Fluorescence Image Processing Research Articles

Mapping the distribution of tension across paxillin upon shear stress with FRET-based biosensor

Paxillin communicates with multiple signalling molecules in focal adhesions (FAs) and participates in the intracellular force transmission upon shear stress. Thus, paxillin is likely to contribute to establishing the shear stress induced-cell polarity. However, it is still unclear whether the tension across FAs proteins can direct the polarity establishments by providing spatial features, due to a lack of efficient manners. This work proposes a visualization approach containing a DNA-encoded biosensor and fluorescent image processing algorithm to collect the spatiotemporal features of tension across paxillin. The results indicate that the tension across paxillin shows polarity between the upstream and downstream zones of the cell along the direction of shear stress, which was mediated by the membrane fluidity and integrity of the cytoskeleton. It demonstrates that the spatial information from the upper surface of cells upon shear stress can be transmitted to the interior of FAs on the basal layer by the architecture consisting of plasma membrane and cytoskeleton. Paxillin is a potential participant in activating cell polarity by providing a spatial mechanical guide to related signaling molecules upon shear stress.Graphical

Transepithelial Electrical Impedance Increase Following Porous Substrate Electroporation Enables Label-Free Delivery.

Porous substrate electroporation (PSEP) is a promising new method for intracellular delivery, yet fundamentals of PSEP are not well understood, especially the intermediate processes leading to delivery. PSEP is an electrical method, yet the relationship between PSEP and electrical impedance remains underexplored. In this study, a device capable of measuring impedance and performing PSEP is developed and the changes in transepithelial electrical impedance (TEEI) are monitored. These measurements show TEEI increases following PSEP, unlike other electroporation methods. The authors then demonstrate how cell culture conditions and electrical waveforms influence this response. More importantly, TEEI response features are correlated with viability and delivery efficiency, allowing prediction of outcomes without fluorescent cargo, imaging, or image processing. This label-free delivery also allows improved temporal resolution of transient processes following PSEP, which the authors expect will aid PSEP optimization for new cell types and cargos.

BRCycle-GAN: A Near-Infrared Fluorescence Image Processing Network Based on a Small Training Set

BRCycle-GAN: A Near-Infrared Fluorescence Image Processing Network Based on a Small Training Set

A real time detection of polymer type in polymeric materials using fluorescent properties and image processing technique

Identification of polymer type in unknown samples is an important issue in industry, which is accomplished using experimental methods such as spectrophotometric and microscopic methods, as well as measuring the physical and thermal characteristics of polymer. On the other hand, when it comes to industrial level and large sample sizes, aforementioned methods are laborious and costly. A common method to tackle the problem is to use chemical solvents and substances. Substantial use of chemicals, on the other hand, may lead to serious health and environmental issues. Therefore, the aim of this study is to introduce a promising clean, fast and facile approach in identification of polymeric materials that enables industry to bridge traditional process with predictive power of software tools. A special focus has been done on textile polymeric fibers and yarns. To address described issues, polymeric materials have been irradiated under ultraviolet (UV) light source. As a result, an augmented color difference appears between different polymer types, because of their different fluorescent activity. Created color difference was then, detected using a digital camera sensor, which works based on principals of image processing technique in python environment. Experimental observations showed good potential of using optical tools in polymer detection approaches.

Modeling and experimental investigation of fiber orientation in cast and 3D-printed cementitious composites

Fiber orientation is process-related and has significant impacts on material performance. However, existing studies only consider the impact of individual factors on the fiber orientation, either boundary impacts or flow field impacts. This work aims to study fiber orientation by considering the combined impact of these two factors. Analytical models were first constructed, which revealed that when the flow of material is negligible, fiber orientation is impacted by the boundary conditions. When the flow field is non-negligible, fiber orientation is affected by the direction of flow streamlines. Specimens were then prepared using fluorescence image processing and μ-computed tomography scanning for fiber orientation analysis. The results indicate that fiber orientation could be controlled by varying boundary constraints and flow fields. Mechanical results reveal that when the designed condition achieves directional-orientated fibers, the mechanical property of specimens could be enhanced with the applied stress direction being the same as the fiber orientation. This work provides a novel insight to tailor fiber-reinforced cementitious material properties by designing the boundary constraints and flow fields in the cast and printing processes.

