Sequencing technologies and applications have pushed the limits and enabled novel studies of biological mechanisms, evolutionary relationships and communication networks between cells. The technical developments leading to single cell RNA-sequencing have enabled detection of rare cell populations while spatial resolution added insights into larger biological environments, like tissues and organs. Massively parallel sequencing has paved the way for integrated high-throughput analyses including that of studying gene expression, protein expression and mapping of microbial communities. This thesis starts with an introduction describing the technical and biological advancements made in recent years with focus on spatially resolved approaches. Then, a summary of recent accomplishments is presented, which enabled ongoing work in a novel field of spatial hostmicrobiome profiling. Lastly, the concluding remarks include both a future perspective and a short reflection on the current developments in the spatial multi-omics field. 16S sequencing is often used for taxonomic classification of bacteria. In Paper I, this sequencing technique was used to study the aerodigestive microbiome in pediatric lung transplant recipients. Many of these patients regretfully reject the organ after transplant, but the underlying cause is, in many cases, unknown. In this paper, multiple factors influencing rejection were examined including that of the aerodigestive microbiome. Pediatric lung transplant recipients often suffer from gastrointestinal dysmotility and the focus of this study was also to analyze changes in the microbiome in relation to irregular gastric muscle movements. The results showed that lung transplant recipients had, in general, lower microbial diversity in the gastric fluid and throat and also that the microbial overlap between lung and gastric sampling sites was significantly less in transplant recipients compared to controls. In addition, gastrointestinal dysmotility was shown to influence the gastric microbiome in lung transplant recipients, but, given the small sample size available in this study, the correlation to patient outcome could not be examined. Integrated analysis of the transcriptome and the antibody-based proteome in the same tissue section was enabled using the method developed in Paper II. Spatial Multi- Omics (SM-Omics) uses a barcoded glass array to capture mRNA and antibody-based expression of selected proteins in the same section. The antibody-based profiling of the tissue section was enabled by either immunofluorescence or DNA-barcoded antibodies that were then decoded by sequencing. The protocol was scaled-up using an automated liquidhandling system. Using this method, simultaneous profiling of the transcriptome and multiplexed protein values was determined in both the mouse brain cortex and mouse spleen. Results showed a high correlation in spatial pattern between gene expression and antibody measurements, independently of the antibody labelling technique. SM-Omics generates a high-plex multi-omics characterization of the tissue in a high throughput manner while exhibiting low technical variation. / Tekniker och applikationer som använder sekvensering har flyttat fram gränsernaoch tillåtit nya undersökningar av biologiska mekanismer, evolutionära släktskap ochkommunikationsnätverk mellan celler. De tekniska utvecklingarna som har lett fram tillRNA-sekvensering av enskilda celler har möjliggjort upptäckten av sällsynta cellpopulationer medan den rumsliga upplösningen har inneburit en ökad förståelse av störrebiologiska miljöer, såsom vävnader och organ. Massively parallel sequencing har banat vägför integrerade analyser med hög kapacitet, vilket inkluderar analys av genuttryck,proteinuttryck och kartläggning av bakteriella samhällen. Den här avhandlingen börjar meden introduktion som beskriver tekniska och biologiska framsteg som gjorts de senaste åren,med fokus på den rumsliga upplösningen. Sedan följer en summering av de senasteprestationerna som har möjliggjort det pågående arbetet i ett nytt fält som avhandlarrumslig profilering av bakterien och dess värd. Slutligen innehåller slutordet både ettframtida perspektiv samt en kort reflektion