Revealing lineage-related signals in single-cell gene expression using random matrix theory.
Revealing lineage-related signals in single-cell gene expression using random matrix theory.
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DOI：
10.1073/pnas.1913931118
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发表时间：
2021-03-16
影响因子：
11.1
通讯作者：
Brenner MP
中科院分区：
文献类型：
--
作者：
Nitzan M;Brenner MP
单细胞技术正在迅速发展，使我们能够衡量细胞群落，组织和完整生物体的异质性和结构。在这样的系统中，基因之间和细胞之间的相关性可以揭示调控相互作用的模式、物理结构和细胞沿着生物过程的时间进展。然而，在嘈杂的高维单细胞数据中识别和梳理这些混合信号通常是一个挑战。在这里，我们表明有可能在单细胞数据的协方差谱中检测谱系的签名，预测它将如何随着发育时间而变化，并预测它如何扩展到检查组织的空间结构。通过单细胞RNA测序产生的细胞群体的基因表达谱包含关于生物状态的丰富信息，包括细胞类型、细胞周期阶段、基因调控模式和在起源组织内的位置。一个主要的挑战是将这些不同生物状态的信息彼此分离，包括区分细胞谱系，因为细胞表达模式的相关性必然受到祖先的污染。在这里，我们使用随机矩阵理论的最新进展，发现在蛋白质遗传学的背景下，以确定单细胞数据中的分化或祖先相关的过程。Qin和科尔韦尔[C.钦湖，加-地J.科尔韦尔，美国国家科学院院刊。Acad. sci. U.S.A. 115，690 - 695（2018）]表明蛋白质序列中的祖先关系在协方差特征值分布中产生幂律特征。我们在scRNA-seq数据中证明了这种特征的存在，并且驱动它们的基因确实与分化和发育途径有关。我们预测存在类似的幂律签名细胞沿着线性轨迹，并证明这线性微分系统。此外，我们概括表明，相同的签名可以出现细胞沿着组织特定的空间轨迹。我们说明这些原则在不同的组织和生物体，包括哺乳动物的表皮和肺，果蝇全胚胎，成年水螅，树突状细胞，肠上皮细胞和细胞进行诱导多能干细胞（iPSC）重编程。我们展示了如何使用这些结果来解释谱系结构沿着iPSC重编程的渐进动态。总之，我们提供了一个框架，可用于在没有先验知识的情况下识别单细胞数据中特定生物过程的特征，并识别与这些过程相关的候选基因。
Single-cell technologies are rapidly advancing, allowing us to gauge the heterogeneity and structure of cellular communities, tissues, and full organisms. The correlations between genes and between cells within such systems can reveal patterns of regulatory interactions, physical structure, and temporal progression of cells along biological processes. However, it is generally a challenge to identify and tease apart these mixed signals within the noisy, high-dimensional single-cell data. Here, we show it is possible to detect a signature for lineage in the covariance spectrum of single-cell data, predict how it will change with developmental time, and predict how it can be extended to examine the spatial structure of a tissue. Gene expression profiles of a cellular population, generated by single-cell RNA sequencing, contains rich information about biological state, including cell type, cell cycle phase, gene regulatory patterns, and location within the tissue of origin. A major challenge is to disentangle information about these different biological states from each other, including distinguishing from cell lineage, since the correlation of cellular expression patterns is necessarily contaminated by ancestry. Here, we use a recent advance in random matrix theory, discovered in the context of protein phylogeny, to identify differentiation or ancestry-related processes in single-cell data. Qin and Colwell [C. Qin, L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A. 115, 690–695 (2018)] showed that ancestral relationships in protein sequences create a power-law signature in the covariance eigenvalue distribution. We demonstrate the existence of such signatures in scRNA-seq data and that the genes driving them are indeed related to differentiation and developmental pathways. We predict the existence of similar power-law signatures for cells along linear trajectories and demonstrate this for linearly differentiating systems. Furthermore, we generalize to show that the same signatures can arise for cells along tissue-specific spatial trajectories. We illustrate these principles in diverse tissues and organisms, including the mammalian epidermis and lung, Drosophila whole-embryo, adult Hydra, dendritic cells, the intestinal epithelium, and cells undergoing induced pluripotent stem cells (iPSC) reprogramming. We show how these results can be used to interpret the gradual dynamics of lineage structure along iPSC reprogramming. Together, we provide a framework that can be used to identify signatures of specific biological processes in single-cell data without prior knowledge and identify candidate genes associated with these processes.
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