- Title
- Label-free macrophage phenotype classification using machine learning methods
- Creator
- Hourani, Tetiana; Perez-Gonzalez, Alexis; Khoshmanesh, Khashayar; Luwor, Rodney; Achuthan, Adrian; Baratchi, Sara; O’Brien-Simpson, Neil; Al-Hourani, Akram
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/199680
- Identifier
- vital:19246
- Identifier
-
https://doi.org/10.1038/s41598-023-32158-7
- Identifier
- ISSN:2045-2322 (ISSN)
- 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 macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity. © 2023, The Author(s).
- Publisher
- Nature Research
- Relation
- Scientific Reports Vol. 13, no. 1 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 The Author(s)
- Rights
- Open Access
- Subject
- MD Multidisciplinary
- Full Text
- Reviewed
- Funder
- 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 biology 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).
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