As high-throughput biology experiments generate increasing amounts of data, the field is naturally turning to data-driven methods for the analysis and extraction of novel insights. These insights into biological systems are crucial for understanding disease progression, drug targets, treatment development, and diagnostics methods, ultimately leading to improving human health and well-being, as well as, deeper insight into cellular biology. Biological data sources such as the genome, transcriptome, proteome, metabolome, and metagenome provide critical information about biological system structure, function, and dynamics. The focus of this licentiate thesis is on proteomics, the study of proteins, which is a natural starting point for understanding biological functions as proteins are crucial functional components of cells. Proteins play a crucial role in enzymatic reactions, structural support, transport, storage, cell signaling, and immune system function. In addition, proteomics has vast data repositories and technical and methodological improvements are continually being made to yield even more data. However, generating proteomic data involves multiple steps, which are prone to errors, making sophisticated models essential to handle technical and biological artifacts and account for uncertainty in the data. In this licentiate thesis, the use of machine learning and probabilistic methods to extract information from mass-spectrometry-based proteomic data is investigated. The thesis starts with an introduction to proteomics, including a basic biological background, followed by a description of how massspectrometry-based proteomics experiments are performed, and challenges in proteomic data analysis. The statistics of proteomic data analysis are also explored, and state-of-the-art software and tools related to each step of the proteomics data analysis pipeline are presented. The thesis concludes with a discussion of future work and the presentation of two original research works. The first research work focuses on adapting Triqler, a probabilistic graphical model for protein quantification developed for data-dependent acquisition (DDA) data, to data-independent acquisition (DIA) data. Challenges in this study included verifying that DIA data conformed with the model used in Triqler, addressing benchmarking issues, and modifying the missing value model used by Triqler to adapt for DIA data. The study showed that DIA data conformed with the properties required by Triqler, implemented a protein inference harmonization strategy, and modified the missing value model to adapt for DIA data. The study concluded by showing that Triqler outperformed current protein quantification techniques. The second research work focused on developing a novel deep-learning based MS2-intensity predictor by incorporating the self-attention mechanism called transformer into Prosit, an established Recurrent Neural Networks (RNN) based deep learning framework for MS2 spectrum intensity prediction. RNNs are a type of neural network that can efficiently process sequential data by capturing information from previous steps, in a sequential manner. The transformer self-attention mechanism allows a model to focus on different parts of its input sequence during processing independently, enabling it to capture dependencies and relationships between elements more effectively. The transformers therefore remedy some of the drawbacks of RNNs, as such, we hypothesized that the implementation of MS2-intensity predictor using transformers rather than RNN would improve its performance. Hence, Prosit-transformer was developed, and the study showed that the model training time and the similarity between the predicted MS2 spectrum and the observed spectrum improved. These original research works address various challenges in computational proteomics and contribute to the development of data-driven life science. / Allteftersom high-throughput experiment genererar allt större mängder data vänder sig området naturligt till data-drivna metoder för analys och extrahering av nya insikter. Dessa insikter om biologiska system är avgörande för att förstå sjukdomsprogression, läkemedelspåverkan, behandlingsutveckling, och diagnostiska metoder, vilket i slutändan leder till en förbättring av människors hälsa och välbefinnande, såväl som en djupare förståelse av cell biologi. Biologiska datakällor som genomet, transkriptomet, proteomet, metabolomet och metagenomet ger kritisk information om biologiska systems struktur, funktion och dynamik. I licentiatuppsats fokusområde ligger på proteomik, studiet av