High-throughput Microscopy Research Articles

HiExM: high-throughput expansion microscopy enables scalable super-resolution imaging.

Expansion microscopy (ExM) enables nanoscale imaging using a standard confocal microscope through the physical, isotropic expansion of fixed immunolabeled specimens. ExM is widely employed to image proteins, nucleic acids, and lipid membranes in single cells; however, current methods limit the number of samples that can be processed simultaneously. We developed High-throughput Expansion Microscopy (HiExM), a robust platform that enables expansion microscopy of cells cultured in a standard 96-well plate. Our method enables ~4.2x expansion of cells within individual wells, across multiple wells, and between plates. We also demonstrate that HiExM can be combined with high-throughput confocal imaging platforms to greatly improve the ease and scalability of image acquisition. As an example, we analyzed the effects of doxorubicin, a known cardiotoxic agent, on human cardiomyocytes (CMs) as measured by Hoechst signal across the nucleus. We show a dose dependent effect on nuclear DNA that is not observed in unexpanded CMs, suggesting that HiExM improves the detection of cellular phenotypes in response to drug treatment. Our method broadens the application of ExM as a tool for scalable super-resolution imaging in biological research applications.

Automated High-Throughput Atomic Force Microscopy Single-Cell Nanomechanical Assay Enabled by Deep Learning-Based Optical Image Recognition.

Mechanical forces are essential for life activities, and the mechanical phenotypes of single cells are increasingly gaining attention. Atomic force microscopy (AFM) has been a standard method for single-cell nanomechanical assays, but its efficiency is limited due to its reliance on manual operation. Here, we present a study of deep learning image recognition-assisted AFM that enables automated high-throughput single-cell nanomechanical measurements. On the basis of the label-free identification of the cell structures and the AFM probe in optical bright-field images as well as the consequent automated movement of the sample stage and AFM probe, the AFM probe tip could be accurately and sequentially moved onto the specific parts of individual living cells to perform a single-cell indentation assay or single-cell force spectroscopy in a time-efficient manner. The study illustrates a promising method based on deep learning for achieving operator-independent high-throughput AFM single-cell nanomechanics, which will benefit the application of AFM in mechanobiology.

Lens-free on-chip 3D microscopy based on wavelength-scanning Fourier ptychographic diffraction tomography

Lens-free on-chip microscopy is a powerful and promising high-throughput computational microscopy technique due to its unique advantage of creating high-resolution images across the full field-of-view (FOV) of the imaging sensor. Nevertheless, most current lens-free microscopy methods have been designed for imaging only two-dimensional thin samples. Lens-free on-chip tomography (LFOCT) with a uniform resolution across the entire FOV and at a subpixel level remains a critical challenge. In this paper, we demonstrated a new LFOCT technique and associated imaging platform based on wavelength scanning Fourier ptychographic diffraction tomography (wsFPDT). Instead of using angularly-variable illuminations, in wsFPDT, the sample is illuminated by on-axis wavelength-variable illuminations, ranging from 430 to 1200 nm. The corresponding under-sampled diffraction patterns are recorded, and then an iterative ptychographic reconstruction procedure is applied to fill the spectrum of the three-dimensional (3D) scattering potential to recover the sample’s 3D refractive index (RI) distribution. The wavelength-scanning scheme not only eliminates the need for mechanical motion during image acquisition and precise registration of the raw images but secures a quasi-uniform, pixel-super-resolved imaging resolution across the entire imaging FOV. With wsFPDT, we demonstrate the high-throughput, billion-voxel 3D tomographic imaging results with a half-pitch lateral resolution of 775 nm and an axial resolution of 5.43 μm across a large FOV of 29.85 mm2 and an imaging depth of >200 μm. The effectiveness of the proposed method was demonstrated by imaging various types of samples, including micro-polystyrene beads, diatoms, and mouse mononuclear macrophage cells. The unique capability to reveal quantitative morphological properties, such as area, volume, and sphericity index of single cell over large cell populations makes wsFPDT a powerful quantitative and label-free tool for high-throughput biological applications.

OpenNucleome for high-resolution nuclear structural and dynamical modeling.

