Cytometry Part A最新文献

Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been found that combines more than two datasets to use together. In classification, five types of white blood cells were identified by using a mixture of four different datasets. In segmentation, four types of white blood cells were determined, and three different neural networks, including CNN (Convolutional Neural Network), UNet and SegNet, were applied. The classification results of the presented study were compared with those of related studies. The balanced accuracy was 98.03%, and the test accuracy of the train-independent dataset was determined to be 97.27%. For segmentation, accuracy rates of 98.9% for train-dependent dataset and 92.82% for train-independent dataset for the proposed CNN were obtained in both nucleus and cytoplasm detection. In the presented study, the proposed method showed that it could detect white blood cells from a train-independent dataset with high accuracy. Additionally, it is promising as a diagnostic tool that can be used in the clinical field, with successful results in classification and segmentation.

Circulating inflammatory cells in eyes have emerged as early indicators of numerous major diseases, yet the monitoring of these cells remains an underdeveloped field. In vivo flow cytometry (IVFC), a noninvasive technique, offers the promise of real-time, dynamic quantification of circulating cells. However, IVFC has not seen extensive applications in the detection of circulating cells in eyes, possibly due to the eye's unique physiological structure and fundus imaging limitations. This study reviews the current research progress in retinal flow cytometry and other fundus examination techniques, such as adaptive optics, ultra-widefield retinal imaging, multispectral imaging, and optical coherence tomography, to propose novel ideas for circulating cell monitoring.

Full spectrum flow cytometry is a powerful tool for immune monitoring on a single-cell level and with currently available machines, panels of 40 or more markers per sample are possible. However, with an increased panel size, spectral unmixing issues arise, and appropriate single stain reference controls are required for accurate experimental results and to avoid unmixing errors. In contrast to conventional flow cytometry, full spectrum flow cytometry takes into account even minor differences in spectral signatures and requires the full spectrum of each fluorochrome to be identical in the reference control and the fully stained sample to ensure accurate and reliable results. In general, using the cells of interest is considered optimal, but certain markers may not be expressed at sufficient levels to generate a reliable positive control. In this case, compensation beads show some significant advantages as they bind a consistent amount of antibody independent of its specificity. In this study, we evaluated two types of manufactured compensation beads for use as reference controls for 30 of the most commonly used and commercially available fluorochromes in full spectrum cytometry and compared them to human and murine primary leukocytes. While most fluorochromes show the same spectral profile on beads and cells, we demonstrate that specific fluorochromes show a significantly different spectral profile depending on which type of compensation beads is used, and some fluorochromes should be used on cells exclusively. Here, we provide a list of important considerations when selecting optimal reference controls for full spectrum flow cytometry.

Over the past decade, the flow cytometry field has witnessed significant advancements in the number of fluorochromes that can be detected. This enables researchers to analyze more than 40 markers simultaneously on thousands of cells per second. However, with this increased complexity and multiplicity of markers, the manual dispensing of antibodies for flow cytometry experiments has become laborious, time-consuming, and prone to errors. An automated antibody dispensing system could provide a potential solution by enhancing the efficiency, and by improving data quality by faithfully dispensing the fluorochrome-conjugated antibodies and by enabling the easy addition of extra controls. In this study, a comprehensive comparison of different liquid handlers for dispensing fluorochrome-labeled antibodies was conducted for the preparation of flow cytometry stainings. The evaluation focused on key criteria including dispensing time, dead volume, and reliability of dispensing. After benchmarking, the I.DOT, a non-contact liquid handler, was selected and optimized in more detail. In the end, the I.DOT was able to prepare a 25-marker panel in 20 min, including the full stain, all FMOs and all single stain controls for cells and beads. Having all these controls improved the validation of the panel, visualization, and analysis of the data. Thus, automated antibody dispensing by dispensers such as the I.DOT reduces time and errors, enhances data quality, and can be easily integrated in an automated workflow to prepare samples for flow cytometry.

Plants are sessile creatures that have to adapt constantly changing environmental circumstances. Plants are subjected to a range of abiotic stressors as a result of unpredictable climate change. Understanding how stress-responsive genes are regulated can help us better understand how plants can adapt to changing environmental conditions. Epigenetic markers that dynamically change in response to stimuli, such as DNA methylation and histone modifications are known to regulate gene expression. Individual cells or particles' physical and/or chemical properties can be measured using the method known as flow cytometry. It may therefore be used to evaluate changes in DNA methylation, histone modifications, and other epigenetic markers, making it a potent tool for researching epigenetics in plants. We explore the use of flow cytometry as a technique for examining epigenetic traits in this thorough discussion. The separation of cell nuclei and their subsequent labeling with fluorescent antibodies, offering information on the epigenetic mechanisms in plants when utilizing flow cytometry. We also go through the use of high-throughput data analysis methods to unravel the complex epigenetic processes occurring inside plant systems.

