Application of regularized Richardson-Lucy algorithm for deconvolution of confocal microscopy images

Gallein potentiates isoniazid's ability to suppress Mycobacterium tuberculosis growth

Mycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis (TB), can be difficult to treat because of drug tolerance. Increased intracellular polyphosphate (polyP) in Mtb enhances tolerance to antibiotics, and capsular polyP in Neisseria gonorrhoeae potentiates resistance to antimicrobials. The mechanism by which bacteria utilize polyP to adapt to antimicrobial pressure is not known. In this study, we found that Mtb adapts to the TB frontline antibiotic isoniazid (INH) by enhancing the accumulation of cellular, extracellular, and cell surface polyP. Gallein, a broad-spectrum inhibitor of the polyphosphate kinase that synthesizes polyP, prevents this INH-induced increase in extracellular and cell surface polyP levels. Gallein and INH work synergistically to attenuate Mtb's ability to grow in in vitro culture and within human macrophages. Mtb when exposed to INH, and in the presence of INH, gallein inhibits cell envelope formation in most but not all Mtb cells. Metabolomics indicated that INH or gallein have a modest impact on levels of Mtb metabolites, but when used in combination, they significantly reduce levels of metabolites involved in cell envelope synthesis and amino acid, carbohydrate, and nucleoside metabolism, revealing a synergistic effect. These data suggest that gallein represents a promising avenue to potentiate the treatment of TB.

PTEN and the PTEN-like phosphatase CnrN have both distinct and overlapping roles in a Dictyostelium chemorepulsion pathway

The directed movement of eukaryotic cells is crucial for processes such as embryogenesis and immune cell trafficking. The enzyme Phosphatase and tensin homolog (PTEN) dephosphorylates phosphatidylinositol 3,4,5-trisphosphate [PI(3,4,5)P 3 ] to phosphatidylinositol 4,5-bisphosphate [PI(4,5)P 2 ]. Dictyostelium discoideum cells require both PTEN and the PTEN-like phosphatase CnrN to locally inhibit Ras activation to induce biased movement of cells away from the secreted chemorepellent protein AprA. Both PTEN and CnrN decrease basal levels of PI(3,4,5)P 3 and increase basal numbers of macropinosomes, and AprA prevents this increase. AprA requires both PTEN and CnrN to increase PI(4,5)P 2 levels, decrease PI(3,4,5)P 3 levels, inhibit proliferation, decrease myosin II phosphorylation, and increase filopod sizes. AprA causes PTEN, similar to CnrN, to localize to the side of the cell towards AprA in an AprA gradient. However, PTEN and CnrN also have distinct roles in some signaling pathways. PTEN, but not CnrN, decreases basal levels of PI(4,5)P 2 , AprA requires PTEN, but not CnrN, to induce cell roundness, and CnrN and PTEN have different effects on the number of filopods and pseudopods, and the sizes of filopods. Together, our results suggest that CnrN and PTEN play unique roles in D. discoideum signaling pathways, and possibly dephosphorylate PI(3,4,5)P 3 in different membrane domains, to mediate chemorepulsion away from AprA.

Gallein and isoniazid act synergistically to attenuate Mycobacterium tuberculosis growth in human macrophages

Mycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis (TB), can be difficult to treat because of drug resistance. Increased intracellular polyphosphate (polyP) in Mtb enhances resistance to antibiotics, and capsular polyP in Neisseria gonorrhoeae potentiates resistance to antimicrobials. The mechanism by which bacteria utilize polyP to adapt to antimicrobial pressure is not known. In this study, we found that Mtb adapts to the TB frontline antibiotic isoniazid (INH) by enhancing the accumulation of cellular, extracellular, and cell surface polyP. Gallein, a broad-spectrum inhibitor of the polyphosphate kinase that synthesizes polyP, prevents this INH-induced increase in extracellular and cell surface polyP levels. Gallein and INH work synergistically to attenuate Mtb's ability to grow in in vitro culture and within human macrophages. Mtb when exposed to INH, and in the presence of INH, gallein inhibits cell envelope formation in most but not all Mtb cells. Metabolomics indicated that INH or gallein have a modest impact on levels of Mtb metabolites, but when used in combination, they significantly reduce levels of metabolites involved in cell envelope synthesis and amino acid, carbohydrate, and nucleoside metabolism, revealing a synergistic effect. These data suggest that gallein represents a promising avenue to potentiate the treatment of TB.

Polyphosphate uses mTOR, pyrophosphate, and Rho GTPase components to potentiate bacterial survival in Dictyostelium

IMPORTANCE

Although most bacteria are quickly killed after phagocytosis by a eukaryotic cell, some pathogenic bacteria escape death after phagocytosis. Pathogenic Mycobacterium species secrete polyP, and the polyP is necessary for the bacteria to prevent their killing after phagocytosis. Conversely, exogenous polyP prevents the killing of ingested bacteria that are normally killed after phagocytosis by human macrophages and the eukaryotic microbe Dictyostelium discoideum. This suggests the possibility that in these cells, a signal transduction pathway is used to sense polyP and prevent killing of ingested bacteria. In this report, we identify key components of the polyP signal transduction pathway in D. discoideum. In cells lacking these components, polyP is unable to inhibit killing of ingested bacteria. The pathway components have orthologs in human cells, and an exciting possibility is that pharmacologically blocking this pathway in human macrophages would cause them to kill ingested pathogens such as Mycobacterium tuberculosis.

Automatic Choroidal Segmentation in Optical Coherence Tomography Images Based on Curvelet Transform and Graph Theory

Background

Automatic segmentation of the choroid on optical coherence tomography (OCT) images helps ophthalmologists in diagnosing eye pathologies. Compared to manual segmentations, it is faster and is not affected by human errors. The presence of the large speckle noise in the OCT images limits the automatic segmentation and interpretation of them. To solve this problem, a new curvelet transform-based K-SVD method is proposed in this study. Furthermore, the dataset was manually segmented by a retinal ophthalmologist to draw a comparison with the proposed automatic segmentation technique.

Methods

In this study, curvelet transform-based K-SVD dictionary learning and Lucy-Richardson algorithm were used to remove the speckle noise from OCT images. The Outer/Inner Choroidal Boundaries (O/ICB) were determined utilizing graph theory. The area between ICB and outer choroidal boundary was considered as the choroidal region.

Results

The proposed method was evaluated on our dataset and the average dice similarity coefficient (DSC) was calculated to be 92.14% ± 3.30% between automatic and manual segmented regions. Moreover, by applying the latest presented open-source algorithm by Mazzaferri et al. on our dataset, the mean DSC was calculated to be 55.75% ± 14.54%.

