Using enhanced number and brightness to measure protein oligomerization dynamics in live cells

Protein dimerization and oligomerization are essential to most cellular functions, yet measurement of the size of these oligomers in live cells, especially when their size changes over time and space, remains a challenge. A commonly used approach for studying protein aggregates in cells is number and brightness (N&B), a fluorescence microscopy method that is capable of measuring the apparent average number of molecules and their oligomerization (brightness) in each pixel from a series of fluorescence microscopy images. We have recently expanded this approach in order to allow resampling of the raw data to resolve the statistical weighting of coexisting species within each pixel. This feature makes enhanced N&B (eN&B) optimal for capturing the temporal aspects of protein oligomerization when a distribution of oligomers shifts toward a larger central size over time. In this protocol, we demonstrate the application of eN&B by quantifying receptor clustering dynamics using electron-multiplying charge-coupled device (EMCCD)-based total internal reflection microscopy (TIRF) imaging. TIRF provides a superior signal-to-noise ratio, but we also provide guidelines for implementing eN&B in confocal microscopes. For each time point, eN&B requires the acquisition of 200 frames, and it takes a few seconds up to 2 min to complete a single time point. We provide an eN&B (and standard N&B) MATLAB software package amenable to any standard confocal or TIRF microscope. The software requires a high-RAM computer (64 Gb) to run and includes a photobleaching detrending algorithm, which allows extension of the live imaging for more than an hour

Single-Molecule Localization, Dynamics and Interactions of DNA Replication and Repair Proteins Revealed by Live-Cell Super-Resolution Microscopy.

The error-free progression of DNA replication is essential for all organisms. Approximately 80 known human diseases are caused by malfunction in DNA replication, and deficiencies in DNA repair mechanisms can also lead to severe consequences such as increased antibiotic resistance in bacteria and cancers in humans. A better understanding of DNA replication and repair requires knowledge of the key players along relevant pathways at the molecular level and in the cellular context. This characterization calls for a technique with superior sensitivity, accuracy and biocompatibility. In this thesis, we integrate single-molecule super-resolution microscopy and single-particle tracking with genetic and genomic approaches to study two proteins that play a pivotal role in maintaining genomic integrity: MutS and PolC. From prokaryotes to human cells, homologs of the highly conserved mismatch repair (MMR) protein MutS recognize mispaired nucleotides and recruit the proteins responsible for downstream repair. Although the structure and function of MutS have been extensively characterized in biochemical isolation, it remains unclear how MutS efficiently identifies, among millions of correctly paired bases, a single mismatch in the complex and crowded cellular environment. To obtain mechanistic insights into MMR initiation from an in vivo perspective, we applied super-resolution imaging in live Bacillus subtilis cells to follow the motion of single MutS proteins in real time, and we monitored how MutS behavior is affected by sequentially blocking critical steps along the MMR pathway. Our results demonstrate an intimate and dynamic coupling between MutS and the replisome which stages MutS to sites of DNA replication, allowing MutS to scan newly synthesized DNA in anticipation of errors largely free of obstacles. We then turn our focus to DNA replication itself. Specifically, we focused on PolC, one of the two essential DNA polymerases in B. subtilis. Based on photobleaching-assisted microscopy and three-dimensional super-resolution microscopy, we quantified the stoichiometry, intracellular locations and dynamics of PolC. Finally, we extended the application of super-resolution microscopy to the field of renewable energy by tracking single molecules and visualizing guest-host interactions in microporous coordination polymers (MCPs).PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113498/1/liaoy_1.pd

Transcription factor clusters regulate genes in eukaryotic cells

Transcription is regulated through binding factors to gene promoters to activate or repress expression, however, the mechanisms by which factors find targets remain unclear. Using single-molecule fluorescence microscopy, we determined in vivo stoichiometry and spatiotemporal dynamics of a GFP tagged repressor, Mig1, from a paradigm signaling pathway of Saccharomyces cerevisiae. We find the repressor operates in clusters, which upon extracellular signal detection, translocate from the cytoplasm, bind to nuclear targets and turnover. Simulations of Mig1 configuration within a 3D yeast genome model combined with a promoter-specific, fluorescent translation reporter confirmed clusters are the functional unit of gene regulation. In vitro and structural analysis on reconstituted Mig1 suggests that clusters are stabilized by depletion forces between intrinsically disordered sequences. We observed similar clusters of a co-regulatory activator from a different pathway, supporting a generalized cluster model for transcription factors that reduces promoter search times through intersegment transfer while stabilizing gene expression

