# Doubling multiplexed imaging capability via spatial expression pattern-guided protein pairing and computational unmixing

## TL;DR

This paper introduces a new method to double the number of proteins that can be imaged in 3D using fewer fluorescent dyes by leveraging spatial patterns and machine learning.

## Contribution

SEPARATE reduces the number of staining cycles by pairing proteins based on spatial patterns and computationally unmixing their signals.

## Key findings

- Using feature-based distances, the method pairs proteins with high spatial distinction for accurate signal unmixing.
- The approach successfully imaged six proteins using only three fluorophores in 3D.
- Validation showed strong correlation between spatial pattern distinction and unmixing performance.

## Abstract

Three-dimensional multiplexed fluorescence imaging is an indispensable technique in neuroscience. For two-dimensional multiplexed imaging, cyclic immunofluorescence, which involves repeating staining, imaging, and signal removal over multiple cycles, has been widely used. However, the application of cyclic immunofluorescence to three dimensions poses challenges, as a single staining process can take more than 12 hours for thick specimens, and repeating this process for multiple cycles can be prohibitively long. Here, we propose SEPARATE (Spatial Expression PAttern-guided paiRing And unmixing of proTEins), a method that reduces the number of cycles by half by imaging two proteins using a single fluorophore. This is achieved by labeling two proteins with the same fluorophores and unmixing their signals based on their three-dimensional spatial expression patterns, using a neural network. We employ a feature extraction network to quantify the spatial distinction between proteins, with these quantified values, termed feature-based distances, used to identify protein pairs. We then validate the feature extraction network with ten proteins, showing a high correlation between spatial pattern distinction and signal unmixing performance. We finally demonstrate the volumetric multiplexed imaging of six proteins using three fluorophores, pairing them based on feature-based distances and unmixing their signals through protein separation networks.

Spatial expression pattern-guided pairing and computational unmixing of co-labeled protein signals enable volumetric multiplexed imaging with fewer fluorophores, effectively doubling multiplexing capacity.