Simulation monitoring of tetracyclines in wastewater based on fluorescence image processing and machine learning classifier

Antibiotics, as an emerging pollutant, have attracted extensive attention due to their trace residues and toxic side effects in the environment. Here, we constructed a new detection mechanism for tetracyclines in sewage by online trace sensing through machine learning (ML) algorithm and image fingerprint extraction method. The image fingerprint information was obtained from the blue to red specific fluorescence response generated by the interaction between the luminescent metal-organic framework (MOF) with tetracyclines, which greatly improved the ability against background interference. Fluorescence images of samples with different concentrations were collected, and the RGB mean value of sample images was extracted through the program as "fingerprint information". Then k-means clustering model (KCM) and hierarchical clustering model (HCM) were constructed to analyze the differences in fingerprint information of samples with different concentrations by clustering analysis method in machine learning. The clustering results showed good clustering characteristics of samples with similar concentrations, which provided reliable support for rapid detection while avoiding the use of expensive large instruments and time-consuming analysis methods.

Rapid Evaluation of Material Surface Modification by a Dielectric Barrier Discharge Based on Fluorescence Coloring and Image Processing Technologies.

In recent years, improving the surface properties of large-scale insulation by plasma modification has attracted extensive attention with the development of power systems and high-tech industries. However, routine evaluations of the modification effect and uniformity require complicated tests after the plasma is powered off, which waste a lot of time and cannot regulate the modification effect online. In this study, a novel fluorescence-assisted dielectric barrier discharge (DBD) for fabricating a functional film is developed, and a rapid evaluation method of the modification effect and uniformity is proposed based on fluorescence coloring and image processing technologies. The results show that the addition of an organic fluorescent agent in the DBD with hexamethyldisiloxane (HMDSO) has no negative effect on the plasma discharge and modification effect, and the fluorescence-assisted DBD successfully fabricates the functional films with typical chromogenic groups (N-H and S═O) that exhibit typical fluorescence under an UV lamp. According to image processing and parameter extraction, the plasma-assisted fluorescent film is converted into a two-dimensional (2D) color map with nine color levels, and three characteristic parameters are proposed to evaluate the modification effect rapidly and directly. It shows that the warmer the color of the treated sample, the better the hydrophobicity and electrical insulating properties, where the red region represents the optimally modified surface, while the blue region represents the worst one. The area, shape, and integrity of the plasma modification are clarified quantificationally, which provides the possibility of further online evaluation of the modification effect and uniformity by the plasma treatment.

Evaluating anticancer agents on 3D bioprinted organoid tumors (BOT) to reduce cost and accelerate therapeutic discovery.

e13500 Background: Despite recent advances in therapeutics, cancer remains the second leading cause of death worldwide. Next generation cancer models hold the promise of breaking this stranglehold. However, extracting adequate tissue for precision and personalized medicine ex vivo models can be difficult depending on tumor type and tissue site. Mechanisms to expand tissue include patient derived xenografts and patient derived organoids. The former imposes a large time window to establish while the latter is constrained in space by sizescales. Advances in 3D bioprinting have improved the ability to reproduce the three-dimensionality and heterogeneity of the tumor microenvironment. Here we present a novel mechanism to address space and time constrains with 3D bioprinted organoid tumors (BOT) that mimic core needle biopsy tissue to reduce both cost and time to market for novel therapeutics making effective cancer therapy more accessible and equitable. The aim of this study is to produce and use BOT core biopsy tissue in ex vivo precision and personalized medicine applications. Methods: Bioinks prepared with MDA-MB-231 cells, fibrinogen, and thrombin will be deposited layer-by-layer on microporous substrate, in various geometrical configurations, and cured in stages to allow cells and matrix to self-assemble with limited degrees of freedom. Bioprinted organoid tissue will be fully cured to mimic patient biopsy cores, which will then be loaded in ex vivo bioreactors to evaluate sensitivity and resistance of small molecule and biologics. Results: Tissue 3D microarchitecture was validated using high-content fluorescence imaging and custom image processing applications. Diffusion of mock agents, small molecule, and nucleic acid stains was measured 200 mm deep in tissue. Differential activity in spatially distinct regions of intact BOT cores was quantified using advanced image processing modules. Conclusions: Patient derived BOT cores could be used both as a predictive model to screen drugs on an individualized basis and to uncover new therapeutic targets to improve efficacy and reduce toxicity. Due to their ability to replicate the physical and biochemical characteristics of a tumor and its microenvironment, BOT based precision and personalized medicine models can provide more accurate data on drug efficacy and toxicology when compared to in vitro cancer models.

