A dynamic and complex early inflammatory response in blood and cerebrospinal fluid of severe traumatic brain injury patients: A dual platform analysis

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Inflammation plays a central role in TBI pathophysiology, yet the early dynamics of inflammatory mediators in blood and cerebrospinal fluid (CSF) remain incompletely understood. Moreover, analytical methods differ across studies and have rarely been directly compared. Aim To characterize the inflammatory response in blood and CSF during the first week after severe TBI and to assess correlation between electrochemiluminescence (ECL) and proximity extension assay (PEA). Methods A prospective observational study was conducted recruiting adult severe TBI patients (n = 21) requiring neurocritical care. Plasma and CSF samples were collected at two time points: days 1–3 and days 4–8. Orthopedic patients with minor extremity fractures (n = 11) served as controls. Inflammatory mediator levels were quantified using ECL (11 mediators) and PEA (45 mediators). Group differences, temporal changes, and inter-platform correlations were analyzed. Results Both plasma and CSF from TBI patients displayed a pronounced inflammatory response, with multiple mediators significantly altered compared to controls. In plasma, 16 mediators were increased and 5 decreased, while in CSF, 22 were increased and 6 decreased during the first post-injury week. Key mediators (IL-8 and IL-10) were consistently elevated in both compartments, although some variation was observed between the ECL and PEA platforms. When comparing analytic methods, ECL and PEA showed strong cross-platform correlations for IL-8 and IL-10 in both plasma and CSF, whereas IL-13 exhibited weak, non-significant agreement. Platform-related differences across compartments and time points were also observed. Conclusion Our findings show a marked inflammatory response in severe TBI, with distinct temporal patterns and robust neuroinflammation in CSF. While ECL and PEA showed strong correlations for several mediators, platform-dependent variability was noted. These insights improve our understanding of TBI-induced neuroinflammation and may help refine biomarker-driven prognostic and therapeutic strategies. Traumatic brain injury Inflammation Electrochemiluminescence Proximity Extension Assay cytokines chemokines cerebrospinal fluid Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Traumatic brain injury (TBI) is a global health problem and a leading cause of death and disability in young adults.( 1 ) An immediate, primary injury to brain tissue, caused by direct mechanical forces, occurs at the moment of impact. Later, complex secondary injury mechanisms develop over hours, weeks, or even years after the initial trauma and markedly exacerbate the primary injury.( 2 ) These secondary injuries are due to complications such as ischemia, hemorrhages, excitotoxicity and cerebral edema. In addition, an inflammatory response is initiated early post-injury and often persists long after the TBI. This chronic neuroinflammation is linked to white matter atrophy and neurodegeneration.( 3 , 4 ) One way to measure inflammation is by quantifying biomarkers or inflammatory mediators. Some measurable mediators include cytokines and chemokines, which is small proteins that regulate inflammatory signaling and cell trafficking.( 4 ) The importance of early inflammation has been debated, as both detrimental and pre-regenerative mechanisms may be activated. Additionally, the early inflammatory response was recently found to be related to the injury pattern and may be used to identify patients at risk of a poor neurological outcome.( 5 , 6 ). Moreover, most reports have analyzed post-injury blood samples and evaluated a limited number of inflammatory factors. While it is known that both pro- and anti-inflammatory biomarkers/inflammatory mediators fluctuate during the acute and sub-acute phase of TBI, few studies have explored the temporal pattern of these changes in both blood and CSF.( 7 ) Furthermore, the relative contributions of systemic (blood) versus central (CSF) inflammation remain poorly understood in severe TBI. In addition, activation of endogenous retroviruses after CNS injury may trigger type I interferon pathways that may be detected in blood and CSF.( 8 ) Electrochemiluminescence (ECL) and Proximity Extension Assay (PEA) are two technologies commonly used to measure inflammatory biomarkers. The well-established ECL method is utilized in various commercial platforms, enabling the simultaneous quantification of multiple analytes with high sensitivity and specificity.( 9 ) It has been widely applied in research on inflammatory and neurodegenerative disorders.( 10 ) In contrast, PEA is a newer, highly multiplexed platform that employs DNA-labeled antibodies to detect proteins with exceptional specificity and a broad dynamic range.( 11 ) There are claims of a higher sensitivity and a lower threshold for detection with PEA, although the technique has rarely been compared directly to ECL methods.( 5 , 12 ) To our knowledge, no previous study has directly compared these two technologies in severe TBI, particularly with parallel analyses of both plasma and CSF compartments. In the present study, we used and compared two biomarker detection techniques, ECL and PEA, in the measurement of inflammatory mediators in plasma and CSF, from patients with severe TBI at two time points during the first week post-injury. We hypothesized there would be a robust, yet complex inflammatory response with an altered pattern over time and with differences between the two compartments. Methods Study design This study was a prospective observational study that conveniently recruited patients with severe TBI between January 2022– June 2024 at Skåne University Hospital in Lund, Sweden. Patients were recruited from the Neurointensive Care Unit (NICU) within 72 hours of injury. All severe TBI patients aged between 18–80 years, with an expected NICU stay of one week, were eligible for inclusion. Severe TBI was defined by a Glasgow coma score (GCS) of ≤ 8 upon admission to the NICU. A list of exclusion criteria is provided in Suppl. Table 1. A control group comprised of orthopedic patients, aged 18–75 years old and without a history of neurodegenerative disease, undergoing spinal anesthesia for minor fractures in the lower extremities, was also recruited. Blood and CSF were collected at the time of spinal anesthesia. Clinical data collection and outcome assessment Basic demographic and clinical data, including age, sex, cause of injury, and length of NICU stay, were collected. Surviving TBI patients were contacted by phone nine to twelve months post-injury to assess long-term outcome. Functional outcome was evaluated using the Extended Glasgow Outcome Scale (GOS-E). Follow-up interviews were all conducted by the same investigator (SM). Collection of samples Blood and CSF samples were collected simultaneously from the TBI patients twice if possible: first, within three days post injury (early time point) and again four to eight days post injury (late time point). Blood samples from the TBI patients were drawn from a peripheral arterial catheter and allocated into EDTA-coated tubes. CSF was collected from patients who had received an external ventricular drain (EVD) for intracranial pressure (ICP) monitoring. All samples were centrifuged at 2000 rpm (relative centrifugal force (RCF) of 644g) for 10 minutes at 4°C to separate plasma and cellular components. Supernatants were allocated to 500 µl tubes and stored at -80°C within one hour of sampling until analysis. Samples from the control group were collected prior to the onset of orthopedic surgery at the time of spinal anesthesia. CSF was collected using a sterile 2 ml tube during lumbar puncture before injection of spinal anesthetics. Venous blood samples were collected from the patient at the same time as CSF was collected and allocated into EDTA-coated tubes. These samples were prepared and stored in the same way as samples from TBI patients. All samples were handled by two study group participants (SM and OP) using the same approach and protocol as described in previous paragraphs. Biomarker analysis Proximity Extension Assay For PEA analysis, samples were sent on dry ice to SVAR Life Science AB, Malmo, Sweden for biomarker detection using Olink PEA platform (Olink Proteomics AB, Uppsala). For this study, the panel Target 48 Cytokine was used where 1–10 µl of plasma-EDTA and CSF was used for each well. The panel consists of 45 cytokines and chemokines in total. The full list of mediators, protein names and their corresponding detection ranges are provided in Suppl. Table 2. Analyses were performed according to the manufacturer’s protocol. For each inflammatory mediator, data were reported both in NPX units (Normalized Protein eXpression, log2 scale) and in standard concentration units (pg/mL) based on standard curves defined during assay validation. The lower and upper limits of quantification (LLOQ and ULOQ) were provided per analyte, defining the validated quantifiable range. Electrochemiluminescence Biomarker levels in both plasma and CSF were measured at our laboratory using electrochemiluminescence (ECL) immunoassays with the Meso Scale Discovery (MSD; Rockville, MD, USA) V-PLEX Human Proinflammatory Panel 1 and the S-PLEX Human IFN-α2a kit. The V-PLEX panel quantified the following 10 cytokines and chemokines: IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, and TNF-α. The S-PLEX panel was used specifically for IFN-α2a quantification. A complete list of mediators, protein names and their corresponding detection ranges is provided in Suppl. Table 3. All procedures were performed according to the manufacturer's protocol. Before analysis, 60 µl of the SULFO-TAG-labeled antibody mix for each panel was diluted in 2400 µl of the recommended diluent. For both V-PLEX and S-PLEX assays, 50 µl of each sample or standard was loaded into designated wells of 96-well MULTI-SPOT plates and incubated for two hours at room temperature with shaking. Plates were then washed three times using phosphate-buffered saline (PBS) containing 0.05% Tween-20. Subsequently, 25 µl of the prepared detection antibody solution was added and incubated for another two hours. Following a final wash cycle, 150 µl of 2× Read Buffer T was added to each well. Plates were analyzed using the MESO QuickPlex SQ 120 instrument (MSD), and protein concentrations were calculated from standard curves generated from serial dilutions of known calibrators. All samples were run in duplicates. Cross-platform overlap. Nine analytes were measured on both platforms—IFN-γ, IL-10, IL-13, IL-1β, IL-2, IL-4, IL-6, IL-8, TNF-α—enabling direct comparison. IL-12p70 and IFN-α2a were assessed by ECL only. Data handling and processing To ensure data quality and reliability, all cytokine and chemokine measurements were assessed for their validity based on their respective LLOQ and ULOQ. For the PEA dataset, protein measurements were accompanied by LLOQ and ULOQ values. Samples with missing concentration were excluded from further analysis. Protein concentrations below the LLOQ, were handled with an imputation strategy by which the concentrations were replaced with LLOQ/2 to retain as much information as possible while minimizing bias.( 13 ) Conversely, values exceeding the ULOQ were capped at ULOQ, ensuring that extreme values did not disproportionately influence the results. The ECL dataset required a similar approach to handle values below LLOQ and above ULOQ. First, all missing cytokine and chemokine concentration values were removed from the dataset. Then, predefined assay-specific LLOQ and ULOQ values were assigned to each protein to determine whether values were within the quantifiable range. Values below the LLOQ were replaced with LLOQ/2, following common imputation practices in biomarker research.( 13 ) Values above the ULOQ were capped at ULOQ to avoid overestimation of protein concentrations. Statistical Analysis All statistical analyses were performed using Stata version 18 (Stata Corp, College Station, TX, USA). Normal distributions were assessed using the Shapiro-Wilk test. As most variables significantly deviated from normality (p < 0.05), non-parametric tests were applied. The Mann-Whitney U test was used to compare cytokine and chemokine levels between controls and TBI patients at each time point (early time point and late time point) in both plasma and CSF. For the inflammatory mediators that were measured using both ECL and PEA, a Spearman correlation analysis was performed to assess the agreement between platforms. To explore temporal changes within the TBI group, inflammatory mediator levels at time point 1 (days 1–3) were compared to time point 2 (days 4–8) using the Wilcoxon signed-rank test for paired samples. Comparison between plasma and CSF concentrations within the same patient was also performed using the Wilcoxon signed-rank test for paired samples. All statistical analyses were two-tailed. P-values < 0.05 were considered statistically significant. Ethics The study was approved by the Swedish Ethical Review Authority (Dnr 2017/4069; 2017/1049; Dnr 2022-07096-02). All collected samples were stored in a local biobank (# BD27). Patients were pseudonymized once samples were collected. Since all patients had a reduced level of consciousness and, thus, were unable to provide consent, informed consent was given by patient´s next of kin. Written informed consent from the controls was obtained from each patient by written and oral instructions prior to the collection of CSF and blood samples. Results Patient demographics The study included 21 patients with severe TBI and 11 control subjects. The median age of TBI patients was 33 (range 18–78) years, and 71% were males. The most common radiological findings were acute subdural hematoma (57%) and traumatic intracerebral hemorrhage/contusion (57%). Control patients had a median age of 46 (range 21–73) years, and 36% were males. GOS-E was assessed nine to twelve months post-injury with a median score of 4.5 (IQR 1–6). The control patients were most often (91%) operated for a tibial fracture (Table 1). Table 1. Patient Characteristics Characteristic TBI-patients, n=21 Sex, male, n (%) 15 (71) Age, years, median (range) 33 (range 18–78) GCS, n (%) 7-8 6 5 4 3 15 (71) 2 (10) 0 (0) 1 (5) 3 (14) Radiological findings, n (%) tSAH Contusion EDH ASDH Diffuse brain swelling 8 (38) 12 (57) 5 (24) 12 (57) 1 (5) Neurosurgical intervention, n (%) Hematoma evacuation Craniectomy ICP monitoring 21 (100) 11 (52) 6 (29) 20 (95) NICU length of stay, days, median (IQR) 12 (7-19) Infection during study period, n (%) Aspiration pneumonia Urinary tract infection 3 (14) 2 (10) 1 (5) ISS, median (IQR) 27 (24-33) AIS head, median (IQR) 5 (4-5) GOS-E 9-12 months post-injury, median (IQR) 4.5 (1-6) Rotterdam CT Score, n (%) 3 4 5 10 3 (14) 12 (57) 5 (24) 1 (5) Trauma mechanism, n (%) Car accident Bike accident Fall Assault 4 (19) 3 (14) 10 (48) 4 (19) Table 1. Demographic and clinical characteristics of patients with severe traumatic brain injury (TBI; n = 21). Continuous variables are presented as median (range) or (IQR; interquartile range). Percentages are calculated with n = 21 as the denominator. Radiological findings refer to the index CT and are not mutually exclusive; therefore, percentages may sum to >100%. Abbreviations: TBI, traumatic brain injury; GCS, Glasgow Coma Scale; tSAH, traumatic subarachnoid hemorrhage; EDH, epidural hematoma; ASDH, acute subdural hematoma; ICP, intracranial pressure; NICU, neuro-intensive care unit; ISS, Injury Severity Score; AIS, Abbreviated Injury Scale; GOS-E, Glasgow Outcome Scale–Extended; IQR, interquartile range. Patient’s sampling We aimed to obtain repeated samples from each TBI patient at Day 1–3 ( timepoint 1, early time point) and Day 4–8 (timepoint 2, late time point) in both plasma and CSF for all TBI patients (n = 21). In practice, plasma was available for 20 at timepoint 1 and 19 patient were sampled both timepoints in plasma. CSF was obtained in 9 patients overall, with both timepoints in 7 patients. One patient received an EVD later and was sampled only at timepoint 2 (both CSF and plasma). All 11 controls provided single-time-point plasma and lumbar CSF. Proximity Extension Assay (PEA) Inflammatory mediator levels in plasma analyzed by PEA only In plasma, several inflammatory mediators showed significant differences between TBI patients and controls. Of these, 12 were increased and four were decreased at any time point post-injury compared to controls. Mediators that were elevated at both time points included CCL7, CSF1, CSF3, HGF, IL-15, IL-17A, CXCL12 and IL-17C. Others were elevated at the early time point e.g. day 1–3 (CCL2) or only at the late time point e.g. day 4–8 (CXCL11, VEGFA and CCL19). Notably, temporal changes were observed in several mediators. CSF1 and IL-17A levels were significantly higher at the late time point compared to the early time point, whereas CSF3 levels were higher at the early time point compared to late time point. Among the mediators showing decreased concentrations in TBI patients compared to controls: OLR1 and TNFsf12 were consistently reduced across both sampling time points, while TNFsf10 and CCL13 were significantly decreased only at the early time point. Significant inflammatory mediator concentrations in plasma analyzed by PEA only are presented in Fig. 1a. A summary of significant temporal changes in plasma, including direction of change across time points, is provided in Suppl. Table 4. The following mediators were unchanged in plasma compared to controls following TBI: IL-18, MMP12, LTA, FLT3LG, TGFα, IL-2, IL-17F, IL-1β, CXCL10, IL-33, TSLP, IFN-γ, CCL4, IL-13, CCL8, CSF2, IL-4, OSM, MMP1, EGF, IL-7, CXCL9, CCL11, CCL3, and IL-27 (data not shown). Inflammatory mediator levels in CSF analyzed by PEA only In CSF, several mediators were significantly different between TBI patients and controls. Of these, 14 were increased, and six were decreased at any time point post-injury when compared to controls. Cytokines and chemokines that were consistently elevated across both time points included CCL11, CCL13, CCL2, CCL3, CCL4, CCL7, CCL8, CSF3, CXCL10, CXCL11, CXCL9, IL-7, MMP1 and OSM. While these mediators remained elevated across both time points, only two showed significant temporal changes within the TBI group. Both CCL8 and CXCL10 demonstrated a delayed increase, with significantly higher levels during the late time point compared to the early time point. Some mediators had significantly lower levels in TBI patients compared to controls. LTA, TGFα and TNFsf12 remained significantly lower across both time points. FLT3LG and IL-15 were significantly reduced at the early time point, though not at the later time point. On the contrary, VEGFA was only significant reduced at the later timepoint and not at the early. Significantly altered inflammatory mediator concentrations in CSF analyzed by PEA are presented in Fig. 1b. A summary of significant temporal changes in CSF, including direction of change across time points, is provided in Suppl. Table 5. The following mediators remained unchanged in CSF compared to controls following TBI: IL-18, HGF, CSF2, CCL19, MMP12, IL-17A, IL-2, IL-17F, OLR1, IL-33, TSLP, IFN-γ, IL-13, IL-4, TNFsf10, EGF, CSF1, IL-17C, CXCL12 and IL-27 (data not shown). Electrochemiluminescence (ECL) Inflammatory mediator levels in plasma and CSF analyzed by ECL only Two analytes, IL-12p70 and IFN-α2a were analyzed by ECL only. The IL-12p70 levels in plasma of TBI patients did not differ from controls at the early time point but were significantly lower at the late time point. In CSF, no significant differences in IL-12p70 levels were observed at any time point (data not shown). Levels of IL-12p70 in plasma are presented in Suppl. Figure 1. The levels of IFN-α2a in plasma and CSF were all below the LLOQ and, consequently, no further statistical analysis was performed. Comparative analyses (ECL vs. PEA) Inflammatory mediator levels in plasma - analyzed by both ECL and PEA Significantly higher levels of IL-8, IL-10 and TNF-α in TBI patients were seen in ECL analysis at both the early and late time points when compared to controls. IL-10 also showed significantly higher levels at the early time point when compared to the late time point. IL-6 was only elevated in the early timepoint. In the PEA platform IL-6, IL-8, and IL-10 were significantly elevated in TBI patients at both time points. IL-6 and IL-10 showed significantly higher values at the early time point compared to the late time point, whereas IL-8 showed significantly higher levels at the late time point compared to the early. TNF-α levels were only significantly elevated at the late time point in PEA platform. The levels in plasma of IFN-γ, IL-1β, IL-2, IL-4 and IL-13 were not significantly altered by TBI on any platform (data not shown). Inflammatory mediator levels in plasma analyzed by both ECL and PEA are presented in Fig. 2a. A summary of significant temporal changes in plasma, including direction of change across time points, is provided in Suppl. Table 4. Inflammatory mediator levels in CSF - analyzed by ECL and PEA In CSF, ECL analysis showed significantly elevated levels of IL-8 IL-10, TNF-α, IL-1β, IL-2 and IL-4 in TBI patients at both early and late time points. IFN-γ levels were only significantly elevated at the late time point but not at the early time point. PEA analysis showed significantly elevated levels at both time points for IL-6, and IL-8, while IL-10 and IL-1β levels were only significantly elevated at the early time point. The levels in CSF of IL-13 were not significantly altered by TBI on any platform (data not shown). Inflammatory mediator levels in CSF analyzed by both ECL and PEA are presented in Fig. 2b. A summary of significant temporal changes in CSF, including direction of change across time points, is provided in Suppl. Table 5. Correlations between ECL and PEA platforms When comparing overlapping inflammatory mediators between ECL and PEA, nine cytokines and chemokines were analyzed using both techniques (IFN-γ, IL-10, IL-13, IL-1β, IL-2, IL-4, IL-6, IL-8, and TNF-α). Spearman correlation analysis was conducted separately for plasma and CSF samples for both TBI and control groups. Due to tied values or minimal variability in some mediators (notably IL-2 and IL-4), certain correlations were excluded from visual and statistical interpretation. Scatter plots with Spearman ρ and corresponding p-values are presented in Fig. 3a (plasma) and Fig. 3b (CSF). Correlations between the ECL and PEA platform in plasma samples In plasma, strong and statistically significant correlations between the two platforms were observed for IFN-γ (ρ = 0.85), IL-10 (ρ = 0.86), IL-6 (ρ = 0.94), IL-8 (ρ = 0.68), TNF-α (ρ = 0.60). IL-1β (ρ = 0.44) showed moderate yet significant correlations. IL-13 showed weak non-significant correlation (ρ = 0.18, p = 0.26), while IL-2 and IL-4 were excluded due to lack of variability and artificially inflated correlation values (ρ = 1.0), caused by tied ranks. Correlations between the ECL and PEA platform in CSF samples In CSF, the strongest correlation was observed for IL-1β (ρ = 0.81), followed by IL-10 (ρ = 0.75), TNF-α (ρ = 0.71), and IL-8 (ρ = 0.71), all statistically significant. IL-6 displayed a high, non-significant, correlation (ρ = 0.90, p = 0.06). IFN-γ (ρ = 0.26, p = 0.46) and IL-13 (ρ = − 0.14, p = 0.60) showed weak and non-significant correlation and IL-2 and IL-4 were again excluded due to tied values. Variability in results Across the nine overlapping analytes, platform-dependent differences emerged, both by compartment and time. Compartment: For TNF-α, PEA showed an increase only in plasma, whereas ECL detected increases in both plasma and CSF. For IL-6, PEA indicated increases in both plasma and CSF, while ECL showed an increase only in plasma. Temporal: In plasma, IL-6 was increased at both time points by PEA, but only at the early time point by ECL. TNF-α was increased at both time points in ECL, but only at the late time point in PEA. IL-8 showed significant increase in late timepoint compared to early timepoint in PEA, but this was not seen in ECL. In CSF, TNF-α was increased at both time points in ECL (not increased in PEA at any timepoint and IL-6 was increased at both time points in PEA and not increased at either time point in ECL. IL-10 was increased at both time points in ECL, but only at early timepoint by PEA. IL-1β was increased at both timepoints in ECL, but only at early timepoint in PEA. In addition, IL-2, IL-4, and IFN-γ were increased in CSF by ECL but not at either time point by PEA. Correlation between plasma and CSF Unique Changes in Plasma Several mediators showed compartment-specific alterations in plasma but were not significantly changed in CSF. In the PEA analysis, significant increase was observed for: TNF-α, CSF1, HGF, IL-17A, CXCL12, IL-17C and CCL19 compared to controls and compared with CSF. Additionally, OLR1 and TNFsf10 levels were significantly decreased in plasma. In the ECL analysis, IL-6 levels were increased compared to controls and CSF and IL-12p70 was the only mediator found to be uniquely decreased in plasma. Unique Changes in CSF In the CSF compartment, a distinct set of mediators was significantly altered, but not changed in plasma. In the PEA analysis, the level of the following mediators was significantly increased: CCL11, CCL3, CCL4, CCL8, CXCL10, CXCL9, IL-7, MMP1, OSM and IL-1β compared to controls and plasma. Additionally, LTA, FLT3LG and TGFα were significantly decreased compared to plasma. In the ECL analysis, IL-1β, IL-2, IL-4, and IFN-γ were uniquely increased in CSF but not in plasma. Altered and overlapping changes in plasma and CSF Several mediators were significantly altered in both plasma and CSF, suggesting a shared or systemic component of the inflammatory response following TBI. In the PEA analysis, overlapping increased mediators included IL-10, IL-8, IL-6, CCL2, CCL7, CSF3 and CXCL11, all of which were significantly elevated in both plasma and CSF. TNFsf12 was decreased in both compartments. Interestingly, some mediators showed opposite patterns: VEGFA and IL-15 were elevated in plasma but decreased in CSF, while CCL13 was increased in CSF but decreased in plasma. In the ECL panel, IL-8, IL-10 and TNF-α were the only mediators significantly increased in both compartments. A Venn diagram summarizing the overlapping and unique mediator changes in plasma and CSF is presented in Fig. 4 and a table compiling these changes can be seen in Suppl. Table 6. Comparison of mediator levels between CSF and plasma – PEA Among the mediators that were significantly elevated in both plasma and CSF, several of them demonstrated clear differences in compartmental expression. IL-8, IL-6, CCL2, and CCL7 showed significantly higher concentrations in CSF compared to plasma. In contrast, IL-10 and CXCL11 were significantly higher in plasma. CSF3 and TNFsf12 showed no significant compartmental differences. These findings highlight both overlapping and compartment-specific dynamics in the post-TBI inflammatory profile (Fig. 5). Comparison of mediator levels between CSF and plasma - ECL In the ECL analysis, IL-8 levels were significantly higher in CSF compared to plasma (p < 0.001). IL-10 and TNF-α showed no significant compartmental differences. (Fig. 6). Discussion The major findings of the present study were that severe TBI induced a robust, dynamic, and multifaceted inflammatory response in both plasma and CSF, evaluated by two analytical platforms. Notably, we observed significant elevations of inflammatory mediators such as IL-8 and IL-10 in both fluid compartments, while other cytokines and chemokines exhibited compartment- and time-specific variations. These findings underscore the intricate interplay between systemic and central inflammatory responses following TBI. The observed inflammatory activation is likely a consequence of direct tissue injury, blood-brain barrier disruption, and mechanisms that trigger innate immune signaling in both the CNS and the periphery.( 14 ) The orchestration of this inflammatory response is complex and involves a wide range of mediators, including pro- and anti-inflammatory cytokines and chemokines. Notably, inflammation can exert both beneficial and detrimental effects in TBI, but the underlying mechanisms driving this dual role remain poorly understood.( 15 ) We also used two different platforms, ECL and PEA, that showed a variable correlation and measured concentrations between the analytical methods, stressing the importance of awareness when using different techniques when and assessing post-injury inflammation. We analyzed the dynamic inflammatory response in two fluid compartments, plasma and CSF. In plasma, the ECL analysis revealed significantly elevated levels of IL- 6, IL-10, IL-8 and TNF-α in TBI patients with variable dynamics. The PEA platform supported these findings. In addition, PEA analysis identified several other plasma mediators elevated at either or both time points, including CCL2, CCL7, CSF3 and CXCL11. Notably, PEA plasma analysis also displayed unique compartment-specific alterations, only seen in plasma and not CSF. Significant increases in TNF-α, CSF1, HGF, IL-17A, CXCL12, IL-17C and CCL19 were seen, whereas OLR1 and TNFsf10 were decreased. IL-12p70 was uniquely reduced in plasma when measured by ECL. These changes may contribute to the regulation of specific regulatory or homeostatic pathways and support previous findings showing temporal shifts in systemic mediators’ post-injury in both human and animal models.( 16 , 17 ) This early inflammatory signature is unlikely to be explained by infection or extracranial trauma, as few patients had signs of systemic infection or significant extracranial injuries during the study period. In a recent report, the levels of IL-6, IL-8, IL-15, IL-16 and MCP-1, samples taken at variable time points during the first post-injury week, were increased in TBI patients with an unfavorable outcome.