Dissertations / Theses on the topic 'Single cell sequencing data'
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1
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.
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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.
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Salehi, 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.
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Improving our understanding of intra-tumour heterogeneity in cancer has important clinical implications, including an opportunity to understand mechanisms behind relapses and drug resistance. Next generation bulk sequencing is a mature tech- nology that has been used to study subclonal tumour populations at an aggregate level. Inference of populations from bulk sequencing requires sophisticated com- putational deconvolution methods. An alternative is to identify populations directly with single cell sequencing. However, single cell sequencing is a very error-prone process, and this impedes its ability to completely replace bulk sequencing for now.
In this work we present dd-PyClone, a statistical model to combine single cell and bulk sequencing data to study clonal subpopulation architecture and improve clustering assignment and cellular prevalence estimates of a set of genomic loci.
We introduce a single nucleotide variant and copy number aberration aware genotype simulation scheme based on a phylogenetic tree, termed the Generalized Dollo model. This model is an improvement over previous genotype generator models in that it also accounts for the evolutionary process before a rare event (here the single nucleotide variant) occurs.
We show that incorporating genomic loci co-occurrence patterns from single cell sequencing studies in inferring clonal subpopulation structure from bulk se- quencing data is beneficial. Our method outperforms existing methods in simula- tion studies and performs comparably in real dataset benchmarking. We also show that our method is fairly robust as to the choice of hyperparameters and performs reasonably in presence of noise. We hope that our method will further the under- standing of the evolutionary basis of cancer.Science, Faculty of
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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.
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Bampalikis, 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.
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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.
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Ronen, Jonathan. "Integrative analysis of data from multiple experiments." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21612.
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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.
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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.
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Johnson, Travis Steele. "Integrative approaches to single cell RNA sequencing analysis." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586960661272666.
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Borgström, Erik. "Technologies for Single Cell Genome Analysis." Doctoral thesis, KTH, Genteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181059.
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During the last decade high throughput DNA sequencing of single cells has evolved from an idea to one of the most high profile fields of research. Much of this development has been possible due to the dramatic reduction in costs for massively parallel sequencing. The four papers included in this thesis describe or evaluate technological advancements for high throughput DNA sequencing of single cells and single molecules. As the sequencing technologies improve, more samples are analyzed in parallel. In paper 1, an automated procedure for preparation of samples prior to massively parallel sequencing is presented. The method has been applied to several projects and further development by others has enabled even higher sample throughputs. Amplification of single cell genomes is a prerequisite for sequence analysis. Paper 2 evaluates four commercially available kits for whole genome amplification of single cells. The results show that coverage of the genome differs significantly among the protocols and as expected this has impact on the downstream analysis. In Paper 3, single cell genotyping by exome sequencing is used to confirm the presence of fat cells derived from donated bone marrow within the recipients’ fat tissue. Close to hundred single cells were exome sequenced and a subset was validated by whole genome sequencing. In the last paper, a new method for phasing (i.e. determining the physical connection of variant alleles) is presented. The method barcodes amplicons from single molecules in emulsion droplets. The barcodes can then be used to determine which variants were present on the same original DNA molecule. The method is applied to two variable regions in the bacterial 16S gene in a metagenomic sample. Thus, two of the papers (1 and 4) present development of new methods for increasing the throughput and information content of data from massively parallel sequencing. Paper 2 evaluates and compares currently available methods and in paper 3, a biological question is answered using some of these tools.QC 20160127
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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.
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Single-cell RNA-sequencing makes possible to study the gene expression at the level of individual cells. However, one of the main challenges of the single-cell RNA-sequencing analysis today, is the identification and annotation of cell types. The current method consists in manually checking the expression of genes using top differentially expressed genes and comparing them with related cell-type markers available in scientific publications. It is therefore time-consuming and labour intensive. Nevertheless, in the last two years,numerous automatic cell-type identification and annotation tools which use different strategies have been created. But, the lack of specific comparisons of those tools in the literature and especially for immuno-oncologic and oncologic purposes makes difficult for laboratories and companies to know objectively what are the best tools for annotating cell types. In this project, a review of the current tools and an evaluation of R tools were carried out.The annotation performance, the computation time and the ease of use were assessed. After this preliminary results, the best selected R tools seem to be ClustifyR (fast and rather precise) and SingleR (precise) for the correlation-based tools, and SingleCellNet (precise and rather fast) and scPred (precise but a lot of cell types remains unassigned) for the supervised classificationtools. Finally, for the marker-based tools, MAESTRO and SCINA are rather robust if they are provided with high quality markers.
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Kindblom, 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.
