Automated cell differential count in sputum is feasible and comparable to manual cell count in identifying eosinophilia.

INTRODUCTION: Cell differential count (CDC) of induced sputum is considered the gold standard for inflammatory phenotyping of asthma but is not implemented in routine care due to its heavy time- and staff demands. Digital Cell Morphology is a technique where digital images of cells are captured and presented preclassified as white blood cells (neutrophils, eosinophils, lymphocytes, macrophages, and unidentified) and nonwhite blood cells for review. With this study, we wanted to assess the accuracy of an automated CDC in identifying the key inflammatory cells in induced sputum. METHODS: Sputum from 50 patients with asthma was collected and processed using the standard processing protocol with one drop 20% albumin added to hinder cell smudging. Each slide was counted automatically using the CellaVision DM96 and manually by an experienced lab technician. Sputum was classified as eosinophilic or neutrophilic using 3% and 61% cutoffs, respectively. RESULTS: We found a good agreement using intraclass correlation for all target cells, despite significant differences in the cell count rate. The automated CDC had a sensitivity of 65%, a specificity of 93%, and a kappa-coefficient of 0.61 for identification of sputum eosinophilia. In contrast, the automated CDC had a sensitivity of 29%, a specificity of 100%, and a kappa-coefficient of 0.23 for identification of sputum neutrophilia. CONCLUSION: Automated- and manual cell counts of sputum agree with regards to the key inflammatory cells. The automated cell count had a modest sensitivity but a high specificity for the identification of both neutrophil and eosinophil asthma.

Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach.

BACKGROUND: For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now, macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing only indirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confronted with several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneous intensity of staining. Consequently, application of commercial software is largely restricted to very rough analysis modes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thus failing to represent the heterogeneity of tumor microenvironment adequately. METHODS: We describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies. RESULTS: Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels. Subsampling within tumor subregions is possible with results almost identical to full sampling. Mean macrophage size in tumor subregions is 152.5±111.3 µm2. CONCLUSIONS: ROF based approach is successfully applied to detection of IHC stained macrophages in DLBCL tissue samples. The method competes well with existing commercial software kits. In difference to them, it is fully automated, externally repeatable, independent on training data and completely documented. Comparison with gene expression data indicates that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.

Automated Cerebrospinal Fluid Cell Counts Using the New Body Fluid Mode of Sysmex UF-1000i.

BACKGROUND: We evaluated the new body fluid module on Sysmex UF1000-i (UF1000i-BF) for analysis of white blood cell (WBC) and red blood cell (RBC) in cerebrospinal fluid (CSF). METHODS: WBC and RBC counting were compared between UF1000i-BF and Fuchs-Rosenthal counting chamber in 67 CSF samples. This study also included the evaluation of between-day precision, limit of blank (LoB), limit of detection (LoD), functional sensitivity (limit of quantitation, LoQ), carryover and linearity. Diagnostic agreement for differentiation between normal and increased WBC counts (≥5.0 × 10(6) /L) was also assessed. RESULTS: The agreement between UF1000i-BF and manual WBC counts was otpiaml in all CSF samples (r = 0.99; y = 1.05x + 0.09). A modest overestimation was noticed in samples with WBC < 30 × 10(6) /L (r = 0.95; y = 1.21x - 0.15). A good agreement was observed for RBC counts (r = 0.98; y = 1.15x + 0.55), particularly in samples with RBC ≥ 18 × 10(6) /L (r = 0.98; y = 1.01x + 8.90). Between-day precision was good, with coefficient of variations (CVs) lower than 7.2% for both WBC and RBC. The LoBs were 0.1 × 10(6) WBC/L and 1.2 × 10(6) RBC/L, the LoDs were 0.7 × 10(6) WBC/L and 5.5 × 10(6) RBC/L, the LoQs were 2.4 × 10(6) WBC/L and 18.0 × 10(6) RBC/L, respectively. Linearity was excellent (r = 1.00 for both WBC and RBC). Carryover was negligible. Excellent diagnostic agreement was obtained at 4.5 × 10(6) WBC/L cut-off (sensitivity, 100%; specificity, 97.4%). CONCLUSION: The UF1000i-BF provides rapid and accurate WBC and RBC counts in clinically relevant values of CSF cells. The use of UF1000i-BF may hence allow to replace routine optical counting, except for samples displaying abnormal WBC counts or abnormal scattergram distribution, for which differential cell counts may still be required.