( 6 ) It was argued that these changes were due to an endogenous response rather than simply a higher injury burden. In that report, the injury severity score (ISS) was 34. In the present study the median ISS was 27, arguing that the contribution of peripheral injuries to our results is small. In the CSF, both platforms identified increased IL-8, IL-10 and IL-1β in TBI patients, with some differences in the temporal profile during the first week post injury. In addition, a broad CSF-specific inflammatory profile was captured by PEA but also in the ECL analysis. The CSF inflammation profile likely reflects continuous central immune activation, in line with previous studies showing compartmentalized neuroinflammation in TBI patients using CSF sampling and microdialysis.( 18 , 19 ) In the PEA dataset we identified a group of mediators that were elevated in both plasma and CSF, including IL-10, IL-8, IL-6, CCL2, CCL7, CSF3, and CXCL11. This overlap suggests a shared systemic and central inflammatory response following TBI. In contrast, several mediators were uniquely elevated in CSF in the PEA platform, such as CCL11, CCL3, CCL4, CCL8, CXCL10, CXCL9, IL-7, MMP1, OSM, IL-1β and CCL13. In ECL platform IL-1β, IL-2, IL-4, and IFN-γ was uniquely elevated in CSF. These findings highlight that while some mediators reflect a common inflammatory response across compartments, others appear to be predominantly generated within the CNS. Thus, even when inflammatory mediators are present in both plasma and CSF, their source, regulation, and potential functional impact may differ.( 20 ) While blood sampling is more easily accessible, the CSF may represent a more direct window into neuroinflammatory processes and may aid in the characterization of the neuroinflammatory landscape and understanding of cytokine- and chemokine-driven pathology in TBI. After TBI, blood-brain barrier (BBB) permeability increases and blood-borne molecules can enter the parenchyma, while signals released by the TBI itself (e.g., cytokines such as TNF-α) further amplify BBB permeability. Thus, CSF cytokine levels may reflect a mixture of central production and plasma ingress. ( 21 , 22 ) In our study, we compared CSF from an external ventricular drain to CSF obtained via lumbar drainage in controls. There are obvious ethical concerns against obtaining ventricular CSF from controls, yet it is important to acknowledge that protein composition varies between these compartments. Ventricular CSF typically contains higher levels of certain proteins compared to lumbar CSF. This protein gradient may reflect dilution, differential production, or compartment-specific dynamics and should be considered when interpreting CSF biomarker findings.( 23 , 24 ) We observed that several inflammatory mediators were decreased in TBI patients compared to controls. In the PEA analysis, OLR1, TNFsf10, and CCL13 were decreased in plasma, while LTA, FLT3LG, TGFα, VEGFA and IL-15 were decreased in CSF. TNFsf12 was consistently reduced in both plasma and CSF. In the ECL analysis, only IL-12p70 was decreased, and this was observed in plasma. Interestingly, some mediators displayed opposing compartmental patterns. For example, CCL13 was increased in CSF and decrease in plasma, while VEGFA and IL-15 were increased in plasma and decrease in CSF. These divergences may reflect early post-TBI peripheral immunosuppression occurring alongside compartmentalized central inflammation.( 25 ) However, alternative explanations—such as assay differences or sampling-window effects—cannot be excluded. Although our control group consisted of orthopedic patients with minor extremity injuries, we cannot exclude that certain inflammatory mediators may have been elevated in controls compared to completely uninjured individuals, as suggested in previous work.( 5 , 26 ) Using emerging technological platforms, a large number of immune-related proteins can now be evaluated simultaneously, extending well beyond single-analyte assays and enabling deeper insight into neuroinflammation in TBI.( 27 ) Because no single factor capture this biology, individualized, data-driven selection of informative feature is required. Conversely, from large datasets much work is needed to select what factors, and patterns, contribute most to the secondary insult.( 28 , 29 ) While the PEA analysis used in our present report provides multifactorial information, additional factors may also be involved and other proteomics analysis platforms evaluating an even larger number of factors are increasingly used with correlation to neurodegeneration.( 5 , 29 ) Understanding the dynamics of cytokine and chemokine expression following TBI is crucial for developing targeted therapeutic strategies, and since neuroinflammation in TBI is not a uniform process, temporally informed interpretation is also required.( 30 , 31 ) While cytokines and chemokines are often labeled as pro- or anti-inflammatory, such categorization is increasingly recognized as an oversimplification. Following TBI, pro-inflammatory cytokines such as IL-6, TNF-α, and IL-1β are rapidly upregulated and have been widely implicated in BBB disruption, neuronal excitotoxicity, and recruitment of immune cells.( 32 ) These factors are associated with worse clinical outcome.( 33 , 34 ) However, the inflammatory response may also have protective or regenerative roles in certain contexts. For instance, while IL-6 is a key driver of the acute-phase response and has been linked to increased ICP and increased neuronal damage, elevated IL-6 levels have also been associated with better one-year neurological outcome. ( 35 , 36 ) Similarly, TNF-α may worsen injury acutely but support neuroprotection during later phases, as shown in knockout mouse models.( 37 ) On the other hand, IL-10, classically viewed as anti-inflammatory, also demonstrates complex actions. It inhibits pro-inflammatory cytokine production and limits immune cell activation.( 38 ) Elevated IL-10 levels have been associated with both improved outcomes in animal models and worse prognosis in clinical TBI studies. ( 33 , 34 , 39 ) Separately, we evaluated the IFN-α2a response using the ECL platform since IFN-α can be activated secondary to endogenous and selective retroviral activation and can be elevated following severe TBI.( 8 ) However no changes were detected post-injury in TBI patients. It remains uncertain whether IFN-α2a is suitable as a biomarker for post-TBI inflammation, whether any IFN-α response occurs in a narrow temporal window outside our sampling period, or whether levels remain below detection thresholds. A key aim of our present study was to compare the analytical ECL and PEA platforms for cytokine and chemokine quantification, the latter rarely used in TBI studies. Strong and statistically significant correlations were observed for several mediators, particularly IFN-γ, IL-10, IL-6, IL-8, and TNF-α in plasma, and IL-10, IL-1β, IL-8, and TNF-α in CSF. These findings support a consistent rank-order agreement between platforms for key inflammatory biomarkers, even across different biological compartments. Our findings align with recent multi-platform comparisons. For example, Pearson correlations across Alamar NULISA and Olink/Simoa/Millipore panels demonstrated high correlation coefficients (ρ > 0.8) for most overlapping proteins, while proteins with > 50% of samples below the limit of detection—such as IL-2, IL-4, IL-5 and IL-13, showed poor correlations.( 5 ) Similarly, a direct comparison of Olink, MSD, and Luminex platforms in nasal epithelial lining fluid reported high correlations for IL-1α and IL-6 (ρ ≥ 0.9), high for CCL3, CCL4, and MCP1 (ρ ≥ 0.7), and moderate for IFNγ, IL-8, and TNF-α (ρ ≥ 0.5). In contrast, IL-2, IL-4, IL-10, and IL-13 showed poor inter-platform agreement, again largely attributable to low abundance and measurements falling below detection thresholds.( 40 ) In our study, platform correlation was likewise variable. In plasma IL-1β showed moderate correlation and IL-13 displayed weak and non-significant correlation. In CSF IFN-γ and IL-13 displayed weak and non- significant correlation. These discrepancies are likely attributable to differences in platform sensitivity, antibody specificity, calibration standards, and matrix effects. In addition, the relatively small sample size limits statistical power, which contribute to some correlations not reaching significance. The lack of interchangeability in absolute concentrations suggest that direct comparison across platforms should be interpreted with caution ( 41 ). Beyond overall rank-order agreement, we observed platform-dependent differences by compartment and time for TNF-α, IL-6, IL-10, IL-1β, IL-2, IL-4, and IFN-γ. For example, TNF-α was elevated in both plasma and CSF by ECL but only in plasma by PEA, and IL-6 was elevated in both compartments by PEA but only in plasma by ECL. Moreover, IL-2, IL-4, and IFN-γ increased in CSF on ECL but showed no increase on PEA. These divergences are expected when targets are near limits of detection and when antibody epitopes, calibration, or matrix effects differ between assays. Practically, we suggest interpreting results using the platform with the best detectability and precision in the relevant matrix and giving the greatest weight to signals that replicate across methods.( 40 , 42 ) Biology effects vs. assay effects can't be fully disentangled, but the data still indicate a robust inflammatory response with clear compartment- and time-specific patterns across several mediators. Strengths and limitations of the study Our study provides a dual-compartment and dual-platform evaluation of early post-TBI inflammation, using both CSF and plasma alongside two complementary biomarker technologies, ECL and PEA. In addition, two time points were evaluated. The inclusion of CSF sampling in a clinical TBI cohort has been performed less often than in plasma and allowed for an assessment of central neuroinflammation that was found to be more robust than in plasma, that is, more mediators were altered in CSF, and shared analytes were typically higher in CSF than in plasma. In addition, we directly compared ECL and PEA technologies in the context of severe TBI, providing novel insights into their performance and application in neuroinflammatory profiling. This exploratory study has several limitations. The sample size was small, which limits statistical power and generalizability. Our TBI cohort was heterogeneous in terms of injury mechanism and severity, with differences in GCS scores at admission that may influence mediator expression. Moreover, while we used two validated platforms, not all mediators were measured across both platforms. Additionally, platform-specific variations in sensitivity and quantification may influence interpretation. Furthermore, although samples were collected systematically within defined timeframes, individual variation in sampling relative to injury may have influenced cytokine levels, especially in the early time point. Sampling time was also variable, and ideally, daily or highly defined sampling intervals are preferred.( 29 , 43 ) We also did not assess other biological fluids (e.g., microdialysate), which may provide higher spatial although highly focal resolution of central inflammation.( 18 , 36 ) The CSF-serum albumin quotient as a proxy for BBB integrity could also have been included.( 44 ) While multiple inflammatory mediators were evaluated, the analysis remains limited to selected cytokines and chemokines and does not capture the full spectrum of post-TBI immune responses. Moreover, in this exploratory study, we did not correct for multiple comparisons, similar to other recent neuroinflammation studies, to avoid inflating type II errors, acknowledging that this approach increases the risk of false positive.( 10 ) Finally, it is beyond the scope of this descriptive pilot study to provide a mechanistic in-depth review of each cytokine/chemokine and its pathophysiological implications. Rather, we aimed to generate exploratory insights to guide future hypothesis-driven analyses. Conclusions Our data shows that post-TBI inflammation is both time- and mediator-specific. Most mediators peak early and remain persistently elevated, and some exhibit delayed rises. Responses were more pronounced in CSF than in plasma. Several analytes displayed compartment-specific behavior, underscoring that central and systemic inflammation only partially overlap. The comparison between ECL and PEA platforms underscores the need for methodological awareness when assessing post-injury cytokine and chemokine levels. Understanding the complex inflammatory dynamics post-TBI is critical for developing targeted, time-sensitive therapeutic strategies. Future research should continue to explore the intricate dynamics of the inflammatory response in TBI. Abbreviations BBB blood–brain barrier CNS central nervous system CSF cerebrospinal fluid ECL electrochemiluminescence EVD external ventricular drain GCS Glasgow Coma Scale GOS-E Glasgow Outcome Scale–Extended ICP intracranial pressure IQR interquartile range ISS Injury Severity Score LLOQ lower limit of quantification MSD Meso Scale Discovery NICU Neurointensive Care Unit NPX Normalized Protein eXpression PBS phosphate-buffered saline PEA proximity extension assay TBI traumatic brain injury ULOQ upper limit of quantification Declarations Ethics approval and consent to participate The study was approved by the Swedish Ethical Review Authority (Dnr 2017/4069; 2017/1049; Dnr 2022-07096-02). All collected samples were stored in a local biobank (# BD27). Patients were pseudonymized once samples were collected. Since all patients had a reduced level of consciousness and, thus, were unable to provide consent, informed consent was given by patient´s next of kin. Written informed consent from the controls was obtained from each patient by written and oral instructions prior to the collection of CSF and blood samples. Consent for publication Not applicable. Availability of data and materials Anonymized summary data supporting the figures and tables are included in the article. The raw cytokine/chemokine concentration matrices (PEA and ECL) and analysis scripts are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was Funded by Swedish Research Council for Sport Science, Alborada Trust, the foundation Hans-Gabriel and Alice Trolle-Wachtmeister, Hospital Funds available via Skåne University Hospital, Swedish Brain Foundation- all to NM. Authors' contributions OP conducted the study - recruited participants, obtained informed consent from controls/next of kin, collected samples, reviewed patients' medical records, performed the statistical analyses, drafted the manuscript, and prepared all figures and tables. OP and KR jointly performed portions of the mediator analyses. KR, EU and NM critically reviewed the text and provided iterative feedback and revisions. All authors approved the final manuscript. Acknowledgements The authors wish to thank Susanne Månsson for assistance with patient recruitment, sample collection, and follow-up interviews, and the nursing and medical staff at the Neurointensive Care Unit, Skåne University Hospital, for their support in patient care and sample logistics. References James SL, Theadom A, Ellenbogen RG, Bannick MS, Montjoy-Venning W, Lucchesi LR, et al. 