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Single-cell genomic sequencing is a rapidly developing field that will play a vital role in human biology and science in the future. As of now, next-generation sequencing is accelerating in speed and decreasing in cost more quickly than Moore's law. Studies have shown that all cells in the human body have with very high probability a unique genomic signature, due to the somatic evolution which have accumulated mutations starting from the zygotic state. The possible reconstruction of phylogenetic lineage trees would be of vital importance to several fields in medicine, such as the stem cell research field. However, state-of-the-art methods for amplification such as WGA currently suffers from extensive allelic dropout which is troublesome when reconstructing phylogenetic trees. We have constructed a statistical model that can be used to predict site specific allelic dropout. Our results suggests that logistic regression is a suitable method for modelling allelic dropout, and that there is a non-linear relationship between the read depth and distance.
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Henao, 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.
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Evrony, Gilad David. "Single-cell Sequencing Studies of Somatic Mutation in the Human Brain." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10747.
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A major unanswered question in neuroscience is whether there exists genomic variability between individual neurons of the brain, contributing to functional diversity or to an unexplained burden of neurologic disease. To address this question, we developed methods to amplify genomes of single neurons from human brains, achieving >80% genome coverage of single-cells and allowing study of a wide-range of somatic mutation types.
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Ke, 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.
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Improved analytical methods provide new opportunities for both biological research and medical applications. This thesis describes several novel molecular techniques for nucleic acid and protein analysis based on detection or sequencing of amplified single molecules (ASMs). ASMs were generated from padlock probe assay and proximity ligation assay (PLA) through a series of molecular processes. In Paper I, a simple colorimetric readout strategy for detection of ASMs generated from padlock probe assay was used for highly sensitive detection of RNA virus, showing the potential of using padlock probes in the point-of-care diagnostics. In Paper II, digital quantification of ASMs, which were generated from padlock probe assay and PLA through circle-to-circle amplification (C2CA), was used for rapid and sensitive detection of nucleic acids and proteins, aiming for applications in biodefense. In Paper III, digital quantification of ASMs that were generated from PLA without C2CA was shown to be able to improve the precision and sensitivity of PLA when compared to the conventional real-time PCR readout. In Paper IV, a non-optical approach for detection of ASMs generated from PLA was used for sensitive detection of bacterial spores. ASMs were detected through sensing oligonucleotide-functionalized magnetic nanobeads that were trapped within them. Finally, based on in situ sequencing of ASMs generated via padlock probe assay, a novel method that enabled sequencing of individual mRNA molecules in their natural context was established and presented in Paper V. Highly multiplex detection of mRNA molecules was also achieved based on in situ sequencing. In situ sequencing allows studies of mRNA molecules from different aspects that cannot be accessed by current in situ hybridization techniques, providing possibilities for discovery of new information from the complexity of transcriptome. Therefore, it has a great potential to become a useful tool for gene expression research and disease diagnostics.
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Tu, 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.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, May, 2020Cataloged 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
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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.
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Benthic diatoms are often used as indicators of water quality and past environmental conditions. This depends entirely on a reliable taxonomic system. With the advent of DNA techniques, genetic analyses can now be used in tandem with traditional microscopy in order to improve taxonomy and determine evolutionary relationships. This thesis examined a speciose genus of diatoms Neidium (> 300 species) and, using sequence data from molecular markers as well as traditional morphological analyses, investigated phylogenetic relationships. Fresh benthic samples from aquatic ecosystems in Eastern North America were collected; Neidium taxa were examined using light and scanning electron microscopy then compared to the original specimen types. A total of 124 individual cells were retrieved, amplified, and sequenced for four molecular markers (rbcL, 18S, psbA, and psbC). Phylogenetic reconstructions were completed using Maximum likelihood and Bayesian analyses; when compared with morphological analyses this led to the delineation of several novel Neidium species.
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Ziegenhain, 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.
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Glaros, 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.
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O'Neill, Kieran. "Automated analysis of single cell leukemia data." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/50867.
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Acute myeloid leukemia (AML) is a high grade malignancy of non-lymphoid cells of the hematopoietic system. AML is a heterogeneous disease, and numerous attempts have been made to risk-stratify AML so that appropriate treatment can be offered. Single cell analysis methods could provide insights into the biology of AML leading to risk-stratified and functionally tailored treatments and hence improved outcomes. Recent advances in flow cytometry allow the simultaneous measurement of up to 17 antibody markers per cell for up to millions of cells, and it is performed routinely during AML clinical workup. However, despite vast amounts of flow cytometry data being gathered, comprehensive, objective and automated studies of this data have not been undertaken. Another method, strand-seq, elucidates template strand inheritance in single cells, with a range of potential applications, none of which had been automated when this thesis work commenced.
I have developed bioinformatic methods enabling research into AML using both these types of data.
