O diabetes mellitus tipo 1 (DM1) tem etiologia autoimune, enquanto o diabetes mellitus tipo 2 (DM2) e o diabetes mellitus gestacional (DMG) são considerados como distúrbios metabólicos. Neste trabalho, foi realizada análise do transcriptoma das células mononucleares do sangue periférico (do inglês, peripheral mononuclear blood cells - PBMCs), obtidas de pacientes com DM1, DM2 e DMG, realizando análises por module maps a fim de comparar características patogênicas e aspectos gerais do tratamento com anotações disponíveis de genes modulados, tais como: a) análises disponíveis a partir de estudos de associação em larga escala (do inglês genome-wide association studies GWAS); b) genes associados ao diabetes em estudos clássicos de ligação disponíveis em bancos de dados públicos; c) perfis de expressão de células imunológicas fornecidos pelo grupo ImmGen (Immunological Project). Foram feitos microarrays do transcriptoma total da plataforma Agilent (Whole genome onecolor Agilent 4x44k) para 56 pacientes (19 DM1, 20 DM2 e 17 DMG). Para a compreensão dos resultados foram aplicados filtros não-informativos e as listas de genes diferencialmente expressos foram obtidas por análise de partição e análise estatística não-paramétrica (rank products), respectivamente. Posteriormente, análises de enriquecimento funcional foram feitas pelo DAVID e os module maps construídos usando a ferramenta Genomica. As análises funcionais contribuíram para discriminar os pacientes a partir de genes envolvidos na inflamação, em especial DM1 e DMG. Os module maps de genes diferencialmente expressos revelaram: a) genes modulados exibiram perfis de transcrição típicos de macrófagos e células dendríticas, b) genes modulados foram associados com genes previamente descritos como genes de complicação ao diabetes a partir de estudos de ligação e de meta-análises; c) a duração da doença, obesidade, número de gestações, níveis de glicose sérica e uso de medicações, tais como metformina, influenciaram a expressão gênica em pelo menos um tipo de diabetes. Esse é o primeiro estudo de module maps mostrando a influência de padrões epidemiológicos, clínicos, laboratoriais, imunopatogênicos e de tratamento na modulação dos perfis transcricionais em pacientes com os três tipos clássicos de diabetes: DM1, DM2 e DMG. / Type 1 diabetes (T1D) is an autoimmune disease while type 2 (T2D) and gestational diabetes (GDM) are considered as metabolic disturbances. We performed a transcriptome analysis of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients, and we took advantage of the module map approach to compare pathogenic and treatment features of our patient series with available annotation of modulated genes from i) genome-wide association studies; ii) genes provided by diabetes meta-analysis in public databases, iii) immune cell gene expression profiles provided by the ImmGen project. Whole genome one-color Agilent 4x44k microarray was performed for 56 (19 T1D, 20 T2D, 17 GDM) patients. Noninformative filtered and differentially expressed genes were obtained by partitioning and rank product analysis, respectively. Functional analyses were carried out using the DAVID software and module maps were constructed using the Genomica tool. Functional analyses contributed to discriminate patients on the basis of genes involved in inflammation, primarily for T1D and GDM. Module maps of differentially expressed genes revealed that: i) modulated genes exhibited transcription profiles typical of macrophage and dendritic cells, ii) modulated genes were associated with previously reported diabetes complication genes disclosed by association and meta-analysis studies, iii) disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin, influenced gene expression profiles in at least one type of diabetes. This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the modulation of the transcription profiles of T1D, T2D and GDM patients.