Mechanical Regulation in Cell Division and in Neurotransmitter Release

During their lifecycle, cells must produce forces which play important roles in several subcellular processes. Force-producing components are organized into macromolecular assemblies of proteins that are often dynamic, and are constructed or disassembled in response to various signals. The forces themselves may directly be involved in subcellular mechanics, or they may influence mechanosensing proteins either within or outside these structures. These proteins play different roles: they may ensure the stability of the force-producing structure, or they may send signals to a coupled process. The generation and sensing of subcellular forces is an active research topic, and this thesis focusses on the roles of these forces in two key areas: cell division and neurotransmitter release. The first part of the thesis deals with the effect of force on cell wall growth regulation during division in the fission yeast Schizosaccharomyces pombe, a cigar-shaped, unicellular organism. During cytokinesis, the last stage of cell division in which the cell physically divides into two, a tense cytokinetic ring anchored to the cellular membrane assembles and constricts, accompanied by the inward centripetal growth of new cell wall, called septum, in the wake of the inward-moving membrane. The contour of the septum hole maintains its circularity as it reduces in size—an indication of regulated growth. To characterize the cell wall growth process, we performed image analysis on contours of the leading edge of the septum obtained via fluorescence microscopy in the labs of our collaborators. We quantified the deviations from circularity using the edge roughness. The roughness was spatially correlated, suggestive of regulated growth. We hypothesized that the cell wall growers are mechanosensitive and respond to the force exerted by the ring. A mathematical model based on this hypothesis then showed that this leads to corrections of roughness in a curvature-dependent fashion. Thus, one of the roles of ring tension is to communicate with the mechanosensitive septum growth processes and coordinate growth to ensure the daughter cells have a functional cell wall. The second part of the thesis deals with how ring tension is produced and sustained, using experimentally measured ultrastructure of the cytokinetic ring itself. Recent super-resolution experiments have revealed that several key proteins of the fission yeast constricting ring are organized into membrane-anchored complexes called nodes. The force producing protein myosin-II in these nodes exerts pulling forces on polymeric actin filaments that are synthesized from polymerizers residing in the nodes. How these forces are marshalled to generate ring tension, and how such an organization maintains its stability is unclear. Using a mathematical model with coarse-grained representations of actin and myosin, we showed that such a node-based organization reproduces previously measured ring tension values. The model explains the origin of experimentally observed bidirectional motion of the nodes in the ring, and showed that turnover of the nodes rescues the ring from inherent contractile instabilities that would be expected when a force-producing structure is made up of small object that effectively attract one another. Finally, the third part of the thesis deals with the role of forces produced by SNARE proteins at synapses between two neurons during neurotransmission. A key step here is synaptic release, where inside a neuron, membrane-bound compartments called vesicles filled with neurotransmitter fuse with the membrane of the neuron forming a transient fusion pore, and release their contents to the outside of the cell. These neurotransmitter molecules are sensed by another neuron that is physically separate from the neuron in question and this neuron propagates the signal henceforth. Thus, regulation of neurotransmitter release is a key step in neurotransmission. A fusion machinery consisting of several proteins facilitates membrane fusion, and pore nucleation requires the formation of a SNARE protein complex in this machinery, whose role during pore dilation is unclear. Using electrophysiological measurements, our collaborators experimentally measured the statistics of the size of single fusion pores in vitro, and observed that average pore sizes increased with the number of SNARE proteins. Using mathematical modeling, we showed that this effect was due to an entropic crowding force that expands the pore and increases with the number of SNAREs, and counteracts the energy barrier to fusion pore expansion

Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application

Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications

abstract: Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading. To detect and classify objects in video, the objects have to be separated from the background, and then the discriminant features are extracted from the region of interest before feeding to a classifier. Effective object segmentation and feature extraction are often application specific, and posing major challenges for object detection and classification tasks. In this dissertation, we address effective object flow based ROI generation algorithm for segmenting moving objects in video data, which can be applied in surveillance and self driving vehicle areas. Optical flow can also be used as features in human action recognition algorithm, and we present using optical flow feature in pre-trained convolutional neural network to improve performance of human action recognition algorithms. Both algorithms outperform the state-of-the-arts at their time. Medical images and videos pose unique challenges for image understanding mainly due to the fact that the tissues and cells are often irregularly shaped, colored, and textured, and hand selecting most discriminant features is often difficult, thus an automated feature selection method is desired. Sparse learning is a technique to extract the most discriminant and representative features from raw visual data. However, sparse learning with \textit{L1} regularization only takes the sparsity in feature dimension into consideration; we improve the algorithm so it selects the type of features as well; less important or noisy feature types are entirely removed from the feature set. We demonstrate this algorithm to analyze the endoscopy images to detect unhealthy abnormalities in esophagus and stomach, such as ulcer and cancer. Besides sparsity constraint, other application specific constraints and prior knowledge may also need to be incorporated in the loss function in sparse learning to obtain the desired results. We demonstrate how to incorporate similar-inhibition constraint, gaze and attention prior in sparse dictionary selection for gastroscopic video summarization that enable intelligent key frame extraction from gastroscopic video data. With recent advancement in multi-layer neural networks, the automatic end-to-end feature learning becomes feasible. Convolutional neural network mimics the mammal visual cortex and can extract most discriminant features automatically from training samples. We present using convolutinal neural network with hierarchical classifier to grade the severity of Follicular Lymphoma, a type of blood cancer, and it reaches 91\% accuracy, on par with analysis by expert pathologists. Developing real world computer vision applications is more than just developing core vision algorithms to extract and understand information from visual data; it is also subject to many practical requirements and constraints, such as hardware and computing infrastructure, cost, robustness to lighting changes and deformation, ease of use and deployment, etc.The general processing pipeline and system architecture for the computer vision based applications share many similar design principles and architecture. We developed common processing components and a generic framework for computer vision application, and a versatile scale adaptive template matching algorithm for object detection. We demonstrate the design principle and best practices by developing and deploying a complete computer vision application in real life, building a multi-channel water level monitoring system, where the techniques and design methodology can be generalized to other real life applications. The general software engineering principles, such as modularity, abstraction, robust to requirement change, generality, etc., are all demonstrated in this research.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Single molecule fluorescence measurements of complex systems

2017 Summer.Includes bibliographical references.Single molecule methods are powerful tools for investigating the properties of complex systems that are generally concealed by ensemble measurements. Here we use single molecule fluorescent measurements to study two different complex systems: 1/ƒ noise in quantum dots and diffusion of the membrane proteins in live cells. The power spectrum of quantum dot (QD) fluorescence exhibits 1/ƒ noise, related to the intermittency of these nanosystems. As in other systems exhibiting 1/ƒ noise, this power spectrum is not integrable at low frequencies, which appears to imply infinite total power. We report measurements of individual QDs that address this long-standing paradox. We find that the level of 1/ƒβ noise for QDs decays with the observation time. We show that the traditional description of the power spectrum with a single exponent is incomplete and three additional critical exponents characterize the dependence on experimental time. A broad range of membrane proteins display anomalous diffusion on the cell surface. Different methods provide evidence for obstructed subdiffusion and diffusion on a fractal space, but the underlying structure inducing anomalous diffusion has never been visualized due to experimental challenges. We addressed this problem by imaging the cortical actin at high resolution while simultaneously tracking individual membrane proteins in live mammalian cells. Our data show that actin introduces barriers leading to compartmentalization of the plasma membrane and that membrane proteins are transiently confined within actin fences. Furthermore, superresolution imaging shows that the cortical actin is organized into a self-similar fractal

