Search

New treatments are required for advanced prostate cancer; however, there are fewer preclinical models of prostate cancer than other common tumor types to test candidate therapeutics. One opportunity to increase the scope of preclinical studies is to grow tissue from patient-derived xenografts (PDXs) as organoid cultures. Here we report a scalable pipeline for automated seeding, treatment and an analysis of the drug responses of prostate cancer organoids. We established organoid cultures from 5 PDXs with diverse phenotypes of prostate cancer, including castrate-sensitive and castrate-resistant disease, as well as adenocarcinoma and neuroendocrine pathology. We robotically embedded organoids in Matrigel in 384-well plates and monitored growth via brightfield microscopy before treatment with poly ADP-ribose polymerase inhibitors or a compound library. Independent readouts including metabolic activity and live-cell imaging–based features provided robust measures of organoid growth and complementary ways of assessing drug efficacy. Single organoid analyses enabled in-depth assessment of morphological differences between patients and within organoid populations and revealed that larger organoids had more striking changes in morphology and composition after drug treatment. By increasing the scale and scope of organoid experiments, this automated assay complements other patient-derived models and will expedite preclinical testing of new treatments for prostate cancer.

The article elucidates the physical mechanism behind the generation of superior-contrast and high-resolution label-free images using an optical waveguide. Imaging is realized by employing a high index contrast multi-moded waveguide as a partially coherent light source. The modes provide near-field illumination of unlabeled samples, thereby repositioning the higher spatial frequencies of the sample into the far-field. These modes coherently scatter off the sample with different phases and are engineered to have random spatial distributions within the integration time of the camera. This mitigates the coherent speckle noise and enhances the contrast (2-10) × as opposed to other imaging techniques. Besides, the coherent scattering of the different modes gives rise to fluctuations in intensity. The technique demonstrated here is named chip-based Evanescent Light Scattering (cELS). The concepts introduced through this work are described mathematically and the high-contrast image generation process using a multi-moded waveguide as the light source is explained. The article then explores the feasibility of utilizing fluctuations in the captured images along with fluorescence-based techniques, like intensity-fluctuation algorithms, to mitigate poor-contrast and diffraction-limited resolution in the coherent imaging regime. Furthermore, a straight waveguide is demonstrated to have limited angular diversity between its multiple modes and therefore, for isotropic sample illumination, a multiple-arms waveguide geometry is used. The concepts introduced are validated experimentally via high-contrast label-free imaging of weakly scattering nanosized specimens such as extra-cellular vesicles (EVs), liposomes, nanobeads and biological cells such as fixed and live HeLa cells.

Summary Stimulated Raman scattering (SRS) is a nonlinear Raman scattering process that can amplify Raman scattering by up to 108 times under modern microscopy configuration. SRS microscopy has emerged as a powerful chemical imaging technique due to its high chemical, spatial, and temporal resolution. While SRS microscopy was originally designed for biomedical applications, it has drawn increasingly more attention from the materials science community in recent years. The high-speed and high-chemical sensitivity of SRS are attractive for both high-throughput material characterizations and capturing transient dynamics in chemical transport and reactions. It has been explored in various topics, such as 2D materials, energy storage and conversion, and polymerizations with great success. In this review, we discuss principles, instrumentation, and current applications of SRS microscopy in materials science, followed by our perspectives on future exciting topics to be studied by SRS microscopy.

Measurement of gene expression at the single-cell level has led to important advances in the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell gene expression approaches have shed light on the high level of transcriptional heterogeneity of individual cells, both at baseline and in response to experimental or environmental perturbations. We have developed a method for High-Content Imaging (HCI)-based quantification of transcript abundance at the single-cell level in primary human immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, which we abbreviate as hcHCR, combines the high sensitivity of the hybridization chain reaction (HCR) for the visualization of mRNA molecules in single cells, with the speed, scalability, and technical reproducibility of HCI. We first tested eight microscopy-compatible attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in a 384-well format and documented the ability of the method to detect increased or decreased transcript abundance at the single-cell level in thousands of cells for each experimental condition by HCI. Furthermore, we demonstrated the feasibility of multiplexing gene expression measurements by simultaneously assaying the abundance of two transcripts per cell, both at baseline and in response to an experimental stimulus. Finally, we tested the robustness of the assay to technical and biological variation. We anticipate that hcHCR will be a suitable and cost-effective assay for low- to medium-throughput chemical, genetic or functional genomic screens in primary human cells, with the possibility of performing personalized screens or screens on cells obtained from patients with a specific disease.

