Project Details
Description
Advancements in the field of cytometry have enabled researchers to go beyond the study of the average of cell populations to high-dimensional analysis of single cells. In some cases, e.g. when a minority subpopulation drives a disease, insight into cell to cell heterogeneity is crucial for the understanding of biological phenomena.
The non-destructive, in-situ nature of microscopy allows us to locate cells in their spatial and temporal context. Through live cell imaging, we can follow cells over time and correlate events and interactions to changes in cell behavior.
While techniques for automatically digitizing certain single-cell parameters of spatiotemporal cell processes have existed for over 30 years, recent developments enable the automatic acquisition and statistical analysis of high-dimensional single-cell data.
By building on previous work in Nordenfelt and Swaminathan Lab to standardize automated acquisition pipelines, this dissertation aims to use image analysis and machine learning to characterize and predict the behaviors of cells and relate these behaviors to biological outcomes.
The non-destructive, in-situ nature of microscopy allows us to locate cells in their spatial and temporal context. Through live cell imaging, we can follow cells over time and correlate events and interactions to changes in cell behavior.
While techniques for automatically digitizing certain single-cell parameters of spatiotemporal cell processes have existed for over 30 years, recent developments enable the automatic acquisition and statistical analysis of high-dimensional single-cell data.
By building on previous work in Nordenfelt and Swaminathan Lab to standardize automated acquisition pipelines, this dissertation aims to use image analysis and machine learning to characterize and predict the behaviors of cells and relate these behaviors to biological outcomes.
| Status | Active |
|---|---|
| Effective start/end date | 2021/07/01 → … |