A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels

Abstract

Histopathological work in clinical labs relying on immunostaining of proteins represents a bottleneck in processing medical tissue samples. Multiplexed spatial proteomics imaging can increase interpretive power but cannot cost-effectively sample the entire proteomic retinue important to diagnostic medicine or drug development. 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 Preprint. Under review.

Expanding the coverage of spatial proteomics

“…Examples include predicting subcellular components from unlabeled microscope images (2–5), virtual histological staining of tissue images (6–8), and predicting immunofluorescence or directly inferring cell types from immunohistochemically stained images (9, 10). This concept can be extended to predict a large number of biomarkers from images of a smaller number (11, 12). For example, Wu et al (13) described a method to select 7 markers out of 40 that enabled accurate prediction of cell types in a number of tissues, and showed the effectiveness of the approach by imaging only those 7.…”