Suliana Manley

Abstract Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset.

Super-resolution microscopy (SRM) is gaining popularity in biosciences; however, claims about optical resolution are contested and often misleading. In this Viewpoint, experts share their views on resolution and common trade-offs, such as labelling and post-processing, aiming to clarify them for biologists and facilitate deeper understanding and best use of SRM.

1 INTRODUCTION Interferometric scattering (iSCAT) microscopy is a label-free technique relying on common-path interferometry between the light scattered by single molecules and light reflected by the coverslip. Imperfections in the coverslip produce a high and spatially nonuniform background in iSCAT measurements.