It is a microbial world we live in: microbes outnumber other organisms by several orders of magnitude, and they have great importance for the environment. However, environmental microbes are notoriously difficult to grow in the laboratory, and using culture independent techniques is necessary to expand our view. In this thesis, I apply metagenomics and single-cell genomics to environmental samples from ancient human remains and lakes. First, I used metagenomics to learn about bacteria from a Neanderthal’s bone and the gut of Ötzi, a frozen natural mummy. Both were exploratory studies where the main question was what kind of bacteria are present. I found out that Streptomyces dominated this particular Neanderthal fossil, and the DNA lacked the damage that is often seen in ancient samples. Ötzi's gut sample was dominated by Clostridia and fungi belonging to Basidiomycota. Second, ten single-cell amplified genomes of freshwater Alphaproteobacterium LD12 and three metagenomes from Swedish lakes were sequenced. Comparative metagenomics allowed hypothesizing about which functions are important for microbe proliferation in freshwater, pointing to osmoregulation and transport proteins and, possibly, to different strategies of metabolizing sugars. I also focused on SAR11 sister-groups in oceans and lakes. Phylogenies and sequence evolutionary distance estimates indicated the existence of microclusters within LD12, showing variation in abundance between lakes. The most striking difference was the relative amount of recombination compared to mutation, the estimated r/m ratio. SAR11 marine and their freshwater cousins are found at the opposite extremes of the r/m range, lowest and highest, respectively. The genetic background or sequence diversity did not explain the observed dramatic difference, so it is possibly connected to environmental adaptation or population dynamics. In addition, I have spent a substantial amount of effort benchmarking available metagenomic methods, for example fragment recruitment of metagenomes to reference genomes. In conclusion, my exploratory metagenomic studies have shed some light on the bacteria present in ancient human remains; comparative metagenomics has suggested the importance of substrate preference on functional differences between lakes and oceans; finally, single-cell genomes have allowed some insight into molecular evolutionary processes taking place in the freshwater LD12 bacterium.

Tumour development has long been recognised as an evolutionary process during which cells accumulate mutations and evolve into a mix of genetically distinct cell subpopulations. The resulting genetic intra-tumour heterogeneity poses a major challenge to cancer therapy, as it increases the chance of drug resistance. To study tumour evolution in more detail, reliable approaches to infer the life histories of tumours are needed. This dissertation focuses on computational methods for inferring trees of tumour evolution from single-cell and multi-sample sequencing data. Recent advances in single-cell sequencing technologies have promised to reveal tumour heterogeneity at a much higher resolution, but single-cell sequencing data is inherently noisy, making it unsuitable for analysis with classic phylogenetic methods. The first part of the dissertation describes OncoNEM, a novel probabilistic method to infer clonal lineage trees from noisy single nucleotide variants of single cells. Simulation studies are used to validate the method and to compare its performance to that of other methods. Finally, OncoNEM is applied in two case studies. In the second part of the dissertation, a comprehensive collection of existing multi-sample approaches is used to infer the phylogenies of metastatic breast cancers from ten patients. In particular, shallow whole-genome, whole exome and targeted deep sequencing data are analysed. The inference methods comprise copy number and point mutation based approaches, as well as a method that utilises a combination of the two. To improve the copy number based inference, a novel allele-specific multi-sample segmentation algorithm is presented. The results are compared across methods and data types to assess the reliability of the different methods. In summary, this thesis presents substantial methodological advances to understand tumour evolution from genomic profiles of single cells or related bulk samples.

Signalling states of cells are heterogeneous even within clonally derived populations due to cell cycle status and their local microenvironment. As a result multiplexed single-cell signal transduction measurements represent a powerful tool which could potentiate a much greater understanding of subcellular processes. However, multiplexed single-cell analysis remains challenging due to several factors, most notably the low copy number of analytes present, difficulties in cellular manipulation and the availability of well characterized and stable probes for use in intact cells. In order to address these issues, a capillary electrophoresis system with laser induced fluorescence (CE-LIF) suitable for screening methods to facilitate single cell analysis was designed and assembled. This system has the requisite sensitivity for single-cell analysis, with the capability of detecting down to 10000 molecules of fluorescein, and has been designed to reduce the time required for analyte separations compared to similar systems by integrating a compact detection module which allows for shorter electrophoretic separation distances. This system has been employed to develop a method to determine the sampling efficiency of laser-based cell lysis of single cells allowing more accurate quantitative measurements of fluorescent peptides from single cells. Furthermore, a fluorescent probe based on amyloid precursor protein (β-APP peptide) has been designed and conditions were found which allowed resolution of enzyme-modified versions within single cells. To identify the enzymatic conversion products produced, a novel method was developed employing bulk cell samples in conjunction with LC-MS. As a testament to the resolution afforded by this technique, the peptide fragments identified from single cells represented peptides which differed by single uncharged amino acids. Together, the methods here developed are able to provide higher quality quantitative data and more informative analysis of fluorescent signal transduction reporters in single cells and represents progress towards being able to obtain highly multiplexed data needed for accurate cellular models.

