Pseudotime Analysis Of ScRNA-seq Studies Reveals The Determinants Of Cell Lineage Specification

The determining mechanism of cell state and fate has always been a fundamental issue in the field of biology.A thorough understanding of this issue is not only conducive to understanding the universal laws of cell differentiation and development,but also has profound significance in analyzing the pathogenesis of diseases.The development of sequencing technology has contributed to the study of fate determining mechanisms.The high granularity gene expression changes generated by single cell RNA sequencing are conducive to further exploring gene expression patterns and specific changes in genes under different cell states,deepening understanding of gene expression changes,and better revealing key transcription factors that regulate cell fate.Biological processes controlled by mutually inhibiting and self activating gene circuits are important models for studying the mechanism of fate determination.This genetic circuit controls the determination of branching points in cell development,which can explain how the fate of two mutually exclusive cells reaches steady-state from progenitor cells.In this genetic circuit,two opposing fate determining transcription factors inhibit the activity of each other through various molecular mechanisms,ultimately specifically selecting cell fate at developmental branching points.Modeling genetic circuits using bistable circuits can mathematically represent biological processes.The bistable circuit maintains its original stable state without external trigger signals.Under the action of external input trigger signals,the stable state of the circuit will change,and the initiation and stabilization of the gene circuit is also completed under the stimulation of a series of molecular signals.Based on a comprehensive understanding of specific system theoretical models and the study of biological processes regulated by gene circuits,this paper attempts to reveal the determinants of cell differentiation and lineage specifications based on pseudotemporal analysis of single cell RNA sequencing,and proposes a prediction algorithm based on Gaussian process regression to identify and characterize specific genes,CF-CTCD.The algorithm identifies hypothetical determinants of core circuit construction by analyzing single cell sequencing data.CF-CTCD is applied to simulated gene regulatory network data and successfully detects the genes Mashl,Hes5,Scl,and Olig2 that control cell fate transitions.In addition,this article uses CF-CTCD to screen core transcription factors on real mouse and human single cell data.The CF-CTCD cell fate determinant prediction algorithm provides a new idea for exploring the cellular dynamics of dual gene circuits,and provides a practical and effective computational method for directed differentiation protocols in lineage differentiation research.