Establishing Image-Based Profiling Methods for Single-Cell Transcriptomics and Morphometrics in the Cephalochordate Amphioxus

The cephalochordate amphioxus occupies a key phylogenetic position for comparative studies elucidating the origins of the chordate body plan and the emergence of vertebrate novelties during evolution. However, as a model system, the amphioxus is still challenging to culture in the lab, and consequently, the repertoire of techniques available for this model organism is still limited. This thesis exploits the simple morphology, small size and transparency of amphioxus embryos and tissues to advance the field of quantitative imaging in the assignation of homology across the large evolutionary distance separating the amphioxus from vertebrates (approx. 520MY). Advancements in the field of light microscopy mean that we are able to visualise events below the diffraction limit of light – at sub-cellular resolution – and in three dimensions, generating such a large volume of imaging data that manual analysis becomes unfeasible. Recent developments in the field of machine learning facilitate high-throughput methodologies for image segmentation, allowing a myriad of modalities of data to be extracted and analysed from microscopy images of biological tissues, crucial for the analysis of transcriptional signatures and morphogenesis in animal tissues. This thesis firstly establishes a set of computational tools for use in the adult amphioxus to build a positional atlas of cell types in the brain, optimising open-source technologies into a pipeline for the 3D visualisation and analysis of transcriptional signatures in segmented nuclei. Next, I assemble a pipeline for automatic whole-cell segmentation and apply this to the study of morphogenesis in the developing amphioxus notochord, recapitulating previous single-cell morphometric (scMorph) analyses as a proof of principle and then dissecting the changes to notochord structure resulting from pharmacological perturbation. Taken together, this thesis builds a repertoire of user-friendly frameworks designed to extract cell and nuclear morphology, along with transcriptional data, from high-resolution confocal images of amphioxus embryos and adult tissues. These tools aim to accurately predict homology with vertebrates at the single-cell level. This comprehensive set of methodologies can be effectively applied to other model systems which face similar challenges in comparative studies due to substantial evolutionary gaps and diverse anatomical structures.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Schwimmer, Michael

Advisor dc:contributor.advisor

• Benito Gutierrez, Elia

• Zoology
• Development
• Imaging
• Machine Learning
• Trancriptomics
• Morphometrics
• Amphioxus

Licence

• https://www.repository.cam.ac.uk/bitstreams/cf016540-b708-4170-ad7b-2e3bf31428d0/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.114518

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/377828