Global Trends in Marine Plankton Diversity across Kingdoms of Life.

The ocean is home to myriad small planktonic organisms that underpin the functioning of marine ecosystems. However, their spatial patterns of diversity and the underlying drivers remain poorly known, precluding projections of their responses to global changes. Here we investigate the latitudinal gradients and global predictors of plankton diversity across archaea, bacteria, eukaryotes, and major virus clades using both molecular and imaging data from Tara Oceans. We show a decline of diversity for most planktonic groups toward the poles, mainly driven by decreasing ocean temperatures. Projections into the future suggest that severe warming of the surface ocean by the end of the 21st century could lead to tropicalization of the diversity of most planktonic groups in temperate and polar regions. These changes may have multiple consequences for marine ecosystem functioning and services and are expected to be particularly significant in key areas for carbon sequestration, fisheries, and marine conservation. VIDEO ABSTRACT.

Transcriptional Bursting and Co-bursting Regulation by Steroid Hormone Release Pattern and Transcription Factor Mobility.

Genes are transcribed in a discontinuous pattern referred to as RNA bursting, but the mechanisms regulating this process are unclear. Although many physiological signals, including glucocorticoid hormones, are pulsatile, the effects of transient stimulation on bursting are unknown. Here we characterize RNA synthesis from single-copy glucocorticoid receptor (GR)-regulated transcription sites (TSs) under pulsed (ultradian) and constant hormone stimulation. In contrast to constant stimulation, pulsed stimulation induces restricted bursting centered around the hormonal pulse. Moreover, we demonstrate that transcription factor (TF) nuclear mobility determines burst duration, whereas its bound fraction determines burst frequency. Using 3D tracking of TSs, we directly correlate TF binding and RNA synthesis at a specific promoter. Finally, we uncover a striking co-bursting pattern between TSs located at proximal and distal positions in the nucleus. Together, our data reveal a dynamic interplay between TF mobility and RNA bursting that is responsive to stimuli strength, type, modality, and duration.

Multiscale chromatin dynamics and high entropy in plant iPSC ancestors.

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.

High-throughput imaging of mRNA at the single-cell level in human primary immune cells.

Measurement of gene expression at the single-cell level has advanced the study of transcriptional regulation programs in healthy and disease states. In particular, single-cell 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 relative changes in transcript abundance at the single-cell level in human primary immune cells and have validated its performance under multiple experimental conditions to demonstrate its general applicability. This method, named hcHCR, combines the sensitivity of the hybridization chain reaction (HCR) for the visualization of RNA in single cells, with the speed, scalability, and reproducibility of HCI. We first tested eight cell attachment substrates for short-term culture of primary human B cells, T cells, monocytes, or neutrophils. We then miniaturized HCR in 384-well format and documented the ability of the method to detect changes in 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 three transcripts per cell 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 suitable for low- to medium-throughput chemical or functional genomics screens in primary human cells, with the possibility of performing screens on cells obtained from patients with a specific disease.

Allele-level visualization of transcription and chromatin by high-throughput 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 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.

Back to the future for drought tolerance.

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.

ANDA: an open-source tool for automated image analysis of in vitro neuronal cells.

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.

Laser ablation tomography (LATscan) as a new tool for anatomical studies of woody plants.

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.

Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks.

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.

An integrated pipeline for high-throughput screening and profiling of spheroids using simple live image analysis of frame to frame variations.

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.

High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease.

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.

HiPLA: High-throughput imaging proximity ligation assay.

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.

HiCTMap: Detection and analysis of chromosome territory structure and position by high-throughput imaging.

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.

High-throughput confocal imaging of differentiated 3D liver-like spheroid cellular stress response reporters for identification of drug-induced liver injury liability.

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.

High-throughput imaging: Focusing in on drug discovery in 3D.

3D organotypic culture models such as organoids and multicellular tumor spheroids (MCTS) are becoming more widely used for drug discovery and toxicology screening. As a result, 3D culture technologies adapted for high-throughput screening formats are prevalent. While a multitude of assays have been reported and validated for high-throughput imaging (HTI) and high-content screening (HCS) for novel drug discovery and toxicology, limited HTI/HCS with large compound libraries have been reported. Nonetheless, 3D HTI instrumentation technology is advancing and this technology is now on the verge of allowing for 3D HCS of thousands of samples. This review focuses on the state-of-the-art high-throughput imaging systems, including hardware and software, and recent literature examples of 3D organotypic culture models employing this technology for drug discovery and toxicology screening.

Use of HCA in subproteome-immunization and screening of hybridoma supernatants to define distinct antibody binding patterns.

