10X Genomics (United States)

Abstract While advances in single cell genomics have helped to chart the cellular components of tumor ecosystems, it has been more challenging to characterize their specific spatial organization and functional interactions. Here, we combine single cell RNA-seq, spatial transcriptomics by Slide- seq, and in situ multiplex RNA analysis, to create a detailed spatial map of healthy and dysplastic colon cellular ecosystems and their association with disease progression. We profiled inducible genetic CRC mouse models that recapitulate key features of human CRC, assigned cell types and epithelial expression programs to spatial tissue locations in tumors, and computationally used them to identify the regional features spanning different cells in the same spatial niche. We find that tumors were organized in cellular neighborhoods, each with a distinct composition of cell subtypes, expression programs, and local cellular interactions. Comparing to scRNA-seq and Slide-seq data from human CRC, we find that both cell composition and layout features were conserved between the species, with mouse neighborhoods correlating with malignancy and clinical outcome in human patient tumors, highlighting the relevance of our findings to human disease. Our work offers a comprehensive framework that is applicable across various tissues, tumors, and disease conditions, with tools for the extrapolation of findings from experimental mouse models to human diseases.

BackgroundThe tumor microenvironment (TME) is composed of highly heterogeneous cell types that dynamically interact with each other. To more precisely distinguish and characterize cellular diversity and disease states within tissues, technologies with single-cell resolution are required. The Visium HD Spatial Gene Expression assay (Visium HD) is a high-resolution spatial technology that utilizes a whole-transcriptome probe panel, resolving data at the single-cell scale within intact tissue sections. This technology offers a more comprehensive understanding of cellular behavior in and around tumors, potentially leading to new insights into disease progression and therapeutic responses.MethodsHuman breast cancer and lung cancer tissue sections preserved using three common methods: formalin-fixed, paraffin-embedded (FFPE), fresh frozen (FF), and PFA-fixed, OCT-embedded (fixed frozen or FxF), were spatially profiled using the Visium HD Spatial Gene Expression assay. Tissues were mounted on glass slides and stained with hematoxylin and eosin (H&E). Probe pairs were hybridized to target RNA and ligated within the tissue samples and transferred to spatially-barcoded Visium HD slides using the CytAssist instrument. The Visium HD capture area consists of approximately 12 million 2 µm x 2 µm spatially barcoded squares without gaps. Data was binned to 8 µm x 8 µm for visualization and analysis.ResultsIn this study, we characterized the whole-transcriptome profiles of various cancer tissues preserved using three fixation methods. Our findings demonstrate the capability of Visium HD to spatially resolve oncogenes and immune cells within multiple tumor tissues. In breast cancer samples, we successfully identified breast glandular and myoepithelial cell clusters, which closely mimic the distinct spatial localization observed in ductal regions identified by H&E staining. Additionally, we observed high expression levels of cancer biomarkers SPP1, MYC and MZB1 in these breast cancer samples. In lung cancer samples, we mapped the expression of EPCAM back to epithelial cells within the tissue.ConclusionsThe Visium HD Spatial Gene Expression assay unravels spatial heterogeneity and maps the TME of FFPE, FF and FxF human breast cancer and lung cancer samples at single-cell scale resolution. This allows for deeper analyses of cellular interactions and the discovery of potential novel gene markers. This technology enables innovative insights into disease pathology and clinical translational research, extending its capabilities to a wide range of biological applications.