Signalling states of cells are heterogeneous even within clonally derived populations due to cell cycle status and their local microenvironment. As a result multiplexed single-cell signal transduction measurements represent a powerful tool which could potentiate a much greater understanding of subcellular processes. However, multiplexed single-cell analysis remains challenging due to several factors, most notably the low copy number of analytes present, difficulties in cellular manipulation and the availability of well characterized and stable probes for use in intact cells. In order to address these issues, a capillary electrophoresis system with laser induced fluorescence (CE-LIF) suitable for screening methods to facilitate single cell analysis was designed and assembled. This system has the requisite sensitivity for single-cell analysis, with the capability of detecting down to 10000 molecules of fluorescein, and has been designed to reduce the time required for analyte separations compared to similar systems by integrating a compact detection module which allows for shorter electrophoretic separation distances. This system has been employed to develop a method to determine the sampling efficiency of laser-based cell lysis of single cells allowing more accurate quantitative measurements of fluorescent peptides from single cells. Furthermore, a fluorescent probe based on amyloid precursor protein (β-APP peptide) has been designed and conditions were found which allowed resolution of enzyme-modified versions within single cells. To identify the enzymatic conversion products produced, a novel method was developed employing bulk cell samples in conjunction with LC-MS. As a testament to the resolution afforded by this technique, the peptide fragments identified from single cells represented peptides which differed by single uncharged amino acids. Together, the methods here developed are able to provide higher quality quantitative data and more informative analysis of fluorescent signal transduction reporters in single cells and represents progress towards being able to obtain highly multiplexed data needed for accurate cellular models.

Bienertia sinuspersici is one of three terrestrial plants identified to perform C4 photosynthesis in a single chlorenchyma cell by compartmentation of organelles and photosynthetic enzymes. This thesis describes a study on the distribution of photosynthetic proteins and their corresponding transcripts in an attempt to understand the regulatory mechanisms underlying their differential accumulation in two types of chloroplast. The patterns of photosynthetic enzymes and transcripts accumulation in developing leaves were examined by using immunolocalization and in situ hybridization. The polypeptides of Rubisco large subunit (RbcL) and pyruvate Pi dikinase (PPDK) accumulate equally in all chloroplasts before the formation of two intracellular cytoplasmic compartments: the central (CCC) and peripheral (PCC) cytoplasmic compartment. The differential accumulation of these enzymes is not completed until the mature stage, indicating that the transition from C3 to C4 photosynthesis occurs at the very late stage of leaf development. In mature chlorenchyma cells, RbcL accumulates 20-fold more in the CCC than in the PCC while PPDK demonstrates a concentration gradient that is lowest in chloroplasts in the center of the CCC and highest in the PCC chloroplasts. The pattern of rbcL transcript accumulation follows that of its polypeptides in developing leaves, suggesting the expression of this gene is controlled at the transcriptional and/or mRNA stability level. The quantitative real-time PCR results of rbcL transcripts from isolated chloroplasts of each compartment further supported this observation. Bioinformatics tools were used to predict possible structual motifs on rbcL mRNA as an attempt to speculate on their role in its distribution and identify the presence of secondary structures in the 5??? untranslated region (UTR) that may function in the regulation of gene expression. Detailed examination of the ultrastructure of the unique intracellular organization in the single-cell C4 system showed various vesicles in close proximity to chloroplasts in both compartments in mature chlorenchyma cells of B. sinuspersici under H2O2 treatments. To further understand the biogenesis of these vesicles, imunolocalization and transient expression of green fluorescent protein (GFP) experiments were performed. These studies identified stroma-filled tubules (stromules) as the structure that participates in the formation of sequestering vesicles (autophagosomes) containing cytosol and organelles. Fluorescent microscopic analyses using autophagosome-specific and autophagic vacuole markers only showed partial overlapping of two fluorescent signals. These results suggest that the formation of autophagosome and autophagic vacuole occur independently. The contribution of stromules to autophagosome formation and the involvement of chloroplastic envelopes in this process provide another level of regulation in the compartmentation of photosynthetic enzymes in single-cell C4 system. Collectively, the findings in this thesis enhance our overall understanding on the development and function of single-cell C4 photosynthesis.

