Cited by 1,865Memory and Neural MechanismsSpatial Cognition and NavigationNeuroscience and Neuropharmacology Research

The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.

Perineuronal nets stabilize the grid cell network

Open access

Christensen, Ane Charlotte, Lensjø, Kristian Kinden, Lepperød, Mikkel Elle, Dragly, Svenn-Arne, Sutterud, Halvard, Blackstad, Jan Sigurd, Fyhn, Marianne, Hafting, Torkel·2021·Nature Communications

DOI 10.1038/s41467-020-20241-wPMC PMC7801665

Published version of a preprint

Open this paper in CoChat — free account Open PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41467-020-20241-w

Cites

76 of 76 references verified0 citation errors · 0 not checkable

Statistics

Fig. 1 Degradation of perineuronal nets alters synaptic connections and neuron spiking activity. a Schematic outline of parahippocampal areas. Sagittal section from rat brain stained for WFA+ PNNs (cyan) and PV+ neurons (red). PNNs are strongly expressed in MEC and parasubiculum (PaS), while there is weaker staining in presubiculum (PrS) and postrhinal cortex (POR). The overlap between PNNs and PV+ neurons is high. Right, high-magnification image of PV+ neuron in MEC, note that PNN enwrap the cell soma and large parts of proximal dendrites. b Histological verification of chABC activity. Anti-chondroitin sulfate (3B3) antibody (magenta) label 6-sulfated unsaturated disaccharide (C-6-S) stubs left from enzymatic degradation of PNNs by chABC. Intact PNN staining (cyan) is greatly reduced after chABC injection and C-6-S stubs shows the full extent of the injection area. c VGAT expressing puncta on a PV+ cell in MEC 5 days after injection of aCSF (control, top panel) or chABC (lower panel). d The number of VGAT expressing puncta on PV soma is reduced after local chABC treatment in MEC (VGAT: mean ± s.e.m.; Control (n = 31 cells) 63.29 ± 4.038; chABC (n = 32) 46.34 ± 2.48, p = 0.002; VGLUT1: Control (n = 18) 60.94 ± 5.48; chABC (n = 32) 49.44 ± 3.13, p = 0.078; VGLUT2: Control (n = 27) 20.54 ± 2.64; chABC (n = 32) 20.74 ± 2.35, p = 0.67. N = 3 animals, Mann–Whitney U test (two sided). e Illustration of open field recording setup and timeline for experiments. f Cumulative distribution of mean spike rates for broad- and narrow-spiking units from controls and chABC-treated rats. Both unit types showed reduced spike rates in animals with disrupted PNNs (median (n); control broad spiking 1.16 Hz (296); chABC broad spiking 1.01 Hz (287), p = 0.01, U = 47688; control narrow spiking 2.46 Hz (73); chABC narrow spiking 1.52 Hz (177), p = 0.03, U = 7586, Mann–Whitney U test (two sided). Insets: violin plot shows min to max and median (large black line). ns = not significant, *p < 0.05, **p < 0.01. Source data are provided as a Source Data file.CC BY · PubMed Central

Fig. 2 Grid cells show reduced spatial coding after PNN removal. a, b Example grid cells from control and chABC-treated rats. Color-coded rate maps (top), running path with spikes superimposed (middle) and spatial autocorrelation maps (bottom) for four grid cells in controls (a) and four grid cells in chABC-treated rats (b). The number above the rate map is maximum firing rate and the number above the autocorrelation map is gridness score. c Removing PNNs did not reduce gridness in the familiar environment (median; Control 0.63; chABC 0.56, p = 0.694). d The dispersed spiking outside grid cell fields caused the spatial specificity of grid cells to decrease (median; Control 0.57; chABC 0.45, p < 0.0001), in addition to reducing the e spatial information (median; Control 0.36; chABC 0.2, p < 0.0001). f Maximum firing rate was reduced (median; Control 9.32; chABC 6.93, p = 0.009). g Grid cell bursting ratio was measured as the number of bursting events divided by the number of single-spike events. Rats with disrupted PNNs showed a large reduction in bursting event ratio (median; Control 0.08; chABC 0.02, p < 0.0001). h Spiking variability was measured as the coefficient of variation (CV) of interspike intervals (ISI). The method used here calculates CV of ISI using only spikes from passes though grid fields (Supplementary Fig. 5) (median; Control 0.99; chABC 0.82, p < 0.001). Violin plots show min to max and median (black line). Width of graph corresponds to number of samples for each value. Control n = 86, chABC n = 63. Mann–Whitney U test (two sided). **p < 0.01, ***p < 0.001, ****p < 0.0001. Source data are provided as a Source Data file.CC BY · PubMed Central

