Fluorescence Microscopy Image Analysis Research Articles

Spatial analysis of tissue immunity and vascularity by light sheet fluorescence microscopy.

The pathogenesis of cancer and cardiovascular diseases is subjected to spatiotemporal regulation by the tissue microenvironment. Multiplex visualization of the microenvironmental components, including immune cells, vasculature and tissue hypoxia, provides critical information underlying the disease progression and therapy resistance, which is often limited by imaging depth and resolution in large-volume tissues. To this end, light sheet fluorescence microscopy, following tissue clarification and immunostaining, may generate three-dimensional high-resolution images at a whole-organ level. Here we provide a detailed description of light sheet fluorescence microscopy imaging analysis of immune cell composition, vascularization, tissue perfusion and hypoxia in mouse normal brains and hearts, as well as brain tumors. We describe a procedure for visualizing tissue vascularization, perfusion and hypoxia with a transgenic vascular labeling system. We provide the procedures for tissue collection, tissue semi-clearing and immunostaining. We further describe standard methods for analyzing tissue immunity and vascularity. We anticipate that this method will facilitate the spatial illustration of structure and function of the tissue microenvironmental components in cancer and cardiovascular diseases. The procedure requires 1-2 weeks and can be performed by users with expertise in general molecular biology.

Cellulose nanocrystal stabilized pickering nanoemulsions and their coalescence stability studied by fluorescence microscopy

Cellulose nanocrystals (CNCs) have been used as Pickering emulsifiers by forming rigid interfacial layer coated around oil droplets. Sulfated and desulfated CNCs were used to emulsify squalane oil to produce Pickering nanoemulsions. The formation of CNCs-Pickering nanoemulsions was investigated and their stability against mechanical stresses was studied using fluorescence microscopy with a lab-made image analysis program. The formation of Pickering nanoemulsions using CNC particles was tuned by screening their surface charges with cations. Sulfated and desulfated CNC nanoemulsions showed submicron droplet size of 619 and 555 nm, respectively. The mechanical stresses could induce droplet encounter leading to physical instability. The sulfated CNCs displayed superior stability than the desulfated CNCs in terms of droplet aggregation after treatment with mechanical stresses. Fluorescence microscopy image analysis revealed the physical instability of Pickering nanoemulsions at the microscopic level, which is a useful tool for studying the destabilization mechanisms.

Effect of RONS-Induced Intracellular Redox Homeostasis in 6-NBDG/Glucose Uptake in C2C12 Myotubes and Single Isolated Skeletal Muscle Fibres.

The glucose uptake in skeletal muscle is essential to produce energy through ATP, which is needed by this organ to maintain vital functions. The impairment of glucose uptake compromises the metabolism and function of skeletal muscle and other organs and is a feature of diabetes, obesity, and ageing. There is a need for research to uncover the mechanisms involved in the impairment of glucose uptake in skeletal muscle. In this study, we adapted, developed, optimised, and validated a methodology based on the fluorescence glucose analogue 6-NBDG, combined with a quantitative fluorescence microscopy image analysis, to determine the glucose uptake in two models of skeletal muscle cells: C2C12 myotubes and single fibres isolated from muscle. It was proposed that reactive oxygen and nitrogen species (RONS) and redox homeostasis play an important role in the modulation of intracellular redox signalling pathways associated with glucose uptake. In this study, we prove that the prooxidative intracellular redox environment under oxidative eustress produced by RONS such as hydrogen peroxide and nitric oxide improves glucose uptake in skeletal muscle cells. However, when oxidation is excessive, oxidative distress occurs, and cellular viability is compromised, although there might be an increase in the glucose uptake. Based on the results of this study, the determination of 6-NBDG/glucose uptake in myotubes and skeletal muscle cells is feasible, validated, and will contribute to improve future research.

Deep-learning applications in the automated image analysis of giant unilamellar vesicles.

