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Automatic Morphological Subtyping Reveals New Roles of Caspases in Mitochondrial Dynamics

Overview of attention for article published in PLoS Computational Biology, October 2011
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Title
Automatic Morphological Subtyping Reveals New Roles of Caspases in Mitochondrial Dynamics
Published in
PLoS Computational Biology, October 2011
DOI 10.1371/journal.pcbi.1002212
Pubmed ID
Authors

Jyh-Ying Peng, Chung-Chih Lin, Yen-Jen Chen, Lung-Sen Kao, Young-Chau Liu, Chung-Chien Chou, Yi-Hung Huang, Fang-Rong Chang, Yang-Chang Wu, Yuh-Show Tsai, Chun-Nan Hsu

Abstract

Morphological dynamics of mitochondria is associated with key cellular processes related to aging and neuronal degenerative diseases, but the lack of standard quantification of mitochondrial morphology impedes systematic investigation. This paper presents an automated system for the quantification and classification of mitochondrial morphology. We discovered six morphological subtypes of mitochondria for objective quantification of mitochondrial morphology. These six subtypes are small globules, swollen globules, straight tubules, twisted tubules, branched tubules and loops. The subtyping was derived by applying consensus clustering to a huge collection of more than 200 thousand mitochondrial images extracted from 1422 micrographs of Chinese hamster ovary (CHO) cells treated with different drugs, and was validated by evidence of functional similarity reported in the literature. Quantitative statistics of subtype compositions in cells is useful for correlating drug response and mitochondrial dynamics. Combining the quantitative results with our biochemical studies about the effects of squamocin on CHO cells reveals new roles of Caspases in the regulatory mechanisms of mitochondrial dynamics. This system is not only of value to the mitochondrial field, but also applicable to the investigation of other subcellular organelle morphology.

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Geographical breakdown

Country Count As %
Germany 2 2%
Portugal 1 <1%
Netherlands 1 <1%
Finland 1 <1%
United Kingdom 1 <1%
United States 1 <1%
Unknown 126 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 27%
Researcher 29 22%
Student > Master 17 13%
Student > Bachelor 9 7%
Student > Postgraduate 5 4%
Other 17 13%
Unknown 20 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 29%
Biochemistry, Genetics and Molecular Biology 28 21%
Engineering 10 8%
Computer Science 9 7%
Medicine and Dentistry 8 6%
Other 18 14%
Unknown 22 17%