Goto

Collaborating Authors

Science


Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Science

The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. [eabd4570][1], p. [eabd4585][2]; see also p. [404][3] ### INTRODUCTION Interindividual clinical variability is vast in humans infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), ranging from silent infection to rapid death. Three risk factors for life-threatening coronavirus disease 2019 (COVID-19) pneumonia have been identified—being male, being elderly, or having other medical conditions—but these risk factors cannot explain why critical disease remains relatively rare in any given epidemiological group. Given the rising toll of the COVID-19 pandemic in terms of morbidity and mortality, understanding the causes and mechanisms of life-threatening COVID-19 is crucial. ### RATIONALE B cell autoimmune infectious phenocopies of three inborn errors of cytokine immunity exist, in which neutralizing autoantibodies (auto-Abs) against interferon-γ (IFN-γ) (mycobacterial disease), interleukin-6 (IL-6) (staphylococcal disease), and IL-17A and IL-17F (mucocutaneous candidiasis) mimic the clinical phenotypes of germline mutations of the genes that encode the corresponding cytokines or receptors. Human inborn errors of type I IFNs underlie severe viral respiratory diseases. Neutralizing auto-Abs against type I IFNs, which have been found in patients with a few underlying noninfectious conditions, have not been unequivocally shown to underlie severe viral infections. While searching for inborn errors of type I IFN immunity in patients with life-threatening COVID-19 pneumonia, we also tested the hypothesis that neutralizing auto-Abs against type I IFNs may underlie critical COVID-19. We searched for auto-Abs against type I IFNs in 987 patients hospitalized for life-threatening COVID-19 pneumonia, 663 asymptomatic or mildly affected individuals infected with SARS-CoV-2, and 1227 healthy controls from whom samples were collected before the COVID-19 pandemic. ### RESULTS At least 101 of 987 patients (10.2%) with life-threatening COVID-19 pneumonia had neutralizing immunoglobulin G (IgG) auto-Abs against IFN-ω (13 patients), against the 13 types of IFN-α (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three individual type I IFNs. These auto-Abs neutralize high concentrations of the corresponding type I IFNs, including their ability to block SARS-CoV-2 infection in vitro. Moreover, all of the patients tested had low or undetectable serum IFN-α levels during acute disease. These auto-Abs were present before infection in the patients tested and were absent from 663 individuals with asymptomatic or mild SARS-CoV-2 infection ( P < 10−16). They were present in only 4 of 1227 (0.33%) healthy individuals ( P < 10−16) before the pandemic. The patients with auto-Abs were 25 to 87 years old (half were over 65) and of various ancestries. Notably, 95 of the 101 patients with auto-Abs were men (94%). ### CONCLUSION A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men. In these patients, adaptive autoimmunity impairs innate and intrinsic antiviral immunity. These findings provide a first explanation for the excess of men among patients with life-threatening COVID-19 and the increase in risk with age. They also provide a means of identifying individuals at risk of developing life-threatening COVID-19 and ensuring their enrolment in vaccine trials. Finally, they pave the way for prevention and treatment, including plasmapheresis, plasmablast depletion, and recombinant type I IFNs not targeted by the auto-Abs (e.g., IFN-β). ![Figure][4] Neutralizing auto-Abs to type I IFNs underlie life-threatening COVID-19 pneumonia. We tested the hypothesis that neutralizing auto-Abs against type I IFNs may underlie critical COVID-19 by impairing the binding of type I IFNs to their receptor and the activation of the downstream responsive pathway. Neutralizing auto-Abs are represented in red, and type I IFNs are represented in blue. In these patients, adaptive autoimmunity impairs innate and intrinsic antiviral immunity. ISGs, IFN-stimulated genes; TLR, Toll-like receptor; IFNAR, IFN-α/β receptor; pSTAT, phosphorylated signal transducers and activators of transcription; IRF, interferon regulatory factor. Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-ω (IFN-ω) (13 patients), against the 13 types of IFN-α (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men. [1]: /lookup/doi/10.1126/science.abd4570 [2]: /lookup/doi/10.1126/science.abd4585 [3]: /lookup/doi/10.1126/science.abe7591 [4]: pending:yes


Inborn errors of type I IFN immunity in patients with life-threatening COVID-19

Science

The immune system is complex and involves many genes, including those that encode cytokines known as interferons (IFNs). Individuals that lack specific IFNs can be more susceptible to infectious diseases. Furthermore, the autoantibody system dampens IFN response to prevent damage from pathogen-induced inflammation. Two studies now examine the likelihood that genetics affects the risk of severe coronavirus disease 2019 (COVID-19) through components of this system (see the Perspective by Beck and Aksentijevich). Q. Zhang et al. used a candidate gene approach and identified patients with severe COVID-19 who have mutations in genes involved in the regulation of type I and III IFN immunity. They found enrichment of these genes in patients and conclude that genetics may determine the clinical course of the infection. Bastard et al. identified individuals with high titers of neutralizing autoantibodies against type I IFN-α2 and IFN-ω in about 10% of patients with severe COVID-19 pneumonia. These autoantibodies were not found either in infected people who were asymptomatic or had milder phenotype or in healthy individuals. Together, these studies identify a means by which individuals at highest risk of life-threatening COVID-19 can be identified. Science , this issue p. [eabd4570][1], p. [eabd4585][2]; see also p. [404][3] ### INTRODUCTION Clinical outcomes of human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection range from silent infection to lethal coronavirus disease 2019 (COVID-19). Epidemiological studies have identified three risk factors for severe disease: being male, being elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, and tissue- and body-level mechanisms underlying life-threatening COVID-19 is of the utmost biological and medical importance. ### RATIONALE We established the COVID Human Genetic Effort ([www.covidhge.com][4]) to test the general hypothesis that life-threatening COVID-19 in some or most patients may be caused by monogenic inborn errors of immunity to SARS-CoV-2 with incomplete or complete penetrance. We sequenced the exome or genome of 659 patients of various ancestries with life-threatening COVID-19 pneumonia and 534 subjects with asymptomatic or benign infection. We tested the specific hypothesis that inborn errors of Toll-like receptor 3 (TLR3)– and interferon regulatory factor 7 (IRF7)–dependent type I interferon (IFN) immunity that underlie life-threatening influenza pneumonia also underlie life-threatening COVID-19 pneumonia. We considered three loci identified as mutated in patients with life-threatening influenza: TLR3 , IRF7 , and IRF9 . We also considered 10 loci mutated in patients with other viral illnesses but directly connected to the three core genes conferring influenza susceptibility: TICAM1/TRIF , UNC93B1 , TRAF3 , TBK1 , IRF3 , and NEMO/IKBKG from the TLR3-dependent type I IFN induction pathway, and IFNAR1 , IFNAR2 , STAT1 , and STAT2 from the IRF7- and IRF9-dependent type I IFN amplification pathway. Finally, we considered various modes of inheritance at these 13 loci. ### RESULTS We found an enrichment in variants predicted to be loss-of-function (pLOF), with a minor allele frequency <0.001, at the 13 candidate loci in the 659 patients with life-threatening COVID-19 pneumonia relative to the 534 subjects with asymptomatic or benign infection ( P = 0.01). Experimental tests for all 118 rare nonsynonymous variants (including both pLOF and other variants) of these 13 genes found in patients with critical disease identified 23 patients (3.5%), aged 17 to 77 years, carrying 24 deleterious variants of eight genes. These variants underlie autosomal-recessive (AR) deficiencies ( IRF7 and IFNAR1 ) and autosomal-dominant (AD) deficiencies ( TLR3 , UNC93B1 , TICAM1 , TBK1 , IRF3 , IRF7 , IFNAR1 , and IFNAR2 ) in four and 19 patients, respectively. These patients had never been hospitalized for other life-threatening viral illness. Plasmacytoid dendritic cells from IRF7-deficient patients produced no type I IFN on infection with SARS-CoV-2, and TLR3−/−, TLR3+/−, IRF7−/−, and IFNAR1−/− fibroblasts were susceptible to SARS-CoV-2 infection in vitro. ### CONCLUSION At least 3.5% of patients with life-threatening COVID-19 pneumonia had known (AR IRF7 and IFNAR1 deficiencies or AD TLR3, TICAM1, TBK1, and IRF3 deficiencies) or new (AD UNC93B1, IRF7, IFNAR1, and IFNAR2 deficiencies) genetic defects at eight of the 13 candidate loci involved in the TLR3- and IRF7-dependent induction and amplification of type I IFNs. This discovery reveals essential roles for both the double-stranded RNA sensor TLR3 and type I IFN cell-intrinsic immunity in the control of SARS-CoV-2 infection. Type I IFN administration may be of therapeutic benefit in selected patients, at least early in the course of SARS-CoV-2 infection. ![Figure][5] Inborn errors of TLR3- and IRF7-dependent type I IFN production and amplification underlie life-threatening COVID-19 pneumonia. Molecules in red are encoded by core genes, deleterious variants of which underlie critical influenza pneumonia with incomplete penetrance, and deleterious variants of genes encoding biochemically related molecules in blue underlie other viral illnesses. Molecules represented in bold are encoded by genes with variants that also underlie critical COVID-19 pneumonia. Clinical outcome upon infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ranges from silent infection to lethal coronavirus disease 2019 (COVID-19). We have found an enrichment in rare variants predicted to be loss-of-function (LOF) at the 13 human loci known to govern Toll-like receptor 3 (TLR3)– and interferon regulatory factor 7 (IRF7)–dependent type I interferon (IFN) immunity to influenza virus in 659 patients with life-threatening COVID-19 pneumonia relative to 534 subjects with asymptomatic or benign infection. By testing these and other rare variants at these 13 loci, we experimentally defined LOF variants underlying autosomal-recessive or autosomal-dominant deficiencies in 23 patients (3.5%) 17 to 77 years of age. We show that human fibroblasts with mutations affecting this circuit are vulnerable to SARS-CoV-2. Inborn errors of TLR3- and IRF7-dependent type I IFN immunity can underlie life-threatening COVID-19 pneumonia in patients with no prior severe infection. [1]: /lookup/doi/10.1126/science.abd4570 [2]: /lookup/doi/10.1126/science.abd4585 [3]: /lookup/doi/10.1126/science.abe7591 [4]: https://www.covidhge.com [5]: pending:yes


Erratum for the Report "Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances" by R. Van Klink, D. E. Bowler, K. B. Gongalsky, A. B. Swenge, A. Gentile, J. M. Chase

