Typical analytical methods for evaluating the protein and gene expression of cells rely on the molecular analysis of analytes from a large number of cells. These methods, however, do not reveal insight into the heterogeneous nature of cells and instead only provide average results of the bulk population. Moreover, these methods are insensitive to low copy number proteins or mRNA that may in fact play a significant role in a cell's phenotypic state. The ability to discern the true distributions of protein and gene expression in a cell population will give rise to the effective identification of unique subpopulations. This allows for a deeper understanding of disease progression, molecular pathways, and cellular differentiation by revealing previously undetectable states of cells. Therefore, it is essential that technologies capable of isolating and comprehensively analyzing many single cells in parallel be developed. In this dissertation, I present new methods and technologies for integrating the capture and isolation of single cells with comprehensive, quantitative on-chip molecular analysis within a single microfluidic platform. I first present the design, development, and function of polymethyldisiloxane (PDMS) microfluidic devices capable of trapping and fully isolating many single cells along a series of compartments. Additionally, I discuss various methods for trapping (dielectrophoretic or hydrodynamic) and lysing (electric-field or chemical) of the cells directly in individual, nanoliter-sized compartments. Subsequently, I present methods for the fabrication, assembly, and application of high-density antibody- conjugated microbead arrays for single-cell proteome analysis. I then discuss how such arrays can be integrated directly into the PDMS devices using a protective patterning scheme to overcome current challenges in producing patterned, modified surfaces in PDMS devices. I also present methods we have developed which could enable analysis of single-cell gene expression by directly sequencing the mRNA transcripts captured from each single cell on the device. Finally, I present my work on electric field-directed assembly of enzyme-nanoparticle layers for biosensor applications. Together, these methods and devices constitute key advancements towards the development of a technology capable of fully integrating single-cell capture and comprehensive molecular analysis

Insulin resistance is the defining feature of the metabolic syndrome and the primary defect leading to type 2 diabetes. Insulin resistance is a pathological state in which the normal physiological function of insulin is impaired in target tissues including skeletal muscle, adipose tissue, and liver. Thiazolidinediones, synthetic ligands of the PPAR[gamma] nuclear receptor, are clinically potent insulin sensitizing drugs; however, not all individuals respond to treatment. Although PPAR[gamma] -mediated gene regulation is the predominant mode of thiazolidinedione-enhanced insulin sensitivity, the exact mechanisms by which PPARgamma activation leads to insulin sensitization or by which insulin sensitization is prevented are poorly understood. Addressing these expansive but fundamental questions concerning multi- tissue insulin resistance and PPAR[gamma] ligand-mediated insulin sensitization necessitates a comprehensive system- wide approach. The advent and maturation of the gene expression microarray allows for high-throughput transcript measurements. The massively parallel nature of microarrays, though, requires robust statistical analysis in order to obtain sensitive, accurate, and reliable results. With these tools, we characterize human subjects whom range in insulin sensitivity and relate their clinical phenotypes with functional pathway alterations. Multi-tissue gene expression profiles are further interrogated for distinctions between thiazolidinedione treatment responder and non-responder subjects. We further characterize the insulin sensitivity and multi-tissue gene expression profiles of lean and insulin resistant, obese Zucker rats treated with one of four PPAR[gamma] ligands, in order to identify functional pathways which are necessary for effective insulin sensitization.A robust statistical approach is formulated, through which sensitivity analysis of leading microarray statistical testers on various controlled datasets demonstrates that a global variance modeling methodology is advantageous towards increased sensitivity for the detection of true differential expression. With this devised approach, we identify pathway alterations in multiple tissues which are characteristic of insulin resistance, TZD-induced insulin sensitization, and potential TZD responsiveness. We also identify transcriptional biomarkers of insulin resistance and response signatures of PPAR[gamma] ligand treatment. These new insights suggest new targets for the therapy of type 2 diabetes, and measures to decrease risk for existing treatments

