Particle Filtering Methods for Subcellular Motion Analysis

Advances in fluorescent probing and microscopic imaging technology have revolutionized biology in the past decade and have opened the door for studying subcellular dynamical processes. However, accurate and reproducible methods for processing and analyzing the images acquired for such studies are still lacking. Since manual image analysis is time consuming, potentially inaccurate, and poorly reproducible, many biologically highly relevant questions are either left unaddressed, or are answered with great uncertainty. The subject of this thesis is particle filtering methods and their application for multiple object tracking in different biological imaging applications. Particle filtering is a technique for implementing recursive Bayesian filtering by Monte Carlo sampling. A fundamental concept behind the Bayesian approach for performing inference is the possibility to encode the information about the imaging system, possible noise sources, and the system dynamics in terms of probability density functions. In this thesis, a set of novel PF based metho

Super-resolution microscopy live cell imaging and image analysis

Novel fundamental research results provided new techniques going beyond the diffraction limit. These recent advances known as super-resolution microscopy have been awarded by the Nobel Prize as they promise new discoveries in biology and live sciences. All these techniques rely on complex signal and image processing. The applicability in biology, and particularly for live cell imaging, remains challenging and needs further investigation. Focusing on image processing and analysis, the thesis is devoted to a significant enhancement of structured illumination microscopy (SIM) and super-resolution optical fluctuation imaging (SOFI)methods towards fast live cell and quantitative imaging. The thesis presents a novel image reconstruction method for both 2D and 3D SIM data, compatible with weak signals, and robust towards unwanted image artifacts. This image reconstruction is efficient under low light conditions, reduces phototoxicity and facilitates live cell observations. We demonstrate the performance of our new method by imaging long super-resolution video sequences of live U2-OS cells and improving cell particle tracking. We develop an adapted 3D deconvolution algorithm for SOFI, which suppresses noise and makes 3D SOFI live cell imaging feasible due to reduction of the number of required input images. We introduce a novel linearization procedure for SOFI maximizing the resolution gain and show that SOFI and PALM can both be applied on the same dataset revealing more insights about the sample. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of the sample through the estimation of molecular parameters. For quantifying the outcome of our super-resolutionmethods, the thesis presents a novel methodology for objective image quality assessment measuring spatial resolution and signal to noise ratio in real samples. We demonstrate our enhanced SOFI framework by high throughput 3D imaging of live HeLa cells acquiring the whole super-resolution 3D image in 0.95 s, by investigating focal adhesions in live MEF cells, by fast optical readout of fluorescently labelled DNA strands and by unraveling the nanoscale organization of CD4 proteins on a plasma membrane of T-cells. Within the thesis, unique open-source software packages SIMToolbox and SOFI simulation tool were developed to facilitate implementation of super-resolution microscopy methods

Development of swept, confocally-aligned planar excitation (SCAPE) microscopy for high-speed, volumetric imaging of biological tissue

With the wide-spread adoption of exogenous fluorescent indicators – and more recently genetically encoded fluorescent proteins – over the past two decades, there exists a diverse chemical toolkit with which to probe biological systems. Individual cell types and sub-cellular compartments can be targeted in an increasingly wide range of model organisms. However, imaging these samples is often an exercise in balancing the needs of any given experiment against the constraints of the chosen imaging technology. For example, a volume of brain tissue is host to neurons, glia, vascular compartments and red blood cells that all occupy discrete locations in 3D space, but must work together to support healthy organ function. Single-cell activity on the order of milliseconds can trigger downstream processes that unfold over the course of multiple seconds or even minutes. The development of a technique capable of providing depth-resolved, volumetric imaging with scalable spatiotemporal resolution is crucial to developing a proper understanding of such biological systems. Bottlenecks in the throughput of existing technologies stem from a combination of inefficient illumination and volume acquisition strategies, and insufficient sensor read-out speeds. Light sheet microscopy is a promising solution, but individual designs tend to be highly specialized to specific types of samples and do not easily adapt to a wide range of experimental settings. In this thesis, I detail my work in developing swept, confocally-aligned planar excitation (SCAPE) microscopy from a first-generation prototype into a versatile, easy-to-reproduce, easy-to-use system for high-speed, 3D imaging. The first chapter introduces the challenges of designing optical systems capable of high-speed, volumetric imaging. An introduction to design choices faced in the construction of fluorescence microscopes, and current approaches to 3D imaging are discussed. The second chapter describes the progression from the 1st to 2nd generation SCAPE system. Improvements made through ray-tracing models and an enhanced optomechanical design are described, and results from this system in a number of model organisms are presented. The third chapter presents results from a range of biological applications to which SCAPE microscopy has been applied. Work in imaging the zebrafish heart to demonstrate the system’s improved imaging speed, the C. elegans to show the system’s resolution, and finally a number of examples of large field-of-view and high-resolution structural imaging are all described. Finally, the fourth chapter concludes with an overview of the work that lies ahead to both further develop of SCAPE microscopy, as well as to bring the existing system’s strengths to bear in a wider range of environments

