Background There is a need for functional genome-wide annotation of the protein-coding genes to get a deeper understanding of mammalian biology. Here, a new annotation strategy is introduced based on dimensionality reduction and density-based clustering of whole-body co-expression patterns. This strategy has been used to explore the gene expression landscape in pig, and we present a whole-body map of all protein-coding genes in all major pig tissues and organs. Results An open-access pig expression map (www.rnaatlas.org) is presented based on the expression of 350 samples across 98 well-defined pig tissues divided into 44 tissue groups. A new UMAP-based classification scheme is introduced, in which all protein-coding genes are stratified into tissue expression clusters based on body-wide expression profiles. The distribution and tissue specificity of all 22,342 protein-coding pig genes are presented. Conclusions Here, we present a new genome-wide annotation strategy based on dimensionality reduction and density-based clustering. A genome-wide resource of the transcriptome map across all major tissues and organs in pig is presented, and the data is available as an open-access resource (www.rnaatlas.org), including a comparison to the expression of human orthologs.
Filopodia are multifunctional finger-like plasma membrane protrusions with bundles of actin filaments that exist in virtually all cell types. It has been known for some time that hyaluronan synthesis activity induces filopodial growth. However, because of technical challenges in the studies of these slender and fragile structures, no quantitative analyses have been performed so far to indicate their association with hyaluronan synthesis. In this work we comprehensively address the direct quantification of filopodial traits, covering for the first time length and density measurements in a series of human cancer cell lines with variable levels of hyaluronan synthesis. The synthesis and plasma membrane binding of hyaluronan were manipulated with hyaluronan synthase 3 (HAS3) and hyaluronan receptor CD44 overexpression, and treatments with mannose, 4-methylumbelliferone (4-MU), and glucosamine. The results of this work show that the growth of filopodia was associated with the levels of hyaluronan synthesis but was not dependent on CD44 expression. The results confirm the hypothesis that abundance and length of filopodia in cancer cells is associated with the activity of hyaluronan synthesis.
A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.
An important quest for the life science community is to deliver a complete annotation of the human building-blocks of life, the genes and the proteins. Here, we report on a genome-wide effort to annotate all protein-coding genes based on single cell transcriptomics data representing all major tissues and organs in the human body, integrated with data from bulk transcriptomics and antibody-based tissue profiling. Altogether, 25 tissues have been analyzed with single cell transcriptomics resulting in genome-wide expression in 444 single cell types using a strategy involving pooling data from individual cells to obtain genome-wide expression profiles of individual cell type. We introduce a new genome-wide classification tool based on clustering of similar expression profiles across single cell types, which can be visualized using dimensional reduction maps (UMAP). The clustering classification is integrated with a new “tau” score classification for all protein-coding genes, resulting in a measure of single cell specificity across all cell types for all individual genes. The analysis has allowed us to annotate all human protein-coding genes with regards to function and spatial distribution across individual cell types across all major tissues and organs in the human body. A new version of the open access Human Protein Atlas (www.proteinatlas.org) has been launched to enable researchers to explore the new genome-wide annotation on an individual gene level.
Cancer is a highly heterogeneous disease in need of accurate and non-invasive diagnostic tools. Here, we describe a novel strategy to explore the proteome signature by comprehensive analysis of protein levels using a pan-cancer approach of patients representing the major cancer types. Plasma profiles of 1,463 proteins from more than 1,400 cancer patients representing altogether 12 common cancer types were measured in minute amounts of blood plasma collected at the time of diagnosis and before treatment. AI-based disease prediction models allowed for the identification of a set of proteins associated with each of the analyzed cancers. By combining the results from all cancer types, a panel of proteins suitable for the identification of all individual cancer types was defined. The results are presented in a new open access Human Disease Blood Atlas. The implication for cancer precision medicine of next generation plasma profiling is discussed.
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