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Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing

Nature Biotechnology (2024)Cite this article

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Abstract

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial–mesenchymal transition with the potential to predict tumor prognosis and progression.

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Fig. 1: Decoder-seq workflow.
Fig. 2: Characterization of 3D dendrimeric DNA arrays.
Fig. 3: Spatially resolved transcriptome analysis of MOBs with Decoder-seq.
Fig. 4: Decoder-seq resolves the mouse hippocampus at near-cellular resolution.
Fig. 5: Resolving spatial heterogeneity in RCCs with Decoder-seq.
Fig. 6: Deciphering the potential spatial evolutionary trajectory of tumor invasion in RCCs.

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Data availability

Decoder-seq datasets (including raw sequences, expression matrices, Space Ranger output and image files), one 10x Visium dataset and ISS datasets have been deposited in NCBI’s Gene Expression Omnibus archive under accession number GSE235896 (ref. 66). DBiT data were obtained from NCBI (GSE137986), and Stereo-seq data of the MOB were downloaded from the China National GeneBank DataBase (https://db.cngb.org/stomics/mosta/download/). The MOB scRNA-seq data were downloaded from NCBI (GSE121891). The Ex-ST and Slide-seqV2 MOB datasets were downloaded from https://data.mendeley.com/datasets/nrbsxrk9mp/1 and https://singlecell.broadinstitute.org/single_cell/study/SCP354/slide-seq-study#study-download, respectively.

The Slide-seqV2 datasets for Olfr gene analysis were downloaded from NCBI (GSE169012). Two public 10x Visium MOB datasets were obtained from the 10x Genomics website (https://www.10xgenomics.com/resources/datasets/adult-mouse-olfactory-bulb-1-standard-1) and NCBI (GSE153859).

The single-cell mouse hippocampus reference was obtained from the DropViz website (http://dropviz.org/?_state_id_=2b493beb4e780071). The Stereo-seq and MERFISH mouse hippocampus datasets were obtained from https://db.cngb.org/stomics/mosta/download/ and https://download.brainimagelibrary.org/29/3c/293cc39ceea87f6d/coronal_2/220501_wb3_co2_15_5z18R_merfish5/, respectively. The scRNA-seq reference datasets for ccRCC and chRCC were obtained from the European Genome–Phenome Archive (EGAD00001008030) and NCBI (GSE159115 andGSE152938), respectively. Bulk RNA-seq data for ccRCC and chRCC were downloaded from TCGA (https://portal.gdc.cancer.gov). Source data are provided with this paper.

Code availability

Code related to this manuscript can be found at https://github.com/songjiajia2018/Decoder-seq.git, https://github.com/songjiajia2018/seg2knn.git and https://github.com/songjiajia2018/dynamicst.git.

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Acknowledgements

We thank D. Yang and S. Yang for their assistance in AFM characterization, X. Xu for early discussions in experimental design, X. Lin and Z. Wu for their assistance in bioinformatics analysis and W. Shi, J. Feng and S. Huang for the preparation of microfluidic chips. C.Y. was supported by the National Natural Science Foundation of China (22293031 and 21927806), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZLCX20212601) and the Fundamental Research Funds for the Central Universities (20720210001 and 20720220005). L.W. was supported by the National Natural Science Foundation of China (22004083) and Shanghai Rising-Star Program (23QA1408200). J.S. was supported by the National Natural Science Foundation of China (22104080). J.C. was supported by the China Postdoctoral Science Foundation (2021M702167).

Author information

Author notes

  1. These authors contributed equally: Jiao Cao, Zhong Zheng, Di Sun.

Authors and Affiliations

  1. Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Jiao Cao, Zhong Zheng, Di Sun, Xin Chen, Rui Cheng, Tianpeng Lv, Yu An, Junhua Zheng, Jia Song, Lingling Wu & Chaoyong Yang

  2. The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, State Key Laboratory of Physical Chemical of Solid Surfaces, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China

    Chaoyong Yang

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Contributions

C.Y. conceived the project. C.Y., L.W. and J.C. designed the experiments. J.S. designed the bioinformatics analysis. J.C., Z.Z. and T.L. performed the experiments. J.C., L.W., J.S., Z.Z., D.S. and R.C. analyzed the data. J.C., X.C. and Y.A. fabricated microfluidic devices. T.L. prepared the microfluidic chip. Z.Z. collected the biopsy samples. L.W. and J.C. wrote the paper. C.Y., L.W., J.S. and J.Z. revised the paper and supervised the project.

Corresponding authors

Correspondence to Junhua Zheng, Jia Song, Lingling Wu or Chaoyong Yang.

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Competing interests

C.Y. is a co-founder of Dynamic Biosystems. The other authors declare no competing interests.

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Nature Biotechnology thanks Qing Nie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Spatially-localized cDNA synthesis.