Computational Image Analysis For Axonal Transport, Phenotypic Profiling, And Digital Pathology

Recent advances in fluorescent probes, microscopy, and imaging platforms have revolutionized biology and medicine, generating multi-dimensional image datasets at unprecedented scales. Traditional, low-throughput methods of image analysis are inadequate to handle the increased “volume, velocity, and variety” that characterize the realm of big data. Thus, biomedical imaging requires a new set of tools, which include advanced computer vision and machine learning algorithms. In this work, we develop computational image analysis solutions to biological questions at the level of single-molecules, cells, and tissues. At the molecular level, we dissect the regulation of dynein-dynactin transport initiation using in vitro reconstitution, single-particle tracking, super-resolution microscopy, live-cell imaging in neurons, and computational modeling. We show that at least two mechanisms regulate dynein transport initiation neurons: (1) cytoplasmic linker proteins, which are regulated by phosphorylation, increase the capture radius around the microtubule, thus reducing the time cargo spends in a diffusive search; and (2) a spatial gradient of tyrosinated alpha-tubulin enriched in the distal axon increases the affinity of dynein-dynactin for microtubules. Together, these mechanisms support a multi-modal recruitment model where interacting layers of regulation provide efficient, robust, and spatiotemporal control of transport initiation. At the cellular level, we develop and train deep residual convolutional neural networks on a large and diverse set of cellular microscopy images. Then, we apply networks trained for one task as deep feature extractors for unsupervised phenotypic profiling in a different task. We show that neural networks trained on one dataset encode robust image phenotypes that are sufficient to cluster subcellular structures by type and separate drug compounds by the mechanism of action, without additional training, supporting the strength and flexibility of this approach. Future applications include phenotypic profiling in image-based screens, where clustering genetic or drug treatments by image phenotypes may reveal novel relationships among genetic or pharmacologic pathways. Finally, at the tissue level, we apply deep learning pipelines in digital pathology to segment cardiac tissue and classify clinical heart failure using whole-slide images of cardiac histopathology. Together, these results demonstrate the power and promise of computational image analysis, computer vision, and deep learning in biological image analysis

3D Organization of Eukaryotic and Prokaryotic Genomes

There is a complex mutual interplay between three-dimensional (3D) genome organization and cellular activities in bacteria and eukaryotes. The aim of this thesis is to investigate such structure-function relationships. A main part of this thesis deals with the study of the three-dimensional genome organization using novel techniques for detecting genome-wide contacts using next-generation sequencing. These so called chromatin conformation capture-based methods, such as 5C and Hi-C, give deep insights into the architecture of the genome inside the nucleus, even on a small scale. We shed light on the question how the vastly increasing Hi-C data can generate new insights about the way the genome is organized in 3D. To this end, we first present the typical Hi-C data processing workflow to obtain Hi-C contact maps and show potential pitfalls in the interpretation of such contact maps using our own data pipeline and publicly available Hi-C data sets. Subsequently, we focus on approaches to modeling 3D genome organization based on contact maps. In this context, a computational tool was developed which interactively visualizes contact maps alongside complementary genomic data tracks. Inspired by machine learning with the help of probabilistic graphical models, we developed a tool that detects the compartmentalization structure within contact maps on multiple scales. In a further project, we propose and test one possible mechanism for the observed compartmentalization within contact maps of genomes across multiple species: Dynamic formation of loops within domains. In the context of 3D organization of bacterial chromosomes, we present the first direct evidence for global restructuring by long-range interactions of a DNA binding protein. Using Hi-C and live cell imaging of DNA loci, we show that the DNA binding protein Rok forms insulator-like complexes looping the B. subtilis genome over large distances. This biological mechanism agrees with our model based on dynamic formation of loops affecting domain formation in eukaryotic genomes. We further investigate the spatial segregation of the E. coli chromosome during cell division. In particular, we are interested in the positioning of the chromosomal replication origin region based on its interaction with the protein complex MukBEF. We tackle the problem using a combined approach of stochastic and polymer simulations. Last but not least, we develop a completely new methodology to analyze single molecule localization microscopy images based on topological data analysis. By using this new approach in the analysis of irradiated cells, we are able to show that the topology of repair foci can be categorized depending the distance to heterochromatin

