Spatially resolved multiomics on the neuronal effects induced by spaceflight

Masarapu, Yuvarani; Cekanaviciute, Egle; Andrusivová, Žaneta; Westholm, Jakub Orzechowski; Björklund, Åsa K.; Fallegger, Robin; Badia-i-Mompel, Pau; Boyko, Valery; Vasisht, Shubha; Saravia-Butler, Amanda; Gebre, Samrawit G.; Lázár, Enikő; Bergmann, Olaf; Taylor, Deanne; Wallace, Douglas C.; Sylvén, Christer; Sáez-Rodríguez, Julio; Galazka, Jonathan M.; Costes, Sylvain V.; Giacomello, Stefania

doi:10.21203/rs.3.rs-2865086/v1

Cited by 3 publications

(2 citation statements)

References 68 publications

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“…Kasumi representations can easily be adapted to explore tissue-specific common and differential patterns between conditions, even beyond association to clinical features. Furthermore, while we considered view compositions capturing a single spatial context, Kasumi can be deployed with more complex compositions addressing different spatial and functional contexts and different omics technologies [34][35][36][37][38] .…”

Section: Discussionmentioning

confidence: 99%

Learning tissue representation by identification of persistent local patterns in spatial omics data

Tanevski,

Vulliard,

Hartmann

et al. 2024

Preprint

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Spatial omics data provide rich molecular and structural information about tissues, enabling novel insights into the structure-function relationship. In particular, it facilitates the analysis of the local heterogeneity of tissues and holds promise to improve patient stratification by association of finer-grained representations with clinically relevant features. Here, we introduce Kasumi, a method for the identification of spatially localized neighborhoods of intra- and intercellular relationships, persistent across samples and conditions. We learn compressed explainable representations while preserving relevant biological signals that are readily deployable for data exploration and hypothesis generation, facilitating translational tasks. We address tasks of patient stratification for disease progression and response to treatment in cancer on data coming from different spatial antibody-based multiplexed proteomics platforms. Kasumi outperforms related neighborhood analysis approaches and offers explanations at the level of cell types or directly from the measurements, of the spatial coordination and multivariate relationships underlying observed disease progression and response to treatment. We show that persistent local patterns form spatially contiguous regions of different sizes. However, the abundance of the persistent local patterns is not associated with their relative importance in downstream tasks. We show that non-abundant, localized structural and functional relationships in the tissue are strongly associated with unfavorable outcomes in disease progression and response to treatment.

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Section: Discussionmentioning

confidence: 99%

Learning tissue representation by identification of persistent local patterns in spatial omics data

Tanevski,

Vulliard,

Hartmann

et al. 2024

Preprint

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“…To this end, we also implemented a multivariate, multi-view modelling approach to learn spatial relationships across distinct types of features or spatial contexts (represented as views) 16 . Our approach can learn complex relationships, such as relationships between ligand expressions and pathways 16 , or cell types and pathways 39,40 (Fig. 2C), as well as jointly model any combination of views (Fig.…”

Section: Liana+'s Spatial Componentmentioning

confidence: 99%

LIANA+: an all-in-one cell-cell communication framework

Dimitrov,

Schäfer,

Farr

et al. 2023

Preprint

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The growing availability of single-cell and spatially-resolved transcriptomics has led to the rapidly growing popularity of methods to infer cell-cell communication. Many approaches have emerged, each capturing only a partial view of the complex landscape of cell-cell communication.Here, we present LIANA+, a scalable framework to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially-resolved data. Beyond integrating and extending established methodologies and a rich knowledge base, LIANA+ enables novel analyses using diverse molecular mediators, including those measured in multi-omics data. Accessible as an open-source Python package athttps://github.com/saezlab/liana-py, LIANA+ provides a comprehensive set of synergistic components to study cell-cell communication.Abstract Figure

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LIANA+ provides an all-in-one framework for cell–cell communication inference

Dimitrov,

Schäfer,

Farr

et al. 2024

Nat Cell Biol

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The growing availability of single-cell and spatially resolved transcriptomics has led to the development of many approaches to infer cell–cell communication, each capturing only a partial view of the complex landscape of intercellular signalling. Here we present LIANA+, a scalable framework built around a rich knowledge base to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially resolved data. By extending and unifying established methodologies, LIANA+ provides a comprehensive set of synergistic components to study cell–cell communication via diverse molecular mediators, including those measured in multi-omics data. LIANA+ is accessible at https://github.com/saezlab/liana-py with extensive vignettes (https://liana-py.readthedocs.io/) and provides an all-in-one solution to intercellular communication inference.

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Spatially resolved multiomics on the neuronal effects induced by spaceflight

Cited by 3 publications

References 68 publications

Learning tissue representation by identification of persistent local patterns in spatial omics data

Learning tissue representation by identification of persistent local patterns in spatial omics data

LIANA+: an all-in-one cell-cell communication framework

LIANA+ provides an all-in-one framework for cell–cell communication inference

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