Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges

Journal of genetics and genomics/Journal of Genetics and Genomics, Год журнала: 2023, Номер 50(9), С. 625 - 640

Опубликована: Март 27, 2023

The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology, from the invention of microscope 350 years ago recent emergence single-cell sequencing, which scientific community has been able visualize at an unprecedented resolution. Most recently, Spatially Resolved Transcriptomics (SRT) technologies have filled gap probing spatial or even three-dimensional organization molecular foundation behind mysteries life, including origin different cellular populations developed totipotent cells human diseases. In this review, we introduce progress challenges on SRT perspectives bioinformatic tools, as well representative applications. With currently fast-moving promising results early adopted research projects, can foresee bright future such new tools understanding most profound analytical level.

Язык: Английский

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STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization

Nucleic Acids Research, Год журнала: 2023, Номер 52(D1), С. D1053 - D1061

Опубликована: Ноя. 11, 2023

Abstract Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their locations at the single-cell level, generating detailed biological insight into processes. A comprehensive database could facilitate sharing transcriptomic data streamline acquisition process for researchers. Here, we present Spatial TranscriptOmics DataBase (STOmicsDB), a that serves as one-stop hub transcriptomics. STOmicsDB integrates 218 manually curated datasets representing 17 species. We annotated cell types, identified regions genes, performed cell-cell interaction analysis these datasets. features user-friendly interface rapid visualization millions cells. To further reusability interoperability data, developed standards archiving constructed system. Additionally, offer distinctive capability customizing dedicated sub-databases researchers, assisting them visualizing analyses. believe contribute research insights field, including archiving, sharing, analysis. is freely accessible https://db.cngb.org/stomics/.

Язык: Английский

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Mapping cells through time and space with moscot

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Май 11, 2023

Abstract Single-cell genomics technologies enable multimodal profiling of millions cells across temporal and spatial dimensions. Experimental limitations prevent the measurement all-encompassing cellular states in their native dynamics or tissue niche. Optimal transport theory has emerged as a powerful tool to overcome such constraints, enabling recovery original context. However, most algorithmic implementations currently available have not kept up pace with increasing dataset complexity, so that current methods are unable incorporate information scale single-cell atlases. Here, we introduce multi-omics optimal (moscot), general scalable framework for applications genomics, supporting multimodality all applications. We demonstrate moscot’s ability efficiently reconstruct developmental trajectories 1.7 million mouse embryos 20 time points identify driver genes first heart field formation. The moscot formulation can be used dimensions well: To this, enrich transcriptomics datasets by mapping from profiles liver sample, align multiple coronal sections brain. then present moscot.spatiotemporal, new approach leverages gene expression uncover spatiotemporal embryogenesis. Finally, disentangle lineage relationships novel murine, time-resolved pancreas development using paired measurements chromatin accessibility, finding evidence shared ancestry between delta epsilon cells. Moscot is an easy-to-use, open-source python package extensive documentation at https://moscot-tools.org .

Язык: Английский

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Cell–cell communication: new insights and clinical implications

Signal Transduction and Targeted Therapy, Год журнала: 2024, Номер 9(1)

Опубликована: Авг. 7, 2024

Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, physiological regulation. Cells communicate direct contact or at a distance using ligand-receptor interactions. So cellular encompasses two processes: signal conduction generation intercellular transmission signals, transduction reception procession signals. Deciphering networks critical understanding metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by detailed description mechanisms molecule importance main signaling pathways they mediate maintaining biological functions. Then systematically introduce series human diseases caused abnormalities progress clinical applications. Finally, summarize various methods monitoring interactions, including imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, single-cell technologies. These aim to illustrate how functions depend on these interactions complexity regulatory regulate crucial processes, homeostasis, immune responses diseases. In addition, this enhances our processes occur after cell-cell binding, highlighting its application discovering new therapeutic targets biomarkers related precision medicine. This collective provides foundation developing targeted drugs personalized treatments.

