| 11. |
- Fernandez Navarro, Jose, 1982-
(författare)
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Computational methods for analysis and visualization of spatially resolved transcriptomes
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Characterizing the expression level of genes (transcriptome) in cells and tis- sues is essential for understanding the biological processes of multicellular or- ganisms. RNA sequencing (RNA-seq) has gained traction in the last decade as a powerful tool that provides an accurate quantitative representation of the transcriptome in tissues. RNA-seq methods are, however, limited by the fact that they provide an average representation of the transcriptome across the tissue. Single cell RNA sequencing (scRNA-seq) provides quantitative gene expression levels of individual cells. This enables the molecular characteri- zation of cell types in health, disease and developmental tissues. However, scRNA-seq lacks the spatial context needed to understand how cells interact and their microenvironment. Current methods that provide spatially resolved gene expression levels are limited by a low throughput and the fact that the target genes must be known in advance.Spatial Transcriptomics (ST) is a novel method that combines high-resolution imaging with high-throughput sequencing. ST provides spatially resolved gene expression levels in tissue sections. The first part of the work presented in this thesis (Papers I, II, III and IV) revolves around the ST method and the development of the computational tools required to process, analyse and visualize ST data.Furthermore, the ST method was utilized to construct a three-dimensional (3D) molecular atlas of the adult mouse brain using 75 consecutive coronal sections (Paper V). We show that the molecular clusters obtained by unsu- pervised clustering of the atlas highly correlates with the Allen Brain Atlas. The molecular clusters provide new insights in the organization of regions like the hippocampus or the amygdala. We show that the molecular atlas can be used to spatially map single cells (scRNA-seq) onto the clusters and that only a handful of genes is required to define the brain regions at a molecular level.Finally, the hippocampus and the olfactory bulb of transgenic mice mim- icking the Alzheimer’s disease (AD) were spatially characterized using the ST method (Paper VI). Dierential expression analysis revealed genes central in areas highly cited as important in AD including lipid metabolism, cellular bioenergetics, mitochondrial function, stress response and neurotransmission.
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| 12. |
- Fernandez Navarro, Jose, 1982-, et al.
(författare)
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Spatial Transcriptomics characterization of Alzheimer’s disease in the adult mouse brain
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Alzheimer’s disease (AD) is a devastating neurological disease associated with progressive loss of mental skills, cognitive and physical functions. Here, our goal was to uncover novel and known molecular targets in the structured layers of the hippocampus and olfactory bulbs that may contribute to hippocampal synaptic dysfunction and smelling defects in AD mice. Spatial Transcriptomics was used to identify high confidence genes that were differentially regulated in AD mice relative to controls. A discussion of how these genes may contribute to AD pathology is provided.
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| 13. |
- Fernandez Navarro, Jose, et al.
(författare)
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Spatial Transcriptomics Reveals Genes Associated with Dysregulated Mitochondrial Functions and Stress Signaling in Alzheimer Disease
- 2020
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Ingår i: iScience. - : Elsevier Inc.. - 2589-0042. ; 23:10
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Tidskriftsartikel (refereegranskat)abstract
- Cellular Neuroscience; Omics; Transcriptomics Alzheimer disease (AD) is a devastating neurological disease associated with progressive loss of mental skills and cognitive and physical functions whose etiology is not completely understood. Here, our goal was to simultaneously uncover novel and known molecular targets in the structured layers of the hippocampus and olfactory bulbs that may contribute to early hippocampal synaptic deficits and olfactory dysfunction in AD mice. Spatially resolved transcriptomics was used to identify high-confidence genes that were differentially regulated in AD mice relative to controls. A diverse set of genes that modulate stress responses and transcription were predominant in both hippocampi and olfactory bulbs. Notably, we identify Bok, implicated in mitochondrial physiology and cell death, as a spatially downregulated gene in the hippocampus of mouse and human AD brains. In summary, we provide a rich resource of spatially differentially expressed genes, which may contribute to understanding AD pathology.
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| 14. |
- Fernandez Navarro, Jose, et al.
