Perineuronal nets stabilize the grid cell network
Open accessPublished version of a preprint
Abstract
Grid cells are part of a widespread network which supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. It has been suggested that PNNs stabilize synaptic connections and long-term memories, but their role in the grid cell network remains elusive. We show that removal of PNNs leads to lower inhibitory spiking activity, and reduces grid cells' ability to create stable representations of a novel environment. Furthermore, in animals with disrupted PNNs, exposure to a novel arena corrupted the spatiotemporal relationships within grid cell modules, and the stored representations of a familiar arena. Finally, we show that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons. Together this work suggests that PNNs provide a key stabilizing element for the grid cell network.
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In V1 visual cortex, ChABC decreases both excitatory and inhibitory inputs to PV interneurons.
“and in V1 visual cortex, ChABC decreases both excitatory and inhibitory inputs to PV interneurons [46, 47].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
In Results, “Degradation of PNNs in MEC” (page unavailable), the paper reports that chABC reduced inhibitory VGAT-positive inputs onto PV+ cells in the medial entorhinal cortex. It does not establish the claimed effect in V1, and it found no significant reduction in excitatory VGLUT1- or VGLUT2-positive inputs. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBoth hippocampus and perirhinal cortex are rich in PV interneurons enwrapped with PNNs, and PNNs are also present on some hippocampal pyramidal neurons.
“Both hippocampus and perirhinal cortex are rich in PV interneurons enwrapped with PNNs, and PNNs are also present on some hippocampal pyramidal neurons [46, 101, 102].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
The Introduction says PNNs condense around maturing PV+ cells, and the Discussion reports PNNs co-localized with other cell types in hippocampal CA2; its cited reference names CA2 pyramidal neurons. The paper does not establish that the hippocampus and perirhinal cortex are rich in PNN-enwrapped PV interneurons, and it does not discuss the perirhinal cortex. Page numbers were unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAll social memory associated regions are highly populated with PNN-surrounded PV neurons.
“All the social memory associated regions are highly populated with PNN-surrounded PV neurons [46, 66, 130, 131].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
The Results (Degradation of PNNs in MEC, Fig. 1a) report dense PNN expression overlapping with PV-positive neurons in the medial entorhinal cortex, with strong staining also in the parasubiculum. The paper does not identify all social-memory-associated regions or show that they are all highly populated with PNN-surrounded PV neurons; the same figure describes weaker staining in two other regions. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn the entorhinal cortex, ChABC reduces inhibitory inputs.
“while in the entorhinal cortex, ChABC reduces inhibitory inputs”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
Following PNN removal in entorhinal cortex there are distorted spatial representations in downstream hippocampal neurons.
“Following PNN removal in entorhinal cortex there are distorted spatial representations in downstream hippocampal neurons (Fig. 2C) [46].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
Heightened network plasticity caused by PNN removal interferes with stored spatial representations and perhaps memories.
“This suggests that PNNs contribute to ensure a rigid grid cell network, which is essential for new representations to form, and that the heightened network plasticity caused by PNN removal interferes with stored spatial representations and perhaps memories.”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
Degradation of PNNs in medial entorhinal cortex destabilizes the grid cell networks leading to impaired representations of new environments.
“However, degradation of PNNs in medial entorhinal cortex, where PV positive neurons are enwrapped in particularly dense PNNs, destabilizes the grid cell networks leading to impaired representations of new environments [46].”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
PNNs contribute to ensuring a rigid grid cell network that is essential for new representations to form.
“This suggests that PNNs contribute to ensure a rigid grid cell network, which is essential for new representations to form, and that the heightened network plasticity caused by PNN removal interferes with stored spatial representations and perhaps memories.”
From The extracellular matrix and perineuronal nets in memory (2022) · cites this paper as [46]
The higher performance in prokaryotes is consistent with previous observations regarding the availability of evolutionary information in prokaryotes compared to Eukarya.
“The higher performance in prokaryotes is consistent with previous observations regarding the availability of evolutionary information in prokaryotes compared to Eukarya27 (Supplementary Fig. 1).”
From Improved prediction of protein-protein interactions using AlphaFold2 (2021) · cites this paper as [27]
The Results section on PNN removal and the Discussion address grid-cell activity in rats, not evolutionary information or performance comparisons between prokaryotes and Eukarya. Searches for those terms and the claim’s distinctive phrase found no passage addressing the claim in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredAnother recently published method obtains an AUC of 0.76 on this set.
“Another recently published method obtains AUC 0.76 on this set27.”
From Improved prediction of protein-protein interactions using AlphaFold2 (2021) · cites this paper as [27]
The Results section reports 0.76 as a median spatial correlation for control grid cells, not as an AUC. A search for “AUC 0.76” and “area under the curve” found no passage reporting an AUC for a method on the cited set; the paper’s reported analyses concern grid-cell activity. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe test set used 1661 protein complexes with known interfaces from a recent study.
“We used 1661 protein complexes with known interfaces from a recent study27 to test the developed pipeline.”
From Improved prediction of protein-protein interactions using AlphaFold2 (2021) · cites this paper as [27]
The Results section on grid cell representations in familiar environments reports recorded neuronal units, not protein complexes. Searches for “1661 protein complexes with known interfaces” and “protein complexes interface pipeline test set” found no passage in our copy addressing the claimed test set. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe study of AF2's ability to separate interacting and non-interacting proteins here contains more extensive data than recent studies.
“The study of AF2s ability to separate interacting and non-interacting proteins here contains more extensive data than recent studies27.”
From Improved prediction of protein-protein interactions using AlphaFold2 (2021) · cites this paper as [27]
The Results section, “Reduced pairwise grid cell stability in a novel environment” (page unavailable), discusses correlations between rat grid cells after perineuronal-net degradation. Searches for AF2 and for interacting versus non-interacting proteins found no passage addressing that topic or comparing the extent of data with recent studies. (LLM, conf=0.78)
Experimental — accuracy not yet measuredPairing the correct sequences should result in MSAs containing inter-chain co-evolutionary information.
“Pairing the correct sequences should result in MSAs containing inter-chain co-evolutionary information27.”
From Improved prediction of protein-protein interactions using AlphaFold2 (2021) · cites this paper as [27]
The Methods section on spatiotemporal pairwise cross-correlations and the Results section on grid-cell stability concern correlations between neuronal firing patterns, not paired sequences. Searches for MSAs, inter-chain co-evolutionary information, and protein-sequence pairing found no passage addressing the claim; page numbers were not available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredPLCG2 polymorphisms are associated with both reduced and increased risk of Alzheimer's disease and with longevity.
“PLCG2 polymorphisms are associated with both reduced and increased risk of Alzheimer’s disease (AD) and with longevity.”
