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The regulation of cell physiology depends largely upon interactions of functionally distinct proteins and cellular components. These interactions may be transient or long-lived, but often affect protein motion. Measurement of protein dynamics within a cellular environment, particularly while perturbing protein function with small molecules, may enable dissection of key interactions and facilitate drug discovery; however, current approaches are limited by throughput with respect to data acquisition and analysis. As a result, studies using super-resolution imaging are typically drawing conclusions from tens of cells and a few experimental conditions tested. We addressed these limitations by developing a high-throughput single-molecule tracking (htSMT) platform for pharmacologic dissection of protein dynamics in living cells at an unprecedented scale (capable of imaging >106 cells/day and screening >104 compounds). We applied htSMT to measure the cellular dynamics of fluorescently tagged estrogen receptor (ER) and screened a diverse library to identify small molecules that perturbed ER function in real time. With this one experimental modality, we determined the potency, pathway selectivity, target engagement, and mechanism of action for identified hits. Kinetic htSMT experiments were capable of distinguishing between on-target and on-pathway modulators of ER signaling. Integrated pathway analysis recapitulated the network of known ER interaction partners and suggested potentially novel, kinase-mediated regulatory mechanisms. The sensitivity of htSMT revealed a new correlation between ER dynamics and the ability of ER antagonists to suppress cancer cell growth. Therefore, measuring protein motion at scale is a powerful method to investigate dynamic interactions among proteins and may facilitate the identification and characterization of novel therapeutics.

Lens-free on-chip microscopy (LFOCM) is a powerful computational imaging technology that combines high-throughput capabilities with cost efficiency. However, in LFOCM, the phase recovered by iterative phase retrieval techniques is generally wrapped into the range of − π to π , necessitating phase unwrapping to recover absolute phase distributions. Moreover, this unwrapping process is prone to errors, particularly in areas with large phase gradients or low spatial sampling, due to the absence of reliable initial guesses. To address these challenges, we propose a novel biplane phase retrieval (BPR) method that integrates phase unwrapping results obtained at different propagation distances to achieve accurate absolute phase reconstruction. The effectiveness of BPR is validated through live-cell imaging of HeLa cells, demonstrating improved quantitative phase imaging (QPI) accuracy when compared to conventional off-axis digital holographic microscopy. Furthermore, time-lapse imaging of COS-7 cells in vitro highlights the method's robustness and capability for long-term quantitative analysis of large cell populations. [ABSTRACT FROM AUTHOR]

The regulation of cell physiology depends largely upon interactions of functionally distinct proteins and cellular components. These interactions may be transient or long-lived, but often affect protein motion. Measurement of protein dynamics within a cellular environment, particularly while perturbing protein function with small molecules, may enable dissection of key interactions and facilitate drug discovery; however, current approaches are limited by throughput with respect to data acquisition and analysis. As a result, studies using super-resolution imaging are typically drawing conclusions from tens of cells and a few experimental conditions tested. We addressed these limitations by developing a high-throughput single-molecule tracking (htSMT) platform for pharmacologic dissection of protein dynamics in living cells at an unprecedented scale (capable of imaging >106 cells/day and screening >104 compounds). We applied htSMT to measure the cellular dynamics of fluorescently tagged estrogen receptor (ER) and screened a diverse library to identify small molecules that perturbed ER function in real time. With this one experimental modality, we determined the potency, pathway selectivity, target engagement, and mechanism of action for identified hits. Kinetic htSMT experiments were capable of distinguishing between on-target and on-pathway modulators of ER signaling. Integrated pathway analysis recapitulated the network of known ER interaction partners and suggested potentially novel, kinase-mediated regulatory mechanisms. The sensitivity of htSMT revealed a new correlation between ER dynamics and the ability of ER antagonists to suppress cancer cell growth. Therefore, measuring protein motion at scale is a powerful method to investigate dynamic interactions among proteins and may facilitate the identification and characterization of novel therapeutics.

The transport of intensity equation (TIE) is a well-established phase retrieval technique that enables incoherent diffraction limit-resolution imaging and is compatible with widely available brightfield microscopy hardware. However, existing TIE methods encounter difficulties in decoupling the independent contributions of phase and aberrations to the measurements in the case of unknown pupil function. Additionally, spatially nonuniform and temporally varied aberrations dramatically degrade the imaging performance for long-term research. Hence, it remains a critical challenge to realize the high-throughput quantitative phase imaging (QPI) with aberration correction under partially coherent illumination. To address these issues, we propose a novel method for high-throughput microscopy with annular illumination, termed as transport-of-intensity QPI with aberration correction (TI-AC). By combining aberration correction and pixel super-resolution technique, TI-AC is made compatible with large pixel-size sensors to enable a broader field of view. Furthermore, it surpasses the theoretical Nyquist-Shannon sampling resolution limit, resulting in an improvement of more than two times. Experimental results demonstrate that the half-width imaging resolution can be improved to ~345 nm across a 10× field of view of 1.77 mm2 (0.4 NA). Given its high-throughput capability for QPI, TI-AC is expected to be adopted in biomedical fields, such as drug discovery and cancer diagnostics.

A new generation of cell sorters combines cellular imaging with sorting cells based on brightfield or fluorescence microscopy information. Specialized imaging technologies allow for the acquisition of blur-free images of cells in flow. Rapid image analysis is key to high-speed sorting and can be realized by either simple parameter measurements or complex machine-learning analysis; each with trade-offs either in the complexity of phenotypes that can be distinguished or speed, respectively. The emergence of image-activated cell sorting heralds a new era for biomedical research, since it enables high-throughput screens and the enrichment of rare cells based on subcellular phenotypes. Commercialization or replication of these instruments provides robust, fast, and accessible platforms to meet the individual research needs of users. Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings. [ABSTRACT FROM AUTHOR]

