Automatically Improving Cell Segmentation in Time-Lapse Microscopy Images Using Temporal Context From Tracking and Lineaging

Over the past decade biologists and microscopists have produced truly amazing movies, showing in wonderful detail the dynamics of living cells and subcellular structures. Access to this degree of detail in living cells is a key aspect of current biological research. This wealth of data and potential discovery is constrained by a lack of software tools. The standard approach to biological image analysis begins with segmentation to identify individual cells, tracking to maintain cellular identities over time, and lineaging to identify parent-daughter relationships. This thesis presents new algorithms for improving the segmentation, tracking and lineaging of live cell time-lapse microscopy images. A new ''segmentation from lineage'' algorithm feeds lineage or other high-level behavioral information back into segmentation algorithms along with temporal context provided by the multitemporal association tracker to create a powerful iterative learning algorithm that significantly improves segmentation and tracking results. A tree inference algorithm is used to improve automated lineage generation by integrating known cellular behavior constraints as well as fluorescent signals if available. The ''learn from edits'' technique uses tracking information to propagate user corrections to automatically correct further tracking mistakes. Finally, the new pixel replication algorithm is used for accurately partitioning touching cells using elliptical shape models. These algorithms are integrated into the LEVER lineage editing and validation software, providing user interfaces for automated segmentation, tracking and lineaging, as well as the ability to easily correct the automated results. These algorithms, integrated into LEVER, have identified key behavioral differences in embryonic and adult neural stem cells. Edit-based and functional validation techniques are used to evaluate and compare the new algorithms with current state of the art segmentation and tracking approaches. All the software as well as the image data and analysis results are released under a new open source/open data model built around Gitlab and the new CloneView interactive web tool.Ph.D., Electrical Engineering -- Drexel University, 201

Reticulon Homology Domain Containing Protein Families of the Endoplasmic Reticulum

