Davran Sabirov1†
Sergei Ogurcov2†
Irina Baichurina1*†Nataliya Blatt1†
Albert Rizvanov1†
Yana Mukhamedshina1,3†- 1Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
- 2Neurosurgical Department No. 2, Republic Clinical Hospital, Kazan, Russia
- 3Department of Histology, Cytology, and Embryology, Kazan State Medical University, Kazan, Russia
To date, a large number of studies are being carried out in the field of neurotrauma, researchers not only establish the molecular mechanisms of the course of the disorders, but are also involved in the search for effective biomarkers for early prediction of the outcome and therapeutic intervention. Particular attention is paid to traumatic brain injury and spinal cord injury, due to the complex cascade of reactions in primary and secondary injury that affect pathophysiological processes and regenerative potential of the central nervous system. Despite a wide range of methods available methods to study biomarkers that correlate with the severity and degree of recovery in traumatic brain injury and spinal cord injury, development of reliable test systems for clinical use continues. In this review, we evaluate the results of recent studies looking for various molecules acting as biomarkers in the abovementioned neurotrauma. We also summarize the current knowledge of new methods for studying biological molecules, analyzing their sensitivity and limitations, as well as reproducibility of results. In this review, we also highlight the importance of developing reliable and reproducible protocols to identify diagnostic and prognostic biomolecules.
Introduction
Neurotrauma is a serious public health problem worldwide due to the high disability of a young working-age population and the high cost of medical support (DeVivo, 2012; Rubiano et al., 2015; Asmamaw et al., 2019). The problem of functional recovery of patients with traumatic brain injury (TBI) and spinal cord injury (SCI) is especially relevant due to low regenerative potential of the central nervous system (CNS). Primary neurotrauma, leading to cell necrosis in the area of a traumatic force application, is replaced by secondary, even more catastrophic damage to the nervous tissue. A complex cascade of inflammatory, toxic and vascular reactions causes the death of neurons and glial cells, accompanying progressive structural changes in brain and spinal cord tissues (Gaudet and Fonken, 2018; Orr and Gensel, 2018).
However, the severity of posttraumatic reactions and the intrinsic regenerative potential of the nervous tissue may be different for individual subjects, which may determine the degree of damage and functional outcomes of the subacute period of neurotrauma. Sometimes it is difficult to establish the severity of the injury applying neuroimaging methods used in the clinic (MRI, CT) (Amyot et al., 2015; Dalkilic et al., 2017). With the help of a functional neurological examination, the clinical assessment of the severity by American Spinal Injury Association (ASIA) Impairment Scale (AIS) grade and the Glasgow Outcome Scale (GOS)/Glasgow Coma Scale (GCS) in SCI and TBI accordingly is of great importance (Weir et al., 2012; Betz et al., 2019). However, the baseline clinical examination is often difficult to perform during the acute period of neurotrauma, and the variability in spontaneous recovery within each grade of AIS and GOSE is very high (Sandwell and Markandaya, 2015).
At the same time, quantitative and qualitative changes in the molecular composition of cerebrospinal fluid (CSF) and blood serum caused by cell damage or an increased permeability of cell membranes and, in general, violations of the blood-brain barrier can make it possible to detect differences in the severity of pathophysiological processes after TBI and SCI (Halford et al., 2017). Given the above, the identification of reliable biomarkers in these biological fluids that can provide an objective and accurate diagnosis of neurotrauma, predict functional outcomes and, in particular, monitor the effectiveness of therapy, may be of decisive importance in medical support (Capirossi et al., 2020).
To date, there is a sufficient number of studies that search for biomarkers of neurotrauma of varying severity. To this end, researchers use various methods and approaches that differ in time, complexity, and cost. However, the clinic still does not have access to a test system that has the ability to accurately and easily diagnose and predict damage to the brain and spinal cord. This review is aimed at describing potential biomarkers correlated with the degree of neurotrauma, with a focus on the sensitivity and objectivity of the methods used and the reproducibility of the results.
Enzyme-linked immunosorbent assay
Enzyme-linked immunosorbent assay (ELISA) has been long known to be used to search for neurotrauma biomarkers. However, the earliest works primarily focused on etiopathogenesis, and only subsequently made a conclusion about the possibility of diagnostic, prognostic and therapeutic significance of the obtained data (Segal and Brunnemann, 1993; Wang et al., 1995; Segal et al., 1997; Kil et al., 1999). Later works, dating back to the beginning of the 2000s according to the sources known to us, already preferred to identify clinical correlates of elevated blood serum/CSF cytokines/autoantibody or other proteins in patients with neurotrauma (Fassbender et al., 2000; Pleines et al., 2001; Hayes et al., 2002; Franz et al., 2003).
Since 2018, the U.S. Food and Drug Administration (FDA) has approved marketing of a first rapid ELISA to assess mild TBI by serum glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) levels. Using the abovementioned Banyan BTI™ test Lewis et al. (2020) showed that in the first 6 h after TBI the median serum concentrations of GFAP and UCH-L1 were significantly higher in patients with acute unfavorable neurological outcome detected by computer tomography (CT+). However, it should be noted that UCH-L1 and GFAP level did not show any significant association with an outcome 3 months after injury (Diaz-Arrastia et al., 2014). Levels of GFAP and UCH-L1 expressions are also being actively studied in SCI (Kwon et al., 2010, 2017; Yokobori et al., 2015). The results of these studies are consistent and indicate a positive correlation between CSF levels of GFAP and UCH-L1 in the acute period of SCI and the severity of damage.
The standard ELISA method has low sensitivity and is poorly suited for searching biomarkers of neurotrauma due to the low circulating concentration of the brain-specific proteins in blood, since the blood-brain barrier limits their diffusion (Marchi et al., 2003). In this regard, new methods for conducting ELISA are being developed (Li et al., 2016; Hu et al., 2020; He et al., 2021). O’Connell et al. (2020) in attempt to determine the advantage of digital ELISA over conventional ELISA methods compared the levels of neuron-specific proteins in TBI patients no more than 24 h after injury. The authors showed that digital ELISA measures of neurofilament light chain (NF-L) and tau protein had a greater diagnostic efficiency and 100% sensitivity compared to the 7.7% in those estimated by conventional ELISA (O’Connell et al., 2020).
Czeiter et al. (2020) assessed levels of 6 serum biomarkers in TBI, two of which -S100B and NSE- were measured using an electrochemiluminescence immunoassay, which shows higher sensitivity, reduction matrix effects, and requires lower sample volumes (Bolton et al., 2020). The study found that the abovementioned biomarkers correlated with the trauma-related intracranial findings on CT and the requirement for hospitalization in a general ward or intensive care unit. However, it is worth noting that the half-life of S100B is ∼90 min, in contrast to NSE, which has a half-life of ∼24 h, thus reducing the clinical utility of S100B as a neurotrauma biomarker (Ghanem et al., 2001; Kang et al., 2021).
Over the past decade, many studies have been conducted using traditional or improved ELISA in search for and quantitative assesment of neurotrauma biomarkers (Table 1). Most of the ELISA kits used are not designed or approved for human use and therefore are not regulated, often leading to a marked variability of results over time among test kits and different laboratories using them (Feng et al., 2019). In addition, there is an understanding that for more informative data it is necessary to obtain results for several biomarkers. Simultaneous measurement of multiple biomarkers with ELISA when run in parallel is time consuming, increases the risk of errors and requires large sample costs often collected in small volume due to their value (Ray et al., 2005). In this regard, the development of multiplex immunoassay technologies, discussed below, made for solving some of the problems and brought the research related molecular diagnostics in neurotrauma to a new level.
