La caractérisation d’échantillons biologiques s’effectue très souvent au microscope optique. Or, il est techniquement difficile d’isoler quelques cellules rares parmi une culture hétérogène en se basant strictement sur des caractéristiques observables au microscope, comme la localisation, la morphologie ou le déplacement, car il n’existe pas nécessairement de marqueur moléculaire unique qui leur sont associés. Afin de répondre à cet enjeux, le laboratoire dans lequel j’ai effectué mon stage de maîtrise a récemment développé la Single Cell Magneto Optical Capture (scMOCa), qui utilise des réactifs communs et un laser de faible puissance pour attacher des billes ferromagnétiques à la membrane plasmique cellulaire et permet d’isoler magnétiquement les cellules d’intérêt. Le présent ouvrage rapporte l’application de la scMOCa à la migration de cellules métastatiques ainsi que les adaptations apportées à la technique nécessaires à la réalisation du projet. Notamment, le volume de cellules uniques capturé a été augmenté d’un facteur d’environ 250 grâce à l’automatisation de la technique et à l’étude du photoblanchiement de la fluorescéine, phénomène à la base de la scMOCa. Brièvement, l’expérience consiste à capturer les cellules uniques présentant les phénotypes migratoires les plus importants, définis par l’analyse de leur trajectoire, parmi une culture hétérogène de cellules métastatique. Les résultats de l’expérience démontrent une conservation des phénotypes migratoires après plusieurs mitoses. Aussi, l’expression génétique relative fait ressortir des gènes et groupes de gènes propres à la migration cellulaire. / The characterization of biological samples depends heavily on the optical microscope. However, it is technically challenging to isolate rare cells among a heterogeneous culture solely based on visual inspection at the microscope. Indeed, characteristics like location, morphology or displacement do not necessairly have specific related molecular markers. In order to solve this issue, the laboratory where I accomplished my master internship developped the Single Cell Magneto Optical Capture (scMOCa) wich uses commun reagents and a low powered laser to attach ferromagnetic beads on the cell plasma membrane and isolate the cells of interest with magnetic tools. The present work reports the application of scMOCa to the metastatic cell migration and the implemented adaptations to the technique in order to carry out the project, especially by increasing the number of single cells being isolated by a factor of 250. This adaptation requiered the study of photobleaching, phenomenon at the foundation of scMOCa. Briefly, the experiment consists to capture the cells presenting the most important migratory phenotypes, defined by their track analysis, among a heterogeneous metastatic cell culture. The experimental results show that the migratory phenotypes are preserved after several cell divisions. Also, the relative gene expression highlights some genes and gene groups owned to cellular migration.

<p>Skeletal muscle takes up nearly 40% of total body mass, is critical for daily function by</p> <p>providing balance, supports breathing, movement, and energy expenditure. Preserving</p> <p>skeletal muscle can also significantly improve one’s quality by maintaining balance, movement</p> <p>and improving metabolic health [1, 2]. This becomes more imperative with age, as skeletal muscle mass naturally declines, and further compounds decline in quality of life and health [1, 2]. Thus, it is critical to understand the physiology of skeletal muscle and the underlying cellular and</p> <p>molecular mechanisms that contribute to normal function. Using mouse models to further our</p> <p>understanding, this dissertation leverages single-cell RNA-sequencing (scRNA-seq) to dissect the</p> <p>cellular and molecular underpinnings of skeletal muscle injury and repair. Specifically, chapter 1</p> <p>provides an overview of skeletal muscle structure, muscle regeneration, and the current state of</p> <p>scRNA-seq literature in muscle regeneration. In chapter 2, I will discuss the large-scale scRNAseq of regenerating muscle which identified dynamic population of resident and infiltrating cells. In chapter 3, I will discuss the potential immunomodulatory role of MuSCs and leveraging scRNAseq data to understand the cellular mechanisms that govern successful muscle regeneration. Finally, in chapter 4 I will discuss the role of the transcription factor Sox11, which was identified by scRNA-seq and was specific to differentiating MuSCs. Thus, this dissertation spans the cellular and molecular components of muscle regeneration.</p>

