Current methods for epigenomic profiling are limited in their ability to obtain genome-wide information with spatial resolution. We introduce spatial ATAC, a method that integrates transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture to perform spatially resolved epigenomics. We show that spatial ATAC enables the discovery of the regulatory programs underlying spatial gene expression during mouse organogenesis, lineage differentiation and in human pathology.

Each cancer is unique, and therefore the use of general treatments are often suboptimal. If we can understand the mechanisms of cancer development, we might be able to develop effective treatments tailored to each patient. Our bodies are complex three-dimensional structures and how things are organized correlate with proper functioning. Technologies for biological research have escalated enormously in the last years. Going from bulk analysis of tissues to the advent of single cell sequencing and spatially resolved transcriptomics has initiated a new era in biological research. The technology Spatial Transcriptomics (ST) combines histology with next-generation sequencing, making it possible to map which genes that are active at thousands of sub-areas in a tissue section. 

In Paper I, ST was combined with an in-house developed artificial intelligence method to explore the landscape of prostate cancer tissue. We identified a gene expression-based tumor signature in healthy tissue areas not possible to recognize through visual assessment, indicating that the genotype changes before phenotype. A gradient of the tumor microenvironment was also identified. In Paper II, prostate cancer tissue from three patients were investigated before and after androgen deprivation therapy using ST. All patients treated with this therapy long enough will reach a clinically defined stage called castration-resistant prostate cancer. We could see that only a set of cancer cells across the tissue responded to the treatment, which allowed comparison of gene expression program in responding versus non-responding cells. By understanding the underlying mechanisms to resistance, it might be possible to target these cells and decrease relapse risk. In Paper III, we inferred copy number variation from ST data allowing for the generation of genome integrity maps in cancerous tissue of prostate, breast, brain, and skin, and in a lymph node. This allowed us to identify tumor clones not recognizable histologically, indicating how genomic instability can be initiated and spread before visible for the naked eye. In Paper IV, we developed a method for spatial ATAC-seq by fusing the ST-technology with ATAC-seq, enabling the analyses of accessible chromatin while preserving histological information. The Visium platform by 10x Genomics was used and we demonstrate a similar capture efficiency to single-cell ATACseq.

Varje cancer är unik, vilket medför att generella behandlingar ofta är suboptimala. Om vi kan förstå mekanismerna bakom cancerutveckling kan vi öka chansen att utveckla effektiva behandlingar anpassade till varje patient. Våra kroppar är tredimensionella strukturer och hur saker är organiserade inom oss korrelerar med funktion. Teknologier för biologisk forskning har eskalerat enormt de senaste åren. Att gå från bulkanalys av vävnader till begynnelsen a vsingle-cell-sekvensering och spatialt upplöst transkriptomik har initierat en ny era inom biologisk forskning. Teknologin Spatial Transkriptomics (ST) kombinerar histologi med next-generation sequencing, vilket möjliggör att kartlägga vilka gener som är aktiva på tusentals subdelar av ett vävnadssnitt.

I Artikel I kombinerades ST med en in-house-utvecklad artificiell intelligens metod för att kunna utforska landskapet av prostatacancervävnad. Vi identifierade genuttrycksbaserade tumörsignaturer inom ett område som via visuell inspektion ej var tumör, vilket indikerar att genotypen ändras innan fenotypen. En gradient av tumörmikroomgivningen identifierades också. I Artikel II undersöktes prostatacancervävnad från tre patienter före och efter androgendeprivationsterapi med ST. Alla patienter behandlade med denna terapi under tillräckligt lång tid kommer nå en kliniskt definierad nivå som kallas kastreringsresistent prostatacancer. Vi kunde se att endast vissa cancerceller i vävnaden svarade på behandling, vilket möjliggjorde jämförelse av genuttrycksprogram mellan svarande och ickesvarande celler. Genom att förstå de underliggande resistensmekanismerna kan det bli möjligt att målsöka dessa celler och minska risken för återfall. I Artikel III, så drog vi slutsatser gällande copy number variation utifrån ST-datan, vilket möjliggjorde en kartläggning av genomintegritet i cancervävnad av prostata, bröst, hjärna, och hud, samt i en lymfnod. Detta medförde att vi kunde identifiera tumörkloner som inte kunde identifieras histologiskt, och indikerar att genom instabilitet kan initieras och spridas innan synligt för det nakna ögat. I Artikel IV utvecklade vi en metod för spatial ATAC-sekvensering genom att kombinera ST-teknologin med ATAC-sekvensering, vilket möjliggör analys av tillgängligt kromatin medan histologisk information bevaras. För detta användes Visium-plattformen av 10x Genomics och vi uppvisar ett liknande antal uppfångade molekyler som vid single-cell ATAC-sekvensering. 

Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer(1). Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics(2) 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.

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. Spatial heterogeneity in prostate cancer can contribute to its resistance to androgen deprivation therapy (ADT). Here, the authors analyse prostate cancer samples before and after ADT using Spatial Transcriptomics, and find heterogeneous pre-treatment tumour cell populations and stromal cells that are associated with resistance.

Simple Summary</p> Prostate cancer has very varied appearances when examined under the microscope, and it is difficult to distinguish clinically significant cancer from indolent disease. In this study, we use computer analyses inspired by neurons, so-called 'neural networks', to gain new insights into the connection between how tissue looks and underlying genes which program the function of prostate cells. Neural networks are 'trained' to carry out specific tasks, and training requires large numbers of training examples. Here, we show that a network pre-trained on different data can still identify biologically meaningful regions, without the need for additional training. The neural network interpretations matched independent manual assessment by human pathologists, and even resulted in more refined interpretation when considering the relationship with the underlying genes. This is a new way to automatically detect prostate cancer and its genetic characteristics without the need for human supervision, which means it could possibly help in making better treatment decisions.</p> Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H & E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H & E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.</p>

Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor micro- environment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.

Current methods for epigenomic profiling are limited in the ability to obtain genome wide

information with spatial resolution. Here we introduce spatial ATAC, a method that integrates

transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture

to perform spatially resolved epigenomics. We show that spatial ATAC enables the discovery

of the regulatory programs underlying spatial gene expression during mouse organogenesis,

lineage differentiation and in human pathology.

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 gain molecular insight into 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. 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 an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.