Dissertations / Theses on the topic 'Single cell sequencing data'
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Ross, Edith. "Inferring tumour evolution from single-cell and multi-sample data." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274604.
Full textSalehi, Sohrab. "dd-PyClone : improving clonal subpopulation inference from single cells and bulk sequencing data." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/56179.
Full textScience, Faculty of
Graduate
Lavagi, Ilaria Verfasser], and Eckhard [Akademischer Betreuer] [Wolf. "Analysis of blastomere of bovine embryos during genome activation by evaluation of single-cell RNA sequencing data / Ilaria Lavagi ; Betreuer: Eckhard Wolf." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2018. http://d-nb.info/1167160541/34.
Full textBampalikis, Dimitrios. "Recognizing biological and technical differences in scRNAseq : A comparison of two protocols." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-366169.
Full textRonen, Jonathan. "Integrative analysis of data from multiple experiments." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21612.
Full textThe 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.
Büttner, Maren [Verfasser], Fabian J. [Akademischer Betreuer] Theis, Julien [Gutachter] Gagneur, Fabian J. [Gutachter] Theis, and Peter V. [Gutachter] Kharchenko. "Statistical data integration for single-cell RNA-sequencing - batch effect correction and lineage inference / Maren Büttner ; Gutachter: Julien Gagneur, Fabian J. Theis, Peter V. Kharchenko ; Betreuer: Fabian J. Theis." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/119244194X/34.
Full textJohnson, Travis Steele. "Integrative approaches to single cell RNA sequencing analysis." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586960661272666.
Full textBorgström, Erik. "Technologies for Single Cell Genome Analysis." Doctoral thesis, KTH, Genteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181059.
Full textQC 20160127
Raoux, Corentin. "Review and Analysis of single-cell RNA sequencing cell-type identification and annotation tools." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297852.
Full textKindblom, Marie, and Hakim Ezeddin Al. "Phylogenetic fatemapping: estimating allelic dropout probability in single cell genomic sequencing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186453.
Full textHenao, Diaz Emanuela. "Towards single-cell exome sequencing with spatial resolution in tissue sections." Thesis, KTH, Skolan för bioteknologi (BIO), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-150564.
Full textEvrony, Gilad David. "Single-cell Sequencing Studies of Somatic Mutation in the Human Brain." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10747.
Full textKe, Rongqin. "Detection and Sequencing of Amplified Single Molecules." Doctoral thesis, Uppsala universitet, Molekylära verktyg, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183141.
Full textTu, Ang A. (Ang Andy). "Recovery of T cell receptor variable sequences from 3' barcoded single-cell RNA sequencing libraries." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127888.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 107-112).
Heterogeneity of the immune system has increasingly necessitated the use of high-resolution techniques, including flow cytometry, RNA-seq, and mass spectrometry, to decipher the immune underpinnings of various diseases such as cancer and autoimmune disorders. In recent years, high-throughput single-cell RNA sequencing (scRNA-seq) has gained popularity among immunologists due to its ability to effectively characterize thousands of individual immune cells from tissues. Current techniques, however, are limited in their ability to elucidate essential immune cell features, including variable sequences of T cell antigen receptors (TCRs) that confer antigen specificity. Incorporation of TCR sequencing into scRNA-seq data could identify cells with shared antigen-recognition, further elucidating dynamics of antigen-specific immune responses in T cells.
In the first part of this thesis work, we develop a strategy that enables simultaneous analysis of TCR sequences and corresponding full transcriptomes from 32 barcoded scRNA-seq samples. This approach is compatible with common 32 scRNA-seq methods, and adaptable to processed samples post hoc. We applied the technique to identify transcriptional signatures associated with clonal T cells from murine and human samples. In both cases, we observed preferential phenotypes among subsets of expanded T cell clones, including cytotoxic T cell states associated with immunization against viral peptides. In the second part of the thesis, we apply the strategy to a 12-patient study of peanut food allergy to characterize T helper cell responses to oral immunotherapy (OIT). We identified clonal T cells associated with distinct subsets of T helper cells, including Teff, Treg, and Tfh, as well as Th1, Th2, and Th17 signatures.