TABLE 1. Candidate biomarkers that are detected by immunoanalysis in neurotrauma.
Multiplex immunoassay
It was previously suggested that the development of a multiplex immunoassay to measure biomarkers of neurotrauma could be an important step in advancing research, as it would allow a faster and cheaper detection of required proteins in the smaller sample volumes (Berger et al., 2009). However, despite these advantages, this technology has not yet been introduced into clinical practice and so far, remains in demand only at the stage of fundamental research of new biomarkers and therapeutic targets.
Korley et al. (2019) measured GFAP, UCH-L1, NF-L, and total tau levels in patient plasma samples up to 24 h after TBI using an ultrasensitive 4-plex immunoassay. GFAP, NF-L and UCH-L1 values correlated with the detection of traumatic intracranial pathology on CT. Of a particular interest was the data showing that the sensitivity of NF-L measurement using the multiplex immunoassay kit was comparable to that of a singleplex analysis of similar samples (Korley et al., 2019). Many researchers showed interest in the abovementioned multiplex immunoassay kit for registering neurology biomarkers. The previously mentioned study by Czeiter et al. (2020) confirmed the correlation of GFAP, UCH-L1, NF-L and total tau levels with TBI severity. The most pronounced changes were found in the expression of GFAP, which level achieved the highest discrimination for predicting CT abnormalities within 24 h after TBI (Czeiter et al., 2020). However, a comprehensive analysis of GFAP in conjunction with the abovementioned proteins did not provide additional value for predicting CT+, confirming the data of previous works (Gill et al., 2018).
Stein et al. (2013) conducted a pilot study to simultaneously assess the level of 21 plasma cytokines of chronic SCI patients using multiplex immunoassay. It was found that there was a significant increase in the levels of MIF, CXCL9, MCSF, IL-3 and SCGF-β in chronic SCI patients compared to uninjured control, nevertheless, no correlation was found with any clinical characteristics. In another study, a 25-plex immunoassay kit was used to search for CSF biomarkers to stratify SCI severity and predict an outcome (Kwon et al., 2017). At 24 h post-injury the level of IL-6 was significantly different between patients with baseline AIS grades of A to C, and changes of IL-6, IL-8 and MCP-1 levels correlated with improvement of AIS grade over 6 months. Our recent pilot study, using extended multiplex analysis of 40 serum analytes of patients at 2 weeks post-SCI showed a large elevation of IFNγ (>52 fold), CCL27 (>13 fold), and CCL26 (>8 fold) (Ogurcov et al., 2021). At the same time the levels of cytokines CXCL5, CCL11, CXCL11, IL10, TNFα, and MIF were different between patients with baseline AIS grades of A or B.
Unfortunately, the low analytical sensitivity of multiplex immunoassay has not yet made it possible to make a significant breakthrough in molecular diagnostics of neurotrauma, moreover, this method is not cheap (Korley et al., 2019). Multiplex immunoassays often reduce analytical sensitivity due to interference between different antibodies, analytes, and assay diluents; variability of the production process; and incompatibility between different limits of quantitation (Ellington et al., 2009). Nevertheless, subsequent work to improve the analytical characteristics of multiplex immunoassay should contribute to its wider introduction into clinical practice, acquiring not only high diagnostic, but also a practical importance for correcting the therapeutic intervention plan.
Proteomic analysis
Neurotrauma leads to a change in synthesis and secretion of many proteins, associated with the complexity and dynamism of posttraumatic processes. In this regard, the use of quantitative proteomics, which helps to recognize differentially expressed proteins in TBI and SCI, can not only identify potential diagnostic biomarkers, but also predict the goals and mechanisms of treatment (Zhou et al., 2020). For research purposes large-scale proteomics is most often used to identify posttraumatic changes in the qualitative and quantitative composition of proteins in CSF and blood serum (Poulos et al., 2020) (Figure 1). Moghieb et al. (2016) initially assessed changes in protein expression in injured rat spinal cord tissue and then determined whether these changes were reflected in CSF or blood serum. During the study, 12 proteins were identified as biomarkers of SCI, of which only transferrin, triosephosphate isomerase 1, cathepsin D, and astrocytic phosphoprotein PEA-15 were found to be elevated in CSF in both rodents and humans with SCI 24 h and 7 days after damage (Moghieb et al., 2016). Similar approach was taken by Skinnider et al. (2021) who evaluated the expression of 491 proteins in CSF and blood serum from patients with acute SCI and, in parallel, in a porcine model of SCI. The authors showed that level of GFAP in CSF correlated with both the initial severity of injury and more adverse neurological outcomes in both humans and pigs (Skinnider et al., 2021).
FIGURE 1. Possibilities of using blood and cerebrospinal fluid as biomarkers of neurotrauma.
Haqqani et al. (2007) applied gel-free proteomic approaches to identify peripheral “surrogate markers” in serum blood samples obtained from children during the acute period of severe TBI. The authors found an increase in the level of proteins such as S100β, neuron-specific gamma-enolase, amyloid beta (A4) precursor protein, alpha-spectrin, and cleaved tau protein, which confirms previously obtained data (Olsson et al., 2004; Ringger et al., 2004). Based on proteomic analysis of blood serum within the first 24 h Anada et al. (2018) proposed a panel of 10 biomarkers for diagnosing patients with TBI of different severity which may be complementary to the Glasgow Coma Scale (GCS) system. The authors found that the level of kininogen, apolipoprotein E and zinc-alpha-2-glycoprotein can be used as protein signatures to differentiate the injury severity (Anada et al., 2018).
It should be noted that blood has the highest priority as a source of biomarkers, since sampling procedure has a low degree of invasiveness, does not lead to significant risks and does not require additional manipulations. However, the key problem is that the blood serum proteome reflects the collective expression of all tissues and cell types of the body and, accordingly, does not have a high specificity for nervous tissue; in addition, the problem also lies in the high dynamic range of proteins and peptides (Azar et al., 2017).
In order to identify biomarkers of neurotrauma using proteomic analysis, it is advisable to sample cerebrospinal fluid, because CSF maintains direct contact with the CNS. CSF has a unique advantage over plasma, saliva, and other fluid sources in its ability to reflect the biochemical changes that occur during neurotrauma. Thus it is widely used in proteomic analysis for the determination of biomarkers in TBI. Among 484 identified proteins Halford et al. (2017) found 232 unique enzymes for the acute period of severe TBI. Proteins associated with astrocyte injury (ALDOC, GLNA, BLBP and PEA15) were identified and confirmed for presence and quantification using two independent approaches: Immunoblotting with scaled densitometry and multiple reaction monitoring-mass spectrometry (MRM-MS). Comparison of the two methods showed similar detection limits and interquartile ranges for ALDOC, BLBP, and GFAP, further confirming the trends obtained with both approaches. However, the authors noted a wide dynamic range of biomarker concentrations, reflecting the greater clinical heterogeneity of TBI (Halford et al., 2017). In other investigation, the two approaches were also used to identify TBI biomarkers in CSF obtained microvesicles/exosomes (MVs/Es) (Manek et al., 2018). Several known TBI biomarkers were found to be present, such as αII-spectrin and GFAP degradation products, UCH-L1 and GFAP at higher concentrations in CSF obtained MVs/Es after TBI compared to similar samples in non-TBI control patients.
