Vision Transformers as a Framework for Imaging Fluorescence Correlation Spectroscopy

Authors

Sim, S. R., Wang, Z., Zhou, S., Pandey, S., Wohland, T.

Abstract

Fluorescence correlation spectroscopy (FCS) is widely used for quantifying molecular dynamics in live cells. However, conventional FCS relies on the autocorrelation function (ACF), a lossy transformation that discards higher-order information, mathematically couples distinct biophysical parameters, and requires long measurements (~50,000 frames, or ~1 minute) that obscure short-lived transient events. To overcome this information bottleneck, we introduce ImFCSViT, a spatiotemporal Vision Transformer-based framework that bypasses the ACF representation entirely, operating directly on raw fluorescence image stacks. By introducing a dedicated normalization token to preserve global intensity statistics, our approach simultaneously extracts four biophysical parameters (diffusion coefficient D, particle density N, particle brightness B, and PSF structure factor {sigma}0) in a single forward pass. Requiring only 2.5 s of data, and potentially down to 1 s (a 1-2 order reduction in data requirement), our approach accurately recovers diffusion coefficients while utilizing its spatial field-of-view to reveal spatially heterogeneous, slow-diffusing sub-populations in lipid bilayers and live-cell plasma membranes that are invisible to classical FCS. By leveraging the data-driven representational learning capabilities of deep learning, we present a flexible, generalizable, and powerful tool for mapping complex, multi-parameter spatiotemporal dynamics in biological systems.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 25 Oct 2026.

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