Carbon Chemist

Tag: Fluorescence

  • Astrocytes found to play pivotal role in epileptic neuronal hyperactivity

    Astrocytes found to play pivotal role in epileptic neuronal hyperactivity

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    Epilepsy, where patients suffer from unexpected seizures, affects roughly 1% of the population. These seizures often involve repetitive and excessive neuronal firing, with the trigger behind this still poorly understood.

    Now, researchers at Tohoku University have monitored astrocyte activity using fluorescence calcium sensors, discovering that astrocyte activity starts approximately 20 seconds before the onset of epileptic neuronal hyperactivity. This suggests that astrocytes play a significant part in triggering epileptic seizures, facilitating the hyper-drive of the neural circuit.

    The findings were detailed in the journal Glia on April 9, 2024.

    Astrocytes are non-neuronal glial cells that occupy almost half of the brain. They have been shown to control the local ionic and metabotropic environment in the brain. Yet, since they do not exhibit electrical activity that can be easily monitored, their role in the function of the brain has largely been neglected. Fluorescence sensor proteins are changing this, revealing more about the mesmerizing activity of astrocytes.

    “Astrocytes appear to have a determinant role in controlling the state of neuronal activity and synaptic plasticity both in physiological and pathophysiological situations,” says Professor Ko Matsui of the Super-network Brain Physiology lab at Tohoku University, who led the research. “Therefore, astrocytes could be considered as a new therapeutic target for epilepsy treatment.”

    When brain tissue makes contact with metals such as copper, it is known to induce inflammation that leads to acute symptomatic seizures, which occurs a few times per day in mice. Matsui and his team observed these events, where they discovered that astrocyte activity may be the trigger for neuronal hyperactivity. Astrocytes can also be activated by low-amplitude direct current stimulation. The researchers noticed that such a stimulation induced a robust increase in the astrocyte calcium, which was followed by an epileptic neuronal hyperactivity episode. When the metabolic activity of the astrocytes was blocked by applying fluorocitrate, the magnitude of the epileptic neuronal hyperactivity was significantly reduced. These all point to the fact that astrocytes have the potential to control neuronal activity.

    Lead study investigator Shun Araki emphasizes that with appropriate guidance, astrocytes’ functions could be harnessed to address a range of neurological conditions. This includes not only epilepsy but also potentially enhancing cognitive abilities beyond natural limitations.

    Source:

    Journal reference:

    Araki, S., et al. (2024). Astrocyte switch to the hyperactive mode. GLIA. doi.org/10.1002/glia.24537.

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  • Revolutionizing hepatocyte count for researchers

    Revolutionizing hepatocyte count for researchers

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    Could you start by giving us an overview of the new CellDrop app and its significance in the realm of hepatocyte analysis?

    The Hepatocyte app on the latest version of CellDrop software is the first automated cell counter algorithm designed specifically to count hepatocytes (liver cells). This is also the first cell-counting app from DeNovix that uses machine-learning algorithms to analyze and count cells.

    What are the primary challenges in counting hepatocytes automatically, and how does the CellDrop hepatocyte app address these issues? 

    Counting hepatocytes with automated cell counters presents significant challenges. These cells are not only large and irregularly shaped, but they may also exhibit auto-fluorescence and contain multiple nuclei. Furthermore, hepatocyte samples often include other cell types, such as lymphocytes and erythrocytes, complicating the counting process.

    These factors significantly complicate the algorithms used by even the most advanced image-based cell counters, making it difficult to differentiate single cells from clusters or to identify cells of interest amidst debris. With the CellDrop Hepatocyte app, we worked with experts in the field and trained a machine-learning algorithm to count hepatocytes the way they would on a microscope using trypan blue.

    Then, we added the CellDrop’s dual channel fluorescence capabilities to the algorithm to better distinguish live vs dead and debris vs cells.

