From nanometers to centimeters: Imaging across spatial scales with smart computer-aided microscopy

Microscopes have been an invaluable tool throughout the history of the life sciences, as they allow researchers to observe the miniscule details of living systems in space and time. However, modern biology studies complex and non-obvious phenotypes and their distributions in populations and thus requires that microscopes evolve from visual aids for anecdotal observation into instruments for objective and quantitative measurements. To this end, many cutting-edge developments in microscopy are fuelled by innovations in the computational processing of the generated images. Computational tools can be applied in the early stages of an experiment, where they allow for reconstruction of images with higher resolution and contrast or more colors compared to raw data. In the final analysis stage, state-of-the-art image analysis pipelines seek to extract interpretable and humanly tractable information from the high-dimensional space of images. In the work presented in this thesis, I performed super-resolution microscopy and wrote image analysis pipelines to derive quantitative information about multiple biological processes. I contributed to studies on the regulation of DNMT1 by implementing machine learning-based segmentation of replication sites in images and performed quantitative statistical analysis of the recruitment of multiple DNMT1 mutants. To study the spatiotemporal distribution of DNA damage response I performed STED microscopy and could provide a lower bound on the size of the elementary spatial units of DNA repair. In this project, I also wrote image analysis pipelines and performed statistical analysis to show a decoupling of DNA density and heterochromatin marks during repair. More on the experimental side, I helped in the establishment of a protocol for many-fold color multiplexing by iterative labelling of diverse structures via DNA hybridization. Turning from small scale details to the distribution of phenotypes in a population, I wrote a reusable pipeline for fitting models of cell cycle stage distribution and inhibition curves to high-throughput measurements to quickly quantify the effects of innovative antiproliferative antibody-drug-conjugates. The main focus of the thesis is BigStitcher, a tool for the management and alignment of terabyte-sized image datasets. Such enormous datasets are nowadays generated routinely with light-sheet microscopy and sample preparation techniques such as clearing or expansion. Their sheer size, high dimensionality and unique optical properties poses a serious bottleneck for researchers and requires specialized processing tools, as the images often do not fit into the main memory of most computers. BigStitcher primarily allows for fast registration of such many-dimensional datasets on conventional hardware using optimized multi-resolution alignment algorithms. The software can also correct a variety of aberrations such as fixed-pattern noise, chromatic shifts and even complex sample-induced distortions. A defining feature of BigStitcher, as well as the various image analysis scripts developed in this work is their interactivity. A central goal was to leverage the user's expertise at key moments and bring innovations from the big data world to the lab with its smaller and much more diverse datasets without replacing scientists with automated black-box pipelines. To this end, BigStitcher was implemented as a user-friendly plug-in for the open source image processing platform Fiji and provides the users with a nearly instantaneous preview of the aligned images and opportunities for manual control of all processing steps. With its powerful features and ease-of-use, BigStitcher paves the way to the routine application of light-sheet microscopy and other methods producing equally large datasets

From nanometers to centimeters: Imaging across spatial scales with smart computer-aided microscopy

