An efficient missing tag identification approach in RFID collisions

Radio frequency identification technology has been widely used to verify the presence of items in many applications such as warehouse management and supply chain logistics. In these applications, the challenge of how to timely identify the missing tags (namely tag searching or missing tag identification) is a key focus. Existing missing tag identification solutions have not achieved their full potentials because collision slots have not been well explored. In this paper, we propose an approach named collision resolving based missing tag identification (CR-MTI) to break through the performance bottleneck of existing missing tag identification protocols. In CR-MTI, multiple tags are allowed to respond with different binary strings in a collision slot. Then, the reader can verify them together by using the bit tracking technology and particularly designed string, thereby significantly improve the time efficiency. CR-MTI also reduces the number of messages transmitted by the reader using customized coding. We further explore the optimal parameter settings to maximize the performance of our proposed CR-MTI. Extensive simulation results show that our proposed CR-MTI outperforms prior art in terms of time efficiency, total executive time and communication complexity

Node Cardinality Estimation in the Internet of Things Using Privileged Feature Distillation

The Internet of Things (IoT) is emerging as a critical technology to connect resource-constrained devices such as sensors and actuators as well as appliances to the Internet. In this paper, we propose a novel methodology for node cardinality estimation in wireless networks such as the IoT and Radio-Frequency IDentification (RFID) systems, which uses the privileged feature distillation (PFD) technique and works using a neural network with a teacher-student model. The teacher is trained using both privileged and regular features, and the student is trained with predictions from the teacher and regular features. We propose node cardinality estimation algorithms based on the PFD technique for homogeneous as well as heterogeneous wireless networks. We show via extensive simulations that the proposed PFD based algorithms for homogeneous as well as heterogeneous networks achieve much lower mean squared errors in the computed node cardinality estimates than state-of-the-art protocols proposed in prior work, while taking the same number of time slots for executing the node cardinality estimation process as the latter protocols.Comment: 15 pages, 17 figures, journal pape

Secure and efficient data extraction for ubiquitous computing applications

Ubiquitous computing creates a world where computers have blended seamlessly into our physical environment. In this world, a computer is no longer a monitor-and-keyboard setup, but everyday objects such as our clothing and furniture. Unlike current computer systems, most ubiquitous computing systems are built using small, embedded devices with limited computational, storage and communication abilities. A common requirement for many ubiquitous computing applications is to utilize the data from these small devices to perform more complex tasks. For critical applications such as healthcare or medical related applications, there is a need to ensure that only authorized users have timely access to the data found in the small device. In this dissertation, we study the problem of how to securely and efficiently extract data from small devices.;Our research considers two categories of small devices that are commonly used in ubiquitous computing, battery powered sensors and battery free RFID tags. Sensors are more powerful devices equipped with storage and sensing capabilities that are limited by battery power, whereas tags are less powerful devices with limited functionalities, but have the advantage of being operable without battery power. We also consider two types of data access patterns, local and remote access. In local data access, the application will query the tag or the sensor directly for the data, while in remote access, the data is already aggregated at a remote location and the application will query the remote location for the necessary information, The difference between local and remote access is that in local access, the tag or sensor only needs to authenticate the application before releasing the data, but in remote access, the small device may have to perform additional processing to ensure that the data remains secure after being collected. In this dissertation, we present secure and efficient local data access solutions for a single RFID tag, multiple RFID tags, and a single sensor, and remote data access solutions for both RFID tag and sensor

