Circulating cell clusters aggravate the hemorheological abnormalities in COVID-19

Microthrombi and circulating cell clusters are common microscopic findings in patients with coronavirus disease 2019 (COVID-19) at different stages in the disease course, implying that they may function as the primary drivers in disease progression. Inspired by a recent flow imaging cytometry study of the blood samples from patients with COVID-19, we perform computational simulations to investigate the dynamics of different types of circulating cell clusters, namely white blood cell (WBC) clusters, platelet clusters, and red blood cell clusters, over a range of shear flows and quantify their impact on the viscosity of the blood. Our simulation results indicate that the increased level of fibrinogen in patients with COVID-19 can promote the formation of red blood cell clusters at relatively low shear rates, thereby elevating the blood viscosity, a mechanism that also leads to an increase in viscosity in other blood diseases, such as sickle cell disease and type 2 diabetes mellitus. We further discover that the presence of WBC clusters could also aggravate the abnormalities of local blood rheology. In particular, the extent of elevation of the local blood viscosity is enlarged as the size of the WBC clusters grows. On the other hand, the impact of platelet clusters on the local rheology is found to be negligible, which is likely due to the smaller size of the platelets. The difference in the impact of WBC and platelet clusters on local hemorheology provides a compelling explanation for the clinical finding that the number of WBC clusters is significantly correlated with thrombotic events in COVID-19 whereas platelet clusters are not. Overall, our study demonstrates that our computational models based on dissipative particle dynamics can serve as a powerful tool to conduct quantitative investigation of the mechanism causing the pathological alterations of hemorheology and explore their connections to the clinical manifestations in COVID-19.     

3D surface analysis and classification in neuroimaging segmentation

This work emphasizes new algorithms for 3D edge and corner detection used in surface extraction and new concept of image segmentation in neuroimaging based on multidimensional shape analysis and classification. We propose using of NifTI standard for describing input data which enables interoperability and enhancement of existing computing tools used widely in neuroimaging research. In methods section we present our newly developed algorithm for 3D edge and corner detection, together with the algorithm for estimating local 3D shape. Surface of estimated shape is analyzed and segmented according to kernel shapes.     

Computer-aided 2D and 3D quantification of human stem cell fate from in vitro samples using Volocity high performance image analysis software

Accurate automated cell fate analysis of immunostained human stem cells from 2- and 3-dimensional (2D-3D) images would improve efficiency in the field of stem cell research. Development of an accurate and precise tool that reduces variability and the time needed for human stem cell fate analysis will improve productivity and interpretability of the data across research groups. In this study, we have created protocols for high performance image analysis software Volocity? to classify and quantify cytoplasmic and nuclear cell fate markers from 2D-3D images of human neural stem cells after in vitro differentiation. To enhance 3D image capture efficiency, we optimized the image acquisition settings of an Olympus FV10i? confocal laser scanning microscope to match our quantification protocols and improve cell fate classification. The methods developed in this study will allow for a more time efficient and accurate software based, operator validated, stem cell fate classification and quantification from 2D and 3D images, and yield the highest ≥94.4% correspondence with human recognized objects.     

The kinetics of Theileria parva infection and lymphocyte transformation in vitro

Theileriaparva is an intracellular protozoan parasite that causes a fatal lymphoproliferative disease of cattle known as East Coast Fever. The parasite infects host lymphocytes causing their transformation and uncontrolled proliferation. Infiltration of major organs with parasitized lymphoblasts results in most cases in death within 3 weeks. Although both T and B lymphocytes are susceptible to infection, the majority of cell lines arising from infection of peripheral blood mononuclear cells in vitro are of T cell lineage. To explore the basis of this phenotypic bias we have followed the very early stages of parasite development in vitro at the single cell level. Peripheral blood mononuclear cells were infected and stained for both surface phenotype and intracellular parasite antigen and analysed by flow cytometry. Although the parasite antigen was detected intracellularly as early as 6h p.i., our data indicate that parasite infection does not lead to cell transformation in all instances. Rather, specific cell types appear to undergo selection very early after infection and expansion of particular cell subsets results in survival and growth of only a small proportion of the cells originally parasitized.     

