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

A Simple and Sensitive High-Content Assay for the Characterization of Antiproliferative Therapeutic Antibodies

Monoclonal antibodies (mAbs) have become a central class of therapeutic agents in particular as antiproliferative compounds. Their often complex modes of action require sensitive assays during early, functional characterization. Current cell-based proliferation assays often detect metabolites that are indicative of metabolic activity but do not directly account for cell proliferation. Measuring DNA replication by incorporation of base analogues such as 5-bromo-2-deoxyuridine (BrdU) fills this analytical gap but was previously restricted to bulk effect characterization in enzyme-linked immunosorbent assay formats. Here, we describe a cell-based assay format for the characterization of antiproliferative mAbs regarding potency and mode of action in a single experiment. The assay makes use of single cell-based high-content-analysis (HCA) for the reliable quantification of replicating cells and DNA content via 5-ethynyl-2-deoxyuridine (EdU) and 4,6-diamidino-2-phenylindole (DAPI), respectively, as sensitive measures of antiproliferative mAb activity. We used trastuzumab, an antiproliferative therapeutic antibody interfering with HER2 cell surface receptor-mediated growth signal transduction, and HER2-overexpressing cell lines BT474 and SKBR3 to demonstrate up to 10-fold signal-to-background (S/B) ratios for treated versus untreated cells and a shift in cell cycle profiles indicating antibody-induced cell cycle arrest. The assay is simple, cost-effective, and sensitive, providing a cell-based format for preclinical characterization of therapeutic mAbs

EDAM-bioimaging : The ontology of bioimage informatics operations, topics, data, and formats

International audienceThe ontology of bioimage informatics operations, topics, data, and formats What? EDAM-bioimaging is an extension of the EDAM ontology, dedicated to bioimage analysis, bioimage informatics, and bioimaging. Why? EDAM-bioimaging enables interoperable descriptions of software, publications, data, and workflows, fostering reliable and transparent science. How? EDAM-bioimaging is developed in a community spirit, in a welcoming collaboration between numerous bioimaging experts and ontology developers. How can I contribute? We need your expertise! You can help by reviewing parts of EDAM-bioimaging, posting comments with suggestions, requirements, or needs for clarification, or participating in a Taggathon or another hackathon. Please see https://github.com/edamontology/edam-bioimaging#contributing. EDAM-bioimaging is developed in an interdisciplinary open collaboration supported by the hosting institutions, participating individuals, and NEUBIAS COST Action (CA15124) and ELIXIR-EXCELERATE (676559) funded by the Horizon 2020 Framework Programme of the European Union. https://github.com/edamontology/edam-bioimaging @edamontology /edamontology/edam-bioimagin

BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples.

Light-sheet imaging of cleared and expanded samples creates terabyte-sized datasets that consist of many unaligned three-dimensional image tiles, which must be reconstructed before analysis. We developed the BigStitcher software to address this challenge. BigStitcher enables interactive visualization, fast and precise alignment, spatially resolved quality estimation, real-time fusion and deconvolution of dual-illumination, multitile, multiview datasets. The software also compensates for optical effects, thereby improving accuracy and enabling subsequent biological analysis

HIF-driven SF3B1 induces KHK-C to enforce fructolysis and heart disease.

Fructose is a major component of dietary sugar and its overconsumption exacerbates key pathological features of metabolic syndrome. The central fructose-metabolising enzyme is ketohexokinase (KHK), which exists in two isoforms: KHK-A and KHK-C, generated through mutually exclusive alternative splicing of KHK pre-mRNAs. KHK-C displays superior affinity for fructose compared with KHK-A and is produced primarily in the liver, thus restricting fructose metabolism almost exclusively to this organ. Here we show that myocardial hypoxia actuates fructose metabolism in human and mouse models of pathological cardiac hypertrophy through hypoxia-inducible factor 1α (HIF1α) activation of SF3B1 and SF3B1-mediated splice switching of KHK-A to KHK-C. Heart-specific depletion of SF3B1 or genetic ablation of Khk, but not Khk-A alone, in mice, suppresses pathological stress-induced fructose metabolism, growth and contractile dysfunction, thus defining signalling components and molecular underpinnings of a fructose metabolism regulatory system crucial for pathological growth

Cristae-dependent quality control of the mitochondrial genome.

[Figure: see text]

Cristae-dependent quality control of the mitochondrial genome.

Mitochondrial genomes (mtDNA) encode essential subunits of the mitochondrial respiratory chain. Mutations in mtDNA can cause a shortage in cellular energy supply, which can lead to numerous mitochondrial diseases. How cells secure mtDNA integrity over generations has remained unanswered. Here, we show that the single-celled yeast Saccharomyces cerevisiae can intracellularly distinguish between functional and defective mtDNA and promote generation of daughter cells with increasingly healthy mtDNA content. Purifying selection for functional mtDNA occurs in a continuous mitochondrial network and does not require mitochondrial fission but necessitates stable mitochondrial subdomains that depend on intact cristae morphology. Our findings support a model in which cristae-dependent proximity between mtDNA and the proteins it encodes creates a spatial “sphere of influence,” which links a lack of functional fitness to clearance of defective mtDNA