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[Preprint]. 2025 Jan 17:rs.3.rs-5656204.
doi: 10.21203/rs.3.rs-5656204/v1.

Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples

Nejla Ozirmak Lermi  1   2 , Max Molina Ayala  3   2 , Sharia Hernandez Ruiz  3 , Wei Lu  3 , Khaja Khan  3 , Alejandra Serrano  3 , Idania Lubo  3 , Leticia Hamana  3 , Katarzyna Tomczak  3 , Sean Barnes  3 , Jinzhuang Dou  1 , Qingnan Liang  1 ; RTI Team; Maria Gabriela Raso  3 , Ximing Tang  3 , Mei Jiang  3 , Beatriz Sanchez-Espiridion  3 , Annikka Weissferdt  4 , John Heymach  5 , Jianjun Zhang  5 , Boris Sepesi  6 , Tina Cascone  5 , Anne Tsao  5 , Mehmet Altan  5 , Reza Mehran  4 , Don Gibbons  5 , Ignacio Wistuba  3 , Cara Haymaker  3 , Ken Chen  1 , Luisa M Solis Soto  3
Affiliations

Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples

Nejla Ozirmak Lermi et al. Res Sq. .

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Abstract

Imaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-μm sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma tumor samples in tissue microarrays to compare the performance of the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, and hematoxylin and eosin staining data for the same samples. In addition to objective assessment of automatic cell segmentation and phenotyping, we performed pixel-resolution manual evaluation of phenotyping to carry out pathologically meaningful comparison between ST platforms. Our study detailed the intricate differences between the ST platforms, revealed the importance of parameters such as tissue age and probe design in determining the data quality, and suggested reliable workflows for accurate spatial profiling and molecular discovery.

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Conflict of interest statement

Competing interests C.H. declares research funding to institution from Sanofi, BTG, Iovance, Obsidian, KSQ, EMD Serono, Takeda, Genentech, BMS, Summit Therapeutics, Artidis, Immunogenesis and Novartis; scientific advisory board member of Briacell with stock options; personal fees from Regeneron outside the scope of the submitted work. LS declares travel support for participation in 10x Genomic Pathology Day event and participation in NanoString Roadshow event, both unrelated to this work. M.A. declares research funding to institution from Genentech, Nektar Therapeutics, Merck, GlaxoSmithKline, Novartis, Jounce Therapeutics, Bristol Myers Squibb, Eli Lilly, Adaptimmune, Shattuck Lab, Gilead, Verismo therapeutics, Lyell; scientific advisory board member of GlaxoSmithKline, Shattuck Lab, Bristol Myers Squibb, AstraZeneca, Insightec, Regeneron, Genprex; personal fees from AstraZeneca, Nektar Therapeutics, SITC; participation of safety review committee for Nanobiotix-MDA Alliance, Henlius outside the scope of the submitted work. J.Z. declares research funding from Johnson and Johnson, Helius, Merck, Novartis and Summit, honoraria and consulting fees from AstraZeneca, BeiGene, Catalyst, GenePlus, Helius, Innovent, Johnson and Johnson, Novartis, Takeda and Varian outside the submitted work. D.L.G. has served on scientific advisory committees for Sanofi, Menarini Ricerche, Onconova, and Eli Lilly, and has received research support from Takeda, NGM Biopharmaceuticals, Boehringer Ingelheim and AstraZeneca. T.C. has received over the past 24 months speaker fees/honoraria (including travel/meeting expenses) from ASCO Post, AstraZeneca, Bio Ascend, Bristol Myers Squibb, Clinical Care Options, IDEOlogy Health, Medical Educator Consortium, Medscape, OncLive, PEAK Medicals, PeerView, Physicians’ Education Resource, Targeted Oncology; advisory role/consulting fees (including travel/meeting expenses) from AstraZeneca, Bristol Myers Squibb, Genentech, Merck, oNKo-innate, Pfizer, and RAPT Therapeutics; institutional research funding from AstraZeneca and Bristol Myers Squibb. All other authors declare no conflicts of interest related to the study.

