Characterization of microglia behaviour in healthy and pathological conditions with image analysis tools.

Microglia are very sensitive to changes in the environment and respond through morphological, functional and metabolic adaptations. To depict the modifications microglia undergo under healthy and pathological conditions, we developed free access image analysis scripts to quantify microglia morphologies and phagocytosis. Neuron-glia cultures, in which microglia express the reporter tdTomato, were exposed to excitotoxicity or excitotoxicity + inflammation and analysed 8 h later. Neuronal death was assessed by SYTOX staining of nucleus debris and phagocytosis was measured through the engulfment of SYTOX+ particles in microglia. We identified seven morphologies: round, hypertrophic, fried egg, bipolar and three 'inflamed' morphologies. We generated a classifier able to separate them and assign one of the seven classes to each microglia in sample images. In control cultures, round and hypertrophic morphologies were predominant. Excitotoxicity had a limited effect on the composition of the populations. By contrast, excitotoxicity + inflammation promoted an enrichment in inflamed morphologies and increased the percentage of phagocytosing microglia. Our data suggest that inflammation is critical to promote phenotypical changes in microglia. We also validated our tools for the segmentation of microglia in brain slices and performed morphometry with the obtained mask. Our method is versatile and useful to correlate microglia sub-populations and behaviour with environmental changes.

CLEMSite, a software for automated phenotypic screens using light microscopy and FIB-SEM.

In recent years, Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has emerged as a flexible method that enables semi-automated volume ultrastructural imaging. We present a toolset for adherent cells that enables tracking and finding cells, previously identified in light microscopy (LM), in the FIB-SEM, along with the automatic acquisition of high-resolution volume datasets. We detect the underlying grid pattern in both modalities (LM and EM), to identify common reference points. A combination of computer vision techniques enables complete automation of the workflow. This includes setting the coincidence point of both ion and electron beams, automated evaluation of the image quality and constantly tracking the sample position with the microscope's field of view reducing or even eliminating operator supervision. We show the ability to target the regions of interest in EM within 5 µm accuracy while iterating between different targets and implementing unattended data acquisition. Our results demonstrate that executing volume acquisition in multiple locations autonomously is possible in EM.

The Image Data Explorer: Interactive exploration of image-derived data.

Many bioimage analysis projects produce quantitative descriptors of regions of interest in images. Associating these descriptors with visual characteristics of the objects they describe is a key step in understanding the data at hand. However, as many bioimage data and their analysis workflows are moving to the cloud, addressing interactive data exploration in remote environments has become a pressing issue. To address it, we developed the Image Data Explorer (IDE) as a web application that integrates interactive linked visualization of images and derived data points with exploratory data analysis methods, annotation, classification and feature selection functionalities. The IDE is written in R using the shiny framework. It can be easily deployed on a remote server or on a local computer. The IDE is available at https://git.embl.de/heriche/image-data-explorer and a cloud deployment is accessible at https://shiny-portal.embl.de/shinyapps/app/01_image-data-explorer.

A Hitchhiker's guide through the bio-image analysis software universe.

Modern research in the life sciences is unthinkable without computational methods for extracting, quantifying and visualising information derived from microscopy imaging data of biological samples. In the past decade, we observed a dramatic increase in available software packages for these purposes. As it is increasingly difficult to keep track of the number of available image analysis platforms, tool collections, components and emerging technologies, we provide a conservative overview of software that we use in daily routine and give insights into emerging new tools. We give guidance on which aspects to consider when choosing the platform that best suits the user's needs, including aspects such as image data type, skills of the team, infrastructure and community at the institute and availability of time and budget.

High-speed fluorescence image-enabled cell sorting.

Fast and selective isolation of single cells with unique spatial and morphological traits remains a technical challenge. Here, we address this by establishing high-speed image-enabled cell sorting (ICS), which records multicolor fluorescence images and sorts cells based on measurements from image data at speeds up to 15,000 events per second. We show that ICS quantifies cell morphology and localization of labeled proteins and increases the resolution of cell cycle analyses by separating mitotic stages. We combine ICS with CRISPR-pooled screens to identify regulators of the nuclear factor κB (NF-κB) pathway, enabling the completion of genome-wide image-based screens in about 9 hours of run time. By assessing complex cellular phenotypes, ICS substantially expands the phenotypic space accessible to cell-sorting applications and pooled genetic screening.

Volumetric morphometry reveals spindle width as the best predictor of mammalian spindle scaling.

