Imaging technologies for the detection of multiple stains in proteomics

Laser-based scanners and charge-coupled device (CCD) camera systems are evolving to have greater functional capabilities for capturing images from a range of staining technologies used in gel electrophoresis and electroblotting. Digitizing Coomassie Brilliant Blue (CBB) stained gels and silver stained gels has now become possible using a laser-based gel scanner, the FLA-5000 fluorescent image analyzer system. Also, a simultaneous dual fluorescent imaging function has been incorporated into the FLA-5000 system, utilizing dichroic mirrors with both the optical system and the emission filter. In the workflow of routine proteomics research, the relationship between SYPRO dye staining and fluorescent detection using the FLA-5000 system have become symbiotic. Additionally in many cases, subsequent staining of the gel with CBB is useful for future research, and thus imaging instruments should be able to handle both staining formats. Digitizing the CBB stained gel can now be easily performed by the FLA-5000 fluorescent image analyzer system using a fluorescent board as an epi-illumination background. A cooled CCD camera system has the potential of imaging not only chemiluminescent membranes but also digitizing molecular weight markers and fluorescent detection of SYPRO dye-stained gels. With Multi Gauge software version 2.0 it is now a simple task to combine two images into one, as commonly required in dual detection experiments. The LAS-3000 system was designed to capture chemiluminescent images and to digitize the images automatically. Thus, new capabilities added to gel imaging systems make them capable of detecting and displaying multiple signals more conveniently.     

Mapping fluorophore distributions in three dimensions by quantitative multiple angle-total internal reflection fluorescence microscopy.

The decay of evanescent field intensity beyond a dielectric interface depends upon beam incident angle, enabling the 3-d distribution of fluorophores to be deduced from total internal reflection fluorescence microscopy (TIRFM) images obtained at multiple incident angles. Instrumentation was constructed for computer-automated multiple angle-TIRFM (MA-TIRFM) using a right angle F2 glass prism (n(r) 1.632) to create the dielectric interface. A laser beam (488 nm) was attenuated by an acoustooptic modulator and directed onto a specified spot on the prism surface. Beam incident angle was set using three microstepper motors controlling two rotatable mirrors and a rotatable optical flat. TIRFM images were acquired by a cooled CCD camera in approximately 0.5 degree steps for >15 incident angles starting from the critical angle. For cell studies, cells were grown directly on the glass prisms (without refractive index-matching fluid) and positioned in the optical path. Images of the samples were acquired at multiple angles, and corrected for angle-dependent evanescent field intensity using "reference" images acquired with a fluorophore solution replacing the sample. A theory was developed to compute fluorophore z-distribution by inverse Laplace transform of angle-resolved intensity functions. The theory included analysis of multiple layers of different refractive index for cell studies, and the anisotropic emission from fluorophores near a dielectric interface. Instrument performance was validated by mapping the thickness of a film of dihexyloxacarbocyanine in DMSO/water (n(r) 1.463) between the F2 glass prism and a plano-convex silica lens (458 mm radius, n(r) 1.463); the MA-TIRFM map accurately reproduced the lens spherical surface. MA-TIRFM was used to compare with nanometer z-resolution the geometry of cell-substrate contact for BCECF-labeled 3T3 fibroblasts versus MDCK epithelial cells. These studies establish MA-TIRFM for measurement of submicroscopic distances between fluorescent probes and cell membranes.     

White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis

White Blood Cells play an important role in observing the health condition of an individual. The opinion related to blood disease involves the identification and characterization of a patient's blood sample. Recent approaches employ Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and merging of CNN and RNN models to enrich the understanding of image content. From beginning to end, training of big data in medical image analysis has encouraged us to discover prominent features from sample images. A single cell patch extraction from blood sample techniques for blood cell classification has resulted in the good performance rate. However, these approaches are unable to address the issues of multiple cells overlap. To address this problem, the Canonical Correlation Analysis (CCA) method is used in this paper. CCA method views the effects of overlapping nuclei where multiple nuclei patches are extracted, learned and trained at a time. Due to overlapping of blood cell images, the classification time is reduced, the dimension of input images gets compressed and the network converges faster with more accurate weight parameters. Experimental results evaluated using publicly available database show that the proposed CNN and RNN merging model with canonical correlation analysis determines higher accuracy compared to other state-of-the-art blood cell classification techniques.     

