A Comprehensive Proteomic and Bioinformatic Analysis of Human Spinal Cord Injury Plasma Identifies Proteins...

Spinal cord injury (SCI) is a major cause of disability, with complications postinjury often leading to lifelong health issues with the need for extensive treatment. Neurological outcome post-SCI can be variable and difficult to predict, particularly in incompletely injured patients. The identification of specific SCI biomarkers in blood may be able to improve prognostics in the field. This study has utilized proteomic and bioinformatic methodologies to investigate differentially expressed proteins in plasma samples across human SCI cohorts with the aim of identifying candidate prognostic biomarkers and biological pathway alterations that relate to neurological outcome. Blood samples were taken, following informed consent, from American Spinal Injury Association impairment scale (AIS) grade C "improvers" (those who experienced an AIS grade improvement) and "nonimprovers" (no AIS change) and AIS grade A and D at <2 weeks ("acute") and ∼3 months ("subacute") postinjury. The total protein concentration from each sample was extracted, with pooled samples being labeled and nonpooled samples treated with ProteoMiner^™ beads. Samples were then analyzed using two 4-plex isobaric tag for relative and absolute quantification (iTRAQ) analyses and a label-free experiment for comparison before quantifying with mass spectrometry. Data are available via ProteomeXchange with identifiers PXD035025 and PXD035072 for the iTRAQ and label-free experiments, respectively. Proteomic datasets were analyzed using OpenMS (version 2.6.0). R (version 4.1.4) and, in particular, the R packages MSstats (version 4.0.1) and pathview (version 1.32.0) were used for downstream analysis. Proteins of interest identified from this analysis were further validated by enzyme-linked immunosorbent assay. The data demonstrated proteomic differences between the cohorts, with the results from the iTRAQ approach supporting those of the label-free analysis. A total of 79 and 87 differentially abundant proteins across AIS and longitudinal groups were identified from the iTRAQ and label-free analyses, respectively. Alpha-2-macroglobulin, retinol-binding protein 4 (RBP4), serum amyloid A1, peroxiredoxin 2 (PRX-2), apolipoprotein A1, and several immunoglobulins were identified as biologically relevant and differentially abundant, with potential as individual candidate prognostic biomarkers of neurological outcome. Bioinformatics analyses revealed that the majority of differentially abundant proteins were components of the complement cascade and most interacted directly with the liver. Many of the proteins of interest identified using proteomics were detected only in a single group and therefore have potential as binary (present or absent) biomarkers, RBP4 and PRX-2 in particular. Additional investigations into the chronology of these proteins and their levels in other tissues (cerebrospinal fluid in particular) are needed to better understand the underlying pathophysiology, including any potentially modifiable targets. Pathway analysis highlighted the complement cascade as being significant across groups of differential functional recovery.