Search

Microglial research has advanced considerably in recent decades yet has been constrained by a rolling series of dichotomies such as "resting versus activated" and "M1 versus M2." This dualistic classification of good or bad microglia is inconsistent with the wide repertoire of microglial states and functions in development, plasticity, aging, and diseases that were elucidated in recent years. New designations continuously arising in an attempt to describe the different microglial states, notably defined using transcriptomics and proteomics, may easily lead to a misleading, although unintentional, coupling of categories and functions. To address these issues, we assembled a group of multidisciplinary experts to discuss our current understanding of microglial states as a dynamic concept and the importance of addressing microglial function. Here, we provide a conceptual framework and recommendations on the use of microglial nomenclature for researchers, reviewers, and editors, which will serve as the foundations for a future white paper.

After spinal cord injury (SCI), normally innocuous visceral or somatic stimuli can trigger uncontrolled reflex activation of sympathetic circuitry, causing pathological dysautonomia. We show that remarkable structural remodeling and plasticity occur within spinal autonomic circuitry, creating abnormal sympathetic reflexes that promote dysautonomia. However, when mice are treated early after SCI with human-equivalent doses of the US Food and Drug Administration (FDA)-approved drug gabapentin (GBP), it is possible to block multi-segmental excitatory synaptogenesis and abolish sprouting of autonomic neurons that innervate immune organs and sensory afferents that trigger pain and autonomic dysreflexia (AD). This "prophylactic GBP" regimen decreases the frequency and severity of AD and protects against SCI-induced immune suppression. These benefits persist even 1 month after stopping treatment. GBP could be repurposed to prevent dysautonomia in at-risk individuals with high-level SCI.

The rapid growth in data sharing presents new opportunities across the spectrum of biomedical research. Global efforts are underway to develop practical guidance for implementation of data sharing and open data resources. These include the recent recommendation of 'FAIR Data Principles', which assert that if data is to have broad scientific value, then digital representations of that data should be Findable, Accessible, Interoperable and Reusable (FAIR). The spinal cord injury (SCI) research field has a long history of collaborative initiatives that include sharing of preclinical research models and outcome measures. In addition, new tools and resources are being developed by the SCI research community to enhance opportunities for data sharing and access. With this in mind, the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health (NIH) hosted a workshop on October 5-6, 2016 in Bethesda, MD, in collaboration with the Open Data Commons for Spinal Cord Injury (ODC-SCI) titled "Preclinical SCI Data: Creating a FAIR Share Community". Workshop invitees were nominated by the workshop steering committee (co-chairs: ARF and VPL; members: AC, KDA, MSB, KF, LBJ, PGP, JMS), to bring together junior and senior level experts including preclinical and basic SCI researchers from academia and industry, data science and bioinformatics experts, investigators with expertise in other neurological disease fields, clinical researchers, members of the SCI community, and program staff representing federal and private funding agencies. The workshop and ODC-SCI efforts were sponsored by the International Spinal Research Trust (ISRT), the Rick Hansen Institute, Wings for Life, the Craig H. Neilsen Foundation and NINDS. The number of attendees was limited to ensure active participation and feedback in small groups. The goals were to examine the current landscape for data sharing in SCI research and provide a path to its future. Below are highlights from the workshop, including perspectives on the value of data sharing in SCI research, workshop participant perspectives and concerns, descriptions of existing resources and actionable directions for further engaging the SCI research community in a model that may be applicable to many other areas of neuroscience. This manuscript is intended to share these initial findings with the broader research community, and to provide talking points for continued feedback from the SCI field, as it continues to move forward in the age of data sharing.

Spinal cord injury (SCI) research is a data-rich field that aims to identify the biological mechanisms resulting in loss of function and mobility after SCI, as well as develop therapies that promote recovery after injury. SCI experimental methods, data and domain knowledge are locked in the largely unstructured text of scientific publications, making large scale integration with existing bioinformatics resources and subsequent analysis infeasible. The lack of standard reporting for experiment variables and results also makes experiment replicability a significant challenge. To address these challenges, we have developed RegenBase, a knowledge base of SCI biology. RegenBase integrates curated literature-sourced facts and experimental details, raw assay data profiling the effect of compounds on enzyme activity and cell growth, and structured SCI domain knowledge in the form of the first ontology for SCI, using Semantic Web representation languages and frameworks. RegenBase uses consistent identifier schemes and data representations that enable automated linking among RegenBase statements and also to other biological databases and electronic resources. By querying RegenBase, we have identified novel biological hypotheses linking the effects of perturbagens to observed behavioral outcomes after SCI. RegenBase is publicly available for browsing, querying and download.Database URL:http://regenbase.org.

Efforts to understand spinal cord injury (SCI) and other complex neurotrauma disorders at the pre-clinical level have shown progress in recent years. However, successful translation of basic research into clinical practice has been slow, partly because of the large, heterogeneous data sets involved. In this sense, translational neurological research represents a "big data" problem. In an effort to expedite translation of pre-clinical knowledge into standards of patient care for SCI, we describe the development of a novel database for translational neurotrauma research known as Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI). We present demographics, descriptive statistics, and translational syndromic outcomes derived from our ongoing efforts to build a multi-center, multi-species pre-clinical database for SCI models. We leveraged archived surgical records, postoperative care logs, behavioral outcome measures, and histopathology from approximately 3000 mice, rats, and monkeys from pre-clinical SCI studies published between 1993 and 2013. The majority of animals in the database have measures collected for health monitoring, such as weight loss/gain, heart rate, blood pressure, postoperative monitoring of bladder function and drug/fluid administration, behavioral outcome measures of locomotion, and tissue sparing postmortem. Attempts to align these variables with currently accepted common data elements highlighted the need for more translational outcomes to be identified as clinical endpoints for therapeutic testing. Last, we use syndromic analysis to identify conserved biological mechanisms of recovery after cervical SCI between rats and monkeys that will allow for more-efficient testing of therapeutics that will need to be translated toward future clinical trials.

The lack of reproducibility in many areas of experimental science has a number of causes, including a lack of transparency and precision in the description of experimental approaches. This has far-reaching consequences, including wasted resources and slowing of progress. Additionally, the large number of laboratories around the world publishing articles on a given topic make it difficult, if not impossible, for individual researchers to read all of the relevant literature. Consequently, centralized databases are needed to facilitate the generation of new hypotheses for testing. One strategy to improve transparency in experimental description, and to allow the development of frameworks for computer-readable knowledge repositories, is the adoption of uniform reporting standards, such as common data elements (data elements used in multiple clinical studies) and minimum information standards. This article describes a minimum information standard for spinal cord injury (SCI) experiments, its major elements, and the approaches used to develop it. Transparent reporting standards for experiments using animal models of human SCI aim to reduce inherent bias and increase experimental value.

Spinal cord injury (SCI) affects hundreds of thousands of people in the United States, and while some effects of the injury are broadly recognized (deficits to locomotion, fine motor control, and quality of life), the systemic consequences of SCI are less well‐known. The spinal cord regulates systemic immunological and visceral functions; this control is often disrupted by the injury, resulting in viscera including the gut, spleen, liver, bone marrow, and kidneys experiencing local tissue inflammation and physiological dysfunction. The extent of pathology depends on the injury level, severity, and time post‐injury. In this review, we describe immunological and metabolic consequences of SCI across several organs. Since infection and metabolic disorders are primary reasons for reduced lifespan after SCI, it is imperative that research continues to focus on these deleterious aspects of SCI to improve life span and quality of life for individuals with SCI. [ABSTRACT FROM AUTHOR]