A Single-Cell Immune Map of Normal and Cancerous Breast Reveals an Expansion of Phenotypic States Driven by the Tumor Microenvironment

Knowledge of the phenotypic states of immune cells in the tumor microenvironment is essential to understand immunological mechanisms of cancer progression, responses to cancer immunotherapy, and the development of novel rational treatments. Yet, this knowledge is opaque to traditional bulk sequencing methods, and novel single-cell RNA sequencing (scRNA-seq) methods which could potentially address these questions introduce complex patterns of error into data that are poorly characterized. This dissertation describes a computational framework, SEQC, built to facilitate rapid and agile analysis of scRNA-seq approaches that utilize molecular barcodes. It combines SEQC with a clustering and normalization method, BISCUIT, and approaches to examine phenotypic diversity and gene variation. These methods are applied to address the unique computational challenges inherent to analysis of single-cell RNA-seq data derived from multiple patients with diverse phenotypes. This dissertation describes an experiment comprising scRNA-seq of over 47,000 immune cells collected from primary breast carcinomas, matched normal breast tissue, peripheral blood, and using these computational approaches. This atlas revealed significant similarity between normal and tumor tissue resident immune cells. However, it also describes continuous tumor-specific phenotypic expansions driven by distinct environmental cues. These results argue against discrete activation states in T cells and the polarization model of macrophage activation in cancer, and have important implications for characterizing tumor-infiltrating immune cells