abstract: Within the last decade there has been remarkable interest in single-cell metabolic analysis as a key technology for understanding cellular heterogeneity, disease initiation, progression, and drug resistance. Technologies have been developed for oxygen consumption rate (OCR) measurements using various configurations of microfluidic devices. The technical challenges of current approaches include: (1) deposition of multiple sensors for multi-parameter metabolic measurements, e.g. oxygen, pH, etc.; (2) tedious and labor-intensive microwell array fabrication processes; (3) low yield of hermetic sealing between two rigid fused silica parts, even with a compliance layer of PDMS or Parylene-C. In this thesis, several improved microfabrication technologies are developed and demonstrated for analyzing multiple metabolic parameters from single cells, including (1) a modified "lid-on-top" configuration with a multiple sensor trapping (MST) lid which spatially confines multiple sensors to micro-pockets enclosed by lips for hermetic sealing of wells; (2) a multiple step photo-polymerization method for patterning three optical sensors (oxygen, pH and reference) on fused silica and on a polyethylene terephthalate (PET) surface; (3) a photo-polymerization method for patterning tri-color (oxygen, pH and reference) optical sensors on both fused silica and on the PET surface; (4) improved KMPR/SU-8 microfabrication protocols for fabricating microwell arrays that can withstand cell culture conditions. Implementation of these improved microfabrication methods should address the aforementioned challenges and provide a high throughput and multi-parameter single cell metabolic analysis platform. / Dissertation/Thesis / M.S. Electrical Engineering 2014

