Nanoparticle Induced Cell Magneto-Rotation for the Multiplexed Monitoring of Morphology, Stress and Drug Sensitivity of Suspended Single Cancer Cells.

The metastatic process of a cancer relies on the transformation of some of the primary tumor cells into cells capable of migrating through the Extra-Cellular Matrix (ECM), surrounding the tumor, into the bloodstream and the lymph nodes, and then settle in distant tissue, growing new secondary tumors. By identifying, characterizing and quantifying these cells, the progression of cancer in a patient during therapy can be more accurately assessed. Here we describe the development of a new method for quantitative real time monitoring of cell size and morphology, on single live suspended cancer cells, unconfined in three dimensions. The enabling cell magnetorotation (CM) method is made possible by nanoparticle induced cell magnetization. Using a rotating magnetic field, the magnetically labeled cells are actively rotated, then imaged, using a high definition CCD camera. Under proper conditions, the rotation period of a magnetic object is proportional to its shape factor. We demonstrate first that the rotational period, when measured in real-time, can serve to track cellular response to drugs, cytotoxic agents and other chemical stimuli. In addition, while cells are rotated, they exhibit very specific morphological activities, even without a chemical stimulus. Described also is how to multiplex the CM method, to image several dozens to several thousands of cells simultaneously, and using morphology to classify cells into different phenotypic categories, with each phenotype being correlated with malignancy level. The intrinsic tumor heterogeneity, at the cellular level, can be visualized with relationship graphs. Shown is the ability to monitor cell morphological changes over long periods of time, in real time, in order to detect the metastatic potential for heterogeneous populations of cancer cells, using tools from statistical analysis methods. The method relies on unsupervised Machine Learning algorithms which do not require human inputs. Overall it is demonstrated that the CM method can be used as a diagnostic tool to evaluate the phenotypical heterogeneity in a cell population in general, and in a cancer cell population in particular. This fast and high throughput method promises to efficiently assess the efficacy of personalized therapeutic strategies.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111434/1/relbez_1.pd

Toward precision medicine with nanopore technology

Currently, when patients are diagnosed with cancer, they often receive a treatment based on the type and stage of the tumor. However, different patients may respond to the same treatment differently, due to the variation in their genomic alteration profile. Thus, it is essential to understand the effect of genomic alterations on cancer drug efficiency and engineer devices to monitor these changes for therapeutic response prediction. Nanopore-based detection technology features devices containing a nanometer-scale pore embedded in a thin membrane that can be utilized for DNA sequencing, biosensing, and detection of biological or chemical modifications on single molecules. Overall, this project aims to evaluate the capability of the biological nanopore, alpha-hemolysin, as a biosensor for genetic and epigenetic biomarkers of cancer. Specifically, we utilized the nanopore to (1) study the effect of point mutations on C-kit1 G-quadruplex formation and its response to CX-5461 cancer drug; (2) evaluate the nanopore\u27s ability to detect cytosine methylation in label-dependent and label-independent manners; and (3) detect circulating-tumor DNA collected from lung cancer patients\u27 plasma for disease detection and treatment response monitoring. Compared to conventional techniques, nanopore assays offer increased flexibility and much shorter processing time

Predictive Modelling Approach to Data-Driven Computational Preventive Medicine

This thesis contributes novel predictive modelling approaches to data-driven computational preventive medicine and offers an alternative framework to statistical analysis in preventive medicine research. In the early parts of this research, this thesis presents research by proposing a synergy of machine learning methods for detecting patterns and developing inexpensive predictive models from healthcare data to classify the potential occurrence of adverse health events. In particular, the data-driven methodology is founded upon a heuristic-systematic assessment of several machine-learning methods, data preprocessing techniques, models’ training estimation and optimisation, and performance evaluation, yielding a novel computational data-driven framework, Octopus. Midway through this research, this thesis advances research in preventive medicine and data mining by proposing several new extensions in data preparation and preprocessing. It offers new recommendations for data quality assessment checks, a novel multimethod imputation (MMI) process for missing data mitigation, a novel imbalanced resampling approach, and minority pattern reconstruction (MPR) led by information theory. This thesis also extends the area of model performance evaluation with a novel classification performance ranking metric called XDistance. In particular, the experimental results show that building predictive models with the methods guided by our new framework (Octopus) yields domain experts' approval of the new reliable models’ performance. Also, performing the data quality checks and applying the MMI process led healthcare practitioners to outweigh predictive reliability over interpretability. The application of MPR and its hybrid resampling strategies led to better performances in line with experts' success criteria than the traditional imbalanced data resampling techniques. Finally, the use of the XDistance performance ranking metric was found to be more effective in ranking several classifiers' performances while offering an indication of class bias, unlike existing performance metrics The overall contributions of this thesis can be summarised as follow. First, several data mining techniques were thoroughly assessed to formulate the new Octopus framework to produce new reliable classifiers. In addition, we offer a further understanding of the impact of newly engineered features, the physical activity index (PAI) and biological effective dose (BED). Second, the newly developed methods within the new framework. Finally, the newly accepted developed predictive models help detect adverse health events, namely, visceral fat-associated diseases and advanced breast cancer radiotherapy toxicity side effects. These contributions could be used to guide future theories, experiments and healthcare interventions in preventive medicine and data mining

