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MetabolomicsUnraveling the Heterogeneity of Hyperuricemia Using Untargeted Metabolomics and Statistical Modeling

Background. Hyperuricemia is the second most prevalent metabolic disorder globally and exhibits pronounced heterogeneity in clinical practice. Only about 10% of individuals with asymptomatic hyperuricemia (HUA) progress to gout, yet the lack of specific biomarkers and precise therapeutic targets ...

2025-10-05 | MTBLS13094 | MetaboLights
MetabolomicsIntegrative Modeling of Quantitative Plasma Lipoprotein, Metabolic, and Amino Acid Data Reveals a Multiorgan Pathological Signature of SARS-CoV-2 Infection
The metabolic effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on human blood plasma were characterized using multi-platform metabolic phenotyping with Nuclear Magnetic Resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Quantitative mea...
2021-01-29 | MTBLS2014 | MetaboLights
MetabolomicsA microfluidic chip recapitulating the human neurovascular unit with a functional blood-brain barrier for modeling gut-brain interactions

The blood-brain barrier (BBB) is essential for central nervous system homeostasis, but most current in vitro models lack structural and functional fidelity. We developed a physiologically relevant human neurovascular unit microfluidic chip (hNVU-on-a-chip) incorporating brain microvascular endoth...

2025-10-28 | MTBLS13230 | MetaboLights
MetabolomicsA novel strategy for mining and identification of acylcarnitines using data-independent-acquisition (DIA) based retention time prediction modeling and pseudo-characteristic fragmentation ions matching
It is a challenging work to screen, identify, and quantify acylcarnitines in complex biological samples. A method, based on the retention time (RT) prediction and data-independent acquisition strategies, was proposed for the large-scale identification of acylcarnitines using liquid chromatography co...
2021-10-06 | MTBLS1932 | MetaboLights
TranscriptomicsQuantitative modeling of transcription factor binding specificities using DNA shape
Accurate predictions of the DNA binding specificities of transcription factors (TFs) are necessary for understanding gene regulatory mechanisms. Traditionally, predictive models are built based on nucleotide sequence features. Here, we employed three- dimensional DNA shape information obtained on a ...
ORGANISM(S): Homo sapiens 
2014-11-04 | E-GEOD-59845 | biostudies-arrayexpress
TranscriptomicsModeling Familial Cancer with iPSC Approaches
In vitro modeling of human disease has recently become feasible with the adoption of induced pluripotent stem cell (iPSC) technology. Here, we established patient-derived iPSCs from an Li-Fraumeni Syndrome (LFS) family and investigated the role of mutant p53 in the development of osteosarcoma (OS). ...
ORGANISM(S): Homo sapiens 
2015-04-10 | E-GEOD-58123 | biostudies-arrayexpress
GenomicsModeling protein destiny in developing tomato fruit
Modeling protein destiny in developing tomato fruit
PRJNA528656 | ENA
ProteomicsHuman tear fluid proteome dataset for usage as a spectral library and for protein modeling
This article provides a detailed dataset of human tear fluid proteins. Samples were fractionated by sodium dodecyl sulfate (SDS) gel electrophoresis resulting in 48 fractions that were spiked with an indexed retention time (iRT) peptide standard. These data are based on a data-dependent acquisition ...
ORGANISM(S): Homo sapiens (Human) 
2019-08-15 | PXD011075 | Pride
Dia Protein modeling Tear fluid Spectral library Dda Lc-ms/ms 
TranscriptomicsHuman iPSC-based Modeling of Late-Onset Disease using Progerin-induced Aging
Reprogramming somatic cells to induced pluripotent stem cells (iPSCs) sets their identity back to an embryonic age. This presents a fundamental hurdle for modeling late-onset disorders using iPSC-derived cells. We therefore developed a strategy to induce age-like features in multiple iPSC-derived li...
ORGANISM(S): Homo sapiens 
2014-01-23 | E-GEOD-52431 | biostudies-arrayexpress
ProteomicsBayesian proteoform modeling improves protein quantification of global proteomic measurements
Data used for the implementation of a Bayesian Proteoform Quantification model (BP-Quant) that uses statistically derived peptide signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimate...
ORGANISM(S): Mus Musculus (ncbitaxon:10090) 
2021-06-24 | MSV000087701 | MassIVE
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