Web-based, standalone tools and R packages dedicated to different-omics tasks such as feature selection, sample classification, multivariate approaches in data integration and meta-analysis.
\r\n\t
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More recently his work pertains to spatial adaptation to climate change, spatial responses in wine growing regions to climate change, the geographies of viticulture and wine, artificial intelligence and machine learning to predict patterns of natural processes and hazards, historical ethnic enclaves in American cities and regions, and environmental adaptations of 19th century European immigrants to North America's landscapes.",institutionString:"Texas State University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"6",institution:{name:"Texas State University",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"12",title:"Environmental Sciences",slug:"environmental-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"194667",firstName:"Marijana",lastName:"Francetic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/194667/images/4752_n.jpg",email:"marijana@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Thereby, a great number of wealth data in many kidney and cardiovascular conditions was generated, however these findings were neither translated nor reached the clinical setting and are still enclosed in peer-reviewed literature and across general scope expression profiling databases. Simultaneously it has become apparent that the existing systems to integrate and correlate this data are either inadequate or non-existent. Due to the multi-factorial molecular phenotype of disease, it is evident that development of novel therapeutic and disease detection approaches should be based upon the study of the entire “System” simultaneously. Figure 1 gives a general overview in the fundamental difference between conventional and systems approaches, whereby in the context of conventional approaches a hypothesis is put forward that is assumed to be of importance in the disease or biological condition. This hypothesis is then tested and either validated or refuted based on the outcome of this hypothesis-driven methodology. Yet, it is obvious that it is easy to investigate any hypothesis and then choose the one that appears most correct, in the real world constraints such as time and financial resources do not allow for such an approach, and hypotheses are usually generated on a best-guess basis which can lead to a substantial amount of bias, resulting in skewed or partial insights and can often be misleading. In order to avoid such scenarios, research driven by the data itself rather than a hypothesis has been proposed a long time ago, but could not be properly implemented due to the lack of unbiased large-scale data or the ability to integrate disparate data in the first place. Additionally, a successful systems approach requires underlying prior knowledge, such as physicochemical parameters in how molecules interact with each other, what reactions they are involved in and other unconnected information. This knowledge has only slowly been accumulated through conventional research and has only over the last 10–15 years been available to such an extent where a systems approach became feasible. Data-driven systems biology-based diagnostic and prognostic models consisting of relevant panels of molecules—key branches of the cellular network, appear to more accurately reflect pathophysiology than traditional hypothesis-driven approaches, consequently, may have a much higher chance of success and implementation in the clinical setting. Of the most pronounced effects is the crossing between research borders and the urge for multidisciplinary integration of biology, chemistry, computing sciences, mathematics, and medicine to tackle the complexity of such system. To get a holistic view of a system’s biology, multiple and different types of observations must be combined, such as clinical which includes pathological, demographical, epidemiological, and as well as molecular, which includes large-scale genotyping, gene expression, proteomics, metabolomics, and lipidomics data. The downside of such an approach in disease analytics or data integration is the rise in complexity both in output as well as in methods needed to generate those, and the skills required to interpret and contextualise outcome parameters. However, biological and disease models generated this way allow for a higher confidence in generating testable hypotheses, disease classifications on a molecular level and identification of overlapping and divergent pathways of malignant conditions. Ultimately, the removal of bias and integration of all available data, both clinical and biological, leads to a far better understanding of disease and enables the identification of intervention points with higher confidence and accuracy.
Overview of general differences between conventional and Systems Biology approaches in biological and disease analysis research. Red arrows show the path of the conventional hypothesis-driven methodology including testing of a hypothesis, usually employing lab-based investigations, and re-adjusting the hypothesis dependent on outcomes. Blue arrows denote a systems approach, where data are integrated and analysed, producing a model system and a hypothesis that can be verified using conventional methods. Outputs of such an approach are usually fed back into the model or the data analysis stream to refine models, adjust hypothesis or confirm the established model.
The standard resource for disease taxonomy relies primarily on the International Classification of Diseases (ICD) which displays information on diseases and health conditions, and a continuous monitoring of the associated epidemiological statistical trends World Health Organisation [1]. The foundations of the ICD disease classification relies mainly in a type of evidence-based medicine with distinction of clinical features, including patient symptoms, histological assessment, and evaluation of risk factors [2]. While widely used in the clinical setting, in the era of “big-data” and precision medicine, its rigid hierarchical structure lacks the flexibility needed to accommodate the fast and expanding molecular-insights of disease-phenotypes captured across many -omics platforms [3]. Moreover, to support this notion of undefined disease boundaries across current disease classification, we can observe the existence of co-occurring conditions that if seen as a unified biological network, could provide information about common multi-functional genes, cellular pathways, as well the impact of lifestyle [4]. Additionally, analysis of disease progression with the presence of overlapping conditions through evaluation of temporal correlation and disease progression patterns condensed from a population can become useful in the prediction and prevention at the patient’s individual level in future disease-associate events [5].
Further disease taxonomy refinement can be achieved by applying network analysis [3] of combined disease phenotypes sourced from ICD-9 with protein-protein interactions (PPI’s) data from STRING [6] and additional curation efforts of gene-disease associations (GDA) from several data sources. The network analysis allowed for reclassified of pancreatic cancer into 11 subclasses, which is consistent with the number of molecular subtypes observed in the Bailey et al. [7] study. They also proposed the use of such approach in drug repurposing, for instance therapy with metformin, a well-known agent used to treat type 2 diabetes mellitus (T2DM), that could regulate the imbalanced status of the microbiota community in the gut mucosae, a known cause of pathological chronic bowel inflammation as occurs in Crohn’s disease and ulcerative colitis [8], and also act as preventable agent to reduce the risk of colorectal cancer. Moreover, molecular profiling associated with histologic assessment seems to yield enhanced probabilistic scores in graft survival predictions. For instance, joint integration of multi-center histology features in renal biopsies and gene-array data yielded a new molecular score system able to predict renal graft survival [9] and improving the diagnosis of antibody-mediated rejection of transplanted in hearts [10]. Such approaches can also be implemented to assess disease trajectory, treatment selection and monitoring in many neoplasms, and could be specially tailored for cases where the tumour primary site is of unknown origin [11].
Over the last 15 years, the rise of systems biology as a research field has changed how we look at human normal physiological function and has helped to uncover disease complexity. Now scientists use systems biology approaches to understand the big picture of how all the pieces interact in an organism. The inference of genotype-phenotype relationships boosted by the assembly of a high-quality human genome opened the avenue for the development of reference maps of interactome networks, [12] consisting of binary association pairs, for instance PPI’s, protein-DNA/RNA, or protein-metabolite interactions. Figure 2 shows the essential biological molecular interactions governing cell behaviour in an over-simplified biological system. A curated compilation of high-quality sources of binary interactions is considered a prime resource in the Systems Biology field and thereby enabling a deeper understanding of the larger picture—be it at the level of the organism, organ, tissue, or cell—by putting its components together. It’s in stark contrast to decades of reductionist biology, which merely focuses on the properties of its individual components [13]. Most disease conditions exhibit expression of complex disease phenotypes [13], such as obesity, metabolic syndrome, autoimmune diseases and renal diseases.
