Project description:Ex-vivo drug sensitivity screening (DSS) only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. Here we developed a machine-learning framework to deconvolute bulk RNA expression matched with bulk drug sensitivity into cell subtype composition and cell subtype drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples more accurately than current state-of-the-art methods. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies
Project description:Ex-vivo drug sensitivity screening (DSS) only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. Here we developed a machine-learning framework to deconvolute bulk RNA expression matched with bulk drug sensitivity into cell subtype composition and cell subtype drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples more accurately than current state-of-the-art methods. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies
Project description:Target deconvolution is a crucial but costly and time-consuming task that hinder large-scale profiling for drug discovery. We present a matrix-augmented pooling strategy (MAPS) which mixes multiple drugs into samples with optimized permutation and delineates targets of each drug simultaneously with mathematical processing. We validated this strategy with thermal proteome profiling (TPP) testing of 15 drugs concurrently, increasing experimental throughput by 60x while maintaining high sensitivity and specificity. Benefiting from the lower cost and higher throughput of MAPS, we perform target deconvolution of the 15 drugs across 5 cell lines. Our profiling revealed drug-target interactions can differ vastly in targets and binding affinity across cell lines. We further validated BRAF and CSNK2A are the potential off-targets of bafetinib and abemaciclib, respectively. This work presents the largest target profiling of structurally diverse drugs across multiple cell lines to date.
Project description:Recently, numerous efforts have been devoted to identifying drug targets and binding sites in complex proteomes, which is of great importance in modern drug discovery. In this study, we developed a robust lysine reactivity profiling method to systematically study drug-binding targets and binding sites at the proteome level. This method is based on the principle that binding of a drug to a specific region of target proteins will change the reactivity of lysine residues that are located at this region, and these changes can be detected with an enrichable and lysine reactive probe. Coupled with data-independent acquisition (DIA), the known target proteins and corresponding binding sites were successfully revealed from K562 cell lysates for three model drugs: geldanamycin, staurosporine, and dasatinib. In addition, the drug-induced conformational changes of certain targets were also revealed by our method during the screening of staurosporine. The screening sensitivity of our method revealed from the screening of stuarosporine and dasatinib was comparable with that of thermal proteome profiling (TPP) or machine learning-based limited proteolysis (LiP-Quant). Overall, 21 and 4 kinase targets, including adenosine 5′-triphosphate (ATP)-binding targets, were identified for staurosporine and dasatinib in K562 cell lysates, respectively. We found that target proteins identified by TPP, LiP-Quant, and our method were complementary, emphasizing that the development of new methods that probe different properties of proteins is of great importance in drug target deconvolution. We also envision further applications of our method in proteome-wide probing multiple events that involve lysine reactivity changes.