I present flowBin, a method for faithfully recombining multitube flow cytometry data. I present flowType-DP, a new version of flowType, able to process flow cytometry and other single cell data having more than 12 markers (including flowBin output). I demonstrate the application of flowBin to AML data, for digitally isolating abnormal cells, and classifying AML patients. I also use flowBin in conjunction with flowType to find cell types associated with clinically relevant gene mutations in AML.
I present BAIT, a software package for accurately detecting sister chromatid exchanges in strand-seq data. I present functionality to place unbridged contigs in late-build genomes into their correct location, and have, with collaborators, published the corrected locations of more than half the unplaced contigs in the current build of the mouse genome. I present contiBAIT, a software package for assembling early-build genomes which consist entirely of unanchored, unbridged contigs. ContiBAIT has the potential to dramatically improve the quality of many model organism genomes at low cost.
These developments enable rapid, automated, objective and reproducible deep profiling of AML flow cytometry data, subclonal cell analysis of AML cytogenetics, and improvements to model organisms used in AML research.Science, Faculty of
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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.
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Nir, Oaz. "Single-cell morphological data reveals signaling network architecture." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/58457.
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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2010.Cataloged 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.
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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.
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Humoral 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.
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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.
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The recent technology of single-cell RNA sequencing can be used to investigate molecular, transcriptional, changes in cells as they develop. I reviewed the literature on the technology, and made a large scale quantitative comparison of the different implementations of single cell RNA sequencing to identify their technical limitations. I investigate how to model transcriptional changes during cellular development. The general forms of expression changes with respect to development leads to nonparametric regression models, in the forms of Gaussian Processes. I used Gaussian process models to investigate expression patterns in early embryonic development, and compared the development of mice and humans. When using in vivo systems, ground truth time for each cell cannot be known. Only a snapshot of cells, all being in different stages of development can be obtained. In an experiment measuring the transcriptome of zebrafish blood precursor cells undergoing the development from hematopoietic stem cells to thrombocytes, I used a Gaussian Process Latent Variable model to align the cells according to the developmental trajectory. This way I could investigate which genes were driving the development, and characterise the different patterns of expression. With the latent variable strategy in mind, I designed an experiment to study a rare event of murine embryonic stem cells entering a state similar to very early embryos. The GPLVM can take advantage of the nonlinear expression patterns involved with this process. The results showed multiple activation events of genes as cells progress towards the rare state. An essential feature of cellular biology is that precursor cells can give rise to multiple types of progenitor cells through differentiation. In the immune system, naive T-helper cells differentiate to different sub-types depending on the infection. For an experiment where mice were infected by malaria, the T-helper cells develop into two cell types, Th1 and Tfh. I model this branching development using an Overlapping Mixture of Gaussian Processes, which let me identify both which cells belong to which branch, and learn which genes are involved with the different branches. Researchers have now started performing high-throughput experiments where spatial context of gene expression is recorded. Similar to how I identify temporal expression patterns, spatial expression patterns can be identified nonparametrically. To enable researchers to make use of this technique, I developed a very fast method to perform a statistical test for spatial dependence, and illustrate the result on multiple data sets.
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Subramanian, Parimalam Sangamithirai. "Dissecting gene expression of single cells ｗith reduced perturbation." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263616.
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Kinchen, 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.
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Colonic stromal cells provide critical structural support but also regulate immunity, tolerance and inflammatory responses in the mucosa. Substantial variability and plasticity of mucosal stromal cells has been reported but a paucity of distinct marker genes exist to identify distinct cell states. Here single-cell RNA-sequencing is used to document heterogeneity and subtype specific markers of individual colonic stromal cells in human and mouse. Marker-free transcriptional clustering of fibroblast-like cells derived from healthy human tissue reveals distinct populations corresponding to myofibroblasts and three transcriptionally and functionally dissimilar populations of fibroblasts. A SOX6 high fibroblast subset occupies a position adjacent to the epithelial basement membrane and expresses multiple epithelial morphogens including WNT5A and BMP2. Additional fibroblast subtypes show specific enrichment for chemokine signalling and prostaglandin E2 synthesis respectively. In ulcerative colitis, substantial remodelling occurs with depletion of the SOX6 high population and emergence of an immune enriched population expressing genes associated with fibroblastic reticular cells including CCL19, CCL21 and IL33. A large murine dataset comprising over 7,000 colonic mesenchymal cells from an acute colitis model and matched healthy controls reveals strong preservation of the SOX6 high and myofibroblast transcriptional signatures. Unsupervised pseudotemporal ordering is used to relate fibroblast subsets to one another producing a branched developmental hierarchy that includes a potential progenitor population with mesothelial characteristics at its origin. This work provides a molecular basis for re-classification of colonic stromal cells and identifies pathological changes in these cells underpinning inflammation in UC.
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Makowski, 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 textAbstract:
The advent of high-throughput sequencing has brought about the creation of an unprecedented amount of research data. Analytical methodology has not been able to keep pace with the plethora of data being produced. Two assays, ImmunoSEQ and the cytokinesisblock micronucleus (CBMN), that both produce count data and have few methods available to analyze them are considered.
