Cell morphology encodes essential information on many underlying biological processes. It is commonly used by clinicians and researchers in the study, diagnosis, prognosis, and treatment of human diseases. Quantification of cell morphology has seen tremendous advances in recent years. However, effectively defining morphological shapes and evaluating the extent of morphological heterogeneity within cell populations remain challenging. Here we present a protocol and software for the analysis of cell and nuclear morphology from fluorescence or bright-field images using the VAMPIRE algorithm (https://github.com/kukionfr/VAMPIRE_open). This algorithm enables the profiling and classification of cells into shape modes based on equidistant points along cell and nuclear contours. Examining the distributions of cell morphologies across automatically identified shape modes provides an effective visualization scheme that relates cell shapes to cellular subtypes based on endogenous and exogenous cellular conditions. In addition, these shape mode distributions offer a direct and quantitative way to measure the extent of morphological heterogeneity within cell populations. This protocol is highly automated and fast, with the ability to quantify the morphologies from 2D projections of cells seeded both on 2D substrates or embedded within 3D microenvironments, such as hydrogels and tissues. The complete analysis pipeline can be completed within 60 minutes for a dataset of ~20,000 cells/2,400 images.

细胞形态学对许多潜在生物过程的基本信息进行编码。它被临床医生和研究人员广泛用于人类疾病的研究、诊断、预后和治疗。近年来，细胞形态的量化取得了巨大进步。然而，有效地定义形态形状和评估细胞群内形态异质性的程度仍然具有挑战性。在这里，我们提出了一个协议和软件，用于使用 VAMPIRE 算法（https://github.com/kukionfr/VAMPIRE_open）从荧光或亮场图像分析细胞和核形态。该算法能够根据沿细胞和核轮廓的等距点将细胞分析和分类为形状模式。检查细胞形态在自动识别的形状模式中的分布提供了一种有效的可视化方案，该方案根据内源性和外源性细胞条件将细胞形状与细胞亚型联系起来。此外，这些形状模式分布提供了一种直接和定量的方法来测量细胞群内形态异质性的程度。该协议是高度自动化和快速的，能够量化在 2D 基板上播种或嵌入 3D 微环境（如水凝胶和组织）中的细胞的 2D 投影的形态。对于约 20,000 个细胞/2,400 张图像的数据集，可以在 60 分钟内完成完整的分析流程。