Cellular function and physiology are largely established through regulated gene expression. The first step in gene expression, transcription of the genomic DNA into RNA, is a process that is highly aligned at the levels of initiation, elongation and termination. In eukaryotes, protein-coding genes are exclusively transcribed by RNA polymerase II (Pol II). Upon transcription of the first 15-20 nucleotides (nt), the emerging nascent RNA 5’ end is modified with a 7-methylguanosyl cap. This is one of several RNA modifications and processing steps that take place during transcription, i.e. co-transcriptionally. For example, protein-coding sequences (exons) are often disrupted by non-coding sequences (introns) that are removed by RNA splicing. The two transesterification reactions required for RNA splicing are catalyzed through the action of a large macromolecular machine, the spliceosome. Several non-coding small nuclear RNAs (snRNAs) and proteins form functional spliceosomal subcomplexes, termed snRNPs. Sequentially with intron synthesis different snRNPs recognize sequence elements within introns, first the 5’ splice site (5‘ SS) at the intron start, then the branchpoint and at the end the 3’ splice site (3‘ SS). Multiple conformational changes and concerted assembly steps lead to formation of the active spliceosome, cleavage of the exon-intron junction, intron lariat formation and finally exon-exon ligation with cleavage of the 3’ intron-exon junction. Estimates on pre-mRNA splicing duration range from 15 sec to several minutes or, in terms of distance relative to the 3‘ SS, the earliest detected splicing events were 500 nt downstream of the 3‘ SS. However, the use of indirect assays, model genes and transcription induction/blocking leave the question of when pre-mRNA splicing of endogenous transcripts occurs unanswered. In recent years, global studies concluded that the majority of introns are removed during the course of transcription. In principal, co-transcriptional splicing reduces the need for post-transcriptional processing of the pre-mRNA. This could allow for quicker transcriptional responses to stimuli and optimal coordination between the different steps. In order to gain insight into how pre-mRNA splicing might be functionally linked to transcription, I wanted to determine when co-transcriptional splicing occurs, how transcripts with multiple introns are spliced and if and how the transcription termination process is influenced by pre-mRNA splicing. I chose two yeast species, S. cerevisiae and S. pombe, to study co-transcriptional splicing. Small genomes, short genes and introns, but very different number of intron-containing genes and multi-intron genes in S. pombe, made the combination of both model organisms a promising system to study by next-generation sequencing and to learn about co-transcriptional splicing in a broad context with applicability to other species. I used nascent RNA-Seq to characterize co-transcriptional splicing in S. pombe and developed two strategies to obtain single-molecule information on co-transcriptional splicing of endogenous genes: (1) with paired-end short read sequencing, I obtained the 3’ nascent transcript ends, which reflect the position of Pol II molecules during transcription, and the splicing status of the nascent RNAs. This is detected by sequencing the exon-intron or exon-exon junctions of the transcripts. Thus, this strategy links Pol II position with intron splicing of nascent RNA. The increase in the fraction of spliced transcripts with further distance from the intron end provides valuable information on when co-transcriptional splicing occurs. (2) with Pacific Biosciences sequencing (PacBio) of full-length nascent RNA, it is possible to determine the splicing pattern of transcripts with multiple introns, e.g. sequentially with transcription or also non-sequentially. Part of transcription termination is cleavage of the nascent transcript at the polyA site. The splicing status of cleaved and non-cleaved transcripts can provide insights into links between splicing and transcription termination and can be obtained from PacBio data. I found that co-transcriptional splicing in S. pombe is similarly prevalent to other species and that most introns are removed co-transcriptionally. Co-transcriptional splicing levels are dependent on intron position, adjacent exon length, and GC-content, but not splice site sequence. A high level of co-transcriptional splicing is