Gide, T. N. | bioRxiv Science

bioRxiv2025

OS2CR-Diff: A Self-Refining Diffusion Framework for CD8 Imputation from One-Step Inference to Conditional Representation

Stain imputation in multiplex immunofluorescence (mIF) imaging addresses the challenge of missing or damaged biomarker channels by reconstructing target biomarker images from a limited set of available stains. This approach offers a faster and more efficient alternative to full-panel staining, enabling detailed analysis of the tumour microenvironment. Existing One-Step Inference Models (OSIMs), primarily based on generative adversarial networks (GAN) or autoencoders, often generate suboptimal images with significant artifacts or reduced signal intensity. These limitations impair visual interpretability and reliability of the downstream immunotherapy response assessment. The challenge is further amplified when imputing cytoplasmic biomarkers such as CD8 from commonly used stains such as DAPI, due to the limited spatial correlation and the inherently complex structure of cytoplasmic signals. To address these limitations, we propose a self-refining diffusion model, OS2CR-Diff, which utilises the results from OSIMs as additional conditional representations. Unlike prior studies that rely on a single or limited conditional inputs, OS2CR-Diff incorporates three conditional inputs: the OSIM-imputed target biomarker image, OSIM-imputed complementary biomarker images, and non-antibody-stained images. Furthermore, we propose a feature fusion module that employs a cross-gated attention mechanism to effectively integrate these inputs, enabling context-aware feature refinement and improving the quality and reliability of imputed biomarker images. We evaluated OS2CR-Diff for CD8 biomarker imputation on mIF images of melanoma tissues. Our method out-performed state-of-the-art methods, achieving a 73.4% increase in the Structural Similarity Index Measure (SSIM), a 28.9% gain in the Peak Signal-to-Noise Ratio (PSNR), a 61.2% improvement in Mean Absolute Error (MAE), and significantly lower false positive rates compared to OSIM.

bioinformatics↗

bioRxiv2025

AdSI-MIMO: Adaptive stain imputation with multi-input and multi-output learning for multiplex immunofluorescence imaging

Multiplex immunofluorescence (mIF) imaging plays a crucial role in studying multiple biomarkers and their interactions within the tumour microenvironment. However, acquiring mIF images that include all desired biomarkers presents significant challenges due to the need for specialised equipment and costly reagents, which increase technical complexity, time and expense. Stain imputation offers a promising solution by synthesising target biomarker images using generative models, thereby eliminating the need for additional staining procedures. Existing deep learning-based stain imputation methods lack flexibility in generating multiple biomarker images from various input combinations. To overcome this limitation, we propose AdSI-MIMO, a novel stain imputation framework for mIF images. Our method features a multibranch deep learning architecture capable of generating multiple outputs and incorporates an adaptive progressive masking strategy to accommodate the varying combinations of input biomarkers. This approach not only improves the quality of the generated biomarker images, but also eliminates the need to train separate models for each target biomarker. We evaluated AdSI-MIMO using two datasets, including a local dataset comprising mIF images from 257 melanoma patients and a public dataset of 55 urothelial carcinoma samples. Our method achieved substantial improvements over state-of-the-art methods, particularly in the imputation of key T-cell and activation biomarkers, such as CD8 and PD-L1. Specifically, across both public and proprietary datasets, our model achieved a 18.4% improvement in Pearson Correlation Coefficient (Pearson-r) for CD8 imputation and a 48.1% improvement for PD-L1 imputation under various input biomarker configurations on the external test set.

bioinformatics↗