CELL THERAPYLabel-free Macrophage
Subtyping
About VisionSort
ThinkCyte’s VisionSort platform combines the strengths of conventional flow cytometry fluorescencesignals with a novel morphometric cellular analysis measure. The dual-mode analytical capability
can be used to identify and sort phenotypically defined cell populations, label free, using machinelearning approaches.

INTRODUCTION
As monocytes migrate into tissues, they differentiate into macrophages.
Upon activation, macrophages can polarize and adopt two very different
and biologically opposing phenotypes, termed M1 and M2. M1 macrophages
are considered classically activated macrophages that mediate proinflammatory responses while M2 macrophages mediate anti-inflammatoryresponses, both by cytokine secretion. The balance of M1 and/or M2
macrophage polarization is known to be a mediator of physiologicalresponses in tumor progression and as such, the ability to isolate cells in thetwo polarization states in purified populations is crucial for understanding
the dynamics of tumor progression. We show here that VisionSort can beused to isolate pure populations of M1 and M2 macrophages label-free,yielding “untouched” cells ready for downstream R&D applications.

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Label-free Macrophage Subtyping

2 OF 2RESULTS
Monocytes were isolated from human
PBMCs using anti-CD14 magnetic
beads. The cells were cultured in media supplemented with M-CSF (50 ng/ml) for 4 days. On day 4, the media wasreplaced with supplemented media
containing LPS (10 ng/mL) and IFNG
(50 ng/mL) to induce M1 differentiation
or IL-4 (10 ng/mL) to induce M2a differentiation. Cells were dissociated with a
non-enzymatic solution and harvested.
Cells stimulated to M1 and M2a differentiation were mixed, stained with fluorescently labeled antibodies to recognize different polarization states (CD80for M1 and CD209 for M2a) and used totrain a supervised machine learningclassifier on VisionSort (Figure 1). The
resulting classifier showed excellent
reproducibility with an AUC = 0.878 +/0.001 (n=6). Unsupervised analysis correlated with a segregation of the M1 vs.
M2a populations (Figure 2).

Figure 1. Generation and evaluation of the machine learning classifier for macrophage
polarization states. Macrophages were labeled with fluorescently tagged CD80 and CD209 antibodies to define the M1 and M2a subtypes. Morphological profiles from the different groundtruth states were used to generate a machine learning classifier on VisionSort, which showed
high discriminatory power and reproducibility when applied to test samples with an AUC of0.878 +/- 0.002 (n=6).

SUMMARY
M1 and M2a macrophages were classified with high accuracy (AUC = 0.878+/- 0.001) using machine learning. This
supervised modeling was confirmed byunsupervised, unbiased UMAP analysis.
This study demonstrates the ability ofVisionSort to identify and differentiate
between M1 from M2a macrophages,
yielding an approach that can be used
to isolate phenotypically defined macrophages from complex mixtures label-free for downstream R&D.

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Figure 2. Unbiased evaluation of polarized macrophage populations. Morphological profilesof M1 and M2a macrophages were assessed by unsupervised machine learning on VisionSortand visualized using UMAP. The two cell populations showed distinct morphometric differences. Live: M0 (CD80- CD209-), M1:CD80+ CD209-, M2a: CD80- CD209+.

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