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多尺度动力学模型研究从早期胸腺祖细胞状态到谱系定型的 T 细胞发育

Multi-scale Dynamical Modeling of T Cell Development from an Early Thymic Progenitor State to Lineage Commitment.

机构信息

Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.

Division of Biology and Biological Engineering, 156-29, California Institute of Technology, Pasadena, CA 91125, USA.

出版信息

Cell Rep. 2021 Jan 12;34(2):108622. doi: 10.1016/j.celrep.2020.108622.

Abstract

Intrathymic development of committed progenitor (pro)-T cells from multipotent hematopoietic precursors offers an opportunity to dissect the molecular circuitry establishing cell identity in response to environmental signals. This transition encompasses programmed shutoff of stem/progenitor genes, upregulation of T cell specification genes, proliferation, and ultimately commitment. To explain these features in light of reported cis-acting chromatin effects and experimental kinetic data, we develop a three-level dynamic model of commitment based upon regulation of the commitment-linked gene Bcl11b. The levels are (1) a core gene regulatory network (GRN) architecture from transcription factor (TF) perturbation data, (2) a stochastically controlled chromatin-state gate, and (3) a single-cell proliferation model validated by experimental clonal growth and commitment kinetic assays. Using RNA fluorescence in situ hybridization (FISH) measurements of genes encoding key TFs and measured bulk population dynamics, this single-cell model predicts state-switching kinetics validated by measured clonal proliferation and commitment times. The resulting multi-scale model provides a mechanistic framework for dissecting commitment dynamics.

摘要

胸腺内多能造血前体细胞向定向祖细胞(pro)-T 细胞的发育为解析细胞特性提供了机会,使其能够响应环境信号建立细胞身份。这种转变包括干细胞/祖细胞基因的程序性关闭、T 细胞特异性基因的上调、增殖,最终的定向分化。为了根据报道的顺式作用染色质效应和实验动力学数据解释这些特征,我们基于与定向分化相关基因 Bcl11b 的调控,建立了一个三级动态定向分化模型。这三个层次分别是:(1)来自转录因子(TF)扰动数据的核心基因调控网络(GRN)结构,(2)一个受随机控制的染色质状态门控,(3)一个经实验克隆生长和定向分化动力学检测验证的单细胞增殖模型。该单细胞模型使用 RNA 荧光原位杂交(FISH)测量编码关键 TF 的基因,并测量批量群体动力学,预测状态转换动力学,该动力学通过测量的克隆增殖和定向分化时间得到验证。由此产生的多尺度模型为解析定向分化动力学提供了一个机制框架。

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