Label-free macrophage phenotype classification using machine learning methods

T Hourani; A Perez-Gonzalez; K Khoshmanesh; R Luwor; AA Achuthan; S Baratchi; NM O’Brien-Simpson; A Al-Hourani

Journal article

Label-free macrophage phenotype classification using machine learning methods

T Hourani, A Perez-Gonzalez, K Khoshmanesh, R Luwor, AA Achuthan, S Baratchi, NM O’Brien-Simpson, A Al-Hourani

Scientific Reports | Published : 2023

DOI: 10.1038/s41598-023-32158-7

Abstract

Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macr..

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University of Melbourne Researchers

Alexis Gonzalez Author

Rodney Luwor Author

Adrian Achuthan Author

Sara Baratchi Author

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Grants

Awarded by Centre of Excellence for Electromaterials Science, Australian Research Council

Funding Acknowledgements

TH was supported by an Australian Government Research Training Program Scholarship. The National Health and Medical Research Council (NHMRC) of Australia and Australian Research Council (ARC) are thanked for the financial support over many years for the immunology, microbiology, peptide chemistry and chemical biol-ogy studies reported in the authors' laboratories. N.M. OS is the recipient of NHMRC funding (APP1142472, APP1158841, APP1185426), ARC funding (DP210102781, DP160101312, LE200100163), Cancer Council Victoria funding (APP1163284) and Australian Dental Research Foundation funding and research is supported by the Division of Basic and Clinical Oral Sciences and Centre for Oral Health Research at The Melbourne Dental School. AAA was supported by a grant from the National Health and Medical Research Council (1159901). SB and KK were supported by Australian Research Council (LP190100728).