Mechanism of Cytoskeleton Modification by Histone Methyltransferase SETD2

In order for the busy and crowded cell to have a semblance of organization, it leverages a complex and dynamic network of polymers, the cytoskeleton, to provide structure and serve as molecular roads for cargo transport. Two main polymer systems, microtubules and actin filaments, provide long- and short-range transport, respectively. Additionally, microtubules form the mitotic spindle and primary cilia, while actin filaments are critical for cell migration and muscle contraction. How cytoskeletal elements have such diverse functional roles is in part due to post-translational modifications, where specific chemical modifications signal for protein interactions and particular motor protein motility. For example, tubulin methylation is only found on mitotic spindles, the microtubule-based bipolar structure that separates chromosomes during cell division and is enzymatically added by SETD2. SETD2 canonically modifies histones, specifically histone 3 at lysine 36, and is the only enzyme that can tri-methylate this residue. Knock-out of SETD2 results in histone- and/or microtubule-dependent genetic instability leading to cancer-driving mitotic defects like multipolar spindles and micronuclei formation. Mutations in SETD2 are implicated in cancer, most commonly in the kidney cancer clear cell renal cell carcinoma (ccRCC), with SETD2 mutations occurring in 10-15% of all ccRCC cases. Thus far, the role of SETD2 in cancer has only been studied in a histone methylation context, but the contribution of cytoskeletal methylation remains unclear. Studies using tumor cells from ccRCC patients demonstrated that when the level of the SETD2 gene product is less than normal (haploinsufficiency), there is a loss of tubulin methylation and genomic instability, whereas total SETD2 inactivation results in a loss of histone methylation. This stepwise model for the loss of SETD2 functionality describes histone and tubulin methylation at the gene level but does not describe the enzymatic regulation of SETD2 amongst its substrates biochemically. Moreover, specific ccRCC mutations have a differential impact on either histone or tubulin methylation in cells, where a R2510H mutation, found in a domain important for regulating protein-protein interactions of SETD2 (the Set2 Rpb1 Interacting SRI domain), retains histone methylation but not tubulin methylation. As such, there remains a significant realm of tubulin-dependent processes that drive ccRCC pathologies that remain unexplored. In this study, I used in vitro biochemical reconstitution with recombinant proteins to determine how SETD2 recognizes and methylates tubulin in addition to actin. By exploiting known tubulin-targeting agents, I found that SETD2 preferentially methylates the dimeric form of tubulin over microtubule polymers and, using recombinant single-isotype tubulin, I demonstrated that methylation is restricted to lysine 40 of alpha-tubulin. Moreover, by introducing pathogenic mutations into SETD2 to probe the recognition of histone and tubulin substrates, I found that particular mutations within the SRI domain tune histone and tubulin methylation by regulating protein-protein interactions with tubulin or RNA Polymerase II. Lastly, I found that tubulin substrate recognition requires the negatively-charged C-terminal tail of alpha-tubulin. Curiously, the SRI domain does not play a similar regulatory role with actin substrate suggesting an alternative recognition site, but our collaborative work found that actin methylation by SETD2 is necessary for cell motility and actin dynamics at the cell periphery. Future studies into tubulin and actin chemical modifications are required to understand the nuanced interactions and crosstalk amongst histone, tubulin, and actin chemical codes in cells and their implications for cancer and disease progression.PHDChemical BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167990/1/skearns_1.pd

Optogenetics and Computer Vision for C. elegans Neuroscience and Other Biophysical Applications

This work presents optogenetics and real-time computer vision techniques to non-invasively manipulate and monitor neural activity with high spatiotemporal resolution in awake behaving Caenorhabditis elegans. These methods were employed to dissect the nematode's mechanosensory and motor circuits and to elucidate the neural control of wave propagation during forward locomotion. Additionally, similar computer vision methods were used to automatically detect and decode fluorescing DNA origami nanobarcodes, a new class of fluorescent reporter constructs

Genome-wide microscopy screening identifies links across processes including a conserved connection between DNA damage control and the microtubule cytoskeleton

Previous PhD students in the lab created a method for large-scale, high-content microscopy screening of a cell library consisting of over 3000 single mutant strains of the fission yeast, Schizosaccharomyces pombe. Each strain has one nonessential gene knocked-out, allowing investigation of the resulting phenotypes. I report the implementation and completion of this screen; developing methods to ensure reliable and accurate results through inclusion of many controls across multiple screening repeats. In total, over 4.5 million images from approximately 19 000 biologically independent cell populations were imaged and analysed. All strains screened contained GFP-labelled tubulin (GFP-Atb2) allowing visualisation of the microtubule polymer network and its organisation in cells, a feature that is conserved across eukaryotes and simplified in S. pombe, making it easy to study. Examination of cell outlines and microtubule patterns was used to study three cell processes: the shape of cells, the organisational pattern of interphase microtubules and the cell cycle stage of cells, as judged by microtubule pattern. Comparison with extensive data from wild-type cells led to the identification of 262 factors that influence one or more of these cell processes. I go on to biologically validate some of the outcomes from the screen, leading to a publication in Developmental Cell reporting the screen, its findings and the online genomic resource SYSGRO. I then focus on a group of mutants that suggest a connection between the DNA damage response (DDR) and microtubule organisation. From here I show that the DDR induces elongation of microtubule bundles in response to the DDR kinases, ATM and ATR. I begin to reveal factors that may mediate this response and finally, I provide evidence to suggest that the same mechanism is conserved in cultured human cells (Hc3716-hTERT), which may go some way to explaining clinical results showing a beneficial effect of microtubule destabilisation in conjunction with cancer therapies