The intricate structural organization of the human nucleus is fundamental to cellular function and gene regulation. Recent advancements in experimental techniques, including high-throughput sequencing and microscopy, have provided valuable insights into nuclear organization. Computational modeling has played significant roles in interpreting experimental observations by reconstructing high-resolution structural ensembles and uncovering organization principles. However, the absence of standardized modeling tools poses challenges for furthering nuclear investigations. We present OpenNucleome-an open-source software designed for conducting GPU-accelerated molecular dynamics simulations of the human nucleus. OpenNucleome offers particle-based representations of chromosomes at a resolution of 100 KB, encompassing nuclear lamina, nucleoli, and speckles. This software furnishes highly accurate structural models of nuclear architecture, affording the means for dynamic simulations of condensate formation, fusion, and exploration of non-equilibrium effects. We applied OpenNucleome to uncover the mechanisms driving the emergence of 'fixed points' within the nucleus-signifying genomic loci robustly anchored in proximity to specific nuclear bodies for functional purposes. This anchoring remains resilient even amidst significant fluctuations in chromosome radial positions and nuclear shapes within individual cells. Our findings lend support to a nuclear zoning model that elucidates genome functionality. We anticipate OpenNucleome to serve as a valuable tool for nuclear investigations, streamlining mechanistic explorations and enhancing the interpretation of experimental observations.

OpenNucleome for high-resolution nuclear structural and dynamical modeling

The intricate structural organization of the human nucleus is fundamental to cellular function and gene regulation. Recent advancements in experimental techniques, including high-throughput sequencing and microscopy, have provided valuable insights into nuclear organization. Computational modeling has played significant roles in interpreting experimental observations by reconstructing high-resolution structural ensembles and uncovering organization principles. However, the absence of standardized modeling tools poses challenges for furthering nuclear investigations. We present OpenNucleome—an open-source software designed for conducting GPU-accelerated molecular dynamics simulations of the human nucleus. OpenNucleome offers particle-based representations of chromosomes at a resolution of 100 KB, encompassing nuclear lamina, nucleoli, and speckles. This software furnishes highly accurate structural models of nuclear architecture, affording the means for dynamic simulations of condensate formation, fusion, and exploration of non-equilibrium effects. We applied OpenNucleome to uncover the mechanisms driving the emergence of ‘fixed points’ within the nucleus—signifying genomic loci robustly anchored in proximity to specific nuclear bodies for functional purposes. This anchoring remains resilient even amidst significant fluctuations in chromosome radial positions and nuclear shapes within individual cells. Our findings lend support to a nuclear zoning model that elucidates genome functionality. We anticipate OpenNucleome to serve as a valuable tool for nuclear investigations, streamlining mechanistic explorations and enhancing the interpretation of experimental observations.

Mantis: High-throughput 4D imaging and analysis of the molecular and physical architecture of cells.

High-throughput dynamic imaging of cells and organelles is essential for understanding complex cellular responses. We report Mantis, a high-throughput 4D microscope that integrates two complementary, gentle, live-cell imaging technologies: remote-refocus label-free microscopy and oblique light-sheet fluorescence microscopy. Additionally, we report shrimPy (Smart High-throughput Robust Imaging and Measurement in Python), an open-source software for high-throughput imaging, deconvolution, and single-cell phenotyping of 4D data. Using Mantis and shrimPy, we achieved high-content correlative imaging of molecular dynamics and the physical architecture of 20 cell lines every 15 min over 7.5 h. This platform also facilitated detailed measurements of the impacts of viral infection on the architecture of host cells and host proteins. The Mantis platform can enable high-throughput profiling of intracellular dynamics, long-term imaging and analysis of cellular responses to perturbations, and live-cell optical screens to dissect gene regulatory networks.