The endoplasmic reticulum (ER) is the largest membrane bound organelle in a cell and has multiple responsibilities. Execution of the various duties performed by the ER requires it to be shaped in a rather complex and intricate manner. ER’s two major structural motives, namely sheets and tubules, play very complex yet not fully understood role in giving ER its overall structure and function. The ratio of sheet and tubule conformations differ significantly within cell types and during cell cycle. Such a balance is possible only with a well-functioning set of factors that constantly communicate with each other throughout a cell cycle. These factors are specifically responsible for either shaping the ER sheets or tubules in addition to factors that keep the dynamic nature of the ER sound. During mitosis, ER undergoes a major transformation in its structure, where the sheet-tubule ratio shifts more towards tubules. Specific factors keep this process sound by acting actively during the stage of mitosis for proper cell division to occur. Although research on such factors are still on-going, many in-depth details on such factors (e.g. their precise localization) and their mechanism of action plus novel factors for ER shaping still needs to be resolved using techniques involving high end light and electron microscopy. In addition, a constant battle in data analysis for answering key questions also persists. Development of tools to study and analyse data on the lines of image analysis and processing is an unmet need that needs simultaneous attention. The research in this thesis focuses on three family of proteins that we uncover as responsible candidates in shaping the ER. To aid the study, this thesis also discusses the development of a software platform for analysis of microscopic data generated during this study. In this research, Reticulon family of proteins (RTN) were characterised using high-end microscopic techniques. We showed RTN4A and RTN4B to localize to ER tubules and sheet edges using pre-embedding immuno electron microscopy (immuno-EM) and electron tomography. Using qPCR, RTN4A and RTN4B were observed to be the most expressed isoforms in neurons and epithelial cells respectively. FAM134C, a poorly characterised protein was identified as one of the RTN4B interacting proteins. FAM134C localised to the ER where it specifically resided at high curvature ER (sheet edges and tubules) similar to RTN4B. FAM134C, similar to the RTN4B also had the capability to promote ER tubules upon overexpression. In addition, another family of proteins belonging to receptor expression enhancing protein (REEP), namely REEP3 and REEP4 were studied for shaping ER during mitotic stage of cell cycle. REEP3 and REEP4 collectively were observed both in tubulating peripheral ER during mitosis and clearing tubular ER from the chromatin for a normal mitosis to take place. Collectively, this work elaborates on proteins belonging to three classes that shape and position the ER specifically either in interphase or during stages of cell division. Our findings also throws light on the role of different domains in each of these proteins such as the reticulon homology domain (RHD) that was observed to be present in all these proteins under study. The RHD previously known for inserting partially and unsymmetrically in the outer leaflet of the ER gives a strong indication for proteins like RTN4B and FAM134C to localize to ER thus tubulating ER upon overexpression conditions. We uncovered the RHD’s crucial role in ER shaping and positioning in REEP3/4 during mitosis.Endoplasma- eli solulimakalvosto (engl. endoplasmic reticulum, ER) on solun suurin kalvon rajoittama organelli, ja sillä on useita tehtäviä. Pystyäkseen suoriutumaan eri tehtävistään ER:n rakenne on monimuotoinen ja alati muuntautuva. ER:n päärakenteet, laatat ja tubulukset, muodostavat monimutkaisen verkoston, eikä niiden kaikkia toimintoja täysin vielä tunneta. Laattojen ja tubulusten määrällinen suhde on erilainen eri solutyypeissä ja solusyklin vaiheissa. ER:n toimivan tasapainon saavuttamiseksi tarvitaan useita tekijöitä, jotka ovat vuorovaikutuksessa keskenään koko solusyklin ajan. Osa näistä tekijöistä osallistuu ER:n rakenteen muokkaamiseen ja osa on vastuussa ER:n dynaamisesta luonteesta. Solunjakautumisen aikana ER:n rakenne muuttuu, ja tubulaarisia rakenteita muodostuu suhteellisesti enemmän. Eri tekijät toimivat aktiivisesti solunjakautumisen eri vaiheissa mahdollistaen näin solujen jakautumisen. Nämä tekijät ovat edelleen tutkimuksen kohteena, ja yksityiskohtien esim. tarkan paikantumisen ja toimintamekanismin selvittämiseksi sekä vielä tuntemattomien, ER:n rakenteeseen vaikuttavien tekijöiden löytämiseksi, on käytettävä kehittyneitä tekniikoita kuten valo- ja elektronimikroskopiaa. Myös tietoaineiston analysoinnin täytyy edelleen kehittyä pystyäksemme vastaamaan tärkeisiin kysymyksiin, ja sen vuoksi sekä kuvankäsittelyyn että kuvien analysointiin tarvittavien ohjelmien kehittämiseen on kiinnitettävä erityistä huomiota. Tässä väitöskirjatutkimuksessa tutkittiin kolmea proteiiniperhettä, joiden osoitettiin vaikuttavan ER:n rakenteeseen. Tutkimuksen aikana otettujen mikroskooppikuvien analysointi oli tämän tutkimuksen kannalta oleellista ja tästä johtuen työssä käsitellään myös kuvankäsittelyohjelmiston kehittämistä. Tässä väitöskirjatutkimuksessa karakterisoitiin Reticulon-proteiineja (RTN) uusilla, kehittyneillä mikroskooppisilla tekniikoilla. Immuunielektronimikroskopialla ja elektronitomografialla osoitettiin RTN4A:n ja RTN4B:n paikallistuvan ER:n tubuluksiin ja laattojen reunoille. Kvantitatiivisella polymeraasiketjureaktiolla pystyttiin osoittamaan, että RTN4A on eniten ilmennetty muunnos hermosoluissa ja RTN4B vastaavasti pintakudossoluissa. Vähän tutkittu proteiini, FAM134C, tunnistettiin yhdeksi RTN4B:n kanssa vuorovaikutuksessa olevista proteiineista ja se paikallistettiin samankaltaisiin ER:n rakenteisiin kuin RTN4B (laattojen reunat ja tubulukset). FAM134C:n ja RTN4B:n ylituotto aikaansai tubulaaristen rakenteiden muodostamista. Lisäksi, ER:n rakenneproteiiniryhmän REEP (engl. receptor expression enhancing protein) proteiinien REEP3 ja REEP4 vaikutusta ER:n rakenteeseen tutkittiin solunjakautumisvaiheen aikana. Solunjakautumisvaiheessa REEP3 ja REEP4 paikallistettiin tubulaariseen, perifeeraaliseen ER:ään. Nämä proteiinit tarvittiin myös tubulaarisen ER:n irrottamiseksi kromatiinista normaalin solunjakautumisen aikaansaamiseksi. Tämä tutkimus syventää tietoja kolmen ER:ä muokkaavan proteiiniperheen proteiineista ja niiden paikallistumisesta ja vaikutuksista ER:n rakenteeseen niin kasvuvaiheessa kuin solunjakautumisen eri vaiheissa. Tulokset antavat myös lisätietoa eri domeenien rooleista näissä proteiineissa, esim. retikulonihomologia-domeenista (RHD), jonka löydettiin kaikista näistä proteiineista. RHD:n tiedetään olevan osittain kaksoiskalvorakenteen sisällä ja siten aiheuttavan kalvojen kaarevoitumista: tämä havaittiin myös ER:n tubuloitumisena ylituotettaessa RTN4B:tä tai FAM134C:tä. Tutkimuksen mukaan RHD:lla oli ratkaiseva rooli ER:n REEP 3/4 rakenneproteiinien toiminnassa solunjakautumisen aikana

