281 research outputs found

    Computational approaches for single-cell omics and multi-omics data

    Get PDF
    Single-cell omics and multi-omics technologies have enabled the study of cellular heterogeneity with unprecedented resolution and the discovery of new cell types. The core of identifying heterogeneous cell types, both existing and novel ones, relies on efficient computational approaches, including especially cluster analysis. Additionally, gene regulatory network analysis and various integrative approaches are needed to combine data across studies and different multi-omics layers. This thesis comprehensively compared Bayesian clustering models for single-cell RNAsequencing (scRNA-seq) data and selected integrative approaches were used to study the cell-type specific gene regulation of uterus. Additionally, single-cell multi-omics data integration approaches for cell heterogeneity analysis were investigated. Article I investigated analytical approaches for cluster analysis in scRNA-seq data, particularly, latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP) models. The comparison of LDA and HDP together with the existing state-of-art methods revealed that topic modeling-based models can be useful in scRNA-seq cluster analysis. Evaluation of the cluster qualities for LDA and HDP with intrinsic and extrinsic cluster quality metrics indicated that the clustering performance of these methods is dataset dependent. Article II and Article III focused on cell-type specific integrative analysis of uterine or decidual stromal (dS) and natural killer (dNK) cells that are important for successful pregnancy. Article II integrated the existing preeclampsia RNA-seq studies of the decidua together with recent scRNA-seq datasets in order to investigate cell-type-specific contributions of early onset preeclampsia (EOP) and late onset preeclampsia (LOP). It was discovered that the dS marker genes were enriched for LOP downregulated genes and the dNK marker genes were enriched for upregulated EOP genes. Article III presented a gene regulatory network analysis for the subpopulations of dS and dNK cells. This study identified novel subpopulation specific transcription factors that promote decidualization of stromal cells and dNK mediated maternal immunotolerance. In Article IV, different strategies and methodological frameworks for data integration in single-cell multi-omics data analysis were reviewed in detail. Data integration methods were grouped into early, late and intermediate data integration strategies. The specific stage and order of data integration can have substantial effect on the results of the integrative analysis. The central details of the approaches were presented, and potential future directions were discussed.  Laskennallisia menetelmiä yksisolusekvensointi- ja multiomiikkatulosten analyyseihin Yksisolusekvensointitekniikat mahdollistavat solujen heterogeenisyyden tutkimuksen ennennäkemättömällä resoluutiolla ja uusien solutyyppien löytämisen. Solutyyppien tunnistamisessa keskeisessä roolissa on ryhmittely eli klusterointianalyysi. Myös geenien säätelyverkostojen sekä eri molekyylidatatasojen yhdistäminen on keskeistä analyysissä. Väitöskirjassa verrataan bayesilaisia klusterointimenetelmiä ja yhdistetään eri menetelmillä kerättyjä tietoja kohdun solutyyppispesifisessä geeninsäätelyanalyysissä. Lisäksi yksisolutiedon integraatiomenetelmiä selvitetään kattavasti. Julkaisu I keskittyy analyyttisten menetelmien, erityisesti latenttiin Dirichletallokaatioon (LDA) ja hierarkkiseen Dirichlet-prosessiin (HDP) perustuvien mallien tutkimiseen yksisoludatan klusterianalyysissä. Kattava vertailu näiden kahden mallin sekä olemassa olevien menetelmien kanssa paljasti, että aihemallinnuspohjaiset menetelmät voivat olla hyödyllisiä yksisoludatan klusterianalyysissä. Menetelmien suorituskyky riippui myös kunkin analysoitavan datasetin ominaisuuksista. Julkaisuissa II ja III keskitytään naisen lisääntymisterveydelle tärkeiden kohdun stroomasolujen ja NK-immuunisolujen solutyyppispesifiseen analyysiin. Artikkelissa II yhdistettiin olemassa olevia tuloksia pre-eklampsiasta viimeisimpiin yksisolusekvensointituloksiin ja löydettiin varhain alkavan pre-eklampsian (EOP) ja myöhään alkavan pre-eklampsian (LOP) solutyyppispesifisiä vaikutuksia. Havaittiin, että erilaistuneen strooman markkerigeenien ilmentyminen vähentyi LOP:ssa ja NK-markkerigeenien ilmentyminen lisääntyi EOP:ssa. Julkaisu III analysoi strooman ja NK-solujen alapopulaatiospesifisiä geeninsäätelyverkostoja ja niiden transkriptiofaktoreita. Tutkimus tunnisti uusia alapopulaatiospesifisiä säätelijöitä, jotka edistävät strooman erilaistumista ja NK-soluvälitteistä immunotoleranssia Julkaisu IV tarkastelee yksityiskohtaisesti strategioita ja menetelmiä erilaisten yksisoludatatasojen (multi-omiikka) integroimiseksi. Integrointimenetelmät ryhmiteltiin varhaisen, myöhäisen ja välivaiheen strategioihin ja kunkin lähestymistavan menetelmiä esiteltiin tarkemmin. Lisäksi keskusteltiin mahdollisista tulevaisuuden suunnista

