Estimating the time-dependent RNA kinetic rates in the cell cycle

Die Menge an RNA in Eukaryonten wird durch ihre kinetischen Transkriptions-, Verarbeitungs- und Abbauraten bestimmt. Diese kinetischen Raten wurden bereits ausführlich in Zellpopulationen untersucht, allerdings unter der Annahme, dass diese in verschiedenen Zelltypen identisch sind. Die Genexpression ist jedoch während biologischer Prozesse wie z.B der Zellproliferation, Zelldifferenzierung und Zellteilung hochdynamisch. Die Untersuchung der RNA- Kinetikraten in Einzelzellen, die sich in verschiedenen Phasen desselben dynamischen Prozesses befinden, kann uns ein umfangreicheres Bild davon geben, wie RNA-Kinetikraten die Genexpression zeitabhängig koordinieren. In diesem Projekt, Wir haben die Methode der RNA- Stoffwechselmarkierung und der biochemischen Nukleosidkonversion mit der Einzelzell-RNA- Sequenzierung kombiniert. Wir leiteten ein zeitabhängiges kinetisches Geschwindigkeitsmodell ab und schätzten RNA-Transkriptions- und - Abbauraten über den zeitlichen Verlauf des Zellzyklus ab. Dabeiverwendeten wir Näherungen basierend auf der Lösung des resultierenden Differentialgleichungssystems. Wir fanden heraus, dass Transkriptions- und Abbauraten der meisten zyklischen Gene hochdynamisch sind. Unterschiedliche kinetische Regulationsmuster formen spezifische Genexpressionsprofile. Etwa 89 % der 377 von uns analysierten zyklischen Gene werden durch dynamische Transkriptions- und Abbauraten reguliert. Während der dynamischen Transkriptionsrate beobachteten wir auch, dass einige zyklische Gene durch dynamische Zerfallsraten angetrieben wurden. Unsere Studie bekräftigt die Bedeutung der zeitlichen Regulation von der Genexpression durch Produktion und Zerfall. Darüber hinaus hat die von uns entwickelte Methode das Potenzial, an verschiedene biologische Prozesse angepasst zu werden. Unser Ansatz in dieser Studie kann die Untersuchung der zeitlichen Genexpressionsregulation und der RNS- Kinetikraten voranbringen.RNA abundance in eukaryotes is determined by its kinetic rates of transcription, processing and degradation. Each of the kinetic rates has been extensively studied in bulk cell populations assuming they are equal in different cells. However, gene expression is highly dynamic during biological processes such as cell proliferation, cell differentiation, and cell division. Investigation of RNA kinetic rates in individual cells which are in different phases of the same dynamic process can give us a more comprehensive picture of how RNA kinetic rates coordinate gene expression in a time-dependent manner. In this project, we adapted the RNA metabolic labeling and biochemical nucleoside conversion method to droplet- based single-cell RNA sequencing. We derived a time- dependent kinetic rate model and estimated RNA transcription and degradation rates over the time course of the cell cycle using approximations based on the solution of the resulting system of differential equations. We found that transcription and degradation rates of most cycling genes are highly dynamic. Different kinetic regulation patterns shape specific gene expression profiles. Around 89% of the 377 cycling genes we analyzed are regulated by dynamic transcription and degradation rates. While dynamic transcription rate was prevalent, we also observed some cycling genes were driven by dynamic decay rates. Our study underscores the importance of temporal gene expression regulation by both production and decay. Moreover, the method we developed has the potential to be adapted to different biological processes. We suggest that our approach can advance the study of temporal gene expression regulation and RNA kinetic rates

Recurrent pregnancy loss is associated with a pro-senescent decidual response during the peri-implantation window

During the implantation window, the endometrium becomes poised to transition to a pregnant state, a process driven by differentiation of stromal cells into decidual cells (DC). Perturbations in this process, termed decidualization, leads to breakdown of the feto-maternal interface and miscarriage, but the underlying mechanisms are poorly understood. Here, we reconstructed the decidual pathway at single-cell level in vitro and demonstrate that stromal cells first mount an acute stress response before emerging as DC or senescent DC (snDC). In the absence of immune cell-mediated clearance of snDC, secondary senescence transforms DC into progesterone-resistant cells that abundantly express extracellular matrix remodelling factors. Additional single-cell analysis of midluteal endometrium identified DIO2 and SCARA5 as marker genes of a diverging decidual response in vivo. Finally, we report a conspicuous link between a pro-senescent decidual response in peri-implantation endometrium and recurrent pregnancy loss, suggesting that pre-pregnancy screening and intervention may reduce the burden of miscarriage