From Multi-omics analyses reveal novel effects of PLCγ2 deficiency in the mouse brain (2023) · cites this paper as [7]
The Results sections on PNN degradation and hippocampal place coding (page unavailable) concern grid-cell activity in rats, not human genetic associations. Searches for PLCG2 polymorphisms, Alzheimer’s disease risk, and longevity found no passage addressing the claim in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Cites
Microstructure of a spatial map in the entorhinal cortex
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., & Moser, E. I. (2005) · Nature · doi:10.1038/nature03721 · Cited by 4,068 · see citing works
Self-citationMechanisms of Gamma Oscillations
Buzsáki, G., & Wang, X.-J. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150444 · Cited by 2,796 · see citing works
Critical period plasticity in local cortical circuits
Hensch, T. K. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1787 · Cited by 2,323 · see citing works
Path integration and the neural basis of the 'cognitive map'
McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I., & Moser, M.-B. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1932 · Cited by 2,024 · see citing works
Reactivation of Ocular Dominance Plasticity in the Adult Visual Cortex
Pizzorusso, T., Medini, P., Berardi, N., Chierzi, S., Fawcett, J. W., & Maffei, L. (2002) · Science · doi:10.1126/science.1072699 · Cited by 1,679 · see citing works
The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells
Muller, R., & Kubie, J. (1987) · The Journal of Neuroscience · doi:10.1523/jneurosci.07-07-01951.1987 · Cited by 1,581 · see citing works
Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex
Sargolini, F., Fyhn, M., Hafting, T., McNaughton, B. L., Witter, M. P., Moser, M.-B., & Moser, E. I. (2006) · Science · doi:10.1126/science.1125572 · Cited by 1,485 · see citing works
Self-citationThree groups of interneurons account for nearly 100% of neocortical GABAergic neurons
Rudy, B., Fishell, G., Lee, S., & Hjerling‐Leffler, J. (2011) · Developmental Neurobiology · doi:10.1002/dneu.20853 · Cited by 1,420 · see citing works
Spatial representation in the entorhinal cortex
Fyhn, M., Molden, S., Witter, M. P., Moser, E. I., & Moser, M.-B. (2004) · Science · doi:10.1126/science.1099901 · Cited by 1,339 · see citing works
Self-citationIntracellular Features Predicted by Extracellular Recordings in the Hippocampus In Vivo
Henze, D. A., Borhegyi, Z., Csicsvari, J., Mamiya, A., Harris, K. D., & Buzsáki, G. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.1.390 · Cited by 942 · see citing works
Independent Codes for Spatial and Episodic Memory in Hippocampal Neuronal Ensembles
Leutgeb, S., Leutgeb, J. K., Barnes, C. A., Moser, E. I., McNaughton, B. L., & Moser, M. (2005) · Science · doi:10.1126/science.1114037 · Cited by 911 · see citing works
Barthó, P., Hirase, H., Monconduit, L., Zugaro, M. B., Harris, K. D., & Buzsáki, G. (2004) · Journal of Neurophysiology · doi:10.1152/jn.01170.2003 · Cited by 888 · see citing works
Droettboom, M., Caswell, T. A., Hunter, J., Firing, E., Nielsen, J. H., Varoquaux, N., Root, B., De Andrade, E. S., Elson, P., Dale, D., et al. (2014) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.592536 · Cited by 861 · see citing works
Accurate Path Integration in Continuous Attractor Network Models of Grid Cells
Burak, Y., & Fiete, I. R. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000291 · Cited by 779 · see citing works
Mapping of a non-spatial dimension by the hippocampal-entorhinal circuit
Aronov, D., Nevers, R., & Tank, D. W. (2017) · Nature · doi:10.1038/nature21692 · Cited by 773 · see citing works
Perineuronal Nets Protect Fear Memories from Erasure
Gogolla, N., Caroni, P., Lüthi, A., & Herry, C. (2009) · Science · doi:10.1126/science.1174146 · Cited by 760 · see citing works
Hippocampal remapping and grid realignment in entorhinal cortex
Fyhn, M., Hafting, T., Treves, A., Moser, M.-B., & Moser, E. I. (2007) · Nature · doi:10.1038/nature05601 · Cited by 749 · see citing works
Self-citationParvalbumin-expressing basket-cell network plasticity induced by experience regulates adult learning
Donato, F., Rompani, S. B., & Caroni, P. (2013) · Nature · doi:10.1038/nature12866 · Cited by 703 · see citing works
The entorhinal grid map is discretized
Stensola, H., Stensola, T., Solstad, T., Frøland, K., Moser, M., & Moser, E. I. (2012) · Nature · doi:10.1038/nature11649 · Cited by 702 · see citing works
Speed cells in the medial entorhinal cortex
Kropff, E., Carmichael, J. E., Moser, M.-B., & Moser, E. I. (2015) · Nature · doi:10.1038/nature14622 · Cited by 668 · see citing works
Development of the Spatial Representation System in the Rat
Langston, R. F., Ainge, J. A., Couey, J. J., Canto, C. B., Bjerknes, T. L., Witter, M. P., Moser, E. I., & Moser, M. (2010) · Science · doi:10.1126/science.1188210 · Cited by 667 · see citing works
Development of the Hippocampal Cognitive Map in Preweanling Rats
Wills, T. J., Cacucci, F., Burgess, N., & O'Keefe, J. (2010) · Science · doi:10.1126/science.1188224 · Cited by 616 · see citing works
Relating Hippocampal Circuitry to Function
Lisman, J. E. (1999) · Neuron · doi:10.1016/s0896-6273(00)81085-5 · Cited by 607 · see citing works
Extracellular matrix molecules and synaptic plasticity
Dityatev, A., & Schachner, M. (2003) · Nature Reviews Neuroscience · doi:10.1038/nrn1115 · Cited by 564 · see citing works
Brain extracellular matrix affects AMPA receptor lateral mobility and short-term synaptic plasticity
Frischknecht, R., Heine, M., Perrais, D., Seidenbecher, C. I., Choquet, D., & Gundelfinger, E. D. (2009) · Nature Neuroscience · doi:10.1038/nn.2338 · Cited by 554 · see citing works
Whishaw, I. Q., & Vanderwolf, C. H. (1973) · Behavioral Biology · doi:10.1016/s0091-6773(73)80041-0 · Cited by 508 · see citing works