proteiner, vilket är en naturlig startpunkt för att förstå biologiska funktioner eftersom proteiner är avgörande funktionella komponenter i celler. Dessa proteiner spelar en avgörande roll i enzymatiska reaktioner, strukturellt stöd, transport, lagring, cellsignalering och immunsystemfunktion. Dessutom har proteomik har stora dataarkiv och tekniska samt metodologiska förbättringar görs kontinuerligt för att ge ännu mer data. Men för att generera proteomisk data krävs flera steg, som är felbenägna, vilket gör att sofistikerade modeller är väsentliga för att hantera tekniska och biologiska artefakter och för att ta hänsyn till osäkerhet i data. I denna licentiatuppsats undersöks användningen av maskininlärning och probabilistiska metoder för att extrahera information från masspektrometribaserade proteomikdata. Avhandlingen börjar med en introduktion till proteomik, inklusive en grundläggande biologisk bakgrund, följt av en beskrivning av hur masspektrometri-baserade proteomikexperiment utförs och utmaningar i proteomisk dataanalys. Statistiska metoder för proteomisk dataanalys utforskas också, och state-of-the-art mjukvara och verktyg som är relaterade till varje steg i proteomikdataanalyspipelinen presenteras. Avhandlingen avslutas med en diskussion om framtida arbete och presentationen av två original forskningsarbeten. Det första forskningsarbetet fokuserar på att anpassa Triqler, en probabilistisk grafisk modell för proteinkvantifiering som utvecklats för datadependent acquisition (DDA) data, till data-independent acquisition (DIA) data. Utmaningarna i denna studie inkluderade att verifiera att DIA-datas egenskaper överensstämde med modellen som användes i Triqler, att hantera benchmarking-frågor och att modifiera missing-value modellen som användes av Triqler till DIA-data. Studien visade att DIA-data överensstämde med de egenskaper som krävdes av Triqler, implementerade en proteininferensharmoniseringsstrategi och modifierade missing-value modellen till DIA-data. Studien avslutades med att visa att Triqler överträffade nuvarande state-of-the-art proteinkvantifieringsmetoder. Det andra forskningsarbetet fokuserade på utvecklingen av en djupinlärningsbaserad MS2-intensitetsprediktor genom att inkorporera self-attention mekanismen som kallas för transformer till Prosit, en etablerad Recurrent Neural Network (RNN) baserad djupinlärningsramverk för MS2 spektrum intensitetsprediktion. RNN är en typ av neurala nätverk som effektivt kan bearbeta sekventiell data genom att bevara och använda dolda tillstånd som fångar information från tidigare steg på ett sekventiellt sätt. Självuppmärksamhetsmekanismen i transformer tillåter modellen att fokusera på olika delar av sekventiellt data samtidigt under bearbetningen oberoende av varandra, vilket gör det möjligt att fånga relationer mellan elementen mer effektivt. Genom detta lyckas Transformer åtgärda vissa nackdelar med RNN, och därför hypotiserade vi att en implementation av en ny MS2-intensitetprediktor med transformers istället för RNN skulle förbättra prestandan. Därmed konstruerades Prosit-transformer, och studien visade att både modellträningstiden och likheten mellan predicerat MS2-spektrum och observerat spektrum förbättrades. Dessa originalforskningsarbeten hanterar olika utmaningar inom beräkningsproteomik och bidrar till utvecklingen av datadriven livsvetenskap. / <p>QC 2023-05-22</p>

No description available.

Biomedical research relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence or magnetic activated cell sorting. Labelling processes potentially alter the cells’ properties and should be avoided, especially when purifying cells for clinical applications. A promising alternative is the label-free identification of cells based on physical properties. Sorting real-time deformability cytometry (soRT-DC) is a microfluidic technique for label-free analysis and sorting of single cells. In soRT-FDC, bright-field images of cells are analyzed by a deep neural net (DNN) to obtain a sorting decision, but sorting was so far only demonstrated for blood cells which show clear morphological differences and are naturally in suspension. Most cells, however, grow in tissues, requiring dissociation before cell sorting which is associated with challenges including changes in morphology, or presence of aggregates. Here, we introduce methods to improve robustness of analysis and sorting of single cells from nervous tissue and provide DNNs which can distinguish visually similar cells. We employ the DNN for image-based sorting to enrich photoreceptor cells from dissociated retina for transplantation into the mouse eye.

