Technology for Single Cell Protein Analysis in Immunology and Cancer Prognostics

The first chapter of this thesis deals with automating data gathering for single cell microfluidic tests. The programs developed saved significant amounts of time with no loss in accuracy. The technology from this chapter was applied to experiments in both Chapters 4 and 5.

The second chapter describes the use of statistical learning to prognose if an anti-angiogenic drug (Bevacizumab) would successfully treat a glioblastoma multiforme tumor. This was conducted by first measuring protein levels from 92 blood samples using the DNA-encoded antibody library platform. This allowed the measure of 35 different proteins per sample, with comparable sensitivity to ELISA. Two statistical learning models were developed in order to predict whether the treatment would succeed. The first, logistic regression, predicted with 85% accuracy and an AUC of 0.901 using a five protein panel. These five proteins were statistically significant predictors and gave insight into the mechanism behind anti-angiogenic success/failure. The second model, an ensemble model of logistic regression, kNN, and random forest, predicted with a slightly higher accuracy of 87%.

The third chapter details the development of a photocleavable conjugate that multiplexed cell surface detection in microfluidic devices. The method successfully detected streptavidin on coated beads with 92% positive predictive rate. Furthermore, chambers with 0, 1, 2, and 3+ beads were statistically distinguishable. The method was then used to detect CD3 on Jurkat T cells, yielding a positive predictive rate of 49% and false positive rate of 0%.

The fourth chapter talks about the use of measuring T cell polyfunctionality in order to predict whether a patient will succeed an adoptive T cells transfer therapy. In 15 patients, we measured 10 proteins from individual T cells (~300 cells per patient). The polyfunctional strength index was calculated, which was then correlated with the patient's progress free survival (PFS) time. 52 other parameters measured in the single cell test were correlated with the PFS. No statistical correlator has been determined, however, and more data is necessary to reach a conclusion.

Finally, the fifth chapter talks about the interactions between T cells and how that affects their protein secretion. It was observed that T cells in direct contact selectively enhance their protein secretion, in some cases by over 5 fold. This occurred for Granzyme B, Perforin, CCL4, TNFa, and IFNg. IL- 10 was shown to decrease slightly upon contact. This phenomenon held true for T cells from all patients tested (n=8). Using single cell data, the theoretical protein secretion frequency was calculated for two cells and then compared to the observed rate of secretion for both two cells not in contact, and two cells in contact. In over 90% of cases, the theoretical protein secretion rate matched that of two cells not in contact.

The Multiscale Systems Immunology project: software for cell-based immunological simulation.

BACKGROUND: Computer simulations are of increasing importance in modeling biological phenomena. Their purpose is to predict behavior and guide future experiments. The aim of this project is to model the early immune response to vaccination by an agent based immune response simulation that incorporates realistic biophysics and intracellular dynamics, and which is sufficiently flexible to accurately model the multi-scale nature and complexity of the immune system, while maintaining the high performance critical to scientific computing. RESULTS: The Multiscale Systems Immunology (MSI) simulation framework is an object-oriented, modular simulation framework written in C++ and Python. The software implements a modular design that allows for flexible configuration of components and initialization of parameters, thus allowing simulations to be run that model processes occurring over different temporal and spatial scales. CONCLUSION: MSI addresses the need for a flexible and high-performing agent based model of the immune system.

Peanut allergy and allergic airways inflammation.

Asthma is a major risk cofactor for anaphylactic deaths in children with peanut allergy. Peanut allergy is generally thought to be a lifelong condition, but some children outgrow their coexistent asthma. It has recently been shown that children who have ‘outgrown’ their asthma symptoms may have ongoing eosinophilic airways inflammation. The need for regular inhaled corticosteroid treatment in peanut allergic children and adolescents who have outgrown their asthma is however unclear. The aims of our study were to look at fractional exhaled nitric oxide levels (FeNO), as a non-invasive marker of eosinophilic airways inflammation, in peanut allergic children and assess whether children with outgrown asthma had elevated levels. Children with peanut allergy were recruited at two pediatric allergy clinics in Belfast, UK. Exhaled nitric oxide levels (FeNO) were measured using the Niox Mino in all children. Of the 101 peanut allergic children who consented for enrolment in the study, 94 were successfully able to use the NIOX Mino. Age range was 4–15 yr (median 10 yr); 61% were boys. Thirty (32%) had never wheezed, 37 (39%) had current treated asthma, 20 (21%) had at least 1 wheezing episode within the last year but were not taking any regular asthma medication (wheeze no treatment), and 7 (7%) had outgrown asthma. All children with outgrown asthma had elevated levels of FeNO (>35 ppb), and 75% of children defined as ‘wheeze no treatment’ had elevated FeNO levels (>35 ppb). Outgrown asthma and children defined as ‘wheeze no treatment’ had higher levels of FeNO than those with no history of wheeze or current treated asthma (p = 0.003). In children with peanut allergy, we found that those who had outgrown asthma had elevated FeNO levels in keeping with ongoing eosinophilic airways inflammation.