    Type 3 adenylyl cyclase, neuronal primary cilia, and hippocampus-dependent memory formation

    Get PDF
    Primary cilia are microtubule-based cellular antennae present in most vertebrate cells including neurons. Neuronal primary cilia have abundant expression of G-protein coupled receptors (GPCRs) and downstream cAMP signaling components such as type 3 adenylyl cyclase (AC3). The deflects of neuronal cilia is associated with many memory-related disorders, such as intellectual disability. Thus far, little is known about how neuronal primary cilia regulate neuronal activity and affect hippocampal memory formation. Episodic memory is thought to be encoded by sparsely distributed memory-eligible neurons in the hippocampus and neocortex. However, it is not clear how memory-eligible neurons interact with one another to form and retrieve a memory. The objectives of my dissertation are to determine the roles of AC3 in regulating cortical protein phosphorylation, to examine the cellular mechanism of episodic memory formation, and to examine how neuronal primary cilia regulate trace fear memory formation. Project 1: Compare protein phosphorylation levels in the prefrontal cortex between AC3 knockout (KO) and wildtype (WT) mice. AC3 represents a key enzyme mediating ciliary cAMP signaling in neurons and is genetically associated with major depressive disorder (MDD) and autism spectrum disorders (ASD). The major downstream effector protein of cAMP in cells is protein kinase A (PKA), whose activation leads to the phosphorylation of numerous proteins to propagate the signaling downstream. In my mass spectrometry-based phosphoproteomic study using conditional AC3 KO mice, I identified thousands of peptides from prefrontal cortical tissues, some of which are differentially phosphorylated in AC3 WT and KO samples. In addition, this effort led to identification of over two hundred proteins, whose phosphorylation were sex-biased. Surprisingly, a high percentage of these targets (31%) are autism-associated proteins/genes. Hence, this study provides the first phosphoproteomic evidence suggesting that sex-biased protein phosphorylation may contribute to the sexual dimorphism of autism. Project 2: Investigate how hippocampal neurons are recruited to interact with each other to encode a trace fear memory. Using in vivo calcium imaging in freely behaving mice, I found that a small portion of highly active hippocampal neurons (termed primed neurons) are actively engaged in memory formation and retrieval. I found that induction of activity synchronization among primed neurons from random dynamics is critical for trace memory formation and retrieval. My work has provided direct in vivo evidence to challenge the long-held paradigm that activation and re-activation of memory cells encodes and retrieves memory, respectively. These findings support a new mechanistic model for associative memory formation, in that primed neurons connect with each other to forge a new circuit, bridging a conditional stimulus with an unconditional stimulus. Project 3: Develop an analytical method to identify primed neurons and determine the roles of neuronal primary cilia on hippocampal neuronal priming and trace memory formation. Neuronal primary cilia are “cellular antennae” which sense and transduce extracellular signals into neuronal soma. However, to date little is known about how neuronal primary cilia influence neuronal functions and hippocampal memory. I utilized conditional Ift88 knockout mice (to ablate cilia) as loss-of-function models. I found that inducible conditional Ift88 KOs display more severe learning deficits compared to their littermate controls. Cilia-ablated mice showed reduced overall neuronal activity, decreased number of primed neurons, and failed to form burst synchronization. These data support the conclusion that alteration of neuronal primary cilia impairs trace fear memory by decreasing hippocampal neuronal priming and the formation of burst synchronization. This study also provides evidence to support the importance of burst synchronization among primed neurons on memory formation and retrieval