Biomolecular interactions, including protein-protein, protein-DNA, and protein-ligand interactions, are of special importance in all biological systems. These interactions may occer during the loading of biomolecules to interfaces, the translocation of biomolecules through transmembrane protein pores, and the movement of biomolecules in a crowded intracellular environment. The molecular interaction of a protein with its binding partners is crucial in fundamental biological processes such as electron transfer, intracellular signal transmission and regulation, neuroprotective mechanisms, and regulation of DNA topology. In this dissertation, a customized surface plasmon resonance (SPR) has been optimized and new theoretical and label free experimental methods with related analytical calculations have been developed for the analysis of biomolecular interactions. Human neuroglobin (hNgb) and cytochrome c from equine heart (Cyt c) proteins have been used to optimize the customized SPR instrument. The obtained Kd value (~13 µM), from SPR results, for Cyt c-hNgb molecular interactions is in general agreement with a previously published result. The SPR results also confirmed no significant impact of the internal disulfide bridge between Cys 46 and Cys 55 on hNgb binding to Cyt c. Using SPR, E. coli topoisomerase I enzyme turnover during plasmid DNA relaxation was found to be enhanced in the presence of Mg2+. In addition, a new theoretical approach of analyzing biphasic SPR data has been introduced based on analytical solutions of the biphasic rate equations. In order to develop a new label free method to quantitatively study protein-protein interactions, quartz nanopipettes were chemically modified. The derived Kd (~20 µM) value for the Cyt c-hNgb complex formations matched very well with SPR measurements (Kd ~16 µM). The finite element numerical simulation results were similar to the nanopipette experimental results. These results demonstrate that nanopipettes can potentially be used as a new class of a label-free analytical method to quantitatively characterize protein-protein interactions in attoliter sensing volumes, based on a charge sensing mechanism. Moreover, the molecule-based selective nature of hydrophobic and nanometer sized carbon nanotube (CNT) pores was observed. This result might be helpful to understand the selective nature of cellular transport through transmembrane protein pores.

[ES] Los biosensores son dispositivos analíticos con aplicabilidad en diferentes campos y con numerosas ventajas frente a otros métodos analíticos convencionales, como son el uso de pequeños volúmenes de muestra y reactivos, su sensibilidad y su rápida respuesta, sin necesidad de pretratamiento de la muestra, equipos caros o personal especializado. Sin embargo, se trata de un campo de investigación relativamente nuevo en el que todavía queda mucho camino por andar. Esta Tesis doctoral pretende aportar un granito de arena a este campo de conocimiento mediante el estudio del potencial de diferentes materiales porosos como transductores para el desarrollo de biosensores ópticos con respuesta en tiempo real y sin marcajes. Los materiales propuestos van desde aquellos artificialmente sintetizados, como silicio poroso (SiP), nanofibras (NFs) poliméricas o membranas poliméricas comerciales, hasta materiales naturales con propiedades fotónicas que todavía no habían sido explotadas para el sensado, como son los exoesqueletos de biosílice de diatomeas. Todos ellos tienen en común la simplicidad en su obtención, evitando costosos y laboriosos procesos de nanofabricación. Para su estudio, se analizará su respuesta óptica y, en aquellos casos en los que ésta permita llevar a cabo experimentos de detección, se desarrollarán estrategias para su biofuncionalización y su implementación en experimentos de biosensado. En el caso del SiP y las NFs se han optimizado los parámetros de fabricación para obtener una respuesta óptica adecuada que permita su interrogación. A continuación, se ha llevado a cabo su biofuncionalización empleando métodos covalentes y no covalentes, así como diferentes bioreceptores (aptámeros de ADN y anticuerpos) para estudiar su potencial y sus limitaciones como biosensores. En el caso de las membranas comerciales y el exoesqueleto de sílice de diatomeas, se ha caracterizado su respuesta óptica y se han llevado a cabo experimentos de sensado de índice de refracción para estudiar su sensibilidad. Así mismo, se ha desarrollado un método de funcionalización de la superficie del exoesqueleto de diatomeas basado en el uso de polielectrolitos catiónicos. Como resultado, se ha demostrado el potencial tanto de NFs para el desarrollo de biosensores, como el de membranas comerciales para sensores cuya aplicación no requiera una elevada sensibilidad pero sí un bajo coste. Además, se ha puesto de manifiesto el gran potencial del exoesqueleto de diatomeas para el desarrollo de sensores basados en su respuesta óptica. Por el contrario, las limitaciones encontradas en el desarrollo de biosensores basados en SiP han evidenciado la necesidad de un estudio riguroso y la optimización de la estructura de materiales porosos previamente a ser usados en (bio)sensado. / [CA] Els biosensors són dispositius