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

Deciphering Leukaemogenic Mechanisms through System-Scale Analysis of Single-Cell RNA Sequencing Data

Haematopoietic stem cells are responsible for producing and sustaining the diverse array of cell types present in the adult blood system. This complex process requires the strict regulation of haematopoietic fate decisions and differentiation trajectories in order to maintain a healthy state. Haematological malignancies such as leukaemia are associated with various perturbations that disrupt this regulation and drive aberrant cell fate decisions, leading to disease. Much of this dysregulation is proposed to occur at the transcriptional level, and recent technological advancements in single-cell sequencing have made it possible to study the transcriptional effects of leukaemic perturbations at the scale of individual haematopoietic stem and progenitor cells. However, the mechanisms through which specific perturbations lead to dysregulation of the blood system remain poorly understood. The primary aim of this work was to build an integrative computational framework for the analysis and comparison of leukaemic perturbations of the murine blood system as measured by single-cell RNA sequencing. Presented in Chapter 3, this framework aims to dissect the perturbation response across different scales – from individual genes to specific progenitor cell types to the entire blood system – and allow informative comparisons to be made about the similarities and differences between several perturbations. In total, eight genetic perturbations known to associate with leukaemia were analysed, resulting in novel biological insights concerning the behaviour of coordinated gene modules and the cellular abundance shifts driven by them. As many leukaemic drivers act directly upon the most immature long-term haematopoietic stem cells, a highly targeted analysis of these cells was performed across the leukaemic perturbations. In Chapter 4 a novel computational pipeline was built to link FACS-sorted cell populations and single-cell transcriptional landscapes. Using this, the cellular and molecular responses of the perturbations were investigated, resulting in several novel hypotheses. For example, the data suggests that many leukaemic perturbations gain a competitive advantage against wild-type cells by pushing their MPP1 cells into more active states. Additionally the data suggests that increases in the transcriptional variability of blood stem cells is associated with pro-erythroid fate decision shifts and vice-versa. Many different types of haematopoietic perturbations exist and can drive disease progression in the blood system. Chapter 5 focuses on single-cell RNA sequencing data from three further perturbations in various settings, including an infection model of Malaria and a model susceptible to endogenous DNA damage by aldehydes. These analyses have driven and validated bodies of experimental work, and comparing them to the previously described perturbation models highlighted both conserved changes and differences in the response of the haematopoietic system across different perturbation settings. The final project aimed to improve upon current computational methods for cellular trajectory inference from single-cell data. Whilst high-throughput experiments allow for the sequencing of large cell numbers, this is balanced by the sparse and noisy nature of the returned data. Current methods perform poorly on such datasets and either cannot deal with large cell numbers or cannot extract enough relevant signal from sparse count matrices. A new computational tool was designed to work best on these large, sparse datasets, and infer the most likely cellular trajectories through snapshot sequencing data using an iterative process. In Chapter 6 this algorithm was applied to different systems including adult haematopoiesis, and was compared to state-of-the-art methods. Overall, this thesis has investigated the transcriptional consequences of numerous preleukaemic perturbations on the haematopoietic stem and progenitor cell compartment at the single-cell level. New methods have been built for integration of single-cell perturbation experiments and their analysis across different biological scales. This has revealed novel biological insights regarding the mechanisms underpinning leukaemic transformation of the blood system

Studying Myeloid Cell Heterogeneity After Spinal Cord Injury via Time-Resolved Single-Cell RNA Sequencing

