Abstract：
:Phenotypic heterogeneity is an important trait for the development and survival of many microorganisms including the yeast Cryptococcus spp., a deadly pathogen spread worldwide. Here, we have applied scanning electron microscopy (SEM) to define four Cryptococcus spp. capsule morphotypes, namely Regular, Spiky, Bald, and Phantom. These morphotypes were persistently observed in varying proportions among yeast isolates. To assess the distribution of such morphotypes we implemented an automated pipeline capable of (1) identifying potentially cell-associated objects in the SEM-derived images; (2) computing object-level features; and (3) classifying these objects into their corresponding classes. The machine learning approach used a Random Forest (RF) classifier whose overall accuracy reached 85% on the test dataset, with per-class specificity above 90%, and sensitivity between 66 and 94%. Additionally, the RF model indicates that structural and texture features, e.g., object area, eccentricity, and contrast, are most relevant for classification. The RF results agree with the observed variation in these features, consistently also with visual inspection of SEM images. Finally, our work introduces morphological variants of Cryptococcus spp. capsule. These can be promptly identified and characterized using computational models so that future work may unveil morphological associations with yeast virulence.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Lopes W,Cruz GNF,Rodrigues ML,Vainstein MH,Kmetzsch L,Staats CC,Vainstein MH,Schrank Adoi
10.1038/s41598-020-59276-wsubject
Has Abstractpub_date
2020-02-11 00:00:00pages
2362issue
1issn
2045-2322pii
10.1038/s41598-020-59276-wjournal_volume
10pub_type
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