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19 pages, 5501 KiB  
Article
Detecting Nanotopography Induced Changes in Cell Migration Directions Using Oxygen Sensors
by Muting Wang and Stella W. Pang
Biosensors 2024, 14(8), 389; https://doi.org/10.3390/bios14080389 - 12 Aug 2024
Viewed by 302
Abstract
This study investigates the oxygen (O2) consumption of single cells during changes in their migration direction. This is the first integration of nanotopographies with an O2 biosensor in a platform, allowing the real-time monitoring of O2 consumption in cells [...] Read more.
This study investigates the oxygen (O2) consumption of single cells during changes in their migration direction. This is the first integration of nanotopographies with an O2 biosensor in a platform, allowing the real-time monitoring of O2 consumption in cells and the ability to distinguish cells migrating in the same direction from those migrating in the opposite direction. Advanced nanofabrication technologies were used to pattern nanoholes or nanopillars on grating ridges, and their effects were evaluated using fluorescence microscopy, cell migration assays, and O2 consumption analysis. The results revealed that cells on the nanopillars over grating ridges exhibited an enhanced migration motility and more frequent directional changes. Additionally, these cells showed an increased number of protrusions and filopodia with denser F-actin areas and an increased number of dotted F-actin structures around the nanopillars. Dynamic metabolic responses were also evident, as indicated by the fluorescence intensity peaks of platinum octaethylporphyrin ketone dye, reflecting an increased O2 consumption and higher mitochondria activities, due to the higher energy required in response to directional changes. The study emphasizes the complex interplay between O2 consumption and cell migration directional changes, providing insights into biomaterial science and regenerative medicine. It suggests innovative designs for biomaterials that guide cell migration and metabolism, advocating nanoengineered platforms to harness the intricate relationships between cells and their microenvironments for therapeutic applications. Full article
(This article belongs to the Special Issue Nanotechnology-Based Optical Sensors for Biomedical Applications)
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<p>Fabrication technology of (<b>a</b>) grating, (<b>b</b>) nanoholes and nanopillars on grating ridges, and (<b>c</b>) hard polydimethylsiloxane replica.</p>
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<p>(<b>a</b>) Schematic of O<sub>2</sub> detection setup. Scanning electron micrographs (SEMs) of (<b>b</b>) grating with 6/4 μm ridge width/trench width and 4.5 μm deep, (<b>c</b>) 280 nm wide and 500 nm deep nanoholes on ridges of grating, and (<b>d</b>) 280 nm wide and 500 nm deep nanopillars on ridges of grating.</p>
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<p>(<b>a</b>) SEMs of MC3T3-E1 cells migrated on surfaces of grating, nanoholes on grating ridges, and nanopillars on grating ridges. (<b>b</b>) Numbers of protrusions per cell, (<b>c</b>) filopodia per cell, and (<b>d</b>) cell aspect ratio analysis from SEMs. Mean values are represented by × and median values are represented by–in (<b>b</b>,<b>c</b>). One-way ANOVA with Tukey’s post hoc test, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Brightfield and fluorescent imaging of MC3T3-E1 cells on surfaces of (<b>a</b>) grating, (<b>b</b>) nanoholes on grating ridges, and (<b>c</b>) nanopillars on grating ridges. (<b>d</b>) F-actin index of MC3T3-E1 cells on surfaces of gratings, nanoholes on grating ridges, and nanopillars on grating ridges. One-way ANOVA with Tukey’s post hoc test, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>(<b>a</b>) Migration speed, (<b>b</b>) total migration distance, and (<b>c</b>) percentage of MC3T3-E1 cells that changed migrating directions on surfaces of gratings, nanoholes on grating ridges, and nanopillars on grating ridges during 16 h. One-way ANOVA with Tukey’s post hoc test, NS—not significant, * <span class="html-italic">p</span> &lt; 0.05, and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>(<b>a</b>) Brightfield image. Fluorescence signals of (<b>b</b>) platinum octaethylporphyrin ketone (PtOEPK) dye, (<b>c</b>) mitochondria, and (<b>d</b>) merged images of cells.</p>
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<p>Changes in fluorescence intensity of PtOEPK dye around MC3T3-E1 cells during migration. (<b>a</b>) Unidirectional and (<b>b</b>) bidirectional cell migration on surfaces of gratings, nanoholes on grating ridges, and nanopillars on grating ridges. (<b>c</b>) Fluorescence intensity of PtOEPK dye over time during migration direction changes on surface of nanopillars on grating ridges. Colored dots indicate cell positions and arrows indicate direction of migration. (<b>d</b>) Fluorescence signals of PtOEPK dye and mitochondria of MC3T3-E1 cell varied as it changed migration directions.</p>
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12 pages, 3899 KiB  
Article
Hybrid Polystyrene–Plasmonic Systems as High Binding Density Biosensing Platforms
by Charles M. Darr, Juiena Hasan, Cherian Joseph Mathai, Keshab Gangopadhyay, Shubhra Gangopadhyay and Sangho Bok
Int. J. Mol. Sci. 2024, 25(16), 8603; https://doi.org/10.3390/ijms25168603 - 7 Aug 2024
Viewed by 311
Abstract
Sensitive, accurate, and early detection of biomarkers is essential for prompt response to medical decisions for saving lives. Some infectious diseases are deadly even in small quantities and require early detection for patients and public health. The scarcity of these biomarkers necessitates signal [...] Read more.
Sensitive, accurate, and early detection of biomarkers is essential for prompt response to medical decisions for saving lives. Some infectious diseases are deadly even in small quantities and require early detection for patients and public health. The scarcity of these biomarkers necessitates signal amplification before diagnosis. Recently, we demonstrated single-molecule-level detection of tuberculosis biomarker, lipoarabinomannan, from patient urine using silver plasmonic gratings with thin plasma-activated alumina. While powerful, biomarker binding density was limited by the surface density of plasma-activated carbonyl groups, that degraded quickly, resulting in immediate use requirement after plasma activation. Therefore, development of stable high density binding surfaces such as high binding polystyrene is essential to improving shelf-life, reducing binding protocol complexity, and expanding to a wider range of applications. However, any layers topping the plasmonic grating must be ultra-thin (<10 nm) for the plasmonic enhancement of adjacent signals. Furthermore, fabricating thin polystyrene layers over alumina is nontrivial because of poor adhesion between polystyrene and alumina. Herein, we present the development of a stable, ultra-thin polystyrene layer on the gratings, which demonstrated 63.8 times brighter fluorescence compared to commercial polystyrene wellplates. Spike protein was examined for COVID-19 demonstrating the single-molecule counting capability of the hybrid polystyrene-plasmonic gratings. Full article
(This article belongs to the Special Issue Recent Advances on Bioreceptors and Nanomaterial-Based Biosensors)
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<p>The structure of plasmonic gratings consists of Ag, Al<sub>2</sub>O<sub>3</sub>, and polystyrene on top of PMSSQ.</p>
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<p>The relationship between the film thickness and the concentration (Inset: the relationship in low concentration).</p>
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<p>Optical profilometry after tape test of thin polystyrene films over (<b>a</b>) unsilanized alumina, (<b>b</b>) TMCS, (<b>c</b>) P Silane, (<b>d</b>) N Silane, and (<b>e</b>) G Silane.</p>
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<p>Atomic force microscopy of polystyrene over alumina-coated gratings (<b>a</b>–<b>d</b>) and flat silver (<b>e</b>–<b>h</b>): (<b>a</b>,<b>e</b>) as-prepared ALD alumina; (<b>b</b>,<b>f</b>) 5 nm polystyrene; (<b>c</b>,<b>g</b>) 7 nm polystyrene; and (<b>d</b>,<b>h</b>) 9 nm polystyrene. (Scale bar = 1 µm).</p>
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<p>FTIR spectra of (<b>a</b>) commercial Nunc plates and PS resin and (<b>b</b>) 5 nm and 50 nm PS films.</p>
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<p>Fluorescence intensity of PS coated grating, grating without PS, and commercial plates.</p>
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<p>Schematic of the rectangular wells of the 24-well adapter showing the maximum incidence angles, α = 19.6° and β = 24.1°.</p>
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<p>(<b>a</b>) A fluorescence image of 100 fg/mL of S-protein with 6 μm × 6 μm grids, and (<b>b</b>) single molecule counting from plasmonic grating with various concentrations of spike protein between 1 fg/mL and 10 pg/mL.</p>
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31 pages, 3609 KiB  
Review
Fluorogenic RNA-Based Biosensors of Small Molecules: Current Developments, Uses, and Perspectives
by Janine Kehrli, Claire Husser and Michael Ryckelynck
Biosensors 2024, 14(8), 376; https://doi.org/10.3390/bios14080376 - 1 Aug 2024
Viewed by 576
Abstract
Small molecules are highly relevant targets for detection and quantification. They are also used to diagnose and monitor the progression of disease and infectious processes and track the presence of contaminants. Fluorogenic RNA-based biosensors (FRBs) represent an appealing solution to the problem of [...] Read more.