Fig. 3 Reduced grid cell stability in novel environment. a Rate maps of five consecutive 20 min recording sessions from six individual grid cells, recorded in control animals (top) and chABC animals (bottom) during a novel environment experiment. Color code indicates spike rate. Rows correspond to individual units and columns correspond to environment. Maximum spike rate (left) and gridness (right) is denoted above each rate map. b Gridness score is reduced in the novel environment in animals treated with chABC (median (n); Control 0.65 (35); chABC 0.44 (24); p = 0.004, U = 606, Mann–Whitney U test (two sided)). c Spatial correlation measured for blocks of 5-min recordings in the novel environment sessions I and II, measured against the novel environment session III. Only units reaching gridness threshold in Familiar I and that could be identified in all five recording sessions were included. Animals treated with chABC showed reduced spatial correlations in the novel environment (mean ± s.e.m.; Control 0.59 ± 0.03; chABC 0.37 ± 0.04; main effect of group: F(1, 30) = 14.22, p = 0.0007; interaction: F(7, 210) = 4.905, p < 0.0001, Control n = 16; chABC n = 16, two-way repeated measures ANOVA with group and time as factors). Points indicate mean; shaded area represents s.e.m. d Spatial stability of the same units as in c, measured by the within-trial spatial correlation of grid cell rate maps (0–10 min correlated with 10–20 min). Points indicate mean; shaded area represents s.e.m. Spatial stability decreased from Familiar I to Novel I in the chABC-treated group, but not in the control group (median; Control Familiar I 0.67; Control Novel I 0.54, p = 0.086; chABC Familiar I 0.65; chABC Novel I 0.45, p = 0.015, Mann–Whitney U test (two sided)). e Spatial stability was similar for both groups in Familiar I (median; Control 0.67; chABC 0.65, p = 0.629). After exploring the novel environment, chABC animals showed reduced spatial stability upon returning to the familiar environment (Familiar II) (median; Control 0.72; chABC 0.53, p = 0.018, Control n = 16; chABC n = 16, Mann–Whitney U test). f Rate maps of two grid cells when introduced to the novel environment for three consecutive days. g Spatial correlation of grid cells from chABC-treated animals was decreased in the novel environment when comparing day 1 with day 2 (median; Control 0.76, n = 12; chABC 0.41, n = 11, p = 0.004, Mann–Whitney U test (two sided)). Rate map from the third novel session (Novel III) on the first day was used for correlations with day 2. n.s. = not significant, *p < 0.05, **p < 0.01. Source data are provided as a Source Data file.CC BY · PubMed Central

Fig. 4 Changes in theta oscillations after PNN removal. a Power spectrum of local field potential in MEC shows that theta oscillations in chABC rats have increased power (dB/Hz) (median; Control familiar 6.12; chABC familiar 4.18; Control novel 7.17; chABC novel 4.8) and lower peak frequency (median; Control familiar 8.39; chABC familiar 8.27; Control novel 8.39; chABC novel 8.09) (solid line: familiar, dotted line: novel, blue: control, red: chABC). Shaded area represents 95% confidence interval. b Mean theta frequency for all animals during recordings in familiar and novel environments (dark colored line). Note the decrease in theta frequency during first exposure to a novel environment in both groups. Dots indicate mean; error bars represent bootstrapped 95% confidence interval. c The mean theta power was similar when the animal was immobile, but the chABC group showed increased theta power during movement. d Power score shows increased correlation between theta power and running speed in animals treated with chABC for both familiar and novel environments (median familiar; Control 0.14 (n = 148 recording sessions); chABC 0.31(n = 75) (U = 2800, p < 0.0001). Novel; Control 0.10 (n = 33); chABC 0.27 (n = 24) (U = 102, p < 0.0001)). e Theta frequency increased slightly with running speed in both groups, but the peak frequency was lower in the chABC-treated group. f Frequency score shows increased correlation between theta frequency and running speed in chABC-treated animals for both familiar and novel environment (median familiar; Control 0.14 (n = 148); chABC 0.20 (n = 75) (U = 3100, p < 0.0001). Novel; Control 0.12 (n = 33); chABC 0.19 (n = 24) (U = 200, p = 0.002)). Number of animals: Control n = 7, chABC n = 7. Mann–Whitney U test (two sided) **p < 0.01, ****p < 0.0001. Source data are provided as a Source Data file.CC BY · PubMed Central

Fig. 5 PNN removal disrupt the temporal relation between grid cells. a, b Pairwise temporal cross-correlation of grid cells with brighter color showing stronger correlation. Each line represents a cell pair, sorted by maximum value of the central peak. Values range from −1 to 1. Only cells that could be followed through the novel environment recording sessions were included. c Pairwise temporal stability calculated by correlations of pairwise correlations across recording sessions. Grid cells in the familiar environment prior to any change in environmental condition was identified and correlated with the first familiar session at the day of the novel environment experiment. This is seen in the graph as Familiar vs Familiar I. The pairwise stability is significant lower for the chABC-treated group from the familiar to the novel environment (median Familiar I vs Novel I; Control 0.36 (n = 51); chABC 0.21 (n = 45), U = 874, p = 0.045). Furthermore, grid cells from chABC-treated rats show reduced correlations in the familiar environment after the animals have explored the novel environment (median Familiar I vs Familiar II; Control 0.52 (n = 51); chABC 0.20 (n = 29), U = 529, p = 0.036) (see Supplementary Table 5 for statistics). Violin plots show median (black line); colored shades represent 95th percentile. d Pairwise spatial stability calculated across sessions as in c. The pairwise spatial correlations are significant lower in chABC-treated rats than controls when going from the familiar to the novel environment (median; Familiar I vs Novel I; Control 0.10 (n = 51); chABC 0.05 (n = 45), U = 800, p = 0.011) (Supplementary Table 6). All tests are Mann–Whitney U tests (two sided), *p < 0.05, ****p < 0.0001. Spatial pairwise correlations are also reduced in the chABC throughout all sessions in the novel environment (Supplementary Fig. 11). Source data are provided as a Source Data file.CC BY · PubMed Central

Fig. 6 Continuous attractor model of the grid cell network with altered inhibition. a Schematic outline of the model. Each excitatory unit (green) is connected to distant inhibitory neurons (dark blue), which in turn inhibit excitatory neurons locally. b A population of excitatory neurons (EX) receive external excitatory drive and feedback inhibition by a population of inhibitory neurons (IN). c PNN removal was simulated by reducing the excitatory to inhibitory synaptic strength (lower panel). Rate maps of excitatory neurons show grid pattern for both control and the reduced inhibition network. d The grid cells in the reduced inhibition network show increased out-of-field rate and thereby lower specificity than control, as seen with logarithmic color scale. Note the increased number of green spikes representing rate outside the field. e The average firing rate of both inhibitory and excitatory neurons are lower in the reduced inhibition network (median, inhibitory control: 57; inhibitory reduced inhibition: 41.7; U = 29,940,515, n = 6858 and excitatory control: 1.11; excitatory reduced inhibition: 0.82; U = 15,509,839.5, n = 5398, p < 0.0001, Mann–Whitney U test (two sided)). Source data are provided as a Source Data file.CC BY · PubMed Central

