ARTICLE DOI: 10.1038/s41467-018-06353-4 OPEN Cell-specific proteome analyses of human bone marrow reveal molecular features of age- dependent functional decline Marco L. Hennrich 1,2, Natalie Romanov 1, Patrick Horn2,3, Samira Jaeger4, Volker Eckstein3, Violetta Steeples5, Fei Ye1,2, Ximing Ding1,2,3, Laura Poisa-Beiro2,3, Mang Ching Lai1,2, Benjamin Lang 1, Jacqueline Boultwood5, Thomas Luft3, Judith B. Zaugg 1,2, Andrea Pellagatti 5, Peer Bork 1,2,6, Patrick Aloy4,7, Anne-Claude Gavin1,2 & Anthony D. Ho 2,3 1234567890():,; Diminishing potential to replace damaged tissues is a hallmark for ageing of somatic stem cells, but the mechanisms remain elusive. Here, we present proteome-wide atlases of age- associated alterations in human haematopoietic stem and progenitor cells (HPCs) and five other cell populations that constitute the bone marrow niche. For each, the abundance of a large fraction of the ~12,000 proteins identified is assessed in 59 human subjects from different ages. As the HPCs become older, pathways in central carbon metabolism exhibit features reminiscent of the Warburg effect, where glycolytic intermediates are rerouted towards anabolism. Simultaneously, altered abundance of early regulators of HPC differ- entiation reveals a reduced functionality and a bias towards myeloid differentiation. Ageing causes alterations in the bone marrow niche too, and diminishes the functionality of the pathways involved in HPC homing. The data represent a valuable resource for further ana- lyses, and for validation of knowledge gained from animal models. 1 European Molecular Biology Laboratory (EMBL), Structural and Computational Biology Unit, Meyerhofstrasse 1, Heidelberg D69117, Germany. 2 Molecular Medicine Partnership Unit (MMPU), Meyerhofstrasse 1, Heidelberg D69117, Germany. 3 Department of Medicine V, Heidelberg University, Heidelberg D69120, Germany. 4 Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona,08028 Catalonia, Spain. 5 Radcliffe Department of Medicine, University of Oxford and Oxford BRC Haematology Theme, Oxford OX3 9DU, UK. 6 Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg D97074, Germany. 7 Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, 08010 Catalonia, Spain. These authors contributed equally: Marco L. Hennrich, Natalie Romanov, Patrick Horn. Correspondence and requests for materials should be addressed to A.-C.G. (email: [email protected]) or to A.D.H. (email: [email protected]) NATURE COMMUNICATIONS | (2018) 9:4004 | DOI: 10.1038/s41467-018-06353-4 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-06353-4 geing of stem cells has been considered as the underlying mainly for progenitors, but do contain a significant percentage of Acause for ageing of tissues and organs, especially in a stem cells. These cell populations constitute 94.2% (±2.8%) of all biological system that is characterized by a high turnover mononuclear cells in the bone marrow. such as haematopoiesis1,2. In humans, anaemia, decreased com- To simultaneously assess the molecular alterations associated petence of the adaptive immune system, an expansion of myeloid with ageing at both spatial (cell population) and temporal cells at the expense of lymphopoiesis, and a higher frequency of (ageing) resolution, we combined two complementary mass haematologic malignancies have been reported to be hallmarks of spectrometry (MS)-based quantification methods. The cell type- ageing3–5. specific changes were measured by a label-free technique adapted The age-associated phenotypes are initiated at the very top of from Schwanhausser et al.22 (see Methods) and the age-associated the haematopoietic hierarchy, i.e., in the haematopoietic stem and ones by isobaric labelling using the tandem mass tag (TMT)23 progenitor cells (HPCs)2,6. With age, the HPC population (Supplementary Fig. 2a,b). The six different cell populations were undergo both quantitative (e.g., an increase in number) and analysed separately. Tryptic digests were processed in batches of functional changes (e.g., a decreased ability to repopulate the five human subjects, each labelled with a different TMT, and were bone marrow3,4,7,8). Transcriptomic studies have provided a combined (Fig. 1c) with a TMT-labelled cell population-specific blueprint of the underlying molecular mechanisms and indicated internal standard to ensure an accurate quantification across all that genes associated with cell cycle, myeloid lineage specification, samples. In total, we were able to identify 12,158 proteins (Fig. 1d, as well as with myeloid malignancies were up-regulated in old e; Supplementary Fig. 2b and Supplementary Data 1) (i.e., in all HPCs, when compared to young ones5,9,10. The aforementioned human subjects