Tophat method cellprofiler manual#
Tophat method cellprofiler software#

Tophat method cellprofiler software#

While most software provide segmentation, feature extraction, and classification modules, they are limited in their image processing 15, 20 and types of feature analysis 15, 16, 18, 19, 20, 21, 22, 23. In this paper, we introduce MitoMo, which is open-source, provides a user-friendly graphical user interface (GUI) that does not require programming knowledge, can easily be adapted to any laboratory, and is flexible in allowing users to import pre-segmented images from any image processing software.īecause of limitations in existing software, there is an unmet need for software that can perform an integrated multi-feature analysis of morphology, motion, texture, and morphogenesis. Some require that users know programming languages and have access to commercial image processing software not routinely available in all labs 18, 19. While mitochondrial analysis software is continually evolving, some existing programs have limitations with respect to accessibility.

Tophat method cellprofiler manual#

Time-consuming manual protocols 17 are being replaced by software that provides automated analysis of mitochondrial features, making rapid high content analysis feasible.

In diseases such as cancer, mitochondria phenotypes have been shown to vary between tumors, and used to classify types of cancer 15, 16.īecause of their importance in homeostasis, stress, and human disease, there is need for technologies to analyze and quantify changes in mitochondrial morphology and dynamic behavior. For example, changes in mitochondrial morphology, mainly fragmentation, and abnormal dynamics in axonal transport in neurons have been reported in HD patients 11. Moreover, mitochondrial morphology and dynamics are altered in common neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD) 11 and may vary within subclasses of diseases such as cancer, diabetes, myopathies and metabolic diseases 7, 11, 12, 13, 14. Mitochondria have an intrinsic ability to sense their state of health, and when stressed, induce compensatory quality-control mechanisms, such as stress-induced mitochondrial hyperfusion (SIMH) or fission and degradation of damaged mitochondria (mitophagy) 6, 8, 9, 10, making them excellent organelles for evaluating cell health. These morphologies are related to the metabolic state and bioenergetics of the cell and vary during processes such as cell division and differentiation 3, 7. During fusion and fission 4, mitochondria transition between morphological classes that include small puncta, tubes, networks, and “donuts” or rings 5, 6. Mitochondrial phenotypes (morphology, dynamics, and organizational patterns) vary significantly in different cell types. Mitochondria are dynamic organelles capable of regulating cell fate, homeostasis, survival, and disease in eukaryotic cells 1, 2, 3. MitoMo unlocks new information on mitochondrial phenotypes and dynamics by enabling deep analysis of mitochondrial features in any cell type and can be applied to a broad spectrum of research problems in cell biology, drug testing, toxicology, and medicine. Biological applications are presented including: (1) establishing normal phenotypes of mitochondria in different cell types (2) quantifying stress-induced mitochondrial hyperfusion in cells treated with an environmental toxicant, (3) tracking morphogenesis in mitochondria undergoing swelling, and (4) evaluating early changes in cell health when morphological abnormalities are not apparent. MitoMo allows analysis of mitochondrial morphogenesis in time-lapse videos to study early progression of cellular stress.

Our pixel-based approach for motion analysis evaluates the magnitude and direction of motion of: (1) molecules within mitochondria, (2) individual mitochondria, and (3) distinct morphological classes of mitochondria.

MitoMo integrated software overcomes these bottlenecks by automating rapid unbiased quantitative analysis of mitochondrial morphology, texture, motion, and morphogenesis and advances machine-learning classification to predict cell health by combining features. Mitochondrial image analysis is typically done on still images using slow manual methods or automated methods of limited types of features. There is a critical need for better analytical methods to study mitochondria in normal and diseased states.