Description of the essential known relationships/interactions in an over-simplified biological system. Transcription factor (TF), microRNA (miRNA), post-translational modifications (PTMs). The illustration does not account for epigenetic modifications, for instance DNA methylation and histone modifications known to occur and regulate gene expression. Dark coloured arrows denote entity associations, while self-circular arrows describe self-pair interactions or modifications.
Using the words of Ronald Germain to provide a definition of Systems Biology, he advocates that: “There are an endless number of definitions, it’s even worse than the elephant,” that infamous elephant that stymies the attempts of blind men to describe it because each feels just one part, “Some people think of it as bioinformatics, taking an enormous amount of information and processing it.” “The other school of thought thinks of it as computational biology, computing on how the systems work. You need both parts.” Ironically, to best understand this novel approach, we should take a reductionist approach to defining its parts. The system, it seems, is more than the sum of its parts [14]. Systems Biology requires comprehensive data at all molecular levels, a profound understanding of biological systems, data-criteria based assessment and in-deep understanding of the limitations of the techniques used in the experimental setup. Moreover, systems biology requires prior knowledge either published or sourced from biological databases and newly predicted and frequent molecular events requires further in vivo/vitro validation [15]. Systems Biology is cross-disciplinary: “[…] a scientific approach that combines the principles of engineering, mathematics, physics, and computer science with extensive experimental data to develop a quantitative as well as a deep conceptual understanding of biological phenomena, permitting prediction and accurate simulation of complex (emergent) biological behaviours” (Ronald Germain in [14]). Furthermore, systems biology promotes understanding of the functional roles and interplays of all molecules in cells in health and disease. Also provides a framework for large-scale data-driven analysis and predictions based on prior knowledge of experimentally identified interactions and pathways [16]. Thus, more relevant that the underlying high-throughput screening methods, including genomics, proteomics, metabolomics, and also bioinformatics approaches is the use of such methods in a integrative manner to holistically understand how nonlinear processes and their outcomes are regulated in a biological system [17].
Over the last 10 years, major efforts to reclassify diseases based on molecular insights from advances in molecular biology, bioinformatics and high-throughput screening yielded novel disease subtypes among many disease conditions. The use of multiple data types, including clinical endpoints—omics and ontology-based data have been used to reconstitute disease phenotypes, classify and to refine disease-relationships [18]. Nevertheless, the development of a molecular-based disease taxonomy that links global molecular networks with pathological phenotype landscapes remains elusive. Systems medicine can be perceived as a multi-disciplinary collaborative effort driven by the application of systems biology approaches, which includes methodological workflows from high-throughput-omics technologies to generate data, warehousing management systems for data flow and handling and methods for data analytics and interpretation in the context of biomedical research [19]. Ultimately, with further adoption of a systems-based approach patients will benefit of a measurable improvement of their health status since processes of disease onset and progression will be mechanistically identified, leading to new insights regarding disease-disease boundaries, and disease subtyping which facilitates ideal pharmacological interventions as drug repurposing [20]. For instance, the identification of digoxin, a drug used as therapy for atrial fibrillation and congestive heart failure [21] as potential drug candidate for pharmacological intervention in medulloblastoma subtypes 3 and 4 [22]. The authors of the study implemented an integrative systems biology approach using genomic data and collating existing drug-drug, drug-targets interactions information into a tridimensional functional-drug network. This approach involved handling omic data sets such as DNA-seq—mutated genes, copy-number variation (CNV)—repeated sections of the genome, RNA-seq and methylation profiles, combined with clinical measurements of patient outcomes (survival data) and fused using network-based and probabilistic methods that yielded a network composite with disrupted driver signalling networks and potential drug candidates [22].
The advent of new high-throughput technologies (sequencing, array-based and mass spectrometry) led to an explosion of available data, not only by the number of experiments performed, but also by the data density obtained per experiment. Here, we will provide description of detection platforms handling molecular datasets; for medical imaging data types and analysis strategies please see the following review [23].
Microarray technologies have been widely used in research for primary screening, including gene expression profiling and providing genotype-phenotype relationship. Moreover, if properly designed, microarrays will not only provide information on gene expression and expressed single nucleotide polymorphisms (SNPs), but also detect exon junctions and fusion genes [24]. However, identical to PCR-based techniques, the design of probes requires prior knowledge. Therefore, microarrays are mostly applied in the quantification of known sequences and not for the discovery of new variants, transcripts or other unknown features [25]. Microarrays have numerous limitations. For instance, they render an indirect measurement of the relative concentration of a particular nucleic acid sequence [26]. Another limitation is based that a DNA-array can only detect sequences that the array was designed for. In addition, non-coding RNA’s that are not yet recognised as expressed are typically not represented on an array [26]. Microarrays are still considered a reliable technique for routine and/or initial screening that allows multiplex quantitation of microRNAs and gene probes expression in a fast, simple and affordable way. Nevertheless, the continuous drop in the cost of NGS at a level that virtually matches the cost of DNA microarray-based platforms, thus is foreseen that DNA-arrays will be fully replaced by sequencing methods within the next decade [26].
The use of omics technologies, including quantitative proteomics methods aims to identify and quantify the dynamics of protein abundance, in order to gain a deeper understanding of the associated biological functions. Thereby, the quantification of the expression level and state of all proteins at a given time can characterise physiological-states at the cellular-level [27]. Mass spectrometry (MS) technology, particularly tandem mass spectrometry (MS/MS), has been utilised as a discovery engine in proteomics [28]. This technology allows for identification and simultaneously quantification of hundreds or even thousands of proteins in an experimental setup, which enables real-time comparisons for instance between two or more physiological states [29]. Furthermore, peptide sequence composition will directly impact on ionisation efficiency, and their intensities observed in a spectrum often do not reflect their abundances, [30] thereby many label-free or label-based quantitation methods have arisen to allow comparative proteomic analysis. For instance, label-free proteomic approaches such as ion intensity, spectral counting have a simplified workflow when compared to labelling techniques; have no theoretical limit concerning multiplexing capability providing an improved proteome coverage, but lower quantification accuracy when compared with labelling methods (e.g. iTRAQ: isobaric tags for relative and absolute quantitation, SILAC: stable isotope labelling by/with amino acids in cell culture) [30]. In proteomics, several algorithms have been developed to query and cross compare MS data. The most popular used to identify proteins from raw MS data are for instance, MASCOT, SeQuest, OMSSA, X!Tandem [31], Andromeda [32], MS-GF [33], Paragon [34] and more recently, Morpheus [35] and an improved SEQUEST-like algorithm—ProLuCID [36]. The rise in the number of algorithms and specialised computational tools for analysis of MS-based proteomics data sets led to the development of workflows/pipelines such as PEAKS [37], MaxQuant [38], OpenMS Proteomics Pipeline (TOPP) [39], Trans-Proteomic Pipeline (TPP) [40] and others for further downstream data analysis—Perseus [41].
