Wireless closed-loop optogenetics across the entire dorsoventral spinal cord in mice

Wireless closed-loop optogenetics across the entire dorsoventral spinal cord in mice

Abstract

Optoelectronic systems can exert precise control over targeted neurons and pathways throughout the brain in untethered animals, but similar technologies for the spinal cord are not well established. In the present study, we describe a system for ultrafast, wireless, closed-loop manipulation of targeted neurons and pathways across the entire dorsoventral spinal cord in untethered mice. We developed a soft stretchable carrier, integrating microscale light-emitting diodes (micro-LEDs), that conforms to the dura mater of the spinal cord. A coating of silicone–phosphor matrix over the micro-LEDs provides mechanical protection and light conversion for compatibility with a large library of opsins. A lightweight, head-mounted, wireless platform powers the micro-LEDs and performs low-latency, on-chip processing of sensed physiological signals to control photostimulation in a closed loop. We use the device to reveal the role of various neuronal subtypes, sensory pathways and supraspinal projections in the control of locomotion in healthy and spinal-cord injured mice.

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Acknowledgements

We thank B. Schneider for providing viral vectors, and L. Batti and S. Pagès from the ALICe platform for light-sheet imaging. Financial support was provided by a Consolidator Grant from the European Research Council (ERC-2015-CoG HOW2WALKAGAIN 682999), the Swiss National Science Foundation (subsidies 310030_130850, CRSII5_183519, BSCGI0 1578000) and the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant agreement no. 785907 (Human Brain Project SGA2) and the Bertarelli Foundation. C.K. is supported by a Marie Skłodowska-Curie postdoctoral fellowship and HFSP long-term fellowship (LT001278/2017-L). S.S. and C.I.D.Z. are supported by grants from BIG (Erasmus MC), Medical-NeuroDelta and INTENSE (LSH-NWO).

Author information

Author notes

  1. These authors contributed equally: Claudia Kathe, Frédéric Michoud, Philipp Schönle.

  2. These authors jointly supervised this work: Stéphanie P. Lacour, Grégoire Courtine.

Affiliations

  1. Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, Swiss Federal Institute of Technology (EPFL), Geneva, Switzerland

    Claudia Kathe, Andreas Rowald, Jimmy Ravier, Leonie Asboth, Thomas H. Hutson, Jérôme Gandar, Quentin Barraud & Grégoire Courtine

  2. Defitech Center for Interventional Neurotherapies (NeuroRestore), University Hospital Lausanne (CHUV), University of Lausanne and EPFL, Lausanne, Switzerland

    Claudia Kathe, Andreas Rowald, Jimmy Ravier, Leonie Asboth, Thomas H. Hutson, Jérôme Gandar, Quentin Barraud & Grégoire Courtine

  3. Bertarelli Foundation Chair in Neuroprosthetic Technology, Laboratory for Soft Bioelectronic Interfaces, Institute of Microenginnering, Institute of Bioengineering, Centre for Neuroprosthetics, EPFL, Geneva, Switzerland

    Frédéric Michoud, Ivan Furfaro, Valentina Paggi, Kyungjin Kim, Noaf Alwahab & Stéphanie P. Lacour

  4. Integrated Systems Laboratory, Department of Information Technology and Electrical Engineering, Swiss Institute of Technology Zurich, Zurich, Switzerland

