Multiple myeloma (MM) is the second-most common haematological malignancy and is characterised by the clonal expansion of abnormal plasma cells within the bone marrow, as well as the production of aberrant monoclonal immunoglobulin. Though durable remission is possible, MM has traditionally been considered incurable, with relapse occurring in almost all patients. Currently, prognosis is determined using a score derived from serum markers (lactate dehydrogenase, beta-2-microglobulin and albumin), as well as cytogenetic abnormalities in the plasma cell clone. With 7 new treatment agents being approved by the FDA since 2012, novel prognostic tools are needed to guide therapy for individual patients. In this pilot study, we developed a multi-omics approach to investigate molecular differences among different risk groups as well as between relapse and newly diagnosed patients. Plasma cells were isolated using CD138 microbeads from patient bone marrow aspirate samples and then subjected to proteomics and lipidomics profiling. Interestingly, while both proteomics and lipidomics results were able to separate relapse from non-relapse patients, the molecular profiles of plasma cells were virtually indistinguishable among traditional prognostic risk groups. A down-regulation trend was observed in relapse compared to non-relapse patients in both proteomics and lipidomics profiles. Out of ~4300 identified proteins 172 were down-regulated while 22 were up-regulated. Untargeted lipidomics showed similar pattern with 187 features down-regulated and 94 up-regulated. Lipid set enrichment on targeted lipidomics indicated significant down-regulation of phosphocholines. Proteomics pathway enrichment revealed significant down-regulation of sphingolipid de novo biosynthesis in relapsed plasma cells, and up-regulation of TCR and NF-kB signalling. This pilot study supports the feasibility and utility of a full multi-omics study in plasma cells to further understand the systems changes in relapsed MM, with a goal of developing biomarkers for detecting and predicting relapse, guiding the therapeutic use of currently available treatments, as well as new therapy development.