Application of Lacunarity for Quantification of Single Molecule Localization Microscopy Images

Bálint Barna H Kovács; Dániel Varga; Dániel Sebők; Hajnalka Majoros; Róbert Polanek; Tibor Pankotai; Katalin Hideghéty; Ákos Kukovecz; Miklós Erdélyi

doi:10.3390/cells11193105

Application of Lacunarity for Quantification of Single Molecule Localization Microscopy Images

Cells. 2022 Oct 2;11(19):3105. doi: 10.3390/cells11193105.

Authors

Bálint Barna H Kovács¹, Dániel Varga¹, Dániel Sebők², Hajnalka Majoros^{3

4}, Róbert Polanek^{5

6}, Tibor Pankotai^{3

4

7}, Katalin Hideghéty^{5

6}, Ákos Kukovecz², Miklós Erdélyi¹

Affiliations

¹ Department of Optics and Quantum Electronics, University of Szeged, 6720 Szeged, Hungary.
² Department of Applied and Environmental Chemistry, University of Szeged, 6720 Szeged, Hungary.
³ Institute of Pathology, Albert Szent-Györgyi Medical School, University of Szeged, 6725 Szeged, Hungary.
⁴ Centre of Excellence for Interdisciplinary Research, Development and Innovation, University of Szeged, 6723 Szeged, Hungary.
⁵ Biomedical Applications Group, ELI-ALPS Research Institute, ELI-HU Non-Profit Ltd., 6728 Szeged, Hungary.
⁶ Department of Oncotherapy, University of Szeged, 6720 Szeged, Hungary.
⁷ Genome Integrity and DNA Repair Group, Hungarian Centre of Excellence for Molecular Medicine (HCEMM), University of Szeged, 6728 Szeged, Hungary.

Abstract

The quantitative analysis of datasets achieved by single molecule localization microscopy is vital for studying the structure of subcellular organizations. Cluster analysis has emerged as a multi-faceted tool in the structural analysis of localization datasets. However, the results it produces greatly depend on the set parameters, and the process can be computationally intensive. Here we present a new approach for structural analysis using lacunarity. Unlike cluster analysis, lacunarity can be calculated quickly while providing definitive information about the structure of the localizations. Using simulated data, we demonstrate how lacunarity results can be interpreted. We use these interpretations to compare our lacunarity analysis with our previous cluster analysis-based results in the field of DNA repair, showing the new algorithm's efficiency.

Keywords: dSTORM; lacunarity; quantitative analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cluster Analysis
DNA Repair
Microscopy* / methods
Single Molecule Imaging*

Grants and funding

This research was partially supported by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme (no. TKP2021-NVA-19); the Bolyai János Research Scholarship of the Hungarian Academy of Sciences (BO/27/20); the UNKP-22-5-SZTE-318 and UNKP-21-5-SZTE-563 grants; the Recovery and Resilience Facility of the European Union within the framework of the Széchenyi Plan Plus programme (National Laboratory for Renewable Energy project, no. RRF-2.3.1-21-2022-00009). The project has received funding from the EU’s Horizon 2020 research and innovation program under grant agreement No. 739593.