(a) Fluorescence image of a MOB tissue section stained with nucleic acid dyes (SYBR Green). (b) Fluorescence image of cDNA containing Cy3-labeled dCTP after permeabilization, RT and tissue removal. (c) Magnified view of the section outlined in white dashed box in (a). (d) Magnified view of the section outlined in white dashed box in (b). (e–g) Magnified views of the sections outlined in white dashed box in (c). (e’-g’) Magnified views of the sections outlined in white dashed box in (d). The distribution of fluorescent cDNAs showed a similar pattern to that of the nucleic acid-stained tissue section after tissue removal, indicating that the cDNAs were localized to the areas under the cells and there was negligible diffusion. White triangle arrowheads point to the nuclei.

Source data

Extended Data Fig. 2 Quality evaluation and comparison of MOB data.

(a) Distribution of cDNA amplicon sizes. (b) Distribution of DNA sequencing library sizes. (c) Gene consistency of results generated from two replicate MOB sections 50-μm-spot Decoder-seq (Spearman’s correlation coefficient R = 0.96, p < 0.001). (d) Distribution of detected UMIs. UMIs(left) and genes (right) per spot from two replicate MOB sections with 50-μm-spot Decoder-seq. (e-f) Average UMI (left) and gene (right) number per spot (e) and per μm2 (f) for 50-μm-spot Decoder-seq and 55-μm-spot 10× Visium in varying sequencing reads. (g) Distribution of detected UMIs (left) and genes (right) from two replicate MOB sections with 15-μm-spot Decoder-seq.

Source data

Extended Data Fig. 3 Spatial gene expression patterns of region-specific marker genes in MOBs.

(a) Spatial gene expression (scaled) patterns of region-specific marker genes as determined with 15-μm-spot Decoder-seq. The marker genes and corresponding regions were Pcp4 (GCL), Cdhr1 and Eomes (MCL), Kit (EPL), Nrsn1 (GL), and Ptn and Omp (ONL). (b) Spatial gene expression (scaled) patterns of region-specific marker genes in MOBs as determined with 50-μm-spot Decoder-seq. The marker genes and corresponding regions were Sox11 (RMS), Cdhr1 and Eomes (MCL), Kit (EPL), Nrsn1 (GL), and Ptn and Omp (ONL). (c) Unsupervised clustering of the MOB section detected by Decoder-seq at 50 μm spatial resolution (left), and magnified views of RMS regions (right, black dashed box).

Extended Data Fig. 4 An integration and comparison of Decoder-seq data with single-cell data and other similar spatial methods.

(a, b) Integration of scRNA-seq data with Ex-ST data and Slide-seqV2 data, respectively. Cell types represented by different colors belong to scRNA-seq and black spots belong to Ex-ST data or Slide-seqV2. (c, d) Spatial distribution of spots annotated by cell types from scRNA-seq in Ex-ST (c) and Slide-seqV2 (d). The Ex-ST was merged with its corresponding DAPI image (blue). Slide-seqV2 only displays a partial region for easy comparison. (e) Comparison of relative abundances of major cell types detected by scRNA-seq, Decoder-seq, Ex-ST, and Slide-seqV2. The cell-type abundances revealed by these four methods are almost similar, with slight discrepancies that may arise from partial release and capture of tissue mRNA using SRT method and cell losses during tissue cell dissociation in scRNA-seq. (f) Comparison of the Spearman’s correlation coefficient, using two-tailed, two-sample t-test, among the degree of similarity between the average feature expression profiles of the single-cell reference and the average feature expression profiles of each cell type of scRNA-seq-based-annotation in Decoder-seq, Ex-ST, or Slide-seqV2, respectively.

Source data

Extended Data Fig. 5 Analysis of the Olfr gene in the MOBs.

(a) GO enrichment analysis of upregulated genes of glomeruli spots comparing to other spots in GL and ONL. (b) Statistical analysis of the disparities in distribution of layer-enriched spots, namely LC, with Olfr1033 or Olfr32 expression across ONL, GL, and other layers of the MOB (chi-squared test). (c) Spatial expression pattern of Olfr1033 and Olfr32 in the MOB derived from ISH data. The ISH data were subdivided into 15 regions from interior to exterior, and the Olfr1033 or Olfr32 gene expression in each region was normalized according to the region area and scaled from 0 to 1. (d, e) Spatial visualization of the raw count of Olfr1033 or Olfr32 genes in Decoder-seq (15 μm, n = 2) and Decoder-seq (50 μm, n = 2) (d), and 10x Visium (55 μm), Slide-seqV2 (10 μm), and Stereo-seq (bin21, 15 μm) (e). In Slide-seqV2, the white arrows point to the spot expressing marker genes of GL (low-magnification on left), and the Olfr1033 or Olfr32 (high-magnification on right). In Stereo-seq, magnified views of a portion within the white dashed boxes on the corresponding left. (f) Correlation matrix of UCell score-based LC-enriched GO terms (categorized as micro-programs) based on profiles across ONL and GL spots. The black dashed box represents the GO meta-programs; the black arrow indicates an LC-specific meta-program containing terms enriched in both LG and LO. The black dashed arrow shows the major function of LC-specific meta-program. (g) Spatial visualization of LC-specific meta-programs of MOBs in 15 μm-spot Decoder-seq (n = 3). (h) Violin plots with statistical analysis using two-tailed, two-sample t-test of the UCell score of LC-specific meta-programs in LC comparing to non-LC layers across the ONL and GL layers. LC: layer cluster; LG: LC compared to non-LC in GL; LO: LC compared to non-LC in ONL.