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05:16:18","extension":"xml","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194818,"visible":true,"origin":"","legend":"","description":"","filename":"a829209cf1af4ea0b93c475de7d30ffb1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/f913f375d4891060a0b24eb8.xml"},{"id":95499629,"identity":"36a8e19d-4c1e-45e3-aed1-ecf23e2c24c0","added_by":"auto","created_at":"2025-11-10 05:16:18","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":201747,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/05cc410ae4769d9272d087bf.html"},{"id":95528221,"identity":"cfd3bb4f-305a-4bef-8b8f-aff510f7c672","added_by":"auto","created_at":"2025-11-10 10:15:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159341,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1a. Levels of inflammatory mediator in plasma - analyzed by PEA platform.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoxplots displaying inflammatory mediator concentrations in plasma samples measured only using the proximity extension assay (PEA) platform. Only cytokines and chemokines that exhibited statistical significance in any comparison are included. The x-axis of each graph represents different groups (Control, TBI TP1: early time point, day 1-3; TBI TP2: later time point, day 4-8), while the y-axis shows inflammatory mediator concentrations in pg/mL. Notably, 12 were increased while 4 were decreased in TBI patients. Boxplots display the median and interquartile range to illustrate group distributions. Significant differences between groups (p \u0026lt; 0.05) are indicated by *.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1b. Levels of inflammatory mediator in CSF - analyzed by PEA platform.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoxplots displaying inflammatory mediator concentrations in cerebrospinal fluid (CSF) samples measured only using the proximity extension assay (PEA) platform. Only cytokines and chemokines that exhibited statistical significance in any comparison are included. The x-axis of each graph represents different groups (Control, TBI TP1: early time point, day 1-3; TBI TP2: later time point, day 4-8), while the y-axis shows inflammatory mediator concentrations in pg/mL. Notably, 14 were increased while 6 were decreased in TBI patients. Boxplots display the median and interquartile range to illustrate group distributions. Significant differences between groups (p \u0026lt; 0.05) are indicated by *.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/51f47f9345366a16ae44f4a5.png"},{"id":95499600,"identity":"15429c23-63f7-4d41-bde1-83d077eeebe7","added_by":"auto","created_at":"2025-11-10 05:16:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2a. Levels of Inflammatory mediator in plasma analyzed by both ECL and PEA platform.\u003c/strong\u003e Boxplots displaying inflammatory mediator concentrations in plasma samples measured using both electrochemiluminescence (ECL) and proximity extension assay (PEA). Only cytokines and chemokines that exhibited statistical significance in any comparison are included. The x-axis of each graph represents different groups (Control, TBI TP1: early time point, day 1-3; TBI TP2: later time point, day 4-8), and platforms are separate, while the y-axis shows inflammatory mediator concentrations in pg/mL. Boxplots display the median, interquartile range, and individual data points to illustrate group distributions. Significant differences between groups (p \u0026lt; 0.05) are indicated by *.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2b. Levels of inflammatory mediator in CSF analyzed by both ECL and PEA platform.\u003c/strong\u003e Boxplots displaying cytokine and chemokine concentrations in cerebrospinal fluid (CSF) samples measured using electrochemiluminescence (ECL) and proximity extension assay (PEA). Only cytokines and chemokines that exhibited statistical significance in any comparison are included. The x-axis of each graph represents different groups (Control, TBI TP1: early time point, day 1-3; TBI TP2: later time point, day 4-8), and platforms are separate, while the y-axis shows inflammatory mediator concentrations in pg/mL. Boxplots display the median and interquartile range to illustrate group distributions. Significant differences between groups (p \u0026lt; 0.05) are indicated by *.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/7ea400e061a0da2ab4a8a918.png"},{"id":95499601,"identity":"37a5416e-aed2-4503-a938-f5845480ba1b","added_by":"auto","created_at":"2025-11-10 05:16:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125112,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3a. Correlations for inflammatory mediators in plasma between the ECL and PEA platform. \u003c/strong\u003eScatter plots with fitted regression lines illustrating the Spearman correlation between electrochemiluminescence (ECL) and proximity extension assay (PEA) measurements for nine overlapping mediators in plasma samples. Each dot represents an individual sample. Spearman’s correlation coefficient (ρ) and corresponding p-value presented within each plot. Significant correlations were observed for IFN-γ, IL-10, IL-1β IL-6, IL-8, and TNF-α, with the strongest correlations for IL-6 (ρ = 0.94), IL-10 (ρ = 0.86), and IFN-γ (ρ = 0.85). IL-2 and IL-4 displayed artificially inflated correlations (ρ = 1.00 or undefined) due to low variability in PEA measurements. The x-axis shows mediator concentrations measured by PEA, and the y-axis shows concentrations measured by ECL, both measured in pg/mL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3b. Correlations\u003c/strong\u003e \u003cstrong\u003efor inflammatory mediators in CSF between the ECL and PEA platforms.\u003c/strong\u003e Scatter plots with fitted regression lines illustrating the Spearman correlation between electrochemiluminescence (ECL) and proximity extension assay (PEA) measurements for nine overlapping mediators in CSF samples. Each dot represents an individual sample. Spearman's correlation coefficient (ρ) and corresponding p-value are shown within each plot. Significant correlations were observed for IL-10, IL-1β, IL-8, and TNF-α. Strong correlations were seen for IL-10 (ρ = 0.75), IL-1β (ρ = 0.81), IL-8 (ρ = 0.71) and TNF-α (ρ = 0.71). IL-2 and IL-4 displayed artificially inflated correlations (ρ = 1.00 or undefined) due to low variability in PEA measurements. The x-axis shows mediator concentrations measured by PEA, and the y-axis shows concentrations measured by ECL, both measured in pg/mL.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/8e63bfd4c4aaeff4e099c747.png"},{"id":95499606,"identity":"e31708ca-d725-4a64-9c7b-95c1c40bf103","added_by":"auto","created_at":"2025-11-10 05:16:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":143169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4. Venn diagrams showing mediators significantly altered in plasma and CSF following TBI assessed separately for the PEA and ECL platforms.\u003c/strong\u003e Significant mediators are grouped by direction of change—either increased or decreased—within each biological compartment, plasma or cerebrospinal fluid (CSF). Overlapping areas represent mediators altered in both plasma and CSF. The top panel presents results from the proximity extension assay (PEA) platform, while the bottom panel shows electrochemiluminescence (ECL) platform. Asterisks (*) indicate mediators exhibiting opposite regulation between plasma and CSF. In the PEA panel, IL-10, IL-8, IL-6, CCL2, CCL7, CSF3, and CXCL11 were significantly elevated in both plasma and CSF, while TNFsf12 was decreased in both compartments. In the ECL panel, IL-10, IL-8, and TNF-α were significantly increased in both plasma and CSF.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/116b4443a363de1cd45df45c.png"},{"id":95527901,"identity":"ca6f1fc0-db1f-4f7f-8b9a-f69cdc3825c9","added_by":"auto","created_at":"2025-11-10 10:15:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5. Comparison of mediator concentrations between plasma and CSF for mediators significantly elevated in both compartments using the PEA platform.\u003c/strong\u003e Boxplots display concentrations (pg/mL) for IL-10, IL-8, IL-6, CCL2, CCL7, and CXCL11, measured using the proximity extension assay (PEA) platform. The x-axis represents the fluid compartment, and the y-axis shows mediator concentrations in pg/mL. Boxplots indicate the median, interquartile range, and individual data points. Wilcoxon signed-rank tests revealed significantly higher concentrations of IL-6, IL-8, CCL2, and CCL7 in CSF, while IL-10 and CXCL11 were significantly higher in plasma. Significant differences (p \u0026lt; 0.05) are indicated by *.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/52fdadcf0b45be0630e48dc5.png"},{"id":95499608,"identity":"676a0bcd-a738-4b8b-ac67-38b2d7963463","added_by":"auto","created_at":"2025-11-10 05:16:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 6. IL-8 concentrations in plasma and CSF measured using the ECL platform. \u003c/strong\u003eBoxplots displaying IL-8 concentrations (pg/mL) in plasma and CSF of TBI patients measured using the electrochemiluminescence (ECL) platform. Wilcoxon signed-rank tests revealed significantly higher concentrations of IL-8 levels in CSF compared to plasma. Notably, all CSF values reached the upper limit of quantification (ULOQ) and were therefore capped at this threshold. The x-axis represents the compartment fluid, and the y-axis indicates protein concentration. Boxplots illustrate the median, interquartile range, and individual data points to show distribution across compartments. Significant differences (p \u0026lt; 0.05) are indicated by *.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/b8f2650c58193248fd1884f6.png"},{"id":96261732,"identity":"89c43e74-8e14-4cd1-bbe5-58d1ee6be4ca","added_by":"auto","created_at":"2025-11-19 08:00:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2254428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/678efc87-920f-48f1-b89f-dfc80685743e.pdf"},{"id":95499599,"identity":"227c931b-1ad8-48ec-a91d-e7e3e3ce80ef","added_by":"auto","created_at":"2025-11-10 05:16:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":93401,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7903521/v1/b05381ef5d0ef4b271423f08.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A dynamic and complex early inflammatory response in blood and cerebrospinal fluid of severe traumatic brain injury patients: A dual platform analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraumatic brain injury (TBI) is a global health problem and a leading cause of death and disability in young adults.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) An immediate, primary injury to brain tissue, caused by direct mechanical forces, occurs at the moment of impact. Later, complex secondary injury mechanisms develop over hours, weeks, or even years after the initial trauma and markedly exacerbate the primary injury.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) These secondary injuries are due to complications such as ischemia, hemorrhages, excitotoxicity and cerebral edema. In addition, an inflammatory response is initiated early post-injury and often persists long after the TBI. This chronic neuroinflammation is linked to white matter atrophy and neurodegeneration.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) One way to measure inflammation is by quantifying biomarkers or inflammatory mediators. Some measurable mediators include cytokines and chemokines, which is small proteins that regulate inflammatory signaling and cell trafficking.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The importance of early inflammation has been debated, as both detrimental and pre-regenerative mechanisms may be activated. Additionally, the early inflammatory response was recently found to be related to the injury pattern and may be used to identify patients at risk of a poor neurological outcome.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Moreover, most reports have analyzed post-injury blood samples and evaluated a limited number of inflammatory factors. While it is known that both pro- and anti-inflammatory biomarkers/inflammatory mediators fluctuate during the acute and sub-acute phase of TBI, few studies have explored the temporal pattern of these changes in both blood and CSF.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Furthermore, the relative contributions of systemic (blood) \u003cem\u003eversus\u003c/em\u003e central (CSF) inflammation remain poorly understood in severe TBI. In addition, activation of endogenous retroviruses after CNS injury may trigger type I interferon pathways that may be detected in blood and CSF.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eElectrochemiluminescence (ECL) and Proximity Extension Assay (PEA) are two technologies commonly used to measure inflammatory biomarkers. The well-established ECL method is utilized in various commercial platforms, enabling the simultaneous quantification of multiple analytes with high sensitivity and specificity.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) It has been widely applied in research on inflammatory and neurodegenerative disorders.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) In contrast, PEA is a newer, highly multiplexed platform that employs DNA-labeled antibodies to detect proteins with exceptional specificity and a broad dynamic range.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) There are claims of a higher sensitivity and a lower threshold for detection with PEA, although the technique has rarely been compared directly to ECL methods.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) To our knowledge, no previous study has directly compared these two technologies in severe TBI, particularly with parallel analyses of both plasma and CSF compartments.\u003c/p\u003e\u003cp\u003eIn the present study, we used and compared two biomarker detection techniques, ECL and PEA, in the measurement of inflammatory mediators in plasma and CSF, from patients with severe TBI at two time points during the first week post-injury. We hypothesized there would be a robust, yet complex inflammatory response with an altered pattern over time and with differences between the two compartments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThis study was a prospective observational study that conveniently recruited patients with severe TBI between January 2022\u0026ndash; June 2024 at Sk\u0026aring;ne University Hospital in Lund, Sweden. Patients were recruited from the Neurointensive Care Unit (NICU) within 72 hours of injury. All severe TBI patients aged between 18\u0026ndash;80 years, with an expected NICU stay of one week, were eligible for inclusion. Severe TBI was defined by a Glasgow coma score (GCS) of \u0026le;\u0026thinsp;8 upon admission to the NICU. A list of exclusion criteria is provided in Suppl. Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003eA control group comprised of orthopedic patients, aged 18\u0026ndash;75 years old and without a history of neurodegenerative disease, undergoing spinal anesthesia for minor fractures in the lower extremities, was also recruited. Blood and CSF were collected at the time of spinal anesthesia.