BioImageXD: an open, general-purpose and high-throughput image-processing platform

ProDiVis: a method to normalize fluorescence signal localization in 3D specimens

A common problem in confocal microscopy is the decrease in intensity of excitation light and emission signal from fluorophores as they travel through 3D specimens, resulting in decreased signal detected as a function of depth. Here, we report a visualization program compatible with widely used fluorophores in cell biology to facilitate image interpretation of differential protein disposition in 3D specimens. Glioblastoma cell clusters were fluorescently labeled for mitochondrial complex I (COXI), P2X7 receptor (P2X7R), β-Actin, Ki-67, and DAPI. Each cell cluster was imaged using a laser scanning confocal microscope. We observed up to ∼70% loss in fluorescence signal across the depth in Z-stacks. This progressive underrepresentation of fluorescence intensity as the focal plane deepens hinders an accurate representation of signal location within a 3D structure. To address these challenges, we developed ProDiVis: a program that adjusts apparent fluorescent signals by normalizing one fluorescent signal to a reference signal at each focal plane. ProDiVis serves as a free and accessible, unbiased visualization tool to use in conjunction with fluorescence microscopy images and imaging software.

CoPixie, a novel algorithm for single-particle track colocalization, enables efficient quantification of telomerase dynamics at telomeres

Single-particle imaging and tracking can be combined with colocalization analysis to study the dynamic interactions between macromolecules in living cells. Indeed, single-particle tracking has been extensively used to study protein-DNA interactions and dynamics. Still, unbiased identification and quantification of binding events at specific genomic loci remains challenging. Herein, we describe CoPixie, a new software that identifies colocalization events between a theoretically unlimited number of imaging channels, including single-particle movies. CoPixie is an object-based colocalization algorithm that relies on both pixel and trajectory overlap to determine colocalization between molecules. We employed CoPixie with live-cell single-molecule imaging of telomerase and telomeres, to test the model that cancer-associated POT1 mutations facilitate telomere accessibility. We show that POT1 mutants Y223C, D224N or K90E increase telomere accessibility for telomerase interaction. However, unlike the POT1-D224N mutant, the POT1-Y223C and POT1-K90E mutations also increase the duration of long-lasting telomerase interactions at telomeres. Our data reveal that telomere elongation in cells expressing cancer-associated POT1 mutants arises from the dual impact of these mutations on telomere accessibility and telomerase retention at telomeres. CoPixie can be used to explore a variety of questions involving macromolecular interactions in living cells, including between proteins and nucleic acids, from multicolor single-particle tracks.

Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images

Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically from multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train new Cellpose models. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (across 9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new Cellpose model and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated Cellpose masks (p<0.0001) indicating greater reproducibility between Cellpose masks. There was also a high correlation (R2>0.9) between Cellpose and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from Cellpose and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, Cellpose provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.

Motivation for using data-driven algorithms in research: A review of machine learning solutions for image analysis of micrographs in neuroscience

Machine learning is a powerful tool that is increasingly being used in many research areas, including neuroscience. The recent development of new algorithms and network architectures, especially in the field of deep learning, has made machine learning models more reliable and accurate and useful for the biomedical research sector. By minimizing the effort necessary to extract valuable features from datasets, they can be used to find trends in data automatically and make predictions about future data, thereby improving the reproducibility and efficiency of research. One application is the automatic evaluation of micrograph images, which is of great value in neuroscience research. While the development of novel models has enabled numerous new research applications, the barrier to use these new algorithms has also decreased by the integration of deep learning models into known applications such as microscopy image viewers. For researchers unfamiliar with machine learning algorithms, the steep learning curve can hinder the successful implementation of these methods into their workflows. This review explores the use of machine learning in neuroscience, including its potential applications and limitations, and provides some guidance on how to select a fitting framework to use in real-life research projects.

A review of open-source image analysis tools for mammalian cell culture: algorithms, features and implementations

Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell imaging analysis, but they are not widely adopted by biologists, especially those lacking an interactive platform. To address the issue, we compile and review existing open-source cell image processing tools that provide interactive interfaces for management and prediction tasks. We highlight the prediction tools that can detect, segment, and track different mammalian cell morphologies across various image modalities and present a comparison of algorithms and unique features of these tools, whether they work locally or in the cloud. This would guide non-experts to determine which is best suited for their purposes and, developers to acknowledge what is worth further expansion. In addition, we provide a general discussion on potential implementations of the tools for a more extensive scope, which guides the reader to not restrict them to prediction tasks only. Finally, we conclude the article by stating new considerations for the development of interactive cell imaging tools and suggesting new directions for future research.

cRel and Wnt5a/Frizzled 5 Receptor-Mediated Inflammatory Regulation Reveal Novel Neuroprotectin D1 Targets for Neuroprotection

Wnt5a triggers inflammatory responses and damage via NFkB/p65 in retinal pigment epithelial (RPE) cells undergoing uncompensated oxidative stress (UOS) and in experimental ischemic stroke. We found that Wnt5a-Clathrin-mediated uptake leads to NFkB/p65 activation and that Wnt5a is secreted in an exosome-independent fashion. We uncovered that docosahexaenoic acid (DHA) and its derivative, Neuroprotectin D1 (NPD1), upregulate c-Rel expression that, as a result, blunts Wnt5a abundance by competing with NFkB/p65 on the Wnt5a promoter A. Wnt5a increases in ischemic stroke penumbra and blood, while DHA reduces Wnt5a abundance with concomitant neuroprotection. Peptide inhibitor of Wnt5a binding, Box5, is also neuroprotective. DHA-decreased Wnt5a expression is concurrent with a drop in NFkB-driven inflammatory cytokine expression, revealing mechanisms after stroke, as in RPE cells exposed to UOS. Limiting the Wnt5a activity via Box5 reduces stroke size, suggesting neuroprotection pertinent to onset and progression of retinal degenerations and stroke consequences. NPD1 disrupts Wnt5a feedback loop at two sites: (1) decreasing FZD5, thus Wnt5a internalization, and (2) by enhancing cREL activity, which competes with p65/NFkB downstream endocytosis. As a result, Wnt5a expression is reduced, and so is its inflammatory signaling in RPE cells and neurons in ischemic stroke.