Recent progress in the field of machine learning allowed scientists to develop software to analyze a great amount of image data efficiently. Trained artificial neural networks can segment, analyze, and classify the target objects in complex images. Face recognition technology in our cell phones is an everyday experience now. We have been developing automated image analysis software to analyze lipid vesicle images efficiently. Giant unilamellar vesicles (GUV), characterized by a diameter greater than 1 µm are routinely used for reconstitution studies. They are used to study a wide range of physiological activities involving lipid membranes. One of the great advantages of the GUV system is that they can be readily imaged, and studied by optical microscopy due to their size much greater than the resolution of visible light's diffraction limit. Fluorescence microscopy image analysis can reveal the protein-lipid interaction, phase separation on the membranes, and mechanical deformation of the membranes. Such analysis typically involves the manual analysis by researchers to identify the target vesicles to analyze and perform quantitative analysis. Our approach to introducing deep-learning can automate the process to help researchers to analyze a great amount of vesicle image data efficiently. Our work shows that with some training, scientists can introduce the deep-learning as a module for their image analysis for various purposes. We discuss the progress in the field of software analysis of vesicle images and show our example works of deep learning applications. Deep-learning assisted identification, and classification of vesicle states and their connection with quantitative analysis will be shown. We suggest practical ways to implement it for various purposes of image analysis. We envision such modules can be potentially introduced in the classroom to teach future scientists to learn machine learning applications in biophysics.

Single-Particle Tracking of Virus Entry in Live Cells.

Novel imaging technologies such as single-particle tracking provide tools to study the intricate process of virus infection in host cells. In this chapter, we provide an overview of studies in which single-particle tracking technologies were applied for the analysis of the viral entry pathways in the context of the live host cell. Single-particle tracking techniques have been dependent on advances in the fluorescent labeling microscopy method and image analysis. The mechanistic and kinetic insights offered by this technique will provide a better understanding of virus entry and may lead to a rational design of antiviral interventions.

Asymmetric survival in single-cell lineages of cyanobacteria in response to photodamage.

Oxygenic photosynthesis is driven by the coupled action of the light-dependent pigment-protein complexes, photosystem I and II, located within the internal thylakoid membrane system. However, photosystem II is known to be prone to photooxidative damage. Thus, photosynthetic organisms have evolved a repair cycle to continuously replace the damaged proteins in photosystem II. However, it has remained difficult to deconvolute the damage and repair processes using traditional ensemble approaches. Here, we demonstrate an automated approach using time-lapse fluorescence microscopy and computational image analysis to study the dynamics and effects of photodamage in single cells at subcellular resolution in cyanobacteria. By growing cells in a two-dimensional layer, we avoid shading effects, thereby generating uniform and reproducible growth conditions. Using this platform, we analyzed the growth and physiology of multiple strains simultaneously under defined photoinhibitory conditions stimulated by UV-A light. Our results reveal an asymmetric cellular response to photodamage between sibling cells and the generation of an elusive subcellular structure, here named a 'photoendosome,' derived from the thylakoid which could indicate the presence of a previously unknown photoprotective mechanism. We anticipate these results to be a starting point for further studies to better understand photodamage and repair at the single-cell level.

Iclots: Open-Source, Artificial Intelligence-Enabled Software for Analyses of Microscopy-Based Data in Hematology