Ultra High Content Image Analysis and Phenotype Profiling of 3D Cultured Micro-Tissues

In many situations, 3D cell cultures mimic the natural organization of tissues more closely than 2D cultures. Conventional methods for phenotyping such 3D cultures use either single or multiple simple parameters based on morphology and fluorescence staining intensity. However, due to their simplicity many details are not taken into account which limits system-level study of phenotype characteristics. Here, we have developed a new image analysis platform to automatically profile 3D cell phenotypes with 598 parameters including morphology, topology, and texture parameters such as wavelet and image moments. As proof of concept, we analyzed mouse breast cancer cells (4T1 cells) in a 384-well plate format following exposure to a diverse set of compounds at different concentrations. The result showed concentration dependent phenotypic trajectories for different biologically active compounds that could be used to classify compounds based on their biological target. To demonstrate the wider applicability of our method, we analyzed the phenotypes of a collection of 44 human breast cancer cell lines cultured in 3D and showed that our method correctly distinguished basal-A, basal-B, luminal and ERBB2+ cell lines in a supervised nearest neighbor classification method.     

White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis

White Blood Cells play an important role in observing the health condition of an individual. The opinion related to blood disease involves the identification and characterization of a patient's blood sample. Recent approaches employ Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and merging of CNN and RNN models to enrich the understanding of image content. From beginning to end, training of big data in medical image analysis has encouraged us to discover prominent features from sample images. A single cell patch extraction from blood sample techniques for blood cell classification has resulted in the good performance rate. However, these approaches are unable to address the issues of multiple cells overlap. To address this problem, the Canonical Correlation Analysis (CCA) method is used in this paper. CCA method views the effects of overlapping nuclei where multiple nuclei patches are extracted, learned and trained at a time. Due to overlapping of blood cell images, the classification time is reduced, the dimension of input images gets compressed and the network converges faster with more accurate weight parameters. Experimental results evaluated using publicly available database show that the proposed CNN and RNN merging model with canonical correlation analysis determines higher accuracy compared to other state-of-the-art blood cell classification techniques.     

Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations—deformable and non-deformable sRBCs—utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R² = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring.     

Characterization and optimization of a simple, repeatable system for the long term in vitro culture of aligned myotubes in 3D

Increased recent research activity in exercise physiology has dramatically improved our understanding of skeletal muscle development and physiology in both health and disease. Advances in bioengineering have enabled the development of biomimetic 3D in vitro models of skeletal muscle which have the potential to further advance our understanding of the fundamental processes that underpin muscle physiology. As the principle structural protein of the extracellular matrix, collagen-based matrices are popular tools for the creation of such 3D models but the custom nature of many reported systems has precluded their more widespread adoption. Here we present a simple, reproducible iteration of an established 3D in vitro model of skeletal muscle, demonstrating both the high levels of reproducibility possible in this system and the improved cellular architecture of such constructs over standard 2D cell culture techniques. We have used primary rat muscle cells to validate this simple model and generate comparable data to conventional established cell culture techniques. We have optimized culture parameters for these cells which should provide a template in this 3D system for using muscle cells derived from other donor species and cell lines.     

Linkage disequilibrium between the juvenile neuronal ceroid lipofuscinosis gene and marker loci on chromosome 16p 12.1.

The neuronal ceroid lipofuscinoses (NCL; Batten disease) are a collection of autosomal recessive disorders characterized by the accumulation of autofluorescent lipopigments in the neurons and other cell types. Clinically, these disorders are characterized by progressive encephalopathy, loss of vision, and seizures. CLN3, the gene responsible for juvenile NCL, has been mapped to a 15-cM region flanked by the marker loci D16S148 and D16S150 on human chromosome 16. CLN2, the gene causing the late-infantile form of NCL (LNCL), is not yet mapped. We have used highly informative dinucleotide repeat markers mapping between D16S148 and D16S150 to refine the localization of CLN3 and to test for linkage to CLN2. We find significant linkage disequilibrium between CLN3 and the dinucleotide repeat marker loci D16S288 (chi 2(7) = 46.5, P < .005), D16S298 (chi 2(6) = 36.6, P < .005), and D16S299 (chi 2(7) = 73.8, P < .005), and also a novel RFLP marker at the D16S272 locus (chi 2(1) = 5.7, P = .02). These markers all map to 16p12.1. The D16S298/D16S299 haplotype "5/4" is highly overrepresented, accounting for 54% of CLN3 chromosomes as compared with 8% of control chromosomes (chi 2 = 117, df = 1, P < .001). Examination of the haplotypes suggests that the CLN3 locus can be narrowed to the region immediately surrounding these markers in 16p12.1. Analysis of D16S299 in our LNCL pedigrees supports our previous finding that CLN3 and CLN2 are different genetic loci. This study also indicates that dinucleotide repeat markers play a valuable role in disequilibrium studies.     

An automated method of differential red blood cell classification with application to the diagnosis of anemia.

