{"id":{"repo_id":"anu","oai_identifier":"oai:openresearch-repository.anu.edu.au:1885/733808497"},"canonical_url":"https://search.dev.ndltd.org/etd/anu/oai:openresearch-repository.anu.edu.au:1885/733808497","repository":{"repo_id":"anu","name":"Australian National University","base_url":"https://openresearch-repository.anu.edu.au/server/oai/request"},"display":{"title":"Platelet phenotype distinguishes ITP from isolated thrombocytopenia","abstract":"Platelets orchestrate haemostasis through receptor-mediated adhesion, aggregation, and secretion. Platelet dysfunction can lead to bleeding and/or clotting disorders but is challenging to diagnose clinically. This thesis focuses on immune thrombocytopenia (ITP), a disorder marked by immune-mediated platelet destruction and impaired thrombopoiesis. Patients with ITP display heterogeneous clinical trajectories and bleeding severity, often poorly correlated with platelet count, necessitating new biomarkers beyond platelet count to predict bleeding risk. A comprehensive analysis of 103 ITP patients, 22 thrombocytopenic controls, and 123 healthy donors (HDs) revealed elevated expression of glycoprotein (GP) VI and platelet-bound immunoglobulin G in symptomatic patients. Soluble biomarkers, including soluble (s) GPVI, thrombopoietin, and citrullinated histone-DNA complexes, were elevated in patients with bleeding. Integrin aIIbb3 activation remained functional but viscoelastic assays showed significantly impaired clot formation parameters. The 'platelet A10' derived measure, reflecting platelet contribution to clot formation, correlated with bleeding severity irrespective of count, underscoring the importance of platelet quality. Immune profiling demonstrated increased circulating CD8+CD38+ T cells, reduced memory B cells, and increased classical monocytes, indicating broad immune disruption. Multivariable modelling using probabilistic principal component analysis generated composite scores that integrated platelet and immune markers, outperforming platelet count in bleeding prediction. Machine learning further improved accuracy (AUC 0.92), challenging thrombocytopenia-centric models and advocating a novel approach that integrates multidimensional platelet and immune data with machine learning to assess bleeding risk in ITP. To complement conventional platelet autoantibody detection methods, a functional assay was developed to evaluate autoantibody-induced platelet receptor shedding. To assess the molecular pathways of platelet-autoantibody engagement, soluble receptor release was measured in the presence of an FcgRIIa-blocking antibody or a broad-spectrum metalloproteinase inhibitor, revealing that shedding was primarily driven by metalloproteinase-dependent proteolysis downstream of platelet activation. An ELISA quantifying sGPVI, a marker of platelet activation, was refined, which demonstrated its robust sensitivity and reproducibility across clinical disorders. These refinements established a HD reference range, with intra- and inter-assay coefficients of variation below 15%, and stability across multiple freeze-thaw cycles. TLT-1 has emerged as a sensitive platelet activation marker due to its rapid surface upregulation during platelet stimulation. The mechanistic studies revealed that, while human GPVI is exclusively cleaved by a disintegrin and metalloproteinase domain (ADAM)10, TLT-1 shedding involved both ADAM10 and ADAM17. Results showed that TLT-1 release during coagulation was FXa-dependent, suggesting a coordinated FXa-driven shedding for GPVI and TLT-1 to modulate platelet activation. Elevated sTLT-1 levels were associated with trauma-induced coagulopathy, injury severity, and mortality, underscoring its relevance in thrombo-inflammatory platelet responses. These findings challenge conventional diagnostic paradigms and advocate for precision medicine approaches using integrated biomarker panels. Validating these biomarkers and developing streamlined assays are essential for clinical translation. By bridging platelet biology and immunopathology, this work contributes to evolving views of ITP as a disorder of integrated platelet-immune dysregulation and provides a framework for therapies that address both platelet dysfunction and immune abnormalities. This dual-axis approach harmonises mechanistic discovery with clinical innovation, paving the way for improved risk assessment in ITP and related disorders.","abstract_html":"Platelets orchestrate haemostasis through receptor-mediated adhesion, aggregation, and secretion. Platelet dysfunction can lead to bleeding and/or clotting disorders but is challenging to diagnose clinically. This thesis focuses on immune thrombocytopenia (ITP), a disorder marked by immune-mediated platelet destruction and impaired thrombopoiesis. Patients with ITP display heterogeneous clinical trajectories and bleeding severity, often poorly correlated with platelet count, necessitating new biomarkers beyond platelet count to predict bleeding risk. A comprehensive analysis of 103 ITP patients, 22 thrombocytopenic controls, and 123 healthy donors (HDs) revealed elevated expression of glycoprotein (GP) VI and platelet-bound immunoglobulin G in symptomatic patients. Soluble biomarkers, including soluble (s) GPVI, thrombopoietin, and citrullinated histone-DNA complexes, were elevated in patients with bleeding. Integrin aIIbb3 activation remained functional but viscoelastic assays showed significantly impaired clot formation parameters. The 'platelet A10' derived measure, reflecting platelet contribution to clot formation, correlated with bleeding severity irrespective of count, underscoring the importance of platelet quality. Immune profiling demonstrated increased circulating CD8+CD38+ T cells, reduced memory B cells, and increased classical monocytes, indicating broad immune disruption. Multivariable modelling