酵母自体荧光寿命显微图像的自动分析方法

Automated Analysis Methods for Autofluorescence Lifetime Microscopic Images of Yeast

酿酒酵母是最具有吸引力的微生物之一,监测其在不同生长时期的新陈代谢状态变化对基础生物学和工业研究都具有重要意义。依据酵母的生成曲线规律培养了不同时间的酵母细胞,基于荧光寿命显微成像(FLIM)进行自体荧光寿命图像的采集,并提出了一种基于机器学习的自动分析方法,可无标记快速鉴别年轻和衰老的酵母细胞。首先,采用深度监督U-Net实现酵母细胞的自动分割;然后,提取每个酵母细胞的荧光寿命特征和形态特征;最后,采用无监督聚类方法实现分类。实验结果表明,酵母的衰老伴随着新陈代谢的变化。FLIM作为一种无标记成像技术可应用于酵母细胞的代谢分析中,结合自动化分析流程可快速准确地区分具有不同代谢差异的细胞,为后续单细胞的筛选奠定了基础。

Saccharomyces cerevisiae is one of the most attractive microorganisms,and monitoring changes in its metabolic state at different growth periods is significant for both basic biology and industrial research.In this paper,yeast cells are cultured for different periods based on the yeast generation curve rule,autofluorescence lifetime images are collected using fluorescence lifetime imaging microscopy(FLIM),and an automatic analysis method based on machine learning is proposed,which can rapidly identify young and senile yeast cells without markers.First,a deep-supervised U-Net is applied to automatically segment yeast cells.Then,the features of fluorescence lifetime and morphology of each yeast cell are extracted.Finally,the classification is achieved using the unsupervised clustering method.The experimental results reveal that yeast senescence is accompanied by changes in metabolism.FLIM,as a label-free imaging technique,can be used for the metabolic analysis of yeast cells.When combined with the automated analysis process,it can swiftly and accurately distinguish cells with different metabolic differences,laying the foundation for subsequent screening of single cells.