High-dimensional proteomic analysis for pathophysiological classification of traumatic brain injury

Abstract

Pathophysiology and outcomes after traumatic brain injury (TBI) are complex and heterogeneous. Current classifications are uninformative about pathophysiology. Proteomic approaches with fluid-based biomarkers are ideal for exploring complex disease mechanisms, because they enable sensitive assessment of an expansive range of processes potentially relevant to TBI pathophysiology. We used novel high-dimensional, multiplex proteomic assays to assess altered plasma protein expression in acute TBI. We analysed samples from 88 participants from the BIO-AX-TBI cohort [n = 38 moderate-severe TBI (Mayo Criteria), n = 22 non-TBI trauma and n = 28 non-injured controls] on two platforms: Alamar NULISA™ CNS Diseases and OLINK® Target 96 Inflammation. Patient participants were enrolled after hospital admission, and samples were taken at a single time point ≤10 days post-injury. Participants also had neurofilament light, GFAP, total tau, UCH-L1 (all Simoa®) and S100B (Millipore) data. The Alamar panel assesses 120 proteins, most of which were previously unexplored in TBI, plus proteins with known TBI specificity, such as GFAP. A subset (n = 29 TBI and n = 24 non-injured controls) also had subacute (10 days to 6 weeks post-injury) 3 T MRI measures of lesion volume and white matter injury (fractional anisotropy). Differential expression analysis identified 16 proteins with TBI-specific significantly different plasma expression. These were neuronal markers (calbindin 2, UCH-L1 and visinin-like protein 1), astroglial markers (S100B and GFAP), neurodegenerative disease proteins (total tau, pTau231, PSEN1, amyloid-beta-42 and 14-3-3γ), inflammatory cytokines (IL16, CCL2 and ficolin 2) and cell signalling- (SFRP1), cell metabolism- (MDH1) and autophagy-related (sequestome 1) proteins. Acute plasma levels of UCH-L1, PSEN1, total tau and pTau231 were correlated with subacute lesion volume. Sequestome 1 was positively correlated with white matter fractional anisotropy, whereas CCL2 was inversely correlated. Neuronal, astroglial, tau and neurodegenerative proteins were correlated with each other, IL16, MDH1 and sequestome 1. Exploratory clustering (k means) by acute protein expression identified three TBI subgroups that differed in injury patterns, but not in age or outcome. One TBI cluster had significantly lower white matter fractional anisotropy than control-predominant clusters but had significantly lower lesion subacute lesion volumes than another TBI cluster. Proteins that overlapped on two platforms had excellent (r > 0.8) correlations between values. We identified TBI-specific changes in acute plasma levels of proteins involved in neurodegenerative disease, inflammatory and cellular processes. These changes were related to patterns of injury, thus demonstrating that processes previously studied only in animal models are also relevant in human TBI pathophysiology. Our study highlights how proteomic approaches might improve classification and understanding of TBI pathophysiology, with implications for prognostication and treatment development.

Keywords: biomarker; inflammation; neurodegeneration; neuroimaging.

Figure 1

Demographic and clinical characteristics of cohorts. (A) Age and sex distribution of the cohorts. (B) Duration of hospital stay (in days) by age for the traumatic brain injury (TBI) cohort. (C) Distribution of clinical descriptors of injury severity [pupil reactivity (BDNR = bilaterally dilated and non-reactive; BR = bilaterally reactive; UDNR = unilaterally dilated and non-reactive), CT Marshall grade and pre-hospital Glasgow Coma Scale (GCS)] within the TBI cohort. (D) Distribution of functional outcome, assessed by Glasgow Outcome Scale Extended (GOS-E) at 6 months (6m) and 12 months (12m) after TBI. (E) Distribution of injury type within the non-TBI trauma (NTT) cohort.

