MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data

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bioRxiv. 2025 Jul 21;. doi: 10.1101/2025.02.10.637323

MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data Feliciano Lab

Ashesh A, Carrara F, Zubarev I, Galinova V, Croft M, Pezzotti M, Gong D, Casagrande F, Colombo E, Giussani S, Restelli E, Cammarota E, Battagliotti JM, Klena N, Di Sante M, Adhikari R, Feliciano D, Pigino G, Taverna E, Harschnitz O, Maghelli N, Scherer N, Nogare DE, Deschamps J, Pasqualini F, Jug F

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Abstract

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscope’s optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce MicroSplit, a computational multiplexing technique based on deep learning that allows multiple cellular structures to be imaged in a single fluorescent channel and then unmixed computationally, allowing faster imaging and reduced photon exposure. We show that MicroSplit efficiently separates up to four superimposed noisy structures into distinct denoised fluorescent image channels. Furthermore, using Variational Splitting Encoder-Decoder (VSE) networks, our approach can sample diverse predictions from a trained posterior of solutions. The diversity of these samples scales with the uncertainty in a given input, allowing us to estimate the true prediction errors by computing the variability between posterior samples. We demonstrate the robustness of MicroSplit across various datasets and noise levels and show its utility to image more, image faster, and improve downstream analysis. We provide MicroSplit along with all associated training and evaluation datasets as open resources, enabling life scientists to benefit from the potential of computational multiplexing and accelerate the pace of scientific discovery.

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MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data

Abstract

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