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Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics

Nature Protocols volume 20, pages 643–689 (2025)Cite this article

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Abstract

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Key points

  • The protocol repurposes an Illumina NovaSeq 6000 flow cell for spatial transcriptomics, generating high-resolution datasets and integrating a streamlined data-analysis pipeline.

  • Leveraging commonly available Illumina equipment, the protocol offers researchers ultra-high, submicrometer resolution in spatial transcriptomics analysis with a comprehensive analytical pipeline, whole-transcriptome coverage, rapid turnaround, cost efficiency and versatility.

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Fig. 1: Overview of experimental procedures.
Fig. 2: An overview of computational procedures.
Fig. 3: Example outputs from the Illumina SAV application.
Fig. 4: Flow cell disassembly and dicing.
Fig. 5: Liquid handling in Seq-Scope chip surface treatment.
Fig. 6: Tissue-freezing chamber.
Fig. 7: Tissue sectioning and attachment.
Fig. 8: Liquid- and chip-handling procedure.
Fig. 9: Seq-Scope adapter frame and silicone isolator used in Part 3: library construction.
Fig. 10: Conceptual representation of the NovaScope workflow based on the ‘Request’ option.
Fig. 11: Spatial arrangement between tiles in the NovaSeq 6000 S4 flow cell.
Fig. 12: An output sbcd image from Step 168 illustrates the distribution of all spatial barcodes (i.e., HDMIs).
Fig. 13: An output smatch image from Step 172, showing the distribution of spatial barcodes (i.e., HDMIs) that matched to the 2nd-Seq FASTQ files.
Fig. 14: An output sge image from Step 174, showing the distribution of spatial barcodes (HDMIs) that align to the reference genome.
Fig. 15: Comparison between sge (left) and aligned histology (right) images.
Fig. 16: An exemplary multidimensional clustering result obtained by using Seurat.
Fig. 17: An exemplary output image from Step 191, illustrating distinct zonation of hepatocellular factors.
Fig. 18: An exemplary pixel-level output image from LDA-based analysis showing clear zonation of hepatocellular factors.
Fig. 19: An exemplary pixel-level output image from Seurat-based analysis showing hepatocellular and non-parenchymal cell factors.
Fig. 20: An exemplary black-and-white segmentation image.
Fig. 21: An exemplary overlay analysis to inspect the cell segmentation performance.
Fig. 22: An exemplary multidimensional clustering result obtained by using Seurat, based on the single-cell data, segmented through Watershed implemented in ImageJ.
Fig. 23: An exemplary multidimensional clustering result obtained by using Seurat, based on the single-cell data, segmented through Cellpose.
Fig. 24: Seq-Scope analysis using shallowly sequenced liver data (~163 million 2nd-Seq read inputs).

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Data availability

All source data described in this protocol are available online via public repositories, such as Zenodo and Deep Blue Data: minimal test run dataset (https://doi.org/10.5281/zenodo.10835761), shallow-sequenced liver dataset (https://doi.org/10.5281/zenodo.10840696), deep-sequenced liver dataset (https://doi.org/10.7302/tw62-4f97) and exemplary downstream analysis input (https://doi.org/10.5281/zenodo.10841777).

Code availability

All source code described in this protocol is available online via GitHub: NovaScope pipeline, v1.0.0 (https://github.com/seqscope/novascope) and NEDA, v1.0.0 (https://github.com/seqscope/NovaScope-exemplary-downstream-analysis).

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Acknowledgements

The work was supported by the Taubman Institute Innovation Project (to H.M.K. and J.H.L.), the NIH (T32AG000114 to Y.K. and C.-S.C., K01AG061236 to M.K., R01DK118631 to G.J., R01AG079163 to M.K. and J.H.L., R01HG011031 and HHSN268201800002I to H.M.K. and R01DK133448 and UH3CA268091 to J.H.L.), Technology Transfer Talent Network (T3N) Postdoctoral Fellowship (to Y.K.) and a Gleen Foundation Core grant (to J.H.L.).