    Single Cell Expression Analysis for Understanding the Development of Glaucoma

    Get PDF
    Glaucoma is characterized as a group of eye diseases where the progressive damage of neurons, particularly Retinal Ganglion Cells (RGCs), leads to vision loss. This disease affects more than 70 million people worldwide, with approximately 10% being bilaterally blind, making it the leading cause of irreversible blindness in the world. The initiation and progression of the disease is still unknown, but studies have suggested the involvement of particular cell types in the retina that relate to the pathogenesis of glaucoma. Single cell RNA sequencing (RNA-seq) analysis is a new technology that provides insight into the gene expression profiles of different cell types. In this study, we employed it to elucidate the transcriptomic changes in various cell types during glaucoma progression. ABCA1-/- mice were used as a normal tension glaucoma model. Single cell RNA-seq experiments were conducted on three wild type (WT) and five knockout (KO) retinal tissues. The data of 62,479 cells were integrated and major cell types were identified, including Müller glia, astrocytes, microglia and RGCs. Ontological analysis suggested strong activation of neuroinflammation and senescence related pathways in KO samples, with specific pathways identified affecting certain cell types. Evidence of macrophage invasion further suggests a knockout-induced inflammatory response, accompanied by sub-type specific RGC degeneration due to excitotoxicity. P2Y6-/- mice were used as a high intraocular pressure (IOP) glaucoma model. 105,772 cells from three WT and three KO retinal tissues were analysed using single cell RNA-seq, with major cell types identified such as RGCs and glial cells. Neuroinflammation and senescence pathways activation was again observed, along with angiogenesis, hypoxia and fibrosis activities activated in knockout glial population. pathogenesis, thus provided data to support future interests in developing potential therapeutical targets in the area. pathogenesis, thus provided data to support future interests in developing potential therapeutical targets in the area

    Structured data abstractions and interpretable latent representations for single-cell multimodal genomics

    Get PDF
    Single-cell multimodal genomics involves simultaneous measurement of multiple types of molecular data, such as gene expression, epigenetic marks and protein abundance, in individual cells. This allows for a comprehensive and nuanced understanding of the molecular basis of cellular identity and function. The large volume of data generated by single-cell multimodal genomics experiments requires specialised methods and tools for handling, storing, and analysing it. This work provides contributions on multiple levels. First, it introduces a single-cell multimodal data standard — MuData — designed to facilitate the handling, storage and exchange of multimodal data. MuData provides interfaces that enable transparent access to multimodal annotations as well as data from individual modalities. This data structure has formed the foundation for the multimodal integration framework, which enables complex and composable workflows that can be naturally integrated with existing omics-specific analysis approaches. Joint analysis of multimodal data can be performed using integration methods. In order to enable integration of single-cell data, an improved multi-omics factor analysis model (MOFA+) has been designed and implemented building on the canonical dimensionality reduction approach for multi-omics integration. Inferring later factors that explain variation across multiple modalities of the data, MOFA+ enables the modelling of latent factors with cell group-specific patterns of activity. MOFA+ model has been implemented as part of the respective multi-omics integration framework, and its utility has been extended by software solutions that facilitate interactive model exploration and interpretation. The newly improved model for multi-omics integration of single cells has been applied to the study of gene expression signatures upon targeted gene activation. In a dataset featuring targeted activation of candidate regulators of zygotic genome activation (ZGA) — a crucial transcriptional event in early embryonic development, — modelling expression of both coding and non-coding loci with MOFA+ allowed to rank genes by their potency to activate a ZGA-like transcriptional response. With identification of Patz1, Dppa2 and Smarca5 as potent inducers of ZGA-like transcription in mouse embryonic stem cells, these findings have contributed to the understanding of molecular mechanisms behind ZGA and laid the foundation for future research of ZGA in vivo. In summary, this work’s contributions include the development of data handling and integration methods as well as new biological insights that arose from applying these methods to studying gene expression regulation in early development. This highlights how single-cell multimodal genomics can aid to generate valuable insights into complex biological systems

    A Bayesian Methodology for Estimation for Sparse Canonical Correlation

    Full text link
    It can be challenging to perform an integrative statistical analysis of multi-view high-dimensional data acquired from different experiments on each subject who participated in a joint study. Canonical Correlation Analysis (CCA) is a statistical procedure for identifying relationships between such data sets. In that context, Structured Sparse CCA (ScSCCA) is a rapidly emerging methodological area that aims for robust modeling of the interrelations between the different data modalities by assuming the corresponding CCA directional vectors to be sparse. Although it is a rapidly growing area of statistical methodology development, there is a need for developing related methodologies in the Bayesian paradigm. In this manuscript, we propose a novel ScSCCA approach where we employ a Bayesian infinite factor model and aim to achieve robust estimation by encouraging sparsity in two different levels of the modeling framework. Firstly, we utilize a multiplicative Half-Cauchy process prior to encourage sparsity at the level of the latent variable loading matrices. Additionally, we promote further sparsity in the covariance matrix by using graphical horseshoe prior or diagonal structure. We conduct multiple simulations to compare the performance of the proposed method with that of other frequently used CCA procedures, and we apply the developed procedures to analyze multi-omics data arising from a breast cancer study

