Cell-type-specific isolation of ribosome-associated mRNA from complex tissues
Abstract
Gene profiling techniques allow the assay of transcripts from organs, tissues, and cells with an unprecedented level of coverage. However, most of these approaches are still limited by the fact that organs and tissues are composed of multiple cell types that are each unique in their patterns of gene expression. To identify the transcriptome from a single cell type in a complex tissue, investigators have relied upon physical methods to separate cell types or in situ hybridization and immunohistochemistry. Here, we describe a strategy to rapidly and efficiently isolate ribosome-associated mRNA transcripts from any cell type in vivo. We have created a mouse line, called RiboTag, which carries an Rpl22 allele with a floxed wild-type C-terminal exon followed by an identical C-terminal exon that has three copies of the hemagglutinin (HA) epitope inserted before the stop codon. When the RiboTag mouse is crossed to a cell-type-specific Cre recombinase-expressing mouse, Cre recombinase activates the expression of epitope-tagged ribosomal protein RPL22 ha , which is incorporated into actively translating polyribosomes. Immunoprecipitation of polysomes with a monoclonal antibody against HA yields ribosome-associated mRNA transcripts from specific cell types. We demonstrate the application of this technique in brain using neuron-specific Cre recombinase-expressing mice and in testis using a Sertoli cell Cre recombinase-expressing mouse.
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
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?
Genetic tagging methods such as TRAP and Ribo-tag begin to overcome obstacles of physical enrichment but are limited to the analysis of mRNA expression.
“The recent invention of genetic tagging methods, such as TRAP (Heiman et al., 2008b) and Ribo-tag (Sanz et al., 2009), begin to overcome these obstacles, but these strategies have been limited to the analysis of mRNA expression.”
From Cell-Type-Based Analysis of MicroRNA Profiles in the Mouse Brain (2012) · cites this paper as Sanz et al., 2009
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Global analysis of protein localization in budding yeast
Huh, W.-K., Falvo, J. V., Gerke, L. C., Carroll, A. S., Howson, R. W., Weissman, J. S., & O'Shea, E. K. (2003) · Nature · doi:10.1038/nature02026 · Cited by 4,248 · cited 1× · see citing works
“Based on examination of large-scale yeast and mammalian protein tagging studies (9, 10), four core ribosome protein subunits (RPS17, RPS25, RPL22, and RPL36) that localized to the appropriate cellular compartments (nucleolus and cytoplasm) when C-terminally tagged with GFP were identified.”
BackgroundThe RIN: an RNA integrity number for assigning integrity values to RNA measurements
Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., Lightfoot, S., Menzel, W., Granzow, M., & Ragg, T. (2006) · BMC Molecular Biology · doi:10.1186/1471-2199-7-3 · Cited by 2,623 · see citing works
A translational profiling approach for the molecular characterization of CNS cell types
Heiman, M., Schaefer, A., Gong, S., Peterson, J. D., Day, M., Ramsey, K. E., Suárez-Fariñas, M., Schwarz, C., Stephan, D. A., Surmeier, D. J., et al. (2008) · Cell · doi:10.1016/j.cell.2008.10.028 · Cited by 1,134 · see citing works
Application of a translational profiling approach for the comparative analysis of CNS cell types
Doyle, J. P., Dougherty, J. D., Heiman, M., Schmidt, E. F., Stevens, T. R., Ma, G., Bupp, S., Shrestha, P., Shah, R. D., Doughty, M. L., et al. (2008) · Cell · doi:10.1016/j.cell.2008.10.029 · Cited by 912 · see citing works
Molecular taxonomy of major neuronal classes in the adult mouse forebrain
Sugino, K., Hempel, C. M., Miller, M. N., Hattox, A. M., Shapiro, P., Wu, C., Huang, Z. J., & Nelson, S. B. (2006) · Nature Neuroscience · doi:10.1038/nn1618 · Cited by 521 · see citing works
FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains
Lobo, M. K., Karsten, S. L., Gray, M., Geschwind, D. H., & Yang, X. W. (2006) · Nature Neuroscience · doi:10.1038/nn1654 · Cited by 411 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Astrocyte scar formation aids central nervous system axon regeneration
Anderson, M. A., Burda, J. E., Ren, Y., Ao, Y., O’Shea, T. M., Kawaguchi, R., Coppola, G., Khakh, B. S., Deming, T. J., & Sofroniew, M. V. (2016) · Nature · doi:10.1038/nature17623 · Cited by 1,851 · see citing works
Epigenomic Signatures of Neuronal Diversity in the Mammalian Brain
Mo, A., Mukamel, E. A., Davis, F. P., Luo, C., Henry, G. L., Picard, S., Urich, M. A., Nery, J. R., Sejnowski, T. J., Lister, R., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.018 · Cited by 739 · see citing works
Chai, H., Diaz-Castro, B., Shigetomi, E., Monte, E., Octeau, J. C., Yu, X., Cohn, W., Rajendran, P. S., Vondriska, T. M., Whitelegge, J. P., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.029 · Cited by 769 · see citing works
The aging astrocyte transcriptome from multiple regions of the mouse brain.
