机器学习模型用于细分和分类蓝菌细胞
Clair A Huffine1,2,3, Zachary L Maas1,4,5, Anton Avramov2,3
1BioFrontiers Institute, University of Colorado, Boulder, CO 80309, USA.
bioRxiv : the preprint server for biology
|December 23, 2024
概括
这项研究介绍了Cypose,一种使用机器学习 (ML) 的新软件,用于准确的菌细胞细分和表型分类. 赛波斯改进了对密集的蓝菌群和丝状蓝菌的高通量分析.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 生物技术是生物技术.
背景情况:
- 时间缩短显微镜使单细胞研究蓝藻细菌的新陈代谢和生理学.
- 由于对比度较低,在密集的殖民地中准确识别单个蓝藻细菌细胞对传统的细分方法具有挑战性.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于细分单个蓝藻细菌细胞和分类细胞表型.
- 介绍Cypose,一个集成ML模型的软件包,用于增强蓝藻细菌图像分析.
主要方法:
- 使用Cellpose框架开发基于ML的细分模型.
- 实现了一个卷积神经网络 (Cyclass) 用于细胞表型分类.
- 将ML模型与准确性和稳定性的传统方法进行比较.
主要成果:
- 囊模型在细分单个蓝藻细菌细胞方面表现出卓越的表现,包括具有多种形态的细胞,并从溶解细胞中区分活细胞.
- 这些模型被证明对像尘埃和细胞碎片这样的成像文物有很强的抵抗力.
- 该分类模型准确地从图像中直接识别出不同的细胞表型.
结论:
- Cypose提供了第一个基于ML的解决方案,用于准确的蓝藻细分和分类.
- 这些模型显著提高了细胞细分的准确性,使密集和丝状蓝藻菌群的高通量分析成为可能.
相关概念视频
01:30Bacterial Phylum Cyanobacteria
Cyanobacteria are a diverse group of oxygenic, phototrophic bacteria that played a pivotal role in converting Earth’s atmosphere from anoxic to oxygen-rich billions of years ago. They exhibit remarkable morphological diversity, ranging from unicellular forms to filamentous types, with cell sizes varying between 0.5 μm and 100 μm. Cyanobacteria are classified into five groups: Chroococcales (unicellular, dividing by binary fission), Pleurocapsales (unicellular, dividing by...
01:28Methods of Classification and Identification
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...