Snake venom-defined fibrin architecture dictates fibroblast survival and differentiation

Fibrin is the provisional matrix formed after injury, setting the trajectory for the subsequent stages of wound healing. It is commonly used as a wound sealant and a natural hydrogel for three-dimensional (3D) biophysical studies. However, the traditional thrombin-driven fibrin systems are poorly controlled. Therefore, the precise roles of fibrin's biophysical properties on fibroblast functions, which underlie healing outcomes, are unknown. Here, we establish a snake venom-controlled fibrin system with precisely and independently tuned architectural and mechanical properties. Employing this defined system, we show that fibrin architecture influences fibroblast survival, spreading phenotype, and differentiation. A fine fibrin architecture is a key prerequisite for fibroblast differentiation, while a coarse architecture induces cell loss and disengages fibroblast's sensitivity towards TGF-β1. Our results demonstrate that snake venom-controlled fibrin can precisely control fibroblast differentiation. Applying these biophysical principles to fibrin sealants has translational significance in regenerative medicine and tissue engineering.

GEMA-An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices

Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.

Cytoskeletal vimentin regulates cell size and autophagy through mTORC1 signaling

The nutrient-activated mTORC1 (mechanistic target of rapamycin kinase complex 1) signaling pathway determines cell size by controlling mRNA translation, ribosome biogenesis, protein synthesis, and autophagy. Here, we show that vimentin, a cytoskeletal intermediate filament protein that we have known to be important for wound healing and cancer progression, determines cell size through mTORC1 signaling, an effect that is also manifested at the organism level in mice. This vimentin-mediated regulation is manifested at all levels of mTOR downstream target activation and protein synthesis. We found that vimentin maintains normal cell size by supporting mTORC1 translocation and activation by regulating the activity of amino acid sensing Rag GTPase. We also show that vimentin inhibits the autophagic flux in the absence of growth factors and/or critical nutrients, demonstrating growth factor-independent inhibition of autophagy at the level of mTORC1. Our findings establish that vimentin couples cell size and autophagy through modulating Rag GTPase activity of the mTORC1 signaling pathway.

A Plasmid-Based Fluorescence Reporter System for Monitoring Oxidative Damage in E. coli

Quantitating intracellular oxidative damage caused by reactive oxygen species (ROS) is of interest in many fields of biological research. The current systems primarily rely on supplemented oxygen-sensitive substrates that penetrate the target cells, and react with ROS to produce signals that can be monitored with spectroscopic or imaging techniques. The objective here was to design a new non-invasive analytical strategy for measuring ROS-induced damage inside living cells by taking advantage of the native redox sensor system of E. coli. The developed plasmid-based sensor relies on an oxygen-sensitive transcriptional repressor IscR that controls the expression of a fluorescent marker in vivo. The system was shown to quantitatively respond to oxidative stress induced by supplemented H₂O₂ and lowered cultivation temperatures. Comparative analysis with fluorescence microscopy further demonstrated that the specificity of the reporter system was equivalent to the commercial chemical probe (CellROX). The strategy introduced here is not dependent on chemical probes, but instead uses a fluorescent expression system to detect enzyme-level oxidative damage in microbial cells. This provides a cheap and simple means for analysing enzyme-level oxidative damage in a biological context in E. coli.

Mistic: An open-source multiplexed image t-SNE viewer

Understanding the complex ecology of a tumor tissue and the spatiotemporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immuno-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. Here, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be t-SNE or UMAP coordinates. This grouped view of all images allows an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype, and can help select images for subsequent downstream analysis. Currently, there is no freely available tool to generate such image t-SNEs.

Software tools and platforms in Digital Pathology: a review for clinicians and computer scientists

At the end of the twentieth century, a new technology was developed that allowed an entire tissue section to be scanned on an objective slide. Originally called virtual microscopy, this technology is now known as Whole Slide Imaging (WSI). WSI presents new challenges for reading, visualization, storage, and analysis. For this reason, several technologies have been developed to facilitate the handling of these images. In this paper, we analyze the most widely used technologies in the field of digital pathology, ranging from specialized libraries for the reading of these images to complete platforms that allow reading, visualization, and analysis. Our aim is to provide the reader, whether a pathologist or a computational scientist, with the knowledge to choose the technologies to use for new studies, development, or research.

Smart imaging to empower brain-wide neuroscience at single-cell levels

A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to 'smart' imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.

Vimentin Suppresses Inflammation and Tumorigenesis in the Mouse Intestine

Vimentin has been implicated in wound healing, inflammation, and cancer, but its functional contribution to intestinal diseases is poorly understood. To study how vimentin is involved during tissue injury and repair of simple epithelium, we induced colonic epithelial cell damage in the vimentin null (Vim^−/−) mouse model. Vim^−/− mice challenged with dextran sodium sulfate (DSS) had worse colitis manifestations than wild-type (WT) mice. Vim^−/− colons also produced more reactive oxygen and nitrogen species, possibly contributing to the pathogenesis of gut inflammation and tumorigenesis than in WT mice. We subsequently describe that CD11b⁺ macrophages served as the mainly cellular source of reactive oxygen species (ROS) production via vimentin-ROS-pSTAT3–interleukin-6 inflammatory pathways. Further, we demonstrated that Vim^−/− mice did not develop colitis-associated cancer model upon DSS treatment spontaneously but increased tumor numbers and size in the distal colon in the azoxymethane/DSS model comparing with WT mice. Thus, vimentin has a crucial role in protection from colitis induction and tumorigenesis of the colon.

Profiling Immune Cells in the Kidney Using Tissue Cytometry and Machine Learning

The immune system governs key functions that maintain renal homeostasis through various effector cells that reside in or infiltrate the kidney. These immune cells play an important role in shaping adaptive or maladaptive responses to local or systemic stress and injury. We increasingly recognize that microenvironments within the kidney are characterized by a unique distribution of immune cells, the function of which depends on this unique spatial localization. Therefore, quantitative profiling of immune cells in intact kidney tissue becomes essential, particularly at a scale and resolution that allow the detection of differences between the various "nephro-ecosystems" in health and disease. In this review, we discuss advancements in tissue cytometry of the kidney, performed through multiplexed confocal imaging and analysis using the Volumetric Tissue Exploration and Analysis (VTEA) software. We highlight how this tool has improved our understanding of the role of the immune system in the kidney and its relevance in the pathobiology of renal disease. We also discuss how the field is increasingly incorporating machine learning to enhance the analytic potential of imaging data and provide unbiased methods to explore and visualize multidimensional data. Such novel analytic methods could be particularly relevant when applied to profiling immune cells. Furthermore, machine-learning approaches applied to cytometry could present venues for nonexhaustive exploration and classification of cells from existing data and improving tissue economy. Therefore, tissue cytometry is transforming what used to be a qualitative assessment of the kidney into a highly quantitative, imaging-based "omics" assessment that complements other advanced molecular interrogation technologies.