Small molecules are highly relevant targets for detection and quantification. They are also used to diagnose and monitor the progression of disease and infectious processes and track the presence of contaminants. Fluorogenic RNA-based biosensors (FRBs) represent an appealing solution to the problem of detecting these targets. They combine the portability of molecular systems with the sensitivity and multiplexing capacity of fluorescence, as well as the exquisite ligand selectivity of RNA aptamers. In this review, we first present the different sensing and reporting aptamer modules currently available to design an FRB, together with the main methodologies used to discover modules with new specificities. We next introduce and discuss how both modules can be functionally connected prior to exploring the main applications for which FRB have been used. Finally, we conclude by discussing how using alternative nucleotide chemistries may improve FRB properties and further widen their application scope. Full article
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<p>Principle of fluorogenic biosensors for small molecules. (<b>a</b>) General working principle of a fluorogenic biosensor. In absence of the ligand, the biosensor is in a dark conformation preventing fluorescence emission. In the presence of the ligand, the biosensor undergoes a structural rearrangement leading to fluorescence emission. (<b>b</b>) Working principle of a biosensor exploiting intercalating dye displacement. The sensing nucleic acid is turned fluorescent upon staining by a nonspecific fluorogenic intercalating dye. The recognition of the target small molecule leads to dye displacement and concomitant fluorescence reduction. (<b>c</b>) Nucleic-acid-based fluorogenic biosensor. The molecule is made of a light-up aptamer (green) functionally connected to a sensing aptamer (blue). In absence of target ligand, the structure of the light-up aptamer is destabilized, and its fluorescence capacity is abrogated. However, the binding of the target ligand to the sensing aptamer, induces a structure switching that the transducer module (yellow) transmits to the light-up aptamer. The latter is then stabilized; it recovers its capacity to bind its fluorogen and forms a fluorescent complex. Arrows symbolize molecular recognition events between RNA and target ligand or specific fluorogen.</p>
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<p>Main approaches to isolate light-up aptamers. (<b>a</b>) Systematic evolution of ligands by exponential enrichment (SELEX). During this in vitro selection process, an oligonucleotide (DNA/RNA/XNA) library is generated and incubated with beads coated with the aptamer ligand. Aptamers that remain attached to the beads after multiple washing steps are recovered. Each round of selection allows the gradual enrichment of aptamer based on their affinity for the target. (<b>b</b>) Fluorescence-activated cell sorting (FACS)-based selection of aptamers displayed on beads and particles. DNA genes contained in the library are individualized and amplified, at the surface of the particles, followed by in vitro transcription and incubation with the fluorogen. FACS is used to sort particles coated with the aptamer of interest (i.e., the most fluorescent particles). Aptamers are therefore directly selected for their ability to activate the fluorogen fluorescence. (<b>c</b>) Microfluidic-assisted in vitro compartmentalization. The starting DNA library is encapsulated into water-in-oil droplets (2.5 pL) using a droplet generator microfluidic chip. The emulsion is collected, and DNA contained in each droplet is amplified by PCR. Droplets are then fused with a droplet containing all necessary reagents for in vitro transcription as well as the fluorogen (green dot). Droplets are finally reinjected into a sorting device, where fluorescence emission is measured, and the droplets are sorted accordingly.</p>
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<p>Structural organization of the principal light-up aptamers and their cognate fluorogens. The sequence and two-dimensional structure of the light-up aptamer is represented according to crystal structures and to Leontis–Westhof representation. The fluorogen is represented by a black dot. Possible entry points usable to design a fluorogenic RNA-based biosensor are highlighted in red. (<b>a</b>) Spinach2 aptamer [<a href="#B65-biosensors-14-00376" class="html-bibr">65</a>] and its circular permuted version (Cp-Spinach2) [<a href="#B75-biosensors-14-00376" class="html-bibr">75</a>], (<b>b</b>) iSpinach aptamer [<a href="#B48-biosensors-14-00376" class="html-bibr">48</a>], (<b>c</b>) Mango-III aptamer [<a href="#B53-biosensors-14-00376" class="html-bibr">53</a>], (<b>d</b>) Mango-II aptamer [<a href="#B52-biosensors-14-00376" class="html-bibr">52</a>], (<b>e</b>) Squash aptamer [<a href="#B67-biosensors-14-00376" class="html-bibr">67</a>], (<b>f</b>) RhoBAST aptamer [<a href="#B61-biosensors-14-00376" class="html-bibr">61</a>], (<b>g</b>) DIR2s aptamer [<a href="#B45-biosensors-14-00376" class="html-bibr">45</a>], (<b>h</b>) Pepper aptamer [<a href="#B59-biosensors-14-00376" class="html-bibr">59</a>], and (<b>i</b>) Corn aptamer [<a href="#B43-biosensors-14-00376" class="html-bibr">43</a>]. (<b>j</b>) Chemical structure of the main fluorogens specifically activated by a light-up RNA aptamer. The fluorogenic part of the molecule is shadowed in a color matching its emission wavelength. Aptamer/fluorogen compatible pairs are listed in <a href="#biosensors-14-00376-t001" class="html-table">Table 1</a>.</p>
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<p>Capture-SELEX. Nucleic acid (DNA/RNA) libraries are incubated with a complementary biotinylated capture-oligonucleotide and incubated with streptavidin-coated beads. The library can be either unstructured (left scheme in the box) or 3WJ-scaffolded (right scheme in the box). The library is immobilized on the beads through the capture-oligonucleotide. Upon extensive washing, the addition of the target molecule induces a structure-switching event that undocks the variants of interest. The latter are then recovered and used for subsequent rounds of selection and/or sequence identification.</p>
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<p>Main design strategies to generate fluorogenic RNA-based biosensors. (<b>a</b>) Direct allosteric control: the sensor aptamer (in blue) is directly connected to the light-up aptamer (in green) by merging essential stems through a communication module (CM in orange). The latter transiently destabilizes the light-up aptamer in absence of the ligand but enables its stabilization upon ligand binding, leading to the restoration of fluorogen binding capacity and to fluorescence emission. (<b>b</b>) Microfluidic-assisted screening used to select FRBs with optimal communication module. The same microfluidic-assisted pipeline described on <a href="#biosensors-14-00376-f002" class="html-fig">Figure 2</a>c is used but iterative rounds of positive and negative selections are performed. During a positive selection, the droplets containing a light-up/fluorogen fluorescence signal in the presence of the ligand (over stabilized variants or optimized FRB) are sorted and the dark droplets (destabilized variants) are discarded. Then, negative selection is performed in absence of ligand and droplets remaining dark (expected FRB) are sorted, while fluorescent droplets (ligand-independent over stabilized variant) are discarded. (<b>c</b>) Strand displacement-mediated control. The sensing aptamer (in blue) is connected to the light-up aptamer (in green) replacing the natural riboswitch regulation platform. Transducer sequence (in orange) is designed so that a strand invades and destabilizes the light-up aptamer in absence of ligand. The binding of the ligand triggers the displacement of the invading strand and leads to refolding of the light-up and the fluorogen fluorescence activation. (<b>d</b>) Mixed control. The sensing aptamer (in blue) is directly connected to a ribozyme (in brown) through a CM (in orange). One strand of the light-up aptamer (in green) is sequestered by the ribozyme, preventing its folding and abrogating its fluorogenic capacity. Upon recognition of the ligand, the ribozyme self-cleaves and releases the light-up strand that refolds and activates the fluorescence of the fluorogen. Arrows symbolize molecular recognition between the RNA and the target ligand.</p>
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<p>Exploiting RNA modularity. (<b>a</b>) Schemes of different additional modules that can be linked to an FRB to increase their accuracy and their stability in living cells. The Tornado stem-loop is shown as expected upon sequence processing. (<b>b</b>) FRB-derived constructs optimized biosensing in bacteria and (<b>c</b>) in mammalian cells. The modular architecture of the constructs is shown in the upper part. The FRB or the light-up aptamer are connected to other modules via their available stems, depending on the application. The illustration shows only a few possible constructions, highlighting the modularity and variety available for adapting in vivo expression. For example, adding a light-up aptamer allows the construction of a ratiometric biosensor in mammalian cells (right). The Tornado system is represented in a stepwise manner (bottom, right). A linear construct is first generated by transcription. Then, ribozymes surrounding the FRB construct self-cleave and release an anticodon-like structure later recognized by a cellular ligase that catalyzes the circularization of the construct. This circularization event is symbolized by the arrow.</p>
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<p>Examples of FRBs applications. (<b>a</b>) SAM-Pepper/RhoBAST ratiometric sensor (adapted from [<a href="#B76-biosensors-14-00376" class="html-bibr">76</a>]). The SAM sensor (blue) exerts an allosteric control on the folding of the light-up Pepper (green). The FRB is inserted into an F30 scaffold (orange) together with the light-up RhoBAST (red). In the absence of SAM, the SAM-Pepper aptamer remains unfolded and dark. Meanwhile, RhoBAST is properly folded and able to activate the fluorescence of TMR-DN (RhoBAST’s cognate fluorogen). In the presence of SAM, the SAM-Pepper aptamer is folded and Pepper activates HBC fluorescence. Correcting the Pepper/HBC fluorescence by that of RhoBAST/TMR-DN allows for normalizing SAM-related fluorescence by the amount of produced biosensor, therefore reducing the impact of cell-to-cell expression variability. The recognition event between the target molecule and the RNA is symbolized by the arrow. (<b>b</b>) FluorMango: a fluoride-specific FRB (adapted from [<a href="#B101-biosensors-14-00376" class="html-bibr">101</a>]). The secondary structure of the biosensor is shown (left), with the sensing aptamer module from crcB riboswitch (in blue) fused to Mango-III light-up aptamer (in green) via a communication module (in orange) identified upon µIVC-seq screening. The addition of the FRB to a mixture containing fluoroacetate as fluorinated substrate and various bacterial strains enables monitoring defluorination activity in real time (right). A defluorinating strain (i.e., <span class="html-italic">Caballeronia</span> sp. S22) can easily be discriminated from a non-defluorinating one (i.e., <span class="html-italic">Pseudomonas fluorescens</span>). Note that a small background is caused by the free fluoride brought by the fluoroacetate substrate.</p>
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13 pages, 4047 KiB  
Article
Chaperone Copolymer-Assisted Catalytic Hairpin Assembly for Highly Sensitive Detection of Adenosine
by Yazhen Liao, Xiaoxue Yin, Wenqian Liu, Zhenrui Du and Jie Du
Polymers 2024, 16(15), 2179; https://doi.org/10.3390/polym16152179 - 31 Jul 2024
Viewed by 378
Abstract
Adenosine is an endogenous molecule that plays a vital role in biological processes. Research indicates that abnormal adenosine levels are associated with a range of diseases. The development of sensors capable of detecting adenosine is pivotal for early diagnosis of disease. For example, [...] Read more.
Adenosine is an endogenous molecule that plays a vital role in biological processes. Research indicates that abnormal adenosine levels are associated with a range of diseases. The development of sensors capable of detecting adenosine is pivotal for early diagnosis of disease. For example, elevated adenosine levels are closely associated with the onset and progression of cancer. In this study, we designed a novel DNA biosensor utilizing chaperone copolymer-assisted catalytic hairpin assembly for highly sensitive detection of adenosine. The functional probe comprises streptavidin magnetic beads, an aptamer, and a catalytic chain. In the presence of adenosine, it selectively binds to the aptamer, displacing the catalytic chain into the solution. The cyclic portion of H1 hybridizes with the catalytic strand, while H2 hybridizes with the exposed H1 fragment to form an H1/H2 complex containing a G-quadruplex. Thioflavin T binds specifically to the G-quadruplex, generating a fluorescent signal. As a nucleic acid chaperone, PLL-g-Dex expedites the strand exchange reaction, enhancing the efficiency of catalytic hairpin assembly, thus amplifying the signal and reducing detection time. The optimal detection conditions were determined to be a temperature of 25 °C and a reaction time of 10 min. Demonstrating remarkable sensitivity and selectivity, the sensor achieved a lowest limit of detection of 9.82 nM. Furthermore, it exhibited resilience to interference in complex environments such as serum, presenting an effective approach for rapid and sensitive adenosine detection. Full article
(This article belongs to the Special Issue Biopolymer-Based Materials in Medical Applications)
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<p>Chemical structure of chaperone copolymer PLL-<span class="html-italic">g</span>-Dex.</p>
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<p>Schematic diagram of a biosensor based on (<b>A</b>) streptavidin magnetic beads functional probe, and (<b>B</b>) PLL-<span class="html-italic">g</span>-Dex assisted catalytic hairpin assembly for adenosine detection.</p>
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<p>Fluorescence emission spectrum under different conditions: (a) MBs-Aptamer/tDNA + H1 + H2 + K<sup>+</sup>; (b) MBs-Aptamer/tDNA + H1 + H2 + K<sup>+</sup> + Target; (c) MBs-Aptamer/tDNA + H1 + H2 + K<sup>+</sup> + PLL-<span class="html-italic">g</span>-Dex; and (d) MBs-Aptamer/tDNA + H1 + H2 + K<sup>+</sup>+ PLL-<span class="html-italic">g</span>-Dex + Target.</p>
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<p>Optimization of experimental conditions. (<b>A</b>) Sequence of H1 and H2. (<b>B</b>) Concentration of H1 and H2. (<b>C</b>) Concentration of PLL-<span class="html-italic">g</span>-Dex. (<b>D</b>) ThT concentration. (<b>E</b>) K<sup>+</sup> concentration. (<b>F</b>) Reaction temperature. (<b>G</b>) Reaction time.</p>
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<p>(<b>A</b>) Fluorescence spectra of different adenosine concentrations (a–l: 0, 25, 50, 100, 150, 200, 300, 400, 500, 600, 1000, and 2000 nM). (<b>B</b>) The correlation between adenosine concentrations and fluorescence signal intensity (the inset shows the linear calibration curve of adenosine vs. fluorescence intensity in the range of 0–600 nM). The error bars represent standard deviations obtained from triplicate experiments.</p>
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<p>(<b>A</b>) Fluorescence spectrum diagram of the relationship between different types of nucleosides and fluorescence intensity. (<b>B</b>) Diagram of the relationship between the fluorescence intensity difference and different nucleoside types at the excitation wavelength of 420 nm. Statistical significance was calculated by the one-way ANOVA. (**** <span class="html-italic">p</span> &lt; 0.0001).</p>
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27 pages, 7574 KiB  
Review
Far-Red Fluorescent Proteins: Tools for Advancing In Vivo Imaging
by Angyang Shang, Shuai Shao, Luming Zhao and Bo Liu
Biosensors 2024, 14(8), 359; https://doi.org/10.3390/bios14080359 - 24 Jul 2024
Viewed by 709
Abstract
Far-red fluorescent proteins (FPs) have emerged as indispensable tools in in vivo imaging, playing a pivotal role in elucidating fundamental mechanisms and addressing application issues in biotechnology and biomedical fields. Their ability for deep penetration, coupled with reduced light scattering and absorption, robust [...] Read more.
