Individual Blood Cells Research Articles

Relationship of Hematological Profiles with the Serum Complement System in Patients with Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder identified by hematological abnormalities including anemia, leukopenia, and thrombocytopenia. Complement system disturbance is implicated in the pathogenesis of SLE. In this work, we aim to study how a full assessment of the complement system, which includes the evaluation of its three pathways, relates to blood cell counts in a population of patients with SLE. New-generation functional assays of the classical, alternative, and lectin pathways of the complement system were conducted in 284 patients with SLE. Additionally, serum levels of inactive molecules (C1q, C2, C3, C4, factor D) and activated molecules (C3a), as well as regulators (C1-inhibitor and factor H), were evaluated. Complete blood cell counts were analyzed. Multivariable linear regression analysis was performed to study the relationship of hematological profiles with this full characterization of the complement system. After multivariable adjustments that included age, sex, SLICC-DI (damage), and SLEDAI (activity) scores, as well as the use of aspirin, prednisone, methotrexate, azathioprine, and mycophenolate mofetil, several relationships were observed between the C pathways and the individual products and blood cells profile. Lower values of C1q and C2 were associated with lower hemoglobin levels. Lower leukocyte counts showed significantly lower values of C4, C1 inhibitor, C3, factor D, and alternative pathway functional levels. Neutrophil counts showed significant negative relationships only with the alternative pathway and C1-inh. In the case of lymphocytes, associations were found, especially with functional tests of the classical and alternative pathways, as well as with C2, C4, C3, and C3a. On the contrary, for platelets, significance was only observed, after multivariable adjustment, with lower C2 concentrations. In conclusion, the serum complement system and hematological profile in SLE are independently linked, after adjustment for disease activity and damage. These relationships are basically negative and are predominantly found in lymphocytes.

Remote Disease Diagnosis through IoMT-Enhanced Blood Cell Classification with Deep Learning

Background For the purpose of diagnosing diseases and developing treatment plans, blood cell pictures must be accurately classified. This procedure can be greatly enhanced by automated systems that make use of deep learning and the Internet of Medical Things (IoMT). Objective In order to improve illness detection and increase healthcare accessibility, this work suggests an IoMT-based system for remote blood cell picture transmission and classification utilizing deep learning algorithms. Methods High-resolution pictures of blood cells are captured by an IoMT tiny camera and wirelessly sent to a cloud-based infrastructure. The blood cells are divided into groups according to a, deeplearning classification algorithm, including neutrophils, lymphocytes, monocytes, and eosinophils. Results The IoMT-enabled system excels in transmitting and analyzing blood cell images, achieving precise classification. Utilizing deep learning models with multi-scale feature extraction and attention mechanisms, the system demonstrates robust performance. Numerical results showcase a high accuracy of approximately 97.21%, along with noteworthy precision, recall, and F1 scores for individual blood cell classes. Eosinophil, Lymphocyte, Monocyte, and Neutrophil classes exhibit strong performance metrics, emphasizing the system's effectiveness in accurate blood cell classification. Conclusion By combining IoMT and deep learning with blood cell image analysis, diagnostic accessibility and efficiency are improved. The suggested approach has the potential to completely transform healthcare by facilitating prompt interventions, individualized treatment regimens, and better patient outcomes. It is essential to continuously enhance and validate the system in order to maximize its efficacy and dependability in a variety of healthcare settings.

Optimization of Formaldehyde Fixative Concentration for Individual Blood Cells to Develop a Stabilized Blood Control for Automated Hematology Analyzers.

In a modern haematology laboratory, the complete and differential counts of blood are performed using complex haematology auto analyzers. In order to ensure the accuracy and reliability of test results, various regulatory authorities have prescribed the use of stabilized blood controls. The major pitfalls of these blood controls are their short shelf life. This could be due to the fact that they are prepared by a common cocktail of fixatives which acts on the discrete cells in various ways and would result in either under-fixation or over-fixation of various cells. Thus, in the present study, we have explored and optimized fixative and buffering for individual cells to achieve stable blood control. Blood cells were isolated using the centrifugation technique and were fixed individually with different concentrations of formaldehyde. After fixation, cells were pooled. Analysis of cell count was done till six months. Cells were also analysed morphologically to see the effect of fixation and storage on cell morphology. In the present study we compared the effect of the concentration of formaldehyde fixative for individual cells in the blood and their role in enhancing the shelf life and maintaining the morphology of the cells when suspended in plasma or suitable buffers post-fixation. It was observed that WBCs can be better fixed with 3 and 3.5% formaldehyde in a buffered solution, whereas RBCs and Platelets can be optimally fixed with 2.5% formaldehyde in a buffered solution.

