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

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

6. 6.
   Massively parallel digital transcriptional profiling of single cells · 2017

   No notice
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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

11. 11.
    A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors · 2020

    Corrected
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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

19. 19.
    Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging · 2018

    No notice
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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

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 (opens in a new tab)

33. 33.
    Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity · 2018

    No notice
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 (opens in a new tab)

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 (opens in a new tab)

36. 36.
    Massive and parallel expression profiling using microarrayed single-cell sequencing · 2016

    Not checked
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 (opens in a new tab)

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 (opens in a new tab)

39. 39.
    Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method · 2015

    No notice
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 (opens in a new tab)

41. 41.

    Giesen C, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods. 2014;11:417-422.

    Not in our library10.1038/nmeth.2869 (opens in a new tab)

42. 42.

    Merritt CR, et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 2020;38:586-599.

    Not in our library10.1038/s41587-020-0472-9 (opens in a new tab)

43. 43.

    Schulz D, et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 2018;6:531.

    Not in our library10.1016/j.cels.2018.04.004 (opens in a new tab)

44. 44.

    10x Genomics. https://www.10xgenomics.com/products/spatial-proteomics.

    Not in our library

45. 45.
    A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries · 2011

    No notice
46. 46.

    Rohland N, Reich D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 2012;22:939-946.

    Not in our library10.1101/gr.128124.111 (opens in a new tab)

47. 47.

    Salmén F, et al. Barcoded solid-phase RNA capture for spatial transcriptomics profiling in mammalian tissue sections. Nat. Protoc. 2018;13:2501-2534.

    Not in our library10.1038/s41596-018-0045-2 (opens in a new tab)

48. 48.

    Wong K, Navarro JF, Bergenstråhle L, Ståhl PL, Lundeberg J. ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets. Bioinformatics. 2018;34:1966-1968.

    Not in our library10.1093/bioinformatics/bty030 (opens in a new tab)

49. 49.

    Äijö, T. et al. Splotch: Robust estimation of aligned spatial temporal gene expression data. 10.1101/757096.

    Not in our library

50. 50.

    Lein ES, et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature. 2007;445:168-176.

    Not in our library10.1038/nature05453 (opens in a new tab)

51. 51.

    Kang H, et al. An important role of neural activity-dependent CaMKIV signaling in the consolidation of long-term memory. Cell. 2001;106:771-783.

    Not in our library10.1016/s0092-8674(01)00497-4 (opens in a new tab)

52. 52.

    Mann AP, et al. Identification of a peptide recognizing cerebrovascular changes in mouse models of Alzheimer’s disease. Nat. Commun. 2017;8:1403.

    Not in our library10.1038/s41467-017-01096-0 (opens in a new tab)

53. 53.

    TotalSeqTM. https://www.biolegend.com/en-us/totalseq.

    Not in our library

54. 54.
    Neuronal organization of olfactory bulb circuits · 2014

    No notice
55. 55.
    Molecular Architecture of the Mouse Nervous System · 2018

    No notice
56. 56.

    Stickels RR, et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 2021;39:313-319.

    Not in our library10.1038/s41587-020-0739-1 (opens in a new tab)

57. 57.

    Chen, A. et al. Large field of view-spatially resolved transcriptomics at nanoscale resolution. Preprint at 10.1101/2021.01.17.427004 (2021).

    Not in our library

58. 58.

    Su G, et al. Spatial multi-omics sequencing for fixed tissue via DBiT-seq. STAR Protoc. 2021;2:100532.

    Not in our library10.1016/j.xpro.2021.100532 (opens in a new tab)

59. 59.

    Navarro JF, Sjöstrand J, Salmén F, Lundeberg J, Ståhl PL. ST Pipeline: an automated pipeline for spatial mapping of unique transcripts. Bioinformatics. 2017;33:2591-2593.

    Not in our library10.1093/bioinformatics/btx211 (opens in a new tab)

60. 60.

    Dobin A, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15-21.

    Not in our library10.1093/bioinformatics/bts635 (opens in a new tab)

61. 61.
    HTSeq—a Python framework to work with high-throughput sequencing data · 2014

    No notice
62. 62.

    Costea PI, Lundeberg J, Akan P. TagGD: fast and accurate software for DNA Tag generation and demultiplexing. PLoS ONE. 2013;8:e57521.

    Not in our library10.1371/journal.pone.0057521 (opens in a new tab)

63. 63.

    Schindelin J, et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods. 2012;9:676-682.

    Not in our library10.1038/nmeth.2019 (opens in a new tab)

64. 64.
    scikit-image: image processing in Python · 2014

    No notice
65. 65.

    Svensson V, Teichmann SA, Stegle O. SpatialDE: identification of spatially variable genes. Nat. Methods. 2018;15:343-346.

    Not in our library10.1038/nmeth.4636 (opens in a new tab)

66. 66.

    Jones, E., Peterson, P. & Oliphant, T. SciPy: Open Source Scientific Tools for Python. Scipyhttp://www.scipy.org/ (2001).

    Not in our library

67. 67.

    Lord SJ, Velle KB, Mullins RD, Fritz-Laylin LK. SuperPlots: communicating reproducibility and variability in cell biology. J. Cell Biol. 2020;219:e202001064.

    Not in our library10.1083/jcb.202001064 (opens in a new tab)

68. 68.

    Svensson V, et al. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods. 2017;14:381-387.

    Not in our library10.1038/nmeth.4220 (opens in a new tab)

69. 69.

    Berg S, et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods. 2019;16:1226-1232.

    Not in our library10.1038/s41592-019-0582-9 (opens in a new tab)

70. 70.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. 1995;57:289-300.

    Not in our library

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