Department of Cellular and Tissue Genomics - Oncology, Genentech

Cell state transitions are at the core of biology: they determine how cells differentiate into each cell type in our body, respond to their environment, change in disease and are altered by treatments. Understanding the rules that govern such cell state decisions is at the heart of learning how living organisms work, and is key for designing long-lasting treatments to reverse disease one cell at a time.

Recent advances in single-cell genomics have unlocked the potential to dissect cell state transitions with unprecedented scale and resolution. First, single-cell genomics allows deep characterization of cell states as high-dimensional phenotypes, without needing to decide a priori what aspects of the cell state to experimentally quantify. Second, pooled screens coupled with single-cell readouts enable dissecting the roles of increasingly large numbers of perturbations in parallel and in a directly causal direction, allowing us to learn the latent manifold of cell states, as well as the connections between such states as measured by experimental perturbations. These fundamental advances in our quantitative and predictive understanding of cell state transitions crucially depend on the development of computational models that are able to extract generalizable principles for how cells work.

In this primer, I will describe the analytical challenges and opportunities for inferring cell state transitions from a variety of different single-cell screening modalities, including genetic -, chemical - and treatment-based perturbations. I will focus on key questions in this domain, including accurately quantifying the effects of individual perturbations, predicting the additivity and non-linearity of combinatorial perturbations, predicting how a perturbation’s effect depends on a cell’s existing cell state and modeling how collections of cells respond to perturbations. Finally, I will summarize how advances in computational approaches may synergize with our ever-expanding experimental ability to measure increasingly high-resolution cell states across multiple modalities, as well as time and space.

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