Toward a morphodynamic model of the cell: Signal processing for cell modeling

From a systems biology perspective, the cell is the principal element of information integration. Therefore, understanding the cell in its spatiotemporal context is the key to unraveling many of the still unknown mechanisms of life and disease. This article reviews image processing aspects relevant to the quantification of cell morphology and dynamics. We cover both acquisition (hardware) and analysis (software) related issues, in a multiscale fashion, from the detection of cellular components to the description of the entire cell in relation to its extracellular environment. We then describe ongoing efforts to integrate all this vast and diverse information along with data about the biomechanics of the cell to create a credible model of cell morphology and behavior.Carlos Ortiz-de-Solorzano and Arrate Muñoz-Barrutia were supported by the Spanish Ministry of Economy and Competitiveness grants with reference DPI2012-38090-C03-02 and TEC2013-48552-C02, respectively. Michal Kozubek was supported by the Czech Science Foundation (302/12/G157)

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

Application of Bioimage Informatics to Quantification of Focal Adhesions and Invadopodia

The development of the ability to fluorescently label functional proteins and visualize their subcellular localization using microscopy in living cells, has made it possible to study a wide range of single cell phenomena. To understand the results of imaging assays, cell biologists have used manual methods for determining the quantitative properties of the cellular structures visualized fluorescent microscopy. As the quantity and complexity of the images that can be collected using fluorescence microscopy has increased, a new subfield of Bioinformatics has developed, named Bioimage Informatics, which specializes in adapting and developing new methods to quantify the image sets resulting from biological assays. In this thesis, I describe the application and development of Bioimage Informatic methods to the analysis of Focal Adhesions and Invadopodia. Focal Adhesions are subcellular protein complexes, whose role include acting as the points of contact for cellular motility and sensing the outside environment. Focal Adhesions have traditionally been analyzed using manual methods, which has limited the number of Focal Adhesions that could be analyzed and the depth of properties that could be collected. I have developed a set of methods which can identify, track and quantify Focal Adhesion properties from live cell image sets. This Focal Adhesion analysis framework has been expanded to include spatial and global methods for describing Focal Adhesion morphology. I have also developed a system for quantifying Invadopodia properties. Invadopodia are subcellular protein complexes present in metastatic cancer cells, which actively degrade the extracellular matrix, allowing migration of cancer cells away from primary tumors. This analysis system has two parts, one which can follow single Invadopodia and assess their properties and a complementary component which assesses degradation behavior in cell populations.Doctor of Philosoph