Conclusions

A significant similarity was observed between automatic and manual segmentations. Automatic segmentation of the choroidal layer could be also utilized in large-scale quantitative studies of the choroid.

Starvation Induces Extracellular Accumulation of Polyphosphate in Dictyostelium discoideum to Inhibit Macropinocytosis, Phagocytosis, and Exocytosis

Dictyostelium discoideum is a soil-dwelling unicellular eukaryote that accumulates extracellular polyphosphate (polyP). At high cell densities, when the cells are about to overgrow their food supply and starve, the corresponding high extracellular concentrations of polyP allow the cells to preemptively anticipate starvation, inhibit proliferation, and prime themselves to begin development. In this report, we show that starved D. discoideum cells accumulate cell surface and extracellular polyP. Starvation reduces macropinocytosis, exocytosis, and phagocytosis, and we find that these effects require the G protein-coupled polyP receptor (GrlD) and two enzymes, Polyphosphate kinase 1 (Ppk1), which is required for synthesizing intracellular polyP, cell surface polyP, and some of the extracellular polyP, and Inositol hexakisphosphate kinase (I6kA), which is required for cell surface polyP and polyP binding to cells, and some of the extracellular polyP. PolyP reduces membrane fluidity, and we find that starvation reduces membrane fluidity; this effect requires GrlD and Ppk1, but not I6kA. Together, these data suggest that in starved cells, extracellular polyP decreases membrane fluidity, possibly as a protective measure. In the starved cells, sensing polyP appears to decrease energy expenditure from ingestion, and decrease exocytosis, and to both decrease energy expenditures and retain nutrients.

Starvation induces extracellular accumulation of polyphosphate in Dictyostelium discoideum to inhibit macropinocytosis, phagocytosis, and exocytosis

Dictyostelium discoideum is a soil-dwelling unicellular eukaryote that accumulates extracellular polyphosphate (polyP). At high cell densities, when the cells are about to overgrow their food supply and starve, the corresponding high extracellular concentrations of polyP allow the cells to preemptively anticipate starvation, inhibit proliferation, and prime themselves to begin development. In this report, we show that starved D. discoideum cells accumulate cell surface and extracellular polyP. Starvation reduces macropinocytosis, exocytosis, and phagocytosis, and we find that these effects require the G protein-coupled polyP receptor (GrlD) and two enzymes, Polyphosphate kinase 1 (Ppk1), which is required for synthesizing intracellular polyP, cell surface polyP, and some of the extracellular polyP, and Inositol hexakisphosphate kinase (I6kA), which is required for cell surface polyP and polyP binding to cells, and some of the extracellular polyP. PolyP reduces membrane fluidity, and we find that starvation reduces membrane fluidity, and this effect requires GrlD and Ppk1 but not I6kA. Together, these data suggest that in starved cells, extracellular polyP decreases membrane fluidity, possibly as a protective measure. In the starved cells, sensing polyP appears to decrease energy expenditure from ingestion, and decrease exocytosis, to both decrease energy expenditures and retain nutrients.

BCM3D 2.0: accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations

Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for observing individual cell behaviors in large bacterial communities called biofilms. Recent progress in machine-learning-based image analysis is providing this capability with ever-increasing accuracy. Leveraging the capabilities of deep convolutional neural networks (CNNs), we recently developed bacterial cell morphometry in 3D (BCM3D), an integrated image analysis pipeline that combines deep learning with conventional image analysis to detect and segment single biofilm-dwelling cells in 3D fluorescence images. While the first release of BCM3D (BCM3D 1.0) achieved state-of-the-art 3D bacterial cell segmentation accuracies, low signal-to-background ratios (SBRs) and images of very dense biofilms remained challenging. Here, we present BCM3D 2.0 to address this challenge. BCM3D 2.0 is entirely complementary to the approach utilized in BCM3D 1.0. Instead of training CNNs to perform voxel classification, we trained CNNs to translate 3D fluorescence images into intermediate 3D image representations that are, when combined appropriately, more amenable to conventional mathematical image processing than a single experimental image. Using this approach, improved segmentation results are obtained even for very low SBRs and/or high cell density biofilm images. The improved cell segmentation accuracies in turn enable improved accuracies of tracking individual cells through 3D space and time. This capability opens the door to investigating time-dependent phenomena in bacterial biofilms at the cellular level.

Incorporating the image formation process into deep learning improves network performance

We present Richardson–Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson–Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson–Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN’s performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.

Richardson–Lucy Network (RLN) combines the traditional Richardson–Lucy iteration with deep learning for improved deconvolution. RLN is more generalizable, offers fewer artifacts and requires less computing time than alternative approaches.

3D super-resolution live-cell imaging with radial symmetry and Fourier light-field microscopy

Live-cell imaging reveals the phenotypes and mechanisms of cellular function and their dysfunction that underscore cell physiology, development, and pathology. Here, we report a 3D super-resolution live-cell microscopy method by integrating radiality analysis and Fourier light-field microscopy (rad-FLFM). We demonstrated the method using various live-cell specimens, including actins in Hela cells, microtubules in mammary organoid cells, and peroxisomes in COS-7 cells. Compared with conventional wide-field microscopy, rad-FLFM realizes scanning-free, volumetric 3D live-cell imaging with sub-diffraction-limited resolution of ∼150 nm (x-y) and 300 nm (z), milliseconds volume acquisition time, six-fold extended depth of focus of ∼6 µm, and low photodamage. The method provides a promising avenue to explore spatiotemporal-challenging subcellular processes in a wide range of cell biological research.

TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images

Simple Summary

Microscopy is central to many areas of biomedical science research, including cancer research, and is critical for understanding basic pathophysiology, mechanisms of action, and treatment response. However, analysis of the numerous images generated from microscopy readouts is usually performed manually, a process that is tedious and time-consuming. Moreover, manual analysis of microscopy images may limit both accuracy and reproducibility. Here, we used an artificial intelligence approach to analyze tunnelling nanotubes (TNTs), a feature of cancer cells that may contribute to their aggressiveness, but which are hard to identify and count. Our approach labeled and detected TNTs and cancer cells from microscopy images and generated TNT-to-cell ratios comparable to those of human experts. Continued refinement of this process will provide a new approach to the analysis of TNTs. Additionally, this approach has the potential to enhance drug screens intended to assess therapeutic efficacy of experimental agents and to reproducibly assess TNTs as a potential biomarker of response to cancer therapy.

Abstract

Background: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. Methods: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. Results: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). Conclusions: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.

