Predict the time and path of single cell differentiation during cell differentiation.

,(),,,Inference of differentiation time for single cell transcriptomes using cell population reference data,Nature Communications。
the study uses the development of a computing toolkit (iCpSc) to integrate single-cell and group cell transcriptome data to predict the differentiation time and path of single cells during cell differentiation, and to identify important regulatory factors and signaling pathways through gene regulatory network analysis.
single-cell transcription sequencing technique is used as a powerful method to analyze the cellular heterogeneity of the development and reprogramming processes.
the key goal of analyzing intercellular heterogeneity is to find unknown cell states or to reconstruct the developmental trajectory of cell lineage.
single-cell transcription group data may contain biological or abiotic interference factors (such as cell cycles), which are often removed by existing computational biology methods based on the judgment of the analyst.
in many cases, cell cycle regulation plays an important role in development and cell differentiation, such as the length of G1 and M, which regulates the fate of nerve cells.
studies show that neurodevelopment is a step-by-step process in mouse embryo development.
recent studies have clarified the molecular and signaling pathways that are more involved in the decision of neural fate, however, whether there are other factors and how these factors interact to regulate the fate of nerves has not been determined.
has shown that through in vitro culture of mouse embryonic stem cells, nerve cells can be induced step by step, and good simulation of the process of nerve differentiation in the body.
in the study, researchers developed a computing toolkit (iCpSc) to integrate single-cell and group cell transcription data to predict the differentiation time and path of single cells during cell differentiation, demonstrating the advantages of iCpSc by testing in simulated data sets and literature data sets.
to further test the effectiveness of this method, the researchers used an in vitro induction model of neural differentiation in mice to select 8 representative points in time at very dense points in time to produce single-cell transcription groups and corresponding group cell transcription group data, and use iCpSc to predict the differentiation time and path of each single cell. After
, the relevant genes ("timer" genes) were differentiated using relevant analysis, and cell cycle regulatory factors were found to be concentrated.
to find key regulatory genes that coordinate cell cycles and nerve cell differentiation by building gene regulatory networks.
finally, the role of Fyn and M in controlling nerve cell differentiation time was verified by the CRISPR/Cas9 gene knockout experiment and small molecule inhibitor experiment.
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