Skip to content
naulon

No retraction, correction or concern found when last checked on Nov 4, 2026.

SM-Omics is an automated platform for high-throughput spatial multi-omics

S. Vickovic, B. Lötstedt, J. Klughammer and 4 more

Nature Communications · 2022 · CC BY

doi:10.1101/2020.10.14.338418

Abstract

The spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial transcriptomics has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of spatial transcriptomics at scale, by presenting Spatial Multi-Omics (SM-Omics) as a fully automated, high-throughput all-sequencing based platform for combined and spatially resolved transcriptomics and antibody-based protein measurements. SM-Omics uses DNA-barcoded antibodies, immunofluorescence or a combination thereof, to scale and combine spatial transcriptomics and spatial antibody-based multiplex protein detection. SM-Omics allows processing of up to 64 in situ spatial reactions or up to 96 sequencing-ready libraries, of high complexity, in a ~2 days process. We demonstrate SM-Omics in the mouse brain, spleen and colorectal cancer model, showing its broad utility as a high-throughput platform for spatial multi-omics.

References 70

  1. 1.

    Jovanovic M, et al. Immunogenetics. Dynamic profiling of the protein life cycle in response to pathogens. Science. 2015;347:1259038.

    Not in our library10.1126/science.1259038

  2. 2.

    Rabani M, et al. High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies. Cell. 2014;159:1698-1710.

    Not in our library10.1016/j.cell.2014.11.015

  3. 3.

    Chick JM, et al. Defining the consequences of genetic variation on a proteome-wide scale. Nature. 2016;534:500-505.

    Not in our library10.1038/nature18270

  4. 4.

    Zhang B, et al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014;513:382-387.

    Not in our library10.1038/nature13438

  5. 5.

    Stoeckius M, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods. 2017;14:865-868.

    Not in our library10.1038/nmeth.4380

  6. 6.
  7. 7.

    Macosko EZ, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202-1214.

    Not in our library10.1016/j.cell.2015.05.002

  8. 8.

    Klein AM, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161:1187-1201.

    Not in our library10.1016/j.cell.2015.04.044

  9. 9.

    Habib N, et al. Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science. 2016;353:925-928.

    Not in our library10.1126/science.aad7038

  10. 10.

    Habib N, et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods. 2017;14:955-958.

    Not in our library10.1038/nmeth.4407

  11. 11.
  12. 12.

    Darmanis S, et al. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep. 2016;14:380-389.

    Not in our library10.1016/j.celrep.2015.12.021

  13. 13.

    Hanahan D, Coussens LM. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell. 2012;21:309-322.

    Not in our library10.1016/j.ccr.2012.02.022

  14. 14.

    Bodenmiller B. Multiplexed epitope-based tissue imaging for discovery and healthcare applications. Cell Syst. 2016;2:225-238.

    Not in our library10.1016/j.cels.2016.03.008

  15. 15.

    Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;348:aaa6090.

    Not in our library10.1126/science.aaa6090

  16. 16.

    Lubeck E, Coskun AF, Zhiyentayev T, Ahmad M, Cai L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods. 2014;11:360-361.

    Not in our library10.1038/nmeth.2892

  17. 17.

    Eng C-HL, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature. 2019;568:235-239.

    Not in our library10.1038/s41586-019-1049-y

  18. 18.

    Lee JH, et al. Highly multiplexed subcellular RNA sequencing in situ. Science. 2014;343:1360-1363.

    Not in our library10.1126/science.1250212

  19. 19.
  20. 20.

    Keren L, et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell. 2018;174:1373-1387.e19.

    Not in our library10.1016/j.cell.2018.08.039

  21. 21.

    Codeluppi S, et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods. 2018;15:932-935.

    Not in our library10.1038/s41592-018-0175-z

  22. 22.

    Moffitt JR, et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science. 2018;362:749-750.

    Not in our library10.1126/science.aau5324

  23. 23.

    Ke R, et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods. 2013;10:857-860.

    Not in our library10.1038/nmeth.2563

  24. 24.

    Ståhl PL, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353:78-82.

    Not in our library10.1126/science.aaf2403

  25. 25.

    Rodriques SG, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019;363:1463-1467.

    Not in our library10.1126/science.aaw1219

  26. 26.

    Vickovic S, et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods. 2019;16:987-990.

    Not in our library10.1038/s41592-019-0548-y

  27. 27.

    Wang X, et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science. 2018;361:328-329.

    Not in our library10.1126/science.aat5691

  28. 28.

    Maynard KR, et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 2021;24:425-436.

    Not in our library10.1038/s41593-020-00787-0

  29. 29.

    Thrane K, Eriksson H, Maaskola J, Hansson J, Lundeberg J. Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma. Cancer Res. 2018;78:5970-5979.

    Not in our library10.1158/0008-5472.can-18-0747

  30. 30.

    Asp M, et al. Spatial detection of fetal marker genes expressed at low level in adult human heart tissue. Sci. Rep. 2017;7:12941.

    Not in our library10.1038/s41598-017-13462-5

  31. 31.

    Asp M, et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell. 2019;179:1647-1660.e19.

    Not in our library10.1016/j.cell.2019.11.025

  32. 32.

    Maniatis S, et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science. 2019;364:89-93.

    Not in our library10.1126/science.aav9776

  33. 33.
  34. 34.

    Berglund E, et al. Automation of spatial transcriptomics library preparation to enable rapid and robust insights into spatial organization of tissues. BMC Genomics. 2020;21:298.

    Not in our library10.1186/s12864-020-6631-z

  35. 35.

    Jemt A, et al. An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries. Sci. Rep. 2016;6:37137.

    Not in our library10.1038/srep37137

  36. 36.
  37. 37.

    Lein E, Borm LE, Linnarsson S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science. 2017;358:64-69.

    Not in our library10.1126/science.aan6827

  38. 38.

    Gerdes MJ, et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA. 2013;110:11982-11987.

    Not in our library10.1073/pnas.1300136110

  39. 39.
  40. 40.

    Angelo M, et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 2014;20:436-442.

    Not in our library10.1038/nm.3488

All 70 references

Cited by papers we hold 4

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.