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

Combining computer simulations and deep learning to understand and predict protein structural dynamics

Molecular dynamics simulations provide a means to characterize the ensemble of structures that a protein adopts in solution. These structural ensembles provide crucial information about how proteins function, and these ensembles also reveal potential drug binding sites that are not observable from static protein structures (i.e. cryptic pockets). However, analyzing these high- dimensional datasets to understand protein function remains challenging. Additionally, finding cryptic pockets using simulation data is slow and expensive, which makes the appeal of computationally screening for cryptic pockets limited to a narrow set of circumstances. In this thesis, I develop deep learning based methods to overcome these challenges. First, I develop a deep learning algorithm, called DiffNets, to deal with the high-dimensionality of structural ensembles. DiffNets takes structural ensembles from similar systems with different biochemical properties and learns to highlight structural features that distinguish the systems, ultimately connecting structural signatures to their associated biochemical properties. Using DiffNets, I provide structural insights that explain how naturally occurring genetic variants of the oxytocin receptor alter signaling. Additionally, DiffNets help reveal how a SARS-CoV-2 protein involved in immune evasion becomes activated. Next, I use MD simulations to hunt for cryptic pockets across the SARS-CoV-2 proteome, which led to the discovery of more than 50 new potential druggable sites. Because this effort required an extraordinary amount of resources, I developed a deep learning approach to predict sites of cryptic pockets from single protein structures. This approach reduces the time to identify if a protein has a cryptic pocket by ~10,000-fold compared to the next best method

Protein sensing using solid-state nanopore

Cytokines are small-molecule signaling proteins involved in cell-cell regulation. The detection of low-abundance cytokines is challenging since the currently available techniques are limited by sensitivity and are time-consuming. Nanopore sensing is an emerging technique in nanotechnology that is catalyzing key breakthroughs in many areas, including the analysis and study of proteins at the single-molecule level. Solid-state nanopore sensing has the advantage of analyzing small copy numbers of biomolecules, such as DNA, with high throughput. However, protein detection using nanopores is still in in infancy because the mechanisms of native protein translocation inside the solid-state nanopore are highly complicated. The goal of this project is to develop a novel solid-state nanopore device for identification and quantification of cancer cytokines directly from cell culture. Vascular endothelial growth factor (VEGF) is chosen as a model cytokine due to its high abundance in cancerous tissue, and its well-characterized molecular structure. Firstly, we used a nanopore sensor to monitor individual VEGF proteins in solution while simultaneously obtaining tertiary and quaternary structural information. Next, we used the translocation signature to identify VEGF secreted directly from the culture media of the breast cancer cell line. A series of DNA and RNA aptamers was screened to selectively bind to secreted VEGF, enhancing the detection rate and creating a unique translocation signature for easy protein discrimination. Finally, we integrated the nanopore with a hard microfluidic device designed to facilitate the on-chip sample preparation prior to nanopore sensing. This nanopore-microfluidic device may allow scientists and clinicians to directly detect biomarkers secreted from a small population of cultured cells, which would revolutionize cancer diagnostics and prognostics.2020-10-22T00:00:00