A-144 Three-wavelength Polarization Imaging Method for the Analysis of Individual Human Blood Cells With Dye-Enhanced Dispersion of Dilution Buffer

Abstract Background Performing basic hematology analysis in a near-patient setting enables more rapid clinical decision making in the process of diagnosis. Traditional blood analyzers in central labs use a complex combination of sensing principles and chemical modification of the sample to derive the necessary parameters for a complete blood count (CBC). To reduce cost and maintenance, varying approaches have been employed to determine a CBC with a single-step measurement technique. One common option is the use of optical microscopy to deliver highly reliable results, examples are fluorescence microscopy, digital holographic microscopy, or spatial light interference microscopy. However, most of these technologies lack a proper determination of cellular-level RBC parameters. Therefore, a non-interferometric, quantitative-phase measurement technique was developed to derive relevant CBC parameters from a single measurement, comparable to a standard lab analyzer. Methods The optical setup employed by this method enables the parallel acquisition of brightfield and differential interference contrast (DIC) images by polarization-sensitive cameras. By using three cameras with individual optical filters, quantitative phase and absorption information can be calculated, which enables the determination of mean cellular volume (MCV), mean cellular hemoglobin (MCH), and mean cellular hemoglobin concentration (MCHC) for red blood cells (RBCs). The whole-blood sample is diluted in a buffer containing a non-cell-penetrating dye to enhance optical refraction contrast and therefore precision in the dependent calculated quantities. The sample is then introduced to a glass flow cell, and the cells are allowed to sediment to the bottom for imaging. Machine learning is applied to the derived images to classify unstained native white blood cells (WBCs) to yield a 5-part differential, which distinguishes between neutrophil, monocyte, lymphocyte, eosinophil and basophil. The algorithm is trained by imaging FACS-validated, purified WBC subgroups. All blood samples are measured on an ADVIA® 2120 Hematology System for control. Results A one-step, optical CBC hematology system based on quantitative phase and absorption image analysis was developed. The correlations for MCV and MCHC compared to a standard lab CBC analyzer were 0.77 and 0.84. The accuracy for the 5-part differential WBC classification was approximately 0.8. To enable a visual inspection of the classification results, a pseudo-colored image of each cell was generated and uploaded in a review tool. Conclusion With a more-refined, less-expensive optical setup, a high-quality CBC analyzer suitable for use at the point of care can be derived with the presented method. A possible simplification could be the use of low-cost but high-resolution Fourier ptychography microscopy (FPM) technology. Imaging of blood samples on a single-cell level offers the potential for the analysis of even more morphological parameters, for example, disease-related changes in the cell membrane. Also, the adhesion behavior of the WBCs in the glass flow cell could indicate cell activation status and therefore give information about infection processes. *Trademark disclaimer: “ADVIA, and all associated marks are trademarks of Siemens Healthcare Diagnostics, Inc. or its affiliates. All other trademarks and brands are the property of their respective owners.”

Single-cell multi-omics of mitochondrial DNA disorders reveals dynamics of purifying selection across human immune cells.

Pathogenic mutations in mitochondrial DNA (mtDNA) compromise cellular metabolism, contributing to cellular heterogeneity and disease. Diverse mutations are associated with diverse clinical phenotypes, suggesting distinct organ- and cell-type-specific metabolic vulnerabilities. Here we establish a multi-omics approach to quantify deletions in mtDNA alongside cell state features in single cells derived from six patients across the phenotypic spectrum of single large-scale mtDNA deletions (SLSMDs). By profiling 206,663 cells, we reveal the dynamics of pathogenic mtDNA deletion heteroplasmy consistent with purifying selection and distinct metabolic vulnerabilities across T-cell states in vivo and validate these observations in vitro. By extending analyses to hematopoietic and erythroid progenitors, we reveal mtDNA dynamics and cell-type-specific gene regulatory adaptations, demonstrating the context-dependence of perturbing mitochondrial genomic integrity. Collectively, we report pathogenic mtDNA heteroplasmy dynamics of individual blood and immune cells across lineages, demonstrating the power of single-cell multi-omics for revealing fundamental properties of mitochondrial genetics.