In vitro model systems for exploring oral biofilms: From single-species populations to complex multi-species communities

Numerous in vitro biofilm model systems are available to study oral biofilms. Over the past several decades, increased understanding of oral biology and advances in technology have facilitated more accurate simulation of intraoral conditions and have allowed for the increased generalizability of in vitro oral biofilm studies. The integration of contemporary systems with confocal microscopy and 16S rRNA community profiling has enhanced the capabilities of in vitro biofilm model systems to quantify biofilm architecture and analyse microbial community composition. In this review, we describe several model systems relevant to modern in vitro oral biofilm studies: the constant depth film fermenter, Sorbarod perfusion system, drip-flow reactor, modified Robbins device, flowcells and microfluidic systems. We highlight how combining these systems with confocal microscopy and community composition analysis tools aids exploration of oral biofilm development under different conditions and in response to antimicrobial/anti-biofilm agents. The review closes with a discussion of future directions for the field of in vitro oral biofilm imaging and analysis.

Computational Methods for Single-Cell Imaging and Omics Data Integration

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

A digital pathology tool for quantification of color features in histologic specimens

In preclinical research, histological analysis of tissue samples is often limited to qualitative or semiquantitative scoring assessments. The reliability of this analysis can be impaired by the subjectivity of these approaches, even when read by experienced pathologists. Furthermore, the laborious nature of manual image assessments often leads to the analysis being restricted to a relatively small number of images that may not accurately represent the whole sample. Thus, there is a clear need for automated image analysis tools that can provide robust and rapid quantification of histologic samples from paraffin-embedded or cryopreserved tissues. To address this need, we have developed a color image analysis algorithm (DigiPath) to quantify distinct color features in histologic sections. We demonstrate the utility of this tool across multiple types of tissue samples and pathologic features, and compare results from our program to other quantitative approaches such as color thresholding and hand tracing. We believe this tool will enable more thorough and reliable characterization of histological samples to facilitate better rigor and reproducibility in tissue-based analyses.

Investigation of Different Free Image Analysis Software for High-Throughput Droplet Detection

Droplet microfluidics has revealed innovative strategies in biology and chemistry. This advancement has delivered novel quantification methods, such as droplet digital polymerase chain reaction (ddPCR) and an antibiotic heteroresistance analysis tool. For droplet analysis, researchers often use image-based detection techniques. Unfortunately, the analysis of images may require specific tools or programming skills to produce the expected results. In order to address the issue, we explore the potential use of standalone freely available software to perform image-based droplet detection. We select the four most popular software and classify them into rule-based and machine learning-based types after assessing the software's modules. We test and evaluate the software's (i) ability to detect droplets, (ii) accuracy and precision, and (iii) overall components and supporting material. In our experimental setting, we find that the rule-based type of software is better suited for image-based droplet detection. The rule-based type of software also has a simpler workflow or pipeline, especially aimed for non-experienced users. In our case, CellProfiler (CP) offers the most user-friendly experience for both single image and batch processing analyses.

ZFTool: A Software for Automatic Quantification of Cancer Cell Mass Evolution in Zebrafish

Background: Zebrafish (Danio rerio) is a model organism for the study of human cancer. Compared with the murine model, the zebrafish model has several properties ideal for personalized therapies. The transparency of the zebrafish embryos and the development of the pigment-deficient ”casper“ zebrafish line give the capacity to directly observe cancer formation and progression in the living animal. Automatic quantification of cellular proliferation in vivo is critical to the development of personalized medicine. Methods: A new methodology was defined to automatically quantify the cancer cellular evolution. ZFTool was developed to establish a base threshold that eliminates the embryo autofluorescence, automatically measures the area and intensity of GFP (green-fluorescent protein) marked cells, and defines a proliferation index. Results: The proliferation index automatically computed on different targets demonstrates the efficiency of ZFTool to provide a good automatic quantification of cancer cell evolution and dissemination. Conclusion: Our results demonstrate that ZFTool is a reliable tool for the automatic quantification of the proliferation index as a measure of cancer mass evolution in zebrafish, eliminating the influence of its autofluorescence.

A graphical user interface (NWUSA) for Raman spectral processing, analysis and feature recognition

It is a practical necessity for non-professional users to interpret biologically derived Raman spectral information for obtaining accurate and reliable analytical results. An integrated Raman spectral analysis software (NWUSA) was developed for spectral processing, analysis, and feature recognition. It provides a user-friendly graphical interface to perform the following preprocessing tasks: spectral range selection, cosmic ray removal, polynomial fitting based background subtraction, Savitzky-Golay smoothing, area-under-curve normalization, mean-centered procedure, as well as multivariate analysis algorithms including principal component analysis (PCA), linear discriminant analysis, partial least squares-discriminant analysis, support vector machine (SVM), and PCA-SVM. A spectral dataset obtained from two different samples was utilized to evaluate the performance of the developed software, which demonstrated that the analysis software can quickly and accurately achieve functional requirements in spectral data processing and feature recognition. Besides, the open-source software can not only be customized with more novel functional modules to suit the specific needs, but also benefit many Raman based investigations, especially for clinical usages.

Neighborhood Impact Factor to Study Cell-Fate Decision-Making in Cellular Communities

Cell-fate determination is a function of cell-intrinsic and -extrinsic signaling cues. Understanding the design principles governing fate control in multicellular systems remains difficult to understand and analyze. To address the current challenges of spatial analysis of potential signaling events, we have developed a pipeline for assessment of the neighboring cells at defined areas in the vicinity of target cells using a newly defined concept of Neighborhood Impact Factor. We have used our pipeline to interrogate cellular decision-making in a genetically derived multi-lineage liver organoid from induced pluripotent stem cells. We examined endothelial versus hepatocyte fate determination for cells with similar expression level of an engineered driver gene circuit. Our analysis suggests that the relative level of gene expression to the neighbor population can control the final fate choice in our engineered liver multicellular system.