av den nuvarande utvecklingen inom fälten förrumslig mång-omik. 16S-sekvensering används ofta för att taxonomiskt klassificera bakterier. Dennasekvenseringsteknik användes i artikel I för att studera mikrobiomet i luft- ochmatspjälkningskanalen hos barn med transplanterad lunga. Dessvärre är det vanligt medavstötning av lungan efter transplantationen hos många av dessa patienter, men denunderliggande orsaken till avstötningen är, i många fall, okänd. I denna studie undersöktesflertalet faktorer, inklusive mikrobiomet i luft- och matspjälkningskanalen, som kan tänkaspåverka bortstötningen. Barn med transplanterad lunga lider ofta av störningar i magtarmkanalens rörelser och artikelns fokus var därmed även att analysera förändringar imikrobiomet i relation till dessa avvikande rörelser i mag-tarmkanalen. Resultatet visade attpatienter med transplanterad lunga generellt hade lägre bakteriell mångfald i magsaft ochhals, samt att det bakteriella överlappet mellan lunga och magsaft var signifikant mindre ipatienter med transplanterad lunga jämfört med kontrollerna. För övrigt visade det sig attstörningar i mag-tarmkanalens rörelser påverkade magsaftens mikrobiom hos patientermed transplanterad lunga, men på grund av studiens storlek på urvalet, kunde det inteundersökas hur detta korrelerade till utfallet hos patienterna. Integrerad analys av transkriptomet och antikroppsbaserad analys av proteomet isamma vävnadssnitt har möjliggjorts genom metoden som utvecklats i artikel II. SpatialMulti-Omics (SM-Omics) använder ett avkodningsbart mönster av korta DNA-segment påen glasyta för att fånga mRNA och antikroppsbaserat uttryck av utvalda proteiner frånsamma vävnadssnitt. Den antikroppsbaserade profileringen av vävnadssnittet uppnåddesgenom antingen immunofluorescens eller antikroppar märkta med DNA-segment somkunde avkodas genom sekvensering. Protokollet skalades upp genom ett automatiseratsystem för att behandla vätskor. Genom användning av denna metod kunde simultanprofilering av transkriptomet och flertalet proteiner uppnås i både hjärnbarken och mjältenhos en mus. Resultaten visade en hög korrelation i det rumsliga mönstret mellangenuttrycket och de antikroppsbaserade mätningarna, oberoende av hur antikropparnahade märkts. SM-Omics genererar en storskalig karaktärisering av vävnaden av flera omikermed hög kapacitet samtidigt som den har låg teknisk variation. / <p>QC 2021-02-02</p>

In situ hybridization (ISH) has played a crucial role in developing a spatial transcriptomic understanding of emerging model organisms in the past, but advancing high-throughput RNA-sequencing (RNA-seq) technology has pushed this method into the shadows, leading to a loss of data resolution. This shift in research towards the exclusive use of RNA-seq neglects essential considerations for transcriptomic studies including the spatial and temporal expression of transcripts, available budget, experimental design needs, and validation of data. A synergy of spatiotemporal transcriptomic techniques is needed, using the bulk and unbiased analysis of RNA-seq and the visual validation and spatiotemporal resolution of ISH. Integration of this synergistic approach can improve our molecular understanding of non-model organisms and establish the background data needed for advancing research techniques. A prime example lies within an emerging model of the marine science and symbiosis fields, where I present a case study on a threatened coral reef keystone – the cnidarian-dinoflagellate symbiosis. Establishing a whole-mount ISH protocol for the emerging cnidarian model Aiptasia (sea anemone) will help future studies reveal the gene regulation underpinning the establishment, persistence, and breakdown of this complex symbiotic relationship.