While significant effort has been devoted to developing novel in vitro microscopy-based hematology assays, a clear need exists for new software-based tools to analyze the data-rich images and videos obtained thereof. Whereas traditional manual analysis of cellular microscopy data is time-consuming and error-prone, advanced computational methods require substantial coding expertise. Popular general-purpose bioimage analysis software including ImageJ, Ilastik, and CellProfiler have robust image processing and visualization algorithms, however, they cannot adapt to hematology-based tools, e,g. time course-based experiments utilizing commercially available flow chambers or novel microfluidic devices, without significant additional calculations or coding/scripting. Accordingly, we developed a user-adaptable suite of tools packaged into an open-source, standalone software, "iCLOTS” (interactive cellular assay labeled observation and tracking software). iCLOTS comprises pre-defined workflows based upon well-validated image processing and artificial intelligence (AI) algorithms for specific use with blood cell-based assays. Designed as a post-processing software, iCLOTS is suitable for previously-collected data or data collected with iCLOTS capabilities in mind. iCLOTS blood cell adhesion, cell deformability, fluid flow-based velocity experiments, and microfluidic-centric applications generate automatically labeled quantitative numerical and graphical data from user-provided files. To assist researchers in interpreting potentially large datasets comprising multiple metrics which describe up to thousands of cells, our software also capitalizes on advances in the burgeoning field of machine learning (ML), a subset of AI. ML clustering algorithms mathematically characterize previously imperceptible data groupings, enabling generation of novel hypotheses with clear implications for digital pathology, personalized medicine, and establishing clinical diagnostic reference ranges (Figure 1A). We demonstrate iCLOTS’ capabilities as a research-enabling platform by reporting new discoveries related to inherited platelet disorders, sepsis, and sickle cell disease. For example, we investigate Hermansky-Pudlak syndrome (HPS), an hereditary platelet disorder that results in platelet dysfunction with prolonged bleeding. Analysis of fluorescence microscopy images reveals increased spreading and phosphatidylserine (PS) exposure via annexin V labeling in platelets obtained from patients with HPS as compared to healthy patient controls on collagen-coated surfaces (n=2,674 and n=1,112 platelets, respectively). PS exposure on the surface of activated platelets confers a procoagulant surface necessary for hemostasis (Kay and Grinstein, Lipid-mediated Protein Signaling, 2013). ML clustering using k-means algorithms automatically separates all single-platelet data points into mathematically optimized clusters describing low and high PS-exposure groupings, in a method akin to flow cytometry with objective boundaries. We find a significantly greater proportion of HPS platelets in the high-PS-exposure cluster as compared to healthy control platelets (*p < 0.001 via Chi-squared test, Figure 1B). iCLOTS is presented to the hematology community as an interactive, standalone software that enables all hematologists to discover a new level of automated analyses for their microscopy-based data. Researchers and clinicians can explore questions about both fundamental cell processes and patho/physiology via measures of biological functionality, cell mechanical properties, properties of cell suspensions, and/or microchannel behavior. Designed to work with all blood cell types and a range of experimental systems, iCLOTS produces detailed numerical results describing a range of cell characteristics and behaviors which may be further assessed by ML algorithms capable of detection of healthy/clinical sample dichotomies and/or significant subpopulations of blood cells. Built by hematologists, for hematologists, iCLOTS balances versatility and automation with plug-and-play capabilities, requiring no computational expertise, thereby enabling a new level of single-cell resolution data analysis free for the entire field of hematology to use/download. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Alginic Acid Beads Containing Fluorescent Solvatochromic Dyes Display an Emission Color Response to a Cationic Surfactant.

Lipophilic fluorescent dyes can be employed as sensors for surfactants present in concentrations above the critical micellar concentration (CMC) where the dyes are monodispersed in micelles. However, the surfactant concentration range over which these dyes are effective is narrowed because by the sigmoidal nature of their responses. To overcome this limitation, we developed a novel sensor material comprised of a labeled fluorescent solvatochromic dye covalently bonded to alginate gel, which is known to strongly adsorb cationic surfactants. We hypothesized that the dye-alginate conjugate would undergo fluorescent color changes in response to binding of surfactants which alter the polarity of the surrounding environment. Indeed, addition of the representative cationic surfactant, cetylpyridinium chloride (CPC), to an aqueous solution of the alginate conjugated fluorescent solvatochromic dye leads to a visible fluorescent color change when the concentration of CPC is below the CMC. The average values of the color appearance parameter, referred to as a hue, of light emitted from gels, calculated by analysis of fluorescence microscopy images using ImageJ software, were found to be approximately linearly dependent on the concentration of CPC encapsulated in the alginate-fluorescent dye complex. This finding shows that absorbed CPC can be quantitatively determined over a wide concentration range in the form of simple fluorescence wavelength or visible responses.

Abstract 1530: Derivatives of betulinic acid act as modulators of the androgen receptor and report cytotoxicity towards cancer cell lines

