Radial glia and radial glia-like cells: Their role in neurogenesis and regeneration DOI Creative Commons
Yamil Miranda-Negrón, José E. García‐Arrarás

Frontiers in Neuroscience, Journal Year: 2022, Volume and Issue: 16

Published: Nov. 16, 2022

Radial glia is a cell type traditionally associated with the developing nervous system, particularly formation of cortical layers in mammalian brain. Nonetheless, some these cells, or closely related types, called radial glia-like cells are found adult central system structures, functioning as neurogenic progenitors normal homeostatic maintenance and response to injury. The heterogeneity nowadays being probed molecular tools, primarily by expression specific genes that define types. Similar markers have identified non-vertebrate organisms. In this review, we focus on processes during homeostasis We highlight our results using model echinoderm

Language: Английский

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

Mengnan Cheng,

Yujia Jiang, Jiangshan Xu

et al.

Journal of genetics and genomics/Journal of Genetics and Genomics, Journal Year: 2023, Volume and Issue: 50(9), P. 625 - 640

Published: March 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.

Language: Английский

Citations

75

STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization DOI Creative Commons
Zhicheng Xu, Weiwen Wang, Tao Yang

et al.

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D1053 - D1061

Published: Nov. 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/.

Language: Английский

Citations

59

Mapping cells through time and space with moscot DOI Creative Commons
Dominik Klein, Giovanni Palla, Marius Lange

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: May 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 .

Language: Английский

Citations

52

Cell–cell communication: new insights and clinical implications DOI Creative Commons

Jimeng Su,

Ying Song,

Zhipeng Zhu

et al.

Signal Transduction and Targeted Therapy, Journal Year: 2024, Volume and Issue: 9(1)

Published: Aug. 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.

Language: Английский

Citations

40

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics DOI
Gunsagar S. Gulati,

Jeremy Philip D’Silva,

Yunhe Liu

et al.

Nature Reviews Molecular Cell Biology, Journal Year: 2024, Volume and Issue: 26(1), P. 11 - 31

Published: Aug. 21, 2024

Language: Английский

Citations

33

Spatiotemporal omics for biology and medicine DOI
Longqi Liu, Ao Chen, Yuxiang Li

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(17), P. 4488 - 4519

Published: Aug. 1, 2024

Language: Английский

Citations

27

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases DOI Creative Commons
Paul Kießling, Christoph Kuppe

Genome Medicine, Journal Year: 2024, Volume and Issue: 16(1)

Published: Jan. 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.

Language: Английский

Citations

24

Microglia-astrocyte crosstalk in the amyloid plaque niche of an Alzheimer’s disease mouse model, as revealed by spatial transcriptomics DOI Creative Commons
Anna Mallach, Magdalena Zielonka, Veerle van Lieshout

et al.

Cell Reports, Journal Year: 2024, Volume and Issue: 43(6), P. 114216 - 114216

Published: May 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.

Language: Английский

Citations

22

Mapping cells through time and space with moscot DOI Creative Commons
Dominik Klein, Giovanni Palla, Marius Lange

et al.

Nature, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 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.

Language: Английский

Citations

5

Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics DOI Creative Commons
Lulu Shang, Peijun Wu, Xiang Zhou

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 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.

Language: Английский

Citations

2