Deep Learning Methods for Detection and Tracking of Particles in Fluorescence Microscopy Images

Studying the dynamics of sub-cellular structures such as receptors, filaments, and vesicles is a prerequisite for investigating cellular processes at the molecular level. In addition, it is important to characterize the dynamic behavior of virus structures to gain a better understanding of infection mechanisms and to develop novel drugs. To investigate the dynamics of fluorescently labeled sub-cellular and viral structures, time-lapse fluorescence microscopy is the most often used imaging technique. Due to the limited spatial resolution of microscopes caused by diffraction, these very small structures appear as bright, blurred spots, denoted as particles, in microscopy images. To draw statistically meaningful biological conclusions, a large number of such particles need to be analyzed. However, since manual analysis of fluorescent particles is very time consuming, fully automated computer-based methods are indispensable. We introduce novel deep learning methods for detection and tracking of multiple particles in fluorescence microscopy images. We propose a particle detection method based on a convolutional neural network which performs image-to-image mapping by density map regression and uses the adaptive wing loss. For particle tracking, we present a recurrent neural network that exploits past and future information in both forward and backward direction. Assignment probabilities across multiple detections as well as the probabilities for missing detections are computed jointly. To resolve tracking ambiguities using future information, several track hypotheses are propagated to later time points. In addition, we developed a novel probabilistic deep learning method for particle tracking, which is based on a recurrent neural network mimicking classical Bayesian filtering. The method includes both aleatoric and epistemic uncertainty, and provides valuable information about the reliability of the computed trajectories. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. Moreover, we developed a convolutional Long Short-Term Memory neural network for combined particle tracking and colocalization analysis in two-channel microscopy image sequences. The network determines colocalization probabilities, and colocalization information is exploited to improve tracking. Short and long-term temporal dependencies of object motion as well as image intensities are taken into account to compute assignment probabilities jointly across multiple detections. We also introduce a deep learning method for probabilistic particle detection and tracking. For particle detection, temporal information is integrated to regress a density map and determine sub-pixel particle positions. For tracking, a fully Bayesian neural network is presented that mimics classical Bayesian filtering and takes into account both aleatoric and epistemic uncertainty. Uncertainty information of individual particle detections is considered. Network training for the developed deep learning-based particle tracking methods relies only on synthetic data, avoiding the need of time-consuming manual annotation. We performed an extensive evaluation of our methods based on image data of the Particle Tracking Challenge as well as on fluorescence microscopy images displaying virus proteins of HCV and HIV, chromatin structures, and cell-surface receptors. It turned out that the methods outperform previous methods

Applications of High-Throughput Sequencing Data Analysis in Transcriptional Studies

High-throughput sequencing has become one of the most powerful tools for studies in genomics, transcriptomics, epigenomics, and metagenomics. In recent years, HTS protocols for enhancing the understanding of the diverse cellular roles of RNA have been designed, such as RNA-Seq, CLIP-Seq, and RIP-Seq. In this work, we explore the applications of HTS data analysis in transcriptional studies. First, the differential expression analysis of RNA-Seq data is discussed and applied to a sheep RNA-Seq dataset to examine the biological mechanisms of the sheep resistance to worm infection. We develop an automatic pipeline to analyze the RNA-Seq dataset, and use a negative binomial model for gene expression analysis. Functional analysis is conducted over the differentially expressed genes, and a broad range of mechanisms providing protection against the parasite are identified in the resistant sheep breed. This study provides insights into the underlying biology of sheep host resistance. Then, a deep learning method is proposed to predict the RNA binding protein binding preferences using CLIP-Seq data. The proposed method uses a deep convolutional autoencoder to effectively learn the robust sequence features, and a softmax classifier to predict the RBP binding sites. To demonstrate the efficacy of the proposed method, we evaluate its performance over a dataset containing 31 CLIP-Seq experiments. This benchmarking shows that the proposed method improves the prediction performance in terms of AUC, compared with the existing methods. The analysis also shows that the proposed method is able to provide insights to identify new RBP binding motifs. Therefore, the proposed method will be of great help in understanding the dynamic regulations of RBPs in various biological processes and diseases. Finally, a database is created to facilitate the reuse of the public available mouse RNA-Seq dataset. The metadata of the publicly available mouse RNA-Seq datasets is manually curated and is served by a well-designed website. The database can be scaled up in the future to serve more types of HTS data