In many metabolomics studies the identification and quantification of metabolites mainly rely on the application of analytical methods based on mass spectrometry (MS) (either coupled with a liquid or gas-chromatograph) and nuclear magnetic resonance (NMR) spectroscopy [42]. Metabolites are defined as small molecules, usually less than 1000 Da, which suffer several changes during cellular metabolism [43]. The selection of a particular platform depends upon the aims of the experimental study and is typically driven by establishing a compromise among sensitivity, specificity, and scanning speed [44]. Metabolomics approaches can be globally split either by the full range measurement/analysis of all compounds in a given sample—untargeted metabolomics, or targeted metabolomics, in which a set of predefined and biochemically well-characterised compounds are measured in a sample [44]. MS has become an essential method for non-targeted profiling of metabolites in complex bio-samples, particularly low-abundance metabolites, due to its high sensitivity and selectivity capabilities when using liquid chromatography (LC) coupled to tandem MS/MS [45]. Metabolomics data from NMR and MS platforms are complex because they usually contain thousands distinct peaks therefore, multivariate statistical analysis plays an important role in metabolomics for reducing data dimensions, differentiating similar spectra, and in the development of predictive models [46]. Metabolomics is used as a screening tool in current healthcare settings, and could be greatly utilised to monitor therapy efficacy, and assess potential drug side-effects [47].
In the field of biomedical research adopting an unbiased approach or “hypothesis-free” (depending of the author and field of study, also defined as hypothesis-generating approach, data-driven research, or discovery research) to research can bring several benefits when compared with the widely used scientific approach—hypothesis-driven research (traditional approach). In which, the latter, in some cases encourages poor scientific practices by forcing/imposing qualitative and weak hypotheses that Are not prepared for strong statistical inference or quantitative analysis (QA) modelling, thereby in such cases an explicitly exploratory approach should be set as default [48]. In order to overcome this problem, large-scale approaches such as expression profiling started to become very popular in the mid ‘90s, and beginning of 2000, with the advent, rapid development and availability of high-throughput mass spectrometry, other methods followed [49]. Computational methods to analyse this flood of data were developed accordingly, however the majority only focused on one specific technology or experimental setup and up to this day are very often not interchangeable in other technological platforms. Large-scale approaches employed in omics research need a different analysis methodology, which is especially true if integrative analysis techniques are employed. True integrative (as opposed to integrating linear relationship data such as gene-protein data) approaches go beyond simple data fusion and gave rise to the field of Systems Biology. On the other hand, hypothesis-generating research (systems biology-derived hypotheses) and hypothesis-driven research are complementary, thus combining both approaches will certainly sustain more chances of a complete understanding of complex biological systems, than either approach on its own [48]. With the advent of high-throughput technologies their application in the biomedical field was a foreseen logical step. However, until recently integration of multi-omic data was not a common approach in former analysis workflows. The literature and publicly available databases are awash with data, yet the main approach of integrating all this information in a disease-specific context is traditionally based on meta-analysis at best or cannot be accomplished using standard computational methods. This molecular information can then be integrated in a further stage by means of meta-analysis or by cross-normalisation of data from different acquisition platforms [50]. A combinatorial stepwise data integration (Figure 3) approach can be used in order to incorporate data from different biological layers of information to predict phenotypic outcomes [51]. On the other side, by recreating the cell environment and dynamics by describing their interactions on a qualitative and quantitative manner and relying on underlying data (prior biological knowledge) for connectivity, e.g. PPI’s, molecular co-occurrence, ontologies and enzymatic reactions [52]. Large-scale data sets for instance derived from multi-omics platforms may also be used to infer novel relationships by network learning approaches using Bayesian inference models [51] and extracting molecular information from multi-layered networks. This approach (as in many others) is challenging since it requires enough statistical power, higher number of samples to deduce all the possible interactions. Another challenge is due to the lack of uniformisation regarding the ‘gold ‘standards (criterions for evaluation) for accepting or rejecting relationships of the inferred model; however the ability to recreate a well-accepted interaction can at least be used for benchmarking methods in biological systems [53].
Purposed workflow for a data-driven approach. Data generation from omics platforms plus existing biological information (a), development of a multi-omics database (b), selection of suitable modelling methods (c), model validation and use for hypothesis-generating research (d), lead optimization and candidate selection (e).
Databases form the basis for most applications in bioinformatics. The number of biological databases available now is enormous, the journal of Nucleic Acids Research (NAR) catalogues a total of 1737 molecular biology databases (2018 edition) [54]. The 2018 edition contains an enormous set of 181 papers that describe the adding of 82 new biological databases, 84 updates and as well 15 databases published elsewhere. However, a prominent issue concerns that many databases are not maintained over time and abandoned, yet they persist in database listings. There are many different types of databases, ranging from primary databases containing sequence data such as nucleic acid or protein; secondary databases or also known as pattern databases hosts, that results from the analysis of the sequences held in primary databases.
The Gene Expression Omnibus (GEO) [55] is a public repository that functions as both warehouse of raw microarray and other gene-based high-throughput data, and additionally serves as a platform for gene differential expression (DE) analysis using the GEO2R tool across a multitude of experimental conditions of user-submitted pre-processed data sets. In the same way, the European counterpart for storing of high-throughput genomics exists such as the European Bioinformatics Institute (EMBL-EBI) throughout the ArrayExpress database [56]. These data resources are both in compliance with community guidelines for description of an experimental setup for microarray and high-throughput NGS experiment. Comparatively, there is currently much less support for sharing of proteomics and metabolomics data sets despite the increasing demand. Public efforts for proteomic data sharing yielded the Proteomics Identification Database (PRIDE) that contains over 10,100 user-submitted MS-based raw proteomic data sets (September 2018) [57]. PeptideAtlas [58] handles re-analysed data sets via the TPP pipeline to provide end-users a consistently view over their data. MetaboLights [59] hosts user-submitted metabolomics experiments, which currently houses 439 experiments (November 2018). The standards for reporting proteomics and metabolomics experiments are coordinated by the Human Proteome Organisation’s Proteomics Standards Initiative (HUPO-PSI), and Metabolomics Standards Initiative (MSI) respectively.
