Graduate Program in Bioinformatics, Boston University, Boston, MA, USA; Center for Network Systems Biology, Boston University, Boston, MA, USA.
Center for Network Systems Biology, Boston University, Boston, MA, USA.
Cell Rep Methods. 2024 Mar 25;4(3):100729. doi: 10.1016/j.crmeth.2024.100729. Epub 2024 Mar 14.
Understanding the dynamic expression of proteins and other key molecules driving phenotypic remodeling in development and pathobiology has garnered widespread interest, yet the exploration of these systems at the foundational resolution of the underlying cell states has been significantly limited by technical constraints. Here, we present DESP, an algorithm designed to leverage independent estimates of cell-state proportions, such as from single-cell RNA sequencing, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. We applied DESP to an in vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell-state signatures from bulk-level measurements of both the proteome and transcriptome, providing insights into transient regulatory mechanisms. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell-state level using established bulk-level workflows.
理解在发育和病理生物学中驱动表型重塑的蛋白质和其他关键分子的动态表达引起了广泛关注，但由于技术限制，这些系统在基础的细胞状态分辨率下的探索受到了很大限制。在这里，我们提出了 DESP，这是一种算法，旨在利用细胞状态比例的独立估计值，例如来自单细胞 RNA 测序的比例，来解析细胞状态对批量分子测量（尤其是定量蛋白质组学）的相对贡献，这些测量是并行记录的。我们将 DESP 应用于上皮-间充质转化的体外模型，并证明其能够从蛋白质组和转录组的批量水平测量中准确重建细胞状态特征，从而深入了解瞬态调控机制。DESP 为模拟批量和单细胞分子测量之间的关系提供了一个可推广的计算框架，使我们能够使用现有的批量水平工作流程在细胞状态水平上研究蛋白质组和其他分子谱。