Size is a fundamental cellular trait that is important in determining phytoplankton physiological and ecological processes. Fossil coccospheres, the external calcite structure produced by the excretion of interlocking plates by the phytoplankton coccolithophores, can provide a rare window into cell size in the past. Coccospheres are delicate however and are therefore poorly preserved in sediment. We demonstrate a novel technique combining imaging flow cytometry and cross-polarised light (ISX+PL) to rapidly and reliably visually isolate and quantify the morphological characteristics of coccospheres from marine sediment by exploiting their unique optical and morphological properties. Imaging flow cytometry combines the morphological information provided by microscopy with high sample numbers associated with flow cytometry. High throughput imaging overcomes the constraints of labour-intensive manual microscopy and allows statistically robust analysis of morphological features and coccosphere concentration despite low coccosphere concentrations in sediments. Applying this technique to the fine-fraction of sediments, hundreds of coccospheres can be visually isolated quickly with minimal sample preparation. This approach has the potential to enable rapid processing of down-core sediment records and/or high spatial coverage from surface sediments and may prove valuable in investigating the interplay between climate change and coccolithophore physiological/ecological response.

Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been identified in many physiological and pathological contexts, driven either by biochemical signaling or by mechanical forces within the tissue. In both cases, competition has generally been characterized based on the elimination of loser cells at the population level, but significantly less attention has been focused on determining how single-cell dynamics and interactions regulate population-wide changes. In this review, we describe quantitative strategies and outline the outstanding challenges in understanding the single cell rules governing tissue-scale competition dynamics. We propose quantitative metrics to characterize single cell behaviors in competition and use them to distinguish the types and outcomes of competition. We describe how such metrics can be measured experimentally using a novel combination of high-throughput imaging and machine learning algorithms. We outline the experimental challenges to quantify cell fate dynamics with high-statistical precision, and describe the utility of computational modeling in testing hypotheses not easily accessible in experiments. In particular, cell-based modeling approaches that combine mechanical interaction of cells with decision-making rules for cell fate choices provide a powerful framework to understand and reverse-engineer the diverse rules of cell competition.

Porcine epidemic diarrhea virus (PEDV) is an emerging porcine coronavirus that causes a tremendous economic burden on the swine industry. The assessment of PEDV-neutralizing antibody levels provides a valuable tool to assess and predict herd immunity. We evaluated the performance of a PEDV imaging cytometry–based high-throughput neutralization test (HTNT) and compared the HTNT to a fluorescent focus neutralization (FFN) assay using serum samples from pigs of known PEDV infection status ( n = 159). Estimates of diagnostic sensitivity and specificity for HTNT and FFN assays derived from receiver-operator characteristic (ROC) curve analyses showed that both PEDV FFN and HTNT provided excellent diagnostic performance. However, in the laboratory, imaging cytometry provided an objective and semi-automated approach that removed human subjectivity from the testing process and reduced the read-time of a 96-well plate to

Fluorescence microscopy is widely used for high content screening in 2D cell cultures and 3D models. In particular, 3D tissue models are gaining major relevance in modern drug development. Enabling direct multiparametric evaluation of complex samples, fluorescence lifetime imaging (FLIM) adds a further level to intensity imaging by the sensitivity of the fluorescence lifetime to the microenvironment. However, the use of FLIM is limited amongst others by the acquisition of sufficient photon numbers without phototoxic effects in live cells. Herein, we developed a new cluster-based analysis method to enhance insight, and significantly speed up analysis and measurement time for the accurate translation of fluorescence lifetime information into pharmacological pathways. Methods: We applied a fluorescently-labeled dendritic core-multishell nanocarrier and its cargo Bodipy as molecules of interest (MOI) to human cells and reconstructed human tissue. Following the sensitivity and specificity assessment of the fitting-free Cluster-FLIM analysis of data in silico and in vitro, we evaluated the dynamics of cellular molecule uptake and intracellular interactions. For 3D live tissue investigations, we applied multiphoton (mp) FLIM. Owing to Cluster-FLIM's statistics-based fitting-free analysis, we utilized this approach for automatization. Results: To discriminate the fluorescence lifetime signatures of 5 different fluorescence species in a single color channel, the Cluster-FLIM method requires only 170, respectively, 90 counts per pixel to obtain 95% sensitivity (hit rate) and 95% specificity (correct rejection rate). Cluster-FLIM revealed cellular interactions of MOIs, representing their spatiotemporal intracellular fate. In a setting of an automated workflow, the assessment of lysosomal trapping of the MOI revealed relevant differences between normal and tumor cells, as well as between 2D and 3D models. Conclusion: The automated Cluster-FLIM tool is fitting-free, providing images with enhanced information, contrast, and spatial resolution at short exposure times and low fluorophore concentrations. Thereby, Cluster-FLIM increases the applicability of FLIM in high content analysis of target molecules in drug development and beyond.