Dictyostelium discoideum cells retain nutrients when the cells are about to overgrow their food source

Dictyostelium discoideum is a unicellular eukaryote that eats bacteria, and eventually outgrows the bacteria. D. discoideum cells accumulate extracellular polyphosphate (polyP), and the polyP concentration increases as the local cell density increases. At high cell densities, the correspondingly high extracellular polyP concentrations allow cells to sense that they are about to outgrow their food supply and starve, causing the D. discoideum cells to inhibit their proliferation. In this report, we show that high extracellular polyP inhibits exocytosis of undigested or partially digested nutrients. PolyP decreases plasma membrane recycling and apparent cell membrane fluidity, and this requires the G protein-coupled polyP receptor GrlD, the polyphosphate kinase Ppk1 and the inositol hexakisphosphate kinase I6kA. PolyP alters protein contents in detergent-insoluble crude cytoskeletons, but does not significantly affect random cell motility, cell speed or F-actin levels. Together, these data suggest that D. discoideum cells use polyP as a signal to sense their local cell density and reduce cell membrane fluidity and membrane recycling, perhaps as a mechanism to retain ingested food when the cells are about to starve.

This article has an associated First Person interview with the first author of the paper.

Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy

A main determinant of the spatial resolution of live-cell super-resolution (SR) microscopes is the maximum photon flux that can be collected. To further increase the effective resolution for a given photon flux, we take advantage of a priori knowledge about the sparsity and continuity of biological structures to develop a deconvolution algorithm that increases the resolution of SR microscopes nearly twofold. Our method, sparse structured illumination microscopy (Sparse-SIM), achieves ~60-nm resolution at a frame rate of up to 564 Hz, allowing it to resolve intricate structures, including small vesicular fusion pores, ring-shaped nuclear pores formed by nucleoporins and relative movements of inner and outer mitochondrial membranes in live cells. Sparse deconvolution can also be used to increase the three-dimensional resolution of spinning-disc confocal-based SIM, even at low signal-to-noise ratios, which allows four-color, three-dimensional live-cell SR imaging at ~90-nm resolution. Overall, sparse deconvolution will be useful to increase the spatiotemporal resolution of live-cell fluorescence microscopy.

Blind deconvolution in autocorrelation inversion for multiview light-sheet microscopy

Combining the information coming from multiview acquisitions is a problem of great interest in light-sheet microscopy. Aligning the views and increasing the resolution of their fusion can be challenging, especially if the setup is not fully calibrated. Here, we tackle these issues by proposing a new reconstruction method based on autocorrelation inversion that avoids alignment procedures. On top of this, we add a blind deconvolution step to improve the resolution of the final reconstruction. Our method permits us to achieve inherently aligned, highly resolved reconstructions while, at the same time, estimating the unknown point-spread function of the system.

Visualizing minute details in light-sheet and confocal microscopy data by combining 3D rolling ball filtering and deconvolution

We developed an open-source deconvolution software that stunningly increases the visibility of minute details, as for example, neurons or nerve fibers in light-sheet microscopy or confocal microscopy data by combining rolling ball background subtraction in three directions with deconvolution using a synthetic or measured point spread function. Via automatic block-wise processing image stacks of virtually unlimited size can be deconvolved even on small computers with 8 or 16 GB RAM. By parallelization and optional GPU-acceleration, the software works with high speed: On a PC equipped with a state-of-the-art NVidia graphic board a three dimensional (3D)-stack of about 1 billion voxels can be deconvolved within 5 to 10 minutes. The implemented variation of the Richardson-Lucy deconvolution algorithm preserves the photogrammetry of the image data by using flux-preserving regularization, an approach that to our knowledge has not been applied for deconvolving microscopy data before.

Iterative-Trained Semi-Blind Deconvolution Algorithm to Compensate Straylight in Retinal Images

The optical quality of an image depends on both the optical properties of the imaging system and the physical properties of the medium in which the light travels from the object to the final imaging sensor. The analysis of the point spread function of the optical system is an objective way to quantify the image degradation. In retinal imaging, the presence of corneal or cristalline lens opacifications spread the light at wide angular distributions. If the mathematical operator that degrades the image is known, the image can be restored through deconvolution methods. In the particular case of retinal imaging, this operator may be unknown (or partially) due to the presence of cataracts, corneal edema, or vitreous opacification. In those cases, blind deconvolution theory provides useful results to restore important spatial information of the image. In this work, a new semi-blind deconvolution method has been developed by training an iterative process with the Glare Spread Function kernel based on the Richardson-Lucy deconvolution algorithm to compensate a veiling glare effect in retinal images due to intraocular straylight. The method was first tested with simulated retinal images generated from a straylight eye model and applied to a real retinal image dataset composed of healthy subjects and patients with glaucoma and diabetic retinopathy. Results showed the capacity of the algorithm to detect and compensate the veiling glare degradation and improving the image sharpness up to 1000% in the case of healthy subjects and up to 700% in the pathological retinal images. This image quality improvement allows performing image segmentation processing with restored hidden spatial information after deconvolution.

Super-resolution imaging of flat-mounted whole mouse cornea

Super-resolution microscopy revolutionized biomedical research with significantly improved imaging resolution down to the molecular scale. To date, only limited studies reported multi-color super-resolution imaging of thin tissue slices mainly because of unavailable staining protocols and incompatible imaging techniques. Here, we show the first super-resolution imaging of flat-mounted whole mouse cornea using single-molecule localization microscopy (SMLM). We optimized immunofluorescence staining protocols for β-Tubulin, Vimentin, Peroxisome marker (PMP70), and Histone-H4 in whole mouse corneas. Using the optimized staining protocols, we imaged these four intracellular protein structures in the epithelium and endothelium layers of flat-mounted mouse corneas. We also achieved simultaneous two-color spectroscopic SMLM (sSMLM) imaging of β-Tubulin and Histone-H4 in corneal endothelial cells. The spatial localization precision of sSMLM in these studies was around 20-nm. This work sets the stage for investigating multiple intracellular alterations in corneal diseases at a nanoscopic resolution using whole corneal flat-mount beyond cell cultures.

Polyphosphate is an extracellular signal that can facilitate bacterial survival in eukaryotic cells

Polyphosphate is a linear chain of phosphate residues and is present in organisms ranging from bacteria to humans. Pathogens such as Mycobacterium tuberculosis accumulate polyphosphate, and reduced expression of the polyphosphate kinase that synthesizes polyphosphate decreases their survival. How polyphosphate potentiates pathogenicity is poorly understood. Escherichia coli K-12 do not accumulate detectable levels of extracellular polyphosphate and have poor survival after phagocytosis by Dictyostelium discoideum or human macrophages. In contrast, Mycobacterium smegmatis and Mycobacterium tuberculosis accumulate detectable levels of extracellular polyphosphate, and have relatively better survival after phagocytosis by D. discoideum or macrophages. Adding extracellular polyphosphate increased E. coli survival after phagocytosis by D. discoideum and macrophages. Reducing expression of polyphosphate kinase 1 in M. smegmatis reduced extracellular polyphosphate and reduced survival in D. discoideum and macrophages, and this was reversed by the addition of extracellular polyphosphate. Conversely, treatment of D. discoideum and macrophages with recombinant yeast exopolyphosphatase reduced the survival of phagocytosed M. smegmatis or M. tuberculosis D. discoideum cells lacking the putative polyphosphate receptor GrlD had reduced sensitivity to polyphosphate and, compared to wild-type cells, showed increased killing of phagocytosed E. coli and M. smegmatis Polyphosphate inhibited phagosome acidification and lysosome activity in D. discoideum and macrophages and reduced early endosomal markers in macrophages. Together, these results suggest that bacterial polyphosphate potentiates pathogenicity by acting as an extracellular signal that inhibits phagosome maturation.

