Huffine Clair A, Maas Zachary L, Avramov Anton, Brininger Chris, Cameron Jeffrey C, Tay Jian Wei
BioFrontiers Institute, University of Colorado, Boulder, CO 80309, USA.
Department of Biochemistry, University of Colorado, Boulder, CO 80309, USA.
bioRxiv. 2024 Dec 12:2024.12.11.628068. doi: 10.1101/2024.12.11.628068.
Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.
延时显微镜最近已被用于在单细胞水平上研究蓝细菌的代谢和生理学。然而，在明场图像中识别单个细胞仍然是一项重大挑战。由于相邻细胞之间缺乏对比度，传统的基于强度的分割算法在识别密集菌落中的单个细胞时表现不佳。在这里，我们描述了一个新开发的名为Cypose的软件包，它使用机器学习（ML）模型来解决两个特定任务：单个蓝细菌细胞的分割和细胞表型的分类。分割模型基于Cellpose框架，而分类则使用名为Cyclass的卷积神经网络进行。据我们所知，这些是第一个开发的基于ML的蓝细菌分割和分类模型。与其他方法相比，我们的分割模型表现出更好的性能，能够分割具有不同形态表型的细胞，以及区分活细胞和裂解细胞。我们还发现我们的模型对成像伪像（如灰尘和细胞碎片）具有鲁棒性。此外，分类模型仅使用图像作为输入就能识别不同的细胞表型。总之，这些模型提高了细胞分割的准确性，并能够对密集的蓝细菌菌落和丝状蓝细菌进行高通量分析。