3D live imaging and phenotyping of CAR-T cell mediated-cytotoxicity using high-throughput Bessel oblique plane microscopy

Clarification of the cytotoxic function of T cells is crucial for understanding human immune responses and immunotherapy procedures. Here, we report a high-throughput Bessel oblique plane microscopy (HBOPM) platform capable of 3D live imaging and phenotyping of chimeric antigen receptor (CAR)-modified T-cell cytotoxicity against cancer cells. The HBOPM platform has the following characteristics: an isotropic subcellular resolution of 320 nm, large-scale scouting over 400 interacting cell pairs, long-term observation across 5 hours, and quantitative analysis of the Terabyte-scale 3D, multichannel, time-lapse image datasets. Using this advanced microscopy platform, several key subcellular events in CAR-T cells are captured and comprehensively analyzed; these events include the instantaneous formation of immune synapses and the sustained changes in the microtubing morphology. Furthermore, we identify the actin retrograde flow speed, the actin depletion coefficient, the microtubule polarization and the contact area of the CAR-T/target cell conjugates as essential parameters strongly correlated with CAR-T-cell cytotoxic function. Our approach will be useful for establishing criteria for quantifying T-cell function in individual patients for all T-cell-based immunotherapies.

Phenotypic heterogeneity follows a growth-viability tradeoff in response to amino acid identity

In their natural environments, microorganisms mainly operate at suboptimal growth conditions with fluctuations in nutrient abundance. The resulting cellular adaptation is subject to conflicting tasks: growth or survival maximisation. Here, we study this adaptation by systematically measuring the impact of a nitrogen downshift to 24 nitrogen sources on cellular metabolism at the single-cell level. Saccharomyces lineages grown in rich media and exposed to a nitrogen downshift gradually differentiate to form two subpopulations of different cell sizes where one favours growth while the other favours viability with an extended chronological lifespan. This differentiation is asymmetrical with daughter cells representing the new differentiated state with increased viability. We characterise the metabolic response of the subpopulations using RNA sequencing, metabolic biosensors and a transcription factor-tagged GFP library coupled to high-throughput microscopy, imaging more than 800,000 cells. We find that the subpopulation with increased viability is associated with a dormant quiescent state displaying differences in MAPK signalling. Depending on the identity of the nitrogen source present, differentiation into the quiescent state can be actively maintained, attenuated, or aborted. These results establish amino acids as important signalling molecules for the formation of genetically identical subpopulations, involved in chronological lifespan and growth rate determination.

High-throughput ultrastructural analysis of macular telangiectasia type 2.

Macular Telangiectasia type 2 (MacTel), is an uncommon form of late-onset, slowly-progressive macular degeneration. Associated with regional Müller glial cell loss in the retina and the amino acid serine synthesized by Müller cells, the disease is functionally confined to a central retinal region - the MacTel zone. We have used high-throughput multi-resolution electron microscopy techniques, optimized for disease analysis, to study the retinas from two women, mother and daughter, aged 79 and 48 years respectively, suffering from MacTel. In both eyes, the principal observations made were changes specific to mitochondrial structure both outside and within the MacTel zone in all retinal cell types, with the exception of those in the retinal pigment epithelium (RPE). The lesion areas, which are a hallmark of MacTel, extend from Bruch's membrane and the choriocapillaris, through all depths of the retina, and include cells from the RPE, retinal vascular elements, and extensive hypertrophic basement membrane material. Where the Müller glial cells are lost, we have identified a significant population of microglial cells, exclusively within the Henle fiber layer, which appear to ensheathe the Henle fibers, similar to that seen normally by Müller cells. Since Müller cells synthesize retinal serine, whereas retinal neurons do not, we propose that serine deficiency, required for normal mitochondrial function, may relate to mitochondrial changes that underlie the development of MacTel. With mitochondrial changes occurring retina-wide, the question remains as to why the Müller cells are uniquely susceptible within the MacTel zone.