Abstract 2292: Novel method for the detection and evaluation of disease biomarkers on white blood cells from liquid biopsies

Abstract Purpose: Blood based liquid biopsies of circulating tumor cells (CTCs) are a non-invasive tool with proven effectiveness in the prognosis, treatment selection, and stratification of cancer patients. In this study we demonstrate a novel application of the RareCyte platform, which was developed to find and analyze rare CTCs from blood samples, in evaluating white blood cell (WBC) sub-populations for disease biomarkers. Experimental Procedures: Using AccuCyte®, an unbiased density-based method for collecting nucleated cells from whole blood, we transferred the nucleated cells to slides and stained for CD14, CD66b, and CK/EpCAM to identify monocytes (CD14+/CD66b-), granulocytes (CD14-/CD66b+), and CTCs (CK/EpCAM+), as well as a drug-target biomarker to evaluate its expression across all cells. These slides were then imaged with CyteFinder®, an automated multiparameter immunofluorescent (IF) microscopy system that applies machine learning algorithms for cell identification. Each slide contains millions of cells, so we developed novel scanning and analysis algorithms to quantify WBC sub-populations in addition to rare CTCs. Results: In addition to detection and enumeration of CTCs, the RareCyte system was able to detect individual white blood cells and quantify monocyte and granulocyte subpopulations. Our results correlated very well with standard CBC (Complete Blood Count) readings from multiple donors. Additionally, the monocyte and granulocyte populations were evaluated for expression of a drug-target biomarker. While expression was observed on both granulocytes and monocytes, monocytes had a significantly higher expression of the drug-target biomarker, a critical finding for future implementation of this drug. Conclusions: Our results show that through modifications to the RareCyte platform we can identify rare and non-rare cell populations simultaneously and evaluate expression of biomarkers and drug-targets across multiple cells of interest. This new application combines the benefits of high throughput data analysis of flow cytometry with the specificity and sensitivity of rare cell detection. Additionally, high-resolution multiplexed immunofluorescent imaging provides cell morphology and biomarker localization information. Our ability to add multiple biomarkers in conjunction with CTC and other rare cell detection provides multiple avenues for future research. One possible application is characterizing white blood cells that cluster with CTCs, which are known to portend a worse prognosis for patients. Our modified workflow could be implemented to precisely characterize these WBCs that contribute to the metastatic process. Utilization of the RareCyte platform for both rare and non-rare cell analysis presents an exciting opportunity for a deeper evaluation of patients and their prognosis through liquid biopsies. Citation Format: Erin Bayer, Jon Ladd, Joshua Nordberg, Arturo B. Ramirez, Jessica A. Baker, Thomas Daly. Novel method for the detection and evaluation of disease biomarkers on white blood cells from liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2292.

An improved Vision Transformer model for the recognition of blood cells

Leukemia is a common, multiple and dangerous blood disease, whose early diagnosis and treatment are very important. At present, the diagnosis of leukemia heavily relies on morphological examination of blood cell images by pathologists, which is tedious and time-consuming. Meanwhile, the diagnostic results are highly subjective, which may lead to misdiagnosis and missed diagnosis. To address the gap above, we proposed an improved Vision Transformer model for blood cell recognition. First, a faster R-CNN network was used to locate and extract individual blood cell slices from original images. Then, we split the single-cell image into multiple image patches and put them into the encoder layer for feature extraction. Based on the self-attention mechanism of the Transformer, we proposed a sparse attention module which could focus on the discriminative parts of blood cell images and improve the fine-grained feature representation ability of the model. Finally, a contrastive loss function was adopted to further increase the inter-class difference and intra-class consistency of the extracted features. Experimental results showed that the proposed module outperformed the other approaches and significantly improved the accuracy to 91.96% on the Munich single-cell morphological dataset of leukocytes, which is expected to provide a reference for physicians' clinical diagnosis.