Computational solutions for addressing heterogeneity in DNA methylation data

DNA methylation, a reversible epigenetic modification, has been implicated with various bi- ological processes including gene regulation. Due to the multitude of datasets available, it is a premier candidate for computational tool development, especially for investigating hetero- geneity within and across samples. We differentiate between three levels of heterogeneity in DNA methylation data: between-group, between-sample, and within-sample heterogeneity. Here, we separately address these three levels and present new computational approaches to quantify and systematically investigate heterogeneity. Epigenome-wide association studies relate a DNA methylation aberration to a phenotype and therefore address between-group heterogeneity. To facilitate such studies, which necessar- ily include data processing, exploratory data analysis, and differential analysis of DNA methy- lation, we extended the R-package RnBeads. We implemented novel methods for calculating the epigenetic age of individuals, novel imputation methods, and differential variability analysis. A use-case of the new features is presented using samples from Ewing sarcoma patients. As an important driver of epigenetic differences between phenotypes, we systematically investigated associations between donor genotypes and DNA methylation states in methylation quantitative trait loci (methQTL). To that end, we developed a novel computational framework –MAGAR– for determining statistically significant associations between genetic and epigenetic variations. We applied the new pipeline to samples obtained from sorted blood cells and complex bowel tissues of healthy individuals and found that tissue-specific and common methQTLs have dis- tinct genomic locations and biological properties. To investigate cell-type-specific DNA methylation profiles, which are the main drivers of within-group heterogeneity, computational deconvolution methods can be used to dissect DNA methylation patterns into latent methylation components. Deconvolution methods require pro- files of high technical quality and the identified components need to be biologically interpreted. We developed a computational pipeline to perform deconvolution of complex DNA methyla- tion data, which implements crucial data processing steps and facilitates result interpretation. We applied the protocol to lung adenocarcinoma samples and found indications of tumor in- filtration by immune cells and associations of the detected components with patient survival. Within-sample heterogeneity (WSH), i.e., heterogeneous DNA methylation patterns at a ge- nomic locus within a biological sample, is often neglected in epigenomic studies. We present the first systematic benchmark of scores quantifying WSH genome-wide using simulated and experimental data. Additionally, we created two novel scores that quantify DNA methyla- tion heterogeneity at single CpG resolution with improved robustness toward technical biases. WSH scores describe different types of WSH in simulated data, quantify differential hetero- geneity, and serve as a reliable estimator of tumor purity. Due to the broad availability of DNA methylation data, the levels of heterogeneity in DNA methylation data can be comprehensively investigated. We contribute novel computational frameworks for analyzing DNA methylation data with respect to different levels of hetero- geneity. We envision that this toolbox will be indispensible for understanding the functional implications of DNA methylation patterns in health and disease.DNA Methylierung ist eine reversible, epigenetische Modifikation, die mit verschiedenen biologischen Prozessen wie beispielsweise der Genregulation in Verbindung steht. Eine Vielzahl von DNA Methylierungsdatensätzen bildet die perfekte Grundlage zur Entwicklung von Softwareanwendungen, insbesondere um Heterogenität innerhalb und zwischen Proben zu beschreiben. Wir unterscheiden drei Ebenen von Heterogenität in DNA Methylierungsdaten: zwischen Gruppen, zwischen Proben und innerhalb einer Probe. Hier betrachten wir die drei Ebenen von Heterogenität in DNA Methylierungsdaten unabhängig voneinander und präsentieren neue Ansätze um die Heterogenität zu beschreiben und zu quantifizieren. Epigenomweite Assoziationsstudien verknüpfen eine DNA Methylierungsveränderung mit einem Phänotypen und beschreiben Heterogenität zwischen Gruppen. Um solche Studien, welche Datenprozessierung, sowie exploratorische und differentielle Datenanalyse beinhalten, zu vereinfachen haben wir die R-basierte Softwareanwendung RnBeads erweitert. Die Erweiterungen beinhalten neue Methoden, um das epigenetische Alter vorherzusagen, neue Schätzungsmethoden für fehlende Datenpunkte und eine differentielle Variabilitätsanalyse. Die Analyse von Ewing-Sarkom Patientendaten wurde als Anwendungsbeispiel für die neu entwickelten Methoden gewählt. Wir untersuchten Assoziationen zwischen Genotypen und DNA Methylierung von einzelnen CpGs, um sogenannte methylation quantitative trait loci (methQTL) zu definieren. Diese stellen einen wichtiger Faktor dar, der epigenetische Unterschiede zwischen Gruppen induziert. Hierzu entwickelten wir ein neues Softwarepaket (MAGAR), um statistisch signifikante Assoziationen zwischen genetischer und epigenetischer Variation zu identifizieren. Wir wendeten diese Pipeline auf Blutzelltypen und komplexe Biopsien von gesunden Individuen an und konnten gemeinsame und gewebespezifische methQTLs in verschiedenen Bereichen des Genoms lokalisieren, die mit unterschiedlichen biologischen Eigenschaften verknüpft sind. Die Hauptursache für Heterogenität innerhalb einer Gruppe sind zelltypspezifische DNA Methylierungsmuster. Um diese genauer zu untersuchen kann Dekonvolutionssoftware die DNA Methylierungsmatrix in unabhängige Variationskomponenten zerlegen. Dekonvolutionsmethoden auf Basis von DNA Methylierung benötigen technisch hochwertige Profile und die identifizierten Komponenten müssen biologisch interpretiert werden. In dieser Arbeit entwickelten wir eine computerbasierte Pipeline zur Durchführung von Dekonvolutionsexperimenten, welche die Datenprozessierung und Interpretation der Resultate beinhaltet. Wir wendeten das entwickelte Protokoll auf Lungenadenokarzinome an und fanden Anzeichen für eine Tumorinfiltration durch Immunzellen, sowie Verbindungen zum Überleben der Patienten. Heterogenität innerhalb einer Probe (within-sample heterogeneity, WSH), d.h. heterogene Methylierungsmuster innerhalb einer Probe an einer genomischen Position, wird in epigenomischen Studien meist vernachlässigt. Wir präsentieren den ersten Vergleich verschiedener, genomweiter WSH Maße auf simulierten und experimentellen Daten. Zusätzlich entwickelten wir zwei neue Maße um WSH für einzelne CpGs zu berechnen, welche eine verbesserte Robustheit gegenüber technischen Faktoren aufweisen. WSH Maße beschreiben verschiedene Arten von WSH, quantifizieren differentielle Heterogenität und sagen Tumorreinheit vorher. Aufgrund der breiten Verfügbarkeit von DNA Methylierungsdaten können die Ebenen der Heterogenität ganzheitlich beschrieben werden. In dieser Arbeit präsentieren wir neue Softwarelösungen zur Analyse von DNA Methylierungsdaten in Bezug auf die verschiedenen Ebenen der Heterogenität. Wir sind davon überzeugt, dass die vorgestellten Softwarewerkzeuge unverzichtbar für das Verständnis von DNA Methylierung im kranken und gesunden Stadium sein werden

Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow

Small RNA-Seq has emerged as a powerful tool in transcriptomics, gene expression profiling and biomarker discovery. Sequencing cell-free nucleic acids, particularly microRNA (miRNA), from liquid biopsies additionally provides exciting possibilities for molecular diagnostics, and might help establish disease-specific biomarker signatures. The complexity of the small RNA-Seq workflow, however, bears challenges and biases that researchers need to be aware of in order to generate high-quality data. Rigorous standardization and extensive validation are required to guarantee reliability, reproducibility and comparability of research findings. Hypotheses based on flawed experimental conditions can be inconsistent and even misleading. Comparable to the well-established MIQE guidelines for qPCR experiments, this work aims at establishing guidelines for experimental design and pre-analytical sample processing, standardization of library preparation and sequencing reactions, as well as facilitating data analysis. We highlight bottlenecks in small RNA-Seq experiments, point out the importance of stringent quality control and validation, and provide a primer for differential expression analysis and biomarker discovery. Following our recommendations will en-courage better sequencing practice, increase experimental transparency and lead to more reproducible small RNA-Seq results. This will ultimately enhance the validity of biomarker signatures, and allow reliable and robust clinical predictions

Identification of Cell Types in scRNA-seq Data via Enhanced Local Embedding and Clustering

Identifying specific cell types is a significant step for studying diseases and potentially leading to better diagnosis, drug discovery, and prognosis. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, which are categorized in the form of unsupervised learning methods, are the most suitable approach in scRNA-seq data analysis when the cell types have not been characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensional nature of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a pipeline to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques such as modified locally linear embedding (MLLE) and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We evaluate the intra- and inter-cluster performance based on the Silhouette score before performing a biological assessment. We further performed gene enrichment analysis across biological databases to evaluate the proposed method\u27s performance. As such, our results show that MLLE combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different experiments

Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi

Transcriptome Sequencing for Precise and Accurate Measurement of Transcripts and Accessibility of TCGA for Cancer Datasets and Analysis

Next-generation sequencing (NGS) technologies are now well established and have become a routine analysis tool for its depth, coverage, and cost. RNA sequencing (RNA-Seq) has readily replaced the conventional array-based approaches and has become method of choice for qualitative and quantitative analysis of transcriptome, quantification of alternative spliced isoforms, identification of sequence variants, novel transcripts, and gene fusions, among many others. The current chapter discusses the multi-step transcriptome data analysis processes in detail, in the context of re-sequencing (where a reference genome is available). We have discussed the processes including quality control, read alignment, quantification of gene from read level, visualization of data at different levels, and the identification of differentially expressed genes and alternatively spliced transcripts. Considering the data that are freely available to the public, we also discuss The Cancer Genome Atlas (TCGA), as a resource of RNA-Seq data on cancer for selection and analysis in specific contexts of experimentation. This chapter provides insights into the applicability, data availability, tools, and statistics for a beginner to get familiar with RNA-Seq data analysis and TCGA