Virulence Factor Cargo and Host Cell Interactions of Shiga Toxin-Producing Escherichia coli Outer Membrane Vesicles

Outer membrane vesicles (OMVs), nanoparticles released by Shiga toxin-producing Escherichia coli (STEC), have been identified as novel efficient virulence tools of these pathogens. STEC O157 OMVs carry a cocktail of virulence factors including Shiga toxin 2a (Stx2a), cytolethal distending toxin V (CdtV), EHEC hemolysin, flagellin, and lipopolysaccharide. OMVs are taken up by human intestinal epithelial and microvascular endothelial cells, the major targets during STEC infection, and deliver the virulence factors into host cells. There the toxins separate from OMVs and are trafficked via different pathways to their target compartments, i.e., the cytosol (Stx2a-A subunit), nucleus (CdtV-B subunit), and mitochondria (EHEC hemolysin). This leads to a toxin-specific host cell injury and ultimately apoptotic cell death. Besides their cytotoxic effects, STEC OMVs trigger an inflammatory response via their lipopolysaccharide and flagellin components. In this chapter, we describe methods for the isolation and purification of STEC OMVs, for the detection of OMV-associated virulence factors, and for the analysis of OMV interactions with host cells including OMV cellular uptake and intracellular trafficking of OMVs and OMV-delivered toxins.

Protein quality control through endoplasmic reticulum-associated degradation maintains haematopoietic stem cell identity and niche interactions

Stem cells need to be protected from genotoxic and proteotoxic stress to maintain a healthy pool throughout life^1–3. Little is known about the proteostasis mechanism that safeguards the stem cells. Here, we report Endoplasmic Reticulum-Associated Degradation (ERAD) as a protein quality checkpoint that controls hematopoietic stem cell (HSC)-niche interaction and determines the fate of HSC. SEL1L-HRD1 complex, the most conserved branch of ERAD⁴, is highly expressed in HSC. Deletion of Sel1l led to niche displacement of HSC, complete loss of HSC identity, and allowed highly efficient donor-HSC engraftment without irradiation. Mechanistic studies identified MPL, the master regulator of HSC identity⁵, as a bona-fide ERAD substrate that became aggregated in the ER upon ERAD deficiency. Restoration of MPL signaling with an agonist partially rescued the number and reconstitution capacity of Sel1l-deficient HSCs. Our study defines ERAD as an essential proteostasis mechanism to safeguard a healthy stem cell pool through regulating the stem cell-niche interaction.

Activation of TNFR1 and TLR4 following oxygen glucose deprivation promotes mitochondrial fission in C6 astroglial cells

Astrocytes have emerged as active players in the innate immune response triggered by various types of insults. Recent literature suggests that mitochondria are key participants in innate immunity. The present study investigates the role of ischemia-induced innate immune response on p65/PGC-1α mediated mitochondrial dynamics in C6 astroglial cells. OGD conditions induced astroglial differentiation in C6 cells and increased the expression of hypoxia markers; HIF-1α, HO-1 and Cox4i2. OGD conditions resulted in induction of innate immune response in terms of expression of TNFR1 and TLR4 along with increase in IL-6 and TNF-α levels. OGD conditions resulted in decreased expression of I-κB with a concomitant increase in phos-p65 levels. The expression of PGC-1α, a key regulator of mitochondrial biogenesis, was also increased. Immunochemical staining suggested that phos-p65 and PGC-1α was co-localized. Studies on mitochondrial fusion (Mfn-1) and fission (DRP1) markers revealed shift toward fission. In addition, mitochondrial membrane potential decreased with increased DNA degradation and apoptosis confirming mitochondrial fission under OGD conditions. However, inhibition of phos-p65 by MG132 reduced the co-localization of phos-p65/ PGC-1α and significantly increased the Mfn-1 expression. The findings demonstrate the involvement of TNFR1 and TLR4 mediated immune response followed by interaction between phos-p65 and PGC-1α in promoting fission in C6 cells under hypoxic condition.

Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates

Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called "-oids" (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells' behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools.

Sphingomyelinases D From Loxosceles Spider Venoms and Cell Membranes: Action on Lipid Rafts and Activation of Endogenous Metalloproteinases

Loxosceles spider venom contains Sphingomyelinase D (SMase D), the key toxin causing pathology. SMase D hydrolyzes the main component of lipid rafts, sphingomyelin, which changes the membrane microenvironment resulting in the activation of endogenous metalloproteinase from the ADAMs family. Alterations in membrane microenvironment of lipid rafts contribute to the activation of several cell surface molecules. Serine proteinases convertases acting on the pro-domain of membrane metalloproteinases, such as ADAMs, increase the cleavage and the release of proteins ectodomains and receptors located at the cell surface areas containing lipid rafts. We, therefore, investigated the interaction of SMases D with these membrane microdomains (lipid rafts) in human keratinocytes, to better understand the molecular mechanism of SMases D action, and identify the ADAM(s) responsible for the cleavage of cell surface molecules. Using specific inhibitors, we observed that ADAMs 10 and 17 are activated in the cell membrane after SMase D action. Furthermore, proproteins convertases, such as furin, are involved in the SMase D induced ADAMs activation. One of the signaling pathways that may be involved in the activation of these proteases is the MAPK pathway, since phosphorylation of ERK1/2 was observed in cells treated with SMase D. Confocal analysis showed a strong colocalization between SMase D and GM1 ganglioside present in rafts. Analysis of structural components of rafts, such as caveolin-1 and flotillin-1, showed that the action of SMase D on cell membranes leads to a reduction in caveolin-1, which is possibly degraded by toxin-induced superoxide production in cells. The action of the toxin also results in flotilin-1 increased detection in the cell membrane. These results indicate that SMases D from Loxosceles venoms alter membrane rafts structure, leading to the activation of membrane bound proteases, which may explain why the lipase action of this toxin can result in proteolytic cleavage of cell surface proteins, ultimately leading to pathology.

