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Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord. Differential expression analysis of single-cell transcriptomics allows scientists to dissect cell-type-specific responses to biological perturbations. Here, the authors show that many commonly used methods are biased and can produce false discoveries.

Single-cell transcriptomics provides an opportunity to characterize cell-type-specific transcriptional networks, intercellular signaling pathways and cellular diversity with unprecedented resolution by profiling thousands of cells in a single experiment. However, owing to the unique statistical properties of scRNA-seq data, the optimal measures of association for identifying gene–gene and cell–cell relationships from single-cell transcriptomics remain unclear. Here, we conducted a large-scale evaluation of 17 measures of association for their ability to reconstruct cellular networks, cluster cells of the same type and link cell-type-specific transcriptional programs to disease. Measures of proportionality were consistently among the best-performing methods across datasets and tasks. Our analysis provides data-driven guidance for gene and cell network analysis in single-cell transcriptomics.Among 17 measures of association tested, measures of proportionality consistently performed well for inference of gene and cellular networks, cell clusters and links to disease from scRNA-seq data. In contrast, several widely used measures of association performed well on only a subset of tasks.

A spinal cord injury usually spares some components of the locomotor circuitry. Deep brain stimulation (DBS) of the midbrain locomotor region and epidural electrical stimulation of the lumbar spinal cord (EES) are being used to tap into this spared circuitry to enable locomotion in humans with spinal cord injury. While appealing, the potential synergy between DBS and EES remains unknown. Here, we report the synergistic facilitation of locomotion when DBS is combined with EES in a rat model of severe contusion spinal cord injury leading to leg paralysis. However, this synergy requires high amplitudes of DBS, which triggers forced locomotion associated with stress responses. To suppress these undesired responses, we link DBS to the intention to walk, decoded from cortical activity using a robust, rapidly calibrated unsupervised learning algorithm. This contingency amplifies the supraspinal descending command while empowering the rats into volitional walking. However, the resulting improvements may not outweigh the complex technological framework necessary to establish viable therapeutic conditions. Deep brain stimulation and epidural electrical stimulation of the spinal cord enable locomotion in humans with spinal cord injury (SCI) but the potential synergy between both approaches is unclear. The authors show that a complex technological approach is required to enable volitional walking in rats with SCI.

We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensional space. We validate our method on single-cell RNA sequencing, chromatin accessibility and imaging transcriptomics datasets, and show that Augur outperforms existing methods based on differential gene expression. Augur identified the neural circuits restoring locomotion in mice following spinal cord neurostimulation. The cell types affected by biological perturbations in complex tissues are uncovered by single-cell analysis.

After spinal cord injury, tissue distal to the lesion contains undamaged cells that could support or augment recovery. Targeting these cells requires a clearer understanding of their injury responses and capacity for repair. Here, we use single nucleus RNA sequencing to profile how each cell type in the lumbar spinal cord changes after a thoracic injury in mice. We present an atlas of these dynamic responses across dozens of cell types in the acute, subacute, and chronically injured spinal cord. Using this resource, we find rare spinal neurons that express a signature of regeneration in response to injury, including a major population that represent spinocerebellar projection neurons. We characterize these cells anatomically and observed axonal sparing, outgrowth, and remodeling in the spinal cord and cerebellum. Together, this work provides a key resource for studying cellular responses to injury and uncovers the spontaneous plasticity of spinocerebellar neurons, uncovering a potential candidate for targeted therapy. Matson et al. performed single nucleus sequencing of the “spared” spinal cord tissue distal to an injury in mice. They found that spinocerebellar neurons expressed a pro-regenerative gene signature and showed axon outgrowth after injury.

Astrocytes are essential for the development and homeostatic maintenance of the central nervous system (CNS). They are also critical players in the CNS injury response during which they undergo a process referred to as “reactive astrogliosis.” Diversity in astrocyte morphology and gene expression, as revealed by transcriptional analysis, is well-recognized and has been reported in several CNS pathologies, including ischemic stroke, CNS demyelination, and traumatic injury. This diversity appears unique to the specific pathology, with significant variance across temporal, topographical, age, and sex-specific variables. Despite this, there is limited functional data corroborating this diversity. Furthermore, as reactive astrocytes display significant environmental-dependent plasticity and fate-mapping data on astrocyte subsets in the adult CNS is limited, it remains unclear whether this diversity represents heterogeneity or plasticity. As astrocytes are important for neuronal survival and CNS function post-injury, establishing to what extent this diversity reflects distinct established heterogeneous astrocyte subpopulations vs. environmentally dependent plasticity within established astrocyte subsets will be critical for guiding therapeutic development. To that end, we review the current state of knowledge on astrocyte diversity in the context of three representative CNS pathologies: ischemic stroke, demyelination, and traumatic injury, with the goal of identifying key limitations in our current knowledge and suggesting future areas of research needed to address them. We suggest that the majority of identified astrocyte diversity in CNS pathologies to date represents plasticity in response to dynamically changing post-injury environments as opposed to heterogeneity, an important consideration for the understanding of disease pathogenesis and the development of therapeutic interventions.