Far-red fluorescent proteins (FPs) have emerged as indispensable tools in in vivo imaging, playing a pivotal role in elucidating fundamental mechanisms and addressing application issues in biotechnology and biomedical fields. Their ability for deep penetration, coupled with reduced light scattering and absorption, robust resistance to autofluorescence, and diminished phototoxicity, has positioned far-red biosensors at the forefront of non-invasive visualization techniques for observing intracellular activities and intercellular behaviors. In this review, far-red FPs and their applications in living systems are mainly discussed. Firstly, various far-red FPs, characterized by emission peaks spanning from 600 nm to 650 nm, are introduced. This is followed by a detailed presentation of the fundamental principles enabling far-red biosensors to detect biomolecules and environmental changes. Furthermore, the review accentuates the superiority of far-red FPs in multi-color imaging. In addition, significant emphasis is placed on the value of far-red FPs in improving imaging resolution, highlighting their great contribution to the advancement of in vivo imaging. Full article
(This article belongs to the Section Optical and Photonic Biosensors)
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<p>(<b>A</b>) Time-lapse images of mCherry-tubulin in MCF-7 breast cancer cells 5 min before the launch (T-300 s) of the rocket and during the real microgravity (r-µg) phase (T + 177 s−T + 402 s). The green arrows indicate changes in α-tubulin [<a href="#B54-biosensors-14-00359" class="html-bibr">54</a>] (reproduced with permission from Copyright 2019, Multidisciplinary Digital Publishing Institute). (<b>B</b>) Images of Glb1-2A-mCherry in indicated tissue sections from Glb1<sup>+/m</sup> mice. The white boxes show cells with mutually exclusive signals for mCherry and Lamin B1 [<a href="#B55-biosensors-14-00359" class="html-bibr">55</a>]. (<b>C</b>) Images of Glb1-2A-mCherry in a cohort of Glb1<sup>+/m</sup> mice at indicated ages [<a href="#B55-biosensors-14-00359" class="html-bibr">55</a>]. The images in (<b>B</b>,<b>C</b>) were reproduced with permission from Copyright 2022, Springer Nature.</p>
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<p>Structure diagrams of two conformations of mPlum as indicated. The water molecule inside the red circle mediates the hydrogen bonding [<a href="#B57-biosensors-14-00359" class="html-bibr">57</a>] (reproduced with permission from Copyright 2022, American Chemical Society).</p>
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<p>(<b>A</b>) Structure diagrams of the eqFP611 chromophore [<a href="#B61-biosensors-14-00359" class="html-bibr">61</a>]. (<b>B</b>) Structure diagrams of the chromophore within the β-can fold of eqFP611. The β-can fold starts from blue, increasing to green with increasing residue number. The eqFP611 side chains are in yellow and the chromophore is in magenta [<a href="#B61-biosensors-14-00359" class="html-bibr">61</a>]. The images in (<b>A</b>,<b>B</b>) were reproduced with permission from Copyright 2003, Elsevier.</p>
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<p>(<b>A</b>) Images of Katushka-labeled <span class="html-italic">E. coli</span> TOP10 bacteria in mice [<a href="#B16-biosensors-14-00359" class="html-bibr">16</a>] (reproduced with permission from Copyright 2019, Springer Nature). (<b>B</b>) Time-lapse images of mito::mKate2 in Hela cells [<a href="#B67-biosensors-14-00359" class="html-bibr">67</a>]. (<b>C</b>) Images of mito::mKate2 in hind paw of CAG-mito::mKate2<sup>+</sup> transgenic founder progeny as compared to a negative littermate [<a href="#B67-biosensors-14-00359" class="html-bibr">67</a>]. The images in (<b>B</b>,<b>C</b>) were reproduced with permission from Copyright 2018, Wiley.</p>
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<p>Structure diagrams of Katushka in a cis fluorescent state at pH 8.5 and in a trans nonfluorescent state at pH 5.0. Hydrogen bonds are shown as blue dashed lines, water (W) is shown as red spheres, and van der Waals contacts are shown as black “eyelashes” [<a href="#B68-biosensors-14-00359" class="html-bibr">68</a>] (reproduced with permission from Copyright 2011, Wiley).</p>
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<p>(<b>A</b>) Structure diagrams of the Neptune chromophore. The conjugated π system of the chromophore and side chain changes are shown in stick representation with nitrogen in blue and oxygen in red, and the van der Waals surfaces of water oxygen atoms are depicted as dotted spheres colored light blue [<a href="#B30-biosensors-14-00359" class="html-bibr">30</a>]. (<b>B</b>) Detailed model of the hydrogen-bonding network. Hydrogen atoms are attached to donors with solid lines and to acceptors with dotted lines [<a href="#B30-biosensors-14-00359" class="html-bibr">30</a>]. The images in (<b>A</b>,<b>B</b>) were reproduced with permission from Copyright 2009, Elsevier.</p>
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<p>(<b>A</b>) Scheme of FRET between EGFP-PTS1 and mCherry-PEX5 [<a href="#B93-biosensors-14-00359" class="html-bibr">93</a>]. (<b>B</b>) Images of EGFP-PTS1 and mCherry-PEX5 in pex5<sup>−/−</sup> cells [<a href="#B93-biosensors-14-00359" class="html-bibr">93</a>]. The images in (<b>A</b>,<b>B</b>) were reproduced with permission from Copyright 2020, Multidisciplinary Digital Publishing Institute.</p>
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<p>(<b>A</b>) Scheme of FRET-GFPRed [<a href="#B94-biosensors-14-00359" class="html-bibr">94</a>]. (<b>B</b>) Images of FRET-GFPRed and FRET-CFPYPet in two Hela cells of one field before and after ionomycin stimulation [<a href="#B94-biosensors-14-00359" class="html-bibr">94</a>]. The images in (<b>A</b>,<b>B</b>) were reproduced with permission from Copyright 2020, Multidisciplinary Digital Publishing Institute. (<b>C</b>) Scheme of Booster-PKA [<a href="#B95-biosensors-14-00359" class="html-bibr">95</a>]. (<b>D</b>) Time-lapse images of Booster-PKA and EKAREV in Hela cells before and after treatments with indicated stimulants and inhibitors [<a href="#B95-biosensors-14-00359" class="html-bibr">95</a>]. The images in (<b>C</b>,<b>D</b>) were reproduced with permission from Copyright 2020, American Chemical Society. (<b>E</b>) Scheme of mKate2-DEVD-iRFP. (<b>F</b>) Time-lapse images of the donor mKate2 before (0 h) and after indicated treatments [<a href="#B98-biosensors-14-00359" class="html-bibr">98</a>] (reproduced with permission from Copyright 2022, Springer Nature), Individual cells in (<b>F</b>) are numbered.</p>
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<p>(<b>A</b>) Scheme of the mLumin-BiFC system [<a href="#B43-biosensors-14-00359" class="html-bibr">43</a>]. (<b>B</b>) Images of mLumin-BiFC signals in COS-7 cells with indicated proteins. Scale bar: 20 μm [<a href="#B43-biosensors-14-00359" class="html-bibr">43</a>]. (<b>C</b>) Images of three pairs of protein interactions in the same living cells with indicated BiFC systems. Scale bar: 10 μm [<a href="#B43-biosensors-14-00359" class="html-bibr">43</a>]. The images in (<b>A</b>–<b>C</b>) were reproduced with permission from Copyright 2009, Elsevier. (<b>D</b>) Scheme of the mNeptune-BiFC system [<a href="#B101-biosensors-14-00359" class="html-bibr">101</a>]. (<b>E</b>) Images of mNeptune-BiFC signals in live cells with indicated proteins [<a href="#B101-biosensors-14-00359" class="html-bibr">101</a>]. (<b>F</b>) Images of mNeptune-BiFC signals in live mice injected subcutaneously with indicated cells [<a href="#B101-biosensors-14-00359" class="html-bibr">101</a>]. The images in (<b>D</b>–<b>F</b>) were reproduced with permission from Copyright 2014, Oxford University Press.</p>
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<p>(<b>A</b>) Scheme of the cpFusionRed-based voltage sensor. S1–S4 are transmembrane voltage-sensitive domains (VSDs). The red barrel is FusionRed. The green and red arrows represent excitation and emission light, respectively [<a href="#B105-biosensors-14-00359" class="html-bibr">105</a>]. (<b>B</b>) Images of fluorescence changes to single voltage steps and trains of 2.5 Hz and 5 Hz in voltage-clamped PC12 cells [<a href="#B105-biosensors-14-00359" class="html-bibr">105</a>]. The images in (<b>A</b>,<b>B</b>) were reproduced with permission from Copyright 2017, Public Library of Science.</p>
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<p>(<b>A</b>) Scheme of mRFP-eGFP-LC3 [<a href="#B51-biosensors-14-00359" class="html-bibr">51</a>]. (<b>B</b>) Images of mRFP-eGFP-LC3 in primary neurons. APs (double arrowheads), ALs (arrowhead), and pa-ALs (asterisk) are shown as indicated [<a href="#B51-biosensors-14-00359" class="html-bibr">51</a>]. The images in (<b>A</b>,<b>B</b>) were reproduced with permission from Copyright 2022, Springer Nature. (<b>C</b>) Scheme of mt-Keima [<a href="#B115-biosensors-14-00359" class="html-bibr">115</a>]. (<b>D</b>) Images of mt-Keima and mito-QC in the mouse heart following the exhaustive exercise protocol. Mitolysosomes (white arrows) and mitochondria (white boxes) are shown as indicated. Scale bar: 20 μm [<a href="#B115-biosensors-14-00359" class="html-bibr">115</a>]. The images in (<b>C</b>,<b>D</b>) were reproduced with permission from Copyright 2021, Taylor &amp; Francis.</p>
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<p>(<b>A</b>) Images of fluorescent signals in the mKate2, KO2, and AzG channels throughout the mice embryo. The higher magnification images within the white boxes are shown in the original article. Scale bar: 1 mm [<a href="#B67-biosensors-14-00359" class="html-bibr">67</a>] (reproduced with permission from Copyright 2018, Wiley). (<b>B</b>) Images of fluorescent signals in the CFP, mMiCy, EGFP, YFP, dKEIMA570, and mKeima channels in one Vero cell. Scale bar: 10 μm [<a href="#B49-biosensors-14-00359" class="html-bibr">49</a>] (reproduced with permission from Copyright 2006, Springer Nature).</p>
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<p>(<b>A</b>) Images of NFA-labeled tumor cells in a nude mouse acquired at wavelengths between 585 and 620 nm [<a href="#B125-biosensors-14-00359" class="html-bibr">125</a>] (reproduced with permission from Copyright 2020, Society of Photo-Optical Instrumentation Engineers). (<b>B</b>) Images of rsFusionRed2–F-Tractin in live U2OS cells with MoNaLISA nanoscopy [<a href="#B127-biosensors-14-00359" class="html-bibr">127</a>] (reproduced with permission from Copyright 2018, Springer Nature).</p>
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54 pages, 3588 KiB  
Review
The Roles of White Adipose Tissue and Liver NADPH in Dietary Restriction-Induced Longevity
by Leah E. Jamerson and Patrick C. Bradshaw
Antioxidants 2024, 13(7), 820; https://doi.org/10.3390/antiox13070820 - 8 Jul 2024
Viewed by 1083
Abstract
Dietary restriction (DR) protocols frequently employ intermittent fasting. Following a period of fasting, meal consumption increases lipogenic gene expression, including that of NADPH-generating enzymes that fuel lipogenesis in white adipose tissue (WAT) through the induction of transcriptional regulators SREBP-1c and CHREBP. SREBP-1c knockout [...] Read more.