Durch die vorliegende Arbeit wurde gezeigt, dass Rhodobacter sphaeroides das Potenzial besitzt, umweltverträglich photoheterotroph Wasserstoff als alternativer, erneuerbarer Energieträger zu erzeugen. Aus genomischen und transkriptomischen Erkenntnissen konnten Rückschlüsse auf Ansatzpunkte für weitere Optimierungen getroffen werden. Durch ein neues Minimalmedium, welches zukünftig sogar einen Beitrag zur Abfallbeseitigung leisten kann, wurde ein wichtiger Schritt hinsichtlich der industriellen Anwendbarkeit von R. sphaeroides für die biologische Wasserstoffproduktion gemacht.:Danksagung Datenverfügbarkeit Inhaltsverzeichnis Abbildungsverzeichnis Tabellenverzeichnis Abkürzungsverzeichnis 1. Einleitung 1.1 Wasserstoff 1.1.1 Wasserstoff als Energieträger 1.1.2 Herstellung von Wasserstoff 1.1.2.1 Konventionelle Wasserstoffproduktion 1.1.2.2 Biologische Wasserstoffproduktion 1.1.2.3 Biologische Wasserstoffproduktion aus Abfällen 1.2 Photosynthetische Bakterien 1.2.1 Rhodobacter sphaeroides im Kontext der biologischen Wasserstoffproduktion 1.2.2 An der Wasserstoffproduktion beteiligte Enzyme 1.3 Third Generation-Sequencing Technologien 2. Zielstellung 3. Material 3.1 Chemikalien 3.2 Medien und Pufferlösungen 3.2.1 Van Niel´s Yeast Medium 3.2.2 Medium nach Krujatz et al. (2014) 3.2.3 RÄ-Medium nach Mougiakos et al. (2019) 3.2.4 PY (Peptone Yeast) Agarmedium 3.2.5 2x YT Medium 3.2.6 LB Medium 3.2.7 GYCC Medium 3.2.8 SOB Medium 3.2.9 SOC Medium 3.2.10 Pufferlösungen 3.3 Mikroorganismen 3.4 Molekularbiologische Reagenzien und Primer 3.5 Plasmide 3.5.1 pCas9 3.5.2 pRKPOL2 3.5.3 pSUPPOL2Sca 3.5.4 pBBRBB-Ppuf843-1200-DsRed 3.5.5 pBBR_cas9_NT 3.6 Geräte 4. Methoden 4.1 Rhodobacter sphaeroides Dauerkultur in Van Niel´s Yeast Medium 112 (ohne Wasserstoffproduktion) 4.2 Rhodobacter sphaeroides Batch-Kultivierung 4.2.1 Kultivierung in Medium nach Krujatz et al. (2014); Vollmedium mit Wasserstoffproduktion 4.2.2 Kultivierung in Fruchtsaftmedium 4.3 Rhodobacter sphaeroides Kultivierung mit kontinuierlicher Aufzeichnung von Temperatur, pH, optischer Dichte, Wasserstoffproduktion und Gasanalyse 4.4 Zellernte 4.5 Nukleinsäureextraktion mit dem MasterPureTM Complete RNA and DNA Purification Kit 4.6 DNase-Abbau 4.7 RNase-Abbau 4.8 Qualitätskontrolle der RNA und DNA mit dem Agilent 2100 Bioanalyzer 4.9 Reverse Transkription und Probenaufreinigung 4.10 qRT-Polymerasekettenreaktion 4.11 Etablierung der CRISPR-Cas9- Methodik bei Rhodobacter sphaeroides – Gen-Knockout der Hydrogenase Untereinheit hupL mit CRISPR-Cas9 4.11.1 Anzucht der Escherichia coli Stämme mit und ohne Plasmid 4.11.2 Plasmid Extraktion mit GeneJET Plasmid Miniprep Kit (#K0502, Thermo Scientific) 4.11.3 Restriktionsverdau zur Vektorlinearisierung 4.11.4 Design der guideRNA 4.11.5 Phosphorylierung der guideRNA 4.11.6 Ligation der guideRNA in pCas9 4.11.7 Transformation pCas9_hupL1/hupL2 in Escherichia coli JM109 durch chemische Kompetenz 4.11.8 Colony-PCR zum Insertnachweis hupL1&2 in pCas9 mit GoTaq® G2 Green Master Mix (Promega) 4.11.9 Konstruktion weiterer Vektoren mit CRISPR-Cas9 Maschinerie aus pCas9_hupL1/2 4.12 Genomeditierung in Rhodobacter sphaeroides 4.12.1 Transformation durch chemische Kompetenz mit PEG-Methode 4.12.2 Transformation durch chemische Kompetenz nach Hanahan et al. (1991) 4.12.3 Konjugation mit Escherichia coli S17-1 4.12.4 Elektroporation 4.12.5 Bioballistische Genomeditierung mit PDS-1000/He Particle Delivers System (BIORAD) 4.12.6 Konjugation mit Escherichia coli S17-1 nach Mougiakos et al. (2019) 65 4.13 Probenvorbereitung für Sequenzierungen 4.13.1 Illumina MiSeq (Genomsequenzierung) 4.13.2 MinION (Genomsequenzierung) 4.13.3 Illumina HiSeq (Transkriptomsequenzierung) 4.14 Bioinformatische Methoden 4.14.1 Genomsequenzierung (Re-Sequenzierung) 4.14.2 Transkriptom-Datenanalyse 5. Ergebnisse und Diskussion 5.1 Schrittweise Reduktion des Vollmediums nach Krujatz et al. (2014) zum Fruchtsaft-Minimalmedium 5.2 Untersuchung der Wasserstoffproduktion in Fruchtsaft-Minimalmedium 5.3 Kontinuierliche Aufzeichnung von Prozessdaten im 1,2 L Bioreaktor 5.3.1 Vergleich der Reaktorläufe in Vollmedium nach Krujatz et al. (2014), Trauben- und Ananas-Minimalmedium der Stämme DSM 158 und SubH2 5.3.2 Prozessgasanalyse 5.4 Analyse des Genoms 5.4.1 Multiples Sequenzalignment der kompletten genomischen Assemblies von Rhodobacter sphaeroides 5.4.2 MiSeq-Sequenzierung des Stammes Rhodobacter sphaeroides 2.4.1. SubH2 5.4.2.1 Bioinformatische Funktionsanalyse von SNPs 5.4.2.2 SNP-Analyse mittels Homology-Modeling 5.4.3 Genomische Architekturanalyse mittels MinION Sequenzierung der Rhodobacter sphaeroides Stämme DSM 158 und 2.4.1. SubH2 5.4.4 Vergleich der MiSeq- und