Abstract Colorectal cancer (CRC) is the second-deadliest cancer in the world, yet a deeper understanding of spatial patterns of gene expression in the tumor microenvironment (TME) remains elusive. Here, we introduce the Visium HD platform (10x Genomics) and use it to investigate human CRC and normal adjacent mucosal tissues from formalin fixed paraffin embedded (FFPE) samples. The first assay available on Visium HD is a probe-based spatial transcriptomics workflow that was developed to enable whole transcriptome single cell scale analysis. We demonstrate highly refined unsupervised spatial clustering in Visium HD data that aligns with the hallmarks of colon tissue morphology and is notably improved over earlier Visium assays. Using serial sections from the same FFPE blocks we generate a single cell atlas of our samples, then we integrate the data to comprehensively characterize the immune cell types present in the TME, specifically at the tumor periphery. We observed enrichment of two pro-tumor macrophage subpopulations with differential gene expression profiles that were localized within distinct tumor regions. Further characterization of the T cells present in one of the samples revealed a clonal expansion that we were able to localize in the tissue using in situ gene expression analysis. In situ analysis also allowed us to perform in-depth characterization of the microenvironment of the clonally expanded T cell population and we identified a third macrophage subpopulation with gene expression profiles consistent with an anti-tumor response. Our study provides a comprehensive map of the cellular composition of the CRC TME and identifies phenotypically and spatially distinct immune cell populations within it. We show that the single cell-scale resolution afforded by Visium HD and the whole transcriptome nature of the assay allows investigations into cellular function and interaction at the tumor periphery in FFPE tissues, which has not been previously possible.

Emerging spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ sequencing (dRNA HybISS, Cartana part of 10xGenomics) to compare male and female healthy mouse kidneys and the male kidney injury and repair timecourse. A pre-selected panel of 200 genes is used to identify cell state dynamics patterns during injury and repair. We develop a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting dataset allows us to resolve 13 kidney cell types within distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. At late timepoints after injury, C3+ leukocytes are enriched near pro-inflammatory, failed-repair proximal tubule cells. Integration of snRNA-seq dataset from the same injury and repair samples also allows us to impute the spatial localization of genes not directly measured by dRNA HybISS.

Abstract Super-resolution chromatin imaging techniques are emerging as powerful tools for spatial genomics. However, it remains challenging to visualize the genome on the order of tens of nanometers, a scale that can reveal rich structural detail about the nuclear organization. Here we introduce Decode-PAINT, a multiplexed in situ method, which leverages rapid diffraction-limited pre-decoding and DNA-PAINT (DNA points accumulation for imaging nanoscale topography). Decode-PAINT visualizes multiple discrete genomic regions at a resolution close to the nucleosome scale with a simple and easy-to-implement optical setup, reduces total imaging time by omitting multiple rounds of single-molecule recording, and enables the interrogation of the ultrastructure of the targets with <12 nm lateral and <20 nm axial resolution. We showcase the ability of our approach to perform multiplexed, high-resolution profiling of chromatin structure by performing targeted imaging of nine regions of active and inactive X chromosomes in individual cells. In short, Decode-PAINT accelerates in situ structural genomics.

Abstract Spatial transcriptomics (ST) is a powerful new approach to characterize the cellular and molecular architecture of the tumor microenvironment. Previous single-cell RNA-sequencing (scRNA-seq) studies of pancreatic ductal adenocarcinoma (PDAC) have revealed a complex immunosuppressive environment characterized by numerous cancer associated fibroblasts (CAFs) subtypes that contributes to poor outcomes. Nonetheless, the evolutionary processes yielding that microenvironment remain unknown. Pancreatic intraepithelial neoplasia (PanIN) is a premalignant lesion with potential to develop into PDAC, but the formalin-fixed and paraffin-embedded (FFPE) specimens required for PanIN diagnosis preclude scRNA-seq profiling. We developed a new experimental pipeline for FFPE ST analysis of PanINs that preserves clinical specimens for diagnosis. We further developed novel multi-omics analysis methods for threefold integration of imaging, ST, and scRNA-seq data to analyze the premalignant microenvironment. The integration of ST and imaging enables automated cell type annotation of ST spots at a single-cell resolution, enabling spot selection and deconvolution for unique cellular components of the tumor microenvironment (TME). Overall, this approach demonstrates that PanINs are surrounded by the same subtypes of CAFs present in invasive PDACs, and that the PanIN lesions are predominantly of the classical PDAC subtype. Moreover, this new experimental and computational protocol for ST analysis suggests a biological model in which CAF-PanIN interactions promote inflammatory signaling in neoplastic cells which transitions to proliferative signaling as PanINs progress to PDAC. Summary Pancreatic intraepithelial neoplasia (PanINs) are pre-malignant lesions that progress into pancreatic ductal adenocarcinoma (PDAC). Recent advances in single-cell technologies have allowed for detailed insights into the molecular and cellular processes of PDAC. However, human PanINs are stored as formalin-fixed and paraffin-embedded (FFPE) specimens limiting similar profiling of human carcinogenesis. Here, we describe a new analysis protocol that enables spatial transcriptomics (ST) analysis of PanINs while preserving the FFPE blocks required for clinical assessment. The matched H&E imaging for the ST data enables novel machine learning approaches to automate cell type annotations at a single-cell resolution and isolate neoplastic regions on the tissue. Transcriptional profiles of these annotated cells enable further refinement of imaging-based cellular annotations, showing that PanINs are predominatly of the classical subtype and surrounded by PDAC cancer associated fibroblast (CAF) subtypes. Applying transfer learning to integrate ST PanIN data with PDAC scRNA-seq data enables the analysis of cellular and molecular progression from PanINs to PDAC. This analysis identified a transition between inflammatory signaling induced by CAFs and proliferative signaling in PanIN cells as they become invasive cancers. Altogether, this integration of imaging, ST, and scRNA-seq data provides an experimental and computational approach for the analysis of cancer development and progression.