The spatial arrangement of the genome within the nucleus is a pivotal aspect of cellular organization and function with implications for gene expression and regulation. While all genome organization features, such as loops, domains, and radial positioning, are nonrandom, they are characterized by a high degree of single-cell variability. Imaging approaches are ideally suited to visualize, measure, and study single-cell heterogeneity in genome organization. Here, we describe two methods for the detection of DNA and RNA of individual gene alleles by fluorescence in situ hybridization (FISH) in a high-throughput format. We have optimized combined DNA/RNA FISH approaches either using simultaneous or sequential detection of DNA and nascent RNA. These optimized DNA and RNA FISH protocols were implemented in a 384-well plate format alongside automated image and data analysis and enable accurate detection of individual gene alleles and their gene expression status across a large cell population. We successfully visualized MYC and EGFR DNA and nascent RNA with allele-level resolution in multiple cell types, and we determined the radial position of active and inactive MYC and EGFR alleles. These optimized DNA/RNA detection approaches are versatile and sensitive tools for mapping of chromatin features and gene activity at the single-allele level and at high throughput. [ABSTRACT FROM AUTHOR]

Objective: In preclinical studies, high-throughput positron emission tomography (PET) imaging, known as simultaneous multiple animal scanning, can reduce the time spent on animal experiments, the cost of PET tracers, and the risk of synthesis of PET tracers. It is well known that the image quality acquired by high-throughput imaging depends on the PET system. Herein, we investigated the influence of large field of view (FOV) PET scanner on high-throughput imaging. Methods: We investigated the influence of scanning four objects using a small animal PET scanner with a large FOV. We compared the image quality acquired by four objects scanned with the one acquired by one object scanned using phantoms and animals. We assessed the image quality with uniformity, recovery coefficient (RC), and spillover ratio (SOR), which are indicators of image noise, spatial resolution, and quantitative precision, respectively. For the phantom study, we used the NEMA NU 4-2008 image quality phantom and evaluated uniformity, RC, and SOR, and for the animal study, we used Wistar rats and evaluated the spillover in the heart and kidney. Results: In the phantom study, four phantoms had little effect on imaging quality, especially SOR compared with that for one phantom. In the animal study as well, four rats had little effect on spillover from the heart muscle and kidney cortex compared with that for one rat. Conclusions: This study demonstrated that an animal PET scanner with a large FOV was suitable for high-throughput imaging. Thus, the large FOV PET scanner can support drug discovery and bridging research through rapid pharmacological and pathological evaluation. [ABSTRACT FROM AUTHOR]

Summary: Global agriculture faces increasing pressure to produce more food with fewer resources. Drought, exacerbated by climate change, is a major agricultural constraint costing the industry an estimated US$80 billion per year in lost production. Wild relatives of domesticated crops, including wheat (Triticum spp.) and barley (Hordeum vulgare L.), are an underutilized source of drought tolerance genes. However, managing their undesirable characteristics, assessing drought responses, and selecting lines with heritable traits remains a significant challenge. Here, we propose a novel strategy of using multi‐trait selection criteria based on high‐throughput spectral images to facilitate the assessment and selection challenge. The importance of measuring plant capacity for sustained carbon fixation under drought stress is explored, and an image‐based transpiration efficiency (iTE) index obtained via a combination of hyperspectral and thermal imaging, is proposed. Incorporating iTE along with other drought‐related variables in selection criteria will allow the identification of accessions with diverse tolerance mechanisms. A comprehensive approach that merges high‐throughput phenotyping and de novo domestication is proposed for developing drought‐tolerant prebreeding material and providing breeders with access to gene pools containing unexplored drought tolerance mechanisms. [ABSTRACT FROM AUTHOR]

Abstract Background Imaging of in vitro neuronal differentiation and measurements of cell morphologies have led to novel insights into neuronal development. Live-cell imaging techniques and large datasets of images have increased the demand for automated pipelines for quantitative analysis of neuronal morphological metrics. Results ANDA is an analysis workflow that quantifies various aspects of neuronal morphology from high-throughput live-cell imaging screens of in vitro neuronal cell types. This tool automates the analysis of neuronal cell numbers, neurite lengths and neurite attachment points. We used chicken, rat, mouse, and human in vitro models for neuronal differentiation and have demonstrated the accuracy, versatility, and efficiency of the tool. Conclusions ANDA is an open-source tool that is easy to use and capable of automated processing from time-course measurements of neuronal cells. The strength of this pipeline is the capability to analyse high-throughput imaging screens.

Background: Imaging of in vitro neuronal differentiation and measurements of cell morphologies have led to novel insights into neuronal development. Live-cell imaging techniques and large datasets of images have increased the demand for automated pipelines for quantitative analysis of neuronal morphological metrics. Results: ANDA is an analysis workflow that quantifies various aspects of neuronal morphology from high-throughput live-cell imaging screens of in vitro neuronal cell types. This tool automates the analysis of neuronal cell numbers, neurite lengths and neurite attachment points. We used chicken, rat, mouse, and human in vitro models for neuronal differentiation and have demonstrated the accuracy, versatility, and efficiency of the tool. Conclusions: ANDA is an open-source tool that is easy to use and capable of automated processing from time-course measurements of neuronal cells. The strength of this pipeline is the capability to analyse high-throughput imaging screens. [ABSTRACT FROM AUTHOR]

We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively low-carrier frequency holograms—deep learning assisted variational Hilbert quantitative phase imaging (DL-VHQPI). The method, incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation, reliably and robustly recovers the quantitative phase information of the test objects. It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system. Compared to the conventional end-to-end networks (without a physical model), the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization. The DL-VHQPI is quantitatively studied by numerical simulation. The live-cell experiment is designed to demonstrate the method's practicality in biological research. The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.

