利用未剪切轉錄建構情境依賴的基因調控網路可解釋調控動態及細胞軌跡

Abstract

在多樣的生物現象中，基因調控網路掌控複雜的基因表現，包含細胞發育、決策細胞命運，以及癌化。單細胞定序技術，比起以往大批RNA定序，提供基因表現較高的解析度，但是同時測量到更多的雜訊，以及更稀疏的表現量，這讓基因調控網路的推論更加有挑戰性。跨不同細胞型態要推論完整的基因調控網路也是相當困難。這邊我們提出情境依賴基因調控網路（CDGRN），它可以從單細胞RNA定序資料來解決這個問題。基因調控網路可以被拆解成子圖，它對應到不同的轉錄情境。每個子圖是由共同活躍的調控配對組成，其中包含由一群細胞共享的轉錄因子，以及他們的目標基因。在不同細胞群體，每個調控配對的活性是由高斯混合模型推得，當中使用了剪切及未剪切轉錄的表現量。我們發現在所有情境下基因表現的聯集提供了足夠的資訊以建構細胞分化軌跡。CDGRN建立了分子層級基因調控與巨觀層級細胞分化之間的連結。在整個發育過程的各個情境中，細胞週期、細胞分化，或是組織特有功能有過度表現這些功能。更令人驚訝的是，我們發現CDGRN的網路亂度會隨著分化過程下降，這暗示了分化的方向。總結而言，我們利用了單細胞RNA定序技術的優勢，並建立了分子調控與分化軌跡之間的連結。情境依賴的網路亂度或許暗示了在特定情境下的細胞成熟度。CDGRN模型被釋出在https://github.com/yuehhua/CDGRNs.jl。

Gene regulatory networks govern the complex gene expression programs in various biological phenomena, including cell development, cell fate decision, and oncogenesis. Single-cell techniques provide higher resolution in gene expression than traditional bulk RNA sequencing, but also incur more noise and sparser expression measurements, making it challenging to infer gene regulatory networks from such profiles. Inference of a complete gene regulatory network across different cell types is also difficult. Here, we propose to address the problem by constructing context-dependent gene regulatory networks (CDGRN) from single-cell RNA sequencing data. A gene regulatory network is decomposed into subgraphs that correspond to distinct transcriptomic contexts. Each subgraph is composed of the consensus active regulation pairs of transcription factors and their target genes shared by a group of cells. The activities of each regulation pair in different cell groups are inferred by a Gaussian mixture model using both the spliced and unspliced transcript expression levels. We find that the union of gene regulation pairs in all contexts provides sufficient information for the reconstruction of differentiation trajectories. CDGRN allows establishing the connection between gene regulation at the molecular level and cell differentiation at the macroscopic level. Functions specific to the cell cycle, cell differentiation, or tissue-specific functions are enriched throughout the developmental progression in each context. Surprisingly, we observe that the network entropy of CDGRN decreases with differentiation progression, implying directionality in differentiation. In conclusion, we leverage the advantage of single-cell RNA sequencing and establish a connection between molecular regulation and differentiation trajectory. Context-dependent network entropy may indicate the maturity of cells in certain contexts. The CDGRN model is available at https://github.com/yuehhua/CDGRNs.jl