Characterizing mouse platelet heterogeneity across diverse disease models using spectral flow cytometry and high-dimensional analysis.
[BACKGROUND] Routine platelet assessment based on count and mean platelet volume overlooks heterogeneity of platelet subpopulations that influence disease outcomes.
APA
Gautam D, Clarke EM, et al. (2026). Characterizing mouse platelet heterogeneity across diverse disease models using spectral flow cytometry and high-dimensional analysis.. Research and practice in thrombosis and haemostasis, 10(2), 103371. https://doi.org/10.1016/j.rpth.2026.103371
MLA
Gautam D, et al.. "Characterizing mouse platelet heterogeneity across diverse disease models using spectral flow cytometry and high-dimensional analysis.." Research and practice in thrombosis and haemostasis, vol. 10, no. 2, 2026, pp. 103371.
PMID
41783863
Abstract
[BACKGROUND] Routine platelet assessment based on count and mean platelet volume overlooks heterogeneity of platelet subpopulations that influence disease outcomes. Different platelet subtypes are associated with diverse pathological conditions, highlighting the need to define and characterize them. Human studies face limitations due to interindividual variability and challenges in acquiring matched controls. Moreover, the small and anucleate nature of platelets constrain conventional single-cell analysis approaches. These gaps highlight the need for a mouse-specific flow cytometry panel to enable detailed investigation of platelet heterogeneity in preclinical models.
[OBJECTIVES] To develop and validate a mouse-specific spectral flow cytometry panel integrated with a high-dimensional analysis pipeline for comprehensive characterization of platelet subpopulations and activation states under physiological and pathological conditions.
[METHODS] A 12-marker spectral panel was optimized and integrated with the PlateletProfiler pipeline for multidimensional clustering and receptor expression profiling. The workflow was applied to conditions known to alter platelet dynamics, including agonist-induced activation and three mouse models of disease: lipopolysaccharide-induced inflammation, driven myeloproliferative neoplasms, and breast cancer.
[RESULTS] Four major platelet subpopulations-resting, primed, aggregatory, and procoagulant-were identified, representing a continuum of activation. Lipopolysaccharide exposure increased primed and aggregatory subsets, mice showed aggregatory and procoagulant fractions, and tumor-bearing mice exhibited increased procoagulant platelets. Across models, platelets displayed upregulation of activation and procoagulant markers. All disease models displayed elevated thiazole orange-positive reticulated platelets.
[CONCLUSIONS] This integrated and scalable workflow provides a robust platform for investigating disease-associated changes in platelet heterogeneity. The PlateletProfiler pipeline is compatible with both mouse and human datasets, supporting broad experimental and translational applications.
[OBJECTIVES] To develop and validate a mouse-specific spectral flow cytometry panel integrated with a high-dimensional analysis pipeline for comprehensive characterization of platelet subpopulations and activation states under physiological and pathological conditions.
[METHODS] A 12-marker spectral panel was optimized and integrated with the PlateletProfiler pipeline for multidimensional clustering and receptor expression profiling. The workflow was applied to conditions known to alter platelet dynamics, including agonist-induced activation and three mouse models of disease: lipopolysaccharide-induced inflammation, driven myeloproliferative neoplasms, and breast cancer.
[RESULTS] Four major platelet subpopulations-resting, primed, aggregatory, and procoagulant-were identified, representing a continuum of activation. Lipopolysaccharide exposure increased primed and aggregatory subsets, mice showed aggregatory and procoagulant fractions, and tumor-bearing mice exhibited increased procoagulant platelets. Across models, platelets displayed upregulation of activation and procoagulant markers. All disease models displayed elevated thiazole orange-positive reticulated platelets.
[CONCLUSIONS] This integrated and scalable workflow provides a robust platform for investigating disease-associated changes in platelet heterogeneity. The PlateletProfiler pipeline is compatible with both mouse and human datasets, supporting broad experimental and translational applications.