A Simple Approach for Counting CD4+ T Cells Based on a Combination of Magnetic Activated Cell Sorting and Automated Cell Counting Methods

Frequent tests for CD4+ T cell counting are important for the treatment of patients with immune deficiency; however, the routinely used fluorescence-activated cell-sorting (FACS) gold standard is costly and the equipment is only available in central hospitals. In this study, we developed an alternative simple approach (shortly named as the MACS-Countess system) for CD4+ T cell counting by coupling magnetic activated cell sorting (MACS) to separate CD4+ T cells from blood, followed by counting the separated cells using CountessTM, an automated cell-counting system. Using the cell counting protocol, 25 µL anti-CD4 conjugated magnetic nanoparticles (NP-CD4, BD Bioscience) were optimized for separating CD4+ T cells from 50 µL of blood in PBS using a DynamagTM-2 magnet, followed by the introduction of 10 µL separated cells into a CountessTM chamber slide for automated counting of CD4+ T cells. To evaluate the reliability of the developed method, 48 blood samples with CD4+ T cell concentrations ranging from 105 to 980 cells/µL were analyzed using both MACS-Countess and FACS. Compared with FACS, MACS-Countess had a mean bias of 3.5% with a limit of agreement (LoA) ranging from −36.4% to 43.3%, which is close to the reliability of the commercial product, PIMA analyzer (Alere), reported previously (mean bias 0.2%; LoA ranging from −42% to 42%, FACS as reference). Further, the MACS-Countess system requires very simple instruments, including only a magnet and an automated cell counter, which are affordable for almost every lab located in a limited resource region.

Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet

The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.

Automated cell count in body fluids: a review

Body fluid cell counting provides valuable information for the diagnosis and treatment of a variety of conditions. Chamber cell count and cellularity analysis by optical microscopy are considered the gold-standard method for cell counting. However, this method has a long turnaround time and limited reproducibility, and requires highly-trained personnel. In the recent decades, specific modes have been developed for the analysis of body fluids. These modes, which perform automated cell counting, are incorporated into hemocytometers and urine analyzers. These innovations have been rapidly incorporated into routine laboratory practice. At present, a variety of analyzers are available that enable automated cell counting for body fluids. Nevertheless, these analyzers have some limitations and can only be operated by highly-qualified laboratory professionals. In this review, we provide an overview of the most relevant automated cell counters currently available for body fluids, the interpretation of the parameters measured by these analyzers, their main analytical features, and the role of optical microscopy as automated cell counters gain ground.

Pseudothrombocytopenia Inducing Nonindicated Platelet Transfusion after Cardiac Surgery

Pseudothrombocytopenia (PTCP) is an in vitro phenomenon of low platelet count caused by the agglutination of platelets, leading to false low platelet counts in automated cell counting. Typically, ethylenediaminetetraacetic acid (EDTA) mediates this platelet clumping. PTCP has little clinical significance, but misdiagnosis may lead to unnecessary diagnostic tests and treatment. In this case report, we present a 65-year-old Caucasian female suffering from multiple complications during and after cardiac surgery. During her postoperative stay at the ICU, she was diagnosed with thrombocytopenia and an inadequate response to platelet supplementation.

An Advanced Approach to Detect Edges of Digital Images for Image Segmentation

Image segmentation has been an active topic of research for many years. Edges characterize boundaries, and therefore, detection of edges is a problem of fundamental importance in image processing. Edge detection in images significantly reduces the amount of data and filters out useless information while preserving the important structural properties in an image. Edges carry significant information about the image structure and shape, which is useful in various applications related with computer vision. In many applications, the edge detection is used as a pre-processing step. Edge detection is highly beneficial in automated cell counting, structural analysis of the image, automated object detection, shape analysis, optical character recognition, etc. Different filters are developed to find the gradients and detect edges. In this chapter, a new filter (kernel) is proposed, and the compass operator is applied on it to detect edges more efficiently. The results are compared with some of the previously proposed filters both qualitatively and quantitatively.

Laocoön: a tool for high-throughput automated cell counting

STRUCTURED ABSTRACTMotivationThere are current programs and plugins that automatically count the number of cells in a given image. However, many of these processes are not entirely automatic, as they require user input to specify a region of interest and are also frequently inaccurate.ResultsThis project presents laocoön, a Python package specifically designed to automatically and efficiently count the number of fluorescently-labelled cells in images. This package not only allows for reliable cell counting, but returns the proportion of cells in each cell cycle relative to all the cells in the DAPI channel, which is currently used for research purposes, but could ultimately be utilized for clinical purposes.Availability and ImplementationThis package, its corresponding execution instructions, and further information about the underlying algorithms, are currently available in the GitHub repository https://github.com/edukait/laocoon under the MIT license and can be run on the command terminal of any operating system. Alternatively, laocoön is available in the Python Package Index (PyPi), so the user can use the pip command to immediately download the [email protected]

Comparison between optical microscopy and automation for cytometric analysis of pericardial fluids in a cohort of adult subjects undergoing cardiac surgery

AimsLimited information is available on number and type of cells present in the pericardial fluid (PF). Current evidence and has been garnered with inaccurate application of guidelines for analysis of body fluids. This study was aimed at investigating the performance of automate cytometric analysis of PF in adult subjects.MethodsSeventy-four consecutive PF samples were analysed with Sysmex XN with a module for body fluid analysis (XN-BF) and optical microscopy (OM). The study also encompassed the assessment of limit of blank, limit of detection and limit of quantitation (LoQ), imprecision, carryover and linearity of XN-BF module.ResultsXN-BF parameters were compared with OM for the following cell classes: total cells (TC), leucocytes (white blood cell [WBC]), polymorphonuclear (PMN) and mononuclear (MN) cells. The relative bias were −4.5%, 71.2%, 108.2% and −47.7%, respectively. Passing and Bablok regression yielded slope comprised between 0.06 for MN and 5.8 for PMN, and intercept between 0.7 for PMN and 220.3 for MN. LoQ was comprised between 3.8×106 and 6.0×106 cells/L for WBC and PMN. Linearity was acceptable and carryover negligible.ConclusionsPF has a specific cellular composition. Overall, automated cell counting can only be suggested for total number of cells, whereas OM seems still the most reliable option for cell differentiation.