While triple-negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigate TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We perform an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures leads to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identifies nine TNBC spatial archetypes, further validated in external datasets. Several spatial archetypes are associated with disease outcome and characterized by potentially actionable features. In this work, we provide a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy.

Cancer is the second leading cause of death in the world, claiming nearly 10 million lives in 2020 alone. One of the main issues in anti-cancer treatment is the heterogeneity of the tumor microenvironment (TME). The TME consists of different cells that are critical for cancer development. Understanding the interactions and identity of these cells is vital to discovering the mechanisms for tumorigenesis. To fundamentally understand the development and mechanisms of the disease will help us in designing novel treatments moving forward. To study the TME, we need methods that both provide extensive information about the cellular profiles and their spatial location, in order to understand how they interact with each other. Single-cell RNA-seq (scRNA-seq) has provided extensive insights into the cellular composition of tumors. However, it requires dissociation of the cells and thus does not retain spatial information. There are several methods to study spatially resolved gene expression in tissues, but one that allows for untargeted and whole-transcriptome wide analysis is the in situ capturing method, Spatial transcriptomics (ST). Although this method allows us to know the location of the gene expression, the resolution is too low for single-cell analysis. With an initial capturing area of 100 μm, 3-30 cells are captured in each spot resulting in a mixture of cells giving rise to the gene expression. At this resolution, it is challenging to confidentially profile the cells, thus making it difficult to explore the cellular interactions fully. To fundamentally explore the TME, improvements need to be made.

In Paper I, we aimed to bridge the gap between ST and scRNA-seq by designing a new array with a capturing area of 2 μm. This new design increased the number of capture areas from 1007 to over 1.4 million and with over a 4000-fold improved resolution. We managed to get spatially resolved gene expression from mouse olfactory bulb (MOB) and breast tumor tissue at a sub-cellular resolution with this new design. Despite a low capture efficiency of around 1.3% per bead, we were able to identify differently expressed (DE) signatures specific to morphological layers, profile specific cell types and explore sub-cellular features. Paper II focuses on the information obtained from the widely available histological images. By integrating the spatial gene expression data from 23 different breast cancer patients with their morphological images via deep learning, we could predict gene expression on different samples solely from their histological images. This was further validated on external samples to ensure that it was applicable to other clinical data. In Paper III, we explored the biology of HER2-positive breast tumors by combining scRNA-seq with ST data from eight different HER2-positive patients. With this combinatorial approach, we studied the interactions of tumor-associated cell types and found tertiary lymphoid (TL)-like structures which have been shown to hold certain predictive power in treatment outcome. From this, we constructed a predictive model that could infer the presence of these TL-like structures across different tissue types and technical platforms. This was validated on external samples from breast cancer, rheumatoid arthritis and melanoma. Lastly, in Paper IV, we sought to improve upon the reproducibility and robustness of the method by automating the 10x Visium protocol on a robotic platform. To benchmark the protocol, we compared identical samples prepared both manually and with the automated approach and achieved high correlation scores of 0.995 and 0.990. By adapting the protocol on a Bravo Liquid Handling Platform, we were able to increase the throughput and robustness of the method and reduce hands-on time by over 80%.

Cancer är världens näst vanligaste dödsorsak med nästan 10 miljoner dödsoffer under 2020. Ett av de största problemen med att behandla cancer är den höga graden av heterogenitet som finns inom mikromiljön av tumören. Tumörens mikromiljö består av flera olika celler som är avgörande för tumörens utveckling. Att veta identiteten på cellerna samt hur de interagerar är vitalt för att upptäcka de underliggande mekanismerna av cancerutveckling. Att fundamentalt förstå mekanismerna och utvecklingen av cancer ligger till grund för att vi ska kunna utveckla nya behandlingar i framtiden. För att kunna studera tumörens mikromiljö så krävs det metoder som både tillhandahåller omfattande information kring cellernas profil samt hur de är distribuerade jämtemot varandra för att förstå hur de interagerar. Med singel-cell RNA-sekvensering (scRNA-seq) så har man fått en omfattande bild av tumörers cellulära uppbyggnad men för att kunna utföra det krävs det att man dissocierar cellerna, vilket i sin tur gör att den spatiala informationen går förlorad. Det finns flera metoder som tillåter spatialt upplöst transkriptomik i vävnader men en som tillhandahåller opartisk analys av hela transkriptomet är en metod som blev döpt till Spatial Transcriptomics (ST). Även om metoden bevarar den spatiala koordinaten av genuttrycket så är upplösningen för låg för att kunna urskilja enskilda celler. Arean som fångar upp mRNAt är 100 µm, vilket fångar omkring 3–30 celler. Detta innebär att varje datapunkt innehåller genuttryck från flera celler vilket försvårar möjligheten att identifiera cellerna. För att förstå cellernas interaktioner och fundamentalt utforska tumörers mikromiljö så krävs det att metoden utvecklas. 