Type 2 diabetes mellitus is a highly prevalent human disease often preceded by a period of hyperinsulinemia and insulin resistance. In both pathologic states, tissues involved in energy storage and metabolism demonstrate impaired physiological responses to insulin. Several cell and animal models have been developed to study the action of insulin and its effects in key metabolic tissues. In order to identify commonalities and differences between these model systems and relate these back to human disease, we have undertaken a unique functional genomics approach. Gene expression microarrays allow the simultaneous measurement of thousands of transcripts. To demonstrate normal physiology in a variety of systems, we apply expression profiling to characterize the effects of insulin in the tissues of C57/BL6 mice, lean Zucker rats, and lean human subjects. We further compare these results with previously published data in 3T3-L1 adipocytes. Comparative analysis across several organisms provides novel insights into the transcriptional responses of three key tissues: skeletal muscle, adipose, and liver. In addition, we apply a similar approach to study models of insulin resistance. In 3T3-L1 adipocytes, we revisit TNF- alpha-induced resistance and compare this with several whole-organism models. In order to interpret the volumes of data collected from these experiments, however, we need a reliable and efficient tool to effectively manage this data. Interpretation of high-throughput expression data is a bioinformatics problem requiring careful integration of statistics, mathematical modeling, computational infrastructure, and biology. As gene expression and promoter microarrays become increasingly prevalent, there is a greater need for precise and accessible analytical tools. These tools not only need to encapsulate mathematically robust methods, but also be simple enough so that statistical details can be sufficiently separated from biological interpretation. Utilizing a Bayesian framework, we have developed such an approach, dubbed VAMPIRE, and implemented it as a web-based, database- driven application. The tools presented here represent a unique solution to the interpretation of gene expression data. These advances have subsequently allowed us to piece together a comprehensive picture of the physiological responses to prolonged hyperinsulinemia, and to suggest novel targets for further study of type 2 diabetes

Transcription factors determine cell fate during differentiation and are capable of reprogramming distinct cell types. They are regulated by multiple mechanisms, many of which control their stability and their ability to bind to DNA. Although a variety of approaches allow for the detection of the changing landscape of transcription factors during differentiation, the majority of these screening approaches, eg. microarray, RNA sequencing, quantitative PCR, western blot, do not address whether the detected transcription factors are stable or even capable of binding to DNA. Moreover, antibodies are not available or are insufficient at characterizing thousands of different transcription factors. New advances have been made in nucleotide detection and these methods include end -labeled fluorescent dyes. Using this technology, we proposed an alternative electrophoretic mobility shift assay (EMSA) protocol using the Odessey® imaging system to supplement the shortcomings of the previously mentioned techniques. By using EMSA oligonucleotides with end- labeled fluorescent tags that can be visualized at a wavelength of 700nm with the Odessey® imaging system, results can be observed within three hours. This protocol provides a potentially scalable platform to screen for novel transcription factor binding profiles including those that function in embryonic stem cells or other cell lineages

A numerical simulator for modeling heterogeneous catalysis is developed to study the transient response of gas-solid kinetics in porous catalysts under composition modulation. A lumped system and a one-dimensional model are built in this numerical simulator. The numerical simulator, Transcat, is a general solver, which can handle unlimited components, arbitrary kinetics and various type of boundary conditions. An original algorithm of element constraint method is developed and also successfully integrated in Transcat to ensure the conservation of total mass and element in a numerical computation. With multiple numerical methods and the theory of stiffness-reduced modeling, Transcat can compute stiff problems efficiently. This general solver is validated by case studies of partial-equilibrium assumption, steady-state profiles and analytical solution of Fisher-Kolmogorov equation. Carbon monoxide oxidation over Pt/Al₂O₃ catalyst under periodic forcing is studied by numerical simulation using Transcat. Transcat is capable of fitting the experiment data and explain the behavior of CO oxidation under transient operation conditions. Frequency optimization of various input signal to maximize the turnover rate of CO is performed in a series of numerical experiments in Transcat. At optimum frequency, the characteristic spatiotemporal pattern of turnover rate is continuous, symmetric and boundary-attached, and the saturated storage of CO over the catalytic site is at minimum