Dietary restriction (DR) protocols frequently employ intermittent fasting. Following a period of fasting, meal consumption increases lipogenic gene expression, including that of NADPH-generating enzymes that fuel lipogenesis in white adipose tissue (WAT) through the induction of transcriptional regulators SREBP-1c and CHREBP. SREBP-1c knockout mice, unlike controls, did not show an extended lifespan on the DR diet. WAT cytoplasmic NADPH is generated by both malic enzyme 1 (ME1) and the pentose phosphate pathway (PPP), while liver cytoplasmic NADPH is primarily synthesized by folate cycle enzymes provided one-carbon units through serine catabolism. During the daily fasting period of the DR diet, fatty acids are released from WAT and are transported to peripheral tissues, where they are used for beta-oxidation and for phospholipid and lipid droplet synthesis, where monounsaturated fatty acids (MUFAs) may activate Nrf1 and inhibit ferroptosis to promote longevity. Decreased WAT NADPH from PPP gene knockout stimulated the browning of WAT and protected from a high-fat diet, while high levels of NADPH-generating enzymes in WAT and macrophages are linked to obesity. But oscillations in WAT [NADPH]/[NADP+] from feeding and fasting cycles may play an important role in maintaining metabolic plasticity to drive longevity. Studies measuring the WAT malate/pyruvate as a proxy for the cytoplasmic [NADPH]/[NADP+], as well as studies using fluorescent biosensors expressed in the WAT of animal models to monitor the changes in cytoplasmic [NADPH]/[NADP+], are needed during ad libitum and DR diets to determine the changes that are associated with longevity. Full article
(This article belongs to the Special Issue Oxidative Stress in Adipose Tissue)
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<p>The citratrate-α-ketoglutarate shuttle and the citrate–pyruvate shuttle compete for cytoplasmic citrate. Both can generate cytoplasmic NADPH, but the citrate–pyruvate shuttle also synthesizes cytoplasmic acetyl-CoA and transfers cytoplasmic NADH-reducing equivalents into the mitochondrial matrix. The citrate–pyruvate shuttle relies upon pyruvate carboxylase (PC) to regenerate oxaloacetate to react with the pyruvate-derived acetyl-CoA. In the liver, both shuttles may operate simultaneously as the citrate–pyruvate shuttle can only provide roughly half of the NADPH required for fatty acid synthesis. In WAT, the remainder of the NADPH for fatty acid synthesis not provided by the citrate–pyruvate shuttle is likely synthesized by the PPP. Transport reactions and unnamed Krebs cycle reactions are shown as dashed arrows, while other chemical reactions are shown as solid arrows. Enzyme names are shown in blue font, metabolite names are shown in black font, coenzyme names are shown in maroon font, and transporter names and gaseous co-reactants and co-products are shown in gray font.</p>
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<p>Serine biosynthesis pathway, the folate cycle, the methionine cycle, and the transsulfuration pathway. Serine is synthesized in a 3-enzyme pathway from the glycolytic intermediate 3-phosphoglycerate, as shown shaded in gray. In most tissues, with the known exception of liver, serine is imported into mitochondria and metabolized by SHMT2 to drive the folate cycle, shown as the circle of yellow arrows in the figure, in the clockwise direction. Formate, a one-carbon intermediate, and tetrahydrofolate are exported from mitochondria and react in the cytoplasm to from 10-formyltetrahydrofolate (10-formylTHF). Cytoplasmic one-carbon units can either be used for NADPH generation with the release of CO<sub>2</sub> or in metabolism, where they are commonly used for methylation reactions or nucleotide synthesis. NADPH oxidation is used to regenerate cytoplasmic tetrahydrofolate (THF) from dihydrofolate. To be used for methylation reactions, the one-carbon unit is funneled into the methionine cycle, shown shaded as a light tan or cream color in the figure. Homocysteine can either be methylated to methionine and re-enter the methionine cycle or react with serine to enter the transsulfuration pathway, as shown shaded in light green in the figure, leading to cysteine and α-ketobutyrate synthesis. Different forms of folate are shown with a white font and blue background. Amino acid names are shown in red font. Other metabolite names are shown in green font, with the exception of coenzyme names that are shown in a purple font. Gaseous products are shown in light blue font. Enzyme names are shown in black font. Single chemical reactions are shown as solid arrows, while multiple reactions are shown as a dashed arrow. Metabolites that are transported into or out of the mitochondrial matrix are boxed.</p>
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<p>Important transcriptional regulators that play a role in WAT NADPH and lipid metabolism that appear to drive DR-mediated longevity and whether these transcriptional regulators are activated during the feeding or fasting portion of the DR diet. After consuming a meal on the DR diet, the transcriptional regulators SREBP-1c, CHREBP, and PPAR-γ are induced in WAT, which leads to the expression of FGF21, PGC-1α, and lipogenic genes, including cytoplasmic NADPH-generating enzymes. Together, this leads to increased NADPH levels, HDAC3 inhibition, increased mitochondrial biogenesis, the browning of WAT, and fatty acid cycling. For further details, see the following references [<a href="#B145-antioxidants-13-00820" class="html-bibr">145</a>,<a href="#B353-antioxidants-13-00820" class="html-bibr">353</a>]. During the fasting portion of the DR diet, decreased insulin signaling leads to the transcriptional activation of FOXO3, while decreased amino acid levels lead to ISR and ATF4 activation that leads to increased expression of ATF3, which contributes to the browning of WAT, with each contributing to longevity. Dotted arrows represent causation, while solid arrows represent increased or decreased levels and/or activity.</p>
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13 pages, 4266 KiB  
Article
Directed Evolution of Protein-Based Sensors for Anaerobic Biological Activation of Methane
by Ehsan Bahrami Moghadam, Nam Nguyen, Yixi Wang and Patrick C. Cirino
Biosensors 2024, 14(7), 325; https://doi.org/10.3390/bios14070325 - 30 Jun 2024
Viewed by 862
Abstract
Microbial alkane degradation pathways provide biological routes for converting these hydrocarbons into higher-value products. We recently reported the functional expression of a methyl-alkylsuccinate synthase (Mas) system in Escherichia coli, allowing for the heterologous anaerobic activation of short-chain alkanes. However, the enzymatic activation [...] Read more.
Microbial alkane degradation pathways provide biological routes for converting these hydrocarbons into higher-value products. We recently reported the functional expression of a methyl-alkylsuccinate synthase (Mas) system in Escherichia coli, allowing for the heterologous anaerobic activation of short-chain alkanes. However, the enzymatic activation of methane via natural or engineered alkylsuccinate synthases has yet to be reported. To address this, we employed high-throughput screening to engineer the itaconate (IA)-responsive regulatory protein ItcR (WT-ItcR) from Yersinia pseudotuberculosis to instead respond to methylsuccinate (MS, the product of methane addition to fumarate), resulting in genetically encoded biosensors for MS. Here, we describe ItcR variants that, when regulating fluorescent protein expression in E. coli, show increased sensitivity, improved overall response, and enhanced specificity toward exogenously added MS relative to the wild-type repressor. Structural modeling and analysis of the ItcR ligand binding pocket provide insights into the altered molecular recognition. In addition to serving as biosensors for screening alkylsuccinate synthases capable of methane activation, MS-responsive ItcR variants also establish a framework for the directed evolution of other molecular reporters, targeting longer-chain alkylsuccinate products or other succinate derivatives. Full article
(This article belongs to the Section Biosensors and Healthcare)
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<p>Regulatory mechanisms of ItcR and variant selection strategy. (<b>a</b>) Transcriptional regulation by ItcR from <span class="html-italic">Y. pseudotuberculosis</span>: ItcR represses the transcription of genes under the control of the promoter P<span class="html-italic">ccl</span>. Repression is relieved upon binding itaconate (IA). <span class="html-italic">Ccl</span>, <span class="html-italic">Ich</span>, and <span class="html-italic">Ict</span> are <span class="html-italic">Y. pseudotuberculosis</span> genes involved in itaconate catabolism [<a href="#B13-biosensors-14-00325" class="html-bibr">13</a>]. (<b>b</b>) ItcR variants showing an enhanced induced response to methylsuccinate (MS) were isolated by placing a reporter gene (that encodes the mCherry red fluorescent protein) under the control of P<span class="html-italic">ccl</span>, enabling the high-throughput screening of variants libraries.</p>
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<p>Phylogenetic tree showing the evolution of MS-responsive variants originating from WT-ItcR. The indicated fold improved values represent the fold enhancement in mCherry RFP fluorescence in the presence of the indicated concentration of MS relative to WT-ItcR. SSM: site-saturation mutagenesis.</p>
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<p>MS-induced RFP expression in <span class="html-italic">E. coli</span> harboring the “Var7” MS biosensor system. Normalized fluorescence intensity (“y”, RFU/OD<sub>600</sub>) is plotted against MS concentration (that which was added to the culture broth), “x”. Data were fitted to Equation (1), as shown (R<sup>2</sup> &gt; 0.99).</p>
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<p>Sensitivity (mM) vs. background RFP (RFU/OD<sub>600</sub>) of each ItcR variant. Sensitivity is defined as the concentration at half of the saturation signal (‘<span class="html-italic">k</span>’ in Equation (1)). Background refers to the absolute normalized fluorescence measured in the absence of the inducer. Data are the average of 3 values, and error bars represent the range. ♦ represents WT-ItcR, with IA as the inducer; <tt>■</tt> represents variant Var1; ● represents responsive variants derived from Var1; <tt>▲</tt> represents Var10, derived from SSM.</p>
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<p>Overlays of binding pocket residues and ligand poses resulting from the molecular docking of (<b>a</b>) WT-ItcR with MS (S-isomer) vs. IA, and (<b>b</b>) Var7 with MS (S-isomer) vs. WT-ItcR with IA. WT-ItcR and Var7 binding pocket residues are shown in sand and dark green, respectively. C-C bonds in IA and MS are depicted in light green and gray, respectively. O atoms are red, and N atoms are blue. For ease of visualization, only the most relevant binding pocket residues are included. More detailed structures are presented in <a href="#app1-biosensors-14-00325" class="html-app">Figure S8</a>.</p>
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15 pages, 2370 KiB  
Article
A Reliable System for Quantitative G-Protein Activation Imaging in Cancer Cells
by Elena Mandrou, Peter A. Thomason, Peggy I. Paschke, Nikki R. Paul, Luke Tweedy and Robert H. Insall
Cells 2024, 13(13), 1114; https://doi.org/10.3390/cells13131114 - 27 Jun 2024
Viewed by 591
Abstract
Fluorescence resonance energy transfer (FRET) biosensors have proven to be an indispensable tool in cell biology and, more specifically, in the study of G-protein signalling. The best method of measuring the activation status or FRET state of a biosensor is often fluorescence lifetime [...] Read more.