IMAGE ANALYSIS AS A RAPID PATHOGEN DETECTION METHOD FOR USE IN POULTRY INDUSTRY

An image analysis system was developed and evaluated as a method for rapid detection of Salmonella typhimurium in pure culture and in chicken washes. A direct immunomagnetic separation and immunofluorescent staining technique was developed to capture and identify target cells. Digital images were acquired and segmented into background and bacteria. Bacteria were enumerated using a custom designed image analysis software. The image analyses results were compared with manual enumeration. A correlation coefficient of 0.78 was established between manual and image analysis counts. In addition, the difference between the manual and the image analysis bacterial counts in individual images was low. Image analysis took an average of 15 s to analyze an image. The results indicate that the proposed system has the potential to be used as a rapid screening procedure for bacterial detection in the food industry.     

Standardization of Size,Shape and Internal Structure of Spinal Cord Images: Comparison of Three Transformation Methods

Functional fluorescence imaging has been widely applied to analyze spatio-temporal patterns of cellular dynamics in the brain and spinal cord. However, it is difficult to integrate spatial information obtained from imaging data in specific regions of interest across multiple samples, due to large variability in the size, shape and internal structure of samples. To solve this problem, we attempted to standardize transversely sectioned spinal cord images focusing on the laminar structure in the gray matter. We employed three standardization methods, the affine transformation (AT), the angle-dependent transformation (ADT) and the combination of these two methods (AT+ADT). The ADT is a novel non-linear transformation method developed in this study to adjust an individual image onto the template image in the polar coordinate system. We next compared the accuracy of these three standardization methods. We evaluated two indices, i.e., the spatial distribution of pixels that are not categorized to any layer and the error ratio by the leave-one-out cross validation method. In this study, we used neuron-specific marker (NeuN)-stained histological images of transversely sectioned cervical spinal cord slices (21 images obtained from 4 rats) to create the standard atlas and also to serve for benchmark tests. We found that the AT+ADT outperformed other two methods, though the accuracy of each method varied depending on the layer. This novel image standardization technique would be applicable to optical recording such as voltage-sensitive dye imaging, and will enable statistical evaluations of neural activation across multiple samples.     

Estimating Skin Resistance to Gas Diffusion in Apples and Potatoes

Banks, N. H. 1985. Estimating skin resistance to gas diffusionin apples and potatoes.?J. exp. Bot. 36: 1842-1850.Two typesof analysis of the rate of efflux of pre-ndash;loaded ethanefrom apples and potatoes into a container of known volume werecompared as methods for estimating skin resistance to gas diffusion(R). An 'exponential model' was fitted to transformed data obtainedover a 24 h period whereas a 'linear model' was fitted to rawdata obtained over a 100 s period. R values for individual applesobtained using the two methods were in close agreement. R washigher in Golden Delicious than in Discovery apples but valuesfor individual fruit within each variety were quite variable.The exponential analysis was unsuitable for estimating R inpotatoes but the linear analysis yielded reproducible R estimates.A computer simulation of ethane efflux from an 'ideal' organshowed that varying the size of container used in either analysisin the range of 250-9000 cm³ would have no detrimental effectson estimates of R. Key words: Skin resistance, gas diffusion, apples, potatoes     

A tiger cannot change its stripes: using a three-dimensional model to match images of living tigers and tiger skins

The tiger is one of many species in which individuals can be identified by surface patterns. Camera traps can be used to record individual tigers moving over an array of locations and provide data for monitoring and studying populations and devising conservation strategies. We suggest using a combination of algorithms to calculate similarity scores between pattern samples scanned from the images to automate the search for a match to a new image. We show how using a three-dimensional surface model of a tiger to scan the pattern samples allows comparison of images that differ widely in camera angles and body posture. The software, which is free to download, considerably reduces the effort required to maintain an image catalogue and we suggest it could be used to trace the origin of a tiger skin by searching a central database of living tigers'' images for matches to an image of the skin.     

