Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data

Tanevski, Jovan; Flores, Ricardo O. Ramirez; Gábor, Attila; Schapiro, Denis; Sáez-Rodríguez, Julio

doi:10.1101/2020.05.08.084145

Cited by 24 publications

(18 citation statements)

References 53 publications

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“…Thus, a threshold signifying conserved spatial gene regulation between transmitters and receivers (with e.g. mistyR 67 ) can be used to define putative true positive interactions.…”

Section: *1 Number Of Inferred Interactions Between Cell Clusters; Average Cell Inferencementioning

confidence: 99%

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Dimitrov

Türei

Boys

et al. 2021

Preprint

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The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.

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“…Thus, a threshold signifying conserved spatial gene regulation between transmitters and receivers (with e.g. mistyR 67 ) can be used to define putative true positive interactions.…”

Section: *1 Number Of Inferred Interactions Between Cell Clusters; Average Cell Inferencementioning

confidence: 99%

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Dimitrov

Türei

Boys

et al. 2021

Preprint

Self Cite

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“…We next estimated to what extent the spatial neighborhood of specific cell-types in the tissue influenced their gene expression, using MISTy 16 . MISTy models the expression of cell-type markers using spatially contextualized views, for example in the detection spot itself (intraview) or the surrounding (paraview).…”

Section: Integrative Multi-omic Analysis Of the Healthy Human Heartmentioning

confidence: 99%

Spatial multi-omic map of human myocardial infarction

Kuppe

Flores

et al. 2020

Preprint

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Myocardial infarction is a leading cause of mortality. While advances in the acute treatment have been made, the late-stage mortality is still high, driven by an incomplete understanding of cardiac remodeling processes1,2. Here we used single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of different physiological zones and timepoints of human myocardial infarction and human control myocardium to generate an integrative high-resolution map of cardiac remodeling. This approach allowed us to increase spatial resolution of cell-type composition and provide spatially resolved insights into the cardiac transcriptome and epigenome with identification of distinct cellular zones of injury, repair and remodeling. We here identified and validated mechanisms of fibroblast to myofibroblast differentiation that drive cardiac fibrosis. Our study provides an integrative molecular map of human myocardial infarction and represents a reference to advance mechanistic and therapeutic studies of cardiac disease.

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“…Spatial transcriptomics technologies provide this information and hence help to prioritize the most likely ligand–receptor interactions. Fundamental questions about cell communication in tissues, such as how secreted ligands act on neighboring cells, can be addressed by analyzing spatially resolved data, combining data‐driven (Sun et al, 2020; preprint: Tanevski et al, 2020) with prior knowledge‐based (Browaeys et al, 2019; Liu et al, 2019; Efremova et al, 2020) approaches. OmniPath provides a framework to support these endeavors.…”

Section: Discussionmentioning

confidence: 99%

Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis

Türei

Valdeolivas

Gul

et al. 2021

Molecular Systems Biology

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Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single-cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter-and intracellular signaling, as well as transcriptional and post-transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath's web service (https://omnipathdb.org/), a Cytoscape plug-in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell-cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intraand intercellular processes for data analysis, as we demonstrate in applications studying SARS-CoV-2 infection and ulcerative colitis.

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Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial data

Cited by 24 publications

References 53 publications

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

Spatial multi-omic map of human myocardial infarction

Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis

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