The genetic manipulability and short lifespan of C. elegans make it an important model for aging research. Widely applied methods for measurements of worm aging based on manual observation are labor intensive and low-throughput. Here, we describe the Worm Collective Activity Monitoring Platform (WormCamp), a system for assaying aging in C. elegans by monitoring activity of populations of worms in standard 24-well plates. We show that metrics based on the rate of decline in collective activity can be used to estimate the average lifespan and locomotor healthspan in the population. Using the WormCamp, we assay a panel of highly divergent natural isolates of C. elegans and show that both lifespan and locomotor healthspan display substantial heritability. To facilitate analysis of large numbers of worms, we developed a robotic imaging system capable of simultaneous automated monitoring of activity, lifespan, and locomotor healthspan in up to 2,304 populations containing a total of ~90,000 animals. We applied the automated system to conduct a large-scale RNA interference screen for genes that affect lifespan and locomotor healthspan. The WormCamp system is complementary to other current automated methods for assessing C. elegans aging and is well suited for efficiently screening large numbers of conditions.

The HyperionTM Imaging System is a novel technology that uses imaging mass cytometry (IMC) to improve upon current methods of tissue imaging, enabling sub-cellular spatial resolution and acquisition of up to 37 proteins on a single tissue slide. The technology is fairly new, and thus we want to explore the types of analysis possible with these data. Here, we introduce an analysis pipeline to utilize machine learning-based analysis for IMC data using data from a muscle invasive bladder cancer patient cohort. We also propose a novel augmentation method to handle the challenge of low number of tissue samples from IMC studies. Our augmentation method was validated and shown to perform better than when only using the original data. Both our pipeline and augmentation method show promise for applications in future research studies and clinical evaluation of this technology. Our results indicate the feasibility of using the proposed framework with a more robust data set to identify prognostic features, which is an important foundation for further clinical research.

NeutrophilExtracellular Trap (NET) formation (NETosis) is a unique process that occurs in response to numerous stimuli. To investigate NETosis, we created a method that can be used easily without the need for complex programming abilities and commercial software packages. This article describes a fully automated assay to quantify NETosis using fluorescence live imaging on an automated widefield inverted microscope. Herein, we describe (1) sample preparation, (2) required equipment for automated acquisition, and finally (3) analysis of NETosis using the readily available image analysis software Fiji (ImageJ2). This protocol can be adapted to evaluate NETosis after different stimuli, and can be easily modified to allow high‐throughput acquisition and analysis using a multi‐well plate format. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Neutrophil isolation and plate setup Basic Protocol 2: Microscope and acquisition setup for automated high throughput imaging Basic Protocol 3: Analysis of NETosis and apoptosis data