Source data

Extended Data Fig. 6 Comparison between Decoder-seq, MERFISH, and Stereo-seq data.

(a) A spatial single-cell atlas of mouse hippocampus derived from Stereo-seq data (left) and MERFISH data (right). (b) Comparison of relative abundances of major cell types detected by Decoder-seq, Stereo-seq, and MERFISH. Slight variations in the relative abundance of certain cell populations might be attributed to technological differences between imaging-based and sequencing-based methodologies, as well as their use of different sample sections. For example, neurogenesis appears more abundant in both Decoder-seq and Stereo-seq datasets, while being relatively rare in the MERFISH dataset. Neurogenesis mainly occurs in SGZ of the hippocampal dentate gyrus, where various cell types such as astrocytes, interneurons, endothelial cells, dentate, oligodendrocytes, cajal-retzius, and others are densely stacked together. For MERFISH, assigning mRNAs based on a small number of marker genes (1147 genes for 123 subclass markers) to cell types, especially in tissue regions of high-density cell populations, may result in a loss in certain cell types. Accurately assigning bins to cells also remains computationally challenging in Stereo-seq. However, since Stereo-seq is essentially a spatially barcoding array-based sequencing method, our Decoder-seq exhibits greater similarity in spatial distribution of cells to Stereo-seq than MERFISH. Overall, Decoder-seq with 15 μm spots demonstrated near cellular resolution and potential for finer-scale spatial analysis. SGZ, sub-granular zone.

Source data

Extended Data Fig. 7 Resolving the spatial heterogeneity in RCCs with Decoder-seq, related to Fig. 5.

(a) Unsupervised clustering of Decoder-seq RCC data (upper panel); the proportion of each cluster in each sample (lower panel). (b) The spatial pie plot of CARD-inferred cell type composition.   (c) CN unsupervised clustering of the shared cluster. (d) H&E images of ccRCC-2-L, ccRCC-3-L, and chRCC-1-L. Distinct categories by pathological annotation for morphological regions, including normal kidney (dark blue), stroma (green), blood vessel (red), and RCC (black). (e) Bubble plot showing differential analysis of CN 1 and CN 2 based on UCell scoring with identified marker genes. (f) Comparison of Treg abundance in CN 1 and CN 2. (g) Comparison of CIBERSORTx-inferred Treg abundance in ccRCC and chRCC subtype samples based on data from TCGA. (h) Spatial projection of the angiogenic pathway in each tissue section.

Source data

Extended Data Fig. 8 The Decoder-seq data with a resolution of 10 μm.

(a) AutoCAD representations of PDMS chips with 10 μm width barcoding channels. Upper: 50 channels of 10 μm in width; Lower: 96 channels of 10 μm in width. (b) Fluorescence images of 3D dendrimeric DNA arrays consisting of 10 μm spots. Alexa 488-labeled DNA barcode Y was ligated to the immobilized DNA barcode X at the intersections of the first set and the second set of flows, generating square fluorescent spots. (c) An H&E image of the MOB and its corresponding spatial UMI map by RNA captures from a single-cell resolution. The spatial UMI count map derived from 10 μm-spot Decoder-seq exhibited fine structures with MOB morphology. (d) Spatial raw expression of Ptgds, Fabp7, Doc2g, and Pcp4 in MOB section.

Supplementary information

Supplementary Information

Supplementary Figs. 1–16, Tables 1–5, 8, 9, 14 and 15, Methods and Decoder-seq protocol.

Reporting Summary

Supplementary Tables 6, 7 and 10–13.

Supplementary Table 6. List of all Olfr genes identified in 10x Visium data and Decoder-seq data. Supplementary Table 7a. Dendrite-enriched genes identified by Decoder-seq along with fold change enrichment and false discovery rate-corrected q value (P < 0.05, log (fold change) > 0.8). Supplementary Table 7b. Soma-enriched genes identified by Decoder-seq along with fold change enrichment and false discovery rate-corrected q value (P < 0.05, log (fold change) < −0.8). Supplementary Table 7c. List of genes that overlap with Ainsley et al.33, Tushev et al.32, Nakayama et al.34 and Slide-seqV2. Supplementary Table 10. List of cell signatures for cell scoring. Supplementary Table 11. List of spatially upregulated genes. Supplementary Table 12. GO enrichment analysis of spatially upregulated genes. Supplementary Table 13. List of dEMT genes.

Supplementary Data

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Cao, J., Zheng, Z., Sun, D. et al. Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-023-02086-y

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