New Insights into Cerebral Vessel Disease Landscapes at Single-Cell Resolution: Pathogenetic and Therapeutic Perspectives.

Cerebrovascular diseases are a leading cause of death and disability globally. The development of new therapeutic targets for cerebrovascular diseases (e.g., ischemic, and hemorrhagic stroke, vascular dementia) is limited by a lack of knowledge of the cellular and molecular biology of health and disease conditions and the factors that cause injury to cerebrovascular structures. Here, we describe the role of advances in omics technology, particularly RNA sequencing, in studying high-dimensional, multifaceted profiles of thousands of individual blood and vessel cells at single-cell resolution. This analysis enables the dissection of the heterogeneity of diseased cerebral vessels and their atherosclerotic plaques, including the microenvironment, cell evolutionary trajectory, and immune response pathway. In animal models, RNA sequencing permits the tracking of individual cells (including immunological, endothelial, and vascular smooth muscle cells) that compose atherosclerotic plaques and their alteration under experimental settings such as phenotypic transition. We describe how single-cell RNA transcriptomics in humans allows mapping to the molecular and cellular levels of atherosclerotic plaques in cerebral arteries, tracking individual lymphocytes and macrophages, and how these data can aid in identifying novel immune mechanisms that could be exploited as therapeutic targets for cerebrovascular diseases. Single-cell multi-omics approaches will likely provide the unprecedented resolution and depth of data needed to generate clinically relevant cellular and molecular signatures for the precise treatment of cerebrovascular diseases.

Analysis of Single Blood Entities Using an Ultramicroelectrode through Single-Entity Electrochemistry

The research was conducted with the aim of analyzing individual human blood cells in the presence of redox species based on single-entity electrochemistry. Various blood cells (e.g., red blood cell, white blood cell, and platelets) perform a role directly related to survival, such as oxygen transport, hemostasis, and immune response, as they move through the whole body’s blood vessels. However, with the current lab-on-a-chip typed biosensor technology, the factors that can be measured using a small amount of blood are limited to uric acid, cholesterol, glucose, or hemoglobin in the blood. In clinical laboratories, common methods for measuring individual blood counts are automated hematology analyzers based on the automated Coulter cell counting method or flow cytometry, which produce a huge amount of blood count results, but are limited. For this reason, it is proposed a method for detecting individual blood cells that can be applied to a biosensor simply and quickly.References Ran, An. et al. (2021) ‘Emerging point-of-care technologies for anemia detection’, Lab on a Chip, 21(10), pp. 1843-1865.J-H, Lin. et al. (2020) ‘A Multifunctional Microfluidic Device for Blood Typing and Primary Screening of Blood Diseases’ ACS Sensors, 5(10), pp. 3082-3090.J, Lee. et al. (2020) ‘Determination of Serotonin Concentration in Single Human Platelets through Single-Entity Electrochemistry’, ACS Sensors, 5(7), pp. 1943-1948.T.L.T, Ho. et al. (2018) ‘Determining mean corpuscular volume and red blood cell count using electrochemical collision events’, Biosensors and Bioelectronics. 110, pp. 155-159.A-C, Villani. et al. (2017) ‘Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors’, Science. 356, No.6335.

Effect of Conical Spiral Flow Channel and Impeller Parameters on Flow Field and Hemolysis Performance of an Axial Magnetic Blood Pump