correlated with high gene expression. In addition, I identified low abundance circular RNAs in intron-containing, as well as intronless genes, which could be side-products of RNA transcription and splicing. The analysis of co-transcriptional splicing patterns of 88 endogenous S. cerevisiae genes showed that the majority of intron splicing occurs within 100 nt downstream of the 3‘ SS. Saturation levels vary, and confirm results of a previous study. The onset of splicing is very close to the transcribing polymerase (within 27 nt) and implies that spliceosome assembly and conformational rearrangements must be completed immediately upon synthesis of the 3‘ SS. For S. pombe genes with multiple introns, most detected transcripts were completely spliced or completely unspliced. A smaller fraction showed partial splicing with the first intron being most often not spliced. Close to the polyA site, most transcripts were spliced, however uncleaved transcripts were often completely unspliced. This suggests a beneficial influence of pre-mRNA splicing for efficient transcript termination. Overall, sequencing of nascent RNA with the two strategies developed in this work offers significant potential for the analysis of co-transcriptional splicing, transcription termination and also RNA polymerase pausing by profiling nascent 3’ ends. I could define the position of pre-mRNA splicing during the process of transcription and provide evidence for fast and efficient co-transcriptional splicing in S. cerevisiae and S. pombe, which is associated with highly expressed genes in both organisms. Differences in S. pombe co-transcriptional splicing could be linked to gene architecture features, like intron position, GC-content and exon length.

To investigate the influence of the internal micellar structure on the course of enzymatic cross-linking especially in the initial phase of the reaction, casein micelles isolated from raw milk via ultracentrifugation were incubated with microbial transglutaminase (mTG) in comparison with non-micellar sodium caseinate. Reactive lysine and glutamine residues were identified using a label-free approach, based on the identification of isopeptides within tryptic hydrolysates by targeted HRMS as well as manual monitoring of fragmentation spectra. Identified reactive sites were furthermore weighted by tracking the formation of isopeptides over an incubation time of 15, 30, 45 and 60 min, respectively. Fifteen isopeptides formed in the early stage of mTG cross-linking of caseins were identified and further specified concerning the position of lysine and glutamine residues involved in the reaction. The results revealed lysine K176 and glutamine Q175 of β-casein as the most reactive residues, which might be located in a highly flexible region of the molecule based on different possible reaction partners identified in this study. Except for the isopeptide αₛ₁ K34–αₛ₂ Q101 in sodium caseinate (SC), all reactive sites were detected in micellar and in non-micellar casein, indicating that the initial phase of enzymatic cross-linking is not affected by micellar aggregation of caseins.

Auf die Entwicklung der Hochdurchsatz-Sequenzierung (HTS) folgte eine Reihe von speziellen Erweiterungen, die erlauben verschiedene zellbiologischer Aspekte wie Genexpression, DNA-Methylierung, etc. zu messen. Die Analyse dieser Daten erfordert die Entwicklung von Algorithmen, die einzelne Experimenteberücksichtigen oder mehrere Datenquellen gleichzeitig in betracht nehmen. Der letztere Ansatz bietet besondere Vorteile bei Analyse von einzelligen RNA-Sequenzierung (scRNA-seq) Experimenten welche von besonders hohem technischen Rauschen, etwa durch den Verlust an Molekülen durch die Behandlung geringer Ausgangsmengen, gekennzeichnet sind. Um diese experimentellen Defizite auszugleichen, habe ich eine Methode namens netSmooth entwickelt, welche die scRNA-seq-Daten entrascht und fehlende Werte mittels Netzwerkdiffusion über ein Gennetzwerk imputiert. Das Gennetzwerk reflektiert dabei erwartete Koexpressionsmuster von Genen. Unter Verwendung eines Gennetzwerks, das aus Protein-Protein-Interaktionen aufgebaut ist, zeige ich, dass netSmooth anderen hochmodernen scRNA-Seq-Imputationsmethoden bei der Identifizierung von Blutzelltypen in der Hämatopoese, zur Aufklärung von Zeitreihendaten unter Verwendung eines embryonalen Entwicklungsdatensatzes und für die Identifizierung von Tumoren der Herkunft für scRNA-Seq von Glioblastomen überlegen