Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp.

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

SUBMITTER: Lopes W 

PROVIDER: S-EPMC7012869 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature