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Nielsen, A. B.

Publications and source records attributed to Nielsen, A. B..

3 recordsLinked to original sources

In depth profiling of the cancer proteome from the flowthrough of standard RNA-preparation kits for precision oncology

Cancer is a highly heterogeneous disease, even within the same patient. Biopsies taken from different regions of a tumor may stand in stark molecular contrast to each other. Therefore, the ability to generate meaningful data from multiple platforms using the same biopsy is crucial for translating multi-omics characterizations into the clinic. However, it is generally a cumbersome and lengthy procedure to generate DNA, RNA and protein material from the same biopsy. The Qiagen AllPrep kit is an accessible, straightforward, and widely used kit in clinics worldwide to process biopsies and generate genomic and transcriptomic data from tumors. We aimed to determine if high-quality proteomics data could also be obtained from the remaining material. Here, we investigated procedures for generating deep and quantitatively accurate proteomic information in high throughput from Qiagen AllPrep flowthroughs. With a number of refinements, we obtain in excess of 10,000 quantified proteins, from 60 samples per day, achieving a substantial coverage of the total proteome. Additionally, we successfully characterize the tumors using phosphoproteomics. Combining a standard kit with in-depth proteomics will be an attractive approach for clinics seeking to implement multi-omics-based precision oncology.

cancer biology↗

Population Genomics of Stone Age Eurasia

Western Eurasia witnessed several large-scale human migrations during the Holocene1-5. To investigate the cross-continental impacts we shotgun-sequenced 317 primarily Mesolithic and Neolithic genomes from across Northern and Western Eurasia. These were imputed alongside published data to obtain diploid genotypes from >1,600 ancient humans. Our analyses revealed a Great Divide genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers (HGs) were highly genetically differentiated east and west of this zone, and the impact of the neolithisation was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacements of HGs in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, while east of the Urals relatedness remained high until [~]4,000 BP, consistent with persistence of localised HG groups. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 BP resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive but we demonstrate that HGs from the Middle Don region contributed ancestry to them. Yamnaya-groups later admixed with individuals associated with the Globular Amphora Culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a Neolithic steppe cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations.

evolutionary biology↗

Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making

The promise of precision medicine is to deliver personalized treatment based on the unique physiology of each patient. This concept was fueled by the genomic revolution, but it is now evident that integrating other types of omics data, like proteomics, into the clinical decision-making process will be essential to accomplish precision medicine goals. However, quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across myriad biomedical databases and publications makes this exceptionally difficult. To address this, we developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG also incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to illustrate how the CKG may support, enrich and accelerate clinical decision-making. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/084897v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@433689org.highwire.dtl.DTLVardef@b18068org.highwire.dtl.DTLVardef@1656084org.highwire.dtl.DTLVardef@a38201_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