Fig. 7 Altered hippocampal place code after PNN degradation in MEC. a Examples of place cells recorded in control and chABC-treated rats. Top row: color-coded rate maps; bottom row: running path with spikes superimposed. Maximum spike rate is denoted above the rate maps. b Place cells show reduced in-field rate (median; Control 2.97; chABC 2.03, p = 0.004) and increased out-of field rate in chABC-treated rats (median; Control 0.19; chABC 0.34, p = 0.004). Violin plots show min to max and median (black line). Width of graph corresponds to number of samples for each value. **p < 0.01, ****p < 0.0001 (Mann–Whitney U test (two sided)). c Rate maps of five consecutive 20 min recording sessions during a novel environment experiment. Rows correspond to individual units and columns to environment condition. d Spatial correlation between blocks of 5 min recordings in a novel environment (Novel I and Novel II) against the last 20 min (Novel III) (mean ± s.e.m.; Control 0.65 ± 0.03; chABC 0.46 ± 0.09; main effect of group: F(1, 40) = 6.011, p = 0.0187, Control n = 30; chABC n = 14, two-way repeated measures ANOVA with group and time as factors). Points indicate mean; shaded area represents s.e.m. e Within a session the place code is stable for both groups measured as spatial correlation between first and last 10 min of the session. f The power spectral density (PSD) plot show stronger theta oscillations in chABC-treated animals (red lines) compared to control (blue lines). Lines indicate median; shaded area represent 95% confidence interval. g Mean theta power in hippocampus correlated with running speeds from familiar and novel environment recordings. h Peak theta frequency increases with running speed. Source data are provided as a Source Data file.CC BY · PubMed Central

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Self-citation
Mechanisms of Gamma Oscillations
Buzsáki, G., & Wang, X.-J. (2012) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062111-150444 · Cited by 2,796
Critical period plasticity in local cortical circuits
Hensch, T. K. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1787 · Cited by 2,323
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
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
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
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
Self-citation
Three 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
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
Self-citation
Intracellular 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