High-contrast multifocus microscopy with a single camera and z-splitter prism

Optical microscopy has been an indispensable tool for studying complex biological systems, but is often hampered by problems of speed and complexity when performing 3D volumetric imaging. Here, we present a multifocus imaging strategy based on the use of a simple z-splitter prism that can be assembled from off-the-shelf components. Our technique enables a widefield image stack to be distributed onto a single camera and recorded simultaneously. We exploit the volumetric nature of our image acquisition by further introducing a novel extended-volume 3D deconvolution strategy to suppress far-out-of-focus fluorescence background to significantly improve the contrast of our recorded images, conferring to our system a capacity for quasi-optical sectioning. By swapping in different z-splitter configurations, we can prioritize high speed or large 3D field-of-view imaging depending on the application of interest. Moreover, our system can be readily applied to a variety of imaging modalities in addition to fluorescence, such as phase-contrast and darkfield imaging. Because of its simplicity, versatility, and performance, we believe our system will be a useful tool for general biological or biomedical imaging applications.

Fundamentals of Microscopy

The light (or optical) microscope is the icon of science. The aphorism "seeing is believing" is often quoted in scientific papers involving microscopy. Unlike many scientific instruments, the light microscope will deliver an image however badly it is set up. Fluorescence microscopy is a widely used research tool across all disciplines of biological and biomedical science. Most universities and research institutions have microscopes, including confocal microscopes. This introductory paper in a series detailing advanced light microscopy techniques explains the foundations of both electron and light microscopy for biologists and life scientists working with the mouse. An explanation is given of how an image is formed. A description is given of how to set up a light microscope, whether it be a brightfield light microscope on the laboratory bench, a widefield fluorescence microscope, or a confocal microscope. These explanations are accompanied by operational protocols. A full explanation on how to set up and adjust a microscope according to the principles of Köhler illumination is given. The importance of Nyquist sampling is discussed. Guidelines are given on how to choose the best microscope to image the particular sample or slide preparation that you are working with. These are the basic principles of microscopy that a researcher must have an understanding of when operating core bioimaging facility instruments, in order to collect high-quality images. © 2020 The Authors. Basic Protocol 1: Setting up Köhler illumination for a brightfield microscope Basic Protocol 2: Aligning the fluorescence bulb and setting up Köhler illumination for a widefield fluorescence microscope Basic Protocol 3: Generic protocol for operating a confocal microscope.

From whole-organ imaging to in-silico blood flow modeling: A new multi-scale network analysis for revisiting tissue functional anatomy

We present a multi-disciplinary image-based blood flow perfusion modeling of a whole organ vascular network for analyzing both its structural and functional properties. We show how the use of Light-Sheet Fluorescence Microscopy (LSFM) permits whole-organ micro-vascular imaging, analysis and modelling. By using adapted image post-treatment workflow, we could segment, vectorize and reconstruct the entire micro-vascular network composed of 1.7 million vessels, from the tissue-scale, inside a ∼ 25 × 5 × 1 = 125mm3 volume of the mouse fat pad, hundreds of times larger than previous studies, down to the cellular scale at micron resolution, with the entire blood perfusion modeled. Adapted network analysis revealed the structural and functional organization of meso-scale tissue as strongly connected communities of vessels. These communities share a distinct heterogeneous core region and a more homogeneous peripheral region, consistently with known biological functions of fat tissue. Graph clustering analysis also revealed two distinct robust meso-scale typical sizes (from 10 to several hundred times the cellular size), revealing, for the first time, strongly connected functional vascular communities. These community networks support heterogeneous micro-environments. This work provides the proof of concept that in-silico all-tissue perfusion modeling can reveal new structural and functional exchanges between micro-regions in tissues, found from community clusters in the vascular graph.

Design and evaluation of an accurate CNR-guided small region iterative restoration-based tumor segmentation scheme for PET using both simulated and real heterogeneous tumors

Tumor delineation accuracy directly affects the effectiveness of radiotherapy. This study presents a methodology that minimizes potential errors during the automated segmentation of tumors in PET images. Iterative blind deconvolution was implemented in a region of interest encompassing the tumor with the number of iterations determined from contrast-to-noise ratios. The active contour and random forest classification-based segmentation method was evaluated using three distinct image databases that included both synthetic and real heterogeneous tumors. Ground truths about tumor volumes were known precisely. The volumes of the tumors were in the range of 0.49-26.34 cm³, 0.64-1.52 cm³, and 40.38-203.84 cm³ respectively. Widely available software tools, namely, MATLAB, MIPAV, and ITK-SNAP were utilized. When using the active contour method, image restoration reduced mean errors in volumes estimation from 95.85 to 3.37%, from 815.63 to 17.45%, and from 32.61 to 6.80% for the three datasets. The accuracy gains were higher using datasets that include smaller tumors for which PVE is known to be more predominant. Computation time was reduced by a factor of about 10 in the smaller deconvolution region. Contrast-to-noise ratios were improved for all tumors in all data. The presented methodology has the potential to improve delineation accuracy in particular for smaller tumors at practically feasible computational times. Graphical abstract Evaluation of accurate lesion volumes using CNR-guided and ROI-based restoration method for PET images.

Fluorescence imaging reversion using spatially variant deconvolution

Fluorescence imaging opens new possibilities for intraoperative guidance and early cancer detection, in particular when using agents that target specific disease features. Nevertheless, photon scattering in tissue degrades image quality and leads to ambiguity in fluorescence image interpretation and challenges clinical translation. We introduce the concept of capturing the spatially-dependent impulse response of an image and investigate Spatially Adaptive Impulse Response Correction (SAIRC), a method that is proposed for improving the accuracy and sensitivity achieved. Unlike classical methods that presume a homogeneous spatial distribution of optical properties in tissue, SAIRC explicitly measures the optical heterogeneity in tissues. This information allows, for the first time, the application of spatially-dependent deconvolution to correct the fluorescence images captured in relation to their modification by photon scatter. Using experimental measurements from phantoms and animals, we investigate the improvement in resolution and quantification over non-corrected images. We discuss how the proposed method is essential for maximizing the performance of fluorescence molecular imaging in the clinic.

