Cell profiling with dynamic features for high-throughput images
Subpopulation heterogeneity has been spawning intense studies at genetic and molecular level due to its occurrence at all biological levels from cells to tissues. We envisioned studying this biological phenomenon through image based profiling methods incorporating motility based features. We develop...
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| Format: | Thesis |
| Language: | English |
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2014
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| Online Access: | http://hdl.handle.net/10356/60567 |
| _version_ | 1826123598826307584 |
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| author | Merlin Veronika Arokiamary James |
| author2 | Rajapakse Jagath Chandana |
| author_facet | Rajapakse Jagath Chandana Merlin Veronika Arokiamary James |
| author_sort | Merlin Veronika Arokiamary James |
| collection | NTU |
| description | Subpopulation heterogeneity has been spawning intense studies at genetic and molecular level due to its occurrence at all biological levels from cells to tissues. We envisioned studying this biological phenomenon through image based profiling methods incorporating motility based features. We developed population profiling methods for analysing subpopulations arising in single-cell lines by introducing motility based dynamic features. Combination of these features with morphological features improved the accuracy of classification of cell states. We introduced unsupervised methods so that prior training data is not required. Also the use of motility features for identifying membrane dynamics and its correlation with whole cell dynamics were investigated. We were able to identify subpopulations of cells with similar dynamic profiles but having different membrane patterns. The profiling pipeline using dynamic features were demonstrated by identifying mitotic phases in cells undergoing cell-cycle. Cells passing through mitotic division exhibit motility characteristics unique to each phase which were utilized for phase recognition. The methods were validated with real image data and the results compared well with ground truth. |
| first_indexed | 2024-10-01T06:06:58Z |
| format | Thesis |
| id | ntu-10356/60567 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:06:58Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/605672020-11-01T11:32:25Z Cell profiling with dynamic features for high-throughput images Merlin Veronika Arokiamary James Rajapakse Jagath Chandana School of Computer Science and Engineering Singapore-MIT Alliance Programme DRNTU::Engineering::Computer science and engineering Subpopulation heterogeneity has been spawning intense studies at genetic and molecular level due to its occurrence at all biological levels from cells to tissues. We envisioned studying this biological phenomenon through image based profiling methods incorporating motility based features. We developed population profiling methods for analysing subpopulations arising in single-cell lines by introducing motility based dynamic features. Combination of these features with morphological features improved the accuracy of classification of cell states. We introduced unsupervised methods so that prior training data is not required. Also the use of motility features for identifying membrane dynamics and its correlation with whole cell dynamics were investigated. We were able to identify subpopulations of cells with similar dynamic profiles but having different membrane patterns. The profiling pipeline using dynamic features were demonstrated by identifying mitotic phases in cells undergoing cell-cycle. Cells passing through mitotic division exhibit motility characteristics unique to each phase which were utilized for phase recognition. The methods were validated with real image data and the results compared well with ground truth. Doctor of Philosophy (SCE) 2014-05-28T07:20:18Z 2014-05-28T07:20:18Z 2011 2011 Thesis http://hdl.handle.net/10356/60567 en 156 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Merlin Veronika Arokiamary James Cell profiling with dynamic features for high-throughput images |
| title | Cell profiling with dynamic features for high-throughput images |
| title_full | Cell profiling with dynamic features for high-throughput images |
| title_fullStr | Cell profiling with dynamic features for high-throughput images |
| title_full_unstemmed | Cell profiling with dynamic features for high-throughput images |
| title_short | Cell profiling with dynamic features for high-throughput images |
| title_sort | cell profiling with dynamic features for high throughput images |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/60567 |
| work_keys_str_mv | AT merlinveronikaarokiamaryjames cellprofilingwithdynamicfeaturesforhighthroughputimages |