Show all 76 references

Sorted by citations

Items 201–300 of 1,004

A robust normalized local filter to estimate compositional heterogeneity directly from cryo-EM maps
Forsberg, B. O., Shah, P. N. M., & Burt, A. (2023) · Nature Communications · doi:10.1038/s41467-023-41478-1 · Cited by 45
The landscape of genomic structural variation in Indigenous Australians
Reis, A. L. M., Rapadas, M., Hammond, J. W., Gamaarachchi, H., Stevanovski, I., Kumaheri, M. A., Chintalaphani, S. R., Dissanayake, D. S. B., Siggs, O. M., Hewitt, A. W., et al. (2023) · Nature · doi:10.1038/s41586-023-06842-7 · Cited by 51
Protease Resistance of ex vivo Amyloid Fibrils implies the proteolytic Selection of disease-associated Fibril Morphologies
Schönfelder, J., Pfeiffer, P., Pradhan, T., Bijzet, J., Hazenberg, B., Schönland, S., Hegenbart, U., Reif, B., Haupt, C., & Fändrich, M. (2021) · bioRxiv · doi:10.1080/13506129.2021.1960501 · Cited by 45
The Effect of Preanalytical and Physiological Variables on Cell-Free DNA Fragmentation
van der Pol, Y., Moldován, N., Verkuijlen, S. A. W. M., Ramaker, J., Boers, D., Onstenk, W., de Rooij, J., Bahce, I., Pegtel, D. M., & Mouliere, F. C. (2022) · Clinical Chemistry · doi:10.1093/clinchem/hvac029 · Cited by 45
Rapid and automated design of two-component protein nanomaterials using ProteinMPNN
de Haas, R. J., Brunette, N., Goodson, A., Dauparas, J., Yi, S. Y., Yang, E. C., Dowling, Q., Nguyen, H., Kang, A., Bera, A. K., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314646121 · Cited by 42
maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks
Cazares, T. A., Rizvi, F. W., Iyer, B., Chen, X., Kotliar, M., Bejjani, A. T., Wayman, J. A., Donmez, O. A., Wronowski, B., Parameswaran, S., et al. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010863 · Cited by 44
Increased RNA and Protein Degradation Is Required for Counteracting Transcriptional Burden and Proteotoxic Stress in Human Aneuploid Cells
Ippolito, M. R., Zerbib, J., Eliezer, Y., Reuveni, E., Viganò, S., Feudis, G. D., Shulman, E. D., Kadmon, A. S., Slutsky, R., Chang, T., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0309 · Cited by 43
Plastic pollution: how can the global health community fight the growing problem?
Bidashimwa, D., Hoke, T., Huynh, T. B., Narkpitaks, N., Priyonugroho, K., Ha, T. T., Burns, A. M., & Weissman, A. (2023) · BMJ Global Health · doi:10.1136/bmjgh-2023-012140 · Cited by 47
Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning
Vinogradov, A. A., Chang, J. S., Onaka, H., Goto, Y., & Suga, H. (2022) · ACS Central Science · doi:10.1021/acscentsci.2c00223 · Cited by 43
Epigenetic modulation of neuroblastoma enhances T cell and NK cell immunogenicity by inducing a tumor-cell lineage switch
Cornel, A. M., Dünnebach, E., Hofman, D. A., Das, S., Sengupta, S., van den Ham, F., Wienke, J., Strijker, J. G. M., van den Beemt, D. A. M. H., Essing, A. H. W., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-005002 · Cited by 43
Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics
Farrell, S., Mani, M., & Goyal, S. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100581 · Cited by 47
Spatial gene expression at single-cell resolution from histology using deep learning with GHIST
Fu, X., Cao, Y., Bian, B., Wang, C., Graham, J. D., Pathmanathan, N., Patrick, E., Kim, J., & Yang, J. Y. H. (2025) · Nature Methods · doi:10.1038/s41592-025-02795-z · Cited by 51
Plasmonic LAMP: Improving the Detection Specificity and Sensitivity for SARS‐CoV‐2 by Plasmonic Sensing of Isothermally Amplified Nucleic Acids
Ye, H., Nowak, C. M., Liu, Y., Li, Y., Zhang, T., Bleris, L. G., & Qin, Z. (2022) · Small · doi:10.1002/smll.202107832 · Cited by 42
Structure and Protein-Protein Interactions of Ice Nucleation Proteins Drive Their Activity
Hartmann, S., Ling, M., Dreyer, L. S. A., Zipori, A., Finster, K., Grawe, S., Jensen, L. Z., Borck, S., Reicher, N., Drace, T., et al. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.872306 · Cited by 43
Relating pathogenic loss-of-function mutations in humans to their evolutionary fitness costs
Agarwal, I., Fuller, Z. L., Myers, S., & Przeworski, M. (2023) · eLife · doi:10.7554/elife.83172 · Cited by 40
Single-cell reference mapping to construct and extend cell-type hierarchies
Michielsen, L., Lotfollahi, M., Strobl, D. C., Sikkema, L., Reinders, M. J. T., Theis, F. J., & Mahfouz, A. E. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad070 · Cited by 43
Active learning of the thermodynamics-dynamics trade-off in protein condensates
An, Y., Webb, M. A., & Jacobs, W. M. (2024) · Science Advances · doi:10.1126/sciadv.adj2448 · Cited by 45
Evolutionary-scale enzymology enables exploration of a rugged catalytic landscape
(2025) · doi:10.1126/science.adu1058 · Cited by 42
Core and rod structures of a thermophilic cyanobacterial light-harvesting phycobilisome
Kawakami, K., Hamaguchi, T., Hirose, Y., Kosumi, D., Miyata, M., Kamiya, N., & Yonekura, K. (2022) · Nature Communications · doi:10.1038/s41467-022-30962-9 · Cited by 43
Saccades are locked to the phase of alpha oscillations during natural reading
Pan, Y., Popov, T. G., Frisson, S., & Jensen, O. B. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3001968 · Cited by 42
Phase variation as a major mechanism of adaptation in Mycobacterium tuberculosis complex
Vargas, R., Luna, M. J., Freschi, L., Marin, M. G., Froom, R., Murphy, K. C., Campbell, E. A., Ioerger, T. R., Sassetti, C. M., & Farhat, M. R. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2301394120 · Cited by 40
Feedforward growth rate control mitigates gene activation burden
Barajas, C., Huang, H.-H., Gibson, J., Sandoval, L., & Vecchio, D. D. (2022) · Nature Communications · doi:10.1038/s41467-022-34647-1 · Cited by 42