Microscopes have been an invaluable tool throughout the history of the life sciences, as they allow researchers to observe the miniscule details of living systems in space and time. However, modern biology studies complex and non-obvious phenotypes and their distributions in populations and thus requires that microscopes evolve from visual aids for anecdotal observation into instruments for objective and quantitative measurements. To this end, many cutting-edge developments in microscopy are fuelled by innovations in the computational processing of the generated images. Computational tools can be applied in the early stages of an experiment, where they allow for reconstruction of images with higher resolution and contrast or more colors compared to raw data. In the final analysis stage, state-of-the-art image analysis pipelines seek to extract interpretable and humanly tractable information from the high-dimensional space of images. In the work presented in this thesis, I performed super-resolution microscopy and wrote image analysis pipelines to derive quantitative information about multiple biological processes. I contributed to studies on the regulation of DNMT1 by implementing machine learning-based segmentation of replication sites in images and performed quantitative statistical analysis of the recruitment of multiple DNMT1 mutants. To study the spatiotemporal distribution of DNA damage response I performed STED microscopy and could provide a lower bound on the size of the elementary spatial units of DNA repair. In this project, I also wrote image analysis pipelines and performed statistical analysis to show a decoupling of DNA density and heterochromatin marks during repair. More on the experimental side, I helped in the establishment of a protocol for many-fold color multiplexing by iterative labelling of diverse structures via DNA hybridization. Turning from small scale details to the distribution of phenotypes in a population, I wrote a reusable pipeline for fitting models of cell cycle stage distribution and inhibition curves to high-throughput measurements to quickly quantify the effects of innovative antiproliferative antibody-drug-conjugates. The main focus of the thesis is BigStitcher, a tool for the management and alignment of terabyte-sized image datasets. Such enormous datasets are nowadays generated routinely with light-sheet microscopy and sample preparation techniques such as clearing or expansion. Their sheer size, high dimensionality and unique optical properties poses a serious bottleneck for researchers and requires specialized processing tools, as the images often do not fit into the main memory of most computers. BigStitcher primarily allows for fast registration of such many-dimensional datasets on conventional hardware using optimized multi-resolution alignment algorithms. The software can also correct a variety of aberrations such as fixed-pattern noise, chromatic shifts and even complex sample-induced distortions. A defining feature of BigStitcher, as well as the various image analysis scripts developed in this work is their interactivity. A central goal was to leverage the user's expertise at key moments and bring innovations from the big data world to the lab with its smaller and much more diverse datasets without replacing scientists with automated black-box pipelines. To this end, BigStitcher was implemented as a user-friendly plug-in for the open source image processing platform Fiji and provides the users with a nearly instantaneous preview of the aligned images and opportunities for manual control of all processing steps. With its powerful features and ease-of-use, BigStitcher paves the way to the routine application of light-sheet microscopy and other methods producing equally large datasets

Learning Multimodal Structures in Computer Vision

A phenomenon or event can be received from various kinds of detectors or under different conditions. Each such acquisition framework is a modality of the phenomenon. Due to the relation between the modalities of multimodal phenomena, a single modality cannot fully describe the event of interest. Since several modalities report on the same event introduces new challenges comparing to the case of exploiting each modality separately. We are interested in designing new algorithmic tools to apply sensor fusion techniques in the particular signal representation of sparse coding which is a favorite methodology in signal processing, machine learning and statistics to represent data. This coding scheme is based on a machine learning technique and has been demonstrated to be capable of representing many modalities like natural images. We will consider situations where we are not only interested in support of the model to be sparse, but also to reflect a-priorily known knowledge about the application in hand. Our goal is to extract a discriminative representation of the multimodal data that leads to easily finding its essential characteristics in the subsequent analysis step, e.g., regression and classification. To be more precise, sparse coding is about representing signals as linear combinations of a small number of bases from a dictionary. The idea is to learn a dictionary that encodes intrinsic properties of the multimodal data in a decomposition coefficient vector that is favorable towards the maximal discriminatory power. We carefully design a multimodal representation framework to learn discriminative feature representations by fully exploiting, the modality-shared which is the information shared by various modalities, and modality-specific which is the information content of each modality individually. Plus, it automatically learns the weights for various feature components in a data-driven scheme. In other words, the physical interpretation of our learning framework is to fully exploit the correlated characteristics of the available modalities, while at the same time leverage the modality-specific character of each modality and change their corresponding weights for different parts of the feature in recognition

CNS-Native Myeloid Cells Drive Immune Suppression in the Brain Metastatic Niche through Cxcl10

