美国弗雷德-哈钦森癌症中心Manu Setty研究组取得进展。他们使用Mellon技术量化单细胞表型图谱中的细胞状态密度。2024年6月18日，国际学术期刊《自然-方法学》发表了这一成果。

研究人员介绍了一种从单细胞数据的高维表示中估算细胞状态密度的算法-Mellon。研究通过剖析分化系统的密度情况展示了Mellon的应用，揭示了主要细胞类型对应的高密度区域与低密度、罕见的过渡状态交织在一起的统一模式。研究证据表明，增强子引物和主调节因子的激活与这些过渡状态的出现有关。

Mellon可以灵活地对时间序列数据进行时间插值，提供发育过程中细胞状态动态的详细视图。Mellon可在各种单细胞数据模式下进行密度估算，并与细胞数量成线性比例。该工作强调了细胞状态密度在理解分化过程中的重要性，以及Mellon在揭示生物轨迹机制方面的潜力。

研究人员表示，细胞状态密度是细胞随表型变化的分布特征，对于揭示各种生物过程的诱导机制至关重要。

附：英文原文

Title: Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon

Author: Otto, Dominik J., Jordan, Cailin, Dury, Brennan, Dien, Christine, Setty, Manu

Issue&Volume: 2024-06-18

Abstract: Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon’s efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.

DOI: 10.1038/s41592-024-02302-w

Source: https://www.nature.com/articles/s41592-024-02302-w