The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
Monosomes actively translate synaptic mRNAs in neuronal processes
Open accessPublished version of a preprint
Abstract
Monosomes translate proteins in neurons: Like all other cells, neurons use different proteins to process information and respond to stimuli. To meet the huge demands for new proteins in their large and complex cell volume, neurons have moved the protein templates—messenger RNAs (mRNAs)—and the protein synthesis machines—ribosomes—out to synapses to make proteins locally. During protein synthesis, multiple ribosomes can form a structure known as a polysome, which produces multiple protein copies from a single mRNA. Working in rodent preparations, Biever et al. found that solitary, mRNA-associated ribosomes, or monosomes, are a substantial source of proteins in neuronal processes. Many synaptic proteins are made on single ribosomes, which may solve the problem of limited space in tiny synaptic compartments. Science , this issue p. eaay4991
Overview
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
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
The Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400
InterProScan 5: genome-scale protein function classification
Jones, P., Binns, D., Chang, H.-Y., Fraser, M., Li, W., McAnulla, C., McWilliam, H., Maslen, J., Mitchell, A., Nuka, G., et al. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu031 · Cited by 9,301
Cited by
The integrated stress response: From mechanism to disease
Costa-Mattioli, M., & Walter, P. (2020) · Science · doi:10.1126/science.aat5314 · Cited by 1,617
Intracellular mRNA transport and localized translation
Das, S., Vera, M., Gandin, V., Singer, R. H., & Tutucci, E. (2021) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00356-8 · Cited by 382
Spatially resolved single-cell translatomics at molecular resolution
(2023) · doi:10.1126/science.add3067 · Cited by 198
RNA transport and local translation in neurodevelopmental and neurodegenerative disease
Fernandopulle, M. S., Lippincott-Schwartz, J., & Ward, M. E. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00785-2 · Cited by 183
Protein synthesis, degradation, and energy metabolism in T cell immunity
Marchingo, J. M., & Cantrell, D. A. (2022) · Cellular & Molecular Immunology · doi:10.1038/s41423-021-00792-8 · Cited by 169
Metadata
- Authors
- Biever, A., Glock, C., Tushev, G., Ciirdaeva, E., Dalmay, T., Langer, J. D., & Schuman, E. M.
- Venue
- Science
- Published
- 2020-01-31
- Volume
- 367
- Issue
- 6477
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3004317045
- Cited by
- 267
- References
- 116
- FWCI
- 10.17
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Monosomes actively translate synaptic mRNAs in neuronal processes. https://app.cochat.ai/papers/10.1126/science.aay4991
- The paper
- Biever, A., Glock, C., Tushev, G., Ciirdaeva, E., Dalmay, T., Langer, J. D., & Schuman, E. M. (2020). Monosomes actively translate synaptic mRNAs in neuronal processes. Science. https://doi.org/10.1126/science.aay4991
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.