    How does the CellDrop app’s performance compare with traditional methods of hepatocyte counting in terms of speed, accuracy, and reliability?

    The Hepatocyte app’s counting speed is much faster than a human, and the area being counted is about 3.5 times larger than what a manual hemocytometer would count, yielding a larger and, therefore, more representative sample, but I think the main advantage is consistency.

    The way I count cells might be slightly different than the way you count cells or even the way I count cells on Friday afternoon might be slightly different than how I would have counted them on Tuesday morning. The Hepatocyte app eliminates all that and standardizes counts between people and labs.

    Could you discuss the app’s potential applications in drug discovery, toxicology, and virology research and how it might change the landscape in these fields?

    Cell counts need to be very accurate and consistent in these workflows where you are using liver models to study the effect of a drug or pathogen. Since hepatocyte cells come directly from a model organism and then are plated and experimentally treated, small variations in the density or viability of the cells between runs can have a statistically significant effect on the result.

    This, of course, requires the researchers to collect more data to confirm these differences and slows the whole process down. I hope that the standardization the CellDrop Hepatocyte app provides will streamline the process for our customers.

    What features have been incorporated into the app to ensure ease of use for researchers, and how accessible is it for varying laboratory setups?

    The CellDrop has an autofocus capability that will ensure that focal planes are consistent, and the Hepatocyte app has a new auto-thresholding feature for determining live/dead fluorescent thresholds. These tools, along with the machine learning count algorithm, should ensure that hepatocyte counts are fast, easy, and consistent.

    Of course, microscope optics, fast processors and machine learning algorithms are not cheap. However, we have tried to make the CellDrop accessible to any lab’s budget by offering a pay-as-you-go option that allows users to buy the CellDrop hardware at a much lower cost and pay a small price as they use counts, eventually paying off the device. This allows labs to spread out the cost over time and based on use.

    What has been the initial feedback from early users of the app, and how has this influenced any further development?

    It was quite exciting and a little stressful the first time I took the Hepatocyte app out in the field for a real beta test. Luckily, we had help from a lot of good scientists refining the algorithm along the way, and the lab was thrilled with the results.

    Since then, we have received a lot of positive feedback, which has been incredibly rewarding after the many months spent in trial and error, fine-tuning the software to meet our expectations.

    Looking ahead, what enhancements or additional features can users expect to see in future versions of the CellDrop app?

    We would like to continue exploring the power of machine learning for cell counting. There are other fields where certain samples, or parts of samples, are difficult to count using traditional cell counting algorithms. We are also studying whether we can apply a similar model to those cell types.

    As the lead on this project, what has been the most rewarding aspect of developing this app, and what are your aspirations for its impact on the scientific community?

    My heart is still in the lab, so I have really enjoyed working closely with hepatocyte researchers and processing/analyzing the samples used in algorithm development.

    I also got to do a lot of the hands-on work training the model itself and see it start to deliver solid and consistent results. I see the tremendous potential machine learning algorithms have for scientific research. It has been a pleasure playing a small part in pushing this technology along.

    Before I started working at DeNovix, I spent a lot of time in graduate school in labs working as a researcher. Cell counting sounds simple, but everybody does it differently. As people work through complicated workflows, we can confirm that they got their cell count right. Helping people like this is really rewarding to me.

    What insights did you gain from developing a product aimed at solving a problem you previously faced as a researcher?

    The main takeaway for me was the critical importance of consistency. No matter the method—be it a machine learning algorithm, the initial approach to cell counting, or manual counting—there will invariably be challenges and ambiguities at the fringes. There are always going to be questions and uncertainties in those gray areas.

    As long as you are doing things consistently, which is what a cell counter does by definition, I think you can work out everything else about the cell count part of the process. This consistency means that, while I may not intervene in later stages of your experiment, such as when you’re performing a western blot or adding antibodies and encountering issues, I can guarantee that the cell counts you receive will be reliable and consistent.