Deep Learning for Detection and Segmentation in High-Content Microscopy Images

High-content microscopy led to many advances in biology and medicine. This fast emerging technology is transforming cell biology into a big data driven science. Computer vision methods are used to automate the analysis of microscopy image data. In recent years, deep learning became popular and had major success in computer vision. Most of the available methods are developed to process natural images. Compared to natural images, microscopy images pose domain specific challenges such as small training datasets, clustered objects, and class imbalance. In this thesis, new deep learning methods for object detection and cell segmentation in microscopy images are introduced. For particle detection in fluorescence microscopy images, a deep learning method based on a domain-adapted Deconvolution Network is presented. In addition, a method for mitotic cell detection in heterogeneous histopathology images is proposed, which combines a deep residual network with Hough voting. The method is used for grading of whole-slide histology images of breast carcinoma. Moreover, a method for both particle detection and cell detection based on object centroids is introduced, which is trainable end-to-end. It comprises a novel Centroid Proposal Network, a layer for ensembling detection hypotheses over image scales and anchors, an anchor regularization scheme which favours prior anchors over regressed locations, and an improved algorithm for Non-Maximum Suppression. Furthermore, a novel loss function based on Normalized Mutual Information is proposed which can cope with strong class imbalance and is derived within a Bayesian framework. For cell segmentation, a deep neural network with increased receptive field to capture rich semantic information is introduced. Moreover, a deep neural network which combines both paradigms of multi-scale feature aggregation of Convolutional Neural Networks and iterative refinement of Recurrent Neural Networks is proposed. To increase the robustness of the training and improve segmentation, a novel focal loss function is presented. In addition, a framework for black-box hyperparameter optimization for biomedical image analysis pipelines is proposed. The framework has a modular architecture that separates hyperparameter sampling and hyperparameter optimization. A visualization of the loss function based on infimum projections is suggested to obtain further insights into the optimization problem. Also, a transfer learning approach is presented, which uses only one color channel for pre-training and performs fine-tuning on more color channels. Furthermore, an approach for unsupervised domain adaptation for histopathological slides is presented. Finally, Galaxy Image Analysis is presented, a platform for web-based microscopy image analysis. Galaxy Image Analysis workflows for cell segmentation in cell cultures, particle detection in mice brain tissue, and MALDI/H&E image registration have been developed. The proposed methods were applied to challenging synthetic as well as real microscopy image data from various microscopy modalities. It turned out that the proposed methods yield state-of-the-art or improved results. The methods were benchmarked in international image analysis challenges and used in various cooperation projects with biomedical researchers

Local Cytoskeletal and Organelle Interactions Impact Molecular Motor-Driven Early Endosomal Trafficking

Molecular motors generate the force needed for long-distance transport of cargos and organelles in the cell. How motor proteins attach to a diverse array of cargos and navigate to the correct location in the cell with enough fidelity to maintain organelle integrity is only starting to be understood. Studying the properties of individual motors, and their fine-tuning by regulatory molecules, is one area of active investigation in vitro. However, the organization of the cell, and the variability of the environment within a single cell, cannot be fully reconstituted in vitro. We investigated the effects of the crowded intracellular environment on early endosomal trafficking. Live-cell imaging of an endosomal cargo (endocytosed epidermal growth factor-conjugated quantum dots) combined with high-resolution tracking was used to analyze the heterogeneous motion of individual endosomes. The motile population of endosomes moved towards the perinuclear region in directed bursts of microtubule-based, dynein-dependent transport interrupted by longer periods of diffusive motion. Actin network density did not affect motile endosomes during directed runs or diffusive interruptions. Simultaneous two-color imaging was used to correlate changes in endosomal movement with potential obstacles to directed runs. Termination of directed runs spatially correlated with microtubule-dense regions, encounters with other endosomes, and interactions with the endoplasmic reticulum, suggesting these interactions interrupt directed transport. Early endosomal and lysosomal interactions with the ER were characterized by dramatic deformation and tubulation of the ER. During a subset of run terminations, we also observed merging and splitting of endosomes, and reversals in direction at speeds up to ten-fold greater than characteristic in vitro motor velocities. These observations suggest endosomal membrane tension is high during directed run termination. Our results indicate that the crowded cellular environment significantly impacts the motor-driven motility of organelles. Rather than simply acting as impediments to movement, interactions of trafficking cargos with intracellular obstacles may facilitate communication between membrane-bound compartments or contribute to the generation of membrane tension necessary for fusion and fission of endosomal membranes or remodeling of the endoplasmic reticulum