Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem

A robust algorithm for segmenting fluorescence images and its application to single-molecule counting

La microscopie par fluorescence de cellules vivantes produit de grandes quantités de données. Ces données sont composées d’une grande diversité au niveau de la forme des objets d’intérêts et possèdent un ratio signaux/bruit très bas. Pour concevoir un pipeline d’algorithmes efficaces en traitement d’image de microscopie par fluorescence, il est important d’avoir une segmentation robuste et fiable étant donné que celle-ci constitue l’étape initiale du traitement d’image. Dans ce mémoire, je présente MinSeg, un algorithme de segmentation d’image de microscopie par fluorescence qui fait peu d’assomptions sur l’image et utilise des propriétés statistiques pour distinguer le signal par rapport au bruit. MinSeg ne fait pas d’assomption sur la taille ou la forme des objets contenus dans l’image. Par ce fait, il est donc applicable sur une grande variété d’images. Je présente aussi une suite d’algorithmes pour la quantification de petits complexes dans des expériences de microscopie par fluorescence de molécules simples utilisant l’algorithme de segmentation MinSeg. Cette suite d’algorithmes a été utilisée pour la quantification d’une protéine nommée CENP-A qui est une variante de l’histone H3. Par cette technique, nous avons trouvé que CENP-A est principalement présente sous forme de dimère.Live-cell fluorescence microscopy produces high amounts of data with a high variability in shapes at low signal-to-noise ratio. An efficient design of image analysis pipelines requires a reliable and robust initial segmentation step that needs little parameter fine-tuning. Here, I present a segmentation algorithm called MinSeg for fluorescence image data that relies on minimal assumptions about the image, and uses statistical considerations to distinguish signal from background. More importantly, the algorithm does not make assumptions about feature size or shape, and is thus universally applicable. I also present a pipeline for the quantification of small complexes with single-molecule fluorescence microscopy using this segmentation algorithm as the first step of the workflow. This pipeline was used for the quantification of a small histone H3 variant protein called CENP-A. We found that the CENP-A nucleosomes are dimers

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

Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments

Optical microscopy provides rich spatio-temporal information characterizing in vivo molecular motion. However, effective forces and other parameters used to summarize molecular motion change over time in live cells due to latent state changes, e.g., changes induced by dynamic micro-environments, photobleaching, and other heterogeneity inherent in biological processes. This study focuses on techniques for analyzing Single Particle Tracking (SPT) data experiencing abrupt state changes. We demonstrate the approach on GFP tagged chromatids experiencing metaphase in yeast cells and probe the effective forces resulting from dynamic interactions that reflect the sum of a number of physical phenomena. State changes are induced by factors such as microtubule dynamics exerting force through the centromere, thermal polymer fluctuations, etc. Simulations are used to demonstrate the relevance of the approach in more general SPT data analyses. Refined force estimates are obtained by adopting and modifying a nonparametric Bayesian modeling technique, the Hierarchical Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT applications. The HDP-SLDS method shows promise in systematically identifying dynamical regime changes induced by unobserved state changes when the number of underlying states is unknown in advance (a common problem in SPT applications). We expand on the relevance of the HDP-SLDS approach, review the relevant background of Hierarchical Dirichlet Processes, show how to map discrete time HDP-SLDS models to classic SPT models, and discuss limitations of the approach. In addition, we demonstrate new computational techniques for tuning hyperparameters and for checking the statistical consistency of model assumptions directly against individual experimental trajectories; the techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One publication available freely as an open-access article at http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763

Spatiotemporal localization of proteins in microorganisms via photoactivated localization microscopy