and in the six cell populations), which represents knowledge on the various mechanistic aspects of HPC ageing was 77% of the currently detectable human proteome24 (see mostly, if not exclusively, gained by studies in murine models of Methods). The number of proteins characterized in each specific ageing and has yet to be validated in human subjects. cell population varied from 6340 in ERPs to 9454 in MSCs. Additionally, changes in the HPC microenvironment—the The technical reproducibility for the TMT-based quantifica- bone marrow niche—also influence haematological ageing. tion, measured in triplicate, was high (averaged Pearson's Whereas alterations in adhesion molecules, which are expressed correlation coefficient = 0.94; median coefficient of variation = in the cellular niche, and which are essential for homing and 4.2%; details in Methods). A principal component analysis maintenance of HPCs, have been described, how they vary with showed that age accounted for a small but sizeable fraction of the ageing process has not been defined11–16. In previous studies, the overall inter-individual variability (between 3.1 and 21.8% we demonstrated specific transcriptomics and epigenetic altera- depending on the cell population). Based on these principal tions characteristic for ageing of human mesenchymal stem/ component analyses, we excluded samples that deviated sig- stromal cells (MSCs)17,18, while other groups indicated that dif- nificantly from the majority due to various technical issues ferent cellular elements in the marrow such as monocytes and (Supplementary Fig. 2c). After removing those outliers, we were macrophages could also play major roles19–21. Whereas these able to analyse a total of 270 samples, and acquired 7375 protein various mechanisms of ageing have been studied in a few, indi- abundance profiles across the human cohort (TMT quantifica- vidual cell populations constituting the bone marrow, our tion), and 6952 across the cell populations (label-free quantifica- understanding of the roles of intrinsic mechanisms, i.e., in the tion). Although we could not capture the least abundant HPCs, vs. extrinsic ones, such as in the marrow niche, has proteins25, our analyses broadly cover the main functional and remained fragmented. compartmental protein categories (Supplementary Fig. 3). The overarching goal of this study is therefore to acquire a The quality of the dataset was validated by the inter-individual systems understanding of the molecular mechanisms involved in variability in each of the different cell populations studied ageing of human HPCs, as well as those in the cell populations (biological variability). While protein abundance fluctuated comprising the marrow niche. As cell functions are more directly among the different samples within the same cell population, characterized by their proteins than their transcript complements, proteins within specific pathways or protein complexes that were we performed a comprehensive and quantitative proteomics expected to be co-expressed showed coherent changes across analysis of the HPCs and their niche in a large cohort of human donors (Supplementary Fig. 4)26,27. Furthermore, hierarchical subjects from different age groups. The underlying datasets clustering based on all proteins quantified by label-free should represent not only a valuable resource for mechanistic quantification yielded a distinct pattern permitting separation analyses and for validation of knowledge gained from animal into clusters that recapitulate the known lineage relationship models, but also provide an atlas of proteomic signatures of (Supplementary Fig. 5). human ageing processes within the cellular network of the bone The quality of the dataset was further confirmed by the marrow. The systemic data should build a foundation for a better abundance profiles of known cell type-specific markers of the understanding of age-related diseases such as myelodysplastic corresponding subpopulations (Fig. 2a and Supplementary Fig. 6), syndromes (MDS) in the future. based on which the respective cell types were isolated (Supple- mentary Fig. 1). All validation analyses indicated that the proteomics datasets were reliable and of appropriate quality to Results address questions on age-dependent differences across cell Multi-scale proteomics profiling of human bone marrow cells. populations. Bone marrow samples of high quality and sufficient quantity from 59 human subjects, 45 male and 14 female, were available for proteomics analysis (Fig. 1a, b). Their age ranged from 20 to The proteomic landscape of the human marrow niche. Recent 60 years with a median of 33.2 years, as depicted in Fig. 1b human proteome atlases are still largely