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Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone. ispartof: NATURE BIOTECHNOLOGY vol:40 issue:4 pages:476-+ ispartof: location:United States status: published

Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. Here, we provide an unsupervised approach to study spatial genome integrity in situ to describe previously unidentified clonal relationships. We employed spatially resolved transcriptomics to infer spatial copy number variations in >120 000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.

Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed High-Definition Spatial Transcriptomics (HDST), which captures RNA from histological tissue sections on a dense spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2μm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues., Editorial summary A dense, spatially-barcoded bead array captures RNA from histological tissue sections for spatially-resolved gene expression analysis.

Introduction: Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. In order to obtain spatial information of clonal genetic events, prior studies have used methods such as laser capture microdissection, which results in assessment of small regions or even single cells. These studies have an inherent bias as a limited number of regions per tissue section can be retrieved and examined. Furthermore, because investigators have selected such regions based on morphology, previous studies have limited their analyses to histologically defined tumour areas while excluding regions populated by benign cells. The possibility to perform unsupervised genome and tissue-wide analysis would therefore provide an important contribution to delineate clonal events. We sought study spatial genome integrity in situ to gain molecular insight into clonal relationships. Materials and Methods: We employed spatially resolved transcriptomics (Visium, 10x Genomics) to infer spatial copy number variations in >120 000 spatial regions across multiple organs, including three whole axial prostates and additional tissues from skin, breast and brain tumors. We used this information to deduce clonal relationships between regions harboring 5-20 cells. Results: We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue. We perform an in-depth spatial analysis of cancers that includes an unprecedented interrogation of up to 50,000 tissue domains in a single patient, and 120,000 tissue domains across 10 patients. In a prostate section, we observed that many CNVs occurred in histologically benign luminal epithelial cells, most notably in chromosomes 8 and 10. This clone constituted a region of exclusively benign acinar cells branching off a duct lined by largely copy neutral cells. The changes in these cells were shared with the nearby intermediate risk prostate cancer cells in the same tissue section. We observed similar findings in another patient’s cutaneous squamous cell carcinoma (cSCC), wherein benign squamous epithelial had alterations in chromosomes 1 and 12 that were shared with nearby cSCC. Our results suggest a model for how genomic instability arises in histo-pathologically benign tissue that may represent early events in cancer evolution. Furthermore the spatial information allowed us to identify small clonal units not evident from morphology and hence would be overlooked by pathologists. Conclusions: We present the first large-scale, comprehensive atlas of genomic evolution at high spatial resolution in prostate cancer. Our study adds an important new approach to the armamentarium of cancer molecular pathology. We highlight the power of an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for focal therapy in prostate cancer. Citation Format: Andrew Erickson, Emelie Berglund, Mengxiao He, Maja Marklund, Reza Mirzazadeh, Niklas Schultz, Linda Kvastad, Alma Andersson, Ludvig Bergenstråhle, Joseph Bergenstråhle, Ludvig Larsson, Alia Shamikh, Elisa Basmaci, Teresita Diaz De Ståhl, Timothy Rajakumar, Kim Thrane, Andrew L. Ji, Paul A. Khavari, Firaz Tarish, Anna Tanoglidi, Jonas Maaskola, Richard Colling, Tuomas Mirtti, Freddie Hamdy, Dan J. Woodcock, Thomas Helleday, Ian G. Mills, Alastair D. Lamb, Joakim Lundenberg. The spatial landscape of clonal somatic mutations in benign and malignant tissue [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 2171.

The molecular mechanisms underlying lethal castration-resistant prostate cancer remain poorly understood, with intratumoral heterogeneity a likely contributing factor. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in needle biopsies collected before and after treatment with androgen deprivation therapy. By doing so, we are able to couple clinical responsiveness and morphological information such as Gleason score to transcriptome-wide data. Our data-driven analysis of transcriptomes identifies several distinct intratumoral cell populations, characterized by their unique gene expression profiles. Certain cell populations present before treatment exhibit gene expression profiles that match those of resistant tumor cell clusters, present after treatment. We confirm that these clusters are resistant by the localization of active androgen receptors to the nuclei in cancer cells post-treatment. Our data also demonstrates that most stromal cells adjacent to resistant clusters do not express the androgen receptor, and we identify differentially expressed genes for these cells. Altogether, this study shows the potential to increase the power in predicting resistant tumors.