Dissertação(mestrado)- Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia e Ciência de Alimentos, Escola de Química e Alimentos, 2009. / Submitted by Caroline Silva (krol_bilhar@hotmail.com) on 2012-08-23T22:10:06Z No. of bitstreams: 1 elisane-dissertao.pdf: 1678353 bytes, checksum: 827dc6807ea87ca51e1133f6f1dd934b (MD5) / Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2012-09-09T17:36:09Z (GMT) No. of bitstreams: 1 elisane-dissertao.pdf: 1678353 bytes, checksum: 827dc6807ea87ca51e1133f6f1dd934b (MD5) / Made available in DSpace on 2012-09-09T17:36:09Z (GMT). No. of bitstreams: 1 elisane-dissertao.pdf: 1678353 bytes, checksum: 827dc6807ea87ca51e1133f6f1dd934b (MD5) Previous issue date: 2009 / O glicerol gerado na síntese de biodiesel pode ser usado na produção de biomassa, para suplementação na alimentação, agregando valor a este subproduto. Este trabalho teve como objetivos: selecionar uma cepa de levedura promissora para produção de biomassa como fonte de proteínas, comparando a utilização de glicerol comercial puro e glicerol bruto proveniente da síntese de biodiesel, como principal fonte de carbono; selecionar o meio de cultivo, através do teste de Tukey; avaliar a composição do meio de cultivo de forma a maximizar a concentração de biomassa e conteúdo proteico através da técnica de planejamento experimental. Foram utilizadas as leveduras Yarrowia lipolytica NRRL YB- 423, Candida lipolytica NRRL Y-1095, Candida utilis NRRL Y-900 e Candida rugosa NRRL Y-95, certificadas como GRAS (Generally Recognized As Safe). Os cultivos foram realizados em frascos agitados, conduzidos em incubadora rotatória a 30°C e 180 rpm. As leveduras Yarrowia lipolytica YB-423 e Candida lipolytica Y-1095 apresentaram maior produção de proteína total em meio contendo glicerol puro, cerca de 1,9 e 1,7 g/L, respectivamente. Quando utilizado meio contendo glicerol bruto, proveniente da produção de biodiesel, não houve diferença estatisticamente significativa quanto à produção de proteína total entre as leveduras, mas quanto ao conteúdo proteico a levedura Candida utilis Y-900 apresentou diferença em relação às demais cepas. Quando avaliada a biomassa máxima, a levedura Yarrowia lipolytica YB-423 apresentou melhor resultado(14,7 g/L). Desta forma, a levedura Yarrowia lipolytica YB-423 foi selecionada para produção de biomassa como fonte de proteínas. Com esta levedura, foram estudados quatro meios de cultivo com composição distinta, sendo selecionado aquele com 50 g/L de glicerol, 5,5 g/L de hidrogenofosfato de amônio, 5,5 g/L de dihidrogenofosfato de potássio, 1 g/L de sulfato de amônio, 0,25 g/L de sulfato de magnésio, 0,021 g/L de cloreto de cálcio dihidratado, 1 g/L de extrato de levedura, 1 g/L de peptona, ajustado a pH 5,5, obtendo-se 18,2 % de conteúdo proteico, 17,8 g/L de biomassa máxima e 3,1 g/L de produção de proteína total. Um planejamento experimental fracionário 2v 5-1 foi utilizado para determinar as variáveis que mais influenciaram na produção de biomassa e no conteúdo proteico. Os parâmetros estudados foram pH inicial do meio de cultivo e as concentrações de glicerol, hidrogenofosfato de amônio, peptona e extrato de levedura. Posteriormente, as variáveis que mais influenciaram foram avaliadas em um planejamento experimental completo 23, fixando-se as concentrações de hidrogenofosfato de amônio (5,5 g/L) e peptona (1,5 g/L). Através da técnica de superfície de resposta, a condição para maximização do conteúdo proteico validada pelo modelo empírico foi de 33,9 g/L de glicerol, pH inicial de 4,8 e 0,6 g/L de extrato de levedura, obtendo-se um conteúdo proteico de 20,1± 0,6%, com uma biomassa máxima de 19,5 ± 1,0 g/L. / The glycerol generated in the biodiesel synthesis can be used in the production of biomass, for diet supplementation, adds value to this by-product. This work aims to: select a promising strain of yeast for production of biomass as a source of protein, comparing the use of pure commercial glycerol and crude glycerol from biodiesel synthesis as the main carbon source; select the culture media through the Tukey test; evaluate the composition of the culture medium in order to maximize the concentration of biomass and protein content by the experimental design technique. The yeast Yarrowia lipolytica YB-423, Candida lipolytica NRRL Y-1095, Candida utilis Y-900 and Candida rugosa Y-95, certified as GRAS (Generally Recognized As Safe) were used. The cultures were performed in shaken flasks, conducted in rotary incubator at 30°C and 180 rpm. Yarrowia lipolytica YB- 423 and Candida lipolytica Y-1095 showed higher production of total protein in the medium containing pure glycerol, about 1.9 and 1.7 g/L, respectively. In medium containing crude glycerol derived from the production of biodiesel, the yeasts showed no statistically significant difference in the production of total protein, but the protein content of the yeast Candida utilis Y-900 showed a difference in relation to the other strains. When assessing the maximum biomass, the yeast Yarrowia lipolytica YB-423 showed the best result (14.7 g/L). Therefore, the yeast Yarrowia lipolytica YB-423 was selected for production of biomass as a source of protein. With this yeast, we studied four culture media with different compositions, thereby selecting that with 50 g/L of glycerol, 5.5 g/L of ammonium hydrogen phosphate, 5.5 g/L of potassium dihydrogen phosphate, 1 g/L of ammonium sulphate, 0.25 g/L magnesium sulphate, 0.021 g/L dihydrated calcium chloride, 1 g/L yeast extract, 1 g/L peptone, pH adjusted to 5.5, resulting in 18.2% of protein content, 17.8 g/L of maximum biomass and 3.1 g/L of total protein production. A 2v 5-1 fractional experimental design was used to determine the variables that most influenced the production of biomass and protein content. The parameters studied were initial pH of the culture medium, glycerol concentrations, ammonium hydrogen phosphate, peptone and yeast extract. Subsequently, the variables that most influenced were evaluated in a full experimental design 23, setting up the concentrations of ammonium hydrogen phosphate (5.5 g/L) and peptone (1,5 g/L). Through the technique of surface response, the condition for maximizing the protein content validated by empirical model was 33.9 g/L glycerol, initial pH of 4.8 and 0.6 g/L yeast extract, resulting in a protein content of 20.1 ± 0.6%, with a maximum biomass of 19.5 ± 1.0 g/L.

