Dataset Information

Proteomic Profiling of Human Omental and Subcutaneous Adipose Tissue in Individuals with a Broad Range of Body-Mass Indexes (BMIs)

ABSTRACT: Obesity is a major public health challenge affecting an ever-increasing proportion of the US population. It is associated with a range of serious comorbidities and is estimated to have cost the US over $173 billion in medical expenses in 2019 alone. The causal mechanisms of comorbidities are not well understood, nor are the roles of different adipose depot locations. To facilitate a deeper understanding of these factors, we deliver a comprehensive and quantitative proteomic dataset presented at the peptide and protein level. Data was acquired from matched subcutaneous and omental adipose tissues from metabolically healthy individuals with no comorbidities and covering a wide range of BMIs . Adipose tissue samples were collected during elective surgeries and immediately processed for histology or flash frozen and stored at -80oC until proteomic analysis by data-independent acquisition LC-MS/MS . Internal and external quality control systems ensured high quality data. All data presented are available via ProteomeXchange – from raw data through to processed quantitative values for each peptide and protein measured. This dataset will allow new insights into biological differences between lean and obese states – captured before the onset of comorbidities. Matched intra-individual sampling across fat depots additionally offers a rare opportunity to uncover depot-specific physiological signatures.

ORGANISM(S): Homo Sapiens

SUBMITTER: Alex Zelter

PROVIDER: PXD067514 | panorama | Sun Jan 18 00:00:00 GMT 2026

REPOSITORIES: PanoramaPublic

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Project description:We have studied protein expression in mouse embryonic stem (mES) cells- R1-9 and identified the largest complement of proteins reported thus far for mES cells, using on-line LC-MS/MS analysis. With support from a transcriptome profile of the same mES cell line, we arrived at an integrated dataset of 9,370 protein expressions that may be useful to understand the stem cell dynamics. 6,278 protein identifications (IDs) were based on two or more peptide signatures and 3,092 with single peptide signatures. These single peptide-based protein IDs were also supported by transcriptome data. The proteomic analysis demonstrated the expression of 1,369 genes (among 9,370) that were not seen in RNA transcripts. Pathway analysis of the protein IDs in our dataset, using KEGG and BioCarta and their gene ontology classification, revealed high representation of the proteins belonging to many regulatory pathways and showed enrichment for protein IDs involved in transcription regulation, signal transduction, cell-cycle, and differentiation. Interestingly, the data support the expression of 1,299 putative open reading frames. In silico domain comparisons of some of these sequences matched with regulatory proteins such as transcription factors or signal transduction molecules. Over 60% of the transcript complement is represented in our protein dataset implying a corresponding richness of the dynamic network of proteins in a pluripotent mES cells.have thus arrived at a dataset of 10428 expressed proteins with high confidence which was also verified by microarray analysis of the expressed mRNAs R1-9 ES cells. Pathway analysis of these expressed proteins was carried out using KEGG, IPA and their gene ontology classification revealed the presence of large number of transcription regulators, signal transducers, cell cycle and differentiation molecules along with other general classes of proteins belonging to a number of regulatory pathways. The database also includes IDs of proteins corresponding to many still unidentified / unannotated ORFs from the mouse genome and with putative regulatory functions. Our study thus represents the largest database of expressed proteins in mouse ES cells that would be a valuable molecular resource for experimental designs in targeted proteomics investigations using mES cell lines.

Dataset Information

Proteomic Profiling of Human Omental and Subcutaneous Adipose Tissue in Individuals with a Broad Range of Body-Mass Indexes (BMIs)

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