The function of cellular structures at the mesoscale is dependent on their geometry and proportionality to cell size. The mitotic spindle is a good example why length and shape of intracellular organelles matter. Spindle length determines the distance over which chromosomes will segregate, and spindle shape ensures bipolarity. While we still lack a systematic and quantitative understanding of subcellular morphology, new imaging techniques and volumetric data analysis promise novel insights into scaling relations across different species. Here, we introduce Spindle3D, an open-source plug-in that allows for the quantitative, consistent, and automated analysis of 3D fluorescent data of spindles and chromatin. We systematically analyze different mammalian cell types, including somatic cells, stem cells, and one- and two-cell embryos, to derive volumetric relations of spindle, chromatin, and the cell. Taken together, our data indicate that mitotic spindle width is a robust indicator of spindle volume, which correlates linearly with chromatin and cell volume both within single cell types and across mammalian species.

OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies.

The rapid pace of innovation in biological imaging and the diversity of its applications have prevented the establishment of a community-agreed standardized data format. We propose that complementing established open formats such as OME-TIFF and HDF5 with a next-generation file format such as Zarr will satisfy the majority of use cases in bioimaging. Critically, a common metadata format used in all these vessels can deliver truly findable, accessible, interoperable and reusable bioimaging data.

Whole-body integration of gene expression and single-cell morphology.

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets.

An open-source semi-automated robotics pipeline for embryo immunohistochemistry.

A significant challenge for developmental systems biology is balancing throughput with controlled conditions that minimize experimental artifacts. Large-scale developmental screens such as unbiased mutagenesis surveys have been limited in their applicability to embryonic systems, as the technologies for quantifying precise expression patterns in whole animals has not kept pace with other sequencing-based technologies. Here, we outline an open-source semi-automated pipeline to chemically fixate, stain, and 3D-image Drosophila embryos. Central to this pipeline is a liquid handling robot, Flyspresso, which automates the steps of classical embryo fixation and staining. We provide the schematics and an overview of the technology for an engineer or someone equivalently trained to reproduce and further improve upon Flyspresso, and highlight the Drosophila embryo fixation and colorimetric or antibody staining protocols. Additionally, we provide a detailed overview and stepwise protocol for our adaptive-feedback pipeline for automated embryo imaging on confocal microscopes. We demonstrate the efficiency of this pipeline compared to classical techniques, and how it can be repurposed or scaled to other protocols and biological systems. We hope our pipeline will serve as a platform for future research, allowing a broader community of users to build, execute, and share similar experiments.

BigDataProcessor2: a free and open-source Fiji plugin for inspection and processing of TB sized image data.

SUMMARY: Modern bioimaging and related areas such as sensor technology have undergone tremendous development over the last few years. As a result, contemporary imaging techniques, particularly electron microscopy (EM) and light sheet microscopy, can frequently generate datasets attaining sizes of several terabytes (TB). As a consequence, even seemingly simple data operations such as cropping, chromatic- and drift-corrections and even visualisation, poses challenges when applied to thousands of time points or tiles. To address this we developed BigDataProcessor2-a Fiji plugin facilitating processing workflows for TB sized image datasets. AVAILABILITY AND IMPLEMENTATION: BigDataProcessor2 is available as a Fiji plugin via the BigDataProcessor update site. The application is implemented in Java and the code is publicly available on GitHub (https://github.com/bigdataprocessor/bigdataprocessor2). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An open-source high-content analysis workflow for CFTR function measurements using the forskolin-induced swelling assay.

MOTIVATION: The forskolin-induced swelling (FIS) assay has become the preferential assay to predict the efficacy of approved and investigational CFTR-modulating drugs for individuals with cystic fibrosis (CF). Currently, no standardized quantification method of FIS data exists thereby hampering inter-laboratory reproducibility. RESULTS: We developed a complete open-source workflow for standardized high-content analysis of CFTR function measurements in intestinal organoids using raw microscopy images as input. The workflow includes tools for (i) file and metadata handling; (ii) image quantification and (iii) statistical analysis. Our workflow reproduced results generated by published proprietary analysis protocols and enables standardized CFTR function measurements in CF organoids. AVAILABILITY AND IMPLEMENTATION: All workflow components are open-source and freely available: the htmrenamer R package for file handling https://github.com/hmbotelho/htmrenamer; CellProfiler and ImageJ analysis scripts/pipelines https://github.com/hmbotelho/FIS_image_analysis; the Organoid Analyst application for statistical analysis https://github.com/hmbotelho/organoid_analyst; detailed usage instructions and a demonstration dataset https://github.com/hmbotelho/FIS_analysis. Distributed under GPL v3.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera: A semi-quantitative, high throughput, microscopy-based assay expands existing approaches to measure SARS-CoV-2 specific antibody levels in human sera.