Boisvert, M. M., Erikson, G. A., Shokhirev, M. N., & Allen, N. J. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.039 · Cited by 702 · see citing works
Function and therapeutic value of astrocytes in neurological diseases
Lee, H.-G., Wheeler, M. A., & Quintana, F. J. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00390-x · Cited by 662 · see citing works
The Central Dogma Decentralized: New Perspectives on RNA Function and Local Translation in Neurons
Holt, C. E., & Schuman, E. M. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.036 · Cited by 525 · see citing works
Determinants of resident tissue macrophage identity and function
Blériot, C., Chakarov, S., & Ginhoux, F. (2020) · Immunity · doi:10.1016/j.immuni.2020.05.014 · Cited by 507 · see citing works
Spiegel, I., Mardinly, A. R., Gabel, H. W., Bazinet, J. E., Couch, C. H., Tzeng, C. P., Harmin, D. A., & Greenberg, M. E. (2014) · Cell · doi:10.1016/j.cell.2014.03.058 · Cited by 416 · see citing works
“To determine whether neuronal activity driven by sensory experience in vivo also induces shared early-response genes and distinct sets of late-response genes in inhibitory and excitatory neurons, we generated mice heterozygous for alleles of either Emx1-Cre (expressed specifically in excitatory neurons) or Gad2-Cre (e…”
MethodInfluentialSrinivasan, R., Lu, T.-Y., Chai, H., Xu, J., Huang, B. S., Golshani, P., Coppola, G., & Khakh, B. S. (2016) · Neuron · doi:10.1016/j.neuron.2016.11.030 · Cited by 443 · see citing works
Detecting Activated Cell Populations Using Single-Cell RNA-Seq
Wu, Y. E., Pan, L., Zuo, Y., Li, X., & Hong, W. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.026 · Cited by 438 · see citing works
The mammalian ribo-interactome reveals ribosome functional diversity and heterogeneity
Simsek, D., Tiu, G. C., Flynn, R. A., Byeon, G. W., Leppek, K., Xu, A. F., Chang, H. Y., & Barna, M. (2017) · Cell · doi:10.1016/j.cell.2017.05.022 · Cited by 439 · see citing works
Leptin signaling in astrocytes regulates hypothalamic neuronal circuits and feeding
Kim, J. G., Suyama, S., Koch, M., Jin, S., Argente-Arizon, P., Argente, J., Liu, Z.-W., Zimmer, M. R., Jeong, J. K., Szigeti-Buck, K., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3725 · Cited by 336 · see citing works
Microglial translational profiling reveals a convergent APOE pathway from aging, amyloid, and tau
Kang, S. S., Ebbert, M. T. W., Baker, K. E., Cook, C., Wang, X., Sens, J. P., Kocher, J.-P., Petrucelli, L., & Fryer, J. D. (2018) · Journal of Experimental Medicine · doi:10.1084/jem.20180653 · Cited by 260 · see citing works
Dietrich, M. O., Liu, Z.-W., & Horvath, T. L. (2013) · Cell · doi:10.1016/j.cell.2013.09.004 · Cited by 306 · see citing works
De Schepper, S., Ge, J. Z., Sierksma, A., Crowley, G., Ferreira, L. S. S., Garceau, D., Toomey, C. E., Sokolova, D., Rueda-Carrasco, J., Shin, S.-H., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01257-z · Cited by 295 · see citing works
