Enhanced Spatial Transcriptomics Analysis of Mouse Lung Tissues Reveals Cell-Specific Gene Expression Changes Associated with Pulmonary Hypertension

Abstract

Spatial transcriptomics technologies have emerged as powerful tools for understanding cellular identity and function within the natural spatial context of tissues. Traditional transcriptomics techniques, such as bulk and single-cell RNA sequencing, lose this spatial information, which is critical for addressing many biological questions. Here, we present a protocol for high-resolution spatial transcriptomics using fixed frozen mouse lung sections mounted on 10X Genomics Xenium slides. This method integrates multiplexed fluorescent in situ hybridization (FISH) with high-throughput imaging to reveal the spatial distribution of mRNA molecules in lung tissue sections, allowing detailed analysis of gene expression changes in a mouse model of pulmonary hypertension (PH). We compared two tissue preparation methods, fixed frozen and fresh frozen, for compatibility with the Xenium platform. Our fixed frozen approach, utilizing a free-floating technique to mount thin lung sections onto Xenium slides at room temperature, preserved tissue integrity and maximized the imaging area, resulting in high-fidelity spatial transcriptomics data. Using a predesigned 379-gene mouse panel, we identified 40 major lung cell types. We detected key cellular changes in PH, including an increase in arterial endothelial cells (AECs) and fibroblasts, alongside a reduction in capillary endothelial cells (CAP1 and CAP2). Through differential gene expression analysis, we observed markers of endothelial-to-mesenchymal transition and fibroblast activation in PH lungs. High-resolution spatial mapping further confirmed increased arterialization in the distal microvasculature. These findings underscore the utility of spatial transcriptomics in preserving the native tissue architecture and enhancing our understanding of cellular heterogeneity in disease. Our protocol provides a reliable method for integrating spatial and transcriptomic data using fixed frozen lung tissues, offering significant potential for future studies in complex diseases such as PH.

Keywords

Spatial transcriptomics / Pulmonary hypertension / Fixed frozen tissue / Xenium platform / Endothelial cells / Arterialization / Mesenchymal transition

Author Contributions

X.M., H.Z., P.C., B.L., J.W. and J.Z. prepared the mouse brain samples, and conducted the tissue mounting and Xenium spatial transcriptomics experiments. P.C., H.Z., analyzed data and prepared the figures, A.A.D., A.L.F., O.R., S.Q., Z.D. revised the manuscript, M.B.F., Z.D. and S.Q. conceived the study, secured funding and wrote the manuscript.

Ethics Statement

All animal care and study protocols were reviewed and approved by the Institutional Animal Care and Use Committee of the University of Arizona (#19-513).

Informed Consent Statement

Not applicable.

Data Availability Statement

The xenium processed data were available at NCBI GEO dataset (GSE277936). Scripts used for analysis are available on GitHub (https://github.com/DaiZYlab/enhancedXenium (accessed on 5 January 2025)). Other data, analytical methods, and materials that support the findings of this study will be available to other researchers from the corresponding authors on reasonable request.

Funding

NIH grant R01MH128192 (S.Q.), R01EY035138 (S.Q.), R21AG078700 (S.Q.), Institute of Mental Health Research (IMHR, Level 1 funding, S.Q.) and institution startup fund from The University of Arizona to S.Q., and in part by NIH grant R01HL158596, R01HL162794, R01HL169509, R01HL170096 to Z.D., R01HL133085 to O.R., R01HL16479-01 to A.L.F, and R01HL160941 to A.A.D., and Dept. of Defense PR230296 to A.A.D.

Declaration of Competing Interest

The authors declare no competing interests.

References

[1] You Y, Fu Y, Li L, Zhang Z, Jia S, Lu S, et al. Systematic comparison of sequencing-based spatial transcriptomic methods. Nat. Methods 2024, 21, 1743-1754, doi:10.1038/s41592-024-02325-3.

[2] Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 2015, 348, aaa6090. doi:10.1126/science.aaa6090.

[3] Eng C-HL, Lawson M, Zhu Q, Dries R, Koulena N, Takei Y, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 2019, 568, 235-239. doi:10.1038/s41586-019-1049-y.

[4] Stickels RR, Murray E, Kumar P, Li J, Marshall JL, Di Bella DJ, et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 2021, 39, 313-319. doi:10.1038/s41587-020-0739-1.

[5] Dai Z, Li M, Wharton J, Zhu MM, Zhao Y-Y. Prolyl-4 Hydroxylase 2 (PHD2) Deficiency in Endothelial Cells and Hematopoietic Cells Induces Obliterative Vascular Remodeling and Severe Pulmonary Arterial Hypertension in Mice and Humans Through Hypoxia-Inducible Factor-2α. Circulation 2016, 133, 2447-2458. doi:10.1161/CIRCULATIONAHA.116.021494.

[6] Ma X, Chen P, Wei J, Zhang J, Chen C, Zhao H, et al. Protocol for Xenium spatial transcriptomics studies using fixed frozen mouse brain sections. STAR Protocols 2024, 5, 103420. doi:10.1016/j.xpro.2024.103420.

[7] Janesick A, Shelansky R, Gottscho AD, Wagner F, Williams SR, Rouault M, et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat. Commun. 2023, 14, 8353. doi:10.1038/s41467-023-43458-x.

[8] Haga Y, Sakamoto Y, Kajiya K, Kawai H, Oka M, Motoi N, et al. Whole-genome sequencing reveals the molecular implications of the stepwise progression of lung adenocarcinoma. Nat. Commun. 2023, 14, 8375. doi:10.1038/s41467-023-43732-y.

[9] Liu B, Yi D, Xia X, Ramirez K, Zhao H, Cao Y, et al. General Capillary Endothelial Cells Undergo Reprogramming Into Arterial Endothelial Cells in Pulmonary Hypertension Through HIF-2α/Notch4 Pathway. Circulation 2024, 150, 414-417. doi:10.1161/CIRCULATIONAHA.123.067981.

[10] Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 2024, 42, 293-304. doi:10.1038/s41587-023-01767-y.

[11] Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018, 19, 15. doi:10.1186/s13059-017-1382-0.

[12] Palla G, Spitzer H, Klein M, Fischer D, Schaar AC, Kuemmerle LB, et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 2022, 19, 171-178. doi:10.1038/s41592-021-01358-2.

[13] Guo M, Morley MP, Jiang C, Wu Y, Li G, Du Y, et al. Guided construction of single cell reference for human and mouse lung. Nat. Commun. 2023, 14, 4566. doi:10.1038/s41467-023-40173-5.

[14] Sun X, Perl A-K, Li R, Bell SM, Sajti E, Kalinichenko VV, et al. A census of the lung: CellCards from LungMAP. Dev. Cell 2022, 57, 112-145.e2. doi:10.1016/j.devcel.2021.11.007.

[15] Koenitzer JR, Wu H, Atkinson JJ, Brody SL, Humphreys BD. Single-Nucleus RNA-Sequencing Profiling of Mouse Lung. Reduced Dissociation Bias and Improved Rare Cell-Type Detection Compared with Single-Cell RNA Sequencing. Am. J. Respir. Cell Mol. Biol. 2020, 63, 739-747. doi:10.1165/rcmb.2020-0095MA.

[16] Liu B, Yi D, Li S, Ramirez K, Xia X, Cao Y, et al. Single-cell and Spatial Transcriptomics Identified Fatty Acid-binding Proteins Controlling Endothelial Glycolytic and Arterial Programming in Pulmonary Hypertension. BioRxiv 2024, doi:10.1101/2024.02.11.579846.