Novel planar photonic antennas to address the dynamic nanoarchitecture of biological membranes

The cell membrane is the encompassing protective shield of every cell and it is composed of a multitude of proteins, lipids and other molecules. The organization of the cell membrane is inextricably intertwined with its function, and sensitive to perturbations from the underlying actin cytoskeleton and the extracellular environment at the nano- and the mesoscale. Elucidating the dynamic interplay between lipids and proteins diffusing on the cell membrane, forming transient domains and (re)organizing them according to signals from the juxtaposed inner and outer meshwork, is of paramount interest in fundamental cell biology. The overarching goal of this thesis is to gain deeper insight into how lipids and proteins dynamically organize in biological membranes at the nanoscale. Photonic nano-antennas are metallic nanostructures that localize and enhance the incident optical radiation into highly confined nanometric regions (< 20 nm), leading to greatly enhanced light-matter interactions. In this thesis, we exploit an innovative design of planar gold nano-antenna arrays of different gap sizes (10-45 nm) and embedded in nanometric-size boxes. To elucidate nanoscale diffusion dynamics in biological membranes with high spatiotemporal resolution and single-molecule detection sensitivity, we further combine our nanogap antenna arrays with fluorescence correlation spectroscopy (FCS) in a serial and multiplexed manner. In this dissertation, we first describe the fabrication process of these planar gold nanogap antennas and characterize their performance by means of electron microscopy and FCS of individual molecules in solution. We demonstrate giant fluorescence enhancement factors of up to 104-105 times provided by our planar nanogap antennas in ultra-confined detection volumes and with single molecule detection sensitivity in the micromolar range. Second, we apply these planar plasmonic nano-antennas in combination with FCS for assessing the dynamic organization of mimetic lipid membranes at the nanoscale. For a ternary composition of the model membranes that include unsaturated and saturated lipids together with cholesterol, we resolve transient nanoscopic heterogeneities as small as 10 nm in size, coexisting in both macroscopically phase-separated lipid phases. Third, we add a Hyaluronic Acid (HA) layer on top of the model lipid membranes to emulate the effect of the extracellular environment surrounding native biological membranes. We extend our nano-antenna-FCS approach with atomic force microscopy and spectroscopy. We reveal a distinct influence of HA on the nanoscale lipid organization of mimetic membranes composed of lipids constituting the more ordered lipid phase. Our results indicate a synergistic effect of cholesterol and HA re-organizing biological membranes at the nanoscale. Fourth, we apply our planar nano-antenna platform combined with FCS to elucidate the nanoscale dynamics of different lipids in living cells. With our nanogap antennas we were able to breach into the sub-30 nm spatial scale on living cell membranes for the first time. We provide compelling evidence of short-lived cholesterol-induced ~10 nm nanodomain partitioning in living plasma membranes. Fifth, we demonstrate the multiplexing capabilities of our planar gold nanogap antenna platform combined with FCS in a widefield illumination scheme combined with sCMOS camera detection. Our approach allows recording of fluorescence signal from more than 200 antennas simultaneously. Moreover, we demonstrate multiplexed FCS recording on 50 nano-antennas simultaneously, both in solution as well as in living cells, with a temporal resolution in the millisecond range. The dissertation finishes with a brief discussion of the main results achieved in this research and proposes new avenues for future research in the field.La membrana plasmática separa el entorno intracelular del extracelular y está compuesta por una multitud de diferentes proteínas y lípidos. Su organización está fuertemente interconectada a su función, y es sensible a perturbaciones tanto de la actina cortical posicionada internamente en proximidad con la membrana, así como de una red extracelular en contacto próximo con la membrana exterior. Estas perturbaciones ocurren a distintas escalas temporales y espaciales, llegando a unos pocos nanómetros. Dada la estrecha relación entre la organización de la membrana y su función biológica, es tremendamente importante entender como lípidos y proteínas se organizan dinámicamente a la escala nanométrica y como se ven afectados por su entorno. El objetivo principal de esta tesis doctoral se centra en alcanzar este entendimiento. Las antenas fotónicas son nano-estructuras metálicas que incrementan la radiación electromagnética en regiones nanométricas (< 20 nm) del espacio. En esta tesis doctoral, hemos fabricado y utilizado plataformas con matrices de antenas en oro, y con regiones de confinamiento entre 10-45 nm. Además, hemos combinado estas antenas con la técnica de ¿fluorescence correlation spectroscopy (FCS)¿ a fin de obtener información espaciotemporal a la nano-escala en membranas biológicas, junto a la sensibilidad de detectar moléculas individuales a altas concentraciones. En esta disertación, describimos primero la fabricación de antenas fotónicas y caracterizamos su rendimiento utilizando técnicas de microscopía electrónica y FCS de moléculas individuales en solución. Nuestros resultados demuestran factores de incremento de la fluorescencia entre 104-105, en regiones ultra-confinadas, y una capacidad para detectar moléculas individuales en rango de concentraciones de micro-molares. Una vez validadas nuestras herramientas, nos enfocamos en su uso para el estudio dinámico de la organización de membranas lipídicas miméticas a escala nanométrica. En el caso de composiciones ternarias de lípidos insaturados, saturados y colesterol, hemos descubierto la existencia de heterogeneidades nanoscópicas y transitorias que coexisten tanto en las regiones ordenadas como desordenadas de las membranas lipídicas. El siguiente capítulo contiene resultados enfocados a estudiar el efecto del entorno extracelular en la organización dinámica de este tipo de capas lipídicas. Para ello, y como modelo, preparamos membranas lipídicas cubiertas de ácido hialurónico (HA), un componente abundantemente expresado en la matriz extracelular. Combinando FCS con microscopia y espectroscopia de fuerzas atómicas, logramos resolver la influencia de HA a escala nanométrica en la organización de la fase ordenada de las membranas lipídicas. Nuestros resultados indican la existencia de un efecto sinérgico entre HA y colesterol en el reordenamiento de la membrana a la nano-escala. El siguiente tema de investigación en esta tesis doctoral se enfoca a la aplicación de antenas fotónicas y FCS para el estudio de dominios lipídicos enriquecidos de colesterol en la membrana plasmática de células vivas. La utilización de estas antenas nos ha permitido, por primera vez, remontar la barrera de 30 nm, y demostrar de manera inequívoca la existencia de dominios enriquecidos en colesterol en células vivas con una resolución espacial de 10 nm. Finalmente, hemos demostrado la capacidad de multiplexado de nuestras antenas fotónicas, combinando una iluminación y detección en campo amplio utilizando una camera sCMOS. Describimos la implementación de nuestro esquema, así como también medidas que demuestran la detección simultánea de fluorescencia en más de 200 antenas. De manera importante, demostramos la obtención de curvas de FCS en 50 antenas simultáneamente, tanto en solución como en células vivas. Esta disertación culmina con una breve discusión de los resultados más importantes de esta investigación en el futur