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical data collection and outcome assessment\u003c/h3\u003e\n\u003cp\u003eBasic demographic and clinical data, including age, sex, cause of injury, and length of NICU stay, were collected. Surviving TBI patients were contacted by phone nine to twelve months post-injury to assess long-term outcome. Functional outcome was evaluated using the Extended Glasgow Outcome Scale (GOS-E). Follow-up interviews were all conducted by the same investigator (SM).\u003c/p\u003e\n\u003ch3\u003eCollection of samples\u003c/h3\u003e\n\u003cp\u003eBlood and CSF samples were collected simultaneously from the TBI patients twice if possible: first, within three days post injury (early time point) and again four to eight days post injury (late time point). Blood samples from the TBI patients were drawn from a peripheral arterial catheter and allocated into EDTA-coated tubes. CSF was collected from patients who had received an external ventricular drain (EVD) for intracranial pressure (ICP) monitoring. All samples were centrifuged at 2000 rpm (relative centrifugal force (RCF) of 644g) for 10 minutes at 4\u0026deg;C to separate plasma and cellular components. Supernatants were allocated to 500 \u0026micro;l tubes and stored at -80\u0026deg;C within one hour of sampling until analysis.\u003c/p\u003e\u003cp\u003eSamples from the control group were collected prior to the onset of orthopedic surgery at the time of spinal anesthesia. CSF was collected using a sterile 2 ml tube during lumbar puncture before injection of spinal anesthetics. Venous blood samples were collected from the patient at the same time as CSF was collected and allocated into EDTA-coated tubes. These samples were prepared and stored in the same way as samples from TBI patients.\u003c/p\u003e\u003cp\u003eAll samples were handled by two study group participants (SM and OP) using the same approach and protocol as described in previous paragraphs.\u003c/p\u003e\n\u003ch3\u003eBiomarker analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eProximity Extension Assay\u003c/h2\u003e\u003cp\u003eFor PEA analysis, samples were sent on dry ice to SVAR Life Science AB, Malmo, Sweden for biomarker detection using Olink PEA platform (Olink Proteomics AB, Uppsala). For this study, the panel Target 48 Cytokine was used where 1\u0026ndash;10 \u0026micro;l of plasma-EDTA and CSF was used for each well. The panel consists of 45 cytokines and chemokines in total. The full list of mediators, protein names and their corresponding detection ranges are provided in Suppl. Table\u0026nbsp;2. Analyses were performed according to the manufacturer\u0026rsquo;s protocol. For each inflammatory mediator, data were reported both in NPX units (Normalized Protein eXpression, log2 scale) and in standard concentration units (pg/mL) based on standard curves defined during assay validation. The lower and upper limits of quantification (LLOQ and ULOQ) were provided per analyte, defining the validated quantifiable range.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eElectrochemiluminescence\u003c/h2\u003e\u003cp\u003eBiomarker levels in both plasma and CSF were measured at our laboratory using electrochemiluminescence (ECL) immunoassays with the Meso Scale Discovery (MSD; Rockville, MD, USA) V-PLEX Human Proinflammatory Panel 1 and the S-PLEX Human IFN-α2a kit. The V-PLEX panel quantified the following 10 cytokines and chemokines: IFN-γ, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-13, and TNF-α. The S-PLEX panel was used specifically for IFN-α2a quantification. A complete list of mediators, protein names and their corresponding detection ranges is provided in Suppl. Table\u0026nbsp;3.\u003c/p\u003e\u003cp\u003eAll procedures were performed according to the manufacturer's protocol. Before analysis, 60 \u0026micro;l of the SULFO-TAG-labeled antibody mix for each panel was diluted in 2400 \u0026micro;l of the recommended diluent. For both V-PLEX and S-PLEX assays, 50 \u0026micro;l of each sample or standard was loaded into designated wells of 96-well MULTI-SPOT plates and incubated for two hours at room temperature with shaking. Plates were then washed three times using phosphate-buffered saline (PBS) containing 0.05% Tween-20. Subsequently, 25 \u0026micro;l of the prepared detection antibody solution was added and incubated for another two hours. Following a final wash cycle, 150 \u0026micro;l of 2\u0026times; Read Buffer T was added to each well.\u003c/p\u003e\u003cp\u003ePlates were analyzed using the MESO QuickPlex SQ 120 instrument (MSD), and protein concentrations were calculated from standard curves generated from serial dilutions of known calibrators. All samples were run in duplicates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCross-platform overlap.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNine analytes were measured on both platforms\u0026mdash;IFN-γ, IL-10, IL-13, IL-1β, IL-2, IL-4, IL-6, IL-8, TNF-α\u0026mdash;enabling direct comparison. IL-12p70 and IFN-α2a were assessed by ECL only.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData handling and processing\u003c/h3\u003e\n\u003cp\u003eTo ensure data quality and reliability, all cytokine and chemokine measurements were assessed for their validity based on their respective LLOQ and ULOQ. For the PEA dataset, protein measurements were accompanied by LLOQ and ULOQ values. Samples with missing concentration were excluded from further analysis. Protein concentrations below the LLOQ, were handled with an imputation strategy by which the concentrations were replaced with LLOQ/2 to retain as much information as possible while minimizing bias.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) Conversely, values exceeding the ULOQ were capped at ULOQ, ensuring that extreme values did not disproportionately influence the results.\u003c/p\u003e\u003cp\u003eThe ECL dataset required a similar approach to handle values below LLOQ and above ULOQ. First, all missing cytokine and chemokine concentration values were removed from the dataset. Then, predefined assay-specific LLOQ and ULOQ values were assigned to each protein to determine whether values were within the quantifiable range. Values below the LLOQ were replaced with LLOQ/2, following common imputation practices in biomarker research.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) Values above the ULOQ were capped at ULOQ to avoid overestimation of protein concentrations.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using Stata version 18 (Stata Corp, College Station, TX, USA). Normal distributions were assessed using the Shapiro-Wilk test. As most variables significantly deviated from normality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), non-parametric tests were applied. The Mann-Whitney U test was used to compare cytokine and chemokine levels between controls and TBI patients at each time point (early time point and late time point) in both plasma and CSF. For the inflammatory mediators that were measured using both ECL and PEA, a Spearman correlation analysis was performed to assess the agreement between platforms. To explore temporal changes within the TBI group, inflammatory mediator levels at time point 1 (days 1\u0026ndash;3) were compared to time point 2 (days 4\u0026ndash;8) using the Wilcoxon signed-rank test for paired samples. Comparison between plasma and CSF concentrations within the same patient was also performed using the Wilcoxon signed-rank test for paired samples. All statistical analyses were two-tailed. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eEthics\u003c/h2\u003e\u003cp\u003eThe study was approved by the Swedish Ethical Review Authority (Dnr 2017/4069; 2017/1049; Dnr 2022-07096-02). All collected samples were stored in a local biobank (# BD27). Patients were pseudonymized once samples were collected. Since all patients had a reduced level of consciousness and, thus, were unable to provide consent, informed consent was given by patient\u0026acute;s next of kin. Written informed consent from the controls was obtained from each patient by written and oral instructions prior to the collection of CSF and blood samples.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003ePatient demographics\u003c/h2\u003e\n \u003cp\u003eThe study included 21 patients with severe TBI and 11 control subjects. The median age of TBI patients was 33 (range 18\u0026ndash;78) years, and 71% were males. The most common radiological findings were acute subdural hematoma (57%) and traumatic intracerebral hemorrhage/contusion (57%). Control patients had a median age of 46 (range 21\u0026ndash;73) years, and 36% were males. GOS-E was assessed nine to twelve months post-injury with a median score of 4.5 (IQR 1\u0026ndash;6). The control patients were most often (91%) operated for a tibial fracture (Table\u0026nbsp;1).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Patient Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"627\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTBI-patients, n=21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eSex, male, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e15 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eAge, years, median (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e33 (range 18\u0026ndash;78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eGCS, n (%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e7-8\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e15 (71)\u003c/p\u003e\n \u003cp\u003e2 (10)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e1 (5)\u003c/p\u003e\n \u003cp\u003e3 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eRadiological findings, n (%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003etSAH\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eContusion\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eEDH\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eASDH\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eDiffuse brain swelling\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8 (38)\u003c/p\u003e\n \u003cp\u003e12 (57)\u003c/p\u003e\n \u003cp\u003e5 (24)\u003c/p\u003e\n \u003cp\u003e12 (57)\u003c/p\u003e\n \u003cp\u003e1 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eNeurosurgical intervention, n (%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eHematoma evacuation\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCraniectomy\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eICP monitoring\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e21 (100)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11 (52)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;6 (29)\u003c/p\u003e\n \u003cp\u003e20 (95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eNICU length of stay, days, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e12 (7-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003e\u0026nbsp;Infection during study period, n (%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eAspiration pneumonia\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eUrinary tract infection\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e3 (14)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 (10)\u003c/p\u003e\n \u003cp\u003e1 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eISS, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e27 (24-33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eAIS head, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e5 (4-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eGOS-E 9-12 months post-injury, median (IQR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e4.5 (1-6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eRotterdam CT Score, n (%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (14)\u003c/p\u003e\n \u003cp\u003e12 (57)\u003c/p\u003e\n \u003cp\u003e5 (24)\u003c/p\u003e\n \u003cp\u003e1 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.3121%;\"\u003e\n \u003cp\u003eTrauma mechanism, n (%)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCar accident\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBike accident\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFall\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eAssault\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56.6879%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (19)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (14)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (48)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (19)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Demographic and clinical characteristics of patients with severe traumatic brain injury (TBI; n = 21). Continuous variables are presented as median (range) or (IQR; interquartile range). Percentages are calculated with n = 21 as the denominator. Radiological findings refer to the index CT and are not mutually exclusive; therefore, percentages may sum to \u0026gt;100%. Abbreviations: TBI, traumatic brain injury; GCS, Glasgow Coma Scale; tSAH, traumatic subarachnoid hemorrhage; EDH, epidural hematoma; ASDH, acute subdural hematoma; ICP, intracranial pressure; NICU, neuro-intensive care unit; ISS, Injury Severity Score; AIS, Abbreviated Injury Scale; GOS-E, Glasgow Outcome Scale\u0026ndash;Extended; IQR, interquartile range.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003ePatient\u0026rsquo;s sampling\u003c/h2\u003e\n \u003cp\u003eWe aimed to obtain repeated samples from each TBI patient at Day 1\u0026ndash;3 ( timepoint 1, early time point) and Day 4\u0026ndash;8 (timepoint 2, late time point) in both plasma and CSF for all TBI patients (n\u0026thinsp;=\u0026thinsp;21). In practice, plasma was available for 20 at timepoint 1 and 19 patient were sampled both timepoints in plasma. CSF was obtained in 9 patients overall, with both timepoints in 7 patients. One patient received an EVD later and was sampled only at timepoint 2 (both CSF and plasma). All 11 controls provided single-time-point plasma and lumbar CSF.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eProximity Extension Assay (PEA)\u003c/h2\u003e\n \u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eInflammatory mediator levels in plasma analyzed by PEA only\u003c/h2\u003e\n \u003cp\u003eIn plasma, several inflammatory mediators showed significant differences between TBI patients and controls. Of these, 12 were increased and four were decreased at any time point post-injury compared to controls. Mediators that were elevated at both time points included CCL7, CSF1, CSF3, HGF, IL-15, IL-17A, CXCL12 and IL-17C. Others were elevated at the early time point e.g. day 1\u0026ndash;3 (CCL2) or only at the late time point e.g. day 4\u0026ndash;8 (CXCL11, VEGFA and CCL19). Notably, temporal changes were observed in several mediators. CSF1 and IL-17A levels were significantly higher at the late time point compared to the early time point, whereas CSF3 levels were higher at the early time point compared to late time point. Among the mediators showing decreased concentrations in TBI patients compared to controls: OLR1 and TNFsf12 were consistently reduced across both sampling time points, while TNFsf10 and CCL13 were significantly decreased only at the early time point. Significant inflammatory mediator concentrations in plasma analyzed by PEA only are presented in Fig. 1a. A summary of significant temporal changes in plasma, including direction of change across time points, is provided in Suppl. Table 4.