Cell-Type-Based Analysis of MicroRNA Profiles in the Mouse Brain
He, M., Liu, Y., Wang, X., Zhang, M. Q., Hannon, G. J., & Huang, Z. J. (2012) · Neuron · doi:10.1016/j.neuron.2011.11.010 · Cited by 277 · see citing works
The translation of non-canonical open reading frames controls mucosal immunity
Jackson, R., Kroehling, L., Khitun, A., Bailis, W., Jarret, A., York, A. G., Khan, O. M., Brewer, J. R., Skadow, M. H., Duizer, C., et al. (2018) · Nature · doi:10.1038/s41586-018-0794-7 · Cited by 242 · see citing works
Itoh, N., Itoh, Y., Tassoni, A., Ren, E., Kaito, M., Ohno, A., Ao, Y., Farkhondeh, V., Johnsonbaugh, H., Burda, J., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1716032115 · Cited by 253 · see citing works
A subset of Kupffer cells regulates metabolism through the expression of CD36
Blériot, C., Barreby, E., Dunsmore, G., Ballaire, R., Chakarov, S., Ficht, X., De Simone, G., Andreata, F., Fumagalli, V., Guo, W., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.08.006 · Cited by 236 · see citing works
Genetic Approaches to Neural Circuits in the Mouse
Josh Huang, Z., & Zeng, H. (2013) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-062012-170307 · Cited by 200 · see citing works
Re-evaluating microglia expression profiles using RiboTag and cell isolation strategies
Haimon, Z., Volaski, A., Orthgiess, J., Boura-Halfon, S., Varol, D., Shemer, A., Yona, S., Zuckerman, B., David, E., Chappell-Maor, L., et al. (2018) · Nature Immunology · doi:10.1038/s41590-018-0110-6 · Cited by 209 · see citing works
Neurons under T Cell Attack Coordinate Phagocyte-Mediated Synaptic Stripping
Di Liberto, G., Pantelyushin, S., Kreutzfeldt, M., Page, N., Musardo, S., Coras, R., Steinbach, K., Vincenti, I., Klimek, B., Lingner, T., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.049 · Cited by 195 · see citing works
Sensory experience regulates cortical inhibition by inducing IGF1 in VIP neurons
Mardinly, A. R., Spiegel, I., Patrizi, A., Centofante, E., Bazinet, J. E., Tzeng, C. P., Mandel-Brehm, C., Harmin, D. A., Adesnik, H., Fagiolini, M., et al. (2016) · Nature · doi:10.1038/nature17187 · Cited by 176 · see citing works
Murine coagulation factor VIII is synthesized in endothelial cells
Everett, L. A., Cleuren, A. C. A., Khoriaty, R. N., & Ginsburg, D. (2014) · Blood · doi:10.1182/blood-2014-02-554501 · Cited by 190 · see citing works
Selective Neuronal Vulnerability in Alzheimer’s Disease: A Network-Based Analysis
Roussarie, J.-P., Yao, V., Rodriguez-Rodriguez, P., Oughtred, R., Rust, J., Plautz, Z., Kasturia, S., Albornoz, C., Wang, W., Schmidt, E. F., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.010 · Cited by 180 · see citing works
A Binary Cre Transgenic Approach Dissects Microglia and CNS Border-Associated Macrophages
