Research And System Implementation Of Cell Spatiotemporal Prediction Method Based On Transcriptome Sequencing Data

Through the time and space analysis of the cell population in the tissue or organ(referred to as the space-time analysis of the cell population for short),we can deeply understand the heterogeneity,differentiation status and interaction of different cell populations in the tissue and organ,and then reveal the mechanism and law behind the life phenomenon.Transcriptome sequencing technology is an important method for spatiotemporal analysis of cell populations,while single cell RNA sequencing(scRNA-seq)and spatial transcriptome sequencing are two important methods for current transcriptome analysis.ScRNA-seq can obtain gene expression information at single-cell resolution,but the spatial information of cells is lost during dissociation.In contrast,spatial transcriptome sequencing can obtain the gene expression information of cell populations in different spatial locations while retaining the cell spatial location information,but its cell resolution is not as good as scRNA-seq.How to effectively use scRNA-seq and spatial transcriptome technology to analyze the development and changes of cells in time and space has become an important challenge in biological research today.Transcriptome sequencing technology is not only used to study cell types in tissues or organs of organisms,but also used in research fields such as embryo and brain development.Based on the transcriptome data of the mouse brain,this study conducted in-depth research on the spatial distribution and temporal development of the mouse brain cell population.The main contributions are as follows:(1)The methods of scRNA-seq and spatial transcriptome sequencing and data processing are systematically analyzed,and the integration analysis methods of the two data are reviewed.The analysis results show that there are some characteristics in the generation and processing of these two sequencing data.For example,in terms of data generation,the former requires tissue dissociation to obtain a single cell,while the latter directly sequencing tissue slices without dissociation;In terms of data processing,the former is based on single cells,while the latter is based on each sequencing site(spot).(2)In order to analyze the spatial distribution of cell populations in tissues,this paper proposes an integrated analysis method-(Integrated Analysis Method Based on Reference Genes,IARG),which integrates scRNA-seq and spatial transcriptome sequencing data.This method uses gene mapping to identify the cell types after clustering the spatial transcriptome cluster analysis,and visualizes the cell populations on the tissue staining image according to the coordinate information of the sequencing sites.Compared to other integrated analysis methods,IARG can more accurately reconstruct the spatial distribution of cell populations.(3)In order to infer the trajectory of cell development,this paper proposes an analysis method based on Minimum Spanning Tree(MST)-(Inference of Trajectory for scRNA-seq Data Based on Mst,TIOM),and conducts a combined analysis of cell differentiation results and spatial information.TIOM calculates cell to cell similarity based on scRNA-seq data,with each cell representing a node and the edges between nodes representing cell to cell familiarity.By constructing a graph,the optimal differentiation path is calculated.Compared to other methods,TIOM can simultaneously display differentiation trajectories and cell distribution within the tissue.(4)A transcriptome Data Analysis System(TDAS)was built.This system has various analysis functions,such as function one:clustering based on scRNA-seq data,searching for common marker genes and cell differentiation trajectory inference(TI)in samples with time series;Function 2:clustering based on spatial transcriptome data,reconstruction of cell spatial distribution,and visualization of TI results on tissue staining images.This study can also be applied to other fields of cell development,such as studying the regulatory mechanisms of genes during cell development.These studies have deepened people’s understanding of the internal development mechanisms of life laws.