For a blood pump, the blood flow channel and impeller parameters directly affect the performance of the pump and the resulting blood circulation. The flow channel in particular has a great impact on the hydraulic performance of the pump (e.g., flow and pressure), which directly determines the overall performance of the blood pump. Traditional bearing-supported blood pumps can cause mechanical damage to blood cells, leading to hemolysis and thrombosis. In this study, therefore, we designed a conical spiral axial blood pump with magnetic levitation. The blood pump was supported by electrodynamic bearings in the radial direction and electromagnetic bearings in the axial direction. The impeller and the front and rear hubs were integrated to minimize blood stagnation and reduce the formation of thrombosis. The hub had a conical spiral flow channel design, which not only reduced the size of the impeller but also increased blood flow and pressure while meeting the design requirements. Computational fluid dynamics (CFD) analysis was used to analyze the flow field of the axial blood pump, a power function model was used to establish a hemolysis prediction model, and the particle tracking method was used to obtain the flow trajectories of individual blood cells, thereby predicting hemolysis-related performance of the blood pump. The simulation results showed that the main high shear stress area in the blood pump was located in the impeller inlet and the clearance between the top of the impeller and the inner chamber of the blood pump. When the hub taper angle of the blood pump was 0.72° and the clearance was 0.3 mm, the average hemolysis prediction value was 0.00216. This prediction value was smaller than that of traditional axial blood pumps. These findings can provide an important reference for the structural design of axial blood pumps and for reducing the hemolysis prediction value.

On the Reliability of CNNs in Clinical Practice: A Computer-Aided Diagnosis System Case Study

Leukocytes classification is essential to assess their number and status since they are the body’s first defence against infection and disease. Automation of the process can reduce the laborious manual process of review and diagnosis by operators and has been the subject of study for at least two decades. Most computer-aided systems exploit convolutional neural networks for classification purposes without any intermediate step to produce an accurate classification. This work explores the current limitations of deep learning-based methods applied to medical blood smear data. In particular, we consider leukocyte analysis oriented towards leukaemia prediction as a case study. In particular, we aim to demonstrate that a single classification step can undoubtedly lead to incorrect predictions or, worse, to correct predictions obtained with wrong indicators provided by the images. By generating new synthetic leukocyte data, it is possible to demonstrate that the inclusion of a fine-grained method, such as detection or segmentation, before classification is essential to allow the network to understand the adequate information on individual white blood cells correctly. The effectiveness of this study is thoroughly analysed and quantified through a series of experiments on a public data set of blood smears taken under a microscope. Experimental results show that residual networks perform statistically better in this scenario, even though they make correct predictions with incorrect information.

How Single-Cell Technologies Have Provided New Insights Into Atherosclerosis.

The development of innovative single-cell technologies has allowed the high-dimensional transcriptomic and proteomic profiling of individual blood and tissue cells. Recent single-cell studies revealed a new cellular heterogeneity of atherosclerotic plaque tissue and allowed a better understanding of distinct immune functional states in the context of atherosclerosis. In this brief review, we describe how single-cell technologies have shed a new light on the cellular composition of atherosclerotic plaques, and their response to diet perturbations or genetic manipulation in mouse models of atherosclerosis. We discuss how single-cell RNA sequencing, cellular indexing of transcriptomes and epitopes by sequencing, transposase-accessible chromatin with high-throughput sequencing, and cytometry by time-of-flight platforms have empowered the identification of discrete immune, endothelial, and smooth muscle cell alterations in atherosclerosis progression and regression. Finally, we review how single-cell approaches have allowed mapping the cellular and molecular composition of human atherosclerotic plaques and the discovery of new immune alterations in plaques from patients with stroke.

Effect of radioiodine therapy (131I) on blood cells of differentiated thyroid carcinoma patients

Thyroid cancer accounts for about 1% of all types of cancers, with about 80% of cases being differentiated and 20% undifferentiated. Our study was conducted on 209 Differentiated Thyroid Carcinoma (DTC) patients treated at the Department of Nuclear Medicine of Nghe An Cancer Hospital with 91.39% of patients having papillary thyroid carcinoma, with the mean age of 47.91 ± 12.08. The geographic distribution of these patients was determined to be 68.42% coastal plains. Patients living in mountainous areas accounted for only 6.22%. For our experiment, treatment for patients including having the tumor removed and thyroid tissue destroyed using 131I. Patients were then prescribed with alternative hormones varying at different doses depending on the degree and stage of cancer. Among 209 patients, 8 patients were treated with 30mCi dose(3.83%); 196 patients treated with the dose of 100–150 mCi (93.78%) and 5 patients treated 200–250 mCi (2.39%). After 3–5 days of treatment, we observed significant changes in blood count with p<0.05. Initially, erythrocytes and hemoglobin count decreased slightly for 30–150 mCi dose patients but with 200–250 mCi dose patients, those indexes fell below the reference level. The platelet index decreased significantly after 3–5 days of treatment (p<0.05). Total leukocytes, lymphocytes and mono count decreased sharply after treatment (p<0.05) but neutrophils changed very little. After 3–6 months of treatment, the blood cell formula gradually stabilized, the recovery of red blood cells took place faster than the white blood cells and platelets. The recovery of blood cells of patients treated with doses of 200–500 mCi took place more slowly (p<0.05). Thus, the use of 131I in the treatment of DTC is a common and effective method, but they may also be at risk of impairing blood cells, especially in high-dose 131I patients. However, this change is still within the allowable limits and after 6 months of treatment, the blood cell counts tend to return to their original values. The speed of recovery depends on the individual blood cell line and the amount of treatment. This research result can be used as a reference for deciding on 131I dosage and supportive measures, interventions in treatment, helping patients avoiding the decrease of blood cells and increase the effectiveness of the treatment process.