Deconvolution of light sheet microscopy recordings

We developed a deconvolution software for light sheet microscopy that uses a theoretical point spread function, which we derived from a model of image formation in a light sheet microscope. We show that this approach provides excellent blur reduction and enhancement of fine image details for image stacks recorded with low magnification objectives of relatively high NA and high field numbers as e.g. 2x NA 0.14 FN 22, or 4x NA 0.28 FN 22. For these objectives, which are widely used in light sheet microscopy, sufficiently resolved point spread functions that are suitable for deconvolution are difficult to measure and the results obtained by common deconvolution software developed for confocal microscopy are usually poor. We demonstrate that the deconvolutions computed using our point spread function model are equivalent to those obtained using a measured point spread function for a 10x objective with NA 0.3 and for a 20x objective with NA 0.45.

Organization of fast and slow chromatin revealed by single-nucleosome dynamics

Understanding chromatin organization and dynamics is important, since they crucially affect DNA functions. In this study, we investigate chromatin dynamics by statistically analyzing single-nucleosome movement in living human cells. Bimodal nature of the mean square displacement distribution of nucleosomes allows for a natural categorization of the nucleosomes as fast and slow. Analyses of the nucleosome-nucleosome correlation functions within these categories along with the density of vibrational modes show that the nucleosomes form dynamically correlated fluid regions (i.e., dynamic domains of fast and slow nucleosomes). Perturbed nucleosome dynamics by global histone acetylation or cohesin inactivation indicate that nucleosome-nucleosome interactions along with tethering of chromatin chains organize nucleosomes into fast and slow dynamic domains. A simple polymer model is introduced, which shows the consistency of this dynamic domain picture. Statistical analyses of single-nucleosome movement provide rich information on how chromatin is dynamically organized in a fluid manner in living cells.

Fourier ring correlation simplifies image restoration in fluorescence microscopy

Fourier ring correlation (FRC) has recently gained popularity among fluorescence microscopists as a straightforward and objective method to measure the effective image resolution. While the knowledge of the numeric resolution value is helpful in e.g., interpreting imaging results, much more practical use can be made of FRC analysis—in this article we propose blind image restoration methods enabled by it. We apply FRC to perform image de-noising by frequency domain filtering. We propose novel blind linear and non-linear image deconvolution methods that use FRC to estimate the effective point-spread-function, directly from the images. We show how FRC can be used as a powerful metric to observe the progress of iterative deconvolution. We also address two important limitations in FRC that may be of more general interest: how to make FRC work with single images (within certain practical limits) and with three-dimensional images with highly anisotropic resolution.

Fourier ring correlation (FRC) analysis is commonly used in fluorescence microscopy to measure effective image resolution. Here, the authors demonstrate that FRC can also be leveraged in blind image restoration methods, such as image deconvolution.

Simultaneous recovery of both bright and dim structures from noisy fluorescence microscopy images using a modified TV constraint

The quality and information content of biological images can be significantly enhanced by postacquisition processing using deconvolution and denoising. However, when imaging complex biological samples, such as neurons, stained with fluorescence labels, the signal level of different structures can differ by several orders of magnitude. This poses a challenge as current image reconstruction algorithms are focused on recovering low signals and generally have sample-dependent performance, requiring tedious manual tuning. This is one of the main hurdles for their wide adoption by nonspecialists. In this work, we modify the general constrained reconstruction method (in our case utilizing a total variation constraint) so that both bright and dim structures can drive the deconvolution with equal force. In this way, we can simultaneously obtain high-quality reconstruction across a wide range of signals within a single image or image sequence. The algorithm is tested on both simulated and experimental data. When compared with current state-of-art algorithms, our algorithm outperforms others in terms of maintaining the resolution in the high-signal areas and reducing artefacts in the low-signal areas. The algorithm was also tested on image sequences where one set of parameters are used to reconstruct all images, with blind evaluation by a group of biologists demonstrating a marked preference for the images produced by our method. This means that our method is suitable for batch processing of image sequences obtained from either spatial or temporal scanning. LAY DESCRIPTION: Fluorescence microscopy images of complex biological samples contain a wide range of signal levels. This signal variation leads current reconstruction algorithms, which aim to enhance the quality of the raw images, to have sample-dependent performance. In this work, we design a new optimization that allows the reconstruction to "pay equal eqattention to" both bright and dim structures. In this way, we can simultaneously recover both bright and dim structures within a single image or image sequence, as validated when the algorithm was quantitatively tested on both simulated and experimental data. When our method was evaluated alongside current state of art algorithms by a group of biologists, our algorithm was considered qualitatively superior.

IOCBIO Sparks detection and analysis software

Analysis of calcium sparks in cardiomyocytes can provide valuable information about functional changes of calcium handling in health and disease. As a part of the calcium sparks analysis, sparks detection and characterization is necessary. Here, we describe a new open-source platform for automatic calcium sparks detection from line scan confocal images. The developed software is tailored for detecting only calcium sparks, allowing us to design a graphical user interface specifically for this task. The software enables detecting sparks automatically as well as adding, removing, or adjusting regions of interest marking each spark. The results of the analysis are stored in an SQL database, allowing simple integration with statistical tools. We have analyzed the performance of the algorithm using a large set of synthetic images with varying spark sizes and noise levels and also compared the analysis results with results obtained by software established in the field. The use of our software is illustrated by an analysis of the effect of isoprenaline (ISO) on spark frequency, amplitude, and spatial and temporal characteristics. For that, cardiomyocytes from C57BL/6 mice were used. We demonstrated an increase in spark frequency, tendency of having larger spark amplitudes, sparks with a longer duration, and occurrence of multiple sparks from the same site in the presence of ISO. We also show that the duration and the width of sparks with the same amplitude were similar in the absence and presence of ISO. The software was released as an open source repository and is available for free use and collaborative development.

An improved Richardson-Lucy iterative algorithm for C-scan image restoration and inclusion size measurement

The accuracy of measuring inclusion size in direct C-scan image of immersion ultrasonic testing is restricted by the lateral resolution of the focused transducer, even if a high frequency is used, and the blurred edge due to scattering of sound waves at inclusions. In this work, an improved image restoration method that is based on the Richardson-Lucy (RL) iterative algorithm is proposed, which is used to restore the C-scan image and improve the accuracy of inclusion size measurement in immersion ultrasonic testing. For the improved RL iterative algorithm, the point spread function (PSF) is derived based on the multi-Gaussian beam model and Kirchhoff approximation, which considers the propagation properties of sound waves at water-steel interface and the spectral characteristics of the transducer with high frequency. In order to determine the final iteration number, the relationship between final iteration number and size of the inclusion in the image is established by restoring the simulated C-scan image and further calibrated with size correction factor. The size correction factor considers the effect of sound attenuation and electro-mechanical transformation encountered in practical testing equipment. Experimental results show that the inclusion sizes measured in restored C-scan images agree well with the optical micrograph results, which prove the effectiveness of the proposed method.