Phytoplankton are aquatic, microscopically small primary producers, accounting for almost half of the worldwide carbon fixation. As early indicators of environmental change, they play a crucial role in water quality management. Human activities like climate change, eutrophication, or international shipping traffic strongly impact diversity of these organisms. Phytoplankton monitoring is a crucial step in the recognition of changes in community composition. The common standard for monitoring programs is manual microscopic counting, which strongly limits sample number and sampling frequency. In contrast, high-throughput technologies like standard flow cytometry (FCM) are restricted to a low taxonomic resolution, which makes them unsuitable for the identification of indicator species. Imaging flow cytometers (IFC) could overcome these limitations as they combine microscopy and high-throughput analysis. In comparison to single fluorescence values, image information not only allows for a wide variety of possibilities to characterize different species as well as immediate and fast measurements but also provides an archivable data output. Taxonomic resolution of IFC (ImageStream X Mk II) was proven comparable to standard FCM (FACSAria II) by the help of numerical evaluations. This is demonstrated on different levels of taxonomic differentiation of laboratory grown cultures in this study. Phytoplankton species discrimination by an imaging flow cytometer could be useful as supportive tool to make machine-learning classifications more robust, reliable, and flexible. Furthermore, this study provides examples, demonstrating the possibility of discrimination between species with similar fluorescence properties, strains, and even subpopulations. In contrast to standard FCM, each cell is not only represented as a dot in a cytogram but is also linked to microscopic brightfield and the author presents a new way to visualize this as image-based cytograms. The source code is supplied and could be useful for all kind of IFC data in general. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Recent advances in optical clearing and light-sheet microscopy have provided unprecedented access to structural and molecular information from intact tissues. However, current light-sheet microscopes have imposed constraints on the size, shape, number of specimens, and compatibility with various clearing protocols. Here we present a multi-immersion open-top light-sheet microscope that enables simple mounting of multiple specimens processed with a variety of clearing protocols, which will facilitate wide adoption by preclinical researchers and clinical laboratories. In particular, the open-top geometry provides unsurpassed versatility to interface with a wide range of accessory technologies in the future., Light-sheet microscopes are increasingly used for imaging cleared tissues, but have imposed constraints on sample geometries and protocols. Here the authors present a multi-immersion open-top light-sheet microscope to overcome these limitations and enable high-throughput imaging of samples processed with various clearing protocols.

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 (high-throughput imaging proximity ligation assay), 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.

We describe a protocol for live-cell high-throughput (HTP) screening of yeast mutant strains carrying fluorescent protein markers for subcellular compartments of choice using automated confocal microscopy. This procedure, which combines HTP genetics and microscopy, results in the acquisition of thousands of images that can be analyzed in a systematic and quantitative way to identify morphology defects in the tagged subcellular compartments. This HTP protocol is readily adapted for screening any combination of markers and can be expanded to different growth conditions or higher order mutant genetic backgrounds.

Summary The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sezary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy. As iCellCnn is not restricted to the diagnosis of SS, we expect such weakly supervised approaches to tap the diagnostic potential of IFC by providing automatic data-driven diagnosis of diseases with so-far unknown morphological manifestations.

Conventional light and electron microscopy are the most widely used techniques for examining plant reproductive tissues; however, they are time-consuming or expensive. The anther is the male part of the plant reproductive system. Structural changes drive development, and any structural defect may lead to an increase in fertility or cause sterility; thus, quick detection of structural changes is crucial in reproductive biology. We optimized an existing low-temperature SEM alternative to examine the internal structure of hydrated, fresh-frozen anthers. In contrast with the original technique, our method does not require precooling adhesion (ethanol to fix the specimen), and the cryo-sectioning can be conducted at atmospheric pressure. In addition to enabling the differentiation between aerial and liquid-filled intercellular spaces, this method is expected to facilitate the detection of quick (during a day) developmental changes in plant reproductive tissues, which is a current challenge using conventional approaches.•This method allows the high-throughput imaging of fresh-frozen plant reproductive samples collected every 10 min, which is important for developmental studies.•The cryo-images of samples with thickness ranging from 0.2 to 3 mm can be well-preserved at 800X magnification.•This method does not require chemical processing, critical point drying, customized cryo-accessories, controlled temperature cold stages, or metal coating. This simplified method does not require highly skilled personnel, and it is suitable in most microscopy laboratories., Graphical abstract Image, graphical abstract

As a typical multicellular model organism, the zebrafish has been increasingly used in biological research. Despite the efforts to develop automated zebrafish larva imaging systems, existing ones are still defective in terms of reliability and automation. This paper presents an improved zebrafish larva high-throughput imaging system, which makes improvements to the existing designs in the following aspects. Firstly, a single larva extraction strategy is developed to make larva loading more reliable. The aggregated larvae are identified, classified by their numbers and patterns, and separated by the aspiration pipette or water stream. Secondly, the dynamic model of larva motion in the capillary is established and an adaptive robust controller is designed for decelerating the fast-moving larva to ensure the survival rate. Thirdly, rotating the larva to the desired orientation is automated by developing an algorithm to estimate the larva's initial rotation angle. For validating the improved larva imaging system, a real-time heart rate monitoring experiment is conducted as an application example. Experimental results demonstrate that the goals of the improvements have been achieved. With these improvements, the improved zebrafish larva imaging system remarkably reduces human intervention and increases the efficiency and success/survival rates of larva imaging.

