  Here we present a structural similarity index measure (SSIM) guided
conditional Generative Adversarial Network (cGAN) that generatively performs
image-to-image (i2i) synthesis to generate photo-accurate protein channels in
multiplexed spatial proteomics images. This approach can be utilized to
accurately generate missing spatial proteomics channels that were not included
during experimental data collection either at the bench or the clinic.
Experimental spatial proteomic data from the Human BioMolecular Atlas Program
(HuBMAP) was used to generate spatial representations of missing proteins
through a U-Net based image synthesis pipeline. HuBMAP channels were
hierarchically clustered by the (SSIM) as a heuristic to obtain the minimal set
needed to recapitulate the underlying biology represented by the spatial
landscape of proteins. We subsequently prove that our SSIM based architecture
allows for scaling of generative image synthesis to slides with up to 100
channels, which is better than current state of the art algorithms which are
limited to data with 11 channels. We validate these claims by generating a new
experimental spatial proteomics data set from human lung adenocarcinoma tissue
sections and show that a model trained on HuBMAP can accurately synthesize
channels from our new data set. The ability to recapitulate experimental data
from sparsely stained multiplexed histological slides containing spatial
proteomic will have tremendous impact on medical diagnostics and drug
development, and also raises important questions on the medical ethics of
utilizing data produced by generative image synthesis in the clinical setting.
The algorithm that we present in this paper will allow researchers and
clinicians to save time and costs in proteomics based histological staining
while also increasing the amount of data that they can generate through their
experiments.
