Abstract
Single-cell RNA sequencing (scRNAseq) technologies have been beneficial in revealing and describing cellular heterogeneity within mammalian tissues, including solid tumors. However, many of these techniques apply poly(A) selection of RNA, and thus have primarily focused on determining the gene signatures of eukaryotic cellular components of the tumor microenvironment. Microbiome analysis has revealed the presence of microbial ecosystems, including bacteria and fungi, within human tumor tissues from major cancer types. Imaging data have revealed that intratumoral bacteria may be located within epithelial and immune cell types. However, as bacterial RNA typically lacks a poly(A) tail, standard scRNAseq approaches have limited ability to capture this microbial component of the tumor microenvironment. To overcome this, we describe the invasion–adhesion-directed expression sequencing (INVADEseq) approach, whereby we adapt 10x Genomics 5′ scRNAseq protocol by introducing a primer that targets a conserved region of the bacterial 16S ribosomal RNA gene in addition to the standard primer for eukaryotic poly(A) RNA selection. This ‘add-on’ approach enables the generation of eukaryotic and bacterial DNA libraries at eukaryotic single-cell level resolution, utilizing the 10x barcode to identify single cells with intracellular bacteria. The INVADEseq method takes 30 h to complete, including tissue processing, sequencing and computational analysis. As an output, INVADEseq has shown to be a reliable tool in human cancer cell lines and patient tumor specimens by detecting the proportion of human cells that harbor bacteria and the identities of human cells and intracellular bacteria, along with identifying host transcriptional programs that are modulated on the basis of associated bacteria.
Key points
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Invasion–adhesion-directed expression sequencing uses a modified 10x Genomics 5′ single-cell RNA sequencing protocol, introducing a primer targeting the bacterial 16S ribosomal RNA gene as well as the standard primer for eukaryotic poly(A) RNA selection to identify cell-associated bacteria and the host transcriptome.
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Invasion–adhesion-directed expression thus overcomes the limited ability of standard single-cell RNA sequencing approaches to capture the microbial component of the tumor microenvironment, facilitating analysis of host–bacterial interactions at the single-cell level.
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Data availability
Raw sequences data from INVADEseq bacterial 16S rRNA and human (GEX) gene libraries are available in the NCBI Sequence Read Archive repository under the Bioproject accession number PRJNA811533.
Code availability
Code for data processing and analysis of single-cell RNAseq data is available at https://github.com/FredHutch/Galeano-Nino-Bullman-Intratumoral-Microbiota_2022.
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Acknowledgements
This research was supported by the Genomics and Bioinformatics Shared Resource of the Fred Hutch/University of Washington Cancer Consortium (P30 CA015704) and the Scientific Computing Infrastructure at Fred Hutch funded by ORIP grant S10OD028685. Research reported in this publication was supported by the National Institute of Dental and Craniofacial Research of the National Institutes of Health under award number R01 DE027850 (to C.D.J.), the National Cancer Institute under award number R00 CA229984-03 (to S.B.), the Interdisciplinary Training Grant in Cancer T32 CA080416 (to J.L.G.N.) and the Cancer Research Institute Irvington Postdoctoral Fellowship (CRI Award CRI4208 to J.L.G.N.). Special thanks to H. Johnston for guidance and helpful discussions. We thank A. Baryiames for help with bacterial co-culture experiments. The use of patient specimens for this work was approved by the Fred Hutchinson Cancer Center Institutional Review Board under the following protocol numbers: RG 1006552 and 1006974.
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Contributions
J.L.G.N., C.D.J. and S.B. designed the study and wrote the paper. H.W. performed the computational analysis of INVADEseq data, S.S.M., M.F. and H.S. provided computational guidance. K.D.L. performed INVADEseq experiments and C.S. performed library preparation and sequencing. K.D.L. performed bacteria and cell line co-cultures. J.L.G.N. and H.W. performed gene expression pathway analysis of INVADEseq data. J.L.G.N. and H.W. performed statical analysis. C.S., M.F., H.S., S.S.M., K.D.L. and H.W. provided edits to the paper. All authors contributed to the final version of the paper.
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S.B. has consulted for GlaxoSmithKline and BiomX. C.D.J. has consulted for Series Therapeutics and Azitra. S.B. is an inventor on US Patent Application no: PCT/US2018/042966, submitted by the Broad Institute and Dana-Farber Cancer Institute, that covers targeting of Fusobacterium for treatment of CRC cancer.