ImmunoSEQ is a sequencing assay that measures the beta T-cell receptor (TCR) repertoire. The ImmunoSEQ assay was used to describe the TCR repertoires of patients that have undergone hematopoietic stem cell transplantation (HSCT). Several different methods for spectratype analysis were extended to the TCR sequencing setting then applied to these data to demonstrate different ways the data set can be analyzed. The different methods include CDR3 distribution perturbation, Oligoscores, Simpson's diversity, Shannon diversity, Kullback-Liebler divergence, a non-parametric method and a proportion logit transformation method. Herein we also demonstrate adapting compositional data analysis methods to the TCR sequencing setting. The various methods were compared when analyzing a set of 13 subjects who underwent hematopoietic stem cell transplantation. The eight subjects who developed graft versus host disease were compared to the five who did not. There was no little overlap in the results of the different methods showing that researchers must choose the appropriate method for their research question of interest.
The CBMN assay measures the rate of micronuclei (MN) formation in a sample of cells and can be paired with gene expression or methylation assays to determine association between MN formation and other genetic markers. Herein we extended the generalized monotone incremental forward stagewise (GMIFS) method to the situation where the response is count data and there are more independent variables than there are samples. Our Poisson GMIFS method was compared to a popular alternative, glmpath, by using simulations and applying both to real data. Simulations showed that both methods perform similarly in accurately choosing truly significant variables. However, glmpath appears to overfit compared to our GMIFS method. Finally, when both methods were applied to two data sets GMIFS appeared to be more stable than glmpath.
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Li, Mengyao. "P53 dynamics: single-cell imaging data analysis and modeling." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/59.
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The p53 protein plays a central role in controlling the fate of cancer cells. At moderate levels of DNA damage, the concentration of the phosphorylated form of p53 undergoes temporal oscillation with a period of a few hours. In Dr. Shi’s lab, single-cell measurements were carried out using the p53-YFP fusion proteins and time-lapse fluorescence microscopy. We report here a detailed study of the image data. From the time series of the p53 concentration in individual cells, we deduce the amplitude and period of the oscillation. The pulse-to-pulse and cell-to-cell variability of the oscillation is characterized. We then carry out a computational study of a mathematical model that involves a negative feedback loop between p53 and Mdm2 proteins. We have determined the phase diagram of the model, and studied the sensitivity of the properties of the oscillating state against the model parameters. Although only p53 concentration is measured in the experiment, we show that careful analysis of the pulse shape can nevertheless yield valuable information on the underlying molecular processes, and shed light on the possible origin of the observed cellto- cell variations.
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Hie, Brian. "Stitching and sketching large-scale single-cell transcriptomic data." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121734.
Full textAbstract:
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged 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
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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.
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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.
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Zhang, 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 textAbstract:
With the completion of human genome sequencing project and the rapid development of sequencing technologies, our capacity in tackling with genetic and genomic changes that underlie human diseases has never been greater. The recent successes in identifying disease causal single nucleotide variations (SNVs) for Mendelian disorders using whole exome sequencing may bring us one step further to understand the pathogenesis of Mendelian diseases. However, many hurdles need to be overcome before the promises can become widespread reality.
In this study, we investigated various strategies and designed a toolkit named PriSNV for SNV identification and prioritization, respectively. The SNV identification pipeline including read alignment, PCR duplication removal, indel realignment, base quality score recalibration, SNV and genotype calling was examined by simulation and real sequencing data. By incorporating sequencing errors and small indels, most of the read alignment software can achieve satisfied results. Nonetheless, the reads with medium size and large indels are prone to be wrongly mapped to the reference genome due to the limitation of gap opening strategies of available read alignment software. In addition, although mapping quality can only reflect certain information of the mapping error rate, it is still important to be adopted to filter out obvious read alignment errors. The PCR duplication removal, indel realignment and base quality score recalibration have proven to be necessary and can substantially reduce the false positive SNV calls. Based on the same quality criterion, Varscan performs as the most sensitive software for SNV calling, unfortunately at mean time the false positive calls are enriched in its result.
In order to prioritize the small subset of functionally important variants from tens of thousands of variants in whole human exome, we developed a toolkit called PriSNV, a systematic prioritization pipeline that makes use of information on variant quality, gene candidacy based on the number of novel nonsynonymous mutations in a gene, gene functional annotation, known involvement in the disease or relevant pathways, and location in linkage regions. Prediction of functional impact of the coding variants is also used to aid the search for causal mutations in Mendelian disorders. For the patient affected by Chron's disease, the candidate genes can be substantially reduced from 9615 to 3 by the gene selection strategies implemented in PriSNV.
