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

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.

Molecular Imaging with Aquaporin-Based Reporter Genes: Quantitative Considerations from Monte Carlo Diffusion Simulations

“…type and Aqp1-expressing CHO cells. We varied cellular radii from 5 to 25 μm to represent both small (e.g., monocytes 42 ) and large cell-types (e.g., adipocytes). We set the upper bound of the intracellular volume fraction as 0.67, which corresponds to typical packing of cells in pellets and many tissues.…”

Out of distribution detection in deep learning-based scattering pattern classification

“…Cells were recovered from healthy donors after obtaining informed consent, in accordance with relevant guidelines and regulations. For PBMC a standard density gradient separation was performed together with a cell class isolation based on a negative selection procedure with specific Ab-coupled magnetic beads [20,21]. Whereas the THP-1 cell line (ATCC, Manassas, VA, USA) was directly cultured in RPMI-1640 medium, supplemented with 10% FBS, 1% L-Glu and 1% penicillin/streptomycin.…”

“…For image acquisition, we utilized a small angle light scattering apparatus [19][20][21][22], combined with the previously mentioned microfluidic single cell alignment device to obtain biophysical single cell information (see Fig. 1).…”

“…1). [19][20][21][22][23][24][25][26] Figure 1. Experimental setup and typical scattering pattern snapshots.…”

“…In more detail, a microfluidic device combined with an optical single cell investigation readout (combination of forward-and side-direction) was used for data recording of different cell classes. [19][20][21] Machine learning uses the scattering information as input to predict the searched for cell types. A prediction model was trained for a set of human peripheral blood cells.…”