    Fluorescent methods to detect and discover peptide and protein interactions in vitro

    Get PDF
    The unique properties of peptide and protein interactions have been exploited in the field of medicine for uses like protein-targeted therapeutic, and diagnostic agents. However, such developments would not be possible without an understanding of these biomolecules. As such, it essential to develop methods that allow the study of these unique interactions. In this work, small fluorescent molecules were utilised for the development of such techniques. A novel probe, CyMT was developed for multimodal mass spectrometry and fluorescence imaging within a single sample. The probe proved to be successful in the imaging of isolated insulin protein samples, first with fluorescence microscopy, followed immediately with mass spectrometry imaging. A second probe, NpMT was also designed and synthesised for future testing. Following from previous work on the development of a novel fluorescent redox sensor for protein tagging, the assessment of novel fluorescent scaffolds was performed. The fluorescent properties of FCR1 and NpFR1 were assessed computationally for compatibility with the HaloTag protein tagging system. This work revealed the suitability of FCR1 for future incorporation into the HaloTag. Further computational studies on NpFR1 helped to uncover the most suitable computation methods that would allow for further assessment and future developments of this sensor. Finally, efforts were directed towards developing a novel peptide-receptor binding assay. The fluorescence probe, BNp-COOH, was designed with two functional moieties - one for solid-phase peptide synthesis attachment, and another as a pull-down partner. The fluorescence allows identification of receptor binding events, while the pull-down partner enables subsequent isolation and characterisation of the interaction. The work outlined here, describes a novel set of tools for the study of peptide and protein interactions. These tools have the potential to enhance our understanding of these interactions

    XVI Agricultural Science Congress 2023: Transformation of Agri-Food Systems for Achieving Sustainable Development Goals

    Get PDF
    The XVI Agricultural Science Congress being jointly organized by the National Academy of Agricultural Sciences (NAAS) and the Indian Council of Agricultural Research (ICAR) during 10-13 October 2023, at hotel Le Meridien, Kochi, is a mega event echoing the theme “Transformation of Agri-Food Systems for achieving Sustainable Development Goals”. ICAR-Central Marine Fisheries Research Institute takes great pride in hosting the XVI ASC, which will be the perfect point of convergence of academicians, researchers, students, farmers, fishers, traders, entrepreneurs, and other stakeholders involved in agri-production systems that ensure food and nutritional security for a burgeoning population. With impeding challenges like growing urbanization, increasing unemployment, growing population, increasing food demands, degradation of natural resources through human interference, climate change impacts and natural calamities, the challenges ahead for India to achieve the Sustainable Development Goals (SDGs) set out by the United Nations are many. The XVI ASC will provide an interface for dissemination of useful information across all sectors of stakeholders invested in developing India’s agri-food systems, not only to meet the SDGs, but also to ensure a stable structure on par with agri-food systems around the world. It is an honour to present this Book of Abstracts which is a compilation of a total of 668 abstracts that convey the results of R&D programs being done in India. The abstracts have been categorized under 10 major Themes – 1. Ensuring Food & Nutritional Security: Production, Consumption and Value addition; 2. Climate Action for Sustainable Agri-Food Systems; 3. Frontier Science and emerging Genetic Technologies: Genome, Breeding, Gene Editing; 4. Livestock-based Transformation of Food Systems; 5. Horticulture-based Transformation of Food Systems; 6. Aquaculture & Fisheries-based Transformation of Food Systems; 7. Nature-based Solutions for Sustainable AgriFood Systems; 8. Next Generation Technologies: Digital Agriculture, Precision Farming and AI-based Systems; 9. Policies and Institutions for Transforming Agri-Food Systems; 10. International Partnership for Research, Education and Development. This Book of Abstracts sets the stage for the mega event itself, which will see a flow of knowledge emanating from a zeal to transform and push India’s Agri-Food Systems to perform par excellence and achieve not only the SDGs of the UN but also to rise as a world leader in the sector. I thank and congratulate all the participants who have submitted abstracts for this mega event, and I also applaud the team that has strived hard to publish this Book of Abstracts ahead of the event. I wish all the delegates and participants a very vibrant and memorable time at the XVI ASC
    corecore