Image Analysis for Sliding Motility of <i>Clostridium perfringens</i>

Chopdekar, Nidhi

Image Analysis for Sliding Motility of Clostridium perfringens

dc.contributor.author	Chopdekar, Nidhi	en
dc.contributor.committeechair	Chen, Jing	en
dc.contributor.committeemember	Melville, Stephen B.	en
dc.contributor.committeemember	Hauf, Silke	en
dc.contributor.department	Biological Sciences	en
dc.date.accessioned	2024-06-12T19:05:57Z	en
dc.date.available	2024-06-12T19:05:57Z	en
dc.date.issued	2024-05-07	en
dc.description.abstract	The research investigates the sliding motility of Clostridium perfringens by employing machine learning-based image segmentation techniques and tracking to extract key quantitative characteristics of the movement of the bacteria. C. perfringens cells maintain end-to-end connections after cell divisions and form elongated chains that expand in a one-dimensional fashion. Cells in the elongating chains are pushed by each other to achieve a sliding movement at potentially high speeds. However, these chains are susceptible to breakage due to stress accumulation from rapid growth, which would undermine efficiency of the passive sliding motility. Utilizing AI-powered image analysis, this research aims to obtain detailed quantification of these dynamics and generate data for future mechanistic studies of the sliding motility. Results from this work highlight the effectiveness of machine learning in detecting individual cells from microscopy images. The accurately segmented cells enable enhanced tracking and detailed analysis of bacterial motility. The results generate useful quantitative data such as growth rate, velocity, and division frequency of C. perfringens.	en
dc.description.abstractgeneral	The research project explores the movement of Clostridium perfringens, a bacterium often responsible for food poisoning, by using machine learning techniques to observe and analyze how each bacterial cell moves within its colony. These bacteria form long, chain-like structures that help them move more rapidly. However, these chains can break when they become too long and undergo too much stress. By applying artificial intelligence-based tools to automatically detect and track cells in time-lapse microscopy videos, the project provides useful data of how these bacteria slide, grow and divide. These data will help us understand the chain-based bacterial sliding in C. perfringens and its underlying mechanism.	en
dc.description.degree	Master of Science	en
dc.format.medium	ETD	en
dc.format.mimetype	application/pdf	en
dc.identifier.uri	https://hdl.handle.net/10919/119406	en
dc.language.iso	en	en
dc.publisher	Virginia Tech	en
dc.rights	Creative Commons Attribution-NonCommercial 4.0 International	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc/4.0/	en
dc.subject	image analysis	en
dc.subject	image segmentation	en
dc.subject	sliding motility	en
dc.subject	live-cell images	en
dc.subject	cell tracking	en
dc.title	Image Analysis for Sliding Motility of <i>Clostridium perfringens</i>	en
dc.type	Thesis	en
dc.type.dcmitype	Text	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	masters	en
thesis.degree.name	Master of Science	en