| 1. |
- Andersson, Alma, et al.
(författare)
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Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions
- 2021
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Andersson, Alma, et al.
(författare)
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Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships
- 2020
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and myeloid cells, co-localization of macrophage and T-cell subsets. A model is constructed to infer presence of tertiary lymphoid structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define novel interactions between tumor-infiltrating cells in breast cancer and provide tools generalizing across tissues and diseases.
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| 3. |
- He, B., et al.
(författare)
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Integrating spatial gene expression and breast tumour morphology via deep learning
- 2020
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Ingår i: Nature Biomedical Engineering. - : Nature Research. - 2157-846X. ; 4:8, s. 827-834
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 4. |
- Kvastad, Linda, et al.
(författare)
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The spatial RNA integrity number assay for in situ evaluation of transcriptome quality
- 2021
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Ingår i: Communications Biology. - : Springer Nature. - 2399-3642. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Salmén, Fredrik, et al.
(författare)
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Multidimensional transcriptomics provides detailed information about immune cell distribution an identity in HER2+ breast tumors
- 2018
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The comprehensive analysis of tumor tissue heterogeneity is crucial for determining specific disease states and establishing suitable treatment regimes. Here, we analyze tumor tissue sections from ten patients diagnosed with HER2+ breast cancer. We obtain and analyze multidimensional, genome-wide transcriptomics data to resolve spatial immune cell distribution and identity within the tissue sections. Furthermore, we determine the extent of immune cell infiltration in different regions of the tumor tissue, including invasive cancer regions. We combine cross-sectioning and computational alignment to build three-dimensional images of the transcriptional landscape of the tumor and its microenvironment. The three-dimensional data clearly demonstrates the heterogeneous nature of tumor-immune interactions and reveal interpatient differences in immune cell infiltration patterns. Our study shows the potential for an improved stratification and description of the tumor-immune interplay, which is likely to be essential in treatment decisions.
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| 6. |
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| 7. |
- Stenbeck, Linnea, 1992-
(författare)
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Deconvolution of Spatial Gene Expression in Cancer
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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%.
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| 8. |
- Stenbeck, Linnea, et al.
(författare)
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Enabling automated and reproducible spatially resolved transcriptomics at scale
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 9. |
- Stenbeck, Linnea, et al.
(författare)
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Enabling automated and reproducible spatially resolved transcriptomics at scale
- 2022
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Ingår i: Heliyon. - : Elsevier BV. - 2405-8440. ; 8:6, s. e09651-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 10. |
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| 11. |
- Vickovic, Sanja, et al.
(författare)
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High-definition spatial transcriptomics for in situ tissue profiling
- 2019
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Ingår i: Nature Methods. - : NATURE PUBLISHING GROUP. - 1548-7091 .- 1548-7105. ; 16:10, s. 987-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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