Figures

Extended Data Fig. 1 |
Extended Data Fig. 1 |. Concordance of ST platforms with DSP WTA.
Scatter plots of the average expression of overlapping genes for the ST platforms and DSP WTA with the same cohorts of ICON2 a, and MESO2 b, TMAs. The blue lines represent linear regression. The Pearson correlation coefficient (R) is provided in the top left corner of each plot.
Extended Data Fig. 2 |
Extended Data Fig. 2 |. Comparison of gene count detection among the different ST platforms.
Scatter plots of the average expression of overlapping genes for the different ST platforms with matched cohorts of ICON2 a, and MESO2 b, TMAs. The green lines represent linear regression. The Pearson correlation coefficient (R) is provided in the top left corner of each plot.
Extended Data Fig. 3 |
Extended Data Fig. 3 |. Cell-type annotation for the ICON TMAs on the CosMx platform using a manual cluster annotation approach.
a, Correlation matrix of the assigned cell types in the ICON TMAs showing high correlation of multiple cell types. Clusters were manually labeled with the corresponding cell types based on the most highly expressed genes in the clusters. b, Dot plot of the expression of selected cell marker genes in the assigned cell types in the ICON TMAs.
Extended Data Fig. 4 |
Extended Data Fig. 4 |. Cell-type annotation for the ICON2 TMA on the MERFISH platform using a manual cluster annotation approach.
a, Correlation matrix of the assigned cell types in the ICON2 TMA showing high correlation of multiple cell types. Clusters were manually labeled with the corresponding cell types based on the most highly expressed genes in the clusters. b, Dot plot of the expression of selected cell marker genes in the assigned cell types in the ICON2 TMA.
Extended Data Fig. 5 |
Extended Data Fig. 5 |. Cell-type annotation for the ICON2 TMA on the Xenium-UM platform using a manual cluster annotation approach.
a, Correlation matrix of the assigned cell types in the Xenium-UM ICON2 TMA showing distinct separation of multiple cell types. Clusters were manually labeled with the corresponding cell types based on the most highly expressed genes in the clusters. b, Dot plot of the expression of selected cell marker genes in the assigned cell types in the ICON2 TMA.
Extended Data Fig. 6 |
Extended Data Fig. 6 |. Cell-type annotation for the ICON2 TMA on the Xenium-MM platform using a manual cluster annotation approach.
a, Correlation matrix of the assigned cell types in the Xenium-MM ICON2 TMA showing distinct separation of multiple cell types. Clusters were manually labeled with corresponding cell types based on the most highly expressed genes in the clusters. b, Dot plot of the expression of selected cell marker genes in the assigned cell types in the ICON2 TMA.
Extended Data Fig. 7 |
Extended Data Fig. 7 |. Cell-type annotation for the MESO TMAs on the CosMx platform using a transfer label from scRNA-seq.
a, Correlation matrix of the assigned cell types in the CosMx MESO TMAs showing distinct separation of multiple cell types. Cell types were labeled using transferring label approach from scRNA-seq data. b, Dot plot of the expression levels for selected cell marker genes in the assigned cell types.
Extended Data Fig. 8 |
Extended Data Fig. 8 |. Cell-type annotation for the MESO2 TMA on the MERFISH platform using transferring label approach from scRNA-seq data.
a, Correlation matrix of the assigned cell types in the MERFISH MESO2 TMA showing distinct separation of multiple cell types. Cell types were labeled using a transferring label approach from scRNA-seq data. b, Dot plot of the expression levels for selected cell marker genes in the assigned cell types.
Extended Data Fig. 9 |
Extended Data Fig. 9 |. Cell-type annotation for the MESO TMAs on the Xenium-UM platform using a transfer label from scRNA-seq.