\u003c/p\u003e\n \u003cp\u003eThe following mediators were unchanged in plasma compared to controls following TBI: IL-18, MMP12, LTA, FLT3LG, TGF\u0026alpha;, IL-2, IL-17F, IL-1\u0026beta;, CXCL10, IL-33, TSLP, IFN-\u0026gamma;, CCL4, IL-13, CCL8, CSF2, IL-4, OSM, MMP1, EGF, IL-7, CXCL9, CCL11, CCL3, and IL-27 (data not shown).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eInflammatory mediator levels in CSF analyzed by PEA only\u003c/h2\u003e\n \u003cp\u003eIn CSF, several mediators were significantly different between TBI patients and controls. Of these, 14 were increased, and six were decreased at any time point post-injury when compared to controls. Cytokines and chemokines that were consistently elevated across both time points included CCL11, CCL13, CCL2, CCL3, CCL4, CCL7, CCL8, CSF3, CXCL10, CXCL11, CXCL9, IL-7, MMP1 and OSM. While these mediators remained elevated across both time points, only two showed significant temporal changes within the TBI group. Both CCL8 and CXCL10 demonstrated a delayed increase, with significantly higher levels during the late time point compared to the early time point. Some mediators had significantly lower levels in TBI patients compared to controls. LTA, TGF\u0026alpha; and TNFsf12 remained significantly lower across both time points. FLT3LG and IL-15 were significantly reduced at the early time point, though not at the later time point. On the contrary, VEGFA was only significant reduced at the later timepoint and not at the early. Significantly altered inflammatory mediator concentrations in CSF analyzed by PEA are presented in Fig.\u0026nbsp;1b. A summary of significant temporal changes in CSF, including direction of change across time points, is provided in Suppl. Table\u0026nbsp;5.\u003c/p\u003e\n \u003cp\u003eThe following mediators remained unchanged in CSF compared to controls following TBI: IL-18, HGF, CSF2, CCL19, MMP12, IL-17A, IL-2, IL-17F, OLR1, IL-33, TSLP, IFN-\u0026gamma;, IL-13, IL-4, TNFsf10, EGF, CSF1, IL-17C, CXCL12 and IL-27 (data not shown).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003eElectrochemiluminescence (ECL)\u003c/h2\u003e\n \u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003eInflammatory mediator levels in plasma and CSF analyzed by ECL only\u003c/h2\u003e\n \u003cp\u003eTwo analytes, IL-12p70 and IFN-\u0026alpha;2a were analyzed by ECL only. The IL-12p70 levels in plasma of TBI patients did not differ from controls at the early time point but were significantly lower at the late time point. In CSF, no significant differences in IL-12p70 levels were observed at any time point (data not shown). Levels of IL-12p70 in plasma are presented in Suppl. Figure\u0026nbsp;1.\u003c/p\u003e\n \u003cp\u003eThe levels of IFN-\u0026alpha;2a in plasma and CSF were all below the LLOQ and, consequently, no further statistical analysis was performed.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003eComparative analyses (ECL vs. PEA)\u003c/h2\u003e\n \u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003eInflammatory mediator levels in plasma - analyzed by both ECL and PEA\u003c/h2\u003e\n \u003cp\u003eSignificantly higher levels of IL-8, IL-10 and TNF-\u0026alpha; in TBI patients were seen in ECL analysis at both the early and late time points when compared to controls. IL-10 also showed significantly higher levels at the early time point when compared to the late time point. IL-6 was only elevated in the early timepoint.\u003c/p\u003e\n \u003cp\u003eIn the PEA platform IL-6, IL-8, and IL-10 were significantly elevated in TBI patients at both time points. IL-6 and IL-10 showed significantly higher values at the early time point compared to the late time point, whereas IL-8 showed significantly higher levels at the late time point compared to the early. TNF-\u0026alpha; levels were only significantly elevated at the late time point in PEA platform. The levels in plasma of IFN-\u0026gamma;, IL-1\u0026beta;, IL-2, IL-4 and IL-13 were not significantly altered by TBI on any platform (data not shown). Inflammatory mediator levels in plasma analyzed by both ECL and PEA are presented in Fig. 2a. A summary of significant temporal changes in plasma, including direction of change across time points, is provided in Suppl. Table 4.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\"\u003e\n \u003ch2\u003eInflammatory mediator levels in CSF - analyzed by ECL and PEA\u003c/h2\u003e\n \u003cp\u003eIn CSF, ECL analysis showed significantly elevated levels of IL-8 IL-10, TNF-\u0026alpha;, IL-1\u0026beta;, IL-2 and IL-4 in TBI patients at both early and late time points. IFN-\u0026gamma; levels were only significantly elevated at the late time point but not at the early time point.\u003c/p\u003e\n \u003cp\u003ePEA analysis showed significantly elevated levels at both time points for IL-6, and IL-8, while IL-10 and IL-1\u0026beta; levels were only significantly elevated at the early time point. The levels in CSF of IL-13 were not significantly altered by TBI on any platform (data not shown). Inflammatory mediator levels in CSF analyzed by both ECL and PEA are presented in Fig.\u0026nbsp;2b. A summary of significant temporal changes in CSF, including direction of change across time points, is provided in Suppl. Table\u0026nbsp;5.\u003c/p\u003e\n \u003cdiv id=\"Sec23\"\u003e\n \u003ch2\u003eCorrelations between ECL and PEA platforms\u003c/h2\u003e\n \u003cp\u003eWhen comparing overlapping inflammatory mediators between ECL and PEA, nine cytokines and chemokines were analyzed using both techniques (IFN-\u0026gamma;, IL-10, IL-13, IL-1\u0026beta;, IL-2, IL-4, IL-6, IL-8, and TNF-\u0026alpha;). Spearman correlation analysis was conducted separately for plasma and CSF samples for both TBI and control groups. Due to tied values or minimal variability in some mediators (notably IL-2 and IL-4), certain correlations were excluded from visual and statistical interpretation. Scatter plots with Spearman \u0026rho; and corresponding p-values are presented in Fig. 3a (plasma) and Fig. 3b (CSF).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\"\u003e\n \u003ch2\u003eCorrelations between the ECL and PEA platform in plasma samples\u003c/h2\u003e\n \u003cp\u003eIn plasma, strong and statistically significant correlations between the two platforms were observed for IFN-\u0026gamma; (\u0026rho;\u0026thinsp;=\u0026thinsp;0.85), IL-10 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.86), IL-6 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.94), IL-8 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.68), TNF-\u0026alpha; (\u0026rho;\u0026thinsp;=\u0026thinsp;0.60). IL-1\u0026beta; (\u0026rho;\u0026thinsp;=\u0026thinsp;0.44) showed moderate yet significant correlations. IL-13 showed weak non-significant correlation (\u0026rho;\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.26), while IL-2 and IL-4 were excluded due to lack of variability and artificially inflated correlation values (\u0026rho;\u0026thinsp;=\u0026thinsp;1.0), caused by tied ranks.\u003c/p\u003e\n \u003cdiv id=\"Sec25\"\u003e\n \u003ch2\u003eCorrelations between the ECL and PEA platform in CSF samples\u003c/h2\u003e\n \u003cp\u003eIn CSF, the strongest correlation was observed for IL-1\u0026beta; (\u0026rho;\u0026thinsp;=\u0026thinsp;0.81), followed by IL-10 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.75), TNF-\u0026alpha; (\u0026rho;\u0026thinsp;=\u0026thinsp;0.71), and IL-8 (\u0026rho;\u0026thinsp;=\u0026thinsp;0.71), all statistically significant. IL-6 displayed a high, non-significant, correlation (\u0026rho;\u0026thinsp;=\u0026thinsp;0.90, p\u0026thinsp;=\u0026thinsp;0.06). IFN-\u0026gamma; (\u0026rho;\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.46) and IL-13 (\u0026rho; = \u0026minus;\u0026thinsp;0.14, p\u0026thinsp;=\u0026thinsp;0.60) showed weak and non-significant correlation and IL-2 and IL-4 were again excluded due to tied values.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\"\u003e\n \u003ch2\u003eVariability in results\u003c/h2\u003e\n \u003cp\u003eAcross the nine overlapping analytes, platform-dependent differences emerged, both by compartment and time. Compartment: For TNF-\u0026alpha;, PEA showed an increase only in plasma, whereas ECL detected increases in both plasma and CSF. For IL-6, PEA indicated increases in both plasma and CSF, while ECL showed an increase only in plasma. Temporal: In plasma,\u003c/p\u003e\n \u003cp\u003eIL-6 was increased at both time points by PEA, but only at the early time point by ECL. TNF-\u0026alpha; was increased at both time points in ECL, but only at the late time point in PEA. IL-8 showed significant increase in late timepoint compared to early timepoint in PEA, but this was not seen in ECL. In CSF, TNF-\u0026alpha; was increased at both time points in ECL (not increased in PEA at any timepoint and IL-6 was increased at both time points in PEA and not increased at either time point in ECL. IL-10 was increased at both time points in ECL, but only at early timepoint by PEA. IL-1\u0026beta; was increased at both timepoints in ECL, but only at early timepoint in PEA. In addition, IL-2, IL-4, and IFN-\u0026gamma; were increased in CSF by ECL but not at either time point by PEA.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\"\u003e\n \u003ch2\u003eCorrelation between plasma and CSF\u003c/h2\u003e\n \u003cdiv id=\"Sec28\"\u003e\n \u003ch2\u003eUnique Changes in Plasma\u003c/h2\u003e\n \u003cp\u003eSeveral mediators showed compartment-specific alterations in plasma but were not significantly changed in CSF. In the PEA analysis, significant increase was observed for: TNF-\u0026alpha;, CSF1, HGF, IL-17A, CXCL12, IL-17C and CCL19 compared to controls and compared with CSF. Additionally, OLR1 and TNFsf10 levels were significantly decreased in plasma. In the ECL analysis, IL-6 levels were increased compared to controls and CSF and IL-12p70 was the only mediator found to be uniquely decreased in plasma.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\"\u003e\n \u003ch2\u003eUnique Changes in CSF\u003c/h2\u003e\n \u003cp\u003eIn the CSF compartment, a distinct set of mediators was significantly altered, but not changed in plasma. In the PEA analysis, the level of the following mediators was significantly increased: CCL11, CCL3, CCL4, CCL8, CXCL10, CXCL9, IL-7, MMP1, OSM and IL-1\u0026beta; compared to controls and plasma. Additionally, LTA, FLT3LG and TGF\u0026alpha; were significantly decreased compared to plasma. In the ECL analysis, IL-1\u0026beta;, IL-2, IL-4, and IFN-\u0026gamma; were uniquely increased in CSF but not in plasma.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAltered and overlapping changes in plasma and CSF\u003c/h3\u003e\n\u003cp\u003eSeveral mediators were significantly altered in both plasma and CSF, suggesting a shared or systemic component of the inflammatory response following TBI. In the PEA analysis, overlapping increased mediators included IL-10, IL-8, IL-6, CCL2, CCL7, CSF3 and CXCL11, all of which were significantly elevated in both plasma and CSF. TNFsf12 was decreased in both compartments. Interestingly, some mediators showed opposite patterns: VEGFA and IL-15 were elevated in plasma but decreased in CSF, while CCL13 was increased in CSF but decreased in plasma. In the ECL panel, IL-8, IL-10 and TNF-\u0026alpha; were the only mediators significantly increased in both compartments. A Venn diagram summarizing the overlapping and unique mediator changes in plasma and CSF is presented in Fig. 4 and a table compiling these changes can be seen in Suppl. Table 6.\u003c/p\u003e\n\u003cdiv id=\"Sec31\"\u003e\n \u003ch2\u003eComparison of mediator levels between CSF and plasma \u0026ndash; PEA\u003c/h2\u003e\n \u003cp\u003eAmong the mediators that were significantly elevated in both plasma and CSF, several of them demonstrated clear differences in compartmental expression. IL-8, IL-6, CCL2, and CCL7 showed significantly higher concentrations in CSF compared to plasma. In contrast, IL-10 and CXCL11 were significantly higher in plasma. CSF3 and TNFsf12 showed no significant compartmental differences. These findings highlight both overlapping and compartment-specific dynamics in the post-TBI inflammatory profile (Fig. 5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec32\"\u003e\n \u003ch2\u003eComparison of mediator levels between CSF and plasma - ECL\u003c/h2\u003e\n \u003cp\u003eIn the ECL analysis, IL-8 levels were significantly higher in CSF compared to plasma (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). IL-10 and TNF-\u0026alpha; showed no significant compartmental differences. (Fig. 6).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe major findings of the present study were that severe TBI induced a robust, dynamic, and multifaceted inflammatory response in both plasma and CSF, evaluated by two analytical platforms. Notably, we observed significant elevations of inflammatory mediators such as IL-8 and IL-10 in both fluid compartments, while other cytokines and chemokines exhibited compartment- and time-specific variations. These findings underscore the intricate interplay between systemic and central inflammatory responses following TBI. The observed inflammatory activation is likely a consequence of direct tissue injury, blood-brain barrier disruption, and mechanisms that trigger innate immune signaling in both the CNS and the periphery.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) The orchestration of this inflammatory response is complex and involves a wide range of mediators, including pro- and anti-inflammatory cytokines and chemokines. Notably, inflammation can exert both beneficial and detrimental effects in TBI, but the underlying mechanisms driving this dual role remain poorly understood.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) We also used two different platforms, ECL and PEA, that showed a variable correlation and measured concentrations between the analytical methods, stressing the importance of awareness when using different techniques when and assessing post-injury inflammation.\u003c/p\u003e\u003cp\u003eWe analyzed the dynamic inflammatory response in two fluid compartments, plasma and CSF. In plasma, the ECL analysis revealed significantly elevated levels of IL- 6, IL-10, IL-8 and TNF-α in TBI patients with variable dynamics. The PEA platform supported these findings. In addition, PEA analysis identified several other plasma mediators elevated at either or both time points, including CCL2, CCL7, CSF3 and CXCL11. Notably, PEA plasma analysis also displayed unique compartment-specific alterations, only seen in plasma and not CSF. Significant increases in TNF-α, CSF1, HGF, IL-17A, CXCL12, IL-17C and CCL19 were seen, whereas OLR1 and TNFsf10 were decreased. IL-12p70 was uniquely reduced in plasma when measured by ECL. These changes may contribute to the regulation of specific regulatory or homeostatic pathways and support previous findings showing temporal shifts in systemic mediators\u0026rsquo; post-injury in both human and animal models.