An atlas of the bone marrow bone proteome in patients with dysproteinemias.

Multiple myeloma (MM) bone disease is a significant cause of morbidity but there is a paucity of data on the impact of malignant plasma cells on adjacent trabecular bone within the BM. Here, we characterize the proteome of trabecular bone tissue from BM biopsies of 56 patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering (SMM), newly diagnosed (NDMM), relapsed MM (RMM), and normal controls. Proteins involved in extracellular matrix (ECM) formation and immunity pathways were decreased in SMM and active MM. Among the proteins most decreased were immunoglobulins, type IV collagen, and TIMP3, suggesting increased immunoparesis and decreased ECM remodelling within trabecular bone. Proteins most increased in SMM/MM were APP (enhances osteoclast activity), ENPP1 (enhances bone mineralization), and MZB1 (required for normal plasmablast differentiation). Pathway analyses showed that proteins involved in gamma -carboxylation, a pathway implicated in osteocalcin function, osteoblast differentiation, and normal hematopoiesis, were also overexpressed in SMM/MM. This study is the first comprehensive proteomic atlas of the BM bone proteome in dysproteinemias. We identify new key proteins and pathways for MM bone disease and potentially impaired hematopoiesis, and show for the first time that gamma -carboxylation pathways are increased in the bone tissue of SMM/MM.

A High-Throughput Workflow for FFPE Tissue Proteomics.

Laser capture microdissection (LCM) has become an indispensable tool for mass spectrometry-based proteomic analysis of specific regions obtained from formalin-fixed paraffin-embedded (FFPE) tissue samples in both clinical and research settings. Low protein yields from LCM samples along with laborious sample processing steps present challenges for proteomic analysis without sacrificing protein and peptide recovery. Automation of sample preparation workflows is still under development, especially for samples such as laser-capture microdissected tissues. Here, we present a simplified and rapid workflow using adaptive focused acoustics (AFA) technology for sample processing for high-throughput FFPE-based proteomics. We evaluated three different workflows: standard extraction method followed by overnight trypsin digestion, AFA-assisted extraction and overnight trypsin digestion, and AFA-assisted extraction simultaneously performed with trypsin digestion. The use of AFA-based ultrasonication enables automated sample processing for high-throughput proteomic analysis of LCM-FFPE tissues in 96-well and 384-well formats. Further, accelerated trypsin digestion combined with AFA dramatically reduced the overall processing times. LC-MS/MS analysis revealed a slightly higher number of protein and peptide identifications in AFA accelerated workflows compared to standard and AFA overnight workflows. Further, we did not observe any difference in the proportion of peptides identified with missed cleavages or deamidated peptides across the three different workflows. Overall, our results demonstrate that the workflow described in this study enables rapid and high-throughput sample processing with greatly reduced sample handling, which is amenable to automation.