(författare)
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ST viewer : a tool for analysis and visualization of spatial transcriptomics datasets
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 35:6, s. 1058-1060
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Tidskriftsartikel (refereegranskat)abstract
- Motivation Spatial Transcriptomics (ST) is a technique that combines high-resolution imaging with spatially resolved transcriptome-wide sequencing. This novel type of data opens up many possibilities for analysis and visualization, most of which are either not available with standard tools or too complex for normal users. Results Here, we present a tool, ST Viewer, which allows real-time interaction, analysis and visualization of Spatial Transcriptomics datasets through a seamless and smooth user interface. Availability and implementation The ST Viewer is open source under a MIT license and it is available at https://github.com/SpatialTranscriptomicsResearch/st_viewer. Supplementary information Supplementary data are available at Bioinformatics online.
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| 15. |
- Flannick, Jason, et al.
(författare)
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Data Descriptor : Sequence data and association statistics from 12,940 type 2 diabetes cases and controls
- 2017
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (> 80% of low-frequency coding variants in similar to ~82 K Europeans via the exome chip, and similar to ~90% of low-frequency non-coding variants in similar to ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
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| 16. |
- Fuchsberger, Christian, et al.
(författare)
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The genetic architecture of type 2 diabetes
- 2016
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 536:7614, s. 41-47
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Tidskriftsartikel (refereegranskat)abstract
- The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
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| 17. |
- Gallart-Ayala, H., et al.
(författare)
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Data Analysis in Transcriptomics and Metabolomics Clinical Applications
- 2018
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Ingår i: Comprehensive Analytical Chemistry. - : Elsevier. - 9780444640444 ; , s. 613-641
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Bokkapitel (refereegranskat)abstract
- In the last couple of decades, transcriptomics and metabolomics technologies have been used for the simultaneous detection and quantification of hundreds to thousands of transcripts and metabolites in several clinical studies. Depending on the aim of the study or its design, different data analysis methodologies and pipelines have been applied to extract relevant information from these studies and aid in their interpretation. Using selected examples in the clinical context, this chapter reviews some of these data analysis strategies commonly applied to transcriptomics and metabolomics datasets, both separately and in an integrated manner.
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| 18. |
- Giacomello, Stefania, et al.
(författare)
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Spatially resolved transcriptome profiling in model plant species
- 2017
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Ingår i: Nature Plants. - : Springer Science and Business Media LLC. - 2055-026X .- 2055-0278. ; 3:6
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Tidskriftsartikel (refereegranskat)abstract
- Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study highresolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes highthroughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.
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| 19. |
- Granholm, Viktor, 1986-, et al.
(författare)
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Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics
- 2013
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Ingår i: Journal of Proteomics. - : Elsevier BV. - 1874-3919 .- 1876-7737. ; 80, s. 123-131
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Tidskriftsartikel (refereegranskat)abstract
- The analysis of a shotgun proteomics experiment results in a list of peptide-spectrum matches (PSMs) in which each fragmentation spectrum has been matched to a peptide in a database. Subsequently, most protein inference algorithms rank peptides according to the best-scoring PSM for each peptide. However, there is disagreement in the scientific literature on the best method to assess the statistical significance of the resulting peptide identifications. Here, we use a previously described calibration protocol to evaluate the accuracy of three different peptide-level statistical confidence estimation procedures: the classical Fisher's method, and two complementary procedures that estimate significance, respectively, before and after selecting the top-scoring PSM for each spectrum. Our experiments show that the latter method, which is employed by MaxQuant and Percolator, produces the most accurate, well-calibrated results.
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| 20. |
- Granholm, Viktor, 1986-, et al.
(författare)
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Fast and accurate database searches with MS-GF+percolator
- 2014
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 13:2, s. 890-897
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Tidskriftsartikel (refereegranskat)abstract
- One can interpret fragmentation spectra stemming from peptides in mass-spectrometry-based proteomics experiments using so-called database search engines. Frequently, one also runs post-processors such as Percolator to assess the confidence, infer unique peptides, and increase the number of identifications. A recent search engine, MS-GF+, has shown promising results, due to a new and efficient scoring algorithm. However, MS-GF+ provides few statistical estimates about the peptide-spectrum matches, hence limiting the biological interpretation. Here, we enabled Percolator processing for MS-GF+ output and observed an increased number of identified peptides for a wide variety of data sets. In addition, Percolator directly reports p values and false discovery rate estimates, such as q values and posterior error probabilities, for peptide-spectrum matches, peptides, and proteins, functions that are useful for the whole proteomics community.
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