Fluorescence resonance energy transfer (FRET) biosensors have proven to be an indispensable tool in cell biology and, more specifically, in the study of G-protein signalling. The best method of measuring the activation status or FRET state of a biosensor is often fluorescence lifetime imaging microscopy (FLIM), as it does away with many disadvantages inherent to fluorescence intensity-based methods and is easily quantitated. Despite the significant potential, there is a lack of reliable FLIM-FRET biosensors, and the data processing and analysis workflows reported previously face reproducibility challenges. Here, we established a system in live primary mouse pancreatic ductal adenocarcinoma cells, where we can detect the activation of an mNeonGreen-Gαi3-mCherry-Gγ2 biosensor through the lysophosphatidic acid receptor (LPAR) with 2-photon time-correlated single-photon counting (TCSPC) FLIM. This combination gave a superior signal to the commonly used mTurquoise2-mVenus G-protein biosensor. This system has potential as a platform for drug screening, or to answer basic cell biology questions in the field of G-protein signalling. Full article
(This article belongs to the Section Cell Methods)
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<p>Comparison of signal intensity and photobleaching between the mNeonGreen- mCherry and the mTurquoise2-mVenus G-Protein Biosensors. (<b>A</b>) Schematic of the mNeonGreen- mCherry biosensor plasmid expressing all three subunits of the heterotrimeric G-protein complex under the control of the CMV promoter to achieve similar expression levels. Representative signal intensity images of PDAC cells transiently transfected with the (<b>B</b>) mNeonGreen-mCherry and (<b>C</b>) mTurquoise2-mVenus biosensor taken with our 2-photon TCSPC FLIM system (TriMScope). In both (<b>B</b>,<b>C</b>), intensity scales are shown on the bottom right. (<b>D</b>) Quantification of signal intensity comparisons between the two biosensors. Signal intensity was measured on three different days for each biosensor (n = 3), with 3 cells per biosensor quantified each day (n = 9 datapoints per biosensor). (<b>E</b>) Quantification of photobleaching for the two biosensors. Solid lines represent the mean signal, while the shaded area represents the range of values of three replicates. In both (<b>D</b>,<b>E</b>), y-axes show arbitrary intensity units.</p>
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<p>The mNeonGreen-mCherry G-protein biosensor is activated by serum (FCS; fetal calf serum), and LPA. For serum stimulation, a final concentration of medium with 3.3% FCS was used. The final concentration of the LPAR1/3 inhibitor (Ki16425) used was 10 μM. Final LPA concentration used was 1 μM. The independent <span class="html-italic">t</span>-test was used to calculate statistical significance with the statannot Python package (*: 1.00 × 10<sup>−2</sup> &lt; <span class="html-italic">p</span> ≤ 5.00 × 10<sup>−2</sup>). The <span class="html-italic">p</span> values for the serum stimulation, LPA stimulation, and inhibitor vs. vehicle are 0.02, 0.03, and 0.04, respectively. The data visualisation web tool SuperPlotsofData [<a href="#B26-cells-13-01114" class="html-bibr">26</a>] was used to present the data. The different colours used show separate replicates, with the larger dot indicating the mean. Distribution per replicate is also shown. Cohen’s d (Cd) was calculated with the numpy (v. 1.26.4) package in Python, and reports the effect size. LPA; lysophosphatidic acid. The inhibitor vehicle was dimethylsulfoxide (DMSO).</p>
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<p>Examples of signal intensity (in grayscale) and false-coloured lifetime images. (<b>A</b>) PDAC cells expressing mNeonGreen-Gαi3 only. (<b>B</b>,<b>C</b>) PDAC cells expressing the full G-protein biosensor. (<b>B</b>) PDAC cells pre- and post-addition of starvation medium. (<b>C</b>) PDAC cells pre- and post-serum stimulation. Signal intensity colour bar from 0 to 450 arbitrary intensity units. Lifetime colour bar from 2000 to 3500 picoseconds. Scale bar, 50 μm. (Note that for quantification, only segmented cell membranes were used).</p>
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<p>Different approaches to FLIM data visualisation. Data from this work were used here for demonstration purposes. (<b>A</b>) Paired point plot example. (<b>B</b>) Box plot example. (<b>C</b>) Bar plot example. The plots were created using Python and the pandas, matplotlib, statannot and seaborn packages. Note that statistical annotations are shown here for demonstration purposes only. (****: 1.00 × 10<sup>−4</sup> &lt; <span class="html-italic">p</span> ≤ 1.00 × 10<sup>−5</sup>); ns = not significant.</p>
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<p>The LPA receptor inhibitor (Ki16425) cannot prevent G-protein activation after serum stimulation in PDAC cells even at high concentrations. Graph showing a lifetime increase after serum stimulation in cell samples treated with Ki16425 or vehicle (DMSO). Samples were treated with either 10 μΜ (<b>left</b> panel) or 100 μΜ (<b>right</b> panel) Ki16425. The independent <span class="html-italic">t</span>-test was used to calculate statistical significance with the statannot Python package (n.s: not significant). The data visualisation web tool SuperPlotsofData [<a href="#B26-cells-13-01114" class="html-bibr">26</a>] was used to present the data. The different colours used show separate replicates, with the larger dot indicating the mean. Distribution per replicate is also shown. LPA; lysophosphatidic acid. DMSO; dimethylsulfoxide.</p>
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15 pages, 2501 KiB  
Article
Detection of Ovarian Cancer Biomarker Lysophosphatidic Acid Using a Label-Free Electrochemical Biosensor
by Nataliia Ivanova, Soha Ahmadi, Edmund Chan, Léa Fournier, Sandro Spagnolo and Michael Thompson
Electrochem 2024, 5(2), 243-257; https://doi.org/10.3390/electrochem5020015 - 18 Jun 2024
Viewed by 746
Abstract
Electrochemical biosensors are valued for their sensitivity and selectivity in detecting biological molecules. Having the advantage of generating signals that can be directly or indirectly proportional to the concentration of the target analyte, these biosensors can achieve specificity by utilizing a specific biorecognition [...] Read more.
Electrochemical biosensors are valued for their sensitivity and selectivity in detecting biological molecules. Having the advantage of generating signals that can be directly or indirectly proportional to the concentration of the target analyte, these biosensors can achieve specificity by utilizing a specific biorecognition surface designed to recognize the target molecule. Electrochemical biosensors have garnered substantial attention, as they can be used to fabricate compact, cost-effective devices, making them promising candidates for point-of-care testing (POCT) devices. This study introduces a label-free electrochemical biosensor employing a gold screen-printed electrode (SPE) to detect lysophosphatidic acid (LPA), a potential early ovarian cancer biomarker. We employed the gelsolin–actin system, previously introduced by our group, in combination with fluorescence spectrometry, as a biorecognition element to detect LPA. By immobilizing a gelsolin–actin complex on an SPE, we were able to quantify changes in current intensity using cyclic voltammetry and differential pulse voltammetry, which was directly proportional to the LPA concentration in the solution. Our results demonstrate the high sensitivity of the developed biosensor for detecting LPA in goat serum, with a limit of detection (LOD) and a limit of quantification (LOQ) of 0.9 µM and 2.76 µM, respectively, highlighting its potential as a promising tool for early-stage diagnosis of ovarian cancer. Full article
(This article belongs to the Collection Feature Papers in Electrochemistry)
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<p>Steps in the synthesis of DTT<sub>COOH</sub> from Ox–DTT.</p>
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<p>Schematic representation of the label-free electrochemical biosensor for LPA detection.</p>
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<p>Bar chart shows the percentages of decrease in current of SPEs after modification with 4 mM DTT<sub>COOH</sub> at pH 10, 7, and 4 using DPV. Error bars represent the standard deviation of three replicates.</p>
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<p>(<b>A</b>,<b>B</b>) Biorecognition surface with DTT<sub>COOH</sub>. (<b>A</b>) Bar chart of bare gold electrodes and after modification with DTT<sub>COOH</sub>, Ni-NTA, gelsolin-actin. Error bars represent the standard deviation of three replicates. (<b>B</b>) Representative differential pulse voltammogram of bare SPEs, and after modification with DTT<sub>COOH</sub>, Ni-NTA, and gelsolin–actin. (<b>C</b>,<b>D</b>) Biorecognition surface with MUA. (<b>C</b>) Bar chart of bare gold electrodes and after modification with MUA, Ni-NTA, gelsolin-actin. Error bars represent the standard deviation of three replicates. (<b>D</b>) Representative differential pulse voltammogram of bare SPEs, and after modification with MUA, Ni-NTA, and gelsolin–actin.</p>
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<p>The influence of scan rate based on the electrochemical response (CV) of the gold SPE after modifications with DTT<sub>COOH</sub>, Ni-NTA, and gelsolin–actin using 10 mM [Fe(CN)<sub>6</sub>]<sup>3−/4−</sup> as the redox probe, which contained 0.5 M KCl as the supporting electrolyte. CV experiments started at open circuit potential (OCP), with positive initial scan polarity and varying scan rate from 0.01 to 1.0 V/s. (<b>A</b>) Cyclic voltammograms at different scan rates, (<b>B</b>) graph of the peak potential separation (ΔE<sub>P</sub> = E<sub>P</sub>(ox) − E<sub>P</sub>(red)) vs. scan rate, (<b>C</b>) graph of i<sub>P</sub>(ox)/i<sub>P</sub>(red) vs. scan rate, and (<b>D</b>) graph of current vs. square root of scan rate, with linear equation of y = 10.761x + 1.0018 and R<sup>2</sup> = 0.9922 for the oxidation signals (orange dots), and linear equation of y = −7.071x − 1.4545 and R<sup>2</sup> = 0.9626 for reduction signals (blue dots). (<b>E</b>) Schematic illustration of a screen-printed electrode with a gold working electrode (4 mm diameter), a gold auxiliary electrode, and a silver reference electrode (E<sub>Standard Hydrogen Electrode (SHE)</sub> = E<sub>Ag/AgCl</sub> + 0.197 V).</p>
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<p>(<b>A</b>–<b>E</b>) Images of the contact angle measurements of (<b>A</b>) bare SPE, (<b>B</b>) after modification with DTT<sub>COOH</sub>, (<b>C</b>) NHS/EDC, (<b>D</b>) Ni-NTA, and (<b>E</b>) gelsolin–actin. (<b>F</b>) Bar chart shows the contact angle of bare SPEs and after modification with DTT<sub>COOH</sub> or MUA, NHS/EDC, Ni-NTA, and gelsolin–actin. Error bars represent the standard deviation of three replicates.</p>
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<p>Evaluation of the developed electrochemical biosensor using different LPA concentrations in PBS. All electrochemical experiments were carried out in 10 mM [Fe(CN)<sub>6</sub>]<sup>3−/4−</sup> as the redox probe, which contained 0.5 M KCl as the supporting electrolyte, using SPE with a gold working electrode (4 mm diameter), a gold auxiliary electrode, and a silver reference electrode (E<sub>Standard Hydrogen Electrode (SHE)</sub> = E<sub>Ag/AgCl</sub> + 0.197 V). (<b>A</b>) Representative cyclic voltammogram of the developed biosensor after incubation in 0.25 µM (blue line), 1 µM (grey line), 5 µM (yellow line), and 10 µM (orange line) LPA solution for 20 min at room temperature. (<b>B</b>) Changes in current intensity obtained from CV were used to draw the calibration curve. The error bars represent three replicate measurements.</p>
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<p>(<b>A</b>) Changes in cyclic voltammetry current intensity after incubation of the developed biosensor in goat serum for 15, 30, and 60 min. (<b>B</b>) Evaluation of developed electrochemical biosensor using different LPA concentrations (0.25, 1, 5, and 10 µM) in goat serum. Changes in current intensity obtained using cyclic voltammetry were used to draw the calibration curve. The estimated LOD of 0.9 µM and LOQ of 2.76 µM with STEYX (standard error of the predicted y-value for each x in a regression) of 0.0378 were calculated using regression analysis with a 98% confidence level. The error bars represent the standard deviation of three replicates.</p>
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12 pages, 2334 KiB  
Article
Europium Nanoparticle-Based Lateral Flow Strip Biosensors for the Detection of Quinoxaline Antibiotics and Their Main Metabolites in Fish Feeds and Tissues
by Qing Mei, Biao Ma, Yun Fang, Yunfei Gong, Jiali Li and Mingzhou Zhang
Biosensors 2024, 14(6), 292; https://doi.org/10.3390/bios14060292 - 4 Jun 2024
Viewed by 906
Abstract
Olaquindox (OLA) and quinocetone (QCT) have been prohibited in aquatic products due to their significant toxicity and side effects. In this study, rapid and visual europium nanoparticle (EuNP)-based lateral flow strip biosensors (LFSBs) were developed for the simultaneous quantitative detection of OLA, QCT, [...] Read more.
