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A major challenge for stem cell engineering is achieving a holistic understanding of the molecular networks and biological processes governing cell differentiation. To address this challenge, we describe a computational approach that combines gene expression analysis, previous knowledge from proteomic pathway informatics and cell signaling models to delineate key transitional states of differentiating cells at high resolution. Our network models connect sparse gene signatures with corresponding, yet disparate, biological processes to uncover molecular mechanisms governing cell fate transitions. This approach builds on our earlier CellNet and recent trajectory-defining algorithms, as illustrated by our analysis of hematopoietic specification along the erythroid lineage, which reveals a role for the EGF receptor family member, ErbB4, as an important mediator of blood development. We experimentally validate this prediction and perturb the pathway to improve erythroid maturation from human pluripotent stem cells. These results exploit an integrative systems perspective to identify new regulatory processes and nodes useful in cell engineering. An advanced CellNet pipeline models the dynamics of stem cell differentiation.

Functional gene disruption is a central tenet of cancer research, where novel drug targets are often identified and validated through cell-growth based knockdown studies or screens. Short hairpin RNA (shRNA)-mediated mRNA knockdown is widely used in both academic and pharmaceutical settings. However, off-target effects of shRNAs as well as interference with endogenous small RNA processing have been reported. We show here that lentiviral delivery of both gene-specific and non-targeting control shRNAs impair in vitro cell growth in a sequence independent manner. In addition, exogenous shRNAs induce a depressed cell-cycle-gene expression signature that is also shRNA-sequence independent and present across several studies. Further, we observe an shRNA mediated general repression of microRNAs belonging to polycistronic genetic clusters, including microRNAs from established oncogenic microRNA clusters. The collective impact of these observations is particularly relevant for cancer research, given the widespread historical use of shRNAs and the common goal of interrogating genes that regulate proliferation. We therefore recommend that when employing shRNA for target validation, care be taken to titrate shRNA dose, use hairpin-expressing controls, perform gene-of-interest rescue experiments and/or corroborate shRNA-derived results by small interfering RNA (siRNA) knockdown or CRISPR/Cas9-mediated genetic knockout. Minimizing these deleterious sequence independent effects will improve research fidelity and help address reported challenges in experimental reproducibility.