In contrast to the abovementioned studies, Streijger et al. (2017) conducted a targeted proteomic analysis of CSF samples obtained from patients with acute SCI using MRM-MS. The authors identified 27 potential biomarkers of neurotrauma (baseline AIS A, B, or C), with triosephosphate isomerase having the strongest association with SCI severity. An earlier study by Sengupta et al. (2014) used difference gel electrophoresis and mass spectrometry (MS) to compare the CSF proteomic profile of patients at days 1–8 and 15–60 after SCI. The authors identified 8 proteins whose expression level depended on SCI severity (AIS A vs. AIS C, D). According to the results, these proteins were involved in various molecular pathways, including DNA repair, protein phosphorylation, tRNA transcription, iron transport, mRNA metabolism, immune response, and lipid and ATP catabolism, while their level profiles changed over time after SCI (Sengupta et al., 2014).
The active development of proteomic analysis methods plays an important role in the discovery of biomarkers in patients with neurotrauma. However, proteomic analysis requires careful sample preparation, which takes a long time, about several days. Preliminary application of large-scale proteomics and subsequent targeted MS or microarray-based methods, often in combination with gel electrophoresis of CSF samples and, to a lesser extent, blood serum of patients with SCI and TBI, allows more efficient search for potential biomarkers that can be used to predict clinical outcomes. However, one of the main limitations of proteomic analysis is the presence of potential age-related proteins, presented individually. In this regard, significant further work is required, since none of the potential biomarkers is ready for a routine use in clinic practice.
Transcriptomic analysis
Signaling cascades of primary and secondary damage triggered by neurotrauma of the CNS contribute to the development of inflammatory reactions and cell death; however, the disclosure of molecular mechanisms is still limited. In this regard, the analysis of transcriptome changes during TBI and SCI can provide key insights into the mechanisms and pathways associated with these pathologies, which will be extremely useful for improving the effectiveness of regenerative therapy and pharmacological screening. Studies using RNA-seq technology provide good coverage of neurodegeneration processes in humans (Alzheimer’s disease, Parkinson’s disease) (Hossein-Nezhad et al., 2016; D’Erchia et al., 2017), while such studies in CNS neurotrauma are most often performed on animal models (Du et al., 2018; Zhou et al., 2019). Thus, studies of animal spinal cord transcriptome at various stages of TBI and SCI helped developing a system analysis program used to identify key determinants in global gene networks, immune response associated enriched groups of genes, cytokine/chemokine activity, the MHC protein complex, processing and antigen presentation, translation, ion channel activity and small GTPase-mediated signal transduction (Chen et al., 2013; Zhong et al., 2016; Meng et al., 2017; Shi et al., 2017; Wang Q. et al., 2019; Li et al., 2019). There are also studies that utilize the method of transcriptomic profiling of animal spinal cord to reveal regenerative mechanisms against the background of various therapy options use (Duan et al., 2015).
Michael et al. (2005) analyzed 1,200 genes in 7 patients with TBI, of which the expression 104 genes were differentially changed when compared with the control group of healthy people. The authors indicated that most often significant differences were observed for genes that control the regulation of transcription, intermediate and energy metabolism, signaling, and intercellular adhesion (Michael et al., 2005). Kyritsis et al. (2021) exmained global gene expression in peripheral blood leukocytes during the acute phase of SCI and identified 197 genes which expression changed after injury, including in direct relation to the severity of SCI. In a similar but earlier study, Wang F. et al. (2019) found that differential gene expression in patients with incomplete SCI with capacity to recover motor function was significantly enriched in the neurotrophin TRK receptor signaling pathway. At the same time, the greatest difference between the groups of patients with incomplete/complete SCI and healthy people was in the expression level of EPHA4, CDK16, BAD, MAP2 Normal 2, EGR and RHOB genes (Wang F. et al., 2019).
In addition to the global gene expression, active targeted studies are being conducted on post-transcriptional regulators that affect gene expression. For example, it was recently established that long non-coding RNAs (lncRNAs) play an important role in a wide range of biological processes and are expressed, among other things, in CNS neurotrauma. Yang et al. (2019) studied the profile of lncRNAs and mRNA in human contusion tissue after TBI. Alterations in the expression of 99 lncRNAs and 63 mRNAs were found in the area of TBI compared to control samples (Yang et al., 2019). In the TBI group, the five were most significantly up-regulated and down-regulated lncRNAs, three of which played an important role in the immune system, representing peptides derived from extracellular proteins that are HLA class II beta chain paralogs (Chowdhary et al., 2015). Thus, the authors established a correlation between lncRNAs and mRNA and concluded that overexpressed lncRNAs were also involved in the pathological process of TBI (Yang et al., 2019).
Earlier studies showed that miRNAs downregulate many more targets than previously thought, thus helping to determine the expression of tissue-specific genes in humans (Lim et al., 2005). Therefore, miRNA profiling is often used, which, unlike mRNA, are more specific and accurate, and because of their size, are more stable in plasma, since they are predominantly located in exosomes. miRNAs are highly expressed in the CNS, can cross the blood-brain barrier, are stable in peripheral biofluids, and can provide information about brain damage. There are a lot of clinical studies on the identification of miRNAs in blood (plasma, serum) or CSF in TBI (Table 2), while similar studies in SCI are few (Lim et al., 2005; Tigchelaar et al., 2019).
TABLE 2. MiRNA biomarkers TBI in different biological sources from human.
For example, Mitra et al. (2017) determined the level of miRNAs circulating in blood plasma on days 5 and 30 after TBI of various severity. miR-142-3p and miR-423-3p showed the highest potential clinical relevance for identifying mild TBI patients with post-concussion syndromes. In another study, miRNA expression in the serum of these patients was also measured to determine the differences between mild and severe TBI (Di Pietro et al., 2017, 2018). miR-425-5p and miR-21 have been shown to be reliable predictors of a favorable 6-month outcome in the first 12 h after mild TBI. miR-335 has been proposed as a promising biomarker for polytrauma-associated severe TBI. A year later, a similar study was published by Qin et al. (2018) in which the authors also assessed differences in the miRNA expression in blood plasma in the first 24 h after receiving mild, moderate, and severe TBI. In this study miR-3195 and miR-328-5p expression levels were higher in the severe TBI group than in the mild and moderate TBI groups (Qin et al., 2018). In addition to the blood serum miRNA, CSF miRNA was also studied within 48 h after TBI (Bhomia et al., 2016). Ten miRNAs, including miR-328, were found to be overexpressed by real-time PCR when compared TBI to healthy controls. The authors concluded that with an increase in the degree of damage detected on CT, more miRNAs are secreted into the serum, which is an indirect indicator of the severity of damage to the nervous tissue.
Early studies of miRNAs in SCI are based on the study of the miRNA profile of injured tissue in rat and mouse animal models (Liu et al., 2009; Strickland et al., 2011; Yunta et al., 2012; Baichurina et al., 2021). More recent studies by Tigchelaar et al., 2017 and Tigchelaar et al., 2019 are devoted to searching for miRNAs as biomarkers of SCI in blood serum and CSF of pigs and humans. The latest work on the assessment of the small RNAs profile, including miRNAs, in the acute patients (days 1 and 5) showed that, depending on the severity of damage, the concentrations of small RNAs increase in CSF in the first 24 h after neurotrauma, followed by a decrease in their concentration level on day 3 to the values observed in non-SCI control patients. It was found that miR-133 and miR-145-3p are statistically significantly increased in the serum of patients with SCI and also show severity-dependent expression. It should be noted that half of the miRNAs that were differentially expressed depending on the severity in blood serum of pigs also showed similar expression pattern in humans after SCI (Tigchelaar et al., 2017, 2019). However, the main cross-species difference was that in contrast to the experiments on pigs, in SCI patients the biggest change in the miRNA profile was observed in CSF, showing a correlation between the expression of certain serum miRNAs with injury severity and neurological outcome.
