Image based phenotypic profiling of single-cell responses to perturbations

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): Increasingly, disease states and responses to therapeutics are seen to be comprised of a heterogeneous mix of cellular states and responses. Insight into the nature of diverse cellular responses to perturbations has immediate applications to pharmacology and disease treatment. Identifying physiologically or clinically important subpopulations, such as cancer drug-resistant or hormone-insensitive cells, is. an important step towards identifying diagnostic biomarkers and developing targeted therapies. Measuring high-dimensional physiological phenotypes of large numbers of individual cells in diverse conditions will enable the characterization of heterogeneous cellular responses to perturbations, and the identification of discrete subpopulations of distinct phenotypic states. Our long term objects are therefore: 1) enabling the detection and comparison of single-cell responses to broad ranges of perturbations; 2) elucidating physiological mechanisms of cellular perturbations; and 3) identifying physiologically important subpopulations. We propose to use immunofluorescence microscopy to monitor complex cellular responses to systematic drug treatments. For the proposed aims, drug treatments are ideal choices for perturbations as they are fast- acting, titratable, and reliable methods for eliciting diverse cellular responses. The specific aims are to: 1. Increase the discriminative capacity of single-cell phenotypic readouts. Single-cell phenotypes, measured independently from small numbers of fluorescent markers, may not distinguish complex cellular states. We will expand readout capacity by increasing the number of markers and quality of readouts per cell, and by correlating readouts of different markers from different cells. 2. Classify perturbation effects using single-cell phenotypic readouts. Classifying the effects of perturbations requires the extraction of informative features from single-cell phenotypic readouts. We will apply new methods for feature selection and drug profiling to the problems of identifying multiphasic and off-target drug effects, and predicting the response to combinations of drug treatments. 3. Represent population heterogeneity as subpopulations of distinct phenotypic states. We will characterize drugs in terms of their effects on these subpopulations of cells.