In general, our results for SNV identification can help the biologists to realize the limitation of available software and shed light on the development of new strategies for accurately identifying SNV calls in the future. PriSNV, the software we developed for SNV prioritization, can provide significant help to biologists in prioritizing SNV calls in a systematic way and reducing search space for further analysis and experimental verification.published_or_final_version
Paediatrics and Adolescent Medicine
Master
Master of Philosophy
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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.
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Die Analyse von NGS Daten ist ein zentraler Aspekt der modernen genomischen Forschung. Bei der Extraktion von Daten aus den beiden am häufigsten verwendeten Quellorganismen bestehen jedoch vielfältige Problemstellungen.
Im ersten Kapitel wird ein neuartiger Ansatz vorgestellt welcher einen Abstand zwischen Krebszellinienkulturen auf Grundlage ihrer kleinen genomischen Varianten bestimmt um die Kulturen zu identifizieren. Eine Voll-Exom sequenzierte Kultur wird durch paarweise Vergleiche zu Referenzdatensätzen identifiziert so ein gemessener Abstand geringer ist als dies bei nicht verwandten Kulturen zu erwarten wäre. Die Wirksamkeit der Methode wurde verifiziert, jedoch verbleiben Einschränkung da nur das Sequenzierformat des Voll-Exoms unterstützt wird.
Daher wird im zweiten Kapitel eine publizierte Modifikation des Ansatzes vorgestellt welcher die Unterstützung der weitläufig genutzten Bulk RNA sowie der Panel-Sequenzierung ermöglicht. Die Ausweitung der Technologiebasis führt jedoch zu einer Verstärkung von Störeffekten welche zu Verletzungen der mathematischen Konditionen einer Abstandsmetrik führen. Daher werden die entstandenen Verletzungen durch statistische Verfahren zuerst quantifiziert und danach durch dynamische Schwellwertanpassungen erfolgreich kompensiert.
Das dritte Kapitel stellt eine neuartige Daten-Aufwertungsmethode (Data-Augmentation) vor welche das Trainieren von maschinellen Lernmodellen in Abwesenheit von neoplastischen Trainingsdaten ermöglicht. Ein abstraktes Abstandsmaß wird zwischen neoplastischen Entitäten sowie Entitäten gesundem Ursprungs mittels einer transkriptomischen Dekonvolution hergestellt. Die Ausgabe der Dekonvolution erlaubt dann das effektive Vorhersagen von klinischen Eigenschaften von seltenen jedoch biologisch vielfältigen Krebsarten wobei die prädiktive Kraft des Verfahrens der des etablierten Goldstandard ebenbürtig ist.The 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.
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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.
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Hu, 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 textAbstract:
Das Herz eines Zebrafishs ist bemerkenswert, da es sich nach einer Verletzung vollständig regenerieren kann. Der Regenerationsprozess wird von Fibrose begleitet - der Bildung von überschüssigem Gewebe der extrazellulären Matrix (ECM). Anders als bei Säugetieren ist die Fibrose im Zebrafish nur transient. Viele Signalwege wurden identifiziert, die an der Herzregeneration beteiligt sind. Allerdings sind die Zelltypen, insbesondere Nicht-Kardiomyozyten, die für die Regulation des Regenerationsprozesses verantwortlich sind, weitgehend unbekannt. In dieser Arbeit haben wir systematisch alle Zelltypen des gesunden und des verletzten Zebrafischherzens mithilfe einer auf Mikrofluidik basierenden Hoch-Durchsatz- Einzelzell-RNA-Sequenzierung bestimmt. Wir fanden eine große Heterogenität von ECM-produzierenden Zellen, einschließlich einer Reihe neuer Fibroblasten, die nach einer Verletzung mit unterschiedlicher Dynamik auftreten. Wir konnten aktivierte Fibroblasten beschreiben und Fibroblasten-Subtypen mit einer pro-regenerativen Funktion identifizieren.
Darüber hinaus haben wir eine Methode entwickelt, um die Transkriptomanalyse und die Rekonstruktion von Zell-Verwandtschaften auf Einzelzellebene zu kombinieren. Unter Verwendung der CRISPR-Cas9-Technologie führten wir zufällige Mutationen in bekannte und ubiquitär transkribierte DNA-Loci während der Embryonalentwicklung von Zebrafischen ein. Diese Mutationen dienten als zellspezifische, permanente und vererbbare “Barcodes”, die zu einem späteren Zeitpunkt erfasst werden konnten. Mit maßgeschneiderten Analysealgorithmen konnten wir dann Stammbäume der sequenzierten Einzelzellen erstellen. Mit dieser neuen Methode haben wir gezeigt, dass im sich regenerierenden Zebrafischherz ECM-produzierende Zellpopulationen entweder mit dem Epi- oder mit dem Endokardium verwandt sind. Zusätzlich entdeckten wir, dass vom Endokardium abgeleitete Zelltypen vom Wnt-Signalweg abhängig sind.The 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.