MinION Genomanalysen 5.5 Analyse des Transkriptoms 5.6 Analyse der Genexpression mit qRT-PCR im Vergleich mit der Wasserstoffproduktion 5.7 CRISPR-Cas9 zum Plasmid-basierten hupL Knock-out 5.7.1 Erstellung der Plasmide pCas9_hupL1 und pCas9_hupL2 5.7.2 PEG-basierte Transformation nach Fornari et al. (1982) 5.7.3 Transformation mittels Elektroporation 5.7.4 Erstellung weiterer Vektoren mit CRISPR-Cas9_Maschinerie aus pCas9_hupL1&2 5.7.5 Transformation mittels Konjugation I 5.7.6 Bioballistische Transformation 5.7.7 Problembehandlung zur Transformation 5.7.8 Transformation mittels Konjugation II 6 Zusammenfassung 7 Ausblick 8 Summary Literaturverzeichnis Anhangsverzeichnis Anhang Versicherung

Transcriptome analysis based on deep expressed sequence tag (EST) sequencing allows quantitative comparisons of gene expression across multiple species. Using pyrosequencing, we generated over 7 million ESTs from four stages of developing seeds of Ricinus communis, Brassica napus, Euonymus alatus and Tropaeolum majus, which differ in their storage tissue for oil, their ability to photosynthesize and in the structure and content of their triacylglycerols (TAG). The larger number of ESTs in these 16 datasets provided reliable estimates of the expression of acyltransferases and other enzymes expressed at low levels. Analysis of EST levels from these oilseeds revealed both conserved and distinct species-specific expression patterns for genes involved in the synthesis of glycerolipids and their precursors. Independent of the species and tissue type, ESTs for core fatty acid synthesis enzymes maintained a conserved stoichiometry and a strong correlation in temporal profiles throughout seed development. However, ESTs associated with non-plastid enzymes of oil biosynthesis displayed dissimilar temporal patterns indicative of different regulation. The EST levels for several genes potentially involved in accumulation of unusual TAG structures were distinct. Comparison of expression of members from multi-gene families allowed the identification of specific isoforms with conserved function in oil biosynthesis. In all four oilseeds, ESTs for Rubisco were present, suggesting its possible role in carbon metabolism, irrespective of light availability. Together, these data provide a resource for use in comparative and functional genomics of diverse oilseeds. Expression data for more than 350 genes encoding enzymes and proteins involved in lipid metabolism are available at the 'ARALIP' website ().

Genome-wide studies are prone to false positives due to inherently low priors and statistical power. One approach to ameliorate this problem is to seek validation of reported candidate genes across independent studies: genes with repeatedly discovered effects are less likely to be false positives. Inversely, genes reported only as many times as expected by chance alone, while possibly representing novel discoveries, are also more likely to be false positives. We show that, across over 30 genome-wide studies that reported Drosophila and Daphnia genes with possible roles in thermal adaptation, the combined lists of candidate genes and orthologous groups are rapidly approaching the total number of genes and orthologous groups in the respective genomes. This is consistent with the expectation of high frequency of false positives. The majority of these spurious candidates have been identified by one or a few studies, as expected by chance alone. In contrast, a noticeable minority of genes have been identified by numerous studies with the probabilities of such discoveries occurring by chance alone being exceedingly small. For this subset of genes, different studies are in agreement with each other despite differences in the ecological settings, genomic tools and methodology, and reporting thresholds. We provide a reference set of presumed true positives among Drosophila candidate genes and orthologous groups involved in response to changes in temperature, suitable for cross-validation purposes. Despite this approach being prone to false negatives, this list of presumed true positives includes several hundred genes, consistent with the “omnigenic” concept of genetic architecture of complex traits.