using probabilistic principal component analysis generated composite scores that integrated platelet and immune markers, outperforming platelet count in bleeding prediction. Machine learning further improved accuracy (AUC 0.92), challenging thrombocytopenia-centric models and advocating a novel approach that integrates multidimensional platelet and immune data with machine learning to assess bleeding risk in ITP. To complement conventional platelet autoantibody detection methods, a functional assay was developed to evaluate autoantibody-induced platelet receptor shedding. To assess the molecular pathways of platelet-autoantibody engagement, soluble receptor release was measured in the presence of an FcgRIIa-blocking antibody or a broad-spectrum metalloproteinase inhibitor, revealing that shedding was primarily driven by metalloproteinase-dependent proteolysis downstream of platelet activation. An ELISA quantifying sGPVI, a marker of platelet activation, was refined, which demonstrated its robust sensitivity and reproducibility across clinical disorders. These refinements established a HD reference range, with intra- and inter-assay coefficients of variation below 15%, and stability across multiple freeze-thaw cycles. TLT-1 has emerged as a sensitive platelet activation marker due to its rapid surface upregulation during platelet stimulation. The mechanistic studies revealed that, while human GPVI is exclusively cleaved by a disintegrin and metalloproteinase domain (ADAM)10, TLT-1 shedding involved both ADAM10 and ADAM17. Results showed that TLT-1 release during coagulation was FXa-dependent, suggesting a coordinated FXa-driven shedding for GPVI and TLT-1 to modulate platelet activation. Elevated sTLT-1 levels were associated with trauma-induced coagulopathy, injury severity, and mortality, underscoring its relevance in thrombo-inflammatory platelet responses. These findings challenge conventional diagnostic paradigms and advocate for precision medicine approaches using integrated biomarker panels. Validating these biomarkers and developing streamlined assays are essential for clinical translation. By bridging platelet biology and immunopathology, this work contributes to evolving views of ITP as a disorder of integrated platelet-immune dysregulation and provides a framework for therapies that address both platelet dysfunction and immune abnormalities. This dual-axis approach harmonises mechanistic discovery with clinical innovation, paving the way for improved risk assessment in ITP and related disorders.","abstract_has_math":false,"creators":["Ali, Sidra"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T00:55:07Z","subjects":[],"languages":["en_AU"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1885/733808497","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Ali, Sidra"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-04-15T03:52:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-04-15T03:52:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:type","label":"Dc Type","values":["Thesis (PhD)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_AU"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1885/733808497"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Platelets orchestrate haemostasis through receptor-mediated adhesion, aggregation, and secretion. Platelet dysfunction can lead to bleeding and/or clotting disorders but is challenging to diagnose clinically. This thesis focuses on immune thrombocytopenia (ITP), a disorder marked by immune-mediated platelet destruction and impaired thrombopoiesis. Patients with ITP display heterogeneous clinical trajectories and bleeding severity, often poorly correlated with platelet count, necessitating new biomarkers beyond platelet count to predict bleeding risk. A comprehensive analysis of 103 ITP patients, 22 thrombocytopenic controls, and 123 healthy donors (HDs) revealed elevated expression of glycoprotein (GP) VI and platelet-bound immunoglobulin G in symptomatic patients. Soluble biomarkers, including soluble (s) GPVI, thrombopoietin, and citrullinated histone-DNA complexes, were elevated in patients with bleeding. Integrin aIIbb3 activation remained functional but viscoelastic assays showed significantly impaired clot formation parameters. The 'platelet A10' derived measure, reflecting platelet contribution to clot formation, correlated with bleeding severity irrespective of count, underscoring the importance of platelet quality. Immune profiling demonstrated increased circulating CD8+CD38+ T cells, reduced memory B cells, and increased classical monocytes, indicating broad immune disruption. Multivariable modelling using probabilistic principal component analysis generated composite scores that integrated platelet and immune markers, outperforming platelet count in bleeding prediction. Machine learning further improved accuracy (AUC 0.92), challenging thrombocytopenia-centric models and advocating a novel approach that integrates multidimensional platelet and immune data with machine learning to assess bleeding risk in ITP. To complement conventional platelet autoantibody detection methods, a functional assay was developed to evaluate autoantibody-induced platelet receptor shedding. To assess the molecular pathways of platelet-autoantibody engagement, soluble receptor release was measured in the presence of an FcgRIIa-blocking antibody or a broad-spectrum metalloproteinase inhibitor, revealing that shedding was primarily driven by metalloproteinase-dependent proteolysis downstream of platelet activation. An ELISA quantifying sGPVI, a marker of platelet activation, was refined, which demonstrated its robust sensitivity and reproducibility across clinical disorders. These refinements established a HD reference range, with intra- and inter-assay coefficients of variation below 15%, and stability