Advances in single-cell genomics now enable large-scale comparisons of cell states across two or more experimental conditions. Numerous statistical tools are available to identify individual genes, proteins or chromatin regions that differ between conditions, but many experiments require inferences at the level of cell types, as opposed to individual analytes. We developed Augur to prioritize the cell types within a complex tissue that are most responsive to an experimental perturbation. In this protocol, we outline the application of Augur to single-cell RNA-seq data, proceeding from a genes-by-cells count matrix to a list of cell types ranked on the basis of their separability following a perturbation. We provide detailed instructions to enable investigators with limited experience in computational biology to perform cell-type prioritization within their own datasets and visualize the results. Moreover, we demonstrate the application of Augur in several more specialized workflows, including the use of RNA velocity for acute perturbations, experimental designs with multiple conditions, differential prioritization between two comparisons, and single-cell transcriptome imaging data. For a dataset containing on the order of 20,000 genes and 20 cell types, this protocol typically takes 1–4 h to complete. This protocol provides a step-by-step workflow for prioritizing the cell types most responsive to an experimental perturbation in single-cell data and describes various applications of the pipeline in five case studies.

Micro- and nanoscale metallic glasses offer exciting opportunities for both fundamental research and applications in healthcare, micro-engineering, optics and electronics. The scientific and technological challenges associated with the fabrication and utilization of nanoscale metallic glasses, however, remain unresolved. Here, we present a simple and scalable approach for the fabrication of metallic glass fibres with nanoscale architectures based on their thermal co-drawing within a polymer matrix with matched rheological properties. Our method yields well-ordered and uniform metallic glasses with controllable feature sizes down to a few tens of nanometres, and aspect ratios greater than 1010. We combine fluid dynamics and advanced in situ transmission electron microscopy analysis to elucidate the interplay between fluid instability and crystallization kinetics that determines the achievable feature sizes. Our approach yields complex fibre architectures that, combined with other functional materials, enable new advanced all-in-fibre devices. We demonstrate in particular an implantable metallic glass-based fibre probe tested in vivo for a stable brain–machine interface that paves the way towards innovative high-performance and multifunctional neuro-probes.Metallic glasses possess intriguing functional properties, but controlled fabrication with nanoscale feature sizes remains challenging. Thermal co-drawing within a viscosity-matched polymer matrix enables the fabrication of uniform metallic glass fibres with feature sizes down to a few tens of nanometres, arbitrary transverse geometries and aspect ratios greater than 1010.

Differential accessibility (DA) analysis of single-cell epigenomics data enables the discovery of regulatory programs that establish cell type identity and steer responses to physiological and pathophysiological perturbations. While many statistical methods to identify DA regions have been developed, the principles that determine the performance of these methods remain unclear. As a result, there is no consensus on the most appropriate statistical methods for DA analysis of single-cell epigenomics data. Here, we present a systematic evaluation of statistical methods that have been applied to identify DA regions in single-cell ATAC-seq (scATAC-seq) data. We leverage a compendium of scATAC-seq experiments with matching bulk ATAC-seq or scRNA-seq in order to assess the accuracy, bias, robustness, and scalability of each statistical method. The structure of our experiments also provides the opportunity to define best practices for the analysis of scATAC-seq data beyond DA itself. We leverage this understanding to develop an R package implementing these best practices. Protocol registration The Stage 1 protocol for this Registered Report was accepted in principle on 25th October 2023. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.24541816.v1 . The authors conduct a comprehensive assessment of statistical methods for identifying differentially accessible (DA) regions in scATAC-seq data, using a compendium of scATAC-seq experiments paired with bulk ATAC-seq or scRNA-seq from the same cells to evaluate accuracy, bias, robustness, and scalability.

[...]the absence of cell type annotations from deposited data hinders the re-use of these datasets to address new biological questions. [...]the subjectivity inherent in cell type annotation implies that without the annotations established by the original authors, future investigators will be unable to reproduce published analyses. [...]we argue that this critical piece of metadata should be required for public data deposition. [...]a recommendation by journals that reviewers inspect deposited data for the presence of appropriate cell-level metadata has the potential to significantly improve reproducibility. The manual and subjective process by which cell types are annotated and the limitations of existing computational tools both have the potential to introduce error into published annotations. [...]for some forms of re-analysis, investigators may find it beneficial to update cell type annotations from scratch, either from a count matrix or the raw reads themselves [13, 14].