VENNTURE–A Novel Venn Diagram Investigational Tool for Multiple Pharmacological Dataset Analysis

As pharmacological data sets become increasingly large and complex, new visual analysis and filtering programs are needed to aid their appreciation. One of the most commonly used methods for visualizing biological data is the Venn diagram. Currently used Venn analysis software often presents multiple problems to biological scientists, in that only a limited number of simultaneous data sets can be analyzed. An improved appreciation of the connectivity between multiple, highly-complex datasets is crucial for the next generation of data analysis of genomic and proteomic data streams. We describe the development of VENNTURE, a program that facilitates visualization of up to six datasets in a user-friendly manner. This program includes versatile output features, where grouped data points can be easily exported into a spreadsheet. To demonstrate its unique experimental utility we applied VENNTURE to a highly complex parallel paradigm, i.e. comparison of multiple G protein-coupled receptor drug dose phosphoproteomic data, in multiple cellular physiological contexts. VENNTURE was able to reliably and simply dissect six complex data sets into easily identifiable groups for straightforward analysis and data output. Applied to complex pharmacological datasets, VENNTURE’s improved features and ease of analysis are much improved over currently available Venn diagram programs. VENNTURE enabled the delineation of highly complex patterns of dose-dependent G protein-coupled receptor activity and its dependence on physiological cellular contexts. This study highlights the potential for such a program in fields such as pharmacology, genomics, and bioinformatics

Quantitative phase-field model for phase transformations in multi-component alloys

A quantitative phase-field model, based on a grand potential functional is derived for the case of multi-component alloys along with the thin-interface asymptotics. In addition, a framework is developed, which allows the coupling of the thermodynamic databases to the phase-field model through the construction of simplistic free energies

Quantitative phase-field model for phase transformations in multi-component alloys

Phase-field modeling has spread to a variety of applications involving phase transformations. While the method has wide applicability, derivation of quantitative predictions requires deeper understanding of the coupling between the system and model parameters. The book highlights a novel phase-field model based on a grand-potential formalism allowing for an elegant and efficient solution to problems in phase transformations

Asynchronous spike event coding scheme for programmable analogue arrays and its computational applications