ist. netSmooth hat einen freien Parameter, die Diffusionsdistanz, welche durch datengesteuerte Metriken optimiert werden kann. So kann netSmooth auch dann eingesetzt werden, wenn der optimale Diffusionsabstand nicht explizit mit Hilfe von externen Referenzdaten optimiert werden kann. Eine integrierte Analyse ist auch relevant wenn multi-omics Daten von mehrerer Omics-Protokolle auf den gleichen biologischen Proben erhoben wurden. Hierbei erklärt jeder einzelne dieser Datensätze nur einen Teil des zellulären Systems, während die gemeinsame Analyse ein vollständigeres Bild ergibt. Ich entwickelte eine Methode namens maui, um eine latente Faktordarstellungen von multiomics Daten zu finden. / The development of high throughput sequencing (HTS) was followed by a swarm of protocols utilizing HTS to measure different molecular aspects such as gene expression (transcriptome), DNA methylation (methylome) and more. This opened opportunities for developments of data analysis algorithms and procedures that consider data produced by different experiments. Considering data from seemingly unrelated experiments is particularly beneficial for Single cell RNA sequencing (scRNA-seq). scRNA-seq produces particularly noisy data, due to loss of nucleic acids when handling the small amounts in single cells, and various technical biases. To address these challenges, I developed a method called netSmooth, which de-noises and imputes scRNA-seq data by applying network diffusion over a gene network which encodes expectations of co-expression patterns. The gene network is constructed from other experimental data. Using a gene network constructed from protein-protein interactions, I show that netSmooth outperforms other state-of-the-art scRNA-seq imputation methods at the identification of blood cell types in hematopoiesis, as well as elucidation of time series data in an embryonic development dataset, and identification of tumor of origin for scRNA-seq of glioblastomas. netSmooth has a free parameter, the diffusion distance, which I show can be selected using data-driven metrics. Thus, netSmooth may be used even in cases when the diffusion distance cannot be optimized explicitly using ground-truth labels. Another task which requires in-tandem analysis of data from different experiments arises when different omics protocols are applied to the same biological samples. Analyzing such multiomics data in an integrated fashion, rather than each data type (RNA-seq, DNA-seq, etc.) on its own, is benefitial, as each omics experiment only elucidates part of an integrated cellular system. The simultaneous analysis may reveal a comprehensive view.

Site-specific tyrosine-type recombinases are effective tools for genome engineering, with the first engineered variants having demonstrated therapeutic potential. So far, adaptation to new DNA target site selectivity of designer-recombinases has been achieved mostly through iterative cycles of directed molecular evolution. While effective, directed molecular evolution methods are laborious and time consuming. To accelerate the development of designer-recombinases I evaluated two sequencing approaches and gathered the sequence information of over two million Cre-like recombinase sequences evolved for 89 different target sites. With this information I first investigated the sequence compositions and residue changes of the recombinases to further our understanding of their target site selectivity. The complexity of the data led me to a generative deep learning approach. Using the sequence data I trained a conditional variational autoencoder called RecGen (Recombinase Generator) that is capable of generating novel recombinases for a given target site. With computational evaluation of the sequences I revealed that known recombinases functional on the desired target site are generally more similar to the RecGen predicted recombinases than other recombinase libraries. Additionally, I could experimentally show that predicted recombinases for known target sites are at least as active as the evolved recombinases. Finally, I also experimentally show that 4 out of 10 recombinases predicted for novel target sites are capable of excising their respective target sites. As a bonus to RecGen I also developed a new method capable of accurate sequencing of recombinases with nanopore sequencing while simultaneously counting DNA editing events. The data of this method should enable the next development iteration of RecGen.

