Lineage tracing involves identifying the ancestors and descendants of every cell in a population and is an important tool for studying biological processes. However, it is often not feasible to track and measure single cells over time because single-cell methods are destructive to samples. This motivates using computational methods for constructing lineages of cells from independent samples collected over time. This talk will cover two directions that we have explored for improving optimal transport methods for computational lineage tracing.

Dysregulation of the immune response to infection can lead to sepsis, a condition with high mortality. We identified a monocyte transcriptional state that is expanded during sepsis and validated its power in discriminating septic patients from controls using public transcriptomic data from multiple geographic locations. We identified a panel of surface markers for isolation and quantification of the monocyte state, characterized its epigenomic and functional phenotypes, and proposed a model for its induction from human bone marrow.