Environmental enrichment, new neurons and the neurobiology of individuality
Kempermann, G. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0120-x · Cited by 479 · see citing works
From grid cells to place cells: A mathematical model
Solstad, T., Moser, E. I., & Einevoll, G. T. (2006) · Hippocampus · doi:10.1002/hipo.20244 · Cited by 477 · see citing works
Hippocampus-independent phase precession in entorhinal grid cells
Hafting, T., Fyhn, M., Bonnevie, T., Moser, M., & Moser, E. I. (2008) · Nature · doi:10.1038/nature06957 · Cited by 471 · see citing works
Self-citationRecurrent inhibitory circuitry as a mechanism for grid formation
Couey, J. J., Witoelar, A., Zhang, S., Zheng, K., Ye, J., Dunn, B. A., Czajkowski, R., Moser, M., Moser, E. I., Roudi, Y., et al. (2013) · Nature Neuroscience · doi:10.1038/nn.3310 · Cited by 413 · see citing works
Favuzzi, E., Marques-Smith, A., Deogracias, R., Winterflood, C. M., Sánchez-Aguilera, A., Mantoan, L., Maeso, P., Fernandes, C., Ewers, H., & Rico, B. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.028 · Cited by 397 · see citing works
The perineuronal net and the control of CNS plasticity
Wang, D., & Fawcett, J. (2012) · Cell and Tissue Research · doi:10.1007/s00441-012-1375-y · Cited by 373 · see citing works
Specific evidence of low-dimensional continuous attractor dynamics in grid cells
Yoon, K., Buice, M. A., Barry, C., Hayman, R., Burgess, N., & Fiete, I. R. (2013) · Nature Neuroscience · doi:10.1038/nn.3450 · Cited by 296 · see citing works
Lensjø, K. K., Lepperød, M. E., Dick, G., Hafting, T., & Fyhn, M. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.2504-16.2016 · Cited by 281 · see citing works
Self-citationFeedback Inhibition Enables Theta-Nested Gamma Oscillations and Grid Firing Fields
Pastoll, H., Solanka, L., van Rossum, M. C. W., & Nolan, M. F. (2013) · Neuron · doi:10.1016/j.neuron.2012.11.032 · Cited by 278 · see citing works
Cellular mechanisms of spatial navigation in the medial entorhinal cortex
Schmidt‐Hieber, C., & Häusser, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3340 · Cited by 273 · see citing works
Perineuronal Nets Suppress Plasticity of Excitatory Synapses on CA2 Pyramidal Neurons
Carstens, K. E., Phillips, M. L., Pozzo-Miller, L., Weinberg, R. J., & Dudek, S. M. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.0245-16.2016 · Cited by 271 · see citing works
The emergence of grid cells: Intelligent design or just adaptation?
Kropff, E., & Treves, A. (2008) · Hippocampus · doi:10.1002/hipo.20520 · Cited by 254 · see citing works
Perineuronal Nets Enhance the Excitability of Fast-Spiking Neurons
Balmer, T. S. (2016) · eneuro · doi:10.1523/eneuro.0112-16.2016 · Cited by 220 · see citing works
Grid cell firing patterns signal environmental novelty by expansion
Barry, C., Ginzberg, L. L., O'Keefe, J. M., & Burgess, N. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209918109 · Cited by 219 · see citing works
Tewari, B. P., Chaunsali, L., Campbell, S. L., Patel, D. C., Goode, A. E., & Sontheimer, H. (2018) · Nature Communications · doi:10.1038/s41467-018-07113-0 · Cited by 208 · see citing works
Hales, J. B., Schlesiger, M. I., Leutgeb, J. K., Squire, L. R., Leutgeb, S., & Clark, R. E. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.10.009 · Cited by 204 · see citing works
Aggrecan Directs Extracellular Matrix-Mediated Neuronal Plasticity
Rowlands, D., Lensjø, K. K., Dinh, T., Yang, S., Andrews, M. R., Hafting, T., Fyhn, M., Fawcett, J. W., & Dick, G. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1122-18.2018 · Cited by 204 · see citing works
Self-citationXia, F., Richards, B. A., Tran, M. M., Josselyn, S. A., Takehara‐Nishiuchi, K., & Frankland, P. W. (2017) · eLife · doi:10.7554/elife.27868 · Cited by 202 · see citing works
Computational Modeling Methods for Neuroscientists
Destexhe, A., & Huguenard, J. R. (2009) · The MIT Press eBooks · doi:10.7551/mitpress/9780262013277.001.0001 · Cited by 190 · see citing works
Behavior-Dependent Coordination of Multiple Theta Dipoles in the Hippocampus
Montgomery, S. M., Betancur, M. I., & Buzsáki, G. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.4339-08.2009 · Cited by 190 · see citing works
Environmental novelty is signaled by reduction of the hippocampal theta frequency
Jeewajee, A., Lever, C., Burton, S., O'Keefe, J. M., & Burgess, N. (2007) · Hippocampus · doi:10.1002/hipo.20394 · Cited by 189 · see citing works
Local and Distant Input Controlling Excitation in Layer II of the Medial Entorhinal Cortex
Fuchs, E. C., Neitz, A., Pinna, R., Melzer, S., Caputi, A., & Monyer, H. (2015) · Neuron · doi:10.1016/j.neuron.2015.11.029 · Cited by 179 · see citing works
Happel, M. F. K., Niekisch, H., Castiblanco Rivera, L. L., Ohl, F. W., Deliano, M., & Frischknecht, R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310272111 · Cited by 176 · see citing works
Dordek, Y., Soudry, D., Meir, R., & Derdikman, D. (2016) · eLife · doi:10.7554/elife.10094 · Cited by 175 · see citing works
Impaired path integration in mice with disrupted grid cell firing
Gil, M., Ancau, M., Schlesiger, M. I., Neitz, A., Allen, K., De Marco, R. J., & Monyer, H. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0039-3 · Cited by 174 · see citing works
Connecting multiple spatial scales to decode the population activity of grid cells
Stemmler, M., Mathis, A., & Herz, A. V. M. (2015) · Science Advances · doi:10.1126/science.1500816 · Cited by 170 · see citing works
Lin, Y.-T., Hsieh, T.-Y., Tsai, T.-C., Chen, C.-C., Huang, C.-C., & Hsu, K.-S. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1896-17.2017 · Cited by 165 · see citing works
Semaphorin 3A Binds to the Perineuronal Nets via Chondroitin Sulfate Type E Motifs in Rodent Brains
Dick, G., Tan, C. L., Alves, J. N., Ehlert, E. M. E., Miller, G. M., Hsieh-Wilson, L. C., Sugahara, K., Oosterhof, A., van Kuppevelt, T. H., Verhaagen, J., et al. (2013) · Journal of Biological Chemistry · doi:10.1074/jbc.m111.310029 · Cited by 160 · see citing works