across multiple freeze-thaw cycles. TLT-1 has emerged as a sensitive platelet activation marker due to its rapid surface upregulation during platelet stimulation. The mechanistic studies revealed that, while human GPVI is exclusively cleaved by a disintegrin and metalloproteinase domain (ADAM)10, TLT-1 shedding involved both ADAM10 and ADAM17. Results showed that TLT-1 release during coagulation was FXa-dependent, suggesting a coordinated FXa-driven shedding for GPVI and TLT-1 to modulate platelet activation. Elevated sTLT-1 levels were associated with trauma-induced coagulopathy, injury severity, and mortality, underscoring its relevance in thrombo-inflammatory platelet responses. These findings challenge conventional diagnostic paradigms and advocate for precision medicine approaches using integrated biomarker panels. Validating these biomarkers and developing streamlined assays are essential for clinical translation. By bridging platelet biology and immunopathology, this work contributes to evolving views of ITP as a disorder of integrated platelet-immune dysregulation and provides a framework for therapies that address both platelet dysfunction and immune abnormalities. This dual-axis approach harmonises mechanistic discovery with clinical innovation, paving the way for improved risk assessment in ITP and related disorders."]},{"key":"dc:title","label":"Title","values":["Platelet phenotype distinguishes ITP from isolated thrombocytopenia"]}]}],"canonical_facts":{"dc:creator":["Ali, Sidra"],"dc:date.accessioned":["2026-04-15T03:52:21Z"],"dc:date.available":["2026-04-15T03:52:21Z"],"dc:date.issued":["2026"],"dc:description.abstract":["Platelets orchestrate haemostasis through receptor-mediated adhesion, aggregation, and secretion. Platelet dysfunction can lead to bleeding and/or clotting disorders but is challenging to diagnose clinically. This thesis focuses on immune thrombocytopenia (ITP), a disorder marked by immune-mediated platelet destruction and impaired thrombopoiesis. Patients with ITP display heterogeneous clinical trajectories and bleeding severity, often poorly correlated with platelet count, necessitating new biomarkers beyond platelet count to predict bleeding risk. A comprehensive analysis of 103 ITP patients, 22 thrombocytopenic controls, and 123 healthy donors (HDs) revealed elevated expression of glycoprotein (GP) VI and platelet-bound immunoglobulin G in symptomatic patients. Soluble biomarkers, including soluble (s) GPVI, thrombopoietin, and citrullinated histone-DNA complexes, were elevated in patients with bleeding. Integrin aIIbb3 activation remained functional but viscoelastic assays showed significantly impaired clot formation parameters. The 'platelet A10' derived measure, reflecting platelet contribution to clot formation, correlated with bleeding severity irrespective of count, underscoring the importance of platelet quality. Immune profiling demonstrated increased circulating CD8+CD38+ T cells, reduced memory B cells, and increased classical monocytes, indicating broad immune disruption. Multivariable modelling using probabilistic principal component analysis generated composite scores that integrated platelet and immune markers, outperforming platelet count in bleeding prediction. Machine learning further improved accuracy (AUC 0.92), challenging thrombocytopenia-centric models and advocating a novel approach that integrates multidimensional platelet and immune data with machine learning to assess bleeding risk in ITP. To complement conventional platelet autoantibody detection methods, a functional assay was developed to evaluate autoantibody-induced platelet receptor shedding. To assess the molecular pathways of platelet-autoantibody engagement, soluble receptor release was measured in the presence of an FcgRIIa-blocking antibody or a broad-spectrum metalloproteinase inhibitor, revealing that shedding was primarily driven by metalloproteinase-dependent proteolysis downstream of platelet activation. An ELISA quantifying sGPVI, a marker of platelet activation, was refined, which demonstrated its robust sensitivity and reproducibility across clinical disorders. These refinements established a HD reference range, with intra- and inter-assay coefficients of variation below 15%, and stability across multiple freeze-thaw cycles. TLT-1 has emerged as a sensitive platelet activation marker due to its rapid surface upregulation during platelet stimulation. The mechanistic studies revealed that, while human GPVI is exclusively cleaved by a disintegrin and metalloproteinase domain (ADAM)10, TLT-1 shedding involved both ADAM10 and ADAM17. Results showed that TLT-1 release during coagulation was FXa-dependent, suggesting a coordinated FXa-driven shedding for GPVI and TLT-1 to modulate platelet activation. Elevated sTLT-1 levels were associated with trauma-induced coagulopathy, injury severity, and mortality, underscoring its relevance in thrombo-inflammatory platelet responses. These findings challenge conventional diagnostic paradigms and advocate for precision medicine approaches using integrated biomarker panels. Validating these biomarkers and developing streamlined assays are essential for clinical translation. By bridging platelet biology and immunopathology, this work contributes to evolving views of ITP as a disorder of integrated platelet-immune dysregulation and provides a framework for therapies that address both platelet dysfunction and immune abnormalities. This dual-axis approach harmonises mechanistic discovery with clinical innovation, paving the way for improved risk assessment in ITP and related disorders."],"dc:identifier.uri":["https://hdl.handle.net/1885/733808497"],"dc:language.iso":["en_AU"],"dc:title":["Platelet phenotype distinguishes ITP from isolated thrombocytopenia"],"dc:type":["Thesis (PhD)"]},"updated_at":"2026-07-24T00:55:07Z"}