Science

In the Report, “[Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances][1],” the following corrections have been made. After publication, some errors in the data underlying the analyses, and the processing of it, were brought to the authors’ attention. The most important was a mistake in the processing of the Environmental Change Network moth data ([ 1 ][2]) from the UK (Datasource_ID 1006). The authors made two errors in processing these data: (i) They neglected to recode abundance counts with error code ‘101’ (indicating no sample taken) as missing data values and they instead entered into the analysis as values of 101, and (ii) they did not sufficiently account for the change in sampling protocol over time; moths were sampled all nights of the year in the first years but only on nights with favorable weather in later years. This led to higher average counts per night in later years because there were fewer data from the nights with low moth counts. These two errors produced a false-positive trend for this dataset. The authors have fixed these issues by removing the error code and retaining only the summer months during which sampling was consistent over time. Furthermore, they revisited all the other datasets in the analysis to check for any other errors in the data, including error codes, missing zeros, duplicate values, outliers, and sampling effort consistency across plots, and corrected these when necessary. They found inconsistencies in the source data of dataset 502 ([ 2 ][3]) and removed all years with missing species. As a result, the authors excluded 8 (out of 30) plots from this dataset because they no longer met the inclusion criteria. Dataset 1424 ([ 3 ][4]) was duplicated in dataset 1347 ([ 4 ][5]) and was thus removed because the latter provided more years of data. The authors retained 165 datasets and 1668 plots. In all, they made changes to 22 of these datasets. All corrections and their effect on the random-effects estimate of each dataset are detailed in the supplementary materials, and all figures and tables in the supplementary materials, as well as in data S1 and S2 and in the repository ([ 5 ][6]), have been replaced. It was also brought to the authors’ attention that they should have been clearer regarding exclusion of non-insects from datasets comprising both insects and non-insect invertebrates, as well as datasets with variable sampling frequencies. They have now added an additional explanation to the methods section of the supplementary materials. In brief, they excluded non-insect invertebrate data as much as possible but not at the cost of also excluding insects. The authors have rerun all models presented in the original paper with the corrected data and found that none of the major qualitative conclusions of the paper changed. The quantitative estimates have changed somewhat, however: The average decline for terrestrial insects across all data are now –1.11% per year (–10.56% per decade) and the increase for freshwater insects is now +1.16% per year (+12.24% per decade), both well within the 95% credible intervals of the previous estimates. In the geographic analysis, Europe now shows weak evidence for a decline of terrestrial insects of –0.76% per year (–7.3% per decade, P = 0.947), which is perpetuated across all time slices of Fig. 3 in the paper (ranging between moderate and strong evidence). Overall, the authors found more strengthening of trends than weakening of trends. For example, there is now weak evidence for a decline of terrestrial biomass and for a positive effect of increasing temperatures on terrestrial insect abundances. They also found weak evidence for a negative effect of last year of sampling on the trend estimates, suggesting that trends are more negative in datasets with more recent data. This matches the progressively more negative trends in the European terrestrial data. All old and new model estimates, presented as the percentage change per year, and a detailed description of the changes to the materials and methods, can be found on Zenodo (). 1. [↵][7]S. Rennie, J. Adamson, R. Anderson, C. Andrews, J. Bater, N. Bayfield, K. Beaton, D. Beaumont, S. Benham, V. Bowmaker, C. Britt, R. Brooker, D. Brooks, J. Brunt, G. Common, R. Cooper, S. Corbett, N. Critchley, P. Dennis, J. Dick, B. Dodd, N. Dodd, N. Donovan, J. Easter, M. Flexen, A. Gardiner, D. Hamilton, P. Hargreaves, M. Hatton-Ellis, M. Howe, J. Kahl, M. Lane, S. Langan, D. Lloyd, B. McCarney, Y. McElarney, C. McKenna, S. McMillan, F. Milne, L. Milne, M. Morecroft, M. Murphy, A. Nelson, H. Nicholson, D. Pallett, D. Parry, I. Pearce, G. Pozsgai, A. Riley, R. Rose, S. Schafer, T. Scott, L. Sherrin, C. Shortall, R. Smith, P. Smith, R. Tait, C. Taylor, M. Taylor, M. Thurlow, A. Turner, K. Tyson, H. Watson, M. Whittaker, I. Woiwod, C. Wood, UK Environmental Change Network (ECN) Moth Data: 1992-2015, NERC Environmental Information Data Centre (2018); . 2. [↵][8]NERC Centre for Population Biology Imperial College, Global population dynamics database, Version 2 (2010); . 3. [↵][9]1. J. M. McCarthy, 2. C. L. Hein, 3. J. D. Olden, 4. M. J. Vander Zanden , Coupling long-term studies with meta-analysis to investigate impacts of non-native crayfish on zoobenthic communities. Freshw. Biol. 51, 224–235 (2006). 10.1111/j.1365-2427.2005.01485.x [OpenUrl][10][CrossRef][11] 4. [↵][12]J. Magnuson, C. S. E. Stanley, North Temperate Lakes LTER: Benthic macroinvertebrates 1981-current, Environmental Data Initiative (2010); . 5. [↵][13]R. van Klink, D. E. Bowler, J. M. Chase, O. Comay, M. M. Driessen, S. K. M. Ernest, A. Gentile, F. Gilbert, K. B. Gongalky, G. Pe’er, I. Pe’er, V. H. Resh, A. B. Swengel, S. R. Swengel, T. J. Valone, R. Vermeulen, T. Wepprich, J. Wiedmann, A global database of long-term changes in insect assemblages, Knowledge Network for Biocomplexity (KNB) (2020); . [1]: https://science.sciencemag.org/content/368/6489/417 [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #ref-4 [6]: #ref-5 [7]: #xref-ref-1-1 "View reference 1 in text" [8]: #xref-ref-2-1 "View reference 2 in text" [9]: #xref-ref-3-1 "View reference 3 in text" [10]: {openurl}?query=rft.jtitle%253DFreshw.%2BBiol.%26rft.volume%253D51%26rft.spage%253D224%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1365-2427.2005.01485.x%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [11]: /lookup/external-ref?access_num=10.1111/j.1365-2427.2005.01485.x&link_type=DOI [12]: #xref-ref-4-1 "View reference 4 in text" [13]: #xref-ref-5-1 "View reference 5 in text"


