Abstract
BackgroundConversion of human somatic cells into induced pluripotent stem cells (iPSCs) is often an inefficient, time consuming and expensive process. Also, the tendency of iPSCs to revert to their original somatic cell type over time continues to be problematic. A computational model of iPSCs identifying genes/molecules necessary for iPSC generation and maintenance could represent a crucial step forward for improved stem cell research. The combination of substantial genetic relationship data, advanced computing hardware and powerful nonlinear modeling software could make the possibility of artificially-induced pluripotent stem cells (aiPSC) a reality. We have developed an unsupervised deep machine learning technology, called DeepNEU that is based on a fully-connected recurrent neural network architecture with one network processing layer for each input. DeepNEU was used to simulate aiPSC systems using a defined set of reprogramming transcription factors. Genes/proteins that were reported to be essential in human pluripotent stem cells (hPSC) were used for system modelling.ResultsThe Mean Squared Error (MSE) function was used to assess system learning. System convergence was defined at MSE < 0.001. The markers of human iPSC pluripotency (N = 15) were all upregulated in the aiPSC final model. These upregulated/expressed genes in the aiPSC system were entirely consistent with results obtained for iPSCs.ConclusionThis research introduces and validates the potential use of aiPSCs as computer models of human pluripotent stem cell systems. Disease-specific aiPSCs have the potential to improve disease modeling, prototyping of wet lab experiments, and prediction of genes relevant and necessary for aiPSC production and maintenance for both common and rare diseases in a cost-effective manner.
Highlights
Conversion of human somatic cells into induced pluripotent stem cells is often an inefficient, time consuming and expensive process
Yamanaka’s group showed that they could turn back the differentiation clock of somatic fibroblasts, first in mice [4], and in humans [5, 6]. Their advance was achieved through the induced overexpression of just four key transcription factors (Oct4, Sox2, Klf4 and c-Myc) to generate embryonic stem-like cells, which were later referred to as induced pluripotent stem cells [4,5,6]
Simulation of the artificially-induced pluripotent stem cells (aiPSC) model Studies have shown that induced pluripotent stem cells (iPSCs) express many factors that are consistent with the signature of undifferentiated human ES cells
Summary
Conversion of human somatic cells into induced pluripotent stem cells (iPSCs) is often an inefficient, time consuming and expensive process. Yamanaka’s group showed that they could turn back the differentiation clock of somatic fibroblasts, first in mice [4], and in humans [5, 6]. Their advance was achieved through the induced overexpression of just four key transcription factors (Oct, Sox, Klf and c-Myc) to generate embryonic stem-like cells, which were later referred to as induced pluripotent stem cells (iPSCs) [4,5,6]. In 2012, professor Yamanaka won the Nobel prize for his contribution to the field of cellular reprogramming and regenerative medicine
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