Genome mining unveils a class of ribosomal peptides with two amino termini
Ren, H., Dommaraju, S. R., Huang, C., Cui, H., Pan, Y., Nešić, M., Zhu, L., Šarlah, D., Mitchell, D. A., & Zhao, H. (2023) · Nature Communications · doi:10.1038/s41467-023-37287-1 · Cited by 41
Epithelial–Mesenchymal Plasticity and Endothelial–Mesenchymal Transition in Cutaneous Wound HealingPreprint
Vu, R., Dragan, M., Sun, P., Werner, S., & Dai, X. (2022) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041237 · Cited by 42
Molecular organization of fibroin heavy chain and mechanism of fibre formation in Bombyx mori
Moreno-Tortolero, R. O., Luo, Y., Parmeggiani, F., Skaer, N., Walker, R., Serpell, L. C., Holland, C., & Davis, S. A. (2024) · Communications Biology · doi:10.1038/s42003-024-06474-1 · Cited by 45
When adaptive radiations collide: Different evolutionary trajectories between and within island and mainland lizard clades
Patton, A. H., Harmon, L. J., Castañeda, M. D. R., Frank, H. K., Donihue, C. M., Herrel, A., & Losos, J. B. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2024451118 · Cited by 40
Phage-Encoded Sigma Factors Alter Bacterial Dormancy
Schwartz, D. A., Lehmkuhl, B. K., & Lennon, J. T. (2022) · mSphere · doi:10.1128/msphere.00297-22 · Cited by 40
Genome-Resolved Metaproteomics Decodes the Microbial and Viral Contributions to Coupled Carbon and Nitrogen Cycling in River Sediments
Rodríguez-Ramos, J. A., Borton, M., McGivern, B. B., Smith, G. J., Solden, L., Shaffer, M., Daly, R. A., Purvine, S. O., Nicora, C., Eder, E., et al. (2022) · mSystems · doi:10.1128/msystems.00516-22 · Cited by 40
Epidemiological impact and cost-effectiveness analysis of COVID-19 vaccination in Kenya
Orangi, S., Ojal, J., Brand, S. P. C., Orlendo, C., Kairu, A., Aziza, R., Ogero, M. O., Agweyu, A., Warimwe, G. M., Uyoga, S. M., et al. (2022) · BMJ Global Health · doi:10.1136/bmjgh-2022-009430 · Cited by 40
Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders
Sadeghi, I., Gispert, J. D., Palumbo, E., Muñoz-Aguirre, M., Wucher, V., D’Argenio, V., Santpere, G., Navarro, A., Guigó, R., & Vilor‐Tejedor, N. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.037 · Cited by 42
Inflammatory Bowel Disease-Associated Gut Commensals Degrade Components of the Extracellular Matrix
Porras, A. M., Zhou, H., Shi, Q., Xiao, X., Bank, J. L. C., Longman, R., & Brito, I. (2022) · mBio · doi:10.1128/mbio.02201-22 · Cited by 39
Interaction of human keratinocytes and nerve fiber terminals at the neuro-cutaneous unit
Erbacher, C., Britz, S., Dinkel, P., Klein, T., Sauer, M., Stigloher, C., & Üçeyler, N. (2023) · eLife · doi:10.7554/elife.77761 · Cited by 40
Autotrophic adaptive laboratory evolution of the acetogen Clostridium autoethanogenum delivers the gas-fermenting strain LAbrini with superior growth, products, and robustness
Ingelman, H., Heffernan, J. K., Harris, A., Brown, S. D., Shaikh, K. M., Saqib, A. Y., Pinheiro, M. J., de Lima, L. A., Martinez, K. R., González-García, R. A., et al. (2024) · New Biotechnology · doi:10.1016/j.nbt.2024.06.002 · Cited by 41
Increasing intracellular dNTP levels improves prime editing efficiency
Liu, P., Ponnienselvan, K., Nyalile, T., Oikemus, S., Joynt, A. T., Iyer, S., Kelly, K. L., Guo, D., Kyawe, P. P., Vanderleeden, E., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02405-x · Cited by 45
Rolling circle RNA synthesis catalyzed by RNA
Kristoffersen, E. L., Burman, M., Noy, A., & Holliger, P. (2022) · eLife · doi:10.7554/elife.75186 · Cited by 38
SIX1 reprograms myogenic transcription factors to maintain the rhabdomyosarcoma undifferentiated state
Hsu, J. Y., Danis, E. P., Nance, S., O’Brien, J., Gustafson, A. L., Wessells, V. M., Goodspeed, A. E., Talbot, J. C., Amacher, S. L., Jedlicka, P., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110323 · Cited by 40
T cell repertoire in peripheral blood as a potential biomarker for predicting response to concurrent cetuximab and nivolumab in head and neck squamous cell carcinoma
Wang, X., Muzaffar, J., Kirtane, K., Song, F., Johnson, M. J., Schell, M. J., Li, J., Yoder, S., Conejo-Garcia, J. R., Guevara-Patino, J. A., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-004512 · Cited by 40
Restoration of p53 activity via intracellular protein delivery sensitizes triple negative breast cancer to anti-PD-1 immunotherapy
Yang, Z., Sun, J. K., Lee, M. M., & Chan, M. K. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-005068 · Cited by 39
Macrophage GSK3β-deficiency inhibits the progression of hepatocellular carcinoma and enhances the sensitivity of anti-PD1 immunotherapy
Sun, G., Liu, H., Zhao, J., Zhang, J., Huang, T., Sun, G., Zhao, S., Zhang, Z., Cao, H., Rong, D., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-005655 · Cited by 38
Slow release of a synthetic auxin induces formation of adventitious roots in recalcitrant woody plants
Roth, O., Yechezkel, S., Serero, O., Eliyahu, A., Vints, I., Tzeela, P., Carignano, A., Janacek, D. P., Peters, V., Kessel, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02065-3 · Cited by 39
Mapping human tissues with highly multiplexed RNA in situ hybridization
Kalhor, K., Chen, C.-J., Lee, H. S., Cai, M., Nafisi, M., Que, R. A., Palmer, C. R., Yuan, Y., Zhang, Y., Li, X., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46437-y · Cited by 40
Stiffness anisotropy coordinates supracellular contractility driving long-range myotube-ECM alignment
Skillin, N. P., Kirkpatrick, B. E., Herbert, K. M., Nelson, B. R., Hach, G. K., Günay, K. A., Khan, R. M., DelRio‬, F. W., White, T. J., & Anseth, K. S. (2024) · Science Advances · doi:10.1126/sciadv.adn0235 · Cited by 39
Asgard archaea modulate potential methanogenesis substrates in wetland soil