    How has your cell counting technology been received by the scientific community?

    Our cell counter showed up on the Lab Rats Reddit recently. Someone had uploaded an image of cells they had counted using our device, recognizable by the software interface. They sought assistance with identifying a problem, and before I could offer my support, ten other individuals had already provided answers. Witnessing the community engagement and seeing our technology in action, functioning well and facilitating discussions, was truly rewarding.

    About DeNovix Inc.

    Welcome to DeNovix

    Award-Winning Products for Life Science

    Our multi-award winning products include the Reviewers’ Choice Life Science Product of the Year and Platinum Seal awarded- DS-11 Series Spectrophotometer / Fluorometer and CellDrop™ Automated Cell Counter. CellDrop is the first instrument of its kind to Count Cells Without Slides. These powerful instruments integrate patented DeNovix technology with easy-to-use software designed by life scientists for life scientists.

    Researchers tell us they love the industry leading performance, smart-phone-like operation, and the flexible connectivity of the instruments. When support is needed, the DeNovix team is here to help. DeNovix received the prestigious Life Sciences Customer Service of the Year based on independent reviews posted by scientists worldwide!

    CellDrop: Sustainable Laboratory Product of the Year

    The CellDrop Automated Cell Counter has been awarded Sustainable Laboratory Product of the Year in the SelectScience® Scientists’ Choice Awards®!

    CellDrop’s patented DirectPipette™ technology distinguishes it as the only cell counter to eliminate the need for cell counting slides. This innovation saves millions of single-use plastic slides from use and disposal each year.

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  • New insights into cardiac structure and disease repair

    New insights into cardiac structure and disease repair

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    In a recent study published in the journal Nature, a large team of researchers from the United States (U.S.) used single-cell ribonucleic acid (RNA) sequencing combined with high-resolution fluorescence in situ hybridization to determine the identities of the various cell types that coordinate spatially to give rise to the complex morphological structure of the heart.

    Study: Spatially organized cellular communities form the developing human heart. Image Credit: beerkoff / ShutterstockStudy: Spatially organized cellular communities form the developing human heart. Image Credit: beerkoff / Shutterstock

    Background

    Each complex structure of the human heart has specific roles that contribute to efficient cardiac function, and the interruption of any of these functions can lead to congenital disabilities such as congenital heart disease in children and cardiac diseases such as valvulopathies and cardiomyopathies in adults. However, despite the critical role of the heart in the human body, the organization and function of the cardiac structures and how they interact with one another remain poorly understood.

    About the study

    In the present study, the researchers used a single-cell RNA sequencing (scRNAseq) approach along with multiplexed error-robust fluorescence in situ hybridization (MER-FISH). This strategy allowed them to combine single-cell transcriptomes and spatial biology and visualize, analyze, and quantify the RNA transcripts of a large number of genes from a single cell.

    They began by identifying the cell lineages that were part of the developing heart, which helped determine how the various cardiac cell types assemble into complex structures and coordinate to regulate the function of the human heart. The scRNAseq was conducted in replicates and analyzed for human hearts in various stages of growth, starting from nine weeks and going up to 16 weeks post-conception.

    The obtained single cells, over 140 million in number, were transcriptionally categorized into five cell compartments: cardiomyocytes, endothelial, mesenchymal, neuronal, and blood. Within these cell compartments, analysis of gene markers helped identify 12 cell classes, with subsequent clustering analyses identifying 39 populations and 75 subpopulations of cells.

    MER-FISH was then used to spatially map the heart cells and explore the cellular mechanisms through which the remodeling and morphogenesis of the heart, including the ventricular wall development, were directed. The organization of the cells identified using scRNAseq, especially during developmental periods such as the compaction of the myocardial wall, was explored using MER-FISH imaging.

