Analytical tools for single-molecule fluorescence imaging in cellulo

Abstract

Recent technological advances in cutting-edge ultrasensitive fluorescence microscopy have allowed single-molecule imaging experiments in living cells across all three domains of life to become commonplace. Single-molecule live-cell data is typically obtained in a low signal-to-noise ratio (SNR) regime sometimes only marginally in excess of 1, in which a combination of detector shot noise, suboptimal probe photophysics, native cell autofluorescence and intrinsically underlying stochasticity of molecules result in highly noisy datasets for which underlying true molecular behaviour is nontrivial to discern. The ability to elucidate real molecular phenomena is essential in relating experimental single-molecule observations to both the biological system under study as well as offering insight into the fine details of the physical and chemical environments of the living cell. To confront this problem of faithful signal extraction and analysis in a noise-dominated regime, the 'needle in a haystack' challenge, such experiments benefit enormously from a suite of objective, automated, high-throughput analysis tools that can home in on the underlying 'molecular signature' and generate meaningful statistics across a large population of individual cells and molecules. Here, I discuss the development and application of several analytical methods applied to real case studies, including objective methods of segmenting cellular images from light microscopy data, tools to robustly localize and track single fluorescently-labelled molecules, algorithms to objectively interpret molecular mobility, analysis protocols to reliably estimate molecular stoichiometry and turnover, and methods to objectively render distributions of molecular parameters.

Mapping the 3D genome through high speed single-molecule tracking of functional transcription factors in single living cells

“…Fluorescent proteins probes are more challenging due to poorer photophysical properties, though Moerner reported axial precisions of ~40 nm using yellow fluorescent protein by employing a double helix PSF method with 30 ms per frame sampling [32]. However, since GFP emits approximately an order of magnitude fewer photons on average than YFP prior to photobleaching, and our method involves much faster sampling, also by close to an order of magnitude compared to Moerner's YFP study, our reported axial precision is close to expectation based on the effective signal-to-noise ratio [33]. Our calibration using single GFP molecules also in many ways represents a worst case for axial resolution as many of the transcription factors we image are clustered [7].…”

Staphylococcus aureus toxin LukSF dissociates from its membrane receptor target to enable renewed ligand sequestration

“…The probability distribution of mS* stoichiometry values in live cells in the absence of mF is shown in Fig. 3 C , rendered using a kernel density estimation that generates an objective distribution that does not depend on the size and location of subjective histogram bins (54). We measured a broad range of stoichiometry values, spanning a range from only a few mS* molecules per focus to several 10s of molecules, with a mean of ∼30 molecules per focus.…”

Single-Organelle Quantification Reveals Stoichiometric and Structural Variability of Carboxysomes Dependent on the Environment

“…In brief, candidate bright fluorescent foci were identified in images using morphological transformation and thresholding. The sub-pixel centroids of these foci were determined using iterative Gaussian masking and their intensity quantified as the summed intensity inside a 5-pixel radius region of interest (ROI) corrected for the mean background intensity inside a surrounding 17 x 17 pixel ROI (Delalez et al, 2010;Leake, 2014). Foci were accepted and tracked through time if they had a signal-to-noise ratio, defined as the mean intensity in the circular ROI divided by the standard deviation in the outer ROI, over 0.4.…”