Our group developed more specialised databases resources in several disease conditions handling pre-selected data sets containing DE molecules. In nephrology, we developed the Chronic Kidney Disease database (CKDdb) [60] storing microRNA, genomics, peptidomics, proteomics and metabolomics information relevant to CKD, collected from over 300 studies in the literature and integrated into the Pan-omics Analysis DataBase (PADB). The PADB framework (
Many modern high-throughput technologies lead to the generation of exceptionally large-scale and complex datasets, which includes PPI’s, protein-DNA interactions, kinase-substrate interactions, qualitative and quantitative genetic-interactions gene co-expression [64]. The “Big Data” challenge can be fulfilled by the development of Bioinformatics tools to handle these large-datasets to reduce their complexity to a level that enables rationale interpretation and in this way is more likely to provide new biological insights to the Life Sciences. The compilation (not an exhaustive list) of many web-based, standalone tools and R-based packages are described in Table 1. They allow the accomplishment of different-omics tasks such as feature selection, sample classification, multivariate methods. Cytoscape [65] is a tool primarily designed for network visualisation and analysis and has useful plugins available through the hosting website. Cytoscape makes use of a wide wealth variety of plugins to extend its functionality which are designed by the scientific community. The platform counts with several freely available apps/plugins (over 300 apps available on November 2018) for a diverse array of uses and analysis types.
Name | Description | Webpage | Ref. |
---|---|---|---|
iClusterPlus | Integrative clustering | [84] | |
mixomics | Data integration (CCA,PLS,PCA) | [85] | |
omicade4 | MCIA and CIA | [86] | |
pwOmics | Pathway-based integration of omics | [87] | |
PRESTO | Dimensionality reduction of multivariate data | [88] | |
caret | Classification and regression training | cran.r-project.org/web/packages/caret | — |
GEO2R | Identify DE genes using GEOquery & limma R packages | [55] | |
Metabo Analyst | Metabolomics analysis | metaboanalyst.ca | [89] |
Networkanalyst/INMEX | Integration of gene DE via network approaches | networkanalyst.ca | [90] |
ExAtlas | Meta-analysis & visualisation of gene DE | [91] | |
Elastic net | Gene DE with fitted GLM | [92] | |
ATHENA | Integration of genomics with clinical data | [93] | |
Network propagation | Gene DE, mutations, PPI’s | [94] |
Web-based, standalone tools and R packages dedicated to different-omics tasks such as feature selection, sample classification, multivariate approaches in data integration and meta-analysis.
PMA, Penalised Multivariate Analysis; RGCCA, Regularised and Sparse Generalised Canonical Correlation Analysis for Multiblock Data; caret, Classification and REgression Training; ATHENA, Analysis Tool for Heritable and Environmental Network Associations; CCA, Canonical-Correlation Analysis; PLS, Partial Least Squares; PCA, Principal Component Analysis; CIA, Co-Inertia Analysis; MCIA, Multiple Co-Inertia Analysis; GO, gene ontology; DE, differential expression; GLM, generalised linear models.
The Gene Ontology (GO) consortium [66] aims to capture the increasing knowledge on gene function in a controlled vocabulary applicable to a wide range of organisms. GO represents genes and gene products attributes on matters of their associated biological processes (BP), cellular components (CC) and molecular functions (MF). GO is considered roughly hierarchical, with ‘child’ elements (terms) being more specific than their ‘parent’ elements (terms), nevertheless, a ‘child’ element (term) might have more than one parent element. The ClueGO app [67] is used for the integration and visualisation of GO and pathway terms sourced from KEGG [68], WikiPathways [69] and Reactome [70]. The resultant ClueGO network is established based in kappa statistics which shows the agreement on how any given gene and/or gene products pairs share similar terms. The ClueGO analysis output is conditioned by thresholding of the kappa coefficient, in which a higher coefficient conducts only to the visualisation of close-related terms with very identical gene products. While, lower kappa coefficients will let visualisation of less associated terms.
The conclusion of the Human Genome Project led to the massification of research related with uncovering genotype—disease phenotype associations [71]. This event translated in a disparate growth in the number of publications and on the other side a limited and slow paced biocuration of these newly discovered evidences. Currently, DisGeNET [72] unifies biomedical literature evidence based on GDA collated from a multitude of databases. This database makes use of the Medical Subjects Headings (MeSH) tree structure for disease classification by a Unified Medical Language System. The potential of the database is extended by disgenet2r package and optional programmatic access.
STRING database [6] collates molecular information to cover both known and predicted PPI’s. All molecular interaction data is originally from primary interaction databases such as IntAct [73], BioGRID [74] and additional text-mining, coexpression and high-throughput experiments and computationally predicted PPIs. The up-to-date database version 10.5 comprises nearly 26 million PPI with a confidence score greater than 0.9 of more than 9 million proteins across 2031 organisms. GeneMANIA is another source for PPIs analysis and is accessible via web interface [75], and also as a Cytoscape app that can be used to detect related genes of a input query by means of a “guilt-by-association” strategy, which explores the realisation that a protein function can be obtained from another by seeing whether it interacts with another of known function. The app uses a large database of functional interaction networks, indexing 2152 association networks containing more than 500 million interactions mapped to 166,084 genes from nine organisms.
Multi-omics datasets might not only contain protein and gene data, but also expression profiles of chemical compounds. While it is easy and straightforward to combine protein/DNA/RNA expression data using common identifiers, this is not the case for metabolism end-products—metabolites. This requires a guilt-by-association, which explores the rationale that metabolites are frequently produced by enzymes and a shift in metabolite expression can reflect an up-stream shift in protein or gene expression. This involves semantic searches in enzyme repositories—BRENDA to identify potential proteins and has some inherent pitfalls such as uncertainty which enzyme/isoform is responsible for the metabolic change. Additionally, the same compound could also be generated by several proteins, which adds to the uncertainty. Therefore, metabolic datasets are often treated as separate entities in multi-omics studies and analysed independently and then converged only at the level of final outcomes [76]. The MetScape 3 app [77] for the Cytoscape can perform joint analysis of both metabolomic and gene expression data and allows visualisation of the entire fused network, or by selecting custom views based on metabolic pathways When dealing with large-scale datasets, there is the option to use a concept file based on pre-computed gene set enrichment analysis (GSEA), along with statistical and fold-change thresholds.
