3D Microscopy Images Research Articles

Graft-versus-host disease suppression by mesenchymal stem cells as determined by cryo-imaging

Background & Aim GVHD limits success of allogeneic bone marrow transplantation (alloBMT). Mesenchymal stem cells (MSCs) has been shown to alleviate GVHD, but the biodistribution of the exogenous stem cells and mechanisms behind their protective effects remain to be fully elucidated. We hypothesize that allogeneic T-cells proliferate in secondary lymphoid organs (SLOs) and that therapeutic MSCs home there and suppress T-cell proliferation. Methods, Results & Conclusion We used multi-spectral mouse cryo-imaging (CryoVizTM) to quantitatively evaluate T-cell proliferation, MSC homing, and putative suppression of T-cells by MSCs. Cryo-imaging enables assessments of stem cell biodistribution, homing, and engraftment in whole-mouse with single-cell sensitivity. Briefly, a frozen mouse is sectioned and imaged in a tiled, automated fashion, providing ana¬tomical brightfield and molecular fluorescence, 3D mi-croscopic imaging, to detect labeled cells. Machine learning software accurately detects fluorescent clusters, enabling counting of injected cells. There were four groups of mice: alloBMT (with GVHD), syngeneic BMT (synBME without GVHD), alloBMT+MSCs (GVHD with MSC treatment), and synBMT+MSCs. T-cells were labeled with green-fluorescent CFSE dye, and MSCs were labeled with red-fluorescent quantum dots and tail vein injected. Cryo-imaging was performed on days 3, 4 and 5 post BMT. We examined the bio-distribution of MSCs and T-cells (Fig-A). MSCs homed to SLOs and localized in spleen marginal zone where they could come in contact with T-cells (Fig-B). When cells divide, CFSE dye is distributed to daughter cells and cell signal intensity decreases. Hence reduced CFSE signal intensity in the alloBMT group as compared to the synBMT group is consistent with a greater proliferation of T-cells as a precursor to GVHD (Figs-C-and-D). With MSCs, CFSE signal remained high, indicating a reduction in T-cell proliferation (Fig-E). Similar results were obtained in lymph nodes. Quantitative assays showed homing of about 3,000-4,000 MSCs in alloBMT, significantly more than in synBMT. There was a significant reduction in T-cell proliferation and SLO volumes when the alloBMT group was treated with MSCs. Treatment gave results between alloBMT (with GVHD) and syngeneic BMT (without GVHD). In conclusion, multispectral cryo-imaging allowed us to determine that only a few thousand MSCs could significantly affect T-cell proliferation in SLOs. This is likely one mechanism for MSC suppression of GVHD.

Optimization of autogenous shrinkage and microstructure for Ultra-High Performance Concrete (UHPC) based on appropriate application of porous pumice

Abstract In this study, optimization of autogenous shrinkage and microstructure for Ultra-High Performance Concrete (UHPC) based on appropriate application of porous pumice is addressed. The pumices with different water absorption rates are utilized to replace river sand by 10%, 20% and 30%, and its effect on the properties of the developed UHPC is investigated. The obtained experimental results show that the inclusion of pre-wetting pumice (particle size distribution is 0.6–1.25 mm) can significantly reduce the autogenous shrinkage of UHPC, without decrease the mechanical properties. Mercury Intrusion Porosimetry and Computed Tomography results indicate that the incorporation of 0.6–1.25 mm hydrous pumice can refine the pore structure and increase the fraction of interconnected pores. Environmental Scanning Electron Microscope, Super-high Magnification Lens Zoom 3D Microscope and Electron BackScattered Diffraction images revealed that the interfacial transition zone (ITZ) skeleton between hydrous pumice and UHPC paste is obviously improved.

A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

Time-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes, i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel denoising algorithm, based on sparse coding, that can both enhance very faint and noisy nuclei signal but simultaneously detect nuclei position accurately. Furthermore our method is based on a limited number of parameters, with only one being critical, which is the approximate size of the objects of interest. We also show that our denoising method coupled with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, we tested our method on two datasets from the cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96:96% recall for the C. elegans dataset. Besides, in the Drosophila dataset, our method achieved very high recall (99:3%).

Accurate registration of in vivo time-lapse images.