Cisco Science: Using Omics To Answer A Range Of Key Questions

Coregonines, including cisco (Coregonus artedi), kiyi (Coregonus kiyi), and bloater (Coregonus hoyi), are a focus for prey fish conservation and restoration efforts throughout the Laurentian Great Lakes. However, fundamental questions about coregonine ecology and genetics remain. For example, we know little about how the early life stages of coregonines respond to environmental change at either the genotypic or phenotypic level. We also have limited knowledge about how to identify different species at the larval stage and the genetic relationships among species, which makes the different species difficult to study at the larval stage. To increase the probability for success in restoration efforts, current and future research need to integrate traditional and novel approaches to better understand what leads to current and future coregonine successes. We used DNA and RNA omics tools, genomics and transcriptomics to boost our comprehension of current coregonine populations and to help understand how C. artedi may respond to environmental change. During the winter of 2017, we conducted a pilot experiment to evaluate how C. artedi eggs may respond to increased light exposure resulting from current and expected reductions in annual ice and snow cover due to global warming. We used transcriptomics to assess differences in gene expression between a continuous light and continuous dark treatment. Our results indicate that light is an environmental factor that could lead to earlier hatch dates, smaller yolk sacs, changes in mortality and differential gene expression in metabolic related and other functionally important genes. In 2018, we sampled larval coregonines in the Apostle Islands of Lake Superior each week from hatch in May until late July. We used genomic sequencing to genetically identify 197 larvae to species: C. artedi, C. hoyi, and C. kiyi. The larval demographic characteristics of each species was assessed and revealed that length ranges, growth rates, yolk sac condition, and effective population size varied among species. Larvae of all three species were found throughout the entirety of the Apostle Islands and the genetic diversity within each species appears high. The results from our pilot experiment and field observations help advance our understanding of the important early life stages of coregonines and how changes in light exposure or growth rates could affect their success or failure in a changing climate

T-cell receptor repertoire sequencing in health and disease

The adaptive immune systems of jawed vertebrates are based upon lymphocytes bearing a huge variety of antigen receptors. Produced by somatic DNA recombination, these receptors are clonally expressed on T- and B-lymphocytes, where they are used to help detect and control infections and help maintain regular bodily function. Full understanding of various aspects of the immune system relies upon accurate measurement of the individual receptors that make up these repertoires. In order to obtain such data, protocols were developed to permit unbiased amplification, high-throughput deep-sequencing, and error-correcting bioinformatic analysis of T-cell receptor sequences. These techniques have been applied to peripheral blood samples to further characterise aspects of the TCR repertoire of healthy individuals, such as V(D)J TCR gene usage and pairing distributions. A large number of sequences are also found to be shared across multiple individuals, including sequences matching receptors belonging to known and proposed T-cell subsets making use of invariant rearrangements. The resolution provided also permitted detection of low-frequency recombination events that use unexpected gene segments, or contained alternative splicing events. Deep-sequencing was further used to study the effect of HIV infection, and subsequent antiretroviral therapy, upon the TCR repertoire. HIV-patient repertoires are typified by marked clonal inequality and perturbed population structures, relative to healthy controls. The data presented support a model in which HIV infection drives expansion of an subset of CD8+ clones, which -- in combination with the virally-mediated loss of CD4+ cells -- is responsible for driving repertoires towards an idiosyncratic population with low diversity. Moreover these altered repertoire features do not significantly recover after three months of therapy. Deep-sequencing therefore presents opportunities to investigate the properties of TCR repertoires both in health and disease, which could be useful when analysing a wide variety of immune phenomena

Development and application of molecular and computational tools to image copper in cells