Abstract INTRODUCTION Triterpenoids are naturally occurring substances with a broad spectrum of beneficial effects for human health. Betulinic acid belongs to the group of pentacyclic triterpenoids and is well known for its antitumor activities. The library of about 1000 betulinic acid-derived triterpenes was collected and analyzed for cytotoxic activity on cancer and non-cancer cell lines (A549, CCRF-CEM, CEM-DNR, HCT116, K562, K562-TAX, U2OS, BJ and MRC-5) in our Institute over the last 20 years. Steroidal triterpenes are precursors of cholesterol, the precursor of testosterone, the fundamental ligand of the androgen receptor that is the key regulator in several physiological and pathological processes. Finding new modulators of androgen receptor is critical for the treatment of various androgen-dependent diseases, including prostate cancer or anabolic deficiencies. Betulinic acid and its derivatives showed structural similarity with steroids and therefore were selected to be tested for androgen receptor activity modulation. METHODS The stably transfected cell line HEK 293T GFP-AR was used to determine the translocation activity of the androgen receptor from the cytoplasm to the nucleus after the treatment with the derivatives. The high throughput fluorescent microscopy device and image analysis comprised the data, and compounds with the proved activity were selected for subsequent analysis. The luciferase reporter gene assay was further used to evaluate the effect of compounds on the transcriptional activity of the androgen receptor. The MTS assay was performed to assess the cytotoxicity. RESULTS Betulinic acid and its eight derivatives were identified to have 5-6 - fold higher translocation activity of AR (ratio cytoplasm GFP intensity/nuclear GFP intensity) than the negative control. Validation on luminescence luciferase reporter assay confirmed 4-5 - fold induction for five derivatives, whereas three derivatives were not active. Interestingly only two of them showed substantial cytotoxicities (IC50&amp;lt; 50 µM) as they possess significant inhibitory activity against all tested cancer cell lines with favorable therapeutic index against the non-cancer cell lines. CONCLUSION Derivatives of betulinic acid were proven to have modulatory activity on the androgen receptor pathway. The two derivatives of betulinic acid also displayed cytotoxicity towards almost all of the tested cancer cell lines. There could be not only AR modulation responsible for anticancer activity as the derivatives of betulinic acid are well know disruptors of mitochondrion functions as well. The synergy of these two mechanisms of action could potentiate the antitumor activity of the selected compounds. This work was supported by ENOCH CZ.02.1.01/0.0/0.0/16_019/0000868, CZ-OPENSCREEN - LM2018130, EATRIS-CZ - LM2018133. Citation Format: Alzbeta Srovnalova, Sona Gurska, Milan Urban, Jan Sarek, Jiri Rehulka, Petr Dzubak, Marian Hajduch. Derivatives of betulinic acid act as modulators of the androgen receptor and report cytotoxicity towards cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1530.

PHLIP Peptides Target Acidity in Activated Macrophages.

Acidity can be a useful alternative biomarker for the targeting of metabolically active cells in certain diseased tissues, as in acute inflammation or aggressive tumors. We investigated the targeting of activated macrophages by pH low insertion peptides (pHLIPs), an established technology for targeting cell-surface acidity. The uptake of fluorescent pHLIPs by activated macrophages was studied in cell cultures, in a mouse model of lung inflammation, and in a mouse tumor model. Fluorescence microscopy, whole-body and organ imaging, immunohistochemistry, and FACS analysis were employed. We find that cultured, activated macrophages readily internalize pHLIPs. The uptake is higher in glycolytic macrophages activated by LPS and INF-γ compared to macrophages activated by IL-4/IL-13. Fluorescent pHLIPs target LPS-induced lung inflammation in mice. In addition to marking cancer cells within the tumor microenvironment, fluorescent pHLIPs target CD45+, CD11b+, F4/80+, and CD206+ tumor-associated macrophages with no significant targeting of other immune cells. Also, fluorescent pHLIPs target CD206-positive cells found in the inguinal lymph nodes of animals inoculated with breast cancer cells in mammary fat pads. pHLIP peptides sense low cell surface pH, which triggers their insertion into the cell membrane. Unlike cancerous cells, activated macrophages do not retain inserted pHLIPs on their surfaces, instead their highly active membrane recycling moves the pHLIPs into endosomes. Targeting activated macrophages in diseased tissues may enable clinical visualization and therapeutic opportunities.

Nanoapatites Doped and Co-Doped with Noble Metal Ions as Modern Antibiofilm Materials for Biomedical Applications against Drug-Resistant Clinical Strains of Enterococcus faecalis VRE and Staphylococcus aureus MRSA.

