Amster, I. J. | bioRxiv Science

bioRxiv2024

Glycoinformatic profiling of label-free intact heparan sulfate oligosaccharides

Heparan sulfates (HS) are a group of heterogenous linear, sulfated polysaccharides that play a role in in health and many diseases including cancer, cardiovascular, and kidney diseases. The structural variety of HS has greatly challenged the development and utility of HS analytics, particularly for native structures, leaving a significant gap in HS technologies for clinical application. Mass spectrometry (MS)-based profiling with bioinformatics offers a top-down approach that can retain variety in large data sets. Using healthy human plasmas, we developed an MS glycoprofiling approach for native HS oligosaccharides, which retains the structural complexity of each individual HS chain and generates an HS index (or Heparan-ome) for each patient. As a proof of concept, analysis of 56 plasma samples ranging from 6 groups of kidney disease patients revealed a new subset cluster (20%, 4/20) of membranous glomerulopathy (MG) patients with distinct HS profiles, highlighting the potential of HS glycoprofiling as a powerful new approach into clinical practice, which warrants future development into clinical diagnostics of kidney and other diseases. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/613784v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@6dac34org.highwire.dtl.DTLVardef@449da1org.highwire.dtl.DTLVardef@c8eb88org.highwire.dtl.DTLVardef@df4deb_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Large-scale untargeted metabolomics studies suffer from individual variation, batch effects and instrument variability, making comparisons of common spectral features across studies difficult. One solution is to compare studies after compound identification. However, compound identification is expensive and time consuming. We successfully identify common spectral features across multiple studies, with a generalizable experimental design approach. First, we included an anchor strain, PD1074, during sample and data collection. Second, we collected data in blocks with multiple controls. These anchors enabled us to successfully integrate three studies of Caenorhabditis elegans for nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) data from five different assays. We found 34% and 14% of features to be significant in LC-MS and NMR, respectively. Between 20-50% of spectral features differ in a mutant and among a set of genetically diverse natural strains, suggesting this reduced set of spectral features are excellent targets for compound identification. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/485859v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@84abe1org.highwire.dtl.DTLVardef@3066fforg.highwire.dtl.DTLVardef@1ba166corg.highwire.dtl.DTLVardef@164cf0_HPS_FORMAT_FIGEXP M_FIG C_FIG Fourteen C. elegans strains are used in three individual studies. PD1074, the anchor control strain (orange), is grown alongside test strains (green, yellow, purple). Multiple biological replicates of PD1074 captures environmental variation in growth conditions. Non-polar and polar metabolic data across the three studies (i.e., natural strains, central metabolism mutants, and UGT mutants) were collected by nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Data acquisition controls in each block included biological reference material and pooled PD1074 samples. Biological replicates of PD1074 (n = 42 for LC-MS, n = 52 for NMR) were included in all batches. Meta-analysis provided comparable inferences to mixed effects models, and the estimated relative effects of each test strain to PD1074 and straightforward comparisons of test strains across experiments.

systems biology↗