Copper is a trace element which is essential for many biological processes. A deficiency or excess of copper(I) ions, which is its main oxidation state of copper in cellular environment, is increasingly linked to the development of neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease (PD and AD). The regulatory mechanisms for copper(I) are under active investigation and lysosomes which are best known as cellular “incinerators” have been found to play an important role in the trafficking of copper inside the cell. Therefore, it is important to develop reliable experimental methods to detect, monitor and visualise this metal in cells and to develop tools that allow to improve the data quality of microscopy recordings. This would enable the detailed exploration of cellular processes related to copper trafficking through lysosomes. The research presented in this thesis aimed to develop chemical and computational tools that can help to investigate concentration changes of copper(I) in cells (particularly in lysosomes), and it presents a preliminary case study that uses the here developed microscopy image quality enhancement tools to investigate lysosomal mobility changes upon treatment of cells with different PD or AD drugs. Chapter I first reports the synthesis of a previously reported copper(I) probe (CS3). The photophysical properties of this probe and functionality on different cell lines was tested and it was found that this copper(I) sensor predominantly localized in lipid droplets and that its photostability and quantum yield were insufficient to be applied for long term investigations of cellular copper trafficking. Therefore, based on the insights of this probe a new copper(I) selective fluorescent probe (FLCS1) was designed, synthesized, and characterized which showed superior photophysical properties (photostability, quantum yield) over CS3. The probe showed selectivity for copper(I) over other physiological relevant metals and showed strong colocalization in lysosomes in SH-SY5Y cells. This probe was then used to study and monitor lysosomal copper(I) levels via fluorescence lifetime imaging microscopy (FLIM); to the best of my knowledge this is the first copper(I) probe based on emission lifetime. Chapter II explores different computational deep learning approaches for improving the quality of recorded microscopy images. In total two existing networks were tested (fNET, CARE) and four new networks were implemented, tested, and benchmarked for their capabilities of improving the signal-to-noise ratio, upscaling the image size (GMFN, SRFBN-S, Zooming SlowMo) and interpolating image sequences (DAIN, Zooming SlowMo) in z- and t-dimension of multidimensional simulated and real-world datasets. The best performing networks of each category were then tested in combination by sequentially applying them on a low signal-to-noise ratio, low resolution, and low frame-rate image sequence. This image enhancement workstream for investigating lysosomal mobility was established. Additionally, the new frame interpolation networks were implemented in user-friendly Google Colab notebooks and were made publicly available to the scientific community on the ZeroCostDL4Mic platform. Chapter III provides a preliminary case study where the newly developed fluorescent copper(I) probe in combination with the computational enhancement algorithms was used to investigate the effects of five potential Parkinson’s disease drugs (rapamycin, digoxin, curcumin, trehalose, bafilomycin A1) on the mobility of lysosomes in live cells.Open Acces

Viral Diversity by Deep Sequencing: Approaches to Analyzing Effects of Anti-HIV Treatments

HIV is a deadly virus responsible for the AIDS pandemic, which has claimed countless lives since its origins in the early 1980s. A cure for HIV is still elusive - HIV can exist as a diverse and dynamic population that adapts quickly to immune and drug pressures, making elimination of infection difficult. Advances in antiretroviral (ARV) therapy have resulted in effective control of HIV for some but not all patients. This dissertation reports case studies of the response of viral populations to selection pressures exerted by emerging anti-HIV therapies. Deep sequencing technology was used to probe viral swarms at high-resolution, which helped make clinically relevant conclusions. Further, novel computational approaches were implemented to control procedural noise and carefully interpret signal. In one study, we examine HIV integrase inhibitors (INIs), which are among the latest ARV drugs. INIs act at a pre-integration level by aborting viral integration, which would normally lead to lasting infection. Raltegravir (RAL) is the only FDA-approved INI to date. Investigating drug resistance is crucial to informing future course of ARV therapy. We describe evolving HIV swarms in patients exhibiting a switch in RAL-resistance profiles. To understand implications of RAL administration, we analyzed the pre-therapy or treatment-naïve context for the viral populations in-depth. Our findings suggest that predominant mutations arise only in presence of RAL - in its absence, they do not constitute fit polymorphisms. For all their effectiveness, drugs have not eradicated HIV. A recent clinical case, however, involving transfer of HIV-resistant cells to an infected patient, resulted for the first time in possible cure. This emphasized the importance of gene-modification and cell-based therapies to treat HIV. One such strategy showing promise uses an antisense to target HIV. The approach has been safe although clinical efficacy has not been fully determined. In support of one such study, we deep-sequenced viral swarms in the presence of antisense-modified cells. Encouragingly, we observed minority strains harboring evidence of antisense pressure in vivo, demonstrating the potential of alternative therapy. Finally, this dissertation underscores the significance of rare signatures in HIV populations, and outlines methods to investigate them