Brain metastasis (br-met) develops in an immunologically unique br-met niche. Central nervous system-native myeloid cells (CNS-myeloids) and bone-marrow-derived myeloid cells (BMDMs) cooperatively regulate brain immunity. The phenotypic heterogeneity and specific roles of these myeloid subsets in shaping the br-met niche to regulate br-met outgrowth have not been fully revealed. Applying multimodal single-cell analyses, we elucidated a heterogeneous but spatially defined CNS-myeloid response during br-met outgrowth. We found Ccr2+ BMDMs minimally influenced br-met while CNS-myeloid promoted br-met outgrowth. Additionally, br-met-associated CNS-myeloid exhibited downregulation of Cx3cr1. Cx3cr1 knockout in CNS-myeloid increased br-met incidence, leading to an enriched interferon response signature and Cxcl10 upregulation. Significantly, neutralization of Cxcl10 reduced br-met, while rCxcl10 increased br-met and recruited VISTAHi PD-L1+ CNS-myeloid to br-met lesions. Inhibiting VISTA- and PD-L1-signaling relieved immune suppression and reduced br-met burden. Our results demonstrate that loss of Cx3cr1 in CNS-myeloid triggers a Cxcl10-mediated vicious cycle, cultivating a br-met-promoting, immune-suppressive niche

The Role of ID3 and PCB153 in the Hyperproliferation and Dysregulation of Lung Endothelial Cells

Uncontrolled growth of vascular stem cells as a result of endothelial-mesenchymal transition is considered to cause hyper-proliferative vascular remodeling in severe pulmonary arterial hypertension (PAH) patients. Hyperplastic intimal growth is one of the causes of closure of the lumen of pulmonary arterioles. This abnormal vessel remodeling leads to the progressive increase in pressure of the pulmonary arterioles causing severe PAH; and debilitating harm to patients resulting in mortality from right heart failure. Environmental factors, including polychlorinated biphenyls (PCBs), are considered to be involved in hyper-proliferative vascular remodeling because genetic makeup can only explain about 10% of severe PAH cases. PCB involvement in lung toxicity has received attention because (i) they have been reported to accumulate in the lung; (ii) PCBs produce pathological vascular remodeling in the experimental model; high levels of PCBs are found in human lung tissue; and (iii) epidemiological studies show the association between lung toxicity and PCBs; and prevalence of hypertension and elevated concentrations of particularly PCB153. Recent studies identify PCB153 as one of the largest contributors for total PCB body burden in humans. Our previous studies demonstrated PCB153 mediated vascular endothelial dysfunction and activated the inhibitor of differentiation protein 3 (ID3). ID3 is an important determinant of mitogen and reactive oxygen species-induced G1→S phase cell cycle progression. Although phosphorylation of ID3 increases cell growth by antagonizing the transcription of cell cycle inhibitors, still there is a critical gap in understanding the molecular mechanism(s) of pulmonary proliferative vascular remodeling associated with PCB exposure in humans and the role of the transcription regulator ID3. Our overall objective was to investigate ID3 mediated transcriptional reprogramming as a driver of PCB153-induced pathological proliferative vascular remodeling. Stable ectopic expression of ID3 in lung endothelial cells contributed to endothelial-mesenchymal transition (EndMT), cell proliferation, and cell migration. Using an endothelial spheroid assay, an established method to measure aberrant hyper-proliferation of endothelial cells in PAH patients, we show that stable ectopic expression of ID3 increased the number and size of vascular spheres. ID3 overexpressing cells exposed to environmentally relevant concentrations of PCB153 showed a two-fold increase in cell proliferation as determined by MTT, SRB, and BrdU assays. ID3 overexpressing cells showed the loss of VE-cadherin and gain of MMP9 and vimentin, which are markers of EndMT. PCB153 also increased phosphorylation of ID3 in lung endothelial cells. To determine the molecular mechanism by which ID3 contributes to hyper-proliferative endothelial cells, we investigated ID3 transcriptional reprogramming using ChIP-Seq and RNA-Seq technology. We show here for the first time that ID3 is part of a more general mechanism of transcriptional regulation. Our ChIP-Seq data show that ID3 binds to a subset of approximately 1200 target genes. Comprehensive motif analysis of ChIP-Seq data using the MEME Suite software toolkit revealed that ID3 bound to the GAGAGAGAGA motif sequence on genomic DNA. We also show a significant preference of ID3 binding to motifs associated with transcription factors IRF1, BC11A, IRF4, PRDM1, FOXJ3, SMAD4, ZBTB6, GATA1, and STAT2. Using an integrative approach of ChIP-Seq and RNA-Seq data, we identified 19 genes whose promoter region was bound by ID3 and RNA was differentially expressed in ID3 overexpressing cells. In summary, our data demonstrated that PCB153 and/or ID3 induces proliferation of lung endothelial cells via transcriptional reprogramming. Discoveries from these findings will lay the necessary groundbreaking work for testing the efficacy of ID3 antagonists for the prevention and treatment of pathological vascular remodeling as well as provide a new paradigm by which PCBs may contribute to lung vascular toxicity