Indocyanine green fluorescence image processing techniques for breast cancer macroscopic demarcation

Re-operation due to disease being inadvertently close to the resection margin is a major challenge in breast conserving surgery (BCS). Indocyanine green (ICG) fluorescence imaging could be used to visualize the tumor boundaries and help surgeons resect disease more efficiently. In this work, ICG fluorescence and color images were acquired with a custom-built camera system from 40 patients treated with BCS. Images were acquired from the tumor in-situ, surgical cavity post-excision, freshly excised tumor and histopathology tumour grossing. Fluorescence image intensity and texture were used as individual or combined predictors in both logistic regression (LR) and support vector machine models to predict the tumor extent. ICG fluorescence spectra in formalin-fixed histopathology grossing tumor were acquired and analyzed. Our results showed that ICG remains in the tissue after formalin fixation. Therefore, tissue imaging could be validated in freshly excised and in formalin-fixed grossing tumor. The trained LR model with combined fluorescence intensity (pixel values) and texture (slope of power spectral density curve) identified the tumor’s extent in the grossing images with pixel-level resolution and sensitivity, specificity of 0.75 ± 0.3, 0.89 ± 0.2.This model was applied on tumor in-situ and surgical cavity (post-excision) images to predict tumor presence.

Toward cardiac tissue characterization using machine learning and light-scattering spectroscopy.

.Significance: The non-destructive characterization of cardiac tissue composition provides essential information for both planning and evaluating the effectiveness of surgical interventions such as ablative procedures. Although several methods of tissue characterization, such as optical coherence tomography and fiber-optic confocal microscopy, show promise, many barriers exist that reduce effectiveness or prevent adoption, such as time delays in analysis, prohibitive costs, and limited scope of application. Developing a rapid, low-cost non-destructive means of characterizing cardiac tissue could improve planning, implementation, and evaluation of cardiac surgical procedures.Aim: To determine whether a new light-scattering spectroscopy (LSS) system that analyzes spectra via neural networks is capable of predicting the nuclear densities (NDs) of ventricular tissues.Approach: We developed an LSS system with a fiber-optics probe and applied it for measurements on cardiac tissues from an ovine model. We quantified the ND in the cardiac tissues using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks (CNNs) to assess the feasibility of characterizing the ND of tissue via LSS.Results: Spectral clustering revealed distinct groups of spectra correlated to ranges of ND. CNNs classified three groups of spectra with low, medium, or high ND with an accuracy of (mean and standard deviation). Our analyses revealed the sensitivity of the classification accuracy to wavelength range and subsampling of spectra.Conclusions: LSS and machine learning are capable of assessing ND in cardiac tissues. We suggest that the approach is useful for the diagnosis of cardiac diseases associated with changes of ND, such as hypertrophy and fibrosis.

Quantification of spatial metal accumulation patterns in Noccaea caerulescens by X-ray fluorescence image processing for genetic studies