Multiplexed combinatorial drug screening using droplet-based microfluidics

The therapy of most cancers has greatly benefited from the use of targeted drugs. However, their effects are often short-lived since many tumors develop resistance against these drugs. Resistance of tumor cells against drugs can be adaptive or acquired and is often caused by genetic or non-genetic heterogeneity between tumor cells. A potential solution to overcome drug resistance is the use of drug combinations addressing multiple targets at once. Finding potent drug combinations against heterogeneous tumors is challenging. One reason is the high number of possible combinations. Another reason is the possibility of inter-patient heterogeneity in drug responses, making patient tailored treatments necessary. These require screens on patient material, which would drastically benefit from miniaturization, as it is the case in droplet-based microfluidics. However, drug screens in droplets against primary tumor cells have so far only been performed at a modest chemical complexity (55 treatment conditions) and with low content readouts. In this thesis we aimed at developing a droplet-based microfluidic workflow that allows the generation of high numbers of drug combinations in picolitre-sized droplets and their multiplexed analysis. To this end, we have established a pipeline to produce up to 420 drug combinations in droplets. We were able to significantly increase the number of possible combinations by building a microfluidic setup that comprises valve and micro-titer plate based injection of drugs into microfluidic devices for droplet generation Furthermore, we integrated a DNA-based barcoding approach to encode each treatment condition, enabling their multiplexed analyses since all droplets can be stored and processed together, which highly increases the throughput. With the established approach we can perform barcoding of each cells’ transcriptome according to the drugs it was exposed to in the droplet. Thereby, the effects of drug combinations on gene expression can be studied in a highly multiplexed way using RNA-Sequencing. We applied the developed approach to run combinatorial drug screens in droplets and analysed the effects of in total 630 drug combinations on gene expression in K562 cells. The low number of cells needed (max. 2 million cells) for such screens, could enable their application directly on tumor biopsies, thus paving the way for personalized therapy approaches. Since the established workflow is compatible with single cell readouts, we also envision its application to analyse drug resistances in heterogeneous tumor samples on the single cell level

RNA-PROTEIN CONDENSATION PATTERNS THE CYTOSOLIC LANDSCAPE OF A SYNCYTIUM

To function properly and survive in differing environmental conditions, every cell must organize their cytoplasm in one form or another. This requirement is increasingly necessary in large, multinucleated cells. Traditionally, this has been thought to be mainly driven by membrane-bound compartments (i.e. organelles) that help eukaryotic life organize into distinct biochemical spaces. More recently, it’s become apparent that the continuous cytosol is also organized into distinct compartments, through entirely separate means. In the multinucleated Drosophila embryo, approximately 70% of mRNA transcripts were determined to be heterogeneously localized across the cell, and mRNA spatial organization in the multinucleate fungus Ashbya gossypii has implicated phase separating RNA binding proteins (RBPs), where transcript heterogeneity is critical for autonomous nuclear division and polarized growth in these syncytial cells. The ability of RNAs to condense into droplets is in many instances contributing to previously appreciated mRNA localization phenomena. Phase separation enables mRNAs to selectively and efficiently co-localize and be co-regulated allowing control of gene expression in time and space. The work presented here demonstrates that mRNA sequence not only drives the localized condensation of RNA-protein droplets in A. gossypii, but also, governs the identity of these specialized RNA granules. Work in this thesis provides evidence that the RNA binding protein, Whi3, exhibits differential phase-separation behavior depending on which RNA target it binds and that this differential behavior is specified by features within the mRNA sequence. In addition, this work investigates the possibility of an auto feedback mechanism by which Whi3 phase separates with its own mRNA to drive differential crowding within the cytosol to promote droplet condensation in crowded cytosolic regions, thus creating individual territories of cytosol within a common syncytial cytoplasm. These data suggest mechanisms by which cells can employ asymmetric RNA localization, specifically the localization of RNAs via phase-separating RNA binding proteins, to generate functionally distinct domains to achieve efficient, and timely cytosolic organization.Doctor of Philosoph

Investigación de la distribución de los alelos HLA en poblaciones sanas y enfermas mediante la aplicación de nuevas metodologías de secuenciación