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Ma, Sai. "Microfluidics for Genetic and Epigenetic Analysis." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78187.
Full textAbstract:
Microfluidics has revolutionized how molecular biology studies are conducted. It permits profiling of genomic and epigenomic features for a wide range of applications. Microfluidics has been proven to be highly complementary to NGS technology with its unique capabilities for handling small volumes of samples and providing platforms for automation, integration, and multiplexing. In this thesis, we focus on three projects (diffusion-based PCR, MID-RRBS, and SurfaceChIP-seq), which improved the sensitivities of conventional assays by coupling with microfluidic technology. MID-RRBS and SurfaceChIP-seq projects were designed to profiling genome-wide DNA methylation and histone modifications, respectively. These assays dramatically improved the sensitivities of conventional approaches over 1000 times without compromising genomic coverages. We applied these assays to examine the neuronal/glial nuclei isolated from mouse brain tissues. We successfully identified the distinctive epigenomic signatures from neurons and glia. Another focus of this thesis is applying electrical field to investigate the intracellular contents. We report two projects, drug delivery to encapsulated bacteria and mRNA extraction under ultra-high electrical field intensity. We envision rapid growth in these directions, driven by the needs for testing scarce primary cells samples from patients in the context of precision medicine.Ph. D.
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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.
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Pettit, 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.
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El, 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.
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Tatsuoka, 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.
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Yang, 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 textAbstract:
Thesis: S.M., Massachusetts Institute of Technology, Department of Biological Engineering, February, 2021Thesis: 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
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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 textAbstract:
A fundamental challenge in biology is to understand the complex gene regulatory networks which control tissue development in the mammalian embryo, and maintain homoeostasis in the adult. The cell fate decisions underlying these processes are ultimately made at the level of individual cells. Recent experimental advances in biology allow researchers to obtain gene expression profiles at single-cell resolution over thousands of cells at once. These single-cell measurements provide snapshots of the states of the cells that make up a tissue, instead of the population-level averages provided by conventional high-throughput experiments. The aim of this PhD was to investigate the possibility of using this new high resolution data to reconstruct mechanistic computational models of gene regulatory networks. In this thesis I introduce the idea of viewing single-cell gene expression profiles as states of an asynchronous Boolean network, and frame model inference as the problem of reconstructing a Boolean network from its state space. I then give a scalable algorithm to solve this synthesis problem. In order to achieve scalability, this algorithm works in a modular way, treating different aspects of a graph data structure separately before encoding the search for logical rules as Boolean satisfiability problems to be dispatched to a SAT solver. Together with experimental collaborators, I applied this method to understanding the process of early blood development in the embryo, which is poorly understood due to the small number of cells present at this stage. The emergence of blood from Flk1+ mesoderm was studied by single cell expression analysis of 3934 cells at four sequential developmental time points. A mechanistic model recapitulating blood development was reconstructed from this data set, which was consistent with known biology and the bifurcation of blood and endothelium. Several model predictions were validated experimentally, demonstrating that HoxB4 and Sox17 directly regulate the haematopoietic factor Erg, and that Sox7 blocks primitive erythroid development. A general-purpose graphical tool was then developed based on this algorithm, which can be used by biological researchers as new single-cell data sets become available. This tool can deploy computations to the cloud in order to scale up larger high-throughput data sets. The results in this thesis demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the gene regulatory networks that underpin organogenesis. Rapid technological advances in our ability to perform single-cell profiling suggest that my tool will be applicable to other organ systems and may inform the development of improved cellular programming strategies.