analítics amb aplicabilitat en diferents camps i amb nombrosos avantatges enfront d'altres mètodes analítics convencionals, com són l'ús de xicotets volums de mostra i reactius, la seua sensibilitat i la seua ràpida resposta, sense necessitat de pretractament de la mostra, equips cars o personal especialitzat. No obstant això, es tracta d'un camp d'investigació relativament nou en el qual encara queda molt camí per fer. Aquesta Tesi doctoral pretén aportar el seu òbol a aquest camp de coneixement mitjançant l'estudi del potencial de diferents materials porosos com a transductors per al desenvolupament de biosensors òptics amb resposta en temps real i sense marcatges. Els materials proposats van des d'aquells artificialment sintetitzats, com a silici porós (SiP), nanofibras (NFs) polimèriques o membranes polimèriques comercials, fins a materials naturals amb propietats fotòniques que encara no havien sigut explotades per al sensat, com són els exoesquelets de biosílice de diatomees. Tots ells tenen en comú la simplicitat en la seua obtenció, evitant costosos i laboriosos processos de nanofabricació. Per al seu estudi, s'analitzarà la seua resposta òptica i, en aquells casos en els quals aquesta permeta dur a terme experiments de detecció, es desenvoluparan estratègies per a la seua biofuncionalizació i la seua implementació en experiments de biosensat. En el cas del SiP i les NFs s'han optimitzat els paràmetres de fabricació per a obtenir una resposta òptica adequada que permeta la seua interrogació. A continuació, s'ha dut a terme la seua biofuncionalizació emprant mètodes covalents i no covalents, així com diferents bioreceptors (aptàmers d'ADN i anticossos) per a estudiar el seu potencial i les seues limitacions com a biosensors. En el cas de les membranes comercials i l'exoesquelet de sílice de diatomees, s'ha caracteritzat la seua resposta òptica i s'han dut a terme experiments de sensat d'índex de refracció per a estudiar la seua sensibilitat. Així mateix, s'ha desenvolupat un mètode de funcionalizació de la superfície de l'exoesquelet de diatomees basat en l'ús de polielectròlits catiònics. Com a resultat, s'ha demostrat el potencial tant de NFs per al desenvolupament de biosensors, com el de membranes comercials per a sensors amb una aplicació que no requerisca una elevada sensibilitat però sí un baix cost. A més, s'ha posat de manifest el gran potencial de l'exoesquelet de diatomees per al desenvolupament de sensors basats en la seua resposta òptica. Per contra, les limitacions trobades en el desenvolupament de biosensors basats en SiP han evidenciat la necessitat d'un estudi rigorós i l'optimització de l'estructura dels materials porosos prèviament a ser usats en (bio)sensat. / [EN] Biosensors are analytical devices with application in diverse fields and with several advantages relative to other conventional methods, such as the use of small volumes of sample and reagents, their sensitivity and their fast response, without the need of the sample pretreatment, expensive equipments or specialised technicians. Nevertheless, this is a relatively new research field in which there is a long way to go yet. This doctoral Thesis aims at doing its bit to this field of knowledge by studying the potential of different porous materials as transducers for the development of real-time and label-free optical biosensors. The proposed materials range from those artificially synthesised, such as porous silicon (pSi), polymeric nanofibres (NFs) or commercial polymeric membranes, to natural materials with photonic properties that had not been exploited for sensing yet, such as biosilica exoskeletons of diatoms. All of them have in common its simple production, avoiding expensive and laborious nanofabrication processes. For their study, their optical response will be analysed and, in those cases in which such optical response allows performing detection experiments, strategies for their biofunctionalisation and their implementation in biosensing experiments will be developed as well. Regarding pSi and NFs, the fabrication parameters were optimised to get a suitable optical response for their interrogation. Afterwards, their surface functionalisation was carried out by covalent and non-covalent methods, as well as different bioreceptors (DNA aptamers and antibodies), to study their potential and their constraints as biosensors. Concerning commercial membranes and the biosilica exoskeleton of diatoms, their optical response was characterised and refractive index sensing experiments were carried out to study their sensitivity. Additionally, a biofunctionalisation method for the surface of the diatoms exoskeleton was developed based on the use of cationic polyelectrolytes. As a result, it was demonstrated the potential of NFs for the development of biosensors, as well as the potential of commercial membranes for developing sensors for an application that does not require a high sensitivity but a low cost. Furthermore, the great potential of biosilica exoskeleton of diatoms for the development of sensors based on their optical response has been revealed. By contrast, the constraints found in the development of pSi illustrate the importance of an accurate study and optimisation of porous materials structure before using them for (bio)sensing. / Martínez Pérez, P. (2021). Development and Optimization of Experimental Biosensing Protocols Using Porous Optical Transducers [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172541 / TESIS

