Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.

Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here we develop data-driven models of single-cell phenotypic responses to extracellular stimuli by linking gene transcription levels to "morphodynamics" - changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals alter the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and provides a prediction of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data.

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Powering ex vivo tissue models in microfluidic systems.

This Frontiers review analyzes the rapidly growing microfluidic strategies that have been employed in attempts to create physio relevant 'organ-on-chip' models using primary tissue removed from a body (human or animal). Tissue harvested immediately from an organism, and cultured under artificial conditions is referred to as ex vivo tissue. The use of primary (organotypic) tissue offers unique benefits over traditional cell culture experiments, and microfluidic technology can be used to further exploit these advantages. Defining the utility of particular models, determining necessary constituents for acceptable modeling of in vivo physiology, and describing the role of microfluidic systems in tissue modeling processes is paramount to the future of organotypic models ex vivo. Virtually all tissues within the body are characterized by a large diversity of cellular composition, morphology, and blood supply (e.g., nutrient needs including oxygen). Microfluidic technology can provide a means to help maintain tissue in more physiologically relevant environments, for tissue relevant time-frames (e.g., matching the natural rates of cell turnover), and at in vivo oxygen tensions that can be controlled within modern microfluidic culture systems. Models for ex vivo tissues continue to emerge and grow in efficacy as mimics of in vivo physiology. This review addresses developments in microfluidic devices for the study of tissues ex vivo that can serve as an important bridge to translational value.