FRAMEWORK FOR IMPROVING PERFORMANCE OF PROTOCOLS FOR READING RADIO FREQUENCY IDENTIFICATION TAGS

Radio-frequency Identification (RFID) is a highly sought-after wireless technology used to track and manage inventory in the supply chain industry. It has varied applications ranging from automated toll collection and security access management to supply chain logistics. Miniaturization and low tag costs of RFID tags have lead to item-level tagging, where not just the pallet holding products is tagged but each product inside has a tag attached to it. Item-level tagging of goods improves the accuracy of the supply chain but it significantly increases the number of tags that an RFID reader must identify and track. Faster identification is crucial to cutting cost and improving efficiency. Existing RFID protocols were designed to primarily handle static scenarios with both RFID tags and readers not being in motion. This research addresses the problem of inventory tracking within a warehouse in multitude of scenarios that involves mobile tags, multiple readers and high density environments. Mobility models are presented and frameworks are developed for the following scenarios: a) mobile tags on a conveyor belt with multiple fixed readers; b) mobile reader in a warehouse with stationary tags in shelves; and c) high density tag population with Near-Field (NF) communication. The proposed frameworks use information sharing among readers to facilitate protocol state handoff and segregation of tags into virtual zones to improve tag reading rates in mobile tag and mobile reader scenarios respectively. Further, a tag’s ability to listen to its Near-Field neighboring tags transmissions is exploited to assist the reader in resolving collisions and hence enhancing throughput. The frameworks discussed in this research are mathematically modeled with a probabilistic analysis of protocols employed in conjunction with framework. With an increased number of tags to be identified, mathematically understanding the performance of the protocol in these large-scale RFID systems becomes essential. Typically, this analysis is performed using Markov-chain models. However, these analyses suffer from the common state-space explosion problem. Hence, it is essential to come up with a scalable analysis, whose computation model is insensitive to the number of tags. The following research analyzes the performance of tag identification protocols in highly dense tag scenarios, and proposes an empirical formula to estimate the approximate time required to read all the tags in a readers range without requiring protocol execution

True single-cell proteomics using advanced ion mobility mass spectrometry

In this thesis, I present the development of a novel mass spectrometry (MS) platform and scan modes in conjunction with a versatile and robust liquid chromatography (LC) platform, which addresses current sensitivity and robustness limitations in MS-based proteomics. I demonstrate how this technology benefits the high-speed and ultra-high sensitivity proteomics studies on a large scale. This culminated in the first of its kind label-free MS-based single-cell proteomics platform and its application to spatial tissue proteomics. I also investigate the vastly underexplored ‘dark matter’ of the proteome, validating novel microproteins that contribute to human cellular function. First, we developed a novel trapped ion mobility spectrometry (TIMS) platform for proteomics applications, which multiplies sequencing speed and sensitivity by ‘parallel accumulation – serial fragmentation’ (PASEF) and applied it to first high-sensitivity and large-scale projects in the biomedical arena. Next, to explore the collisional cross section (CCS) dimension in TIMS, we measured over 1 million peptide CCS values, which enabled us to train a deep learning model for CCS prediction solely based on the linear amino acid sequence. We also translated the principles of TIMS and PASEF to the field of lipidomics, highlighting parallel benefits in terms of throughput and sensitivity. The core of my PhD is the development of a robust ultra-high sensitivity LC-MS platform for the high-throughput analysis of single-cell proteomes. Improvements in ion transfer efficiency, robust, very low flow LC and a PASEF data independent acquisition scan mode together increased measurement sensitivity by up to 100-fold. We quantified single-cell proteomes to a depth of up to 1,400 proteins per cell. A fundamental result from the comparisons to single-cell RNA sequencing data revealed that single cells have a stable core proteome, whereas the transcriptome is dominated by Poisson noise, emphasizing the need for both complementary technologies. Building on our achievements with the single-cell proteomics technology, we elucidated the image-guided spatial and cell-type resolved proteome in whole organs and tissues from minute sample amounts. We combined clearing of rodent and human organs, unbiased 3D-imaging, target tissue identification, isolation and MS-based unbiased proteomics to describe early-stage β-amyloid plaque proteome profiles in a disease model of familial Alzheimer’s. Automated artificial intelligence driven isolation and pooling of single cells of the same phenotype allowed us to analyze the cell-type resolved proteome of cancer tissues, revealing a remarkable spatial difference in the proteome. Last, we systematically elucidated pervasive translation of noncanonical human open reading frames combining state-of-the art ribosome profiling, CRISPR screens, imaging and MS-based proteomics. We performed unbiased analysis of small novel proteins and prove their physical existence by LC-MS as HLA peptides, essential interaction partners of protein complexes and cellular function

Reliable identification of protein-protein interactions by crosslinking mass spectrometry

