{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/381349"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/381349","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Investigating the cytoskeleton and microtubule-based transport with in situ cryo-electron tomography","abstract":"The cytoskeleton is composed of microtubules, actin, and intermediate filaments, which function both as a cellular scaffold and as tracks for motor proteins that drive intracellular transport. Cytoplasmic dynein, in combination with dynactin and activating adaptors, is a microtubule-based motor that drives long-range transport of many cargo types towards microtubule minus ends. Structural and cellular work to date has revealed how dynein-dynactin complexes form and arrange on microtubules. However, due to limitations of in vitro reconstitution, the molecular details of cargo interaction are not well described. Cryo-electron tomography (cryo-ET) can be applied to cellular samples to resolve macromolecules spatially at nanometer resolution. When many copies of a target are present, sub-tomogram averaging (STA) can be applied to improve resolution. I aimed to use cryo-ET to visualise cargo-bound dynein-dynactin complexes, directly in cells. I also aimed to visualise the specialised cytoskeletal environments in different cellular compartments in which dynein functions. I initially aimed to use cryo-ET to visualise the cellular projections of SH-SY5Y cells, a human neuroblastoma line. Cryo-ET of these projections revealed microtubule bundles and organelles. Immunofluorescence and STA analysis revealed uniform polarity microtubule bundles in MAP2-expressing projections, suggesting that differentiated SH-SY5Y projections showed a mixed neurite phenotype. The signal:noise ratio (SNR) of these tomograms was too low to visualise most macromolecules in the cytoplasm. This made this approach sub-optimal to visualise dynein-dynactin complexes. To address the SNR issues, I next employed cryo-focused ion beam (cryo-FIB) milling to thick rodent hippocampal neurites. This was to 1) target cellular regions with more motor-driven transport and 2) to remove unnecessary cellular noise in attempt to improve tomogram SNR. I found that, even with sample optimisation, cryo-FIB milling of neurites was low-throughput and produced poor quality lamellae. This undermined the aim of visualising dynein-dynactin complexes in situ. I also used a cryo-correlative light and electron microscopy (cryo-CLEM) approach to target the axon initial segment (AIS), providing insights into its microtubule and neurofilament networks. Due to the poor lamella quality, I next pursued more conventional cryo-FIB milling cellular targets. I next investigated cryo-FIB milling of cancer cell lines with dynein-driven cargo clustering systems. This was to increase the number of dynein-dynactin complexes on recognisable cargo, for a targeted cryo-ET approach. I first trialled a dynein and peroxisome clustering system that harnessed the inducible FKBP-FRB binding system. Cryo-CLEM allowed the targeting and identification of possible clusters in lamellae; however, tomograms lacked microtubules, which is an essential pre-requisite for visualising motile dynein-dynactin complexes. I next trialled a sodium arsenite (AS)-induced dynein-driven organelle clustering system. While many instances of cargo in the vicinity of microtubules were observed, no putative dynein-dynactin complexes were observed, likely as a result of the thickness of the lamellae in this dataset which were on average >200nm. However, the tomograms did reveal surprising ribosome localisations; membranes of vesicles of many forms were decorated with ribosomes in AS-treated HeLa cells. STA revealed a subset of these ribosomes that appeared to be non-active, that bound via a novel binding mode. Finally, I trialled a dynein-driven mitochondrial clustering system. This system yielded a large tomogram dataset with many microtubules and mitochondrial cargo, in which I could manually identify possible dynein-dynactin complex densities. Going forward, a computational particle picking and STA approach would confirm if these densities are in fact dynein-dynactin complexes. This work also showed that in situ cryo-ET is promising for identifying dynein-dynactin complex on cargo.","abstract_html":"The cytoskeleton is composed of microtubules, actin, and intermediate filaments, which function both as a cellular scaffold and as tracks for motor proteins that drive intracellular transport. Cytoplasmic dynein, in combination with dynactin and activating adaptors, is a microtubule-based motor that drives long-range transport of many cargo types towards microtubule minus ends. Structural and cellular work to date has revealed how dynein-dynactin complexes form and arrange on microtubules. However, due to limitations of in vitro reconstitution, the molecular details of cargo interaction are not well described. Cryo-electron tomography (cryo-ET) can be applied to cellular samples to resolve macromolecules spatially at nanometer resolution. When many copies of a target are present, sub-tomogram averaging (STA) can be applied to improve resolution. I aimed to use cryo-ET to visualise