This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Wheeler, M. A. et al. Genetically targeted magnetic control of the nervous system. Nat. Neurosci. 19, 756–761 (2016).
Allen, C. B., Celikel, T. & Feldman, D. E. Long-term depression induced by sensory deprivation during cortical map plasticity in vivo. Nat. Neurosci. 6, 291–299 (2003).
Celikel, T., Szostak, V. A. & Feldman, D. E. Modulation of spike timing by sensory deprivation during induction of cortical map plasticity. Nat. Neurosci. 7, 534–541 (2004).
Clem, R. L., Celikel, T. & Barth, A. L. Ongoing in vivo experience triggers synaptic metaplasticity in the neocortex. Science 319, 101–104 (2008).
Kole, K. et al. Transcriptional mapping of the primary somatosensory cortex upon sensory deprivation. Gigascience 6, 1–6 (2017).
Kole, K. et al. Proteomic landscape of the primary somatosensory cortex upon sensory deprivation. Gigascience 6, 1–10 (2017).
Xu, F.-X. et al. Magneto is ineffective in controlling electrical properties of cerebellar Purkinje cells. Nat. Neurosci. https://doi.org/10.1038/s41593-019-0475-3 (2019).
Celikel, T. et al. Select overexpression of homer1a in dorsal hippocampus impairs spatial working memory. Front. Neurosci. 1, 97–110 (2007).
Wang, G., et al. Revaluation of magnetic properties of Magneto. Nat. Neurosci. https://doi.org/10.1038/s41593-019-0473-5 (2019).
Freudenberg, F. et al. GluA1 and its PDZ-interaction: a role in experience-dependent behavioral plasticity in the forced swim test. Neurobiol. Dis. 52, 160–167 (2013).
Freudenberg, F., Marx, V., Seeburg, P. H., Sprengel, R. & Celikel, T. Circuit mechanisms of GluA1-dependent spatial working memory. Hippocampus 23, 1359–1366 (2013).
Voigts, J., Siegle, J. H., Pritchett, D. L. & Moore, C. I. The flexDrive: an ultra-light implant for optical control and highly parallel chronic recording of neuronal ensembles in freely moving mice. Front. Syst. Neurosci. 7, 8 (2013).
Allen, C. B., Celikel, T. & Feldman, D. E. Long-term depression induced by sensory deprivation during cortical map plasticity in vivo. Nat. Neurosci. 6, 291–299 (2003).
Siegle, J. H. et al. Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology. J. Neural Eng. 14, 045003 (2017).
Clem, R. L., Celikel, T. & Barth, A. L. Ongoing in vivo experience triggers synaptic metaplasticity in the neocortex. Science 319, 101–104 (2008).
Brouns, T. & Celikel, T. PASER for automated analysis of neural signals recorded in pulsating magnetic fields. preprint at bioRxiv https://doi.org/10.1101/739409 (2019).
Jun, J. J. et al. Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/101030v2 (2017).
Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M. & Harris, K. D. Fast and accurate spike sorting of high-channel count probes with KiloSort. Adv. Neural Inf. Process. Syst. 30, 4448–4456 (2016).
Yger, P. et al. Fast and accurate spike sorting in vitro and in vivo for up to thousands of electrodes. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/067843v1 (2016).
Dolan, K., Martens, H. C., Schuurman, P. R. & Bour, L. J. Automatic noise-level detection for extra-cellular micro-electrode recordings. Med. Biol. Eng. Comput. 47, 791–800 (2009).
Shan, K. Q., Lubenov, E. V. & Siapas, A. G. Model-based spike sorting with a mixture of drifting t-distributions. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/109850v1 (2017).
Aertsen, A. M., Gerstein, G. L., Habib, M. K. & Palm, G. Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J. Neurophysiol. 61, 900–917 (1989).
Bakštein, E. et al. Methods for automatic detection of artifacts in microelectrode recordings. J. Neurosci. Methods 290, 39–51 (2017).
Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011).
Acknowledgements
We thank the members of the Department of Neurophysiology for stimulating discussions and critical insight on the manuscript. This work was supported by grants from the European Commission (Horizon2020, 660328), European Regional Development Fund (MIND, 122035) and the Netherlands Organisation for Scientific Research (NWO-ALW Open Competition, 824.14.022) to T.C., as well as doctoral fellowships from the Chinese Scholarship Council to Y.Z. and X.Y., and the National Council for Scientific and Technological Development of Brazil (CNPQ) to A.S.L.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
Supplementary Figure 1 Change in magnetic field strength as a function of distance from the permanent magnet.
Measurements were made using a HT-20 Gaussmeter (Hangzhou BST Magnet Co. Ltd., China) and repeated three times at each distance. The error bars are standard deviation. The distance between the target cells and magnet was ~200 ± 100 𝜇m in in vitro and ~8 ± 2 mm in in vivo experiments.
Supplementary Figure 2 Magneto2.0 is cleaved from mCherry but remains primarily in the cytoplasm.