An arginine-rich nuclear localization signal (ArgiNLS) strategy for streamlined image segmentation of single cells

High-throughput volumetric fluorescent microscopy pipelines can spatially integrate whole-brain structure and function at the foundational level of single cells. However, conventional fluorescent protein (FP) modifications used to discriminate single cells possess limited efficacy or are detrimental to cellular health. Here, we introduce a synthetic and nondeleterious nuclear localization signal (NLS) tag strategy, called "Arginine-rich NLS" (ArgiNLS), that optimizes genetic labeling and downstream image segmentation of single cells by restricting FP localization near-exclusively in the nucleus through a poly-arginine mechanism. A single N-terminal ArgiNLS tag provides modular nuclear restriction consistently across spectrally separate FP variants. ArgiNLS performance in vivo displays functional conservation across major cortical cell classes and in response to both local and systemic brain-wide AAV administration. Crucially, the high signal-to-noise ratio afforded by ArgiNLS enhances machine learning-automated segmentation of single cells due to rapid classifier training and enrichment of labeled cell detection within 2D brain sections or 3D volumetric whole-brain image datasets, derived from both staining-amplified and native signal. This genetic strategy provides a simple and flexible basis for precise image segmentation of genetically labeled single cells at scale and paired with behavioral procedures.

The Escherichia coli chromosome moves to the replisome

In Escherichia coli, it is debated whether the two replisomes move independently along the two chromosome arms during replication or if they remain spatially confined. Here, we use high-throughput fluorescence microscopy to simultaneously determine the location and short-time-scale (1 s) movement of the replisome and a chromosomal locus throughout the cell cycle. The assay is performed for several loci. We find that (i) the two replisomes are confined to a region of ~250 nm and ~120 nm along the cell’s long and short axis, respectively, (ii) the chromosomal loci move to and through this region sequentially based on their distance from the origin of replication, and (iii) when a locus is being replicated, its short time-scale movement slows down. This behavior is the same at different growth rates. In conclusion, our data supports a model with DNA moving towards spatially confined replisomes at replication.

An atlas of the tomato epigenome reveals that KRYPTONITE shapes TAD-like boundaries through the control of H3K9ac distribution

In recent years, the exploration of genome three-dimensional (3D) conformation has yielded profound insights into the regulation of gene expression and cellular functions in both animals and plants. While animals exhibit a characteristic genome topology defined by topologically associating domains (TADs), plants display similar features with a more diverse conformation across species. Employing advanced high-throughput sequencing and microscopy techniques, we investigated the landscape of 26 histone modifications and RNA polymerase II distribution in tomato (Solanum lycopersicum). Our study unveiled a rich and nuanced epigenetic landscape, shedding light on distinct chromatin states associated with heterochromatin formation and gene silencing. Moreover, we elucidated the intricate interplay between these chromatin states and the overall topology of the genome. Employing a genetic approach, we delved into the role of the histone modification H3K9ac in genome topology. Notably, our investigation revealed that the ectopic deposition of this chromatin mark triggered a reorganization of the 3D chromatin structure, defining different TAD-like borders. Our work emphasizes the critical role of H3K9ac in shaping the topology of the tomato genome, providing valuable insights into the epigenetic landscape of this agriculturally significant crop species.

Discovery of a novel inhibitor of macropinocytosis with antiviral activity

Several viruses hijack various forms of endocytosis in order to infect host cells. Here, we report the discovery of a molecule with antiviral properties that we named virapinib, which limits viral entry by macropinocytosis. The identification of virapinib derives from a chemical screen using high-throughput microscopy, where we identified chemical entities capable of preventing infection with a pseudotype virus expressing the spike (S)protein from SARS-CoV-2. Subsequent experiments confirmed the capacity of virapinib to inhibit infection by SARS-CoV-2, as well as by additional viruses, such as mpox virus and TBEV. Mechanistic analyses revealed that the compound inhibited macropinocytosis, limiting this entry route for the viruses. Importantly, virapinib has no significant toxicity to host cells. In summary, we present the discovery of a molecule that inhibits macropinocytosis, thereby limiting the infectivity of viruses that use this entry route such as SARS-CoV2.

