MaCADEMIA: a software toolbox for single-molecule fluorescence image quantification

Single-molecule localisation microscopy (SMLM) is a powerful tool in molecular and cellular biology and part of the broad umbrella of super-resolution microscopy. A distinct feature of SMLM, is its capacity for deep quantification, beyond just spatial information, to copy number, stoichiometry, molecular interaction and dynamics in living cells. The hardware required is increasingly straightforward to implement. However, complexity remains in the image analysis where much remains fragmented across specialised software tools. Here, we present MaCADEMIA (Matlab Computational Analysis of Diffusion, Emitter Molecular Intensity and Abundance), a simple yet powerful toolbox of MATLAB functions and associated documentation to enable straightforward extraction of quantitative single-molecule information. MaCADEMIA performs ensemble particle detection achieving localisation accuracies down to 0.5 pixels Root mean squared error (RMSE) across wide signal-to-noise (SNR) ranges on standardised particle tracking benchmarks, consistently matching or exceeding the performance of 14 leading methods. Our integrated workflow enables molecular counting and stoichiometry determination through stepwise photobleaching analysis, mobility measurement via mean squared displacement, colocalisaton analysis and absolute protein copy number quantification using three-dimensional cellular models.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-11-02
DOI
https://doi.org/10.64898/2026.10.30.763064
Primary Topic
Advanced Fluorescence Microscopy Techniques
Type
preprint

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preprint

MaCADEMIA: a software toolbox for single-molecule fluorescence image quantification

Josana Rodrı́guez, Adam J. M. Wollman, Katrina E Crompton, Mark C. Leake et al.
bioRxiv (Cold Spring Harbor Laboratory)
Advanced Fluorescence Microscopy Techniques
preprint

MaCADEMIA: a software toolbox for single-molecule fluorescence image quantification

Josana Rodrı́guez, Adam J. M. Wollman, Katrina E Crompton, Mark C. Leake, Zoe Catchpole, Kate S. Harris, Kirsty Smith, Katherine Sanders, Dominic Alderson, Sophia Johnson, John Packer
preprint en

Abstract

Single-molecule localisation microscopy (SMLM) is a powerful tool in molecular and cellular biology and part of the broad umbrella of super-resolution microscopy. A distinct feature of SMLM, is its capacity for deep quantification, beyond just spatial information, to copy number, stoichiometry, molecular interaction and dynamics in living cells. The hardware required is increasingly straightforward to implement. However, complexity remains in the image analysis where much remains fragmented across specialised software tools. Here, we present MaCADEMIA (Matlab Computational Analysis of Diffusion, Emitter Molecular Intensity and Abundance), a simple yet powerful toolbox of MATLAB functions and associated documentation to enable straightforward extraction of quantitative single-molecule information. MaCADEMIA performs ensemble particle detection achieving localisation accuracies down to 0.5 pixels Root mean squared error (RMSE) across wide signal-to-noise (SNR) ranges on standardised particle tracking benchmarks, consistently matching or exceeding the performance of 14 leading methods. Our integrated workflow enables molecular counting and stoichiometry determination through stepwise photobleaching analysis, mobility measurement via mean squared displacement, colocalisaton analysis and absolute protein copy number quantification using three-dimensional cellular models.

bioRxiv (Cold Spring Harbor Laboratory)
Durham University (GB), Newcastle University (GB)
Royal Society, Engineering and Physical Sciences Research Council, Biotechnology and Biological Sciences Research Council
Advanced Fluorescence Microscopy Techniques
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MaCADEMIA: a software toolbox for single-molecule fluorescence image quantification — Josana Rodrı́guez, Adam J. M. Wollman, et al. · bioRxiv (Cold Spring Harbor Laboratory) (2026) | TGRS Research Map | TGRS