德国慕尼黑亥姆霍兹中心Fabian J. Theis和瑞士苏黎世大学Lucas Pelkmans团队共同合作，近期取得重要工作进展。他们开发了一种称为CAMPA深度学习框架，用来学习一致的亚细胞标志以量化多重蛋白图谱的变化 。相关研究成果2023年5月29日在西安发表于《自然—方法学》杂志上。

据介绍，高度多重成像为理解空间背景如何在多个长度尺度上，塑造基因组及其产物的活动提供了巨大的前景。

研究人员介绍了一种称为CAMPA（用于多路像素分析的条件自动编码器）的深度学习框架，该框架使用条件变分自动编码器来学习分子像素轮廓的表示，这些表示在异质细胞群体和实验扰动中是一致的。对这些像素级表示进行聚类可以识别出一致的亚细胞标志，这些标志可以根据其大小、形状、分子组成和相对空间组织进行定量比较。使用高分辨率多重免疫荧光，这揭示了亚细胞组织如何在RNA合成、RNA加工或细胞大小受到干扰时发生变化，并揭示了无膜细胞器的分子组成与大量RNA合成率的细胞间变异之间的联系。

总之，通过捕捉可解释的细胞表型，研究人员预计CAMPA将极大地加快生物组织多尺度图谱的系统绘制，以确定环境塑造生理和疾病的规则。

附：英文原文

Title: Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps

Author: Spitzer, Hannah, Berry, Scott, Donoghoe, Mark, Pelkmans, Lucas, Theis, Fabian J.

Issue&Volume: 2023-05-29

Abstract: Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization. Using high-resolution multiplexed immunofluorescence, this reveals how subcellular organization changes upon perturbation of RNA synthesis, RNA processing or cell size, and uncovers links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates. By capturing interpretable cellular phenotypes, we anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organization to identify the rules by which context shapes physiology and disease.

DOI: 10.1038/s41592-023-01894-z

Source: https://www.nature.com/articles/s41592-023-01894-z