In vivo imaging experiments often require automated detection and tracking of changes in the specimen. These tasks can be hindered by variations in the position and orientation of the specimen relative to the microscope, as well as by linear and nonlinear tissue deformations. We propose a feature-based registration method, coupled with optimal transformations, designed to address these problems in 3D time-lapse microscopy images. Features are detected as local regions of maximum intensity in source and target image stacks, and their bipartite intensity dissimilarity matrix is used as an input to the Hungarian algorithm to establish initial correspondences. A random sampling refinement method is employed to eliminate outliers, and the resulting set of corresponding features is used to determine an optimal translation, rigid, affine, or B-spline transformation for the registration of the source and target images. Accuracy of the proposed algorithm was tested on fluorescently labeled axons imaged over a 68-day period with a two-photon laser scanning microscope. To that end, multiple axons in individual stacks of images were traced semi-manually and optimized in 3D, and the distances between the corresponding traces were measured before and after the registration. The results show that there is a progressive improvement in the registration accuracy with increasing complexity of the transformations. In particular, sub-micrometer accuracy (2-3 voxels) was achieved with the regularized affine and B-spline transformations.

Artificial neural network filters for enhancing 3D optical microscopy images of neurites.

The ability to extract accurate morphology of labeled neurons from microscopy images is crucial for mapping brain connectivity and for understanding changes in connectivity that underlie learning and neurological disorders. There are, however, two problems, specific to optical microscopy imaging of neurons, which make accurate neuron tracing exceedingly challenging: (i) neurites can appear broken due to inhomogeneous labeling and (ii) neurites can appear fused in 3D due to limited resolution. Here, we propose and evaluate several artificial neural network (ANN) architectures and conventional image enhancement filters with the aim of alleviating both problems. We developed four image quality metrics to evaluate the effects of the proposed filters: normalized intensity in the cross-over regions between neurites, effective radius of neurites, coefficient of variation of intensity along neurites, and local background to neurite intensity ratio. Our results show that ANN-based filters, trained on optimized semi-manual traces of neurites, can significantly outperform conventional filters. In particular, U-Net based filtering can virtually eliminate background intensity, while also reducing the effective radius of neurites to nearly 1 voxel. In addition, this filter significantly decreases intensity in the cross-over regions between neurites and reduces fluctuations of intensity on neurites' centerlines. These results suggest that including an ANN-based filtering step, which does not require substantial extra time or computing power, can be beneficial for automated neuron tracing projects.

Cake layer characterization in Activated Sludge Membrane Bioreactors: Real-time analysis

Activated Sludge Membrane Bioreactors (AS-MBR) are recognized as a commercially competitive alternative to conventional wastewater treatments. However, membrane fouling remains one of the main challenges and disadvantages of the process. This study evaluates the suitability of Optical Coherence Tomography (OCT) in monitoring the cake layer development in-situ in AS-MBR under continuous operation. Real-time direct imaging of the cake layer was feasible when limiting the continuous movement of the AS flocs in the reactor by turning aeration off for few minutes prior to scanning a given membrane area. The cake layer morphology was evaluated using both 2D and 3D image analysis. The 3D analysis respect to 2D analysis provided a more representative characterization of the fouling formed in the system. The non-invasive nature of OCT imaging enabled monitoring fouling development over time, where an increase in thickness and a decrease in roughness was observed in the first 200 h of operation. The 3D OCT image analyses were also compared with the 3D confocal laser scanning microscopy (CLSM) image analyses performed at the end of the study. Results demonstrate that OCT imaging can be applied for online, real-time monitoring and analysis of fouling behavior in AS-MBR systems.

WP1-22 DuraGen™ as an encapsulating material for neural stem cell delivery

ObjectivesAchieving neural regeneration after spinal cord injury (SCI) represents a significant challenge. Neural stem cell (NSC) therapy offers replacement of damaged cells and delivery of pro-regenerative factors, but >95% of cells die when transplanted to sites of neural injury. Biomaterial scaffolds provide cellular protective encapsulation to improve cell survival. However, current available scaffolds are overwhelmingly not approved for human use, presenting a major barrier to clinical translation. Surgical biomaterials offer the unique benefit of being FDA-approved for human implantation. Specifically, a neurosurgical grade material, DuraGen™, used predominantly for human duraplasty has many attractive features of an ideal biomaterial scaffold. Here, we have investigated the use of DuraGen™ as a 3D cell encapsulation device for potential use in combinatorial, regenerative therapies.MethodsPrimary NSCs were seeded into optimised sheets of DuraGen™. NSC growth and fate within DuraGen™ were assessed using 3D microscopic fluorescence imaging techniques.ResultsDuraGen™ supports the survival (ca 95% viability, 12 days) and 3D growth of NSCs. NSC phenotype, proliferative capacity and differentiation into astrocytes, neurons and oligodendrocytes were unaffected by DuraGen™.ConclusionsA ‘combinatorial therapy’, consisting of NSCs protected within a DuraGen™ matrix, offers a potential clinically translatable approach for neural cell therapy.