Soft extracellular matrix enhances inflammatory activation of mesenchymal stromal cells to induce monocyte production and trafficking

Mesenchymal stromal cells (MSCs) modulate immune cells to ameliorate multiple inflammatory pathologies. Biophysical signals that regulate this process are poorly defined. By engineering hydrogels with tunable biophysical parameters relevant to bone marrow where MSCs naturally reside, we show that soft extracellular matrix maximizes the ability of MSCs to produce paracrine factors that have been implicated in monocyte production and chemotaxis upon inflammatory stimulation by tumor necrosis factor-α (TNFα). Soft matrix increases clustering of TNF receptors, thereby enhancing NF-κB activation and downstream gene expression. Actin polymerization and lipid rafts, but not myosin-II contractility, regulate mechanosensitive activation of MSCs by TNFα. We functionally demonstrate that human MSCs primed with TNFα in soft matrix enhance production of human monocytes in marrow of xenografted mice and increase trafficking of monocytes via CCL2. The results suggest the importance of biophysical signaling in tuning inflammatory activation of stromal cells to control the innate immune system.

A low-cost paper-based platform for fast and reliable screening of cellular interactions with materials

A paper-based platform was developed and tested for studies on basic cell culture, material biocompatibility, and activity of pharmaceuticals in order to provide a reliable, robust and low-cost cell study platform. It is based upon a paper or paperboard support, with a nanostructured latex coating to provide an enhanced cell growth and sufficient barrier properties. Wetting is limited to regions of interest using a flexographically printed hydrophobic polydimethylsiloxane layer with circular non-print areas. The nanostructured coating can be substituted for another coating of interest, or the regions of interest functionalized with a material to be studied. The platform is fully up-scalable, being produced with roll-to-roll rod coating, flexographic and inkjet printing methods. Results show that the platform efficiency is comparable to multi-well plates in colorimetric assays in three separate studies: a cell culture study, a biocompatibility study, and a drug screening study. The color intensity is quantified by using a common office scanner or an imaging device and the data is analyzed by a custom computer software without the need for expensive screening or analysis equipment.

Human Enterovirus Group B Viruses Rely on Vimentin Dynamics for Efficient Processing of Viral Nonstructural Proteins

We report that several viruses from the human enterovirus group B cause massive vimentin rearrangements during lytic infection. Comprehensive studies suggested that viral protein synthesis was triggering the vimentin rearrangements. Blocking the host cell vimentin dynamics with β, β'-iminodipropionitrile (IDPN) did not significantly affect the production of progeny viruses and only moderately lowered the synthesis of structural proteins such as VP1. In contrast, the synthesis of the nonstructural proteins 2A, 3C, and 3D was drastically lowered. This led to attenuation of the cleavage of the host cell substrates PABP and G3BP1 and reduced caspase activation, leading to prolonged cell survival. Furthermore, the localization of the proteins differed in the infected cells. Capsid protein VP1 was found diffusely around the cytoplasm, whereas 2A and 3D followed vimentin distribution. Based on protein blotting, smaller amounts of nonstructural proteins did not result from proteasomal degradation but from lower synthesis without intact vimentin cage structure. In contrast, inhibition of Hsp90 chaperone activity, which regulates P1 maturation, lowered the amount of VP1 but had less effect on 2A. The results suggest that the vimentin dynamics regulate viral nonstructural protein synthesis while having less effect on structural protein synthesis or overall infection efficiency. The results presented here shed new light on differential fate of structural and nonstructural proteins of enteroviruses, having consequences on host cell survival.IMPORTANCE A virus needs the host cell in order to replicate and produce new progeny viruses. For this, the virus takes over the host cell and modifies it to become a factory for viral proteins. Irrespective of the specific virus family, these proteins can be divided into structural and nonstructural proteins. Structural proteins are the building blocks for the new progeny virions, whereas the nonstructural proteins orchestrate the takeover of the host cell and its functions. Here, we have shown a mechanism that viruses exploit in order to regulate the host cell. We show that viral protein synthesis induces vimentin cages, which promote production of specific viral proteins that eventually control apoptosis and host cell death. This study specifies vimentin as the key regulator of these events and indicates that viral proteins have different fates in the cells depending on their association with vimentin cages.

Light-Sheet Microscopy and Its Potential for Understanding Developmental Processes

The ability to visualize and quantitatively measure dynamic biological processes in vivo and at high spatiotemporal resolution is of fundamental importance to experimental investigations in developmental biology. Light-sheet microscopy is particularly well suited to providing such data, since it offers exceptionally high imaging speed and good spatial resolution while minimizing light-induced damage to the specimen. We review core principles and recent advances in light-sheet microscopy, with a focus on concepts and implementations relevant for applications in developmental biology. We discuss how light-sheet microcopy has helped advance our understanding of developmental processes from single-molecule to whole-organism studies, assess the potential for synergies with other state-of-the-art technologies, and introduce methods for computational image and data analysis. Finally, we explore the future trajectory of light-sheet microscopy, discuss key efforts to disseminate new light-sheet technology, and identify exciting opportunities for further advances.

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

Food Microstructure and Fat Content Affect Growth Morphology, Growth Kinetics, and Preferred Phase for Cell Growth of Listeria monocytogenes in Fish-Based Model Systems