    Philipp Schönle, Noé Brun & Qiuting Huang

  5. Department of Neuroscience, Erasmus MC, Rotterdam, The Netherlands

    Sadaf Soloukey & Chris I. De Zeeuw

  6. Department of Neurosurgery, Erasmus MC, Rotterdam, The Netherlands

    Sadaf Soloukey

  7. Centre d’Imagerie Biomedicale, EPFL, Lausanne, Switzerland

    Ileana Jelescu

  8. Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland

    Antoine Philippides & Daniel Huber

  9. Netherlands Institute of Neuroscience, Royal Dutch Academy of Arts and Sciences, Amsterdam, The Netherlands

    Chris I. De Zeeuw

  10. Department of Neurosurgery, CHUV, Lausanne, Switzerland

    Grégoire Courtine

Contributions

C.K., F.M., P.S., G.C. and S.P.L. contributed equally to this work. F.M. developed and collaborated with V.P. and K.K. for the production of spinal implants. C.K., F.M., I.F., S.S. and T.H.H. carried out the experiments and analysis. P.S., N.B. and Q.H. developed the head-mounted wireless stimulation device. I.J. performed MRI. A.P. carried out the high-speed X-ray videography. A.R. and J.G. analyzed the imaging data. A.R. and N.A. conducted computational simulations. J.R. generated the figures and conceived the illustrations with contributions from C.K., F.M. and P.S. S.P.L. and G.C. conceived and supervised the study. G.C. and S.P.L. secured funding. G.C. wrote the paper with S.P.L., C.K., F.M. and P.S., and all the authors contributed to its editing.

Corresponding authors

Correspondence to
Stéphanie P. Lacour or Grégoire Courtine.

Ethics declarations

Competing interests

The authors declare competing interests: G.C. and S.P.L. are founders and shareholders of Onward medical, a company with partial relationship to the present work.

Additional information

Peer review information Nature Biotechnology thanks C. J. Heckman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Three-dimensional anatomical model of the mouse.

Step 1: The entire mouse was imaged in a computed tomography (CT) scanner. The resulting reconstruction of the entire skeleton of the mouse is shown. Step 2: We acquired high-speed X-ray videographies of a freely behaving mouse, which allowed to capture the extent of the changes in postures of the mouse during activities of daily living. Step 3: The reconstructed mouse skeleton is morphed onto selected X-ray images to quantify the bending radius of the lumbar spinal cord. Step 4: We adapted an antenna to enable magnetic resonance imaging of the lumbar spinal cord, including the visualisation of the posterior roots. We measured the epidural space in these images. Step 5: We transformed these imaging datasets into finite element models of the mouse lumbar spinal cord, including the vertebra, spinal cord and spinal roots. This computer model provides estimates of the suitable dimensions for an implant in the epidural space of the lumbar spinal cord of mice shown in red.

Extended Data Fig. 2 Fabrication process of the micro-LED array.

a, Main steps of the microfabrication of the micro-LED array: Step 1: Schematic illustration of the micro-LED array microfabrication process. A Ti/Au/Ti film is sputtered on a polyimide substrate and subsequently patterned by photolithography and wet etching. Next, the device interconnects are covered by a 2nd layer of polyimide, and the whole polyimide stack is patterned by photolithography and reactive ion etching (RIE). The preparation is covered with a thin layer of PDMS. The silicone superstrate is patterned to the device layout by photolitography and RIE, exposing the micro-LED integration sites. Then, micro-LEDs are precisely interfaced with the device interconnects. Finally, the micro-LED array is encapsulated with PDMS and released from the silicon carrier. Step 2: Photograph of the 4-inch wafer following the micro-LED array microfabrication process. Step 3: Colorized scanning electron micrograph (45° tilted view) of the micro-LED array surface, highlitghling the fine patterning of the PDMS superstrate and integration of the bare dies. Schematic cross-section of the device, showing three interconnects encapsulated in PDMS, top left inset. Step 4: Photograph of the optoelectronic device laminated on a fingertip. The array hosts 2 independent micro-LED channels, connected via serpentine interconnects that accommodate physiological motion. Step 5: Stress-strain curves for bulk PI, a fully functional implant and bulk PDMS measured under a displacement rate of 100μm/s. In the panel referring to the fully functional implant, the y-axis on the right (blue) also reports the relative resistance measured at the current input of 1 mA. The grey box highlights the strain range under in vivo conditions. b, Downconversion of light to desired wavelength. Step 1: Schematic illustration of the downconversion process using a phosphor-silicone matrix. Blue photons are converted to the desired wavelength (1), transmitted through the matrix (2) or back-scattered (3). Step 2: Photographs of the optoelectronic devices with the introduction of the phosphor-silicone matrix. The phosphor peak emission wavelengths are indicated below the corresponding photographs. Step 3: Optical characterization following downconversion of blue light. Emission spectra of the micro-LED arrays depending on their respective phosphor-silicone matrix implementation. Note the leakage of blue light at 𝜆 = 470 nm (left). Total optical power produced by one micro-LED channel covered with phosphor-silicone matrices with emission peaks at 𝜆 = 590 nm or 𝜆 = 620 nm. The respective optical power of the leaked blue light is depicted at 𝜆 = 470 nm. For reference, the optical power of bare blue LEDs is plotted. Step 4: Characterisation of different wavelength implants in Thy1-ChR2 and vGlut 2 ChrimsonR mice. Only 470 nm wavelength light results in muscle responses in Thy1-ChR2 mice. Only 590 nm and 650 nm wavelengths result in muscle responses in vGlut 2 ChrimsonR mice.