(Invited) Evolution of Redox Couples in Li- and Mn-Rich Cathode Materials and Mitigation of Voltage Fade by Reducing Oxygen Release

Voltage fading is a major problem in battery applications for high-energy Lithium- and Manganese-rich (LMR) layered materials. Because of the complexity in the LMR structure, the voltage fade mechanism is not well understood. Here we conduct both in situ and ex situ studies on a typical LMR material (Li1.2Ni0.15Co0.1Mn0.55O2) during charge-discharge cycling, using multi-length-scale X-ray spectroscopic and 3D electron microscopic imaging techniques. Through probing from surface to bulk, from individual to the whole ensembles of particles, we show that the average valence states of each type of transition metal cations are continuously reduced, which is attributed to the oxygen release from LMR. Such reductions activate the new lower voltage Mn3+/Mn4+ and Co2+/Co3+ redox couples in addition to the original ones like Ni2+/Ni3+, Ni3+/Ni4+, and O2-/O-, directly leading to the voltage fade. We also show that the oxygen release causes microstructural defects such as the formation of large pores within particles which also contributes to the voltage fading. The surface coating and modifications are suggested to be effective in suppressing the voltage fade through reducing the oxygen release.

Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets.

Three-dimensional microscopy is increasingly prevalent in biology due to the development of techniques such as multiphoton, spinning disk confocal, and light sheet fluorescence microscopies. These methods enable unprecedented studies of life at the microscale, but bring with them larger and more complex datasets. New image processing techniques are therefore called for to analyze the resulting images in an accurate and efficient manner. Convolutional neural networks are becoming the standard for classification of objects within images due to their accuracy and generalizability compared to traditional techniques. Their application to data derived from 3D imaging, however, is relatively new and has mostly been in areas of magnetic resonance imaging and computer tomography. It remains unclear, for images of discrete cells in variable backgrounds as are commonly encountered in fluorescence microscopy, whether convolutional neural networks provide sufficient performance to warrant their adoption, especially given the challenges of human comprehension of their classification criteria and their requirements of large training datasets. We therefore applied a 3D convolutional neural network to distinguish bacteria and non-bacterial objects in 3D light sheet fluorescence microscopy images of larval zebrafish intestines. We find that the neural network is as accurate as human experts, outperforms random forest and support vector machine classifiers, and generalizes well to a different bacterial species through the use of transfer learning. We also discuss network design considerations, and describe the dependence of accuracy on dataset size and data augmentation. We provide source code, labeled data, and descriptions of our analysis pipeline to facilitate adoption of convolutional neural network analysis for three-dimensional microscopy data.

Low-cost, readily available 3D microscopy imaging system with variable focus spinner.

A large open aperture in an optical system can capture high-resolution images but yields a shallow depth of field. To overcome this issue, we investigated a low-cost, readily available method for retrofitting microscopy imaging systems to achieve 3D focus scanning in this study. Specifically, a procedure for fabricating variable focus spinners with dissimilar plates was introduced, and a sequence of 12 images was captured in different focal planes. The image scale and phase were corrected, and the in-focus pixels were abstracted by employing the Laplacian operator. Finally, an all-in-focus sharp image was generated, and a depth map was obtained.

Impact of physical confinement on nuclei geometry and cell division dynamics in 3D spheroids

Multicellular tumour spheroids are used as a culture model to reproduce the 3D architecture, proliferation gradient and cell interactions of a tumour micro-domain. However, their 3D characterization at the cell scale remains challenging due to size and cell density issues. In this study, we developed a methodology based on 3D light sheet fluorescence microscopy (LSFM) image analysis and convex hull calculation that allows characterizing the 3D shape and orientation of cell nuclei relative to the spheroid surface. By using this technique and optically cleared spheroids, we found that in freely growing spheroids, nuclei display an elongated shape and are preferentially oriented parallel to the spheroid surface. This geometry is lost when spheroids are grown in conditions of physical confinement. Live 3D LSFM analysis of cell division revealed that confined growth also altered the preferential cell division axis orientation parallel to the spheroid surface and induced prometaphase delay. These results provide key information and parameters that help understanding the impact of physical confinement on cell proliferation within tumour micro-domains.

