Continuous Polony Gels for Tissue Mapping with High Resolution and RNA Capture Efficiency
Open accessAbstract
Current technologies for acquiring spatial transcript information from tissue sections rely on either RNA probes or spatial barcodes. The former methods require a priori knowledge for probeset formulation; the latter have yet to achieve single cell resolution and/or transcript capture efficiencies approaching dissociative, single-cell methods. Here, we describe a novel spatial transcriptome assay called p olony (or DNA cluster)- i nde xe d l ibrary-sequencing (PIXEL-seq). It improves upon other spatial barcoding methods by employing “continuous” polony oligos arrayed across a customized gel surface. In terms of assay performance, PIXEL-seq attains ≤ 1 µm resolution and captures >1,000 unique molecular identifiers/10×10 µm 2 . In other words, this global, naive platform achieves subcellular spatial transcriptome mapping while maintaining high transcript capture efficiencies.
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?
Previous studies have shown that KLK6 and MBP expression is restricted to oligodendrocytes, while ATP1B1 is mainly expressed in neurons and astrocytes.
“Previous studies have shown that KLK6 and MBP expression is restricted to oligodendrocytes, while ATP1B1 is mainly expressed in neurons and astrocytes 19 .”
From SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network (2021) · cites this paper as [19]
The passages on mouse brain expression patterns (Abstract, page unavailable) and the marker-gene list in Figure 3 (PIXEL-seq data analysis, page unavailable) discuss other genes, not the claimed cell-type distributions. Searches for KLK6, MBP, ATP1B1, and oligodendrocytes found no passage in our copy addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · see citing works
Accurate whole human genome sequencing using reversible terminator chemistry
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684 · see citing works
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358 · see citing works
Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
Stickels, R. R., Murray, E., Kumar, P., Li, J., Marshall, J. L., Di Bella, D. J., Arlotta, P., Macosko, E. Z., & Chen, F. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0739-1 · Cited by 1,295 · see citing works
Human Genome Sequencing Using Unchained Base Reads on Self-Assembling DNA Nanoarrays
Drmanac, R., Sparks, A. B., Callow, M. J., Halpern, A. L., Burns, N. L., Kermani, B. G., Carnevali, P., Nazarenko, I., Nilsen, G. B., Yeung, G., et al. (2010) · Science · doi:10.1126/science.1181498 · Cited by 1,212 · see citing works
Quantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183 · see citing works
In situ sequencing for RNA analysis in preserved tissue and cells
Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wählby, C., & Nilsson, M. (2013) · Nature Methods · doi:10.1038/nmeth.2563 · Cited by 934 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- R. Bumgarner, in Curr. Protoc. Mol. Biol. (2013), chap. 22, pp. 22.21.21-22.21.11.
- R. D. Mitra, G. M. Church, In situ localized amplification and contact replication of many individual DNA molecules. Nucleic Acids Res. 27, e34 (1999).
- N. E. Broude, K. Woodward, R. Cavallo, C. R. Cantor, D. Englert, DNA microarrays with stem-loop DNA probes: preparation and applications. Nucleic Acids Res. 29, e92 (2001).
- A. Chen, S. Liao, M. Cheng, K. Ma, L. Wu, Y. Lai, J. Yang, W. Li, J. Xu, S. Hao, X. Chen, X. Liu, X. Huang, F. Lin, X. Tang, Z. Li, Y. Hong, D. Fu, Y. Jiang, J. Peng, S. Liu, M. Shen, C. Liu, Q. Li, Z. Wang, Z. Wang, X. Huang, Y. Yuan, G. Volpe, C. Ward, P. Muñoz-Cánoves, J. P. Thiery, F. Zhao, M. Li, H. Kuang, O. Wang, H. Lu, B. Wang, M. Ni, W. Zhang, F. Mu, Y. Yin, H. Yang, M. Lisby, R. J. Cornall, M. Uhlen, M. A. Esteban, Y. Li, L. Liu, J. Wang, X. Xu, Large field of view-spatially resolved transcriptomics at nanoscale resolution. bioRxiv, 2021.2001.2017.427004 (2021).
- Y. Hao, S. Hao, E. Andersen-Nissen, W. M. Mauck, S. Zheng, A. Butler, M. J. Lee, A. J. Wilk, C. Darby, M. Zagar, P. Hoffman, M. Stoeckius, E. Papalexi, E. P. Mimitou, J. Jain, A. Srivastava, T. Stuart, L. B. Fleming, B. Yeung, A. J. Rogers, J. M. McElrath, C. A. Blish, R. Gottardo, P. Smibert, R. Satija, Integrated analysis of multimodal single-cell data. bioRxiv, 2020.2010.2012.335331 (2020).
- K. Lebrigand, J. Bergenstråhle, K. Thrane, A. Mollbrink, P. Barbry, R. Waldmann, J. Lundeberg, The spatial landscape of gene expression isoforms in tissue sections. bioRxiv, 2020.2008.2024.252296 (2020).
Cited by
Sorted by citations
Exploring tissue architecture using spatial transcriptomics
Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700 · see citing works
Hu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D. J., Lee, E. B., Shinohara, R. T., & Li, M. (2021) · Nature Methods · doi:10.1038/s41592-021-01255-8 · Cited by 1,113 · see citing works
He, S., Bhatt, R., Brown, C., Brown, E. A., Buhr, D. L., Chantranuvatana, K., Danaher, P., Dunaway, D., Garrison, R. G., Geiss, G., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01483-z · Cited by 814 · see citing works
Long, Y., Ang, K. S., Li, M., Chong, K. L. K., Sethi, R., Zhong, C., Xu, H., Ong, Z., Sachaphibulkij, K., Chen, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36796-3 · Cited by 700 · see citing works
Self-citationThe expanding vistas of spatial transcriptomics
Tian, L., Chen, F., & Macosko, E. Z. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01448-2 · Cited by 539 · see citing works
- Authors
- Fu, X., Sun, L., Chen, J. Y., Dong, R., Lin, Y., Palmiter, R. D., Lin, S., & Gu, L.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Washington University in St. Louis (US)
- Corresponding author
- Gu, Liangcai
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2021-03-17
- OpenAlex
- W3138548670
- Cited by
- 64
- References
- 7
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 14 · 2023: 11 · 2022: 23 · 2021: 8
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- University of Washington
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- spatial transcriptomics, single-cell resolution, unique molecular identifiers, tissue sections
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