Buetfering, C., Allen, K., & Monyer, H. (2014) · Nature Neuroscience · doi:10.1038/nn.3696 · Cited by 160 · see citing works
Representation of Geometric Borders in the Developing Rat
Bjerknes, T. L., Moser, E. I., & Moser, M. (2014) · Neuron · doi:10.1016/j.neuron.2014.02.014 · Cited by 156 · see citing works
de Vivo, L., Landi, S., Panniello, M., Baroncelli, L., Chierzi, S., Mariotti, L., Spolidoro, M., Pizzorusso, T., Maffei, L., & Ratto, G. M. (2013) · Nature Communications · doi:10.1038/ncomms2491 · Cited by 148 · see citing works
Brandon, M. P., Koenig, J., Leutgeb, J. K., & Leutgeb, S. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.013 · Cited by 145 · see citing works
Cohen, J. D., Bolstad, M., & Lee, A. K. (2017) · eLife · doi:10.7554/elife.23040 · Cited by 144 · see citing works
Correlation structure of grid cells is preserved during sleep
Gardner, R. J., Lu, L., Wernle, T., Moser, M., & Moser, E. I. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0360-0 · Cited by 134 · see citing works
Lensjø, K. K., Christensen, A. C., Tennøe, S., Fyhn, M., & Hafting, T. (2017) · eneuro · doi:10.1523/eneuro.0379-16.2017 · Cited by 133 · see citing works
Self-citationMiao, C., Cao, Q., Moser, M., & Moser, E. I. (2017) · Cell · doi:10.1016/j.cell.2017.08.050 · Cited by 129 · see citing works
Wouterlood, F. G., H�rtig, W., Br�ckner, G., & Witter, M. P. (1995) · Journal of Neurocytology · doi:10.1007/bf01181556 · Cited by 125 · see citing works
Trettel, S. G., Trimper, J. B., Hwaun, E., Fiete, I. R., & Colgin, L. L. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0359-6 · Cited by 108 · see citing works
Kanter, B. R., Lykken, C. M., Avesar, D., Weible, A. P., Dickinson, J. R., Dunn, B. A., Borgesius, N. Z., Roudi, Y., & Kentros, C. G. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.001 · Cited by 100 · see citing works
Functional properties of stellate cells in medial entorhinal cortex layer II
Rowland, D. C., Obenhaus, H. A., Skytøen, E. R., Zhang, Q., Kentros, C. G., Moser, E. I., & Moser, M. (2018) · eLife · doi:10.7554/elife.36664 · Cited by 90 · see citing works
Differential effects of ongoing EEG beta and theta power on memory formation
Scholz, S., Schneider, S. L., & Rose, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171913 · Cited by 78 · see citing works
Grid Cells Encode Local Positional Information
Ismakov, R., Barak, O., Jeffery, K. J., & Derdikman, D. (2017) · Current Biology · doi:10.1016/j.cub.2017.06.034 · Cited by 68 · see citing works
Perineuronal nets control visual input via thalamic recruitment of cortical PV interneurons
Faini, G., Aguirre, A., Landi, S., Lamers, D., Pizzorusso, T., Ratto, G. M., Deleuze, C., & Bacci, A. (2018) · eLife · doi:10.7554/elife.41520 · Cited by 64 · see citing works
Grid alignment in entorhinal cortex
Si, B., Kropff, E., & Treves, A. (2012) · Biological Cybernetics · doi:10.1007/s00422-012-0513-7 · Cited by 58 · see citing works
Recurrent circuits within medial entorhinal cortex superficial layers support grid cell firing
Zutshi, I., Fu, M. L., Lilascharoen, V., Leutgeb, J. K., Lim, B. K., & Leutgeb, S. (2018) · Nature Communications · doi:10.1038/s41467-018-06104-5 · Cited by 57 · see citing works
Grid scale drives the scale and long-term stability of place maps
Mallory, C. S., Hardcastle, K., Bant, J. S., & Giocomo, L. M. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0055-3 · Cited by 46 · see citing works
Long-Term Plasticity Is Proportional to Theta-Activity
Tsanov, M., & Manahan‐Vaughan, D. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005850 · Cited by 40 · see citing works
Kunkel, S., Morrison, A., Weidel, P., Eppler, J. M., Sinha, A., Schenck, W., Schmidt, M., Vennemo, S. B., Jordan, J., Peyser, A., et al. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.259534 · Cited by 24 · see citing works
Self-citationCarretero (1998) · doi:10.1175/1520-0477(1998)079
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Chklovski, A., Parks, D. H., Woodcroft, B. J., & Tyson, G. W. (2023) · Nature Methods · doi:10.1038/s41592-023-01940-w · Cited by 1,705 · see citing works
Improved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works
SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway
Willett, B. J., Grove, J., MacLean, O. A., Wilkie, C., De Lorenzo, G., Furnon, W., Cantoni, D., Scott, S., Logan, N., Ashraf, S., et al. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01143-7 · Cited by 785 · see citing works
Li, B., Deng, A., Li, K., Hu, Y., Li, Z., Shi, Y., Xiong, Q., Liu, Z., Guo, Q., Zou, L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28089-y · Cited by 566 · see citing works
Sampling alternative conformational states of transporters and receptors with AlphaFold2
del Alamo, D., Sala, D., Mchaourab, H. S., & Meiler, J. (2022) · eLife · doi:10.7554/elife.75751 · Cited by 511 · see citing works
Brown, C., Mullet, J. I., Hindi, F., Stoll, J. E., Gupta, S., Choi, M., Keenum, I. M., Vikesland, P. J., Pruden, A. J., & Zhang, L. (2022) · Applied and Environmental Microbiology · doi:10.1128/aem.00991-22 · Cited by 525 · see citing works
Scaffolding protein functional sites using deep learning
Wang, J., Lisanza, S., Juergens, D., Tischer, D., Watson, J. L., Castro, K. M., Ragotte, R., Saragovi, A., Milles, L. F., Baek, M., et al. (2022) · Science · doi:10.1126/science.abn2100 · Cited by 487 · see citing works
Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction
Li, F., Yuan, L., Lu, H., Li, G., Chen, Y., Engqvist, M. K. M., Kerkhoven, E. J., & Nielsen, J. (2022) · Nature Catalysis · doi:10.1038/s41929-022-00798-z · Cited by 466 · see citing works