Summary: Traditionally, botanists study plant anatomy by carefully sectioning samples, histological staining to highlight tissues of interests, then imaging slides under light microscopy. This approach generates significant details; however, this workflow is laborious, particularly in woody vines (lianas) with heterogeneous anatomies, and ultimately yields two‐dimensional (2D) images. Laser ablation tomography (LATscan) is a high‐throughput imaging system that yields hundreds of images per minute. This method has proven useful for studying the structure of delicate plant tissues; however, its utility in understanding the structure of woody tissues is underexplored.We report LATscan‐derived anatomical data from several stems of lianas (c. 20 mm) of seven species and compare these results with those obtained through traditional anatomical techniques.LATscan successfully allows the description of tissue composition by differentiating cell type, size, and shape, but also permits the recognition of distinct cell wall composition (e.g. lignin, suberin, cellulose) based on differential fluorescent signals on unstained samples.LATscan generate high‐quality 2D images and 3D reconstructions of woody plant samples; therefore, this new technology is useful for both qualitative and quantitative analyses. This high‐throughput imaging technology has the potential to bolster phenotyping of vegetative and reproductive anatomy, wood anatomy, and other biological systems. [ABSTRACT FROM AUTHOR]

Abstract Background Deep-learning–based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of training data, which is difficult to generate for bacterial cell images. Here, we present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac). Results We have developed SyMBac, a tool that allows for rapid, automatic creation of arbitrary amounts of training data, combining detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, and is capable of training accurate image segmentation models. The major advantages of our approach are as follows: (1) synthetic training data can be generated virtually instantly and on demand; (2) these synthetic images are accompanied by perfect ground truth positions of cells, meaning no data curation is required; (3) different biological conditions, imaging platforms, and imaging modalities can be rapidly simulated, meaning any change in one’s experimental setup no longer requires the laborious process of manually generating new training data for each change. Deep-learning models trained with SyMBac data are capable of analysing data from various imaging platforms and are robust to drastic changes in cell size and morphology. Our benchmarking results demonstrate that models trained on SyMBac data generate more accurate cell identifications and precise cell masks than those trained on human-annotated data, because the model learns the true position of the cell irrespective of imaging artefacts. We illustrate the approach by analysing the growth and size regulation of bacterial cells during entry and exit from dormancy, which revealed novel insights about the physiological dynamics of cells under various growth conditions. Conclusions The SyMBac approach will help to adapt and improve the performance of deep-learning–based image segmentation models for accurate processing of high-throughput timelapse image data.

TrypTag was a 4-year project to tag the N- and C-termini of almost all Trypanosoma brucei proteins with a fluorescent protein and record the subcellular localisation through images and manual annotation. We highlight the new routes to cell biological discovery this transformative resource is enabling for parasitologists and cell biologists. [ABSTRACT FROM AUTHOR]

Islet microencapsulation is a promising solution to diabetes treatment, but its quality control based on manual microscopic inspection is extremely low-throughput, highly variable, and laborious. This study presents a high-throughput islet-encapsulation quality screening system based on lens-free on-chip imaging with a wide field-of-view of 18.15 cm2, which is more than 100× larger than that of a lens-based optical microscope, enabling it to image and analyze ∼8000 microcapsules in a single frame. Custom-written image reconstruction and processing software provides the user with clinically important information, such as microcapsule count, size, intactness, and information on whether each capsule contains an islet. This high-throughput and cost-effective platform can be useful for researchers to develop better encapsulation protocols as well as perform quality control prior to transplantation.

Structured illumination microscopy (SIM) is a robust wide-field optical nanoscopy technique. Several approaches are implemented to improve SIM's resolution capability (∼2-fold). However, achieving a high resolution with a large field of view (FOV) is still challenging. We present tilt-mirror-based multi-periodic SIM for large-FOV super-resolution microscopy. The sample is illuminated by a multi-periodic structured pattern generated by six-beam interference using a custom-designed mirror mount. We achieve 3.16-fold resolution improvement while using a 20×/0.40 numerical-aperture objective that supports a large FOV (0.53 mm × 0.34 mm). This overcomes the high-space-bandwidth product challenge, achieving 9.98-fold improvement. mMP-SIM decouples illumination and collection paths, enabling scalable super-resolution over a large FOV. By using a 28×/0.80 numerical-aperture objective lens, an optical resolution of 170 nm over a 0.40 mm × 0.25 mm imaging area is demonstrated. The proof-of-principle experimental demonstration is performed for both fluorescent beads and a biosample like U2OS (human bone osteosarcoma) cells.

Background: Deep-learning–based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of training data, which is difficult to generate for bacterial cell images. Here, we present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac). Results: We have developed SyMBac, a tool that allows for rapid, automatic creation of arbitrary amounts of training data, combining detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, and is capable of training accurate image segmentation models. The major advantages of our approach are as follows: (1) synthetic training data can be generated virtually instantly and on demand; (2) these synthetic images are accompanied by perfect ground truth positions of cells, meaning no data curation is required; (3) different biological conditions, imaging platforms, and imaging modalities can be rapidly simulated, meaning any change in one's experimental setup no longer requires the laborious process of manually generating new training data for each change. Deep-learning models trained with SyMBac data are capable of analysing data from various imaging platforms and are robust to drastic changes in cell size and morphology. Our benchmarking results demonstrate that models trained on SyMBac data generate more accurate cell identifications and precise cell masks than those trained on human-annotated data, because the model learns the true position of the cell irrespective of imaging artefacts. We illustrate the approach by analysing the growth and size regulation of bacterial cells during entry and exit from dormancy, which revealed novel insights about the physiological dynamics of cells under various growth conditions. Conclusions: The SyMBac approach will help to adapt and improve the performance of deep-learning–based image segmentation models for accurate processing of high-throughput timelapse image data. [ABSTRACT FROM AUTHOR]

Abstract Background Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes—opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms—effectively limiting the extraction of curated observations from field deployments. Holography offers relatively simple coherent microscopy designs with non-intrusive 3-D image information, and rapid frame rates that support data-driven plankton imaging tasks. Classification benchmarks across different domains have been set with transfer learning approaches, focused on repurposing pre-trained, state-of-the-art deep learning models as classifiers to learn new image features without protracted model training times. Combining the data production of holography, digital image processing, and computer vision could improve in-situ monitoring of plankton communities and contribute to sampling the diversity of microbial eukaryotes. Results Here we use a light and portable digital in-line holographic microscope (The HoloSea) with maximum optical resolution of 1.5 μm, intensity-based object detection through a volume, and four different pre-trained convolutional neural networks to classify > 3800 micro-mesoplankton (> 20 μm) images across 19 classes. The maximum classifier performance was quickly achieved for each convolutional neural network during training and reached F1-scores > 89%. Taking classification further, we show that off-the-shelf classifiers perform strongly across every decision threshold for ranking a majority of the plankton classes. Conclusion These results show compelling baselines for classifying holographic plankton images, both rare and plentiful, including several dinoflagellate and diatom groups. These results also support a broader potential for deployable holographic microscopes to sample diverse microbial eukaryotic communities, and its use for high-throughput plankton monitoring.