Generative Modelling of Cortical Receptor Distributions from Cytoarchitectonic Images in the Macaque Brain

Neurotransmitter receptor densities are relevant for understanding the molecular architecture of brain regions. Quantitative in vitro receptor autoradiography, has been introduced to map neurotransmitter receptor distributions of brain areas. However, it is very time and cost-intensive, which makes it challenging to obtain whole-brain distributions. At the same time, high-throughput light microscopy and 3D reconstructions have enabled high-resolution brain maps capturing measures of cell density across the whole human brain. Aiming to bridge gaps in receptor measurements for building detailed whole-brain atlases, we study the feasibility of predicting realistic neurotransmitter density distributions from cell-body stainings. Specifically, we utilize conditional Generative Adversarial Networks (cGANs) to predict the density distributions of the M2 receptor of acetylcholine and the kainate receptor for glutamate in the macaque monkey’s primary visual (V1) and motor cortex (M1), based on light microscopic scans of cell-body stained sections. Our model is trained on corresponding patches from aligned consecutive sections that display cell-body and receptor distributions, ensuring a mapping between the two modalities. Evaluations of our cGANs, both qualitative and quantitative, show their capability to predict receptor densities from cell-body stained sections while maintaining cortical features such as laminar thickness and curvature. Our work underscores the feasibility of cross-modality image translation problems to address data gaps in multi-modal brain atlases.

Spontaneous single-nucleotide substitutions and microsatellite mutations have distinct distributions of fitness effects.

The fitness effects of new mutations determine key properties of evolutionary processes. Beneficial mutations drive evolution, yet selection is also shaped by the frequency of small-effect deleterious mutations, whose combined effect can burden otherwise adaptive lineages and alter evolutionary trajectories and outcomes in clonally evolving organisms such as viruses, microbes, and tumors. The small effect sizes of these important mutations have made accurate measurements of their rates difficult. In microbes, assessing the effect of mutations on growth can be especially instructive, as this complex phenotype is closely linked to fitness in clonally evolving organisms. Here, we perform high-throughput time-lapse microscopy on cells from mutation-accumulation strains to precisely infer the distribution of mutational effects on growth rate in the budding yeast, Saccharomyces cerevisiae. We show that mutational effects on growth rate are overwhelmingly negative, highly skewed towards very small effect sizes, and frequent enough to suggest that deleterious hitchhikers may impose a significant burden on evolving lineages. By using lines that accumulated mutations in either wild-type or slippage repair-defective backgrounds, we further disentangle the effects of 2 common types of mutations, single-nucleotide substitutions and simple sequence repeat indels, and show that they have distinct effects on yeast growth rate. Although the average effect of a simple sequence repeat mutation is very small (approximately 0.3%), many do alter growth rate, implying that this class of frequent mutations has an important evolutionary impact.

Pixel-super-resolved lens-free quantitative phase microscopy with partially coherent illumination

Lens-free on-chip microscopy (LFOCM) has been widely utilized in digital pathology, drug screening, point-of-care testing (POCT), and quantitative phase imaging (QPI) due to its high throughput imaging capability and compactness. Initially, coherent laser sources were used in LFOCM to generate interference fringes to reconstruct the intensity and phase information of an object. The use of partially coherent light-emitting diodes (LEDs) in LFOCM offers a more portable and cost-effective alternative to conventional coherent illumination sources. However, the coherence-gating effect from a relatively low degree of coherence may cause a blur of high-frequency information in holograms, leading to an inaccurate object recovery. Thus, we present a pixel-super-resolved lens-free quantitative phase microscopy (PSR-LFQPM) with partially coherent illumination, which not only compensates for the impact of low coherence without increasing the volume of the system but also suppresses the theoretical Nyquist-Shannon sampling resolution limit imposed by the sensor pixel size (0.9 μm). Based on the partially coherent imaging model, we integrate the spatial coherence transfer function (SCTF) obtained from the pre-calibrated LED source distribution during the iteration process to obtain an accurate high-resolution recovery. Applying PSR-LFQPM to image living HeLa cells in vitro, we achieve real-time dynamic high-throughput QPI performance (half-pitch resolution of 780 nm with a 1.41-fold improvement compared to results without considering the effect of coherence) across a wide FOV (19.53 mm2). The proposed method provides a compact, low-cost, and high-throughput lens-free on-chip microscopy system for biomedical and POCT applications.

Mantis: high-throughput 4D imaging and analysis of the molecular and physical architecture of cells.

