Accurate measurements of protein interactions in cells via improved spatial image cross-correlation spectroscopy

The sensitive detection of protein interactions in living cells is an important first step toward understanding each of the multitude of cellular processes that are regulated by such interactions. Spatial image cross-correlation spectroscopy (ICCS) is one method used to measure protein-protein interactions from the analysis of two-channel fluorescence microscopy images. In spatial ICCS, cross-correlation of fluctuations in fluorescence intensity recorded as images from two independent wavelength detection channels in a fluorescence microscope is used to determine the average number of interacting particles in the imaged region. Even in situations where the particle number density is relatively high, ICCS provides an accurate measure of molecular interactions. However, it was shown previously that the method suffers from relatively high detection limits of interacting particles (approximately 20%) and can be perturbed by heterogeneous spatial distributions of the fluorescent particles within the images. Here, we demonstrate new approaches to circumvent some of the limitations of ICCS. Spatial scrambling of pixel blocks within fluorescence images was investigated as a way of extending the detection of spatial ICCS to measure lower interaction fractions as well as colocalization within cells. We also show that 'mean-intensity-padding' of regions of interest within fluorescence images is a feasible method of applying ICCS to arbitrarily selected areas of the cell with boundaries or edge morphologies that would be impossible to analyze with conventional ICCS. Using these newly developed strategies we were able to measure the fraction of actin that interacts with alpha-actinin in the leading edge of a migrating cell.     

Accuracy and dynamic range of spatial image correlation and cross-correlation spectroscopy

We present a comprehensive study of the accuracy and dynamic range of spatial image correlation spectroscopy (ICS) and image cross-correlation spectroscopy (ICCS). We use simulations to model laser scanning microscopy imaging of static subdiffraction limit fluorescent proteins or protein clusters in a cell membrane. The simulation programs allow us to control the spatial imaging sampling variables and the particle population densities and interactions and introduce and vary background and counting noise typical of what is encountered in digital optical microscopy. We systematically calculate how the accuracy of both image correlation methods depends on practical experimental collection parameters and characteristics of the sample. The results of this study provide a guide to appropriately plan spatial image correlation measurements on proteins in biological membranes in real cells. The data presented map regimes where the spatial ICS and ICCS provide accurate results as well as clearly showing the conditions where they systematically deviate from acceptable accuracy. Finally, we compare the simulated data with standard confocal microscopy using live CHO cells expressing the epidermal growth factor receptor fused with green fluorescent protein (GFP/EGFR) to obtain typical values for the experimental variables that were investigated in our study. We used our simulation results to estimate a relative precision of 20% for the ICS measured receptor density of 64 microm(-2) within a 121 x 98 pixel subregion of a single cell.     

New algorithm to determine true colocalization in combination with image restoration and time-lapse confocal microscopy to MAP kinases in mitochondria

The subcellular localization and physiological functions of biomolecules are closely related and thus it is crucial to precisely determine the distribution of different molecules inside the intracellular structures. This is frequently accomplished by fluorescence microscopy with well-characterized markers and posterior evaluation of the signal colocalization. Rigorous study of colocalization requires statistical analysis of the data, albeit yet no single technique has been established as a standard method. Indeed, the few methods currently available are only accurate in images with particular characteristics. Here, we introduce a new algorithm to automatically obtain the true colocalization between images that is suitable for a wide variety of biological situations. To proceed, the algorithm contemplates the individual contribution of each pixel's fluorescence intensity in a pair of images to the overall Pearsońs correlation and Manders' overlap coefficients. The accuracy and reliability of the algorithm was validated on both simulated and real images that reflected the characteristics of a range of biological samples. We used this algorithm in combination with image restoration by deconvolution and time-lapse confocal microscopy to address the localization of MEK1 in the mitochondria of different cell lines. Appraising the previously described behavior of Akt1 corroborated the reliability of the combined use of these techniques. Together, the present work provides a novel statistical approach to accurately and reliably determine the colocalization in a variety of biological images.     