A major challenge in characterizing plankton communities is the collection, identification and quantification of samples in a time-efficient way. The classical manual microscopy counts are gradually being replaced by high throughput imaging and nucleic acid sequencing. DNA sequencing allows deep taxonomic resolution (including cryptic species) as well as high detection power (detecting rare species), while RNA provides insights on function and potential activity. However, these methods are affected by database limitations, PCR bias, and copy number variability across taxa. Recent developments in high-throughput imaging applied in situ or on collected samples (high-throughput microscopy, Underwater Vision Profiler, FlowCam, ZooScan, etc) has enabled a rapid enumeration of morphologically-distinguished plankton populations, estimates of biovolume/biomass, and provides additional valuable phenotypic information. Although machine learning classifiers generate encouraging results to classify marine plankton images in a time efficient way, there is still a need for large training datasets of manually annotated images. Here we provide workflow examples that couple nucleic acid sequencing with high-throughput imaging for a more complete and robust analysis of microbial communities. We also describe the publicly available and collaborative web application EcoTaxa, which offers tools for the rapid validation of plankton by specialists with the help of automatic recognition algorithms. Finally, we describe how the field is moving with citizen science programs, unmanned autonomous platforms with in situ sensors, and sequencing and digitalization of historical plankton samples.

Despite the widespread popularity of the "scratch assay," where a pipette is dragged manually through cultured tissue to create a gap to study cell migration and healing, it carries significant drawbacks. Its heavy reliance on manual technique can complicate quantification, reduce throughput, and limit the versatility and reproducibility. We present an open-source, low-cost, accessible, robotic scratching platform that addresses all of the core issues. Compatible with nearly all standard cell culture dishes and usable directly in a sterile culture hood without specialized training, our robot makes highly reproducible scratches in a variety of complex cultured tissues with high throughput. Moreover, the robot demonstrates precise removal of tissues for sculpting arbitrary tissue and wound shapes, enabling complex co-culture experiments. This system significantly improves the usefulness of the conventional scratch assay and opens up new possibilities in complex tissue engineering for realistic wound healing and migration research., Competing Interests: Declaration of interests D.J.C. and Y.L. have filed patent applications based on the method developed in this work., (Copyright © 2024. Published by Elsevier Inc.)

Plant protoplasts provide starting material for of inducing pluripotent cell masses that are competent for tissue regeneration in vitro, analogous to animal induced pluripotent stem cells (iPSCs). Dedifferentiation is associated with large-scale chromatin reorganisation and massive transcriptome reprogramming, characterised by stochastic gene expression. How this cellular variability reflects on chromatin organisation in individual cells and what factors influence chromatin transitions during culturing are largely unknown. Here, we used high-throughput imaging and a custom supervised image analysis protocol extracting over 100 chromatin features of cultured protoplasts. The analysis revealed rapid, multiscale dynamics of chromatin patterns with a trajectory that strongly depended on nutrient availability. Decreased abundance in H1 (linker histones) is hallmark of chromatin transitions. We measured a high heterogeneity of chromatin patterns indicating intrinsic entropy as a hallmark of the initial cultures. We further measured an entropy decline over time, and an antagonistic influence by external and intrinsic factors, such as phytohormones and epigenetic modifiers, respectively. Collectively, our study benchmarks an approach to understand the variability and evolution of chromatin patterns underlying plant cell reprogramming in vitro., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

Background: Plankton are foundational to marine food webs and an important feature for characterizing ocean health. Recent developments in quantitative imaging devices provide in-flow high-throughput sampling from bulk volumes--opening new ecological challenges exploring microbial eukaryotic variation and diversity, alongside technical hurdles to automate classification from large datasets. However, a limited number of deployable imaging instruments have been coupled with the most prominent classification algorithms--effectively limiting the extraction of curated observations from field deployments. Holography offers relatively simple coherent microscopy designs with non-intrusive 3-D image information, and rapid frame rates that support data-driven plankton imaging tasks. Classification benchmarks across different domains have been set with transfer learning approaches, focused on repurposing pre-trained, state-of-the-art deep learning models as classifiers to learn new image features without protracted model training times. Combining the data production of holography, digital image processing, and computer vision could improve in-situ monitoring of plankton communities and contribute to sampling the diversity of microbial eukaryotes. Results: Here we use a light and portable digital in-line holographic microscope (The HoloSea) with maximum optical resolution of 1.5 µm, intensity-based object detection through a volume, and four different pre-trained convolutional neural networks to classify > 3800 micro-mesoplankton (> 20 µm) images across 19 classes. The maximum classifier performance was quickly achieved for each convolutional neural network during training and reached F1-scores > 89%. Taking classification further, we show that off-the-shelf classifiers perform strongly across every decision threshold for ranking a majority of the plankton classes. Conclusion: These results show compelling baselines for classifying holographic plankton images, both rare and plentiful, including several dinoflagellate and diatom groups. These results also support a broader potential for deployable holographic microscopes to sample diverse microbial eukaryotic communities, and its use for highthroughput plankton monitoring. [ABSTRACT FROM AUTHOR]