An aptamer-based sensing platform for luteinising hormone pulsatility measurement

Normal fertility in human involves highly orchestrated communication across the hypothalamic-pituitary-gonadal (HPG) axis. The pulsatile release of Luteinising Hormone (LH) is a critical element for downstream regulation of sex steroid hormone synthesis and the production of mature eggs. Changes in LH pulsatile pattern have been linked to hypothalamic dysfunction, resulting in multiple reproductive and growth disorders including Polycystic Ovary Syndrome (PCOS), Hypothalamic Amenorrhea (HA), and delayed/precocious puberty. Therefore, assessing the pulsatility of LH is important not only for academic investigation of infertility, but also for clinical decisions and monitoring of treatment. However, there is currently no clinically available tool for measuring human LH pulsatility. The immunoassay system is expensive and requires large volumes of patient blood, limiting its application for LH pulsatility monitoring. In this thesis, I propose a novel method using aptamer-enabled sensing technology to develop a device platform to measure LH pulsatility. I first generated a novel aptamer binding molecule against LH by a nitrocellulose membrane-based in vitro selection then characterised its high affinity and specific binding properties by multiple biophysical/chemical methods. I then developed a sensitive electrochemical-based detection method using this aptamer. The principal mechanism is that structure switching upon binding is associated with the electron transfer rate changes of the MB redox label. I then customised this assay to numerous device platforms under our rapid prototyping strategy including 96 well automated platform, continuous sensing platform and chip-based multiple electrode platform. The best-performing device was found to be the AELECAP (Automated ELEctroChemical Aptamer Platform) – a 96-well plate based automatic micro-wire sensing platform capable of measuring a series of low volume luteinising hormone within a short time. Clinical samples were evaluated using AELECAP. A series of clinical samples were measured including LH pulsatility profile of menopause female (high LH amplitude), normal female/male (normal LH amplitude) and female with hypothalamic amenorrhea (no LH pulsatility). Total patient numbers were 12 of each type, with 50 blood samples collected every 10 mins in 8 hours. Results showed that the system can distinguish LH pulsatile pattern among the cohorts and pulsatility profiles were consistent with the result measured by clinical assays. AELECAP shows high potential as a novel approach for clinical aptamer-based sensing. AELECAP competes with current automated immunometric assays system with lower costs, lower reagent use, and a simpler setup. There is potential for this approach to be further developed as a tool for infertility research and to assist clinicians in personalised treatment with hormonal therapy.Open Acces

Novel biomarkers to guide therapy in chronic inflammatory diseases

In this thesis, we focused on the role of the epigenetic modifications in Inflammatory Bowel Disease (IBD) and other immune mediated diseases such as rheumatoid arthritis. In particular, we elaborated on aberrant DNA methylation and investigated its potential to predict therapy response to biological treatment. Furthermore, we explored other novel biomarkers to biological response with microbial signatures and single cell transcriptomics in IBD