Optical Coherence Tomography Angiography in the Thirteen-Lined Ground Squirrel.

PurposeTo assess the performance of two spectral-domain optical coherence tomography-angiography systems in a natural model of hypoperfusion: the hibernating thirteen-lined ground squirrel (13-LGS).MethodsUsing a high-speed (130 kHz) OCT-A system (HS-OCT-A) and a commercial OCT (36 kHz; Bioptigen Envisu; BE-OCT-A), we imaged the 13-LGS retina throughout its hibernation cycle. Custom software was used to extract the superior, middle, and deep capillary plexus (SCP, MCP, and DCP, respectively). The retinal vasculature was also imaged with adaptive optics scanning light ophthalmoscopy (AOSLO) during torpor to visualize individual blood cells. Finally, correlative histology with immunolabeled or DiI-stained vasculature was performed.ResultsDuring euthermia, vessel density was similar between devices for the SCP and MCP (P = 0.88, 0.72, respectively), with a small difference in the DCP (−1.63 ± 1.54%, P = 0.036). Apparent capillary dropout was observed during torpor, but recovered after forced arousal, and this effect was exaggerated in high-speed OCT-A imaging. Based on cell flux measurements with AOSLO, increasing OCT-A scan duration by ∼1000× would avoid the apparent capillary dropout artifact. High correspondence between OCT-A (during euthermia) and histology enabled lateral scale calibration.ConclusionsWhile the HS-OCT-A system provides a more efficient workflow, the shorter interscan interval may render it more susceptible to the apparent capillary dropout artifact. Disambiguation between capillary dropout and transient ischemia can have important implications in the management of retinal disease and warrants additional diagnostics.Translational RelevanceThe 13-LGS provides a natural model of hypoperfusion that may prove valuable in modeling the utility of OCT-A in human pathologies associated with altered blood flow.

A hybrid of light-field and light-sheet imaging to study myocardial function and intracardiac blood flow during zebrafish development

Biomechanical forces intimately contribute to cardiac morphogenesis. However, volumetric imaging to investigate the cardiac mechanics with high temporal and spatial resolution remains an imaging challenge. We hereby integrated light-field microscopy (LFM) with light-sheet fluorescence microscopy (LSFM), coupled with a retrospective gating method, to simultaneously access myocardial contraction and intracardiac blood flow at 200 volumes per second. While LSFM allows for the reconstruction of the myocardial function, LFM enables instantaneous acquisition of the intracardiac blood cells traversing across the valves. We further adopted deformable image registration to quantify the ventricular wall displacement and particle tracking velocimetry to monitor intracardiac blood flow. The integration of LFM and LSFM enabled the time-dependent tracking of the individual blood cells and the differential rates of segmental wall displacement during a cardiac cycle. Taken together, we demonstrated a hybrid system, coupled with our image analysis pipeline, to simultaneously capture the myocardial wall motion with intracardiac blood flow during cardiac development.

Blood Flow Velocimetry in a Microchannel During Coagulation Using Particle Image Velocimetry and Wavelet-Based Optical Flow Velocimetry.