Transcriptomic analysis is expensive and takes a long time to complete. Nevertheless, the above examples indicate the prospect of using microRNA as a diagnostic tool. It is assumed that if one selects microRNAs that can characterize different degrees of neurotrauma, one can create an effective panel for accurate diagnosis. Unfortunately, studying the profile of microRNAs in biological fluids is a rather difficult task, which is associated with their low concentration and the lack of standard approaches for the isolation and analysis of miRNAs. In addition, one of the limitations of serum or plasma miRNA studies is the difficulty in determining the origin of a particular miRNA and identifying its effects.
Metabolic analysis
Metabolomics is defined as a method for identifying metabolites synthesized by biological and physiological systems and is a phenotypic expression of genome and proteome. The use of high-throughput metabolomics methods may be useful in discovering new biomarkers associated with homeostasis disorders after neurotrauma (Wolahan et al., 2016).
Glenn et al. (2013) examined CSF of patients with severe and mild TBI in acute period (24 h) using proton nuclear magnetic resonance spectroscopic analysis. The study showed that the concentration lactate, propylene glycol and glutamine was significantly increased, and the concentration of total creatinine significantly decreased after TBI. The authors found that α-glucose was a stronger predictor cerebral metabolic rate of oxygen, increased intracranial pressure and Glasgow Outcome Scale–Extended. Taking into account the increase in propylene glycol these results, suggest changes in glucose metabolism after TBI (Glenn et al., 2013). In a later work, Orešič et al. (2016) performed a metabolomic analysis based on two-dimensional gas chromatography coupled to time-of-flight mass spectrometry of serum and brain microdialysate from patients with acute TBI (12 h). The study was focused on two groups of patients with similar TBI but from different regions—Finland and United Kingdom. The authors found an increase in the blood serum of two medium-chain fatty acids (decanoic and octanoic acids) and sugar derivatives, most of which were also found in high concentrations in brain microdialysis of patients with TBI. The authors concluded that the serum metabolites were sensitive to the severity of TBI and predicted patient outcomes (Orešič et al., 2016).
In another study Wu et al. (2016) applied a metabolomic profiling method using a differential chemical isotope labeling liquid chromatography mass spectrometry with a universal metabolome standard. CSF and sera samples from 30 patients were obtained at 3 time points (∼24, 48 and 72 h) after SCI. There were 6 CSF metabolites (uridine, imidazoleacetic acid, methionine sulfoxide, arginine, cystathionine, and homocarnosine) and 4 serum metabolites (uridine, 4-hydroxyproline, N1, N12-diacetylspermine, and glycylproline), the level of which correlated with the severity of SCI AIS A, B and C. Metabolic pathway analysis revealed a predominant dysregulation of arginine-proline metabolism after SCI (Wu et al., 2016). In a more recent pilot study by Singh et al. (2018) the correlation between the metabolic profile in the blood serum of patients with SCI and neurological recovery was analyzed using proton nuclear magnetic resonance spectroscopic. It was found that significant differences in metabolites between SCI and the control group were characteristic of 15 metabolites, of which 7 were statistically significantly different and belonged to two classes of organic compounds: amino acids (valine, isoleucine, glycine), ketone bodies (acetone, succinate, acetate) and lactate (Singh et al., 2018).
Thus, the possibility of using metabolomic profiling of CSF and blood serum in neurotrauma to study pathophysiological processes and search for biomarkers that predict disease outcomes is being considered. Using the analysis of the metabolomic profile, it is possible to draw a conclusion about which of the metabolic pathways is impaired and where therapy should be directed. However, the number of such studies is not large and requires continued work in this direction with the inclusion of a larger sample of patients in order to effectively search for reliable biomarkers. The metabolites that have already been identified in TBI and SCI are very diverse, and were found in studies using different measurement methods, which does not allow comparison of the results. In addition, in order to develop a panel of biomarkers for use in diagnostics, the absolute amount of a particular metabolite must first be established, and then analysis should be carried out in larger samples and control populations.
Conclusion
The multicomponent nature of the processes that occurs during neurotrauma and forms complex microenvironment is the main barrier in the development of effective diagnostic and prognostic tools. In addition, dynamic post-traumatic processes and existing differences between the types of injury (for example, contusion, crush, etc.) as well as their location are also critically important for the search for potential biomarkers of neurotrauma. On the other hand, high sensitivity of components to changes in external factors (low robustness) of the most accessible and widely used ELISA methods adds complexity to the interpretation and requires validation of the results obtained. In this regard, mass spectrometric analysis methods, primarily based on MRM technology, are increasingly being used in clinical practice to improve the detection selectivity of target biomarker proteins, the search for which in neurotrauma, however, has not yet been completed. To include transcriptomic analysis in the arsenal of laboratory diagnostics, apparently, it will take even more time, which is necessary to establish a reliable picture of gene regulation and their influence on specific links of pathogenesis. When solving the existing technical problems associated with sample preparation and the features of various devices, as well as increasing the interlaboratory reproducibility of results and objectivity of the data obtained, the possibility of creating in the future ready-made panels/chips for sequencing or sets of specific primers for the detection of diagnostic transcripts in TBI and SCI is not ruled out. Given the above, it is worth noting that, before choosing the most effective and clinically feasible approach, it is necessary to carry out important work to establish common analytical protocols for determining diagnostic and prognostic biomolecules, since the results of research work available to date are often not comparable with each other and have low reproducibility. Unfortunately, we cannot fully compare the technologies mentioned in our review, since each of them is suitable for a specific task. It is possible to analyze the presented technologies only in terms of cost and turn-around time, but not the result. The detected biomolecules belong to different classes and, accordingly, have different diagnostic and prognostic capabilities in neurotrauma.
Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Funding
The work is carried out in accordance with the Strategic Academic Leadership Program “Priority 2030” of the Kazan Federal University of the Government of the Russian Federation.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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References
Ahadi, R., Khodagholi, F., Daneshi, A., Vafaei, A., Mafi, A. A., Jorjani, M., et al. (2015). Diagnostic value of serum levels of GFAP, pNF-H, and NSE compared with clinical findings in severity assessment of human traumatic spinal cord injury. Spine 40, E823–E830. doi:10.1097/BRS.0000000000000654
Al Nimer, F., Thelin, E., Nyström, H., Dring, A. M., Svenningsson, A., Piehl, F., et al. (2015). Comparative assessment of the prognostic value of biomarkers in traumatic brain injury reveals an independent role for serum levels of neurofilament light. PloS One 10, e0132177. doi:10.1371/journal.pone.0132177
Amyot, F., Arciniegas, D. B., Brazaitis, M. P., Curley, K. C., Diaz-Arrastia, R., Gandjbakhche, A., et al. (2015). A review of the effectiveness of neuroimaging modalities for the detection of traumatic brain injury. J. Neurotrauma 32, 1693–1721. doi:10.1089/neu.2013.3306
Anada, R. P., Wong, K. T., Jayapalan, J. J., Hashim, O. H., and Ganesan, D. (2018). Panel of serum protein biomarkers to grade the severity of traumatic brain injury. Electrophoresis 39, 2308–2315. doi:10.1002/elps.201700407
Asmamaw, Y., Yitayal, M., Debie, A., and Handebo, S. (2019). The costs of traumatic head injury and associated factors at University of Gondar Specialized Referral Hospital, Northwest Ethiopia. BMC Public Health 19, 1399. doi:10.1186/s12889-019-7800-3
Azar, S., Hasan, A., Younes, R., Najdi, F., Baki, L., Ghazale, H., et al. (2017). “Biofluid proteomics and biomarkers in traumatic brain injury,” in Methods in molecular biology. Editor N. J. Clifton (New York, NY: Humana Press), 45–63. doi:10.1007/978-1-4939-6952-4_3
Baichurina, I., Valiullin, V., James, V., Rizvanov, A., and Mukhamedshina, Y. (2021). The study of cerebrospinal fluid microRNAs in spinal cord injury and neurodegenerative diseases: Methodological problems and possible solutions. Int. J. Mol. Sci. 23, 114. doi:10.3390/ijms23010114
Berger, R. P., Ta’asan, S., Rand, A., Lokshin, A., and Kochanek, P. (2009). Multiplex assessment of serum biomarker concentrations in well-appearing children with inflicted traumatic brain injury. Pediatr. Res. 65, 97–102. doi:10.1203/PDR.0b013e31818c7e27
Betz, R., Biering-Sørensen, F., Burns, S. P., Donovan, W., Graves, D. E., Guest, J., et al. (2019). The 2019 revision of the international standards for neurological classification of spinal cord injury (ISNCSCI)—what’s new? Spinal Cord. 57, 815–817. doi:10.1038/s41393-019-0350-9
Bhomia, M., Balakathiresan, N. S., Wang, K. K., Papa, L., and Maheshwari, R. K. (2016). A panel of serum MiRNA biomarkers for the diagnosis of severe to mild traumatic brain injury in humans. Sci. Rep. 6, 28148. doi:10.1038/srep28148
Bolton, J. S., Chaudhury, S., Dutta, S., Gregory, S., Locke, E., Pierson, T., et al. (2020). Comparison of ELISA with electro-chemiluminescence technology for the qualitative and quantitative assessment of serological responses to vaccination. Malar. J. 19, 159. doi:10.1186/s12936-020-03225-5
Capirossi, R., Piunti, B., Fernández, M., Maietti, E., Rucci, P., Negrini, S., et al. (2020). Early CSF biomarkers and late functional outcomes in spinal cord injury. A pilot study. Int. J. Mol. Sci. 21, 9037. doi:10.3390/ijms21239037
Chen, K., Deng, S., Lu, H., Zheng, Y., Yang, G., Kim, D., et al. (2013). RNA-seq characterization of spinal cord injury transcriptome in acute/subacute phases: A resource for understanding the pathology at the systems level. PLoS One 8, e72567. doi:10.1371/journal.pone.0072567
Chowdhary, V. R., Dai, C., Tilahun, A. Y., Hanson, J. A., Smart, M. K., Grande, J. P., et al. (2015). A central role for HLA-DR3 in anti-smith antibody responses and glomerulonephritis in a transgenic mouse model of spontaneous lupus. J. Immunol. 195, 4660–4667. doi:10.4049/jimmunol.1501073
Czeiter, E., Amrein, K., Gravesteijn, B. Y., Lecky, F., Menon, D. K., Mondello, S., et al. (2020). Blood biomarkers on admission in acute traumatic brain injury: Relations to severity, CT findings and care path in the CENTER-TBI study. EBioMedicine 56, 102785. doi:10.1016/j.ebiom.2020.102785
D’Erchia, A. M., Gallo, A., Manzari, C., Raho, S., Horner, D. S., Chiara, M., et al. (2017). Massive transcriptome sequencing of human spinal cord tissues provides new insights into motor neuron degeneration in ALS. Sci. Rep. 71 (7), 1–20. doi:10.1038/s41598-017-10488-7
Dalkilic, T., Fallah, N., Noonan, V. K., Salimi Elizei, S., Dong, K., Belanger, L., et al. (2017). Predicting injury severity and neurological recovery after acute cervical spinal cord injury: A comparison of cerebrospinal fluid and magnetic resonance imaging biomarkers. J. Neurotrauma 35, 435–445. doi:10.1089/neu.2017.5357
DeVivo, M. J. (2012). Epidemiology of traumatic spinal cord injury: Trends and future implications. Spinal Cord. 50, 365–372. doi:10.1038/sc.2011.178
Di Pietro, V., Porto, E., Ragusa, M., Barbagallo, C., Davies, D., Forcione, M., et al. (2018). Salivary MicroRNAs: Diagnostic markers of mild traumatic brain injury in contact-sport. Front. Mol. Neurosci. 11, 290. doi:10.3389/fnmol.2018.00290
Di Pietro, V., Ragusa, M., Davies, D., Su, Z., Hazeldine, J., Lazzarino, G., et al. (2017). MicroRNAs as novel biomarkers for the diagnosis and prognosis of mild and severe traumatic brain injury. J. Neurotrauma 34, 1948–1956. doi:10.1089/neu.2016.4857
Diaz-Arrastia, R., Wang, K. K. W., Papa, L., Sorani, M. D., Yue, J. K., Puccio, A. M., et al. (2014). Acute biomarkers of traumatic brain injury: Relationship between plasma levels of ubiquitin C-terminal hydrolase-L1 and glial fibrillary acidic protein. J. Neurotrauma 31, 19–25. doi:10.1089/neu.2013.3040
Du, H., Shi, J., Wang, M., An, S., Guo, X., and Wang, Z. (2018). Analyses of gene expression profiles in the rat dorsal horn of the spinal cord using RNA sequencing in chronic constriction injury rats. J. Neuroinflammation 15, 280. doi:10.1186/s12974-018-1316-0
Duan, H., Ge, W., Zhang, A., Xi, Y., Chen, Z., Luo, D., et al. (2015). Transcriptome analyses reveal molecular mechanisms underlying functional recovery after spinal cord injury. Proc. Natl. Acad. Sci. U. S. A. 112, 13360–13365. doi:10.1073/pnas.1510176112
Ellington, A. A., Kullo, I. J., Bailey, K. R., and Klee, G. G. (2009). Measurement and quality control issues in multiplex protein assays: A case study. Clin. Chem. 55, 1092–1099. doi:10.1373/clinchem.2008.120717
Fassbender, K., Schneider, S., Bertsch, T., Schlueter, D., Fatar, M., Ragoschke, A., et al. (2000). Temporal profile of release of interleukin-1beta in neurotrauma. Neurosci. Lett. 284, 135–138. doi:10.1016/S0304-3940(00)00977-0