Hyperspectral stimulated Raman scattering (hSRS) is a label-free microspectroscopic modality that enables live-cell metabolic imaging with chemical specificity. Yet, hSRS in the CH region has low throughput and poor chemical specificity, which limits its application to a broader range of metabolic studies. We propose a high-content, high-throughput hSRS imaging method by a sparsity-driven spectral unmixing and active spectral sub-sampling. We unprecedently generate chemical maps of four major metabolic species (lipid, protein, nucleic acid and carbohydrate) in a Mia PaCa-2 cell using seven spectral frames in the CH region, improving the acquisition speed by over an order of magnitude.

Photoacoustic (PA) tomography (PACT) ’s capability is evidently influenced by the availability of imaging probes such as the genetically encoded proteins with near-infrared optical absorption (NIR-GEP). We present a new PACT screening platform specially designed for high throughput imaging and quantification of PA signal strengths from randomly mutated NIR-GEP candidates expressed in Escherichia coli colonies.The new platform holds promise to facilitate research on the next generation NIR PA imaging probes.

Advances in genome engineering and high throughput imaging technologies have enabled genome-scale screens of single cells for a variety of phenotypes, including subcellular morphology and protein localization. We constructed TheCellVision.org, a freely available and web-accessible image visualization and data browsing tool that serves as a central repository for fluorescence microscopy images and associated quantitative data produced by high-content screening experiments. Currently, TheCellVision.org hosts 575,590 images and associated analysis results from two published high-content screening (HCS) projects focused on the budding yeast Saccharomyces cerevisiae. TheCellVision.org allows users to access, visualize and explore fluorescence microscopy images, and to search, compare, and extract data related to subcellular compartment morphology, protein abundance, and localization. Each dataset can be queried independently or as part of a search across multiple datasets using the advanced search option. The website also hosts computational tools associated with the available datasets, which can be applied to other projects and cell systems, a feature we demonstrate using published images of mammalian cells. Providing access to HCS data through websites such as TheCellVision.org enables new discovery and independent re-analyses of imaging data.

Understanding the rules that govern neuronal dynamics throughout the brain to subserve behavior and cognition remains one of the biggest challenges in neuroscience research. Recent technical advances enable the recording of increasingly larger neuronal populations to produce increasingly more sophisticated datasets. Despite bold and important open-science and data-sharing policies, these datasets tend to include unique data acquisition methods, behaviors, and file structures. Discrepancies between experimental protocols present key challenges in comparing data between laboratories and across different brain regions and species. Here, we discuss our recent efforts to create a standardized and high-throughput research platform to address these issues. The McGill-Mouse-Miniscope (M3) platform is an initiative to combine miniscope calcium imaging with standardized touchscreen-based animal behavioral testing. The goal is to curate an open-source and standardized framework for acquiring, analyzing, and accessing high-quality data of the neuronal dynamics that underly cognition throughout the brain in mice, marmosets, and models of disease. We end with a discussion of future developments and a call for users to adopt this standardized approach.

Tumor marker detection contributes to the early diagnosis of cancers. However, due to the lack of detection specificity, its results cannot act as a direct evidence to confirm cancer occurrence in clinic. Joint detection of tumor markers may improve the detection specificity. As a trial for clinical diagnosis of hepatocellular carcinoma, α-fetoprotein, α-l-fucosidase and ferritin have been combined and detected with a label-free, phase sensitive and high throughput imaging ellipsometry biosensor (IEB). Eighty-two sera have been quantitatively detected with IEB and the results are in agreement with the clinical standard approaches. Evaluated by receiver operating characteristic analysis, the specificity of joint detection improves remarkably with IEB for hepatocellular carcinoma. It can be foreseen that the joint detection of tumor markers with IEB has a potential for clinical cancer diagnosis., Highlights • Three tumor markers are combined to detect hepatocellular carcinoma. • Sera detection results are consistent with clinical standard methods. • The joint detection improves the detection specificity of hepatocellular carcinoma. • The imaging ellipsometry biosensor shows a potential for cancer diagnosis.

Multiplexed asymmetric-detection time-stretch optical microscopy (multi-ATOM) has recently been developed to enable high-throughput quantitative phase imaging flow cytometry, from which single-cell biophysical properties can be measured at large scale. However, it lacks the ability to link such biophysical knowledge to biomolecular signatures at the single-cell precision for validation and correlative multi-scale single-cell analysis. We report a high-throughput multimodal system that integrates multi-ATOM with multiplexed 1-D fluorescence imaging/detection, termed FluorATOM; and applied it to perform synchronized biophysical and biomolecular phenotyping of rare breast circulating tumor cells detected in peripheral blood in a mouse xenograft at a throughput of >10,000 cell/sec.