O diabetes mellitus tipo 1 (DM1) tem etiologia autoimune, enquanto o diabetes mellitus tipo 2 (DM2) e o diabetes mellitus gestacional (DMG) são considerados como distúrbios metabólicos. Neste trabalho, foi realizada análise do transcriptoma das células mononucleares do sangue periférico (do inglês, peripheral mononuclear blood cells - PBMCs), obtidas de pacientes com DM1, DM2 e DMG, realizando análises por module maps a fim de comparar características patogênicas e aspectos gerais do tratamento com anotações disponíveis de genes modulados, tais como: a) análises disponíveis a partir de estudos de associação em larga escala (do inglês genome-wide association studies GWAS); b) genes associados ao diabetes em estudos clássicos de ligação disponíveis em bancos de dados públicos; c) perfis de expressão de células imunológicas fornecidos pelo grupo ImmGen (Immunological Project). Foram feitos microarrays do transcriptoma total da plataforma Agilent (Whole genome onecolor Agilent 4x44k) para 56 pacientes (19 DM1, 20 DM2 e 17 DMG). Para a compreensão dos resultados foram aplicados filtros não-informativos e as listas de genes diferencialmente expressos foram obtidas por análise de partição e análise estatística não-paramétrica (rank products), respectivamente. Posteriormente, análises de enriquecimento funcional foram feitas pelo DAVID e os module maps construídos usando a ferramenta Genomica. As análises funcionais contribuíram para discriminar os pacientes a partir de genes envolvidos na inflamação, em especial DM1 e DMG. Os module maps de genes diferencialmente expressos revelaram: a) genes modulados exibiram perfis de transcrição típicos de macrófagos e células dendríticas, b) genes modulados foram associados com genes previamente descritos como genes de complicação ao diabetes a partir de estudos de ligação e de meta-análises; c) a duração da doença, obesidade, número de gestações, níveis de glicose sérica e uso de medicações, tais como metformina, influenciaram a expressão gênica em pelo menos um tipo de diabetes. Esse é o primeiro estudo de module maps mostrando a influência de padrões epidemiológicos, clínicos, laboratoriais, imunopatogênicos e de tratamento na modulação dos perfis transcricionais em pacientes com os três tipos clássicos de diabetes: DM1, DM2 e DMG. / Type 1 diabetes (T1D) is an autoimmune disease while type 2 (T2D) and gestational diabetes (GDM) are considered as metabolic disturbances. We performed a transcriptome analysis of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients, and we took advantage of the module map approach to compare pathogenic and treatment features of our patient series with available annotation of modulated genes from i) genome-wide association studies; ii) genes provided by diabetes meta-analysis in public databases, iii) immune cell gene expression profiles provided by the ImmGen project. Whole genome one-color Agilent 4x44k microarray was performed for 56 (19 T1D, 20 T2D, 17 GDM) patients. Noninformative filtered and differentially expressed genes were obtained by partitioning and rank product analysis, respectively. Functional analyses were carried out using the DAVID software and module maps were constructed using the Genomica tool. Functional analyses contributed to discriminate patients on the basis of genes involved in inflammation, primarily for T1D and GDM. Module maps of differentially expressed genes revealed that: i) modulated genes exhibited transcription profiles typical of macrophage and dendritic cells, ii) modulated genes were associated with previously reported diabetes complication genes disclosed by association and meta-analysis studies, iii) disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin, influenced gene expression profiles in at least one type of diabetes. This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the modulation of the transcription profiles of T1D, T2D and GDM patients.

Cette thèse de doctorat s’inscrit dans le développement et l’utilisation de méthodes mathématiques et informatiques qui exploitent les données temporelles d’expression des gènes issues de puces à ADN afin de rationaliser et de modéliser les réseaux de régulation génique. Dans cette optique, nous nous sommes principalement intéressés aux données d’expression des gènes de la drosophile (Drosophila melanogaster) pendant son développement, du stade embryonnaire au stade adulte. Nous avons également étudié des données concernant le développement d’autres eucaryotes supérieurs, la réponse d’une bactérie soumises à différents stress et le cycle cellulaire d’une levure. Ce travail a été réalisé selon trois volets principaux :la détection des stades de développement et des perturbations, les classifications de profils d’expression et la modélisation de réseaux de régulation.<p><p>Premièrement, l’observation des données d’expression utilisées nous a conduits à approfondir l’étude des phénomènes survenant lors des changements de stades de développement de la drosophile. Dans ce but, deux méthodes de détection automatique de ces changements ont été développées et appliquées aux données temporelles disponibles sur le développement d’eucaryotes supérieurs. Elles ont également été appliquées à des données temporelles relatives à des perturbations externes de bactéries. Cette étude à montré qu’une formulation mathématique simple permettait de retrouver les instants expérimentaux où une perturbation ou un changement de stade de développement est observé, à partir uniquement des profils d’expression. Par ailleurs, la réponse à une perturbation externe s’avère non distinguable d’une succession de stades de développement, sur la base des seuls profils temporels d’expression.