Laboratory workflow for optimizing leukocyte count and differential in synovial fluids on Sysmex XN-1000.

INTRODUCTION: Falsely elevated synovial white blood cell (WBC) counts using automated hematology analyzers have been reported particularly in the setting of joint arthroplasty. We evaluated the implementation of a laboratory workflow based on Sysmex XN-1000-automated cell counting and scattergram interpretation. METHODS: WBC and differential were measured for 76 synovial fluid samples (29 native joints and 47 with joint arthroplasties) with Sysmex XN-1000 and manual methods. All scattergrams were evaluated for possible incorrect WBC and/or differential according to our implemented workflow. A specific finding was the "banana-shape" scattergram, which indicates possible interferences. The European Bone & Joint Infection Society (EBJIS) criteria were applied to identify possible prosthetic joint infections (PJIs) in patients with joint arthroplasties. RESULTS: Correlation between automated and manual WBC counts, calculated for samples with WBC count <50 000/µL, was higher for native joints (r = 0.938) compared with patients known with arthroplasty (r = 0.906). Scattergrams classified as OK showed overall a higher correlation compared with scattergrams, which were interpreted as NOT OK. "Banana-shape" scattergrams (n = 19) showed falsely elevated automated WBC count, and the patterns were mainly seen in prosthesis patients (17/19 [89%]). Six of 47 (13%) patients with joint arthroplasties were reclassified from "confirmed" to "unlikely" PJI according to the EBJIS criteria. CONCLUSION: Our workflow based on scattergram interpretation resulted in accurate WBC counts in synovial fluid using automated/and or manual methods. It is important to identify the presence of "banana-shape" scattergrams to avoid overestimated automated WBC counts. Overall, automated synovial WBC count can be used, even for patients with arthroplasty, but after visual inspection of the scattergram to exclude possible interferences.

Image Analysis Using the Fluorescence Imaging of Nuclear Staining (FINS) Algorithm.

Finding appropriate image analysis techniques for a particular purpose can be difficult. In the context of the analysis of immunocytochemistry images, where the key information lies in the number of nuclei containing co-localised fluorescent signals from a marker of interest, researchers often opt to use manual counting techniques because of the paucity of available tools. Here, we present the development and validation of the Fluorescence Imaging of Nuclear Staining (FINS) algorithm for the quantification of fluorescent signals from immunocytochemically stained cells. The FINS algorithm is based on a variational segmentation of the nuclear stain channel and an iterative thresholding procedure to count co-localised fluorescent signals from nuclear proteins in other channels. We present experimental results comparing the FINS algorithm to the manual counts of seven researchers across a dataset of three human primary cell types which are immunocytochemically stained for a nuclear marker (DAPI), a biomarker of cellular proliferation (Ki67), and a biomarker of DNA damage (γH2AX). The quantitative performance of the algorithm is analysed in terms of consistency with the manual count data and acquisition time. The FINS algorithm produces data consistent with that achieved by manual counting but improves the process by reducing subjectivity and time. The algorithm is simple to use, based on software that is omnipresent in academia, and allows data review with its simple, intuitive user interface. We hope that, as the FINS tool is open-source and is custom-built for this specific application, it will streamline the analysis of immunocytochemical images.

YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images.

Cell counting in fluorescence microscopy is an essential task in biomedical research for analyzing cellular dynamics and studying disease progression. Traditional methods for cell counting involve manual counting or threshold-based segmentation, which are time-consuming and prone to human error. Recently, deep learning-based object detection methods have shown promising results in automating cell counting tasks. However, the existing methods mainly focus on segmentation-based techniques that require a large amount of labeled data and extensive computational resources. In this paper, we propose a novel approach to detect and count multiple-size cells in a fluorescence image slide using You Only Look Once version 5 (YOLOv5) with a feature pyramid network (FPN). Our proposed method can efficiently detect multiple cells with different sizes in a single image, eliminating the need for pixel-level segmentation. We show that our method outperforms state-of-the-art segmentation-based approaches in terms of accuracy and computational efficiency. The experimental results on publicly available datasets demonstrate that our proposed approach achieves an average precision of 0.8 and a processing time of 43.9 ms per image. Our approach addresses the research gap in the literature by providing a more efficient and accurate method for cell counting in fluorescence microscopy that requires less computational resources and labeled data.