Computational multifocal microscopy

Despite recent advances, high performance single-shot 3D microscopy remains an elusive task. By introducing designed diffractive optical elements (DOEs), one is capable of converting a microscope into a 3D "kaleidoscope," in which case the snapshot image consists of an array of tiles and each tile focuses on different depths. However, the acquired multifocal microscopic (MFM) image suffers from multiple sources of degradation, which prevents MFM from further applications. We propose a unifying computational framework which simplifies the imaging system and achieves 3D reconstruction via computation. Our optical configuration omits optical elements for correcting chromatic aberrations and redesigns the multifocal grating to enlarge the tracking area. Our proposed setup features only one single grating in addition to a regular microscope. The aberration correction, along with Poisson and background denoising, are incorporated in our deconvolution-based fully-automated algorithm, which requires no empirical parameter-tuning. In experiments, we achieve spatial resolutions of 0.35um (lateral) and 0.5um (axial), which are comparable to the resolution that can be achieved with confocal deconvolution microscopy. We demonstrate a 3D video of moving bacteria recorded at 25 frames per second using our proposed computational multifocal microscopy technique.

In vivo blind-deconvolution photoacoustic ophthalmoscopy with total variation regularization

Photoacoustic ophthalmoscopy (PAOM) is capable of noninvasively imaging anatomic and functional information of the retina in living rodents. However, the strong ocular aberration in rodent eyes and limited ultrasonic detection sensitivity affect PAOM's spatial resolution and signal-to-noise ratio (SNR) in in vivo eyes. In this work, we report a computational approach to combine blind deconvolution (BD) algorithm with a regularizing constraint based on total variation (BDTV) for PAOM imaging restoration. We tested the algorithm in retinal and choroidal microvascular images in albino rat eyes. The algorithm improved PAOM's lateral resolution by around 2-fold. Moreover, it enabled the improvement in imaging SNR for both major vessels and capillaries, and realized the well-preserved blood vessels' edges simultaneously, which surpasses conventional Richardson-Lucy BD algorithm. The reported results indicate that the BDTV algorithm potentially facilitate PAOM in extracting retinal pathophysiological information by enhancing in vivo imaging quality without physically modifying PAOM's optical configuration.

An overview of state-of-the-art image restoration in electron microscopy

In Life Science research, electron microscopy (EM) is an essential tool for morphological analysis at the subcellular level as it allows for visualization at nanometer resolution. However, electron micrographs contain image degradations such as noise and blur caused by electromagnetic interference, electron counting errors, magnetic lens imperfections, electron diffraction, etc. These imperfections in raw image quality are inevitable and hamper subsequent image analysis and visualization. In an effort to mitigate these artefacts, many electron microscopy image restoration algorithms have been proposed in the last years. Most of these methods rely on generic assumptions on the image or degradations and are therefore outperformed by advanced methods that are based on more accurate models. Ideally, a method will accurately model the specific degradations that fit the physical acquisition settings. In this overview paper, we discuss different electron microscopy image degradation solutions and demonstrate that dedicated artefact regularisation results in higher quality restoration and is applicable through recently developed probabilistic methods.

Measure and model a 3-D space-variant PSF for fluorescence microscopy image deblurring

Conventional deconvolution methods assume that the microscopy system is spatially invariant, introducing considerable errors. We developed a method to more precisely estimate space-variant point-spread functions from sparse measurements. To this end, a space-variant version of deblurring algorithm was developed and combined with a total-variation regularization. Validation with both simulation and real data showed that our PSF model is more accurate than the piecewise-invariant model and the blending model. Comparing with the orthogonal basis decomposition based PSF model, our proposed model also performed with a considerable improvement. We also evaluated the proposed deblurring algorithm. Our new deblurring algorithm showed a significantly better signal-to-noise ratio and higher image quality than those of the conventional space-invariant algorithm.

3D in vivo imaging with extended-focus optical coherence microscopy

Optical coherence microscopy (OCM) has unique advantages of non-invasive 3D imaging without the need of exogenous labels for studying biological samples. However, the imaging depth of this technique is limited by the tradeoff between the depth of focus (DOF) and high lateral resolution in Gaussian optics. To overcome this limitation, we have developed an extended-focus OCM (xf-OCM) imaging system using quasi-Bessel beam illumination to extend the DOF to ∼100 μm, about 3-fold greater than standard OCM. High lateral resolution of 1.6 μm ensured detailed identification of structures within live animal samples. The insensitivity to spherical aberrations strengthened the capability of our xf-OCM system in 3D biological imaging.

Photoacoustic microscopy in vivo using synthetic-aperture focusing technique combined with three-dimensional deconvolution

Acoustic-resolution photoacoustic microscopy (ARPAM) plays an important role in studying the microcirculation system of biological tissues with deep penetration. High lateral resolution of ARPAM is achieved by using a high numerical aperture acoustic transducer. The deteriorated lateral resolution in the out-of-focus region can be alleviated by synthetic aperture focusing technique (SAFT). Previously, we reported a three-dimensional (3D) deconvolution ARPAM to improve both lateral and axial resolutions in the focus region. In this study, we present our extension of resolution enhancement to the out-of-focus region based on two-dimensional SAFT combined with the 3D deconvolution (SAFT+Deconv). In both the focus and out-of-focus regions, depth-independent lateral resolution provided by SAFT, together with inherently depth-independent axial resolution, ensures a depth-independent point spread function for 3D deconvolution algorithm. Imaging of 10 μm polymer beads shows that SAFT+Deconv ARPAM improves the -6 dB lateral resolutions from 65-700 μm to 20-29 μm, and the -6 dB axial resolutions from 35-42 μm to 12-19 μm in an extended depth of focus (DOF) of ∼2 mm. The signal-to-noise ratio is also increased by 6-30 dB. The resolution enhancement in three dimensions is validated by in vivo imaging of a mouse's dorsal subcutaneous microvasculature. Our results suggest that SAFT+Deconv ARPAM may allow fine spatial resolution with deep penetration and extended DOF for biomedical photoacoustic applications.