<p><p>Deuxièmement, en raison des dimensions du problème constitué par les données d’expression de plusieurs milliers de gènes et de l’impossibilité de distinguer le rôle dans la régulation des gènes qui présentent des profils d’expression similaires, il s’est avéré nécessaire de classifier les gènes selon leurs profils d’expression. En nous basant sur les résultats obtenus lors de la détection des stades de développement, la démarche suivie est de regrouper les gènes qui présentent des profils temporels d’expression aux comportements similaires non seulement au cours de la série temporelle complète, mais également dans chacun des stades de développement. Dans cette optique, trois distances ont été proposées et utilisées dans une classification hiérarchique des données d’expression de la drosophile.<p><p>Troisièmement, des structures de modèles linéaires et non linéaires ainsi que des méthodes d’estimation et de réduction paramétriques ont été développées et utilisées pour reproduire les données d’expression du développement de la drosophile. Les résultats de ce travail ont montré qu’avec une structure de modèle linéaire simple, la reproduction des profils expérimentaux était excellente et que, dans ce cas, le réseau de régulation génique de la drosophile pouvait se contenter d’une faible connectivité (en moyenne 3 connexions par classe de gènes) et ce, sans hypothèse a priori. Toutefois, les modèles linéaires ont ensuite sérieusement été remis en question par des analyses de robustesse aux perturbations paramétriques et de stabilité des profils après extrapolation dans le temps. Dès lors, quatre structures de modèles non linéaires et cinq méthodes de réduction paramétrique ont été proposées et utilisées pour concilier les critères de reproduction des données, de robustesse et de stabilité des réseaux identifiés. En outre, ces méthodes de modélisation ont été appliquées à un sous-ensemble de 20 gènes impliqués dans le développement musculaire de la drosophile et pour lesquels 36 interactions ont été validées expérimentalement, ainsi qu’à des profils synthétiques bruités. Nous avons pu constater que plus de la moitié des connexions et non-connexions sont retrouvées par trois modèles non linéaires. Les résultats de cette étude ont permis d’éliminer certaines structures de modèle et méthodes de réduction et ont mis en lumière plusieurs directions futures à suivre dans la démarche de modélisation des réseaux de régulation génique. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

L’approche analytique d’interrogation sur puce par spectrométrie de masse est une techniqueparticulièrement bien adaptée à l’analyse multiplexée requise pour la recherche de biomarqueurs dansle diagnostic moderne. L’objectif a été de contribuer aux développements technologiques etméthodologiques d’une plateforme d’analyse baptisée SUPRA-MS (Imagerie par Résonance desPlasmons de Surface en array combinée à la Spectrométrie de Masse). Des puces d’or compatiblesavec la SPRi et la MS ont été conçues et réalisées à l’aide des techniques de dépôt sous vide. Uneétude originale couplant la SPRi avec l’AFM a permis d’établir une relation entre le signal SPRmesuré et la quantité réelle de protéines fixées sur des puces nanostructurées. Nous avons ensuitedéveloppé une procédure d’immobilisation en format array (16 à λ6 spots) par liaison covalente enmonocouche des anticorps spécifiques, dirigés contre un biomarqueur du cancer du sein (LAG3) pourune analyse multiplexée d’échantillons biologiques. Du plasma humain contenant 300 ng/mL deLAGγ est injecté à la surface de la puce. L’injection est suivie en temps réel par SPRi et conduit à unecapture du biomarqueur à l’échelle de la femtomole par spots. Un traitement collectif des spots parspray pour la digestion in situ des protéines et le dépôt de matrice en vue d’une interrogation MS enMALDI-TOF a été mis au point par l’équipe du Dr Patrick Ducoroy (CLIPP-CHU Dijon). Lesrésultats MS obtenues ont permis 100 % d’identification du biomarqueur. Cette technologie sansmarquages spécifiques est particulièrement bien adaptée à la caractérisation fine des biomarqueurs et àla discrimination de variants protéiques. / The analytic approach of interrogation on chip by mass spectrometry is a suitable technique tomultiplexed analysis required for biomarker research in modern diagnosis. The aim was to contributeto the technological and methodological developments of analysis platform called SUPRA-MS(Surface Plasmon Resonance in Array coupled to Mass Spectrometry) whose goal is to provideadditional data to assay on the target protein by mass spectrometry. At first, gold chips compatiblewith SPRi and MS were designed and fabricated using vacuum deposition techniques. An originalstudy coupling SPRi with AFM has established a relationship between the SPR signal measured andthe real amount of proteins bound to nanostructured chips. We developed an immobilization procedurein array format (spots 16-96) by covalent monolayer of specific antibodies directed against the proteinLAG3, a biomarker of breast cancer for multiplex analysis of human biological samples (plasma).Human plasma containing 300 ng/mL of LAG3 is injected to the chip surface. The injection ismonitored in real time by SPRi and leads to the biomarker capture at the femtomole scale. After thebiosensing step, a collective treatment of spots by spray for in situ protein digestion and matrixdeposition in view of a MS analysis was developed by Dr. Patrick Ducoroy's team (CLIPP-CHUDijon). The MS and MS-MS analysis by MALDI-TOF was developed to analyze all spotsautomatically and determine their peptide mapping leading to 100% of the biomarker identification.This technology does not require the use of specific markers, is suitable to the biomarkerscharacterization and discrimination of protein variants.

