{"title":"Determining cellular lineage directed networks in hematopoiesis using single-cell transcriptomic data and volatility-constrained correlation","authors":"Tomoshiro Ochiai , Jose C. Nacher","doi":"10.1016/j.biosystems.2024.105248","DOIUrl":null,"url":null,"abstract":"<div><p>Single-cell transcriptome sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling the analysis of expression profiles at an individual cell level. This technology has shown promise in uncovering new cell types, gene functions, cell differentiation, and trajectory inference through the study of various biological processes, such as hematopoiesis. Recent scRNA-seq analysis of mouse bone marrow cells has provided a network model of hematopoietic lineage. However, all data analyses have predicted undirected network maps for the associated cell trajectories. Moreover, the debate regarding the origin of basophil cells still persists. In this work, we apply the Volatility Constrained (VC) correlation method to predict not only the network structure but also the causality or directionality between the cell types present in the hematopoietic process. Our findings suggest a dual origin of basophils, from both granulocyte/macrophage and erythrocyte progenitors, the latter being a trajectory less explored in previous research. The proposed approach and predictions may assist in developing a complete hematopoietic process map, impacting our understanding of hematopoiesis and providing a robust directional network framework for further biomedical research.</p></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"242 ","pages":"Article 105248"},"PeriodicalIF":2.0000,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0303264724001333","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Single-cell transcriptome sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling the analysis of expression profiles at an individual cell level. This technology has shown promise in uncovering new cell types, gene functions, cell differentiation, and trajectory inference through the study of various biological processes, such as hematopoiesis. Recent scRNA-seq analysis of mouse bone marrow cells has provided a network model of hematopoietic lineage. However, all data analyses have predicted undirected network maps for the associated cell trajectories. Moreover, the debate regarding the origin of basophil cells still persists. In this work, we apply the Volatility Constrained (VC) correlation method to predict not only the network structure but also the causality or directionality between the cell types present in the hematopoietic process. Our findings suggest a dual origin of basophils, from both granulocyte/macrophage and erythrocyte progenitors, the latter being a trajectory less explored in previous research. The proposed approach and predictions may assist in developing a complete hematopoietic process map, impacting our understanding of hematopoiesis and providing a robust directional network framework for further biomedical research.
期刊介绍:
BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.