Source data

Extended Data Fig. 3 Ultraminiaturized, battery powered, head-mounted, wireless recording and stimulation platform.

Step 1: System overview: the implants (9 stainless steel wires for ground and 4 differential EMG recording channels, micro-LEDs connected by 3 copper wires) are connected by a 16 pin Omnetics connector to the PCB assembly of the wireless headstage platform. Step 2: Illustration of the antenna design as integrated on the PCB of the head-stage. Step 3: Head-stage wireless link reliability assessment in a typical laboratory/office environment based on received signal strength indication (RSSI) measurements versus line-of-sight distance. A sufficient link is maintained for over 10 m of distance. Step 4: Verification of the LED pulsed current driver performance: LED current can be controlled in 1 mA steps and is monitored on-chip for each issued pulse to verify micro-LED-implant condition and ensure experiment validity. Step 5: Tablet user interface for pulse(-train) stimulation and configuration ranges for all parameters. Step 6: Measurement of closed-loop performance: a EMG signal, simulated as a short sine-wave burst, the triggering of the software algorithm (SW trig), and the issued LED current pulse (I LED) have been acquired by an oscilloscope to measure the delay between the beginning of EMG activity and the closed-loop response. A delay of 11.3 ms is caused by the signal processing, which includes characteristics preventing spiking caused by noise (low-pass). A delay of 0.9 ms is caused by the construction of signal to drive the LED activation. Step 7: Tablet user interface for closed-loop experiments, consisting of an experiment parameter configuration interface and a live preview of all four acquired EMG traces.

Source data

Extended Data Fig. 4 Long-term biointegration of the micro-LED array.

Step 1: Stepwise surgical insertion of the micro-LED array into the epidural space. Micro-LED array overlaying a spinal cord surrogate, bottom right inset. Step 2: Post-mortem evaluation of foreign body responses within the dorsal horn of spinal segments located below the micro-LED array. Coronal spinal cord sections of mice implanted for 1, 4 or 6 weeks were stained against Iba1 and GFAP proteins, and compared with the spinal cord of non-implanted mice. Histogram plots report the fluorescent staining intensity quantified within the dorsal horns. The photograph shows the staining from a window within the dorsal horn, as shown in the scheme (n = 6 healthy mice, n=4 for each timepoint post-implantation one-way ANOVA, Iba1 p = 0.64, GFAP p = 0.36, mean±s.e.m.). Step 3: Schematic illustrating the post-mortem evaluation of spinal cord circularity. The bar graph reports the mean circularity (n=6 mice, one-way ANOVA, p = 0.95, mean±s.e.m.). Step 4: Photographs show examples of subcutaneous wires taken in mice that were implanted for 4 weeks.

Source data

Extended Data Fig. 5 The micro-LED array does not alter motor functions and behaviors.