Quantitative Immunofluorescent Imaging of Immune Cells in Mucosal Tissues.

Understanding the interplay between commensals, pathogens, and immune cells in the skin and mucosal tissues is critical to improve prevention and treatment of a myriad of diseases. While high-parameter flow cytometry is the current gold standard for immune cell characterization in blood, it is less suitable for mucosal tissues, where structural and spatial information is lost during tissue disaggregation. Immunofluorescence overcomes this limitation, serving as an excellent alternative for studying immune cells in mucosal tissues. However, the use of immunofluorescent microscopy for analyzing clinical samples is hampered by a lack of high-throughput quantitative analysis techniques. In this chapter, we describe methods for sectioning, staining, and imaging whole sections of human foreskin tissue. We also describe methods to automate immune cell quantification from immunofluorescent images, including image preprocessing and methods to quantify both circular and irregularly shaped immune cells using open-source software.

TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images.

BACKGROUND: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. METHODS: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. RESULTS: The algorithm detected 49.9% of human expert-identified TNTs, counted TNTs, and calculated the number of TNTs per cell, or TNT-to-cell ratio (TCR); it detected TNTs that were not originally detected by the experts. The model had 0.41 precision, 0.26 recall, and 0.32 f-1 score on a test dataset. The predicted and true TCRs were not significantly different across the training and test datasets (p = 0.78). CONCLUSIONS: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.

An image dataset related to automated macrophage detection in immunostained lymphoma tissue samples.

BACKGROUND: We present an image dataset related to automated segmentation and counting of macrophages in diffuse large B-cell lymphoma (DLBCL) tissue sections. For the classification of DLBCL subtypes, as well as for providing a prognosis of the clinical outcome, the analysis of the tumor microenvironment and, particularly, of the different types and functions of tumor-associated macrophages is indispensable. Until now, however, most information about macrophages has been obtained either in a completely indirect way by gene expression profiling or by manual counts in immunohistochemically (IHC) fluorescence-stained tissue samples while automated recognition of single IHC stained macrophages remains a difficult task. In an accompanying publication, a reliable approach to this problem has been established, and a large set of related images has been generated and analyzed. RESULTS: Provided image data comprise (i) fluorescence microscopy images of 44 multiple immunohistostained DLBCL tumor subregions, captured at 4 channels corresponding to CD14, CD163, Pax5, and DAPI; (ii) "cartoon-like" total variation-filtered versions of these images, generated by Rudin-Osher-Fatemi denoising; (iii) an automatically generated mask of the evaluation subregion, based on information from the DAPI channel; and (iv) automatically generated segmentation masks for macrophages (using information from CD14 and CD163 channels), B-cells (using information from Pax5 channel), and all cell nuclei (using information from DAPI channel). CONCLUSIONS: A large set of IHC stained DLBCL specimens is provided together with segmentation masks for different cell populations generated by a reference method for automated image analysis, thus featuring considerable reuse potential.

Pre- and postanalytical errors in haematology.

The majority of errors in laboratory medicine occur in the pre- and postanalytical phases of the testing process. Although the causes of these errors are largely common to all laboratory medicine specialties, it is important for the haematology laboratory to understand the particular impact of some on automated counting. The preanalytical phase is the stage of greatest risk but preanalytical errors may go undetected until postanalytical validation and interpretation. The challenges in the postanalytical phase include the standardisation of reference intervals against which results can be interpreted and the impact of just a small difference in reference interval for a key analyte such as haemoglobin concentration. Quality indicators against which pre- and postanalytical error incidence are measured are a source of information that can be used to improve services but laboratories struggle to collect good quality data.

Cutoff Value for Correcting White Blood Cell Count for Nucleated Red Blood Cells: What is it? Why is it Important?

Nucleated red blood cells (RBCs) are normally observed in the peripheral blood of neonates and during pregnancy. Under other conditions, the presence of nucleated RBCs in circulating blood indicates disorder in the blood-producing mechanism. The increased presence of nucleated RBCs, however, falsely elevates the leukocyte count, as measured by most automated hematology analyzers, warranting a manual correction of the leukocyte count. For a long time, cutoff values for correcting white blood cell (WBC) count for the presence of nucleated RBCs have been used regularly, particularly in developing countries. However, because those values are largely subjective, they can vary widely between laboratories worldwide. These varied cutoff values include 1, 5, 10, 20, and 50; it appears that the numbers 5 and 10 are the most common values used in corrections; the reasons require further elucidation. In this article, we discuss the merits of correcting the WBC count for nucleated RBCs at certain cutoff points.

