Our platform includes both novel and previously described components, which we outline below. Here, we provide a network biology platform, CellNet, which assesses the fidelity of cell fate conversions and generates specific hypotheses aimed at improving derived cell populations. However, such global analyses do not provide an intuitive or a quantitative means for diagnosing the deficiencies of engineered cells, nor do they provide a systematic approach to prioritize interventions to improve derivations of the desired populations. The molecular similarity of engineered populations is typically assessed by semiquantitative PCR, array-based expression profiling, or RNA sequencing followed by simple clustering analysis. To what extent does a cell population engineered in vitro resemble the corresponding target cell or tissue in both molecular and functional terms? While functional complementation via transplantation in live animals has been used to assess the ability of engineered cells to mimic the physiology of their native counterparts, such experiments are technically challenging, lack quantitative rigor, and provide limited insights when judging human tissue function in animal hosts. The widespread practice of cellular engineering has raised critical questions about the relationship of the derived cells to their native counterparts. 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.
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. 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). 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. Somatic 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.
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