a, Kinematic analysis of locomotor function reveals there are no gait deficits after micro-LED implantation. Step 1: The absence of influences on locomotor performance was evaluated using longitudinal recordings of whole-body kinematics during walking along a corridor. Markers are attached to the skin overlaying anatomical landmarks to record whole-body kinematics with the optoelectronic Vicon system. Step 2: Sequence of leg movements during walking reconstructed from the 3D coordinates of the markers. Step 3: The kinematics is morphed onto the anatomical model of the mouse and an envelope is added over the bony structure to obtain a realistic reconstruction of leg movements during walking. The resulting chronophotography-like sequences of locomotor movements illustrate the absence of differences between gait patterns recorded before and after the implantation. Step 4: A total of 78 parameters were calculated from kinematic recordings (Supplementary Table 1). Step 5: A principal component (PC) analysis was applied to the calculated parameters, and represented in the new space created by PC1 and PC2. Each dot represents the mean and SEM values of many mice (n > 5 mice) with averaged data from many gait cycles (n > 10 pre mouse). The bar plot reports the mean values of scores on PC1, which captures the more pronounced differences between gait cycles recorded at different time-points (n=6-9 mice/timepoint, one-way ANOVA, p= 0.16, mean±s.e.m.). Step 6: Color-coded representation of factor loadings with the highest level of correlation of PC1. Step 7: Functional clusters of highly correlated parameters were extracted from PC1 loadings representing gait parameters that describe differences between groups the most. Bar plots reporting mean values of calculated gait parameters with high correlations with PC1. This highly-sensitive statistical analysis failed to detect measurable changes in gait patterns following the implantation of the micro-LED array (n=6-9 mice, one-way ANOVAs, Stride length p= 0.42; Step height p=0.46; Amplitude of ankle elevation p=0.13; Amplitude of Ankle Speed p=0.13, mean±s.e.m.). b, Implantation of the mico-LED had no effect on exploratory behaviours or fine locomotor function. Step 1: The potential impact of the micro-LED array on exploratory behaviours was measured in the open field paradigm. Body trajectories measured during 20 min of home cage monitoring are shown before and after implantation of micro-LED array. The bar plot reports the quantification of distance covered during 20 min period of observation (n = 6, two-sided unpaired t-test, p = 0.58, mean±s.e.m.). Step 2: Mice were tested on the accelerating rotarod paradigm. The bar plot reports quantification of the time to fall off (n = 12 mice per timepoint, n=6 mice at week 4, one-way ANOVA, p = 0.44, mean±s.e.m.). Step 3: Mice walked along a horizontal ladder with unevenly spaced rungs. The bar plot reports quantification of the percentage of foot falls (n = 8 mice, one-way ANOVA, p = 0.08, mean±s.e.m.). Step 4: Mice climbed up a 1-meter long vertical rope. The bar plot reports quantification of the time to complete the climb (n = 5 mice, one-way ANOVA, p = 0.60, mean±s.e.m.).

Source data

Extended Data Fig. 6 Spatially targeted photostimulation elicits robust muscle responses.

Step 1: Timeline detailing key experiments. Step 2: Post-moterm verification of ChR2 expression. Photographs show the robust expession of ChR2 in the spinal cord and dorsal root ganglia of Thy1-ChR2 mice. ChR2 is expressed in proprioceptive neurons of the dorsal root ganglia, as illustrated with the overlap between ChR2-eYFP with parvalbumin mRNA (HCR). Step 3: Experimental set up to evaluate muscle responses and leg kinematics when delivering single pulses of photostimulation while the mouse is suspended in the air in a robotic body weight support system. Leg movements are reconstructed following one pulse of photostimulation delivered over the rostral versus caudal lumbar spinal cord. Step 4: Associated muscle responses in the iliopsoas and gastrocnemius medialis muscles are shown following one pulse of photostimulation delivered over the rostral versus caudal lumbar spinal cord. Step 5: The bar plot reports the mean range of motion at the knee and hip following photostimulation over the rostral and caudal lumbar spinal cord (n = 10 mice, two-sided unpaired t-test, p= 0.2445 and p = 0.3153 respectively, mean±s.e.m.). Note that the iliopsoas muscle is activated by photostimulation of rostral lumbar segments, as expected based on the anatomical position of the motoneurons innervating this muscle within these spinal segments and pre-motor interneurons, as well as by photostimulation of caudal lumbar segments through the activation of primary afferent fibres embedded in the rostral posterior roots that travel along the spinal cord, in the vicinity of the caudal LED channel. The different location at which photostimulation depolarizes afferent fibers is reflected in the increase in the latency of muscle responses with photostimulation over caudal versus rostral lumbar segments and the clear singluar wave. Step 6: The plots report the area under the curve (AUC) of rectified muscle responses of the illiopsoas and grastrocnemius following photostimulation of the rostral versus caudal lumbar spinal cord with increasing intensity (n = 5 mice, one-way ANOVA, , p < 0.0001, mean±s.e.m.).