Single cell RNA sequencing allows expression profiles for individual cells to be obtained thus offering unprecedented insight into the behavior of individual cells. Insight gained from exploration of individual cells has implications in both cancer and developmental biology. Much of the power of these models is derived from the shear amount and granularity of the data that can be collected; however, with this power comes the deleterious introduction of batch effects. Samples sequenced on different days, by different technicians can show variance that cannot be attributed to biological condition, but rather is only due to the batch in which it was sequenced. These batch effects can cause alterations to the perceived relationships between the main effect and the outcome of interest, for instance cancer status, the main effect of cancer status may be hidden by the unwanted and unmodeled variance. Two known methods for the correction of batch effects in bulk RNA sequencing data are ComBat-Seq and Surrogate Variable Analysis; in this work, we demonstrate that when cell-type is known, inclusion of that covariate in the ComBat-Seq results in an appropriate correction of the batch effect. We also demonstrate that when cell-type is not known, SVA can be used to infer cell-type information form the latent structure of the count matrix with some loss of accuracy compared to the correction with cell type. This cell type information can be used in place of the actual cell-type covariate information to correct single cell RNA sequencing data with ComBat-Seq; inclusion of surrogate variables helps the accuracy of the correction in certain scenarios. Additionally, in the case where cell-type is not known, and the cell proportions are balanced between batches we demonstrate that ComBat-Seq can be used naive to cell-type information. The efficacy of this procedure is demonstrated with two simulated datasets and a dataset containing Jurkat and t293 cells. These results are then compared to Harmony, a recently reported batch correction algorithm. The procedure, herein reported, has benefits over harmony in certain situations such as when a counts matrix is needed for further analysis or when there is thought to be substantial intra-cell-type variability across different batches.

Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing cellular heterogeneity in biological samples. Various scRNA-seq protocols have been developed that can measure the transcriptome from thousands of cells in a single experiment. With these methods readily available, the ability to transform raw data into biological understanding of complex systems is now a rate-limiting step. In this dissertation, I introduce novel computational software and tools which enhance preprocessing and clustering of scRNA-seq data and evaluate their performance compared to existing methods. First, I present scruff, an R/Bioconductor package that preprocesses data generated from scRNA-seq protocols including CEL-Seq or CEL-Seq2 and reports comprehensive data quality metrics and visualizations. scruff rapidly demultiplexes, aligns, and counts the reads mapped to genomic features with deduplication of unique molecular identifier (UMI) tags and provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Second, I present Celda, a novel Bayesian hierarchical model that can perform simultaneous co-clustering of genes into transcriptional modules and cells into subpopulations for scRNA-seq data. Celda identified novel cell subpopulations in a publicly available peripheral blood mononuclear cell (PBMC) dataset and outperformed a PCA-based approach for gene clustering on simulated data. Third, I extend the application of Celda by developing a multimodal clustering method that utilizes both mRNA and protein expression information generated from single-cell sequencing datasets with multiple modalities, and demonstrate that Celda multimodal clustering captured meaningful biological patterns which are missed by transcriptome- or protein-only clustering methods. Collectively, this work addresses limitations present in the computational analyses of scRNA-seq data by providing novel methods and solutions that enhance scRNA-seq data preprocessing and clustering.

Following stroke there is a robust increase in the proliferation of neural stem and progenitor cells (NSPCs) that ectopically migrate from the subventricular zone (SVZ) to surround the site of damage induced by stroke (infarct). Previous in vivo studies by our lab and others have shown that a majority of migrating NSPCs when labelled prior to stroke become astrocytes surrounding the infarct. In contrast, our lab has shown that the majority of NSPCs when labelled after stroke become neurons surrounding the infarct. This thesis aims to elucidate the contributions of intrinsic changes that can alter the temporal fate of the NSPCs. The NSPCs were fate mapped in this study using the nestin-CreERT2 mouse model and strokes were induced using the photothrombosis model within the cortex. In alignment with our previous findings, fate-mapping the NSPCs using a single injection of tamoxifen treatment revealed a temporal-specific switch in neuronal fate when NSPCs were labeled at timepoints greater than 7 days following stroke. Single cell RNA sequencing and histological analysis identified significant differences in the proportion of populations of NSPCs and their progeny labeled at the SVZ in the absence or presence of a stroke. NSPCs labelled after stroke were comprised of a reduced proportion of quiescent neural stem cells alongside an accompanied increase in doublecortin-expressing neuroblasts. The RNA transcriptional profile of the NSPCs labelled also revealed NSPCs and their progeny labeled after stroke had an overall enrichment for a neuronal transcription profile in all of the labeled cells with a reduction in astrocytic gene expression in quiescent and activated neural stem cells. Furthermore, we highlight the presence of perturbed transcriptional dynamics of neuronal genes, such as doublecortin following stroke. Altogether, our study reveals following a stroke there is a sustained intrinsic regulated neuronal-fated response in the NSPCs that reside in the SVZ that may not be exclusive from extrinsic regulation. This work raises the challenge to learn how to harness the potential of this response to improve recovery following stroke through examining their contributions to recovery.