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) This early inflammatory signature is unlikely to be explained by infection or extracranial trauma, as few patients had signs of systemic infection or significant extracranial injuries during the study period. In a recent report, the levels of IL-6, IL-8, IL-15, IL-16 and MCP-1, samples taken at variable time points during the first post-injury week, were increased in TBI patients with an unfavorable outcome.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) It was argued that these changes were due to an endogenous response rather than simply a higher injury burden. In that report, the injury severity score (ISS) was 34. In the present study the median ISS was 27, arguing that the contribution of peripheral injuries to our results is small. In the CSF, both platforms identified increased IL-8, IL-10 and IL-1β in TBI patients, with some differences in the temporal profile during the first week post injury. In addition, a broad CSF-specific inflammatory profile was captured by PEA but also in the ECL analysis. The CSF inflammation profile likely reflects continuous central immune activation, in line with previous studies showing compartmentalized neuroinflammation in TBI patients using CSF sampling and microdialysis.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) In the PEA dataset we identified a group of mediators that were elevated in both plasma and CSF, including IL-10, IL-8, IL-6, CCL2, CCL7, CSF3, and CXCL11. This overlap suggests a shared systemic and central inflammatory response following TBI. In contrast, several mediators were uniquely elevated in CSF in the PEA platform, such as CCL11, CCL3, CCL4, CCL8, CXCL10, CXCL9, IL-7, MMP1, OSM, IL-1β and CCL13. In ECL platform IL-1β, IL-2, IL-4, and IFN-γ was uniquely elevated in CSF. These findings highlight that while some mediators reflect a common inflammatory response across compartments, others appear to be predominantly generated within the CNS. Thus, even when inflammatory mediators are present in both plasma and CSF, their source, regulation, and potential functional impact may differ.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) While blood sampling is more easily accessible, the CSF may represent a more direct window into neuroinflammatory processes and may aid in the characterization of the neuroinflammatory landscape and understanding of cytokine- and chemokine-driven pathology in TBI. After TBI, blood-brain barrier (BBB) permeability increases and blood-borne molecules can enter the parenchyma, while signals released by the TBI itself (e.g., cytokines such as TNF-α) further amplify BBB permeability. Thus, CSF cytokine levels may reflect a mixture of central production and plasma ingress. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn our study, we compared CSF from an external ventricular drain to CSF obtained via lumbar drainage in controls. There are obvious ethical concerns against obtaining ventricular CSF from controls, yet it is important to acknowledge that protein composition varies between these compartments. Ventricular CSF typically contains higher levels of certain proteins compared to lumbar CSF. This protein gradient may reflect dilution, differential production, or compartment-specific dynamics and should be considered when interpreting CSF biomarker findings.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eWe observed that several inflammatory mediators were decreased in TBI patients compared to controls. In the PEA analysis, OLR1, TNFsf10, and CCL13 were decreased in plasma, while LTA, FLT3LG, TGFα, VEGFA and IL-15 were decreased in CSF. TNFsf12 was consistently reduced in both plasma and CSF. In the ECL analysis, only IL-12p70 was decreased, and this was observed in plasma. Interestingly, some mediators displayed opposing compartmental patterns. For example, CCL13 was increased in CSF and decrease in plasma, while VEGFA and IL-15 were increased in plasma and decrease in CSF. These divergences may reflect early post-TBI peripheral immunosuppression occurring alongside compartmentalized central inflammation.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) However, alternative explanations\u0026mdash;such as assay differences or sampling-window effects\u0026mdash;cannot be excluded.\u003c/p\u003e\u003cp\u003eAlthough our control group consisted of orthopedic patients with minor extremity injuries, we cannot exclude that certain inflammatory mediators may have been elevated in controls compared to completely uninjured individuals, as suggested in previous work.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eUsing emerging technological platforms, a large number of immune-related proteins can now be evaluated simultaneously, extending well beyond single-analyte assays and enabling deeper insight into neuroinflammation in TBI.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) Because no single factor capture this biology, individualized, data-driven selection of informative feature is required. Conversely, from large datasets much work is needed to select what factors, and patterns, contribute most to the secondary insult.(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) While the PEA analysis used in our present report provides multifactorial information, additional factors may also be involved and other proteomics analysis platforms evaluating an even larger number of factors are increasingly used with correlation to neurodegeneration.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) Understanding the dynamics of cytokine and chemokine expression following TBI is crucial for developing targeted therapeutic strategies, and since neuroinflammation in TBI is not a uniform process, temporally informed interpretation is also required.(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) While cytokines and chemokines are often labeled as pro- or anti-inflammatory, such categorization is increasingly recognized as an oversimplification. Following TBI, pro-inflammatory cytokines such as IL-6, TNF-α, and IL-1β are rapidly upregulated and have been widely implicated in BBB disruption, neuronal excitotoxicity, and recruitment of immune cells.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) These factors are associated with worse clinical outcome.(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) However, the inflammatory response may also have protective or regenerative roles in certain contexts. For instance, while IL-6 is a key driver of the acute-phase response and has been linked to increased ICP and increased neuronal damage, elevated IL-6 levels have also been associated with better one-year neurological outcome. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) Similarly, TNF-α may worsen injury acutely but support neuroprotection during later phases, as shown in knockout mouse models.(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) On the other hand, IL-10, classically viewed as anti-inflammatory, also demonstrates complex actions. It inhibits pro-inflammatory cytokine production and limits immune cell activation.(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) Elevated IL-10 levels have been associated with both improved outcomes in animal models and worse prognosis in clinical TBI studies. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eSeparately, we evaluated the IFN-α2a response using the ECL platform since IFN-α can be activated secondary to endogenous and selective retroviral activation and can be elevated following severe TBI.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) However no changes were detected post-injury in TBI patients. It remains uncertain whether IFN-α2a is suitable as a biomarker for post-TBI inflammation, whether any IFN-α response occurs in a narrow temporal window outside our sampling period, or whether levels remain below detection thresholds.\u003c/p\u003e\u003cp\u003eA key aim of our present study was to compare the analytical ECL and PEA platforms for cytokine and chemokine quantification, the latter rarely used in TBI studies. Strong and statistically significant correlations were observed for several mediators, particularly IFN-γ, IL-10, IL-6, IL-8, and TNF-α in plasma, and IL-10, IL-1β, IL-8, and TNF-α in CSF. These findings support a consistent rank-order agreement between platforms for key inflammatory biomarkers, even across different biological compartments. Our findings align with recent multi-platform comparisons. For example, Pearson correlations across Alamar NULISA and Olink/Simoa/Millipore panels demonstrated high correlation coefficients (ρ\u0026thinsp;\u0026gt;\u0026thinsp;0.8) for most overlapping proteins, while proteins with \u0026gt;\u0026thinsp;50% of samples below the limit of detection\u0026mdash;such as IL-2, IL-4, IL-5 and IL-13, showed poor correlations.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Similarly, a direct comparison of Olink, MSD, and Luminex platforms in nasal epithelial lining fluid reported high correlations for IL-1α and IL-6 (ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.9), high for CCL3, CCL4, and MCP1 (ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.7), and moderate for IFNγ, IL-8, and TNF-α (ρ\u0026thinsp;\u0026ge;\u0026thinsp;0.5). In contrast, IL-2, IL-4, IL-10, and IL-13 showed poor inter-platform agreement, again largely attributable to low abundance and measurements falling below detection thresholds.(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) In our study, platform correlation was likewise variable. In plasma IL-1β showed moderate correlation and IL-13 displayed weak and non-significant correlation. In CSF IFN-γ and IL-13 displayed weak and non- significant correlation. These discrepancies are likely attributable to differences in platform sensitivity, antibody specificity, calibration standards, and matrix effects. In addition, the relatively small sample size limits statistical power, which contribute to some correlations not reaching significance. The lack of interchangeability in absolute concentrations suggest that direct comparison across platforms should be interpreted with caution (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Beyond overall rank-order agreement, we observed platform-dependent differences by compartment and time for TNF-α, IL-6, IL-10, IL-1β, IL-2, IL-4, and IFN-γ. For example, TNF-α was elevated in both plasma and CSF by ECL but only in plasma by PEA, and IL-6 was elevated in both compartments by PEA but only in plasma by ECL. Moreover, IL-2, IL-4, and IFN-γ increased in CSF on ECL but showed no increase on PEA. These divergences are expected when targets are near limits of detection and when antibody epitopes, calibration, or matrix effects differ between assays. Practically, we suggest interpreting results using the platform with the best detectability and precision in the relevant matrix and giving the greatest weight to signals that replicate across methods.(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) Biology effects vs. assay effects can't be fully disentangled,\u003c/p\u003e\u003cp\u003ebut the data still indicate a robust inflammatory response with clear compartment- and time-specific patterns across several mediators.\u003c/p\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations of the study\u003c/h2\u003e\u003cp\u003eOur study provides a dual-compartment and dual-platform evaluation of early post-TBI inflammation, using both CSF and plasma alongside two complementary biomarker technologies, ECL and PEA. In addition, two time points were evaluated. The inclusion of CSF sampling in a clinical TBI cohort has been performed less often than in plasma and allowed for an assessment of central neuroinflammation that was found to be more robust than in plasma, that is, more mediators were altered in CSF, and shared analytes were typically higher in CSF than in plasma. In addition, we directly compared ECL and PEA technologies in the context of severe TBI, providing novel insights into their performance and application in neuroinflammatory profiling.\u003c/p\u003e\u003cp\u003eThis exploratory study has several limitations. The sample size was small, which limits statistical power and generalizability. Our TBI cohort was heterogeneous in terms of injury mechanism and severity, with differences in GCS scores at admission that may influence mediator expression. Moreover, while we used two validated platforms, not all mediators were measured across both platforms. Additionally, platform-specific variations in sensitivity and quantification may influence interpretation. Furthermore, although samples were collected systematically within defined timeframes, individual variation in sampling relative to injury may have influenced cytokine levels, especially in the early time point. Sampling time was also variable, and ideally, daily or highly defined sampling intervals are preferred.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) We also did not assess other biological fluids (e.g., microdialysate), which may provide higher spatial although highly focal resolution of central inflammation.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) The CSF-serum albumin quotient as a proxy for BBB integrity could also have been included.(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) While multiple inflammatory mediators were evaluated, the analysis remains limited to selected cytokines and chemokines and does not capture the full spectrum of post-TBI immune responses. Moreover, in this exploratory study, we did not correct for multiple comparisons, similar to other recent neuroinflammation studies, to avoid inflating type II errors, acknowledging that this approach increases the risk of false positive.