Quantifying Subpixel Accuracy: An Experimental Method for Measuring Accuracy in Image-Correlation-Based, Single-Particle Tracking

Single-particle tracking (SPT) is a range of powerful analysis techniques that measure particle motion from video microscopy image sequences. SPT is used to study the behavior of motor proteins and associated organelle transport within a cell. Many SPT algorithms deliver subpixel accurate measurements with noisy data corresponding to sub-10-nm resolution. Image-correlation techniques have been shown to be the most accurate method of tracking extended objects. However, to date, it has not been possible to determine the level of error when measuring the motion of an arbitrary particle with this method. In this article we derive a method for experimentally determining the accuracy of image-correlation-based SPT. We then apply this technique to a series of confocal fluorescence microscope image sequences of mitochondria, demonstrating the possibility of making measurements accurate to 5 nm when working with extended objects within live cells. In doing so we show that for particles with a low signal/noise ratio, the accuracy can vary by a factor of 2, corresponding to different particle shapes for a given signal/noise ratio. Use of the presented technique will allow researchers to quantify the accuracy of their measurements on a per-particle basis. This in turn will allow the selection of the most accurately tracked particles, helping to push the accuracy of spatial measurements well below the diffraction limit. This is particularly important for the study of molecular motors whose step size is a similar scale to these limits.     

Tracking Image Correlation: Combining Single-Particle Tracking and Image Correlation

The interactions and coordination of biomolecules are crucial for most cellular functions. The observation of protein interactions in live cells may provide a better understanding of the underlying mechanisms. After fluorescent labeling of the interacting partners and live-cell microscopy, the colocalization is generally analyzed by quantitative global methods. Recent studies have addressed questions regarding the individual colocalization of moving biomolecules, usually by using single-particle tracking (SPT) and comparing the fluorescent intensities in both color channels. Here, we introduce a new method that combines SPT and correlation methods to obtain a dynamical 3D colocalization analysis along single trajectories of dual-colored particles. After 3D tracking, the colocalization is computed at each particle’s position via the local 3D image cross correlation of the two detection channels. For every particle analyzed, the output consists of the 3D trajectory, the time-resolved 3D colocalization information, and the fluorescence intensity in both channels. In addition, the cross-correlation analysis shows the 3D relative movement of the two fluorescent labels with an accuracy of 30 nm. We apply this method to the tracking of viral fusion events in live cells and demonstrate its capacity to obtain the time-resolved colocalization status of single particles in dense and noisy environments.     

Imaging molecular interactions in living cells

Hormones integrate the activities of their target cells through receptor-modulated cascades of protein interactions that ultimately lead to changes in cellular function. Understanding how the cell assembles these signaling protein complexes is critically important to unraveling disease processes, and to the design of therapeutic strategies. Recent advances in live-cell imaging technologies, combined with the use of genetically encoded fluorescent proteins, now allow the assembly of these signaling protein complexes to be tracked within the organized microenvironment of the living cell. Here, we review some of the recent developments in the application of imaging techniques to measure the dynamic behavior, colocalization, and spatial relationships between proteins in living cells. Where possible, we discuss the application of these different approaches in the context of hormone regulation of nuclear receptor localization, mobility, and interactions in different subcellular compartments. We discuss measurements that define the spatial relationships and dynamics between proteins in living cells including fluorescence colocalization, fluorescence recovery after photobleaching, fluorescence correlation spectroscopy, fluorescence resonance energy transfer microscopy, and fluorescence lifetime imaging microscopy. These live-cell imaging tools provide an important complement to biochemical and structural biology studies, extending the analysis of protein-protein interactions, protein conformational changes, and the behavior of signaling molecules to their natural environment within the intact cell.     