Probing the Role of Peptidoglycan Metabolism in Helicobacter pylori's Helical Shape

Helical cell shape is important for Helicobacter pylori's ability to colonize the human stomach. Cell shape in this Gram‐negative pathogen is determined by the cell wall, which is composed of peptidoglycan (PG), a polymer of disaccharides with an attached pentapeptide stem that can join glycan strands by crosslinking with an adjacent peptide stem. Maintaining helical shape requires the activity of two endopeptidases, two carboxypeptidases, and several non‐enzymatic proteins. This suggests that structural modification of the PG is essential for shape maintenance, but it is unclear whether structural modification alone is sufficient or if modifications act in concert with PG synthesis and/or turnover. The most widely‐adopted metabolic probe for labeling new PG synthesis is either a small fluorophore or clickable alkyne conjugated to D‐alanine, which is present in the two terminal positions in the PG peptide stem. However, this probe appears to be mainly incorporated through transpeptidation reactions, which do occur at new PG synthesis sites, but may also occur during synthesis‐independent modification of the cell wall. We generated a strain of H. pylori that expresses Pseudomonas putida N‐acetylmuramic acid (MurNAc ) recycling enzymes AmgK and MurU and is capable of incorporating the newly‐developed MurNAc‐alkyne probe into the cell wall. We are employing quantitative analysis of 3D structured illumination microscopy (SIM) images to define the different regions of the helical cell using Gaussian curvature and to measure whether D‐alanine‐alkyne and MurNAc‐alkyne are preferentially enriched at positive or negative curvature (major or minor helical axis of a helical cell, respectively). Preliminary results indicate that the two probes show different curvature enrichment and suggest that crosslinking activity occurs independently of new PG synthesis and/or crosslinking rates during synthesis differ at different parts of the cell. These results suggest a role for spatial regulation of both structural variation and new PG synthesis in maintaining H. pylori's helical cell shape. Furthermore, these results demonstrate the value of employing both metabolic probes to investigate cell growth and shape maintenance.Support or Funding InformationThis material is based upon work supported by the National Institutes of Health R01 AI094839 and U01 CA221230, the National Science Foundation Graduate Research Fellowship under Grant No. DGE‐0718124 and DGE‐1256082, and the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Voxel-based plaque classification in coronary intravascular optical coherence tomography images using decision trees.

Intravascular Optical Coherence Tomography (IVOCT) is a high contrast, 3D microscopic imaging technique that can be used to assess atherosclerosis and guide stent interventions. Despite its advantages, IVOCT image interpretation is challenging and time consuming with over 500 image frames generated in a single pullback volume. We have developed a method to classify voxel plaque types in IVOCT images using machine learning. To train and test the classifier, we have used our unique database of labeled cadaver vessel IVOCT images accurately registered to gold standard cryo-images. This database currently contains 300 images and is growing. Each voxel is labeled as fibrotic, lipid-rich, calcified or other. Optical attenuation, intensity and texture features were extracted for each voxel and were used to build a decision tree classifier for multi-class classification. Five-fold cross-validation across images gave accuracies of 96 % ± 0.01 %, 90 ± 0.02% and 90 % ± 0.01 % for fibrotic, lipid-rich and calcified classes respectively. To rectify performance degradation seen in left out vessel specimens as opposed to left out images, we are adding data and reducing features to limit overfitting. Following spatial noise cleaning, important vascular regions were unambiguous in display. We developed displays that enable physicians to make rapid determination of calcified and lipid regions. This will inform treatment decisions such as the need for devices (e.g., atherectomy or scoring balloon in the case of calcifications) or extended stent lengths to ensure coverage of lipid regions prone to injury at the edge of a stent.