Transcription factors (TF) are critical for the regulation of gene expression since they control if gene’s DNA is transcribed into RNA [78]. A compendium on non-redundant TF and TF binding sites can be found at JASPAR [79]. The number of human TF ranges from 1500 to 2600, depending on source and stringency [78]. Direct analysis of modulated events due to TFs is not only valuable but might shed light on hidden elements that conventional pathway analysis cannot reveal. However, many TF binding sites and modulated genes are very hypothetical and often a random guess. Therefore, network-based analysis and interpretation involving TF elements should be taken with caution. CyTargetLinker [80] for extends existing biological networks by adding interactions associated with regulatory elements such as TF-target, miRNA-target or drug-targets. The application requires a loaded network with network attributes preferentially mapped to Ensembl, NCBI gene, UniProt, miRBase or DrugBank. Similarly, in CluePedia [81] users can perform miRNA analysis, by matching it to target-genes via selection of different database resources custom versions. Users can upload a list of genes and query the app to perform gene/miRNA enrichments. Then it will generate a miRNA-target interaction network that can be reused for inline integration with GO and pathway term clustering [81] within ClueGO.
PathVisio [82] allows drawing, edition, and visualisation of pathways handling gene, protein and metabolite data that can be further cross-mapped via the BridgeDb [83]. Inference of relevant pathways is based on an archive of pre-existent pathway maps from WikiPathways [69] and Reactome [70], establishing pathway over-representation based on a Z-score statistical procedure under the hypergeometric distribution and a P-value ranking based on a permutation procedure (randomisation test) that compares actual and permuted Z-scores. Pathways with a permuted P < 0.05 are considered significant by default.
KEGG is an integrated database resource of biological systems integrating genomic, compound and functional information. KEGG allows analysis of datasets from high-throughput omics technologies by uploading a list of genes/proteins or metabolites along with optional statistical scores and fold-change values. After converting to KEGG internal identifiers, the molecular data is matched (KEGG mapper) into a collection of curated pathways, covering metabolism, signalling transduction pathways, specific pathways for several disease conditions and drug development.
The availability of large-scale multi-omics data has opened the avenue to gain an unrivalled insight in disease-associated molecular pathophysiological changes. Simultaneously it has become apparent that systems to integrate and correlate this data are either inadequate or non-existent. The literature and publicly available databases are awash with data, yet the main approach of integrating all this information in a disease-specific context is traditionally based on meta-analysis at best or cannot be accomplished using standard computational methods. In order to better model complex organisms, samples from multiple tissues of the same individuals should be studied simultaneously using omics data, which will require the development of novel analysis methods. Acquiring the relevant tissues and/or body fluid sources from Human study cohorts can of course be difficult, thereby comparative systems biology may help identify which organisms may be similar enough in each aspect to be used as models. It is sometimes suggested that omics technologies and systems biology have failed to deliver many breakthrough enhancements to the treatment of complex diseases. In some cases, it may be that in fact such diseases are not truly one disease from a system or reductionist point-of-view, but several with the same or similar phenotypic end-points—i.e., with the current terminology they are unknown subtypes of disease. If this is the case, then the overlap between the systems is poor and statistical methods which the approach relies on require very large cohorts for identification of these subtypes and subsequent description of each system. Other possibilities are that longitudinal data or samples from different tissues are required. Other relevant concerns arise from biomarker validation studies, such as correlated observations (i.e. multiple observations per patient), multiplicity (testing multiple biomarkers or endpoints), multiple clinical endpoints (interest in more than one relevant endpoint) and selection bias (from retrospective data or observational study). Data-driven investigations using systems biology approaches, although offer complete views over the function of biological systems in health and disease its limited by the state of completeness of prior biological information.
The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP7/2007–2013 under grant agreement FP7-PEOPLE-2013-ITN-608332. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The authors declare that there is no conflict of interest regarding the publication of this manuscript.
Spinal cord injury (SCI) is an important clinical problem with significant socioeconomic impact worldwide.
\nSCI is a catastrophic event involving damage to the spinal cord (SC) that causes morphological and physiological changes leading to biomechanical and functional disorders in patients [1]. This condition induces acute and chronic inflammatory processes that can result in temporary or permanent repercussions including paraplegia, quadriplegia, or even death [2].
\nThe pathophysiology of SCI is very complicated, and it consists of a primary and a secondary phase. The primary phase occurs immediately after the damage to the SC causing cell death at the epicenter of the injury as well as the beginning of the pro-inflammatory response [3]. The secondary phase starts 2 hours after the damage and can last up to 6 months. During this phase the extent of the injury increases in response to the augmented pro-inflammatory factors which contribute to induce local edema, ischemia, vascular alterations, ionic dysregulations, and oxidative stress [3, 4]. These prejudicial mechanisms persist during the chronic stages of the injury, and although their intensity is diminished, the neurological function continues to decline [5]. Most of the post-traumatic neuronal degeneration involves an uncontrollable cascade of destructive mechanisms that are still incompletely understood and remain a challenge for scientists [6].
\nThe current therapy for SCI involves surgical decompression and steroid administration; however, both of them only show minimal efficiency, and the need for an effective therapy is continuously rising [7]. Therefore, the transplantation of stem cells as a novel therapeutic approach has received increasing attention due to their promising results in neurological recovery in SCI [8, 9, 10]. Among them, mesenchymal stem cells (MSCs) demonstrate to be a valuable promising therapy due to their significant autocrine and paracrine activity which help to induce the proliferation and differentiation of different cell types and to exert immunomodulatory effects in the microenvironment of the host [6, 11]. MSCs, anti-inflammatory molecules, and trophic factors are capable of supporting axonal growth to promote angiogenesis, remyelination, and protection against apoptotic cell death [12]. Furthermore, MSCs possess a varied spectrum of therapeutic properties such as neuroprotection after glutamate excitotoxicity [13, 14], reduction in protein levels associated with stress and reactive oxygen species [15] and pro-inflammatory cytokines [16], M1 macrophage polarization to the M2 pro-repair activated phenotype [17], secretion of neurotrophic factors [16, 18, 19], and their ability to produce numerous exosomes.
\nIn addition, MSCs have minimal immunoreactivity towards the host as well as a limited chance of developing a tumor and are particularly appealing due to their wide range of advantages over other types of stem cells [20]. Finally, we will discuss clinical trials of improvement using autologous and allogeneic MSCs after acute and chronic SCI.
\nMSCs are adult stem cells with self-renewing and differentiation abilities. These cells can be isolated from different sources (bone marrow, adipose tissue, umbilical cord (UC), and amniotic fluid) and are easily preserved without raising any ethical issue [21]. Mammalian bone marrow is the best understood niche that harbors hematopoietic stem cells (HSCs), and MSCs are believed to provide the basis for the physical structures of the niche [22]. Moreover, MSCs are defined as multipotent cells that are thought to regulate the self-renewal, proliferation, and differentiation of the HSCs through the production of cytokines and intracellular signals that are initiated by cell-to-cell interaction. Lastly, MSCs can differentiate into cells from different lineages, such as osteoblasts, cartilage cells, fibroblasts, muscle cells, fat cells, and neurons [23, 24].
\nMost researchers have suggested minimal criteria to define MSCs. The International Society for Cellular Therapy (ISCT) established specific criteria in order to identify unique populations of MSCs [25].