Food microstructure significantly affects microbial growth dynamics, but knowledge concerning the exact influencing mechanisms at a microscopic scale is limited. The food microstructural influence on Listeria monocytogenes (green fluorescent protein strain) growth at 10°C in fish-based food model systems was investigated by confocal laser scanning microscopy. The model systems had different microstructures, i.e., liquid, xanthan (high-viscosity liquid), aqueous gel, and emulsion and gelled emulsion systems varying in fat content. Bacteria grew as single cells, small aggregates, and microcolonies of different sizes (based on colony radii [size I, 1.5 to 5.0 μm; size II, 5.0 to 10.0 μm; size III, 10.0 to 15.0 μm; and size IV, ≥15 μm]). In the liquid, small aggregates and size I microcolonies were predominantly present, while size II and III microcolonies were predominant in the xanthan and aqueous gel. Cells in the emulsions and gelled emulsions grew in the aqueous phase and on the fat-water interface. A microbial adhesion to solvent assay demonstrated limited bacterial nonpolar solvent affinities, implying that this behavior was probably not caused by cell surface hydrophobicity. In systems containing 1 and 5% fat, the largest cell volume was mainly represented by size I and II microcolonies, while at 10 and 20% fat a few size IV microcolonies comprised nearly the total cell volume. Microscopic results (concerning, e.g., growth morphology, microcolony size, intercolony distances, and the preferred phase for growth) were related to previously obtained macroscopic growth dynamics in the model systems for an L. monocytogenes strain cocktail, leading to more substantiated explanations for the influence of food microstructural aspects on lag phase duration and growth rate.IMPORTANCEListeria monocytogenes is one of the most hazardous foodborne pathogens due to the high fatality rate of the disease (i.e., listeriosis). In this study, the growth behavior of L. monocytogenes was investigated at a microscopic scale in food model systems that mimic processed fish products (e.g., fish paté and fish soup), and the results were related to macroscopic growth parameters. Many studies have previously focused on the food microstructural influence on microbial growth. The novelty of this work lies in (i) the microscopic investigation of products with a complex composition and/or structure using confocal laser scanning microscopy and (ii) the direct link to the macroscopic level. Growth behavior (i.e., concerning bacterial growth morphology and preferred phase for growth) was more complex than assumed in common macroscopic studies. Consequently, the effectiveness of industrial antimicrobial food preservation technologies (e.g., thermal processing) might be overestimated for certain products, which may have critical food safety implications.

IA-Lab: A MATLAB framework for efficient microscopy image analysis development, applied to quantifying intracellular transport of internalized peptide-drug conjugate

This work presents a MATLAB-based software package for high-throughput microscopy image analysis development, making such development more accessible for a large user community. The toolbox provides a GUI and a number of analysis workflows, and can serve as a general framework designed to allow for easy extension. For a new application, only a minor part of the object-oriented code needs to be replaced by new components, making development efficient. This makes it possible to quickly develop solutions for analysis not available in existing tools. We show its use in making a tool for quantifying intracellular transport of internalized peptide-drug conjugates.

The code is freely available as open source on GitHub (https://github.com/amcorrigan/ia-lab)

New software for automated cilia detection in cells (ACDC)

Background

Primary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. Typically, quantitative cilia analysis is done manually, which is very time-consuming. While some open-source and commercial image analysis software applications can segment input data, they still require the user to optimize many parameters, suffer from user bias, and often lack rigorous performance quality assessment (e.g., false positives and false negatives). Further, optimal parameter combinations vary in detection accuracy depending on cilia reporter, cell type, and imaging modality. A good automated solution would analyze images quickly, robustly, and adaptably—across different experimental data sets—without significantly compromising the accuracy of manual analysis.

Methods

To solve this problem, we developed a new software for automated cilia detection in cells (ACDC). The software operates through four main steps: image importation, pre-processing, detection auto-optimization, and analysis. From a data set, a representative image with manually selected cilia (i.e., Ground Truth) is used for detection auto-optimization based on four parameters: signal-to-noise ratio, length, directional score, and intensity standard deviation. Millions of parameter combinations are automatically evaluated and optimized according to an accuracy ‘F1’ score, based on the amount of false positives and false negatives. Afterwards, the optimized parameter combination is used for automated detection and analysis of the entire data set.

Results

The ACDC software accurately and adaptably detected nuclei and primary cilia across different cell types (NIH3T3, RPE1), cilia reporters (AcTub, Smo-GFP, Arl13b), and image magnifications (60×, 40×). We found that false-positive and false-negative rates for Arl13b-stained cilia were 1–6%, yielding high F1 scores of 0.96–0.97 (max. = 1.00). The software detected significant differences in mean cilia length between control and cytochalasin D-treated cell populations and could monitor dynamic changes in cilia length from movie recordings. Automated analysis offered up to a 96-fold speed enhancement compared to manual analysis, requiring around 5 s/image, or nearly 18,000 cilia analyzed/hour.

Conclusion

The ACDC software is a solution for robust automated analysis of microscopic images of ciliated cells. The software is extremely adaptable, accurate, and offers immense time-savings compared to traditional manual analysis.

Electronic supplementary material

The online version of this article (10.1186/s13630-019-0061-z) contains supplementary material, which is available to authorized users.

Imaging, Visualization, and Computation in Developmental Biology

Embryonic development is highly complex and dynamic, requiring the coordination of numerous molecular and cellular events at precise times and places. Advances in imaging technology have made it possible to follow developmental processes at cellular, tissue, and organ levels over time as they take place in the intact embryo. Parallel innovations of in vivo probes permit imaging to report on molecular, physiological, and anatomical events of embryogenesis, but the resulting multidimensional data sets pose significant challenges for extracting knowledge. In this review, we discuss recent and emerging advances in imaging technologies, in vivo labeling, and data processing that offer the greatest potential for jointly deciphering the intricate cellular dynamics and the underlying molecular mechanisms. Our discussion of the emerging area of “image-omics” highlights both the challenges of data analysis and the promise of more fully embracing computation and data science for rapidly advancing our understanding of biology.

Quantitative bioimage analytics enables measurement of targeted cellular stress response induced by celastrol-loaded nanoparticles

The cellular stress response, which provides protection against proteotoxic stresses, is characterized by the activation of heat shock factor 1 and the formation of nuclear stress bodies (nSBs). In this study, we developed a computerized method to quantify the formation and size distribution of nSBs, as stress response induction is of interest in cancer research, neurodegenerative diseases, and in other pathophysiological processes. We employed an advanced bioimaging and analytics workflow to enable quantitative detailed subcellular analysis of cell populations even down to single-cell level. This type of detailed analysis requires automated single cell analysis to allow for detection of both size and distribution of nSBs. For specific induction of nSB we used mesoporous silica nanoparticles (MSNs) loaded with celastrol, a plant-derived triterpene with the ability to activate the stress response. To enable specific targeting, we employed folic acid functionalized nanoparticles, which yields targeting to folate receptor expressing cancer cells. In this way, we could assess the ability to quantitatively detect directed and spatio-temporal nSB induction using 2D and 3D confocal imaging. Our results demonstrate successful implementation of an imaging and analytics workflow based on a freely available, general-purpose software platform, BioImageXD, also compatible with other imaging modalities due to full 3D/4D and high-throughput batch processing support. The developed quantitative imaging analytics workflow opens possibilities for detailed stress response examination in cell populations, with significant potential in the analysis of targeted drug delivery systems related to cell stress and other cytoprotective cellular processes.

