| 1. |
- Alvez, Maria Bueno, et al.
(författare)
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Next generation pan-cancer blood proteome profiling using proximity extension assay
- 2023
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Karlsson, Max, et al.
(författare)
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Genome-wide annotation of protein-coding genes in pig
- 2022
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Ingår i: BMC Biology. - : Springer Nature. - 1741-7007. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Karlsson, Max, et al.
(författare)
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Genome-wide single cell annotation of the human protein-coding genes
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 4. |
- Kotol, David, et al.
(författare)
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Absolute Quantification of Pan-Cancer Plasma Proteomes Reveals Unique Signature in Multiple Myeloma
- 2023
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 15:19
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Tidskriftsartikel (refereegranskat)abstract
- Mass spectrometry based on data-independent acquisition (DIA) has developed into a powerful quantitative tool with a variety of implications, including precision medicine. Combined with stable isotope recombinant protein standards, this strategy provides confident protein identification and precise quantification on an absolute scale. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1800 blood plasma samples representing 15 different cancer types. We successfully performed an absolute quantification of 253 proteins in multiplex. The assay had low intra-assay variability with a coefficient of variation below 20% (CV = 17.2%) for a total of 1013 peptides quantified across almost two thousand injections. This study identified a potential biomarker panel of seven protein targets for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of markers, including the complement C1 complex, JCHAIN, and CD5L, resulted in a prediction model with an AUC of 0.96 for the identification of multiple myeloma patients across various cancer patients. All these proteins are known to interact with immunoglobulins.
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| 5. |
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| 6. |
- Royer, Patrick, et al.
(författare)
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Plasma proteomics for prediction of subclinical coronary artery calcifications in primary prevention
- 2024
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Ingår i: American Heart Journal. - : Elsevier BV. - 0002-8703 .- 1097-6744. ; 271, s. 55-67
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Tidskriftsartikel (refereegranskat)abstract
- Background and aims: Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors. Methods: Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS. Results: The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors. Conclusions: A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.
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| 7. |
- Yang, Hong, et al.
(författare)
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Integrative proteo-transcriptomic characterization of advanced fibrosis in chronic liver disease across etiologies
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Various causes of chronic hepatic injury and inflammation can lead to fibrosis and cirrhosis, potentially predisposing individuals to hepatocellular carcinoma. Despite extensive research, the molecular mechanisms underlying liver fibrosis and its associated progression to cancer remain incompletely understood. In this study, we employed an integrated proteotranscriptomics approach to characterize the molecular pathophysiology of liver fibrosis in both liver and plasma samples from 330 individuals. This cohort included 40 healthy subjects and 290 patients with histologically characterized fibrosis due to chronic viral infection, alcohol consumption, or metabolic-dysfunction associated steatotic liver disease. We demonstrated that pathways related to extracellular matrix alterations, immune response, inflammation, and metabolism are dysregulated in advanced hepatic fibrosis, regardless of the underlying cause. Additionally, our analysis of peritumoral hepatic tissues revealed transcription signatures linked to cell proliferation, survival, and inflammation in hepatocellular carcinoma. Furthermore, we observed extensive remodeling of the plasma proteome linked with severe fibrosis and identified 132 circulating proteomic signatures associated with advanced fibrosis by integrative analysis of plasma proteomics with hepatic transcriptomics. We finally developed predictive models using machine learning to facilitate the non-invasive detection of advanced fibrosis and cirrhosis.
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