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.

Single cell classification of macrophage subtypes by label-free cell signatures and machine learning / Dannhauser, David; Rossi, Domenico; De Gregorio, Vincenza; Netti, Paolo Antonio; Terrazzano, Giuseppe; Causa, Filippo. - In: ROYAL SOCIETY OPEN SCIENCE. - ISSN 2054-5703. - 9:9(2022), p. 220270. [10.1098/rsos.220270]

Single cell classification of macrophage subtypes by label-free cell signatures and machine learning

Dannhauser, David
Primo
;
De Gregorio, Vincenza;Netti, Paolo Antonio;Terrazzano, Giuseppe;Causa, Filippo
Ultimo
2022

Abstract

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.
2022
Single cell classification of macrophage subtypes by label-free cell signatures and machine learning / Dannhauser, David; Rossi, Domenico; De Gregorio, Vincenza; Netti, Paolo Antonio; Terrazzano, Giuseppe; Causa, Filippo. - In: ROYAL SOCIETY OPEN SCIENCE. - ISSN 2054-5703. - 9:9(2022), p. 220270. [10.1098/rsos.220270]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/895433
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