Diese Arbeit beschreibt die Kombination von Phagen-Display-Biopanning und Illumina Next-Generation DNA-Sequencing (NGS) zur Identifizierung peptidbasierter Adhäsionsdomänen für Polypropylen (PP). Eine Biopanning-Runde gefolgt von NGS liefert PP-bindende Peptide, die durch Sanger-Sequenzierung nicht erkennbar sind. NGS bietet den Vorteil eines enorm umfangreichen Datensatzes, welcher tiefgreifende Sequenzanalysen erlaubt. Die selektierten Sequenzen werden als wasserbasierte Primer für PP–Metallhaftung zur Vorbehandlung von PP-Oberflächen eingesetzt und erhöhen die Haftfestigkeit um 100 % gegenüber nicht vorbehandeltem PP. / This thesis describes the combination of phage display biopanning and Illumina Next-Generation DNA-Sequencing (NGS) to identify peptide-based adhesion domains for polypropylene (PP). One round of biopanning followed by NGS yields PP-binding peptides that are undetectable by Sanger sequencing. NGS has the advantage of an extensive data set, which allows in-depth sequence analysis. The selected peptide sequences are then used as water-based primers for PP metal adhesion for the pretreatment of PP surfaces and increase the adhesion by 100% compared to non-pretreated PP.

The detection of plasma cell–free tumor DNA (ctDNA) is prognostic in colorectal cancer (CRC) and has potential for early prediction of disease recurrence. In clinical routine, ctDNA-based diagnostics are limited by the low concentration of ctDNA and error rates of standard next-generation sequencing (NGS) approaches. We evaluated the potential to increase the stability and yield of plasma cell–free DNA (cfDNA) for routine diagnostic purposes using different blood collection tubes and various manual or automated cfDNA extraction protocols. Sensitivity for low-level ctDNA was measured in KRAS-mutant cfDNA using an error-reduced NGS procedure. To test the applicability of rapid evaluation of ctDNA persistence in clinical routine, we prospectively analyzed postoperative samples of 67 CRC (stage II) patients. ctDNA detection was linear between 0.0045 and 45%, with high sensitivity (94%) and specificity (100%) for mutations at 0.1% VAF. The stability and yield of cfDNA were superior when using Streck BCT tubes and a protocol by Zymo Research. Sensitivity for ctDNA increased 1.5-fold by the integration of variant reads from triplicate PCRs and with PCR template concentration. In clinical samples, ctDNA persistence was found in ∼9% of samples, drawn 2 weeks after surgery. Moreover, in a retrospective analysis of 14 CRC patients with relapse during adjuvant therapy, we successfully detected ctDNA (median 0.38% VAF; range 0.18–5.04% VAF) in 92.85% of patients significantly prior (median 112 days) to imaging-based surveillance. Using optimized pre-analytical conditions, the detection of postoperative ctDNA is feasible with excellent sensitivity and allows the prediction of CRC recurrence in routine oncology testing.

African cichlids are well established models for studying social hierarchies in teleosts and elucidating the effects social dominance has on gene expression. Ascension in the social hierarchy has been found to increase plasma levels of steroid hormones, follicle stimulating hormone (Fsh) and luteinizing hormone (Lh) as well as gonadosomatic index (GSI). Furthermore, the expression of genes related to gonadotropins and steroidogenesis and signaling along the brain-pituitary-gonad axis (BPG-axis) is affected by changes of an animal’s social status. In this study, we use RNA-sequencing to obtain an in-depth look at the transcriptomes of testes and pituitaries from dominant and subordinate male Nile tilapia living in long-term stable social hierarchies. This allows us to draw conclusions about factors along the brain-pituitary-gonad axis that are involved in maintaining dominance over weeks or even months. We identify a number of genes that are differentially regulated between dominant and subordinate males and show that in high-ranking fish this subset of genes is generally upregulated. Genes differentially expressed between the two social groups comprise growth factors, related binding proteins and receptors, components of Wnt-, Tgfβ- and retinoic acid-signaling pathway, gonadotropin signaling and steroidogenesis pathways. The latter is backed up by elevated levels of 11-ketotestosterone, testosterone and estradiol in dominant males. Luteinizing hormone (Lh) is found in higher concentration in the plasma of long-term dominant males than in subordinate animals. Our results both strengthen the existing models and propose new candidates for functional studies to expand our understanding of social phenomena in teleost fish.