High-throughput dynamic imaging of cells and organelles is essential for understanding complex cellular responses. We report Mantis, a high-throughput 4D microscope that integrates two complementary, gentle, live-cell imaging technologies: remote-refocus label-free microscopy and oblique light-sheet fluorescence microscopy. Additionally, we report shrimPy, an open-source software for high-throughput imaging, deconvolution, and single-cell phenotyping of 4D data. Using Mantis and shrimPy, we achieved high-content correlative imaging of molecular dynamics and the physical architecture of 20 cell lines every 15 minutes over 7.5 hours. This platform also facilitated detailed measurements of the impacts of viral infection on the architecture of host cells and host proteins. The Mantis platform can enable high-throughput profiling of intracellular dynamics, long-term imaging and analysis of cellular responses to perturbations, and live-cell optical screens to dissect gene regulatory networks.

SmartEM: machine-learning guided electron microscopy.

Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a singlebeam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM allocates the proper imaging time for each region of interest - scanning all pixels equally rapidly, then re-scanning small subareas more slowly where a higher quality signal is required to achieve accurate segmentability, in significantly less time. We demonstrate that this pipeline achieves a 7-fold acceleration of image acquisition time for connectomics using a commercial single-beam SEM. We apply SmartEM to reconstruct a portion of mouse cortex with the same accuracy as traditional microscopy but in less time.

Depth-enhanced high-throughput microscopy by compact PSF engineering

High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers throughput and increases photobleaching and photodamage. Point spread function (PSF) engineering is an optical method that enables various 3D imaging capabilities, yet it has not been implemented in high-throughput microscopy due to the cumbersome optical extension it typically requires. Here we demonstrate compact PSF engineering in the objective lens, which allows us to enhance the imaging depth of field and, combined with deep learning, recover 3D information using single snapshots. Beyond the applications shown here, this work showcases the usefulness of high-throughput microscopy in obtaining training data for deep learning-based algorithms, applicable to a variety of microscopy modalities.

P37 Drug repurposing for treatment of recessive dystrophic epidermolysis bullosa cutaneous squamous cell carcinoma

Abstract Background Recessive dystrophic epidermolysis bullosa (RDEB) is an inherited skin disorder that leads to severe wounding and blistering of the skin. Patients with RDEB develop aggressive cutaneous squamous cell carcinoma (cSCC) with a mortality rate of approximately 90% by age 55 years. The current primary treatment is wide excision of tumours, often leading to amputations. Identifying new therapeutic avenues for RDEB cSCC remains a priority. Here, we approach this in two ways. Firstly, we identified a SMAD4-dependent sphingosine pathway response to endogenous transforming growth factor (TGF)-β, essential for RDEB cSCC proliferation which could be targeted as a potential therapeutic strategy. Secondly, we utilized a US Food and Drug Administration (FDA) screen of > 3000 drugs to identify potential druggable pathways that could be used to treat RDEB cSCC. Methods RDEB cSCC cell lines were used to test Siponimod, a sphingosine-1 receptor modulator, on proliferation, clonogenicity and invasion in vitro. Siponimod was used in vivo to test tumour growth effect of targeting the sphingosine pathway. We carried out an FDA-approved drug screen on four RDEB cSCC cell lines using the Opera Phenix high-throughput microscope to identify targets. Results Treatment of RDEB cSCC cell lines with Siponimod showed compromised proliferation compared with untreated cells in addition to significant reduction in colony formation. There was a significant decrease in the invasive capability of RDEB SCC cells. Treatment of normal human keratinocytes showed no effect on proliferation. Our FDA-approved screen identified multiple drugs that target MEK, mechanistic target of rapamycin complex, histone deacetylase, signal transducer and activator of transcription, and topoisomerase I/II in the top 50 hits. The most common target was DNA damage/repair pathways, but cytoskeletal signalling, transmembrane transporters and proteases were identified as other potential avenues for treatment of RDEB cSCC. Conclusions There is an unmet need for therapeutic options in RDEB cSCC. Here, we have demonstrated the potential of repurposing previously licensed drugs to identify treatment avenues for RDEB cSCC.