This work is the result of the definition, design and evaluation of a novel method to interconnect the computational elements - commonly known as Configurable Analogue Blocks (CABs) - of a programmable analogue array. This method is proposed for total or partial replacement of the conventional methods due to serious limitations of the latter in terms of scalability. With this method, named Asynchronous Spike Event Coding (ASEC) scheme, analogue signals from CABs outputs are encoded as time instants (spike events) dependent upon those signals activity and are transmitted asynchronously by employing the Address Event Representation (AER) protocol. Power dissipation is dependent upon input signal activity and no spike events are generated when the input signal is constant. On-line, programmable computation is intrinsic to ASEC scheme and is performed without additional hardware. The ability of the communication scheme to perform computation enhances the computation power of the programmable analogue array. The design methodology and a CMOS implementation of the scheme are presented together with test results from prototype integrated circuits (ICs)

SOX2 Co-Occupies Distal Enhancer Elements with Distinct POU Factors in ESCs and NPCs to Specify Cell State

SOX2 is a master regulator of both pluripotent embryonic stem cells (ESCs) and multipotent neural progenitor cells (NPCs); however, we currently lack a detailed understanding of how SOX2 controls these distinct stem cell populations. Here we show by genome-wide analysis that, while SOX2 bound to a distinct set of gene promoters in ESCs and NPCs, the majority of regions coincided with unique distal enhancer elements, important cis-acting regulators of tissue-specific gene expression programs. Notably, SOX2 bound the same consensus DNA motif in both cell types, suggesting that additional factors contribute to target specificity. We found that, similar to its association with OCT4 (Pou5f1) in ESCs, the related POU family member BRN2 (Pou3f2) co-occupied a large set of putative distal enhancers with SOX2 in NPCs. Forced expression of BRN2 in ESCs led to functional recruitment of SOX2 to a subset of NPC-specific targets and to precocious differentiation toward a neural-like state. Further analysis of the bound sequences revealed differences in the distances of SOX and POU peaks in the two cell types and identified motifs for additional transcription factors. Together, these data suggest that SOX2 controls a larger network of genes than previously anticipated through binding of distal enhancers and that transitions in POU partner factors may control tissue-specific transcriptional programs. Our findings have important implications for understanding lineage specification and somatic cell reprogramming, where SOX2, OCT4, and BRN2 have been shown to be key factors

Analysis of large-scale molecular biological data using self-organizing maps

Modern high-throughput technologies such as microarrays, next generation sequencing and mass spectrometry provide huge amounts of data per measurement and challenge traditional analyses. New strategies of data processing, visualization and functional analysis are inevitable. This thesis presents an approach which applies a machine learning technique known as self organizing maps (SOMs). SOMs enable the parallel sample- and feature-centered view of molecular phenotypes combined with strong visualization and second-level analysis capabilities. We developed a comprehensive analysis and visualization pipeline based on SOMs. The unsupervised SOM mapping projects the initially high number of features, such as gene expression profiles, to meta-feature clusters of similar and hence potentially co-regulated single features. This reduction of dimension is attained by the re-weighting of primary information and does not entail a loss of primary information in contrast to simple filtering approaches. The meta-data provided by the SOM algorithm is visualized in terms of intuitive mosaic portraits. Sample-specific and common properties shared between samples emerge as a handful of localized spots in the portraits collecting groups of co-regulated and co-expressed meta-features. This characteristic color patterns reflect the data landscape of each sample and promote immediate identification of (meta-)features of interest. It will be demonstrated that SOM portraits transform large and heterogeneous sets of molecular biological data into an atlas of sample-specific texture maps which can be directly compared in terms of similarities and dissimilarities. Spot-clusters of correlated meta-features can be extracted from the SOM portraits in a subsequent step of aggregation. This spot-clustering effectively enables reduction of the dimensionality of the data in two subsequent steps towards a handful of signature modules in an unsupervised fashion. Furthermore we demonstrate that analysis techniques provide enhanced resolution if applied to the meta-features. The improved discrimination power of meta-features in downstream analyses such as hierarchical clustering, independent component analysis or pairwise correlation analysis is ascribed to essentially two facts: Firstly, the set of meta-features better represents the diversity of patterns and modes inherent in the data and secondly, it also possesses the better signal-to-noise characteristics as a comparable collection of single features. Additionally to the pattern-driven feature selection in the SOM portraits, we apply statistical measures to detect significantly differential features between sample classes. Implementation of scoring measurements supplements the basal SOM algorithm. Further, two variants of functional enrichment analyses are introduced which link sample specific patterns of the meta-feature landscape with biological knowledge and support functional interpretation of the data based on the ‘guilt by association’ principle. Finally, case studies selected from different ‘OMIC’ realms are presented in this thesis. In particular, molecular phenotype data derived from expression microarrays (mRNA, miRNA), sequencing (DNA methylation, histone modification patterns) or mass spectrometry (proteome), and also genotype data (SNP-microarrays) is analyzed. It is shown that the SOM analysis pipeline implies strong application capabilities and covers a broad range of potential purposes ranging from time series and treatment-vs.-control experiments to discrimination of samples according to genotypic, phenotypic or taxonomic classifications