Abstract We have optimized an experimental and computational pipeline to adapt spatial transcriptomics (ST) approaches based upon the Visium (10x Genomics) technology to infer cellular composition and intercellular interactions of FFPE clinical specimens. We apply this technology to deliver an approach to examine pancreatic intraepithelial neoplasia (PanIN) to identify intrinsic and extrinsic mechanisms that are associated with the progression of these pre-malignant lesions to invasive carcinoma. Currently, most pancreatic cancers are diagnosed at an advanced stage that reflects in dismal survival rates and a better understanding of PanINs biology will provide valuable insights for early therapeutic interventions. Thus, we used PanINs as our model system to implement the FFPE ST workflow. Our workflow for FFPE ST analysis facilitates sectioning of small regions (5mm in diameter) from a paraffin block that are stained and imaged with H&E and concurrently measured for genome-wide transcriptional profiling. Subsequently, the image is used for automated cell annotation using an algorithm, CODA, trained to identify normal and neoplastic pancreatic cell types. CODA identified the normal pancreatic histological regions (ducts, acini, islets of Langerhans, stroma), as well as the neoplastic cells. This automated analysis enables isolation of specific spots for differential expression analysis to pinpoint the transcriptional changes that occur within neoplastic cells along ducts in PanIn and their changes between high-grade and low-grade lesions. The spatial gene expression analysis identified clusters that mapped to the cell types annotated by CODA and the marker genes of each cluster matched known markers for the correspondent cell type. Although PanINs are very small in size (< 1mm), we found specific clusters accurately mapped to these lesions in each sample. Overall, the spatial sequencing data presented enough depth and complexity to allow differential expression and pathway analysis. We observed a significant number of deregulated genes in PanINs compared to normal ducts. Some deregulated genes are known PanIN markers, but potential new markers were also identified. Moreover, the integration of CODA with gene expression changes enables us to verify that unique stromal regions annotated with CODA and associated with PanIns are in fact heterogeneous and formed by distinct cell subtypes. Altogether, our workflow combining automated cell annotation with STA from the same section provides a methodology to precisely examine the sample architecture while measuring heterogeneity at the transcriptional level. This combined approach can be applied to different FFPE tumor types to leverage the use of large bioarchives of samples not previously accessible to genome-wide spatial methods. Citation Format: Alexander T. Bell, Kohei Fujikura, Jacob Stern, Rena Chan, James Chell, Stephen Williams, Ashley Kiemen, Elizabeth M. Jaffee, Denis Wirtz, Laura D. Wood, Elana J. Fertig, Luciane T. Kagohara. Spatial transcriptomics for FFPE characterizes the molecular and cellular architecture of malignant changes in pancreatic pre-malignant lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 637.