Peripheral electrosensory imaging by weakly electric fish

Different species have developed different solutions to the problem of constructing a representation of the environment from sensory images projected onto sensory surfaces. Comprehension of how these images are formed is an essential first step in understanding the representation of external reality by a given sensory system. Modeling of the electrical sensory images of objects began with the discovery of electroreception and continues to provide general insights into the mechanisms of imaging. Progress in electric image research has made it possible to establish the physical basis of electric imaging, as well as methods to accurately predict the electric images of objects alone and as a part of a natural electric scene. In this review, we show the following. (1) The internal low resistance of the fish’s body shapes the image in two different ways: by funneling the current generated by the electric organ to the sensory surface, it increases the fields rostrally, thus enhancing the perturbation produced by nearby objects; and by increasing the projected image. (2) The electric fish’s self-generated currents are modified by capacitive objects in a distinctive manner. These modulations can be detected by different receptor types, yielding the possibility of “electric color.” (3) The effects of different objects in a scene interact with each other, generating an image that is different from the simple addition of the images of individual objects, thus causing strong contextual effects.     

Feature extraction from three-dimensional images in quantitative microscopy

Different methods are investigated in selecting and generating the appropriate microscope images for analysis of three-dimensional objects in quantitative microscopy. Traditionally, the ‘best’ focused image from a set is used for quantitative analysis. Such an objectively determined image is optimal for the extraction of some features, but may not be the best image for the extraction of all features. Various methods using multiple images are here developed to obtain a tighter distribution for all features.Three different approaches for analysis of images of stained cervical cells were analyzed. In the first approach, features are extracted from each image in the set. The feature values are then averaged to give the final result. In the second approach, a set of varying focused images are reconstructed to obtain a set of in-focus images. Features are then extracted from this set and averaged. In the third approach, a set of images in the three-dimensional scene is compressed into a single two-dimensional image. Four different compression methods are used. Features are then extracted from the resulting two-dimensional image. The third approach is employed on both the raw and transformed images.Each approach has its advantages and disadvantages. The first approach is fast and produces reasonable results. The second approach is more computationally expensive but produces the best results. The last approach overcomes the memory storage problem of the first two approaches since the set of images is compressed into one. The method of compression using the highest gradient pixel produces better results overall than other data reduction techniques and produces results comparable to the first approach.     

The relative importance of orange spot coloration and total length of males in female guppy mate preference

Female mate choice by multiple male traits is an important current topic in animal behavior. However, the relative importance among the multiple cues in female choice is not explored in most cases. Female guppies Poecilia reticulata use both the color saturation of orange spots and the total length of males as mate choice criteria. In the present study, we used digitally modified video playbacks to examine the relative importance of these two male traits to female mate preferences. We initially examined the effective difference in the color saturation of orange spots as well as that in total length between two stimulus male images. Females only showed a strong preference for a bright male image (compared to the dull image) when the difference in color saturation was large (91% versus 25%). Conversely, females only exhibited a preference for larger size when they were presented a choice between two relatively small male images (total length 26.0 mm versus 23.0 mm). When two male images in which both the two traits were modified were presented to females, they prioritized male images possessing higher color saturation of orange spots, indicating the color saturation of male orange spots to be a more important factor than the total length in their mate choice. The color saturation of orange spots may convey more reliable information about the males to the females than their total lengths. These findings imply that females may rank multiple male criteria depending on relative benefits or costs derived from their mate choice based on each criterion.     

A Sensitive,Reproducible and Objective Immunofluorescence Analysis Method of Dystrophin in Individual Fibers in Samples from Patients with Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is characterized by the absence or reduced levels of dystrophin expression on the inner surface of the sarcolemmal membrane of muscle fibers. Clinical development of therapeutic approaches aiming to increase dystrophin levels requires sensitive and reproducible measurement of differences in dystrophin expression in muscle biopsies of treated patients with DMD. This, however, poses a technical challenge due to intra- and inter-donor variance in the occurrence of revertant fibers and low trace dystrophin expression throughout the biopsies. We have developed an immunofluorescence and semi-automated image analysis method that measures the sarcolemmal dystrophin intensity per individual fiber for the entire fiber population in a muscle biopsy. Cross-sections of muscle co-stained for dystrophin and spectrin have been imaged by confocal microscopy, and image analysis was performed using Definiens software. Dystrophin intensity has been measured in the sarcolemmal mask of spectrin for each individual muscle fiber and multiple membrane intensity parameters (mean, maximum, quantiles per fiber) were calculated. A histogram can depict the distribution of dystrophin intensities for the fiber population in the biopsy. This method was tested by measuring dystrophin in DMD, Becker muscular dystrophy, and healthy muscle samples. Analysis of duplicate or quadruplicate sections of DMD biopsies on the same or multiple days, by different operators, or using different antibodies, was shown to be objective and reproducible (inter-assay precision, CV 2–17% and intra-assay precision, CV 2–10%). Moreover, the method was sufficiently sensitive to detect consistently small differences in dystrophin between two biopsies from a patient with DMD before and after treatment with an investigational compound.     