Emergence of the novel pathogenic coronavirus SARS-CoV-2 and its rapid pandemic spread presents challenges that demand immediate attention. Here, we describe the development of a semi-quantitative high-content microscopy-based assay for detection of three major classes (IgG, IgA, and IgM) of SARS-CoV-2 specific antibodies in human samples. The possibility to detect antibodies against the entire viral proteome together with a robust semi-automated image analysis workflow resulted in specific, sensitive and unbiased assay that complements the portfolio of SARS-CoV-2 serological assays. Sensitive, specific and quantitative serological assays are urgently needed for a better understanding of humoral immune response against the virus as a basis for developing public health strategies to control viral spread. The procedure described here has been used for clinical studies and provides a general framework for the application of quantitative high-throughput microscopy to rapidly develop serological assays for emerging virus infections.

An integrated multiomic and quantitative label-free microscopy-based approach to study pro-fibrotic signalling in ex vivo human precision-cut lung slices.

Fibrosis can affect any organ, resulting in the loss of tissue architecture and function with often life-threatening consequences. Pathologically, fibrosis is characterised by the expansion of connective tissue due to excessive deposition of extracellular matrix (ECM) proteins, including the fibrillar forms of collagen. A significant limitation for discovering cures for fibrosis is the availability of suitable human models and techniques to quantify mature fibrillar collagen deposition as close as possible to human physiological conditions.Here we have extensively characterised an ex vivo cultured human lung tissue-derived, precision-cut lung slices (hPCLS) model using label-free second harmonic generation (SHG) light microscopy to quantify fibrillar collagen deposition and mass spectrometry-based techniques to obtain a proteomic and metabolomic fingerprint of hPCLS in ex vivo culture.We demonstrate that hPCLS are viable and metabolically active, with mesenchymal, epithelial, endothelial and immune cell types surviving for at least 2 weeks in ex vivo culture. Analysis of hPCLS-conditioned supernatants showed a strong induction of pulmonary fibrosis-related ECM proteins upon transforming growth factor-ß1 (TGF-ß1) stimulation. This upregulation of ECM proteins was not translated into an increased deposition of fibrillar collagen. In support of this observation, we revealed the presence of a pro-ECM degradation activity in our ex vivo cultures of hPCLS, inhibition of which by a metalloproteinase inhibitor resulted in increased collagen deposition in response to TGF-ß1 stimulation.Together the data show that an integrated approach of measuring soluble pro-fibrotic markers alongside quantitative SHG-based analysis of fibrillar collagen is a valuable tool for studying pro-fibrotic signalling and testing anti-fibrotic agents.

Integrative Imaging Reveals SARS-CoV-2-Induced Reshaping of Subcellular Morphologies.

Pathogenesis induced by SARS-CoV-2 is thought to result from both an inflammation-dominated cytokine response and virus-induced cell perturbation causing cell death. Here, we employ an integrative imaging analysis to determine morphological organelle alterations induced in SARS-CoV-2-infected human lung epithelial cells. We report 3D electron microscopy reconstructions of whole cells and subcellular compartments, revealing extensive fragmentation of the Golgi apparatus, alteration of the mitochondrial network and recruitment of peroxisomes to viral replication organelles formed by clusters of double-membrane vesicles (DMVs). These are tethered to the endoplasmic reticulum, providing insights into DMV biogenesis and spatial coordination of SARS-CoV-2 replication. Live cell imaging combined with an infection sensor reveals profound remodeling of cytoskeleton elements. Pharmacological inhibition of their dynamics suppresses SARS-CoV-2 replication. We thus report insights into virus-induced cytopathic effects and provide alongside a comprehensive publicly available repository of 3D datasets of SARS-CoV-2-infected cells for download and smooth online visualization.

Dense and pleiotropic regulatory information in a developmental enhancer.

Changes in gene regulation underlie much of phenotypic evolution1. However, our understanding of the potential for regulatory evolution is biased, because most evidence comes from either natural variation or limited experimental perturbations2. Using an automated robotics pipeline, we surveyed an unbiased mutation library for a developmental enhancer in Drosophila melanogaster. We found that almost all mutations altered gene expression and that parameters of gene expression-levels, location, and state-were convolved. The widespread pleiotropic effects of most mutations may constrain the evolvability of developmental enhancers. Consistent with these observations, comparisons of diverse Drosophila larvae revealed apparent biases in the phenotypes influenced by the enhancer. Developmental enhancers may encode a higher density of regulatory information than has been appreciated previously, imposing constraints on regulatory evolution.

Tracking cells in epithelial acini by light sheet microscopy reveals proximity effects in breast cancer initiation.

