Image processing tools for petabyte-scale light sheet microscopy data.

Light sheet microscopy is a powerful technique for high-speed three-dimensional imaging of subcellular dynamics and large biological specimens. However, it often generates datasets ranging from hundreds of gigabytes to petabytes in size for a single experiment. Conventional computational tools process such images far slower than the time to acquire them and often fail outright due to memory limitations. To address these challenges, we present PetaKit5D, a scalable software solution for efficient petabyte-scale light sheet image processing. This software incorporates a suite of commonly used processing tools that are optimized for memory and performance. Notable advancements include rapid image readers and writers, fast and memory-efficient geometric transformations, high-performance Richardson-Lucy deconvolution and scalable Zarr-based stitching. These features outperform state-of-the-art methods by over one order of magnitude, enabling the processing of petabyte-scale image data at the full teravoxel rates of modern imaging cameras. The software opens new avenues for biological discoveries through large-scale imaging experiments.

Characterization, comparison, and optimization of lattice light sheets.

Lattice light sheet microscopy excels at the noninvasive imaging of three-dimensional (3D) dynamic processes at high spatiotemporal resolution within cells and developing embryos. Recently, several papers have called into question the performance of lattice light sheets relative to the Gaussian sheets most common in light sheet microscopy. Here, we undertake a theoretical and experimental analysis of various forms of light sheet microscopy, which demonstrates and explains why lattice light sheets provide substantial improvements in resolution and photobleaching reduction. The analysis provides a procedure to select the correct light sheet for a desired experiment and specifies the processing that maximizes the use of all fluorescence generated within the light sheet excitation envelope for optimal resolution while minimizing image artifacts and photodamage. We also introduce a new type of "harmonic balanced" lattice light sheet that improves performance at all spatial frequencies within its 3D resolution limits and maintains this performance over lengthened propagation distances allowing for expanded fields of view.

Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopy.

We presenta robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, for example due to changes in temperature or mechanical drift. It is also useful for automated slide scanning or multiwell plate imaging where the sample(s) to be imaged may not be in the same horizontal plane throughout the image data acquisition. To address the impact of (thermal or mechanical) fluctuations over time in the optical autofocus system itself, we utilize a convolutional neural network (CNN) that is trained over multiple days to account for such fluctuations. To address the trade-off between axial precision and range of the autofocus, we implement orthogonal optical readouts with separate CNN training data, thereby achieving an accuracy well within the 600 nm depth of field of our 1.3 numerical aperture objective lens over a defocus range of up to approximately +/-100 µm. We characterize the performance of this autofocus system and demonstrate its application to automated multiwell plate single molecule localization microscopy.

Many microscopy experiments involve extended imaging of samples over timescales from minutes to days, during which the microscope can 'drift' out of focus. When imaging at high magnification, the depth of field is of the order of one micron and so the imaging system should keep the sample in the focal plane of the microscope objective lens to this precision. Unfortunately, temperature changes in the laboratory can cause thermal expansion of microscope components that can move the focal plane by more than a micron and such changes can occur on a timescale of minutes. This is a particular issue for super-resolved microscopy experiments using single molecule localization microscopy (SMLM) techniques, for which 1000s of images are acquired, and for automated imaging of multiple samples in multiwell plates. It is possible to maintain the sample in the focal plane focus position by either automatically moving the sample or adjusting the imaging system, for example by moving the objective lens. This is called 'autofocus' and is frequently achieved by reflecting a light beam from the microscope coverslip and measuring its position of beam profile as a function of defocus of the microscope. The correcting adjustment is then usually calculated analytically but there is recent interest in using machine learning techniques to determine the required focussing adjustment. Here, we present a system that uses a neural network to determine the required defocus correcting adjustment from camera images of a laser beam that is reflected from the coverslip. Unfortunately, this approach will only work when the microscope is in the same condition as it was when the neural network was trained - and this can be compromised by the same drift of the optical system that causes the defocus needing to be corrected. We show, however, that by training a neural network over an extended period, for example 10 days, this approach can 'learn' about the optical system drifts and provide the required autofocus function. We also show that an optical system utilizing a rectangular slit can make two measurements of the defocus simultaneously, with one measurement being optimized for high accuracy over a limited range (±10 µm) near focus and the other providing lower accuracy but over a much longer range (±100 µm). This robust autofocus system is suitable for automated super-resolved microscopy of arrays of samples in a multiwell plate using SMLM, for which an experiment routinely lasts more than 5 h.