This article describes novel measurements of the velocity of whole blood flow in a microchannel during coagulation. The blood is imaged volumetrically using a simple optical setup involving a white light source and a microscope camera. The images are processed using particle image velocimetry (PIV) and wavelet-based optical flow velocimetry (wOFV), both of which use images of individual blood cells as flow tracers. Measurements of several clinically relevant parameters such as the clotting time, decay rate, and blockage ratio are computed. The high-resolution wOFV results yield highly detailed information regarding thrombus formation and corresponding flow evolution that is the first of its kind.

Assessing the co-variability of DNA methylation across peripheral cells and tissues: Implications for the interpretation of findings in epigenetic epidemiology.

Most epigenome-wide association studies (EWAS) quantify DNA methylation (DNAm) in peripheral tissues such as whole blood to identify positions in the genome where variation is statistically associated with a trait or exposure. As whole blood comprises a mix of cell types, it is unclear whether trait-associated DNAm variation is specific to an individual cellular population. We collected three peripheral tissues (whole blood, buccal epithelial and nasal epithelial cells) from thirty individuals. Whole blood samples were subsequently processed using fluorescence-activated cell sorting (FACS) to purify five constituent cell-types (monocytes, granulocytes, CD4+ T cells, CD8+ T cells, and B cells). DNAm was profiled in all eight sample-types from each individual using the Illumina EPIC array. We identified significant differences in both the level and variability of DNAm between different sample types, and DNAm data-derived estimates of age and smoking were found to differ dramatically across sample types from the same individual. We found that for the majority of loci variation in DNAm in individual blood cell types was only weakly predictive of variance in DNAm measured in whole blood, although the proportion of variance explained was greater than that explained by either buccal or nasal epithelial samples. Covariation across sample types was much higher for DNAm sites influenced by genetic factors. Overall, we observe that DNAm variation in whole blood is additively influenced by a combination of the major blood cell types. For a subset of sites, however, variable DNAm detected in whole blood can be attributed to variation in a single blood cell type providing potential mechanistic insight about EWAS findings. Our results suggest that associations between whole blood DNAm and traits or exposures reflect differences in multiple cell types and our data will facilitate the interpretation of findings in epigenetic epidemiology.

Robust three-dimensional nanotube-in-micropillar array electrodes to facilitate size independent electroporation in blood cell therapy.

Blood is an attractive carrier for plasmid and RNA-based medicine in cell therapy. Electroporation serves as a favorable delivery tool for simple operation, quick internalization, minimum cell culture involvement, and low contamination risk. However, the delivery outcome of electroporation heavily depends on the treated cells such as their type, size, and orientation to the electric field, not ideal for highly heterogeneous blood samples. Herein, a new electroporation system was developed towards effective transfection to cells in blood regardless of their large diversity. By coupling replica molding and infiltration-coating processes, we successfully configured a three-dimensional electrode comprised of a polymer micropillar array on which carbon nanotubes (CNTs) are partially embedded. During electroporation, cells sag between micropillars and deform to form a conformal contact with their top and side surfaces. The implanted CNTs not only provide a robust conductive coating for polymer micropattern but also have their protruded ends face the cell membrane vertically everywhere with maximum transmembrane potential. Regardless of their largely varied sizes and random dispersion, both individual blood cell type and whole blood samples were effectively transfected with plasmid DNA (85% after 24 h and 95% after 72 h, or 2.5-3.0 folds enhancement). High-dose RNA probes were also introduced, which regulate better the expression levels of exogenous and endogenous genes in blood cells. Besides its promising performance on non-viral delivery routes to cell-related studies and therapy, the involved new fabrication method also provides a convenient and effective way to construct flexible electronics with stable micro/nano features on the surface.