cargo-bound dynein-dynactin complexes, directly in cells. I also aimed to visualise the specialised cytoskeletal environments in different cellular compartments in which dynein functions. I initially aimed to use cryo-ET to visualise the cellular projections of SH-SY5Y cells, a human neuroblastoma line. Cryo-ET of these projections revealed microtubule bundles and organelles. Immunofluorescence and STA analysis revealed uniform polarity microtubule bundles in MAP2-expressing projections, suggesting that differentiated SH-SY5Y projections showed a mixed neurite phenotype. The signal:noise ratio (SNR) of these tomograms was too low to visualise most macromolecules in the cytoplasm. This made this approach sub-optimal to visualise dynein-dynactin complexes. To address the SNR issues, I next employed cryo-focused ion beam (cryo-FIB) milling to thick rodent hippocampal neurites. This was to 1) target cellular regions with more motor-driven transport and 2) to remove unnecessary cellular noise in attempt to improve tomogram SNR. I found that, even with sample optimisation, cryo-FIB milling of neurites was low-throughput and produced poor quality lamellae. This undermined the aim of visualising dynein-dynactin complexes in situ. I also used a cryo-correlative light and electron microscopy (cryo-CLEM) approach to target the axon initial segment (AIS), providing insights into its microtubule and neurofilament networks. Due to the poor lamella quality, I next pursued more conventional cryo-FIB milling cellular targets. I next investigated cryo-FIB milling of cancer cell lines with dynein-driven cargo clustering systems. This was to increase the number of dynein-dynactin complexes on recognisable cargo, for a targeted cryo-ET approach. I first trialled a dynein and peroxisome clustering system that harnessed the inducible FKBP-FRB binding system. Cryo-CLEM allowed the targeting and identification of possible clusters in lamellae; however, tomograms lacked microtubules, which is an essential pre-requisite for visualising motile dynein-dynactin complexes. I next trialled a sodium arsenite (AS)-induced dynein-driven organelle clustering system. While many instances of cargo in the vicinity of microtubules were observed, no putative dynein-dynactin complexes were observed, likely as a result of the thickness of the lamellae in this dataset which were on average >200nm. However, the tomograms did reveal surprising ribosome localisations; membranes of vesicles of many forms were decorated with ribosomes in AS-treated HeLa cells. STA revealed a subset of these ribosomes that appeared to be non-active, that bound via a novel binding mode. Finally, I trialled a dynein-driven mitochondrial clustering system. This system yielded a large tomogram dataset with many microtubules and mitochondrial cargo, in which I could manually identify possible dynein-dynactin complex densities. Going forward, a computational particle picking and STA approach would confirm if these densities are in fact dynein-dynactin complexes. This work also showed that in situ cryo-ET is promising for identifying dynein-dynactin complex on cargo.","abstract_has_math":false,"creators":["Johnston, Eleanor"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Carter, Andrew"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-10-31","date_published":"2024-10-31","updated_at":"2026-07-22T22:23:59Z","subjects":["cryo-electron tomography","cryo-FIB SEM","axon initial segment","ribosome"],"languages":["eng"],"rights":[],"rights_urls":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/dc4b0938-46d9-4167-b5fc-140e2ba8c1fc/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.116563","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Carter, Andrew"]},{"key":"dc:creator","label":"Author","values":["Johnston, Eleanor"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024-10-31"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/381349"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["cryo-electron tomography","cryo-FIB SEM","axon initial segment","ribosome"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/dc4b0938-46d9-4167-b5fc-140e2ba8c1fc/download","http://purl.org/NET/rdflicense/allrightsreserved"]},{"key":"dc:rights.embargodate","label":"Dc Rights Embargodate","values":["2026-03-12"]},{"key":"dc:rights.embargotype","label":"Dc Rights Embargotype","values":["embargo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.116563"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/08a8c4ed-f248-4034-9882-9ae5f42b206a/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The cytoskeleton is composed of microtubules, actin, and intermediate filaments, which function both as a cellular scaffold and as tracks for motor proteins that drive intracellular transport. 