Advancing limb neural prostheses

Science

Although sophisticated upper- and lower-limb prostheses have been developed, amputees cannot control them intuitively nor perceive sensations from them. These deficiencies result in serious issues, including risk of falls, decreased mobility, heart fatigue during walking, and lower functionality while grasping. Moreover, the prostheses are not perceived by the users as part of their own body (low embodiment), which increases cognitive burden during use or device abandonment. An ideal man-machine interface should enable effortless bidirectional communication between the user and the prosthesis. Neural prostheses that provide bidirectional interfacing with the residual nervous system exploit the persistence of the central and peripheral neural pathways devoted to motor control and sensing ([ 1 ][1]). A combination of neurotechnologies recently achieved previously unseen capabilities of prosthesis actuation and sensory restoration, but several hurdles need to be overcome before widespread use of these devices. Upper-limb amputees actuate opening and closing of commercial hand prostheses through the contraction of residual muscles, with no possibility to control single digits. Above-knee amputees exploit their residual hip movements to initiate protheses, which then execute predefined patterns of motion. Together with natural actuation, a physiologically plausible sensory feedback from prosthesis to amputee is missing, forcing users to continuously visually inspect their artificial limbs. A key limitation of prostheses actuation is that users must learn the nonintuitive control strategies (for example, contracting the biceps to close the hand). In the case of amputations closer to the hand or foot, the control signals can be extracted from the residual muscles, but when amputations are at the thigh or shoulder level, muscles that control the hand or foot are lost. In these cases, a solution is surgical rerouting of nerves toward the other available muscles, called targeted muscular reinnervation (TMR). After this intervention, the nerves grow into the new muscles and gain the capacity to excite the tissue. When users attempt movement, neural signals contract the chest muscles in the case of shoulder-level amputees ([ 2 ][2]) or residual thigh muscles in the case of the above-knee amputation ([ 3 ][3]) (see the figure). Sensors placed over the skin capture the electrical signal produced by muscle activity, which is then transformed into movement of the robotic arm or leg. Yet, control based on skin surface electrodes ([ 2 ][2], [ 3 ][3]) suffers from instability due to electrode movement or detachment. Further development of the muscle reinnervation concept led to the regenerative peripheral nerve interface approach ([ 4 ][4]). The distal end of a transected peripheral nerve in the arm stump is sutured into a muscle graft, which is implanted with recording wires. These wires are in turn connected to prosthesis controls via cables passing through the skin (which have the potential to become broken or induce infections). This creates a natural amplifier of neural signals of volitional control by transducing them into high-gain myoelectric signals. A high signal-to-noise ratio of this biointerface enabled high-precision control, even for single finger movements in two amputees ([ 4 ][4]). To overcome the problems of skin electrodes and percutaneous wires, tiny implantable myoelectric sensors (IMESs), which capture the muscular signals directly, were developed. These sensors were injected into the reinnervated muscles of three above-elbow amputees and conferred long-term functional use ([ 5 ][5]). Whenever the movement intention of an amputee activates the implanted muscles, IMESs capture and amplify these electrical signals and send them wirelessly to the receiving coil embedded into the prosthesis socket to control the motors of the prostheses. This proof of concept needs to be demonstrated in a bigger cohort of patients and will hopefully increase controllable movements. Alongside actuation, restoring sensory information from the artificial limb to the user is essential for functionality. Natural sensations from the missing extremity can be restored to the brain, inducing so-called phantom sensations, through electrical stimulation of the residual nerves proximal to the amputation. Peripheral nerves contain parallel tubular structures, called fascicles, that transmit different sensations (such as touch and vibration) from specific areas of the limb. Several neural interfaces with different geometries and placement have been developed and preliminarily tested in upper-limb amputees. The stimulation of residual median and ulnar nerves (which innervate fingers and palm), through implanted transverse intrafascicular multichannel electrodes (TIMEs) ([ 6 ][6]), restored tactile feedback from prostheses in four upper-limb amputees. Relying on the restored feedback, users controlled the amount of force exerted with the prosthesis and distinguished between objects with different compliances and shapes. Other neurotechnologies that can restore sensory information include Utah slanted electrode arrays (USEAs). These consist of a grid of micrometric stimulating wires of different length that are implanted in peripheral nerve fibers ([ 7 ][7]) and deliver spatially focused (selective) feedback to hand amputees. In a complementary approach, remarkable stability of several years (in terms of functionality and biocompatibility) was achieved with flat interface nerve electrode (FINE) ([ 8 ][8]) implants. FINEs enable a gentle nerve flattening, placing the active contacts in proximity to the inner fascicles without penetrating the nerve like TIMEs and USEAs do. Thus, FINEs are easier to implant and cause less nerve damage, but at the cost of higher stimulation current to elicit sensations. Stimulation of leg nerves is a different challenge because they are bigger than nerves in the arm, and skin receptors have different density over the foot-sole skin compared with the palm. Through a meticulous surgery, multiple TIMEs were implanted in the sciatic nerve (which innervates the foot and lower leg) and elicited selective foot and leg sensations in three above-knee amputees, augmenting their confidence during walking ([ 9 ][9]). This improved their mobility on stairs, avoidance of falls, and embodiment of the artificial leg while diminishing the cognitive load during use. Contrary to upper-limb amputation, leg amputation introduces additional health complications such as increased cardiovascular fatigue and decreased mobility. Two highly disabled, above-knee amputees equipped with intraneural feedback from prostheses experienced several health benefits, such as diminished cardiovascular fatigue and pain, increased mobility over uneven terrains, and brain load decrease ([ 10 ][10]). In these studies, different sensations were restored in amputees, but there is limited evidence for perceptions of limb position, speed, and torque, namely proprioceptive sensations, which are essential for walking. To overcome this in below-knee amputees, a surgical approach connecting in series two opposing muscle-tendon ensembles (an agonist and an antagonist) was developed ([ 11 ][11]). With this strategy, one muscle contracting and shortening (volitional or electrically activated) induces the stretching of the other in the opposing direction. This linked motion permits the natural body receptors embedded in the muscle-tendon to transmit information about muscle length, speed, and force, which is perceived by the brain as joint proprioception and used for precise stair walking. However, transfer of this elegant solution to more disabled above-knee amputees could represent a considerable challenge. Sensations induced by these different approaches are close to natural but can be perceived as unpleasant electrical tingling. Time-variable electrical stimulation, precisely defined by using computational modeling ([ 12 ][12]), that simulates in silico the responses of tactile neurons innervating the glabrous (hairless) skin was preliminarily demonstrated to induce more natural sensations ([ 7 ][7], [ 13 ][13]) and could therefore potentially increase acceptance of these technologies. Computational modeling could optimize implant geometry and electrical parameters to personalize devices in the future. ![Figure][14] Bidirectional limb neural prostheses Residual motor and sensory neurons in arms and legs of amputees can be used with implants and surgery techniques to confer different sensations and precise motor control of prostheses. Such bidirectional communication and possibly combinations of approaches should improve the quality of life for amputees. GRAPHIC: V. ALTOUNIAN/ SCIENCE Recently, muscular control implants and sensory stimulation were combined in four amputees, showing long-term stability and safety but with limited insights about sensory benefits or cognitive effects ([ 14 ][15]). The quantified long-term demonstration of the symbiotic and beneficial use of such a bidirectional approach needs to be proven. It needs to be explored whether artificial motor and sensory signals together could be intuitively handled by the brain, without sensorimotor conflicts or cognitive overload, hopefully resulting in increased functionality. Neural prostheses for sensory feedback restoration were connected to the stimulator, injecting electrical current into the nerves via percutaneous cables, which increases the probability of infections and has limited robustness. Thus, fully implantable wireless systems need to be developed. These systems should feature: long-term stability and safety of the implants, high battery capacity, stable leads (that currently are prone to breakage), and easy replacement of implants or their parts. Although typical implantable neurostimulators (such as pacemakers) have preprogrammed stimulation protocols, with no need for a continuous transcutaneous communication, for prostheses a high burden of information has to be wirelessly transmitted through the skin to enable bidirectional communication. This demands high battery capacity while maintaining a limited implant size for surgical placement, representing an important technological challenge. Presently, most implants require several hours-long surgeries, and therefore minimally invasive procedures should be developed. Capturing information external to the limb is mandatory to trigger neurofeedback, but prostheses do not have sensors in robotic fingers or under the prosthetic foot. Thus, research efforts are devoted to the development of prosthetic electronic skin, which should be able to accommodate a high density of sensors over flexible polymeric structures ([ 15 ][16]). In the future, these could be imagined as gloves or socks, with robust and high-resolution sensors, placed over the prostheses to transmit different sensory signals such as pressure, movement, and temperature. To date, these studies are mainly proof of concepts regarding an increase of quality of life or technological viability of neural prostheses, performed with their own metrics, making it difficult to objectively