With a population both increasing in number and age, comes a need for new diagnostic tools in the healthcare system, capable of diagnosing and monitoring multiple disorders in a cheap and effective way to provide personalised healthcare. Multiplex label-free biosensors have the potential to rejuvenate the current system. This thesis details the assessment of an ‘in house’ built labelfree array screening technology that has potential to be a point-of-care diagnostic for personalised medicine – the Array Reader. The performance of the Array Reader platform is considered in detail and optimised for both antibody and protein screening arrays. A Global Fit protocol is developed to extract kinetic constants for all protein-protein interactions, assuming a Langmuir adsorption binding model. Standard operating procedures are developed to provide optimised dynamic range, sensitivity, reproducibility and limit of detection of immuno-kinetic assay. A new antibody bio-stack signal amplification strategy is formed, improving the detection limit 60-fold. As a consequence, the bio-stack resulted in a novel method for determining the plasmon field penetration depth, defining the assay sensing volume at the nanoparticle surface. Antibody screening arrays were investigated with an IgG quantification assay to determine total IgG content from serum samples. It relied on the ability of protein A/G to bind antibodies via the Fc region. Specific antigens were used to measure the binding properties of the antibody Fab region. By characterising both regions, we have gained insight into the overall ability of an antibody to trigger an immune response. Protein screening assay were investigated targeting C-reactive protein (CRP), a marker of inflammation. The assays performance characteristics compared favourably with clinically used CRP assays. Finally, an antibody screening array was developed to assess the efficacy of a vaccine against Yersinia pestis in a non-human primate model. The vaccine screening array is an excellent example of the versatility of the platform and just one of many possible applications for the future.

This work deals with the development of a new label-free detection technique called Pulsed Streaming Potential (PSP). Its novelty relies on the adaptation of a classical electrokinetic phenomenon (streaming potential) into a tool which can evaluate molecular interplay in label-free fashion. Implementation of PSP to microfluidic platforms allowed the label-free sensing of binding events to plastic (modified and unmodified) surfaces. It was demonstrated the use of real time PSP in plastic microfluidic platforms for determination of kinetic parameters of the interaction of proteins and plastic surfaces. Moreover, initial change of PSP after adsorption of proteins showed to be proportional to the bulk concentration of proteins and it was used for quantification of Lysozyme in the nanomolar range. Several approaches were studied to manipulate the surface of microfluidic channels in order to improve selectivity of PSP through reduction of non-specific adsorption. These approaches included the fabrication of composite surface of polyacrilic acid (PAA) and polyethylene glycol acrylate (PEGA) on cyclic olefin copolymer microchannels, as well as adsorption of nanospheres on COC-PEGA channels.

Label free detection of biologically relevant binding pairs has provided critical insight into the characterization of reagents used in both therapeutic and diagnostic applications. The Interferometric Reflectance Imaging Sensor (IRIS) platform has been developed for the multiplexed, real-time detection of such binding interactions. Improvements to experimental methodology and analysis applied to the latest iteration of the IRIS provided heretofore unseen binding characterizations with this multiplexed platform. Here, we extend and demonstrate the utility of the IRIS system to (1) evaluate and compare kinetic parameters to those obtained with more traditional label free methods (2) characterize multiple, disease relevant antibodies in multiple disease systems (anthrax, Zika, dengue and plague) (3) determine appropriate binding pairs in multiplexed label free formats and (4) obtain 10-fold improvements to the limits of detection for analyte in solution over previous IRIS iterations. Applications to immunoassay development are discussed throughout with exemplary datasets provided. Observations regarding additional IRIS utilities are also discussed, including qualifications of genetically engineered ligands, evaluating subcloned antibodies and screening unpurified antibody supernatants.