Valentin-Alvarado, L. E., Appler, K. E., Anda, V. D., Schoelmerich, M. C., West-Roberts, J. A., Kivenson, V., Crits‐Christoph, A., Ly, L., Sachdeva, R., Greening, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49872-z · Cited by 34
Unnecessary antibiotic treatment of children hospitalised with respiratory syncytial virus (RSV) bronchiolitis: risk factors and prescription patterns
Obolski, U., Kassem, E., Na’amnih, W., Tannous, S., Kagan, V., & Muhsen, K. (2021) · Journal of Global Antimicrobial Resistance · doi:10.1016/j.jgar.2021.10.015 · Cited by 39
Flow-Cell-Based Technology for Massively Parallel Characterization of Base-Modified DNA Aptamers
Wu, D., Feagin, T., Mage, P. L., Rangel, A. E., Wan, L., Kong, D., Li, A., Coller, J., Eisenstein, M. S., & Soh, H. T. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.1c04777 · Cited by 39
Benchmarking genome assembly methods on metagenomic sequencing data
Zhang, Z., Yang, C., Veldsman, W. P., Fang, X., & Zhang, L. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad087 · Cited by 39
Exploring genome gene content and morphological analysis to test recalcitrant nodes in the animal phylogeny
Juravel, K., Porras, L., Höhna, S., Pisani, D., & Wörheide, G. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282444 · Cited by 38
Gene-level alignment of single-cell trajectories
Sumanaweera, D., Suo, C., Cujba, A.-M., Muraro, D., Dann, E., Polański, K., Steemers, A. S., Lee, W., Oliver, A. J., Park, J.-E., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02378-4 · Cited by 41
A Sensitive and Rapid Wastewater Test for SARS-COV-2 and Its Use for the Early Detection of a Cluster of Cases in a Remote Community
Daigle, J., Racher, K., Hazenberg, J., Yeoman, A., Hannah, H., Duong, D., Mohammed, U., Spreitzer, D., Gregorchuk, B. S. J., Head, B. M., et al. (2022) · Applied and Environmental Microbiology · doi:10.1128/aem.01740-21 · Cited by 38
Physical activity specifically evokes release of cell-free DNA from granulocytes thereby affecting liquid biopsy
Neuberger, E. W. I., Sontag, S., Brahmer, A., Philippi, K. F. A., Radsak, M. P., Wagner, W., & Simon, P. D. (2022) · Clinical Epigenetics · doi:10.1186/s13148-022-01245-3 · Cited by 42
A w Mel Wolbachia variant in Aedes aegypti from field‐collected Drosophila melanogaster with increased phenotypic stability under heat stress
Gu, X., Ross, P. A., Rodriguez‐Andres, J., Robinson, K. L., Yang, Q., Lau, M., & Hoffmann, A. A. (2022) · Environmental Microbiology · doi:10.1111/1462-2920.15966 · Cited by 39
Repeated introduction of micropollutants enhances microbial succession despite stable degradation patterns
Izabel‐Shen, D., Li, S., Luo, T., Wang, J., Li, Y., Sun, Q., Yu, C., & Hu, A. (2022) · ISME Communications · doi:10.1038/s43705-022-00129-0 · Cited by 37
Humoral and T-Cell Immune Response After 3 Doses of Messenger RNA Severe Acute Respiratory Syndrome Coronavirus 2 Vaccines in Fragile Patients: The Italian VAX4FRAIL Study
Corradini, P., Agrati, C., Apolone, G., Mantovani, A., Giannarelli, D., Marasco, V., Bordoni, V., Sacchi, A., Matusali, G., Salvarani, C., et al. (2022) · medRxiv · doi:10.1093/cid/ciac404 · Cited by 35
Resolving tricky nodes in the tree of life through amino acid recoding
Giacomelli, M., Rossi, M. E., Lozano-Fernández, J., Feuda, R., & Pisani, D. (2022) · iScience · doi:10.1016/j.isci.2022.105594 · Cited by 42
Nanoporous, Gas Permeable PEGDA Ink for 3D Printing Organ‐on‐a‐Chip Devices
Karamzadeh, V., Shen, M. L., Shafique, H., Lussier, F., & Juncker, D. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202315035 · Cited by 37
Multinucleated giant cells are hallmarks of ovarian aging with unique immune and degradation-associated molecular signatures
Converse, A., Perry, M. J., Dipali, S. S., Isola, J. V. V., Kelly, E. B., Varberg, J. M., Zelinski, M. B., Gerton, J. L., Stout, M. B., Pritchard, M. T., et al. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003204 · Cited by 38
Super‐resolution analyzing spatial organization of lysosomes with an organic fluorescent probe
Wang, L., Chen, R., Han, G., Liu, X., Huang, T., Diao, J., & Sun, Y. (2022) · Exploration · doi:10.1002/exp.20210215 · Cited by 35
Towards a molecular mechanism underlying mitochondrial protein import through the TOM and TIM23 complexes
Ford, H. C., Allen, W. J., Pereira, G. C., Liu, X., Dillingham, M. S., & Collinson, I. R. (2022) · eLife · doi:10.7554/elife.75426 · Cited by 33
DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
Barissi, S., Sala, A., Wieczór, M., Battistini, F., & Orozco, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac708 · Cited by 37
Exploring the role of intratendinous pressure in the pathogenesis of tendon pathology: a narrative review and conceptual framework
Pringels, L., Cook, J. L., Witvrouw, E., Burssens, A., Bossche, L. V., & Wezenbeek, E. (2022) · British Journal of Sports Medicine · doi:10.1136/bjsports-2022-106066 · Cited by 37
Long divergent haplotypes introgressed from wild sheep are associated with distinct morphological and adaptive characteristics in domestic sheep
Cheng, H., Zhang, Z., Wen, J., Lenstra, J. A., Heller, R., Cai, Y., Guo, Y., Li, M., Li, R., Li, W., et al. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010615 · Cited by 39
Dimensionality and Ramping: Signatures of Sentence Integration in the Dynamics of Brains and Deep Language Models
Desbordes, T., Lakretz, Y., Chanoine, V., Oquab, M., Badier, J., Trébuchon, A., Carron, R., Bénar, C., Dehaene, S., & King, J.-R. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1163-22.2023 · Cited by 38
Soluble and multivalent Jag1 DNA origami nanopatterns activate Notch without pulling force