compare outcomes. They are not clinical studies of safety and efficacy, which are important to achieve the necessary medical certifications. Globally, the regulatory steps are demanding and costly, and when accounting for individualization of devices and smaller volume need, the economic cost is potentially high for end users. This could hinder the widespread use of these technologies. Public health care systems vary considerably, and each carries out its own assessment of health technology to support its decisions regarding reimbursement, mainly by demonstrated cost-effectiveness and benefits that increase “quality-adjusted life-years.” Therefore, it is of paramount importance to plan, from the first steps of bidirectional neural prosthesis testing, how to demonstrate safety and health gains because this would augment the likelihood of device approval. The future of neurotechnological intervention for amputees will be in the personalized and combined use of these technologies. Depending on the amputation level and patients' characteristics, the customized combination of muscular and sensory interventions is likely to benefit amputees in the long term. 1. [↵][17]1. G. S. Dhillon, 2. K. W. Horch , IEEE Trans. Neural Syst. Rehabil. Eng. 13, 468 (2005). [OpenUrl][18][CrossRef][19][PubMed][20][Web of Science][21] 2. [↵][22]1. T. A. Kuiken et al ., JAMA 301, 619 (2009). [OpenUrl][23][CrossRef][24][PubMed][25][Web of Science][26] 3. [↵][27]1. L. J. Hargrove et al ., N. Engl. J. Med. 369, 1237 (2013). [OpenUrl][28][CrossRef][29][PubMed][30][Web of Science][31] 4. [↵][32]1. P. P. Vu et al ., Sci. Transl. Med. 12, eaay2857 (2020). [OpenUrl][33][Abstract/FREE Full Text][34] 5. [↵][35]1. S. Salminger et al ., Sci. Robot. 4, eaaw6306 (2019). [OpenUrl][36] 6. [↵][37]1. S. Raspopovic et al ., Sci. Transl. Med. 6, 222ra19 (2014). [OpenUrl][38][Abstract/FREE Full Text][39] 7. [↵][40]1. J. A. George et al ., Sci. Robot. 4, eaax2352 (2019). [OpenUrl][41] 8. [↵][42]1. D. W. Tan et al ., Sci. Transl. Med. 6, 257ra138 (2014). [OpenUrl][43][Abstract/FREE Full Text][44] 9. [↵][45]1. F. M. Petrini et al ., Sci. Transl. Med. 11, eaav8939 (2019). [OpenUrl][46][Abstract/FREE Full Text][47] 10. [↵][48]1. F. M. Petrini et al ., Nat. Med. 25, 1356 (2019). [OpenUrl][49][CrossRef][50][PubMed][51] 11. [↵][52]1. T. R. Clites et al ., Sci. Transl. Med. 10, eaap8373 (2018). [OpenUrl][53][Abstract/FREE Full Text][54] 12. [↵][55]1. H. Saal et al ., Proc. Natl. Acad. Sci. U.S.A. 114, 28 (2017). [OpenUrl][56] 13. [↵][57]1. G. Valle et al ., Neuron 100, 37 (2018). [OpenUrl][58] 14. [↵][59]1. M. Ortiz-Catalan et al ., N. Engl. J. Med. 382, 1732 (2020). [OpenUrl][60] 15. [↵][61]1. A. Chortos et al ., Nat. Mater. 15, 937 (2016). [OpenUrl][62][CrossRef][63][PubMed][64] Acknowledgments: The author is supported by the European Research Council 2017-STG n.759998 (FeelAgain) and holds shares of SensArs Neuroprosthetics. I thank F. Petrini and M. Capogrosso for their thoughtful input. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: pending:yes [15]: #ref-14 [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: {openurl}?query=rft.jtitle%253DIEEE%2Btransactions%2Bon%2Bneural%2Bsystems%2Band%2Brehabilitation%2Bengineering%2B%253A%2B%2Ba%2Bpublication%2Bof%2Bthe%2BIEEE%2BEngineering%2Bin%2BMedicine%2Band%2BBiology%2BSociety%26rft.stitle%253DIEEE%2BTrans%2BNeural%2BSyst%2BRehabil%2BEng%26rft.aulast%253DDhillon%26rft.auinit1%253DG.%2BS.%26rft.volume%253D13%26rft.issue%253D4%26rft.spage%253D468%26rft.epage%253D472%26rft.atitle%253DDirect%2Bneural%2Bsensory%2Bfeedback%2Band%2Bcontrol%2Bof%2Ba%2Bprosthetic%2Barm.%26rft_id%253Dinfo%253Adoi%252F10.1109%252FTNSRE.2005.856072%26rft_id%253Dinfo%253Apmid%252F16425828%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [19]: /lookup/external-ref?access_num=10.1109/TNSRE.2005.856072&link_type=DOI [20]: /lookup/external-ref?access_num=16425828&link_type=MED&atom=%2Fsci%2F370%2F6514%2F290.atom [21]: /lookup/external-ref?access_num=000233943400005&link_type=ISI [22]: #xref-ref-2-1 "View reference 2 in text" [23]: {openurl}?query=rft.jtitle%253DJAMA%26rft.stitle%253DJAMA%26rft.aulast%253DKuiken%26rft.auinit1%253DT.%2BA.%26rft.volume%253D301%26rft.issue%253D6%26rft.spage%253D619%26rft.epage%253D628%26rft.atitle%253DTargeted%2BMuscle%2BReinnervation%2Bfor%2BReal-time%2BMyoelectric%2BControl%2Bof%2BMultifunction%2BArtificial%2BArms%26rft_id%253Dinfo%253Adoi%252F10.1001%252Fjama.2009.116%26rft_id%253Dinfo%253Apmid%252F19211469%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [24]: /lookup/external-ref?access_num=10.1001/jama.2009.116&link_type=DOI [25]: /lookup/external-ref?access_num=19211469&link_type=MED&atom=%2Fsci%2F370%2F6514%2F290.atom [26]: /lookup/external-ref?access_num=000263229000026&link_type=ISI [27]: #xref-ref-3-1 "View reference 3 in text" [28]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D369%26rft.spage%253D1237%26rft_id%253Dinfo%253Adoi%252F10.1056%252FNEJMoa1300126%26rft_id%253Dinfo%253Apmid%252F24066744%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [29]: /lookup/external-ref?access_num=10.1056/NEJMoa1300126&link_type=DOI [30]: /lookup/external-ref?access_num=24066744&link_type=MED&atom=%2Fsci%2F370%2F6514%2F290.atom [31]: /lookup/external-ref?access_num=000324888200010&link_type=ISI [32]: #xref-ref-4-1 "View reference 4 in text" [33]: {openurl}?query=rft.jtitle%253DScience%2BTranslational%2BMedicine%26rft.stitle%253DSci%2BTransl%2BMed%26rft.aulast%253DVu%26rft.auinit1%253DP.%2BP.%26rft.volume%253D12%26rft.issue%253D533%26rft.spage%253Deaay2857%26rft.epage%253Deaay2857%26rft.atitle%253DA%2Bregenerative%2Bperipheral%2Bnerve%2Binterface%2Ballows%2Breal-time%2Bcontrol%2Bof%2Ban%2Bartificial%2Bhand%2Bin%2Bupper%2Blimb%2Bamputees%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscitranslmed.aay2857%26rft_id%253Dinfo%253Apmid%252F32132217%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [34]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTE6InNjaXRyYW5zbWVkIjtzOjU6InJlc2lkIjtzOjE1OiIxMi81MzMvZWFheTI4NTciO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxNC8yOTAuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [35]: #xref-ref-5-1 "View reference 5 in text" [36]: {openurl}?query=rft.jtitle%253DSci.%2BRobot.%26rft.volume%253D4%26rft.spage%253D6306eaaw%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [37]: #xref-ref-6-1 "View reference 6 in text" [38]: {openurl}?query=rft.jtitle%253DScience%2BTranslational%2BMedicine%26rft.stitle%253DSci%2BTransl%2BMed%26rft.aulast%253DRaspopovic%26rft.auinit1%253DS.%26rft.volume%253D6%26rft.issue%253D222%26rft.spage%253D222ra19%26rft.epage%253D222ra19%26rft.atitle%253DRestoring%2BNatural%2BSensory%2BFeedback%2Bin%2BReal-Time%2BBidirectional%2BHand%2BProstheses%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscitranslmed.3006820%26rft_id%253Dinfo%253Apmid%252F24500407%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [39]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTE6InNjaXRyYW5zbWVkIjtzOjU6InJlc2lkIjtzOjEzOiI2LzIyMi8yMjJyYTE5IjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzcwLzY1MTQvMjkwLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [40]: #xref-ref-7-1 "View reference 7 in text" [41]: {openurl}?query=rft.jtitle%253DSci.%2BRobot.%26rft.volume%253D4%26rft.spage%253D2352eaax%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [42]: #xref-ref-8-1 "View reference 8 in text" [43]: {openurl}?query=rft.jtitle%253DScience%2BTranslational%2BMedicine%26rft.stitle%253DSci%2BTransl%2BMed%26rft.aulast%253DTan%26rft.auinit1%253DD.%2BW.%26rft.volume%253D6%26rft.issue%253D257%26rft.spage%253D257ra138%26rft.epage%253D257ra138%26rft.atitle%253DA%2Bneural%2Binterface%2Bprovides%2Blong-term%2Bstable%2Bnatural%2Btouch%2Bperception%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscitranslmed.3008669%26rft_id%253Dinfo%253Apmid%252F25298320%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTE6InNjaXRyYW5zbWVkIjtzOjU6InJlc2lkIjtzOjE0OiI2LzI1Ny8yNTdyYTEzOCI7czo0OiJhdG9tIjtzOjIyOiIvc2NpLzM3MC82NTE0LzI5MC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [45]: #xref-ref-9-1 "View reference 9 in text" [46]: {openurl}?query=rft.jtitle%253DScience%2BTranslational%2BMedicine%26rft.stitle%253DSci%2BTransl%2BMed%26rft.aulast%253DPetrini%26rft.auinit1%253DF.%2BM.%26rft.volume%253D11%26rft.issue%253D512%26rft.spage%253Deaav8939%26rft.epage%253Deaav8939%26rft.atitle%253DEnhancing%2Bfunctional%2Babilities%2Band%2Bcognitive%2Bintegration%2Bof%2Bthe%2Blower%2Blimb%2Bprosthesis%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscitranslmed.aav8939%26rft_id%253Dinfo%253Apmid%252F31578244%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [47]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTE6InNjaXRyYW5zbWVkIjtzOjU6InJlc2lkIjtzOjE1OiIxMS81MTIvZWFhdjg5MzkiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxNC8yOTAuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [48]: #xref-ref-10-1 "View reference 10 in text" [49]: {openurl}?query=rft.jtitle%253DNat.%2BMed.%26rft.volume%253D25%26rft.spage%253D1356%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41591-019-0567-3%26rft_id%253Dinfo%253Apmid%252F31501600%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [50]: /lookup/external-ref?access_num=10.1038/s41591-019-0567-3&link_type=DOI [51]: /lookup/external-ref?access_num=31501600&link_type=MED&atom=%2Fsci%2F370%2F6514%2F290.atom [52]: #xref-ref-11-1 "View reference 11 in text" [53]: {openurl}?query=rft.jtitle%253DScience%2BTranslational%2BMedicine%26rft.stitle%253DSci%2BTransl%2BMed%26rft.aulast%253DClites%26rft.auinit1%253DT.%2BR.%26rft.volume%253D10%26rft.issue%253D443%26rft.spage%253Deaap8373%26rft.epage%253Deaap8373%26rft.atitle%253DProprioception%2Bfrom%2Ba%2Bneurally%2Bcontrolled%2Blower-extremity%2Bprosthesis%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscitranslmed.aap8373%26rft_id%253Dinfo%253Apmid%252F29848665%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [54]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTE6InNjaXRyYW5zbWVkIjtzOjU6InJlc2lkIjtzOjE1OiIxMC80NDMvZWFhcDgzNzMiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxNC8yOTAuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [55]: #xref-ref-12-1 "View reference 12 in text" [56]: {openurl}?query=rft.jtitle%253DProc.%2BNatl.%2BAcad.%2BSci.%2BU.S.A.%26rft.volume%253D114%26rft.spage%253D28%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [57]: #xref-ref-13-1 "View reference 13 in text" [58]: {openurl}?query=rft.jtitle%253DNeuron%26rft.volume%253D100%26rft.spage%253D37%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [59]: #xref-ref-14-1 "View reference 14 in text" [60]: {openurl}?query=rft.jtitle%253DN.%2BEngl.%2BJ.%2BMed.%26rft.volume%253D382%26rft.spage%253D1732%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [61]: #xref-ref-15-1 "View reference 15 in text" [62]: {openurl}?query=rft.jtitle%253DNat.%2BMater.%26rft.volume%253D15%26rft.spage%253D937%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnmat4671%26rft_id%253Dinfo%253Apmid%252F27376685%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [63]: /lookup/external-ref?access_num=10.1038/nmat4671&link_type=DOI [64]: /lookup/external-ref?access_num=27376685&link_type=MED&atom=%2Fsci%2F370%2F6514%2F290.atom


Weathering the storm

Science

As president, Donald Trump has battered science. But increased spending by Congress and some supportive agency heads have provided relief. Disastrous. Damaging. Catastrophic. Those are just some of the more polite terms that many U.S. scientists use to describe the policies of President Donald Trump. His handling of the COVID-19 pandemic, his repeated public dismissals of scientific expertise, and his disdain for evidence have prompted many researchers to label him the most antiscience president in living memory. Last month, that sense of betrayal led two of the nation's preeminent scientific bodies, the U.S. National Academy of Sciences and the National Academy of Medicine, to issue an uncharacteristically harsh rebuke. Although the 24 September statement did not name Trump, it was clearly aimed at the president. “Policymaking must be informed by the best available evidence without it being distorted, concealed, or otherwise deliberately miscommunicated,” the leaders of the two academies wrote. “We find reports and incidents of the politicization of science, particularly the overriding of evidence and advice from public health officials and derision of government scientists, to be alarming.” Although many U.S. scientists share those sentiments, other aspects of the administration's overall record elicit a more positive response. Ask researchers how federal funding for their fields has fared since Trump took office in January 2017, and they might acknowledge sustained support and even mention new opportunities in some areas. Inquire about what they think of the appointees leading the federal agencies that fund their work, and they will offer some good—even glowing—reviews. Those seemingly contradictory responses reflect the complexity of an $80-billion-a-year system that remains the envy of the world. Any president trying to alter that behemoth has three levers to press—policies, budget requests, and leadership appointments. To analyze Trump's record in each area, Science has talked to dozens of researchers, administrators, and lobbyists. Many asked to remain anonymous because they have ongoing interactions with the administration. Most scientists give Trump exceedingly low marks in an arena where he has perhaps the greatest authority: foreign affairs. His unilateral decisions to pull out of the Paris climate treaty, the Iran nuclear deal, and the World Health Organization are widely seen as damaging not just to global scientific cooperation, but also to the continued health, safety, and prosperity of the planet. Similarly, most scientists think the administration's aggressive efforts to restrict immigration pose a serious threat to the nation's ability to attract scientific talent from around the world. In the domestic arena, Trump's efforts to impose new policies by executive order and rewrite regulations have also drawn sharp criticism from scientists. They say the administration has routinely ignored or suppressed evidence that doesn't support its efforts to roll back environmental regulations, including those aimed at limiting emissions of greenhouse gases. Trump has also threatened the reliability of key demographic data by interfering with the orderly completion of the 2020 census, and by telling the Department of Commerce to exclude undocumented residents from the final count. Biomedical researchers, meanwhile, have been appalled by what they say is a de facto ban on the use of tissue derived from elective abortions in research, as well as orders to cancel a grant that Trump disliked. Such moves, many researchers believe, are designed to advance the president's political agenda at the expense of national interests. Fewer scientists complain about the Trump administration's record on spending. But that's largely because Congress has ignored the deep cuts the White House has proposed in its annual budget requests to Congress (see graphic, p. 280). For example, the National Institutes of Health (NIH), the biggest federal supporter of academic research, has seen its budget rise by 39% in the past 5 years despite deep cuts proposed by Trump. The budget of the National Science Foundation (NSF) has gone up by 17% over the past 3 years, reversing the downward direction that Trump has requested and rising more than twice as fast as it did under former President Barack Obama. Researchers working on artificial intelligence (AI) and in quantum information science are enjoying an even more rapid growth rate. In a rare embrace of large spending increases, the Trump administration has thrown its weight behind a 2-year doubling of those fields, which fuel what it calls “industries of the future.” And Congress seems amenable to the idea. Assessing the president's appointees is more complicated. Scientists have condemned some of Trump's choices at agencies involved in environmental regulation or climate science, citing their meager scientific credentials or views that are outside the mainstream. The appointees are clustered at the Environmental Protection Agency (EPA), the National Oceanic and Atmospheric Administration (NOAA), and the Department of the Interior. The list also includes three recently installed senior officials at the Census Bureau, which is embroiled in controversy over its plans for completing the 2020 census. At the same time, most scientists give high marks to the officials who lead agencies that hand out the bulk of federal research dollars (and are generally not involved in hot-button regulatory issues). That list includes the heads of NIH—Obama-era hold-over Francis Collins—and NSF, where Sethuraman Panchanathan succeeded Obama appointee France Córdova after her 6-year term ended in March. Physical scientists also give good reviews to Paul Dabbar and Chris Fall, who manage the science portfolio at the Department of Energy (DOE). A third group of Trump science appointees remains something of an enigma to the U.S. research community. They include the president's unofficial science adviser, Kelvin Droegemeier; Robert Redfield, head of the Centers for Disease Control and Prevention; and Stephen Hahn, head of the Food and Drug Administration. The trio are considered able scientists and are generally respected by their peers. But Droegemeier, who leads the White House Office of Science and Technology Policy (OSTP), has disappointed many science policy insiders by failing to make good on promises to better coordinate federal policies that affect universities. “I give him an A for effort, and an F for performance,” one observer says. And all three leaders have drawn complaints for their tepid responses when Trump has disputed settled science or attacked their agencies and the scientists who work for them. But such broad strokes paint only a partial picture of how Trump has influenced the U.S. research enterprise. In the following pages, Science looks at how federal science agencies have fared under a president who has repeatedly boasted of “draining the swamp” in the nation's capital. Trump's arrival brought fears of upheaval, but NIH watchers say the agency has managed to stay on course. Collins's warm relationship with congressional leaders has helped win generous budget increases. And Ned Sharpless, Trump's choice to lead its largest institute, the National Cancer Institute, has been “fantastic,” says Jon Retzlaff, chief policy officer for the American Association for Cancer Research. In contrast, researchers say White House pressure caused NIH to launch a damaging crackdown on scientists with foreign ties (p. 282). They also accuse Trump of political meddling in two important issues—fetal tissue research and pandemic research. In June 2019, the White House ended funding for NIH's in-house research using tissue from elective abortions and announced a new ethics review for extramural grants. This year, a 15-member ethics panel dominated by abortion opponents recommended approval of only one of 14 proposals that had passed review. And in April, NIH pulled a grant to the EcoHealth Alliance, a nonprofit organization working on bat viruses with the Chinese group that Trump accused—without evidence—of releasing the SARS-CoV-2 virus driving the pandemic. Those actions “have sent a chilling message to scientists,” says molecular biologist Keith Yamamoto of the University of California, San Francisco. “If problems that you have a real passion to dig into are deemed politically unsound, you could be out of luck. So watch out.” Arriving 2 years into Trump's 4-year term to head OSTP, Droegemeier promised to streamline and improve how the federal government manages academic research. But an interagency panel he created to take on the task—the Joint Committee on the Research Environment (JCORE)—has yet to reach consensus on any of the four areas Droegemeier has targeted. “He came in all fired up, promising to make things happen,” one lobbyist says. “But so far nothing has come out of JCORE, and the research community is very disappointed.” Research advocates do praise OSTP for helping focus more attention on AI and quantum information science. But science lobbyists say the real driver of that initiative has been Michael Kratsios, a scientific neophyte who was nominally in charge of OSTP before Droegemeier joined the administration. Kratsios “came into the job knowing less about science than any previous OSTP head,” one university lobbyist says. “But he was eager to learn, and he listens. He's also figured out how to use his connections to advance the administration's agenda.” ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) AAAS/R&D BUDGET AND POLICY PROGRAM Trump's first energy secretary, Rick Perry, had vowed to eliminate DOE when he ran against Trump in 2016. But Perry surprised the community by becoming a champion of the department's science mission, and his successor, Dan Brouillette, has embraced that role since taking over in December 2019. Observers also credit undersecretary Dabbar for sustaining the political momentum behind several big projects at DOE's 17 national laboratories, including a new atom smasher to study nuclear physics at Brookhaven National Laboratory and a fast-neutron test reactor at Idaho National Laboratory. Despite the Trump administration's distaste for clean energy research and its conviction that private industry is the real engine of innovation, DOE's $7 billion Office of Science has fared well. It benefited handsomely from the administration's embrace of AI and quantum information science, where physicists and engineers try to leverage subtle quantum effects to develop more powerful supercomputers and secure communication systems. In July, for example, DOE announced it would build a prototype quantum network to connect Argonne National Laboratory, Fermi National Accelerator Laboratory, and the University of Chicago. Fall, who was already working for the government when he became head of DOE's basic science shop in May 2019, thinks his office has thrived by avoiding ideological battles over the proper role of government in creating new technologies. “What we don't do is policy,” he says. “I'm doing my level best to keep the Office of Science out of politics.” Given candidate Trump's rhetoric opposing government regulation, his affection for fossil fuels, and his denial of climate change, it's no surprise that EPA has often disregarded science in devising environmental policy. Its approach to regulating particulate air pollution—often called PM2.5 (particulate matter smaller than 2.5 microns in diameter)—contains all the hallmarks of that approach, including appointing people tied to polluting industries to key posts, excluding experts from advisory roles, and using questionable methods to tip the scales when balancing benefits against costs. Soon after his appointment in 2017, then–EPA Administrator Scott Pruitt launched several major changes that would likely help ease regulations of PM2.5, which is linked to increased heart and lung diseases and premature deaths. He banned any EPA-funded scientist from serving on advisory boards that vet proposed regulations, but kept the door open to people associated with polluting industries. (A federal court overturned the ban earlier this year.) Pruitt also installed an industry consultant, Tony Cox, as chairman of the air pollution science committee and abolished an expert panel, led by Christopher Frey of North Carolina State University, that advised the committee on the science of particulate matter. Although Pruitt was forced out of the agency in mid-2018, his replacement, Andrew Wheeler, has followed a similar path. He declined a recommendation from agency scientists to tighten PM2.5 limits, citing a study by the reconstituted committee that found the science behind such a reduction was uncertain. The agency's recent actions “just made the whole thing a charade,” Frey says. EPA officials have also proposed barring the agency from considering certain scientific studies as it develops regulations if the underlying data cannot be made public because of concerns about patient privacy or trade secrets. That's the case for some large studies on how air pollution affects public health, and for many industry-funded reviews of toxic chemicals. Researchers say the rule fails to recognize the legitimate need to protect the confidentiality of some data and will undermine the quality of EPA's rulemaking. Home to some of the country's premier climate scientists, NOAA managed to operate mostly under the radar until August 2019, when Trump announced erroneously that Hurricane Dorian posed a threat to the state of Alabama and apparently used a marker to alter a National Weather Service forecast showing its path. The White House and Commerce Department pushed NOAA's acting administrator, Neil Jacobs, to reprimand weather forecasters for their correction of the president's map and tweets. That political flap, dubbed Sharpiegate, ultimately led to the arrival last month of two new senior political appointees, David Legates and Ryan Maue, who have been dismissive of climate science. “I have grave concerns around these appointments,” says Jonathan White, a retired Navy admiral and CEO of the Consortium for Ocean Leadership. “NOAA has the best [climate] scientists in the government, and I'm very concerned these voices will be muzzled.” As custodian for more than 1.8 million square kilometers of federal land, the Department of the Interior has been a central player in the Trump administration's push for more oil and gas drilling. But critics say department officials have often overlooked, disregarded, or altered the relevant science, enabling them to dismiss the climate impacts of that drilling and discount potential harm to endangered species. One early target was calculations of the economic toll from greenhouse gas emissions. Shortly after Trump took office, the department drastically reduced estimates by the Obama administration of such costs. It did so by considering only direct impacts in the United States and by reducing the dollar value of impacts on future generations. The Trump administration has used the lower price tags to justify rolling back Obama-era limits on methane emissions from oil and gas wells, as well as carbon dioxide from cars and power plants, which fall under the authority of the Department of Transportation and EPA, respectively. But this year, a federal judge ruled the lower estimates were not defensible and that the Interior Department had tried “to erase the scientific and economic facts” used in the previous estimates. The plight of endangered species has received little attention during the Trump administration, with the number of new species being listed for federal protection at an all-time low. The Fish and Wildlife Service, the branch of the Interior Department that decides whether a species is endangered, “just doesn't have the institutional support to really push back when politics gets in the way of science,” says Brett Hartl of the Center for Biological Diversity, which frequently sues federal agencies over endangered species. “They're kind of a forgotten agency.” Agriculture Secretary Sonny Perdue upset scientists with his decision to move two of the agency's research centers—the National Institute of Food and Agriculture (NIFA) and the Economic Research Service (ERS)—from Washington, D.C., to Kansas City, Missouri. According to the Congressional Research Service, roughly 75% of the employees left the Department of Agriculture (USDA) rather than move, and many grants were delayed by several months. Perdue said the new location would bring NIFA and ERS closer to their constituencies and save on rent. But many observers—including congressional Democrats—saw the move as an excuse to shrink ERS and diminish its ability to provide objective monitoring of myriad agricultural trends through its surveys and reports. And they worried the departures of so many veteran staff would deprive USDA of institutional knowledge and expertise that would take years to replace. On the plus side, USDA's decision this year to exempt certain gene-edited crops from its biotechnology regulations, potentially easing research, has been well received, says Karl Anderson, director of government relations for the American Society of Agronomy, the Crop Science Society of America, and the Soil Science Society of America. Anderson also applauds the agency's first ever set of long-range goals, which aim to increase agricultural production by 40% by 2050 while cutting the industry's environmental footprint in half. “I think it's a terrific effort,” he says. The Trump administration's efforts to limit or prohibit scientific collaborations with China and other countries deemed to pose national security risks have set off alarms throughout the academic community. Although separate from the president's attempts to restrict immigration, both efforts run counter to the traditionally open environment that has propelled U.S. science since the end of World War II. Many researchers also regard them as exercises in racial and ethnic stereotyping. The Obama administration pursued a handful of investigations, some later dropped, involving scientists with ties to China. But in the summer of 2018, NIH began to send letters to dozens of universities flagging nearly 200 faculty members believed to have hidden research support from Chinese entities. At the same time, university leaders heard themselves being accused of unwittingly handing over the fruits of federally funded research to China, the United States's chief rival as a scientific and economic superpower. In November 2018, the Department of Justice announced its China Initiative, making it clear that NIH's investigations were part of a broader campaign. Several scientists have been indicted and some have pleaded guilty, although the charges typically involve making false statements to federal officials or covering up their foreign ties rather than passing along sensitive technologies. Several agencies have taken steps aimed at learning who else is funding research by their grantees and then deciding whether those other sources pose a threat to national security. But NIH's actions are widely regarded as the most aggressive and, thus, potentially the most harmful. NSF, for example, insists on full disclosure but only occasionally initiates an investigation, and DOE has told its own scientists they cannot participate in foreign talent recruitment programs but has not altered its rules for grantees. “Agencies are under tremendous pressure from the White House to find guilty people,” says Stanford University physicist Steven Chu, a Nobel Prize winner and former energy secretary under Obama (and a past president of AAAS, Science 's publisher). “NSF has tried to push back, but NIH has almost completely folded.” The country needs to defend itself against military and economic espionage, scientists say, but some worry the administration's actions to date have already damaged the U.S. research enterprise and that additional restrictions could be fatal. “The potential loss is hard to estimate,” Chu says. Noting the outsize contribution of foreign-born scientists to U.S. technical innovation in the past 30 years, he adds, “It's scary to think [what would happen] if you shut that off.” Looking ahead to such research-based challenges as the COVID-19 pandemic and climate change, many scientists crave leadership that respects science. On 2 September, for example, 81 Nobel laureates announced their support for Trump's opponent, Democrat Joe Biden. (So far, Trump has not received such an endorsement, although there was a “Scientists for Trump” group during the 2016 contest.) In their letter, the laureates don't mention any specific policies that Biden has championed over nearly a half-century in public office, including his 8 years as vice president under Obama. But the statement makes clear that they think a Biden administration will do a better job of interacting with the scientific community. “At no time in our nation's history has there been a greater need for our leaders to appreciate the value of science in formulating public policy,” they write in a public letter. “Joe Biden has consistently demonstrated his willingness to listen to experts, his understanding of the value of international collaborations in research, and his respect for the contribution that immigrants make to the intellectual life of our country.” More than a political endorsement, the letter reflects a sense that the federal government has turned its back on science in the past 4 years and their hope that the next president will, in Obama's memorable phrase, “restore science to its rightful place.” [1]: pending:yes