Detecting and quantifying colocalization of cell surface molecules by single particle fluorescence imaging

Single particle fluorescence imaging (SPFI) uses the high sensitivity of fluorescence to visualize individual molecules that have been selectively labeled with small fluorescent particles. The positions of particles are determined by fitting the intensity profile of their images to a 2-D Gaussian function. We have exploited the positional information obtained from SPFI to develop a method for detecting colocalization of cell surface molecules. This involves labeling two different molecules with different colored fluorophores and determining their positions separately by dual wavelength imaging. The images are analyzed to quantify the overlap of the particle images and hence determine the extent of colocalization of the labeled molecules. Simulated images and experiments with a model system are used to investigate the extent to which colocalization occurs from chance proximity of randomly distributed molecules. A method of correcting for positional shifts that result from chromatic aberration is presented. The technique provides quantification of the extent of colocalization and can detect whether colocalized molecules occur singly or in clusters. We have obtained preliminary data for colocalization of molecules on intact cells. Cells often exhibit particulate autofluorescence that can interfere with the measurements; a method for overcoming this problem by triple wavelength imaging is described.     

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology¹ even in complex tissue sections². Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells³, however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgenössische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.     

Quantitative Analysis of Intracellular Fluorescent Foci in Live Bacteria

Fluorescence microscopy has revolutionized in vivo cellular biology. Through the specific labeling of a protein of interest with a fluorescent protein, one is able to study movement and colocalization, and even count individual proteins in a live cell. Different algorithms exist to quantify the total intensity and position of a fluorescent focus. Although these algorithms have been rigorously studied for in vitro conditions, which are greatly different than the in-homogenous and variable cellular environments, their exact limits and applicability in the context of a live cell have not been thoroughly and systematically evaluated. In this study, we quantitatively characterize the influence of different background subtraction algorithms on several focus analysis algorithms. We use, to our knowledge, a novel approach to assess the sensitivity of the focus analysis algorithms to background removal, in which simulated and experimental data are combined to maintain full control over the sensitivity of a focus within a realistic background of cellular fluorescence. We demonstrate that the choice of algorithm and the corresponding error are dependent on both the brightness of the focus, and the cellular context. Expectedly, focus intensity estimation and localization accuracy suffer in all algorithms at low focus to background ratios, with the bacteroidal background subtraction in combination with the median excess algorithm, and the region of interest background subtraction in combination with a two-dimensional Gaussian fit algorithm, performing the best. We furthermore show that the choice of background subtraction algorithm is dependent on the expression level of the protein under investigation, and that the localization error is dependent on the distance of a focus from the bacterial edge and pole. Our results establish a set of guidelines for what signals can be analyzed to give a targeted spatial and intensity accuracy within a bacterial cell.     

A fast global fitting algorithm for fluorescence lifetime imaging microscopy based on image segmentation

Global fitting algorithms have been shown to improve effectively the accuracy and precision of the analysis of fluorescence lifetime imaging microscopy data. Global analysis performs better than unconstrained data fitting when prior information exists, such as the spatial invariance of the lifetimes of individual fluorescent species. The highly coupled nature of global analysis often results in a significantly slower convergence of the data fitting algorithm as compared with unconstrained analysis. Convergence speed can be greatly accelerated by providing appropriate initial guesses. Realizing that the image morphology often correlates with fluorophore distribution, a global fitting algorithm has been developed to assign initial guesses throughout an image based on a segmentation analysis. This algorithm was tested on both simulated data sets and time-domain lifetime measurements. We have successfully measured fluorophore distribution in fibroblasts stained with Hoechst and calcein. This method further allows second harmonic generation from collagen and elastin autofluorescence to be differentiated in fluorescence lifetime imaging microscopy images of ex vivo human skin. On our experimental measurement, this algorithm increased convergence speed by over two orders of magnitude and achieved significantly better fits.     

Coordinate-based colocalization analysis of single-molecule localization microscopy data

Colocalization of differently labeled biomolecules is a valuable tool in fluorescence microscopy and can provide information on biomolecular interactions. With the advent of super-resolution microscopy, colocalization analysis is getting closer to molecular resolution, bridging the gap to other technologies such as fluorescence resonance energy transfer. Among these novel microscopic techniques, single-molecule localization-based super-resolution methods offer the advantage of providing single-molecule coordinates that, rather than intensity information, can be used for colocalization analysis. This requires adapting the existing mathematical algorithms for localization microscopy data. Here, we introduce an algorithm for coordinate-based colocalization analysis which is suited for single-molecule super-resolution data. In addition, we present an experimental configuration for simultaneous dual-color imaging together with a robust approach to correct for optical aberrations with an accuracy of a few nanometers. We demonstrate the potential of our approach for cellular structures and for two proteins binding actin filaments.     

