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Summary: Under caloric restriction, bone marrow adipocytes (BM-Ads) do not decrease in size compared to white adipocytes, suggesting they harbor unique metabolic properties. We compare human primary BM-Ads with paired subcutaneous adipocytes (SC-Ads) using proteomic and lipidomic approaches. We find that, although SC-Ads and BM-Ads share similar morphological features, they possess distinct lipid metabolism. Although BM-Ad shows enrichment in proteins involved in cholesterol metabolism, correlating with increased free cholesterol content, proteins involved in lipolysis were downregulated. In particular, monoacylglycerol lipase expression is strongly reduced in BM-Ads, leading to monoacylglycerol accumulation. Consequently, basal and induced lipolytic responses are absent in BM-Ads, affirming their differences in metabolic fitness upon caloric restriction. These specific metabolic features are not recapitulated in vitro using common protocols to differentiate bone marrow mesenchymal stem cells. Thus, contrary to classical SC-Ads, BM-Ads display a specific lipid metabolism, as they are devoid of lipolytic activity and exhibit a cholesterol-orientated metabolism. : Attané et al. show that, although human bone marrow adipocytes (BM-Ads) resemble classic white adipocytes on a morphological level, their lipid metabolism is highly specific. They are devoid of lipolytic activity, one of the main metabolic functions of white adipocytes, which explains why they behave like a preserved lipid source under energy-demanding conditions. Keywords: bone marrow adipocytes, cholesterol, lipolysis, monoacylglycerol lipase, proteomic, adipose tissue, bone marrow fat

This data article describes a controlled, spiked proteomic dataset for which the “ground truth” of variant proteins is known. It is based on the LC-MS analysis of samples composed of a fixed background of yeast lysate and different spiked amounts of the UPS1 mixture of 48 recombinant proteins. It can be used to objectively evaluate bioinformatic pipelines for label-free quantitative analysis, and their ability to detect variant proteins with good sensitivity and low false discovery rate in large-scale proteomic studies. More specifically, it can be useful for tuning software tools parameters, but also testing new algorithms for label-free quantitative analysis, or for evaluation of downstream statistical methods. The raw MS files can be downloaded from ProteomeXchange with identifier http://www.ebi.ac.uk/pride/archive/projects/PXD001819. Starting from some raw files of this dataset, we also provide here some processed data obtained through various bioinformatics tools (including MaxQuant, Skyline, MFPaQ, IRMa-hEIDI and Scaffold) in different workflows, to exemplify the use of such data in the context of software benchmarking, as discussed in details in the accompanying manuscript [1]. The experimental design used here for data processing takes advantage of the different spike levels introduced in the samples composing the dataset, and processed data are merged in a single file to facilitate the evaluation and illustration of software tools results for the detection of variant proteins with different absolute expression levels and fold change values.