Integrating Deep Learning with Single-Cell Transcriptomics for Predictive Modeling in Stem Cell Therapy

Abstract

Stem cell therapy has tremendous potential to treat a broad range of medical conditions, but technical aspects of this therapeutic approach have hampered its clinical application because differentiation outcomes can be unpredictable, cellular heterogeneity is not fully understood, and predictions of the efficacy of stem cell therapy are cumbersome. Single-cell transcriptomics (scRNA-seq) technology has become a revolutionary tool with which to elucidate gene expression of single cells, and it allows a more in-depth description of cell states and lineage. Nonetheless, scRNA-seq data are of high dimensionality and complexity, and they require sophisticated computation methods to be able to use meaningful patterns to inform therapeutic initiatives. In recent years deep learning has found profound success in learning nonlinear relationships, extracting hierarchical features, and learning large-scale biological data. The paper provides an integrative framework to predict the stem cell therapy outcomes using the combination of single-cell transcriptomics and deep learning. The methodology will use the latest neural network architecture design to estimate differentiation trajectories, make projections of optimal donor cell lines, and even make predictions based on patient therapeutic outcomes. Collective application of these approaches helps not only to increase the accuracy of prediction but also informs biologically about how gene regulatory practices can regulate the fate of stem cells. The framework that is proposed could hasten the designing of individualized regenerative medicine solutions, enhance the safety of treatment, and increase clinical performance.