Single Molecule Localisation Microscopy with sCMOS Cameras

Abstract

Single-molecule localisation microscopy (SMLM) is rapidly becoming a widely used method to achieve super-resolution at the nanometre scale. The development of improved camera technologies has been increasingly important in enabling robust super-resolution of the PALM and STORM type. Here we investigate the performance of scientific complementary metal-oxide semiconductor (sCMOS) cameras in SMLM for nanometre scale imaging. With sCMOS cameras characteristics such as gain, offset and read noise are pixel dependent which can introduce positional bias when localising images of single molecules to determine their localisation. We investigate an algorithm based on a weighted least squares approach to correct this bias and show that it behaves stably in SMLM with typical background photon levels. Our algorithm includes criteria to exclude pixels with high dark currents or excessive read noise from the fitting process. Once signal levels are sufficiently high the pixel dependent properties have little impact on the localisation results and we identify signal levels where using complex algorithms may be unwarranted. We introduce practical strategies to simplify determination of camera maps and provide an efficient software implementation to allow for seamless bias-corrected SMLM with sCMOS cameras. With these algorithms, the characterisation of camera properties can be used to achieve a ∼22% improvement of the localisation precision over standard, un-corrected, algorithms. By comparing the localisation results from both sCMOS and EMCCD cameras, we confirm that sCMOS cameras can perform as well as EMCCDs while exhibiting distinct advantages, such as 20 times larger field of view and faster frame rates. We used an sCMOS based setup to obtain super-resolution images of a range of biological samples and conclude that sCMOS cameras are an attractive alternative to EMCCDs for SMLM.