Spatiotemporal image correlation spectroscopy (STICS) theory, verification, and application to protein velocity mapping in living CHO cells

We introduce a new extension of image correlation spectroscopy (ICS) and image cross-correlation spectroscopy (ICCS) that relies on complete analysis of both the temporal and spatial correlation lags for intensity fluctuations from a laser-scanning microscopy image series. This new approach allows measurement of both diffusion coefficients and velocity vectors (magnitude and direction) for fluorescently labeled membrane proteins in living cells through monitoring of the time evolution of the full space-time correlation function. By using filtering in Fourier space to remove frequencies associated with immobile components, we are able to measure the protein transport even in the presence of a large fraction (>90%) of immobile species. We present the background theory, computer simulations, and analysis of measurements on fluorescent microspheres to demonstrate proof of principle, capabilities, and limitations of the method. We demonstrate mapping of flow vectors for mixed samples containing fluorescent microspheres with different emission wavelengths using space time image cross-correlation. We also present results from two-photon laser-scanning microscopy studies of alpha-actinin/enhanced green fluorescent protein fusion constructs at the basal membrane of living CHO cells. Using space-time image correlation spectroscopy (STICS), we are able to measure protein fluxes with magnitudes of mum/min from retracting lamellar regions and protrusions for adherent cells. We also demonstrate the measurement of correlated directed flows (magnitudes of mum/min) and diffusion of interacting alpha5 integrin/enhanced cyan fluorescent protein and alpha-actinin/enhanced yellow fluorescent protein within living CHO cells. The STICS method permits us to generate complete transport maps of proteins within subregions of the basal membrane even if the protein concentration is too high to perform single particle tracking measurements.     

A practical guide to evaluating colocalization in biological microscopy

Fluorescence microscopy is one of the most powerful tools for elucidating the cellular functions of proteins and other molecules. In many cases, the function of a molecule can be inferred from its association with specific intracellular compartments or molecular complexes, which is typically determined by comparing the distribution of a fluorescently labeled version of the molecule with that of a second, complementarily labeled probe. Although arguably the most common application of fluorescence microscopy in biomedical research, studies evaluating the "colocalization" of two probes are seldom quantified, despite a diversity of image analysis tools that have been specifically developed for that purpose. Here we provide a guide to analyzing colocalization in cell biology studies, emphasizing practical application of quantitative tools that are now widely available in commercial and free image analysis software.     

Fluorescence lifetime imaging microscopy (flimscopy). Methodology development and application to studies of endosome fusion in single cells.

A new method of fluorescence microscopy for cell imaging has been developed that takes advantage of the spatial variations of fluorescence lifetimes in single cells as a source of image contrast, and thus it is named "fluorescence lifetime imaging microscopy (flimscopy)". Since time-resolved fluorescence measurements are sensitive to molecular dynamics and interactions, flimscopy allows the molecular information to be visualized in single cells. In flimscopy measurements, several (nanosecond) time-resolved fluorescence images of a sample are obtained at various delay times after pulsed laser excitation of the microscope's entire field of view. Lifetimes are calculated pixel-by-pixel from these time-resolved images, and the spatial variations of the lifetimes are then displayed in a pseudocolor format (flimscopy image). The total data acquisition time needed to obtain a flimscopy image with the diffraction-limited spatial resolution (approximately 250 nm) is decreased to just approximately 30 s for approximately 300 fluorescent molecules/micron2. This was achieved by developing a high-frequency (400 kHz) nanosecond-gating (9 ns full width at half height)-signal accumulation system. This technique allows the extent of resonance energy transfer to be visualized in single living cells, and is free from the errors due to variations in path length, light scattering, and the number of fluorophores that necessitate complex corrections in steady-state microfluorometry and fluorescence ratio imaging microscopy. Flimscopy was applied here to observe the extent of fusion of individual endosomes in single cells. Results revealed the occurrence of extensive fusion between primary endocytic vesicles and/or sorting endosomes, thereby raising the possibility that the biogenesis of sorting endosomes involves multiple fusions of primary endocytic vesicles.     

