Cytometry Part A最新文献

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.

Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population found in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application to PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analysis of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a β-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images, and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC data set including nine patients and two disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the data set and had a tendency to underestimate CHC counts for regions of interest (ROIs) containing relatively large amounts of cells (>50,000) when using the conventional enumeration method. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the β-VAE embeddings achieved an F1 score of 0.80, matching the average performance of human annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.

Cross reactivities are known for human leukocyte antigen inside HLA-B7 related Cross-Reactive Group (B7CREG). Some CE-IVD flow-cytometry kits use double monoclonal antibodies (mAb) to distinguish HLA-B27 and HLA-B7 but practice reveals more complexes results. This study explores the performances of this test. Analysis of 466 consecutive cases using HLA-B27 IOTest™ kit on a Navios™ cytometer from Beckman-Coulter, partially compared to their genotypes. Expected haplotypes HLA-B27-/HLA-B7- (undoubtedly HLA-B27 negative) and HLA-B27+/HLA-B7- (undoubtedly HLA-B27+) were clearly identified according to the manufacturer's instructions. On the opposite, patients strongly labeled with anti-HLA-B7 showed three different phenotypes regarding anti-HLA-B27 labeling: (1) most of the cases were poorly labeled in accordance with cross reactivity inside B7CREG (HLA-B27-/HLA-B7+ haplotype); (2) rare cases had strong B7 and B27 labeling corresponding to HLA-B27+/HLA-B7+ haplotype; (3) even less cases had strong labeling by anti-HLA-B7 but non for anti-HLA-B27, all expressing HLA-B44 and no B7CREG molecules. Surprisingly, more cases were not labeled with anti-HLA-B7 antibody but partially labeled with anti-HLA-B27 suggesting another cross reactivity out of B7CREG. mAb HLA typing suggests new, cross reactivities of anti-HLA-B27 antibody to more molecules out of B7CREG and of anti-HLA-B7 antibody but not anti-HLA-B27 to HLA-B44 molecule also out of B7CREG. HLA-B27 could surely be excluded in most samples labeled with HLA-B27, below a “grey zone” on intermediate intensity. More comparison is needed in future studies.

Imaging flow cytometry is an attractive method to investigate individual cells by optical properties. However, imaging flow cytometry applications with clinical relevance are scarce so far. Platelet aggregation naturally occurs during blood coagulation to form a clot. However, aberrant platelet aggregation is associated with cardiovascular disease under steady-state conditions in the blood. Several types of so-called antiplatelet drugs are frequently described to reduce the risk of stroke or cardiovascular diseases. However, an efficient monitoring method is missing to identify the presence and frequency of platelet–platelet aggregates in whole blood on a single cell level. In this work, we employed imaging flow cytometry to identify fluorescently labeled platelets in whole blood with a conditional gating strategy. Images were post-processed and aligned. A convolutional neural network was designed to identify platelet–platelet aggregates of two, three, and more than three platelets, and results were validated against various data set properties. In addition, the neural network excluded erythrocyte–platelet aggregates from the results. Based on the results, a parameter for detecting platelet–platelet aggregates, the weighted platelet aggregation, was developed. If employed on a broad scale with proband and patient samples, our method could aid in building a future diagnostic marker for cardiovascular disease and monitoring parameters to optimize drug prescriptions in such patient groups.