The main aim of our research was to investigate antiadhesive and antibiofilm properties of nanocrystalline apatites doped and co-doped with noble metal ions (Ag+, Au+, and Pd2+) against selected drug-resistant strains of Enterococcus faecalis and Staphylococcus aureus. The materials with the structure of apatite (hydroxyapatite, nHAp; hydroxy-chlor-apatites, OH-Cl-Ap) containing 1 mol% and 2 mol% of dopants and co-dopants were successfully obtained by the wet chemistry method. The majority of them contained an additional phase of metallic nanoparticles, in particular, AuNPs and PdNPs, which was confirmed by the XRPD, FTIR, UV–Vis, and SEM–EDS techniques. Extensive microbiological tests of the nanoapatites were carried out determining their MIC, MBC value, and FICI. The antiadhesive and antibiofilm properties of the tested nanoapatites were determined in detail with the use of fluorescence microscopy and computer image analysis. The results showed that almost all tested nanoapatites strongly inhibit adhesion and biofilm production of the tested bacterial strains. Biomaterials have not shown any significant cytotoxic effect on fibroblasts and even increased their survival when co-incubated with bacterial biofilms. Performed analyses confirmed that the nanoapatites doped and co-doped with noble metal ions are safe and excellent antiadhesive and antibiofilm biomaterials with potential use in the future in medical sectors.

A fast open-source Fiji-macro to quantify virus infection and transfection on single-cell level by fluorescence microscopy

The ability to automatically analyze large quantities of image data is a valuable tool for many biochemical assays, as it rapidly provides reliable data. Here, we describe a fast and robust Fiji macro for the analysis of cellular fluorescence microscopy images with single-cell resolution. The macro presented here was validated by successful reconstruction of fluorescent and non-fluorescent cell mixing ratios (for fluorescence fractions ranging between 0 and 100%) and applied to quantify the efficiency of transfection and virus infection inhibition. It performed well compared with manually obtained image quantification data. Its use is not limited to the cases shown here but is applicable for most monolayered cellular assays with nuclei staining. We provide a detailed description of how the macro works and how it is applied to image data. It can be downloaded free of charge and may be used by and modified according to the needs of the user.• Rapid, simple, and reproducible segmentation of eukaryotic cells in confluent cellular assays• Open-source software for use without technical or computational expertise• Single-cell analysis allows identification and quantification of virus infected cell populations and infection inhibition

Using BODIPY FL-Sphingolipid Analogs to Study Sphingolipid Metabolism in Mouse Embryonic Stem Cells.

Sphingolipids are important structural components of cellular membranes. They also function as prominent signaling molecules to control a variety of cellular events, such as cell growth, differentiation, and apoptosis. Impaired sphingolipid metabolism, particularly defects in sphingolipid degradation, has been associated with many human diseases. Fluorescence sphingolipid analogs have been widely used as efficient probes to study sphingolipid metabolism and intracellular trafficking in living mammalian cells. Compared with nitrobenzoxadiazole fluorophores (NBD FL), the boron dipyrromethene difluoride fluorophores (BODIPY FL) have much higher absorptivity and fluorescence quantum. These features allow more intensive labeling of cells for fluorescence microscopy imaging and flow cytometry analysis. Here, we describe a protocol employing BODIPY FL-labeled sphingolipid analogs to elucidate sphingolipid internalization, trafficking, and endocytosis in mouse embryonic stem cells. This protocol was validated in: eLife (2022), DOI: 10.7554/eLife.67452 Graphical abstract.

Selenium-Dependent Read Through of the Conserved 3'-Terminal UGA Stop Codon of HIV-1 nef.

