Cell-Type Specific Single-Cell Signatures Reveal Nephrotoxic Drug Affects
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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.
The prediction and early detection of drug-induced kidney injury is a significant clinical challenge since physicians rely on biomarkers that only become elevated after substantial kidney damage has occurred, limiting opportunities for intervention and patient protection. Our investigation utilized single-cell data and available drug toxicity information to examine how individual kidney cell populations respond to potentially harmful medications. We hypothesized that specific kidney cell subtypes are primarily responsible for observed drug toxicity, which may explain the difficulties in predicting drug-induced kidney injury.
Through comprehensive analysis of 32 distinct kidney cell types, we identified six specific cellular populations that demonstrate differential responses to nephrotoxic compounds. We subsequently developed models that demonstrate superior predictive performance compared to analytical approaches using bulk RNA sequencing data. Our methodology represents a substantial advancement in precision medicine approaches to drug safety. These findings have important implications for clinical practice and patient safety. The cellular signatures we identified may enable earlier detection of kidney injury risk, potentially allowing clinicians to modify treatment regimens before irreversible damage occurs. Our work establishes a foundation for improved drug safety protocols and may contribute to reducing medication-related kidney injury in hospitalized patients.