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Finally, it is beyond the scope of this descriptive pilot study to provide a mechanistic in-depth review of each cytokine/chemokine and its pathophysiological implications. Rather, we aimed to generate exploratory insights to guide future hypothesis-driven analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur data shows that post-TBI inflammation is both time- and mediator-specific. Most mediators peak early and remain persistently elevated, and some exhibit delayed rises. Responses were more pronounced in CSF than in plasma. Several analytes displayed compartment-specific behavior, underscoring that central and systemic inflammation only partially overlap. The comparison between ECL and PEA platforms underscores the need for methodological awareness when assessing post-injury cytokine and chemokine levels. Understanding the complex inflammatory dynamics post-TBI is critical for developing targeted, time-sensitive therapeutic strategies. Future research should continue to explore the intricate dynamics of the inflammatory response in TBI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBBB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eblood\u0026ndash;brain barrier\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCNS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecentral nervous system\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCSF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecerebrospinal fluid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eECL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eelectrochemiluminescence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEVD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eexternal ventricular drain\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlasgow Coma Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGOS-E\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlasgow Outcome Scale\u0026ndash;Extended\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eintracranial pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einterquartile range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eISS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInjury Severity Score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLLOQ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elower limit of quantification\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMeso Scale Discovery\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNICU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeurointensive Care Unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNPX\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNormalized Protein eXpression\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ephosphate-buffered saline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eproximity extension assay\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTBI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etraumatic brain injury\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eULOQ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eupper limit of quantification\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Swedish Ethical Review Authority (Dnr 2017/4069; 2017/1049; Dnr 2022-07096-02). All collected samples were stored in a local biobank (# BD27). Patients were pseudonymized once samples were collected. Since all patients had a reduced level of consciousness and, thus, were unable to provide consent, informed consent was given by patient\u0026acute;s next of kin. Written informed consent from the controls was obtained from each patient by written and oral instructions prior to the collection of CSF and blood samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnonymized summary data supporting the figures and tables are included in the article. The raw cytokine/chemokine concentration matrices (PEA and ECL) and analysis scripts are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was\u0026nbsp;Funded by Swedish Research Council for Sport Science, Alborada Trust, the foundation Hans-Gabriel and Alice Trolle-Wachtmeister, Hospital Funds available via Sk\u0026aring;ne University Hospital, Swedish Brain Foundation- all to NM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOP conducted the study - recruited participants, obtained informed consent from controls/next of kin, collected samples, reviewed patients\u0026apos; medical records, performed the statistical analyses, drafted the manuscript, and prepared all figures and tables. OP and KR jointly performed portions of the mediator analyses. KR, EU and NM critically reviewed the text and provided iterative feedback and revisions. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank Susanne M\u0026aring;nsson for assistance with patient recruitment, sample collection, and follow-up interviews, and the nursing and medical staff at the Neurointensive Care Unit, Sk\u0026aring;ne University Hospital, for their support in patient care and sample logistics.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJames SL, Theadom A, Ellenbogen RG, Bannick MS, Montjoy-Venning W, Lucchesi LR, et al. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology. 2019;18(1):56\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDixon KJ. Pathophysiology of Traumatic Brain Injury. Phys Med Rehabil Clin North Am. 2017;28(2):215\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarklund N, Vedung F, Lubberink M, Tegner Y, Johansson J, Blennow K, et al. Tau aggregation and increased neuroinflammation in athletes after sports-related concussions and in traumatic brain injury patients - A PET/MR study. Neuroimage Clin. 2021;30:102665.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimon DW, McGeachy MJ, Bayır H, Clark RS, Loane DJ, Kochanek PM. The far-reaching scope of neuroinflammation after traumatic brain injury. 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Compr Psychoneuroendocrinol. 2021;7:100052.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeedham EJ, Helmy A, Zanier ER, Jones JL, Coles AJ, Menon DK. The immunological response to traumatic brain injury. J Neuroimmunol. 2019;332:112\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaas AIR, Menon DK, Manley GT, Abrams M, \u0026Aring;kerlund C, Andelic N, et al. Traumatic brain injury: progress and challenges in prevention, clinical care, and research. Lancet Neurol. 2022;21(11):1004\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThelin EP, Hall CE, Gupta K, Carpenter KLH, Chandran S, Hutchinson PJ, et al. Elucidating Pro-Inflammatory Cytokine Responses after Traumatic Brain Injury in a Human Stem Cell Model. J Neurotrauma. 2018;35(2):341\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDyhrfort P, Wettervik TS, Clausen F, Enblad P, Hillered L, Lew\u0026eacute;n A. A Dedicated 21-Plex Proximity Extension Assay Panel for High-Sensitivity Protein Biomarker Detection Using Microdialysis in Severe Traumatic Brain Injury: The Next Step in Precision Medicine? Neurotrauma Rep. 2023;4(1):25\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelmy A, Carpenter KL, Menon DK, Pickard JD, Hutchinson PJ. The cytokine response to human traumatic brain injury: temporal profiles and evidence for cerebral parenchymal production. J Cereb Blood Flow Metab. 2011;31(2):658\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiong Y, Mahmood A, Chopp M. Current understanding of neuroinflammation after traumatic brain injury and cell-based therapeutic opportunities. Chin J Traumatol. 2018;21(3):137\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelmy A, Guilfoyle MR, Carpenter KLH, Pickard JD, Menon DK, Hutchinson PJ. Recombinant human interleukin-1 receptor antagonist promotes M1 microglia biased cytokines and chemokines following human traumatic brain injury. J Cereb Blood Flow Metab. 2016;36(8):1434\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGadani SP, Walsh JT, Lukens JR, Kipnis J. Dealing with Danger in the CNS: The Response of the Immune System to Injury. Neuron. 2015;87(1):47\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChodobski A, Zink BJ, Szmydynger-Chodobska J. Blood-brain barrier pathophysiology in traumatic brain injury. Transl Stroke Res. 2011;2(4):492\u0026ndash;516.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantacruz CA, Vincent J-L, Bader A, Rinc\u0026oacute;n-Guti\u0026eacute;rrez LA, Dominguez-Curell C, Communi D, et al. Association of cerebrospinal fluid protein biomarkers with outcomes in patients with traumatic and non-traumatic acute brain injury: systematic review of the literature. Crit Care. 2021;25(1):278.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRostgaard N, Olsen MH, Ottenheijm M, Drici L, Simonsen AH, Plomgaard P, et al. Differential proteomic profile of lumbar and ventricular cerebrospinal fluid. Fluids Barriers CNS. 2023;20(1):6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHazeldine J, Lord JM, Belli A. Traumatic Brain Injury and Peripheral Immune Suppression: Primer and Prospectus. Front Neurol. 2015;6:235.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAneja A, Landy DC, Mittwede PN, Albano AY, Teasdall RJ, Isla A, et al. Inflammatory cytokines associated with outcomes in orthopedic trauma patients independent of New Injury Severity score: A pilot prospective cohort study. J Orthop Res. 2022;40(7):1555\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindblad C, Pin E, Just D, Al Nimer F, Nilsson P, Bellander B-M, et al. 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Brain Behav Immun. 2012;26(8):1191\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoane DJ, Kumar A. Microglia in the TBI brain: The good, the bad, and the dysregulated. Exp Neurol. 2016;275(0 3):316\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang J, Ran M, Li H, Lin Y, Ma K, Yang Y, et al. New insight into neurological degeneration: Inflammatory cytokines and blood-brain barrier. Front Mol Neurosci. 2022;15:1013933.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen X, Duan XS, Xu LJ, Zhao JJ, She ZF, Chen WW, et al. Interleukin-10 mediates the neuroprotection of hyperbaric oxygen therapy against traumatic brain injury in mice. Neuroscience. 2014;266:235\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar RG, Boles JA, Wagner AK. Chronic Inflammation After Severe Traumatic Brain Injury: Characterization and Associations With Outcome at 6 and 12 Months Postinjury. J Head Trauma Rehabil. 2015;30(6):369\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaheja A, Sinha S, Samson N, Bhoi S, Subramanian A, Sharma P, et al. Serum biomarkers as predictors of long-term outcome in severe traumatic brain injury: analysis from a randomized placebo-controlled Phase II clinical trial. J Neurosurg. 2016;125(3):631\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeiler FA, Thelin EP, Czosnyka M, Hutchinson PJ, Menon DK, Helmy A. Cerebrospinal Fluid and Microdialysis Cytokines in Severe Traumatic Brain Injury: A Scoping Systematic Review. Front Neurol. 2017;8:331.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScherbel U, Raghupathi R, Nakamura M, Saatman KE, Trojanowski JQ, Neugebauer E, et al. Differential acute and chronic responses of tumor necrosis factor-deficient mice to experimental brain injury. Proc Natl Acad Sci U S A. 1999;96(15):8721\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStrle K, Zhou JH, Shen WH, Broussard SR, Johnson RW, Freund GG, et al. Interleukin-10 in the brain. Crit Rev Immunol. 2001;21(5):427\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDi Battista AP, Rhind SG, Hutchison MG, Hassan S, Shiu MY, Inaba K, et al. Inflammatory cytokine and chemokine profiles are associated with patient outcome and the hyperadrenergic state following acute brain injury. J Neuroinflamm. 2016;13(1):40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZetlen HL, Cao KT, Schichlein KD, Knight N, Maecker HT, Nadeau KC, et al. Comparison of multiplexed protein analysis platforms for the detection of biomarkers in the nasal epithelial lining fluid of healthy subjects. J Immunol Methods. 2023;517:113473.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKinski K, Tang H, Wang K, Birchler M, Wright M. Comparison of highly sensitive, multiplex immunoassay platforms for streamlined clinical cytokine quantification. Bioanalysis. 2025;17(1):17\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbe K, Beer JC, Nguyen T, Ariyapala IS, Holmes TH, Feng W, et al. Cross-Platform Comparison of Highly Sensitive Immunoassays for Inflammatory Markers in a COVID-19 Cohort. J Immunol. 2024;212(7):1244\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLassar\u0026eacute;n P, Lindblad C, Frostell A, Carpenter KLH, Guilfoyle MR, Hutchinson PJA, et al. Systemic inflammation alters the neuroinflammatory response: a prospective clinical trial in traumatic brain injury. J Neuroinflamm. 2021;18(1):221.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindblad C, Pin E, Just D, Al Nimer F, Nilsson P, Bellander BM, et al. Fluid proteomics of CSF and serum reveal important neuroinflammatory proteins in blood-brain barrier disruption and outcome prediction following severe traumatic brain injury: a prospective, observational study. Crit Care. 2021;25(1):103.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Traumatic brain injury, Inflammation, Electrochemiluminescence, Proximity Extension Assay, cytokines, chemokines, cerebrospinal fluid","lastPublishedDoi":"10.21203/rs.3.rs-7903521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7903521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e\u003cp\u003eSevere traumatic brain injury (TBI) is associated with high mortality and long-term disability. Inflammation plays a central role in TBI pathophysiology, yet the early dynamics of inflammatory mediators in blood and cerebrospinal fluid (CSF) remain incompletely understood. Moreover, analytical methods differ across studies and have rarely been directly compared.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e\u003cp\u003eTo characterize the inflammatory response in blood and CSF during the first week after severe TBI and to assess correlation between electrochemiluminescence (ECL) and proximity extension assay (PEA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA prospective observational study was conducted recruiting adult severe TBI patients (n\u0026thinsp;=\u0026thinsp;21) requiring neurocritical care. Plasma and CSF samples were collected at two time points: days 1\u0026ndash;3 and days 4\u0026ndash;8. Orthopedic patients with minor extremity fractures (n\u0026thinsp;=\u0026thinsp;11) served as controls. Inflammatory mediator levels were quantified using ECL (11 mediators) and PEA (45 mediators). Group differences, temporal changes, and inter-platform correlations were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eBoth plasma and CSF from TBI patients displayed a pronounced inflammatory response, with multiple mediators significantly altered compared to controls. In plasma, 16 mediators were increased and 5 decreased, while in CSF, 22 were increased and 6 decreased during the first post-injury week. Key mediators (IL-8 and IL-10) were consistently elevated in both compartments, although some variation was observed between the ECL and PEA platforms. When comparing analytic methods, ECL and PEA showed strong cross-platform correlations for IL-8 and IL-10 in both plasma and CSF, whereas IL-13 exhibited weak, non-significant agreement. Platform-related differences across compartments and time points were also observed.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur findings show a marked inflammatory response in severe TBI, with distinct temporal patterns and robust neuroinflammation in CSF. While ECL and PEA showed strong correlations for several mediators, platform-dependent variability was noted. These insights improve our understanding of TBI-induced neuroinflammation and may help refine biomarker-driven prognostic and therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"A dynamic and complex early inflammatory response in blood and cerebrospinal fluid of severe traumatic brain injury patients: A dual platform analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 05:16:13","doi":"10.21203/rs.3.rs-7903521/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"871237fd-2e13-46a1-91d8-0e5232243a85","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T07:56:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 05:16:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7903521","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7903521","identity":"rs-7903521","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}