Язык: Английский

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Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Nature Reviews Molecular Cell Biology, Год журнала: 2024, Номер 26(1), С. 11 - 31

Опубликована: Авг. 21, 2024

Язык: Английский

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Spatiotemporal omics for biology and medicine

Cell, Год журнала: 2024, Номер 187(17), С. 4488 - 4519

Опубликована: Авг. 1, 2024

Язык: Английский

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Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Genome Medicine, Год журнала: 2024, Номер 16(1)

Опубликована: Янв. 18, 2024

Abstract Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome parallel or even same tissue section. This review focuses on recent advancements spatial multi-omics technologies, including novel computational approaches. We discuss low-resolution high-resolution methods which can resolve up 10,000 individual molecules at subcellular level. By applying integrating these techniques, researchers recently gained valuable insights into molecular circuits mechanisms govern cell biology along cardiovascular disease spectrum. provide an overview current approaches, with focus integration datasets, highlighting strengths weaknesses various pipelines. These tools play crucial role analyzing interpreting facilitating discovery new findings, enhancing translational research. Despite nontrivial challenges, such need for standardization experimental setups, analysis, improved tools, application holds tremendous potential revolutionizing our understanding human processes identification biomarkers therapeutic targets. Exciting opportunities lie ahead field will likely contribute advancement personalized medicine diseases.

Язык: Английский

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Microglia-astrocyte crosstalk in the amyloid plaque niche of an Alzheimer’s disease mouse model, as revealed by spatial transcriptomics

Cell Reports, Год журнала: 2024, Номер 43(6), С. 114216 - 114216

Опубликована: Май 30, 2024

The amyloid plaque niche is a pivotal hallmark of Alzheimer's disease (AD). Here, we employ two high-resolution spatial transcriptomics (ST) platforms, CosMx and Spatial Enhanced Resolution Omics-sequencing (Stereo-seq), to characterize the transcriptomic alterations, cellular compositions, signaling perturbations in an AD mouse model. We discover heterogeneity composition niches, marked by increase microglial accumulation. profile alterations glial cells vicinity plaques conclude that response consistent across different brain regions, while astrocytic more heterogeneous. Meanwhile, as density niches increases, astrocytes acquire neurotoxic phenotype play key role inducing GABAergic decreasing glutamatergic hippocampal neurons. thus show accumulation microglia around disrupts signaling, turn imbalance neuronal synaptic signaling.

Язык: Английский

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Mapping cells through time and space with moscot

Nature, Год журнала: 2025, Номер unknown

Опубликована: Янв. 22, 2025

Abstract Single-cell genomic technologies enable the multimodal profiling of millions cells across temporal and spatial dimensions. However, experimental limitations hinder comprehensive measurement under native dynamics in their tissue niche. Optimal transport has emerged as a powerful tool to address these constraints facilitated recovery original cellular context 1–4 . Yet, most optimal applications are unable incorporate information or scale single-cell atlases. Here we introduce multi-omics (moscot), scalable framework for genomics that supports multimodality all applications. We demonstrate capability moscot efficiently reconstruct developmental trajectories 1.7 million from mouse embryos 20 time points. To illustrate space, enrich transcriptomic datasets by mapping profiles liver sample align multiple coronal sections brain. present moscot.spatiotemporal, an approach leverages gene-expression data both dimensions uncover spatiotemporal embryogenesis. also resolve endocrine-lineage relationships delta epsilon previously unpublished mouse, time-resolved pancreas development dataset using paired measurements gene expression chromatin accessibility. Our findings confirmed through validation NEUROD2 regulator progenitor model human induced pluripotent stem cell islet differentiation. Moscot is available open-source software, accompanied extensive documentation.

Язык: Английский

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Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Янв. 26, 2025

An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)—a subset of exhibiting distinct expression patterns within specific types. Celina utilizes varying coefficient model to accurately capture each gene's pattern relation the distribution types across tissue locations, ensuring effective type I error control and high power. proves powerful compared existing methods single-cell resolution stands as only solution spot-resolution transcriptomics. Applied five real datasets, uncovers ct-SVGs associated with tumor progression patient survival lung cancer, identifies metagenes unique linked proliferation immune response kidney detects preferentially expressed near amyloid-β plaques an Alzheimer's model. The authors develop detect (ct-SVGs) These exhibit types, offering insights into transcriptomic mechanism underlying cellular heterogeneity.

Язык: Английский

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