Cancer clone evolution takes place within tissue ecosystem habitats. But, how exactly tumors arise from a few malignant cells within an intact epithelium is a central, yet unanswered question. This is mainly due to the inaccessibility of this process to longitudinal imaging together with a lack of systems that model the progression of a fraction of transformed cells within a tissue. Here, we developed a new methodology based on primary mouse mammary epithelial acini, where oncogenes can be switched on in single cells within an otherwise normal epithelial cell layer. We combine this stochastic breast tumor induction model with inverted light-sheet imaging to study single-cell behavior for up to four days and analyze cell fates utilizing a newly developed image-data analysis workflow. The power of this integrated approach is illustrated by us finding that small local clusters of transformed cells form tumors while isolated transformed cells do not.

There are now drugs to treat many types of cancer, but questions still remain around how these diseases start in the first place. Researchers think that tumor growth begins when a single cell suffers damage to certain sites in its DNA that eventually cause it to divide uncontrollably. That damaged cell, and its descendants, go on to form a lump, or tumor. The trouble with proving this theory is that it is hard to watch it happening in real time. Doctors usually only meet people with cancer when their tumors start to cause health problems. By this point, the tumors contain millions of cells. A way to watch the very beginnings of a cancer could reveal risk factors within a tissue that foster the growth of a tumor. But first, researchers need to test their theory about how the disease begins in the first place. One way to do this is to surround a single cancer cell with healthy cells and watch what happens next. To do this, Alladin, Chaible et al. took healthy cells from the breast tissue of mice and grew them in the laboratory into mini-organs called organoids. These organoids share a lot of features with actual mouse breast tissue; they can even make milk if given the right hormones. Once the organoids were ready, Alladin, Chaible et al then started modifying a small number of single cells inside them by switching on genes called oncogenes, which are known to drive cancer formation in humans. Using fluorescent proteins and a sheet of laser light it was possible to watch what happened to the cells over time. This revealed that, even though all the oncogene-driven single cells received the same signals, not all of them started to divide uncontrollably. In fact, a single modified cell had a low chance of forming a tumor on its own. The more oncogene-driven cells there were near to each other, the more likely they were to form tumors. Alladin, Chaible et al. think that this is because the healthy tissue interacts with the modified, oncogene-driven cells to suppress tumor formation. It is only when a larger number of modified cells group together and start to communicate with each other that they can override the inhibitory messages of the healthy tissue. How healthy tissue stops single modified cells from forming tumors is not yet clear. But, with this new mini-organ system, researchers now have the tools to investigate. In the future, this could lead to new strategies to stop cancer before it has a chance to get started.

Genetic screening identifies a SUMO protease dynamically maintaining centromeric chromatin.

Centromeres are defined by a self-propagating chromatin structure based on stable inheritance of CENP-A containing nucleosomes. Here, we present a genetic screen coupled to pulse-chase labeling that allow us to identify proteins selectively involved in deposition of nascent CENP-A or in long-term transmission of chromatin-bound CENP-A. These include factors with known roles in DNA replication, repair, chromatin modification, and transcription, revealing a broad set of chromatin regulators that impact on CENP-A dynamics. We further identify the SUMO-protease SENP6 as a key factor, not only controlling CENP-A stability but virtually the entire centromere and kinetochore. Loss of SENP6 results in hyper-SUMOylation of CENP-C and CENP-I but not CENP-A itself. SENP6 activity is required throughout the cell cycle, suggesting that a dynamic SUMO cycle underlies a continuous surveillance of the centromere complex that in turn ensures stable transmission of CENP-A chromatin.

Nuclear Pores Assemble from Nucleoporin Condensates During Oogenesis.

The molecular events that direct nuclear pore complex (NPC) assembly toward nuclear envelopes have been conceptualized in two pathways that occur during mitosis or interphase, respectively. In gametes and embryonic cells, NPCs also occur within stacked cytoplasmic membrane sheets, termed annulate lamellae (AL), which serve as NPC storage for early development. The mechanism of NPC biogenesis at cytoplasmic membranes remains unknown. Here, we show that during Drosophila oogenesis, Nucleoporins condense into different precursor granules that interact and progress into NPCs. Nup358 is a key player that condenses into NPC assembly platforms while its mRNA localizes to their surface in a translation-dependent manner. In concert, Microtubule-dependent transport, the small GTPase Ran and nuclear transport receptors regulate NPC biogenesis in oocytes. We delineate a non-canonical NPC assembly mechanism that relies on Nucleoporin condensates and occurs away from the nucleus under conditions of cell cycle arrest.