BackgroundHyperaccumulation of trace elements is a rare trait among plants which is being investigated to advance our understanding of the regulation of metal accumulation and applications in phytotechnologies. Noccaea caerulescens (Brassicaceae) is an intensively studied hyperaccumulator model plant capable of attaining extremely high tissue concentrations of zinc and nickel with substantial genetic variation at the population-level. Micro-X-ray Fluorescence spectroscopy (µXRF) mapping is a sensitive high-resolution technique to obtain information of the spatial distribution of the plant metallome in hydrated samples. We used laboratory-based µXRF to characterize a collection of 86 genetically diverse Noccaea caerulescens accessions from across Europe. We developed an image-processing method to segment different plant substructures in the µXRF images. We introduced the concentration quotient (CQ) to quantify spatial patterns of metal accumulation and linked that to genetic variation.ResultsImage processing resulted in automated segmentation of µXRF plant images into petiole, leaf margin, leaf interveinal and leaf vasculature substructures. The harmonic means of recall and precision (F1 score) were 0.79, 0.80, 0.67, and 0.68, respectively. Spatial metal accumulation as determined by CQ is highly heritable in Noccaea caerulescens for all substructures, with broad-sense heritability (H2) ranging from 76 to 92%, and correlates only weakly with other heritable traits. Insertion of noise into the image segmentation algorithm barely decreases heritability scores of CQ for the segmented substructures, illustrating the robustness of the trait and the quantification method. Very low heritability was found for CQ if randomly generated substructures were compared, validating the approach.ConclusionsA strategy for segmenting µXRF images of Noccaea caerulescens is proposed and the concentration quotient is developed to provide a quantitative measure of metal accumulation pattern, which can be used to determine genetic variation for such pattern. The metric is robust to segmentation error and provides reliable H2 estimates. This strategy provides an avenue for quantifying XRF data for analysis of the genetics of metal distribution patterns in plants and the subsequent discovery of new genes that regulate metal homeostasis and sequestration in plants.

Losartan Inhibits SARS-CoV-2 Replication in Vitro.

SARS-CoV-2 infection is associated with substantial mortality and high morbidity. This study tested the effect of angiotensin II type I receptor blocker, losartan, on SARS-CoV-2 replication and inhibition of the papain-like protease of the virus. The dose-dependent inhibitory effect of losartan, in concentrations from 1μM to 100μM as determined by quantitative cell analysis combining fluorescence microscopy, image processing, and cellular measurements (Cellomics analysis) on SARS-CoV-2 replication was investigated in Vero E6 cells. The impact of losartan on deubiquitination and deISGylation of SARS-CoV-2 papain-like protease (PLpro) were also evaluated. Results: Losartan reduced PLpro cleavage of tetraUbiquitin to diUbiquitin. It was less effective in inhibiting PLpro's cleavage of ISG15-AMC than Ubiquitin-AMC. To determine if losartan inhibited SARS-CoV-2 replication, losartan treatment of SARS-CoV-2 infected Vero E6 was examined. Losartan treatment one hour prior to SARS-CoV-2 infection reduced levels of SARS-CoV-2 nuclear protein, an indicator of virus replication, by 80% and treatment one-hour post-infection decreased viral replication by 70%. Losartan was not an effective inhibitor of deubiquitinase or deISGylase activity of the PLpro but affected the SARS-CoV-2 replication of Vero E6 cells in vitro. As losartan has a favorable safety profile and is currently available it has features necessary for efficacious drug repurposing and treatment of COVID-19.

Fluorescence imaging and Raman spectroscopy applied for the accurate diagnosis of breast cancer with deep learning algorithms.

Deep learning is usually combined with a single detection technique in the field of disease diagnosis. This study focused on simultaneously combining deep learning with multiple detection technologies, fluorescence imaging and Raman spectroscopy, for breast cancer diagnosis. A number of fluorescence images and Raman spectra were collected from breast tissue sections of 14 patients. Pseudo-color enhancement algorithm and a convolutional neural network were applied to the fluorescence image processing, so that the discriminant accuracy of test sets, 88.61%, was obtained. Two different BP-neural networks were applied to the Raman spectra that mainly comprised collagen and lipid, so that the discriminant accuracy of 95.33% and 98.67% of test sets were gotten, respectively. Then the discriminant results of fluorescence images and Raman spectra were counted and arranged into a characteristic variable matrix to predict the breast tissue samples with partial least squares (PLS) algorithm. As a result, the predictions of all samples are correct, with minor error of predictive value. This study proves that deep learning algorithms can be applied into multiple diagnostic optics/spectroscopy techniques simultaneously to improve the accuracy in disease diagnosis.