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Indiana University-Purdue University Indianapolis (IUPUI) / The single cell gel electrophoresis assay (comet assay) is a useful tool for monitoring individuals who may be at risk of DNA damage and the ensuing process of carcinogenesis or other disease states. Leukocytes in blood samples provide a means of obtaining cells for use in the comet assay. However instances may arise when samples must be stored for later analysis. The present study investigated the effects of storage conditions on DNA damage in the form of strand breaks and oxidized bases in rat and human leukocytes using the comet assay. Whole blood and buffy coat samples were stored at room temperature or 4ºC for 1, 2, 24, and 48 hours or cryopreserved at -80ºC for 1 day and 1, 2, 3, and 4 weeks. The results show that the time of storage is limited if the whole blood or buffy coat samples are stored at room temperature or 4ºC. However, if cryopreserved using glycerol or DMSO as the cryoprotectant, the samples may be stored for at least 4 weeks without DNA strand breaks or oxidative damage deviating significantly from the fresh samples.

In forensic casework analysis it is necessary to obtain genetic profiles from increasingly smaller amounts of biological material left behind by perpetrators of crime. The ability to obtain profiles from trace biological evidence is demonstrated with so-called ‘touch DNA evidence’ which is perceived to be the result of DNA obtained from shed skin cells transferred from donor to an object or person during physical contact. However, the current method of recovery of trace DNA involves cotton swabs or adhesive tape to sample an area of interest. This "blindswabbing" approach may result in the recovery of biological material from different individuals resulting in admixed DNA profiles which are often difficult to interpret. Profiles recovered from these samples are reported to be from shed skin cells with no biological basis for that determination. A specialized approach for the isolation of single or few cells from ‘touch DNA evidence’ is necessary to improve the analysis and interpretation of recovered profiles. Here we describe the development of optimized and robust micro volume PCR reactions (1-5 μL) to improve the sensitivity and efficiency of ‘touch DNA’ analysis. These methods will permit not only the recovery of the genetic profile of the donor of the biological material, but permit an identification of the tissue source of origin using mRNA profiling. Results showed that the 3.5 uL amplification volume, a fraction of the standard 25 uL amplification volume, was the most ideal volume for the DNA assay, as it had very minimal evaporation with a 50% profile recovery rate at a single cell equivalent input (~5 pg) with reducing amplification volume alone. Findings for RNA showed that by reducing both amplification steps, reverse transcriptase PCR (20 uL) and body fluid multiplex PCR (25 uL), to iv 5 uL, ideal results were obtained with an increase in sensitivity and detection of six different body fluids down to 50 pg. Once optimized at the trace level, the assays were applied to the collection of single and few cells. DNA findings showed that about 40% of a full profile could be recovered from a single buccal cell, with nearly 80% of a full profile recovered from only two cells. RNA findings from collected skin particles of "touched" surfaces showed accurate skin detection down to 25 particles and detection in one clump of particles. The profiles recovered were of high quality and similar results were able to be replicated through subsequent experiments. More studies are currently underway to optimize these developed assays to increase profile recovery at the single cell level. Methods of doing so include comparing different locations on touched surfaces for highest bio-particle recovery and the development of physical characteristics of bio-particles that would provide the most ideal results