Olaquindox (OLA) and quinocetone (QCT) have been prohibited in aquatic products due to their significant toxicity and side effects. In this study, rapid and visual europium nanoparticle (EuNP)-based lateral flow strip biosensors (LFSBs) were developed for the simultaneous quantitative detection of OLA, QCT, and 3-methyl-quinoxaline-2-carboxylic acid (MQCA) in fish feed and tissue. The EuNP-LFSBs enabled sensitive detection for OLA, QCT, and MQCA with a limit of detection of 0.067, 0.017, and 0.099 ng/mL (R2 ≥ 0.9776) within 10 min. The average recovery of the EuNP-LFSBs was 95.13%, and relative standard deviations were below 9.38%. The method was verified by high-performance liquid chromatography (HPLC), and the test results were consistent. Therefore, the proposed LFSBs serve as a powerful tool to monitor quinoxalines in fish feeds and their residues in fish tissues. Full article
(This article belongs to the Special Issue Immunoassays and Biosensing)
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Figure 1
<p>Detection principle of EuNP-LFSBs. (<b>a</b>(<b>i</b>)) Preparation of three EuNP-mAb probes. The EuNP-mAb probes were obtained by the activated ester method. (<b>a</b>(<b>ii</b>)) Scanning electron microscope images of EuNP-mAbs. (<b>b</b>) Composition of the EuNP-LFSB system. The fabrication of EuNP-LFSBs includes five parts: a sample pad, conjugate pad, NC membrane, absorption pad, and backing card. (<b>c</b>) The EuNP-LFSB detection schematic. The negative results for OLA, QCT, and MQCA were indicated by the appearance of test line 1 (T1), test line 2 (T2), and test line 3 (T3), respectively, and the control line (C) on the LFSBs. On the contrary, the positive result is indicated by the absence of test lines. (<b>d</b>) Visual identification and quantitative analysis of EuNP-LFSBs. The results were observed by naked eyes under UV light and the fluorescence intensity on the EuNP-LFSBs was read and stored by a fluorescent strip reader.</p>
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<p>Optimization of the EuNP-LFSB system. (<b>a</b>) FTIR results for OLA. (<b>b</b>) FTIR results for QCT. (<b>c</b>) FTIR results for MQCA. (<b>d</b>) Influence of various OLA-OVA and EuNP-OLA-mAb concentrations on the T/C value. (<b>e</b>) Influence of various QCT-OVA and EuNP-QCT-mAb concentrations on the T/C value. (<b>f</b>) Influence of various MQCA-BSA and EuNP-MQCA-mAb concentrations on the T/C value. (<b>g</b>) Schematic diagram of three types of mixed probe preparation. (<b>h</b>) Effect of different EuNP-mAb probes mixed ratio on the fluorescence intensity. (<b>i</b>) Position result of the three encapsulated antigens on EuNP-LFSBs. (<b>j</b>) Effect of reaction time on EuNP-LFSBs.</p>
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<p>EuNP-LFSB performance evaluation. (<b>a</b>(<b>i</b>)) Schematic diagram of the sensitivity test. (<b>a</b>(<b>ii</b>)) Schematic diagram of the specificity test. (<b>b</b>) Sensitivity analysis of EuNP-LFSBs. The OLA standard curve was y = 0.3005x + 0.4548, R<sup>2</sup> = 0.9906. The QCT standard curve was y = 0.2837x + 0.6035, R<sup>2</sup> = 0.9924. The MQCA standard curve was y = 0.4036x + 0.5045, R<sup>2</sup> = 0.9914. (<b>c</b>) Sensitivity analysis of AuNP-LFSBs. The OLA standard curve was y = 0.5211x + 0.1582, R<sup>2</sup> = 0.9803. The QCT standard curve was y = 0.3367x + 0.386, R<sup>2</sup> = 0.9884; The MQCA standard curve was y = 0.2981x + 0.1243, R² = 0.9847. (<b>d</b>) Specificity analysis of EuNP-LFSBs. (<b>e</b>) Specificity analysis of AuNPs-LFSBs.</p>
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<p>Testing in actual samples. (<b>a</b>) Schematic diagram of actual sample analysis. (<b>b</b>) The results of EuNP-LFSBs and HPLC for 500 actual samples. (<b>c</b>) Table of concordance between the proposed EuNP-LFSBs and HPLC for 500 actual samples tested (sensitivity values [%] used to detect EuNP-mLFIA are shown in red; specificity values [%] are shown in black). (<b>d</b>) The correlation analysis of EuNP-LFSBs and HPLC. (<b>e</b>) Comparison of the consistency of 500 actual samples detected by EuNP-LFSBs and HPLC.</p>
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14 pages, 2548 KiB  
Article
Development of a Whole-Cell System Based on the Use of Genetically Modified Protoplasts to Detect Nickel Ions in Food Matrices
by Monica De Caroli, Carla Perrotta and Patrizia Rampino
Int. J. Mol. Sci. 2024, 25(11), 6090; https://doi.org/10.3390/ijms25116090 - 31 May 2024
Viewed by 354
Abstract
Heavy metals are dangerous contaminants that constitute a threat to human health because they persist in soils and are easily transferred into the food chain, causing damage to human health. Among heavy metals, nickel appears to be one of the most dangerous, being [...] Read more.
Heavy metals are dangerous contaminants that constitute a threat to human health because they persist in soils and are easily transferred into the food chain, causing damage to human health. Among heavy metals, nickel appears to be one of the most dangerous, being responsible for different disorders. Public health protection requires nickel detection in the environment and food chains. Biosensors represent simple, rapid, and sensitive methods for detecting nickel contamination. In this paper, we report on the setting up a whole-cell-based system, in which protoplasts, obtained from Nicotiana tabacum leaves, were used as transducers to detect the presence of heavy metal ions and, in particular, nickel ions. Protoplasts were genetically modified with a plasmid containing the Green Fluorescent Protein reporter gene (GFP) under control of the promoter region of a sunflower gene coding for a small Heat Shock Protein (HSP). Using this device, the presence of heavy metal ions was detected. Thus, the possibility of using this whole-cell system as a novel tool to detect the presence of nickel ions in food matrices was assessed. Full article
(This article belongs to the Special Issue Whole-Cell System and Synthetic Biology)
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<p>Confocal microscope images of tobacco protoplasts after FDA staining. WT—untransformed protoplasts; p<span class="html-italic">35SGFP</span>—protoplast transformed with p<span class="html-italic">35SGFP</span> plasmid; pPr<span class="html-italic">HSP17.6aGFP</span>—protoplast transformed with pPr<span class="html-italic">HSP17.6aGFP</span> plasmid. (<b>a</b>,<b>c</b>,<b>e</b>) Floating protoplasts; (<b>b</b>,<b>d</b>,<b>f</b>) protoplasts immobilized in K3 medium containing 0.6% agarose. Scale bars: 50 µm. Objective: 10×; zoom: 0.6.</p>
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<p>Evaluation of fluorescence, measured by a fluorometer, after 2, 3, 4, and 7 h from immobilization of untransformed protoplasts (WT), protoplasts transformed with p<span class="html-italic">35SGFP</span> plasmid (p<span class="html-italic">35SGFP</span>), and protoplasts transformed with pPr<span class="html-italic">HSP17.6aGFP</span> plasmid (pPr<span class="html-italic">HSP17.6aGFP</span>). Each value represents the mean of three independent measurements ± SD. Different uppercase letters indicate significant differences among the FU values (<span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Confocal microscope images of immobilized tobacco protoplasts. (<b>a</b>,<b>b</b>) Protoplasts transformed with p<span class="html-italic">35SGFP</span> plasmid; (<b>c</b>,<b>d</b>) protoplasts transformed with pPr<span class="html-italic">HSP17.6aGFP</span> plasmid. (<b>b</b>,<b>d</b>) Bright field. Scale bars: 20 µm. Objective 40×, zoom 2.0.</p>
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<p>Evaluation of fluorescence by fluorometer after 1, 2, 3 and 4 h from protoplast immobilization. Red line: protoplasts transformed with p<span class="html-italic">35SGFP</span> plasmid; violet line: untreated protoplasts transformed with pPr<span class="html-italic">HSP17.6aGFP</span> plasmid; and green line: protoplasts transformed with pPr<span class="html-italic">HSP17.6aGFP</span> plasmid plus metal ion solutions (50 μL of 20 μM each). Each value represents the mean of three independent measurements ± SD.</p>
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<p>Relative fluorescence (expressed as relative fluorescence units, RFUs) at the maximum level of detection of protoplasts transformed with pPr<span class="html-italic">HSP17.6aGFP</span> and treated with different metal ions (50 μL of 20 μM each). Each value represents the mean of three independent measurements ± SD. Uppercase letters indicate statistically different values among the various protoplast groups. Asterisks correspond to statistically different values between the NiCl<sub>2</sub>-treated protoplasts and each other different group (*, significant difference, <span class="html-italic">p</span> &lt; 0.05; **, highly significant difference, <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Confocal microscope images of the immobilized protoplasts transformed with pPr<span class="html-italic">HSP17.6aGFP</span> plasmid in presence of 50 μL of 20 μM AlCl<sub>3</sub> (<b>a</b>), CdSO<sub>4</sub> (<b>b</b>), CoCl<sub>2</sub> (<b>c</b>), CuSO<sub>4</sub> (<b>d</b>), NiCl<sub>2</sub> (<b>e</b>) and ZnSO<sub>4</sub> (<b>f</b>). Scale bars: 20 µm. Objective: 40×; zoom: 2.0.</p>
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<p>Values of fluorescence, expressed as fluorescence units (FUs) of protoplasts transformed with pPr<span class="html-italic">HSP17.6aGFP</span> plasmids and treated with different NiCl<sub>2</sub> concentrations. Each value represents the mean of three independent measurements ± SD.</p>
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<p>Values of fluorescence as expressed in fluorescence units (FUs) of protoplasts transformed with pPr<span class="html-italic">HSP17.6aGFP</span> plasmid plus the addition of different food matrices (in the presence or absence of 20 μM nickel ions) for 2 h. Protoplasts transformed with pPr<span class="html-italic">HSP17.6aGFP</span> plasmid with only nickel ions added (Nickel) and untreated protoplasts (Untreated) were used as controls. Each value represents the mean of three independent measurements ± SD.</p>
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21 pages, 5518 KiB  
Article
Effects of Akt Activator SC79 on Human M0 Macrophage Phagocytosis and Cytokine Production
by Robert J. Lee, Nithin D. Adappa and James N. Palmer
Cells 2024, 13(11), 902; https://doi.org/10.3390/cells13110902 - 24 May 2024
Viewed by 846
Abstract
Akt is an important kinase in metabolism. Akt also phosphorylates and activates endothelial and neuronal nitric oxide (NO) synthases (eNOS and nNOS, respectively) expressed in M0 (unpolarized) macrophages. We showed that e/nNOS NO production downstream of bitter taste receptors enhances macrophage phagocytosis. In [...] Read more.
