Single cell classification of macrophage subtypes by label-free cell signatures and machine learning. Issue 9 (28th September 2022)

• Record Type: Journal Article
• Authors: Dannhauser, David; Rossi, Domenico; De Gregorio, Vincenza; Netti, Paolo Antonio; Terrazzano, Giuseppe; Causa, Filippo
• Is Part Of: Royal Society open science. Volume 9:Issue 9(2022)
• Publication Date: 2022-09-28
• Subjects: single-cell -- label-free -- machine learning -- optical signature -- macrophages; Science -- Periodicals; 500
• DOI: 10.1098/rsos.220270
• Languages: English
• ISSNs: 2054-5703

Abstract

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.