RegenBase: a knowledge base of spinal cord injury biology for translational research

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org)

The past decade has seen accelerating movement from data protectionism in publishing toward open data sharing to improve reproducibility and translation of biomedical research. Developing data sharing infrastructures to meet these new demands remains a challenge. One model for data sharing involves simply attaching data, irrespective of its type, to publisher websites or general use repositories. However, some argue this creates a 'data dump' that does not promote the goals of making data Findable, Accessible, Interoperable and Reusable (FAIR). Specialized data sharing communities offer an alternative model where data are curated by domain experts to make it both open and FAIR. We report on our experiences developing one such data-sharing ecosystem focusing on 'long-tail' preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, non-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommons that is well-embraced by the community it aims to serve. In the present paper, we provide a review of the community-based design template and describe the adoption by the community including a high-level review of current data assets, publicly released datasets, and web analytics. Although odc-sci.org is in its late beta stage of development, it represents a successful example of a specialized data commons that may serve as a model for other fields.

The Life of a Trailing Spouse

In 1981, I published a paper in the first issue of the Journal of Neuroscience with my postdoctoral mentor, Alan Pearlman. It reported a quantitative analysis of the receptive field properties of neurons in reeler mouse visual cortex and the surprising conclusion that although the neuronal somas were strikingly malpositioned, their receptive fields were unchanged. This suggested that in mouse cortex at least, neuronal circuits have very robust systems in place to ensure the proper formation of connections. This had the unintended consequence of transforming me from an electrophysiologist into a cellular and molecular neuroscientist who studied cell adhesion molecules and the molecular mechanisms they use to regulate axon growth. It took me a surprisingly long time to appreciate that your science is driven by the people around you and by the technologies that are locally available. As a professional puzzler, I like all different kinds of puzzles, but the most fun puzzles involve playing with other puzzlers. This is my story of learning how to find like-minded puzzlers to solve riddles about axon growth and regeneration.

Functional long non-coding and circular RNAs in zebrafish

The utility of model organisms to understand the function of a novel transcript/genes has allowed us to delineate their molecular mechanisms in maintaining cellular homeostasis. Organisms such as zebrafish have contributed a lot in the field of developmental and disease biology. Attributable to advancement and deep transcriptomics, many new transcript isoforms and non-coding RNAs such as long noncoding RNA (lncRNA) and circular RNAs (circRNAs) have been identified and cataloged in multiple databases and many more are yet to be identified. Various methods and tools have been utilized to identify lncRNAs/circRNAs in zebrafish using deep sequencing of transcriptomes as templates. Functional analysis of a few candidates such as tie1-AS, ECAL1 and CDR1as in zebrafish provides a prospective outline to approach other known or novel lncRNA/circRNA. New genetic alteration tools like TALENS and CRISPRs have helped in probing for the molecular function of lncRNA/circRNA in zebrafish. Further latest improvements in experimental and computational techniques offer the identification of lncRNA/circRNA counterparts in humans and zebrafish thereby allowing easy modeling and analysis of function at cellular level.

FAIR SCI Ahead: The Evolution of the Open Data Commons for Pre-Clinical Spinal Cord Injury Research

Over the last 5 years, multiple stakeholders in the field of spinal cord injury (SCI) research have initiated efforts to promote publications standards and enable sharing of experimental data. In 2016, the National Institutes of Health/National Institute of Neurological Disorders and Stroke hosted representatives from the SCI community to streamline these efforts and discuss the future of data sharing in the field according to the FAIR (Findable, Accessible, Interoperable and Reusable) data stewardship principles. As a next step, a multi-stakeholder group hosted a 2017 symposium in Washington, DC entitled "FAIR SCI Ahead: the Evolution of the Open Data Commons for Spinal Cord Injury research." The goal of this meeting was to receive feedback from the community regarding infrastructure, policies, and organization of a community-governed Open Data Commons (ODC) for pre-clinical SCI research. Here, we summarize the policy outcomes of this meeting and report on progress implementing these policies in the form of a digital ecosystem: the Open Data Commons for Spinal Cord Injury (ODC-SCI.org). ODC-SCI enables data management, harmonization, and controlled sharing of data in a manner consistent with the well-established norms of scholarly publication. Specifically, ODC-SCI is organized around virtual "laboratories" with the ability to share data within each of three distinct data-sharing spaces: within the laboratory, across verified laboratories, or publicly under a creative commons license (CC-BY 4.0) with a digital object identifier that enables data citation. The ODC-SCI implements FAIR data sharing and enables pooled data-driven discovery while crediting the generators of valuable SCI data.

Data Dissemination: Shortening the Long Tail of Traumatic Brain Injury Dark Data

Translation of traumatic brain injury (TBI) research findings from bench to bedside involves aligning multi-species data across diverse data types including imaging and molecular biomarkers, histopathology, behavior, and functional outcomes. In this review we argue that TBI translation should be acknowledged for what it is: a problem of big data that can be addressed using modern data science approaches. We review the history of the term big data, tracing its origins in Internet technology as data that are "big" according to the "4Vs" of volume, velocity, variety, veracity and discuss how the term has transitioned into the mainstream of biomedical research. We argue that the problem of TBI translation fundamentally centers around data variety and that solutions to this problem can be found in modern machine learning and other cutting-edge analytical approaches. Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data ("dark data") and "long-tail data" (small, specialty TBI datasets undergirding the published literature). We review a few early examples of published articles in both the pre-clinical and clinical TBI research literature to demonstrate how data reuse can drive new discoveries leading into translational therapies. Making TBI data resources more Findable, Accessible, Interoperable, and Reusable (FAIR) through better data stewardship has great potential to accelerate discovery and translation for the silent epidemic of TBI.

Reconnecting Eye to Brain

Although much is known about the regenerative capacity of retinal ganglion cells, very significant barriers remain in our ability to restore visual function following traumatic injury or disease-induced degeneration. Here we summarize our current understanding of the factors regulating axon guidance and target engagement in regenerating axons, and review the state of the field of neural regeneration, focusing on the visual system and highlighting studies using other model systems that can inform analysis of visual system regeneration. This overview is motivated by a Society for Neuroscience Satellite meeting, "Reconnecting Neurons in the Visual System," held in October 2015 sponsored by the National Eye Institute as part of their "Audacious Goals Initiative" and co-organized by Carol Mason (Columbia University) and Michael Crair (Yale University). The collective wisdom of the conference participants pointed to important gaps in our knowledge and barriers to progress in promoting the restoration of visual system function. This article is thus a summary of our existing understanding of visual system regeneration and provides a blueprint for future progress in the field.

Identification of miRNAs involved in DRG neurite outgrowth and their putative targets

Peripheral neurons regenerate their axons after injury. Transcriptional regulation by microRNAs (miRNAs) is one possible mechanism controlling regeneration. We profiled miRNA expression in mouse dorsal root ganglion neurons after a sciatic nerve crush, and identified 49 differentially expressed miRNAs. We evaluated the functional role of each miRNA using a phenotypic analysis approach. To predict the targets of the miRNAs we employed RNA-Sequencing and examined transcription at the isoform level. We identify thousands of differentially expressed isoforms and bioinformatically associate the miRNAs that modulate neurite growth with their putative target isoforms to outline a network of regulatory events underlying peripheral nerve regeneration. MiR-298, let-7a, and let-7f enhance neurite growth and target the majority of isoforms in the differentially expressed network.

Developing a data sharing community for spinal cord injury research

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.