MSCs must be plastic adherent when maintained under standard culture conditions.
MSCs must be positive for CD105, CD90, CD73, CD29, CD44, CD71, and CD106 and be negative for the expression of hematopoietic markers such as CD34, CD45, HLA-DR, CD14, MHC-II, CD11b, and CD14 and express low levels of MHC-I.
MSCs must differentiate in vitro at least in osteoblasts, adipocytes, and chondroblasts [25, 26].
MSCs are well known for their ability to differentiate into numerous cell lineages, but, besides their cell multipotential reprogramming capacity, they promise to be an effective candidate therapy in clinical trials for different human pathologies due to their successful homing, immunomodulation, and tissue repairing [27]. Moreover, exosomes from MSCs are being considered the most important factor of the therapeutic effects of MSCs as they could be used as molecule exchangers and natural drug delivery vehicles [28].
\nSeveral studies have shown that MSCs are capable of migrating selectively and exert homing capabilities to different organs [29, 30]. Even if they are transplanted by local or systemic pathways, MSCs are principally guided to damaged tissues by the coordinate expression of specific receptors and ligands that allow them to reach their desired target and effectuate different mechanisms [31]. Additionally, MSCs possess a high chemotactic activity that increases the recruitment of different cells. Indeed, fibroblasts accelerate migration, proliferation, and integrin expression in response to MSC secretome [32, 33]. Similarly, neutrophils increase migration rate and immunological response when they are stimulated with MSCs after microbial challenge in vitro [34, 35]. In murine SCI models, the MSC-grafted SC has proven to amplify granulocytes and antigen-presenting cell recruitment in early stages by a wide variety of cytokines and chemokines such as CXCL10, CXCL12, CXCL1, and CL5 to boost SC recovery [34, 36].
\nMSCs have proven to regulate the immune response through cell-to-cell contact and by the secretion of soluble mediators including cytokines, prostaglandins, enzymes, and proapoptotic and antiapoptotic molecules [27, 37, 38, 39]. Different studies involving MSC transplantation in exacerbated immune response models such as peritonitis and ulcerative colitis ameliorate inflammation by reducing the expression levels of pro-inflammatory cytokines such as interleukin-1 beta, interleukin-12, interleukin-6, and tumor necrosis factor-α (TNFα). In addition, these cells exert a decrease of the classical phenotype M1 marker and an increase of the alternative phenotype M2, as well as a marked macrophage reprogramming from M1 to M2 [17, 40, 41, 42]. Moreover, MSCs can suppress T cell activation and proliferation by downregulating the expression of costimulatory molecules on the surface of dendritic cells [43], interleukin-10, transforming growth factor-B (TGFβ), nitric oxide, and indoleamine 2,3-dioxygenase enzyme production in response to inflammation as well as interleukin-2 absorption [37, 44, 45]. Similarly, B cell activation can be disturbed, and the regulatory B cell phenotype can be promoted [46, 47].
\nMSCs participate in repairing many tissues, mostly by the secretion of TGFβ and vascular endothelial growth factor (VEGF) to promote angiogenesis [48], extracellular matrix remodeling, and reduction of the scar formation in chronic wounds [49, 50]. Similarly, in pathologies where gliosis, demyelination, and neuroinflammation occur, MSCs have shown neuroprotective activities such as vascular stabilization and angiogenesis by tight junction protein expression [51], neuronal suppression apoptosis [52, 53], glia hypertrophy prevention [54], and promotion of myelinization by the activation of oligodendrocyte precursor cells [53, 55]. Additionally, MSCs promote synaptic transmission [56], neurite outgrowth, and axonal sprouting mostly by excretion of trophic factors including brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) [12, 57]. SCI models motor skills are increased [58] inclusively; bladder and erectile dysfunction improvement have been reported [59, 60].
\nOriginally it was believed that MSCs could exclusively differentiate into cells from the mesodermal lineage [23, 24]; however, in the last 20 years many authors have proven that a proper microenvironment can promote greater plasticity and that MSCs from different sources can differentiate into dermal, neural, or glial cells in vitro and in vivo when they are exposed to neurotrophic factors and specific cytokines [61, 62, 63]. Smooth muscle and endothelial cells derived from MSCs can be detected and improve heart functions in ischemic myocardium models [64]. Also, skin, articular cartilage, and bone regeneration have been reported, but mostly when MSCs are combined with natural and artificial scaffolds or when genetically modified [65, 66, 67]. In order to achieve CNS regeneration, different sources of MSCs and culture methods have been tested in murine SCI models; however, transplants have demonstrated to improve functional recovery by differentiation into neurons, astrocytes, but mostly oligodendrocytes [54, 55, 58].
\nExosomes are extracellular vesicles released by many cells, including MSCs. Their length is between 30 and 100nm, and they can bind to cells through receptor and ligand interaction or by fusion with the target cell membrane to deliver high amounts of cytokines, growth factors, microRNAs, and mRNAs capable of modifying peptide and protein synthesis [60]. Thus, the derived MSC exosomes could be the most attractive therapy in SCI models since neuronal differentiation from MSCs remains poor [58, 68] and several studies show that the regenerative and anti-inflammatory mechanisms are mostly mediated by paracrine factors [27, 36, 69, 70, 71]. Furthermore, MSC exosomes are natural drug delivery vehicles that can be modified and produced in high quantities [28, 72, 73]. MSCs have proven to be safe in many different preclinical studies; however, clinical trials involving exosomes in SCI therapy are not yet recruited due to the fact that optimal MSC culture conditions and protocols for exosome isolation is still to be established (
The promising results of MSCs in preclinical studies encouraged their use in humans; however, the results obtained in the clinical trials still remain controversial and do not replicate what was previously reported in experimental animal studies. In this section we will review the results obtained from the main clinical trials involving MSC transplantation as well as their type of application, properties, limitations, and future directions.