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Lu, 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 textAbstract:
Since the emergence of biology as a quantitative science in the past century, a lot of biological discoveries have been driven by milestone technical advances such as X-ray crystallography, fluorescence microscopy and high-throughput sequencing. Fluorescence microscopy is widely used to explore the nanoscale cellular world because of its superb sensitivity and spatial resolution. However, many species (e.g. lipids, small proteins) are non-fluorescent and are difficult to label without disturbing their native functions. In the first part of the dissertation, we explore using three different contrast mechanisms for label-free imaging of these species – absorption and stimulated emission (Chapter 2), heat generation and diffusion (Chapter 3) and nonlinear scattering (Chapter 4). We demonstrate label-free imaging of blood vessels, cytochromes, drugs for photodynamic therapy, and muscle and brain tissues with three dimensional optical sectioning capability. With the rapid development of high throughput genotyping techniques, genome analysis is currently routinely done genome-wide with single nucleotide resolution. However, a large amount of starting materials are often required for whole genome analysis. The dynamic changes in DNA molecules generate intra-sample heterogeneity. Even with the same genome content, different cells often have very different transcriptome profiles in a functional organism. Such intra-sample heterogeneities in the genome and transcriptome are often masked by ensemble analysis. In this second part of the dissertation, we first introduce a whole genome amplification method with high coverage in sequencing single human cells (Chapter 6). We then use the technique to study meiotic recombinations in sperm cells from an individual (Chapter 7). We further develop a technique that enables digital counting of genome fragments and whole genome haplotyping in single cells (Chapter 8). And we introduce our ongoing efforts on single cell transcriptome analysis (Chapter 9). In the end, we introduce our initial effort in exploring the genome accessibility at the single cell level (Chapter 9). Through the development of techniques probing the single cell genome, transcriptome and possibly epigenome, we hope to provide a toolbox for studying biological processes with genome-wide and single cell resolution.Chemistry and Chemical Biology
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Berardi, Francesco. "NOMA Performance in a 5G NR Single Cell Downlink Scenario." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Find full textAbstract:
In the last years there has been a rapid growth of devices demanding for user access in the network. This fact is thanks to the Internet of Things world and the invention and wide spread of smartphones which have affordable prices, thus, going to decrease the digital divide. Because of this, the spectrum efficiency is getting more and more important and precious. Therefore, there is a need for a new information technology: the fifth generation technology,
5G New Radio.
This thesis is going to introduce the 5G NR and to compare the advantages and drawbacks of Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access systems (NOMA).
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Hu, 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.
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Reddy, 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.
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Single cell RNA technology is a recent technical advancement used to understand the cancertumorgenicity at single cell resolution. In this study we have analyzed the scRNA data from thenon-small cell lung cancer (NSCLC) dataset to facilitate the early identification of NSCLCsubtypes namely, squamous cell carcinoma (SCC) and adenocarcinoma (AC). Non-immunecells, have a major role in tumorigenesis of the malignant tumors, in early stages. Therefore,we have analyzed the major non-immune cells, namely endothelial cells and fibroblast cellsfrom the GSE127465 dataset using SEURAT pipeline. Dimensionality reduction analysis andcluster analysis indicate that AC and SCC subtypes of NSCLC have different fibroblastcompositions. Differential gene expression analysis indicates that AC tumours have shownelevated content of MGP/PTGDS and INMT/MFAP4 fibroblast cells, whereas squamous cellcarcinoma showed an elevated content of COL6A1/COL6A2 and FNDC1/COL12A1 fibroblastcells. The statistical analysis shows that the clustering is statistically significant and not anartefact. Given that the tumour microenvironment is highly dynamic, in this study we haveattempted to understand the tumour microenvironment by scRNA analysis of non-immune cellsat single cell resolution.
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Vuong, 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.
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The initiating events of ovarian cancer remain unknown, but an established risk factor is use of estrogen therapy by post-menopausal women where there is a positive correlation between duration of use and risk for disease. Mouse models of ovarian cancer have shown that exposure to exogenous 17β-estradiol (E2) accelerates tumour onset so this study aims to investigate the E2 signalling mechanisms responsible for sensitizing ovarian epithelial cells to transformation. By developing model systems that are responsive to E2 manipulation, we showed that E2 induces the formation of epithelial dysplasias both in vitro and in vivo. microRNA microarray was used to discover that E2 up-regulates microRNA-378 via the ESR1 pathway, resulting in the down-regulation of a tumour suppressor gene called Disabled-2 (Dab2). E2 suppression of Dab2 was found to result in increased proliferation, loss of contact inhibition, epithelial dysplasia, and increased sensitivity to transformation. This mechanism was also found to be active in mouse fallopian tube epithelium and human ovarian cancer cells. Single-cell RNA sequencing and trajectory analysis was subsequently used to explore additional signalling mechanisms that might contribute to the emergence of dysplastic lesions induced by E2. Multiple molecular signalling pathways dysregulated by E2 were identified and this revealed several possible biomarkers to be investigated for early detection of ovarian cancer. In the context of a current lack of strategies for ovarian cancer prevention or early detection, this work represents a significant advance in our understanding of how E2 promotes ovarian cancer initiation.
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Sarma, Mimosa. "Microfluidic platforms for Transcriptomics and Epigenomics." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90294.