Protein-protein interactions govern most cellular pathways and processes, and multiple technologies have emerged to systematically map them. Assessing the error of interaction networks has been a challenge. Crosslinking mass spectrometry is currently widening its scope from structural analyses of purified multi-protein complexes towards systems-wide analyses of protein-protein interactions (PPIs). Using a carefully controlled large-scale analysis of Escherichia coli cell lysate, we demonstrate that false-discovery rates (FDR) for PPIs identified by crosslinking mass spectrometry can be reliably estimated. We present an interaction network comprising 590 PPIs at 1% decoy-based PPI-FDR. The structural information included in this network localises the binding site of the hitherto uncharacterised protein YacL to near the DNA exit tunnel on the RNA polymerase.TU Berlin, Open-Access-Mittel – 2021DFG, 390540038, EXC 2008: Unifying Systems in Catalysis "UniSysCat"DFG, 392923329, GRK 2473: Bioaktive Peptide - Innovative Aspekte zur Synthese und BiosyntheseDFG, 426290502, Erfassung der strukturellen Organisation des Mycoplasma pneumoniae Proteoms mittels in-Zell Crosslinking-Massenspektrometri

Development and application of quantitative proteomics strategies to analyze molecular mechanisms of neurodegeneration

Neurodegenerative disorders such as Alzheimer’s disease, Huntington’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis or Prion diseases are chronic, incurable and often fatal. A cardinal feature of all neurodegenerative disorders is the accumulation of misfolded and aggregated proteins. Depending on the disease, these aggregated proteins are cell type specific and have distinct cellular localizations, compositions and structures. Despite intensive research, the contribution of protein misfolding and aggregation to the cell type specific toxicity is not completely understood. In recent years, quantitative proteomics has matured into an exceptionally powerful technology providing accurate quantitative information on almost all cellular proteins as well as protein interactions, modifications, and subcellular localizations, which cannot be addressed by other omics technologies. The aim of this thesis is to investigate key features of neurodegeneration such as misfolded proteins and toxic protein aggregates with cutting edge proteomics, presenting a technological “proof of concept” and novel insights into the (patho)physiology of neurodegeneration

Quantification and Simulation of Liquid Chromatography-Mass Spectrometry Data

Computational mass spectrometry is a fast evolving field that has attracted increased attention over the last couple of years. The performance of software solutions determines the success of analysis to a great extent. New algorithms are required to reflect new experimental procedures and deal with new instrument generations. One essential component of algorithm development is the validation (as well as comparison) of software on a broad range of data sets. This requires a gold standard (or so-called ground truth), which is usually obtained by manual annotation of a real data set. Comprehensive manually annotated public data sets for mass spectrometry data are labor-intensive to produce and their quality strongly depends on the skill of the human expert. Some parts of the data may even be impossible to annotate due to high levels of noise or other ambiguities. Furthermore, manually annotated data is usually not available for all steps in a typical computational analysis pipeline. We thus developed the most comprehensive simulation software to date, which allows to generate multiple levels of ground truth and features a plethora of settings to reflect experimental conditions and instrument settings. The simulator is used to generate several distinct types of data. The data are subsequently employed to evaluate existing algorithms. Additionally, we employ simulation to determine the influence of instrument attributes and sample complexity on the ability of algorithms to recover information. The results give valuable hints on how to optimize experimental setups. Furthermore, this thesis introduces two quantitative approaches, namely a decharging algorithm based on integer linear programming and a new workflow for identification of differentially expressed proteins for a large in vitro study on toxic compounds. Decharging infers the uncharged mass of a peptide (or protein) by clustering all its charge variants. The latter occur frequently under certain experimental conditions. We employ simulation to show that decharging is robust against missing values even for high complexity data and that the algorithm outperforms other solutions in terms of mass accuracy and run time on real data. The last part of this thesis deals with a new state-of-the-art workflow for protein quantification based on isobaric tags for relative and absolute quantitation (iTRAQ). We devise a new approach to isotope correction, propose an experimental design, introduce new metrics of iTRAQ data quality, and confirm putative properties of iTRAQ data using a novel approach. All tools developed as part of this thesis are implemented in OpenMS, a C++ library for computational mass spectrometry

Optimization of High Field Asymmetric Waveform Ion Mobility Spectrometry to enhance the comprehensiveness of mass spectrometry-based proteomic analyses

