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The vulval precursor cell (VPC) fate patterning in Caenorhabditis elegans is a classic model experimental system for cell fate determination and patterning in development. Despite its apparent simplicity (six neighboring cells arranged in one dimension) and many experimental and computational efforts, the patterning strategy and mechanism remain controversial due to incomplete knowledge of the complex biology. Here, we carry out a comprehensive computational analysis and obtain a reservoir of all possible network topologies that are capable of VPC fate patterning under the simulation of various biological environments and regulatory rules. We identify three patterning strategies: sequential induction, morphogen gradient and lateral antagonism, depending on the features of the signal secreted from the anchor cell. The strategy of lateral antagonism, which has not been reported in previous studies of VPC patterning, employs a mutual inhibition of the 2° cell fate in neighboring cells. Robust topologies are built upon minimal topologies with basic patterning strategies and have more flexible and redundant implementations of modular functions. By simulated mutation, we find that all three strategies can reproduce experimental error patterns of mutants. We show that the topology derived by mapping currently known biochemical pathways to our model matches one of our identified functional topologies. Furthermore, our robustness analysis predicts a possible missing link related to the lateral antagonism strategy. Overall, we provide a theoretical atlas of all possible functional networks in varying environments, which may guide novel discoveries of the biological interactions in vulval development of Caenorhabditis elegans and related species.

Current functional assessment of biomaterial‐induced stem cell lineage fate in vitro mainly relies on biomarker‐dependent methods with limited accuracy and efficiency. Here a “Mesenchymal stem cell Differentiation Prediction (MeD‐P)” framework for biomaterial‐induced cell lineage fate prediction is reported. MeD‐P contains a cell‐type‐specific gene expression profile as a reference by integrating public RNA‐seq data related to tri‐lineage differentiation (osteogenesis, chondrogenesis, and adipogenesis) of human mesenchymal stem cells (hMSCs) and a predictive model for classifying hMSCs differentiation lineages using the k‐nearest neighbors (kNN) strategy. It is shown that MeD‐P exhibits an overall accuracy of 90.63% on testing datasets, which is significantly higher than the model constructed based on canonical marker genes (80.21%). Moreover, evaluations of multiple biomaterials show that MeD‐P provides accurate prediction of lineage fate on different types of biomaterials as early as the first week of hMSCs culture. In summary, it is demonstrated that MeD‐P is an efficient and accurate strategy for stem cell lineage fate prediction and preliminary biomaterial functional evaluation.

Stem Cell Lineage Fate Highly accurate, efficient, and convenient evaluation for biomaterial‐induced stem cell lineage fate is pivotal for regenerative biomaterials research. In article number 2210637, Binbin Lai, Xuehui Zhang, Xuliang Deng, and co‐workers report MeD‐P, a machine‐learning‐supported intelligent evaluation model, for predicting human mesenchymal stem cell–biomaterial interaction based on RNA‐seq data. This framework shows the potential of artificial intelligence in facilitating biological performance optimization of biomaterials in stem cell lineage fate regulation.

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Human induced pluripotent stem cell (iPSC) technology has opened exciting opportunities for stem-cell-based therapy. However, its wide adoption is precluded by several challenges including low reprogramming efficiency and potential for malignant transformation. Better understanding of the molecular mechanisms of the changes that cells undergo during reprograming is needed to improve iPSCs generation efficiency and to increase confidence for their clinical use safety. Here, we find that dominant negative mutations in STAT3 in patients with autosomal-dominant hyper IgE (Job's) syndrome (AD-HIES) result in greatly reduced reprograming efficiency of primary skin fibroblasts derived from skin biopsies. Analysis of normal skin fibroblasts revealed upregulation and phosphorylation of endogenous signal transducer and activator of transcription 3 (STAT3) and its binding to the NANOG promoter following transduction with OKSM factors. This coincided with upregulation of NANOG and appearance of cells expressing pluripotency markers. Upregulation of NANOG and number of pluripotent cells were greatly reduced throughout the reprograming process of AD-HIES fibroblasts that was restored by over-expression of functional STAT3. NANOGP8, the human-specific NANOG retrogene that is often expressed in human cancers, was also induced during reprogramming, to very low but detectable levels, in a STAT3-dependent manner. Our study revealed the critical role of endogenous STAT3 in facilitating reprogramming of human somatic cells.