A method of automated red cell analysis suitable for the rapid classification of large numbers of red cells from individual blood specimens has been developed, and preliminarily tested on normal bloods and clinically proven cases of anemias and red cell disorders. According to this method digital image processing techniques provide several features relating to shape and internal central pallor configurations of red cells. These features are used with a fully automated decision logic to rapidly provide a quantitative "red cell differential" analysis, a report of the percentage subpopulations of recognized categories of red cells. For each subpopulation, measurements of mean cell area, mean cell hemoglobin content and mean cell hemoglobin density are provided. The nine types of red cell disorders studied with this method were: (a) iron deficiency anemia, (b) the anemia of chronic disease, (c) beta-thalassemia trait, (d) sickle cell anemia, (e) hemoglobin C disease, (f) intravascular hemolysis, (g) hereditary elliptocytosis, (h) hereditary spherocytosis, and (i) megaloblastic anemia due to folic acid deficiency. Preliminary indications are that the red cell differential is useful in distinguishing between these conditions.     

A Novel Atlas-Based Strategy for Understanding Cardiac Dysfunction in Patients with Congenital Heart Disease

Tetralogy of Fallot (TOF) is the most common form of cyanotic congenital heart disease. Infants diagnosed with TOF require surgical interventions to survive into adulthood. However, as a result of postoperative structural malformations and long-term ventricular remodeling, further interventions are often required later in life. To help identify those at risk of disease progression, serial cardiac magnetic resonance (CMR) imaging is used to monitor these patients. However, most of the detailed information on cardiac shape and biomechanics contained in these large four-dimensional (4D) data sets goes unused in clinical practice for lack of efficient and comprehensive quantitative analysis tools. While current global metrics of cardiac size and function, such as indexed ventricular mass and volumes, can identify patients at risk of further complications, they are not adequate to explain the underlying mechanisms causing the postoperative malfunctions, and help cardiologists plan optimal personalized treatments. We are proposing a novel approach that uses 4D ventricular shape models derived from CMR imaging exams to generate statistical atlases of ventricular shape and finite-element models of ventricular biomechanics to identify specific features of cardiac shape and biomechanical properties that explain variations in ventricular function. This study has the potential to discover novel biomarkers that precede adverse ventricular remodeling and dysfunction.     

From spatial-data to 3D models of the developing human brain

Visualisation and interpretation of gene expression data have been crucial to advances in our understanding of mechanisms underlying early brain development. As most developmental processes involve complex changes in size, shape and structure, spatial-data can most readily provide information at multiple levels (cell type, cell location in relation to tissue organisation or body axes, etc.), that can be related to these complex changes. Although three-dimensional (3D) spatial-data are ideal, the restricted availability of suitable tissues makes it difficult to generate these for genes expressed at early human fetal stages. Mapping gene expression data to representative 3D models facilitates combinatorial analysis of multiple expression patterns but does not overcome the problems of sparsely sampled data in time and space. Here we describe software that allows 3D domains to be reconstructed by interpolating between sparse 2D gene expression patterns that have been mapped to 3D representative models of corresponding human developmental stages. A set of procedures are proposed to infer expression domains in these gaps. The procedures, which are connected in a serial way, include components clustering, components tracking, shape matching and points interpolation. Each procedure consists of a graphical user interface and a set of algorithms. Results on exemplar gene data are provided.     

Gene Features Selection for Three-Class Disease Classification via Multiple Orthogonal Partial Least Square Discriminant Analysis and S-Plot Using Microarray Data

Motivation

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes.

Results

Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub’s leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.     

Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields

Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.     

Opposite regulation of vitamin D receptor by ATRA in AML cells susceptible and resistant to vitamin D-induced differentiation

Some leukemic cell lines can be driven to differentiate to monocyte-like cells by 1,25-dihydroxyvitamin D(3) (1,25D) and to granulocyte-like cells by all-trans retinoic acid (ATRA). Acute myloid leukemias (AMLs) are heterogeneous blood malignancies characterized by a block at various stages of hematopoietic differentiation and there are more than 200 known chromosome translocations and mutations in leukemic cells of patients diagnosed with AML. Because of the multiplicity in the genetic lesions causing the disease, AMLs are particularly difficult to treat successfully. In particular, various AML cells to a variable degree respond to 1,25D-based differentiation and only one type of AML undergoes successfully ATRA-based differentiation therapy. In this paper we describe that AML cell line KG-1 is resistant to 1,25D-induced monocytic differentiation, while sensitive to ATRA-induced granulocytic differentiation. We show that KG-1 cells have very low level of VDR protein and that expression of VDR mRNA is upregulated by ATRA. We show for the first time that this regulation is cell context-specific, because in another AML cell line, HL60, VDR mRNA is downregulated by ATRA. ATRA-induced VDR protein in cytosol of KG-1 cells can be further activated by 1,25D to induce monocytic differentiation of these cells.