a, Correlation matrix of the assigned cell types in Xenium-UM MESO TMAs showing distinct separation of multiple cell types. Cell types were labeled using a transferring label approach from scRNA-seq data. b, Dot plot of the expression levels for selected cell marker genes in the assigned cell types.
Extended Data Fig. 10 |
Extended Data Fig. 10 |. Cell-type annotation for the MESO TMAs on the Xenium-MM platform using a transfer label from scRNA-seq.
a, Correlation matrix of the assigned cell types in Xenium-MM MESO TMAs showing distinct separation of multiple cell types. Cell types were labeled using a transferring label approach from scRNA-seq data. b, Dot plot of the expression levels for selected cell marker genes in the assigned cell types.
Fig. 1 |
Fig. 1 |. Experimental design, panel comparison, and nuclear staining with the ST platforms.
a, TMAs containing tissue samples from patients with pleural mesothelioma (n = 22) or non-small cell lung cancer (NSCLC; n = 22). Tumor samples were sectioned at a thickness of 5 μm and submitted to CosMx, MERFISH, Xenium-UM, and Xenium-MM assays based on tissue availability. b, Venn diagram of the genes shared by the panels of the ST platforms. c, CosMx whole images of the ICON2 and MESO2 TMAs stained for morphology markers before FOV selection. Red/white squares display selected FOVs of CosMx. The CosMx FOV, MERFISH, Xenium-UM, and Xenium-MM images display nuclear staining of selected FOV regions and whole tissue cores. Only TMAs with data available from all ST platforms were selected for comparisons.
Fig. 2 |
Fig. 2 |. Technical comparison of the ST platforms.
a and b, Box plots of the transcript counts and uniquely expressed genes per cell captured on each ST platform for each TMA tissue block. Each dot represents a cell. The red diamonds correspond to the average transcript counts and uniquely expressed genes in the blocks. c, Dot plot of the total transcript counts per probe across the ST platforms. Each dot represents a gene (red) or a negative control probe (green). d, Bar graph of FDRs calculated using negative or blank control probes and total read counts. The y-axis represents FDR as a percentage. The gray bars represent unavailable data. The blue and green bars represent FDRs calculated using blank and negative control probes, respectively.
Fig. 3 |
Fig. 3 |. Comparative analysis of cell segmentation across the ST platforms.
a, Images of nuclear staining overlaid with cell boundaries obtained on all platforms using the ICON2 TMA. Cells with red boundaries represent filtered cells after quality control. b, Bubble plot of the mean cell area sizes (μm2) across the tissue blocks. The bubble size corresponds to the difference between the smallest and largest cell areas within each block. c, Percentages of the remaining cells after filtering. The dashed lines indicate transcript count thresholds for: Xenium (10), MERFISH (10), and CosMx (30).
Fig. 4 |
Fig. 4 |. Concordance of RNA levels between ST platforms and bulk RNA-seq data.
Scatter plots of the average expression of overlapping genes in ST platform and bulk RNA-seq data for matched cohorts of the ICON2 (a) and MESO2 (b) TMAs. The red lines represent linear regression. Pearson correlation coefficients (R) are provided in the top left corner of each plot. Outlier genes are annotated.
Fig. 5 |
Fig. 5 |. Cell-type annotation performance of the ST platforms using the ICON2 TMA.
a, UMAP and spatial locations of manually annotated cell-type clusters across the platforms. Clusters are labeled with their corresponding cell types based on top-expressed genes. b, Box plot of the F1-scores representing performance of cell segmentation and cell type annotations in ST platforms. Each dot represents a tissue core.
Fig. 6 |
Fig. 6 |. Cell-type annotation performance of the ST platforms in the MESO2 TMA.
UMAP and spatial locations of annotated cell types across the platforms. The cell-type labels were transferred from scRNA-seq data on matched pleural mesothelioma cohorts using Seurat in R.
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