Abstract 5379: Non-invasive profiling of advanced prostate cancer via multi-parametric liquid biopsy and radiomics

Abstract Introduction: Biomarkers of treatment response in advanced prostate cancer (PC) are urgently needed. Liquid biopsy profiling and radiomic imaging enable non-invasive, repeatable tumor profiling, and combining these modalities could yield powerful new predictive tools. We tested the feasibility of concurrent, multi-parametric molecular analysis of circulating tumor cells (CTCs) and matched plasma cell-free DNA (cfDNA), combined with radiomic analysis of CT scans from the same patients. Methods: Under IRB-approved informed consent, blood was collected from 23 patients with metastatic castrate resistant PC (mCRPC). CTCs were enumerated using the RareCyte and CellSearch platforms, and individual single CTCs and white blood cells (WBCs) were retrieved using RareCyte's integrated robotic micropipette. DNA was extracted from single cells (scDNA), as well as from matched cfDNA and buffy coats (bcDNA), then sequenced using Ampliseq HD Pan-cancer panel. Concurrent radiomic analysis was performed on bone metastatic lesions identified on CT scans from the same patients, and manually segmented regions of interest were queried for a variety of texture metrics. Results: Of 19 patients with both CellSearch and RareCyte enumeration, CTCs were detected in 12 by CellSearch (63%; median: 3/7.5 mL; range 1-343/ 7.5 mL) and in 14 by RareCyte (74%; median: 1/7.5 mL; range 1-363/7.5 mL). Using matched bcDNA as a gold standard, sensitivity and positive predictive value of germline SNP detection were 74% and 93% for scDNA, and 98% and 99% for cfDNA. A total of 48 and 18 somatic alterations were identified across the cohort in CTCs and cfDNA, respectively, including mutations in AR, TP53, CCND3, FGFR1, ALK, and ROS1. Of 14 patients whose single CTCs were recovered by RareCyte, 12 (86%) had detectable somatic mutations in scDNA, and 7 (50%) had detectable somatic mutations in matched cfDNA. While some mutations were concordant between matched scDNA and cfDNA, most were distinct. Radiomic entropy in CT scans was associated with CellSearch CTC-counts (AUC of 0.74) using the FDA-cleared prognostic threshold of <5 vs. ≥5 CTCs/7.5ml. Conclusion: We successfully piloted an integrated, multi-parametric analysis of matched single CTCs, WBCs, plasma cfDNA, and buffy coats, along with matched radiomic analysis of CT scans in patients with mCRPC. Germline variants were detected more readily in cfDNA, whereas somatic mutations in commonly altered genes were detected more frequently in CTCs. CellSearch CTC count, an FDA cleared prognostic biomarker, correlated with radiomic entropy, the first such association to our knowledge between liquid biopsy and radiographic readouts. The results demonstrate the feasibility of this approach and its potential to generate comprehensive molecular data and new biomarker profiles. Prospective validation in a large PC cohort is underway. Citation Format: Gareth J. Morrison, Jonathan Buckley, D. Gigi Ostrow, Bino Varghese, Nolan Ericson, Steven Cen, Alexander Cunha, Yi-Tsung Lu, Tad George, Jeffrey Smith, David Quinn, Vinay Duddalwar, Timothy Triche, Amir Goldkorn. Non-invasive profiling of advanced prostate cancer via multi-parametric liquid biopsy and radiomics [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5379.

Utility of Hematology Histograms

Background: Over the past five decades, hematology analyzers have evolved from semi-automated to fully automated ones. A histogram is a graphic representationof a collection of data based on cell size and/or cell number depicting variations in the process. Objective: To study hematology histograms in relation with major blood cell components and in cases of acute febrile illness. Methods: It was a cross sectional observational study conducted over 200 consecutively collected samples from adults. Histograms were obtained from 3-part automated hematology analyzer, evaluated in relation to the RBCs, WBCs and platelets with their peripheral blood smear (PBS) picture and with the cases with diagnosis of acute febrile illness. Results: In the present study, maximum number of samples were from 15-25 years of age group with a male preponderance. Out of 200 histograms, 102 had combined abnormality of the all 3 blood cell types in varied combinations. Neutrophilia, microcytosis and thrombocytopenia were the most common findings in the respective individual blood cell histograms. Maximum sensitivity of WBC histogram was for neutrophilia (81.08%), that of RBC histogram was for microcytosis (81.3%) while that of platelet histogram was for thrombocytopenia (97.5%). In cases of acute febrile illness, dengue was the most common diagnosis with thrombocytopenia being most common histogram finding. Conclusion: Histograms provided by the automated hematology analyzers are of great diagnostic and morphologic importance. Histograms obtained from automated analyzers should be complementary to hematological parameters and peripheral blood smear examination.