Research description:
How one genome generates a large diversity of cell types, each with unique spatiotemporal gene expression patterns and physiological roles, is an enduring fundamental question in cell and developmental biology. To understand the genome structure-function relationships, it is not sufficient to know the genome sequence and local epigenetic features – we must also consider the large-scale physical architecture of entire chromosomes and their positioning within the nucleus in space and time (4D). Our broad objective is to understand how the entire genome is organized in complex multicellular systems, and how this organization influences the genome’s functional output (Sawh et al., Mol Cell 2020; Sawh and Mango. Current Opinion in Genetics & Development 2022). In the current opportunity, a SUDS scholar will extend our methods of traditional watershed 3D image segmentation to extract quantitative chromosome conformation information from C. elegans embryo spatial omics data. With a large amount of 3D segmented ground truth data in hand, the applicant will develop a threshold-free deep neural network approach to accurately segment anisotropic cell, nuclear, and chromosome objects in C. elegans embryos over developmental time. The position can be in-person, remote, or hybrid depending on the preference of the candidate.
The student will work closely with graduate students and myself, to use and refine 3D semantic image segmentation algorithms on fluorescence images. The student will refine a pre-trained neural network model (e.g. Cellpose 2.0) using already available ground-truth data to develop custom models for C. elegans cell, nuclei, and chromosome volumes.
Year: 2024
Researcher:
Ahilya Sawh, Department of Biochemistry, University of Toronto
Students:
Faisal Shaik, University of Toronto
Waleed Adel Alsarhani, King Abdullah University of Science & Technology