The sixth author’s name is spelled incorrectly. The correct name is: F. Hillmann. The correct citation is: Keizer EM, Valdes ID, Forn-Cuni G, Klijn E, Meijer AH, Hillmann F, et al. (2021) Variation... Show moreThe sixth author’s name is spelled incorrectly. The correct name is: F. Hillmann. The correct citation is: Keizer EM, Valdes ID, Forn-Cuni G, Klijn E, Meijer AH, Hillmann F, et al. (2021) Variation of virulence of five Aspergillus fumigatus isolates in four different infection models. PLoS ONE 16(7): e0252948. https://doi.org/10.1371/journal.pone.0252948Show less
Keizer, E.M.; Valdes, I.D.; Forn Cuni, G.; Klijn, E.; Meijer, A.H.; Hillman, F.; ... ; Cock, H. de 2021
Conidia of Aspergillus fumigatus are inhaled by humans on daily basis. As a consequence, these conidia can cause infections that differ in severity ranging from allergic bronchopulmonary... Show moreConidia of Aspergillus fumigatus are inhaled by humans on daily basis. As a consequence, these conidia can cause infections that differ in severity ranging from allergic bronchopulmonary aspergillosis to invasive aspergillosis. In this study we compared virulence of five A. fumigatus isolates in four different infection models to address the predictive value of different model systems. Two of the A. fumigatus strains were isolated from dogs with a non-invasive sino-nasal aspergillosis (DTO271-B5 and DTO303-F3), while three strains were isolated from human patients with invasive aspergillosis (Af293, ATCC46645 and CEA10). Infection models used encompassed cultured type II A549 lung epithelial cells, Protostelium aurantium amoeba, Galleria melonella larvae and zebrafish embryos. No major differences in virulence between these five strains were observed in the lung epithelial cell model. In contrast, strain ATCC46645 was most virulent in the amoeba and zebrafish model, whereas it was much less virulent in the Galleria infection model. DTO303-F3 was most virulent in the latter model. In general, reference strain Af293 was less virulent as compared to the other strains. Genome sequence analysis showed that this latter strain differed from the other four strains in 136 SNPs in virulence-related genes. Together, our results show that virulence of individual A. fumigatus strains show significant differences between infection models. We conclude that the predictive value of different model systems varies since the relative virulence across fungal strains does not hold up across different infection model systems. Show less
Keizer, E.M.; Valdes, I.D.; Forn Cuni, G.; Klijn, E.; Meijer, A.H.; Hillman, F.; ... ; Cock, H. de 2021
The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells... Show moreThe recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years. Show less
The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells... Show moreThe recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years. Show less