Antibiotic resistance is a quickly emerging public health crisis, accounting for more than 700,000 annual global deaths. Global human antibiotic overuse and misuse has significantly expedited the rate at which bacteria become resistant to antibiotics. A renewed focus on discovering new antibiotics is one approach to addressing this crisis. However, it alone cannot solve the problem: historically, the introduction of a new antibiotic has consistently, and at times rapidly, been followed by the appearance and dissemination of resistant bacteria. It is thus crucial to develop strategies to improve how we select and deploy antibiotics so that we can control and prevent the emergence and transmission of antibiotic resistance. Current gold-standard antibiotic susceptibility tests measure bacterial growth, which can take up to 72 hours. However, bacteria exhibit more immediate measurable phenotypes of antibiotic susceptibility, including changes in transcription, after brief antibiotic exposure. In this dissertation I develop a framework for building a paper-based cell-free toehold sensor antibiotic susceptibility test that can detect differential mRNA expression. I also explore how long-term lab evolution experiments can be used to prospectively uncover transcriptional signatures of antibiotic susceptibility. Paper-based cell-free systems provide an opportunity for developing clinically tractable nucleic-acid based diagnostics that are low-cost, rapid, and sensitive. I develop a computational workflow to rapidly and easily design toehold switch sensors, amplification primers, and synthetic RNAs. I develop an experimental workflow, based on existing paper-based cell-free technology, for screening toehold sensors, amplifying bacterial mRNA, and deploying sensors for differential mRNA detection. I combine this work to introduce a paper-based cell-free toehold sensor antibiotic susceptibility test that can detect fluoroquinolone-susceptible E. coli. Next, I describe a methodology for long-term lab evolution and how it can be used to explore the relationship between a phenotype, such as gene expression, and antibiotic resistance acquisition. Using a set of E. coli strains evolved to acquire tetracycline resistance, I explore how each strain's transcriptome changes as resistance increases. Together, this work provides a set of computational and experimental methods that can be used to study the emergence of antibiotic resistance, and improve upon available methods for properly selecting and deploying antibiotics. / 2023-03-17T00:00:00Z

Le phytopathogène Agrobacterium tumefaciens est l'agent causal de la maladie appelée galle du collet, et est capable d'infecter plus de 90 familles de plantes dicotylédones. Cette ∝-protéobactérie appartient à la famille Rhizobiaceae. A. tumefaciens est un complexe de différentes espèces regroupées en 10 génomovars (G1 à G8 et G13). A. tumefaciens C58 appartient au groupe du G8. Son génome est constitué de 4 réplicons : 1 chromosome circulaire, 1 chromosome linéaire et des 2 plasmides dispensables : pAt (pour A.tumefaciens) et pTi (pour Tumor inducing, qui est requis pour la virulence). Pour explorer de nouveaux aspects du mode de vie d’A. tumefaciens, et en particulier l'interaction entre la bactérie et sa plante hôte, deux approches différentes ont été utilisées pour identifier, caractériser et analyser les gènes qui pourraient jouer un rôle dans l'adaptation des bactéries à la tumeur. Une expérience de l'évolution par des passages en série de trois souches différentes de l'agent pathogène sur la plante hôte Solanum lycopersicum a été effectuée afin de clarifier la dynamique évolutive du génome au cours de l'infection. Parallèlement, une étude de différents transcriptomes (in planta et in vitro) a été réalisée et étudiée pour élucider des gènes bactériens candidats impliqués dans l'interaction de la bactérie avec la plante et divers composés produits dans la tumeur. Ce travail tente de donner une vue plus générale du processus d'adaptation de la bactérie à la niche écologique qui est la tumeur. / Agrobacterium tumefaciens is the causal agent of the plant disease called crowngall, and it’s able to infect more than 90 families of dicotyledonous plants. It is an α-Proteobacterium and belongs to the Rhizobiaceae family. A. tumefaciens is a complex of different species grouped in 10 genomovars (G1 to G8, and G13). A. tumefaciens C58 belongs to the G8 group. Its genome consists in 4 replicons: 1 chromosome circular, 1 chromosome linear and 2 dispensable plasmids: pAt (for A. tumefaciens) and pTi (for Tumor inducing), which is required for virulence. To explore new aspects of the A. tumefaciens lifestyle, and in particular the interaction between the bacteria and its plant host, two different approaches have been used to identify, characterize and analyze genes that could play a role in the adaptation of the bacteria to tumor lifestyle. An evolution experiment by serial passages of three different strains of thepathogen on the host plant Solanum lycopersicum has been carried out to clarify the evolutionary dynamics of the genome during the course of infection. In parallel, a study of different transcriptomes (in planta and in vitro) was performed and studied to elucidate bacterial candidate genes involved in the interaction of the bacteria with the plant and various compounds produced in the tumor. This work attempts to give a more general view of the process of adaptation of the bacteria to the ecological niche that is the tumor.