Smyrlaki, I., Fördős, F., Rocamonde-Lago, I., Wang, Y., Shen, B., Lentini, A., Luca, V. C., Reinius, B., Teixeira, A. I., & Högberg, B. (2024) · Nature Communications · doi:10.1038/s41467-023-44059-4 · Cited by 37
Perineuronal nets as regulators of parvalbumin interneuron function: Factors implicated in their formation and degradation
Santos‐Silva, T., Colodete, D. A. E., Lisboa, J. R., Freitas, I. D. S., Lopes, C. F. B., Hadera, V., Lima, T. S. A., Souza, A. J., & Gomes, F. V. (2024) · Basic & Clinical Pharmacology & Toxicology · doi:10.1111/bcpt.13994 · Cited by 39
Efficacy of convalescent plasma for treatment of COVID-19 in Uganda
Kirenga, B., Byakika‐Kibwika, P., Muttamba, W., Kayongo, A., Namakula, O. L., Mugenyi, L., Kiwanuka, N., Lusiba, J. K., Atukunda, A., Mugume, R., et al. (2021) · BMJ Open Respiratory Research · doi:10.1136/bmjresp-2021-001017 · Cited by 35
Modelling homing suppression gene drive in haplodiploid organisms
Liu, Y., & Champer, J. (2022) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2022.0320 · Cited by 36
A neuromechanical model for Drosophila larval crawling based on physical measurements
Sun, X., Liu, Y., Liu, C., Mayumi, K., Ito, K., Nose, A., & Kohsaka, H. (2022) · BMC Biology · doi:10.1186/s12915-022-01336-w · Cited by 43
Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis
Jia, C., Singh, A., & Grima, R. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010574 · Cited by 35
Genomic Underpinnings of Population Persistence in Isle Royale Moose
Kyriazis, C. C., Beichman, A. C., Brzeski, K. E., Hoy, S. R., Peterson, R. O., Vucetich, J. A., Vucetich, L. M., Lohmueller, K. E., & Wayne, R. K. (2023) · Molecular Biology and Evolution · doi:10.1093/molbev/msad021 · Cited by 36
The molecular consequences of androgen activity in the human breast
Raths, F., Karimzadeh, M., Ing, N., Martinez, A., Yang, Y., Qu, Y., Lee, T.-Y., Mulligan, B., Devkota, S., Tilley, W. T., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100272 · Cited by 39
Comparative effect of metformin versus sulfonylureas with dementia and Parkinson’s disease risk in US patients over 50 with type 2 diabetes mellitus
Newby, D., Linden, A. B., Fernandes, M., Winchester, L. M., Molero, Y., Sproviero, W., Ghose, U., Li, Q. S., Launer, L. J., van Duijn, C. M., et al. (2022) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2022-003036 · Cited by 35
3D melanoma spheroid model for the development of positronium biomarkers
Karimi, H., Moskal, P., Żak, A., & Stępień, E. Ł. (2023) · Scientific Reports · doi:10.1038/s41598-023-34571-4 · Cited by 32
Universal open MHC-I molecules for rapid peptide loading and enhanced complex stability across HLA allotypes
Sun, Y., Young, M. C., Woodward, C. H., Danon, J. N., Truong, H. V., Gupta, S. K., Winters, T. J., Font-Burgada, J., Burslem, G. M., & Sgourakis, N. G. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304055120 · Cited by 38
From molecules to behavior: Implications for perineuronal net remodeling in learning and memory
Sanchez, B., Kraszewski, P., Lee, S., & Cope, E. C. (2023) · Journal of Neurochemistry · doi:10.1111/jnc.16036 · Cited by 41
The landscape of cancer-rewired GPCR signaling axes
Arora, C., Matic, M., Bisceglia, L., Chiaro, P. D., Rosa, N. D. O., Carli, F., Clubb, L. M., Fard, L. A. N., Kargas, G., Diaferia, G. R., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100557 · Cited by 37
Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development
Kim, S., Koppitch, K. A., Parvez, R. K., Guo, J., Achieng, M. A., Schnell, J., Lindström, N. O., & McMahon, A. P. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.07.013 · Cited by 39
Environment-independent distribution of mutational effects emerges from microscopic epistasis
Ardell, S. M., Martsul, A., Johnson, M. S., & Kryazhimskiy, S. (2024) · Science · doi:10.1126/science.adn0753 · Cited by 41
Antibody-lectin chimeras for glyco-immune checkpoint blockade
Stark, J. C., Gray, M. A., Ibarlucea-Benitez, I., Lustig, M., Bond, A., Cho, B., Govil, I., Luu, T., Priestley, M. J., Veth, T. S., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02884-6 · Cited by 41
Cancer and the risk of coronavirus disease 2019 diagnosis, hospitalisation and death: A population‐based multistate cohort study including 4 618 377 adults in Catalonia, Spain
Roel, E., Pistillo, A., Recalde, M., Fernández-Bertolín, S., Aragón, M., Soerjomataram, I., Jenab, M., Puente, D., Prieto-Alhambra, D., Burn, E., et al. (2021) · International Journal of Cancer · doi:10.1002/ijc.33846 · Cited by 17
Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS
Batra, R., Whalen, W., Alvarez-Mulett, S. L., Gómez-Escobar, L. G., Hoffman, K. L., Simmons, W., Harrington, J. S., Chetnik, K., Büyüközkan, M., Benedetti, E., et al. (2022) · PLoS Pathogens · doi:10.1371/journal.ppat.1010819 · Cited by 36
Subcellular spatial transcriptomics identifies three mechanistically different classes of localizing RNAs
Cassella, L., & Ephrussi, A. (2022) · Nature Communications · doi:10.1038/s41467-022-34004-2 · Cited by 36
Lineage plasticity enables low-ER luminal tumors to evolve and gain basal-like traits
Mohamed, G. A., Mahmood, S., Ognjenovic, N. B., Lee, M. K., Wilkins, O. M., Christensen, B. C., Muller, K. E., & Pattabiraman, D. R. (2023) · Breast Cancer Research · doi:10.1186/s13058-023-01621-8 · Cited by 33
Intertumoral lineage diversity and immunosuppressive transcriptional programs in well-differentiated gastroenteropancreatic neuroendocrine tumors
Hoffman, S. E., Dowrey, T. W., Villacorta-Martín, C., Bi, K., Titchen, B. M., Johri, S., DelloStritto, L. K., Patel, M., MacKichan, C., Inga, S., et al. (2023) · Science Advances · doi:10.1126/sciadv.add9668 · Cited by 37
Exploring high-resolution cryo-ET and subtomogram averaging capabilities of contemporary DEDs
