Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.
Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.
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DOI：
10.1038/s41467-023-38637-9
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发表时间：
2023-05-27
影响因子：
16.6
通讯作者：
Roy, Sushmita
中科院分区：
文献类型：
--
作者：
Zhang, Shilu;Pyne, Saptarshi;Pietrzak, Stefan;Halberg, Spencer;McCalla, Sunnie Grace;Siahpirani, Alireza Fotuhi;Sridharan, Rupa;Roy, Sushmita
细胞类型特异性基因表达模式是将转录因子和信号蛋白连接到靶基因的转录基因调控网络（GRNs）的输出。单细胞技术，如单细胞RNA测序（scRNA-seq）和使用测序的转座酶可降解染色质单细胞测定（scATAC-seq），可以以前所未有的细节检查细胞类型特异性基因调控。然而，目前推断细胞类型特异性GRN的方法在整合scRNA-seq和scATAC-seq测量以及对细胞谱系的网络动态建模的能力方面受到限制。为了应对这一挑战，我们开发了单细胞多任务网络推理（scMTNI），这是一种多任务学习框架，用于从scRNA-seq和scATAC-seq数据中推断谱系上每种细胞类型的GRN。使用模拟和真实的数据集，我们表明，scMTNI是一个广泛适用的框架，线性和分支谱系，准确地推断GRN动力学，并确定不同的过程，如细胞重编程和分化的命运转变的关键调节器。细胞类型特异性基因表达模式是将转录因子和信号蛋白连接到靶基因的转录基因调控网络（GRNs）的输出。在这里，作者提出了单细胞多任务网络推理（scMTNI），这是一个多任务学习框架，用于从为不同细胞命运规范轨迹收集的scRNA-seq和scATAC-seq数据集推断细胞类型特异性GRN动态。
Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation. Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Here, the authors present single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer cell type-specific GRN dynamics from scRNA-seq and scATAC-seq datasets collected for diverse cell fate specification trajectories.
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作者：
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影响因子：
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作者：
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