Source data

Extended Data Fig 7 Conversion of emitted light to target any opsin.

a, Targeted photostimulation of vGlut2ON neurons in the spinal cord results in distinct hip versus ankle movements. Step 1: Timeline detailing key experiments. Step 2: Schematic illustration of the experimental procedures to target vGlut2ON spinal neurons with Chrimson expression. Step 3: Leg movements are reconstructed following one pulse of photostimulation delivered over the rostral versus caudal lumbar spinal cord. 620 nm photostimulation in vGlut2 mice, injected with an AAV flex ChrimsonR around motor pools of the iliopsoas and tibialis anterior, results in more specific muscle activation patterns. Rostral photostimulation maximises the hip flexor amplitude and caudal photostimulation maximises the foot flexor amplitude. Step 4: The bar plot reports the mean range of motion at the hip, knee and ankle following photostimulation over the rostral and caudal lumbar spinal cord (n = 10 mice, two-sided unpaired t-test, p = 0.0015; 0.0265; 0.0180 respectively, mean±s.e.m.). Step 5: Post-moterm verification of Chrimson expression. Photographs show the robust expession of Chrimson in the spinal cord in vGlut2 Cre mice around the injection sites, which was assessed with iDISCO. b, Two-colour optogentic activation and inhitition of vGlutON neurons in the spinal cord. Step 1: Timeline detailing key experiments. Step 2: Schematic illustration of the experimental procedures to target vGlut2ON spinal neurons with ChR2 and Jaws expression and the micro-LED array implanted over the injection sites. Step 3: A series of 78 gait parameters (see Supplementary Table 1) calculated from the kinematic recordings are submitted to a PC analysis. Each recorded gait pattern, depicted by each dot, are shown in the new denoised space defined by PC1 and PC2. Step 4: Factors loadings of individual parameters on PC1 (correlation between PC1 and each variable). ar. Step 5: Functional clusters of highly correlated parameters were extracted from PC1 loadings representing gait parameters that describe differences between groups the most. Parameteres relating to step height explained a large variance of the gait cycles, therefore indicating it best describes differences between groups. Step 6: Post-moterm verification of ChR2 and Jaws expression. Photographs show the robust expession of ChR2 and Jaws in the spinal cord in vGlut2 Cre mice at spinal level L3 (targeted with ChR2) and spinal level S1 (targeted with Jaws). Note: Fibers of vGlutON neurons project across many levels of the spinal cord and were therefore detected at both levels.

Extended Data Fig 8 Heat diffusion in the mouse spinal cord and its consequence for motor control.

a, Simulating heat diffusion in the spinal cord tissue. Step 1: Maximum temperature calculated with the Bioheat Transfer model in response to photostimulation protocols eliciting activation of ChR2 (25% duty cycle) or Jaws (95% duty cycle). Each plot has an adjusted scale allowing to view the maximum temperatures. Step 2: Temperature measurements of the LEDs in air with an infrared camera confirming maximum temperatures predicted by the model. b, Heat diffusion measure in vivo with a temperature probe. Step 1: Experimental setup. Step 2: Relationship between incremental duty cycles (every 5%) and the increase in temperature measured after 10 s and 30 s for two wavelenghts delivered over incremental photostimulation intensity (every 50 mW/mm2). Step 3: In vivo measurements of heat decay using a temperature probe inserted in the dorsal horn after 30 s of photostimulation with the settings necessary to target ChR2 (25% duty cycle, wavelength 470 nm, 2 LEDs, left) or Jaws (95% duty cycle, wavelength 620 nm, 2 LEDs, right). The plots report the changes in temperature over time for three levels of intensity. c, Heat affecting locomotion in wildtype mice. Step 1: Timeline detailing key experiments. Step 2: Experimental setup to record leg kinematics during stepping on a treadmill while photostimulation of increasing intensity is delivered over the spinal cord of a mice with a complete SCI. A serotonergic pharmacotherapy is adminstered prior to the experiment to reactivate the lumbar spinal cord below the injury. Step 3: A series of 78 gait parameters (see Supplementary Table 1) calculated from the kinematic recordings are submitted to a PC analysis. Each recorded gait pattern, depicted by each dot, are shown in the new denoised space defined by PC1 and PC2.

