Modeling Cellular Programs and Interactions Underlying Innate Immune Response

Photo of cells.

Thursday, March 21, 2019

To protect the host and maintain tissue homeostasis, the immune system responds not only to a vast array of pathogens, but also to environmental cues, for example, signals of tissue injury or changes in nutrient availability. Innate lymphoid cells (ILCs) are a type of immune cell that play a role in maintaining a stable environment and barrier in mucosal tissues but can also promote inflammation. What causes immune cells to trigger inflammatory pathologies rather than serve a tissue protective function? Through computational analysis of the transcriptomic profiles of tens of thousands of individual tissue-resident ILCs under both homeostatic and inflammatory conditions, my research has focused on identifying novel molecular cues and transcriptional pathways that modulate immune responses in the lung and the skin. ILCs express receptors for a number of peptides produced by the nervous system, suggesting that activation of nerves may modulate ILC function. Supporting this hypothesis, in joint work with experimental collaborators, we found that ILCs respond potently to signaling through one such neuropeptide by amplifying allergic airway inflammation. To decipher transcriptional heterogeneity in skin-resident ILCs in a psoriasis model, we went beyond discrete classification models to a generative probabilistic model that enabled inference of the biological processes at play, as well as the relevance of each process in each cell. The model and our subsequent analysis revealed a potential quiescent ILC state, as well as a novel functional spectrum of ILCs, validated in vivo, ranging from quiescence to a classical ILC2 state to a pathological, mixed ILC3-like state. This work illustrates how adapting models from other data sciences to specific biological contexts can shed light on the space of transcriptional immune states and their transformation during inflammatory responses. I plan to further develop approaches that encode more of the complexity of biological systems, across cell types, states, space and time, and apply them to understanding how metabolic reprogramming and interactions between cell types affect immune regulation of tissue homeostasis.

Samantha Riesenfeld is a postdoctoral associate in Aviv Regev’s lab at the Broad Institute of MIT and Harvard, and a collaborating member of Vijay Kuchroo’s lab at Harvard Medical School. Her research involves developing and using data science approaches to analyze single-cell genomic data. She has been particularly focused on immune cell biology, especially the complex space of transcriptional states of tissue-resident innate lymphoid cells (ILCs) and their dependence on signals from other cell types, including neurons. She has a PhD in Theoretical Computer Science from UC Berkeley, where she was advised by Richard Karp. Her initial biological training was in metagenomics as a postdoc in Katherine Pollard’s lab at the Gladstone Institutes.