Nanoscale characterization of living samples has become essential for modern biology. Atomic force microscopy (AFM) creates topological images of fragile biological structures from biomolecules to living cells in aqueous environments. However, correlating nanoscale structure to biological function of specific proteins can be challenging. To this end we have built and characterized a correlated single molecule localization microscope (SMLM)/AFM that allows localizing specific, labeled proteins within high-resolution AFM images in a biologically relevant context. Using direct stochastic optical reconstruction microscopy (dSTORM)/AFM, we directly correlate and quantify the density of localizations with the 3D topography using both imaging modalities along (F-)actin cytoskeletal filaments. In addition, using photo activated light microscopy (PALM)/AFM, we provide correlative images of bacterial cells in aqueous conditions. Moreover, we report the first correlated AFM/PALM imaging of live mammalian cells. The complementary information provided by the two techniques opens a new dimension for structural and functional nanoscale biology.

With the advent of single-molecule localization microscopy (SMLM) techniques, intracellular proteins can be imaged at unprecedented resolution with high specificity and contrast. These techniques can lead to a better understanding of cell functioning, as they allow, among other applications, counting the number of molecules of a protein specie in a single cell, studying the heterogeneity in protein spatial organization, and probing the spatial interactions between different protein species. However, the use of these techniques for accurate quantitative measurements requires corrections for multiple inherent sources of error, including: overcounting due to multiple localizations of a single fluorophore (i.e., photoblinking), undercounting caused by incomplete photoconversion, uncertainty in the localization of single molecules, sample drift during the long imaging time, and inaccurate image registration in the case of dual-color imaging. In this paper, we review recent efforts that address some of these sources of error in quantitative SMLM and give examples in the context of photoactivated localization microscopy (PALM).

a b s t r a c tSingle molecule localization microscopy (SMLM), which can provide up to an order of magnitude improvement in spatial resolution over conventional fluorescence microscopy, has the potential to be a highly useful tool for quantitative biological experiments. It has already been used for this purpose in varied fields in biology, ranging from molecular biology to neuroscience. In this review article, we briefly review the applications of SMLM in quantitative biology, and also the challenges involved and some of the solutions that have been proposed. Due to its advantages in labeling specificity and the relatively low overcounting caused by photoblinking when photo-activable fluorescent proteins (PA-FPs) are used as labels, we focus specifically on Photo-Activated Localization Microscopy (PALM), even though the ideas presented might be applicable to SMLM in general. Also, we focus on the following three quantitative measurements: single molecule counting, analysis of protein spatial distribution heterogeneity and co-localization analysis.

BackgroundAnalyzing spatial distributions of objects in images is a fundamental task in many biological studies. The relative arrangement of a set of objects with respect to another set of objects contains information about potential interactions between the two sets of objects. If they do not “feel” each other’s presence, their spatial distributions are expected to be independent of one another. Spatial correlations in their distributions are indicative of interactions and can be modeled by an effective interaction potential acting between the points of the two sets. This can be used to generalize co-localization analysis to spatial interaction analysis. However, no user-friendly software for this type of analysis was available so far.ResultsWe present an ImageJ/Fiji plugin that implements the complete workflow of spatial pattern and interaction analysis for spot-like objects. The plugin detects objects in images, infers the interaction potential that is most likely to explain the observed pattern, and provides statistical tests for whether an inferred interaction is significant given the number of objects detected in the images and the size of the space within which they can distribute. We benchmark and demonstrate the present software using examples from confocal and PALM single-molecule microscopy.ConclusionsThe present software greatly simplifies spatial interaction analysis for point patterns, and makes it available to the large user community of ImageJ and Fiji. The presented showcases illustrate the usage of the software.