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A cell, the building block of all life, stores a plethora of information in its genome, epigenome, and transcriptome which needs to be analyzed via various Omic studies. The heterogeneity in a seemingly similar group of cells is an important factor to consider and it could lead us to better understand processes such as cancer development and resistance to treatment, fetal development, and immune response. There is an ever growing demand to be able to develop more sensitive, accurate and robust ways to study Omic information and to analyze subtle biological variation between samples even with limited starting material obtained from a single cell. Microfluidics has opened up new and exciting possibilities that have revolutionized how we study and manipulate the contents of the cell like the DNA, RNA, proteins, etc. Microfluidics in conjunction with Next Gen Sequencing has provided ground-breaking capabilities for handling small sample volumes and has also provided scope for automation and multiplexing. In this thesis, we discuss a number of platforms for developing low-input or single cell Omic technologies. The first part talks about the development of a novel microfluidic platform to carry out single-cell RNA-sequencing in a one-pot method with a diffusion-based reagent swapping scheme. This platform helps to overcome the limitations of conventional microfluidic RNA seq methods reported in literature that use complicated multiple-chambered devices. It also provides good quality data that is comparable to state-of-the-art scRNA-seq methods while implementing a simpler device design that permits multiplexing. The second part talks about studying the transcriptome of innate leukocytes treated with varying levels of LPS and using RNA-seq to observe how innate immune cells undergo epigenetic reprogramming to develop phenotypes of memory cells. The third part discusses a low-cost alternative to produce tn5 enzyme which low-cost NGS studies. And finally, we discuss a microfluidic approach to carrying out low-input epigenomic studies for studying transcription factors. Today, single-cell or low-input Omic studies are rapidly moving into the clinical setting to enable studies of patient samples for personalized medicine. Our approaches and platforms will no doubt be important for transcriptomic and epigenomic studies of scarce cell samples under such settings.Doctor 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.
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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.
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Wireless Data Broadcast (WDB) is known as a highly efficient information delivery mechanism of nearly unlimited scalability. Over the last few years, due to the appealing properties and a wide area of possible application, a large number of new solutions and ideas related to WDB have been proposed. However, most of those solutions employ overly simplified assumptions concerning the system operation and user behaviour, and therefore fail to provide a broader insight into the nature and performance of WDB as found in the real world.
The work presented in this thesis aims to overcome the main limitations of the previous published research works on WDB. In particular, the following contributions are made. (1) We propose a new model of TDM-based single-cell WDB systems, which extends over both broadcast and unicast data retrieval principles, and assumes realistic-impatient user behaviour patterns. Based on this model, we define and derive exact mathematical expressions for several performance measures that seem most appropriate for the analysis of WDB systems. Consequently, we prove that there exists a single broadcast scheduling scheme (Soptimal), which can ensure the optimal system performance with respect to all of the measures at once, as well as the system's throughput, QoS and GoS. (2) The actual search for Soptimal, both in WDB systems of uniform and variable user mobilities, turns out to be a complex non-linear double inequality-constrained optimization problem, without a tractable closed-form solution. However, by exploiting some mathematical properties of the main cost function, we prove that the given optimization problem can be considerably simplified. Based on this simplification, we derive a closed form approximate expression for Soptimal and, consequently, we propose an algorithm for fast, CPU conserving estimation of Soptimal . Experimental results verify that the proposed algorithm requires minimum computation, while providing performance almost identical to Soptimal obtained through numerical estimation. (3) For the completeness of our discussion on single-cell WDB systems, we also consider the possibility of frequency-division-multiplexing (FDM) WDB. We prove that for any given FDM-based broadcast schedule there exists a TDM-based broadcast schedule that results in a better overall system performance. With this proof, we ultimately justify our initial decision to make TDM-WDB the main focus of this work. (Abstract shortened by UMI.)
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Kuut, 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.
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Reddy, 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.
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Genga, 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.
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The definitive endoderm gives rise to several specialized organs, including the thymus. Improper development of the definite endoderm or its derivatives can lead to human disease; in the case of the thymus, immunodeficiency or autoimmune disorders. Human pluripotent stem cells (hPSCs) have emerged as a system to model human development, as study of their differentiation allows for elucidation of the molecular basis of cell fate decisions, under both healthy and impaired conditions. Here, we first developed a CRISPR-effector system to control endogenous gene expression in hPSCs, a novel approach to manipulating hPSC state. Next, the human-specific, loss-of-function phenotypes of candidate transcription factors driving hPSC-to-definitive endoderm differentiation were analyzed through combined CRISPR-perturbation and single-cell RNA-sequencing. This analysis revealed the importance of TGFβ mediators in human definitive endoderm differentiation as well as identified an unappreciated role for FOXA2 in human foregut development. Finally, as the differentiation of definitive endoderm to thymic epithelial progenitors (TEPs) is of particular interest, a single-cell transcriptomic atlas of murine thymus development was generated in anticipation of identifying factors driving later stages of TEP differentiation. Taken together, this dissertation establishes a CRISPR-effector system to interrogate gene and regulatory element function in hPSC differentiation strategies, details the role of specific transcription factors in human endoderm differentiation, and sets the groundwork for future investigations to characterize hPSC-derived TEPs and the factors driving their differentiation.
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Muench, David. "Gfi1-controlled transcriptional circuits in normal and malignant hematopoiesis." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1553250015825734.
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