MSI-based mapping strategies in tumour-heterogeneity

Since the early 2000s, considerable innovations in MS technology and associated gene sequencing systems have enabled the "-omics" revolution. The data collected from multiple omics research can be combined to gain a better understanding of cancer's biological activity. Breast and ovarian cancer are among the most common cancers worldwide in women. Despite significant advances in diagnosis, treatment, and subtype identification, breast cancer remains the world's second leading cause of cancer-related deaths in women, with ovarian cancer ranking fifth. Tumour heterogeneity is a significant hurdle in cancer patient prognosis, response to therapy, and metastasis. As such, heterogeneity is one of the most significant and clinically relevant areas of cancer research nowadays. Metabolic reprogramming is a hallmark of malignancy that has been widely acknowledged in recent literature. Metabolic heterogeneity in tumours poses a challenge in developing therapies that exploit metabolic vulnerabilities. Consequently, it is crucial to approach tumour heterogeneity with an unlabeled yet spatially specific read-out of metabolic and genetic information. The advantage of DESI-MSI technology originates from its untargeted nature, which allows for the investigation of thousands of component distributions, at a micrometre scale, in a single experiment. Most notably, using a DESI-MSI clustering approach could potentially offer novel insights into metabolism, providing a method to characterise metabolically distinct sub-regions and subsequently delineate the underlying genetic drivers through genomic analyses. Hence, in this study, we aim to map the inter-and intra-tumour metabolic heterogeneity in breast and ovarian cancer by integrating multimodal MSI-based mapping strategies, comprising DESI and MALDI, with IMC (Imaging Mass Cytometry) analysis of the tumour section, using CyTOF, and high- throughput genetic characterisation of metabolically-distinct regions by transcriptomics. The multimodal analysis workflow was initially performed using sequential breast cancer Patient-Derived Xenografts (PDX) models and was expanded on primary tumour sections. Moreover, a newly developed DESI-MSI friendly, hydroxypropyl-methylcellulose and polyvinylpyrrolidone (HPMC/PVP) hydrogel-based embedding was successfully established to allow simultaneous preparation and analysis of numerous fresh frozen core-size biopsies in the same Tissue Microarray (TMA) block for the investigation of tumour heterogeneity. Additionally, a single section strategy was combined with DESI-MSI coupled to Laser Capture Microdissection (LCM) application to integrate gene expression analysis and Liquid Chromatography-Mass Spectrometry (LC-MS) on the same tissue segment. The developed single section methodology was then tested with multi-region collected ovarian tumours. DESI-MSI-guided spatial transcriptomics was performed for co-registration of different omics datasets on the same regions of interest (ROIs). This co-registration of various omics could unravel possible interactions between distinct metabolic profiles and specific genetic drivers that can lead to intra-tumour heterogeneity. Linking all these findings from MSI-based or guided various strategies allows for a transition from a qualitative approach to a conceptual understanding of the architecture of multiple molecular networks responsible for cellular metabolism in tumour heterogeneity.Open Acces

Enhancing the forensic comparison process of common trace materials through the development of practical and systematic methods

An ongoing advancement in forensic trace evidence has driven the development of new and objective methods for comparing various materials. While many standard guides have been published for use in trace laboratories, different areas require a more comprehensive understanding of error rates and an urgent need for harmonizing methods of examination and interpretation. Two critical areas are the forensic examination of physical fits and the comparison of spectral data, which depend highly on the examiner’s judgment. The long-term goal of this study is to advance and modernize the comparative process of physical fit examinations and spectral interpretation. This goal is fulfilled through several avenues: 1) improvement of quantitative-based methods for various trace materials, 2) scrutiny of the methods through interlaboratory exercises, and 3) addressing fundamental aspects of the discipline using large experimental datasets, computational algorithms, and statistical analysis. A substantial new body of knowledge has been established by analyzing population sets of nearly 4,000 items representative of casework evidence. First, this research identifies material-specific relevant features for duct tapes and automotive polymers. Then, this study develops reporting templates to facilitate thorough and systematic documentation of an analyst’s decision-making process and minimize risks of bias. It also establishes criteria for utilizing a quantitative edge similarity score (ESS) for tapes and automotive polymers that yield relatively high accuracy (85% to 100%) and, notably, no false positives. Finally, the practicality and performance of the ESS method for duct tape physical fits are evaluated by forensic practitioners through two interlaboratory exercises. Across these studies, accuracy using the ESS method ranges between 95-99%, and again no false positives are reported. The practitioners’ feedback demonstrates the method’s potential to assist in training and improve peer verifications. This research also develops and trains computational algorithms to support analysts making decisions on sample comparisons. The automated algorithms in this research show the potential to provide objective and probabilistic support for determining a physical fit and demonstrate comparative accuracy to the analyst. Furthermore, additional models are developed to extract feature edge information from the systematic comparison templates of tapes and textiles to provide insight into the relative importance of each comparison feature. A decision tree model is developed to assist physical fit examinations of duct tapes and textiles and demonstrates comparative performance to the trained analysts. The computational tools also evaluate the suitability of partial sample comparisons that simulate situations where portions of the item are lost or damaged. Finally, an objective approach to interpreting complex spectral data is presented. A comparison metric consisting of spectral angle contrast ratios (SCAR) is used as a model to assess more than 94 different-source and 20 same-source electrical tape backings. The SCAR metric results in a discrimination power of 96% and demonstrates the capacity to capture information on the variability between different-source samples and the variability within same-source samples. Application of the random-forest model allows for the automatic detection of primary differences between samples. The developed threshold could assist analysts with making decisions on the spectral comparison of chemically similar samples. This research provides the forensic science community with novel approaches to comparing materials commonly seen in forensic laboratories. The outcomes of this study are anticipated to offer forensic practitioners new and accessible tools for incorporation into current workflows to facilitate systematic and objective analysis and interpretation of forensic materials and support analysts’ opinions