Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration

By Michaela Asp, J. Bergenstråhle, J. Lundeberg. Bioessays, 2020

538 citations recorded by the source corpus.

Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method‐specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue‐type dependence, and limited capacity to obtain detailed single‐cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon.

View Paper: https://www.semanticscholar.org/paper/d073747bd8e18e0f5f96d557d3d849283534d881

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Citations in the corpus

Best practices for single-cell analysis across modalities (2023, background): “Alignment of standard spatially naive scRNA-seq data and targeted spatially resolved data enables imputation of the whole transcriptome (measured in standard scRNA-seq) in a spatially resolved manner and attempts to resolve the limitations of targeted feature spaces.”
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, unknown): “The popular 10x Visium platform can capture scRNA-seq scale transcriptomes but uses 55 µ m capture spots that arelargerthantypicalcells(5 – 10 µ m) 18,19 .”
Integrated intra‐ and intercellular signaling knowledge for multicellular omics analysis (2020, unknown): “Or for the resulting networks used to make the conclusions it would be good to show the number of resources from which the nodes/data points were extracted, to make the case that this integrated resource is necessary for this analysis.”
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder (2021, background): “Introduction The functions of complex tissues are fundamentally related to the spatial context of different cell types [1].”
Methods and applications for single-cell and spatial multi-omics (2023, background): “Principles of spatial mono-omics methods for spatial multi-omics developments Methods for spatial mono-omics have progressed tremendously in the last decade with excellent reviews existing for spatial transcriptomics 165,171–173 , (epi)genomics 166,174 , proteomics 86,167 and metabolomics 168,175 .”