Gangavarapu, K., Latif, A. A., Mullen, J. L., Alkuzweny, M., Hufbauer, E., Tsueng, G., Haag, E., Zeller, M., Aceves, C. M., Zaiets, K., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-01769-3 · Cited by 393 · see citing works
Zeng, A. G. X., Bansal, S., Jin, L., Mitchell, A., Chen, W. C., Abbas, H. A., Chan‐Seng‐Yue, M., Voisin, V., van Galen, P., Tierens, A. M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01819-x · Cited by 336 · see citing works
Stout, A. J., Mirliani, A. B., Rittenberg, M. L., Shub, M., White, E. C., Yuen, J. S. K., & Kaplan, D. L. (2022) · Communications Biology · doi:10.1038/s42003-022-03423-8 · Cited by 313 · see citing works
Cooperative sensing of mitochondrial DNA by ZBP1 and cGAS promotes cardiotoxicity
Lei, Y., VanPortfliet, J. J., Chen, Y., Bryant, J. D., Li, Y., Fails, D., Torres-Odio, S., Ragan, K. B., Deng, J., Mohan, A., et al. (2023) · Cell · doi:10.1016/j.cell.2023.05.039 · Cited by 292 · see citing works
Skowronek, P., Thielert, M., Voytik, E., Tanzer, M. C., Hansen, F. M., Willems, S., Karayel, Ö., Brunner, A., Meier, F., & Mann, M. (2022) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2022.100279 · Cited by 278 · see citing works
Distinct cellular dynamics associated with response to CAR-T therapy for refractory B cell lymphoma
Haradhvala, N. J., Leick, M. B., Maurer, K., Gohil, S. H., Larson, R. C., Yao, N., Gallagher, K., Katsis, K., Frigault, M. J., Southard, J., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01959-0 · Cited by 277 · see citing works
Novel antigen-presenting cell imparts Treg-dependent tolerance to gut microbiota
Akagbosu, B., Tayyebi, Z., Shibu, G., Paucar Iza, Y. A., Deep, D., Parisotto, Y. F., Fisher, L., Pasolli, H. A., Thevin, V., Elmentaite, R., et al. (2022) · Nature · doi:10.1038/s41586-022-05309-5 · Cited by 273 · see citing works
Kennedy, N. A., Lin, S., Goodhand, J., Chanchlani, N., Hamilton, B., Bewshea, C., Nice, R., Chee, D., Cummings, F. J. R., Fraser, A., et al. (2021) · Gut · doi:10.1136/gutjnl-2021-324789 · Cited by 272 · see citing works
Irrgang, P., Gerling, J., Kocher, K., Lapuente, D., Steininger, P. A., Habenicht, K. M., Wytopil, M., Beileke, S., Schäfer, S., Zhong, J., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.ade2798 · Cited by 259 · see citing works
Weissbrod, O., Kanai, M., Shi, H., Gazal, S., Peyrot, W. J., Khera, A. V., Okada, Y., The Biobank Japan Project, Matsuda, K., Yamanashi, Y., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01036-9 · Cited by 252 · see citing works
Groves, H. E., Piché‐Renaud, P., Peci, A., Farrar, D. S., Buckrell, S., Bancej, C. M., Sevenhuysen, C., Campigotto, A. J., Gubbay, J. B., & Morris, S. K. (2021) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2021.100015 · Cited by 261 · see citing works
Complete human day 14 post-implantation embryo models from naive ES cells
Oldak, B., Wildschutz, E., Bondarenko, V., Comar, M.-Y., Zhao, C., Aguilera-Castrejon, A., Tarazi, S., Viukov, S. V., Pham, T. X. A., Ashouokhi, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06604-5 · Cited by 232 · see citing works
Large-Scale Study of Antibody Titer Decay following BNT162b2 mRNA Vaccine or SARS-CoV-2 Infection
Israel, A., Shenhar, Y., Green, I., Merzon, E., Golan‐Cohen, A., Schäffer, A. A., Ruppin, E., Vinker, S., & Magen, E. (2021) · Vaccines · doi:10.3390/vaccines10010064 · Cited by 218 · see citing works
Antibody structure prediction using interpretable deep learning
Ruffolo, J. A., Sulam, J., & Gray, J. J. (2022) · Patterns · doi:10.1016/j.patter.2021.100406 · Cited by 216 · see citing works
Poudyal, M., Patel, K., Gadhe, L. G., Sawner, A. S., Kadu, P., Datta, D., Mukherjee, S., Ray, S., Navalkar, A., Maiti, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41864-9 · Cited by 194 · see citing works
The extracellular matrix and perineuronal nets in memory
Fawcett, J. W., Fyhn, M., Jendelova, P., Kwok, J. C. F., Ruzicka, J., & Sorg, B. A. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01634-3 · Cited by 169 · see citing works
Self-citationSARS-CoV-2 vaccination induces mucosal antibody responses in previously infected individuals
Sano, K., Bhavsar, D., Singh, G., Floda, D. L., Srivastava, K., Gleason, C., PARIS Study Group, Amoako, A. A., Andre, D., Beach, K. F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32389-8 · Cited by 174 · see citing works
Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows
Salas, S. M., Kuemmerle, L. B., Langseth, C. M., Tiesmeyer, S., Avenel, C., Hu, T., Rehman, H., Grillo, M., Czarnewski, P., Helgadóttir, S., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02617-2 · Cited by 185 · see citing works
Microglia as hackers of the matrix: sculpting synapses and the extracellular space
Crapser, J. D., Arreola, M. A., Tsourmas, K. I., & Green, K. N. (2021) · Cellular & Molecular Immunology · doi:10.1038/s41423-021-00751-3 · Cited by 178 · see citing works
Structural basis for potent antibody neutralization of SARS-CoV-2 variants including B.1.1.529
Zhou, T., Wang, L., Misasi, J., Pegu, A., Zhang, Y., Harris, D. R., Olia, A. S., Talana, C. A., Yang, E. S., Chen, M., et al. (2022) · Science · doi:10.1126/science.abn8897 · Cited by 160 · see citing works
Molecular and cellular dynamics of the developing human neocortex
Wang, L., Wang, C., Moriano, J., Chen, S., Zuo, G., Cebrián‐Silla, A., Zhang, S., Mukhtar, T., Wang, S., Song, M., et al. (2025) · Nature · doi:10.1038/s41586-024-08351-7 · Cited by 173 · see citing works
Elapsed time since BNT162b2 vaccine and risk of SARS-CoV-2 infection: test negative design study
Israel, A., Merzon, E., Schäffer, A. A., Shenhar, Y., Green, I., Golan-Cohen, A., Ruppin, E., Magen, E., & Vinker, S. (2021) · BMJ · doi:10.1136/bmj-2021-067873 · Cited by 170 · see citing works
Barriers to immune cell infiltration in tumors
Melssen, M. M., Sheybani, N., Leick, K. M., & Slingluff, C. L. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-006401 · Cited by 163 · see citing works
