Evidence map›Paper›PMID 42734752›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

Jinglong Zhang, Kristen J Brennand, Bin Zhang, Minghui Wang, Aiqun Li

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Jinglong ZhangDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Kristen J BrennandDepartments of Psychiatry and Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT, USA.
Bin ZhangDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Minghui WangDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. minghui.wang@mssm.edu.
Aiqun LiDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. aiqun.li@mssm.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Indexed as

Gene Expression ProfilingSingle-Cell AnalysisTranscriptomeAlzheimer DiseaseClustered Regularly Interspaced Short Palindromic RepeatsCRISPR-Cas SystemsHigh-Throughput Nucleotide SequencingHumansInduced Pluripotent Stem CellsNeuronsRNA, Guide, CRISPR-Cas SystemsSingle-Cell Gene Expression AnalysisRNA, Guide, CRISPR-Cas SystemsAlzheimer’s diseaseCRISPRECCITE-seqGene perturbationHuman-induced pluripotent stem cells (hiPSCs)Signaling pathways

Identifiers

PMID42734752

What OpenQuestion holds

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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.