Graphical abstract, Highlights • Human induced pluripotent stem cells in self-renewing or differentiating conditions. • Simple live phase images analysed using CellProfiler and frame to frame subtraction. • Convolutional Neuronal Network predicts culture condition from spheroid morphology., High-throughput imaging methods can be applied to relevant cell culture models, fostering their use in research and translational applications. Improvements in microscopy, computational capabilities and data analysis have enabled high-throughput, high-content approaches from endpoint 2D microscopy images. Nonetheless, trade-offs in acquisition, computation and storage between content and throughput remain, in particular when cells and cell structures are imaged in 3D. Moreover, live 3D phase contrast microscopy images are not often amenable to analysis because of the high level of background noise. Cultures of Human induced pluripotent stem cells (hiPSC) offer unprecedented scope to profile and screen conditions affecting cell fate decisions, self-organisation and early embryonic development. However, quantifying changes in the morphology or function of cell structures derived from hiPSCs over time presents significant challenges. Here, we report a novel method based on the analysis of live phase contrast microscopy images of hiPSC spheroids. We compare self-renewing versus differentiating media conditions, which give rise to spheroids with distinct morphologies; round versus branched, respectively. These cell structures are segmented from 2D projections and analysed based on frame-to-frame variations. Importantly, a tailored convolutional neural network is trained and applied to predict culture conditions from time-frame images. We compare our results with more classic and involved endpoint 3D confocal microscopy and propose that such approaches can complement spheroid-based assays developed for the purpose of screening and profiling. This workflow can be realistically implemented in laboratories using imaging-based high-throughput methods for regenerative medicine and drug discovery.

This protocol allows high throughput imaging of Marchantia gemmae, using a cheap setup made with broadly available lab equipment. It was developed from the 3D-printed Imaging Growth Chamber developed by Mihails Delmans in his PhD work (https://github.com/HaseloffLab/MihailsDelmansThesis). We used a transparent 384 wells plate filled with 1/2 strength Gamborg B5 media with 1.2% agar and placed a single gemma at the centre of the well. Gene frames and coverslips treated with anti-fog spray were used to cover (seal) the wells. This setup can be paired with automated imaging of samples.

Understanding the rules that govern neuronal dynamics throughout the brain to subserve behavior and cognition remain one of the biggest challenges in neuroscience research. Recent technical advances enable the recording of increasingly larger neuronal populations to produce increasingly more sophisticated datasets. Despite bold and important open-science and data-sharing policies, these datasets tend to include unique data acquisition methods, behavior, and file structures. Discrepancies between experimental protocols present several key challenges including the analysis of the data itself, comparison of data collected between laboratories, and for the comparison of dynamics between brain regions and species. Here, we discuss our recent efforts to create a standardized and high-throughput research platform to address these issues. The McGill-Mouse-Marmoset (M3) platform combines miniscope calcium imaging recording in both mice and marmosets with standardized touchscreen-based behavioral testing. The goal is to curate an open-source and standardized framework for acquiring, analyzing, and accessing high-quality data of the neuronal dynamics that underly cognition throughout the brain in mice, marmosets, and models of disease. We end with a discussion of future developments and a call for users to adopt this standardized approach.

In article number https://doi.org/10.1002/advs.201901891, Dan Zhu, Peng Fei, and co‐workers develop an imaging technique, multiangle‐resolved subvoxel selective plane illumination microscopy (termed Mars‐SPIM), that can readily obtain the 3D digital atlas of a cleared whole mouse brain with an isotropic single‐cell resolution, in a short acquisition time of several minutes. It provides an efficient way to implement system‐level cellular analysis, such as the mapping of different neuron populations and tracing of long‐distance neural projections over the entire brain.

Microalgae play a key role in aquatic ecology, and methods providing species determination and enumeration can provide critical information about—for instance—harmful algae blooms (HABs) or spreading of invasive species. A crucial step in current methods is the use of sedimentation. This provides the enrichment needed to achieve statistical counts of sometimes rare species within reasonable timeframes, but it comes with the drawback of aggregating the sample. This is a real challenge for computer-aided identification as particle aggregates can often be erroneously classified. In this paper, we propose an alternative method based on flow-through imaging aided by acoustic-focussing, as this provides better input-data for automated counting-methods while simultaneously removing the need for manual sample preparation. We demonstrate that by acoustically focussing microalgae and other particulates in a fast-flowing water sample, it is possible to analyse up to 8 mL sample per minute with sufficient image quality to discriminate the invasive species Ostreopsis ovata from other particulates in samples taken directly from the Mediterranean. We also showcase the ability to achieve sharp images in flow-through at magnifications up to × 50.

