CytoCensus: mapping cell identity and division in tissues and organs using machine learning
| Autor: | Tamsin J. Samuels, Richard M. Parton, Elizabeth J. Robertson, Ita Costello, Lu Yang, Ilan Davis, Martin Hailstone, Dominic Waithe, Yoav Arava |
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| Přispěvatelé: | Hailstone, Martin [0000-0001-9326-3827], Waithe, Dominic [0000-0003-2685-4226], Samuels, Tamsin J [0000-0003-4670-1139], Arava, Yoav [0000-0002-2562-9409], Robertson, Elizabeth [0000-0001-6562-0225], Parton, Richard M [0000-0002-2152-4271], Davis, Ilan [0000-0002-5385-3053], Apollo - University of Cambridge Repository |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Male
3D cell detection 4D image analysis Time Factors Computer science Mammalian Embryos Mutant Cell computer.software_genre Animals Genetically Modified Machine Learning Tissue Culture Techniques Automation Mice 0302 clinical medicine ex vivo culture Single-cell analysis cell biology Image Processing Computer-Assisted Biology (General) Zebrafish neural stem cells Larva 0303 health sciences Microscopy Video biology D. melanogaster General Neuroscience Vertebrate Brain Embryo General Medicine live imaging Cell identity Neural stem cell Tools and Resources Organoids medicine.anatomical_structure Drosophila melanogaster Phenotype Medicine Identification (biology) Female Stem cell Cell Division Cell type QH301-705.5 Science Machine learning Time-Lapse Imaging General Biochemistry Genetics and Molecular Biology Retina 03 medical and health sciences developmental biology Live cell imaging biology.animal Organoid medicine Animals Image analysis mouse 030304 developmental biology General Immunology and Microbiology business.industry Reproducibility of Results biology.organism_classification Embryo Mammalian Mutation Artificial intelligence business computer Developmental biology 030217 neurology & neurosurgery |
| Zdroj: | eLife eLife, Vol 9 (2020) |
| DOI: | 10.1101/137406 |
| Popis: | A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D ‘point-and-click’ user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues. eLife digest There are around 200 billion cells in the human brain that are generated by a small pool of rapidly dividing stem cells. For the brain to develop correctly, these stem cells must produce an appropriate number of each type of cell in the right place, at the right time. However, it remains unclear how individual stem cells in the brain know when and where to divide. To answer this question, Hailstone et al. studied the larvae of fruit flies, which use similar genes and mechanisms as humans to control brain development. This involved devising a new method for extracting the brains of developing fruit flies and keeping the intact tissue alive for up to 24 hours while continuously imaging individual cells in three dimensions. Manually tracking the division of each cell across multiple frames of a time-lapse is extremely time consuming. To tackle this problem, Hailstone et al. created a tool called CytoCensus, which uses machine learning to automatically identify stem cells from three-dimensional images and track their rate of division over time. Using the CytoCensus tool, Hailstone et al. identified a gene that controls the diverse rates at whichstem cells divide in the brain. Earlier this year some of the same researchers also published a study showing that this gene regulates a well-known cancer-related protein using an unconventional mechanism. CytoCensus was also able to detect cells in other developing tissues, including the embryos of mice. In the future, this tool could aid research into diseases that affect complex tissues, such as neurodegenerative disorders and cancer. |
| Databáze: | OpenAIRE |
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