Spinal cord injury (SCI) is a devastating pathology that affects thousands of individuals annually, resulting in the requirement for long-term physical and medical care and thus significant personal, societal, and economic burdens. The SCI pathology is characterised by an initial mechanical insult, followed by a spatiotemporally dynamic secondary injury. Decades of research have worked to assemble a general picture of this secondary pathology. We now understand that compared to the normal wound healing observed in the periphery, tissue recovery after SCI is dysregulated and results in a chronic wound state characterized by persistent inflammation and functional deficits. The primary drivers of this inflammation are central nervous system (CNS) resident microglia and infiltrating myeloid cells. However, the precise role of these myeloid cell subsets remains unclear as upon crossing the blood-spinal cord barrier (BSCB), infiltrating monocyte-derived macrophages may take on the morphology of microglia, and upregulate canonical microglia markers, making the two populations difficult to distinguish. In this PhD project, I employed single-cell RNA sequencing (scRNAseq) to deconvolute the complex heterogeneity of infiltrating and resident myeloid cells in mouse models of thoracic contusion SCI at an unprecedented resolution. To fully appreciate the temporal dynamics of the pathology, I collected samples across the acute, subacute, and early chronic phases of SCI, plus a sham-injured control. Recent experiments have demonstrated that CNS infiltrating macrophages also take on the transcriptional profiles of microglia, which led me to question whether I had accurately annotated infiltrating macrophages in the dataset. To address this, I repeated the experiment with a transgenic fate-mapping mouse line then integrated these two datasets to generate a time-resolved SCI myeloid cell atlas with definitive ontogeny labelling. With this dataset I generated a putative time resolved map of myeloid cell dynamics across the SCI pathology. Through collaboration, I was also able to verify the expression of select genes via single-molecule fluorescent in situ hybridization (smFISH) and immunofluorescence (IF). A key observation was the persistence of a pro-inflammatory foam cell-like state in both microglia and macrophages, which may contribute to the non-resolving chronic injury. Future studies might investigate the functional relevance of this population, and its suitability as a therapeutic target to reduce the long-term disabilities of SCI patients.Regan Hamel was supported by the Cambridge Trust and is the recipient of a Canadian Scholarship Trust Foundation, an MNI-Cambridge Douglas Avrith Graduate Studentship, and a Rosetrees Trust Studentship

Design and Fabrication of Printed DNA Droplets Arrangement and Detection Inkjet System

This article describes the aims to establish a thermal bubble printhead with simultaneously driving multi-channel for DNA droplet arrangement. It proposed a monolithic CMOS/MEMS system with multi-level output voltage ESD protection system for protected inkjet printhead. High-voltage power, low-voltage logic, and CMOS/MEMS architecture were integrated in inkjet chip. It used bulk micromachining technology (MEMS). On-chip high-voltage electrostatic discharge (HV-ESD), protection design in smart power technology of monolithic inkjet chip is a challenging issue. The nozzle jets interleaving scanning sequence is controlled spatially on the elements to avoid the strong interference with DNA droplets caused by the excitation of the neighbor driven elements. A heating element, disposed on the substrate, includes a conductor loop which does not encompass the heating elements on the substrate. The configuration of the heater jet significantly reduces both electromagnetic and capacitance interference caused by the heating elements. The simulation and experience result have shown in the research. It is reduced nearly half the time compared to the case with traditional scanning sequence. This experiment develops new controlled structure designs of chip for inkjet printheads. A bubble inkjet(TIJ) device is designed, several of the architectures may be adjusted just a small microns to improve and optimize the DNA drop nucleation and generation efficiency. The DNA droplet ejection behavior of the multiplexer inkjet printhead within 60-μm orifice size has been measured beyond 5 kHz operation system, 12 pL capacity of ejected DNA droplet volume

Tracing tumorigenesis in a solid tumor model at single-cell resolution

Characterizing the complex composition of solid tumors is fundamental for understanding tumor initiation, progression and metastasis. While patient-derived samples provide valuable insight, they are heterogeneous on multiple molecular levels, and often originate from advanced tumor stages. Here, we use single-cell transcriptome and epitope profiling together with pathway and lineage analyses to study tumorigenesis from a developmental perspective in a mouse model of salivary gland squamous cell carcinoma. We provide a comprehensive cell atlas and characterize tumor-specific cells. We find that these cells are connected along a reproducible developmental trajectory: initiated in basal cells exhibiting an epithelial-to-mesenchymal transition signature, tumorigenesis proceeds through Wnt-differential cancer stem cell-like subpopulations before differentiating into luminal-like cells. Our work provides unbiased insights into tumor-specific cellular identities in a whole tissue environment, and emphasizes the power of using defined genetic model systems