Application of direct stochastic optical reconstruction microscopy (dSTORM) to the histological analysis of human glomerular disease.

Electron microscopy (EM) following immunofluorescence (IF) imaging is a vital tool for the diagnosis of human glomerular diseases, but the implementation of EM is limited to specialised institutions and it is not available in many countries. Recent progress in fluorescence microscopy now enables conventional widefield fluorescence microscopes to be adapted at modest cost to provide resolution below 50 nm in biological specimens. We show that stochastically switched single-molecule localisation microscopy can be applied to clinical histological sections stained with standard IF techniques and that such super-resolved IF may provide an alternative means to resolve ultrastructure to aid the diagnosis of kidney disease where EM is not available. We have implemented the direct stochastic optical reconstruction microscopy technique with human kidney biopsy frozen sections stained with clinically approved immunofluorescent probes for the basal laminae and immunoglobulin G deposits. Using cases of membranous glomerulonephritis, thin basement membrane lesion, and lupus nephritis, we compare this approach to clinical EM images and demonstrate enhanced imaging compared to conventional IF microscopy. With minor modifications in established IF protocols of clinical frozen renal biopsies, we believe the cost-effective adaptation of conventional widefield microscopes can be widely implemented to provide super-resolved image information to aid diagnosis of human glomerular disease.

Genetic and biased agonist-mediated reductions in ß-arrestin recruitment prolong cAMP signaling at glucagon family receptors.

Receptors for the peptide hormones glucagon-like peptide-1 (GLP-1R), glucose-dependent insulinotropic polypeptide (GIPR), and glucagon (GCGR) are important regulators of insulin secretion and energy metabolism. GLP-1R agonists have been successfully deployed for the treatment of type 2 diabetes, but it has been suggested that their efficacy is limited by target receptor desensitization and downregulation due to recruitment of ß-arrestins. Indeed, recently described GLP-1R agonists with reduced ß-arrestin-2 recruitment have delivered promising results in preclinical and clinical studies. We therefore aimed to determine if the same phenomenon could apply to the closely related GIPR and GCGR. In HEK293 cells depleted of both ß-arrestin isoforms the duration of G protein-dependent cAMP/PKA signaling was increased in response to the endogenous ligand for each receptor. Moreover, in wildtype cells, "biased" GLP-1, GCG, and GIP analogs with selective reductions in ß-arrestin-2 recruitment led to reduced receptor endocytosis and increased insulin secretion over a prolonged stimulation period, although the latter effect was only seen at high agonist concentrations. Biased GCG analogs increased the duration of cAMP signaling, but this did not lead to increased glucose output from hepatocytes. Our study provides a rationale for the development of GLP-1R, GIPR, and GCGR agonists with reduced ß-arrestin recruitment, but further work is needed to maximally exploit this strategy for therapeutic purposes.

The Influence of Peptide Context on Signaling and Trafficking of Glucagon-like Peptide-1 Receptor Biased Agonists.

Signal bias and membrane trafficking have recently emerged as important considerations in the therapeutic targeting of the glucagon-like peptide-1 receptor (GLP-1R) in type 2 diabetes and obesity. In the present study, we have evaluated a peptide series with varying sequence homology between native GLP-1 and exendin-4, the archetypal ligands on which approved GLP-1R agonists are based. We find notable differences in agonist-mediated cyclic AMP signaling, recruitment of ß-arrestins, endocytosis, and recycling, dependent both on the introduction of a His → Phe switch at position 1 and the specific midpeptide helical regions and C-termini of the two agonists. These observations were linked to insulin secretion in a beta cell model and provide insights into how ligand factors influence GLP-1R function at the cellular level.

DDR1 autophosphorylation is a result of aggregation into dense clusters.

