The distinctive distributions of proteins within subcellular compartments both at steady state and during signaling events have essential roles in cell function. Here we describe a method for delineating the complex arrangement of proteins within subcellular structures visualized using point-localization superresolution (PL-SR) imaging. The approach, called pair correlation photoactivated localization microscopy (PC-PALM), uses a pair-correlation algorithm to precisely identify single molecules in PL-SR imaging data sets, and it is used to decipher quantitative features of protein organization within subcellular compartments, including the existence of protein clusters and the size, density and number of proteins in these clusters. We provide a step-by-step protocol for PC-PALM, illustrating its analysis capability for four plasma membrane proteins tagged with photoactivatable GFP (PAGFP). The experimental steps for PC-PALM can be carried out in 3 d and the analysis can be done in ∼6-8 h. Researchers need to have substantial experience in single-molecule imaging and statistical analysis to conduct the experiments and carry out this analysis.

Progressive, technological achievements in the quantitative fluorescence microscopy field are allowing researches from many different areas to start unraveling the dynamic intricacies of biological processes inside living cells. From super-resolution microscopy techniques to tracking of individual proteins, fluorescence microscopy is changing our perspective on how the cell works. Fortunately, a growing number of research groups are exploring single-molecule studies in living cells. However, no clear consensus exists on several key aspects of the technique such as image acquisition conditions, or analysis of the obtained data. Here, we describe a detailed approach to perform single-molecule tracking (SMT) of transcription factors in living cells to obtain key binding characteristics, namely their residence time and bound fractions. We discuss different types of fluorophores, labeling density, microscope, cameras, data acquisition, and data analysis. Using the glucocorticoid receptor as a model transcription factor, we compared alternate tags (GFP, mEOS, HaloTag, SNAP-tag, CLIP-tag) for potential multicolor applications. We also examine different methods to extract the dissociation rates and compare them with simulated data. Finally, we discuss several challenges that this exciting technique still faces.

Pointillistic based super-resolution techniques, such as photoactivated localization microscopy (PALM), involve multiple cycles of sequential activation, imaging, and precise localization of single fluorescent molecules. A super-resolution image, having nanoscopic structural information, is then constructed by compiling all the image sequences. Because the final image resolution is determined by the localization precision of detected single molecules and their density, accurate image reconstruction requires imaging of biological structures labeled with fluorescent molecules at high density. In such image datasets, stochastic variations in photon emission and intervening dark states lead to uncertainties in identification of single molecules. This, in turn, prevents the proper utilization of the wealth of information on molecular distribution and quantity. A recent strategy for overcoming this problem is pair-correlation analysis applied to PALM. Using rigorous statistical algorithms to estimate the number of detected proteins, this approach allows the spatial organization of molecules to be quantitatively described.

Quantitative methods and approaches have been playing an increasingly important role in cell biology in recent years. They involve making accurate measurements to test a predefined hypothesis in order to compare experimental data with predictions generated by theoretical models, an approach that has benefited physicists for decades. Building quantitative models in experimental biology not only has led to discoveries of counterintuitive phenomena but has also opened up novel research directions. To make the biological sciences more quantitative, we believe a two-pronged approach needs to be taken. First, graduate training needs to be revamped to ensure biology students are adequately trained in physical and mathematical sciences and vice versa. Second, students of both the biological and the physical sciences need to be provided adequate opportunities for hands-on engagement with the methods and approaches necessary to be able to work at the intersection of the biological and physical sciences. We present the annual Physiology Course organized at the Marine Biological Laboratory (Woods Hole, MA) as a case study for a hands-on training program that gives young scientists the opportunity not only to acquire the tools of quantitative biology but also to develop the necessary thought processes that will enable them to bridge the gap between these disciplines.

