利用单细胞转录组数据和波动约束相关性确定造血过程中的细胞系定向网络

IF 2 4区 生物学 Q2 BIOLOGY
Tomoshiro Ochiai , Jose C. Nacher
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引用次数: 0

摘要

单细胞转录组测序(scRNA-seq)通过分析单个细胞水平的表达谱,彻底改变了我们对细胞过程的理解。通过对造血等各种生物过程的研究,这项技术有望发现新的细胞类型、基因功能、细胞分化和轨迹推断。最近对小鼠骨髓细胞的 scRNA-seq 分析提供了一个造血系网络模型。然而,所有数据分析都预测了相关细胞轨迹的不定向网络图。此外,关于嗜碱性粒细胞起源的争论依然存在。在这项工作中,我们应用波动约束(VC)相关方法不仅预测了网络结构,还预测了造血过程中细胞类型之间的因果关系或方向性。我们的研究结果表明,嗜碱性粒细胞具有双重起源,既来自粒细胞/巨噬细胞,也来自红细胞祖细胞,而后者是以往研究中较少探讨的一种轨迹。所提出的方法和预测可能有助于绘制完整的造血过程图,影响我们对造血的理解,并为进一步的生物医学研究提供一个强大的定向网络框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determining cellular lineage directed networks in hematopoiesis using single-cell transcriptomic data and volatility-constrained correlation

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.

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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
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.
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