Obr, M., Hagen, W. J. H., Dick, R. A., Yu, L., Kotecha, A., & Schur, F. K. M. (2022) · Journal of Structural Biology · doi:10.1016/j.jsb.2022.107852 · Cited by 34
Deciphering how early life adiposity influences breast cancer risk using Mendelian randomization
Vabistsevits, M., Smith, G. D., Sanderson, E. C. M., Richardson, T. G., Lloyd‐Lewis, B., & Richmond, R. C. (2022) · Communications Biology · doi:10.1038/s42003-022-03272-5 · Cited by 36
Self-assembly of tessellated tissue sheets by expansion and collision
Heinrich, M. A., Alert, R., Wolf, A. E., Košmrlj, A., & Cohen, D. J. (2022) · Nature Communications · doi:10.1038/s41467-022-31459-1 · Cited by 34
SARS‐CoV ‐2 infection impacts carbon metabolism and depends on glutamine for replication in Syrian hamster astrocytes
de Oliveira, L. G., Angelo, Y. D. S., Yamamoto, P., Carregari, V. C., Crunfli, F., Reis‐de‐Oliveira, G., Silva-Costa, L. C., Vendramini, P. H., Duque, É. D. A., dos Santos, N. B., et al. (2022) · Journal of Neurochemistry · doi:10.1111/jnc.15679 · Cited by 35
Evolution of a globally unique SARS-CoV-2 Spike E484T monoclonal antibody escape mutation in a persistently infected, immunocompromised individual
Halfmann, P., Minor, N. R., Haddock, L. A., Maddox, R., Moreno, G. K., Braun, K. M., Baker, D. A., Riemersa, K. K., Prasad, A., Alman, K. J., et al. (2022) · Virus Evolution · doi:10.1093/ve/veac104 · Cited by 36
Assembly of the 81.6 Mb centromere of pea chromosome 6 elucidates the structure and evolution of metapolycentric chromosomes
Macas, J., Robledillo, L. Á., Kreplak, J., Novák, P., Koblížková, A., Vrbová, I., Burstin, J., & Neumann, P. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010633 · Cited by 35
Chromatin fiber breaks into clutches under tension and crowding
Liu, S., Lin, X., & Zhang, B. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac725 · Cited by 34
Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex
Roe, J. M., Dídac Vidal-piñeiro, A., Amlien, I. K., Pan, M., Sneve, M. H., de Schotten, M. T., Friedrich, P., Sha, Z., Francks, C., Eilertsen, E. M., et al. (2023) · eLife · doi:10.7554/elife.84685 · Cited by 33
Splicing quality control mediated by DHX15 and its G-patch activator SUGP1
Feng, Q., Krick, K. S., Chu, J., & Burge, C. B. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113223 · Cited by 35
Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning
Vornholt, T., Mutný, M., Schmidt, G. W., Schellhaas, C., Tachibana, R., Panke, S., Ward, T. R., Krause, A., & Jeschek, M. (2024) · ACS Central Science · doi:10.1021/acscentsci.4c00258 · Cited by 36
First-order visual interneurons distribute distinct contrast and luminance information across ON and OFF pathways to achieve stable behavior
Ketkar, M. D., Gür, B., Molina-Obando, S., Ioannidou, M., Martelli, C., & Silies, M. (2022) · eLife · doi:10.7554/elife.74937 · Cited by 31
Human voltage-gated Na + and K + channel properties underlie sustained fast AP signaling
Wilbers, R., Metodieva, V. D., Duverdin, S., Heyer, D. B., Galakhova, A. A., Mertens, E. J., Versluis, T. D., Baayen, J. C., Idema, S., Noske, D. P., et al. (2023) · Science Advances · doi:10.1126/sciadv.ade3300 · Cited by 32
Mouse α-synuclein fibrils are structurally and functionally distinct from human fibrils associated with Lewy body diseases
Sokratian, A., Zhou, Y., Tatli, M., Burbidge, K. J., Xu, E., Viverette, E., Donzelli, S., Duda, A. M., Yuan, Y., Li, H., et al. (2024) · Science Advances · doi:10.1126/sciadv.adq3539 · Cited by 32
SARS-CoV-2 mRNA vaccinations fail to elicit humoral and cellular immune responses in patients with multiple sclerosis receiving fingolimod
Meyer‐Arndt, L., Braun, J., Fauchère, F., Vanshylla, K., Loyal, L., Henze, L., Kruse, B., Dingeldey, M., Jürchott, K., Mangold, M., et al. (2022) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2022-329395 · Cited by 33
Humoral Immunogenicity of 3 COVID-19 Messenger RNA Vaccine Doses in Patients With Inflammatory Bowel Disease
Schell, T. L., Knutson, K. L., Saha, S., Wald, A. M., Phan, H., Almasry, M., Chun, K., Grimes, I. C., Lutz, M. K., Hayney, M. S., et al. (2022) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izac082 · Cited by 33
Population structure and inbreeding in wild house mice (Mus musculus) at different geographic scales
Morgan, A. P., Hughes, J. J., Didion, J. P., Jolley, W. J., Campbell, K. J., Threadgill, D. W., Bonhomme, F., Searle, J. B., & de Villena, F. P. (2022) · Heredity · doi:10.1038/s41437-022-00551-z · Cited by 28
Low diversity and microdiversity of comammox bacteria in wastewater systems suggest specific adaptations within the Ca. Nitrospira nitrosa cluster
Cotto, I., Vilardi, K. J., Huo, L., Fogarty, E. C., Khunjar, W., Wilson, C., Clippeleir, H. D., Gilmore, K. R., Bailey, E., Lücker, S., et al. (2022) · Water Research · doi:10.1016/j.watres.2022.119497 · Cited by 32

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: Christensen, Ane Charlotte
Lensjø, Kristian Kinden
Lepperød, Mikkel Elle
Dragly, Svenn-Arne
Sutterud, Halvard
Blackstad, Jan Sigurd
Fyhn, Marianne
Hafting, Torkel
Venue: Nature Communications
Publisher: Springer Science and Business Media LLC
Published: 2021-01-11
Volume: 12
Issue: 1
Pages: 253-253
ISSN: 2041-1723 (ISSN-L)
DOI: 10.1038/s41467-020-20241-w
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
FWCI: 189.42
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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
Social Sciences › Psychology › Experimental and Cognitive Psychology › 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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Perineuronal nets stabilize the grid cell network

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks