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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.

A spinal cord injury disrupts communication between the brain and the circuits in the spinal cord that regulate neurological functions. The consequences are permanent paralysis, loss of sensation and debilitating dysautonomia. However, the majority of circuits located above and below the injury remain anatomically intact, and these circuits can reorganize naturally to improve function. In addition, various neuromodulation therapies have tapped into these processes to further augment recovery. Emerging research is illuminating the requirements to reconstitute damaged circuits. Here, we summarize these natural and targeted reorganizations of circuits after a spinal cord injury. We also advocate for new concepts of reorganizing circuits informed by multi-omic single-cell atlases of recovery from injury. These atlases will uncover the molecular logic that governs the selection of 'recovery-organizing' neuronal subpopulations, and are poised to herald a new era in spinal cord medicine. The authors summarize changes in circuits after spinal cord injury and current strategies to target these circuits in order to improve recovery, but also advocate for new concepts of reorganizing circuits informed by multi-omic single-cell atlases.

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.

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.

A spinal cord injury (SCI) disrupts the neuronal projections from the brain to the region of the spinal cord that produces walking, leading to various degrees of paralysis. Here, we aimed to identify brain regions that steer the recovery of walking after incomplete SCI and that could be targeted to augment this recovery. To uncover these regions, we constructed a space–time brain-wide atlas of transcriptionally active and spinal cord-projecting neurons underlying the recovery of walking after incomplete SCI. Unexpectedly, interrogation of this atlas nominated the lateral hypothalamus (LH). We demonstrate that glutamatergic neurons located in the LH (LH Vglut2 ) contribute to the recovery of walking after incomplete SCI and that augmenting their activity improves walking. We translated this discovery into a deep brain stimulation therapy of the LH (DBS LH ) that immediately augmented walking in mice and rats with SCI and durably increased recovery through the reorganization of residual lumbar-terminating projections from brainstem neurons. A pilot clinical study showed that DBS LH immediately improved walking in two participants with incomplete SCI and, in conjunction with rehabilitation, mediated functional recovery that persisted when DBS LH was turned off. There were no serious adverse events related to DBS LH . These results highlight the potential of targeting specific brain regions to maximize the engagement of spinal cord-projecting neurons in the recovery of neurological functions after SCI. Further trials must establish the safety and efficacy profile of DBS LH , including potential changes in body weight, psychological status, hormonal profiles and autonomic functions. Whole-brain anatomical and activity surveys identify the lateral hypothalamus as a key driver of recovery from spinal cord injury, leading to a deep brain stimulation therapy that augments the recovery of walking in humans.

Many connected devices are expected to be deployed during the next few years. Energy harvesting appears to be a good solution to power these devices but is not a reliable power source due to the time-varying nature of most energy sources. It is possible to harvest energy from multiple energy sources to tackle this problem, thus increasing the amount and the consistency of harvested energy. Additionally, a power management system can be implemented to compute how much energy can be consumed and to allocate this energy to multiple tasks, thus adapting the device quality of service to its energy capabilities. The goal is to maximize the amount of measured and transmitted data while avoiding power failures as much as possible. For this purpose, an industrial sensor node platform was extended with a multi-source energy-harvesting circuit and programmed with a novel energy-allocation system for multi-task devices. In this paper, a multi-source energy-harvesting LoRaWAN node is proposed and optimal energy allocation is proposed when the node runs different sensing tasks. The presented hardware platform was built with off-the-shelf components, and the proposed power management system was implemented on this platform. An experimental validation on a real LoRaWAN network shows that a gain of 51% transmitted messages and 62% executed sensing tasks can be achieved with the multi-source energy-harvesting and power-management system, compared to a single-source system.

LoRa is popular for internet of things applications as this communication technology offers both a long range and a low power consumption. However, LoRaWAN, the standard MAC protocol that uses LoRa as physical layer, has the bottleneck of a high downlink latency to achieve energy efficiency. To overcome this drawback we explore the use of wake-up radio combined with LoRa, and propose an adequate MAC protocol that takes profit of both these heterogeneous and complementary technologies. This protocol allows an opportunistic selection of a cluster head that forwards commands from the gateway to the nodes in the same cluster. Furthermore, to achieve self-sustainability, sensor nodes might include an energy harvesting sub-system, for instance to scavenge energy from the light, and their quality of service can be tuned, according to their available energy. To have an effective self-sustaining LoRa system, we propose a new energy manager that allows less fluctuations of the quality of service between days and nights. Latency and energy are modeled in a hybrid manner, i.e., leveraging microbenchmarks on real hardware platforms, to explore the influence of the energy harvesting conditions on the quality of service of this heterogeneous network. It is clearly demonstrated that the cooperation of nodes within a cluster drastically reduces the latency of LoRa base station commands, e.g., by almost 90% compared to traditional LoRa scheme for a 10 nodes cluster.