    The study then aimed to decipher the assembly of these specific cardiovascular cells into the cellular neighborhoods that come together to form the multi-cell structures that contribute to heart function. The scientists also explored the organizational and cellular complexity of specific regions, such as the ventricles, by exploring the cells within the ventricles that were identified, isolated, and mapped using MER-FISH. Additionally, mouse models were used to interrogate the interactions between cells through in vivo experiments, and pluripotent stem cells from humans were used to evaluate the same in in vitro experiments.

    a, Left, schematic of experiment. Right, scRNA-seq identifies a diverse range of distinct cardiac cells that create the developing human heart as displayed by uniform manifold approximation and projection (UMAP) of ~143,000 cells. b, Schematic shows how 238 cardiac-cell-specific genes were spatially identified using MERFISH. Pseudo-coloured dots mark the location of individual molecules of ten specific RNA transcripts. c, Approximately 250,000 MERFISH-identified cardiac cells were clustered into specific cell populations as shown by UMAP and coloured accordingly in d. d, Identified MERFISH cells were spatially mapped across a frontal section of a 13 p.c.w. heart (left) and shown according to major cell classes (right). e, Joint embedding between MERFISH and age-matched scRNA-seq datasets enabled cell label transfer and MERFISH gene imputation. f, Co-occurrence heatmap shows the correspondence of cell annotations of MERFISH cells to those transferred from the 13 p.c.w. scRNA-seq dataset. g, Gene imputation performance was validated spatially by comparing normalized gene expression profiles of marker genes measured by MERFISH with the corresponding imputed gene expression profiles. Epi, epicardial; MV, mitral valve; P–RBC, platelet–red blood cell; TV, tricuspid valve. Scale bar, 250 µm (g). Illustration in a was created using BioRender (https://www.biorender.com).a, Left, schematic of experiment. Right, scRNA-seq identifies a diverse range of distinct cardiac cells that create the developing human heart as displayed by uniform manifold approximation and projection (UMAP) of ~143,000 cells. b, Schematic shows how 238 cardiac-cell-specific genes were spatially identified using MERFISH. Pseudo-coloured dots mark the location of individual molecules of ten specific RNA transcripts. c, Approximately 250,000 MERFISH-identified cardiac cells were clustered into specific cell populations as shown by UMAP and coloured accordingly in d. d, Identified MERFISH cells were spatially mapped across a frontal section of a 13 p.c.w. heart (left) and shown according to major cell classes (right). e, Joint embedding between MERFISH and age-matched scRNA-seq datasets enabled cell label transfer and MERFISH gene imputation. f, Co-occurrence heatmap shows the correspondence of cell annotations of MERFISH cells to those transferred from the 13 p.c.w. scRNA-seq dataset. g, Gene imputation performance was validated spatially by comparing normalized gene expression profiles of marker genes measured by MERFISH with the corresponding imputed gene expression profiles. Epi, epicardial; MV, mitral valve; P–RBC, platelet–red blood cell; TV, tricuspid valve. Scale bar, 250 µm (g). Illustration in a was created using BioRender (https://www.biorender.com).

    Results

    The findings revealed that various cardiac cell types belonged to specific subpopulations that were part of specific communities, with the functional specialization defined according to the anatomical region in which they were present and the cellular ecosystem. The cardiomyocyte lineages were the largest cell compartment identified using MER-FISH. The study also found that cells that belonged to non-cardiomyocyte cell compartments also underwent segregation into populations and subpopulations and contributed to the formation of specific structures and regions of the heart.

    Cardiomyocyte subpopulations in the ventricular regions exhibited an ability to construct complex laminal structures in the ventricular wall and form cellular communities with other subpopulations of cardiac cells. Furthermore, the in vivo and in vitro experiments conducted to understand the interactions between cells revealed that the spatial organization of subpopulations of cardiac cells during the morphogenesis of the ventricular wall was conducted through various signaling pathways.