Die biotechnologischen Entwicklungen der letzten Jahre ermöglichen eine immer detailliertere Untersuchung von genetischen und molekularen Markern mit multiplen komplexen Traits. Allerdings liefern vorhandene statistische Methoden für diese komplexen Analysen oft keine valide Inferenz. Das erste Ziel der vorliegenden Arbeit ist, zwei neue statistische Methoden für Assoziationsstudien von genetischen Markern mit multiplen Phänotypen zu entwickeln, effizient und robust zu implementieren, und im Vergleich zu existierenden statistischen Methoden zu evaluieren. Der erste Ansatz, C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes), ermöglicht die Assoziation von genetischen Varianten mit multiplen Traits in einem gemeinsamen Copula Modell zu untersuchen. Der zweite Ansatz, CIEE (Causal Inference using Estimating Equations), ermöglicht direkte genetische Effekte zu schätzen und testen. C-JAMP wird in dieser Arbeit für Assoziationsstudien von seltenen genetischen Varianten mit quantitativen Traits evaluiert, und CIEE für Assoziationsstudien von häufigen genetischen Varianten mit quantitativen Traits und Ereigniszeiten. Die Ergebnisse von umfangreichen Simulationsstudien zeigen, dass beide Methoden unverzerrte und effiziente Parameterschätzer liefern und die statistische Power von Assoziationstests im Vergleich zu existierenden Methoden erhöhen können - welche ihrerseits oft keine valide Inferenz liefern. Für das zweite Ziel dieser Arbeit, neue genetische und transkriptomische Marker für kardiometabolische Traits zu identifizieren, werden zwei Studien mit genom- und transkriptomweiten Daten mit C-JAMP und CIEE analysiert. In den Analysen werden mehrere neue Kandidatenmarker und -gene für Blutdruck und Adipositas identifiziert. Dies unterstreicht den Wert, neue statistische Methoden zu entwickeln, evaluieren, und implementieren. Für beide entwickelten Methoden sind R Pakete verfügbar, die ihre Anwendung in zukünftigen Studien ermöglichen. / In recent years, the biotechnological advancements have allowed to investigate associations of genetic and molecular markers with multiple complex phenotypes in much greater depth. However, for the analysis of such complex datasets, available statistical methods often don’t yield valid inference. The first aim of this thesis is to develop two novel statistical methods for association analyses of genetic markers with multiple phenotypes, to implement them in a computationally efficient and robust manner so that they can be used for large-scale analyses, and evaluate them in comparison to existing statistical approaches under realistic scenarios. The first approach, called the copula-based joint analysis of multiple phenotypes (C-JAMP) method, allows investigating genetic associations with multiple traits in a joint copula model and is evaluated for genetic association analyses of rare genetic variants with quantitative traits. The second approach, called the causal inference using estimating equations (CIEE) method, allows estimating and testing direct genetic effects in directed acyclic graphs, and is evaluated for association analyses of common genetic variants with quantitative and time-to-event traits. The results of extensive simulation studies show that both approaches yield unbiased and efficient parameter estimators and can improve the power of association tests in comparison to existing approaches, which yield invalid inference in many scenarios. For the second goal of this thesis, to identify novel genetic and transcriptomic markers associated with cardiometabolic traits, C-JAMP and CIEE are applied in two large-scale studies including genome- and transcriptome-wide data. In the analyses, several novel candidate markers and genes are identified, which highlights the merit of developing, evaluating, and implementing novel statistical approaches. R packages are available for both methods and enable their application in future studies.