A machine vision system to predict individual cow feed intake of different feeds in a cowshed

Data on individual feed intake of dairy cows, an important variable for farm management, are currently unavailable in commercial dairies. A real-time machine vision system including models that are able to adapt to multiple types of feed was developed to predict individual feed intake of dairy cows. Using a Red-Green-Blue-Depth (RGBD) camera, images of feed piles of two different feed types (lactating cows' feed and heifers' feed) were acquired in a research dairy farm, for a range of feed weights under varied configurations and illuminations. Several models were developed to predict individual feed intake: two Transfer Learning (TL) models based on Convolutional Neural Networks (CNNs), one CNN model trained on both feed types, and one Multilayer Perceptron and Convolutional Neural Network model trained on both feed types, along with categorical data. We also implemented a statistical method to compare these four models using a Linear Mixed Model and a Generalised Linear Mixed Model, showing that all models are significantly different. The TL models performed best and were trained on both feeds with TL methods. These models achieved Mean Absolute Errors (MAEs) of 0.12 and 0.13 kg per meal with RMSE of 0.18 and 0.17 kg per meal for the two different feeds, when tested on varied data collected manually in a cowshed. Testing the model with actual cows’ meals data automatically collected by the system in the cowshed resulted in a MAE of 0.14 kg per meal and RMSE of 0.19 kg per meal. These results suggest the potential of measuring individual feed intake of dairy cows in a cowshed using RGBD cameras and Deep Learning models that can be applied and tuned to different types of feed.     

Visualization of Individual Reovirus Particles by Low-Temperature, High-Resolution Scanning Electron Microscopy

We used low-temperature, high-resolution scanning electron microscopy (cryo-HRSEM) to visualize surface structures on individual reovirus particles. Both intact virions and two forms of subvirion particles—infectious subvirion particles and cores—were examined, and despite some distortion of particles during specimen preparation and viewing in the microscope, the images obtained by cryo-HRSEM exhibited a level of interpretable detail not routinely achieved by other methods without image averaging. Cryo-HRSEM images of discrete reovirus particles were used to characterize and confirm features of the outer protein capsid of this virus by comparison with image reconstructions previously derived from cryotransmission electron microscopy. Distinct surface features attributable to each of the four outer-capsid proteins were identified. In addition, cryo-HRSEM images confirmed that significant changes occur on the surfaces of individual reovirus particles during disassembly and entry of cells and that the reovirus outer capsid is organized as a left-handed T=13 icosahedron. Several unique capabilities and potential uses suggest that cryo-HRSEM has a place alongside other, more established methods for molecular characterizations of virus particles.     

Development of separable electron spin resonance-computed tomography imaging for multiple radical species: an application to .OH and .NO

A method of separable ESR-CT (electron spin resonance-computed tomography) imaging for multiple radical species was developed and applied to imaging of .OH and .NO. The algorithm was improved by combining filtered back-projection with a modified algebraic reconstruction technique to enhance accuracy and shorten calculation time. With this algorithm, spectral-spatial images of the phantom consisting of 3-carbamoyl-2,2,5,5,-tetramethylpyrrolidine-N-oxyl and 2-phenyl-4,4,5,5,-tetramethylimidazoline-3-oxide-1-oxyl could be obtained in different directions by rotating the spatial axis. The spatial function of individual radicals was extracted by each of the two methods from each spectral-spatial image. The separative 2D images of each radical were individually constructed using the spatial function obtained with the two methods. By comparing the separative images with the phantom sample, the algorithm for separable ESR-CT imaging was established. This ESR-CT technique was combined with L-band ESR spectroscopy and applied to the separative imaging of .OH and .NO, which were spin trapped with 5, 5-dimethyl-1-pyrroline-N-oxide (DMPO) and Fe(2+)-N-methyl-D-glucamine dithiocarbamate complex, respectively. The ESR signal of DMPO-OH decreased gradually during data acquisition, and the decrease was calibrated by extrapolating the signal intensity to the beginning of data sampling. Both the position and size of the individual images for .OH and .NO were in very good agreement with the findings for the sample.     

