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Cell-Type Specific Single-Cell Signatures Reveal Nephrotoxic Drug Affects
Aditi Kuchi1, Jose Miguel Acitores Cortina1, Hongyu Liu1
1Cedars-Sinai Medical System, Department of Computational Biomedicine, 700 N. San Vicente Blvd, West Hollywood, CA 90048, USA.
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.
Insights
Single-cell RNA sequencing reveals specific kidney cell subtypes contribute to drug-induced acute kidney injury (AKI). This approach improves prediction of nephrotoxicity compared to traditional methods.
Area of Science:
- Nephrology
- Genomics
- Toxicology
Background:
- Drug-induced acute kidney injury (AKI) is a significant cause of morbidity and mortality in hospitalized patients.
- Current biomarkers for AKI, such as creatinine levels, are only effective after substantial kidney damage has occurred.
- Understanding the cellular mechanisms of drug-induced AKI is limited, hindering effective prediction and prevention.
Purpose of the Study:
- To investigate the hypothesis that specific kidney cell subtypes are responsible for nephrotoxicity.
- To identify cellular targets for predicting and mitigating drug-induced AKI.
- To explore the utility of single-cell RNA sequencing (scRNAseq) in understanding AKI mechanisms.
Main Methods:
- Utilized single-cell RNA sequencing (scRNAseq) data from the Human Cell Atlas.
- Generated cellular response scores for 32 distinct kidney cell types.
- Developed an ensemble model integrating scRNAseq data to predict drug effects.
Main Results:
- Identified significant gene expression differences in 6 specific kidney cell types, including Indistinct intercalated cells and Epithelial Progenitor cells.
- The developed ensemble model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.6.
- This performance represents a significant improvement over traditional bulk RNA sequencing methods for predicting AKI.
Conclusions:
- Single-cell transcriptomic signatures can elucidate previously unexplained molecular mechanisms of nephrotoxicity.
- scRNAseq offers a powerful tool for dissecting cellular heterogeneity in AKI.
- Identifying susceptible cell subtypes may lead to improved prediction and targeted therapies for drug-induced AKI.
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