Metabolic compartmentation in rainbow trout cardiomyocytes: coupling of hexokinase but not creatine kinase to mitochondrial respiration

Rainbow trout (Oncorhynchus mykiss) cardiomyocytes have a simple morphology with fewer membrane structures such as sarcoplasmic reticulum and t-tubules penetrating the cytosol. Despite this, intracellular ADP diffusion is restricted. Intriguingly, although diffusion is restricted, trout cardiomyocytes seem to lack the coupling between mitochondrial creatine kinase (CK) and respiration. Our aim was to study the distribution of diffusion restrictions in permeabilized trout cardiomyocytes and verify the role of CK. We found a high activity of hexokinase (HK), which led us to reassess the situation in trout cardiomyocytes. We show that diffusion restrictions are more prominent than previously thought. In the presence of a competitive ADP-trapping system, ADP produced by HK, but not CK, was channeled to the mitochondria. In agreement with this, we found no positively charged mitochondrial CK in trout heart homogenate. The results were best fit by a simple mathematical model suggesting that trout cardiomyocytes lack a functional coupling between ATPases and pyruvate kinase. The model simulations show that diffusion is restricted to almost the same extent in the cytosol and by the outer mitochondrial membrane. Furthermore, they confirm that HK, but not CK, is functionally coupled to respiration. In perspective, our results suggest that across a range of species, cardiomyocyte morphology and metabolism go hand in hand with cardiac performance, which is adapted to the circumstances. Mitochondrial CK is coupled to respiration in adult mammalian hearts, which are specialized to high, sustained performance. HK associates with mitochondria in hearts of trout and neonatal mammals, which are more hypoxia-tolerant.

Restricted ADP movement in cardiomyocytes: Cytosolic diffusion obstacles are complemented with a small number of open mitochondrial voltage-dependent anion channels

Adequate intracellular energy transfer is crucial for proper cardiac function. In energy starved failing hearts, partial restoration of energy transfer can rescue mechanical performance. There are two types of diffusion obstacles that interfere with energy transfer from mitochondria to ATPases: mitochondrial outer membrane (MOM) with voltage-dependent anion channel (VDAC) permeable to small hydrophilic molecules and cytoplasmatic diffusion barriers grouping ATP-producers and -consumers. So far, there is no method developed to clearly distinguish the contributions of cytoplasmatic barriers and MOM to the overall diffusion restriction. Furthermore, the number of open VDACs in vivo remains unknown. The aim of this work was to establish the partitioning of intracellular diffusion obstacles in cardiomyocytes. We studied the response of mitochondrial oxidative phosphorylation of permeabilized rat cardiomyocytes to changes in extracellular ADP by recording 3D image stacks of NADH autofluorescence. Using cell-specific mathematical models, we determined the permeability of MOM and cytoplasmatic barriers. We found that only ~2% of VDACs are accessible to cytosolic ADP and cytoplasmatic diffusion barriers reduce the apparent diffusion coefficient by 6-10×. In cardiomyocytes, diffusion barriers in the cytoplasm and by the MOM restrict ADP/ATP diffusion to similar extents suggesting a major role of both barriers in energy transfer and other intracellular processes.

A software tool for tomographic axial superresolution in STED microscopy

A method for generating three-dimensional tomograms from multiple three-dimensional axial projections in STimulated Emission Depletion (STED) superresolution microscopy is introduced. Our STED< method, based on the use of a micromirror placed on top of a standard microscopic sample, is used to record a three-dimensional projection at an oblique angle in relation to the main optical axis. Combining the STED< projection with the regular STED image into a single view by tomographic reconstruction, is shown to result in a tomogram with three-to-four-fold improved apparent axial resolution. Registration of the different projections is based on the use of a mutual-information histogram similarity metric. Fusion of the projections into a single view is based on Richardson-Lucy iterative deconvolution algorithm, modified to work with multiple projections. Our tomographic reconstruction method is demonstrated to work with real biological STED superresolution images, including a data set with a limited signal-to-noise ratio (SNR); the reconstruction software (SuperTomo) and its source code will be released under BSD open-source license.

Working together: spatial synchrony in the force and actin dynamics of podosome first neighbors

Podosomes are mechanosensitive adhesion cell structures that are capable of applying protrusive forces onto the extracellular environment. We have recently developed a method dedicated to the evaluation of the nanoscale forces that podosomes generate to protrude into the extracellular matrix. It consists in measuring by atomic force microscopy (AFM) the nanometer deformations produced by macrophages on a compliant Formvar membrane and has been called protrusion force microscopy (PFM). Here we perform time-lapse PFM experiments and investigate spatial correlations of force dynamics between podosome pairs. We use an automated procedure based on finite element simulations that extends the analysis of PFM experimental data to take into account podosome architecture and organization. We show that protrusion force varies in a synchronous manner for podosome first neighbors, a result that correlates with phase synchrony of core F-actin temporal oscillations. This dynamic spatial coordination between podosomes suggests a short-range interaction that regulates their mechanical activity.

Point spread functions and deconvolution of ultrasonic images

This article investigates the restoration of ultrasonic pulse-echo C-scan images by means of deconvolution with a point spread function (PSF). The deconvolution concept from linear system theory (LST) is linked to the wave equation formulation of the imaging process, and an analytic formula for the PSF of planar transducers is derived. For this analytic expression, different numerical and analytic approximation schemes for evaluating the PSF are presented. By comparing simulated images with measured C-scan images, we demonstrate that the assumptions of LST in combination with our formula for the PSF are a good model for the pulse-echo imaging process. To reconstruct the object from a C-scan image, we compare different deconvolution schemes: the Wiener filter, the ForWaRD algorithm, and the Richardson-Lucy algorithm. The best results are obtained with the Richardson-Lucy algorithm with total variation regularization. For distances greater or equal twice the near field distance, our experiments show that the numerically computed PSF can be replaced with a simple closed analytic term based on a far field approximation.

The location of energetic compartments affects energetic communication in cardiomyocytes

The heart relies on accurate regulation of mitochondrial energy supply to match energy demand. The main regulators are Ca²⁺ and feedback of ADP and P_i. Regulation via feedback has intrigued for decades. First, the heart exhibits a remarkable metabolic stability. Second, diffusion of ADP and other molecules is restricted specifically in heart and red muscle, where a fast feedback is needed the most. To explain the regulation by feedback, compartmentalization must be taken into account. Experiments and theoretical approaches suggest that cardiomyocyte energetic compartmentalization is elaborate with barriers obstructing diffusion in the cytosol and at the level of the mitochondrial outer membrane (MOM). A recent study suggests the barriers are organized in a lattice with dimensions in agreement with those of intracellular structures. Here, we discuss the possible location of these barriers. The more plausible scenario includes a barrier at the level of MOM. Much research has focused on how the permeability of MOM itself is regulated, and the importance of the creatine kinase system to facilitate energetic communication. We hypothesize that at least part of the diffusion restriction at the MOM level is not by MOM itself, but due to the close physical association between the sarcoplasmic reticulum (SR) and mitochondria. This will explain why animals with a disabled creatine kinase system exhibit rather mild phenotype modifications. Mitochondria are hubs of energetics, but also ROS production and signaling. The close association between SR and mitochondria may form a diffusion barrier to ADP added outside a permeabilized cardiomyocyte. But in vivo, it is the structural basis for the mitochondrial-SR coupling that is crucial for the regulation of mitochondrial Ca²⁺-transients to regulate energetics, and for avoiding Ca²⁺-overload and irreversible opening of the mitochondrial permeability transition pore.