Introduction: Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. In order to obtain spatial information of clonal genetic events, prior studies have used methods such as laser capture microdissection, which results in assessment of small regions or even single cells. These studies have an inherent bias as a limited number of regions per tissue section can be retrieved and examined. Furthermore, because investigators have selected such regions based on morphology, previous studies have limited their analyses to histologically defined tumor areas while excluding regions populated by benign cells. The possibility to perform unsupervised genome and tissue-wide analysis would therefore provide an important contribution to delineate clonal events. We sought study spatial genome integrity in situ to gain molecular insight into clonal relationships. Materials and Methods: We employed spatially resolved transcriptomics (Visium, 10x Genomics) to infer spatial copy number variations in >120 000 spatial regions across multiple organs, including three whole axial prostates and additional tissues from skin, breast and brain tumors. We additionally performed in silico assessment of spatial copy number inference. We used this information to deduce clonal relationships between regions harboring 5-20 cells. Results: We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumors and in nearby benign tissue. We perform an in-depth spatial analysis of cancers that includes an unprecedented interrogation of up to 50,000 tissue domains in a single patient, and 120,000 tissue domains across 10 patients. In a prostate section, we observed that many CNVs occurred in histologically benign luminal epithelial cells, most notably in chromosomes 8 and 10. This clone constituted a region of exclusively benign acinar cells branching off a duct lined by largely copy neutral cells. The changes in these cells were shared with the nearby intermediate risk prostate cancer cells in the same tissue section. We observed similar findings in another patient’s cutaneous squamous cell carcinoma (cSCC), wherein benign squamous epithelial had alterations in chromosomes 1 and 12 that were shared with nearby cSCC. Our results suggest a model for how genomic instability arises in histo-pathologically benign tissue that may represent early events in cancer evolution. Furthermore the spatial information allowed us to identify small clonal units not evident from morphology and hence would be overlooked by pathologists. Conclusions: We present the first large-scale, comprehensive atlas of genomic evolution at high spatial resolution in prostate cancer. Our study adds an important new approach to the armamentarium of cancer molecular pathology. We highlight the power of an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for focal therapy in prostate cancer Citation Format: Andrew Erickson, Emelie Berglund, Mengxiao He, Maja Marklund, Reza Mirzazadeh, Niklas Schultz, Ludvig Bergenstråhle, Linda Kvastad, Alma Andersson, Joseph Bergenstråhle, Ludvig Larsson, Alia Shamikh, Elisa Basmaci, Teresita Diaz De Ståhl, Timothy Rajakumar, Kim Thrane, Andrew L. Ji, Paul A. Khavari, Firaz Tarish, Anna Tanoglidi, Jonas Maaskola, Richard Colling, Tuomas Mirtti, Freddie C. Hamdy, Dan J. Woodcock, Thomas Helleday, Ian G. Mills, Alastair D. Lamb, Joakim Lundeberg. The spatial landscape of clonal somatic mutations in benign and malignant tissue [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR016.

BackgroundTechnological developments in the emerging field of spatial transcriptomics have opened up an unexplored landscape where transcript information is put in a spatial context. Clustering commonly constitutes a central component in analyzing this type of data. However, deciding on the number of clusters to use and interpreting their relationships can be difficult.ResultsWe introduce SpatialCPie, an R package designed to facilitate cluster evaluation for spatial transcriptomics data. SpatialCPie clusters the data at multiple resolutions. The results are visualized with pie charts that indicate the similarity between spatial regions and clusters and a cluster graph that shows the relationships between clusters at different resolutions. We demonstrate SpatialCPie on several publicly available datasets.ConclusionsSpatialCPie provides intuitive visualizations of cluster relationships when dealing with Spatial Transcriptomics data.