Author information

Author notes

  1. These authors contributed equally: Yongsung Kim, Weiqiu Cheng, Chun-Seok Cho.

Authors and Affiliations

  1. Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA

    Yongsung Kim, Chun-Seok Cho, Yongha Hwang, Anna Park, Mitchell Schrank, Jer-En Hsu, Angelo Anacleto, Myungjin Kim & Jun Hee Lee

  2. Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA

    Weiqiu Cheng, Yichen Si, Jingyue Xi & Hyun Min Kang

  3. Space Planning and Analysis, University of Michigan Medical School, Ann Arbor, MI, USA

    Yongha Hwang

  4. Biomedical Research Core Facilities Advanced Genomics Core, University of Michigan, Ann Arbor, MI, USA

    Ellen Pedersen, Olivia I. Koues & Thomas Wilson

  5. Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI, USA

    Thomas Wilson

  6. Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA

    Thomas Wilson

  7. Department of Genetics, Harvard Medical School, Boston, MA, USA

    ChangHee Lee

  8. Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

    Goo Jun

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  1. Yongsung Kim
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Contributions

Y.K., C.-S.C., A.P., M.S., J.-E.H., M.K. and J.H.L. developed the experimental part of the protocol. W.C., Y.H., Y.S., J.X., A.A., C.L., G.J. and H.M.K. developed the computational part of the protocol. E.P., O.I.K., T.W., H.M.K. and J.H.L. developed the sequencing part of the protocol. Y.K., W.C., C.-S.C., H.M.K. and J.H.L. prepared the first draft. All authors revised, reviewed and approved the final version.

Corresponding authors

Correspondence to Hyun Min Kang or Jun Hee Lee.

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Competing interests

H.M.K. owns stock in Regeneron Pharmaceuticals. J.H.L. is an inventor on a patent and pending patent applications related to Seq-Scope.

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Nature Protocols thanks Moritz Gerstung and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Cho, C.-S. et al. Cell 184, 3559–3572.e22 (2021): https://doi.org/10.1016/j.cell.2021.05.010

Xi, J. et al. Bioinform. Adv. 2, vbac061 (2022): https://doi.org/10.1093/bioadv/vbac061

Si, Y. et al. Nat. Methods 21, 1843–1854 (2024): https://doi.org/10.1038/s41592-024-02415-2

Do, T. H. et al. Sci. Immunol. 7, eabo2787 (2022): https://doi.org/10.1126/sciimmunol.abo2787

Supplementary information

Supplementary Data 1

3D model (STL) file for the custom frame adapter described in the protocol. The STL file can be used to fabricate the adapter in most 3D printing service centers. We printed the adapter on a Stratasys J850 by using the default white material.

Supplementary Data 2

Sketch drawing and specification of the custom silicone isolator (Grace Bio-Labs, cat. no. JTR25-A-1.0, RD501346). Information in this PDF is sufficient to reproduce the part with the same specifications applied by Grace Bio-Labs.

Supplementary Data 3

README file providing details on running the code and software applied in the protocol

Supplementary Video 1

NovaSeq 6000 S4 flow cell disassembly. The scalpel is used to separate the flow cell into its three main components. This video demonstrates the entire procedure of the flow cell disassembly. During the demonstration, viewers may notice that a small piece was broken off the top layer of the flow cell. This layer is thin and, therefore, prone to breakage. However, as long as the broken piece is outside the imaging area described in Fig. 3d (B02–B10 and T02–T10), it does not interfere with subsequent procedures. Furthermore, even if the breakage damages some imaging areas, other intact areas can still be effectively used without any issues.

Supplementary Video 2

Seq-Scope chip dicing procedure. The chip was diced from the disassembled top and bottom layers of a NovaSeq 6000 S4 flow cell by using a precision glass cutter.

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Kim, Y., Cheng, W., Cho, CS. et al. Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics. Nat Protoc 20, 643–689 (2025). https://doi.org/10.1038/s41596-024-01065-0

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