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.

Aging produces a number of changes in the neuronal structure and function throughout a variety of organisms. These aging-induced changes encompass a wide range of phenotypes, from loss of locomotion ability to defective production of synaptic vesicles. C. elegans is one of the primary systems used to elucidate phenotypes associated with aging processes. Conventional aging studies in C. elegans are typically labor-intensive, low-throughput, and incorporate fluorodeoxyuridine (FUdR) as a sterilizing agent to keep the population age-synchronized throughout the assay. However, FUdR exposure induces lifespan extension, and can potentially mask the phenotypes associated with the natural aging process. In addition, studying cellular or subcellular structures requires anesthetics or adhesives to immobilize nematodes while acquiring high-resolution images. In this platform, we are able to maintain a population (∼1000 worms) age-synchronized throughout its lifespan and perform a series of high-resolution microscopy studies in a drug-free environment. The device is composed of two main interconnected sections, one with the purpose of filtering progeny while keeping the parent population intact, and one for trapping nematodes in individual compartments for microscopy. Immobilization is carried out by decreasing the temperature of the device where nematodes are trapped by placing a heat sink on top of the chip. We were able to perform periodic high-resolution microscopy of fluorescently tagged synapses located at the dorsal side of the nematode's tail throughout the worms' lifespan. To characterize the subtle phenotypes that emerge as nematodes age, computer vision was implemented to perform automated unbiased detection of synapses and quantitative analysis of aging-induced synaptic changes.

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.

A common strategy to measure the efficacy of drug treatment is the in vitro comparison of ensemble readouts with and without treatment, such as proliferation and cell death. A fundamental assumption underlying this approach is that there exists minimal cell-to-cell variability in the response to a drug. Here, we demonstrate that ensemble and non-spatial single-cell readouts applied to primary cells may lead to incomplete conclusions due to cell-to-cell variability. We exposed primary fetal pulmonary artery endothelial cells (PAEC) isolated from healthy newborn sheep and persistent pulmonary hypertension of the newborn (PPHN) sheep to the growth hormone, insulin-like growth factor 1 (IGF-1). We found that IGF-1 increased proliferation and branch points in tube formation assays but not angiogenic signaling proteins at the population level for both cell types. We hypothesized that this molecular ambiguity was due to the presence of cellular sub-populations with variable responses to IGF-1. Using high throughput single-cell imaging, we discovered a spatially localized response to IGF-1. This suggests localized signaling or heritable cell response to external stimuli may ultimately be responsible for our observations. Discovering and further exploring these rare cells is critical to finding new molecular targets to restore cellular function., Summary: Here we demonstrate that inconclusive molecular readouts of drug exposure may be due to a highly heterogeneous cellular response associated with distinct cell lineages within a primary cell population.

Circulating tumor cells (CTCs) represent novel biomarkers, since they are obtainable through a simple and noninvasive blood draw or liquid biopsy. Here, we review the high-definition single-cell analysis (HD-SCA) workflow, which brings together modern methods of immunofluorescence with more sophisticated image processing to rapidly and accurately detect rare tumor cells among the milieu of platelets, erythrocytes, and leukocytes in the peripheral blood. In particular, we discuss progress in methods to measure CTC morphology and subcellular protein expression, and we highlight some initial applications that lead to fundamental new insights about the hematogenous phase of cancer, as well as its performance in early-stage diagnosis and treatment monitoring. We end with an outlook on how to further probe CTCs and the unique advantages of the HD-SCA workflow for improving the precision of cancer care.