\nMost of the first clinical studies describing reporting the application of MSCs were focused on describing the transplantation technique, the safety, and the evidence of any adverse reactions. Moviglia et al. conducted a preliminary report that described the intra-arterial administration of bone marrow (BM) MSCs (BM-MSCs) in two patients with chronic SCI in combination with neurorehabilitation programs. Patient 1 presented paraplegia at the eighth thoracic vertebra (T8) with a sensitive level corresponding to T6, while patient 2 presented severe quadriplegia with a lesion that extended from his third to fifth cervical vertebrae (C3–C5) and a sensitive level corresponding to C2. After 6 months, both patients improved their motor and sensory functions without having any secondary effects. The motor level of patient 1 now corresponds to his first sacral metamere (S1) and his sensitive level to the fourth sacral metamere (S4), while sensory and motor functions from patient 2 reached T1–T2 [74]. Similar results, in terms of the safety, were obtained in a pilot study conducted by Pal et al., where 30 patients with complete cervical or thoracic SCI (ASIA scale rating system class A) received intrathecal injections of MSCs via lumbar puncture and none of them presented any adverse effects in the following 1–3 years [75]. However, only the patients with less than 6 months of thoracic-level injury experienced improvement in their quality of life and degree of independence according to Barthel’s Index (BI). Despite these improvements, there was no significant change in the ASIA score and in magnetic resonance imaging (MRI). Furthermore, due to the homing abilities of the MSCs, Ra et al. also tested the toxicity, tumorigenicity, and therapeutic potential of the intravenous administration of adipose tissue-derived MSCs in eight patients with more than 12 months of SCI. After 3 months the therapy demonstrated to be safe and not promoting tumor growth [76]. In addition, this study described limited motor recovery where only one patient with ASIA A demonstrated improvement to ASIA grade C. Lastly, other studies have also demonstrated the safety and lack of evidence of any severe adverse reactions with the intrathecal administration of MSCs [77, 78].
\nMoving forward, Sykova et al. conducted a nonrandomized phase I/phase II clinical trial comparing the functional improvement and safety of intra-arterial versus intravenous administration of BM-MSCs in 20 patients with SCI at the cervical or thoracic level. The clinical characterization described 15 patients with ASIA grade A and 5 patients with ASIA grade B (incomplete SCI). Both intra-arterial group (n=7) and the intravenous group (n=13) contained patients with acute and chronic phases of SCI. The study found significant functional improvements (motor and sensory) in five acute patients and only in one chronic patient. In the intra-arterial group, all four subacute patients exhibited a significant improvement in their ASIA score or Frankel score as well as a marked recovery of motor and somatosensory evoked potentials (MEPs and SEPs). However, in the intravenous group, only one patient demonstrated an improved ASIA score as well as electrophysiology results. Interestingly most of the patients who had functional improvements received the administration of the MSCs close to the injury site suggesting that the administration route through the vertebral artery or into the cerebrospinal fluid might lead to the best outcome. This study also describes that 3–4 weeks after the injury appears to be the best therapeutic window to administer the cells [79]. These results are also supported by another study carried out by Park et al. where they showed improved motor and sensory function in 5 out of 6 patients who received intraspinal implantation of BM-MSCs 7 days post-injury in combination with granulocyte-macrophage colony-stimulating factor (GM-CSF) [80]. Four patients demonstrated a significant improvement in their American Spinal Injury Association Impairment Scale (AIS) grades from A to C, while one patient improved to AIS B from A. Lastly this study also describes those 3–4 weeks after the injury appears to be the best therapeutic window to administer the cells [79].
\nAs many studies supported the safety of this therapy, more scientists focused on comparing and trying to find different types of MSC transplantation. Geffner et al. reported eight thoracic SCI cases (four acute and four chronic) with the administration of BM-MSCs through many different administration routes such as intravenous, intraspinal, and directly into the spinal canal in order to assure that the cells will reach their target. Over the course of 2 years, patients showed significant improvements in their quality of life measured by the BI score as well as certain motor recovery measured by the ASIA, Ashworth, and Frankel scores. All four patients of the acute group experienced an improvement in the ASIA score from A to C, while chronic patients had a lesser recovery improving from an ASIA score of B–C to C–D. Improvement of bladder control was the most important aspect in augmenting their quality of life; however, there were also many other important motor improvements, which cannot be correctly represented in the ASIA score [81]. In addition, other studies furtherly support the therapeutic potential of MSCs in improving the urinary functions of SCI patients which majorly contributes to increasing their self-care ability [82, 83]. This study also demonstrated the feasibility and safety of multiple administration routes. Moreover, the study conducted by Jeon et al. discussed the effectiveness of the intraspinal application of BM-MSCs in 10 patients with complete cervical SCI. After 6 months 6 patients demonstrated motor improvements in the upper limbs by measuring electrophysiological parameters (electromyography, nerve conduction velocity, SEP, MEP) as well as morphological changes described by magnetic resonance imaging (MRI) at the site of the lesion. In addition, three out of those six patients exhibited a significant increase in the performance of daily tasks. However, the ASIA/Frankel motor grade remained the same and did not reflect these motor improvements. This study also reported the absence of any major adverse effect or neoplasm growth over the course of 3 years, furtherly supporting the safety of this therapy [84].
\nKaramouzian et al. conducted one of the first studies to introduce a control group in the field of MSCs and SCI. This nonrandomized clinical trial discussed their therapeutic potential by comparing the outcome of 11 patients (7 males and 4 females with mean age of 33.3 ± 8.9 years) with complete subacute SCI who received BM-MSC transplantation via lumbar puncture with a control group (n=20). Five patients in the study group and 12 patients in the control group presented spinal fracture at T12 and L1 levels, while the remaining patients presented a lesion between T1 and T11. After almost 3 years of follow-up, five patients of the experimental group and three patients of the control group exhibited noticeable recovery (a two-grade improvement from baseline, i.e., from ASIA A to C); however, the results were statistically borderline, and there is no clear evidence of the therapeutic potential of these MSCs [85]. In a similar study, Dai et al. discussed the effectiveness of BM-MSCs in complete and chronic SCI. This study randomly assigned 40 patients with complete and chronic SCI into a treatment group (n=20) and a control group (n=20). After 6 months of follow-up, 50% of the treatment group demonstrated significant motor recovery as well as an improvement in ASIA score and in electrophysiological examinations. In addition, most of the patients in the treatment group exhibited a significant clinical improvement in terms of the amount of residual urinary volume, pinprick sensory, and light touch, while the control group did not exhibit any significant motor or sensorial improvements [82]. Both of these studies suggest that BM-MSCs might help improve neurological function in complete and chronic SCI, and they present no evidence of any severe complications or major adverse events in any of the patients.
\nAlthough stem cell therapy has demonstrated that they possess the therapeutic potential to be combined with neurorehabilitation, Cheng et al. analyzed the effect of UC-MSCs in comparison with neurorehabilitation and self- healing in 34 patients with thoracolumbar SCI and AIS A grading. Patients were divided into three groups: the UC-MSC treatment group, the rehabilitation group, and the control group. After 6 months around 70% of the patients in the treatment group experienced a significant motor recovery and noticeable improvement in muscle tension and self-care ability which involves an increase in the strength of the abdomen, waist, and lower limbs. Meanwhile, only 36% of the patients treated with neurorehabilitation exhibited certain improvements in these aspects, and the control group showed no significant changes in motor recovery, sensation, or self-care ability. In terms of bladder functions, the treatment group showed a decrease in residual urinary volume and maximum detrusor pressure as well as an increase in bladder capacity and urinary flow in comparison with the other two groups [86]. Later on, El-Kheir et al. decided to compare the use of BM-MSCs in combination with physical therapy with the use of physical therapy alone in 70 chronic cervical and thoracic SCI patients (25 AIS A and 45 AIS B) in a phase I/phase II controlled single-blind clinical trial. After 18 months of follow-up, 46% of the stem cell therapy group exhibited functional improvement and an increase in both motor and dermatome scores by the ASIA and AIS scoring as well as a significant improvement in motor, pinprick, and light touch sensory and functional independence scores over the treated group with physical therapy alone [87]. These studies suggest that BM-MSCs can be combined with additional therapies in order to boost their therapeutic potential.