Waning effectiveness of the third dose of the BNT162b2 mRNA COVID-19 vaccine
Patalon, T., Saciuk, Y., Peretz, A., Perez, G., Lurie, Y., Maor, Y., & Gazit, S. (2022) · Nature Communications · doi:10.1038/s41467-022-30884-6 · Cited by 143 · see citing works
Blebs promote cell survival by assembling oncogenic signalling hubs
Weems, A. D., Welf, E. S., Driscoll, M., Zhou, F., Mazloom-Farsibaf, H., Chang, B.-J., Murali, V. S., Gihana, G. M., Weiss, B. G., Chi, J., et al. (2023) · Nature · doi:10.1038/s41586-023-05758-6 · Cited by 154 · see citing works
Hwang, M., Canzoniero, J. V., Rosner, S., Zhang, G., White, J. R., Belcaid, Z., Cherry, C., Balan, A., Pereira, G., Curry, A., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-004688 · Cited by 150 · see citing works
An atlas of genetic scores to predict multi-omic traits
Xu, Y., Ritchie, S. C., Liang, Y., Timmers, P. R. H. J., Pietzner, M., Lannelongue, L., Lambert, S. A., Tahir, U. A., May-Wilson, S., Foguet, C., et al. (2023) · Nature · doi:10.1038/s41586-023-05844-9 · Cited by 143 · see citing works
Signature of long-lived memory CD8+ T cells in acute SARS-CoV-2 infection
Adamo, S., Michler, J., Zurbuchen, Y., Cervia, C., Taeschler, P., Raeber, M. E., Sain, S. B., Nilsson, J., Moor, A., & Boyman, O. (2021) · Nature · doi:10.1038/s41586-021-04280-x · Cited by 141 · see citing works
Comparative analysis of cell–cell communication at single-cell resolution
Wilk, A. J., Shalek, A. K., Holmes, S., & Blish, C. A. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01782-z · Cited by 139 · see citing works
Biering, S. B., Sarnik, S. A., Wang, E., Zengel, J. R., Leist, S. R., Schäfer, A., Sathyan, V., Hawkins, P., Okuda, K., Tau, C., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01131-x · Cited by 133 · see citing works
Enzyme-less nanopore detection of post-translational modifications within long polypeptides
Martin-Baniandres, P., Lan, W.-H., Stephanie Board, Romero‐Ruiz, M., Garcia-Manyes, S., Qing, Y., & Bayley, H. (2023) · Nature Nanotechnology · doi:10.1038/s41565-023-01462-8 · Cited by 143 · see citing works
Waning Effectiveness of the Third Dose of the BNT162b2 mRNA COVID-19 Vaccine
Patalon, T., Saciuk, Y., Peretz, A., Perez, G., Lurie, Y., Maor, Y., & Gazit, S. (2022) · doi:10.1101/2022.02.25.22271494 · Cited by 143 · see citing works
A comprehensive transcriptome signature of murine hematopoietic stem cell aging
Flohr Svendsen, A., Yang, D., Kim, K., Lazare, S., Skinder, N., Zwart, E., Mura-Meszaros, A., Ausema, A., von Eyss, B., de Haan, G., et al. (2021) · Blood · doi:10.1182/blood.2020009729 · Cited by 138 · see citing works
Wingert, J. C., & Sorg, B. A. (2021) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2021.673210 · Cited by 129 · see citing works
Complex scaffold remodeling in plant triterpene biosynthesis
Peña, R. D. L., Hodgson, H., Liu, C., Stephenson, M. J., Martín, A. C., Owen, C., Harkess, A., Leebens‐Mack, J., Jimenez, L. E., Osbourn, A., et al. (2023) · Science · doi:10.1126/science.adf1017 · Cited by 137 · see citing works
A human embryonic limb cell atlas resolved in space and time
Zhang, B., He, P., Lawrence, J. E. G., Wang, S., Tuck, E., Williams, B. A., Roberts, K., Kleshchevnikov, V., Mamanova, L., Bolt, L., et al. (2024) · Nature · doi:10.1038/s41586-023-06806-x · Cited by 143 · see citing works
Polθ is phosphorylated by PLK1 to repair double-strand breaks in mitosis
Gelot, C., Kovacs, M. T., Miron, S. M., Mylne, E., Haan, A., Boeffard-Dosierre, L., Ghouil, R., Popova, T. G., Dingli, F., Loew, D., et al. (2023) · Nature · doi:10.1038/s41586-023-06506-6 · Cited by 138 · see citing works
Biomolecular condensates can both accelerate and suppress aggregation of α-synuclein
Lipiński, W. P., Visser, B. S., Robu, I., Fakhree, M. A. A., Lindhoud, S., Claessens, M. M. A. E., & Spruijt, E. (2022) · Science Advances · doi:10.1126/sciadv.abq6495 · Cited by 134 · see citing works
Katsman, E., Orlanski, S., Martignano, F., Fox-Fisher, I., Shemer, R., Dor, Y., Zick, A., Eden, A., Petrini, I., Conticello, S. G., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02710-1 · Cited by 127 · see citing works
Allen, R. J., Stockwell, A., Oldham, J. M., Guillén‐Guío, B., Schwartz, D. A., Maher, T. M., Flores, C., Noth, I., Yaspan, B. L., Jenkins, G. R., et al. (2022) · Thorax · doi:10.1136/thoraxjnl-2021-218577 · Cited by 126 · see citing works
Nishijima, S., Stankevič, E., Aasmets, O., Schmidt, T., Nagata, N., Keller, M. I., Ferretti, P., Juel, H. B., Fullam, A., Robbani, S. M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.022 · Cited by 127 · see citing works
Adapt or die: how the pandemic made the shift from EBM to EBM+ more urgent
Greenhalgh, T., Fisman, D. N., Cane, D. J., Oliver, M., & MacIntyre, C. R. (2022) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2022-111952 · Cited by 124 · see citing works
Hunt, A. C., Case, J. B., Park, Y., Cao, L., Wu, K., Walls, A. C., Liu, Z., Bowen, J. E., Yeh, A. H.-W., Saini, S., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abn1252 · Cited by 116 · see citing works
Behavioral origin of sound-evoked activity in mouse visual cortex
Bimbard, C., Sit, T. P. H., Lebedeva, A., Reddy, C. B., Harris, K. D., & Carandini, M. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01227-x · Cited by 112 · see citing works
Jenster, L., Lange, K.-E., Normann, S., Hemdt, A. V., Wuerth, J. D., Schiffelers, L. D. J., Tesfamariam, Y. M., Gohr, F. N., Klein, L., Kaltheuner, I. H., et al. (2022) · The Journal of Experimental Medicine · doi:10.1084/jem.20220837 · Cited by 113 · see citing works
Topological structures and syntenic conservation in sea anemone genomes
Zimmermann, B., Montenegro, J. D., Robb, S. M. C., Fropf, W. J., Weilguny, L., He, S., Chen, S., Lovegrove-Walsh, J., Hill, E. M., Chen, C.-Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-44080-7 · Cited by 106 · see citing works
Zeng, A. G. X., Iacobucci, I., Shah, S., Mitchell, A., Wong, G., Bansal, S., Chen, D., Gao, Q.-S., Kim, H., Kennedy, J. A., et al. (2025) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-24-0342 · Cited by 74 · see citing works
Assessing the Level and Determinants of COVID-19 Vaccine Confidence in Kenya
Orangi, S., Pinchoff, J., Mwanga, D. M., Abuya, T., Hamaluba, M., Warimwe, G. M., Austrian, K., & Barasa, E. (2021) · Vaccines · doi:10.3390/vaccines9080936 · Cited by 109 · see citing works
Caniels, T. G., Bontjer, I., van der Straten, K., Poniman, M., Burger, J. A., Appelman, B., Lavell, A. H. A., Oomen, M., Godeke, G.-J., Valle, C., et al. (2021) · Science Advances · doi:10.1126/sciadv.abj5365 · Cited by 105 · see citing works
Haghighi, M., Caicedo, J. C., Cimini, B. A., Carpenter, A. E., & Singh, S. (2022) · Nature Methods · doi:10.1038/s41592-022-01667-0 · Cited by 109 · see citing works
The spatial landscape of gene expression isoforms in tissue sections
Lebrigand, K., Bergenstråhle, J., Thrane, K., Mollbrink, A., Meletis, K., Barbry, P., Waldmann, R., & Lundeberg, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad169 · Cited by 105 · see citing works
Lupori, L., Totaro, V., Cornuti, S., Ciampi, L., Carrara, F., Grilli, E., Viglione, A., Tozzi, F., Putignano, E., Mazziotti, R. M., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112788 · Cited by 106 · see citing works
A new method for multiancestry polygenic prediction improves performance across diverse populations
Zhang, H., Zhan, J., Jin, J., Zhang, J., Lu, W., Zhao, R., Ahearn, T. U., Yu, Z., O’Connell, J., Jiang, Y., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01501-z · Cited by 101 · see citing works
Rapid and Parallel Adaptive Mutations in Spike S1 Drive Clade Success in SARS-CoV-2
Kistler, K. E., Huddleston, J., & Bedford, T. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2022.03.018 · Cited by 99 · see citing works
Croce, G., Bobisse, S., Moreno, D. L., Schmidt, J., Guillame, P., Harari, A., & Gfeller, D. (2024) · Nature Communications · doi:10.1038/s41467-024-47461-8 · Cited by 102 · see citing works
Unsupervised Spatially Embedded Deep Representation of Spatial TranscriptomicsPreprint
Fu, H., Xu, H., Chong, K., Li, M., Ang, K. S., Lee, H. K., Ling, J., Chen, A., Shao, L., Liu, L., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.15.448542 · Cited by 98 · see citing works
Tiberti, S., Catozzi, C., Croci, O., Ballerini, M., Cagnina, D., Soriani, C., Scirgolea, C., Gong, Z., He, J.-T., Macandog, A. D. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34467-3 · Cited by 100 · see citing works
Insights from complex trait fine-mapping across diverse populationsPreprint
Kanai, M., Ulirsch, J. C., Karjalainen, J. M., Kurki, M., Karczewski, K. J., Fauman, E. B., Wang, Q. S., Jacobs, H., Aguet, F., Ardlie, K., et al. (2021) · medRxiv · doi:10.1101/2021.09.03.21262975 · Cited by 96 · see citing works
Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills
Vassiliadis, P., Beanato, E., Popa, T., Windel, F., Morishita, T., Neufeld, E., Duqué, J., Derosière, G., Wessel, M. J., & Hummel, F. C. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01901-z · Cited by 97 · see citing works
Robust dimethyl‐based multiplex‐DIA doubles single‐cell proteome depth via a reference channel
Thielert, M., Itang, E. C. M., Ammar, C., Rosenberger, F. A., Bludau, I., Schweizer, L., Nordmann, T. M., Skowronek, P., Wahle, M., Zeng, W., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211503 · Cited by 96 · see citing works
De novo design of allosterically switchable protein assemblies
Pillai, A. S., Idris, A., Philomin, A., Weidle, C., Skotheim, R., Leung, P. J. Y., Broerman, A. J., Demakis, C., Borst, A. J., Praetorius, F., et al. (2024) · Nature · doi:10.1038/s41586-024-07813-2 · Cited by 96 · see citing works
Potential Autoimmunity Resulting from Molecular Mimicry between SARS-CoV-2 Spike and Human Proteins
Nunez-Castilla, J., Stebliankin, V., Baral, P., Balbin, C. A., Sobhan, M., Cickovski, T., Mondal, A. M., Narasimhan, G., Chapagain, P., Mathee, K., et al. (2022) · Viruses · doi:10.3390/v14071415 · Cited by 100 · see citing works
Bioprinting microporous functional living materials from protein-based core-shell microgels
Ou, Y., Cao, S., Zhang, Y., Zhu, H., Guo, C., Yan, W., Xin, F., Dong, W., Zhang, Y., Narita, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-022-35140-5 · Cited by 96 · see citing works
Implications of <i>AlphaFold</i> 2 for crystallographic phasing by molecular replacement
McCoy, A. J., Sammito, M. D., & Read, R. J. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798321012122 · Cited by 104 · see citing works
Edelman-Klapper, H., Zittan, E., Shitrit, A. B., Rabinowitz, K. M., Goren, I., Avni‐Biron, I., Ollech, J. E., Lichtenstein, L., Banai-Eran, H., Yanai, H. A., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.10.029 · Cited by 95 · see citing works
Zhao, H., Rasheed, H., Nøst, T. H., Cho, Y., Liu, Y., Bhatta, L., Bhattacharya, A., Initiative, G. B. M.-A., Hemani, G., Smith, G. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100195 · Cited by 89 · see citing works
IFITM3 blocks influenza virus entry by sorting lipids and stabilizing hemifusion
Klein, S., Golani, G., Lolicato, F., Lahr, C. M., Beyer, D., Herrmann, A., Wachsmuth-Melm, M., Reddmann, N., Brecht, R., Hosseinzadeh, M., et al. (2023) · Cell Host & Microbe · doi:10.1016/j.chom.2023.03.005 · Cited by 95 · see citing works
Szego, E. M., Malz, L., Bernhardt, N., Rösen-Wolff, A., Falkenburger, B. H., & Luksch, H. (2022) · eLife · doi:10.7554/elife.81943 · Cited by 94 · see citing works
Directed Evolution of Aptamer Discovery Technologies
Wu, D., Gordon, C. K. L., Shin, J. H., Eisenstein, M. S., & Soh, H. T. (2022) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00724 · Cited by 94 · see citing works
