SpotMAX: A generalist framework for multidimensional automatic spot detection and quantification
11 affiliations
- 1Institute of Functional Epigenetics, Molecular Targets and Therapeutics Center, Helmholtz Zentrum München, Neuherberg, Germany
- 2Helmholtz Zentrum Muenchen
- 3Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Baden-Württemberg, Germany
- 4Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg University, Heidelberg, Baden-Württemberg, Germany
- 5European Molecular Biology Laboratory
- 6Faculty of Medicine, Ludwig-Maximilians Universität München, Munich, Germany
- 7Massachusetts Institute of Technology
- 8University of Helsinki
- 9David H. Koch Institute for Integrative Cancer Research, and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- 10Centre for Hematology and Regenerative Medicine, Department of Medicine Huddinge, Karolinska Institutet, Huddinge, Sweden
- 11Helsinki Institute of Life Science, Institute of Biotechnology, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
Abstract
The analysis of spot-like structures is a widespread task in microscopy-based cell biology. Existing solutions are typically specific to single applications and do not use multi-dimensional information from 5D datasets. Therefore, experimental scientists often resort to subjective manual annotation. Here, we present SpotMAX, a generalist AI-driven framework for automated spot detection and quantification. SpotMAX leverages the full scope of multi-dimensional datasets with an easy-to-use interface and an embedded framework for cell segmentation and tracking. SpotMAX outperforms state-of-the-art tools, and in some cases, even expert human annotators. We applied SpotMAX across diverse experimental questions, ranging from meiotic crossover events in C. elegans to mitochondrial DNA dynamics in S. cerevisiae and telomere length in mouse stem cells, leading to new biological insights. With its flexibility in integrating AI workflows, we anticipate that SpotMAX will become the standard for spot analysis in microscopy data. Source code: https://github.com/SchmollerLab/SpotMAX
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BibTeX
@article{padovani2026spotmax,
title = {{SpotMAX: A generalist framework for multidimensional automatic spot detection and quantification}},
author = {Francesco Padovani and Ivana Čavka and Ana Rita Rodrigues Neves and Cristina Piñeiro López and Nada Al-Refaie and Leonardo Bolcato and Dimitra Chatzitheodoridou and Yagya Chadha and Pablo Lagos and Timon Stegmaier and Xiaofeng A. Su and Jette Lengefeld and Daphne S. Cabianca and Simone Köhler and Kurt M. Schmoller},
journal = {Science Advances},
year = {2026},
month = jul,
doi = {10.1126/sciadv.adw3811},
url = {https://doi.org/10.1126/sciadv.adw3811},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - SpotMAX: A generalist framework for multidimensional automatic spot detection and quantification
AU - Francesco Padovani
AU - Ivana Čavka
AU - Ana Rita Rodrigues Neves
AU - Cristina Piñeiro López
AU - Nada Al-Refaie
AU - Leonardo Bolcato
AU - Dimitra Chatzitheodoridou
AU - Yagya Chadha
AU - Pablo Lagos
AU - Timon Stegmaier
AU - Xiaofeng A. Su
AU - Jette Lengefeld
AU - Daphne S. Cabianca
AU - Simone Köhler
AU - Kurt M. Schmoller
PY - 2026
DA - 2026/07/08
JO - Science Advances
DO - 10.1126/sciadv.adw3811
UR - https://doi.org/10.1126/sciadv.adw3811
AB - The analysis of spot-like structures is a widespread task in microscopy-based cell biology. Existing solutions are typically specific to single applications and do not use multi-dimensional information from 5D datasets. Therefore, experimental scientists often resort to subjective manual annotation. Here, we present SpotMAX, a generalist AI-driven framework for automated spot detection and quantification. SpotMAX leverages the full scope of multi-dimensional datasets with an easy-to-use interface and an embedded framework for cell segmentation and tracking. SpotMAX outperforms state-of-the-art tools, and in some cases, even expert human annotators. We applied SpotMAX across diverse experimental questions, ranging from meiotic crossover events in C. elegans to mitochondrial DNA dynamics in S. cerevisiae and telomere length in mouse stem cells, leading to new biological insights. With its flexibility in integrating AI workflows, we anticipate that SpotMAX will become the standard for spot analysis in microscopy data. Source code: https://github.com/SchmollerLab/SpotMAX
SN - 2375-2548
ER -