Single-molecule localization microscopy

TLDR

Single‑molecule localization microscopy (SMLM) dramatically improves spatial resolution beyond diffraction limits, enabling imaging of biological structures at the molecular scale by computationally localizing individual fluorescent molecules to generate super‑resolution images or trajectories. This primer introduces the basic principles of SMLM and outlines key experimental considerations—fluorescent labeling, sample preparation, hardware, and image acquisition—while previewing future developments in the field. The authors explain how low‑resolution image sequences are computationally processed to reconstruct super‑resolution images or extract quantitative information, and discuss limitations, artifacts, and strategies to mitigate them. The primer highlights biological discoveries enabled by SMLM, underscores its value for newcomers and practitioners, and outlines promising future advances.

Abstract

Single-molecule localization microscopy (SMLM) describes a family of powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scale. In SMLM, individual fluorescent molecules are computationally localized from diffraction-limited image sequences and the localizations are used to generate a super-resolution image or a time course of super-resolution images, or to define molecular trajectories. In this Primer, we introduce the basic principles of SMLM techniques before describing the main experimental considerations when performing SMLM, including fluorescent labelling, sample preparation, hardware requirements and image acquisition in fixed and live cells. We then explain how low-resolution image sequences are computationally processed to reconstruct super-resolution images and/or extract quantitative information, and highlight a selection of biological discoveries enabled by SMLM and closely related methods. We discuss some of the main limitations and potential artefacts of SMLM, as well as ways to alleviate them. Finally, we present an outlook on advanced techniques and promising new developments in the fast-evolving field of SMLM. We hope that this Primer will be a useful reference for both newcomers and practitioners of SMLM.