The individual and global impact of copy-number variants on complex human traits
Auwerx, C., Lepamets, M., Sadler, M. C., Patxot, M., Stojanov, M., Baud, D., Mägi, R., Esko, T., Metspalu, A., Milani, L. A., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.02.010 · Cited by 85 · see citing works
Roden, C., Dai, Y., Giannetti, C. A., Seim, I., Lee, M., Sealfon, R. S. G., McLaughlin, G. A., Boerneke, M. A., Iserman, C., Wey, S. A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac596 · Cited by 94 · see citing works
Subsynaptic positioning of AMPARs by LRRTM2 controls synaptic strength
Ramsey, A. M., Tang, A., LeGates, T. A., Gou, X., Carbone, B. E., Thompson, S. M., Biederer, T., & Blanpied, T. A. (2021) · Science Advances · doi:10.1126/sciadv.abf3126 · Cited by 90 · see citing works
The landscape of metabolic brain alterations in Alzheimer's disease
Batra, R., Arnold, M., Wörheide, M. A., Allen, M., Wang, X., Blach, C., Levey, A. I., Seyfried, N. T., Ertekin‐Taner, N., Bennett, D. A., et al. (2022) · Alzheimer s & Dementia · doi:10.1002/alz.12714 · Cited by 88 · see citing works
Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC
Padovani, F., Mairhörmann, B., Falter‐Braun, P., Lengefeld, J., & Schmoller, K. M. (2022) · BMC Biology · doi:10.1186/s12915-022-01372-6 · Cited by 93 · see citing works
Khosla, M., Murty, N. A. R., & Kanwisher, N. (2022) · Current Biology · doi:10.1016/j.cub.2022.08.009 · Cited by 92 · see citing works
Gazit, S., Saciuk, Y., Perez, G., Peretz, A., Pitzer, V. E., & Patalon, T. (2022) · BMJ · doi:10.1136/bmj-2022-071113 · Cited by 87 · see citing works
Green, A. G., Yoon, C. H., Chen, M. L., Ektefaie, Y., Fina, M., Freschi, L., Gröschel, M. I., Kohane, I. S., Beam, A. L., & Farhat, M. R. (2022) · Nature Communications · doi:10.1038/s41467-022-31236-0 · Cited by 90 · see citing works
NetGO 3.0: Protein Language Model Improves Large-Scale Functional Annotations
Wang, S., You, R., Liu, Y., Xiong, Y., & Zhu, S. (2023) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.04.001 · Cited by 89 · see citing works
Santos‐Medellín, C., Blazewicz, S. J., Pett‐Ridge, J., Firestone, M. K., & Emerson, J. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02207-5 · Cited by 89 · see citing works
Multispecies biofilm architecture determines bacterial exposure to phages
Winans, J. B., Wucher, B. R., & Nadell, C. D. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001913 · Cited by 86 · see citing works
Pan-cancer classification of single cells in the tumour microenvironment
Nofech-Mozes, I., Soave, D. M., Awadalla, P., & Abelson, S. (2023) · Nature Communications · doi:10.1038/s41467-023-37353-8 · Cited by 87 · see citing works
Sampaio, N. M. V., Blassick, C. M., Andreani, V., Lugagne, J., & Dunlop, M. J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2115032119 · Cited by 84 · see citing works
Generation of human alveolar epithelial type I cells from pluripotent stem cells
Burgess, C. L., Huang, J., Bawa, P., Alysandratos, K., Minakin, K., Ayers, L. J., Morley, M. P., Babu, A., Villacorta-Martín, C., Yampolskaya, M., et al. (2024) · Cell stem cell · doi:10.1016/j.stem.2024.03.017 · Cited by 86 · see citing works
Calderón-Franco, D., Sarelse, R., Christou, S., Pronk, M., van Loosdrecht, M. C. M., Abeel, T., & Weissbrodt, D. G. (2022) · Water Research · doi:10.1016/j.watres.2022.118571 · Cited by 84 · see citing works
Chen, Z., King, W. C., Hwang, A., Gerstein, M., & Zhang, J. (2022) · Science Advances · doi:10.1126/sciadv.abq3745 · Cited by 75 · see citing works
CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data
Bae, S., Na, K. J., Koh, J., Lee, D. S., Choi, H., & Kim, Y. T. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac084 · Cited by 75 · see citing works
Contribution of behavioural variability to representational drift
Sadeh, S., & Clopath, C. (2022) · eLife · doi:10.7554/elife.77907 · Cited by 76 · see citing works
Chen, C., Boorla, V. S., Banerjee, D., Chowdhury, R., Cavener, V. S., Nissly, R. H., Gontu, A., Boyle, N. R., Vandegrift, K. J., Nair, M. S., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106480118 · Cited by 79 · see citing works
Steele, L., Olabi, B., Roberts, K., Mazin, P., Koplev, S., Tudor, C. L., Rumney, B. M., Admane, C., Jiang, T., Correa‐Gallegos, D., et al. (2025) · Nature Immunology · doi:10.1038/s41590-025-02267-8 · Cited by 88 · see citing works
BRD2 inhibition blocks SARS-CoV-2 infection by reducing transcription of the host cell receptor ACE2
Samelson, A. J., Tran, Q. D., Robinot, R., Carrau, L., Rezelj, V. V., Kain, A. M., Chen, M., Ramadoss, G. N., Guo, X., Lim, S. A., et al. (2022) · Nature Cell Biology · doi:10.1038/s41556-021-00821-8 · Cited by 76 · see citing works
- Authors
- Christensen, A. C., Lensjø, K. K., Lepperød, M. E., Dragly, S.-A., Sutterud, H., Blackstad, J. S., Fyhn, M., & Hafting, T.
- Affiliations
- University of Oslo (NO)
- Corresponding author
- Hafting, Torkel
- ORCID
- Venue
- Nature Communications
- Published
- 2021-01-11
- Volume
- 12
- Issue
- 1
- Pages
- 253-253
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7801665
- OpenAlex
- W3120392574
- Cited by
- 1,865
- References
- 76
- Citations by year
- 2026: 132 · 2025: 277 · 2024: 340 · 2023: 431 · 2022: 499 · 2021: 170 · 2020: 11 · 2019: 3 · 2018: 2
- FWCI
- 189.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Funders
- Norges Forskningsråd (217920, 231248)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- perineuronal nets, grid cells, extracellular matrix, inhibitory interneurons, spatial memory, navigation, entorhinal cortex, hippocampus, synaptic stability
- MeSH terms
- Grid Cells, Action Potentials, Animals, Computer Simulation, Hippocampus, Male, Models, Neurological, Neurons, Theta Rhythm, Time Factors, Entorhinal Cortex, Rats, Long-Evans, Rats
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