Understanding the properties and functions of complex biological systems depends upon knowing the proteins present and the interactions between them. Recent advances in mass spectrometry have given us greater insights into the participating proteomes, however, monoclonal antibodies remain key to understanding the structures, functions, locations and macromolecular interactions of the involved proteins. The traditional single immunogen method to produce monoclonal antibodies using hybridoma technology are time, resource and cost intensive, limiting the number of reagents that are available. Using a high content analysis screening approach, we have developed a method in which a complex mixture of proteins (e.g., subproteome) is used to generate a panel of monoclonal antibodies specific to a subproteome located in a defined subcellular compartment such as the nucleus. The immunofluorescent images in the primary hybridoma screen are analyzed using an automated processing approach and classified using a recursive partitioning forest classification model derived from images obtained from the Human Protein Atlas. Using an ammonium sulfate purified nuclear matrix fraction as an example of reverse proteomics, we identified 866 hybridoma supernatants with a positive immunofluorescent signal. Of those, 402 produced a nuclear signal from which patterns similar to known nuclear matrix associated proteins were identified. Detailed here is our method, the analysis techniques, and a discussion of the application to further in vivo antibody production.

High-throughput detection and quantification of mitochondrial fusion through imaging flow cytometry.

Mitochondria are highly dynamic organelles whose fusion and fission play an increasingly important role in a number of both normal and pathological cellular functions. Despite the increased interest in mitochondrial dynamics, robust, and quantitative methods to analyze mitochondrial fusion and fission activity in intact cells have not been developed. The current state-of-the art method to measure mitochondrial fusion activity is the polyethylene glycol (PEG) fusion assay in which cells expressing distinct mitochondrially-targeted fluorescent proteins (FPs) are fused together and mitochondrial fusion activity is determined by the rate at which color mixing occurs. Although this assay is useful, cell-cell fusion events are rare, and finding the number of fused cells required to generate statistically rigorous data is both tedious and time-consuming. Furthermore, the data-collection methods available for fluorescence microscopy lead to inherent selection biases that are difficult to control for. To that end, we have developed an unbiased and high-throughput method to detect, image, and analyze fused cells using the Amnis ImagestreamX™ MKII. With IDEAS™ software, we developed algorithms for identifying the fused cells (two nuclei within a single cell), distinguishing them from cell aggregates. Additionally, using the fluorescence localization of the mitochondrially-targeted fluorescent proteins (YFP and DsRed), we applied a modified co-localization algorithm to identify those cells that had a high co-localization score indicating mitochondrial fusion activity. These algorithms were tested using negative controls (FPs associated with fusion deficient mitochondria) and positive controls (cells expressing both FPs in the same mitochondria). Once validated these algorithms could be applied to test samples to evaluate the degree of mitochondrial fusion in cells with various genetic mutations. Ultimately, this new method is the first robust, high-throughput way to directly measure mitochondrial fusion in intact cells. Given how many cellular processes are being linked mitochondrial dynamics, this technique will provide a powerful new tool in the study of this important organelle. © 2016 International Society for Advancement of Cytometry.

High-throughput cell-cycle imaging opens new doors for discovery.

During the life of a cell, numerous essential cellular processes must be coordinated both spatially and temporally, from DNA replication and chromosome segregation to gene expression and cytokinesis. In order to analyze these inherently dynamic and cell-cycle-dependent processes, it is essential to observe the dynamic localization of the cellular machinery throughout the entire cell cycle. Although some coarse features of cell-cycle dynamics can be captured in snapshot imaging, where cellular size or morphology can be used as a proxy for cell-cycle phase, the inherently stochastic nature of ultrastructures in the cell makes the direct visualization of subcellular dynamics an essential tool to differentiate between structural differences that are the result of biologically relevant dynamics versus cell-to-cell variation. With these goals in mind, we have developed a unique high-throughput imaging approach, and have recently applied this to characterize the cell-cycle localization of nearly every protein in the bacterial cell (Kuwada in Mol Microbiol, 95(1), 64-79, 2015). This approach combines large-format sample preparation with automated image capture, processing, and analysis to quantitatively characterize proteome localization of tens of thousands of complete cell cycles.

High-resolution, high-throughput imaging with a multibeam scanning electron microscope.

Electron-electron interactions and detector bandwidth limit the maximal imaging speed of single-beam scanning electron microscopes. We use multiple electron beams in a single column and detect secondary electrons in parallel to increase the imaging speed by close to two orders of magnitude and demonstrate imaging for a variety of samples ranging from biological brain tissue to semiconductor wafers.

Animal PET scanner with a large field of view is suitable for high-throughput scanning of rodents.

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.