Recent development of computational cluster analysis methods for single-molecule localization microscopy images

Yoonsuk Hyun; Doory Kim

doi:10.1016/j.csbj.2023.01.006

Recent development of computational cluster analysis methods for single-molecule localization microscopy images

Comput Struct Biotechnol J. 2023 Jan 9:21:879-888. doi: 10.1016/j.csbj.2023.01.006. eCollection 2023.

Authors

Yoonsuk Hyun¹, Doory Kim^{2

3

4

5}

Affiliations

¹ Department of Mathematics, Inha University, Republic of Korea.
² Department of Chemistry, Hanyang University, Republic of Korea.
³ Research Institute for Convergence of Basic Science, Hanyang University, Republic of Korea.
⁴ Institute of Nano Science and Technology, Hanyang University, Republic of Korea.
⁵ Research Institute for Natural Sciences, Hanyang University, Republic of Korea.

Abstract

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.

Keywords: Cluster analysis; Machine learning; Single-molecule localization microscopy; Super-resolution fluorescence microscopy.

Publication types

Review