Computational deconvolution of drug sensitivity via single-cell–to–bulk transcriptome mapping

Analyses routinely conducted in both fundamental research and biomedical studies often report aggregate signals derived from heterogeneous mixtures of cells. These mixtures reflect the intrinsic anatomical and functional complexity of the tissues and organs from which they are derived. Such bulk measurements obscure cell-specific signals, thereby hindering accurate interpretation of the underlying biological processes. Here, we introduce CLIMB and CLIFF, a two-step machine-learning framework that deconvolves bulk RNA-seq into cell-type composition and expression, and then resolves bulk drug sensitivity measurements into cell subtype- and single-cell-specific profiles. We first validate CLIMB using in-vitro mixtures of leukemic cell lines with experimentally known cell-type proportions, then extend this validation to large-scale, cross-dataset pseudo-bulk analyses built from independent scRNA-seq references across two disease contexts and five cancer types - mimicking the reference-to-target mismatches encountered in real applications - and show that CLIMB accurately infers the cellular composition of bulk samples, outperforming state-of-the-art bulk deconvolution methods. We emphasize CLIMB's effectiveness in acute myeloid leukemia, where it offers highly accurate estimation of leukemic stem cell fractions across three test datasets and three patient cohorts. We then validate CLIFF, our algorithm for estimating cell subtype- and single-cell-specific drug sensitivity, first on the same in-vitro cell line mixtures using experimentally measured drug responses, and then through in-depth, realistic-scale pseudo-bulk simulations. We applied our deconvolution strategy to the beatAML cohort dataset, currently the most extensive database of ex-vivo drug screening data. This analysis generated a cell subtype-resolved drug sensitivity landscape for 121 compounds, revealing multiple compounds putatively active against leukemic stem cells, including midostaurin, A-674563, SNS-032, and foretinib. Collectively, our work introduces a general computational framework for resolving bulk functional measurements at cell-type and single-cell resolution, providing an attractive new computational tool for drug development and precision medicine.