Single cell classification of macrophage subtypes by label-free cell signatures and machine learning
In: Royal Society Open Science, Jg. 9 (2022-09-01), Heft 9
Online
academicJournal
Zugriff:
Pro-inflammatory (M1) and anti-inflammatory (M2) macrophage phenotypes play a fundamental role in the immune response. The interplay and consequently the classification between these two functional subtypes is significant for many therapeutic applications. Albeit, a fast classification of macrophage phenotypes is challenging. For instance, image-based classification systems need cell staining and coloration, which is usually time- and cost-consuming, such as multiple cell surface markers, transcription factors and cytokine profiles are needed. A simple alternative would be to identify such cell types by using single-cell, label-free and high throughput light scattering pattern analyses combined with a straightforward machine learning-based classification. Here, we compared different machine learning algorithms to classify distinct macrophage phenotypes based on their optical signature obtained from an ad hoc developed wide-angle static light scattering apparatus. As the main result, we were able to identify unpolarized macrophages from M1- and M2-polarized phenotypes and distinguished them from naive monocytes with an average accuracy above 85%. Therefore, we suggest that optical single-cell signatures within a lab-on-a-chip approach along with machine learning could be used as a fast, affordable, non-invasive macrophage phenotyping tool to supersede resource-intensive cell labelling.
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Single cell classification of macrophage subtypes by label-free cell signatures and machine learning
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Autor/in / Beteiligte Person: | Dannhauser, David ; Rossi, Domenico ; Vincenza De Gregorio ; Paolo Antonio Netti ; Terrazzano, Giuseppe ; Causa, Filippo |
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Zeitschrift: | Royal Society Open Science, Jg. 9 (2022-09-01), Heft 9 |
Veröffentlichung: | The Royal Society, 2022 |
Medientyp: | academicJournal |
ISSN: | 2054-5703 (print) |
DOI: | 10.1098/rsos.220270 |
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