IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data.
IQCELL: A platform for predicting the effect of gene perturbations on developmental trajectories using single-cell RNA-seq data.
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DOI：
10.1371/journal.pcbi.1009907
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发表时间：
2022-03
影响因子：
4.3
通讯作者：
Zandstra PW
中科院分区：
文献类型：
--
作者：
Heydari T;A Langley M;Fisher CL;Aguilar-Hidalgo D;Shukla S;Yachie-Kinoshita A;Hughes M;M McNagny K;Zandstra PW
来自不同发育系统的单细胞rna测序（scRNA-seq）数据越来越多，为直接从数据中推断基因调控网络（grn）提供了机会。本文介绍了IQCELL，这是一个直接从scRNA-seq数据推断、模拟和研究可执行逻辑grn的平台。这种可执行的grn允许模拟控制发育程序的基本假设，并有助于加速控制干细胞命运的策略设计。我们首先描述了IQCELL的架构。接下来，我们将IQCELL应用于早期小鼠t细胞和红细胞发育的scRNA-seq数据集，并表明该平台可以推断出过去几十年研究中报道的74%以上的因果基因相互作用。我们还将表明，生成的GRN的动态模拟定性地概括了已知基因扰动的影响。最后，我们实现了一个IQCELL基因选择管道，使我们能够在没有先验知识的情况下识别候选基因。我们证明了基于推断集的GRN模拟产生的结果与原始策划列表相似。总之，IQCELL平台提供了一个多功能工具来推断、模拟和研究动态生物系统中可执行的grn。可执行的grn为模拟发育和疾病过程中复杂的细胞内动力学提供了重要的策略。在这里，我们介绍IQCELL，这是一个直接从单细胞测序数据推断、模拟和研究可执行逻辑grn的平台。IQCELL是一个集成平台，包括基因选择模块，构建逻辑grn，并模拟正常和扰动条件下的发育轨迹。我们通过重建早期小鼠t细胞和红细胞发育的grn来证明IQCELL的实用性。我们表明，IQCELL可以“自动”推断出绝大多数基因相互作用，这些基因相互作用是以前几十年的实验研究报告的。IQCELL还为用户提供了一个模拟细胞发育轨迹的平台。我们表明，推断的grn的动态模拟类似于实验观察到的基因表达动态，并捕获遗传扰动研究的影响。IQCELL提供了一个多功能的工具来推断和模拟动态生物系统中的grn。
The increasing availability of single-cell RNA-sequencing (scRNA-seq) data from various developmental systems provides the opportunity to infer gene regulatory networks (GRNs) directly from data. Herein we describe IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from scRNA-seq data. Such executable GRNs allow simulation of fundamental hypotheses governing developmental programs and help accelerate the design of strategies to control stem cell fate. We first describe the architecture of IQCELL. Next, we apply IQCELL to scRNA-seq datasets from early mouse T-cell and red blood cell development, and show that the platform can infer overall over 74% of causal gene interactions previously reported from decades of research. We will also show that dynamic simulations of the generated GRN qualitatively recapitulate the effects of known gene perturbations. Finally, we implement an IQCELL gene selection pipeline that allows us to identify candidate genes, without prior knowledge. We demonstrate that GRN simulations based on the inferred set yield results similar to the original curated lists. In summary, the IQCELL platform offers a versatile tool to infer, simulate, and study executable GRNs in dynamic biological systems. Executable GRNs provide an important strategy to model complex intracellular dynamics in development and disease. Here we introduce IQCELL, a platform to infer, simulate, and study executable logical GRNs directly from single cell sequencing data. IQCELL is an integrative platform that includes modules for gene selection, building logical GRNs, and simulating developmental trajectories under normal and perturbed conditions. We demonstrate the utility of IQCELL by reconstructing GRNs for early mouse T-cell and red blood cell development. We show that IQCELL can “automatically” infer the vast majority of gene interactions previously reported from decades of experimental research. IQCELL also provides users with a platform to simulate the developmental trajectories of cells. We show that dynamic simulations of the inferred GRNs resemble experimentally observed gene expression dynamics and capture the effects of genetic perturbation studies. IQCELL offers a versatile tool to infer and simulate GRNs in dynamic biological systems.