Die embryonale Neurogenese in Drosophila ist eine hochgradig koordinierte Abfolge von Zellschicksalsentscheidungen, die viele Ähnlichkeiten mit der Entwicklung des Nervensystems in Wirbeltieren aufweist. Diese Zellschicksalsentscheidungen sind räumlich und zeitlich koordiniert. Diese Zellen entstehen an stereotypen Positionen in jedem Segment und sind entlang zweier räumlicher Achsen angeordnet: der dorsoventralen und der anteroposterioren Achse. Neuroblasten teilen sich, um stereotype Zelllinien zu bilden, und die Zellen weisen charakteristische Zellmorphologien und -ziele auf, wobei die molekularen Mechanismen, die diese Merkmale bestimmen, noch weitgehend unbekannt sind. Jahrzehnte der Genetik haben einige Faktoren aufgedeckt, die für viele dieser Entscheidungen notwendig sind, aber ein Verständnis der einzelnen neurogenen Linien auf Genomebene war bis vor kurzem in vivo unmöglich. Ich habe mRNA aus Einzelzellen verwendet, um die Transkriptomdynamik von Schicksalsentscheidungen in der frühen Entwicklung des Nervensystems zu untersuchen. Mein Ziel ist es, zu entschlüsseln, wie sich Zellen unterscheiden, wenn Entscheidungen getroffen werden, die für die Entwicklung des Nervensystems wesentlich sind. Ich habe Transkriptomdaten von einzelnen Zellen aus Zehntausenden von Neuroblasten während der gesamten embryonalen Neurogenese erstellt. Es gelang mir, spezifische neurogene Populationen und ihre Genexpressionsprofile entlang ihrer Differenzierungswege zu identifizieren. Ich konnte die komplizierten zeitlichen Achsen, die das sich entwickelnde embryonale Nervensystem formen, teilweise entschlüsseln - ein Prozess, der von der Fliege bis zum Menschen konserviert ist. Diese Arbeit hat die Identifizierung lokalisierter Marker und sogar spezifischer Neuroblasten ermöglicht. Dieses Verständnis kann nun mit Informationen über die einzelnen Zellschicksale kombiniert werden, aus denen diese Neuroblasten hervorgehen, wie z. B. ihre spezifischen neuronalen und glialen Schicksale. / Embryonic neurogenesis in Drosophila is a highly coordinated sequence of cell fate decisions that bears many similarities to the development of the nervous system in vertebrates. These cell fate decisions are spatially and temporally coordinated. These cells arise at stereotypic positions in each segment and are arranged along two spatial axes: the dorsoventral axis and the anteroposterior axis. Neuroblasts divide to give rise to stereotypic lineages and the cells exhibit characteristic cell morphologies, branching patterns, and targets, the molecular mechanisms that determine these characteristics are still largely unknown. Decades of genetics have uncovered some factors necessary for many of these decisions, but understanding individual neurogenic lineages at the genome level has been impossible in vivo until recently. I have used Single cell mRNA to study the transcriptome dynamics that accompany important fate decisions in early nervous system development. My goal is to decipher how cells differ when decisions are made that are essential for nervous system development. This knowledge is invaluable for developing models for the in vivo mechanisms that allow individual cells in the nervous system to specify and differentiate. I have generated transcriptome data of single cells from tens of thousands of neuroblasts throughout embryonic neurogenesis. I was able to identify specific neurogenic populations and their gene expression profiles along their differentiation pathways. I was able to partially decipher the intricate temporal axes that shape the developing embryonic nervous system, a process that is conserved from fly to human. Single-cell transcriptomics has enabled the identification of localized markers and even specific neuroblasts. This understanding can now be combined with information about the individual cell fates that give rise to these neuroblasts, such as their specific neuronal and glial fates.

