The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Single-molecule RNA detection at depth by hybridization chain reaction and tissue hydrogel embedding and clearing
Open accessAbstract
Accurate and robust detection of mRNA molecules in thick tissue samples can reveal gene expression patterns in single cells within their native environment. Preserving spatial relationships while accessing the transcriptome of selected cells is a crucial feature for advancing many biological areas – from developmental biology to neuroscience. However, because of the high autofluorescence background of many tissue samples, it is difficult to detect single-molecule fluorescence in situ hybridization (smFISH) signals robustly in opaque thick samples. Here, we draw on principles from the emerging discipline of dynamic nucleic acid nanotechnology to develop a robust method for multi-color, multi-RNA imaging in deep tissues using single-molecule hybridization chain reaction (smHCR). Using this approach, single transcripts can be imaged using epifluorescence, confocal or selective plane illumination microscopy (SPIM) depending on the imaging depth required. We show that smHCR has high sensitivity in detecting mRNAs in cell culture and whole-mount zebrafish embryos, and that combined with SPIM and PACT (passive CLARITY technique) tissue hydrogel embedding and clearing, smHCR can detect single mRNAs deep within thick (0.5 mm) brain slices. By simultaneously achieving ∼20-fold signal amplification and diffraction-limited spatial resolution, smHCR offers a robust and versatile approach for detecting single mRNAs in situ, including in thick tissues where high background undermines the performance of unamplified smFISH.
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
Imaging individual mRNA molecules using multiple singly labeled probes
Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A., & Tyagi, S. (2008) · Nature Methods · doi:10.1038/nmeth.1253 · Cited by 2,028
Structural and molecular interrogation of intact biological systems
Chung, K., Wallace, J., Kim, S.-Y., Kalyanasundaram, S., Andalman, A. S., Davidson, T. J., Mirzabekov, J. J., Zalocusky, K. A., Mattis, J., Denisin, A. K., et al. (2013) · Nature · doi:10.1038/nature12107 · Cited by 2,008
Self-citationStochastic mRNA Synthesis in Mammalian Cells
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y., & Tyagi, S. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040309 · Cited by 1,737
Visualization of Single RNA Transcripts in Situ
Femino, A. M., Fay, F. S., Fogarty, K., & Singer, R. H. (1998) · Science · doi:10.1126/science.280.5363.585 · Cited by 1,504
Single-cell in situ RNA profiling by sequential hybridization
Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M., & Cai, L. (2014) · Nature Methods · doi:10.1038/nmeth.2892 · Cited by 985
Self-citation
Cited by
Choi, H. M. T., Schwarzkopf, M., Fornace, M. E., Acharya, A., Artavanis, G., Stegmaier, J., Cunha, A., & Pierce, N. A. (2018) · Development · doi:10.1242/dev.165753 · Cited by 1,495
Self-citationThree-dimensional intact-tissue sequencing of single-cell transcriptional states
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620
Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors
Patriarchi, T., Cho, J. R., Merten, K., Howe, M. W., Marley, A., Xiong, W.-H., Folk, R. W., Broussard, G. J., Liang, R., Jang, M. J., et al. (2018) · Science · doi:10.1126/science.aat4422 · Cited by 1,074
Self-citationShah, S., Lubeck, E., Zhou, W., & Cai, L. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.001 · Cited by 671
Self-citationSpatially resolved transcriptomes—next generation tools for tissue exploration
Asp, M., Bergenstråhle, J., & Lundeberg, J. (2020) · BioEssays · doi:10.1002/bies.201900221 · Cited by 666
Bressan, D., Battistoni, G., & Hannon, G. J. (2023) · Science · doi:10.1126/science.abq4964 · Cited by 594
A Comprehensive Roadmap of Murine Spermatogenesis Defined by Single-Cell RNA-Seq
Green, C. D., Ma, Q., Manske, G. L., Shami, A. N., Zheng, X., Marini, S., Moritz, L., Sultan, C., Gurczynski, S. J., Moore, B. B., et al. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.07.025 · Cited by 521
Highly multiplexed in situ protein imaging with signal amplification by Immuno-SABER
Saka, S. K., Wang, Y., Kishi, J. Y., Zhu, A., Zeng, Y., Xie, W., Kirli, K., Yapp, C., Cicconet, M., Beliveau, B. J., et al. (2018) · doi:10.1101/507566 · Cited by 485
Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues
Saka, S. K., Wang, Y., Kishi, J. Y., Zhu, A., Zeng, Y., Xie, W., Kirli, K., Yapp, C., Cicconet, M., Beliveau, B. J., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0207-y · Cited by 481
SABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues
Kishi, J. Y., Lapan, S. W., Beliveau, B. J., West, E. R., Zhu, A., Sasaki, H. M., Saka, S. K., Wang, Y., Cepko, C. L., & Yin, P. (2019) · Nature Methods · doi:10.1038/s41592-019-0404-0 · Cited by 454
The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing
Lein, E., Borm, L. E., & Linnarsson, S. (2017) · Science · doi:10.1126/science.aan6827 · Cited by 383
Thirst-associated preoptic neurons encode an aversive motivational drive.