Sources of variation in cell-type RNA-Seq profiles

Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples

Computational approaches for single-cell omics and multi-omics data

Single-cell omics and multi-omics technologies have enabled the study of cellular heterogeneity with unprecedented resolution and the discovery of new cell types. The core of identifying heterogeneous cell types, both existing and novel ones, relies on efficient computational approaches, including especially cluster analysis. Additionally, gene regulatory network analysis and various integrative approaches are needed to combine data across studies and different multi-omics layers. This thesis comprehensively compared Bayesian clustering models for single-cell RNAsequencing (scRNA-seq) data and selected integrative approaches were used to study the cell-type specific gene regulation of uterus. Additionally, single-cell multi-omics data integration approaches for cell heterogeneity analysis were investigated. Article I investigated analytical approaches for cluster analysis in scRNA-seq data, particularly, latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP) models. The comparison of LDA and HDP together with the existing state-of-art methods revealed that topic modeling-based models can be useful in scRNA-seq cluster analysis. Evaluation of the cluster qualities for LDA and HDP with intrinsic and extrinsic cluster quality metrics indicated that the clustering performance of these methods is dataset dependent. Article II and Article III focused on cell-type specific integrative analysis of uterine or decidual stromal (dS) and natural killer (dNK) cells that are important for successful pregnancy. Article II integrated the existing preeclampsia RNA-seq studies of the decidua together with recent scRNA-seq datasets in order to investigate cell-type-specific contributions of early onset preeclampsia (EOP) and late onset preeclampsia (LOP). It was discovered that the dS marker genes were enriched for LOP downregulated genes and the dNK marker genes were enriched for upregulated EOP genes. Article III presented a gene regulatory network analysis for the subpopulations of dS and dNK cells. This study identified novel subpopulation specific transcription factors that promote decidualization of stromal cells and dNK mediated maternal immunotolerance. In Article IV, different strategies and methodological frameworks for data integration in single-cell multi-omics data analysis were reviewed in detail. Data integration methods were grouped into early, late and intermediate data integration strategies. The specific stage and order of data integration can have substantial effect on the results of the integrative analysis. The central details of the approaches were presented, and potential future directions were discussed.  Laskennallisia menetelmiä yksisolusekvensointi- ja multiomiikkatulosten analyyseihin Yksisolusekvensointitekniikat mahdollistavat solujen heterogeenisyyden tutkimuksen ennennäkemättömällä resoluutiolla ja uusien solutyyppien löytämisen. Solutyyppien tunnistamisessa keskeisessä roolissa on ryhmittely eli klusterointianalyysi. Myös geenien säätelyverkostojen sekä eri molekyylidatatasojen yhdistäminen on keskeistä analyysissä. Väitöskirjassa verrataan bayesilaisia klusterointimenetelmiä ja yhdistetään eri menetelmillä kerättyjä tietoja kohdun solutyyppispesifisessä geeninsäätelyanalyysissä. Lisäksi yksisolutiedon integraatiomenetelmiä selvitetään kattavasti. Julkaisu I keskittyy analyyttisten menetelmien, erityisesti latenttiin Dirichletallokaatioon (LDA) ja hierarkkiseen Dirichlet-prosessiin (HDP) perustuvien mallien tutkimiseen yksisoludatan klusterianalyysissä. Kattava vertailu näiden kahden mallin sekä olemassa olevien menetelmien kanssa paljasti, että aihemallinnuspohjaiset menetelmät voivat olla hyödyllisiä yksisoludatan klusterianalyysissä. Menetelmien suorituskyky riippui myös kunkin analysoitavan datasetin ominaisuuksista. Julkaisuissa II ja III keskitytään naisen lisääntymisterveydelle tärkeiden kohdun stroomasolujen ja NK-immuunisolujen solutyyppispesifiseen analyysiin. Artikkelissa II yhdistettiin olemassa olevia tuloksia pre-eklampsiasta viimeisimpiin yksisolusekvensointituloksiin ja löydettiin varhain alkavan pre-eklampsian (EOP) ja myöhään alkavan pre-eklampsian (LOP) solutyyppispesifisiä vaikutuksia. Havaittiin, että erilaistuneen strooman markkerigeenien ilmentyminen vähentyi LOP:ssa ja NK-markkerigeenien ilmentyminen lisääntyi EOP:ssa. Julkaisu III analysoi strooman ja NK-solujen alapopulaatiospesifisiä geeninsäätelyverkostoja ja niiden transkriptiofaktoreita. Tutkimus tunnisti uusia alapopulaatiospesifisiä säätelijöitä, jotka edistävät strooman erilaistumista ja NK-soluvälitteistä immunotoleranssia Julkaisu IV tarkastelee yksityiskohtaisesti strategioita ja menetelmiä erilaisten yksisoludatatasojen (multi-omiikka) integroimiseksi. Integrointimenetelmät ryhmiteltiin varhaisen, myöhäisen ja välivaiheen strategioihin ja kunkin lähestymistavan menetelmiä esiteltiin tarkemmin. Lisäksi keskusteltiin mahdollisista tulevaisuuden suunnista