<p>With 2.1 million new cases, breast cancer is the most common cancer in women. Triple negative breast cancer (TNBC), which is 15-20% of these breast cancer cases is clinically negative for expression of estrogen and progesterone receptors (ER/PR) and human epidermal growth factor receptor 2 (HER2) receptors<a>.</a> It is characterized by its unique molecular profile, aggressive behavior, distinct patterns of metastasis, and lack of targeted therapies. TNBCs utilize glycolysis for growth, proliferation, invasiveness, chemotherapeutic resistance and hence has poor therapeutic response. There is an urgent need for novel/alternate therapeutic strategies beyond current standard of treatment for this subset of high-risk patients. Electrical pulse-based chemotherapy, known as electrochemotherapy (ECT) could be a viable option for TNBC therapy. ECT involves the local application of precisely controlled electrical pulses to reversibly permeabilize the cell membrane for enhanced uptake. ECT can increase the cytotoxicity of the chemotherapeutics up-to 1000 times, facilitating a potent local cytotoxic effect. </p> <p>The high cost and severe side-effects of conventional chemotherapeutics motivate the application of effective natural compounds. Combining electrical pulses with natural compounds will enhance the treatment efficacy. This dissertation focuses on curcumin, the yellow pigment of natural herb turmeric, that has been used for over 5000 years for its excellent anticancer properties. Previous studies have demonstrated the effectiveness of curcumin for treating multiple cancers, including TNBC, with limited side effects. The potency of curcumin can be enhanced further by combining it with ECT to provide an attractive and cost-effective alternative for TNBC treatment. </p> <p>Towards this we studied the effect of ECT with curcumin on MDA-MB-231 cell line, a human adenocarcinoma epithelial TNBC cell line. We performed various assays, including cell viability, colony forming, cell cycle, apoptosis, H₂O₂ reactive oxygen species (ROS), immunoblotting, real time quantitative PCR (qPCR), and cellular metabolites detection to study the impact of ECT with curcumin on MDA-MB-231 cells. In addition, to better understand the underlying mechanisms, we used high throughput, label-free quantitative proteomics. While several studies have attempted to define the mechanism of action of curcumin on cancer cells, little is known on the action mechanism of the curcumin delivered with electrical pulses. This work unravels the molecular mechanism behind the enhanced effects observed under the ECT-based curcumin therapy in TNBC cells, employing a high-throughput, quantitative, label-free mass spectroscopy-based proteomics approach. The proteomics approach provides information on the thousands of cellular proteins involved in the cellular process, allowing a comprehensive understanding of the electro-curcumin-therapy mechanism. Similar studies were also performed for ECT with cisplatin to compare the efficacy of the electro-curcumin-therapy to the standard stand-alone cisplatin-based therapy.</p> <p>Our results revealed a switch in the metabolism from glycolysis to mitochondrial metabolic pathways. This metabolic switch caused an excessive production of H₂O₂ ROS to inflict apoptotic cell death in MDA-MB-231 cells, demonstrating the potency of this ECT based curcumin therapy. These results encourage further studies to extend the application of ECT for clinical practice.</p>

No / Here we report the identification and approximate quantification of cytochrome P450 (CYP) proteins in human liver microsomes as determined by nano-LC-MS/MS with application of the exponentially modified protein abundance index (emPAI) algorithm during database searching. Protocols based on 1D-gel protein separation and 2D-LC peptide separation gave comparable results. In total, 18 CYP isoforms were unambiguously identified based on unique peptide matches. Further, we have determined the absolute quantity of two CYP enzymes (2E1 and 1A2) in human liver microsomes using stable-isotope dilution mass spectrometry, where microsomal proteins were separated by 1D-gel electrophoresis, digested with trypsin in the presence of either a CYP2E1- or 1A2-specific stable-isotope labeled tryptic peptide and analyzed by LC-MS/MS. Using multiple reaction monitoring (MRM) for the isotope-labeled tryptic peptides and their natural unlabeled analogues quantification could be performed over the range of 0.1-1.5 pmol on column. Liver microsomes from four individuals were analyzed for CYP2E1 giving values of 88-200 pmol/mg microsomal protein. The CYP1A2 content of microsomes from a further three individuals ranged from 165 to 263 pmol/mg microsomal protein. Although, in this proof-of-concept study for CYP quantification, the two CYP isoforms were quantified from different samples, there are no practical reasons to prevent multiplexing the method to allow the quantification of multiple CYP isoforms in a single sample.