Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration

Asp, Michaela; Bergenstråhle, Joseph; Lundeberg, Joakim

doi:10.1002/bies.201900221

Cited by 450 publications

(415 citation statements)

References 62 publications

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“…In summary, we provide a new integrated resource of biological knowledge particularly valuable for network analysis and modeling of bulk and single-cell omics data. Furthermore, with the emergence of spatially resolved omics data 46 , we anticipate that this prior knowledge of interand intracellular communication will be valuable to study tissue architecture.…”

Section: Comprehensive Knowledge For Multicellular Omics Analysismentioning

confidence: 99%

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Türei¹,

Valdeolivas²,

Gul

et al. 2020

Preprint

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Molecular knowledge of biological processes is a cornerstone in the analysis of omics data. Applied to single-cell data, such analyses can provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across different resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources in a single database. It covers the interactions and roles of proteins in inter- and intracellular signal transduction, as well as transcriptional and post-transcriptional regulation. We also provide a comprehensive collection of protein complexes and rich annotations on the properties of proteins, including function, localization, and role in diseases. The resource is available for human, and via homology translation for mouse and rat. The data is accessible via the web service of OmniPath, a Cytoscape plugin, and packages in R/Bioconductor and Python, providing convenient access options for both computational and experimental scientists. Our resource provides a single access point to knowledge spanning intra- and intercellular processes for data analysis, as we demonstrate in applications to study SARS-CoV-2 infection and ulcerative colitis.

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Section: Comprehensive Knowledge For Multicellular Omics Analysismentioning

confidence: 99%

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Türei¹,

Valdeolivas²,

Gul

et al. 2020

Preprint

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“…In contrast, scRNA-seq offers insights into the transcriptome of cells in the higher order of magnitudes, but their spatial origin is to a large extent lost. [17] Spatial Transcriptomics (ST), as described by Ståhl and Sálmen et.al., presents a solution to this dilemma, by providing spatially resolved and transcriptome-wide expression information. [18] Cell interactions and spatial context are key components of the tumor ecosystem, however this space is inhabited by a diverse population of complex cell types that cannot be defined by a few marker genes or surface receptors; hence the benefits of using a technique like ST. [19,20] Although ST does not provide single-cell resolution, this issue can be addressed by leveraging information from scRNA-seq, spatially mapping cell types or clusters by integration of the two data modalities.…”

Section: Introductionmentioning

confidence: 99%

Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

Andersson

Larsson

Stenbeck

et al. 2020

Preprint

Self Cite

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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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“…ST allows for the quantitative spatial distribution of mRNA transcripts using barcoded oligo-dT arrays in histological sections, which can be validated using ISS, a technique that em rolling circle amplification ploys padlock probes and rolling circle amplification to visualize genes which have known primers (9). In the paper, Asp et al not only test their technical proof of concept but also uncover novel cell types, such as clusters of fibrosis-associated fibroblast-like cells and a subpopulation of cardiac muscle cells (37). They also visualized their result by integrating the spatial information into 3D transcriptional maps for other researchers.…”

Section: Single-cell Maps Of the Human Heart Using Stmentioning

confidence: 99%

Single-cell and spatial transcriptomics approaches of cardiovascular development and disease

Roth

Kim

et al. 2020

BMB Rep.

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Recent advancements in the resolution and throughput of single-cell analyses, including single-cell RNA sequencing (scRNA-seq), have achieved significant progress in biomedical research in the last decade. These techniques have been used to understand cellular heterogeneity by identifying many rare and novel cell types and characterizing subpopulations of cells that make up organs and tissues. Analysis across various datasets can elucidate temporal patterning in gene expression and developmental cues and is also employed to examine the response of cells to acute injury, damage, or disruption. Specifically, scRNA-seq and spatially resolved transcriptomics have been used to describe the identity of novel or rare cell subpopulations and transcriptional variations that are related to normal and pathological conditions in mammalian models and human tissues. These applications have critically contributed to advance basic cardiovascular research in the past decade by identifying novel cell types implicated in development and disease. In this review, we describe current scRNA-seq technologies and how current scRNA-seq and spatial transcriptomic (ST) techniques have advanced our understanding of cardiovascular development and disease. [BMB Reports 2020; 53(8): 393-399]

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Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration

Cited by 450 publications

References 62 publications

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Integrated intra- and intercellular signaling knowledge for multicellular omics analysis

Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

Single-cell and spatial transcriptomics approaches of cardiovascular development and disease

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