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1314 Polarity measurements from multiplex imaging suggest immune cell activation
  1. Eric Wu1,
  2. Aaron Mayer2,
  3. Alexandro E Trevino2 and
  4. James Zou1
  1. 1Stanford University, Stanford, CA, USA
  2. 2Enable Medicine, Menlo Park, CA, USA
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.


Background Multiplexed immunofluorescence (mIF) methods image dozens of molecules at subcellular resolution and whole-slide scale, allowing detailed characterization of tumor microenvironments. Although cells exhibit differential subcellular localization of proteins and other molecules depending on their functional states,1–3 these morphological features are difficult to quantify. They are discarded in standard analysis workflows. In this work, we develop a metric defining surface protein polarity from mIF images and apply it to enhance immune cell phenotyping. We define six cell subtypes relating to morphology and measure their relation to patient outcomes.

Methods Our polarity quantification is described in figure 1. Briefly, we apply a polar transformation to cell-centered image patches, denoise, and take the area-under-the-signal-curve, resulting in a polarity score (range 0–1). The score is thresholded to produce a classification into polar and non-polar cells.

We train two models: a 3-layer multilayer perceptron (MLP) neural network that accepts the percent composition of cell types per sample and predicts binary 60-month survival; and a graph-based neural network4 that accepts 3-hop cell neighborhoods and predicts sample-level survival status.

Results We verify that polarity events (figure 1) are correlated with positive patient outcomes, even after controlling for technical artifacts. Introducing polarity-aware cell subtypes improves model performance in predicting survival status across three patient cohorts (table 1). Given that polar expression can indicate antigen engagement during contact with tumor cells,5 we confirm that polar cells are more likely than uniform cells to be adjacent to tumor cells; conversely, cells with tumor cells as direct neighbors are more likely to be polar. Tumor cells are significantly more likely to neighbor polar than non-polar immune cells (figure 2).

Finally, keeping the number of polar cells constant, we permute the spatial arrangement of polar cells. This significantly influences survival predictions (figure 3), suggesting that polar cell contacts, not simply their presence in a sample, are important.

Conclusions We describe a robust, interpretable subcellular morphology metric that reflects macrobiological states. Although labeling polarity events as immune synapses is premature, they represent biologically relevant signals in tumor microenvironments.


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  4. Wu Z, Trevino AE, Wu E, Swanson K, Kim HJ, D’Angio HB, et al. SPACE-GM: geometric deep learning of disease-associated microenvironments from multiplex spatial protein profiles. BioRxiv. 2022 May 13;

  5. Juzans M, Cuche C, Di Bartolo V, Alcover A. Imaging polarized granule release at the cytotoxic T cell immunological synapse using TIRF microscopy: Control by polarity regulators. Methods Cell Biol. 2023;173:1–13.

Abstract 1314 Figure 1

Workflow of computing polarity and Samples of polar and uniform cells

Abstract 1314 Figure 2

Neighborhood composition of polar vs uniform T and B cells. Tumors are significantly more present next to polar cells vs uniform cells. Only neighbor cell types with significant differences between polar and uniform are shown

Abstract 1314 Figure 3

Permutation experiments: Sample 7-hop neighborhood permutations for CD8 T cells. Left is for polar CD8+ T cells, and right is for uniform CD8+ T cells

Abstract 1314 Table 1

Prediction performance on patient survival status increases when introducing the additional polarity-derived cell types. This effect is observed across multiple datasets and disease types

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