Computational 3D imaging to quantify structural components and assembly of protein networks

Traditionally, protein structures have been described by the secondary structure architecture and fold arrangement. However, the relatively novel method of 3D confocal microscopy of fluorescent-protein-tagged networks in living cells allows resolving the detailed spatial organization of these networks. This provides new possibilities to predict network functionality, as structure and function seem to be linked at various scales. Here, we propose a quantitative approach using 3D confocal microscopy image data to describe protein networks based on their nano-structural characteristics. This analysis is constructed in four steps: (i) Segmentation of the microscopic raw data into a volume model and extraction of a spatial graph representing the protein network. (ii) Quantifying protein network gross morphology using the volume model. (iii) Quantifying protein network components using the spatial graph. (iv) Linking these two scales to obtain insights into network assembly. Here, we quantitatively describe the filamentous temperature sensitive Z protein network of the moss Physcomitrella patens and elucidate relations between network size and assembly details. Future applications will link network structure and functionality by tracking dynamic structural changes over time and comparing different states or types of networks, possibly allowing more precise identification of (mal) functions or the design of protein-engineered biomaterials for applications in regenerative medicine. Statement of SignificanceProtein networks are highly complex and dynamic structures that play various roles in biological environments. Analyzing the detailed spatial structure of these networks may lead to new insight into biological functions and malfunctions. Here, we propose a tool set that extracts structural information at two scales of the protein network and allows therefore to address questions such as “how is the network built?” or “how networks grow?”.

Primary fibroblasts derived from sporadic amyotrophic lateral sclerosis patients do not show ALS cytological lesions

Objective: Sporadic amyotrophic lateral sclerosis (sALS) is a fatal neurodegenerative disorder affecting upper and lower motor neurons. In view of the heterogeneous presentation of the disease, one of the current challenges is to identify diagnostic and prognostic markers in order to diagnose sALS at early stage and to stratify patients in trials. In this study, we sought to identify cytological hallmarks of sALS in patient-derived fibroblasts with the aim of finding new clinical-related markers of the disease. Methods: Primary fibroblasts were prospectively collected from patients affected with classical, rapid, and slow forms of sALS. TDP-43 localization, cytoskeleton distribution, mitochondrial network architecture, and stress granules formation were analyzed using 3D fluorescence microscopy and new super-resolution imaging. Intracellular reactive oxygen species (ROS) production was assessed using live imaging techniques. Results: Six sALS patients (two classical, two rapid, and two slow) and four age-matched controls were included. No difference in fibroblasts cell growth, morphology, and distribution was noticed. The analysis of TDP-43 did not reveal any mislocalization nor aggregation of the protein. The cytoskeleton was harmoniously distributed among the cells, without any inclusion noticed, and no difference was observed regarding the mitochondrial network architecture. Basal ROS production and response to induced stress were similar among patient and control fibroblasts. Conclusions: ALS cytological lesions are absent in patient-derived fibroblasts and thus cannot contribute as diagnostic nor prognostic markers of the disease.

Fractal approaches to characterize the structure of capillary suspensions using rheology and confocal microscopy.

The rheological properties of a particle suspension can be substantially altered by adding a small amount of a secondary fluid that is immiscible with the bulk phase. The drastic change in the strength of these capillary suspensions arises due to the capillary forces, induced by the added liquid, leading to a percolating particle network. Using rheological scaling models, fractal dimensions are deduced from the yield stress and from oscillatory strain amplitude sweep data as function of the solid volume fraction. Exponents obtained using aluminum-oxide-based capillary suspensions, with a preferentially wetting secondary fluid, indicate an increase in the particle gel's fractal dimension with increasing particle size. This may be explained by a corresponding relative reduction in the capillary force compared to other forces. Confocal images using a glass model system show the microstructure to consist of compact particle flocs interconnected by a sparse backbone. Thus, using the rheological models two different fractal dimensionalities are distinguished - a lower network backbone dimension (D = 1.86-2.05) and an intrafloc dimension (D = 2.57-2.74). The latter is higher due to the higher local solid volume fraction inside of the flocs compared to the sparse backbone. Both of these dimensions are compared with values obtained by analysis of spatial particle positions from 3D confocal microscopy images, where dimensions between 2.43 and 2.63 are computed, lying between the two dimension ranges obtained from rheology. The fractal dimensions determined via this method corroborate the increase in structural compactness with increasing particle size.

Age of heart disease presentation and dysmorphic nuclei in patients with LMNA mutations.