Feng, F., Thompson, M. P., Thomas, B. E., Duffy, E. R., Kim, J., Kurosawa, S., et al. (2019). A computational solution to improve biomarker reproducibility during long-term projects. PLoS One 14, e0209060. doi:10.1371/journal.pone.0209060
Franz, G., Beer, R., Kampfl, A., Engelhardt, K., Schmutzhard, E., Ulmer, H., et al. (2003). Amyloid beta 1-42 and tau in cerebrospinal fluid after severe traumatic brain injury. Neurology 60, 1457–1461. doi:10.1212/01.WNL.0000063313.57292.00
Gaudet, A. D., and Fonken, L. K. (2018). Glial cells shape pathology and repair after spinal cord injury. Neurotherapeutics 15, 554–577. doi:10.1007/s13311-018-0630-7
Ghanem, G., Loir, B., Morandini, R., Sales, F., Lienard, D., Eggermont, A., et al. (2001). On the release and half‐life of S100B protein in the peripheral blood of melanoma patients. Int. J. Cancer 94, 586–590. doi:10.1002/ijc.1504
Gill, J., Latour, L., Diaz-Arrastia, R., Motamedi, V., Turtzo, C., Shahim, P., et al. (2018). Glial fibrillary acidic protein elevations relate to neuroimaging abnormalities after mild TBI. Neurology 91, e1385–e1389. doi:10.1212/WNL.0000000000006321
Glenn, T. C., Hirt, D., Mendez, G., McArthur, D. L., Sturtevant, R., Wolahan, S., et al. (2013). “Metabolomic analysis of cerebral spinal fluid from patients with severe brain injury,” in Brain edema XV (Vienna: Springer Vienna), 115–119. doi:10.1007/978-3-7091-1434-6_20
Halford, J., Shen, S., Itamura, K., Levine, J., Chong, A. C., Czerwieniec, G., et al. (2017). New astroglial injury-defined biomarkers for neurotrauma assessment. J. Cereb. Blood Flow. Metab. 37, 3278–3299. doi:10.1177/0271678X17724681
Haqqani, A. S., Hutchison, J. S., Ward, R., and Stanimirovic, D. B. (2007). Biomarkers and diagnosis; protein biomarkers in serum of pediatric patients with severe traumatic brain injury identified by ICAT-LC-MS/MS. J. Neurotrauma 24, 54–74. doi:10.1089/neu.2006.0079
Hayes, K. C., Hull, T. C. L., Delaney, G. A., Potter, P. J., Sequeira, K. A. J., Campbell, K., et al. (2002). Elevated serum titers of proinflammatory cytokines and CNS autoantibodies in patients with chronic spinal cord injury. J. Neurotrauma 19, 753–761. doi:10.1089/08977150260139129
He, Z., Huffman, J., Curtin, K., Garner, K. L., Bowdridge, E. C., Li, X., et al. (2021). Composable microfluidic plates (cPlate): A simple and scalable fluid manipulation system for multiplexed enzyme-linked immunosorbent assay (ELISA). Anal. Chem. 93, 1489–1497. doi:10.1021/acs.analchem.0c03651
Hossein-Nezhad, A., Fatemi, R. P., Ahmad, R., Peskind, E. R., Zabetian, C. P., Hu, S. C., et al. (2016). Transcriptomic profiling of extracellular RNAs present in cerebrospinal fluid identifies differentially expressed transcripts in Parkinson’s disease. J. Park. Dis. 6, 109–117. doi:10.3233/JPD-150737
Hu, R., Sou, K., and Takeoka, S. (2020). A rapid and highly sensitive biomarker detection platform based on a temperature-responsive liposome-linked immunosorbent assay. Sci. Rep. 10, 18086. doi:10.1038/s41598-020-75011-x
Johnson, J. J., Loeffert, A. C., Stokes, J., Olympia, R. P., Bramley, H., and Hicks, S. D. (2018). Association of salivary MicroRNA changes with prolonged concussion symptoms. JAMA Pediatr. 172, 65–73. doi:10.1001/jamapediatrics.2017.3884
Kang, C., Jeong, W., Park, J. S., You, Y., Min, J. H., Cho, Y. C., et al. (2021). Comparison of prognostic performance between neuron-specific enolase and S100 calcium-binding protein B obtained from the cerebrospinal fluid of out-of-hospital cardiac arrest survivors who underwent targeted temperature management. J. Clin. Med. 10, 1531. doi:10.3390/jcm10071531
Kil, K., Zang, Y. C., Yang, D., Markowski, J., Fuoco, G. S., Vendetti, G. C., et al. (1999). T cell responses to myelin basic protein in patients with spinal cord injury and multiple sclerosis. J. Neuroimmunol. 98, 201–207. doi:10.1016/S0165-5728(99)00057-0
Korley, F. K., Yue, J. K., Wilson, D. H., Hrusovsky, K., Diaz-Arrastia, R., Ferguson, A. R., et al. (2019). Performance evaluation of a multiplex assay for simultaneous detection of four clinically relevant traumatic brain injury biomarkers. J. Neurotrauma 36, 182–187. doi:10.1089/neu.2017.5623
Kwon, B. K., Stammers, A. M. T., Belanger, L. M., Bernardo, A., Chan, D., Bishop, C. M., et al. (2010). Cerebrospinal fluid inflammatory cytokines and biomarkers of injury severity in acute human spinal cord injury. J. Neurotrauma 27, 669–682. doi:10.1089/neu.2009.1080
Kwon, B. K., Streijger, F., Fallah, N., Noonan, V. K., Bélanger, L. M., Ritchie, L., et al. (2017). Cerebrospinal fluid biomarkers to stratify injury severity and predict outcome in human traumatic spinal cord injury. J. Neurotrauma 34, 567–580. doi:10.1089/neu.2016.4435
Kyritsis, N., Torres-Espín, A., Schupp, P. G., Huie, J. R., Chou, A., Duong-Fernandez, X., et al. (2021). Diagnostic blood RNA profiles for human acute spinal cord injury. J. Exp. Med. 218, e20201795. doi:10.1084/jem.20201795
Lewis, L. M., Papa, L., Bazarian, J. J., Weber, A., Howard, R., and Welch, R. D. (2020). Biomarkers may predict unfavorable neurological outcome after mild traumatic brain injury. J. Neurotrauma 37, 2624–2631. doi:10.1089/neu.2020.7071
Li, C., Yang, Y., Wu, D., Li, T., Yin, Y., and Li, G. (2016). Improvement of enzyme-linked immunosorbent assay for the multicolor detection of biomarkers. Chem. Sci. 7, 3011–3016. doi:10.1039/C5SC04256A
Li, Y., Chen, Y., Li, X., Wu, J., Pan, J.-Y., Cai, R.-X., et al. (2019). RNA sequencing screening of differentially expressed genes after spinal cord injury. Neural Regen. Res. 14, 1583–1593. doi:10.4103/1673-5374.255994
Lim, L. P., Lau, N. C., Garrett-Engele, P., Grimson, A., Schelter, J. M., Castle, J., et al. (2005). Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433, 769–773. doi:10.1038/nature03315
Liu, N.-K., Wang, X.-F., Lu, Q.-B., and Xu, X.-M. (2009). Altered microRNA expression following traumatic spinal cord injury. Exp. Neurol. 219, 424–429. doi:10.1016/j.expneurol.2009.06.015
Ma, S. Q., Xu, X. X., He, Z. Z., Li, X. H., and Luo, J. M. (2019). Dynamic changes in peripheral blood-targeted miRNA expression profiles in patients with severe traumatic brain injury at high altitude. Mil. Med. Res. 6, 12–17. doi:10.1186/s40779-019-0203-z
Manek, R., Moghieb, A., Yang, Z., Kumar, D., Kobessiy, F., Sarkis, G. A., et al. (2018). Protein biomarkers and neuroproteomics characterization of microvesicles/exosomes from human cerebrospinal fluid following traumatic brain injury. Mol. Neurobiol. 55, 6112–6128. doi:10.1007/s12035-017-0821-y
Marchi, N., Rasmussen, P., Kapural, M., Fazio, V., Kight, K., Mayberg, M. R., et al. (2003). Peripheral markers of brain damage and blood-brain barrier dysfunction. Restor. Neurol. Neurosci. 21, 109–121.
