Fig. 1: VirtualMultiplexer is a generative toolkit for synthesizing virtual multiplexed staining.

a, In a typical histopathology workflow, serial tissue sections from a tumour resection are stained with H&E and IHC to highlight tissue morphology and molecular expression of several markers of interest. This time-consuming and tissue-exhaustive process yields unpaired tissue slides that bear the technical risk of suboptimal quality in terms of missing stainings, tissue artefacts and unaligned tissues. b, To mitigate these issues, the VirtualMultiplexer uses generative AI to rapidly render, from a real input H&E image, consistent, reliable and pixel-wise aligned IHC stainings. c, As the generated images are now virtually multiplexed, they are further exploited to train early fusion graph transformers able to predict several clinically relevant endpoints. d, The VirtualMultiplexer was successfully transferred across image scales and patient cohorts and showed potential in being transferred to other tissue types, accelerating clinical applications and discovery.