Este estudo trata das proteínas relacionadas ao desenvolvimento e à formação da parede celular vegetal de cana-de-açúcar, com o objetivo de auxiliar no desenvolvimento de novas tecnologias para a produção de etanol celulósico a partir do bagaço de cana. Com isso, as proteínas de parede celular de entrenós e folhas de plantas com 4 meses de idade em dois estádios de desenvolvimento, juvenil e maduro, foram identificadas. Para extração foi utilizado o método não destrutivo por infiltração a vácuo utilizando dois sais, 0,2 M de CaCl2 e 2 M de LiCl seguido de centrifugação. As amostras complexas foram digeridas, fracionadas, sequenciadas por LC-MSE . Os peptídeos foram processados utilizando o ProteinLynx 2.5 e comparados com a base de dados de ESTs traduzidos de cana e sorgo. A anotação das proteínas foi realizada com base no programa PFAM e dividas em classes funcionais. Apenas as proteínas que apareceram em pelo menos duas das três repetições biológicas foram utilizadas na análise principal. Para prever a localização subcelular das proteínas selecionadas utilizaram-se os softwares: SignalP, TargetP, Predotar e TMHMM. Apenas aquelas proteínas que foram preditas para serem secretadas por dois ou mais programas foram consideradas como proteínas de parede celular (PPC). Ao todo, 543 proteínas foram consideradas como PPC: 205 em entrenós jovens, 143 em entrenós maduros, 124 em folhas jovens e 71 em folhas maduras. Dentre essas proteínas, 365 foram consideradas diferentes, e caracterizadas em dez classes funcionais. A análise estatística compreendeu a análise de PCA e PLS-DA, havendo diferença estatística entre os tratamentos analisados. Neste trabalho, foram encontradas 66 glicosil-hidrolases e 39 peroxidases, sendo 14 e 11 exclusivas de tecidos juvenis, respectivamente. Essas proteínas são conhecidas por terem funções relacionadas à quebra e ao remodelamento dos polissacarídeos da parede celular vegetal, e, portanto, foram indicadas neste estudo como alvo de pesquisas futuras que utilizem as próprias enzimas da planta para otimização da produção do etanol celulósico.Individualmente, este estudo foi o que mais identificou PPCs dentre a literatura existente, além de ter sido pioneiro na utilização da análise quantitativa para PPC. / This study provides information about the proteins of the cell wall of sugarcane at diferente stages of development and formation. The aim of this study is to assist in the development of new technologies for the production of cellulosic ethanol from sugarcane bagasse. Cell wall proteins from 4-month-old internodes and leaves of sugarcane in two developmental stages, juvenile and mature, have been identified. Protein extraction was performed with a non-destructive method by using vacuum infiltration with two salts, 0.2 M CaCl2 and 2 M LiCl, followed by centrifugation. Complex samples were digested, fractionated and sequenced by LC-MSE. Peptides were processed by ProteinLynx 2.5 and compared to the translated sugarcane and sorghum ESTs database. The annotation of the proteins was performed using PFAM and the functional classification was according the one used in other related studies. Only the proteins that appeared in at least two of the three biological replicates were used in the main analysis. In order to predict the subcellular localization of these proteins, SignalP, TargetP, TMHMM and Predotar softwares were used. Only those proteins that were predicted to be secreted by two or more programs were considered as cell wall proteins (PPS). Altogether, 543 proteins were classified as PPC: 205 inimmature internodes, 143 in mature internodes, 124 in young leaves and 71 in matured leaves. Among these proteins, 365 were considered different, and divided into ten functional classes. Statistical analysis was made with PCA and PLSDA, confirming that there were statistical differences among the treatments. In this work, 66 glycoside hydrolases and 39 peroxidases c identified, being 14 and 11 unique to young tissues, respectively. These proteins have their function related to plant cell wall polysaccharides breakdown and remodeling, and, therewith, the glycoside hydrolases and peroxidases found in this study were indicated to be the target of future research using the plant\'s own enzymes to optimize the cellulosic ethanol production. Individually, this study was the one that most identified PPC among the existing literature, and is a pioneer in the use of quantitative analysis for PPCs.

Lung carcinoma is the most prevalent type of cancer in the world, considered to be a relentlessly progressive disease, with dismal mortality rates to patients. Recent advances in targeted therapy hold the premise for the delivery of better, more effective treatments to lung cancer patients, that could significantly enhance their survival rates. Optimizing care delivery through targeted therapies requires the ability to effectively identify and diagnose lung cancer along with identifying the lung cancer cell type specific to each patient, \textit{small cell carcinoma}, \textit{adenocarcinoma}, or \textit{squamous cell carcinoma}. Label free optical imaging techniques such as the \textit{Coherent anti-stokes Raman Scattering microscopy} have the potential to provide physicians with minimally invasive access to lung tumor sites, and thus allow for better cancer diagnosis and sub-typing. To maximize the benefits of such novel imaging techniques in enhancing cancer treatment, the development of new data analysis methods that can rapidly and accurately analyze the new types of data provided through them is essential. Recent studies have gone a long way to achieving those goals but still face some significant bottlenecks hindering the ability to fully exploit the diagnostic potential of CARS images, namely, the streamlining of the diagnosis process was hindered by the lack of ability to automatically detect cancer cells, and the inability to reliably classify them into their respective cell types. More specifically, data analysis methods have thus far been incapable of correctly identifying and differentiating the different non-small cel lung carcinoma cell types, a stringent requirement for optimal therapy delivery. In this study we have addressed the two bottlenecks named above, through designing an image processing framework that is capable of, automatically and accuratly, detecting cancer cells in two and three dimensional CARS images. Moreover, we built upon this capability with a new approach at analyzing the segmented data, that provided significant information about the cancerous tissue and ultimately allowed for the automatic differential classification of non-small cell lung carcinoma cell types, with superb accuracies.

