Evidence map›Paper›PMID 30865899›Full record

ArticleCell reports2019

Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes.

Zhou Fang, Chen Weng, Haiyan Li, Ran Tao, Weihua Mai, Xiaoxiao Liu, Leina Lu, Sisi Lai, Qing Duan, Carlos Alvarez and 6 more

Open access · goldAbstract read
In one paragraph

Article in Cell reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
86citing papers in PubMed, 2 pooled it
4.6field-weighted citation impact, top 4% 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

86 citing papers in PubMed, 2 syntheses or guidelines pooled it, 132 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. ProtectiveiScience · 2026
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26 more citing papers are in PubMed but not listed here.

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

16 authors at 5 institutions in 2 countries.

Zhou FangDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Chen WengDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Haiyan LiDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Ran TaoCenter for Cancer and Immunology Research, Brain Tumor Institute, Children's National Medical Center, Washington, D.C. 20010, USA.
Weihua MaiDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; Department of Neurology, the Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province 519000, China.
Xiaoxiao LiuDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Leina LuDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Sisi LaiDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Qing DuanDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.
Carlos AlvarezDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; The Biomedical Sciences Training Program (BSTP), School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Peter ArvanDivision of Metabolism, Endocrinology, and Diabetes, University of Michigan Medical Center, Ann Arbor, MI 48109, USA.
Anthony Wynshaw-BorisDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
Yun LiDepartment of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA.
Yanxin PeiCenter for Cancer and Immunology Research, Brain Tumor Institute, Children's National Medical Center, Washington, D.C. 20010, USA.
Fulai JinDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; Department of Population and Quantitative Health Sciences, Department of Electrical Engineering and Computer Science, Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH 44106, USA. Electronic address: fxj45@case.edu.
Yan LiDepartment of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA. Electronic address: yxl1379@case.edu.
Case Western Reserve University · USChildren's National · USUniversity of North Carolina at Chapel Hill · USFifth Affiliated Hospital of Sun Yat-sen University · CNUniversity of Michigan–Ann Arbor · US

Funding

Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Mehboob A Hussain · 2013 to 2026
$24.3M
Robust mapping of chromatin loops from sparse or single cell Hi-C data with DeepLoopR01HG009658 · NHGRI · CASE WESTERN RESERVE UNIVERSITY · PI Fulai Jin · 2017 to 2026
$5.1M
Elucidating human beta cell transcriptional regulome with low-input genomic technologiesR01DK113185 · NIDDK · CASE WESTERN RESERVE UNIVERSITY · PI Yan Li · 2018 to 2026
$4.0M
NHGRI NIH HHS R01 HG009658NIDDK NIH HHS P30 DK020572NIDDK NIH HHS R01 DK113185
6 · The paper itself

Abstract

Identification of human disease signature genes typically requires samples from many donors to achieve statistical significance. Here, we show that single-cell heterogeneity analysis may overcome this hurdle by significantly improving the test sensitivity. We analyzed the transcriptome of 39,905 single islets cells from 9 donors and observed distinct β cell heterogeneity trajectories associated with obesity or type 2 diabetes (T2D). We therefore developed RePACT, a sensitive single-cell analysis algorithm to identify both common and specific signature genes for obesity and T2D. We mapped both β-cell-specific genes and disease signature genes to the insulin regulatory network identified from a genome-wide CRISPR screen. Our integrative analysis discovered the previously unrecognized roles of the cohesin loading complex and the NuA4/Tip60 histone acetyltransferase complex in regulating insulin transcription and release. Our study demonstrated the power of combining single-cell heterogeneity analysis and functional genomics to dissect the etiology of complex diseases.

Indexed as

Genetic HeterogeneityTranscriptomeAnimalsCell Cycle ProteinsCells, CulturedChromosomal Proteins, Non-HistoneCohesinsCRISPR-Cas SystemsDiabetes Mellitus, Type 2Gene Expression ProfilingHEK293 CellsHistone AcetyltransferasesHumansInsulinInsulin-Secreting CellsMiceCell Cycle ProteinsChromosomal Proteins, Non-HistoneCohesinsHistone AcetyltransferasesInsulinbioinformaticsCellular heterogeneityCRISPR screendiabetesDrop-seqfunctional genomicsobesitypancreatic isletsingle cellβ cell

Identifiers

PMID30865899
PMCPMC6573026
OpenAlexW2921398065

What OpenQuestion holds

Textmetadata
LicenceTDM
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.