Optimising image capture for low-light widefield quantitative fluorescence microscopy

TL;DR

This paper discusses optimizing low-light fluorescence microscopy for biological imaging, analyzing camera performance, noise sources, and post-processing to improve image quality while minimizing light exposure.

Contribution

It provides a comprehensive analysis of specialized low-light cameras and practical guidance for optimizing quantitative fluorescence microscopy in live biological samples.

Findings

EMCCD, qCMOS, and sCMOS cameras have distinct noise profiles.

Post-processing algorithms significantly improve image quality.

Optimized camera settings enhance quantitative measurements in low-light conditions.

Abstract

Low-light optical imaging refers to the use of cameras to capture images with minimal photon flux. This area has broad application to diverse fields, including optical microscopy for biological studies. In such studies, it is important to reduce the intensity of illumination to reduce adverse effects such as photobleaching and phototoxicity that may perturb the biological system under study. The challenge when minimising illumination is to maintain image quality that reflects the underlying biology and can be used for quantitative measurements. An example is the optical redox ratio which is computed from autofluorescence intensity to measure metabolism. In all such cases, it is critical for researchers to optimise selection and application of scientific cameras to their microscopes, but few resources discuss performance in the low-light regime. In this tutorial, we address the challenges in optical fluorescence imaging at low-light levels for quantitative microscopy, with an emphasis on live biological samples. We analyse the performance of specialised low-light scientific cameras such as the EMCCD, qCMOS, and sCMOS, while considering the differences in platform architecture and the contribution of various sources of noise. The tutorial covers a detailed discussion of user-controllable parameters, as well as the application of post-processing algorithms for denoising. We illustrate these concepts using autofluorescence images of live mammalian embryos captured with a two-photon light sheet fluorescence microscope.