DNA is packaged into the nucleus of a mammalian cell as a nucleoprotein complex called chromatin. Changes in chromatin structure occur during processes that are critical to an understanding of mammalian cell biology such as cell division. Existing fixed-cell techniques have provided insight into chromatin organization in the mammalian nucleus. In addition, fluorescence microscopy techniques have allowed us to study changes in chromatin structure in living cells. However, most of these fluorescence techniques cannot be used for tissue imaging or long-term imaging due to photobleaching. In this thesis, we demonstrate that a label-free technique called Stimulated Raman Scattering (SRS) microscopy can be used to solve these problems and study chromatin structure in living mammalian cells both in culture and in tissue. SRS is a vibrational microscopy technique that takes advantage of intrinsic contrast arising from specific chemical bonds in a molecule. Nucleic acids have specifc phosphate and CH vibrations that can be used to determine their cellular distributions. Imaging at specific phosphate peaks using fingerprint SRS microscopy allows the detection of polytene chromosomes in Drosophila salivary gland cells and condensed chromatin in metaphase mammalian cells. In addition, we develop a technique called multicolor SRS microscopy, in which we image at several wavelengths across the CH vibrational band, and then use linear combination to simultaneously determine the nucleic acid, lipid and protein distributions in living mammalian cells. This technique achieves greater contrast than imaging at the phosphate vibrational peak due to the stronger SRS signal in the high wavenumber CH band and so allows us to determine chromatin structure in interphase mammalian cells. This technique also allows long-term imaging of living mammalian cells and the imaging of tissue such as mouse skin. The technique is used to monitor mammalian cell division in culture and paves the way for similar studies in living tissue. This technique will provide insight into cell division, differentiation and apoptosis during development and in disease models such as cancer.

Mass spectrometry is the primary technology of proteomics. For the analysis of complex proteomes, protein identities and quantities are inferred from their peptides that are generated by cleaving all proteins with the endopeptidase trypsin. But there is one major disadvantage that is due to biophysical differences, different peptides cause different intensities. Miscellaneous approaches have been developed to circumvent this problem based on the chemical or metabolic introduction of heavy stable isotopes. This enables to monitor protein abundance differences of two or more samples on the same tryptic peptides that differ in mass only. Absolute quantification can be achieved similar by spiking-in synthetic isotopical labeled counterparts of a sample’s tryptic peptides. However, labeling technics suffer from high prices, introduced biases, need for extensive manual control, laborious implementation and implementation restrictions. Therefore, a multiplicity of label-free approaches have been developed that profit from instrumental improvements targeting reliability of identifications and reproducibility of quantitative values. No extensive systematic comparison of label-free quantitative parameters has been published so far presumably because of the laborious implementation. An analysis of primary label-free parameters and associated normalization methods is presented here that compares dynamic and linear ranges and accuracies in the estimation of protein amounts. This facilitated the establishment of label-free procedures addressing three fundamental questions in proteomics: what is a sample’s composition, are proteins that share a specific property enriched and what are the differences between two (or more) samples. A new mathematic model is presented that defines and elucidates enrichment. The procedures were applied first to analyze and compare stem cell plasma membrane proteomes. This is an ambitious model for proteomics because of only small amounts of arduous to analyze, partial hydrophobic proteins in a complex proteomic and chemical background. It is of scientific relevance, as membrane proteins are the cell’s communication interface that enable cell type specific processes and hence can be used to define, isolate and quantify those. The success of cell surface proteome enrichment, the quantitative composition of the proteome and the proteomic difference between stem cells isolated from the dental pulp and cultivated in different media is shown. Secondly, the procedures were applied to the analysis of transient protein networks that assemble onto proteo-liposomes in a newly designed recruitment assay that fully recapitulates membrane sorting as seen in vivo. All transmembrane proteins need to be trafficked to other organelles’ membranes by vesicular trafficking. Sorting signals within the cytosolic regions of the protein cargos trigger the formation of trafficking complexes around those. The transient membrane complexes additionally recognize organelle or organelle-domain specific membrane lipids, such as phosphatidylinositol phosphates. Different trafficking ways are characterized by different trafficking complexes. The elucidation of trafficking complexes that form around a transmembrane protein of interest discloses its trafficking routes and involved signaling processes. The synthetic proteo-liposomes were prepared from chemically defined lipids and heterologous expressed cytosolic domains of type I or type II membrane receptors. The proteomic analyses of such samples are challenging because of huge proteomic backgrounds of proteins binding to the liposomes irrespective of the receptor and relatively small amounts and numbers of receptor-specific binders. Though the basic idea is to elucidate sorting machineries and study membrane trafficking processes, such experiments are untargeted and miscellaneous discoveries were achieved. We elucidated that the apical determinant crumbs 2 is a cargo of the retromer complex. This revealed a fameless level of control for the establishment of cell polarity. We found retromer along with the adapter complexes AP 4 and AP 5 trafficking the beta amyloid precursor protein APP. This confirmed recent publications and yielded new insights. Moreover, many more proteins and complexes appeared to associate with the cytosolic part of APP (AICD) in a membrane context-dependent or -independent manner. Among those, some were so far unknown to interact with AICD, like mTORC1 and the PIKFyve complex.