Access to quantitative, robust, yet affordable diagnostic tools is necessary to reduce global infectious disease burden. Manual microscopy has served as a bedrock for diagnostics with wide adaptability, although at a cost of tedious labor and human errors. Automated robotic microscopes are poised to enable a new era of smart field microscopy but current platforms remain cost prohibitive and largely inflexible, especially for resource poor and field settings. Here we present Octopi, a low-cost ($250-$500) and reconfigurable autonomous microscopy platform capable of automated slide scanning and correlated bright-field and fluorescence imaging. Being highly modular, it also provides a framework for new disease-specific modules to be developed. We demonstrate the power of the platform by applying it to automated detection of malaria parasites in blood smears. Specifically, we discovered a spectral shift on the order of 10 nm for DAPI-stained Plasmodium falciparum malaria parasites. This shift allowed us to detect the parasites with a low magnification (equivalent to 10x) large field of view (2.56 mm2) module. Combined with automated slide scanning, real time computer vision and machine learning-based classification, Octopi is able to screen more than 1.5 million red blood cells per minute for parasitemia quantification, with estimated diagnostic sensitivity and specificity exceeding 90% at parasitemia of 50/ul and 100% for parasitemia higher than 150/l. With different modules, we further showed imaging of tissue slice and sputum sample on the platform. With roughly two orders of magnitude in cost reduction, Octopi opens up the possibility of a large robotic microscope network for improved disease diagnosis while providing an avenue for collective efforts for development of modular instruments.One sentence summaryWe developed a low-cost ($250-$500) automated imaging platform that can quantify malaria parasitemia by scanning 1.5 million red blood cells per minute.

Imagine you are a scientist, working on collections. You have your pet taxon and you need information which is distributed in a number of books and publications but also in the specimens deposited in Museums or Herbaria. Instead of paying visits to these establishments, around the world, you wish there was a means to transform all the information you need into a digitized form of the physical objects, you can reach from the screen of your laptop, tablet or cell phone. You dream you were able to watch, inspect and even dissect the type material you need online but also to compare it with others they way sequences are blasted against large databases, these days. You plan to make global research on this taxon and try to derive patterns from both the molecular and organismal level of the biological organization and to link the patterns resulting to the drivers of change for this taxon. This is the vision of the Virtual Museum of Natural History and one of the ways to achieve this vision is to address the “collections on demand” requirement. One of the possible means to address this requirement is the digitization through the use of the micro-ct technology. The micro-CT virtual laboratory (vLab), developed by LifeWatchGreece research infrastructure (RI), makes it possible the online exploration and dissemination of micro-CT datasets, which are only rarely made available to the public due to their large size and a lack of dedicated online platforms for the interactive manipulation of 3D data. This presentation shows the development of such a “collections on demand” function, implemented by the SYNTHESYS+ project (DiSSCo RI), which combines such high-throughput technologies, that is micro-ct and genomics, to address the scientific community’s requirements. We show that this approach to combine patterns deriving from the application of novel techniques, which represent different kinds of observations is possible and we propose certain case studies as examples. The innovation aspects of this function include: Expansion and development of cost models for Collections on Demand; Development of standards and guidelines for exchange of collection-derived imaging data; Construction of new data pipelines and standard workflows, enabling access to complex digital content such as 3D scans; Development of novel molecular lab protocols, workflows and informatics pipelines, to enable large scale; DNA sequencing of NH collections. Expansion and development of cost models for Collections on Demand; Development of standards and guidelines for exchange of collection-derived imaging data; Construction of new data pipelines and standard workflows, enabling access to complex digital content such as 3D scans; Development of novel molecular lab protocols, workflows and informatics pipelines, to enable large scale; DNA sequencing of NH collections.

In recent years, protein kinases have become some of the most significant drug targets in cancer patients. Kinases are known to regulate the activity of many human proteins, and consequently their inhibition has been used to control cancer proliferation. A significant challenge in drug discovery is the rapid and efficient identification of new small molecules. In this study, we propose a novel in silico drug discovery approach to identify kinase targets that impinge on nuclear receptor signaling with data generated using high-content analysis (HCA). A high-throughput imaging dataset was generated from an siRNA human kinome screen on engineered cells that allow direct visualization of effects on estrogen receptor-α or a chimeric progesterone receptor B binding to specific DNA. Two types of kinase descriptors are extracted from these imaging data: first, a population-median-based descriptor and second a bag-of-words (BoW) descriptor that can capture heterogeneity information in the imaging data. Using these descriptors, we provide prediction results of drug-kinase-target interactions based on single-task learning, multi-task learning, and collaborative filtering methods. The best performing model in target-based drug discovery gives an area under the receiver operating characteristic curve (AUC) of 0.86, whereas the best model in ligand-based discovery gives an AUC of 0.79. These promising results suggest that imaging-based information can be used as an additional source of information to existing virtual screening methods, thereby making the drug discovery process more time and cost efficient.