We found that though the TCR repertoires of the patients were remarkably stable, regardless of their clinical outcomes, Th1 and Th2 clonotypes were phenotypically suppressed while Tfh clonotypes were not affected by therapy. Furthermore, we observed that highly activated clones were less likely to be suppressed by OIT than less activated clones. Our work represents one of the most detailed transcriptomic profiles of T helper cells in food allergy. In the last part of the thesis, we leverage the simplicity and adaptability of the method to recover TCR sequences from previously processed scRNA-seq samples derived from HIV patients and a nonhuman primate model of TB. In the HIV study, we recovered expanded clonotypes associated with activated T cells from longitudinal samples from patients with acute HIV infections. In the TB study, we modified the primers used in the method to T cells from TB granulomas of cynomolgus macaques.
We identified not only expanded clonotypes associated with cytotoxic functions, but also clonotypes shared by clusters of activated T cells. In total, these results demonstrate the utility of our method when studying diseases in which clonotype-driven responses are critical to understanding the underlying biology.
by Ang A. Tu.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Biological Engineering
Lefebvre, Keely. "Resolving the Taxonomy and Phylogenetics of Benthic Diatoms from Single Cell Sequencing." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34553.
Full textZiegenhain, Christoph [Verfasser], and Wolfgang [Akademischer Betreuer] Enard. "Improving & applying single-cell RNA sequencing / Christoph Ziegenhain ; Betreuer: Wolfgang Enard." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2017. http://d-nb.info/1151818372/34.
Full textGlaros, Anastasios. "Data-driven Definition of Cell Types Based on Single-cell Gene Expression Data." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297498.
Full textO'Neill, Kieran. "Automated analysis of single cell leukemia data." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50867.
Full textScience, Faculty of
Graduate
Vieth, Beate [Verfasser], and Wolfgang [Akademischer Betreuer] Enard. "Statistical power analysis for single-cell RNA-sequencing / Beate Vieth ; Betreuer: Wolfgang Enard." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1225683033/34.
Full textNir, Oaz. "Single-cell morphological data reveals signaling network architecture." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/58457.
Full textCataloged from PDF version of thesis.
Includes bibliographical references.
Metastasis, the migration of cancer cells from the primary site of tumorigenesis and the subsequent invasion of secondary tissues, causes the vast majority of cancer deaths. To spread, metastatic cells dramatically rearrange their shape in complex, dynamic fashions. Genes encoding signaling proteins that regulate cell shape in normal cells are often mutated in cancer, especially in highly metastatic disease. To study these key signaling proteins in locomotion and metastasis, we develop and validate statistical methods to extract information from highthroughput morphological data from genetic screens. Our contributions fall into three major categories. 1) To define and apply robust statistical measures to identify genes regulating morphological variability. We develop and thoroughly test methods for measuring morphological variability of single-cells populations, and apply these metrics to genetic screens in yeast and fly. We further apply these techniques to subsets of genes involved in cellular processes to study genetic contributions to variability in these processes. We propose new roles for genes as suppressors or enhancers of morphological noise. We validate our findings on the basis of known gene function and network architecture. 2) To perform inference of protein signaling relationships by utilizing high-throughput morphological data. We apply machine-learning techniques to systematically identify genetic interactions between proteins on the basis of image-based data from double-knockout screens.
(cont.) Next, we focus on RhoGTPases and RhoGTPase Activating Proteins (RhoGAPs) in Drosophila., where by using basic knowledge of network architecture we apply our techniques to detect signaling relationships. 3) To integrate expression data with high-throughput morphological data to study the mechanisms for determination of cell morphology. We utilize morphological and microarray data from fly screens. By comparing expression data between control treatment conditions and treatment conditions displaying morphological phenotypes (e.g. high population variability), we identify genes and pathways correlated with this class distinction, thereby validating our previous studies and providing further insight into the determination of morphology. A key challenge in systems biology is to analyze emerging high-throughput image-based data to understand how cellular phenotypes are genetically encoded. Our work makes significant contributions to the literature on high-throughput morphological study and describes a path for future investigation.
by Oaz Nir.
Ph.D.
Gupta, Namita. "Computational Identification of B Cell Clones in High-Throughput Immunoglobulin Sequencing Data." Thesis, Yale University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10633249.
