssonStanford Diabetes Research Center, Stanford University School of Medicine, Stanford, California, United States.ORCID 0000-0001-5376-5128
Chong Y ParkDivision of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
Thomas QuertermousDivision of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
Joshua W KnowlesDivision of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
Jiehan LiDivision of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
Cardiovascular Institute of the South · USUniversity of California, Los Angeles · USKarolinska University Hospital · SEStanford Medicine · US

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
Stanford Center for Connecting DNA Variants to Function and PhenotypeUM1HG011972 · NHGRI · STANFORD UNIVERSITY · PI JESSE M ENGREITZ, THOMAS QUERTERMOUS · 2021 to 2026
$10.5M
Causal variant association mechanisms in TCF21 binding coronary disease lociR01HL134817 · NHLBI · STANFORD UNIVERSITY · PI THOMAS QUERTERMOUS · 2017 to 2026
$6.2M
Systems Genetics Dissection of Non-alcoholic SteatohepatitisR01DK117850 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Aldons Jake Lusis · 2019 to 2026
$4.6M
The SMAD3 signaling network in coronary artery disease riskR01HL139478 · NHLBI · STANFORD UNIVERSITY · PI THOMAS QUERTERMOUS · 2018 to 2026
$3.7M
Mechanisms controlling fructose uptake and utilizationR01DK125260 · NIDDK · STANFORD UNIVERSITY · PI Katrin Jennifer Svensson · 2020 to 2026
$3.5M
Characterization of novel insulin resistance genes by gene editing, high-throughput phenotyping and in vivo studiesR01DK120565 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2019 to 2023
$3.2M
PDGFD regulates a transcriptional network to modulate smooth muscle cell transition and coronary artery disease riskR01HL156846 · NHLBI · STANFORD UNIVERSITY · PI QUERTERMOUS, THOMAS · 2021 to 2024
$2.7M
Identifying tobacco-genetic interactions through study of the aryl hydrocarbon receptor pathway.R01HL151535 · NHLBI · STANFORD UNIVERSITY · PI QUERTERMOUS, THOMAS · 2021 to 2024
$2.7M
Mechanisms of NAT2 Regulation of Insulin Resistance and Mitochondrial DysfunctionR01DK116750 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2019 to 2023
$2.4M
Beyond GWAS of insulin resistance: An integrated approach to translate genetic association to functionR01DK106236 · NIDDK · STANFORD UNIVERSITY · PI KNOWLES, JOSHUA WILEY · 2016 to 2020
$2.4M
American Heart Association-American Stroke Association 18POST34030448American Heart Association-American Stroke Association 23IPA1042031NHGRI NIH HHS UM1 HG011972NHLBI NIH HHS R01 HL134817NHLBI NIH HHS R01 HL139478NHLBI NIH HHS R01 HL145708NHLBI NIH HHS R01 HL151535NHLBI NIH HHS R01 HL156846NIDDK NIH HHS K99 DK111916NIDDK NIH HHS P30 DK116074NIDDK NIH HHS R00 DK111916NIDDK NIH HHS R01 DK106236NIDDK NIH HHS R01 DK107437NIDDK NIH HHS R01 DK116750NIDDK NIH HHS R01 DK117850NIDDK NIH HHS R01 DK120565NIDDK NIH HHS R01 DK125260NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

CROP-Seq combines gene silencing using CRISPR interference with single-cell RNA sequencing. Here, we applied CROP-Seq to study adipogenesis and adipocyte biology. Human preadipocyte SGBS cell line expressing KRAB-dCas9 was transduced with a sgRNA library. Following selection, individual cells were captured using microfluidics at different timepoints during adipogenesis. Bioinformatic analysis of transcriptomic data was used to determine the knockdown effects, the dysregulated pathways, and to predict cellular phenotypes. Single-cell transcriptomes recapitulated adipogenesis states. For all targets, over 400 differentially expressed genes were identified at least at one timepoint. As a validation of our approach, the knockdown of

Indexed as

Metabolic DiseasesRNA, Guide, CRISPR-Cas SystemsAdipocytesAdipogenesisCell DifferentiationCell LineHumansRNA, Guide, CRISPR-Cas SystemsadipocyteadipogenesisCRISPRiSGBSsingle-cell RNA-Seq

Identifiers

PMID37486064
PMCPMC10635647
OpenAlexW4385185892

What OpenQuestion holds

Textmetadata
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.