The HIV-1 nef gene terminates in a 3'-UGA stop codon, which is highly conserved in the main group of HIV-1 subtypes, along with a downstream potential coding region that could extend the nef protein by 33 amino acids, if readthrough of the stop codon occurs. Antisense tethering interactions (ATIs) between a viral mRNA and a host selenoprotein mRNA are a potential viral strategy for the capture of a host selenocysteine insertion sequence (SECIS) element (Taylor et al, 2016) [1]. This mRNA hijacking mechanism could enable the expression of virally encoded selenoprotein modules, via translation of in-frame UGA stop codons as selenocysteine (SeC). Here we show that readthrough of the 3'-terminal UGA codon of nef occurs during translation of HIV-1 nef expression constructs in transfected cells. This was accomplished via fluorescence microscopy image analysis and flow cytometry of HEK 293 cells, transfected with engineered GFP reporter gene plasmid constructs, in which GFP can only be expressed by translational recoding of the UGA codon. SiRNA knockdown of thioredoxin reductase 1 (TR1) mRNA resulted in a 67% decrease in GFP expression, presumably due to reduced availability of the components involved in selenocysteine incorporation for the stop codon readthrough, thus supporting the proposed ATI. Addition of 20 nM sodium selenite to the media significantly enhanced stop codon readthrough in the pNefATI1 plasmid construct, by >100%, supporting the hypothesis that selenium is involved in the UGA readthrough mechanism.

TDAExplore: Quantitative analysis of fluorescence microscopy images through topology-based machine learning

SummaryRecent advances in machine learning have greatly enhanced automatic methods to extract information from fluorescence microscopy data. However, current machine-learning-based models can require hundreds to thousands of images to train, and the most readily accessible models classify images without describing which parts of an image contributed to classification. Here, we introduce TDAExplore, a machine learning image analysis pipeline based on topological data analysis. It can classify different types of cellular perturbations after training with only 20–30 high-resolution images and performs robustly on images from multiple subjects and microscopy modes. Using only images and whole-image labels for training, TDAExplore provides quantitative, spatial information, characterizing which image regions contribute to classification. Computational requirements to train TDAExplore models are modest and a standard PC can perform training with minimal user input. TDAExplore is therefore an accessible, powerful option for obtaining quantitative information about imaging data in a wide variety of applications.

Kinetic Modeling-Assisted Mechanistic Understanding of the Catalytic Ozonation Process Using Cu-Al Layered Double Hydroxides and Copper Oxide Catalysts.

In this study, copper aluminum layered hydroxides (Cu-Al LDHs) and copper oxide (CuO) were utilized as catalysts for heterogeneous catalytic ozonation (HCO). Target compounds oxalate and formate were used with removal by adsorption and oxidation quantified to elucidate the role of the catalyst in contaminant removal. Oxidation of oxalate mostly occurred on the catalyst surface via interaction of surface oxalate complexes with surface-located oxidants. In contrast, the oxidation of formate occurred in the bulk solution as well as on the surface of the catalyst. Measurement of O3 decay kinetics coupled with fluorescence microscopy image analysis corresponding to 7-hydroxycoumarin formation indicates that while surface hydroxyl groups in Cu-Al LDHs facilitate slow decay of O3 resulting in the formation of hydroxyl radicals on the surface, CuO rapidly transforms O3 into surface-located hydroxyl radicals and/or other oxidants. Futile consumption of surface-located oxidants via interaction with the catalyst surface was minimal for Cu-Al-LDHs; however, it becomes significant in the presence of higher CuO dosages. A mechanistic kinetic model has been developed which adequately describes the experimental results obtained and can be used to optimize the process conditions for the application of HCO.

Assessment of natural antioxidants' effect on PDT cytotoxicity through fluorescence microscopy image analysis.

Photodynamic therapy (PDT) is a cancer treatment modality mediated by reactive oxygen species (ROS). However, the intracellular antioxidant defense system antagonizes PDT-generated ROS, impeding PDT efficacy. This study aimed to evaluate the enhancement of PDT cytotoxicity by its combination with natural antioxidants in pro-oxidant concentrations. A rich natural antioxidant mixture originating from Pinus halepensis bark extract was studied for its potential to enhance the efficacy of m-tetrahydroxyphenylchlorin (m-THPC)-PDT on LNCaP prostate cancer cells, in vitro. Various P. halepensis concentrations, at two different incubation times, were used in combination with m-THPC-PDT. Assessment of cellular viability and intracellular ROS levels evaluated the treatments' outcome. A novel method was developed for the assessment of the intracellular ROS levels, based on image analysis and data extraction from fluorescence microscopy images. P. halepensis bark extract increased the intracellular ROS levels in a concentration-dependent but not in an incubation-dependent manner. The higher concentrations used (≥50 μg/ml) reduced cellular viability even by 50%. One hour pretreatment with 30 μg/ml P. halepensis before m-THPC-PDT exceeded the levels of cellular death by approximately 15%. The results provided evidence of the cytotoxic effect of P. halepensis bark extract on LNCaP cells, showing the potential of P. halepensis to be used as an anticancer agent in prostate cancer treatment. The results also provided evidence of enhancement of m-THPC-PDT by P. halepensisbark extract showed the potential to be used as a supplementary agent to improve prostate cancer PDT treatment.