Identifying new, more efficacious anti-seizure medications (ASMs) is challenging, partly due to limitations in animal-based assays. Zebrafish ( Danio rerio ) can serve as a model of chemical and genetic seizures, but methods for detecting seizure-like activity in zebrafish, though powerful, have been hampered by low sensitivity (locomotor/behavioral assays) or low-throughput (tectal electrophysiology or calcium fluorescence microscopy). To address these issues, we developed a novel approach to assay seizure-like activity using combined locomotor and calcium fluorescence features, measured simultaneously from unrestrained larval zebrafish using a 96-well fluorescent plate reader. Using custom software to track fish movement and changes in fluorescence (deltaF/F0) from high-speed time-series (12.6Hz), we trained logistic classifiers using elastic net regression to distinguish seizure-like activity from non-seizure related changes based on event-specific and subject-specific features in response to the GABA A R antagonist, pentylenetetrazole (PTZ). We demonstrate that a classifier trained on combined movement and fluorescence data achieves high accuracy ("PTZ M+F"; area-under-curve receiver-operator characteristic (AUC-ROC): 0.98; F1 score: 0.912) and out-performs classifiers trained on movement ("PTZ M"; AUC-ROC: 0.9, F1: 0.9) or fluorescence features alone ("PTZ F"; AUC-ROC 0.96; F1: 0.87). The rate of classified seizure-like events increases as a dose-response to PTZ (serial dose escalation, 0, 2.5mM, 15mM) and is strongly suppressed by ASM treatment (valproic acid, VPA; tiagabine, TGB). At high-dose PTZ, we show that VPA reduces seizure-like activity defined by either "PTZ M+F" or "PTZ M" classifiers. Meanwhile, TGB selectively reduces events defined by the "PTZ M+F" classifier, paralleling previous reports that TGB reduces electrographic but not locomotor seizures and highlighting the potential for our approach to combine features of previously orthogonal assays. Using ASM benchmark data, we employ bootstrap simulation to calculate the expected statistical power of our method as a function of sample size. We demonstrate that anti-seizure responses (robust strictly standardized mean difference, RSSMD, versus control) with magnitudes similar to those associated with VPA or TGB can be reliably detected (true positive rate (TPR) > 90%) with as few as N=4 biological replicates per group, while maintaining a 5% false positive rate. In a prospective test screen with 3-6 replicates per group and on-plate controls, the anti-seizure effect of 4 out of 5 tested ASMs (CBZ, LEV, LZP, TGB) was detected. In summary, we demonstrate a simple high-throughput approach to whole organism anti-seizure phenotyping combining two previously reported metrics to facilitate screens for novel anti-seizure interventions in zebrafish., Competing Interests: Competing interests. The authors have no competing interests to declare. Declaration of interest: The authors declare that no conflict of interest exists.

Cardiovascular diseases have emerged as one of the leading causes of human mortality, but the discovery of new drugs has been hindered by the absence of suitable in vitro platforms. In recent decades, continuously refined protocols for differentiating human induced pluripotent stem cells (hiPSCs) into hiPSC-derived cardiomyocytes (hiPSC-CMs) have significantly advanced disease modeling and drug screening; however, this has led to an increasing need to monitor the function of hiPSC-CMs. The precise regulation of action potentials (APs) and intracellular calcium (Ca 2+ ) transients is critical for proper excitation-contraction coupling and cardiomyocyte function. These important parameters are usually adversely affected in cardiovascular diseases or under cardiotoxic conditions and can be measured using optical imaging-based techniques. However, this procedure is complex and technologically challenging. We have adapted the IonOptix system to simultaneously measure APs and Ca 2+ transients in hiPSC-CMs loaded with the fluorescent dyes FluoVolt and Rhod 2, respectively. This system serves as a powerful high-throughput platform to facilitate the discovery of new compounds to treat cardiovascular diseases with the cellular phenotypes of abnormal APs and Ca 2+ handling. Here, we present a comprehensive protocol for hiPSC-CM preparation, device setup, optical imaging, and data analysis. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Maintenance and seeding of hiPSC-CMs Basic Protocol 2: Simultaneous detection of action potentials and Ca 2+ transients in hiPSC-CMs., (© 2024 Wiley Periodicals LLC.)

The recent revolution in digital technologies and information processing methods present important opportunities to transform the way optical imaging is performed, particularly toward improving the throughput of microscopes while at the same time reducing their relative cost and complexity. Lensfree computational microscopy is rapidly emerging toward this end, and by discarding lenses and other bulky optical components of conventional imaging systems, and relying on digital computation instead, it can achieve both reflection and transmission mode microscopy over a large field-of-view within compact, cost-effective and mechanically robust architectures. Such high throughput and miniaturized imaging devices can provide a complementary toolset for telemedicine applications and point-of-care diagnostics by facilitating complex and critical tasks such as cytometry and microscopic analysis of e.g., blood smears, Pap tests and tissue samples. In this article, the basics of these lensfree microscopy modalities will be reviewed, and their clinically relevant applications will be discussed.

The recent revolution in digital technologies and information processing methods present important opportunities to transform the way optical imaging is performed, particularly toward improving the throughput of microscopes while at the same time reducing their relative cost and complexity. Lensfree computational microscopy is rapidly emerging toward this end, and by discarding lenses and other bulky optical components of conventional imaging systems, and relying on digital computation instead, it can achieve both reflection and transmission mode microscopy over a large field-of-view within compact, cost-effective and mechanically robust architectures. Such high throughput and miniaturized imaging devices can provide a complementary toolset for telemedicine applications and point-of-care diagnostics by facilitating complex and critical tasks such as cytometry and microscopic analysis of e.g., blood smears, Pap tests and tissue samples. In this article, the basics of these lensfree microscopy modalities will be reviewed, and their clinically relevant applications will be discussed.

Large-scale electron microscopy (EM) allows analysis of both tissues and macromolecules in a semi-automated manner, but acquisition rate forms a bottleneck. We reasoned that a negative bias potential may be used to enhance signal collection, allowing shorter dwell times and thus increasing imaging speed. Negative bias potential has previously been used to tune penetration depth in block-face imaging. However, optimization of negative bias potential for application in thin section imaging will be needed prior to routine use and application in large-scale EM. Here, we present negative bias potential optimized through a combination of simulations and empirical measurements. We find that the use of a negative bias potential generally results in improvement of image quality and signal-to-noise ratio (SNR). The extent of these improvements depends on the presence and strength of a magnetic immersion field. Maintaining other imaging conditions and aiming for the same image quality and SNR, the use of a negative stage bias can allow for a 20-fold decrease in dwell time, thus reducing the time for a week long acquisition to less than 8 h. We further show that negative bias potential can be applied in an integrated correlative light electron microscopy (CLEM) application, allowing fast acquisition of a high precision overlaid LM-EM dataset. Application of negative stage bias potential will thus help to solve the current bottleneck of image acquisition of large fields of view at high resolution in large-scale microscopy.

