Evidence map›Paper›PMID 39277847›Full record

ArticleHuman molecular genetics2024

Deciphering single-cell gene expression variability and its role in drug response.

Sizhe Liu, Liang Chen

Abstract read
In one paragraph

Article in Human molecular genetics, 2024. 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

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

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

2 authors.

Sizhe LiuThomas Lord Department of Computer Science, University of Southern California, 941 Bloom Walk, Los Angeles, CA 90089, United States.
Liang ChenDepartment of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States.ORCID 0000-0001-6164-4553

Funding

The Role of NMD in Cortical Neural Progenitor CellsR01NS125276 · NINDS · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI Sika Zheng · 2022 to 2026
$2.6M
Understanding the NMD regulatory path from genetic variation to phenotypesR01GM137428 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI CHEN, LIANG · 2020 to 2023
$1.6M
NIGMS NIH HHS R01 GM137428NIH HHS R01NS125276NINDS NIH HHS R01 NS125276
6 · The paper itself

Abstract

The effectiveness of drug treatments is profoundly influenced by individual responses, which are shaped by gene expression variability, particularly within pharmacogenes. Leveraging single-cell RNA sequencing (scRNA-seq) data, our study explores the extent of expression variability among pharmacogenes in a wide array of cell types across eight different human tissues, shedding light on their impact on drug responses. Our findings broaden the established link between variability in pharmacogene expression and drug efficacy to encompass variability at the cellular level. Moreover, we unveil a promising approach to enhance drug efficacy prediction. This is achieved by leveraging a combination of cross-cell and cross-individual pharmacogene expression variation measurements. Our study opens avenues for more precise forecasting of drug performance, facilitating tailored and more effective treatments in the future.

Indexed as

Single-Cell AnalysisGene Expression ProfilingGene Expression RegulationHumansPharmacogeneticsSequence Analysis, RNATranscriptomedrug efficacyexpression variationmachine learningpharmacogenesingle-cell RNA-seq

Identifiers

PMID39277847
PMCPMC11578114

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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.