The nucleus acts as a ruler tailoring cell responses to spatial constraints

Science

Single cells continuously experience and react to mechanical challenges in three-dimensional tissues. Spatial constraints in dense tissues, physical activity, and injury all impose changes in cell shape. How cells can measure shape deformations to ensure correct tissue development and homeostasis remains largely unknown (see the Perspective by Shen and Niethammer). Working independently, Venturini et al. and Lomakin et al. now show that the nucleus can act as an intracellular ruler to measure cellular shape variations. The nuclear envelope provides a gauge of cell deformation and activates a mechanotransduction pathway that controls actomyosin contractility and migration plasticity. The cell nucleus thereby allows cells to adapt their behavior to the local tissue microenvironment. Science , this issue p. [eaba2644][1], p. [eaba2894][2]; see also p. [295][3] ### INTRODUCTION The human body is a crowded place. This crowding is even more acute when the regulation of cell growth and proliferation fails during the formation of a tumor. Dealing with the lack of space in crowded environments presents cells with a challenge. This is especially true for immune cells, whose task is to patrol tissues, causing them to experience both acute and sustained deformation as they move. Although changes in tissue crowding and associated cell shape alterations have been known by pathologists to be key diagnostic traits of late-stage tumors since the 19th century, the impact of these changes on the biology of cancer and immune cells remains unclear. Moreover, it is not known whether cells can detect and adaptively respond to deformations in densely packed spaces. ### RATIONALE To test the hypothesis that cells possess an ability to detect and respond to environmentally induced changes in their shape, we fabricated artificial microenvironments that mimic the conditions experienced by tumor and immune cells in a crowded tissue. By combining dynamic confinement, force measurements, and live cell imaging, we were able to quantify cell responses to precisely controlled physical perturbations of their shape. ### RESULTS Our results show that, although cells are surprisingly resistant to compressive forces, they monitor their own shape and develop an active contractile response when deformed below a specific height. Notably, we find that this is achieved by cells monitoring the deformation of their largest internal compartment: the nucleus. We establish that the nucleus provides cells with a precise measure of the extent of their deformation. Once cell compression exceeds the size of the nucleus, it causes the bounding nuclear envelope (NE) to unfold and stretch. The onset of the contractile response occurs when the NE reaches a fully unfolded state. This transition in the mechanical state of the NE and its membranes permits calcium release from internal membrane stores and activates the calcium-dependent phospholipase cPLA2, an enzyme known to operate as a molecular sensor of nuclear membrane tension and a critical regulator of signaling and metabolism. Activated cPLA2 catalyzes the formation of arachidonic acid, an omega-6 fatty acid that, among other processes, potentiates the adenosine triphosphatase activity of myosin II. This induces contractility of the actomyosin cortex, which produces pushing forces to resist physical compression and to rapidly squeeze the cell out of its compressive microenvironment in an “evasion reflex” mechanism. ### CONCLUSION Although the nucleus has traditionally been considered a passive storehouse for genetic material, our work identifies it as an active compartment that rapidly convers mechanical inputs into signaling outputs, with a critical role of its envelope in this sensing function. The nucleus is able to detect environmentally imposed compression and respond to it by generating a signal that is used to change cell behaviors. This phenomenon plays a critical role in ensuring that cells, such as the immune cells within a tumor, can adapt, survive, and efficiently move through a crowded and mechanically heterogeneous microenvironment. Characterizing the full spectrum of signals triggered by nuclear compression has the potential to elucidate mechanisms underlying signaling, epigenetic, and metabolic adaptations of cells to their mechanoenvironment and is thus an exciting avenue for future research. ![Figure][4] The nuclear ruler and its contribution to the “life cycle” of a confined cell. (1) Cell confinement below resting nucleus size, leading to nuclear deformation and to unfolding, and stretching of the nuclear envelope. (2) Nuclear membrane tension increase, which triggers calcium release, cPLA2 activation, and arachidonic acid (ARA) production. (3) Actomyosin force ( F ) generation. (4) Increased cell migratory capacity and escape from confinement. The microscopic environment inside a metazoan organism is highly crowded. Whether individual cells can tailor their behavior to the limited space remains unclear. In this study, we found that cells measure the degree of spatial confinement by using their largest and stiffest organelle, the nucleus. Cell confinement below a resting nucleus size deforms the nucleus, which expands and stretches its envelope. This activates signaling to the actomyosin cortex via nuclear envelope stretch-sensitive proteins, up-regulating cell contractility. We established that the tailored contractile response constitutes a nuclear ruler–based signaling pathway involved in migratory cell behaviors. Cells rely on the nuclear ruler to modulate the motive force that enables their passage through restrictive pores in complex three-dimensional environments, a process relevant to cancer cell invasion, immune responses, and embryonic development. [1]: /lookup/doi/10.1126/science.aba2644 [2]: /lookup/doi/10.1126/science.aba2894 [3]: /lookup/doi/10.1126/science.abe3881 [4]: pending:yes


Transient cortical circuits match spontaneous and sensory-driven activity during development

Science

As the brain develops, it does not simply get bigger. Like a building that depends on temporary scaffolds as its structures are assembled, the developing brain sets up the circuits that characterize the adult brain. Molnár et al. review the current state of knowledge about how brain connections are built and how autonomously established patterns are reshaped by activity from the sensory periphery. With the help of a transient population of neurons, the spontaneous activity of early circuits is molded by increasing inputs from the external world. When these normal developmental interactions are disrupted, consequent miswiring drives dysfunction in the adult brain. Science , this issue p. [eabb2153][1] ### BACKGROUND During early mammalian brain development, transient neurons and circuits form the scaffold for the development of neuronal networks. In the immature cerebral cortex, subplate neurons in the lower cortical layer and Cajal-Retzius cells in the marginal zone lay the foundations for cortical organization in horizontal layers and translaminar radial circuits (“cortical columns”). Patterns of spontaneous activity during early development synchronize local and large-scale cortical networks, which form the functional template for generation of cortical architecture and guide establishment of global thalamocortical and intracortical networks. These networks become established in an autonomous fashion before the arrival of signals from the sensory periphery and before the maturation of cortical circuits. The subplate, which is a transient structure located below the developing cortical plate, orchestrates alignment of these autonomously established pathways by integrating spontaneous and sensory-driven activity patterns during critical stages of early development. ### ADVANCES The subplate contains heterogeneous neuronal populations with distinct characteristics, such as origin, birthdate, neurotransmitters, receptor expression, morphology, projections, firing properties, and their participation in specific intra- and extracortical connectivity. The transformation of this early subplate-driven circuit to the adult-like cortex requires patterned spontaneous activity and depends on the awakening of silent synapses in the cortical plate when thalamic inputs are progressively integrated. Moreover, a subpopulation of the glutamatergic and GABAergic (GABA, γ-aminobutyric acid) subplate neurons has widespread axonal projections that establish early large-scale networks. The early circuits are remodeled when Cajal-Retzius and subplate neurons largely disappear by programmed cell death. Both the programmed cell death and the remodeling of circuits may be also controlled by the transition from spontaneous synchronized burst to sensory-driven activity. ### OUTLOOK Functional impairments of these transient circuits (that include both transient and more permanent cell types) have great clinical relevance. Genetic abnormalities or early pathological conditions such as in utero infection, inflammation, exposure to pharmacological compounds, or hypoxia-ischemia induce functional disturbances in early microcircuits, which may lead to cortical miswiring at later stages and subsequent neurological and psychiatric conditions. A better understanding of the transition from early transient to permanent neuronal circuits will clarify mechanisms driving abnormal distribution and persistence of subplate neurons as interstitial white matter cells in pathophysiological conditions. Exploring the transition from transient to permanent circuits helps us to understand causal foundations of certain pharmaco-resistant epilepsies and psychiatric conditions and to consider new therapeutic strategies to treat such disorders. ![Figure][2] Early spontaneous synchronized neuronal activity sculpts cortical architecture. ( A ) Schematic outlines of brain development from the embryonic stage to adult. ( B and C ) Prenatal cortical circuits are dominated by early-generated, largely transient neurons in the subplate (SP) and marginal zone (MZ) before maturation of cortical plate (CP) neurons. ( D to H ) Transformation of early subplate-driven circuits to the adult-like six-layered cortex requires spontaneous synchronized burst activity (D) that also controls programmed cell death (apoptosis), arrangement of neurites and axons, and formation and awakening of synapses. Most subplate neurons disappear with development; a few survive in rodents as layer (L) 6b neurons or in primates as interstitial white matter (WM) cells (G). During prenatal and early postnatal stages, pathophysiological conditions such as hypoxia-ischemia, drugs, infection or inflammation may alter spontaneous activity [(E) and (F)]. These altered activity patterns may disturb subsequent developmental programs, including apoptosis (H). Surviving subplate neurons that persist in white matter or L6b may support altered circuits that could cause neurological or psychiatric disorders. At the earliest developmental stages, spontaneous activity synchronizes local and large-scale cortical networks. These networks form the functional template for the establishment of global thalamocortical networks and cortical architecture. The earliest connections are established autonomously. However, activity from the sensory periphery reshapes these circuits as soon as afferents reach the cortex. The early-generated, largely transient neurons of the subplate play a key role in integrating spontaneous and sensory-driven activity. Early pathological conditions—such as hypoxia, inflammation, or exposure to pharmacological compounds—alter spontaneous activity patterns, which subsequently induce disturbances in cortical network activity. This cortical dysfunction may lead to local and global miswiring and, at later stages, can be associated with neurological and psychiatric conditions. [1]: /lookup/doi/10.1126/science.abb2153 [2]: pending:yes


Behavioral state coding by molecularly defined paraventricular hypothalamic cell type ensembles

Science

What is the contribution of molecularly defined cell types to neural coding of stimuli and states? Xu et al. aimed to evaluate neural representation of multiple behavioral states in the mouse paraventricular hypothalamus. To achieve this goal, they combined deep-brain two-photon imaging with post hoc validation of gene expression in the imaged cells. The behavioral states could be well predicted by the neural response of multiple neuronal clusters. Some clusters were broadly tuned and contributed strongly to the decoding of multiple behavioral states, whereas others were more specifically tuned to certain behaviors or specific time windows of a behavioral state. Science , this issue p. [eabb2494][1] ### INTRODUCTION Brain function is often compared to an orchestral ensemble, where subgroups of neurons that have similar activity are analogous to different types of instruments playing a musical score. Brains are composed of specialized neuronal subtypes that can be efficiently classified by gene expression profiles measured by single-cell RNA sequencing (scRNA-seq). Are these molecularly defined cell types the “instruments” in the neural ensemble? To address this question, we examined the neural ensemble dynamics of the hypothalamic paraventricular nucleus (PVH), a small brain region that is important for behavior states such as hunger, thirst, and stress. Past work has emphasized specialized behavioral state–setting roles for different PVH cell types, but it is not clear whether the dynamics of the PVH ensemble support this view. ### RATIONALE We considered three possibilities for how PVH neurons could be involved in encoding behavioral states: (i) PVH neurons of a molecularly defined cell type may respond similarly and be specialized for a behavioral state as a “labeled-line,” (ii) molecularly defined cell types may show unrelated activity patterns and be irrelevant to behavioral state coding, and (iii) molecularly defined neurons may respond similarly within a type, but behavioral state may be encoded by combinations of cell types. To evaluate the role of molecularly defined cell types in the neural ensemble, it is important to monitor activity in many individual neurons with subsecond temporal resolution along with quantitative gene expression information about each cell. For this, we developed the CaRMA (calcium and RNA multiplexed activity) imaging platform in which deep-brain two-photon calcium imaging of neuron activity is performed in mice during multiple behavioral tasks. This is followed by ex vivo multiplexed RNA fluorescent in situ hybridization to measure gene expression information in the in vivo–imaged neurons. ### RESULTS We simultaneously imaged calcium activity in hundreds of PVH neurons from 10 cell types across 11 behavioral states. Within a molecularly defined cell type, neurons often showed similar activity patterns such that we could predict functional responses of individual neurons solely from their quantitative gene expression information. Behavioral states could be decoded with high accuracy based on combinatorial assemblies of PVH cell types, which we called “grouped-ensemble coding.” Labeled-line coding was not observed. The neuromodulatory receptor gene neuropeptide receptor neuropeptide Y receptor type 1 ( Npy1r ) was usually the most predictive gene for neuron functional response and was expressed in multiple cell types, analogous to the “conductor” of the PVH neural ensemble. ### CONCLUSION Our results validated molecularly defined neurons as important information processing units in the PVH. We found correspondence between the gene expression hierarchies used for molecularly defined cell type classification and functional activity hierarchies involving coordination by neuromodulation. CaRMA imaging offers a solution to the problem of how to rapidly evaluate the function of the panoply of cell types being uncovered with scRNA-seq. CaRMA imaging bridges a gap between the abstract digital elements typically described in systems neuroscience with the “wetware” associated with traditional molecular neuroscience. Merging these two areas is essential to understanding the relationships of gene expression, brain function, behavior, and ultimately neurological diseases. ![Figure][2] CaRMA imaging reveals combinatorial cell type coding of behavior states. CaRMA imaging records calcium dynamics of PVH neurons across multiple behavioral states followed by gene expression profiling. Combinatorial assemblies of PVH cell types encoded behavioral states. The PVH neural activity ensemble was split by Npy1r expression into two main cell classes that were subdivided into cell types. Thus, neuromodulation coordinates cell types for grouped-ensemble coding to represent different survival behaviors such as eating, drinking, and stress. Brains encode behaviors using neurons amenable to systematic classification by gene expression. The contribution of molecular identity to neural coding is not understood because of the challenges involved with measuring neural dynamics and molecular information from the same cells. We developed CaRMA (calcium and RNA multiplexed activity) imaging based on recording in vivo single-neuron calcium dynamics followed by gene expression analysis. We simultaneously monitored activity in hundreds of neurons in mouse paraventricular hypothalamus (PVH). Combinations of cell-type marker genes had predictive power for neuronal responses across 11 behavioral states. The PVH uses combinatorial assemblies of molecularly defined neuron populations for grouped-ensemble coding of survival behaviors. The neuropeptide receptor neuropeptide Y receptor type 1 (Npy1r) amalgamated multiple cell types with similar responses. Our results show that molecularly defined neurons are important processing units for brain function. [1]: /lookup/doi/10.1126/science.abb2494 [2]: pending:yes


Better, faster, and even cheap

Science

Cryo–electron microscopy (cryo-EM) enables access to structures of proteins that were previously intractable, including large protein complexes such as the ribosome ([ 1 ][1]), integral membrane proteins ([ 2 ][2], [ 3 ][3]), and highly heterogeneous or conformationally dynamic systems ([ 4 ][4]). Each sample is a vitrified layer of protein suspended over a support film on an EM grid. Despite recent advances in cryo-EM (the so-called “resolution revolution”) ([ 5 ][5], [ 6 ][6]), major barriers persist, including loss of the highest-resolution information through electron beam damage and blurring from sample movement (which is most pronounced initially when the sample is least damaged). Typically, tens of thousands of images must be averaged to compensate for signal loss. On page 223 of this issue, Naydenova et al. ([ 7 ][7]) describe a new specimen support film (see the figure) that not only improves both the quality of images and the efficiency of collection, but also does so at a relatively low price. Like much of society, the strengths and weaknesses of cryo-EM have been highlighted by the coronavirus disease 2019 (COVID-19) pandemic. Structural biology has contributed to our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with more than 330 structures deposited into the Protein Data Bank since early March. At the time of writing, 264 of those structures were produced by x-ray crystallography and 72 by cryo-EM. The astonishing throughput of crystallography was demonstrated in recent work on the main SARS-CoV-2 protease, where ∼1500 datasets were collected in a single day ([ 8 ][8]). In contrast, cryo-EM datasets typically require on the order of 24 hours of data collection, even though the process is largely automated ([ 9 ][9]). High-end microscopes are not only under extraordinary demand, but are also associated with very high capital and annual maintenance costs ([ 10 ][10]). These differences have made x-ray crystallography better suited to the rapid turnaround required by the pharmaceutical drug discovery pipeline ([ 11 ][11]). These timelines also present a barrier for academic research. Increasing throughput will lower costs and enable more researchers to use cryo-EM to solve structures and accelerate scientific discovery. Naydenova et al. propose a new specimen support film, dubbed “HexAuFoil,” that provides many advantages over conventional support films. In cryo-EM, images are typically taken of proteins embedded in a layer of vitrified ice suspended over a support films containing holes ∼1 to 2 µm in diameter. By making these holes smaller (200 to 300 nm in diameter) and packing them more tightly together (a nontrivial feat), they substantially increased data throughput. They obtained 200 images for every microscope stage movement (versus a typical 10 to 30 images) and propose that as many as 800 images could be obtained with an appropriately configured microscope. A second approach to increasing throughput that Naydenova et al. addressed is to increase the data quality by minimizing the information loss from both radiation damage and sample movement. They present a fascinating and exhaustive analysis showing that the primary cause of specimen movement during imaging is a buckling of the vitrified ice layer. On the basis of these insights, they created a support film with an optimal substrate thickness relative to the hole diameter, which reduces the total movement of the sample to <1 Å during the course of an exposure. They could mathematically extrapolate the data to a three-dimensional map before the onset of radiation damage. Standard support films have recently been used to obtain the first truly atomic-level cryo-EM reconstructions ([ 12 ][12], [ 13 ][13]) of apoferritin, a very stable test specimen. The advances developed by Naydenova et al. should bring this goal closer for less well-behaved proteins. Although software is also now available to correct for the effects of beam-induced movement ([ 14 ][14]), this approach does not provide the concomitant benefit of faster data collection. High-resolution cryo-EM is still in a state of active method development, its true potential not yet realized. Thus, it is exciting that Naydenova et al. , with one accessible, inexpensive hardware development, will allow all practitioners to acquire better images much more rapidly as soon as the grids become commercially available. The promise is of a future of high-resolution structures of a wide range of proteins, in an ensemble of conformational or compositional states ([ 15 ][15]), produced with much higher throughput. 1. [↵][16]1. A. Brown, 2. S. Shao , Curr. Opin. Struct. Biol. 52, 1 (2018). [OpenUrl][17][CrossRef][18] 2. [↵][19]1. Y. Cheng , Curr. Opin. Struct. Biol. 52, 58 (2018). [OpenUrl][20][CrossRef][21][PubMed][22] 3. [↵][23]1. M. Dong et al ., Nat. Commun. 11, 4137 (2020). [OpenUrl][24] 4. [↵][25]1. T. Nakane, 2. D. Kimanius, 3. E. Lindahl, 4. S. H. W. Scheres , eLife 7, e36861 (2018). [OpenUrl][26][CrossRef][27][PubMed][28] 5. [↵][29]1. W. Kühlbrandt , Science 343, 1443 (2014). [OpenUrl][30][Abstract/FREE Full Text][31] 6. [↵][32]1. Y. Cheng , Science 361, 876 (2018). [OpenUrl][33][Abstract/FREE Full Text][34] 7. [↵][35]1. K. Naydenova, 2. P. Jia, 3. C. J. Russo , Science 370, 223 (2020). [OpenUrl][36][CrossRef][37] 8. [↵][38]1. A. Douangamath et al ., bioRxiv 118117 (2020). 9. [↵][39]1. D. Lyumkis , J. Biol. Chem. 294, 5181 (2019). [OpenUrl][40][Abstract/FREE Full Text][41] 10. [↵][42]1. K. Naydenova et al ., IUCrJ 6, 1086 (2019). [OpenUrl][43][CrossRef][44][PubMed][45] 11. [↵][46]1. G. Scapin, 2. C. S. Potter, 3. B. Carragher , Cell Chem. Biol. 25, 1318 (2018). [OpenUrl][47] 12. [↵][48]1. T. Nakane et al ., bioRxiv 110189 (2020). 13. [↵][49]1. K. M. Yip, 2. N. Fischer, 3. E. Paknia, 4. A. Chari, 5. H. Stark , bioRxiv 106740 (2020). 14. [↵][50]1. D. Tegunov, 2. L. Xue, 3. C. Dienemann, 4. P. Cramer, 5. J. Mahamid , bioRxiv 136341 (2020). 15. [↵][51]1. A. Dance , Nat. Methods 17, 879 (2020). [OpenUrl][52] Acknowledgments: Supported by NIH grants GM103310 and OD019994 and Simons Foundation grant SF349247. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: #ref-14 [15]: #ref-15 [16]: #xref-ref-1-1 "View reference 1 in text" [17]: {openurl}?query=rft.jtitle%253DCurr.%2BOpin.%2BStruct.%2BBiol.%26rft.volume%253D52%26rft.spage%253D1%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.sbi.2018.07.001%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [18]: /lookup/external-ref?access_num=10.1016/j.sbi.2018.07.001&link_type=DOI [19]: #xref-ref-2-1 "View reference 2 in text" [20]: {openurl}?query=rft.jtitle%253DCurr.%2BOpin.%2BStruct.%2BBiol.%26rft.volume%253D52%26rft.spage%253D58%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.sbi.2018.08.008%26rft_id%253Dinfo%253Apmid%252F30219656%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [21]: /lookup/external-ref?access_num=10.1016/j.sbi.2018.08.008&link_type=DOI [22]: /lookup/external-ref?access_num=30219656&link_type=MED&atom=%2Fsci%2F370%2F6513%2F171.atom [23]: #xref-ref-3-1 "View reference 3 in text" [24]: {openurl}?query=rft.jtitle%253DNat.%2BCommun.%26rft.volume%253D11%26rft.spage%253D4137%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [25]: #xref-ref-4-1 "View reference 4 in text" [26]: {openurl}?query=rft.jtitle%253DeLife%26rft.volume%253D7%26rft.spage%253De36861%26rft_id%253Dinfo%253Adoi%252F10.7554%252FeLife.36861%26rft_id%253Dinfo%253Apmid%252F29856314%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [27]: /lookup/external-ref?access_num=10.7554/eLife.36861&link_type=DOI [28]: /lookup/external-ref?access_num=29856314&link_type=MED&atom=%2Fsci%2F370%2F6513%2F171.atom [29]: #xref-ref-5-1 "View reference 5 in text" [30]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DKuhlbrandt%26rft.auinit1%253DW.%26rft.volume%253D343%26rft.issue%253D6178%26rft.spage%253D1443%26rft.epage%253D1444%26rft.atitle%253DThe%2BResolution%2BRevolution%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.1251652%26rft_id%253Dinfo%253Apmid%252F24675944%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [31]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEzOiIzNDMvNjE3OC8xNDQzIjtzOjQ6ImF0b20iO3M6MjI6Ii9zY2kvMzcwLzY1MTMvMTcxLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [32]: #xref-ref-6-1 "View reference 6 in text" [33]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DCheng%26rft.auinit1%253DY.%26rft.volume%253D361%26rft.issue%253D6405%26rft.spage%253D876%26rft.epage%253D880%26rft.atitle%253DSingle-particle%2Bcryo-EM--How%2Bdid%2Bit%2Bget%2Bhere%2Band%2Bwhere%2Bwill%2Bit%2Bgo%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aat4346%26rft_id%253Dinfo%253Apmid%252F30166484%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [34]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIzNjEvNjQwNS84NzYiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxMy8xNzEuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [35]: #xref-ref-7-1 "View reference 7 in text" [36]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DNaydenova%26rft.auinit1%253DK.%26rft.volume%253D370%26rft.issue%253D6513%26rft.spage%253D223%26rft.epage%253D226%26rft.atitle%253DCryo-EM%2Bwith%2Bsub-1%2BA%2Bspecimen%2Bmovement%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.abb7927%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [37]: /lookup/external-ref?access_num=10.1126/science.abb7927&link_type=DOI [38]: #xref-ref-8-1 "View reference 8 in text" [39]: #xref-ref-9-1 "View reference 9 in text" [40]: {openurl}?query=rft.jtitle%253DJ.%2BBiol.%2BChem.%26rft_id%253Dinfo%253Adoi%252F10.1074%252Fjbc.REV118.005602%26rft_id%253Dinfo%253Apmid%252F30804214%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [41]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiamJjIjtzOjU6InJlc2lkIjtzOjExOiIyOTQvMTMvNTE4MSI7czo0OiJhdG9tIjtzOjIyOiIvc2NpLzM3MC82NTEzLzE3MS5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [42]: #xref-ref-10-1 "View reference 10 in text" [43]: {openurl}?query=rft.jtitle%253DIUCrJ%26rft.volume%253D6%26rft.spage%253D1086%26rft_id%253Dinfo%253Adoi%252F10.1107%252FS2052252519012612%26rft_id%253Dinfo%253Apmid%252F31709064%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [44]: /lookup/external-ref?access_num=10.1107/S2052252519012612&link_type=DOI [45]: /lookup/external-ref?access_num=31709064&link_type=MED&atom=%2Fsci%2F370%2F6513%2F171.atom [46]: #xref-ref-11-1 "View reference 11 in text" [47]: {openurl}?query=rft.jtitle%253DCell%2BChem.%2BBiol.%26rft.volume%253D25%26rft.spage%253D1318%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [48]: #xref-ref-12-1 "View reference 12 in text" [49]: #xref-ref-13-1 "View reference 13 in text" [50]: #xref-ref-14-1 "View reference 14 in text" [51]: #xref-ref-15-1 "View reference 15 in text" [52]: {openurl}?query=rft.jtitle%253DNat.%2BMethods%26rft.volume%253D17%26rft.spage%253D879%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx


A new villain in neuronal death

Science

Excitotoxicity is the process by which the excitatory amino acid neurotransmitter glutamate causes neuronal toxicity ([ 1 ][1]). A landmark study in 1987 revealed that Ca2+ influx into neurons through glutamate N -methyl-d-aspartate receptor channels (NMDARs) could trigger excitotoxicity ([ 2 ][2]), making Ca2+ ions potential villains as well as functionally critical signaling molecules in neurons. On page 191 of this issue, Yan et al. ([ 3 ][3]) reveal a newly discovered mechanism of NMDAR-mediated excitotoxicity: a physical interaction with TRPM4, a transient receptor potential channel ([ 4 ][4]). Small-molecule “interface inhibitors” prevent NMDAR-TRPM4 physical coupling and eliminate excitotoxicity in vitro and in vivo. This mechanism of excitotoxicity does not require NMDAR-mediated Ca2+ influx: TRPM4 is the new villain. Consequently, excitotoxicity can be halted without disrupting physiologically crucial NMDAR-mediated Ca2+ signaling. This presents opportunities in the design of therapeutics for disorders involving excitotoxicity, including stroke, epilepsy, and neurodegeneration. Glutamate, released at excitatory synapses in the vertebrate brain, binds to NMDARs to induce Ca2+-dependent forms of synaptic plasticity ([ 5 ][5]) and survival-promoting intracellular signaling pathways involving CREB \[cyclic adenosine monophosphate (cAMP) response element–binding protein\] ([ 6 ][6]). Glutamate can also spill over beyond the synapse and activate neighboring synaptic and extrasynaptic NMDARs. Excessive NMDAR-mediated Ca2+ influx is a widely accepted explanation for excitotoxicity ([ 7 ][7], [ 8 ][8]). A hitherto unrelated ion channel, TRPM4, is activated by cytoplasmic Ca2+ and is permeable to monovalent cations Na+ and K+ (but not to Ca2+), mediating membrane depolarization ([ 9 ][9]). TRPM4 has previously been linked to neuronal death: In mice, inhibition of TRPM4 reduces neurodegeneration, and genetic deletion of Trpm4 protects against glutamate excitotoxicity ([ 10 ][10]). Yan et al. demonstrate a physical interaction between NMDARs and TRPM4 in hippocampal cell cultures and brain tissue from mice. NMDARs are typically composed of GluN1, GluN2, and sometimes GluN3 subunits ([ 11 ][11]). TRPM4 specifically interacts with GluN2A and GluN2B, but not GluN2C/D, GluN1, or GluN3. The site of NMDAR-TRPM4 interaction was narrowed down to a cytoplasmic region of TRPM4, which the authors call TwinF. They further identify a highly conserved isoleucine-rich cytoplasmic sequence in GluN2A and GluN2B to which TwinF binds; they name this I4. A TwinF peptide used to block NMDAR-TRPM4 interaction was neuroprotective in three assays of cell death: (i) NMDAR-evoked excitotoxicity of neuronal cultures, (ii) oxygen-glucose deprivation–evoked cell death in neuronal cultures, and (iii) a small but significant reduction in ischemic brain damage in vivo after middle cerebral artery occlusion (MCAO) in mice. Small-molecule inhibitors that target the precise NMDAR-TRPM4 interaction, called compounds 8 and 19, were identified. In the mouse MCAO model and in retinal degeneration induced by NMDA (the selective NMDAR agonist for which the receptor was named), intraperitoneal delivery of compound 8 offered neuroprotection—a small but significant reduction in infarct volume and cell loss, as well as reduction in NMDAR-TRPM4 interaction by as much as 38%. Thus, these small and simple molecules could potentially be delivered systemically to patients. Rapid delivery would be required in acute excitotoxic injury—for example, after stroke or epilepsy—whereas in chronic neurodegenerative diseases, possible contributions of excitotoxicity to disease progression ([ 8 ][8]) might be mitigated over a longer time course. The authors hypothesize that NMDAR-TRPM4 coupling triggers “CREB shutoff” (CREB dephosphorylation) ([ 6 ][6], [ 12 ][12]). TwinF and compounds 8 and 19 appear to “detoxify” NMDAR signaling by preventing CREB shutoff. In addition, TwinF reduces NMDAR-induced mitochondrial membrane dysfunction. By disrupting NMDAR-TRPM4 coupling, harmful signaling pathways are silenced while neuroprotective signaling pathways are spared (see the figure). The mechanism by which physical coupling of NMDAR-TRPM4 brings about such neuroprotective effects is not yet elucidated, but it does not appear to involve Ca2+ signaling. A surprising finding is that blocking the NMDAR-TRPM4 interaction with TwinF or small-molecule inhibitors has no effect on normal synaptic NMDAR channel function recorded in hippocampal CA1 pyramidal neurons in mouse brain slices. Nor is there any effect on NMDAR-mediated Ca2+ influx and signaling in vitro. TRPM4 channel activity is also unaffected by blocking NMDAR interaction. Conventional NMDAR antagonists typically impair cognitive function ([ 13 ][13]), most likely by blocking NMDAR-mediated synaptic transmission, Ca2+ influx, and plasticity ([ 5 ][5]). Being able to target excitotoxicity while having no detrimental effect on NMDAR ionic current flux or calcium permeability in vitro constitutes an important step. It remains to be confirmed that the small-molecule inhibitors have no detrimental effects in vivo. ![Figure][14] Disrupting glutamate receptor interactions Synaptic N -methyl-d-aspartate receptor channels (NMDARs), activated by glutamate release and coincident membrane depolarization, trigger neuroprotective signaling pathways involving phosphorylated cAMP-responsive element–binding protein (pCREB). Extrasynaptic NMDARs that are activated by synaptic glutamate spillover interact with transient receptor potential monovalent cation channel 4 (TRPM4), triggering excitotoxic cell death. Blocking NMDAR-TRPM4 binding with an N/T inhibitor eliminates neuronal death signaling and enables neuroprotective synaptic signaling. GRAPHIC: KELLIE HOLOSKI/ SCIENCE A physical coupling between TRPM4 and NMDARs in mediating excitotoxicity explains a conundrum in NMDAR research: why NMDARs located at synapses appear to mediate prosurvival signaling, whereas NMDARs located away from the synapse appear to prevent this and indeed trigger prodeath signaling pathways ([ 6 ][6], [ 8 ][8], [ 12 ][12], [ 14 ][15]). Previous explanations for this include the subcellular localization of different signaling molecules or different concentrations of Ca2+ influx at synaptic versus extrasynaptic sites. Because TRPM4 is absent from synapses, this suggests that NMDAR-TRPM4 complexes would only form extrasynaptically, offering a satisfying explanation for this puzzle. Considerable research effort has focused on the role of Ca2+ in NMDAR-mediated excitotoxicity. The study of Yan et al. introduces a new villain, TRPM4. This requires reevaluation of the role of NMDARs in neuronal death. It may redirect research efforts away from Ca2+ and toward physical interactions of NMDARs, and it could provide another argument against developing NMDAR antagonist and channel blocker therapies. It is unknown whether the NMDAR-TRPM4 interaction will prove to be the primary (or even exclusive) excitotoxicity mechanism in a broader range of neurons. For example, neurodegeneration in Huntington's and Parkinson's diseases affects the basal ganglia ([ 14 ][15]), whereas Yan et al. studied hippocampal neurons. There is evidence for acute and chronic excitotoxicity in human neurological disorders ([ 8 ][8]), and so it may be illuminating to screen postmortem human brains from patients with neurodegenerative diseases for NMDAR-TRPM4 interactions. If higher amounts of interaction are seen compared with healthy subjects, can small-molecule protein interaction inhibitors be designed to produce a sufficiently robust yet safe disruption? Another intriguing question is whether NMDAR-TRPM4 interaction is triggered under normal physiological conditions and plays an important functional role, or is instead purely a pathological response to injury. After more than 30 years of research, perhaps Ca2+ signaling will ultimately be shown to have little importance in excitotoxicity—to be a hero and not a villain in neuronal signaling. 1. [↵][16]1. J. W. Olney , Science 164, 719 (1969). [OpenUrl][17][Abstract/FREE Full Text][18] 2. [↵][19]1. D. W. Choi , J. Neurosci. 7, 369 (1987). [OpenUrl][20][Abstract/FREE Full Text][21] 3. [↵][22]1. J. Yan et al ., Science 370, eaay3302 (2020). [OpenUrl][23][CrossRef][24] 4. [↵][25]1. P. Launay et al ., Cell 109, 397 (2002). [OpenUrl][26][CrossRef][27][PubMed][28][Web of Science][29] 5. [↵][30]1. C. Lüscher, 2. R. C. Malenka , Cold Spring Harb. Perspect. Biol. 4, a005710 (2012). [OpenUrl][31][Abstract/FREE Full Text][32] 6. [↵][33]1. G. E. Hardingham, 2. H. Bading , Nat. Rev. Neurosci. 11, 682 (2010). [OpenUrl][34][CrossRef][35][PubMed][36][Web of Science][37] 7. [↵][38]1. M. Arundine, 2. M. Tymianski , Cell Calcium 34, 325 (2003). [OpenUrl][39][CrossRef][40][PubMed][41][Web of Science][42] 8. [↵][43]1. J. Lewerenz, 2. P. Maher , Front. Neurosci. 9, 469 (2015). [OpenUrl][44][CrossRef][45][PubMed][46] 9. [↵][47]1. S. Choi 1. R. Guinamard, 2. C. Simard, 3. L. Sallé , in Encyclopedia of Signaling Molecules, S. Choi, Ed. (Springer, 2016). 10. [↵][48]1. B. Schattling et al ., Nat. Med. 18, 1805 (2012). [OpenUrl][49][CrossRef][50][PubMed][51] 11. [↵][52]1. D. J. A. Wyllie, 2. M. R. Livesey, 3. G. E. Hardingham , Neuropharmacology 74, 4 (2013). [OpenUrl][53][CrossRef][54][PubMed][55] 12. [↵][56]1. G. E. Hardingham, 2. Y. Fukunaga, 3. H. Bading , Nat. Neurosci. 5, 405 (2002). [OpenUrl][57][CrossRef][58][PubMed][59][Web of Science][60] 13. [↵][61]1. S. A. Lipton , Nat. Rev. Drug Discov. 5, 160 (2006). [OpenUrl][62][CrossRef][63][PubMed][64][Web of Science][65] 14. [↵][66]1. M. P. Parsons, 2. L. A. Raymond , Neuron 82, 279 (2014). [OpenUrl][67][CrossRef][68][PubMed][69][Web of Science][70] [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: #ref-10 [11]: #ref-11 [12]: #ref-12 [13]: #ref-13 [14]: pending:yes [15]: #ref-14 [16]: #xref-ref-1-1 "View reference 1 in text" [17]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DOlney%26rft.auinit1%253DJ.%2BW.%26rft.volume%253D164%26rft.issue%253D3880%26rft.spage%253D719%26rft.epage%253D721%26rft.atitle%253DBrain%2BLesions%252C%2BObesity%252C%2Band%2BOther%2BDisturbances%2Bin%2BMice%2BTreated%2Bwith%2BMonosodium%2BGlutamate%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.164.3880.719%26rft_id%253Dinfo%253Apmid%252F5778021%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [18]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjEyOiIxNjQvMzg4MC83MTkiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxMy8xNjguYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [19]: #xref-ref-2-1 "View reference 2 in text" [20]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2BNeuroscience%26rft.stitle%253DJ.%2BNeurosci.%26rft.aulast%253DChoi%26rft.auinit1%253DD.%26rft.volume%253D7%26rft.issue%253D2%26rft.spage%253D369%26rft.epage%253D379%26rft.atitle%253DIonic%2Bdependence%2Bof%2Bglutamate%2Bneurotoxicity%26rft_id%253Dinfo%253Adoi%252F10.1523%252FJNEUROSCI.07-02-00369.1987%26rft_id%253Dinfo%253Apmid%252F2880938%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [21]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Njoiam5ldXJvIjtzOjU6InJlc2lkIjtzOjc6IjcvMi8zNjkiO3M6NDoiYXRvbSI7czoyMjoiL3NjaS8zNzAvNjUxMy8xNjguYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [22]: #xref-ref-3-1 "View reference 3 in text" [23]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DYan%26rft.auinit1%253DJ.%26rft.volume%253D370%26rft.issue%253D6513%26rft.spage%253Deaay3302%26rft.epage%253Deaay3302%26rft.atitle%253DCoupling%2Bof%2BNMDA%2Breceptors%2Band%2BTRPM4%2Bguides%2Bdiscovery%2Bof%2Bunconventional%2Bneuroprotectants%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.aay3302%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [24]: /lookup/external-ref?access_num=10.1126/science.aay3302&link_type=DOI [25]: #xref-ref-4-1 "View reference 4 in text" [26]: {openurl}?query=rft.jtitle%253DCell%26rft.stitle%253DCell%26rft.aulast%253DLaunay%26rft.auinit1%253DP.%26rft.volume%253D109%26rft.issue%253D3%26rft.spage%253D397%26rft.epage%253D407%26rft.atitle%253DTRPM4%2Bis%2Ba%2BCa2%252B-activated%2Bnonselective%2Bcation%2Bchannel%2Bmediating%2Bcell%2Bmembrane%2Bdepolarization.%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS0092-8674%252802%252900719-5%26rft_id%253Dinfo%253Apmid%252F12015988%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [27]: /lookup/external-ref?access_num=10.1016/S0092-8674(02)00719-5&link_type=DOI [28]: /lookup/external-ref?access_num=12015988&link_type=MED&atom=%2Fsci%2F370%2F6513%2F168.atom [29]: /lookup/external-ref?access_num=000175412100014&link_type=ISI [30]: #xref-ref-5-1 "View reference 5 in text" [31]: {openurl}?query=rft.jtitle%253DCold%2BSpring%2BHarb.%2BPerspect.%2BBiol.%26rft_id%253Dinfo%253Adoi%252F10.1101%252Fcshperspect.a005710%26rft_id%253Dinfo%253Apmid%252F22510460%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [32]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTE6ImNzaHBlcnNwZWN0IjtzOjU6InJlc2lkIjtzOjExOiI0LzYvYTAwNTcxMCI7czo0OiJhdG9tIjtzOjIyOiIvc2NpLzM3MC82NTEzLzE2OC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [33]: #xref-ref-6-1 "View reference 6 in text" [34]: {openurl}?query=rft.jtitle%253DNature%2Breviews.%2BNeuroscience%26rft.stitle%253DNat%2BRev%2BNeurosci%26rft.aulast%253DHardingham%26rft.auinit1%253DG.%2BE.%26rft.volume%253D11%26rft.issue%253D10%26rft.spage%253D682%26rft.epage%253D696%26rft.atitle%253DSynaptic%2Bversus%2Bextrasynaptic%2BNMDA%2Breceptor%2Bsignalling%253A%2Bimplications%2Bfor%2Bneurodegenerative%2Bdisorders.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnrn2911%26rft_id%253Dinfo%253Apmid%252F20842175%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [35]: /lookup/external-ref?access_num=10.1038/nrn2911&link_type=DOI [36]: /lookup/external-ref?access_num=20842175&link_type=MED&atom=%2Fsci%2F370%2F6513%2F168.atom [37]: /lookup/external-ref?access_num=000281928500002&link_type=ISI [38]: #xref-ref-7-1 "View reference 7 in text" [39]: {openurl}?query=rft.jtitle%253DCell%2Bcalcium%26rft.stitle%253DCell%2BCalcium%26rft.aulast%253DArundine%26rft.auinit1%253DM.%26rft.volume%253D34%26rft.issue%253D4-5%26rft.spage%253D325%26rft.epage%253D337%26rft.atitle%253DMolecular%2Bmechanisms%2Bof%2Bcalcium-dependent%2Bneurodegeneration%2Bin%2Bexcitotoxicity.%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS0143-4160%252803%252900141-6%26rft_id%253Dinfo%253Apmid%252F12909079%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [40]: /lookup/external-ref?access_num=10.1016/S0143-4160(03)00141-6&link_type=DOI [41]: /lookup/external-ref?access_num=12909079&link_type=MED&atom=%2Fsci%2F370%2F6513%2F168.atom [42]: /lookup/external-ref?access_num=000184927500003&link_type=ISI [43]: #xref-ref-8-1 "View reference 8 in text" [44]: {openurl}?query=rft.jtitle%253DFront.%2BNeurosci.%26rft.volume%253D9%26rft.spage%253D469%26rft_id%253Dinfo%253Adoi%252F10.3389%252Ffnins.2015.00469%26rft_id%253Dinfo%253Apmid%252F26733784%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [45]: /lookup/external-ref?access_num=10.3389/fnins.2015.00469&link_type=DOI [46]: /lookup/external-ref?access_num=26733784&link_type=MED&atom=%2Fsci%2F370%2F6513%2F168.atom [47]: #xref-ref-9-1 "View reference 9 in text" [48]: #xref-ref-10-1 "View reference 10 in text" [49]: {openurl}?query=rft.jtitle%253DNature%2Bmedicine%26rft.stitle%253DNat%2BMed%26rft.aulast%253DSchattling%26rft.auinit1%253DB.%26rft.volume%253D18%26rft.spage%253D1805%26rft.atitle%253DTRPM4%2Bcation%2Bchannel%2Bmediates%2Baxonal%2Band%2Bneuronal%2Bdegeneration%2Bin%2Bexperimental%2Bautoimmune%2Bencephalomyelitis%2Band%2Bmultiple%2Bsclerosis.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnm.3015%26rft_id%253Dinfo%253Apmid%252F23160238%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [50]: /lookup/external-ref?access_num=10.1038/nm.3015&link_type=DOI [51]: /lookup/external-ref?access_num=23160238&link_type=MED&atom=%2Fsci%2F370%2F6513%2F168.atom [52]: #xref-ref-11-1 "View reference 11 in text" [53]: {openurl}?query=rft.jtitle%253DNeuropharmacology%26rft.volume%253D74%26rft.spage%253D4%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.neuropharm.2013.01.016%26rft_id%253Dinfo%253Apmid%252F23376022%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [54]: /lookup/external-ref?access_num=10.1016/j.neuropharm.2013.01.016&link_type=DOI [55]: /lookup/external-ref?access_num=23376022&link_type=MED&atom=%2Fsci%2F370%2F6513%2F168.atom [56]: #xref-ref-12-1 "View reference 12 in text" [57]: {openurl}?query=rft.jtitle%253DNature%2Bneuroscience%26rft.stitle%253DNat%2BNeurosci%26rft.aulast%253DHardingham%26rft.auinit1%253DG.%2BE.%26rft.volume%253D5%26rft.issue%253D5%26rft.spage%253D405%26rft.epage%253D414%26rft.atitle%253DExtrasynaptic%2BNMDARs%2Boppose%2Bsynaptic%2BNMDARs%2Bby%2Btriggering%2BCREB%2Bshut-off%2Band%2Bcell%2Bdeath%2Bpathways.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnn835%26rft_id%253Dinfo%253Apmid%252F11953750%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [58]: /lookup/external-ref?access_num=10.1038/nn835&link_type=DOI [59]: /lookup/external-ref?access_num=11953750&link_type=MED&atom=%2Fsci%2F370%2F6513%2F168.atom [60]: /lookup/external-ref?access_num=000175272000010&link_type=ISI [61]: #xref-ref-13-1 "View reference 13 in text" [62]: {openurl}?query=rft.jtitle%253DNature%2Breviews.%2BDrug%2Bdiscovery%26rft.stitle%253DNat%2BRev%2BDrug%2BDiscov%26rft.aulast%253DLipton%26rft.auinit1%253DS.%2BA.%26rft.volume%253D5%26rft.issue%253D2%26rft.spage%253D160%26rft.epage%253D170%26rft.atitle%253DParadigm%2Bshift%2Bin%2Bneuroprotection%2Bby%2BNMDA%2Breceptor%2Bblockade%253A%2Bmemantine%2Band%2Bbeyond.%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnrd1958%26rft_id%253Dinfo%253Apmid%252F16424917%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [63]: /lookup/external-ref?access_num=10.1038/nrd1958&link_type=DOI [64]: /lookup/external-ref?access_num=16424917&link_type=MED&atom=%2Fsci%2F370%2F6513%2F168.atom [65]: /lookup/external-ref?access_num=000235067900028&link_type=ISI [66]: #xref-ref-14-1 "View reference 14 in text" [67]: {openurl}?query=rft.jtitle%253DNeuron%26rft.volume%253D82%26rft.spage%253D279%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.neuron.2014.03.030%26rft_id%253Dinfo%253Apmid%252F24742457%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [68]: /lookup/external-ref?access_num=10.1016/j.neuron.2014.03.030&link_type=DOI [69]: /lookup/external-ref?access_num=24742457&link_type=MED&atom=%2Fsci%2F370%2F6513%2F168.atom [70]: /lookup/external-ref?access_num=000334506800006&link_type=ISI