One of the goals of neuroscience is to classify all of the neurons in the brain. Neuronal types can be defined using a combination of morphology, electrophysiology, and gene expression profiles. Gene expression profiles allow differentiation between cells that share similar characteristics. Leveraging the advantage of Berghia stephanieae (Gastropoda; Nudibranchia), which has around 28,000 neurons, I constructed high-throughput single-neuron transcriptomes for its whole brain. I produced a single-cell dissociation protocol and a custom data analysis pipeline for data of this nature. Around 129,000 cells were collected from 18 rhinophore ganglia and 20 circumesophageal ring ganglia (brain), consisting of the cerebropleural, pedal, and buccal ganglia. Messenger RNA libraries were constructed using the 10X Genomics’ Chromium platform. After library preparation, around 1,000 cells were recovered and sequenced. The HTStream package was utilized to trim off unwanted sequences from the raw reads and remove PCR duplicates and other contamination, then the salmon alevin package was employed to construct gene-by-cell matrices containing all the transcripts for each gene in each cell. The Seurat pipeline was used to extract this expression data from the matrices, normalize it, and perform dimensionality reduction. The cells were clustered based on similarities in their gene expression profiles. The cells formed eight clusters on a UMAP graph, each having distinct marker genes. Additionally, one cluster was composed of almost exclusively cells from the rhinophore ganglia, accounting for 30% of all rhinophore ganglion cells in the sample. Cells from the rhinophore ganglia are as heteregenous as cells from the rest of the brain, with cells forming six clusters. Cell populations that express the same neurotransmitter were identified for a wide range of both small-molecule neurotransmitters and neuropeptides. In a separate project, the locomotion of Berghia was recorded over 9 days with 2 lighting regimes: LD first and DD first. The results suggest that locomotion of Berghia is governed by circadian clock and that Berghia is nocturnal. Hunger state likely plays a role in modulating this circadian rhythm.

La transcriptómica estudia el nivel de expresión de los genes en distintas condiciones experimentales para tratar de identificar los genes asociados a un fenotipo dado así como las relaciones de regulación entre distintos genes. Los datos ómicos se caracterizan por contener información de miles de variables en una muestra con pocas observaciones. Las tecnologías de alto rendimiento más comunes para medir el nivel de expresión de miles de genes simultáneamente son los microarrays y, más recientemente, la secuenciación de RNA (RNA-seq). Este trabajo de tesis versará sobre la evaluación, adaptación y desarrollo de modelos estadísticos para el análisis de datos de expresión génica, tanto si ha sido estimada mediante microarrays o bien con RNA-seq. El estudio se abordará con herramientas univariantes y multivariantes, así como con métodos tanto univariantes como multivariantes. / Tarazona Campos, S. (2014). Statistical methods for transcriptomics: From microarrays to RNA-seq [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48485 / TESIS / Premios Extraordinarios de tesis doctorales

Indiana University-Purdue University Indianapolis (IUPUI) / The successful treatment and potential eradication of many complex diseases, such as cancer, begins with elucidating the convoluted mapping of molecular profiles to phenotypical manifestation. Our observed molecular profiles (e.g., genomics, transcriptomics, epigenomics) are often high-dimensional and are collected from patient samples falling into heterogeneous disease subtypes. Interpretable learning from such data calls for sparsity-driven models. This dissertation addresses the high dimensionality, sparsity, and heterogeneity issues when analyzing multiple-omics data, where each method is implemented with a concomitant R package. First, we examine challenges in submatrix identification, which aims to find subgroups of samples that behave similarly across a subset of features. We resolve issues such as two-way sparsity, non-orthogonality, and parameter tuning with an adaptive thresholding procedure on the singular vectors computed via orthogonal iteration. We validate the method with simulation analysis and apply it to an Alzheimer’s disease dataset. The second project focuses on modeling relationships between large, matched datasets. Exploring regressional structures between large data sets can provide insights such as the effect of long-range epigenetic influences on gene expression. We present a high-dimensional version of mixture multivariate regression to detect patient clusters, each with different correlation structures of matched-omics datasets. Results are validated via simulation and applied to matched-omics data sets. In the third project, we introduce a novel approach to modeling spatial transcriptomics (ST) data with a spatially penalized multinomial model of the expression counts. This method solves the low-rank structures of zero-inflated ST data with spatial smoothness constraints. We validate the model using manual cell structure annotations of human brain samples. We then applied this technique to additional ST datasets. / 2025-05-22