Allen, W. E., DeNardo, L. A., Chen, M. Z., Liu, C. D., Loh, K. M., Fenno, L. E., Ramakrishnan, C., Deisseroth, K., & Luo, L. (2017) · Science · doi:10.1126/science.aan6747 · Cited by 390
Protection of tissue physicochemical properties using polyfunctional crosslinkers
Park, Y.-G., Sohn, C. H., Chen, R., McCue, M., Yun, D. H., Drummond, G. T., Ku, T., Evans, N. B., Oak, H. C., Trieu, W., et al. (2019) · Nature Biotechnology · doi:10.1038/nbt.4281 · Cited by 421
Self-citationChallenges and emerging directions in single-cell analysis
Yuan, G.-C., Cai, L., Elowitz, M., Enver, T., Fan, G., Guo, G., Irizarry, R., Kharchenko, P., Kim, J., Orkin, S., et al. (2017) · Genome Biology · doi:10.1186/s13059-017-1218-y · Cited by 341
Self-citationMoffitt, J. R., Hao, J., Bambah-Mukku, D., Lu, T., Dulac, C., & Zhuang, X. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617699113 · Cited by 308
Using single-cell genomics to understand developmental processes and cell fate decisions
Griffiths, J. A., Scialdone, A., & Marioni, J. C. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178046 · Cited by 250
Single-cell transcriptomic evidence for dense intracortical neuropeptide networks
Smith, S. J., Sümbül, U., Graybuck, L. T., Collman, F., Seshamani, S., Gala, R., Gliko, O., Elabbady, L., Miller, J. A., Bakken, T. E., et al. (2019) · eLife · doi:10.7554/elife.47889 · Cited by 156
From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
Chen, X., Teichmann, S. A., & Meyer, K. B. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013452 · Cited by 112
Dense functional and molecular readout of a circuit hub in sensory cortex
Condylis, C., Ghanbari, A., Manjrekar, N., Bistrong, K., Yao, S., Yao, Z., Nguyen, T. N., Zeng, H., Tasic, B., & Chen, J. L. (2022) · Science · doi:10.1126/science.abl5981 · Cited by 81
Exploring single cells in space and time during tissue development, homeostasis and regeneration
Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63
Grancharova, T., Gerbin, K. A., Rosenberg, A. B., Roco, C. M., Arakaki, J. E., DeLizo, C. M., Dinh, S. Q., Donovan-Maiye, R. M., Hirano, M., Nelson, A. M., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-94732-1 · Cited by 61
Gerbin, K. A., Grancharova, T., Donovan-Maiye, R. M., Hendershott, M. C., Anderson, H. G., Brown, J. M., Chen, J., Dinh, S. Q., Gehring, J. L., Johnson, G. R., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.001 · Cited by 59
Kohman, R. E., Kunjapur, A. M., Hysolli, E., Wang, Y., & Church, G. M. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201707976 · Cited by 33
Encoding Method of Single-cell Spatial Transcriptomics Sequencing
Zhou, Y., Jia, E., Pan, M., Zhao, X., & Ge, Q. (2020) · International Journal of Biological Sciences · doi:10.7150/ijbs.43887 · Cited by 32
The future of cerebral organoids in drug discovery
Salick, M. R., Lubeck, E., Riesselman, A., & Kaykas, A. (2021) · Seminars in Cell & Developmental Biology · doi:10.1016/j.semcdb.2020.05.024 · Cited by 29
Self-citationImage-based 3D genomics through chromatin tracing
Yang, T., & Wang, S. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00354-y · Cited by 10
Metadata
- Authors
- Shah, S., Lubeck, E., Schwarzkopf, M., He, T.-F., Greenbaum, A., Sohn, C. H., Lignell, A., Choi, H. M. T., Gradinaru, V., Pierce, N. A., & Cai, L.
- Venue
- Development
- Publisher
- The Company of Biologists
- Published
- 2016-08-01
- Volume
- 143
- Issue
- 15
- Pages
- 2862-2867
- ISSN
- 0950-1991 (ISSN-L)
- 1477-9129
- OpenAlex
- W2467652252
- Cited by
- 210
- References
- 5
- FWCI
- 11.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
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.