(a) To confirm that Magneto was effectively released from its fusion with mCherry, N2A cells were transfected with 1: pFUGW-V4trunc-fer-traffick-p2A-mCherry, 2: pcDNA3-Magneto-p2A-mCherry, 3: pcDNA3-flag-TRPV4 and 4: pcDNA3 plasmids. TRPV4 and empty pcDNA3 served as controls; tubulin was used as a loading control. Magneto and TRPV4-expression were analyzed by Western blot using anti-flag and anti-mCherry antibodies. The major protein products identified by the anti-flag antibody were the ~135 kDa Magneto protein and the ~95-kDa flag-TRPV4 protein. The ~160-kDa Magneto-p2A-mCherry product was recognized by both anti-flag and anti-mCherry antibodies. However, the principal protein detected by the anti-mCherry antibody was mCherry. These findings indicate that Magneto was effectively cleaved from its fusion with mCherry. Note that both pFUGW-V4trunc-fer-traffick-p2A-cherry and pcDNA3-Magneto-p2A-mCherry produce the same protein products, indicating that sub-cloning of Magneto into the viral vector pFUGW did not affect the Open Reading Frame of Magneto-p2A-mCherry. (b) I: N2a cells were transfected with pcDNA3-flag-TRPV4 and immunostained using anti-flag antibodies. Note that flag-TRPV4 was observed in the plasma membrane. II–IV: N2a cells were transfected with Magneto-p2A-mCherry plasmid and stained using anti-flag antibodies (II, IV, green). mCherry fluorescence was directly imaged (II, red), DAPI (blue) was used to stain the nuclei (II–III). Note that mCherry was mostly localized to the nucleus, however, also a significant amount was present in the cytoplasm. In contrast to flag-TRPV4, most Magneto expression was found in reticular structures in the cytoplasm, most likely representing the ER (IV). These results indicate that, compared to flag-TRPV4, Magneto is less effectively transported to the plasma membrane of N2a cells. Scale bar, 10 µm.
Supplementary Figure 3
The Western Blot shown in Supplmentary Figure. 2a at different exposures.
Supplementary Figure 4 Isolation quality of single units.
(a) Concatenated spike shapes across the tetrode channels visualized as a density heatmap of time-voltage values. (b) The peak-to-peak amplitude (top) and firing rate (bottom) over the whole recording period in a session, showing the temporal stability of the cluster. (c) Histogram of normalized spike amplitudes. Any amplitude to the left of the dashed vertical line crosses the minimum threshold for spike detection, which is three times the average background signal (voltage). (d) Distribution of running average spike counts in 10 sec bins (with 5 sec overlap). The red line indicates the predicted distribution of spike counts by a Gaussian distribution given the observed variance in spike count. (e) Cross-correlation between two tetrode channels that are maximally dissimilar. The large peak at zero lag indicates that the spike waveforms are temporally aligned. (f) Autocorrelation of spike events.
Supplementary Figure 5 Magnetic stimulation does not alter the temporal correlations of action potentials.
(a) A representative example of spiking correlations between two simultaneously recorded neurons from the same tetrode. Joint peristimulus time histograms represent the temporal correlations across the binary states of magnetic stimulation. (b) Spiking pattern in single units (auto-correlations; left) and across simultaneously recorded units (cross-correlations; right). Gray traces: Correlation in the absence of magnetic stimulation (magnet off), red: during magnetic stimulation (magnet on), blue: the pairwise difference between magnet off–magnet on. Thick traces are population averages; color-coded shadows in the background represent the standard deviation within stimulation condition. Neither single cell spiking correlations (N= 353, P = 0.98, paired t-test), nor spiking correlations across neurons (N= 939, P = 1.00, paired t-test) were altered upon magnetic stimulation. See Supplmentary Figure. 5 for Poincaré analysis of spiking in single neurons across stimulus conditions.
Supplementary Figure 6 Spiking pattern does not change during magnetic stimulation in vivo.
(a) Poincaré plot of interspike intervals (ISI) from a representative neuron under control conditions (magnet off) and during magnetic stimulation (magnet on). Corresponding ISI histograms (bin size = 0.5 ms) are shown on the left and bottom of each plot. (b) Post-stimulus time histograms of a representative neuron for the magnet on and off conditions. (c) Mean normalized ISI Poincaré plot across all neurons (N = 235). Neurons that fired <50 spikes during the period of observation were excluded from the plot (N = 118).
Supplementary information
Rights and permissions
About this article
Cite this article
Kole, K., Zhang, Y., Jansen, E.J.R. et al. Assessing the utility of Magneto to control neuronal excitability in the somatosensory cortex. Nat Neurosci 23, 1044–1046 (2020). https://doi.org/10.1038/s41593-019-0474-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-019-0474-4
This article is cited by
-
Subsecond multichannel magnetic control of select neural circuits in freely moving flies
Nature Materials (2022)
-
Modulating cell signalling in vivo with magnetic nanotransducers
Nature Reviews Methods Primers (2022)
-
Cortical Representation of Touch in Silico
Neuroinformatics (2022)
-
Non-contact long-range magnetic stimulation of mechanosensitive ion channels in freely moving animals
Nature Materials (2021)
-
Uncovering a possible role of reactive oxygen species in magnetogenetics
Scientific Reports (2020)