    The study also found cardiac regions composed of spatially organized combinations of cell populations that segregated together, called cellular communities. These cellular communities varied in the number and types of cell populations, and within these communities, neighbors of each cardiac cell within a 150-micrometer radius were defined. These interacting populations of cells also had distinct cellular signaling pathways.

    Conclusions

    Overall, the study found that cardiomyocytes were the largest compartment of cell types in the developing heart, and all the cell types exhibited distinct structural and regional distributions in the heart. Specific cell populations also formed cellular communities in various combinations, with signaling pathways between the cell populations within the community defining their structure and function. The study helped in understanding the development of the complex structure of the human heart, providing potential avenues to treat structural heart diseases.

    Journal reference:

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  • Innovative Subak tool offers affordable solution for detecting nuclease digestion

    Innovative Subak tool offers affordable solution for detecting nuclease digestion

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    A new tool could reduce costs for diagnosing infectious diseases.

    Biomedical researchers from The University of Texas at Austin have developed a new, less expensive way to detect nuclease digestion – one of the critical steps in many nucleic acid sensing applications, such as those used to identify COVID-19 and other infectious diseases. 

    A new study published in the journal Nature Nanotechnology shows that this low-cost tool, called Subak, is effective at telling when nucleic acid cleavage occurs, which happens when an enzyme called nuclease breaks down nucleic acids, such as DNA or RNA, into smaller fragments. 

    The traditional way of identifying nuclease activity, Fluorescence Resonance Energy Transfer (FRET) probe, costs 62 times more to produce than the Subak reporter. 

    “To make diagnostics more accessible to the public, we have to reduce costs,” said Soonwoo Hong, a Ph.D. student in the lab of Tim Yeh, associate professor in the Cockrell School of Engineering’s Department of Biomedical Engineering, who led the work. “Any improvements in nucleic acid detection will strengthen our testing infrastructure and make it easier to widely detect diseases like COVID-19.”

    The research team – which also included Jennifer Brodbelt, professor of chemistry at UT Austin’s College of Natural Sciences, and MinJun Kim, professor of mechanical engineering in Southern Methodist University’s Lyle School of Engineering – replaced the traditional FRET probe with Subak reporter in a test called DETECTR (DNA endonuclease-targeted CRISPR trans reporter).

    Subak reporters are based on a special class of fluorescent nanomaterials known as silver nanoclusters. They are made up of 13 silver atoms wrapped inside a short DNA strand. This organic/inorganic composite nanomaterial is too small to be visible to the naked eye and ranging from 1 to 3 nanometers (one billionth of a meter) in size.

    Nanomaterials at this length scale, such as semiconductor quantum dots, can be highly luminescent and exhibit different colors. Fluorescent nanomaterials have found applications in TV displays and biosensing, such as the Subak reporters.

    We have very clear evidence from mass spectrometry that transformation from Ag13 to Ag10 underlines the green to red color conversion observed in the sample, after DNA template digestion.”


    Jennifer Brodbelt, professor of chemistry at UT Austin’s College of Natural Sciences

    Subak reporters, which can be synthesized at room temperature in a single-pot reaction, cost just $1 per nanomole to make. In contrast, FRET probe – which employs complex steps to label a donor dye and a quencher – costs $62 per nanomole to produce. 

    “These highly luminescent silver nanoclusters can be called quantum dots as they show strong size-tunable fluorescence emission due to quantum confinement effect,” Yeh said. “No one can precisely tune the cluster size (and the corresponding emission color) until our demonstration of Subak,” which highlights the innovation of this research. 

    In addition to further testing the Subak reporter for nuclease digestion, the team also wants to investigate whether it can be a probe for other biological targets. 

    The work is supported by a National Science Foundation grant to Yeh and Brodbelt.

    Source:

    University of Texas at Austin

    Journal reference:

    Hong, S., et al. (2024). A non-FRET DNA reporter that changes fluorescence colour upon nuclease digestion. Nature Nanotechnology. doi.org/10.1038/s41565-024-01612-6.

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