Evidence map›Paper›PMID 40667011›Full record

ArticlebioRxiv : the preprint server for biology2025

Cell-Type Specific Single-Cell Signatures Reveal Nephrotoxic Drug Affects.

Aditi Kuchi, Jose Miguel Acitores Cortina, Hongyu Liu, Yasaman Fatapour, Jacob Berkowitz, Nicholas P Tatonetti

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

5 · Who and what money

Authors and funding

6 authors.

Aditi KuchiCedars-Sinai Medical System, Department of Computational Biomedicine, 700 N. San Vicente Blvd, West Hollywood, CA 90048, USA.ORCID 0009-0005-4662-5051
Jose Miguel Acitores CortinaCedars-Sinai Medical System, Department of Computational Biomedicine, 700 N. San Vicente Blvd, West Hollywood, CA 90048, USA.ORCID 0009-0007-0361-9334
Hongyu LiuCedars-Sinai Medical System, Department of Computational Biomedicine, 700 N. San Vicente Blvd, West Hollywood, CA 90048, USA.
Yasaman FatapourCedars-Sinai Medical System, Department of Computational Biomedicine, 700 N. San Vicente Blvd, West Hollywood, CA 90048, USA.
Jacob BerkowitzCedars-Sinai Medical System, Department of Computational Biomedicine, 700 N. San Vicente Blvd, West Hollywood, CA 90048, USA.ORCID 0009-0009-8460-7240
Nicholas P TatonettiCedars-Sinai Medical System, Department of Computational Biomedicine, 700 N. San Vicente Blvd, West Hollywood, CA 90048, USA.ORCID 0000-0002-2700-2597

Funding

Precision Pharmacology and Pharmacovigilance: Leveraging AI to address drug safety knowledge gapsR35GM131905 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Nicholas P Tatonetti · 2019 to 2026
$3.3M
NIGMS NIH HHS R35 GM131905
6 · The paper itself

Abstract

Drug-induced acute kidney injury (AKI) affects about 20% of hospitalized AKI patients, a significant contributor to morbidity and mortality. The lack of understanding of the kidney system and functioning of nephrotoxic drugs contributes to hospital-acquired AKI cases. AKI is difficult to predict because of its complex injury mechanism and the numerous pathways through which it manifests. Traditional toxicity biomarkers, like elevated creatinine levels, detect AKI only after significant kidney injury has occurred. Concurrently, advancements in single cell RNA sequencing (scRNAseq) have improved our ability to map cellular heterogeneity within tissues, potentially enabling the study of drug effects at a single cell level. We hypothesized that only particular subtypes of kidney cells may be responsible for observed nephrotoxicity and explain prediction challenges. To test this, we generated cellular response scores for 32 kidney cell types from the Human Cell Atlas and estimated drug effects. We identified significant expression differences in 6 cell types (e.g. Indistinct intercalated cell p = 0.009, Epithelial Progenitor cell, p = 0.04). We also developed an ensemble model that achieved an AUROC of 0.6 across different kidney cell populations - a significant improvement over using traditional bulk RNA sequencing alone. The single-cell transcriptomic signatures we identified potentially reveal unexplained molecular mechanisms of nephrotoxicity.

Indexed as

nephrotoxicitySingle cell RNAseqsingle cell signatures

Identifiers

PMID40667011
PMCPMC12262470

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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.