Proteinmethylierung spielt eine immer größere Rolle in der Regulierung zellulärer Prozesse. Die Entwicklung effizienter proteomweiter Methoden zur Detektion von Methylierung auf Proteinen ist limitiert und technisch schwierig. In dieser Arbeit haben wir einen neuen Hefe-Zwei-Hybrid-Ansatz (Y2H) entwickelt, der Proteine, die miteinander wechselwirken, mit Hilfe von Sequenzierungen der zweiten Generation identifiziert (Y2H-Seq). Der neue Y2H-Seq-Ansatz wurde systematisch mit dem Y2H-Seq-Ansatz verglichen. Dafür wurde ein Bait-Set von 8 Protein-Arginin-Methyltransferasen, 17 Protein-Lysin-Methyltransferasen und 10 Demethylasen gegen 14,268 Prey-Proteine getestet. Der Y2H-Seq-Ansatz ist weniger arbeitsintensiv, hat eine höhere Sensitivität als der Standard Y2H-Matrix-Ansatz und ist deshalb besonders geeignet, um schwache Interaktionen zwischen Substraten und Protein-Methyltransferasen zu detektieren. Insgesamt wurden 523 Wechselwirkungen zwischen 22 Bait-Proteinen und 324 Prey-Pr oteinen etabliert, darunter 11 bekannte Methyltransferasen-Substrate. Netzwerkanalysen zeigen, dass Methyltransferasen bevorzugt mit Transkriptionsregulatoren, DNA- und RNA-Bindeproteinen wechselwirken. Diese Daten repräsentieren das erste proteomweite Wechselwirkungsnetzwerk über Protein-Methyltransferasen und dienen als Ressource für neue potentielle Methylierungssubstrate. In einem in vitro Methylierungsassay wurden exemplarisch mit Hilfe massenspektrometrischer Analysen die methylierten Aminosäurereste einiger Kandidatenproteine bestimmt. Von neun getesteten Proteinen waren sieben methyliert, zu denen gehören SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP und WDR42A. Wahrscheinlich sind viele Methylierungssubstrate im Netzwerk vorhanden. Das vorgestellte Protein-Protein-Wechselwirkungsnetzwerk zeigt, dass Proteinmethylierung sehr unterschiedliche zelluläre Prozesse beeinflusst und ermöglicht die Aufstellung neuer Hypothesen über die Regulierung Molekularer Mechanismen durch Methylierung. / Protein methylation on arginine and lysine residues is a largely unexplored posttranslational modification which regulates diverse cellular processes. The development of efficient proteome-wide approaches for detecting protein methylation is limited and technically challenging. We developed a novel workload reduced yeast-two hybrid (Y2H) approach to detect protein-protein interactions utilizing second generation sequencing. The novel Y2H-seq approach was systematically evaluated against our state of the art Y2H-matrix screening approach and used to screen 8 protein arginine methyltransferases, 17 protein lysine methyltransferases and 10 demethylases against a set of 14,268 proteins. Comparison of the two approaches revealed a higher sensitivity of the new Y2H-seq approach. The increased sampling rate of the Y2H-seq approach is advantageous when assaying transient interactions between substrates and methyltransferases. Overall 523 interactions between 22 bait proteins and 324 prey proteins were identified including 11 proteins known to be methylated. Network analysis revealed enrichment of transcription regulator activity, DNA- and RNA-binding function of proteins interacting with protein methyltransferases. The dataset represents the first proteome-wide interaction network of enzymes involved in methylation and provides a comprehensively annotated resource of potential new methylation substrates. An in vitro methylation assay coupled to mass spectrometry revealed amino acid methylation of candidate proteins. Seven of nine proteins tested were methylated including SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP and WDR42A indicating that the interaction network is likely to contain many putative methyltransferase substrate pairs. The presented protein-protein interaction network demonstrates that protein methylation is involved in diverse cellular processes and can inform hypothesis driven investigation into molecular mechanisms regulated through methylation.

