Direct specification of lymphatic endothelium from mesenchymal progenitors.

During embryogenesis, endothelial cells (ECs) are generally described to arise from a common pool of progenitors termed angioblasts, which diversify through iterative steps of differentiation to form functionally distinct subtypes of ECs. A key example is the formation of lymphatic ECs (LECs), which are thought to arise largely through transdifferentiation from venous endothelium. Opposing this model, here we show that the initial expansion of mammalian LECs is primarily driven by the in situ differentiation of mesenchymal progenitors and does not require transition through an intermediate venous state.

Tracking the gene expression programs and clonal relationships that underlie mast, myeloid, and T lineage specification from stem cells.

T cells develop from hematopoietic progenitors in the thymus and protect against pathogens and cancer. However, the emergence of human T cell-competent blood progenitors and their subsequent specification to the T lineage have been challenging to capture in real time. Here, we leveraged a pluripotent stem cell differentiation system to understand the transcriptional dynamics and cell fate restriction events that underlie this critical developmental process.

LineageOT is a unified framework for lineage tracing and trajectory inference.

Understanding the genetic and epigenetic programs that control differentiation during development is a fundamental challenge, with broad impacts across biology and medicine. Measurement technologies like single-cell RNA-sequencing and CRISPR-based lineage tracing have opened new windows on these processes, through computational trajectory inference and lineage reconstruction. While these two mathematical problems are deeply related, methods for trajectory inference are not typically designed to leverage information from lineage tracing and vice versa.

Learning dynamical information from static protein and sequencing data.

Many complex processes, from protein folding to neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. Although efficient algorithms for cluster detection in high-dimensional spaces have been developed over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions.

Mode Selection in Compressible Active Flow Networks.

Coherent, large-scale dynamics in many nonequilibrium physical, biological, or information transport networks are driven by small-scale local energy input. Here, we introduce and explore an analytically tractable nonlinear model for compressible active flow networks. In contrast to thermally driven systems, we find that active friction selects discrete states with a limited number of oscillation modes activated at distinct fixed amplitudes.

Stochastic cycle selection in active flow networks.

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network.