The collagen receptor DDR1 is a receptor tyrosine kinase that promotes progression of a wide range of human disorders. Little is known about how ligand binding triggers DDR1 kinase activity. We previously reported that collagen induces DDR1 activation through lateral dimer association and phosphorylation between dimers, a process that requires specific transmembrane association. Here we demonstrate ligand-induced DDR1 clustering by widefield and super-resolution imaging and provide evidence for a mechanism whereby DDR1 kinase activity is determined by its molecular density. Ligand binding resulted in initial DDR1 reorganisation into morphologically distinct clusters with unphosphorylated DDR1. Further compaction over time led to clusters with highly aggregated and phosphorylated DDR1. Ligand-induced DDR1 clustering was abolished by transmembrane mutations but did not require kinase activity. Our results significantly advance our understanding of the molecular events underpinning ligand-induced DDR1 kinase activity and provide an explanation for the unusually slow DDR1 activation kinetics.

Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow.

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.

Automated Fluorescence Lifetime Imaging High-Content Analysis of Förster Resonance Energy Transfer between Endogenously Labeled Kinetochore Proteins in Live Budding Yeast Cells.

We describe an open-source automated multiwell plate fluorescence lifetime imaging (FLIM) methodology to read out Förster resonance energy transfer (FRET) between fluorescent proteins (FPs) labeling endogenous kinetochore proteins (KPs) in live budding yeast cells. The low copy number of many KPs and their small spatial extent present significant challenges for the quantification of donor fluorescence lifetime in the presence of significant cellular autofluorescence and photobleaching. Automated FLIM data acquisition was controlled by µManager and incorporated wide-field time-gated imaging with optical sectioning to reduce background fluorescence. For data analysis, we used custom MATLAB-based software tools to perform kinetochore foci segmentation and local cellular background subtraction and fitted the fluorescence lifetime data using the open-source FLIMfit software. We validated the methodology using endogenous KPs labeled with mTurquoise2 FP and/or yellow FP and measured the donor fluorescence lifetimes for foci comprising 32 kinetochores with KP copy numbers as low as ~2 per kinetochore under an average labeling efficiency of 50%. We observed changes of median donor lifetime ≥250 ps for KPs known to form dimers. Thus, this FLIM high-content analysis platform enables the screening of relatively low-copy-number endogenous protein-protein interactions at spatially confined macromolecular complexes.

easySLM-STED: Stimulated emission depletion microscopy with aberration correction, extended field of view and multiple beam scanning.

We demonstrate a simplified set-up for STED microscopy with a straightforward alignment procedure that uses a single spatial light modulator (SLM) with collinear incident excitation and depletion beams to provide phase modulation of the beam profiles and correction of optical aberrations. We show that this approach can be used to extend the field of view for STED microscopy by correcting chromatic aberration that otherwise leads to walk-off between the focused excitation and depletion beams. We further show how this arrangement can be adapted to increase the imaging speed through multibeam excitation and depletion. Fine adjustments to the alignment can be accomplished using the SLM only, conferring the potential for automation.

In vivo label-free mapping of the effect of a photosystem II inhibiting herbicide in plants using chlorophyll fluorescence lifetime.

BACKGROUND: In order to better understand and improve the mode of action of agrochemicals, it is useful to be able to visualize their uptake and distribution in vivo, non-invasively and, ideally, in the field. Here we explore the potential of plant autofluorescence (specifically chlorophyll fluorescence) to provide a readout of herbicide action across the scales utilising multiphoton-excited fluorescence lifetime imaging, wide-field single-photon excited fluorescence lifetime imaging and single point fluorescence lifetime measurements via a fibre-optic probe. RESULTS: Our studies indicate that changes in chlorophyll fluorescence lifetime can be utilised as an indirect readout of a photosystem II inhibiting herbicide activity in living plant leaves at three different scales: cellular (~µm), single point (~1 mm2) and macroscopic (~8 × 6 mm2 of a leaf). Multiphoton excited fluorescence lifetime imaging of Triticum aestivum leaves indicated that there is an increase in the spatially averaged chlorophyll fluorescence lifetime of leaves treated with Flagon EC-a photosystem II inhibiting herbicide. The untreated leaf exhibited an average lifetime of 560 ± 30 ps while the leaf imaged 2 h post treatment exhibited an increased lifetime of 2000 ± 440 ps in different fields of view. The results from in vivo wide-field single-photon excited fluorescence lifetime imaging excited at 440 nm indicated an increase in chlorophyll fluorescence lifetime from 521 ps in an untreated leaf to 1000 ps, just 3 min after treating the same leaf with Flagon EC, and to 2150 ps after 27 min. In vivo single point fluorescence lifetime measurements demonstrated a similar increase in chlorophyll fluorescence lifetime. Untreated leaf presented a fluorescence lifetime of 435 ps in the 440 nm excited chlorophyll channel, CH4 (620-710 nm). In the first 5 min after treatment, mean fluorescence lifetime is observed to have increased to 1 ns and then to 1.3 ns after 60 min. For all these in vivo plant autofluorescence lifetime measurements, the plants were not dark-adapted. CONCLUSIONS: We demonstrate that the local impact of a photosystem II herbicide on living plant leaves can be conveniently mapped in space and time via changes in autofluorescence lifetime, which we attribute to changes in chlorophyll fluorescence. Using portable fibre-optic probe instrumentation originally designed for label-free biomedical applications, this capability could be deployed outside the laboratory for monitoring the distribution of herbicides in growing plants.

Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy.

We present an open source high content analysis instrument utilizing automated fluorescence lifetime imaging (FLIM) for assaying protein interactions using Förster resonance energy transfer (FRET) based readouts of fixed or live cells in multiwell plates. This provides a means to screen for cell signaling processes read out using intramolecular FRET biosensors or intermolecular FRET of protein interactions such as oligomerization or heterodimerization, which can be used to identify binding partners. We describe here the functionality of this automated multiwell plate FLIM instrumentation and present exemplar data from our studies of HIV Gag protein oligomerization and a time course of a FRET biosensor in live cells. A detailed description of the practical implementation is then provided with reference to a list of hardware components and a description of the open source data acquisition software written in µManager. The application of FLIMfit, an open source MATLAB-based client for the OMERO platform, to analyze arrays of multiwell plate FLIM data is also presented. The protocols for imaging fixed and live cells are outlined and a demonstration of an automated multiwell plate FLIM experiment using cells expressing fluorescent protein-based FRET constructs is presented. This is complemented by a walk-through of the data analysis for this specific FLIM FRET data set.

Plasmonic Nanoprobes for Stimulated Emission Depletion Nanoscopy.

Plasmonic nanoparticles influence the absorption and emission processes of nearby emitters due to local enhancements of the illuminating radiation and the photonic density of states. Here, we use the plasmon resonance of metal nanoparticles in order to enhance the stimulated depletion of excited molecules for super-resolved nanoscopy. We demonstrate stimulated emission depletion (STED) nanoscopy with gold nanorods with a long axis of only 26 nm and a width of 8 nm. These particles provide an enhancement of up to 50% of the resolution compared to fluorescent-only probes without plasmonic components irradiated with the same depletion power. The nanoparticle-assisted STED probes reported here represent a ∼2 × 103 reduction in probe volume compared to previously used nanoparticles. Finally, we demonstrate their application toward plasmon-assisted STED cellular imaging at low-depletion powers, and we also discuss their current limitations.

STED nanoscopy with fluorescent quantum dots.

The widely popular class of quantum-dot molecular labels could so far not be utilized as standard fluorescent probes in STED (stimulated emission depletion) nanoscopy. This is because broad quantum-dot excitation spectra extend deeply into the spectral bands used for STED, thus compromising the transient fluorescence silencing required for attaining super-resolution. Here we report the discovery that STED nanoscopy of several red-emitting commercially available quantum dots is in fact successfully realized by the increasingly popular 775 nm STED laser light. A resolution of presently ∼ 50 nm is demonstrated for single quantum dots, and sub-diffraction resolution is further shown for imaging of quantum-dot-labelled vimentin filaments in fibroblasts. The high quantum-dot photostability enables repeated STED recordings with >1,000 frames. In addition, we have evidence that the tendency of quantum-dot labels to blink is largely suppressed by combined action of excitation and STED beams. Quantum-dot STED significantly expands the realm of application of STED nanoscopy, and, given the high stability of these probes, holds promise for extended time-lapse imaging.