Thermal oxidation assessment of Italian extra virgin olive oil using an UltraViolet (UV) induced fluorescence imaging system.

Extra virgin olive oil is a high-quality product with profound health benefits but is susceptible to degradation due to oxidation. Environmental conditions such as temperature, oxygen, and light, promote the oxidation process. From this perspective thermal oxidation stability is of primary concern in terms of food quality and safety. The ability to resist oxidation ensures continued nutritional and economic value. In this study, the thermal oxidation stability of four mono-cultivars of extra virgin olive oil from four different regions of Italy was studied. The samples underwent thermal treatment at 120°C with measurements taken at regular time intervals over 180min. To develop a simplified imaging system, the fluorescence characteristics of the samples during thermal exposure were measured using front-face fluorescence and transmittance spectroscopy in order to assess the changes that occur due to thermal exposure. Standard quality indices including; Peroxide value, acidity, K232, and K270, were also measured following IOC (International Olive Council) procedures. Image processing of both color and fluorescence images was done to ascertain cultivar responses to the thermal treatment. Fluorescence peaks associated with polyphenols, oxidation products, and chlorophyll were identified and monitored, and a comparison made between the different cultivars. Fluorescence peaks were observed at emission wavelengths 435, 465, and 570nm, which are suspected to be products of oxidation and hydrolysis, respectively. The cultivars with a higher concentration of polyphenols showed greater resistance to the formation of oxidation products; an indication that they have a higher thermal stability. The B channel of the RGB color space was identified as being sensitive to changes in UltraViolet (UV) induced fluorescence images due to thermal exposure, and to enable the monitoring of the thermal stability of the different cultivars of extra virgin olive oil.

Rapid adsorption of thin oil slick by modified polypropylene adsorption materials based on hydrophilic induction and hydrophobic capture

Residues oil from oil slick treatment on the water surface cause chronic damage to the environment. These thin oil slicks were labeled with fluorescent. Adsorption characteristics of the modified polypropylene (PP) non-woven fabric PP-GMA-OA/SS were studied. The introduction of hydrophilic groups on the surface of oleophilic materials can accelerate the adsorption of thin oil slicks and produces two strengthening effects. Hydrophilicity is used to induce water molecules to carry oil slicks approach to the fabrics. Hydrophobicity of the fabric is used to quickly capture the oil. Through a method of fluorescence image processing, thin oil slicks were quantified based on integrated optical density (IOD) values. The method verified the rapid adsorption performance of modified PP non-woven fabrics on residual oil slicks based on hydrophilic induction and hydrophobic capture. The material has considerable adsorption capacity and is economical as a supplement material to thin oil slick adsorption.

Image processing in total internal reflection fluorescence microscopy

The paper presents a method for recording fluorescent images of single molecules using the total internal reflection fluorescence microscopy. This method allows determining the location of a molecule with subdiffraction accuracy and a high signal-to-noise ratio of the detected radiation. Methods for computer processing of fluorescence images are considered. The obtained experimental data are presented and the possible areas of application of the obtained results are discussed

High‐Speed Quantification of Pore‐Scale Multiphase Flow of Water and Supercritical CO2 in 2‐D Heterogeneous Porous Micromodels: Flow Regimes and Interface Dynamics

AbstractThe pore‐scale flow of CO2 and water in 2‐D heterogeneous porous micromodels over a Ca range of nearly three orders of magnitude was explored experimentally. The porous geometry is a close reprint of real sandstone, and the experiments were performed under reservoir‐relevant conditions (i.e., 8 MPa and 21 °C), thus ensuring relevance to practical CO2 operations. High‐speed fluorescent microscopy and image processing were employed to achieve temporally and spatially resolved data, providing a unique view of the dynamics underlying this multiphase flow scenario. Under conditions relevant to CO2 sequestration, final CO2 saturation was found to decrease and increase logarithmically with Ca within the capillary and viscous‐fingering regimes, respectively, with a minimum occurring during regime crossover. Specific interfacial length generally scales linearly with CO2 saturation, with higher slopes noted at high Ca due to stronger viscous and inertial forces, as supported by direct pore‐scale observations. Statistical analysis of the interfacial movements revealed that pore‐scale events are controlled by their intrinsic dynamics at low Ca, but overrun by the bulk flow at high Ca. During postfront flow, while permeability is typically correlated with total CO2 saturation in the porous domain (regardless of its mobility), the saturation of active CO2 pathways in the current study correlated very well with permeability. This alternate approach to characterize relative permeability could serve to mitigate hysteresis in relative permeability curves. Taken together, these results provide unique insights that address inconsistent observations in the literature and previously unanswered questions about the underlying flow dynamics of this important multiphase flow scenario.