Full textHumoral immunity is driven by the expansion, somatic hypermutation, and selection of B cell clones. Each clone is the progeny of a single B cell responding to antigen. with diversified Ig receptors. The advent of next-generation sequencing technologies enables deep profiling of the Ig repertoire. This large-scale characterization provides a window into the micro-evolutionary dynamics of the adaptive immune response and has a variety of applications in basic science and clinical studies. Clonal relationships are not directly measured, but must be computationally inferred from these sequencing data. In this dissertation, we use a combination of human experimental and simulated data to characterize the performance of hierarchical clustering-based methods for partitioning sequences into clones. Our results suggest that hierarchical clustering using single linkage with nucleotide Hamming distance identifies clones with high confidence and provides a fully automated method for clonal grouping. The performance estimates we develop provide important context to interpret clonal analysis of repertoire sequencing data and allow for rigorous testing of other clonal grouping algorithms. We present the clonal grouping tool as well as other tools for advanced analyses of large-scale Ig repertoire sequencing data through a suite of utilities, Change-O. All Change-O tools utilize a common data format, which enables the seamless integration of multiple analyses into a single workflow. We then apply the Change-O suite in concert with the nucleotide coding se- quences for WNV-specific antibodies derived from single cells to identify expanded WNV-specific clones in the repertoires of recently infected subjects through quantitative Ig repertoire sequencing analysis. The method proposed in this dissertation to computationally identify B cell clones in Ig repertoire sequencing data with high confidence is made available through the Change-O suite and can be applied to provide insight into the dynamics of the adaptive immune response.
Svensson, Valentine. "Probabilistic modelling of cellular development from single-cell gene expression." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/267937.
Full textSubramanian, Parimalam Sangamithirai. "Dissecting gene expression of single cells with reduced perturbation." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263616.
Full textKinchen, James. "Intestinal stromal cell types in health and inflammatory bowel disease uncovered by single-cell transcriptomics." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:1bf9d8f0-6d09-46f5-9d1e-3c9e0b826618.
Full textMakowski, Mateusz. "High-Throughput Data Analysis: Application to Micronuclei Frequency and T-cell Receptor Sequencing." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3923.
Full textLi, Mengyao. "P53 dynamics: single-cell imaging data analysis and modeling." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/59.
Full textHie, Brian. "Stitching and sketching large-scale single-cell transcriptomic data." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121734.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 57-65).
Researchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems [1]-[7] and every cell type in the human body [8] at an unprecedented scale, with scRNA-seq experiments regularly profiling gene expression in hundreds of thousands or even millions of cells [9]. Leveraging this data to gain unprecedented insight into biology and disease requires algorithms that can scale to the tremendous amount of data being generated and can integrate information across multiple experiments, laboratories, and technologies. Here, we present two algorithms that aim to aid researchers in gaining better insight from scRNA-seq data sets. The first, Scanorama, inspired by algorithms for panorama stitching, achieves accurate integration of heterogeneous scRNA-seq data sets, which we use to integrate a number of large and complex collections of data sets. The second algorithm, geometric sketching, is a sampling approach that aims to evenly cover the low-dimensional manifold spanned by the cells to capture more of the rare transcriptional structure than would uniform subsampling with equal probability for each cell, obtaining sketches that better capture the transcriptional heterogeneity of the original data. Moreover, geometric sketching can be used to improve the computational efficiency of algorithms for single-cell integration, including Scanorama. We anticipate that both algorithms will play an important role in the analysis and interpretation of large-scale single-cell transcriptomic data sets.
by Brian Hie.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Chwastek, Damian. "Elucidating the Contribution of Stroke-Induced Changes to Neural Stem and Progenitor Cells Associated with a Neuronal Fate." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41839.
Full textZhang, Lu, and 张璐. "Identification and prioritization of single nucleotide variation for Mendelian disorders from whole exome sequencing data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48521905.
Full textpublished_or_final_version
Paediatrics and Adolescent Medicine
Master
Master of Philosophy
Otto, Raik. "Distance-based methods for the analysis of Next-Generation sequencing data." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/23267.
