Yampolskaya Maria, Herriges Michael, Ikonomou Laertis, Kotton Darrell, Mehta Pankaj
Department of Physics, Boston University, Boston, MA 02215, USA.
Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA, USA.
bioRxiv. 2023 Jan 25:2023.01.25.525581. doi: 10.1101/2023.01.25.525581.
Advances in single-cell RNA-sequencing (scRNA-seq) provide an unprecedented window into cellular identity. The increasing abundance of data requires new theoretical and computational frameworks for understanding cell fate determination, accurately classifying cell fates from expression data, and integrating knowledge from cell atlases. Here, we present single-cell Type Order Parameters (scTOP): a statistical-physics-inspired approach for constructing "order parameters" for cell fate given a reference basis of cell types. scTOP can quickly and accurately classify cells at a single-cell resolution, generate interpretable visualizations of developmental trajectories, and assess the fidelity of engineered cells. Importantly, scTOP does this without using feature selection, statistical fitting, or dimensional reduction (e.g., UMAP, PCA, etc.). We illustrate the power of scTOP utilizing a wide variety of human and mouse datasets (both and ). By reanalyzing mouse lung alveolar development data, we characterize a transient perinatal hybrid alveolar type 1/alveolar type 2 (AT1/AT2) cell population that disappears by 15 days post-birth and show that it is transcriptionally distinct from previously identified adult AT2-to-AT1 transitional cell types. Visualizations of lineage tracing data on hematopoiesis using scTOP confirm that a single clone can give rise to as many as three distinct differentiated cell types. We also show how scTOP can quantitatively assess the transcriptional similarity between endogenous and transplanted cells in the context of murine pulmonary cell transplantation. Finally, we provide an easy-to-use Python implementation of scTOP. Our results suggest that physics-inspired order parameters can be an important tool for understanding development and characterizing engineered cells.
单细胞RNA测序（scRNA-seq）技术的进步为了解细胞身份提供了前所未有的视角。日益丰富的数据需要新的理论和计算框架，以理解细胞命运决定、从表达数据中准确分类细胞命运，以及整合来自细胞图谱的知识。在此，我们提出了单细胞类型序参（scTOP）：一种受统计物理学启发的方法，用于在给定细胞类型参考基础的情况下构建细胞命运的“序参”。scTOP能够以单细胞分辨率快速且准确地对细胞进行分类，生成发育轨迹的可解释可视化结果，并评估工程细胞的保真度。重要的是，scTOP在不使用特征选择、统计拟合或降维（例如UMAP、PCA等）的情况下就能做到这一点。我们利用各种人类和小鼠数据集（包括 和 ）展示了scTOP的强大功能。通过重新分析小鼠肺肺泡发育数据，我们鉴定出一种出生后15天消失的围产期短暂混合肺泡1型/肺泡2型（AT1/AT2）细胞群体，并表明其转录特征与先前鉴定的成年AT2向AT1转变的细胞类型不同。使用scTOP对造血谱系追踪数据进行可视化显示，单个克隆可产生多达三种不同的分化细胞类型。我们还展示了scTOP如何在小鼠肺细胞移植的背景下定量评估内源性细胞和移植细胞之间的转录相似性。最后，我们提供了一个易于使用的scTOP Python实现。我们的结果表明，受物理学启发的序参可以成为理解发育和表征工程细胞的重要工具。