\nEqually important, El-Kheir et al. described that thoracic SCI patients with smaller lesions and lower duration of the injury had a higher increase of functional improvement in comparison with patients with cervical SCI [87]. Similar results were obtained in the study carried out by Mendonca et al. which demonstrated a statistically significant correlation between the neurological recovery and both the level and size of the injury in patients with chronic (>6 months) thoracic or lumbar SCI. In addition, after the intra-lesion administration of BM-MSCs, all of the 14 patients demonstrated certain improvements in tactile sensitivity and 8 subjects showed some improvement in the motor functions of the lower limbs reflected by an improvement in their ASIA grading from A to B or C [88].
\nAs the amount of beneficial but limited results grew, the amount of the transplanted MSCs and number of administrations started to gain attention. Vaquero et al. conducted a series of clinical trials involving different numbers of MSC administrations in SCI. The first study consisted of a clinical trial involving 12 patients with complete (ASIA A) and chronic thoracic SCI who received two separate transplantations of BM-MSCs in the subarachnoid space. All patients exhibited certain degree of sensorial improvement (pinprick sensitivity and light touch sensitivity), and 50% of the patients showed motor activity below the injury level according to clinical and neurophysiological studies (SEPs and MEPs). More than 30% of the patients improved their AIS A score from A to B or C, and 83% of the patients presented improvement in urodynamic function including possibility of voluntary micturition (5 patients), increased bladder capacity at filling in (8 patients), decreased detrusor pressure at bladder filling in (9 patients), and increased bladder compliance (10 patients). In addition, they hypothesize that the clinical improvement was dose-dependent [89]. The second study consisted of 10 patients with incomplete (ASIA B and C) cervical, thoracic, and lumbar SCI who received 4 subarachnoid administrations of MSCs. Besides the variable degree of sensorial and motor improvement, after 12 months of the first dosage almost all the patients showed noticeable improvements in bowel and bladder control as well as evidence of muscle reinnervation in electromyographic studies. Furthermore, half of them demonstrated a decrease in spasticity by the Penn and Ashworth scales, and after the third dosage of MSCs, all the patients exhibited an increase in the values of neurotrophic factors such as brain-derived neurotrophic factor (BDNF), glial-derived neurotrophic factor (GDNF), ciliary neurotrophic factor (CNF), and neurotrophin 3 (NT-3) and neurotrophin 4 (NT-4). However, the difference with the basal mean concentration was not statistically significant [90]. Lastly, their third study consisted of a phase II clinical trial with the administration of three intrathecal injections of MSCs in nine patients with chronic SCI. However, this time 44.4% of the patients demonstrated important improvements in voluntary muscle contraction, motor power, spasm, spasticity, neuropathic pain, and sexual function (IANR-SCIFRS scale) along with evidence of muscle reinnervation. In addition, more than half of the patients showed improved somatosensory and motor evoked potentials [91]. Taken all together, Vaquero and colleagues demonstrate that the subarachnoid administration of MSCs is a safe procedure and that the clinical improvement may increase in a dose-dependent manner. These results were furtherly supported by a study carried out by Oh et al. where a single intramedullary administration of MSC in 16 patients with chronic SCI ASIA B demonstrated very limited therapeutic efficacy. Only two patients demonstrated enhanced motor recovery [92].
\nOverall, the use of MSCs in SCI appears to be safe and without any major evidence of severe adverse reactions. However, the results obtained in the clinical trials so far do not concrete the promising results obtained in the preclinical trials. This may be due to the fact that preclinical studies normally utilize specific animal models with standardized protocols to produce the injury as well as preestablished treatments and timing of the transplantation which cannot be replicated in a human study. In clinical trials, most of these conditions depend heavily on chance, the traumatic event, and the emergency setting which may differ a lot from the controlled atmosphere of an animal experiment. In addition, there is a great lack of phase III clinical trials due to financial and ethical reasons. As mentioned before, one of the few phase III clinical trials held by Oh et al. showed weak and limited therapeutic efficacy [92]. Further investigation is needed to determine accurate parameters for its clinical use in SCI such as optimal therapeutic protocols involving type, preparation, number of cells administered, timing of transplantation, and administration route.
\nOn the other hand, thanks to the technological revolution, scientists have now started to investigate the use of MSCs in combination with new biomaterials in order to promote tissue repair and to improve cell survival [6]. A study conducted by Xiao et al. analyzed the therapeutic effect of MSC transplantation in combination with a collagen scaffold which is known to support cell migration and adhesion [93]. After the transplant the injury status of the patients changed from ASIA A to ASIA C accompanied by a significant improvement in motor, sensory, and urinary functions [94]. Furthermore, the possibility of combining MSCs with hydrogels is particularly appealing due to their capacity to be injected with minimal invasion and to be loaded with specific drugs that can be furtherly released in a controlled manner [95]. Moreover, MSCs’ ability to induce the production of neurotrophic, immunomodulating, and neuroprotective factors needs further investigation in order to be fully understood and furtherly enhanced, aiming to improve the clinical outcomes. Lastly, the homing properties of the MSCs could be useful to transport specific target drugs to the site of the lesion and thus acting as a vector [96].
\nIn conclusion, MSCs represent a practical therapy in the search for a new treatment for SCI; this may be due to the fact that MSC therapy presents a large spectrum of favorable assets that make it particularly appealing. First, MSCs can be isolated from non-embryonic tissues (BM, adipose tissue, UC, and amniotic fluid, among others) via noninvasive techniques and are easily preserved and expanded in vitro. Furthermore, MSCs possess migratory properties that allow them to be administered through different routes and have minimal immunoreactivity towards the host, as well as a limited chance of developing a tumor. Numerous clinical trials have demonstrated their safety for transplantation in humans as well as their lack of any major side effects. However, an enormous improvement in motor recovery and sensory function is still missing, bringing out that additional investigation and new phase III clinical trials are needed in order to fully understand the mechanism of action of MSCs as well as the pathological mechanisms which prevent the restoration of neural circuits in SCI. Lastly, the use of combinatory strategies with specific drugs, biomaterials, or neurorehabilitation may be the key factor to translate their promising results into the clinical practice.
\nSupporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.
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