Image quality improvement in optical coherence tomography using Lucy-Richardson deconvolution algorithm

Optical coherence tomography (OCT) has the potential for skin tissue characterization due to its high axial and transverse resolution and its acceptable depth penetration. In practice, OCT cannot reach the theoretical resolutions due to imperfections of some of the components used. One way to improve the quality of the images is to estimate the point spread function (PSF) of the OCT system and deconvolve it from the output images. In this paper, we investigate the use of solid phantoms to estimate the PSF of the imaging system. We then utilize iterative Lucy-Richardson deconvolution algorithm to improve the quality of the images. The performance of the proposed algorithm is demonstrated on OCT images acquired from a variety of samples, such as epoxy-resin phantoms, fingertip skin and basaloid larynx and eyelid tissues.

Software tool for 3D extraction of germinal centers

BACKGROUND

Germinal Centers (GC) are short-lived micro-anatomical structures, within lymphoid organs, where affinity maturation is initiated. Theoretical modeling of the dynamics of the GC reaction including follicular CD4+ T helper and the recently described follicular regulatory CD4+ T cell populations, predicts that the intensity and life span of such reactions is driven by both types of T cells, yet controlled primarily by follicular regulatory CD4+ T cells. In order to calibrate GC models, it is necessary to properly analyze the kinetics of GC sizes. Presently, the estimation of spleen GC volumes relies upon confocal microscopy images from 20-30 slices spanning a depth of ~ 20 - 50 μm, whose GC areas are analyzed, slice-by-slice, for subsequent 3D reconstruction and quantification. The quantity of data to be analyzed from such images taken for kinetics experiments is usually prohibitively large to extract semi-manually with existing software. As a result, the entire procedure is highly time-consuming, and inaccurate, thereby motivating the need for a new software tool that can automatically identify and calculate the 3D spot volumes from GC multidimensional images.

RESULTS

We have developed pyBioImage, an open source cross platform image analysis software application, written in python with C extensions that is specifically tailored to the needs of immunologic research involving 4D imaging of GCs. The software provides 1) support for importing many multi-image formats, 2) basic image processing and analysis, and 3) the ExtractGC module, that allows for automatic analysis and visualization of extracted GC volumes from multidimensional confocal microscopy images. We present concrete examples of different microscopy image data sets of GC that have been used in experimental and theoretical studies of mouse model GC dynamics.

CONCLUSIONS

The pyBioImage software framework seeks to be a general purpose image application for immunological research based on 4D imaging. The ExtractGC module uses a novel clustering algorithm for automatically extracting quantitative spatial information of a large number of GCs from a collection of confocal microscopy images. In addition, the software provides 3D visualization of the GCs reconstructed from the image stacks. The application is available for public use at http://sourceforge.net/projects/pybioimage/.

Blind-deconvolution optical-resolution photoacoustic microscopy in vivo

Optical-resolution photoacoustic microscopy (OR-PAM) is becoming a vital tool for studying the microcirculation system in vivo. By increasing the numerical aperture of optical focusing, the lateral resolution of OR-PAM can be improved; however, the depth of focus and thus the imaging range will be sacrificed correspondingly. In this work, we report our development of blind-deconvolution optical-resolution photoacoustic microscopy (BD-PAM) that can provide a lateral resolution ~2-fold finer than that of conventional OR-PAM (3.04 vs. 5.78μm), without physically increasing the system's numerical aperture. The improvement achieved with BD-PAM is demonstrated by imaging graphene nanoparticles and the microvasculature of mice ears in vivo. Our results suggest that BD-PAM may become a valuable tool for many biomedical applications that require both fine spatial resolution and extended depth of focus.

Analysis of molecular movement reveals latticelike obstructions to diffusion in heart muscle cells

Intracellular diffusion in muscle cells is known to be restricted. Although characteristics and localization of these restrictions is yet to be elucidated, it has been established that ischemia-reperfusion injury reduces the overall diffusion restriction. Here we apply an extended version of raster image correlation spectroscopy to determine directional anisotropy and coefficients of diffusion in rat cardiomyocytes. Our experimental results indicate that diffusion of a smaller molecule (1127 MW fluorescently labeled ATTO633-ATP) is restricted more than that of a larger one (10,000 MW Alexa647-dextran), when comparing diffusion in cardiomyocytes to that in solution. We attempt to provide a resolution to this counterintuitive result by applying a quantitative stochastic model of diffusion. Modeling results suggest the presence of periodic intracellular barriers situated ∼1 μm apart having very low permeabilities and a small effect of molecular crowding in volumes between the barriers. Such intracellular structuring could restrict diffusion of molecules of energy metabolism, reactive oxygen species, and apoptotic signals, enacting a significant role in normally functioning cardiomyocytes as well as in pathological conditions of the heart.

Permeabilized rat cardiomyocyte response demonstrates intracellular origin of diffusion obstacles

Intracellular diffusion restrictions for ADP and other molecules have been predicted earlier based on experiments on permeabilized fibers or cardiomyocytes. However, it is possible that the effective diffusion distance is larger than the cell dimensions due to clumping of cells and incomplete separation of cells in fiber preparations. The aim of this work was to check whether diffusion restrictions exist inside rat cardiomyocytes or are caused by large effective diffusion distance. For that, we determined the response of oxidative phosphorylation (OxPhos) to exogenous ADP and ATP stimulation in permeabilized rat cardiomyocytes using fluorescence microscopy. The state of OxPhos was monitored via NADH and flavoprotein autofluorescence. By varying the ADP or ATP concentration in flow chamber, we determined that OxPhos has a low affinity in cardiomyocytes. The experiments were repeated in a fluorometer on cardiomyocyte suspensions leading to similar autofluorescence changes induced by ADP as recorded under the microscope. ATP stimulated OxPhos more in a fluorometer than under the microscope, which was attributed to accumulation of ADP in fluorometer chamber. By calculating the flow profile around the cell in the microscope chamber and comparing model solutions to measured data, we demonstrate that intracellular structures impose significant diffusion obstacles in rat cardiomyocytes.