La grande complexité des échantillons biologiques peut compliquer l'identification des protéines et compromettre la profondeur et la couverture des analyses protéomiques utilisant la spectrométrie de masse. Des techniques de séparation permettant d’améliorer l’efficacité et la sélectivité des analyses LC-MS/MS peuvent être employées pour surmonter ces limitations. La spectrométrie de mobilité ionique différentielle, utilisant un champ électrique élevé en forme d'onde asymétrique (FAIMS), a montré des avantages significatifs dans l’amélioration de la transmission d'ions peptidiques à charges multiples, et ce, en réduisant les ions interférents. Dans ce contexte, l'objectif de cette thèse était d'explorer les capacités analytiques de FAIMS afin d'élargir à la fois la gamme dynamique de détection des protéines/peptides et la précision des mesures en protéomique quantitative par spectrométrie de masse. Pour cela, nous avons systématiquement intégré FAIMS dans des approches classiques en protéomique afin de déterminer les changements dynamiques du protéome humain en réponse à l’hyperthermie. Nous avons d’abord étudié les avantages de FAIMS par rapport à la quantification par marquage isobare (tandem mass tag, TMT). Cette approche permet le marquage d'ions peptidiques avec différents groupements chimiques dont les masses nominales sont identiques mais différant par leur distribution respective d'isotopes stables. Les ions peptidiques marqués par TMT produisent des ions rapporteurs de masses distinctes une fois fragmentés en MS/MS. Malheureusement, la co-sélection d'ions précurseurs conduit souvent à des spectres MS/MS chimériques et une approche plus lente basée sur le MS3 est nécessaire pour une quantification précise. Comme FAIMS améliore l’efficacité de séparation en transmettant sélectivement des ions en fonction de leur voltage de compensation (CV), nous avons obtenu moins de co-sélection de peptides. FAIMS a amélioré la quantification des peptides TMT au niveau MS2 et a permis d’obtenir 68% plus de peptides quantifiés par rapport aux analyses LC-MS/MS classiques, fournissant ainsi un aperçu plus vaste des changements dynamiques du protéome humain en réponse au stress thermique. De plus, nous avons étudié le marquage métabolique par incorporation d’acides aminés marqués par des isotopes stables en culture cellulaire (SILAC). Si des interférences co-éluent avec les isotopes SILAC, la quantification devient imprécise et les contreparties de SILAC peuvent être assignées de manière erronée aux ions interférants du chromatogramme, faussant ainsi le rapport SILAC. Le fractionnement post-ionisation FAIMS pourrait filtrer les ions appartenant au bruit de fond qui pourraient autrement être attribués à une paire ou à un triplet SILAC pour la quantification. Dans ce projet, FAIMS a été particulièrement bénéfique pour les espèces peu abondantes et s’est montré plus performant que le fractionnement par échange de cations (SCX). En outre, FAIMS a permis la séparation des phosphoisomères fréquemment observés dans les extraits complexes de phosphoprotéomes. Le troisième objectif de ce travail de recherche était d'explorer la séparation de l'état de charge et la transmission améliorée de peptides fortement chargés avec FAIMS et son application à l'analyse de peptides SUMOylés. FAIMS pourrait ainsi améliorer la transmission des peptides SUMOylés triplement chargés par rapport aux peptides tryptiques usuels, lesquels sont principalement doublement chargés. Ceci permettait l'enrichissement en phase gazeuse des ions peptides SUMOylés. FAIMS est une approche alternative plus simple pour fractionner les peptides SUMOylés, ce qui réduit les pertes d’échantillon et permet de simplifier le traitement des échantillons, tout en augmentant l’efficacité de séparation de manière plus automatisée et en ajoutant un ordre de grandeur de sensibilité. Le dernier objectif de cette thèse était d’améliorer l’instrumentation de FAIMS en le jumelant aux instruments à la fine pointe de la technologie. Avec un nouveau dispositif FAIMS, développé par nos collaborateurs chez Thermo Fisher Scientific, nous avons montré une amélioration dans la robustesse et la transmission des ions pour la nouvelle interface. Dans des expériences simples en protéomique shotgun, FAIMS a étendu la gamme dynamique d'un ordre de grandeur pour une couverture protéomique plus profonde par rapport aux analyses LC-MS/MS classiques. En outre, le fractionnement en phase gazeuse de FAIMS a généré moins d’analyses chimériques en MS2, ce qui a permis d’obtenir plus d’identifications et une meilleure quantification. Pour ce faire, nous avons directement comparé le LC-FAIMS-MS/MS au LC-MS/MS/MS en utilisant la sélection de précurseur synchrone (SPS) avec et sans fractionnement en phase inverse basique. Des