Label-free microscopy directly images live samples without fluorescent markers, providing a panoramic view of biological structures and functions. However, pursuing high-throughput and high-content live cell imaging requires both higher spatial resolution and larger field of view, which are limited by the optical system. In this study, we custom-built a 5▪/0.35 objective with a field number of 28 mm, providing a space-bandwidth product of 34 megapixels, which is four times greater than that of commercial high-throughput objectives such as the Nikon Lambda series (4▪/0.2, 10▪/0.45, 20▪/0.75). Furthermore, we incorporated Fourier ptychography microscopy (FPM) into the system, achieving a synthetic numerical aperture of 0.72, suitable for digital pathology and long-term live cell observation. To improve the FPM performance, we proposed a phantom-based calibration method for quantitative correction of illumination angle errors. Additionally, this method can also serve as an initial step for the on-line calibration. The remarkable capabilities of our 5▪/0.35 objective have been demonstrated, as well as the effectiveness of computational label-free microscopy. Furthermore, by combining our original FPM with more advanced hardware and algorithms, our FPM can achieve higher performance. • Custom-built a high-throughput objective lens. • Developed a Fourier ptychography microscopy system based on the high-throughput objective lens. • Introduced a phantom-based calibration method for illumination angle correction. • Integrated traditional optical design techniques with computational imaging methods. [ABSTRACT FROM AUTHOR]

Quantification of intracellular bacterial colonies is useful in strategies directed against bacterial attachment, subsequent cellular invasion and intracellular proliferation. An automated, high-throughput microscopy-method was established to quantify the number and size of intracellular bacterial colonies in infected host cells (Detection and quantification of intracellular bacterial colonies by automated, high-throughput microscopy, Ernstsen et al., 2017 [1]). The infected cells were imaged with a 10× objective and number of intracellular bacterial colonies, their size distribution and the number of cell nuclei were automatically quantified using a spot detection-tool. The spot detection-output was exported to Excel, where data analysis was performed. In this article, micrographs and spot detection data are made available to facilitate implementation of the method.

Adaptive stress response pathways play a key role in the switch between adaptation and adversity, and are important in drug-induced liver injury. Previously, we have established an HepG2 fluorescent protein reporter platform to monitor adaptive stress response activation following drug treatment. HepG2 cells are often used in high-throughput primary toxicity screening, but metabolizing capacity in these cells is low and repeated dose toxicity testing inherently difficult. Here, we applied our bacterial artificial chromosome-based GFP reporter cell lines representing Nrf2 activation (Srxn1-GFP and NQO1-GFP), unfolded protein response (BiP-GFP and Chop-GFP), and DNA damage response (p21-GFP and Btg2-GFP) as long-term differentiated 3D liver-like spheroid cultures. All HepG2 GFP reporter lines differentiated into 3D spheroids similar to wild-type HepG2 cells. We systematically optimized the automated imaging and quantification of GFP reporter activity in individual spheroids using high-throughput confocal microscopy with a reference set of DILI compounds that activate these three stress response pathways at the transcriptional level in primary human hepatocytes. A panel of 33 compounds with established DILI liability was further tested in these six 3D GFP reporters in single 48 h treatment or 6 day daily repeated treatment. Strongest stress response activation was observed after 6-day repeated treatment, with the BiP and Srxn1-GFP reporters being most responsive and identified particular severe-DILI-onset compounds. Compounds that showed no GFP reporter activation in two-dimensional (2D) monolayer demonstrated GFP reporter stress response activation in 3D spheroids. Our data indicate that the application of BAC-GFP HepG2 cellular stress reporters in differentiated 3D spheroids is a promising strategy for mechanism-based identification of compounds with liability for DILI. [ABSTRACT FROM AUTHOR]

The larval zebrafish is a promising vertebrate model organism to study neural mechanisms underlying learning and memory due to its small brain and rich behavioral repertoire. Here, we report on a high-throughput operant conditioning system for zebrafish larvae, which can simultaneously train 12 fish to associate a visual conditioned pattern with electroshocks. We find that the learning responses can be enhanced by the visual contrast, not the spatial features of the conditioned patterns, highlighted by several behavioral metrics. By further characterizing the learning curves as well as memory extinction, we demonstrate that the percentage of learners and the memory length increase as the conditioned pattern becomes darker. Finally, little difference in operant learning responses was found between AB wild-type fish and elavl3:H2B-GCaMP6f transgenic fish.

Major progress has been achieved in recent years in three-dimensional microscopy techniques. This applies to the life sciences in general, but specifically the neuroscientific field has been a main driver for developments regarding volume imaging. In particular, scanning electron microscopy offers new insights into the organization of cells and tissues by volume imaging methods, such as serial section array tomography, serial block-face imaging or focused ion beam tomography. However, most of these techniques are restricted to relatively small tissue volumes due to the limited acquisition throughput of most standard imaging techniques. Recently, a novel multi-beam scanning electron microscope technology optimized to the imaging of large sample areas has been developed. Complemented by the commercialization of automated sample preparation robots, the mapping of larger, cubic millimeter range tissue volumes at high-resolution is now within reach. This Mini Review will provide a brief overview of the various approaches to electron microscopic volume imaging, with an emphasis on serial section array tomography and multi-beam scanning electron microscopic imaging.

The spatial arrangement of the genome within the nucleus is a pivotal aspect of cellular organization and function with implications for gene expression and regulation. While all genome organization features, such as loops, domains, and radial positioning, are non-random, they are characterized by a high degree of single-cell variability. Imaging approaches are ideally suited to visualize, measure, and study single-cell heterogeneity in genome organization. Here, we describe two methods for the detection of DNA and RNA of individual gene alleles by fluorescence in situ hybridization (FISH) in a high-throughput format. We have optimized combined DNA/RNA FISH approaches either using simultaneous or sequential detection. These optimized DNA and RNA FISH protocols, implemented in a 384-well plate format alongside automated image and data analysis, enable accurate detection of chromatin loci and their gene expression status across a large cell population with allele-level resolution. We successfully visualized MYC and EGFR DNA and RNA in multiple cell types, and we determined the radial position of active and inactive MYC and EGFR alleles. These optimized DNA/RNA detection approaches are versatile and sensitive tools for mapping of chromatin features and gene activity at the single-allele level and at high throughput., Competing Interests: Conflict of Interest The authors declare no conflicts of interest.