Opportunistic forwarding has emerged as a promising technique to address the problem of unreliable links typical in wireless sensor networks and improve energy efficiency by exploiting multiuser diversity. Timer-based solutions, such as timer-based contention, form promising schemes to allow opportunistic next hop relay selection. However, they can incur significant idle listening and thus reduce the lifetime of the network. To tackle this problem, we propose to exploit emerging wake-up receiver technologies that have the potential to considerably reduce the power consumption of wireless communications. A careful design of MAC protocols is required to efficiently employ these new devices. In this work, we propose Opportunistic Wake-Up MAC (OPWUM), a novel multihop MAC protocol using timer-based contention. It enables the opportunistic selection of the best receiver among its neighboring nodes according to a given metric (e.g., the remaining energy), without requiring any knowledge about them. Moreover, OPWUM exploits emerging wake-up receivers to drastically reduce nodes power consumption. Through analytical study and exhaustive networks simulations, we show the effectiveness of OPWUM compared to the current state-of-the-art protocols using timer-based contention.

Aims Although dipyridamole is a widely used pharmacological stress agent, the direct effects on myocardium are not entirely known. Diabetic cardiomyopathy can be investigated by 2D-strain echocardiography. The aim of this study was to assess myocardial functional reserve after dipyridamole infusion using speckle-tracking echocardiography. Methods Seventy-five patients referred for dipyridamole stress myocardial perfusion gated SPECT (MPGS) were examined by echocardiography to assess a new concept of longitudinal strain reserve (LSR) and longitudinal strain rate reserve (LSRR) respectively defined by the differences of global longitudinal strain (GLS) and longitudinal strain rate between peak stress after dipyridamole and rest. Twelve patients with myocardial ischemia were excluded on the basis of MPGS as gold standard. Results Mean LSR was −2.28±2.19% and was more important in the 28 (44%) diabetic patients (−3.27±1.93%; p = 0.001). After multivariate analyses, only diabetes improved LSR (p = 0.011) after dipyridamole infusion and was not associated with glycaemic control (p = 0.21), insulin therapy (p = 0.46) or duration of the disease (p = 0.80). Conversely, age (p = 0.002) remained associated with a decrease in LSR. LSSR was also correlated to age (p = 0.005). Patients with a LSR < 0% have a better survival after 15 months (log-rank p = 0.0012). Conclusion LSR explored by 2D speckle-tracking echocardiography after dipyridamole infusion is a simple and new concept that provides new insights into the impact of diabetes and age on the myocardium with a potential prognostic value.

A spinal cord injury interrupts pathways from the brain and brainstem that project to the lumbar spinal cord, leading to paralysis. Here we show that spatiotemporal epidural electrical stimulation (EES) of the lumbar spinal cord 1 – 3 applied during neurorehabilitation 4 , 5 (EES REHAB ) restored walking in nine individuals with chronic spinal cord injury. This recovery involved a reduction in neuronal activity in the lumbar spinal cord of humans during walking. We hypothesized that this unexpected reduction reflects activity-dependent selection of specific neuronal subpopulations that become essential for a patient to walk after spinal cord injury. To identify these putative neurons, we modelled the technological and therapeutic features underlying EES REHAB in mice. We applied single-nucleus RNA sequencing 6 – 9 and spatial transcriptomics 10 , 11 to the spinal cords of these mice to chart a spatially resolved molecular atlas of recovery from paralysis. We then employed cell type 12 , 13 and spatial prioritization to identify the neurons involved in the recovery of walking. A single population of excitatory interneurons nested within intermediate laminae emerged. Although these neurons are not required for walking before spinal cord injury, we demonstrate that they are essential for the recovery of walking with EES following spinal cord injury. Augmenting the activity of these neurons phenocopied the recovery of walking enabled by EES REHAB , whereas ablating them prevented the recovery of walking that occurs spontaneously after moderate spinal cord injury. We thus identified a recovery-organizing neuronal subpopulation that is necessary and sufficient to regain walking after paralysis. Moreover, our methodology establishes a framework for using molecular cartography to identify the neurons that produce complex behaviours. Transcriptomic analysis following epidural electrical stimulation of the lumbar spinal cord during neurorehabilitation in mice identifies a population of neurons that orchestrates the restoration of walking following paralysis.