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.

Current methods for spatial transcriptomics are limited by low spatial resolution. Here we introduce a method that integrates spatial gene expression data with histological image data from the same tissue section to infer higher-resolution expression maps. Using a deep generative model, our method characterizes the transcriptome of micrometer-scale anatomical features and can predict spatial gene expression from histology images alone.

In situ RNA capturing has made it possible to record histology and spatial gene expression from the same tissue section, but methods to jointly analyze both kinds of data are still missing. Here, we present XFuse, a scalable deep generative model for spatial data fusion. XFuse can infer high-resolution, full-transcriptome spatial gene expression from histological image data and be used to characterize transcriptional heterogeneity in detailed anatomical structures.

Background Interest in studying the spatial distribution of gene expression in tissues is rapidly increasing. Spatial Transcriptomics is a novel sequencing-based technology that generates high-throughput information on the distribution, heterogeneity and co-expression of cells in tissues. Unfortunately, manual preparation of high-quality sequencing libraries is time-consuming and subject to technical variability due to human error during manual pipetting, which results in sample swapping and the accidental introduction of batch effects. All these factors complicate the production and interpretation of biological datasets. Results We have integrated an Agilent Bravo Automated Liquid Handling Platform into the Spatial Transcriptomics workflow. Compared to the previously reported Magnatrix 8000+ automated protocol, this approach increases the number of samples processed per run, reduces sample preparation time by 35%, and minimizes batch effects between samples. The new approach is also shown to be highly accurate and almost completely free from technical variability between prepared samples. Conclusions The new automated Spatial Transcriptomics protocol using the Agilent Bravo Automated Liquid Handling Platform rapidly generates high-quality Spatial Transcriptomics libraries. Given the wide use of the Agilent Bravo Automated Liquid Handling Platform in research laboratories and facilities, this will allow many researchers to quickly create robust Spatial Transcriptomics libraries.

Spatial transcriptomics and single cell RNA-sequencing offer complementary insights into the transcriptional expression landscape. We here present a probabilistic method that integrates data from both techniques, leveraging their respective strengths in such a way that we are able to spatially map cell types to a tissue. The method is applied to several different types of tissue where the spatial cell type topographies are successfully delineated.

Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.

Tissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays to date have lacked global transcriptome information, throughput or single-cell resolution. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-Seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are randomly deposited into tightly packed individual microsized wells on a slide. The position of each bead is decoded with sequential hybridization using complementary oligonucleotides providing a unique bead-specific spatial address. We then capture, and spatially in situ barcode, RNA from the histological tissue sections placed on the HDST array. HDST recovers hundreds of thousands of transcript-coupled spatial barcodes per experiment at 2 μm resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to spatial analysis of tissues at high resolution.

We create data-driven maps of transcriptomic anatomy with a probabilistic framework for unsupervised pattern discovery in spatial gene expression data. With convolved negative binomial regression we discover patterns which correspond to cell types, microenvironments, or tissue components, and that consist of gene expression profiles and spatial activity maps. Expression profiles quantify how strongly each gene is expressed in a given pattern, and spatial activity maps reflect where in space each pattern is active. Arbitrary covariates and prior hierarchies are supported to leverage complex experimental designs.We demonstrate the method with Spatial Transcriptomics data of mouse brain and olfactory bulb. The discovered transcriptomic patterns correspond to neuroanatomically distinct cell layers. Moreover, batch effects are successfully addressed, leading to consistent pattern inference for multi-sample analyses. On this basis, we identify known and uncharacterized genes that are spatially differentially expressed in the hippocampal field between Ammon’s horn and the dentate gyrus.

Motiviation Spatial Transcriptomics (ST) is a method which combines high resolution tissue imaging with high troughput transcriptome sequencing data. This data must be aligned with the images for correct visualization, a process that involves several manual steps. Results Here we present ST Spot Detector, a web tool that automates and facilitates this alignment through a user friendly interface. Supplementary information Supplementary data are available at Bioinformatics online.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.