Kim, J.-S., Kolesnikov, M., Peled-Hajaj, S., Scheyltjens, I., Xia, Y., Trzebanski, S., Haimon, Z., Shemer, A., Lubart, A., Van Hove, H., et al. (2021) · Immunity · doi:10.1016/j.immuni.2020.11.007 · Cited by 176 · see citing works
A new genetic strategy for targeting microglia in development and disease
McKinsey, G. L., Lizama, C. O., Keown-Lang, A. E., Niu, A., Santander, N., Larpthaveesarp, A., Chee, E., Gonzalez, F. F., & Arnold, T. D. (2020) · eLife · doi:10.7554/elife.54590 · Cited by 176 · see citing works
Voskuhl, R. R., Itoh, N., Tassoni, A., Matsukawa, M. A., Ren, E., Tse, V., Jang, E., Suen, T. T., & Itoh, Y. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821306116 · Cited by 162 · see citing works
Microbiota-modulated CART <sup>+</sup> enteric neurons autonomously regulate blood glucose
Muller, P. A., Matheis, F., Schneeberger, M., Kerner, Z., Jové, V., & Mucida, D. (2020) · Science · doi:10.1126/science.abd6176 · Cited by 154 · see citing works
Single-cell RNA-seq uncovers a robust transcriptional response to morphine by glia
Avey, D., Sankararaman, S., Yim, A. K. Y., Barve, R., Milbrandt, J., & Mitra, R. D. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.08.080 · Cited by 143 · see citing works
McKinley, J. W., Shi, Z., Kawikova, I., Hur, M., Bamford, I. J., Sudarsana Devi, S. P., Vahedipour, A., Darvas, M., & Bamford, N. S. (2019) · Neuron · doi:10.1016/j.neuron.2019.06.013 · Cited by 130 · see citing works
Identification of neurodegenerative factors using translatome–regulatory network analysis
Brichta, L., Shin, W., Jackson-Lewis, V., Blesa, J., Yap, E.-L., Walker, Z., Zhang, J., Roussarie, J.-P., Alvarez, M. J., Califano, A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4070 · Cited by 118 · see citing works
Ribosome Profiling Reveals a Cell-Type-Specific Translational Landscape in Brain Tumors
Gonzalez, C., Sims, J. S., Hornstein, N., Mela, A., Garcia, F., Lei, L., Gass, D. A., Amendolara, B., Bruce, J. N., Canoll, P., et al. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0084-14.2014 · Cited by 116 · see citing works
RNA-binding proteins in neurodegeneration: Seq and you shall receive
Nussbacher, J. K., Batra, R., Lagier-Tourenne, C., & Yeo, G. W. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2015.02.003 · Cited by 115 · see citing works
Genetic dissection of GABAergic neural circuits in mouse neocortex
Taniguchi, H. (2014) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2014.00008 · Cited by 106 · see citing works
Dendritic remodeling of D1 neurons by RhoA/Rho-kinase mediates depression-like behavior
Fox, M. E., Chandra, R., Menken, M. S., Larkin, E. J., Nam, H., Engeln, M., Francis, T. C., & Lobo, M. K. (2020) · Molecular Psychiatry · doi:10.1038/s41380-018-0211-5 · Cited by 100 · see citing works
Glia in neurodegeneration: Drivers of disease or along for the ride?