Ziel dieser Doktorarbeit ist eine umfassende Untersuchung der Heterogenität von Mikroglia aus morphologischer, elektrophysiologischer und transkriptioneller Perspektive mit dem Schwerpunkt auf Unterschiede zwischen weißer und grauer Substanz. Im ersten Kapitel diskutiere ich die morphologische Heterogenität von Mikroglia mit dem Fokus auf Satelliten- und Parenchymale-Mikroglia. Wir führten eine eingehende Analyse mehrerer Hirnareale durch und quantifizierten die Anzahl der Satellitenmikroglia, die mit verschiedenen neuronalen Subtypen in Kontakt stehen. Wir fanden heraus, dass die Anzahl der Satellitenmikroglia stark mit der neuronalen Dichte eines bestimmten Bereichs korreliert. Im zweiten Kapitel dieser Arbeit untersuche ich die transkriptionelle Heterogenität von Mikroglia aus weißer und grauer Substanz, wobei ich die in Gliazellen neu etablierte Patch-seq-Methode anwende. Diese Methode ermöglicht es eine Kombination aus morphologischen, lektrophysiologischen und transkriptionellen Profilen einzelner Zellen zu erhalten, die es erlauben, zelluläre Unterschiede zu charakterisieren. Wir identifizieren einen zellulären Subtyp, wenn wir den Patch-seq-Datensatz mit FACS-basierter Einzelzell-RNA-seq-Datensätzen vergleichen. Dieser Subtyp gehört eindeutig zu dissoziierten Gewebeproben und ist durch die Expression von Stress-assoziierten Genen charakterisiert. Im dritten Kapitel wende ich mich der Frage zu, wie Transkripte mittels SLAM-seq nachverfolgt werden können, die während der Dissoziation des Gewebes entstehen. Das Verfahren ermöglicht es mRNA, die während der Dissoziation der Probe entsteht, metabolisch zu markieren, rechnerisch zu identifizieren und zu entfernen. Indem wir die markierten Transkripte aus dem Mikroglia “entfernen”, beobachten wir, dass ein „aktivierter Mikroglia“-Subtyp zur allgemeinen Mikroglia-Population gehört. / The aim of this doctoral work is to provide a comprehensive study and overview on the topic of the heterogeneity of microglia in the normal adult mouse brain from the morphological, electrophysiological and transcriptional perspective with the focus on differences between white and grey matters. In the first Chapter, I discuss the morphological heterogeneity of microglia in the brain with the focus on two morphologically distinct classes: satellite and parenchymal microglia. We performed an in-depth analysis of multiple brain areas and quantified the number of satellite microglia which is in contact with different neuronal subtypes. We found that satellite microglia numbers are highly correlated with neuronal densities of a certain area, while showing no preferences for any of the neuronal types. In Chapter two of this work, I study transcriptional heterogeneity of microglia from white and grey matters. For this I am employing Patch-seq, which we newly established in glial cells. This method allows a combination of morphological, electrophysiological and transcriptional profiles of single cells to assess their differences. When comparing Patch-seq dataset to the previously published FACS isolated single cell RNA-seq microglia datasets, we find a subtype of cells which uniquely belongs to FACS sample and is characterized by expression of stress-associated genes. This finding points out to the fact of dissociation-related artifacts in the single cell RNA-seq data which are not present in situ. In the third chapter, I identified transcripts which are induced during the dissociation of the tissue by employing the SLAM-seq method. This procedure allows to metabolically label newly transcribed mRNA and computationally remove transcripts from the sample. By removing the labeled transcripts from the dataset of cells isolated from the hippocampus via enzymatic dissociation, we observe that an “activated microglia” subtype merges with the general microglia population.