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Medicina, Departamento de Inmunología, Oftalmología y ORL, leída el 09/03/2021Increasing our knowledge of the HLA system, including both the complete sequence description and the assessment of its diversity at the worldwide human population-level, is of great importance for elucidating the molecular functional mechanisms of the immune system and its regulation in health and disease. Furthermore, assessment of HLA allelic and haplotypic diversity of each human population is essential in the clinical histocompatibility and transplantation setting as well as in the pharmacogenetics, immunotherapy and anthropology fields. Nevertheless, the inherent vast polymorphism and high complexity presented by the HLA system have been an important challenge for its unambiguous and in-depth (high-resolution) characterization by previously available legacy molecular HLA genotyping methods (e.g. SSP, SSO and even SBT). Recent application of novel next-generation sequencing (NGS) technology for high-resolution molecular HLA genotyping has enabled to obtain, at a high-throughput mode and larger scale, full-length and/or extended sequences and genotypes of all major HLA genes, thus overcoming most of these previous limitations. Objectives: I) Characterization of HLA allele and haplotype diversity of all major classical HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 and -DRB3/4/5) by application of NGS of a first representative cohort of the Spanish population that could also serve as a healthy control reference group. Respective statistical analyses were performed for this immunogenetic population data. II) Characterization of HLA allele and haplotype diversity of all major classical HLA genes (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 and -DRB3/4/5) by application of NGS of a respective cohort of multiple sclerosis (MS) patients in the Spanish population (recruited at the Department of Neurology, Hospital Clínic, Barcelona, Catalonia, Spain). A first case-control study was carried out to examine HLA-disease associations with MS in these Spanish population cohorts as well as to attempt a fine-mapping of these allele and haplotype associations by full gene resolution level via NGS. In addition, a second analysis exercise (i.e. test case) of this case-control study was carried out using an alternative healthy control group dataset, exclusively from the Spanish northeastern region of Catalonia in this second case, to evaluate possible differences in the findings of HLA-disease association with MS due to plausible regional HLA genetic variation within mainland Spain (i.e. as a statistical way to try controlling for any possible existing population stratification)...El estudio del sistema HLA, incluyendo la descripción completa de su secuencia y de la diversidad de este complejo HLA a nivel poblacional, es de gran importancia de cara a poder entender los mecanismos moleculares y funciones del sistema inmune así como su regulación en individuos sanos y enfermos. Además, la caracterización exhaustiva de la diversidad de alelos y haplotipos HLA de cada población humana es esencial en el campo de la inmunología de trasplante e histocompatibilidad al igual que en las áreas de farmacogenética e inmunoterapia. El inmenso polimorfismo y gran complejidad que presenta el sistema HLA han sido hasta ahora importantes barreras de cara a poder caracterizarlo en gran detalle (por alta resolución) y sin ambigüedades mediante métodos de genotipaje HLA tradicionales disponibles (como son SSP, SSO o incluso SBT). La reciente aplicación de la novedosa tecnología de secuenciación masiva NGS para el genotipaje molecular HLA por alta resolución ha posibilitado obtener secuencias completas o mucho más extendidas para genotipos de los principales genes de HLA, superándose así estas previas limitaciones. Objetivos: I) Caracterización de la diversidad alélica y haplotípica de los principales genes HLA (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 y -DRB3/4/5) mediante la aplicación de NGS en una primera cohorte representativa de la población española que, igualmente, constituirá una población control de referencia para estudios de asociación de HLA y enfermedades. También, respectivos análisis estadísticos se realizaron para estos resultados de genotipaje HLA. II) Caracterización de la diversidad alélica y haplotípica de los principales genes HLA (HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1 y -DRB3/4/5) mediante la aplicación de NGS en una correspondiente cohorte de pacientes con esclerosis múltiple (EM) de la población española (reclutados y procedentes del Departamento de Neurología del Hospital Clínic (Barcelona, Cataluña)). Un primer estudio de asociación HLA tomando casos (pacientes EM) frente a controles sanos se llevó a cabo para examinar la asociación de genes HLA y la enfermedad de EM en estas cohortes de población española antes mencionadas. Así se buscaba realizar un mapeo fino de las respectivas asociaciones alélicas y haplotípicas de HLA mediante la gran resolución alélica proporcionada por esta metodología de secuenciación masiva. De modo adicional, y como un segundo ejercicio de análisis en este estudio de asociación HLA, se utilizó un grupo control sano alternativo al previo, que incluía individuos procedentes de la región de Cataluña (situada al noreste de España) exclusivamente en este caso, para evaluar así posibles diferencias dadas en la asociación de HLA con EM debido a la probable variación genética en HLA existente a nivel regional dentro del territorio de España...Fac. de MedicinaTRUEunpu