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Optical signature dataset for living macrophages and monocytes

Citation

Dannhauser, David et al. (2022), Optical signature dataset for living macrophages and monocytes, Dryad, Dataset, https://doi.org/10.5061/dryad.1ns1rn8wh

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 a 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.

Methods

Fluid forces 3D align cells from a cell sample to the centreline of a microfluidic device, where a collimated laser beam interacts with passing individual cells. The light interaction reveals significantly different scattering patterns (optical signature) for each macrophage phenotype as well as monocytes, which a camera-based readout system records. The obtained data is processed (dataset) and classified with machine learning to obtain a label-free macrophage phenotype classification. The dataset indicates pooled data of three probands in the last column the cell type (0 - monocyte, 1 - MO-macrophages, 2 - M1-macrophages, 3 - M2-macrophages). In addition, we show the dataset of the PCR analysis and bright-field observations of investigated cells from one porband.