Das Hauptanliegen dieser Arbeit war es valide chemische Verfahren für die Optimierung der Gesamtleistung von Glykan-Microarrays bereitzustellen. Dafür erfolgte eine gründliche Untersuchung jedes einzelnen Prozessschritts innerhalb der Arrayproduktion durch Anwendung komplementärer Methoden der chemischen Oberflächenanalytik. Mit Hilfe von fortgeschrittenen Verfahren der Elektronen-Spektroskopie für die chemische Anlayse (ESCA/XPS) wurden valide quantitative Daten bei der chemischen Charakterisierung der Oberflächen erhalten die mit den häufig eingesetzten qualitativen bzw. indirekten Verfahren (z.B. Kontaktwinkel Goniometrie und Fluoreszenz-Spektroskopie) so nicht erhalten werden können. Die robuste Anbindung von Glykanen auf der Substratoberfläche ist Voraussetzung für eine reproduzierbare Anwendung in der Diagnostik aber auch für die Entwicklung valider quantitativer Charakterisierungsmethoden zur Bewertung der Effizienz der Immobilisierungsreaktionen. Ein Schwerpunkt der Arbeit lag in der Charakterisierung und Optimierung der Glykananbindung an amin-reaktive Oberflächen. Hierzu wurden z.B. spezielle Glykane mit Fluorlabel auf epoxid-funktionalisierten Siliziumoberflächen immobilisiert. Eine Quantifizierung der angebundenen Glykane ist zum Beispiel über die Bestimmung der CF3-Gruppe im hochaufgelösten C1s XPS Spektrum möglich. Die Interaktionen Sonde-Analyt wurden modellhaft mit immobilisierten Glykanen und dem Lektin Concanavalin A mit Verfahren der chemischen Oberflächenanalytik untersucht. Neben der chemischen Charakterisierung frisch präparierter Glykansonden wurde auch das Alterungsverhalten der Glykan-Microarrays untersucht. / The objective of this research is to sidestep many of the initial and current problems of glycan microarray based devices by using new analytical approaches to control molecular engineering. For this purpose, a thorough investigation of each individual step in the array production is carried out by applying complementary methods of surface chemical analysis. New fluorophore-free protocols based on methods of surface analysis as XPS will be developed and validated to enable glycan microarray performance optimization. The advantage of these methods is the direct quantitative access to chemical bonds at high lateral resolution. In contrast to the frequently used qualitative or indirect methods (e.g. contact angle goniometry and fluorescence spectroscopy), valid quantitative data are obtained. The robust binding of glycans on the substrate surface, is a prerequisite for a reproducible application in the diagnostics but also for the development of valid quantitative characterization methods for the evaluation of the efficiency of the immobilization reactions. One focus of the work was the characterization and optimization of the glycan binding to popular amine-reactive surfaces. For this purpose, specific glycans with fluorine-label were immobilized on epoxide-functionalized silicon surfaces. A quantification of the attached glycan molecules is possible, for example, by determining the amount of CF3 groups using the high-resolution C1s XPS spectrum. The interactions between model probe (glycan molecules) and model analyte (lectin concanavalin A) were investigated using powerful methods surface chemical analysis. In addition to the chemical characterization of freshly prepared glycan probes, the aging behavior of the glycan microarrays was also investigated.

In this dissertation, first we consider the problem of testing homogeneity and order in a contaminated normal model, when the data is correlated under some known covariance structure. To address this problem, we developed a moment based homogeneity and order test, and design weights for test statistics to increase power for homogeneity test. We applied our test to microarray about Down’s syndrome. This dissertation also studies a singular Bayesian information criterion (sBIC) for a bivariate hierarchical mixture model with varying weights, and develops a new data dependent information criterion (sFLIC).We apply our model and criteria to birth- weight and gestational age data for the same model, whose purposes are to select model complexity from data.

