Spatial transcriptomics: Technologies, applications and experimental considerations

Wang, Ye; Liu, Bin; Zhao, Gexin; Lee, Yoojin; Buzdin, Anton; Mu, Xiaojing; Zhao, Joseph J; Hong, Chen; Li, Xinmin

doi:10.1016/j.ygeno.2023.110671

Cited by 35 publications

(12 citation statements)

References 52 publications

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“…To address this, we utilized cell deconvolution using RB scRNA-seq dataset to resolve and improve the resolution of our spatial transcriptomic dataset. Alternative spatial transcriptomic systems could be applied to improve RB profiling in future research 87 . For instance, imaging-based spatial systems such as MERSCOPE and Xenium offer spatial profiling at single cell level, but are greatly limited by the number of detected genes.…”

Section: Discussionmentioning

confidence: 99%

Spatial transcriptomic profiling of human retinoblastoma

Wang,

Hung,

Urrutia-Cabrera

et al. 2024

Preprint

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Retinoblastoma (RB) represents one of the most prevalent intraocular cancers in children. Understanding the tumor heterogeneity in RB is important to design better targeted therapies. Here we used spatial transcriptomic to profile human retina and RB tumor to comprehensively dissect the spatial cell-cell communication networks. We found high intratumoral heterogeneity in RB, consisting of 10 transcriptionally distinct subpopulations with varying levels of proliferation capacity. Our results uncovered a complex architecture of the tumor microenvironment that predominantly consisted of cone precursors, as well as glial cells and cancer-associated fibroblasts. We delineated the cell trajectory underlying malignant progression of RB, and identified key signaling pathways driving genetic regulation across RB progression. We also explored the signaling pathways mediating cell-cell communications in RB subpopulations, and mapped the spatial networks of RB subpopulations and region neighbors. Altogether, we constructed the first spatial gene atlas for RB, which allowed us to characterize the transcriptomic landscape in spatially-resolved RB subpopulations, providing novel insights into the complex spatial communications involved in RB progression.

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Section: Discussionmentioning

confidence: 99%

Spatial transcriptomic profiling of human retinoblastoma

Wang,

Hung,

Urrutia-Cabrera

et al. 2024

Preprint

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“…Spatial transcriptomics can reveal the spatial distribution of gene expression in brain tissue, which has important implications for understanding the interactions of cell types and complex molecular processes in damaged areas after stroke. Spatial transcriptomics provide spatially resolved gene expression profiles with single‐cell resolution for in‐depth characterization of cell types and states within tissues 189,190 …”

Section: Discussionmentioning

confidence: 99%

Perspective from single‐cell sequencing: Is inflammation in acute ischemic stroke beneficial or detrimental?

Deng,

Hu,

Zhou

et al. 2023

CNS Neurosci Ther

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BackgroundAcute ischemic stroke (AIS) is a common cerebrovascular event associated with high incidence, disability, and poor prognosis. Studies have shown that various cell types, including microglia, astrocytes, oligodendrocytes, neurons, and neutrophils, play complex roles in the early stages of AIS and significantly affect its prognosis. Thus, a comprehensive understanding of the mechanisms of action of these cells will be beneficial for improving stroke prognosis. With the rapid development of single‐cell sequencing technology, researchers have explored the pathophysiological mechanisms underlying AIS at the single‐cell level.MethodWe systematically summarize the latest research on single‐cell sequencing in AIS.ResultIn this review, we summarize the phenotypes and functions of microglia, astrocytes, oligodendrocytes, neurons, neutrophils, monocytes, and lymphocytes, as well as their respective subtypes, at different time points following AIS. In particular, we focused on the crosstalk between microglia and astrocytes, oligodendrocytes, and neurons. Our findings reveal diverse and sometimes opposing roles within the same cell type, with the possibility of interconversion between different subclusters.ConclusionThis review offers a pioneering exploration of the functions of various glial cells and cell subclusters after AIS, shedding light on their regulatory mechanisms that facilitate the transformation of detrimental cell subclusters towards those that are beneficial for improving the prognosis of AIS. This approach has the potential to advance the discovery of new specific targets and the development of drugs, thus representing a significant breakthrough in addressing the challenges in AIS treatment.

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“…Spatial technologies such as 10X Genomics Xenium, CosMX, Mibi, and MERSCOPE have added a spatial dimension to near-, at, or sub-single-cell resolution 23 .…”

Section: Mainmentioning

confidence: 99%

Thetidyomicsecosystem: Enhancing omic data analyses

Hutchison,

Keyes,

Crowell

et al. 2023

Preprint

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The exponential growth of omic data presents challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor offers an extensive data analysis platform and community, while R tidy programming offers a standard data organisation and manipulation that has revolutionised data science. Bioconductor and tidy R have mostly remained independent; bridging these two ecosystems would streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. Here, we introduce thetidyomicsecosystem—a suite of interoperable software that brings the vast tidy software ecosystem to omic data analysis.

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Spatial transcriptomics: Technologies, applications and experimental considerations

Cited by 35 publications

References 52 publications

Spatial transcriptomic profiling of human retinoblastoma

Spatial transcriptomic profiling of human retinoblastoma

Perspective from single‐cell sequencing: Is inflammation in acute ischemic stroke beneficial or detrimental?

Thetidyomicsecosystem: Enhancing omic data analyses

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