Akt is an important kinase in metabolism. Akt also phosphorylates and activates endothelial and neuronal nitric oxide (NO) synthases (eNOS and nNOS, respectively) expressed in M0 (unpolarized) macrophages. We showed that e/nNOS NO production downstream of bitter taste receptors enhances macrophage phagocytosis. In airway epithelial cells, we also showed that the activation of Akt by a small molecule (SC79) enhances NO production and increases levels of nuclear Nrf2, which reduces IL-8 transcription during concomitant stimulation with Toll-like receptor (TLR) 5 agonist flagellin. We hypothesized that SC79’s production of NO in macrophages might likewise enhance phagocytosis and reduce the transcription of some pro-inflammatory cytokines. Using live cell imaging of fluorescent biosensors and indicator dyes, we found that SC79 induces Akt activation, NO production, and downstream cGMP production in primary human M0 macrophages. This was accompanied by a reduction in IL-6, IL-8, and IL-12 production during concomitant stimulation with bacterial lipopolysaccharide, an agonist of pattern recognition receptors including TLR4. Pharmacological inhibitors suggested that this effect was dependent on Akt and Nrf2. Together, these data suggest that several macrophage immune pathways are regulated by SC79 via Akt. A small-molecule Akt activator may be useful in some infection settings, warranting future in vivo studies. Full article
(This article belongs to the Special Issue Macrophage Activation and Regulation)
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Graphical abstract

Graphical abstract
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<p>Akt isoform expression in M0 macrophages. (<b>A</b>) Expression (transcripts per million, TPM) of Akt isoforms in immune cells in the DIC database; (<b>B</b>) Normalized gene counts of Akt isoforms from GEO dataset GSE122597. (<b>C</b>) Expression of Akt isoforms relative to housekeeping gene UBC determined via performing qPCR on the monocytes and derived M0 macrophages used here; data points show results from 3 independent donors. (<b>D</b>) Imaging cytometry analysis of staining of macrophage markers (CD14, CD68, and CD16) with eNOS and iNOS in M0 macrophages. All markers were significantly above the control (mouse IgG) except iNOS, which was upregulated in M1 macrophages; * <span class="html-italic">p</span> &lt; 0.05 vs. IgG isotype control via one-way ANOVA with Dunnett’s post-test. (<b>E</b>) Fluo-4 Ca<sup>2+</sup> trace (average of n = 3 experiments) showing response to 50 µM histamine inhibited by H1 antagonist cetirizine. The time of addition of histamine ± cetirizine is denoted by the arrow. (<b>F</b>) Immunofluorescence of Akt and eNOS in M0 macrophages. The nuclear DAPI stain is shown in yellow. The scale bar is 5 µm. (<b>G</b>) Isotype control (rabbit and goat serum) staining. All images are representative of images from cells from 3 independent donors collected and imaged on different days. The scale bar is 5 µm.</p>
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<p>Visualization of SC79 Akt activation in M0 macrophages using fluorescent biosensors. (<b>A</b>) Schematic of the AktAR biosensor. Cerulean (cyan fluorescent protein (CFP) variant) and circularly permutated (cp) Venus (YFP variant) surround a forkhead-associated domain (FHA1)-phosphorylated amino acid binding domain and FOXO1 Akt substrate sequence. Akt phosphorylation causes a change in conformation, and a closer proximity of CFP and YFP increases FRET (an increase in the YFP/CFP emission ratio). (<b>B</b>) Representative traces from single experiments of AktAR2 YFP/CFP ratio changes in response to 1–10 µM SC79 ± MK2206 or LY294002. Time of addition of the indicated drugs is denoted by the arrow. (<b>C</b>) Bar graph of the same responses as in B from 4 independent experiments from different donors per condition. (<b>D</b>) Diagram of the TORCAR biosensor. Phosphorylation of the 4EBP1 motif brings CFP and YFP further apart and decreases FRET (an increased CFP/YFP emission ratio). (<b>E</b>) Representative traces from single experiments of TORCAR (or mutated T/A control TORCAR) FRET ratio changes in response to 1–10 µM SC79 ± rapamycin. Time of addition of the indicated drugs is denoted by the arrow. (<b>F</b>) Bar graph of the same responses as in (<b>E</b>) from 4 independent experiments from different donors per condition. Significance was determined via one-way ANOVA with Dunnett’s post-test, comparing values to the vehicle control; * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>SC79 activates NO production via Akt. (<b>A</b>) Bar graph of endpoint DAF-FM fluorescence from 5 independent experiments using macrophages from different donors. Responses were tested with 0.1–10 µg/mL SC79 ± Akt inhibitors MK2206 (10 µg/mL) or GSK690693 (10 µM), PKC inhibitor Gö6983 (10 µM), PKA inhibitor H89 (10 µM), NOS inhibitor L-NAME (10 µM), or inactive D-NAME (10 µM). Significance was determined via one-way ANOVA with Dunnett’s post-test, comparing values to those for HBSS alone; * <span class="html-italic">p</span> &lt; 0.05. (<b>B</b>) DAF-FM fluorescence data with 1 and 10 µg/mL SC79 ± 10 µM CFTR<sub>inh</sub>172 pretreatment. No significant differences were determined via one-way ANOVA. (<b>C</b>) Representative real-time traces of DAF-FM fluorescence, ± L-NAME or D-NAME. Time of addition of the indicated drugs is denoted by the arrow. (<b>D</b>) Data from 5 independent experiments done similarly as in (<b>C</b>). Significance was determined via one-way ANOVA with Dunnett’s post-test, comparing values to those for SC79 alone; * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>SC79 activates cGMP production downstream of NO. (<b>A</b>) Representative traces of cGMP biosensor fluorescence changes with stimulation by 10 µg/mL SC79 vs. the vehicle (0.1% DMSO) only. An upward deflection corresponds to an increase in cGMP levels. Time of addition of the indicated drugs is denoted by the arrow. (<b>B</b>) Bar graph of results from independent experiments done similarly as in (<b>A</b>). Significance was determined via one-way ANOVA with Dunnett’s post-test, comparing values to those for HBSS plus the vehicle (0.1% DMSO) alone; * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>SC79 enhances FITC <span class="html-italic">E. coli</span> phagocytosis, likely via Akt and NO signaling. (<b>A</b>) Images showing phagocytosis of FITC-labeled <span class="html-italic">E. coli</span> (magenta, DAPI nuclear stain in green) in primary human monocyte-derived macrophages, as described [<a href="#B17-cells-13-00902" class="html-bibr">17</a>,<a href="#B19-cells-13-00902" class="html-bibr">19</a>]. (<b>B</b>) FITC fluorescence (indicating macrophage phagocytosis) increased with SC79 treatment, which was blocked by Akt inhibitor MK2206 or GSK690693 and NOS inhibitor L-NAME. Significance was determined via one-way ANOVA with Dunnett’s post-test, comparing all values to those of the control; * <span class="html-italic">p</span> &lt; 0.05. Data are from 5 independent experiments using cells from 5 donors.</p>
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<p>SC79 enhances pHrodo <span class="html-italic">S. aureus</span> phagocytosis, likely via Akt and NO signaling. (<b>A</b>) The phagocytosis of pHrodo <span class="html-italic">S. aureus</span> also increased with 10 µg/mL SC79 and was blocked by MK2206. Note that pHrodo only fluoresces in acidic environments like the phagosome, confirming that internalization reflects phagocytosis. Data were obtained from 5 independent experiments using cells from 5 donors. Significance was determined via one-way ANOVA with Bonferroni’s post-test; ** <span class="html-italic">p</span> &lt; 0.01. (<b>B</b>) The same type of experiments as in A, but testing SC79 ± L-NAME or D-NAME; significance determined via one-way ANOVA with Dunnett’s post-test, comparing all values to those of the control (HBSS + 0.1% DMSO); ** <span class="html-italic">p</span> &lt; 0.01. (<b>C</b>) The same type of experiments as in A and B, but testing SC79 ± guanylyl cyclase inhibitor ODQ or NS2028 or adenylyl cyclase inhibitor KH 7 (all at 10 µM); significance determined via one-way ANOVA with Dunnett’s post-test, comparing all values to those of the control (HBSS + 0.1% DMSO); ** <span class="html-italic">p</span> &lt; 0.01. (<b>D</b>) Micrographs of pHrodo <span class="html-italic">S. aureus</span> phagocytosed in macrophages. Top and bottom rows show images from two different donors. (<b>E</b>) Quantification of 4 independent experiments, as shown in (<b>D</b>), confirming the dose-dependent increase in phagocytosis with SC79; significance was determined via one-way ANOVA with Dunnett’s post-test, comparing all values to those of the control; ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>SC79 enhancement of phagocytosis is not altered by CFTR<sub>inh</sub>172. (<b>A</b>) The same type of FITC <span class="html-italic">E. coli</span> phagocytosis experiments as in <a href="#cells-13-00902-f005" class="html-fig">Figure 5</a>, testing the SC79 ± CFTR<sub>inh</sub>172 pretreatment. (<b>B</b>) The same type of phagocytosis experiments of pHrodo <span class="html-italic">S. aureus</span> as in <a href="#cells-13-00902-f006" class="html-fig">Figure 6</a>, but testing SC79 ± CFTR<sub>inh</sub>172 pretreatment. Significance was determined via one-way ANOVA with Bonferroni’s post-test with paired comparisons; * <span class="html-italic">p</span> &lt; 0.05 vs. 0 µg/mL SC79 (HBSS + 0.1% DMSO vehicle control); n.s. means there was no statistical significance between bracketed groups. Data from 5–6 independent experiments per condition with macrophages from different donors.</p>
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<p>SC79 enhances LPS or bacterial-induced superoxide production. (<b>A</b>) Bar graph of macrophage MitoSox Red fluorescence measured on plate reader (396 nm excitation, 610 nm emission) after 60 min stimulation with LPS or <span class="html-italic">E. coli</span> ± SC79. (<b>B</b>) Bar graph of macrophage dihydroethidium fluorescence (518 nm excitation, 605 nm emission) from experiments similar to those in (<b>A</b>). Data from 4–5 independent experiments per condition with macrophages from different donors; significance determined via one-way ANOVA with Bonferroni’s post-test with paired comparisons (±SC79); * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Reduction in macrophage cytokines with SC79. (<b>A</b>) LDH release into cell culture media. Staurosporine and triton X-100 were controls used to induce apoptotic death (often followed by secondary necrosis in vitro [<a href="#B107-cells-13-00902" class="html-bibr">107</a>]) and nonspecific lysis, respectively. No LDH was observed with SC79 (one-way ANOVA; Dunnett’s post-test; n = 4 experiments per condition from separate donors); * <span class="html-italic">p</span> &gt; 0.05. (<b>B</b>) Macrophage IL-12 (M1 marker) or IL-10 (M2 marker) release determined by performing ELISA after 72 h on M1 cocktail (20 ng/mL IFNγ + 100 ng/mL LPS)- or M2-polarizing IL-4 (20 ng/mL). Significance was determined via one-way ANOVA with Bonferroni’s post-test, comparing values with those of M0 (no stimulation); * <span class="html-italic">p</span> &gt; 0.05; n = 8 experiments per condition from separate donors. (<b>C</b>) Dose response of brusatol or ML385 with IL-6 release. Significance was determined via one-way ANOVA with Bonferroni’s post-test, comparing values with those of the control (no stimulation); * <span class="html-italic">p</span> &gt; 0.05; n = 4 experiments from separate donors. (<b>D</b>–<b>F</b>) Bar graphs of IL-6 (<b>D</b>), IL-8 (<b>E</b>), or IL-12 (<b>F</b>) release with SC79 (10 µg/mL) ± LPS (100 ng/mL) ± 10 nM brusatol or ML385, as indicated. Significance was determined via one-way ANOVA, with Bonferroni’s post-test; * <span class="html-italic">p</span> &lt; 0.05 vs. control (media + vehicle) and <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 between bracketed columns. n = 4–5 experiments from separate donors. (<b>G</b>) The same type of experiments as in D-F but using <span class="html-italic">P. aeruginosa</span>-conditioned media ± SC79 ± ML385. Significance was determined via one-way ANOVA with Bonferroni’s post-test; * <span class="html-italic">p</span> &lt; 0.05 between bracketed columns. (<b>H</b>) IL-6 (green) or IL-8 (magenta) transcript with LPS ± SC79 ± ML385. Significance was tested via one-way ANOVA with Bonferroni’s post-test; * <span class="html-italic">p</span> &lt; 0.05 vs. control and <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 between bracketed columns; n = 4 experiments from separate donors. (<b>I</b>) Nrf2 target transcript levels with SC79 ± brusatol or ML385. Significance was tested via one-way ANOVA with Dunnett’s post-test; * <span class="html-italic">p</span> &lt; 0.05 vs. control; n = 4 experiments from separate donors. (<b>J</b>) TNF transcript levels with LPS ± SC79. Significance was tested via one-way ANOVA with Dunnett’s post-test; * <span class="html-italic">p</span> &lt; 0.05 vs. control; n = 4 experiments from separate donors.</p>
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9 pages, 1757 KiB  
Communication
Point-of-Care Fluorescence Biosensing System for Rapid Multi-Allergen Screening
by Silvia Demuru, Hui Chai-Gao, Yevhen Shynkarenko, Nicola Hermann, Patricia-Daiana Boia, Peter Cristofolini, Bradley Petkus, Silvia Generelli, Samantha Paoletti, Stefano Cattaneo and Loïc Burr
Sensors 2024, 24(11), 3280; https://doi.org/10.3390/s24113280 - 21 May 2024
Viewed by 694
Abstract
With the steady increase in allergy prevalence worldwide, there is a strong need for novel diagnostic tools for precise, fast, and less invasive testing methods. Herein, a miniatured fluorescence-based biosensing system is developed for the rapid and quantitative detection of allergen-specific immunoglobulin-E. An [...] Read more.
