Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da FIOCRUZ (ARCA) |
Texto Completo: | https://www.arca.fiocruz.br/handle/icict/41213 |
Resumo: | Universidade Federal do Rio Grande do Sul. Centro de Biotecnologia. Porto Alegre, RS, Brasil. |
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Lopes, WilliamCruz, Giuliano N. F.Rodrigues, Marcio LourençoVainstein, Mendeli H.Kmetzsch, LiviaStaats, Charley C.Vainstein, Marilene H.Schrank, Augusto2020-05-12T21:02:08Z2020-05-12T21:02:08Z2020LOPES, Willian et al. Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp. Scientific Reports. V.10, n.2362, p. 1-11, 2020.2454-2164https://www.arca.fiocruz.br/handle/icict/4121310.1038/s41598-020-59276-wporNature ResearchCryptococcusMicroscopy, Electron, ScanningVirulence FactorsMachine LearningMicroscopía Electrónica de RastreoFactores de VirulenciaAprendizaje AutomáticoMicroscopia Eletrônica de VarreduraFatores de VirulênciaAprendizado de MáquinaScanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus sppinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleUniversidade Federal do Rio Grande do Sul. Centro de Biotecnologia. Porto Alegre, RS, Brasil.BiomeHub. Florianópolis, SC, Brasil.Fundação Oswaldo Cruz. Instituto Carlos Chagas. Curitiba, PR, Brasil. / Universidade Federal do Rio de Janeiro. Instituto de Microbiologia Paulo Góes. Rio de Janeiro, RJ, Brasil.Universidade Federal do Rio Grande do Sul. Instituto de Física. Departamento de Física. Porto Alegre, RS, Brasil.Universidade Federal do Rio Grande do Sul. Centro de Biotecnologia. Porto Alegre, RS, Brasil.Universidade Federal do Rio Grande do Sul. Centro de Biotecnologia. Porto Alegre, RS, Brasil.Universidade Federal do Rio Grande do Sul. Centro de Biotecnologia. Porto Alegre, RS, Brasil.Universidade Federal do Rio Grande do Sul. Centro de Biotecnologia. Porto Alegre, RS, Brasil.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.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/41213/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINALs41598-020-592.pdfs41598-020-592.pdfapplication/pdf4669903https://www.arca.fiocruz.br/bitstream/icict/41213/2/s41598-020-592.pdfcd1d93312b5aaa78c9fa64318faa30b2MD52TEXTs41598-020-592.pdf.txts41598-020-592.pdf.txtExtracted texttext/plain43204https://www.arca.fiocruz.br/bitstream/icict/41213/3/s41598-020-592.pdf.txtfc10d686c41260336c2ac492d8911edbMD53icict/412132020-05-13 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dc.title.pt_BR.fl_str_mv |
Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp |
title |
Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp |
spellingShingle |
Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp Lopes, William Cryptococcus Microscopy, Electron, Scanning Virulence Factors Machine Learning Microscopía Electrónica de Rastreo Factores de Virulencia Aprendizaje Automático Microscopia Eletrônica de Varredura Fatores de Virulência Aprendizado de Máquina |
title_short |
Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp |
title_full |
Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp |
title_fullStr |
Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp |
title_full_unstemmed |
Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp |
title_sort |
Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp |
author |
Lopes, William |
author_facet |
Lopes, William Cruz, Giuliano N. F. Rodrigues, Marcio Lourenço Vainstein, Mendeli H. Kmetzsch, Livia Staats, Charley C. Vainstein, Marilene H. Schrank, Augusto |
author_role |
author |
author2 |
Cruz, Giuliano N. F. Rodrigues, Marcio Lourenço Vainstein, Mendeli H. Kmetzsch, Livia Staats, Charley C. Vainstein, Marilene H. Schrank, Augusto |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Lopes, William Cruz, Giuliano N. F. Rodrigues, Marcio Lourenço Vainstein, Mendeli H. Kmetzsch, Livia Staats, Charley C. Vainstein, Marilene H. Schrank, Augusto |
dc.subject.other.pt_BR.fl_str_mv |
Cryptococcus |
topic |
Cryptococcus Microscopy, Electron, Scanning Virulence Factors Machine Learning Microscopía Electrónica de Rastreo Factores de Virulencia Aprendizaje Automático Microscopia Eletrônica de Varredura Fatores de Virulência Aprendizado de Máquina |
dc.subject.en.pt_BR.fl_str_mv |
Microscopy, Electron, Scanning Virulence Factors Machine Learning |
dc.subject.es.pt_BR.fl_str_mv |
Microscopía Electrónica de Rastreo Factores de Virulencia Aprendizaje Automático |
dc.subject.decs.pt_BR.fl_str_mv |
Microscopia Eletrônica de Varredura Fatores de Virulência Aprendizado de Máquina |
description |
Universidade Federal do Rio Grande do Sul. Centro de Biotecnologia. Porto Alegre, RS, Brasil. |
publishDate |
2020 |
dc.date.accessioned.fl_str_mv |
2020-05-12T21:02:08Z |
dc.date.available.fl_str_mv |
2020-05-12T21:02:08Z |
dc.date.issued.fl_str_mv |
2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
LOPES, Willian et al. Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp. Scientific Reports. V.10, n.2362, p. 1-11, 2020. |
dc.identifier.uri.fl_str_mv |
https://www.arca.fiocruz.br/handle/icict/41213 |
dc.identifier.issn.pt_BR.fl_str_mv |
2454-2164 |
dc.identifier.doi.none.fl_str_mv |
10.1038/s41598-020-59276-w |
identifier_str_mv |
LOPES, Willian et al. Scanning electron microscopy and machine learning reveal heterogeneity in capsular morphotypes of the human pathogen Cryptococcus spp. Scientific Reports. V.10, n.2362, p. 1-11, 2020. 2454-2164 10.1038/s41598-020-59276-w |
url |
https://www.arca.fiocruz.br/handle/icict/41213 |
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por |
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por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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Nature Research |
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Nature Research |
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