Functional proteometrics for cell migration.

BACKGROUND: Advances in living cellular fluorescence biosensors and computerized microscopy enable a vision of fully automated high-resolution measurements of the detailed intracellular molecular dynamics directly linked to cellular behaviors. Given the heterogeneity of cell populations, a statistically relevant study of molecular-cellular dynamics is a key motivation for improved automation. METHODS: We explored automating computerized, microscope-based data extraction and analyses that monitor cell locomotion, rates of mitoses, and spatiotemporal activities of intracellular proteins via ratiometric fluorescent biosensors in mouse fibroblasts. Novel image processing methods included K-means clustering segmentation preprocessing followed by modified discrete, normalized cross-correlational alignment of two-color images; ratiometric processing for fluorescence resonance energy transfer (FRET) measurements; and intracellular spatial distribution measurements of RhoA GTPase activity. RESULTS: The interdivision time was 19.4 h (mean) +/- 6.0 h (SD) (n = 7) for the GFP-histone cells in the two-by-two field that was scanned for 72 h. After registration and ratioing of the cells with the RhoA biosensor, increases in both cell protrusion and retraction were coincident with to increases in RhoA activity. CONCLUSIONS: These advances lay the foundation for extracting and correlating measurements characterizing the functional relationships of spatial localization and protein activation with features of cell migration such as velocity, polarization, protrusion, retraction, and mitosis.     

Imaging system for morphometric assessment of absorption or fluorescence in stained cells

An image acquisition and processing system has been developed for quantitative microscopy of absorption or fluorescence in stained cells. Three different light transducers are used in the system to exploit the best characteristics of these sensors for different biological measurements. A digital scanner, in the form of a linear array charge-coupled device (CCD), acquires data with high spatial and photometric resolution. A color (RGB) camera is employed when spectral information is required for the segmentation of cellular subcomponents. An image-intensified charged-injection device (CID) camera provides for very low light intensity measurements, primarily for fluorescence-labeled cells. Properties of these transducers, such as contrast transfer function, linearity, and photo-response nonuniformity, have been measured. Two dedicated image processing units were incorporated into the system. The front-end processor, based on a digital signal processor, provides functions such as object detection, raw image calibration, compression, artifact removal, and filtering. The second image processor is associated with the frame memory and includes a histogram processor, a dedicated arithmetic logic unit for image processing functions, and a graphics module for one-bit overlay functions. An interactive program was developed to acquire cell images and to experiment with a range of segmentation algorithms, feature extractions, and other image processing functions. The results of any image operation are displayed on the video monitor. Once a desired processing sequence is determined, the sequence may be stored to become part of a command library and can be executed thereafter as a single instruction.     

Oufti: an integrated software package for high‐accuracy,high‐throughput quantitative microscopy analysis

With the realization that bacteria display phenotypic variability among cells and exhibit complex subcellular organization critical for cellular function and behavior, microscopy has re‐emerged as a primary tool in bacterial research during the last decade. However, the bottleneck in today's single‐cell studies is quantitative image analysis of cells and fluorescent signals. Here, we address current limitations through the development of Oufti, a stand‐alone, open‐source software package for automated measurements of microbial cells and fluorescence signals from microscopy images. Oufti provides computational solutions for tracking touching cells in confluent samples, handles various cell morphologies, offers algorithms for quantitative analysis of both diffraction and non‐diffraction‐limited fluorescence signals and is scalable for high‐throughput analysis of massive datasets, all with subpixel precision. All functionalities are integrated in a single package. The graphical user interface, which includes interactive modules for segmentation, image analysis and post‐processing analysis, makes the software broadly accessible to users irrespective of their computational skills.     

