Library Item
Quantitative Detection of Single Molecules in Fluorescence Microscopy Images
Description:
Fluorescence imaging and counting of single molecules adsorbed or bound to surfaces are being employed in a number of quantitative analysis applications. Reliable molecular counts with knowledge of counting uncertainties, both false-positive and false-negative probabilities, are critical to these applications. By counting stationary single molecules on a surface, spatial criteria may be applied to the image analysis to improve confidence in detection, which is especially critical when detecting single fluorescent labels. In this work, we describe a simple approach to incorporating spatial criteria for counting single molecules by using an intensity threshold to locate regions with multiple, adjacent intense pixels, where the size of these regions is guided by the point-spread function of the microscope. By requiring multiple, spatially correlated bright pixels, false-positive events resulting from random samples of background noise are minimized. The reliability of detection is established by quantitative knowledge of the distributions of background and signals. By measuring and modeling both the background and single-molecule intensity distributions, false-positive and false-negative detection probabilities are estimated for arbitrary threshold parameters by using combinatorial statistics. From this theory, detection parameters can be optimized to minimize false-positive and false-negative probabilities, which can be calculated explicitly. For detection of single rhodamine 6G molecules at a threshold set at 2.5 times the standard deviation above background, the false-negative probability was only 1.5%, determined from distributions of single-molecule intensities on well-populated surfaces, and the false-positive probability from background noise was 2.8 spots per 50 × 50 ?m image. The false-positive events compare favorably with theoretical probabilities calculated using combinatorial statistical analysis and simulated false-positive events counted in images of random noise.