TrpM8-mediated somatosensation in mouse neocortex.

Somatosensation is a complex sense mediated by more than a dozen distinct neural subtypes in the periphery. Although pressure and touch sensation have been mapped to primary somatosensory cortex in rodents, it has been controversial whether pain and temperature inputs are also directed to this area. Here we use a well-defined somatosensory modality, cool sensation mediated by peripheral TrpM8-receptors, to investigate the neural substrate for cool perception in the mouse neocortex. Using activation of cutaneous TrpM8 receptor-expressing neurons, we identify candidate neocortical areas responsive for cool sensation. Initially, we optimized TrpM8 stimulation and determined that menthol, a selective TrpM8 agonist, was more effective than cool stimulation at inducing expression of the immediate-early gene c-fos in the spinal cord. We developed a broad-scale brain survey method for identification of activated brain areas, using automated methods to quantify c-fos immunoreactivity (fos-IR) across animals. Brain areas corresponding to the posterior insular cortex and secondary somatosensory (S2) show elevated fos-IR after menthol stimulation, in contrast to weaker activation in primary somatosensory cortex (S1). In addition, menthol exposure triggered fos-IR in piriform cortex, the amygdala, and the hypothalamus. Menthol-mediated activation was absent in TrpM8-knock-out animals. Our results indicate that cool somatosensory input broadly drives neural activity across the mouse brain, with neocortical signal most elevated in the posterior insula, as well as S2 and S1. These findings are consistent with data from humans indicating that the posterior insula is specialized for somatosensory information encoding temperature, pain, and gentle touch.

Correlation Between Ki-67 Index, World Health Organization Grade and Patient Survival in Glial Tumors With Astrocytic Differentiation.

Background Glioblastoma multiforme (GBM) is a class IV astrocytic tumor, the most malignant of the four groups of World Health Organization (WHO) tumors with astrocytic differentiation. Aim The aim of this study was to estab-lish whether a correlation exists between the Ki-67 index of tumors with astrocytic differentiation, WHO grade, and patient survival. Materials and methods A retrospective non-clinical approach to patient selection was chosen for the aim of the study. A total of 47 patients diagnosed and treated for CNS tumors with astrocytic differentiation in the St. Marina University Hospital, Varna, Bulgaria, from September 2012 to July 2016 were retrospectively included into the study cohort. The cases were tested for their immunohistochemistry (IHC) reaction with Ki-67 after their original Hematoxylin and Eosin and IHC slides were reviewed by a single author and blind coded. The Ki-67 positivity index of the nuclei was estimated after digitalization of the slides and calculated by the ImmunoRatio automated count-ing tool. The individual Ki-67 index and patient survival of each case were statistically compared. Results The histopathological groups, after the blind Ki-67 index automated calculation was carried out, revealed no WHO grade I, two WHO grade II samples, four WHO grade III samples and 41 WHO grade IV cases, and these were included in the analysis. The two samples of WHO grade II astrocytic tumors had a mean Ki-67 index of 25%; however, they comprised tumors with an individual index of 43% and 7%, both individual values with a highly unlikely index for this group. The four samples of WHO grade III had a mean Ki-67 index of 4%, standard deviation ±2.16 (p>0.05), with the lowest index being 1% and the highest one being 6%. Both WHO grade II and III did not include enough samples to allow for a proper statistical analysis of patient survival. The 41 GBM cases had a mean Ki-67 index of 17.34%, standard deviation ±10.79 (p>0.05). Statistical analysis of the Ki-67 index divid-ed dichotomously into two groups and patient survival revealed that cases with a high Ki-67 index had no significant difference in survival when compared to those with low expression. Conclusions Based on the reported results, the mean Ki-67 percentage of positive nuclei in GBM tumor sam-ples cannot be used to estimate the survival of patients. However, Ki-67 remains a valuable IHC pathological tool.

Preliminary evaluation of UF-5000 Body Fluid Mode for automated cerebrospinal fluid cell counting.

