Summary Cytotoxic compounds are important drugs and research tools. Here, we introduce a method, Scalable Time-lapse Analysis of Cell death Kinetics (STACK), to quantify the kinetics of compound-induced cell death in mammalian cells at the population level. STACK uses live and dead cell markers, high-throughput time-lapse imaging, and mathematical modeling to determine the kinetics of population cell death over time. We used STACK to profile the effects of 1,819 bioactive compounds on cell death in two human cancer cell lines, resulting in a large and freely dataset [doi:10.17632/3pnv5wh5jm.2]. 79 potent lethal compounds common to both cell lines caused cell death with widely divergent kinetics. Thirteen compounds triggered cell death within hours, including the metallophore zinc pyrithione (ZP). Mechanistic studies demonstrated that this rapid onset lethal phenotype was caused in human cancer cells by metabolic disruption and ATP depletion. These results provide the first comprehensive survey of cell death kinetics and analysis of rapid onset lethal compounds.

Cell death can be executed by regulated apoptotic and non-apoptotic pathways, including the iron-dependent process of ferroptosis. Small molecules are essential tools for studying the regulation of cell death. Using time-lapse imaging, and a library of 1,833 bioactive compounds, we assembled a large compendium of kinetic cell death modulatory profiles for inducers of apoptosis and ferroptosis. From this dataset we identify dozens of ferroptosis suppressors, including numerous compounds that appear to act via cryptic off-target antioxidant or iron chelating activities. We show that the FDA-approved drug bazedoxifene acts as a potent radical trapping antioxidant inhibitor of ferroptosis both in vitro and in vivo. ATP-competitive mechanistic target of rapamycin (mTOR) inhibitors, by contrast, are on-target ferroptosis inhibitors. Further investigation revealed both mTOR-dependent and mTOR-independent mechanisms that link amino acid metabolism to ferroptosis sensitivity. These results highlight kinetic modulatory profiling as a useful tool to investigate cell death regulation.

A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. Here we train a machine learning model using functional, mutational and structural features, including new genome essentiality metrics, 3D genome organization and enhancer reporter data to identify deleterious variants in non-coding regions. We assess the model for functional correlates by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements in over 3 Mb of genome sequence. Finally, we explore two user cases that involve indels and the disruption of enhancers associated with a developmental disease. We rank variants in the non-coding genome according to their predicted deleteriousness. The model prioritizes non-coding regions associated with regulation of important genes and with cell viability, an in vitro surrogate of essentiality.

Cell death can be executed by regulated apoptotic and non-apoptotic pathways, including the iron-dependent process of ferroptosis. Small molecules are essential tools for studying the regulation of cell death. Using live-cell, time-lapse imaging, and a library of 1,833 small molecules including FDA-approved drugs and investigational agents, we assemble a large compendium of kinetic cell death 'modulatory profiles' for inducers of apoptosis and ferroptosis. From this dataset we identified dozens of small molecule inhibitors of ferroptosis, including numerous investigational and FDA-approved drugs with unexpected off-target antioxidant or iron chelating activities. ATP-competitive mechanistic target of rapamycin (mTOR) inhibitors, by contrast, were on-target ferroptosis inhibitors. Further investigation revealed both mTOR-dependent and mTOR-independent mechanisms linking amino acid levels to the regulation of ferroptosis sensitivity in cancer cells. These results highlight widespread bioactive compound pleiotropy and link amino acid sensing to the regulation of ferroptosis.

The identification of essential regulatory elements is central to the understanding of the consequences of genetic variation. Here we use novel genomic data and machine learning techniques to map essential regulatory elements and to guide functional validation. We train an XGBoost model using 38 functional and structural features, including genome essentiality metrics, 3D genome organization and enhancer reporter STARR-seq data to differentiate between pathogenic and control non-coding genetic variants. We validate the accuracy of prediction by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements. In neurodevelopmental disorders, the model (ncER, non-coding Essential Regulation) maps essential genomic segments within deletions and rearranged topologically associated domains linked to human disease. We show that the approach successfully identifies essential regulatory elements in the human genome.