FRET multiphoton spectral imaging microscopy of 7-ketocholesterol and Nile Red in U937 monocytic cells loaded with 7-ketocholesterol

OBJECTIVE: To show the effect of 7-ketocholesterol (7KC) on cellular lipid content by means of flow cytometry and the interaction of 7KC with Nile Red (NR) via ultraviolet fluorescence resonance energy transfer (FRET) excitation of NR on U937 monocytic cells by means of 2-photon excitation confocal laser scanning microscopy (CLSM). STUDY DESIGN: Untreated and 7KC-treated U937 cells were stained with NR and analyzed by flow cytometry and CLSM. 3D sequences of images were obtained by spectral analysis in a 2-photon excitation CLSM and analyzed by the factor analysis of medical image sequences (FAMIS) algorithm, which provides factor curves and images. Factor images are the result of the FAMIS image processing method, which handles emission spectra. In FRET analysis, preparations are screened at selected UV wavelengths to avoid emission of NR in the absence of 7KC. RESULTS: During 7KC-induced cell death,flow cytometry and CLSM revealed a modification of the cellular lipid content. Factor images show FRET occurrence and subsequent colocalization of 7KC and NR. CONCLUSION: This investigation established the utility of 2-photon excitation CLSM to assess colocalization of 7KC with NR by FRET and to identify and distinguish polar and neutral lipids stained by NR that accumulate from the effect of 7KC.     

Automated method for the rapid and precise estimation of adherent cell culture characteristics from phase contrast microscopy images

The quantitative determination of key adherent cell culture characteristics such as confluency, morphology, and cell density is necessary for the evaluation of experimental outcomes and to provide a suitable basis for the establishment of robust cell culture protocols. Automated processing of images acquired using phase contrast microscopy (PCM), an imaging modality widely used for the visual inspection of adherent cell cultures, could enable the non‐invasive determination of these characteristics. We present an image‐processing approach that accurately detects cellular objects in PCM images through a combination of local contrast thresholding and post hoc correction of halo artifacts. The method was thoroughly validated using a variety of cell lines, microscope models and imaging conditions, demonstrating consistently high segmentation performance in all cases and very short processing times (<1 s per 1,208 × 960 pixels image). Based on the high segmentation performance, it was possible to precisely determine culture confluency, cell density, and the morphology of cellular objects, demonstrating the wide applicability of our algorithm for typical microscopy image processing pipelines. Furthermore, PCM image segmentation was used to facilitate the interpretation and analysis of fluorescence microscopy data, enabling the determination of temporal and spatial expression patterns of a fluorescent reporter. We created a software toolbox (PHANTAST) that bundles all the algorithms and provides an easy to use graphical user interface. Source‐code for MATLAB and ImageJ is freely available under a permissive open‐source license. Biotechnol. Bioeng. 2014;111: 504–517. © 2013 Wiley Periodicals, Inc.     

Automated detection and tracking of individual and clustered cell surface low density lipoprotein receptor molecules.

We have developed a technique to detect, recognize, and track each individual low density lipoprotein receptor (LDL-R) molecule and small receptor clusters on the surface of human skin fibroblasts. Molecular recognition and high precision (30 nm) simultaneous automatic tracking of all of the individual receptors in the cell surface population utilize quantitative time-lapse low light level digital video fluorescence microscopy analyzed by purpose-designed algorithms executed on an image processing work station. The LDL-Rs are labeled with the biologically active, fluorescent LDL derivative dil-LDL. Individual LDL-Rs and unresolved small clusters are identified by measuring the fluorescence power radiated by the sub-resolution fluorescent spots in the image; identification of single particles is ascertained by four independent techniques. An automated tracking routine was developed to track simultaneously, and without user intervention, a multitude of fluorescent particles through a sequence of hundreds of time-lapse image frames. The limitations on tracking precision were found to depend on the signal-to-noise ratio of the tracked particle image and mechanical drift of the microscope system. We describe the methods involved in (i) time-lapse acquisition of the low-light level images, (ii) simultaneous automated tracking of the fluorescent diffraction limited punctate images, (iii) localizing particles with high precision and limitations, and (iv) detecting and identifying single and clustered LDL-Rs. These methods are generally applicable and provide a powerful tool to visualize and measure dynamics and interactions of individual integral membrane proteins on living cell surfaces.