Modeling of LMNA-Related Dilated Cardiomyopathy Using Human Induced Pluripotent Stem Cells

Dilated cardiomyopathy (DCM) is one of the leading causes of heart failure and heart transplantation. A portion of familial DCM is due to mutations in the LMNA gene encoding the nuclear lamina proteins lamin A and C and without adequate treatment these patients have a poor prognosis. To get better insights into pathobiology behind this disease, we focused on modeling LMNA-related DCM using human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CM). Primary skin fibroblasts from DCM patients carrying the most prevalent Finnish founder mutation (p.S143P) in LMNA were reprogrammed into hiPSCs and further differentiated into cardiomyocytes (CMs). The cellular structure, functionality as well as gene and protein expression were assessed in detail. While mutant hiPSC-CMs presented virtually normal sarcomere structure under normoxia, dramatic sarcomere damage and an increased sensitivity to cellular stress was observed after hypoxia. A detailed electrophysiological evaluation revealed bradyarrhythmia and increased occurrence of arrhythmias in mutant hiPSC-CMs on β-adrenergic stimulation. Mutant hiPSC-CMs also showed increased sensitivity to hypoxia on microelectrode array and altered Ca²⁺ dynamics. Taken together, p.S143P hiPSC-CM model mimics hallmarks of LMNA-related DCM and provides a useful tool to study the underlying cellular mechanisms of accelerated cardiac degeneration in this disease.

Protective distal side-to-side neurorrhaphy in proximal nerve injury-an experimental study with rats

Background

Side-to-side neurorrhaphy may protect the denervated end organ and preserve the initial connection with proximal stump. We examined the effect of protective side-to-side anastomosis on nerve and end organ regeneration in proximal nerve injury model.

Methods

The left common peroneal nerve of 24 Sprague Dawley rats was proximally transected. In groups B and C, side-to-side neurorrhaphy was performed distally between the peroneal and tibial nerves without (group B) and with (group C) partial donor nerve axotomy inside the epineural window. Group A served as an unprotected control. After 26 weeks, the proximal transection was repaired with end-to-end neurorrhaphy on all animals. Regeneration was followed during 12 weeks with the walk track analysis. Morphometric studies and wet muscle mass calculations were conducted at the end of the follow-up period.

Results

The results of the walk track analysis were significantly better in groups B and C compared to group A. Groups B and C showed significantly higher wet mass ratios of the tibialis anterior and extensor digitorum longus muscle compared to group A. Group C showed significantly higher morphometric values compared to group A. Group B reached higher values of the fibre count, fibre density, and percentage of the fibre area compared to group A.

Conclusions

Protective distal side-to-side neurorrhaphy reduced muscle atrophy and had an improving effect on the morphometric studies and walk track analysis. Distal side-to-side neurorrhaphy does not prevent the regenerating axons to grow from the proximal stump to achieve distal nerve stump.

Lectin-Recognizable MOF Glyconanoparticles: Supramolecular Glycosylation of ZIF-8 Nanocrystals by Sugar-Based Surfactants

A strategy toward the integration of highly functional microporous materials, such as metal-organic frameworks (MOFs), in composites via biochemical recognition interactions is presented. Postsynthetic modification of zeolitic-imidazolate framework-8 MOF nanocrystals with a maltose-exposing biocompatible surfactant (the so-called "Glyco-MOFs") was performed to confer affinity toward lectin protein concanavalin A. The addition of small amounts of concanavalin A to the colloidal Glyco-MOF dispersion triggers the aggregation of these units into self-limited size supramolecular architectures directed by specific sugar-lectin binding interactions.

A bioimage informatics platform for high-throughput embryo phenotyping

High-throughput phenotyping is a cornerstone of numerous functional genomics projects. In recent years, imaging screens have become increasingly important in understanding gene-phenotype relationships in studies of cells, tissues and whole organisms. Three-dimensional (3D) imaging has risen to prominence in the field of developmental biology for its ability to capture whole embryo morphology and gene expression, as exemplified by the International Mouse Phenotyping Consortium (IMPC). Large volumes of image data are being acquired by multiple institutions around the world that encompass a range of modalities, proprietary software and metadata. To facilitate robust downstream analysis, images and metadata must be standardized to account for these differences. As an open scientific enterprise, making the data readily accessible is essential so that members of biomedical and clinical research communities can study the images for themselves without the need for highly specialized software or technical expertise. In this article, we present a platform of software tools that facilitate the upload, analysis and dissemination of 3D images for the IMPC. Over 750 reconstructions from 80 embryonic lethal and subviable lines have been captured to date, all of which are openly accessible at mousephenotype.org. Although designed for the IMPC, all software is available under an open-source licence for others to use and develop further. Ongoing developments aim to increase throughput and improve the analysis and dissemination of image data. Furthermore, we aim to ensure that images are searchable so that users can locate relevant images associated with genes, phenotypes or human diseases of interest.

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

Lymphatic endothelium stimulates melanoma metastasis and invasion via MMP14-dependent Notch3 and β1-integrin activation

Lymphatic invasion and lymph node metastasis correlate with poor clinical outcome in melanoma. However, the mechanisms of lymphatic dissemination in distant metastasis remain incompletely understood. We show here that exposure of expansively growing human WM852 melanoma cells, but not singly invasive Bowes cells, to lymphatic endothelial cells (LEC) in 3D co-culture facilitates melanoma distant organ metastasis in mice. To dissect the underlying molecular mechanisms, we established LEC co-cultures with different melanoma cells originating from primary tumors or metastases. Notably, the expansively growing metastatic melanoma cells adopted an invasively sprouting phenotype in 3D matrix that was dependent on MMP14, Notch3 and β1-integrin. Unexpectedly, MMP14 was necessary for LEC-induced Notch3 induction and coincident β1-integrin activation. Moreover, MMP14 and Notch3 were required for LEC-mediated metastasis of zebrafish xenografts. This study uncovers a unique mechanism whereby LEC contact promotes melanoma metastasis by inducing a reversible switch from 3D growth to invasively sprouting cell phenotype.