High-throughput Analysis of Synaptic Activity in Electrically Stimulated Neuronal Cultures.

Synaptic dysfunction is a hallmark of various neurodegenerative and neurodevelopmental disorders. To interrogate synapse function in a systematic manner, we have established an automated high-throughput imaging pipeline based on fluorescence microscopy acquisition and image analysis of electrically stimulated synaptic transmission in neuronal cultures. Identification and measurement of synaptic signal fluctuations is achieved by means of an image analysis algorithm based on singular value decomposition. By exploiting the synchronicity of the evoked responses, the algorithm allows disentangling distinct temporally correlated patterns of firing synapse populations or cell types that are present in the same recording. We demonstrate the performance of the analysis with a pilot compound screen and show that the multiparametric readout allows classifying treatments by their spatiotemporal fingerprint. The image analysis and visualization software has been made publicly available on Github ( https://www.github.com/S3Toolbox ). The streamlined automation of multi-well image acquisition, electrical stimulation, analysis, and meta-data warehousing facilitates large-scale synapse-oriented screens and, in doing so, it will accelerate the drug discovery process.

Automated Image Processing Workflow for Morphological Analysis of Fluorescence Microscopy Cell Images

Computerized image analysis of biological cells and tissues is a necessary complement to high-throughput microscopy, allowing researchers to effectively analyze large volumes of cellular data. It has the potential to dramatically improve the throughput and accuracy of measurements and related downstream analyses that may be obtained from images. This study presents a novel workflow for automated analysis of fluorescence microscopy images, which benefits from running multiple segmentation workflows and combining them to produce the best final segmentation. It is tested using a dataset of 42 fluorescence microscopy cells, evaluated against a hand segmented dataset using the F1 score, and critically compared to a single segmentation workflow, which served as a control. The accuracy and reliability of the novel workflow are demonstrated to be superior to the control workflow, which achieved F1 scores of 0.845 and 0.608, respectively. The workflow and example code are available through an open-source software platform.

Imaging of the internal chorion structure of rainbow trout Oncorhynchus mykiss live embryos and the distribution of quantum dots therein: Towards a deeper understanding of potential nanotoxicity

Quantum dots (QDs) are extensively explored in biomedicine but the understanding of the mechanism underlying the uptake of colloidal QDs by biological systems is still poor. The knowledge of their distribution in tissues and their possibilities to cross biological barriers, especially at early stages of organism development, is also still limited. The potential risk of nanoparticle exposure to aquatic organisms is continuously questioned in ecotoxicology. To answer this question, we investigated the interaction (penetration and distribution) of CdSe/ZnS-COOH QDs with the chorion as well as their toxicity, cytotoxicity and genotoxicity in “eye-egg” stage embryos of rainbow trout Oncorhynchus mykiss. Results of the confocal fluorescence microscopy, fluorescence spectroscopy and 3D image analysis of O. mykiss chorion showed that it consists of three different layers and has a hexagonal pattern of pores distribution. The present study has revealed that both the accumulation and the distribution of QDs within the chorion are related to its structure. QDs are accumulated in pore canals of the external membrane of a fertilized O. mykiss egg. Our study has shown that QDs exposure induces bradycardia in O. mykiss and, as a result, cardiac toxicity in embryos. However, embryo survival rates as well as cytotoxicity and genotoxicity effects in the fish exposed to QDs for 1 day were not found to differ significantly from those in 4-day exposed fish. Results of the mathematical modelling showed the adsorption of QDs aggregates on the outer surface of the chorion leading to the oxygen-deficient microenvironment in embryos. These results represent an important step forward towards a better understanding of the mechanisms underlying QDs impacts on living organisms, which is essential for clarifying the protective properties of biological structures, and is valuable for the evaluation of nanotoxicity to fish at early development stages.