I Artikel I var vårt mål att föra ST närmare singel-cell analys genom att ändra den array som används för metoden. Den nya designen medförde en ökning av antalet areor där mRNAt kan fångas från 1007st till över 1,4 millioner och med en över 4000 gånger förbättrad upplösning. Vi kunde med denna array få spatialt upplöst genuttryck från vävnader av mushjärna och brösttumör med en upplösning på 2 µm. Trots en låg verkningsgrad på omkring 1,3% så lyckades vi identifiera skilda genuttryck som var specifika för vissa morfologiska regioner, specifika celltyper samt utforska egenskaper på en subcellulär nivå. Artikel II fokuserar på den information som kan fås genom histologiska bilder vilka är lättillgängliga. Genom att integrera det spatiala genuttrycket från 23 patienter med de tillhörande histologiska bilderna genom djup maskininlärning så kunde vi förutspå genuttrycket på andra prover endast baserat på deras histologiska bilder. Detta validerades på externa prover för att säkerhetsställa att de var applicerbart på kliniska prover. I Artikel III så utforskade vi biologin av HER2-positiva brösttumörer genom att kombinera scRNA-seq data med data från åtta olika HER2-positiva patienter genererat via ST. Med att kombinera de två så kunde vi utforska interaktioner mellan celler i tumören och fann tertiära lymfoid (TL)-liknande strukturer. Det har visats att dessa TL-strukturer är till viss del en förutsägande faktor när det gäller behandling. I och med detta så tog vi fram en modell för att hitta dessa TL-liknande strukturer i data från andra vävnader samt data genererat från en annan plattform. Detta validerades på externa prover från bröstcancer, reumatoid artrit och malignt melanom. Slutligen i Artikel IV så var vårt mål att förbättra reproducerbarheten samt robustheten av metoden genom att tillämpa den på en automatisk plattform. För att säkerhetsställa prestandan av det automatiserade protokollet så jämförde vi identiska prover förberedda både manuellt och automatiserat. Från detta fick vi höga korrelationskoefficienter på 0,995 och 0,990. Genom att anpassa protokollet på den plattform som heter ”Bravo Liquid Handling Platform” så lyckades vi öka robustheten och effektiviteten av metoden samt reducera den praktiska tiden i laboratoriet med över 80%.

Spatial information of tissues is an essential component to reach a holistic overview of gene expression mecha-nisms. The sequencing-based Spatial transcriptomics approach allows to spatially barcode the whole tran-scriptome of tissue sections using microarray glass slides. However, manual preparation of high-quality tissue sequencing libraries is time-consuming and subjected to technical variability. Here, we present an automated adaptation of the 10x Genomics Visium library construction on the widely used Agilent Bravo Liquid Handling Platform. Compared to the manual Visium library preparation, our automated approach reduces hands-on time by over 80% and provides higher throughput and robustness. Our automated Visium library preparation protocol provides a new strategy to standardize spatially resolved transcriptomics analysis of tissues at scale.

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases. While transcriptomics have enhanced our understanding for cancer, spatial transcriptomics enable the characterisation of cellular interactions. Here, the authors integrate single cell data with spatial information for HER2 + tumours and develop tools for the prediction of interactions between tumour-infiltrating cells.

The RNA integrity number (RIN) is a frequently used quality metric to assess the completeness of rRNA, as a proxy for the corresponding mRNA in a tissue. Current methods operate at bulk resolution and provide a single average estimate for the whole sample. Spatial transcriptomics technologies have emerged and shown their value by placing gene expression into a tissue context, resulting in transcriptional information from all tissue regions. Thus, the ability to estimate RNA quality in situ has become of utmost importance to overcome the limitation with a bulk rRNA measurement. Here we show a new tool, the spatial RNA integrity number (sRIN) assay, to assess the rRNA completeness in a tissue wide manner at cellular resolution. We demonstrate the use of sRIN to identify spatial variation in tissue quality prior to more comprehensive spatial transcriptomics workflows. Kvastad et al. develop the spatial RNA Integrity Number (sRIN) assay that evaluates the RNA integrity at cellular resolution. This method improves the resolution of a similar method called the RNA Integrity Number (RIN), demonstrating spatial variation in the quality of RNA samples.

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. 

Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcriptcoupled spatial barcodes at 2-mu m resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

Tissue spatial information is an essential component to reach a holistic overview of gene expression mechanisms. The sequencing-based Spatial transcriptomics approach allows to spatially barcode the whole transcriptome of tissue sections using microarray glass slides. However, manual preparation of high-quality tissue sequencing libraries is time-consuming and subjected to technical variability. Here, we present an automated adaptation of the 10x Genomics Visium library construction on the widely used Agilent Bravo Liquid Handling Platform. Compared to the manual Visium library preparation, our automated approach reduces hands-on time by over 80% and provides higher throughput and robustness. Our automated Visium library preparation protocol provides a new strategy to standardize spatially resolved transcriptomics analysis of tissues at scale.