Development of image analysis software for quantification of viable cells in microchips

Over the past few years, image analysis has emerged as a powerful tool for analyzing various cell biology parameters in an unprecedented and highly specific manner. The amount of data that is generated requires automated methods for the processing and analysis of all the resulting information. The software available so far are suitable for the processing of fluorescence and phase contrast images, but often do not provide good results from transmission light microscopy images, due to the intrinsic variation of the acquisition of images technique itself (adjustment of brightness / contrast, for instance) and the variability between image acquisition introduced by operators / equipment. In this contribution, it has been presented an image processing software, Python based image analysis for cell growth (PIACG), that is able to calculate the total area of the well occupied by cells with fusiform and rounded morphology in response to different concentrations of fetal bovine serum in microfluidic chips, from microscopy images in transmission light, in a highly efficient way.

Exosomes as secondary inductive signals involved in kidney organogenesis

The subfraction of extracellular vesicles, called exosomes, transfers biological molecular information not only between cells but also between tissues and organs as nanolevel signals. Owing to their unique properties such that they contain several RNA species and proteins implicated in kidney development, exosomes are putative candidates to serve as developmental programming units in embryonic induction and tissue interactions. We used the mammalian metanephric kidney and its nephron-forming mesenchyme containing the nephron progenitor/stem cells as a model to investigate if secreted exosomes could serve as a novel type of inductive signal in a process defined as embryonic induction that controls organogenesis. As judged by several characteristic criteria, exosomes were enriched and purified from a cell line derived from embryonic kidney ureteric bud (UB) and from primary embryonic kidney UB cells, respectively. The cargo of the UB-derived exosomes was analysed by qPCR and proteomics. Several miRNA species that play a role in Wnt pathways and enrichment of proteins involved in pathways regulating the organization of the extracellular matrix as well as tissue homeostasis were identified. When labelled with fluorescent dyes, the uptake of the exosomes by metanephric mesenchyme (MM) cells and the transfer of their cargo to the cells can be observed. Closer inspection revealed that besides entering the cytoplasm, the exosomes were competent to also reach the nucleus. Furthermore, fluorescently labelled exosomal RNA enters into the cytoplasm of the MM cells. Exposure of the embryonic kidney-derived exosomes to the whole MM in an ex vivo organ culture setting did not lead to an induction of nephrogenesis but had an impact on the overall organization of the tissue. We conclude that the exosomes provide a novel signalling system with an apparent role in secondary embryonic induction regulating organogenesis.

Systems Biology-Driven Hypotheses Tested In Vivo: The Need to Advancing Molecular Imaging Tools

Processing and interpretation of biological images may provide invaluable insights on complex, living systems because images capture the overall dynamics as a "whole." Therefore, "extraction" of key, quantitative morphological parameters could be, at least in principle, helpful in building a reliable systems biology approach in understanding living objects. Molecular imaging tools for system biology models have attained widespread usage in modern experimental laboratories. Here, we provide an overview on advances in the computational technology and different instrumentations focused on molecular image processing and analysis. Quantitative data analysis through various open source software and algorithmic protocols will provide a novel approach for modeling the experimental research program. Besides this, we also highlight the predictable future trends regarding methods for automatically analyzing biological data. Such tools will be very useful to understand the detailed biological and mathematical expressions under in-silico system biology processes with modeling properties.

Identification of a novel loss-of-function PHEX mutation, Ala720Ser, in a sporadic case of adult-onset hypophosphatemic osteomalacia

Adults presenting with sporadic hypophosphatemia and elevations in circulating fibroblast growth factor-23 (FGF23) concentrations are usually investigated for an acquired disorder of FGF23 excess such as tumor induced osteomalacia (TIO). However, in some cases the underlying tumor is not detected, and such patients may harbor other causes of FGF23 excess. Indeed, coding-region and 3'UTR mutations of phosphate-regulating neutral endopeptidase (PHEX), which encodes a cell-surface protein that regulates circulating FGF23 concentrations, can lead to alterations in phosphate homeostasis, which are not detected until adulthood. Here, we report an adult female who presented with hypophosphatemic osteomalacia and raised serum FGF23 concentrations. The patient and her parents, who were her only first-degree relatives, had no history of rickets. The patient was thus suspected of having TIO. However, no tumor had been identified following extensive localization studies. Mutational analysis of the PHEX coding-region and 3'UTR was undertaken, and this revealed the patient to be heterozygous for a novel germline PHEX mutation (c.2158G>T; p.Ala720Ser). In vitro studies involving the expression of WT and mutant PHEX proteins in HEK293 cells demonstrated the Ala720Ser mutation to impair trafficking of PHEX, with ~20% of the mutant protein being expressed at the cell surface, compared to ~80% cell surface expression for WT PHEX (p<0.05). Thus, our studies have identified a pathogenic PHEX mutation in a sporadic case of adult-onset hypophosphatemic osteomalacia, and these findings highlight a role for PHEX gene analysis in some cases of suspected TIO, particularly when no tumor has been identified.

Culturing substrates influence the morphological, mechanical and biochemical features of lung adenocarcinoma cells cultured in 2D or 3D

Alternative models such as three-dimensional (3D) cell cultures represent a distinct milestone towards capturing the realities of cancer biology in vitro and reduce animal experimentation in the preclinical stage of drug discovery. Significant work remains to be done to understand how substrates used in in vitro alternatives influence cancer cells phenotype and drug efficacy responses, so that to accurately link such models to specific in vivo disease scenarios. Our study describes how the morphological, mechanical and biochemical properties of adenocarcinoma (A549) cells change in response to a 3D environment and varying substrates. Confocal Laser Scanning (LSCM), He-Ion (HIM) and Atomic Force (AFM) microscopies, supported by ELISA and Western blotting, were used. These techniques enabled us to evaluate the shape, cytoskeletal organization, roughness, stiffness and biochemical signatures of cells grown within soft 3D matrices (PuraMatrix™ and Matrigel™), and to compare them to those of cells cultured on two-dimensional glass substrates. Cell cultures are also characterized for their biological response to docetaxel, a taxane-type drug used in Non-Small-Cell Lung Cancer (NSCLC) treatment. Our results offer an advanced biophysical insight into the properties and potential application of 3D cultures of A549 cells as in vitro alternatives in lung cancer research.