Single-cell analysis means to analyze cells on an individual level. This individual analysis enhances the investigation of the heterogeneity among and the classification of individual cells. Single-cell analysis is a broad term and can include various measurements. This thesis utilizes single-cell RNA sequence data that measures RNA sequences representing genes for individual cells. This data is often high-dimensional, with tens of thousands of RNA sequences measured for each cell. Dimension reduction is therefore necessary when analyzing the data. One proposed dimension reduction method is the unsupervised machine learning method variational autoencoders. The scVI framework has previously implemented a variational autoencoder for analyzing single-cell RNA sequence data. The variational autoencoder of the scVI has one latent space with a Gaussian distribution. Several extensions have been made to the scVI framework since its creation. This thesis proposes an additional extension consisting of a variational autoencoder with two latent spaces, called hybridVI. One of these latent spaces has a Gaussian distribution and the other a von Mises-Fisher distribution. The data is separated between these two latent spaces, meaning that some of the genes go through one latent space and the rest go through the other. In this thesis the cell cycle genes go through the von Mises-Fisher latent space and the rest of the genes go through the Gaussian latent space. The motivation behind the von Mises-Fisher latent space is that cell cycle genes are believed to follow a circular distribution. Putting these genes through a von Mises-Fisher latent space instead of a Gaussian latent space could provide additional insights into the data. The main focus of this thesis was to analyze the impact this separation. The analysis consisted of comparing the performance of the hybridVI model, to the original scVI variational autoencoder. The comparison utilized three annotated datasets, one peripheral blood mononuclear cell dataset, one cortex cell dataset, and one B cell dataset collected by the Henriksson lab at Umeå University. The evaluation metrics used were the adjusted rand index, normalized mutual information and a Wilcoxon signed ranks test was used to determine if the results had statistical significance. The results indicate that the size of the dataset was essential for achieving robust and statistically significant results. For the two datasets that yielded statistically significant results, the scVI model performed better than the hybridVI model. However, more research analyzing biological aspects is necessary to declare the hybridVI model’s effect on the biological interpretation of the results. / Individuell cellanalys är en relativt ny metod som möjliggör undersökning av celler på indivudiell nivå. Det här examensarbetet analyserar RNA sekvens data, där RNA sekvenser är specifierade för individuella celler. Den här sortens data är ofta högdimensionell med flera tusen gener noterade för varje cell. För att möjliggöra en analys av den här datan krävs någon form av dimensionreducering. En föreslagen metod är den ovövervakade maskininlärningsmetoden variational autoencoders. Ett ramverk, scVI, har framtagit en variational autoencoder designad för att hantera den här sortens data. Den här modellen har endast en latentrymd med en normalfördelning. Det här examensarbetet föreslår en utökning av det här ramverket med en variational autoencoder med två latentrymder,där den ena är normalfördelad och den andra följer en von Mises-Fisher fördelning. Motiveringen till en sådan fördelning är att cellcykelgener är antagna att tillhöra en cirkulär fördelning. Cellcykelgenerna i datan kan därmed hanteras av den cirkulära latentrymden. Huvudfokuset i den här studien är att undersöka om den här separationen av gener kan förbättra modellens förmåga att hitta korrekta kluster. Experimentet utfördes på tre annoterade dataset, ett som bestod av perifera mononukleära blodceller, ett som bestod av hjärnbarksceller och ett som bestod av B celler insamlat av Henrikssongruppen vid Umeå universitet. Modellen från scVI ramverket jämfördes med den nya metoden med två latentrymder, hybridVI. Måtten som användes för att bedöma de modellerna var adjusted rand index och normaliserad mutual information och ett Wilcoxon Signed-Ranks test användes för att bedöma resultatens statistiska signifikans. Resultaten påvisar att de båda modellerna preseterar bättre och mer konsekvent för större dataset. Två dataset gav statistiskt signifikanta resultat och visade att scVI modellen presterade bättre än hybridmodellen. Det behövs dock en biologisk analys av resultaten för att undersöka vilken modells resultat som har mest biologisk relevans.

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Recent advances in sequencing technology have given access to information extracted on a single cell level. Single cell RNA sequencing enables for transcriptomes to be sequenced, allowing for studies within and between cell types. A recently developed protocol, based on Smart-seq2, and the Proximity ligation essay, allows for the detection of protein data from single cells, in parallel with RNA. The combination of the transcriptomic and proteomic data will enhance researchers’ ability to explore cell states. In this study, we are comparing a new pulldown protocol with the widely-used Smart-seq2, as well as against FACS sorted cells. Our results show differences in the RNA sequenced between the two protocols, as well the prediction of cell cycle state based on their data. Using RNA extracted from the pulldown protocol in different time points, we also calculate the direction of development for the cells. We expect that the incorporation of proteomic data will shed light to relevant biological questions related to the cell function.

Tissue-resident stem cells are present in many adult organs, where they are important for organ homeostasis and repair in response to injury. However, the signals that activate these cells and the mechanisms governing how these cells self-renew or differentiate are highly context dependent and incompletely understood, particularly in non-hematopoietic tissues. In the skin, melanocyte stem cells (McSCs) are responsible for replenishing mature pigmented melanocytes. In mammals, these cells reside in the hair follicle bulge and bulb niches where they are activated during homeostatic hair follicle turnover and following melanocyte destruction, as occurs in vitiligo and other skin hypopigmentation disorders. Recently, we identified adult McSCs in the zebrafish. To elucidate mechanisms governing McSC self-renewal and differentiation fates we analyzed individual transcriptomes from thousands of melanocyte lineage cells during the regeneration process. We identified transcriptional signatures for McSCs, deciphered transcriptional changes and intermediate cell states during regeneration, and analyzed cell-cell signaling changes to discover mechanisms governing melanocyte regeneration. We identified KIT signaling via the RAS/MAPK pathway as a regulator of McSC direct differentiation. Analysis of the scRNAseq dataset also revealed a population of mitfa/aox5 co-expressing cells that divides following melanocyte destruction, likely corresponding to cells that undergo self-renewal. Our findings show how different subpopulations of mitfa-positive cells underlie regeneration and differentiation of at least one subpopulation requires reactivation of developmental KIT signaling to properly reconstitute the melanocyte stripe.