Extended Data Fig 9 Intersectional genetics to target specific pathways and probe their function.

a, Optogenetic silencing of the corticospinal tract in the lumbar spinal cord. Step 1: Timeline detailing key experiments. Step 2: Schematic illustration of the experimental procedures to target corticospinal tract neurons with synaptic projections to the lumbar spinal cord with Jaws. Step 3: Mice are walking freely when the photostimulation is suddenly turned on. Step 4: A series of 78 gait parameters (see Supplementary Table 1) calculated from the kinematic recordings are submitted to a PC analysis. Each dot represents the mean and SEM values of many gait cycles (n > 10 per mouse) are shown in the new denoised space defined by PC1 and PC2. Step 5: Factors loadings of individual parameters on PC1 (correlation between PC1 and each variable). Step 6: Functional clusters of highly correlated parameters were extracted from PC1 loadings representing gait parameters that describe differences between groups the most. Parameteres relating to paw dragging and balance explained a large variance of the gait cycles, therefore indicating it best describes differences between groups. Step 7: Post-mortem verification of Jaws expression. Photographs show the robust expession of Jaws in the lumbar spinal cord, as well as in neurons located in the injected region of the primary motor cortex. b, Optogenetic siliencing of the reticulospinal tract in the lumbar spinal cord. Step 1: Timeline detailing key experiments. Step 2: Schematic illustration of the experimental procedures to target reticulospinal tract neurons with synaptic projections to the lumbar spinal cord with Jaws. Step 3: Mice are walking freely when the photostimulation is suddenly turned on. Step 4: A series of 78 gait parameters (see Supplementary Table 1) calculated from the kinematic recordings are submitted to a PC analysis. Each dot represents the mean and SEM values of many gait cycles (n > 10 pre mouse) are shown in the new denoised space defined by PC1 and PC2. Step 5: Factors loadings of individual parameters on PC1 (correlation between PC1 and each variable). Step 6: Functional clusters of highly correlated parameters were extracted from PC1 loadings representing gait parameters that describe differences between groups the most. Parameteres relating to paw dragging and step height explained a large variance of the gait cycles, therefore indicating it best describes differences between groups. Step 7: Post-moterm verification of Jaws expression. Photographs show the robust expession of Jaws in the lumbar spinal cord, as well as in neurons located in the ventral gigantocellular nucleus.

Extended Data Fig 10 Intersectional genetics to target specific neurons in the spinal cord and dorsal root ganglia.

a, Manipulation of V2a interneurons during swimming. Step 1: Timeline detailing key experiments. Step 2: Schematic illustration of the experimental procedures to express ChrimsonR in V2a interneurons with targeted injection of virus in the lumbar spinal cord of Vsx2 Cre mice. Step 3: Post-mortem verification of Jaws or Chrimson expression. 3D images, including coronal sections showing the robust expession of Jaws or ChrimsonR in the lumbar spinal cord. b, Silencing PVON neurons in afferent fibers. Step 1: Timeline detailing key experiments. Step 2: Schematic illustration of the experimental procedures to express Jaws in PVON located in the dorsal root ganglia using targeted injection of virus in the sciatic nerve. Step 3: Experimental setup to record leg kinematics during stepping on a treadmill in mice with complete SCI. A serotonergic pharmacotherapy is adminstered prior to the experiment to reactivate the lumbar spinal cord below the injury. Step 4: A series of 78 gait parameters (see Supplementary Table 1) calculated from the kinematic recordings are submitted to a PC analysis. Each dot represents the mean and SEM values of many gait cycles (n > 10 pre mouse) are shown in the new denoised space defined by PC1 and PC2. Step 5: Factors loadings of individual parameters on PC1 (corelation between PC1 and each variable). Step 6: Functional clusters of highly correlated parameters were extracted from PC1 loadings representing gait parameters that describe differences between groups the most. Parameteres relating to step length and height explained a large variance of the gait cycles, therefore indicating it best describes differences between groups. Step 7: Post-mortem verification of Jaws expression. Photographs show the robust expession of Jaws in PVON located in the dorsal root ganglia and sciatic nerve.

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Kathe, C., Michoud, F., Schönle, P. et al. Wireless closed-loop optogenetics across the entire dorsoventral spinal cord in mice.
Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-01019-x

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