Reconstructing the circuits that control how immune cells adopt specific states and control an immune response is a major challenge, due to the diversity of cell types, the spectra of cell states, and their temporal changes over the course of the response. The combined advent of massive scale single cell genomics and large scale genetic CRIPSR screens suddenly provide an extraordinary opportunity to reconstruct a systems level model of the complex molecular and cellular processes that unfold during an immune response, including the cell types and states that compose the response, the regulators that control them, how cells affect each other, and how they integrate to form physiological and pathological responses. However, to learn such knowledge from massive, noisy and heterogeneous data there is an enormous need for sophisticated, innovative, robust, and scalable computational methods. These span include early data quality control and processing that addresses noise such as false negatives in single cell RNA-seq profiles, and the inference of the regulators that control cell types, states and temporal transitions, and guidelines for adaptive experimental design, from the number of cells to analyze to the choice of genes to perturb. In particular, because the current capacity to perturb genes in vivo is limited, ranking candidates for perturbation and refining their predictions and ranking as new perturbation data is collected is key for successful discoveries. Finally, because of the complexity of the data and of the underlying biology, achieving insights requires a close partnership between immunologists and computational experts. Unfortunately, successful methods and foundational datasets often remain out of reach for immunologists, absent software and data portals that would serve those. Here, we will leverage our extensive and pioneering expertise in computational biology for systems immunology ? which we developed, harnessed and demonstrated in a long-term and close collaboration with the members of this Program ? to develop and deploy computational methods and tools to bridge the gap between data and knowledge in systems immunology, and apply them in the context of the program?s projects. Specifically, we will develop, establish and maintain cutting-edge tools for the analysis of single cell RNA-seq data including identification of cell types, states, temporal transitions, and the associated pathways and signature, with high efficiency compatible with massive scale data (Aim 1). We will develop, establish and maintain cutting-edge tools to predict key regulators associated with these cell types, states and responses, as they unfold over time, and develop and use methods that rank regulators and select targets for genetic manipulation in CRISPR screens in vivo, followed by adaptive identification of new regulators following additional genetic screens (Aim 2). We will establish and maintain a public portal for all data, analyses and methods we collect, and release software tools as part of our open source Trinity package (Aim 3). Throughout, we will serve as an effective nexus for all project and core leads for all analysis purposes.