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I initially aimed to use cryo-ET to visualise the cellular projections of SH-SY5Y cells, a human neuroblastoma line. Cryo-ET of these projections revealed microtubule bundles and organelles. Immunofluorescence and STA analysis revealed uniform polarity microtubule bundles in MAP2-expressing projections, suggesting that differentiated SH-SY5Y projections showed a mixed neurite phenotype. The signal:noise ratio (SNR) of these tomograms was too low to visualise most macromolecules in the cytoplasm. This made this approach sub-optimal to visualise dynein-dynactin complexes. To address the SNR issues, I next employed cryo-focused ion beam (cryo-FIB) milling to thick rodent hippocampal neurites. This was to 1) target cellular regions with more motor-driven transport and 2) to remove unnecessary cellular noise in attempt to improve tomogram SNR. I found that, even with sample optimisation, cryo-FIB milling of neurites was low-throughput and produced poor quality lamellae. This undermined the aim of visualising dynein-dynactin complexes in situ. I also used a cryo-correlative light and electron microscopy (cryo-CLEM) approach to target the axon initial segment (AIS), providing insights into its microtubule and neurofilament networks. Due to the poor lamella quality, I next pursued more conventional cryo-FIB milling cellular targets. I next investigated cryo-FIB milling of cancer cell lines with dynein-driven cargo clustering systems. This was to increase the number of dynein-dynactin complexes on recognisable cargo, for a targeted cryo-ET approach. I first trialled a dynein and peroxisome clustering system that harnessed the inducible FKBP-FRB binding system. Cryo-CLEM allowed the targeting and identification of possible clusters in lamellae; however, tomograms lacked microtubules, which is an essential pre-requisite for visualising motile dynein-dynactin complexes. I next trialled a sodium arsenite (AS)-induced dynein-driven organelle clustering system. While many instances of cargo in the vicinity of microtubules were observed, no putative dynein-dynactin complexes were observed, likely as a result of the thickness of the lamellae in this dataset which were on average >200nm. However, the tomograms did reveal surprising ribosome localisations; membranes of vesicles of many forms were decorated with ribosomes in AS-treated HeLa cells. STA revealed a subset of these ribosomes that appeared to be non-active, that bound via a novel binding mode. Finally, I trialled a dynein-driven mitochondrial clustering system. This system yielded a large tomogram dataset with many microtubules and mitochondrial cargo, in which I could manually identify possible dynein-dynactin complex densities. Going forward, a computational particle picking and STA approach would confirm if these densities are in fact dynein-dynactin complexes. 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Immunofluorescence and STA analysis revealed uniform polarity microtubule bundles in MAP2-expressing projections, suggesting that differentiated SH-SY5Y projections showed a mixed neurite phenotype. The signal:noise ratio (SNR) of these tomograms was too low to visualise most macromolecules in the cytoplasm. This made this approach sub-optimal to visualise dynein-dynactin complexes. To address the SNR issues, I next employed cryo-focused ion beam (cryo-FIB) milling to thick rodent hippocampal neurites. This was to 1) target cellular regions with more motor-driven transport and 2) to remove unnecessary cellular noise in attempt to improve tomogram SNR. I found that, even with sample optimisation, cryo-FIB milling of neurites was low-throughput and produced poor quality lamellae. This undermined the aim of visualising dynein-dynactin complexes in situ. I also used a cryo-correlative light and electron microscopy (cryo-CLEM) approach to target the axon initial segment (AIS), providing insights into its microtubule and neurofilament networks. Due to the poor lamella quality, I next pursued more conventional cryo-FIB milling cellular targets. I next investigated cryo-FIB milling of cancer cell lines with dynein-driven cargo clustering systems. This was to increase the number of dynein-dynactin complexes on recognisable cargo, for a targeted cryo-ET approach. I first trialled a dynein and peroxisome clustering system that harnessed the inducible FKBP-FRB binding system. Cryo-CLEM allowed the targeting and identification of possible clusters in lamellae; however, tomograms lacked microtubules, which is an essential pre-requisite for visualising motile dynein-dynactin complexes. I next trialled a sodium arsenite (AS)-induced dynein-driven organelle clustering system. While many instances of cargo in the vicinity of microtubules were observed, no putative dynein-dynactin complexes were observed, likely as a result of the thickness of the lamellae in this dataset which were on average >200nm. However, the tomograms did reveal surprising ribosome localisations; membranes of vesicles of many forms were decorated with ribosomes in AS-treated HeLa cells. STA revealed a subset of these ribosomes that appeared to be non-active, that bound via a novel binding mode. Finally, I trialled a dynein-driven mitochondrial clustering system. This system yielded a large tomogram dataset with many microtubules and mitochondrial cargo, in which I could manually identify possible dynein-dynactin complex densities. Going forward, a computational particle picking and STA approach would confirm if these densities are in fact dynein-dynactin complexes. 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