Systematic data analysis pipeline for quantitative morphological cell phenotyping.

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

Rational selection of morphological phenotypic traits to extract essential similarities in chemical perturbation in the ergosterol pathway.

Terbinafine, fluconazole, and amorolfine inhibit fungal ergosterol synthesis by acting on their target enzymes at different steps in the synthetic pathway, causing the accumulation of various intermediates. We found that the effects of these three in- hibitors on yeast morphology were different. The number of morphological parameters commonly altered by these drugs was only approximately 6% of the total. Using a rational strategy to find commonly changed parameters,we focused on hidden essential similarities in the phenotypes possibly due to decreased ergosterol levels. This resulted in higher apparent morphological similarity. Improvements in morphological similarity were observed even when canonical correlation analysis was used to select biologically meaningful morphological parameters related to gene function. In addition to changes in cell morphology, we also observed differences in the synergistic effects among the three inhibitors and in their fungicidal effects against pathogenic fungi possibly due to the accumulation of different intermediates. This study provided a comprehensive understanding of the properties of inhibitors acting in the same biosynthetic pathway.

Application of unimodal probability distribution models for morphological phenotyping of budding yeast.

Morphological phenotyping of the budding yeast Saccharomyces cerevisiae has helped to greatly clarify the functions of genes and increase our understanding of cellular functional networks. It is necessary to understand cell morphology and perform quantitative morphological analysis (QMA) but assigning precise values to morphological phenotypes has been challenging. We recently developed the Unimodal Morphological Data image analysis pipeline for this purpose. All true values can be estimated theoretically by applying an appropriate probability distribution if the distribution of experimental values follows a unimodal pattern. This reliable pipeline allows several downstream analyses, including detection of subtle morphological differences, selection of mutant strains with similar morphology, clustering based on morphology, and study of morphological diversity. In addition to basic research, morphological analyses of yeast cells can also be used in applied research to monitor breeding and fermentation processes and control the fermentation activity of yeast cells.