Full textThe analysis of NGS data is a central aspect of modern Molecular Genetics and Oncology. The first scientific contribution is the development of a method which identifies Whole-exome-sequenced CCL via the quantification of a distance between their sets of small genomic variants. A distinguishing aspect of the method is that it was designed for the computer-based identification of NGS-sequenced CCL. An identification of an unknown CCL occurs when its abstract distance to a known CCL is smaller than is expected due to chance. The method performed favorably during benchmarks but only supported the Whole-exome-sequencing technology. The second contribution therefore extended the identification method by additionally supporting the Bulk mRNA-sequencing technology and Panel-sequencing format. However, the technological extension incurred predictive biases which detrimentally affected the quantification of abstract distances. Hence, statistical methods were introduced to quantify and compensate for confounding factors. The method revealed a heterogeneity-robust benchmark performance at the trade-off of a slightly reduced sensitivity compared to the Whole-exome-sequencing method. The third contribution is a method which trains Machine-Learning models for rare and diverse cancer types. Machine-Learning models are subsequently trained on these distances to predict clinically relevant characteristics. The performance of such-trained models was comparable to that of models trained on both the substituted neoplastic data and the gold-standard biomarker Ki-67. No proliferation rate-indicative features were utilized to predict clinical characteristics which is why the method can complement the proliferation rate-oriented pathological assessment of biopsies. The thesis revealed that the quantification of an abstract distance can address sources of erroneous NGS data analysis.
Andreani, Tommaso [Verfasser]. "From DNA sequences to cell types by detecting regulatory genomic regions in sequencing data / Tommaso Andreani." Mainz : Universitätsbibliothek der Johannes Gutenberg-Universität Mainz, 2021. http://d-nb.info/1230551662/34.
Full textHu, Bo. "Analysis of cellular drivers of zebrafish heart regeneration by single-cell RNA sequencing and high-throughput lineage tracing." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/23324.
Full textThe zebrafish heart has the remarkable capacity to fully regenerate after injury. The regeneration process is accompanied by fibrosis - the formation of excess extracellular matrix (ECM) tissue, at the injury site. Unlike in mammals, the fibrosis of the zebrafish heart is only transient. While many pathways involved in heart regeneration have been identified, the cell types, especially non-myocytes, responsible for the regulation of the regenerative process have largely remained elusive. Here, we systematically determined all different cell types of both the healthy and cryo-injured zebrafish heart in its regeneration process using microfluidics based high-throughput single-cell RNA sequencing. We found a considerable heterogeneity of ECM producing cells, including a number of novel fibroblast cell types which appear with different dynamics after injury. We could describe activated fibroblasts that extensively switch on gene modules for ECM production and identify fibroblast sub- types with a pro-regenerative function. Furthermore, we developed a method that is capable of combining transcriptome analysis with lineage tracing on the single-cell level. Using CRISPR-Cas9 technology, we introduced random mutations into known and ubiquitously transcribed DNA loci during the zebrafish embryonic development. These mutations served as cell-unique, permanent, and heritable barcodes that could be captured at a later stage simultaneously with the transcriptome by high-throughput single-cell RNA sequencing. With custom tailored analysis algorithms, we were then able to build a developmental lineage tree of the sequenced single cells. Using this new method, we revealed that in the regenerating zebrafish heart, ECM contributing cell populations derive either from the epi- or the endocardium. Additionally, we discovered in a functional experiment that endocardial derived cell types are Wnt signaling dependent.
Ma, Sai. "Microfluidics for Genetic and Epigenetic Analysis." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78187.
Full textPh. D.
MAHMOUD, NADY ABDELMOEZ ATTA. "On-chip Electrophoretic Fractionation of Cytoplasmic and Nuclear RNA from Single Cells." Kyoto University, 2019. http://hdl.handle.net/2433/244546.
Full textPettit, Jean-Baptiste Olivier Georges. "Spatial analysis of complex biological tissues from single cell gene expression data." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708750.
Full textEl, Bardisy Shaheer [Verfasser]. "Development of a High-Throughput Single-Cell Sequencing Platform for the Discovery of Shared-Antigen and Neoepitope-Specific T-Cell Receptors / Shaheer El Bardisy." Mainz : Universitätsbibliothek Mainz, 2020. http://d-nb.info/1211519929/34.
Full textTatsuoka, Hisato. "Single-cell Transcriptome Analysis Dissects the Replicating Process of Pancreatic Beta Cells in Partial Pancreatectomy Model." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263543.
Full textYang, Karren Dai. "Learning causal graphs under interventions and applications to single-cell biological data analysis." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130806.
Full textThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
Cataloged from the official PDF version of thesis.
Includes bibliographical references (pages 49-51).
This thesis studies the problem of learning causal directed acyclic graphs (DAGs) in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. The identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes, has previously been studied. This thesis first extends these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them, by defining and characterizing the interventional Markov equivalence class that can be identified from general interventions. Subsequently, this thesis proposes the first provably consistent algorithm for learning DAGs in this setting. Finally, this algorithm as well as related work is applied to analyze biological datasets.
by Karren Dai Yang.