Figure 2

Differential expression analysis identifying traumatic brain injury-specific changes in plasma proteins. (A) Volcano plots showing the differences in plasma protein expression between each pair of groups. Red dots denote significant proteins. (B) Schematic diagram of the categorization of proteins with significant group differences (adjusted P < 0.05). Proteins are categorized based on a between-group comparison coefficient log₂ > 0.58 (equivalent to >1.5× difference between the two groups) and post hoc t-test P < 0.05. Protein names are colour-coded based on biological role/pathway: red = neurodegenerative disease associated; blue = neuronal marker; orange = cytokine/chemokine; purple = tau pathology; green = astroglial marker. Black indicates a range of other roles and pathways (Supplementary Table 2). Nine proteins for which between-group comparison did not meet these criteria for any of the group pairs [traumatic brain injury (TBI) versus control (CON), TBI versus non-TBI trauma (NTT) and NTT versus CON] are not included in the schematic diagram. (C) Boxplots illustrating plasma protein levels in CON, NTT and TBI groups for proteins identified by differential expression analysis to be TBI specific. The y-axis units are NULISA protein quantification (NPQ) units.

Figure 3

Relationship between plasma proteins and neuroimaging features. (A) Comparison of mean fractional anisotropy (FA) z-scores of the whole skeleton and corpus callosum between non-injured healthy controls (CON) and traumatic brain injury (TBI) patients on subacute MRI (10 days to 6 weeks post-injury). *Adjusted P < 0.05. (B) Voxelwise comparison of z-scored mean FA in patients compared with controls on subacute MRI (10 days to 6 weeks post-injury), with significant (P < 0.05) group differences in red, overlaid on the white matter skeleton in green. Results are overlaid on a 1 mm standard brain in Diffusion tensor imaging toolkit space. (C) Correlation matrix of TBI-specific proteins (identified by differential expression analysis) with lesion volume, z-score of the whole skeleton FA (z_wholeskel_FA) and z-score of the corpus callosum (z_CC_FA) on subacute MRI (10 days to 6 weeks post-injury) within the TBI cohort. Proteins are ordered by category. See Supplementary Table 2 for category. Only statistically significant results (false discovery rate adjusted P < 0.05) are shown.

Figure 4

Correlations between proteins. Correlation matrix of traumatic brain injury (TBI)-specific proteins (identified by differential expression analysis) within the TBI cohort. Proteins are ordered by category, separated by a dashed line. See Supplementary Table 2 for category. Only statistically significant results (false discovery rate adjusted P < 0.05) are shown.

Figure 5

Cluster analysis identifying traumatic brain injury subgroups. (A) Cluster analysis identified five clusters, with three TBI-predominant clusters (Clusters 3, 4 and 5) and two non-TBI predominant clusters (Clusters 1 and 2). The black box highlights a set of proteins with marked differently levels between TBI-predominant Clusters 4 and 5. CON = non-injured healthy controls; NTT = non-TBI trauma controls; TBI = traumatic brain injury. (B) Comparison of MRI measures between the clusters. Cluster 5 had significantly lower mean skeleton fractional anisotropy (FA) and mean corpus callosum FA, in comparison to Clusters 1 and 2. In contrast, Cluster 4 had significantly higher lesion volumes than Clusters 1, 2 and 5. *P < 0.05 on Tukey’s post hoc test, performed after a statistically significant effect of cluster was identified using ANOVA. Note that only CON and TBI groups had MRI.

Figure 6

Correlations between assay approaches. Pearson correlation between levels of proteins assessed by two different assay approaches (the Alamar NULISA™ CNS Diseases panel, horizontal, versus OLINK® Target 96 Inflammation panel or Simoa® or Millipore ELISA-based assay, vertical). The correlation coefficient is shown superimposed on a representative circle. Red boxes denote proteins for which >50% samples were below the limit of detection (LOD) of the OLINK panel. We used 50% of samples as the cut-off because this would mean that below the LOD would not be simply from one participant type. Inset are plots of three of the overlapping proteins: IL6 and GFAP, which show excellent correlation between two assay approaches, and IL4, which shows very poor correlation. Note: NEFL/NFL, MAPT/total tau, MCP1/CCL2 and MCP4/CCL13 are the same proteins, but named differently in the different assays.