Globally, cancer is one of the leading causes of mortality, with an estimated 18.1 million cancer cases, 10 million deaths, and 1.9 million new cases diagnosed in 2020 (Sung et al., 2021). However, during the past several decades, cancer survival has improved such that 82% of children and >2/3 of adults diagnosed with cancer will survive beyond five years (World Health Organization (WHO) - Childhood Cancer, 2021). Skeletal muscle atrophy and fibrosis are long-term adverse effects experienced by 80% of cancer survivors for which there is no available therapy (Paulino, 2004). These long-term consequences are related to the toxicity from the cancer treatment, leading to alterations in skeletal muscle function which can lead to comorbidities and increased mortality among cancer survivors (Paulino, 2004; Williams et al., 2016). Thus, novel approaches to address the long-term effects of cancer therapy on skeletal muscle are critically needed. Exercise training is a potential non-pharmacological strategy that improves common cancer- and treatment-related side effects (Mustian et al., 2012). Specifically, exercise programs that combine resistance and endurance training (RET) have been shown to improve muscle strength and cardiovascular fitness in cancer survivors (Tong et al., 2020). The mechanisms responsible for these effects remain unknown. The remarkable plasticity of skeletal muscle relies primarily on muscle stem (satellite) cells (MuSCs) (Lepper et al., 2011) that are regulated, in part, by muscle-resident stromal cells (Bentzinger et al., 2013). These different stromal cell types, including: vascular endothelial cells (ECs), immune cells, and mesenchymal progenitors, also known as fibro-adipogenic progenitors (FAPs), create the muscle stem cell niche (Yin et al., 2013). FAPs possess a dual role as they are involved in skeletal muscle maintenance and regeneration by secreting pro-myogenic trophic factors (Biferali et al., 2019; Joe et al., 2010; Uezumi et al., 2010; Wosczyna et al., 2019), but also contribute to fibrotic and fatty tissue accumulation in chronic degenerative conditions (Uezumi et al., 2010). The divergent features of FAPs highly depend on signals they receive from their microenvironment (Giuliani et al., 2021); however, FAP's contribution to cancer treatment-induced muscle pathology in cancer survivors remains unknown. The overall objective of this thesis is to begin to develop an understanding of the role of FAPs in cancer treatment-induced muscle pathology and to determine if RET represents an effective therapy to prevent the long-term muscle defects of juvenile cancer plus therapy.

Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental “vector fields” using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.

Cell differentiation is the process of a cell developing from one cell type to another. It is of interest to analyse the differentiation from stem cells to different types of mature cells, and discover what genes are involved in regulating the differentiation to specific cells, for instance to get insights to what is causing certain diseases and find potential treatments.  In this project, two mathematical models are developed for analysing blood cell differentiation (haematopoiesis) with methods based on optimal transportation. Optimal transportation is about moving one mass distribution to another at minimal cost. Modelling a sample of cells as point masses placed in a space based on the cells' gene expressions, accessed by single-cell RNA sequencing, optimal transportation is used to find transitions between cells that costs the least in terms of changes in gene expression. With this, cell-to-cell trajectories, from haematopoietic stem cells to mature blood cells, are obtained. With the first model, cells are divided into groups based on their maturity, which is determined by using diffusion pseudotime, and optimal transportation is preformed between groups. The resulting trajectories suggest that haematopoietic stem cells possibly can develop into the same mature cell type in different ways, and that the cell fate for some cell types is decided late on in development. In future work, the gene regulation along the obtained trajectories can be analysed. The second model is developed to be more general than the first, and not be dependent on a group division before preforming optimal transportation. / Celldifferentiering är processen då en cell utvecklas från en celltyp till en annan. Det är av intresse att analysera differentieringen från stamcell till olika typer av mogna celler, och undersöka vilka gener som har betydelse i regleringen av differentieringen till specifika celler, bland annat för att få en inblick i vad som orsakar vissa sjukdomar och hitta potentiella botemedel. I detta projekt utvecklas två matematiska modeller för att analysera blodcellsutveckling (hematopoes) med metoder som är baserade på optimal transport. Optimal transport handlar om att förflytta en massfördelning till en annan till lägst kostnad. Genom att modellera celler som punktmassor, placerade i ett rum baserat på cellernas genuttryck som fås genom singel-cell RNA-sekvensering, används optimal transport för att hitta förflyttningar mellan celler som kostar minst i termer av förändringar i genuttryck. Från detta skapas vägar mellan celler, från hematopoetiska stamceller till mogna celler. I den första modellen delas cellerna upp i grupper baserat på deras mognadsgrad, som bestäms genom att använda pseudotid baserad på en diffusionsavbildning, och optimal transport används sedan mellan grupperna. De resulterande vägarna visar på att hematopoetiska stamceller möjligen kan utvecklas till samma typ av mogen cell på olika sätt, och att cellödet för vissa typer av celler bestäms sent i utvecklingen. I framtida arbete kan genregleringen längs de funna vägarna analyseras. Den andra modellen utvecklas för att vara mer generell än den första, och inte bero på en gruppuppdelning innan optimal transport används.

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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