Gleichman, A. J., & Carmichael, S. T. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104957 · Cited by 90 · see citing works
Molecular basis of dendritic atrophy and activity in stress susceptibility
Francis, T. C., Chandra, R., Gaynor, A., Konkalmatt, P., Metzbower, S. R., Evans, B., Engeln, M., Blanpied, T. A., & Lobo, M. K. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.178 · Cited by 89 · see citing works
DV200 Index for assessing RNA integrity in next‐generation sequencing
Matsubara, T., Soh, J., Morita, M., Uwabo, T., Tomida, S., Fujiwara, T., Kanazawa, S., Toyooka, S., & Hirasawa, A. (2020) · BioMed Research International · doi:10.1155/2020/9349132 · Cited by 97 · see citing works
Chen, W., Zheng, Q., Huang, Q., Ma, S., & Li, M. (2022) · eLife · doi:10.7554/elife.75636 · Cited by 89 · see citing works
Inducible targeting of CNS astrocytes in Aldh1l1-CreERT2 BAC transgenic mice
Winchenbach, J., Düking, T., Berghoff, S. A., Stumpf, S. K., Hülsmann, S., Nave, K.-A., & Saher, G. (2016) · F1000Research · doi:10.12688/f1000research.10509.1 · Cited by 74 · see citing works
Scheckel, C., Imeri, M., Schwarz, P., & Aguzzi, A. (2020) · eLife · doi:10.7554/elife.62911 · Cited by 57 · see citing works
Spatiotemporally resolved protein synthesis as a molecular framework for memory consolidation
Shrestha, P., & Klann, E. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.01.004 · Cited by 54 · see citing works
STOP floxing around: Specificity and leakiness of inducible Cre/loxP systems
Stifter, S. A., & Greter, M. (2020) · European Journal of Immunology · doi:10.1002/eji.202048546 · Cited by 48 · see citing works
Reduced Slc6a15 in Nucleus Accumbens D2-Neurons Underlies Stress Susceptibility
Chandra, R., Francis, T. C., Nam, H., Riggs, L. M., Engeln, M., Rudzinskas, S., Konkalmatt, P., Russo, S. J., Turecki, G., Iniguez, S. D., et al. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3250-16.2017 · Cited by 49 · see citing works
Molecular neuroanatomy: a generation of progress
Pollock, J. D., Wu, D.-Y., & Satterlee, J. S. (2014) · Trends in Neurosciences · doi:10.1016/j.tins.2013.11.001 · Cited by 36 · see citing works
Göcz, B., Rumpler, É., Sárvári, M., Skrapits, K., Takács, S., Farkas, I., Csillag, V., Trinh, S. H., Bardóczi, Z., Ruska, Y., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113749119 · Cited by 35 · see citing works
Chandra, R., Engeln, M., Francis, T. C., Konkalmatt, P., Patel, D., & Lobo, M. K. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.10.024 · Cited by 35 · see citing works
Zhou, L., Xu, Z., Oh, Y., Gamuyao, R., Lee, G., Xie, Y., Cho, H., Lee, S., & Duh, E. J. (2021) · JCI Insight · doi:10.1172/jci.insight.93382 · Cited by 34 · see citing works
Senescence in aging and disorders of the central nervous system
Swenson, B. L., Meyer, C. F., Bussian, T. J., & Baker, D. J. (2019) · Translational Medicine of Aging · doi:10.1016/j.tma.2019.01.002 · Cited by 24 · see citing works
microRNA-33 controls hunger signaling in hypothalamic AgRP neurons
Price, N. L., Fernández-Tussy, P., Varela, L., Cardelo, M. P., Shanabrough, M., Aryal, B., de Cabo, R., Suárez, Y., Horvath, T. L., & Fernández-Hernando, C. (2024) · Nature Communications · doi:10.1038/s41467-024-46427-0 · Cited by 25 · see citing works
“These mice express an unmodi fi ed copy of the core ribosome subunit RPL22 under normal conditions, which is excised in cells expressing CRE, leading to the expression of a modi fi ed RPL22 construct containing multiple HA tags 30,31 .”
BackgroundCanesin, G., Feldbrügge, L., Wei, G., Janovicova, L., Janikova, M., Csizmadia, E., Ariffin, J., Hedblom, A., Herbert, Z. T., Robson, S. C., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104983 · Cited by 19 · see citing works
- Authors
- Sanz, E., Yang, L., Su, T., Morris, D. R., McKnight, G. S., & Amieux, P. S.
- Affiliations
- Pharmacology, University of Washington, Seattle, WA 98195
- Departments of bBiochemistry and
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2009-08-18
- Volume
- 106
- Issue
- 33
- Pages
- 13939-13944
- ISSN
- 0027-8424
- 1091-6490
- Cited by
- 982
- References
- 6
- Language
- en
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