Photoactivated localization microscopy (PALM) is a single molecule fluorescence microscopy technique (SMLM) that relies on the controlled activation and imaging of photo-activatable/convertible fluorescent proteins to determine their position with nanometer scale precision. The analysis of SMLM data is composed of two sequential aspects: the generation of a super-resolution table/image and the subsequent analysis. In recent years, several data analysis packages dedicated to the generation of super-resolved images have been developed. These packages have been extensively characterized and compared in a community-wide effort, therefore allowing researchers to identify optimal solutions for their experiments and providing software developers with a gold standard. On the contrary, the development of data analysis packages dedicated to the study of protein coordinates has been lagging behind, and no comprehensive approach has been developed to date. Here, I present a combination of Fiji and R based scripts for the characterization, filtering and quality assurance of SMLM derived localizations. Furthermore, I demonstrate that specific conventional image analysis techniques can be applied, both quantitatively and qualitatively, to super resolution images. I then apply these analysis tools exemplarily on the characterization of the spatio-temporal localization of a novel DNA repair system in Corynebacterium glutamicum, termed Dip (DNA damage induced protein) C. Finally, I combine the multiple data analysis packages that I developed and/or adapted for the study of specific biological scenarios into a single cohesive pipeline, therefore providing a generalized and comprehensive approach toward the coordinate based analysis of the spatio-temporal localization of proteins in PALM and, in general, in SMLM. Each of the data analysis packages that comprise the pipeline is here presented together with the biological scenario that prompted its development. These include the study of magnetosome formation in Magnetospirillum gryphiswaldense, the study of the chromosome segregation machinery in C. glutamicum and the study of flagellar organization in Trypanosoma brucei.Die photoaktivierte Lokalisationsmikroskopie (PALM) ist eine Einzelmolekül-Fluoreszenzmikroskopie Technik (SMLM), die auf der kontrollierten Aktivierung und Aufnahme von photoaktivierbaren / konvertierbaren fluoreszierenden Proteinen beruht, um ihre Position mit einer Präzision im Nanometerbereich zu bestimmen. Die Analyse von SMLM-Daten besteht aus zwei aufeinander folgenden Aspekten: der Erzeugung einer Tabelle / eines hochauflösenden Bildes und der anschließenden Analyse. In den letzten Jahren wurden mehrere Datenanalysepakete entwickelt, die sich der Berechnung der hochaufgelösten Bilder widmen. Diese Pakete wurden in gemeinschaftsweiten Anstrengungen umfassend charakterisiert und verglichen, sodass Forscher eine optimale Lösung für eigene Experimente wählen können, während Softwareentwicklern einen Goldstandard zur Hand haben. Gegensätzlich wurde jedoch die Entwicklung von Datenanalysepaketen zur spezifischen Untersuchung von Proteinkoordinaten vernachlässigt, so dass in diesem Bereich keine umfassenden Instrumente existieren. In dieser Arbeit präsentiere ich eine Kombination aus Fiji- und R basierten Skripten zur Charakterisierung, Filterung und Qualitätssicherung von SMLM Proteinkoordinaten. Darüber hinaus zeige ich, dass bestimmte konventionelle Bildanalysetechniken sowohl quantitativ als auch qualitativ auf „Superresolution“ Bilder angewandt werden können. Im Folgenden verwende Ich diese Analysewerkzeuge dann beispielhaft zur Charakterisierung der räumlich-zeitlichen Lokalisierung eines neuartigen DNA-Reparatursystems in Corynebacterium glutamicum, welches ich DipC (DNA-Schaden-induziertes Protein) genannt habe. Schließlich kombiniere ich die genannten Datenanalysepakete, die ich für die Untersuchung spezifischer biologischer Szenarien entwickelt und / oder angepasst habe, zu einer einzigen zusammenhängenden Arbeitsroutine. Diese bietet einen allgemeinen und umfassenden Ansatz für die koordinatenbasierte Analyse der räumlich-zeitlichen Lokalisierung von Proteinen aus PALM- und im Allgemeinen aus SMLM-Experimenten. Jedes der Datenanalysepakete, die in beschriebener Routine enthalten sind, wird hier zusammen mit dem biologischen Szenario vorgestellt, das zu ihrer Entwicklung geführt hat. Dazu gehören die Untersuchung der Magnetosomenbildung in Magnetospirillum gryphiswaldense, die Untersuchung der Chromosomensegregationsmaschinerie in C. glutamicum und die Untersuchung der Flagellenorganisation in Trypanosoma brucei