Nuclear shape defects are a distinguishing characteristic in laminopathies, cancers, and other pathologies. Correlating these defects to the symptoms, mechanisms, and progression of disease requires unbiased, quantitative, and high-throughput means of quantifying nuclear morphology. To accomplish this, we developed a method of automatically segmenting fluorescently stained nuclei in 2D microscopy images and then classifying them as normal or dysmorphic based on three geometric features of the nucleus using a package of Matlab codes. As a test case, cultured skin-fibroblast nuclei of individuals possessing LMNA splice-site mutation (c.357-2A>G), LMNA nonsense mutation (c.736 C>T, pQ246X) in exon 4, LMNA missense mutation (c.1003C>T, pR335W) in exon 6, Hutchinson-Gilford Progeria Syndrome, and no LMNA mutations were analyzed. For each cell type, the percentage of dysmorphic nuclei, and other morphological features such as average nuclear area and average eccentricity were obtained. Compared to blind observers, our procedure implemented in Matlab codes possessed similar accuracy to manual counting of dysmorphic nuclei while being significantly more consistent. The automatic quantification of nuclear defects revealed a correlation between in vitro results and age of patients for initial symptom onset. Our results demonstrate the method’s utility in experimental studies of diseases affecting nuclear shape through automated, unbiased, and accurate identification of dysmorphic nuclei.

Improved Three-Dimensional (3D) Resolution of Electron Tomograms Using Robust Mathematical Data-Processing Techniques.

Electron tomography has become an essential tool for three-dimensional (3D) characterization of nanomaterials. In recent years, advances have been made in specimen preparation and mounting, acquisition geometries, and reconstruction algorithms. All of these components work together to optimize the resolution and clarity of an electron tomogram. However, one important component of the data-processing has received less attention: the 2D tilt series alignment. This is challenging for a number of reasons, namely because the nature of the data sets and the need to be coherently aligned over the full range of angles. An inaccurate alignment may be difficult to identify, yet can significantly limit the final 3D resolution. In this work, we present an improved center-of-mass alignment model that allows us to overcome discrepancies from unwanted objects that enter the imaging area throughout the tilt series. In particular, we develop an approach to overcome changes in the total mass upon rotation of the imaging area. We apply our approach to accurately recover small Pt nanoparticles embedded in a zeolite that may otherwise go undetected both in the 2D microscopy images and the 3D reconstruction. In addition to this, we highlight the particular effectiveness of the compressed sensing methods with this data set.

Surface determination of 3D confocal Raman microscopy imaging of the skin

A surface determination method for the application of 3D confocal Raman microscopy on inhomogeneous skin sections has been presented, which is based on depth profiles of the keratin contribution of the acquired Raman spectra. The method was compared to two similar auto-focusing methods that are based on the intensity of the reflected excitation light and Raman spectra, respectively.The measurements were performed on hair follicles containing skin sections of porcine ears ex vivo. The surface determination on such samples is especially challenging due to their different molecular composition and surface inhomogeneity.An advantage of this method is molecular sensitivity, whereby only the surface of the sample will be detected and not the substrate of the microscope slide, in the case of disruptions during the processing of samples. A disadvantage of the method is the increased overall acquisition time if only the surface spectra are to be applied for 2D mapping.

High-Speed "4D" Computational Microscopy of Bacterial Surface Motility.

Bacteria exhibit surface motility modes that play pivotal roles in early-stage biofilm community development, such as type IV pili-driven "twitching" motility and flagellum-driven "spinning" and "swarming" motility. Appendage-driven motility is controlled by molecular motors, and analysis of surface motility behavior is complicated by its inherently 3D nature, the speed of which is too fast for confocal microscopy to capture. Here, we combine electromagnetic field computation and statistical image analysis to generate 3D movies close to a surface at 5 ms time resolution using conventional inverted microscopes. We treat each bacterial cell as a spherocylindrical lens and use finite element modeling to solve Maxwell's equations and compute the diffracted light intensities associated with different angular orientations of the bacterium relative to the surface. By performing cross-correlation calculations between measured 2D microscopy images and a library of computed light intensities, we demonstrate that near-surface 3D movies of Pseudomonas aeruginosa translational and rotational motion are possible at high temporal resolution. Comparison between computational reconstructions and detailed hydrodynamic calculations reveals that P. aeruginosa act like low Reynolds number spinning tops with unstable orbits, driven by a flagellum motor with a torque output of ∼2 pN μm. Interestingly, our analysis reveals that P. aeruginosa can undergo complex flagellum-driven dynamical behavior, including precession, nutation, and an unexpected taxonomy of surface motility mechanisms, including upright-spinning bacteria that diffuse laterally across the surface, and horizontal bacteria that follow helicoidal trajectories and exhibit superdiffusive movements parallel to the surface.