With the steady increase in allergy prevalence worldwide, there is a strong need for novel diagnostic tools for precise, fast, and less invasive testing methods. Herein, a miniatured fluorescence-based biosensing system is developed for the rapid and quantitative detection of allergen-specific immunoglobulin-E. An antibody-based fluorescence assay in a microfluidic-patterned slide, combined with a custom-made portable fluorescence reader for image acquisition and user-friendly software for the data analysis, enables obtaining results for multiple allergens in just ~1 h with only 80 μL of blood serum. The multiplexed detection of common birch, timothy grass, cat epithelia, house dust mite, and dog epithelia shows quantitative IgE-mediated allergic responses to specific allergens in control serum samples with known total IgE concentration. The responses are verified with different control tests and measurements with a commercial fluorescence reader. These results open the door to point-of-care allergy screening for early diagnosis and broader access and for large-scale research in allergies. Full article
(This article belongs to the Special Issue Eurosensors 2023 Selected Papers)
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Figure 1
<p>Portable system for allergen screening. (<b>a</b>) Image showing the microfluidic slide with a zoom on the micropillars; (<b>b</b>) simplified schematic of the biosensing assay on the slide; (<b>c</b>) image of the portable reader with a slide inserted; (<b>d</b>) the fluorescent signals on the micropillars with an image acquired with the portable reader and custom software.</p>
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<p>Portable reader components and comparison. (<b>a</b>) Schematic of the different components inside the developed portable reader; (<b>b</b>) Comparison of the data acquired by InnoScan and the portable reader, analyzing the microfluidic slide with different gradients of the fluorescent dye molecules Atto-BSA. Lower part shows correspondent line profile of the gradient from the lowest to the higher (24 points from about 10 to 5000 dye mol/μm<sup>2</sup>).</p>
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<p>Images showing the custom software features. (<b>a</b>) Portable reader connected to the laptop with the program and login into the user interface; (<b>b</b>) the feature inside the program to acquire the image of the slide; (<b>c</b>) automated pillar recognition; (<b>d</b>) automated fluorescence data extraction and possibility to export in different Excel reports; the colors represent different fluorescence intensities.</p>
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<p>Results for specific IgE detection. (<b>a</b>) Optical image acquired by InnoScan and the portable reader, using a microfluidic slide with different recombinant allergens; (<b>b</b>) Relative fluorescence signals extracted and converted in dye molecules/μm<sup>2</sup> based on the internal Atto BSA dye calibration. Tested with control serum (274 IU/mL total IgE). The dashed lines indicate the signal over which there is a significant IgE signal, highlighting the maximum cross-sensitivity signal from the IgG negative control.</p>
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9 pages, 4244 KiB  
Article
Carbon Dot-Decorated Polystyrene Microspheres for Whispering-Gallery Mode Biosensing
by Anton A. Starovoytov, Evgeniia O. Soloveva, Kamilla Kurassova, Kirill V. Bogdanov, Irina A. Arefina, Natalia N. Shevchenko, Tigran A. Vartanyan, Daler R. Dadadzhanov and Nikita A. Toropov
Photonics 2024, 11(5), 480; https://doi.org/10.3390/photonics11050480 - 20 May 2024
Viewed by 845
Abstract
Whispering gallery mode (WGM) resonators doped with fluorescent materials find impressive applications in biological sensing. They do not require special conditions for the excitation of WGM inside that provide the basis for in vivo sensing. Currently, the problem of materials for in vivo [...] Read more.
Whispering gallery mode (WGM) resonators doped with fluorescent materials find impressive applications in biological sensing. They do not require special conditions for the excitation of WGM inside that provide the basis for in vivo sensing. Currently, the problem of materials for in vivo WGM sensors are substantial since their fluorescence should have stable optical properties as well as they should be biocompatible. To address this we present WGM microresonators of 5–7 μm, where the dopant is made of carbon quantum dots (CDs). CDs are biocompatible since they are produced from carbon and demonstrate bright optical emission, which shows different bands depending on the excitation wavelength. The WGM sensors developed here were tested as label-free biosensors by detecting bovine serum albumin molecules. The results showed WGM frequency shifting, with the limit of detection down to 1016 M level. Full article
(This article belongs to the Special Issue Advancements in Optical Metamaterials)
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<p>Spectra of water solutions: optical density (<b>a</b>) and normalized photoluminescence (<b>b</b>) of BSA (1), CDs (2), and their mixture excited at <math display="inline"><semantics> <mi>λ</mi> </semantics></math> = 488 nm (3).</p>
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<p>(<b>a</b>) SEM image of a polystyrene microsphere. (<b>b</b>) Normalized photoluminescence spectra of CDs solution excited at 405 nm (curve 1) and 488 nm (curve 2), CD-doped microspheres excited at 405 nm (curve 3) and 488 nm (curve 4). Inset shows a microsphere image, corresponding to spectrum 3, which was acquired by a laser scanning confocal microscope.</p>
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<p>(<b>a</b>) Emission spectra of polystyrene microspheres covered with carbon dots at different excitation intensities; (<b>b</b>) input-output characteristic defined as intensities of separate emission lines.</p>
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<p>(<b>a</b>) Emission spectra of CDs doped microspheres before and after adding BSA molecules (<math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>16</mn> </mrow> </msup> </semantics></math> M solution). WGM frequency shift extracted from emission spectra of CDs doped microspheres before and after adding BSA solution: (<b>b</b>)—<math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>15</mn> </mrow> </msup> </semantics></math> M, (<b>c</b>)—<math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>16</mn> </mrow> </msup> </semantics></math> M, (<b>d</b>)—<math display="inline"><semantics> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>18</mn> </mrow> </msup> </semantics></math> M.</p>
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<p>Dependence of WGM frequency shift vs. concentration of added BSA solution in logarithmic scale.</p>
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10 pages, 2018 KiB  
Article
A Förster Resonance Energy Transfer (FRET)-Based Immune Assay for the Detection of Microcystin-LR in Drinking Water
by Alessandro Capo, Angela Pennacchio, Concetta Montagnese, Antonis Hadjiantonis, Panayiota Demosthenous, Alessandro Giusti, Maria Staiano, Sabato D’Auria and Antonio Varriale
Sensors 2024, 24(10), 3204; https://doi.org/10.3390/s24103204 - 17 May 2024
Viewed by 711
Abstract
Cyanobacteria bloom is the term used to describe an abnormal and rapid growth of cyanobacteria in aquatic ecosystems such as lakes, rivers, and oceans as a consequence of anthropic factors, ecosystem degradation, or climate change. Cyanobacteria belonging to the genera Microcystis, Anabaena [...] Read more.
Cyanobacteria bloom is the term used to describe an abnormal and rapid growth of cyanobacteria in aquatic ecosystems such as lakes, rivers, and oceans as a consequence of anthropic factors, ecosystem degradation, or climate change. Cyanobacteria belonging to the genera Microcystis, Anabaena, Planktothrix, and Nostoc produce and release toxins called microcystins (MCs) into the water. MCs can have severe effects on human and animal health following their ingestion and inhalation. The MC structure is composed of a constant region (composed of five amino acid residues) and a variable region (composed of two amino acid residues). When the MC variable region is composed of arginine and leucine, it is named MC-LR. The most-common methods used to detect the presence of MC-LR in water are chromatographic-based methods (HPLC, LC/MS, GC/MS) and immunological-based methods (ELISA). In this work, we developed a new competitive Förster resonance energy transfer (FRET) assay to detect the presence of traces of MC-LR in water. Monoclonal antibody anti-MC-LR and MC-LR conjugated with bovine serum albumin (BSA) were labeled with the near-infrared fluorophores CF568 and CF647, respectively. Steady-state fluorescence measurements were performed to investigate the energy transfer process between anti-MC-LR 568 and MC-LR BSA 647 upon their interaction. Since the presence of unlabeled MC-LR competes with the labeled one, a lower efficiency of FRET process can be observed in the presence of an increasing amount of unlabeled MC-LR. The limit of detection (LoD) of the FRET assay is found to be 0.245 nM (0.245 µg/L). This value is lower than the provisional limit established by the World Health Organization (WHO) for quantifying the presence of MC-LR in drinking water. Full article
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Graphical abstract

Graphical abstract
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<p>Evaluation of binding capability of unlabeled anti-MC-LR. Indirect ELISA test of unlabeled anti-MC-LR vs. MC-LR BSA. The experiments were performed in triplicate at 25 °C.</p>
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<p>Evaluation and comparison of the binding capability of unlabeled anti-MC-LR and labeled anti-MC-LR 568. Indirect ELISA tests of unlabeled anti-MC-LR and labeled anti-MC-LR 568 vs. MC-LR BSA were performed. The experiments were performed in triplicate at 25 °C.</p>
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<p>Spectroscopy characterization. Absorption and emission spectra of anti-MC-LR 568 and MC-LR BSA 647. The spectra were acquired at 25 °C. The fluorescence emission spectra were acquired upon excitation at 647 nm and 565 nm. All measurements were performed in 20 mM sodium phosphate buffer + 150 mM NaCl, pH 7.4. The spectra were normalized to 1.0. The temperature was set at 25 °C.</p>
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<p>FRET efficiency evaluation. FRET efficiency evaluation via a titration experiment using anti-MC-LR 568 and increased concentrations of MC-LR BSA 647. The measurements were performed at 25 °C.</p>
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<p>FRET Competitive assay. FRET competitive immune assay in the absence of and in the presence of unlabeled MC-LR (<b>a</b>). Ratiometric fluorescence emission (F<sub>667</sub>/F<sub>580</sub>) response in the presence of increasing concentrations of unlabeled MC-LR (<b>b</b>). The calibration curves of the assay were obtained through a linear fitting function (<b>c</b>). All measurements were acquired in PBS buffer, pH 7.4, upon excitation at 565 nm.</p>
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