Using established biorepositories for emerging research questions: a feasibility study

Abstract

Background: Proteomics and metabolomics offer substantial potential for advancing kidney transplant research by providing versatile opportunities for gaining insights into the biomolecular processes occurring in donors, recipients, and grafts. To achieve this, adequate quality and numbers of biological samples are required. Whilst access to donor samples is facilitated by initiatives such as the QUOD biobank, an adequately powered biobank allowing exploration of recipient-related aspects in long-term transplant outcomes is missing. Rich, yet unverified resources of recipient material are the serum repositories present in the immunological laboratories of kidney transplant centers that prospectively collect recipient sera for immunological monitoring. However, it is yet unsure whether these samples are also suitable for -omics applications, since such clinical samples are collected and stored by individual centers using non-uniform protocols and undergo an undocumented number of freeze-thaw cycles. Whilst these handling and storage aspects may affect individual proteins and metabolites, it was reasoned that incidental handling/storage artifacts will have a limited effect on a theoretical network (pathway) analysis. To test the potential of such long-term stored clinical serum samples for pathway profiling, we submitted these samples to discovery proteomics and metabolomics.

Methods: A mass spectrometry-based shotgun discovery approach was used to obtain an overview of proteins and metabolites in clinical serum samples from the immunological laboratories of the Dutch PROCARE consortium. Parallel analyses were performed with material from the strictly protocolized QUOD biobank.

Results: Following metabolomics, more than 800 compounds could be identified in both sample groups, of which 163 endogenous metabolites were found in samples from both biorepositories. Proteomics yielded more than 600 proteins in both groups. Despite the higher prevalence of fragments in the clinical, non-uniformly collected samples compared to the biobanked ones (42.5% vs 26.5% of their proteomes, respectively), these fragments could still be connected to their parent proteins. Next, the proteomic and metabolomic profiles were successfully mapped onto theoretical pathways through integrated pathway analysis, which showed significant enrichment of 79 pathways.

Conclusions: This feasibility study demonstrated that long-term stored serum samples from clinical biorepositories can be used for qualitative proteomic and metabolomic pathway analysis, a notion with far-reaching implications for all biomedical, long-term outcome-dependent research questions and studies focusing on rare events.

Keywords: Biobank; Kidney transplantation; Metabolomics; Plasma; Proteomics; Serum.

Fig. 1

Global metabolome analysis of the plasma and serum samples. A Ridgeline plot presenting the distribution of mass to charge ratio (m/z) of compounds identified in plasma and serum samples. B Venn diagram highlighting the distribution of the identified endogenous metabolites per group in numbers and in percentage, evidencing the overlapping and unique metabolites. C Bubble plot of chemical class enrichment analysis of plasma and serum endogenous metabolites (top 15 sets, p < 0.05). The bubble size is correlated with the enrichment score, and a red colour means more significant enrichment. The X-axis depicts the number of metabolites identified in plasma and serum and the Y-axis lists the names of the chemical classes of annotated metabolites

Fig. 2

Global proteome analysis of the plasma and serum samples. A Venn diagram describing common and unique proteins between the plasma and serum samples. B Protein fragments level reported as percentage of total proteome. C Deamidation level of asparagine (N) and glutamine (Q) amino acids. D Oxidation level of Methionine (M) residue in the plasma and serum samples. “ns”; non-significant. (****); statistical significance p ≤ 0.0001

Fig. 3

Visual representation of protein fragments through alluvial plots. The diagram flow shows the multitude of protein fragments with master protein and molecular weight (MW) which were identified in plasma and serum samples. The size of the coloured blocks in the protein column are proportional to the number of fragments identified for that specific master protein. The connecting lines between the protein and MW columns represent the identified fragments. The “MW” column provides information on the MW of the specified fragment, in kDa

Fig. 4

STRING analysis of the extracellular matrix (ECM) receptor interaction pathway. The network displays the interaction between the identified proteins in plasma (A) and serum (B) samples. Each node denotes a gene. The nodes circled with red indicate the genes uniquely identified in the plasma samples. The connecting lines between nodes indicate the evidence of their relationships: red line = fusion evidence; green line = neighbourhood evidence; blue line = co-occurrence evidence; purple line = experimental evidence; yellow line = text-mining evidence; light blue line = database evidence; black line = co-expression evidence