This work describes the functionalization and testing of Si$_3$N$_4$-based photonic crystal slabs (PCS) for label-free biosensing. PCS support optical resonance modes that are sensitive to the local refractive index. Knowing that surface binding events change the local RI, analyte binding to the activated sensor can be detected. Various functionalization recipes were tried, and one was preferred for the biosensing experiments due to its higher yield and uniformity. Additionally, thickness of the topmost sensor layer was studied to assess biosensor performance quantified through sensitivity metrics. On the systems level, a reusable clamping system and customized microfluidic channels were designed, fabricated, and implemented on the PCS biosensors to enable device refurbishment. Proof-of-principle biodetection experiments were carried out using the established functionalization protocol on the in-house fabricated PCS. Conjugation of streptavidin and bovine serum albumin to the sensor surface was observed through wavelength shifts of the resonant modes.

This work describes the functionalization and testing of Si$_3$N$_4$-based photonic crystal slabs (PCS) for label-free biosensing. PCS support optical resonance modes that are sensitive to the local refractive index. Knowing that surface binding events change the local RI, analyte binding to the activated sensor can be detected. Various functionalization recipes were tried, and one was preferred for the biosensing experiments due to its higher yield and uniformity. Additionally, thickness of the topmost sensor layer was studied to assess biosensor performance quantified through sensitivity metrics. On the systems level, a reusable clamping system and customized microfluidic channels were designed, fabricated, and implemented on the PCS biosensors to enable device refurbishment. Proof-of-principle biodetection experiments were carried out using the established functionalization protocol on the in-house fabricated PCS. Conjugation of streptavidin and bovine serum albumin to the sensor surface was observed through wavelength shifts of the resonant modes.

Membrane-bound proteins and charged lipids are separated based on their charge-to-size ratio by electrophoretic-electroosmotic focusing (EEF) method on supported lipid bilayers (SLBs). EEF uses opposing electrophoretic and electroosmotic forces to focus and separate proteins and lipids into narrow bands from an initially homogeneous mixture. Membrane-associated species were focused into specific positions within the SLB in a highly repeatable fashion. The steady-state focusing positions of the proteins could be predicted and controlled by tuning experimental conditions, such as buffer pH, ionic strength, electric field and temperature. Careful tuning of the variables should enable one to separate mixtures of membrane proteins with only subtle differences. The EEF technique was found to be an effective way to separate protein mixtures with low initial concentrations and it overcame diffusive peak broadening problem. A "SLB differentiation" post-separation SLB treatment method was also developed by using magnetic particles to rapidly slice the whole SLB into many small patches after electrophoretic separation, while keeping the majority of materials on surface and avoiding the use of chemical reactions. Label-free detection techniques were also developed based on EEF on SLBs. First, a new separation based label-free detection method was developed based on the change of focusing position of fluorescently labeled ligands. This technique is capable of simultaneous detecting multiple protein competitive binding on the same ligand on SLBs. Low concentration protein can be detected in the presence of interfering proteins and high concentration of BSA. The fluorescent ligands were moved to different focusing positions in a charged SLB patch by different binding proteins. Both free ligand and protein bound ligand concentrations were obtained. Therefore, both protein identity and quantity information were obtained simultaneously. Second, the focusing position of fluorescent biomarkers on SLB was used to monitor the phospholipase D catalyzed hydrolysis of phosphatidylcholine (PC) to form phosphatidic acid (PA), which is involved with the change of charge on the phospholipids. The focusing position of fluorescent membrane-bound biomarker in the EEF experiment is directly determined by the negative charge density on SLB. Other enzyme reactions involved with the change of phospholipids charge can be monitored in a label-free fashion in a similar way.