CellNet: Network Biology Applied to Stem Cell Engineering

• Reconstruction of mammalian gene regulatory networks from 21 cell types and tissues
• Algorithm that assesses quality of engineered cells and suggests improvements
• Persistence of starting cell GRNs and establishment of unintended GRNs is common
• Directed differentiation is superior to conversion in establishing endogenous GRNs

SummarySomatic cell reprogramming, directed differentiation of pluripotent stem cells, and direct conversions between differentiated cell lineages represent powerful approaches to engineer cells for research and regenerative medicine. We have developed CellNet, a network biology platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. Analyzing expression data from 56 published reports, we found that cells derived via directed differentiation more closely resemble their in vivo counterparts than products of direct conversion, as reflected by the establishment of target cell-type gene regulatory networks (GRNs). Furthermore, we discovered that directly converted cells fail to adequately silence expression programs of the starting population and that the establishment of unintended GRNs is common to virtually every cellular engineering paradigm. CellNet provides a platform for quantifying how closely engineered cell populations resemble their target cell type and a rational strategy to guide enhanced cellular engineering.

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