Meng, Q., Zhuang, Y., Ying, Z., Agrawal, R., Yang, X., and Gomez-Pinilla, F. (2017). Traumatic brain injury induces genome-wide transcriptomic, methylomic, and network perturbations in brain and blood predicting neurological disorders. EBioMedicine 16, 184–194. doi:10.1016/j.ebiom.2017.01.046
Michael, D. B., Byers, D. M., and Irwin, L. N. (2005). Gene expression following traumatic brain injury in humans: Analysis by microarray. J. Clin. Neurosci. 12, 284–290. doi:10.1016/j.jocn.2004.11.003
Mitra, B., Rau, T. F., Surendran, N., Brennan, J. H., Thaveenthiran, P., Sorich, E., et al. (2017). Plasma micro-RNA biomarkers for diagnosis and prognosis after traumatic brain injury: A pilot study. J. Clin. Neurosci. 38, 37–42. doi:10.1016/j.jocn.2016.12.009
Moghieb, A., Bramlett, H. M., Das, J. H., Yang, Z., Selig, T., Yost, R. A., et al. (2016). Differential neuroproteomic and systems biology analysis of spinal cord injury. Mol. Cell. Proteomics 15, 2379–2395. doi:10.1074/mcp.M116.058115
O’Connell, G. C., Alder, M. L., Smothers, C. G., Still, C. H., Webel, A. R., and Moore, S. M. (2020). Use of high-sensitivity digital ELISA improves the diagnostic performance of circulating brain-specific proteins for detection of traumatic brain injury during triage. Neurol. Res. 42, 346–353. doi:10.1080/01616412.2020.1726588
Ogurcov, S., Shulman, I., Garanina, E., Sabirov, D., Baichurina, I., Kuznetcov, M., et al. (2021). Blood serum cytokines in patients with subacute spinal cord injury: A pilot study to search for biomarkers of injury severity. Brain Sci. 11, 322. doi:10.3390/BRAINSCI11030322
Okonkwo, D. O., Yue, J. K., Puccio, A. M., Panczykowski, D. M., Inoue, T., McMahon, P. J., et al. (2013). GFAP-BDP as an acute diagnostic marker in traumatic brain injury: results from the prospective transforming research and clinical knowledge in traumatic brain injury study. J. Neurotrauma 30, 1490–1497. doi:10.1089/neu.2013.2883
Olsson, A., Csajbok, L., Ost, M., Höglund, K., Nylén, K., Rosengren, L., et al. (2004). Marked increase of beta-amyloid(1-42) and amyloid precursor protein in ventricular cerebrospinal fluid after severe traumatic brain injury. J. Neurol. 251, 870–876. doi:10.1007/s00415-004-0451-y
Orešič, M., Posti, J. P., Kamstrup-Nielsen, M. H., Takala, R. S. K., Lingsma, H. F., Mattila, I., et al. (2016). Human serum metabolites associate with severity and patient outcomes in traumatic brain injury. EBioMedicine 12, 118–126. doi:10.1016/j.ebiom.2016.07.015
Orr, M. B., and Gensel, J. C. (2018). Spinal cord injury scarring and inflammation: Therapies targeting glial and inflammatory responses. Neurotherapeutics 15, 541–553. doi:10.1007/s13311-018-0631-6
Patz, S., Trattnig, C., Grünbacher, G., Ebner, B., Gülly, C., Novak, A., et al. (2013). More than cell dust: Microparticles isolated from cerebrospinal fluid of brain injured patients are messengers carrying mRNAs, miRNAs, and proteins. J. Neurotrauma 30, 1232–1242. doi:10.1089/NEU.2012.2596
Pleines, U. E., Morganti-Kossmann, M. C., Rancan, M., Joller, H., Trentz, O., and Kossmann, T. (2001). S-100β reflects the extent of injury and outcome, whereas neuronal specific enolase is a better indicator of neuroinflammation in patients with severe traumatic brain injury. J. Neurotrauma 18, 491–498. doi:10.1089/089771501300227297
Poulos, R. C., Hains, P. G., Shah, R., Lucas, N., Xavier, D., Manda, S. S., et al. (2020). Strategies to enable large-scale proteomics for reproducible research. Nat. Commun. 11, 3793. doi:10.1038/s41467-020-17641-3
Qin, X., Li, L., Lv, Q., Shu, Q., Zhang, Y., and Wang, Y. (2018). Expression profile of plasma microRNAs and their roles in diagnosis of mild to severe traumatic brain injury. PLoS One 13, e0204051. doi:10.1371/journal.pone.0204051
Ray, C. A., Bowsher, R. R., Smith, W. C., Devanarayan, V., Willey, M. B., Brandt, J. T., et al. (2005). Development, validation, and implementation of a multiplex immunoassay for the simultaneous determination of five cytokines in human serum. J. Pharm. Biomed. Anal. 36, 1037–1044. doi:10.1016/j.jpba.2004.05.024
Redell, J. B., Moore, A. N., Ward, N. H., Hergenroeder, G. W., and Dash, P. K. (2010). Human traumatic brain injury alters plasma microRNA levels. J. Neurotrauma 27, 2147–2156. doi:10.1089/neu.2010.1481
Ringger, N. C., O’steen, B. E., Brabham, J. G., Silver, X., Pineda, J., Wang, K. K. W., et al. (2004). A novel marker for traumatic brain injury: CSF alphaII-spectrin breakdown product levels. J. Neurotrauma 21, 1443–1456. doi:10.1089/neu.2004.21.1443
Rubiano, A. M., Carney, N., Chesnut, R., and Puyana, J. C. (2015). Global neurotrauma research challenges and opportunities. Nature 527, S193–S197. doi:10.1038/nature16035
Sandwell, S., and Markandaya, M. (2015). “Neurotrauma, prognosis and outcome predictions BT - encyclopedia of trauma care,” in, eds. P. J. Papadakos, and M. L. Gestring (Berlin, Heidelberg: Springer Berlin Heidelberg), 1079–1082. doi: doi:10.1007/978-3-642-29613-0_627
Segal, J. L., and Brunnemann, S. R. (1993). Circulating levels of soluble interleukin 2 receptors are elevated in the sera of humans with spinal cord injury. J. Am. Paraplegia Soc. 16, 30–33. doi:10.1080/01952307.1993.11735881
Segal, J. L., Gonzales, E., Yousefi, S., Jamshidipour, L., and Brunnemann, S. R. (1997). Circulating levels of IL-2R, ICAM-1, and IL-6 in spinal cord injuries. Arch. Phys. Med. Rehabil. 78, 44–47. doi:10.1016/S0003-9993(97)90008-3
Sengupta, M. B., Basu, M., Iswarari, S., Mukhopadhyay, K. K., Sardar, K. P., Acharyya, B., et al. (2014). CSF proteomics of secondary phase spinal cord injury in human subjects: Perturbed molecular pathways post injury. PLoS One 9, e110885. doi:10.1371/journal.pone.0110885
Shahim, P., Gren, M., Liman, V., Andreasson, U., Norgren, N., Tegner, Y., et al. (2016). Serum neurofilament light protein predicts clinical outcome in traumatic brain injury. Sci. Rep. 6 (1), 1–9. doi:10.1038/srep36791
Shi, L. L., Zhang, N., Xie, X. M., Chen, Y. J., Wang, R., Shen, L., et al. (2017). Transcriptome profile of rat genes in injured spinal cord at different stages by RNA-sequencing. BMC Genomics 18, 1–14. doi:10.1186/s12864-017-3532-x
Singh, A., Srivastava, R. N., Agrahari, A., Singh, S., Raj, S., Chatterji, T., et al. (2018). Proton nmr based serum metabolic profile correlates with the neurological recovery in treated acute spinal cord injury (asci) subjects: A pilot study. Clin. Chim. Acta. 480, 150–160. doi:10.1016/j.cca.2018.02.011