Microarray technology is a promising approach for the simultaneous analysis of multiple single nucleotide polymorphisms (SNPs), which are the most abundant form of genetic variation. In this thesis enzyme-assisted microarray-based methods were developed to improve the accuracy and genotype discrimination power of the current methods for SNP genotyping. The improved technology was applied for analysing recessively inherited disease mutations, for Y-chromosomal SNPs in a population study, for an evolutionary analysis of SNPs in flycatchers and for multiplexed quantitative determination of SNP-allele frequencies in pooled DNA samples. A robust attachment chemistry for immobilising oligonucleotides on glass surface was established, based on an evaluation of eight covalent coupling methods. A four-colour fluorescence detection strategy, which enabled a multiplexed quantitative analysis for as little as 2% of a minority allele frequency in pooled samples was generated. Twenty-five Y-chromosomal SNPs were screened in a collection of 300 samples from five Finno-Ugric-speaking populations using minisequencing on microarrays. In these populations six distinct haplotypes were defined by the six SNPs that were polymorphic. Data from five microsatellite markers was combined with the SNP data, revealing shared Y-chromosomal haplotypes between the Finns and the Saami, indicating, in accordance with earlier data, at least two founding Y-chromosomal lineages in these populations. Database screening and subsequent validation of 125 potential SNPs in the highly repetitive type 1 interferon genes and genes coding for proteins in the interferon-related regulatory pathways revealed 25 informative SNPs in the Finnish and Swedish populations. These SNPs were included in a panel for microarray based genotyping that should find a variety of applications in genetic studies due to the important immunoregulatory functions of the IFN family. The significance of sex-chromosome evolution on speciation was investigated in two naturally hybridising flycatcher species (N=459) by analysing a panel of 20 SNPs using minisequencing on microarrays. A strong selection against gene flow across the species boundary of sex-linked genes was observed, as well as a sex-chromosomal influence on male plumage characteristics that have previously been shown to reinforce isolation in these birds. The results suggest a major role for sex-chromosome-mediated isolation of the two flycatcher species.

Clinical immunohistochemistry (IHC) techniques are not yet fully standardized. In this project, a standardization method was developed and tested for proficiency testing (PT) in external quality assurance (EQA) and quality control (QC) in clinical IHC laboratories. The breast cancer markers estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor 2 (HER2) were used as a model system. Digital image analysis (IA) was used in conjunction with new calibrated and standardized cell line microarrays (CLMA). CLMAs built from nine formalin-fixed paraffin-embedded (FFPE) breast cancer cell lines were used for both QC controls and PT samples, instead of traditionally used FFPE tissues, in the standardization of breast cancer IHC. IA was used for measurement of IHC results, and compared to evaluation by the traditional expert-assessment method. Laboratory Score: Reference Score Ratio (LSRSR) was derived from Histo-Scores (HScores) determined by IA. HScores and LSRSRs were examined statistically and evaluated as histograms and boxplots to summarize and rank participant laboratory EQA results, in comparison to a reference sample or reference laboratories in two consecutive Canada-wide EQA runs. LSRSR-derived reference ranges were highly sensitive in evaluating laboratory EQA performance in PT as well as for monitoring of controls for QC. Laboratory on-slide tissue and cell-line IHC QA controls were assessed using IA and Levey Jennings QC charts. These charts were determined to be an excellent way to observe trending in laboratory IHC staining over time, particularly when cell line controls were used. This approach also reduced the time and labor costs for PT evaluation. Overall, cell line calibration controls were functionally equivalent or better than tissue-based controls in QC and PT mainly because of cell line biological homogeneity and sample availability. This study identified an optimal design for preparation of IHC cell line controls and PT samples for breast cancer markers. Optimal, intermediate staining cell line IHC controls were identified for all three breast cancer markers. Using IA with LSRSR and cell line samples is recommended for standardization of IHC methodology. This approach advances QA for diagnostic IHC and when implemented will improve patient care