Computing Interpretable Representations of Cell Morphodynamics

Shape changes (morphodynamics) are one of the principal ways cells interact with their environments and perform key intrinsic behaviours like division. These dynamics arise from a myriad of complex signalling pathways that often organise with emergent simplicity to carry out critical functions including predation, collaboration and migration. A powerful method for analysis can therefore be to quantify this emergent structure, bypassing the low-level complexity. Enormous image datasets are now available to mine. However, it can be difficult to uncover interpretable representations of the global organisation of these heterogeneous dynamic processes. Here, such representations were developed for interpreting morphodynamics in two key areas: mode of action (MoA) comparison for drug discovery (developed using the economically devastating Asian soybean rust crop pathogen) and 3D migration of immune system T cells through extracellular matrices (ECMs). For MoA comparison, population development over a 2D space of shapes (morphospace) was described using two models with condition-dependent parameters: a top-down model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. A variety of landscapes were discovered, describing phenotype transitions during growth, and possible perturbations in the tip growth machinery that cause this variation were identified. For interpreting T cell migration, a new 3D shape descriptor that incorporates key polarisation information was developed, revealing low-dimensionality of shape, and the distinct morphodynamics of run-and-stop modes that emerge at minute timescales were mapped. Periodically oscillating morphodynamics that include retrograde deformation flows were found to underlie active translocation (run mode). Overall, it was found that highly interpretable representations could be uncovered while still leveraging the enormous discovery power of deep learning algorithms. The results show that whole-cell morphodynamics can be a convenient and powerful place to search for structure, with potentially life-saving applications in medicine and biocide discovery as well as immunotherapeutics.Open Acces

Evaluation of the relevance and impact of kinase dysfunction in neurological disorders through proteomics and phosphoproteomics bioinformatics

Phosphorylation is an important post-translational modification that is involved in various biological processes and its dysregulation has in particular been linked to diseases of the central nervous system including neurological disorders. The present thesis characterizes alterations in the phosphoproteome and protein abundance associated with schizophrenia and Parkinson's disease, with the goal of uncovering the underlying disease mechanisms. To support this goal, I eventually created an automated analysis pipeline in R to streamline the analysis process of proteomics and phosphoproteomics data. Mass spectrometry (MS) technology is utilized to generate proteomics and phosphoproteomics data. Study I of the thesis demonstrates an automated R pipeline, PhosPiR, created to perform multi-level functional analyses of MS data after the identification and quantification of the raw spectral data. The pipeline does not require coding knowledge to run. It supports 18 different organisms, and provides analyses of MS intensity data from preprocessing, normalization and imputation, through to figure overviews, statistical analysis, enrichment analysis, PTM-SEA, kinase prediction and activity analysis, network analysis, hub analysis, annotation mining, and homolog alignment. The LRRK2-G2019S mutation, a frequent genetic cause of late onset Parkinson's disease, was investigated in Study II and III. One study investigated the mechanism of LRRK2-G2019S function in brain, and the other identified proteins with significantly altered overall translation patterns in sporadic and LRRK2-G2019S patient samples. Specifically, study II identified that LRRK2 is localized to the small 40S ribosomal subunit and that LRRK2 activity suppresses RNA translation, as validated in cell and animal models of Parkinson's disease and in patient cells. Study III utilized bio-orthogonal non-canonical amino acid tagging to label newly translated proteins in order to identify which proteins were affected by repressed translation in patient samples, using mass spectrometry analysis. The analysis revealed 33 and 30 nascent proteins with reduced synthesis in sporadic and LRRK2-G2019S Parkinson’s cases, respectively. The biological process "cytosolic signal recognition particle (SRP)-dependent co-translational protein targeting to membrane" was functionally significantly affected in both sporadic and LRRK2-G2019S Parkinson's, while "Tubulin/FTsz C-terminal domain superfamily network" was only significantly enriched in LRRK2-G2019S Parkinson’s cases. The findings were validated bytargeted proteomics and immunoblotting. Study IV is conducted to investigate the role of JNK1 in schizophrenia. Wild type and Jnk1-/- mice were used to analyze the phosphorylation profile using LC-MS/MS analysis. 126 proteins associated with schizophrenia were identified to overlap with the significantly differentially phosphorylated proteins in Jnk1-/- mice brain. The NMDAR trafficking pathway was found to be highly enriched, and surface staining of NMDAR subunits in neurons showed that surface expression of both subunits in Jnk1-/- neurons was significantly decreased. Further behavioral tests conducted with MK801 treatment have associated the Jnk1-/- molecular and behavioral phenotype with schizophrenia and neuropsychiatric disease