Analytical tools for single-molecule fluorescence imaging in cellulo

Recent technological advances in cutting-edge ultrasensitive fluorescence microscopy have allowed single-molecule imaging experiments in living cells across all three domains of life to become commonplace. Single-molecule live-cell data is typically obtained in a low signal-to-noise ratio (SNR) regime sometimes only marginally in excess of 1, in which a combination of detector shot noise, sub-optimal probe photophysics, native cell autofluorescence and intrinsically underlying stochasticity of molecules result in highly noisy datasets for which underlying true molecular behaviour is non-trivial to discern. The ability to elucidate real molecular phenomena is essential in relating experimental single-molecule observations to both the biological system under study as well as offering insight into the fine details of the physical and chemical environments of the living cell. To confront this problem of faithful signal extraction and analysis in a noise-dominated regime, the 'needle in a haystack' challenge, such experiments benefit enormously from a suite of objective, automated, high-throughput analysis tools that can home in on the underlying 'molecular signature' and generate meaningful statistics across a large population of individual cells and molecules. Here, I discuss the development and application of several analytical methods applied to real case studies, including objective methods of segmenting cellular images from light microscopy data, tools to robustly localize and track single fluorescently-labelled molecules, algorithms to objectively interpret molecular mobility, analysis protocols to reliably estimate molecular stoichiometry and turnover, and methods to objectively render distributions of molecular parameters

Local image correlation methods for the characterization of subcellular structure and dynamics by confocal and super-resolution microscopy

This thesis work aspires to present a new concept for the application of correlation techniques to the study of the cellular environment. By exploiting local analysis in combination to a fast fit-free technique (the phasor approach) we provide an exhaustive high-resolution analysis of structural and dynamic properties while maintaining a reasonable computation time. The dissertation will be articulated as follows: In CHAPTER 1 we aim to provide the reader with a description of the techniques that will be exploited during the rest of the dissertation together with the open questions and problematics that our techniques will try to answer to. In CHAPTER 2 we present the local analysis concept and its application to a correlation technique capable of measuring size and concentration (ICS). We will show how we coupled ICS to the phasor approach to create a technique (PLICS) for the assessment of size heterogeneity. PLICS will be demonstrated with simulations as well as with cellular samples and will be applied to the study of endocytic vesicles uptake and to the characterization of other organelles. In CHAPTER 3 the concept is extended to two-colors samples for the determination of local inter-structure distance (PLICCS). We will present a pattern analysis method we developed that exploits this information in order to evaluate the relative distribution of the structures imaged in the two channels, comparing it to a random distribution. This method will be validated with simulations and applied to the study of replication-transcription collisions. Successively, we will show that PLICCS can be converted to a localization algorithm for single particle tracking that will be used for tracking membrane receptors in living neurons. CHAPTER 4 will describe the extension of our local analysis to RICS, a correlation technique capable of measuring the diffusion coefficient of a fluorescent probe. The resulting algorithm (L-RICS) provides high resolution diffusion maps that will be used to characterize the diffusion of a fluorescent probe (GFP) within the nucleus and nucleolus of living cells. We will show that the algorithm can be implemented also in non-linear scanning systems. CHAPTER 5 will conclude the dissertation by introducing advanced correlation methods for the analysis of non-Brownian diffusion and their coupling to super-resolution techniques. In particular, we will present a super-resolution correlation technique (SPLIT) recently developed capable of analyzing the cellular environment and a microcamera-based approach (Airyscan comprehensive correlation analysis) we developed for the parallel implementation, in super-resolution, of several complementary correlation techniques