RNA-fluorescence in situ hybridization (RNA-FISH) is an essential and widely used tool for visualizing RNA molecules in intact cells. Recent advances have increased RNA-FISH sensitivity, signal detection efficiency, and throughput. However, detection of endogenous mRNA splice variants has been challenging due to the limits of visualization of RNA-FISH fluorescence signals and due to the limited number of RNA-FISH probes per target. HiFENS (high-throughput FISH detection of endogenous pre-mRNA splicing isoforms) is a method that enables visualization and relative quantification of mRNA splice variants at single-cell resolution in an automated high-throughput manner. HiFENS incorporates HCR (hybridization chain reaction) signal amplification strategies to enhance the fluorescence signal generated by low abundance transcripts or a small number of FISH probes targeting short stretches of RNA, such as single exons. The technique offers a significant advance in high-throughput FISH-based RNA detection and provides a powerful tool that can be used as a readout in functional genomics screens to discover and dissect cellular pathways regulating gene expression and alternative pre-mRNA splicing events., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

In the realm of oncology, the transformative impact of PROTAC (PROteolysis TAgeting Chimeras) technology has been particularly pronounced since its introduction in the 21st century. Initially conceived for cancer treatment, PROTACs have evolved beyond their primary scope, attracting increasing interest in addressing a diverse array of medical conditions. This expanded focus includes not only oncological disorders but also viral infections, bacterial ailments, immune dysregulation, neurodegenerative conditions, and metabolic disorders. This comprehensive review explores the broadening landscape of PROTAC application, highlighting ongoing developments and innovations aimed at deploying these molecules across a spectrum of diseases. Careful consideration of the design challenges associated with PROTACs reveals that, when appropriately addressed, these compounds present significant advantages over traditional therapeutic approaches, positioning them as promising alternatives. To evaluate the efficacy of PROTAC molecules, a diverse array of assays is employed, ranging from High-Throughput Imaging (HTI) assays to Cell Painting assays, CRBN engagement assays, Fluorescence Polarization assays, amplified luminescent proximity homogeneous assays, Timeresolved fluorescence energy transfer assays, and Isothermal Titration Calorimetry assays. These assessments collectively contribute to a nuanced understanding of PROTAC performance. Looking ahead, the trajectory of PROTAC technology suggests its potential recognition as a versatile therapeutic strategy for an expansive range of medical conditions. Ongoing progress in this field sets the stage for PROTACs to emerge as valuable tools in the multifaceted landscape of medical treatments., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Alternative lengthening of telomeres (ALT) is a telomerase-independent and recombination-based mechanism used by approximately 15% of human cancers to maintain telomere length and to sustain proliferation. ALT-positive cells display unique features that could be exploited for tailored cancer therapies. A key limitation for the development of ALT-specific treatments is the lack of an assay to detect ALT-positive cells that is easy to perform and that can be scaled up. One of the most broadly used assays for ALT detection, CCA (C-circle assay), does not provide single-cell information and it is not amenable to High-Throughput Screening (HTS). To overcome these limitations, we developed Native-FISH (N-FISH) as an alternative method to visualize ALT-specific single-stranded telomeric DNA. N-FISH produces single-cell data, can be applied to fixed tissues, does not require DNA isolation or amplification steps, and it can be miniaturized in a 384-well format. This protocol details the steps to perform N-FISH protocol both in a low- and high-throughput format to analyze ALT. While low-throughput N-FISH is useful to assay the ALT state of cell lines, we expect that the miniaturized N-FISH assay coupled with high-throughput imaging will be useful in functional genomics and chemical screens to identify novel cellular factors that regulate ALT and potential ALT therapeutic targets for cancer therapies directed against ALT-positive tumors, respectively., (Copyright © 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)

Highlights • A high-throughput imaging method for visualization of protein-protein interactions. • A panel of 60 lamin A/C interactors were identified. • Progerin alters interaction behavior of a subset of nuclear proteins. • Changes in protein interactions largely correlate with interactor expression. Abstract Protein-protein interactions are essential for cellular structure and function. To delineate how the intricate assembly of protein interactions contribute to cellular processes in health and disease, new methodologies that are both highly sensitive and can be applied at large scale are needed. Here, we develop HiPLA (hi gh-throughput imaging p roximity l igation a ssay), a method that employs the well-established antibody-based proximity ligation assay in a high-throughput imaging screening format as a novel means to systematically visualize protein interactomes. Using HiPLA with a library of antibodies targeting nuclear proteins, we probe the interaction of 60 proteins and associated post-translational modifications (PTMs) with the nuclear lamina in a model of the premature aging disorder Hutchinson-Gilford progeria syndrome (HGPS). We identify a subset of proteins that differentially interact with the nuclear lamina in HGPS. Using HiPLA in combination with quantitative indirect immunofluorescence, we find that the majority of differential interactions are accompanied by corresponding changes in expression of the interacting protein. Taken together, HiPLA offers a novel approach to probe cellular protein-protein interaction at a large scale and reveals mechanistic insights into the assembly of protein complexes. [ABSTRACT FROM AUTHOR]

The larval zebrafish is a promising vertebrate model organism to study neural mechanisms underlying learning and memory due to its small brain and rich behavioral repertoire. Here, we report on a high-throughput operant conditioning system for zebrafish larvae, which can simultaneously train 12 fish to associate a visual conditioned pattern with electroshocks. We find that the learning responses can be enhanced by the visual contrast, not the spatial features of the conditioned patterns, highlighted by several behavioral metrics. By further characterizing the learning curves as well as memory extinction, we demonstrate that the percentage of learners and the memory length increase as the conditioned pattern becomes darker. Finally, little difference in operant learning responses was found between AB wild-type fish and elavl3:H2B-GCaMP6f transgenic fish. [ABSTRACT FROM AUTHOR]