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Nature Protocols thanks Wen Lu and Antoine-Emmanuel Saliba for their contribution to the peer review of this work.
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Key references using this protocol:
Galeano Niño, J. L. et al. Nature 611, 810–817 (2022): https://doi.org/10.1038/s41586-022-05435-0
Supplementary information
Supplementary Information
Supplementary Fig. 1.
Supplementary Table
Supplementary Table 1: scRNAseq: cell-associated microbiome composition in an individual OSCC patient. The table indicates the microbiome composition in a OSCC sample, including the number of UMI transcripts for each bacterium per cell. The relative abundance (%) for each bacterium is plotted in Fig. 3b. The data used to generate this table has been published previously8. Supplementary Table 2: scRNAseq: gene expression profile for an individual OSCC patient. All cluster analysis. Differential expression analysis between cellular groups as shown in Fig. 3c. The analysis was performed using the FindMarker function from the Seurat package based on the Wilcoxon rank sum test. The log2 fold change is calculated based on the difference in the gene expression average between two groups. Adjusted P values were calculated using the Bonferroni correction test for each gene in the dataset. P values <0.05 indicate significant differential gene regulation between experimental conditions. The data used to generate this table have been published previously8. Supplementary Table 3: scRNAseq: GSEA for an individual OSCC patient. All cluster analysis. GSEA between cellular groups as shown in Fig. 3d. The analysis was performed using the FindMarker function from the Seurat package based on the Wilcoxon rank sum test. The log2 fold change is calculated based on the difference in the gene expression average between two groups. Adjusted P values were calculated using the Bonferroni correction test for each gene in the dataset. P values <0.05 indicate significant differential gene regulation between experimental conditions. The data used to generate this table have been published previously8. Supplementary Table 4: scRNAseq. cell-associated microbiome composition from the high bacteria load OSCC cohort (High_Bac). The table indicates the microbiome composition in samples from the High_Bac cohort including the number of UMI transcripts for each bacterium. Relative abundance for each bacterium was plotted in Fig. 4b. The data used to generate this table have been published previously8. Supplementary Table 5: scRNAseq: gene expression profile in specific cell clusters from the high bacteria load OSCC cohort (High_Bac). Integrated data. Differential expression analysis between cellular groups as shown in Fig. 4c. The analysis was performed using the FindMarker function from the Seurat package based on the Wilcoxon rank sum test. The log2 fold change is calculated based on the difference in the gene expression average between two groups. Adjusted P values were calculated using the Bonferroni correction test for each gene in the dataset. P values <0.05 indicate significant differential gene regulation between experimental conditions. The data used to generate this table have been published previously8. Supplementary Table 6: scRNAseq: GSEA for specific cell clusters from the high bacteria load OSCC cohort (High_Bac). Integrated data. GSEA between cellular groups as shown in Fig. 4d. The analysis was performed using the FindMarker function from the Seurat package based on the Wilcoxon Rank Sum test. The log2 fold change is calculated based on the difference in the gene expression average between two groups. Adjusted P values were calculated using the Bonferroni correction test for each gene in the dataset. P values <0.05 indicate significant differential gene regulation between experimental conditions. The data used to generate this table have been published previously8. Supplementary Table 7: scRNAseq: cell-associated microbiome composition from the low bacterial load OSCC cohort (Low_Bac). The table indicates the microbiome composition in samples from the Low_Bac cohort including the number of UMI transcripts for each bacterium. The relative abundance (%) for each bacterium was plotted in Fig. 5b. The data used to generate this table have been published previously8. Supplementary Table 8: scRNAseq: gene expression profile in specific cell clusters from the low bacteria load OSCC cohort (Low_Bac). Integrated data. Differential expression analysis between cellular groups as shown in Fig. 5c. The analysis was performed using the FindMarker function from the Seurat package based on the Wilcoxon rank sum test. The log2 fold change is calculated based on the difference in the gene expression average between two groups. Adjusted P values were calculated using the Bonferroni correction test for each gene in the dataset. P values <0.05 indicate significant differential gene regulation between experimental conditions. The data used to generate this table have been published previously8.
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Galeano Niño, J.L., Wu, H., LaCourse, K.D. et al. INVADEseq to identify cell-adherent or invasive bacteria and the associated host transcriptome at single-cell-level resolution. Nat Protoc 18, 3355–3389 (2023). https://doi.org/10.1038/s41596-023-00888-7
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DOI: https://doi.org/10.1038/s41596-023-00888-7
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