Skinnider, M. A., Rogalski, J., Tigchelaar, S., Manouchehri, N., Prudova, A., Jackson, A. M., et al. (2021). Proteomic portraits reveal evolutionarily conserved and divergent responses to spinal cord injury. Mol. Cell. Proteomics. 20, 100096. doi:10.1016/j.mcpro.2021.100096
Stein, A., Panjwani, A., Sison, C., Rosen, L., Chugh, R., Metz, C., et al. (2013). Pilot study: Elevated circulating levels of the proinflammatory cytokine macrophage migration inhibitory factor in patients with chronic spinal cord injury. Arch. Phys. Med. Rehabil. 94, 1498–1507. doi:10.1016/j.apmr.2013.04.004
Streijger, F., Skinnider, M. A., Rogalski, J. C., Balshaw, R., Shannon, C. P., Prudova, A., et al. (2017). A targeted proteomics analysis of cerebrospinal fluid after acute human spinal cord injury. J. Neurotrauma 34, 2054–2068. doi:10.1089/neu.2016.4879
Strickland, E. R., Hook, M. A., Balaraman, S., Huie, J. R., Grau, J. W., and Miranda, R. C. (2011). MicroRNA dysregulation following spinal cord contusion: Implications for neural plasticity and repair. Neuroscience 186, 146–160. doi:10.1016/j.neuroscience.2011.03.063
Taheri, S., Tanriverdi, F., Zararsiz, G., Elbuken, G., Ulutabanca, H., Karaca, Z., et al. (2016). Circulating MicroRNAs as potential biomarkers for traumatic brain injury-induced hypopituitarism. J. Neurotrauma 33, 1818–1825. doi:10.1089/neu.2015.4281
Thelin, E., Al Nimer, F., Frostell, A., Zetterberg, H., Blennow, K., Nyström, H., et al. (2019). A serum protein biomarker panel improves outcome prediction in human traumatic brain injury. J. neurotrauma 36, 2850–2862. doi:10.1089/neu.2019.6375
Tigchelaar, S., Gupta, R., Shannon, C. P., Streijger, F., Sinha, S., Flibotte, S., et al. (2019). MicroRNA biomarkers in cerebrospinal fluid and serum reflect injury severity in human acute traumatic spinal cord injury. J. Neurotrauma 36, 2358–2371. doi:10.1089/neu.2018.6256
Tigchelaar, S., Streijger, F., Sinha, S., Flibotte, S., Manouchehri, N., So, K., et al. (2017). Serum MicroRNAs reflect injury severity in a large animal model of thoracic spinal cord injury. Sci. Rep. 7, 1376. doi:10.1038/s41598-017-01299-x
Wang, F., Liu, J., Wang, X., Chen, J., Kong, Q., Ye, B., et al. (2019a). The emerging role of lncRNAs in spinal cord injury. Biomed. Res. Int., 3467121. –9. doi:10.1155/2019/3467121
Wang, Q., Ai, H., Liu, J., Xu, M., Zhou, Z., Qian, C., et al. (2019b). Characterization of novel lnc RNAs in the spinal cord of rats with lumbar disc herniation. J. Pain Res. 12, 501–512. doi:10.2147/JPR.S164604
Wang, R., Chen, J., Zhou, S., Li, C., Yuan, G., Xu, W., et al. (1995). Enzyme-linked immunoadsorbent assays for myelin basic protein and antibodies to myelin basic protein in serum and CSF of patients with diseases of the nervous system. Hua xi yi ke da xue xue bao = J. West China Univ. Med. Sci. = Huaxi yike daxue xuebao. 26, 131-4. Available at: http://europepmc.org/abstract/MED/7490015.
Weir, J., Steyerberg, E. W., Butcher, I., Lu, J., Lingsma, H. F., McHugh, G. S., et al. (2012). Does the extended Glasgow outcome scale Add value to the conventional Glasgow outcome scale? J. Neurotrauma 29, 53–58. doi:10.1089/neu.2011.2137
Wolahan, S. M., Hirt, D., Braas, D., and Glenn, T. C. (2016). Role of metabolomics in traumatic brain injury research. Neurosurg. Clin. N. Am. 27, 465–472. doi:10.1016/j.nec.2016.05.006
Wu, Y., Streijger, F., Wang, Y., Lin, G., Christie, S., Mac-Thiong, J.-M., et al. (2016). Parallel metabolomic profiling of cerebrospinal fluid and serum for identifying biomarkers of injury severity after acute human spinal cord injury. Sci. Rep. 6, 38718. doi:10.1038/srep38718
Yang, L.-X., Yang, L.-K., Zhu, J., Chen, J.-H., Wang, Y.-H., and Xiong, K. (2019). Expression signatures of long non-coding RNA and mRNA in human traumatic brain injury. Neural Regen. Res. 14, 632–641. doi:10.4103/1673-5374.247467
Yang, T., Song, J., Bu, X., Wang, C., Wu, J., Cai, J., et al. (2016). Elevated serum miR-93, miR-191, and miR-499 are noninvasive biomarkers for the presence and progression of traumatic brain injury. J. Neurochem. 137, 122–129. doi:10.1111/jnc.13534
Yokobori, S., Zhang, Z., Moghieb, A., Mondello, S., Gajavelli, S., Dietrich, W. D., et al. (2015). Acute diagnostic biomarkers for spinal cord injury: Review of the literature and preliminary research report. World Neurosurg. 83, 867–878. doi:10.1016/j.wneu.2013.03.012
You, W. D., Tang, Q. L., Wang, L., Lei, J., Feng, J. F., Mao, Q., et al. (2016). Alteration of microRNA expression in cerebrospinal fluid of unconscious patients after traumatic brain injury and a bioinformatic analysis of related single nucleotide polymorphisms. Chin. J. Traumatol. = Zhonghua chuang shang za zhi 19, 11–15. doi:10.1016/J.CJTEE.2016.01.004
Yunta, M., Nieto-Díaz, M., Esteban, F. J., Caballero-López, M., Navarro-Ruíz, R., Reigada, D., et al. (2012). MicroRNA dysregulation in the spinal cord following traumatic injury. PLoS One 7, e34534. doi:10.1371/journal.pone.0034534
Zhong, J., Jiang, L., Cheng, C., Huang, Z., Zhang, H., Liu, H., et al. (2016). Altered expression of long non-coding RNA and mRNA in mouse cortex after traumatic brain injury. Brain Res. 1646, 589–600. doi:10.1016/j.brainres.2016.07.002
Zhou, D., Liu, J., Hang, Y., Li, T., Li, P., Guo, S., et al. (2020). TMT-based proteomics analysis reveals the protective effects of Xuefu Zhuyu decoction in a rat model of traumatic brain injury. J. Ethnopharmacol. 258, 112826. doi:10.1016/j.jep.2020.112826
Keywords: biomarkers, traumatic brain injury, spinal cord injury, enzyme-linked immunosorbent assay, proteomic analysis, transcriptomic analysis, metabolic analysis
Citation: Sabirov D, Ogurcov S, Baichurina I, Blatt N, Rizvanov A and Mukhamedshina Y (2022) Molecular diagnostics in neurotrauma: Are there reliable biomarkers and effective methods for their detection?. Front. Mol. Biosci. 9:1017916. doi: 10.3389/fmolb.2022.1017916
Received: 12 August 2022; Accepted: 12 September 2022;
Published: 29 September 2022.
Edited by:
William C. Cho, QEH, Hong Kong SAR, ChinaReviewed by:
Matthew J. Robson, University of Cincinnati, United StatesAndres M. Rubiano, El Bosque University, Colombia
Copyright © 2022 Sabirov, Ogurcov, Baichurina, Blatt, Rizvanov and Mukhamedshina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Irina Baichurina, YmFpY2gucmluYUBtYWlsLnJ1
†These authors have contributed equally to this work