Accurate segmentation of zebrafish from bright-field microscope images is crucial to many applications in the life sciences. Early zebrafish stages are used, and in these stages the zebrafish is partially transparent. This transparency leads to edge ambiguity as is typically seen in the larval stages. Therefore, segmentation of zebrafish objects from images is a challenging task in computational bio-imaging. Popular computational methods fail to segment the relevant edges, which subsequently results in inaccurate measurements and evaluations. Here we present a hybrid method to accomplish accurate and efficient segmentation of zebrafish specimens from bright-field microscope images. We employ the mean shift algorithm to augment the colour representation in the images. This improves the discrimination of the specimen to the background and provides a segmentation candidate retaining the overall shape of the zebrafish. A distance-regularised level set function is initialised from this segmentation candidate and fed to an improved level set method, such that we can obtain another segmentation candidate which preserves the explicit contour of the object. The two candidates are fused using heuristics, and the hybrid result is refined to represent the contour of the zebrafish specimen. We have applied the proposed method on two typical datasets. From experiments, we conclude that the proposed hybrid method improves both efficiency and accuracy of the segmentation of the zebrafish specimen. The results are going to be used for high-throughput applications with zebrafish. [ABSTRACT FROM AUTHOR]

Digital pathology via whole-slide imaging (WSI) systems has recently been approved for the primary diagnostic use in the US. Acquiring whole-slide images with spectral information at each pixel permits the use of multiplexed antibody labeling and allow for the measurement of cellularly resolved chemical information. Here, we report the development of a high-throughput terapixel hyperspectral WSI system using prism-based slit-array dispersion. We demonstrate a slit-array detection scheme for absorption-based measurements and a slit-array projection scheme for fluorescence-based measurements. The spectral resolution and spectral range in the reported schemes can be adjusted by changing the orientation of the slit-array mask. We use our system to acquire 74 5-megapixel brightfield images at different wavelengths in ∼1 s, corresponding to a throughput of 0.375 gigapixels∕s. A terapixel whole-slide spatial–spectral data cube can be obtained in ∼45 min. The reported system is compatible with existing WSI systems and can be developed as an add-on module for whole-slide spectral imaging. It may find broad applications in high-throughput chemical imaging with multiple antibody labeling. The use of slit array for structured illumination may also provide insights for developing highthroughput hyperspectral confocal imaging systems. [ABSTRACT FROM AUTHOR]

The spatial organization of chromosomes in the nuclear space is an extensively studied field that relies on measurements of structural features and 3D positions of chromosomes with high precision and robustness. However, no tools are currently available to image and analyze chromosome territories in a high-throughput format. Here, we have developed High-throughput Chromosome Territory Mapping (HiCTMap), a method for the robust and rapid analysis of 2D and 3D chromosome territory positioning in mammalian cells. HiCTMap is a high-throughput imaging-based chromosome detection method which enables routine analysis of chromosome structure and nuclear position. Using an optimized FISH staining protocol in a 384-well plate format in conjunction with a bespoke automated image analysis workflow, HiCTMap faithfully detects chromosome territories and their position in 2D and 3D in a large population of cells per experimental condition. We apply this novel technique to visualize chromosomes 18, X, and Y in male and female primary human skin fibroblasts, and show accurate detection of the correct number of chromosomes in the respective genotypes. Given the ability to visualize and quantitatively analyze large numbers of nuclei, we use HiCTMap to measure chromosome territory area and volume with high precision and determine the radial position of chromosome territories using either centroid or equidistant-shell analysis. The HiCTMap protocol is also compatible with RNA FISH as demonstrated by simultaneous labeling of X chromosomes and Xist RNA in female cells. We suggest HiCTMap will be a useful tool for routine precision mapping of chromosome territories in a wide range of cell types and tissues. [ABSTRACT FROM AUTHOR]

The selective autophagic removal of mitochondria called mitophagy is an essential physiological signaling for clearing damaged mitochondria and thus maintains the functional integrity of mitochondria and cells. Defective mitophagy is implicated in several diseases, placing mitophagy as a target for drug development. The identification of key regulators of mitophagy as well as chemical modulators of mitophagy requires sensitive and reliable quantitative approaches. Since mitophagy is a rapidly progressing event and sub-microscopic in nature, live cell image-based detection tools with high spatial and temporal resolution is preferred over end-stage assays. We describe two approaches for measuring mitophagy in mammalian cells using stable cells expressing EGFP-LC3 – Mito-DsRed to mark early phase of mitophagy and Mitochondria-EGFP – LAMP1-RFP stable cells for late events of mitophagy. Both the assays showed good spatial and temporal resolution in wide-field, confocal and super-resolution microscopy with high-throughput adaptable capability. A limited compound screening allowed us to identify a few new mitophagy inducers. Compared to the current mitophagy tools, mito-Keima or mito-QC, the assay described here determines the direct delivery of mitochondrial components to the lysosome in real time mode with accurate quantification if monoclonal cells expressing a homogenous level of both probes are established. Since the assay described here employs real-time imaging approach in a high-throughput mode, the platform can be used both for siRNA screening or compound screening to identify key regulators of mitophagy at decisive stages. [ABSTRACT FROM AUTHOR]

High-throughput imaging (HTI) is a powerful tool in the discovery of cellular disease mechanisms. While traditional approaches to identify disease pathways often rely on knowledge of the causative genetic defect, HTI-based screens offer an unbiased discovery approach based on any morphological or functional defects of disease cells or tissues. In this review, we provide an overview of the use of HTI for the study of human disease mechanisms. We discuss key technical aspects of HTI and highlight representative examples of its practical applications for the discovery of molecular mechanisms of disease, focusing on infectious diseases and host–pathogen interactions, cancer, and rare genetic diseases. We also present some of the current challenges and possible solutions offered by novel cell culture systems and genome engineering approaches. [ABSTRACT FROM AUTHOR]