The immune system consists of a complex continuum of cell types that communicate with each other and non-immune tissues in homeostasis, and during infections, autoimmunity and cancer. Conventional transcriptional and functional profiling enabled by cell surface marker sorting has revealed a great deal about how specific cell types operate en masse, yet important transcriptional heterogeneity that exists within cell populations remains unexplored. High-throughput single cell RNA-seq can overcome this limitation by profiling entire transcriptomes of thousands of individual cells, revealing cell-to-cell variation by decoding patterns within populations masked in bulk transcriptomes. We will exploit this to dissect the mouse CD4+ T cell compartment, a heterogeneous white blood cell population that initiates adaptive immune responses.
In AIM 1, we will chart the dynamics of in vivo CD4+ cell states in mouse before, during and after immune response challenges. By sequencing thousands of single cell transcriptomes, we will map the landscape of CD4+ T cell states in an unbiased, quantitative and comprehensive way.
In AIM 2, we will predict key transcription factors, cell surface markers, and signalling molecules, including cytokines/chemokines in each cell state through novel computational approaches. Furthermore, our analyses will establish regulatory modules and networks of gene-gene interactions active in immune responses.
In AIM 3, we will (a) confirm the in vivo impact of new cell states by performing adoptive cell transfer assays; and
(b) validate our predictions of regulatory molecules and interactions using a massively parallel CRISPR/Cas knockout screen in vitro.
This powerful integrated approach combines single cell RNA-sequencing, bioinformatics and genetic engineering to dissect CD4+ T cell states, a central compartment of mammalian adaptive immunity, and reveal basic principles of gene regulation.
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