A new generation of direct electron detectors for transmission electron microscopy (TEM) promises significant improvement over previous detectors in terms of their modulation transfer function (MTF) and detective quantum efficiency (DQE). However, the performance of these new detectors needs to be carefully monitored in order to optimize imaging conditions and check for degradation over time. We have developed an easy-to-use software tool, FindDQE, to measure MTF and DQE of electron detectors using images of a microscope’s built-in beam stop. Using this software, we have determined the DQE curves of four direct electron detectors currently available: the Gatan K2 Summit, the FEI Falcon I and II, and the Direct Electron DE-12, under a variety of total dose and dose rate conditions. We have additionally measured the curves for the Gatan US4000 and TVIPS TemCam-F416 scintillator-based cameras. We compare the results from our new method with published curves.

Live imaging of large biological specimens is fundamentally limited by the short optical penetration depth of light microscopes. To maximize physical coverage, we developed the SiMView technology framework for high-speed in vivo imaging, which records multiple views of the specimen simultaneously. SiMView consists of a light-sheet microscope with four synchronized optical arms, real-time electronics for long-term sCMOS-based image acquisition at 175 million voxels per second, and computational modules for high-throughput image registration, segmentation, tracking and real-time management of the terabytes of multiview data recorded per specimen. We developed one-photon and multiphoton SiMView implementations and recorded cellular dynamics in entire Drosophila melanogaster embryos with 30-s temporal resolution throughout development. We furthermore performed high-resolution long-term imaging of the developing nervous system and followed neuroblast cell lineages in vivo. SiMView data sets provide quantitative morphological information even for fast global processes and enable accurate automated cell tracking in the entire early embryo.

High-resolution movies in the Digital Embryo repository
Nature News: "Fruitfly development, cell by cell" by Lauren Gravitz
Nature Methods Technology Feature: "Faster frames, clearer pictures" by Monya Baker
Andor Insight Awards: Life Sciences Winner

The observation of biological processes in their natural in vivo context is a key requirement for quantitative experimental studies in the life sciences. In many instances, it will be crucial to achieve high temporal and spatial resolution over long periods of time without compromising the physiological development of the specimen. Here, we discuss the principles underlying light sheet-based fluorescence microscopes. The most recent implementation DSLM is a tool optimized to deliver quantitative data for entire embryos at high spatio-temporal resolution. We compare DSLM to the two established light microscopy techniques: confocal and two-photon fluorescence microscopy. DSLM provides up to 50 times higher imaging speeds and a 10-100 times higher signal-to-noise ratio, while exposing the specimens to at least three orders of magnitude less light energy than confocal and two-photon fluorescence microscopes. We conclude with a perspective for future development.

Glucose is an essential source of energy for the brain. Recently, the development of genetically encoded fluorescent biosensors has allowed real time visualization of glucose dynamics from individual neurons and astrocytes. A major difficulty for this approach, even for ratiometric sensors, is the lack of a practical method to convert such measurements into actual concentrations in ex vivo brain tissue or in vivo. Fluorescence lifetime imaging provides a strategy to overcome this. In a previous study, we reported the lifetime glucose sensor iGlucoSnFR-TS (then called SweetieTS) for monitoring changes in neuronal glucose levels in response to stimulation. This genetically encoded sensor was generated by combining the Thermus thermophilus glucose-binding protein with a circularly permuted variant of the monomeric fluorescent protein T-Sapphire. Here, we provide more details on iGlucoSnFR-TS design and characterization, as well as pH and temperature sensitivities. For accurate estimation of glucose concentrations, the sensor must be calibrated at the same temperature as the experiments. We find that when the extracellular glucose concentration is in the range 2-10 mM, the intracellular glucose concentration in hippocampal neurons from acute brain slices is ~20% of the nominal external glucose concentration (~0.4-2 mM). We also measured the cytosolic neuronal glucose concentration in vivo, finding a range of ~0.7-2.5 mM in cortical neurons from awake mice.

We describe a fluorescence in situ hybridization method that permits detection of the localization and abundance of single mRNAs (smFISH) in cleared whole-mount adult Drosophila brains. The approach is rapid and multiplexable and does not require molecular amplification; it allows facile quantification of mRNA expression with subcellular resolution on a standard confocal microscope. We further demonstrate single-mRNA detection across the entire brain using a custom Bessel beam structured illumination microscope (BB-SIM).