Lysosomes are the terminal end of catabolic pathways in the cell, as well as signaling centers performing important functions such as the recycling of macromolecules, organelles, and nutrient adaptation. The importance of lysosomes in human health is supported by the fact that the deficiency of most lysosomal genes causes monogenic diseases called as a group Lysosomal Storage Diseases (LSDs). A common phenotypic hallmark of LSDs is the expansion of the lysosomal compartment that can be detected by using conventional imaging methods based on immunofluorescence protocols or overexpression of tagged lysosomal proteins. These methods require the alteration of the cellular architecture (i.e., due to fixation methods), can alter the behavior of cells (i.e., by the overexpression of proteins), and require sample preparation and the accurate selection of compatible fluorescent markers in relation to the type of analysis, therefore limiting the possibility of characterizing cellular status with simplicity. Therefore, a quantitative and label-free methodology, such as Quantitative Phase Imaging through Digital Holographic (QPI-DH), for the microscopic imaging of lysosomes in health and disease conditions may represent an important advance to study and effectively diagnose the presence of lysosomal storage in human disease. Here we proof the effectiveness of the QPI-DH method in accomplishing the detection of the lysosomal compartment using mouse embryonic fibroblasts (MEFs) derived from a Mucopolysaccharidosis type III-A (MSP-IIIA) mouse model, and comparing them with wild-type (WT) MEFs. We found that it is possible to identify label-free biomarkers able to supply a first pre-screening of the two populations, thus showing that QPI-DH can be a suitable candidate to surpass fluorescent drawbacks in the detection of lysosomes dysfunction. An appropriate numerical procedure was developed for detecting and evaluate such cellular substructures from in vitro cells cultures. Results reported in this study are encouraging about the further development of the proposed QPI-DH approach for such type of investigations about LSDs.

The gold standard of leukocyte differentiation is a manual examination of blood smears, which is not only time and labor intensive but also susceptible to human error. As to automatic classification, there is still no comparative study of cell segmentation, feature extraction, and cell classification, where a variety of machine and deep learning models are compared with home-developed approaches. In this study, both traditional machine learning of K-means clustering versus deep learning of U-Net, U-Net + ResNet18, and U-Net + ResNet34 were used for cell segmentation, producing segmentation accuracies of 94.36% versus 99.17% for the dataset of CellaVision and 93.20% versus 98.75% for the dataset of BCCD, confirming that deep learning produces higher performance than traditional machine learning in leukocyte classification. In addition, a series of deep-learning approaches, including AlexNet, VGG16, and ResNet18, was adopted to conduct feature extraction and cell classification of leukocytes, producing classification accuracies of 91.31%, 97.83%, and 100% of CellaVision as well as 81.18%, 91.64% and 97.82% of BCCD, confirming the capability of the increased deepness of neural networks in leukocyte classification. As to the demonstrations, this study further conducted cell-type classification of ALL-IDB2 and PCB-HBC datasets, producing high accuracies of 100% and 98.49% among all literature, validating the deep learning model used in this study.

In this study we describe three different methods for labeling T lymphocytes with cell trace violet (CTV), in order to track cell division in mouse and human cells, in both the in vitro and in vivo setting. We identified a modified method of CTV labeling that can be applied directly to either conventional or spectral flow cytometry, that maintained lymphocyte viability and function, yet minimized dye spill-over into other fluorochrome channels. Our optimized method for CTV labeling allowed us to identify up to eight cell divisions and the replication index for in vitro-stimulated mouse and human lymphocytes, and the co-expression of T-cell subset markers. Furthermore, the homeostatic trafficking, expansion and division of CTV-labeled congenic donor T cells could be detected using spectral cytometry, in an adoptive T-cell transfer mouse model. Our optimized CTV method can be applied to both in vitro and in vivo settings to examine the behavior and phenotype of activated T cells.

Finding novel methodologies that enhance the precision, agility, and standardization of drug discovery is crucial for studying leishmaniasis. The slide count is the technique most used to assess the leishmanicidal effect of a given drug in vitro. Despite being consolidated in the scientific environment, it presents several difficulties in its execution, assessment, and results. In addition to being laborious, this technique takes time, both for the preparation of the material for analysis and for the counting itself. Our research group suggests a fresh approach to address this requirement, which involves utilizing nuclear labeling with propidium iodide and flow cytometry to determine the quantity of Leishmania sp. parasites present in macrophages in vitro. Our results show that the fluorescence of infected samples increases as the infection rate increases. Using Pearson's Correlation analysis, it was possible to establish a correlation coefficient (Pearson r = 0.9473) that was strongly positive, linear, and directly proportional to the fluorescence and infection rate variables. Thus, it is possible to infer a mathematical equation through linear regression to estimate the number of parasites in each sample using the Relative Fluorescence Units (RFU) values. This new methodology opens space for the possibility of using this methodological resource in the in vitro quantification of Leishmania in macrophages.