La technologie des microarrays demeure à ce jour un outil important pour la mesure de l'expression génique. Au-delà de la technologie elle-même, l'analyse des données provenant des microarrays constitue un problème statistique complexe, ce qui explique la myriade de méthodes proposées pour le pré-traitement et en particulier, l'analyse de l'expression différentielle. Toutefois, l'absence de données de calibration ou de méthodologie de comparaison appropriée a empêché l'émergence d'un consensus quant aux méthodes d'analyse optimales. En conséquence, la décision de l'analyste de choisir telle méthode plutôt qu'une autre se fera la plupart du temps de façon subjective, en se basant par exemple sur la facilité d'utilisation, l'accès au logiciel ou la popularité. Ce mémoire présente une approche nouvelle au problème de la comparaison des méthodes d'analyse de l'expression différentielle. Plus de 800 pipelines d'analyse sont appliqués à plus d'une centaine d'expériences sur deux plateformes Affymetrix différentes. La performance de chacun des pipelines est évaluée en calculant le niveau moyen de co-régulation par l'entremise de scores d'enrichissements pour différentes collections de signatures moléculaires. L'approche comparative proposée repose donc sur un ensemble varié de données biologiques pertinentes, ne confond pas la reproductibilité avec l'exactitude et peut facilement être appliquée à de nouvelles méthodes. Parmi les méthodes testées, la supériorité de la sommarisation FARMS et de la statistique de l'expression différentielle TREAT est sans équivoque. De plus, les résultats obtenus quant à la statistique d'expression différentielle corroborent les conclusions d'autres études récentes à propos de l'importance de prendre en compte la grandeur du changement en plus de sa significativité statistique. / Microarrays remain an important tool for the measurement of gene expression, and a myriad of methods for their pre-processing or statistical testing of differential expression has been proposed in the past. However, insufficient and sometimes contradictory evidence has prevented the emergence of a strong consensus over a preferred methodology. This leaves microarray practitioners to somewhat arbitrarily decide which method should be used to analyze their data. Here we present a novel approach to the problem of comparing methods for the identification of differentially expressed genes. Over eight hundred analytic pipelines were applied to more than a hundred independent microarray experiments. The accuracy of each analytic pipeline was assessed by measuring the average level of co-regulation uncovered across all data sets. This analysis thus relies on a varied set of biologically relevant data, does not confound reproducibility for accuracy and can easily be extended to future analytic pipelines. This procedure identified FARMS summarization and the TREAT gene ordering statistic as algorithms significantly more accurate than other alternatives. Most interestingly, our results corroborate recent findings about the importance of taking the magnitude of change into account along with an assessment of statistical significance.

Plasmonics techniques, such as surface plasmon resonance (SPR) and surface-enhanced Raman scattering (SERS), have been widely used for chemical and biochemical sensing applications. One approach to excite surface plasmons is through the coupling of light into metallic grating nanostructures. Those grating nanostructures can be fabricated using state-of-the-art nanofabrication methods. Laser interference lithography (LIL) is one of those methods that allow the rapid fabrication of nanostructures with a high-throughput. In this thesis, LIL was combined with other microfabrication techniques, such as photolithography and template stripping, to fabricate different types of plasmonic sensors. Firstly, template stripping was applied to transfer LIL-fabricated patterns of one-dimensional nanogratings onto planar supports (e.g., glass slides and plane-cut optical fiber tips). A thin adhesive layer of epoxy resin was used to facilitate the transfer. The UV-Vis spectroscopic response of the nanogratings supported on glass slides demonstrated a strong dependency on the polarization of the incident light. The bulk refractive index sensitivities of the glass-supported nanogratings were dependent on the type of metal (Ag or Au) and the thickness of the metal film. The described methodology provided an efficient low-cost fabrication alternative to produce metallic nanostructures for plasmonic chemical sensing applications. Secondly, we demonstrated a versatile procedure (LIL either alone or combined with traditional laser photolithography) to prepare both large area (i.e. one inch2) and microarrays (μarrays) of metallic gratings structures capable of supporting SPR excitation (and SERS). The fabrication procedure was simple, high-throughput, and reproducible, with less than 5 % array-to-array variations in geometrical properties. The nanostructured gold μarrays were integrated on a chip for SERS detection of ppm-level of 8-quinolinol, an emerging water-borne pharmaceutical contaminant. Lastly, the LIL-fabricated large area nanogratings have been applied for SERS detection of the mixtures of quinolone antibiotics, enrofloxacin, an approved veterinary antibiotic, and one of its active metabolite, ciprofloxacin. The quantification of these analytes (enrofloxacin and ciprofloxacin) in aqueous mixtures were achieved by employing chemometric analysis. The limit of quantification of the method described in this work is in the ppm-level, with <10 % SERS spatial variation. Isotope-edited internal calibration method was attempted to improve the accuracy and reproducibility of the SERS methodology. / Graduate / 2018-02-17