Mapping Diffusion in a Living Cell via the Phasor Approach

Diffusion of a fluorescent protein within a cell has been measured using either fluctuation-based techniques (fluorescence correlation spectroscopy (FCS) or raster-scan image correlation spectroscopy) or particle tracking. However, none of these methods enables us to measure the diffusion of the fluorescent particle at each pixel of the image. Measurement using conventional single-point FCS at every individual pixel results in continuous long exposure of the cell to the laser and eventual bleaching of the sample. To overcome this limitation, we have developed what we believe to be a new method of scanning with simultaneous construction of a fluorescent image of the cell. In this believed new method of modified raster scanning, as it acquires the image, the laser scans each individual line multiple times before moving to the next line. This continues until the entire area is scanned. This is different from the original raster-scan image correlation spectroscopy approach, where data are acquired by scanning each frame once and then scanning the image multiple times. The total time of data acquisition needed for this method is much shorter than the time required for traditional FCS analysis at each pixel. However, at a single pixel, the acquired intensity time sequence is short; requiring nonconventional analysis of the correlation function to extract information about the diffusion. These correlation data have been analyzed using the phasor approach, a fit-free method that was originally developed for analysis of FLIM images. Analysis using this method results in an estimation of the average diffusion coefficient of the fluorescent species at each pixel of an image, and thus, a detailed diffusion map of the cell can be created.     

The Neural Basis of Mark Making: A Functional MRI Study of Drawing

Compared to most other forms of visually-guided motor activity, drawing is unique in that it “leaves a trail behind” in the form of the emanating image. We took advantage of an MRI-compatible drawing tablet in order to examine both the motor production and perceptual emanation of images. Subjects participated in a series of mark making tasks in which they were cued to draw geometric patterns on the tablet''s surface. The critical comparison was between when visual feedback was displayed (image generation) versus when it was not (no image generation). This contrast revealed an occipito-parietal stream involved in motion-based perception of the emerging image, including areas V5/MT+, LO, V3A, and the posterior part of the intraparietal sulcus. Interestingly, when subjects passively viewed animations of visual patterns emerging on the projected surface, all of the sensorimotor network involved in drawing was strongly activated, with the exception of the primary motor cortex. These results argue that the origin of the human capacity to draw and write involves not only motor skills for tool use but also motor-sensory links between drawing movements and the visual images that emanate from them in real time.     

Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling

PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create “variable signal,” whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.     

Object Segmentation and Ground Truth in 3D Embryonic Imaging

Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.     

Auto-align - multi-modality fluorescence microscopy image co-registration

Multi-modality microscopes incorporate multiple microscopy techniques into one module, imaging through a common objective lens. Simultaneous or consecutive image acquisition of a single specimen, using multiple techniques, increases the amount of measurable information available. In order to benefit from each modality, it is necessary to accurately co-register data sets. Intrinsic differences in the image formation process employed by each modality result in images which possess different characteristics. In addition, as a result of using different measurement devices, images often differ in size and can suffer relative geometrical deformations including rotation, scale and translation, making registration a complex problem. Current methods generally rely on manual input and are therefore subject to human error. Here, we present an automated image registration tool for fluorescence microscopy. We show that it successfully registers images obtained via total internal reflection fluorescence (TIRF), or epi-fluorescence, and confocal microscopy. Furthermore, we provide several other applications including channel merging following image acquisition through an emission beam splitter, and lateral stage drift correction. We also discuss areas of membrane trafficking which could benefit from application of Auto-Align. Auto-Align is an essential item in the advanced microscopist's toolbox which can create a synergy of single or multi-modality image data.