Integrating nutrition and immunology: A new frontier

Nutrition is critical to immune defence and parasite resistance, which not only affects individual organisms, but also has profound ecological and evolutionary consequences. Nutrition and immunity are complex traits that interact via multiple direct and indirect pathways, including the direct effects of nutrition on host immunity but also indirect effects mediated by the host's microbiota and pathogen populations. The challenge remains, however, to capture the complexity of the network of interactions that defines nutritional immunology. The aim of this paper is to discuss the recent findings in nutritional research in the context of immunological studies. By taking examples from the entomological literature, we argue that insects provide a powerful tool for examining the network of interactions between nutrition and immunity due to their tractability, short lifespan and ethical considerations. We describe the relationships between dietary composition, immunity, disease and microbiota in insects, and highlight the importance of adopting an integrative and multi-dimensional approach to nutritional immunology. 

Immunology taught by lung dendritic cells.

Dendritic cells (DCs) are leukocytes specialised in the uptake, processing, and presentation of antigen and fundamental in regulating both innate and adaptive immune functions. They are mainly localised at the interface between body surfaces and the environment, continuously scrutinising incoming antigen for the potential threat it may represent to the organism. In the respiratory tract, DCs constitute a tightly enmeshed network, with the most prominent populations localised in the epithelium of the conducting airways and lung parenchyma. Their unique localisation enables them to continuously assess inhaled antigen, either inducing tolerance to inoffensive substances, or initiating immunity against a potentially harmful pathogen. This immunological homeostasis requires stringent control mechanisms to protect the vital and fragile gaseous exchange barrier from unrestrained and damaging inflammation, or an exaggerated immune response to an innocuous allergen, such as in allergic asthma. During DC activation, there is upregulation of co-stimulatory molecules and maturation markers, enabling DC to activate naïve T cells. This activation is accompanied by chemokine and cytokine release that not only serves to amplify innate immune response, but also determines the type of effector T cell population generated. An increasing body of recent literature provides evidence that different DC subpopulations, such as myeloid DC (mDC) and plasmacytoid DC (pDC) in the lungs occupy a key position at the crossroads between tolerance and immunity. This review aims to provide the clinician and researcher with a summary of the latest insights into DC-mediated pulmonary immune regulation and its relevance for developing novel therapeutic strategies for various disease conditions such as infection, asthma, COPD, and fibrotic lung disease.

Immunology-based subspace detectors for anomaly detection

A key problem in high dimensional anomaly detection is that the time spent in constructing detectors by the means of generateand-test is tolerable. In fact, due to the high sparsity. of the data, it is ineffective to construct detectors in the whole data space. Previous investigations have shown that most essentIal patterns can be discovered in different subspaces. This inspires us to construct detectors in signIficant subspaces only for anomaly detection. We first use ENCLUS-based method to discover all significant subspaces and .then use a greedy-growth algorithm to construct detectors in each subspace. The elements used to constItute a detector are gods Instead of data points, which makes the time-consumption irrelevant to the size of the nonnal data. We test the effectiveness and efficiency of our method on both synthetic and benchmark datasets. The results reveal that our method is particularly useful in anomaly detection in high dimensional data spaces.

Computational immunology for anomaly detection

The thesis makes a significant contribution to the issue of anomaly detection by introducing a computational immunology approach. Immunity-based anomaly detection in high dimensional space is systematically investigated and the proposed hybrid method (combining data mining techniques and computational immunology) improves both accuracy and efficiency.

Recent advances in immunology to target cancer, inflammation and infections

Immunology is the branch of biomedical sciences to study of the immune system physiology both in healthy and diseased states. Some aspects of autoimmunity draws our attention to the fact that it is not always associated with pathology. For instance, autoimmune reactions are highly useful in clearing off the excess, unwanted or aged tissues from the body. Also, generation of autoimmunity occurs after the exposure to the non-self antigen that is structurally similar to the self, aided by the stimulatory molecules like the cytokines. Thus, a narrow margin differentiates immunity from auto-immunity as already discussed. Hence, finding answers for how the physiologic immunity turns to pathologic autoimmunity always remains a question of intense interest. However, this margin could be cut down only if the physiology of the immune system is better understood. The individual chapters included in this book will cover all the possible aspects of immunology and pathologies associated with it. The authors have taken strenuous effort in elaborating the concepts that are lucid and will be of reader's interest.