mesures quantitatives comparables ont été obtenues pour toutes les méthodes, à l'exception du fait que FAIMS a parmi d’obtenir un nombre 2,5 fois plus grand de peptides quantifiables par rapport aux expériences sans FAIMS. Globalement, cette thèse met en évidence certains des avantages que FAIMS peut offrir aux expériences en protéomique en améliorant à la fois l'identification et la quantification des peptides.The high complexity of biological samples can confound protein identification and compromise the depth and coverage of mass spectrometry-based proteomic analyses. Separation techniques that provide improved peak capacity and selectivity of LC-MS/MS analyses are often sought to overcome these limitations. High-field asymmetric waveform ion mobility spectrometry (FAIMS), a differential ion mobility device, has shown significant advantages by enhancing the transmission of multiple-charged peptide ions by reducing singly-charged interferences. In this context, the goal of this thesis was to explore the analytical capabilities of FAIMS to extend both the dynamic range of proteins/peptides detection and the precision of quantitative proteomic measurements by mass spectrometry. For this, we systematically integrated FAIMS in standard workflows to monitor the dynamic changes of the human proteome in response to hyperthermia. We first studied the merits of FAIMS to aid isobaric labeling quantification with tandem mass tags (TMT). This approach allows the labeling of peptide ions with different chemical groups of identical nominal masses but differing in their respective distribution of stable isotopes. TMT-labeled peptide ions produce reporter ions of distinct masses once fragmented by MS/MS. Unfortunately, the co-selection of precursor ions often leads to chimeric MS/MS spectra, and a slower MS3 centric approach is needed for precise quantification. Since FAIMS improves peak capacity by selectively transmitting ions based on their compensation voltage (CV), we obtained less peptide co-selection. FAIMS improved TMT quantification at the MS2 level and achieved 68 % more quantified peptides compared to regular LC-MS/MS, providing a deeper insight into the dynamic changes of the human proteome in response to heat stress. Further, we investigated stable isotope labeling by amino acids in cell culture (SILAC) quantification. If interferences co-elute simultaneously with SILAC isotopomers, quantification becomes inaccurate and SILAC counterparts can be missassigned to interfering ions in the highly populated chromatogram, thus skewing the SILAC ratio. FAIMS post-ionization fractionation could filter out background ions that can otherwise be attributed to a SILAC pair/triplet for quantification. In this work, FAIMS was especially beneficial for low abundant species and outperformed the standard strong cation exchange (SCX) fractionation workflow. In addition, FAIMS allowed the separation of phosphoisomers that are frequently observed in complex phosphoproteome extracts. The third aim of this work explored the charge state separation and enhanced transmission of highly charged peptides with FAIMS and its application for SUMOylated peptide analysis. FAIMS could enhance the transmission of triply charged SUMOylated peptides over typical tryptic peptide that are predominantly doubly charged, by applying more negative CVs with FAIMS. This allowed for gas-phase enrichment of SUMOylated peptide ions. FAIMS is an alternate and more straightforward approach to fractionate SUMOylated peptides that reduced sample loss, avoided sample processing, while increasing peak capacity in a more automated manner and added one order of magnitude in sensitivity. The last aim of this thesis was to improve the FAIMS instrumentation by interfacing it to the latest state-of-the-art instruments. With a new FAIMS device developed by our collaborators at Thermo Fisher Scientific, we demonstrate the robustness and the improved ion transmission for the new interface. In simple shotgun proteomics, FAIMS extended the dynamic range by one order of magnitude for deeper proteome coverage compared to regular LC-MS/MS. Moreover, fewer MS2 chimeric scans were generated with FAIMS gas-phase fractionation, which garnered more identifications and better quantification. For this, we directly compared LC-FAIMS-MS/MS to LC-MS/MS/MS using synchronous precursor selection (SPS) with and without basic reverse phase fractionation. Comparable quantitative measurements were obtained for all methods, except that FAIMS provided a 2.5-fold increase in the number of quantifiable peptides compared with non-FAIMS experiments. Overall, this thesis highlights some of the advantages that FAIMS can provide for proteomics experiments by improving both peptide identification and quantification