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy.

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using the analysis of cell membrane receptor clusters as a model, we present a detailed protocol for this image analysis task using Fiji (ImageJ) and Matlab routines to: 1) define regions of interest and design masks adapted to these regions; 2) track the particles in fluorescence microscopy videos; 3) analyze the diffusion and intensity characteristics of selected tracks. The quantitative analysis of the diffusion coefficients, types of motion, and cluster size obtained by fluorescence microscopy and image processing provides a valuable tool to objectively determine particle dynamics and the consequences of modifying environmental conditions. In this article we present detailed protocols for the analysis of these features. The method described here not only allows single-molecule tracking detection, but also automates the estimation of lateral diffusion parameters at the cell membrane, classifies the type of trajectory and allows complete analysis thus overcoming the difficulties in quantifying spot size over its entire trajectory at the cell membrane.

An effective "three-in-one" screening assay for testing drug and nanoparticle toxicity in human endothelial cells.

Evaluating nanoparticle (NP) toxicity in human cell systems is a fundamental requirement for future NP biomedical applications. In this study, we have designed a screening assay for assessing different types of cell death induced by NPs in human umbilical vein endothelial cell (HUVEC) culture. This assay consists of WST-8, LDH and Hoechst 33342 staining, all performed in one well, which enables an evaluation of cell viability, necrosis and apoptosis, respectively, in the same cell sample. The 96-well format and automated processing of fluorescent images enhances the assay rapidity and reproducibility. After testing the assay functionality with agents that induced different types of cell death, we investigated the endothelial toxicity of superparamagnetic iron oxide nanoparticles (SPIONs, 8 nm), silica nanoparticles (SiNPs, 7–14 nm) and carboxylated multiwall carbon nanotubes (CNTCOOHs, 60 nm). Our results indicated that all the tested NP types induced decreases in cell viability after 24 hours at a concentration of 100 μg/ml. SPIONs caused the lowest toxicity in HUVECs. By contrast, SiNPs induced pronounced necrosis and apoptosis. A time course experiment showed the gradual toxic effect of all the tested NPs. CNTCOOHs inhibited tetrazolium derivatives at 100 μg/ml, causing false negative results from the WST-8 and LDH assay. In summary, our data demonstrate that the presented “three-in-one” screening assay is capable of evaluating NP toxicity effectively and reliably. Due to its simultaneous utilization of two different methods to assess cell viability, this assay is also capable of revealing, if NPs interfere with tetrazolium salts.

Combining fluorescence imaging with Hi-C to study 3D genome architecture of the same single cell.

Fluorescence imaging and chromosome conformation capture assays such as Hi-C are key tools for studying genome organization. However, traditionally, they have been carried out independently, making integration of the two types of data difficult to perform. By trapping individual cell nuclei inside a well of a 384-well glass-bottom plate with an agarose pad, we have established a protocol that allows both fluorescence imaging and Hi-C processing to be carried out on the same single cell. The protocol identifies 30,000-100,000 chromosome contacts per single haploid genome in parallel with fluorescence images. Contacts can be used to calculate intact genome structures to better than 100-kb resolution, which can then be directly compared with the images. Preparation of 20 single-cell Hi-C libraries using this protocol takes 5 d of bench work by researchers experienced in molecular biology techniques. Image acquisition and analysis require basic understanding of fluorescence microscopy, and some bioinformatics knowledge is required to run the sequence-processing tools described here.