S.M.
S.M.
S.M. Massachusetts Institute of Technology, Department of Biological Engineering
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Woodhouse, Steven. "Synthesising executable gene regulatory networks in haematopoiesis from single-cell gene expression data." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/269317.
Full textLu, Sijia. "Label-Free Optical Imaging of Chromophores and Genome Analysis at the Single Cell Level." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10563.
Full textChemistry and Chemical Biology
Berardi, Francesco. "NOMA Performance in a 5G NR Single Cell Downlink Scenario." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Find full textHu, Bo [Verfasser]. "Analysis of cellular drivers of zebrafish heart regeneration by single-cell RNA sequencing and high-throughput lineage tracing / Bo Hu." Berlin : Humboldt-Universität zu Berlin, 2021. http://nbn-resolving.de/urn:nbn:de:kobv:11-110-18452/24021-9.
Full textReddy, Veena K. "Analysis of single cell RNA seq data to identify markers for subtyping of non-small cell lung cancer." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18514.
Full textVuong, Nhung. "Molecular Mechanisms by Which Estrogen Causes Ovarian Epithelial Cell Dysplasia." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37286.
Full textSarma, Mimosa. "Microfluidic platforms for Transcriptomics and Epigenomics." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90294.
Full textDoctor of Philosophy
This is the era of personalized medicine which means that we are no longer looking at one-size-fits-all therapies. We are rather focused on finding therapies that are tailormade to every individual’s personal needs. This has become more and more essential in the context of serious diseases like cancer where therapies have a lot of side-effects. To provide tailor-made therapy to patients, it is important to know how each patient is different from another. This difference can be found from studying how the individual is unique or different at the cellular level i.e. by looking into the contents of the cell like DNA, RNA, and chromatin. In this thesis, we discussed a number of projects which we can contribute to advancement in this field of personalized medicine. Our first project, MID-RNA-seq offers a new platform for studying the information contained in the RNA of a single cell. This platform has enough potential to be scaled up and automated into an excellent platform for studying the RNA of rare or limited patient samples. The second project discussed in this thesis involves studying the RNA of innate immune cells which defend our bodies against pathogens. The RNA data that we have unearthed in this project provides an immense scope for understanding innate immunity. This data provides our biologist collaborators the scope to test various pathways in innate immune cells and their roles in innate immune modulation. Our third project discusses a method to produce an enzyme called ‘Tn5’ which is necessary for studying the sequence of DNA. This enzyme which is commercially available has a very high cost associated with it but because we produced it in the lab, we were able to greatly reduce costs. The fourth project discussed involves the study of chromatin structure in cells and enables us to understand how our lifestyle choices change the expression or repression of genes in the cell, a study called epigenetics. The findings of this study would enable us to study epigenomic profiles from limited patient samples. Overall, our projects have enabled us to understand the information from cells especially when we have limited cell numbers. Once we have all this information we can compare how each patient is different from others. The future brings us closer to putting this into clinical practice and assigning different therapies to patients based on such data.
Vlajic, Natalija. "Single-cell and hierarchical wireless data broadcast systems: Modeling, performance analysis, and optimal scheduling." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/29005.
Full textKuut, Gunnar [Verfasser], and Veit [Akademischer Betreuer] Hornung. "Using RNA barcoding and sequencing to study cellular differentiation on a single-cell and population level / Gunnar Kuut ; Betreuer: Veit Hornung." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021. http://d-nb.info/123801707X/34.
Full textReddy, Devulapally Praneeth [Verfasser]. "High-throughput sequencing of human B cell receptor repertoires at single-cell level with preservation of the native antibody heavy and light chain pairs / Praneeth Reddy Devulapally." Berlin : Freie Universität Berlin, 2017. http://d-nb.info/1143596021/34.
Full textGenga, Ryan M. "Towards Understanding the Molecular Basis of Human Endoderm Development Using CRISPR-Effector and Single-Cell Technologies." eScholarship@UMMS, 2019. https://escholarship.umassmed.edu/gsbs_diss/1008.
Full textMuench, David. "Gfi1-controlled transcriptional circuits in normal and malignant hematopoiesis." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1553250015825734.
Full text