BACKGROUND: Cellular analysis of cerebrospinal fluid (CSF) provides important diagnostic information in various medical conditions. The aim of this study was to evaluate the application of Sysmex UF-5000 body fluid mode in cytometric analysis of CSF compared to Light Microscopic (LM). METHODS: Eighty-one consecutive CSF samples were analyzed by UF-5000 body fluid mode and by LM. The study also included the evaluation of: limit of Blank (LoB), limit of Detection (LoD), limit of Quantitation (LoQ), carryover and linearity. RESULTS: For total nucleated cells (TNC-UF) and white blood cells (WBC-UF) LoB, LoD and LoQ were 1×106cells/L, 1.8×106cells/L and 1.9×106cells/L respectively. For red blood cells (RBC) LoB was 2×106cells/L, LoD was 3.5×106cells/L and LoQ was 14×106cells/L respectively. Linearity was excellent, carryover was negligible. The agreement between UF-5000 body fluid mode parameters and manual cell counts was good in all CSF samples with bias ranged between -0.5 and 25.1×106cells/L. The ROC curve analysis showed an area under curve of 0.99 for both TNC-UF and WBC-UF parameters. CONCLUSIONS: The UF-5000 body fluid mode offers rapid and accurate counts in clinically relevant concentration ranges, replacing the LM for most samples. However, in samples with abnormal cell counts or with abnormal scattergram the need for microscopic review remains.

Quantification of neuronal density across cortical depth using automated 3D analysis of confocal image stacks.

A new framework for measuring densities of immunolabeled neurons across cortical layers was implemented that combines a confocal microscopy sampling strategy with automated analysis of 3D image stacks. Its utility was demonstrated by quantifying neuronal density in macaque cortical areas V1 and V2. A series of overlapping confocal image stacks were acquired, each spanning from the pial surface to the white matter. DAPI channel images were automatically thresholded, and contiguous regions that included multiple clumped nuclear profiles were split using k-means clustering of image pixels for a set of candidate k values determined based on the clump's area; the most likely candidate segmentation was selected based on criteria that capture expected nuclear profile shape and size. The centroids of putative nuclear profiles estimated from 2D images were then grouped across z planes in an image stack to identify the positions of nuclei in x-y-z. 3D centroids falling outside user-specified exclusion boundaries were deleted, nuclei were classified by the presence or absence of signal in a channel corresponding to an immunolabeled antigen (e.g., the pan-neuronal marker NeuN) at the nuclear centroid location, and the set of classified cells was combined across image stacks to estimate density across cortical depth. The method was validated by comparison with conventional stereological methods. The average neuronal density across cortical layers was 230 × 103 neurons per mm3 in V1 and 130 × 103 neurons per mm3 in V2. The method is accurate, flexible, and general enough to measure densities of neurons of various molecularly identified types.

Mindray BC-6800 body fluid mode, performance of nucleated cells, and differential count in ascitic and pleural fluids.

INTRODUCTION: An accurate and rapid analysis of cells in body fluids (BFs) is important for diagnosis and follow-up in many pathological conditions. We evaluated the analytical performance of the module BF Mindray BC-6800 (BC-6800-BF) for cytometric analysis of ascitic and pleural fluids. METHODS: A total of 99 ascitic and 45 pleural samples were collected and assessed with BC-6800-BF and optical microscopy. This study also includes the evaluation of limit blank (LoB), limit detection (LoD), limit quantitation, (LoQ), carryover, linearity, and diagnostic concordance between the two methods. RESULTS: For TC-BF, LoB was 1 × 10(6) cells/L, LoD was 3 × 10(6) cells/L, and LoQ was 4 × 10(6) cells/L. Linearity was excellent (r(2) = 0.99) and carryover was negligible. TC-BF performed with the two methods showed Pearson's correlation of 0.99 (P < 0.0001), Passing-Bablok regression y = 1.04x - 1.17, and bias 33.7 cells. In ascitic fluids, polymorphonuclear cells (PMN) showed an area under curve (AUC) of 0.98 (P < 0.0001). In pleural fluids, mononuclear cells (MN) and PMN % displayed an AUC of 0.79 (P < 0.0001) and 0.93 (P < 0.0001), respectively. CONCLUSIONS: BC-6800-BF in ascitic and pleural fluids offers rapid and accurate cell and differential counts in clinically relevant concentration ranges. The use of BC-6800-BF may allow to replace routine optical counting, except for samples displaying abnormal cell counts or abnormal DIFF scattergram.