Cancer-associated fibroblasts undergoing neoadjuvant chemotherapy suppress rectal cancer revealed by single-cell and spatial transcriptomics DOI Creative Commons
Pengfei Qin, Huaxian Chen, Yuhang Wang

et al.

Cell Reports Medicine, Journal Year: 2023, Volume and Issue: 4(10), P. 101231 - 101231

Published: Oct. 1, 2023

Neoadjuvant chemotherapy (NAC) for rectal cancer (RC) shows promising clinical response. The modulation of the tumor microenvironment (TME) by NAC and its association with therapeutic response remain unclear. Here, we use single-cell RNA sequencing spatial transcriptome to examine cell dynamics in 29 patients RC, who are sampled pairwise before after treatment. We construct a high-resolution cellular dynamic landscape remodeled their associations markedly reshapes populations cancer-associated fibroblasts (CAFs), which is strongly associated CAF subsets regulate TME through recruitment crosstalk activate immunity suppress progression multiple cytokines, including CXCL12, SLIT2, DCN. In contrast, epithelial-mesenchymal transition malignant cells upregulated CAF_FAP MIR4435-2HG induction, resulting worse outcomes. Our study demonstrates that inhibits modulates remodeling CAFs.

Language: Английский

Screening cell–cell communication in spatial transcriptomics via collective optimal transport DOI Creative Commons
Zixuan Cang, Yanxiang Zhao, Axel A. Almet

et al.

Nature Methods, Journal Year: 2023, Volume and Issue: 20(2), P. 218 - 228

Published: Jan. 23, 2023

Abstract Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell–cell communication (CCC). However, incorporation of the spatial information complex biochemical processes required in reconstruction CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) infer transcriptomics, which accounts for competition between different ligand receptor species as well distances cells. A collective optimal transport method is developed handle molecular interactions constraints. Furthermore, introduce downstream tools signaling directionality genes regulated using machine learning models. We apply simulation data eight acquired with five show its effectiveness robustness identifying varying resolutions gene coverages. Finally, identifies new CCCs during skin morphogenesis case study human epidermal development.

Language: Английский

Citations

200
CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics DOI Creative Commons
Suoqin Jin, Maksim V. Plikus,

Qing Nie

et al.

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

Published: Nov. 5, 2023

Abstract Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication tissues systematically and with reduced bias. A key challenge is the integration between known molecular interactions measurements into a framework identify analyze complex networks. Previously, we developed computational tool, named CellChat that infers analyzes networks from RNA-sequencing (scRNA-seq) data within easily interpretable framework. quantifies signaling probability two cell groups using simplified mass action-based model, which incorporates core interaction ligands receptors multi-subunit structure along modulation by cofactors. v2 updated version includes direct incorporation of spatial locations cells, if available, infer spatially proximal communication, additional comparison functionalities, expanded database ligand-receptor pairs rich annotations, Interactive Explorer. Here provide step-by-step protocol for can be used both scRNA-seq resolved transcriptomic data, including inference analysis one dataset identification altered across different datasets. The steps applying transcriptomics are described detail. R implementation toolkit tutorials graphic outputs available at https://github.com/jinworks/CellChat . This typically takes around 20 minutes, no specialized prior bioinformatics training required complete task.

Language: Английский

Citations

93
CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics DOI
Suoqin Jin, Maksim V. Plikus, Qing Nie

et al.

Nature Protocols, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Language: Английский

Citations

81
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
A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication DOI Creative Commons
Changde Cheng, Wenan Chen, Hongjian Jin

et al.

Cells, Journal Year: 2023, Volume and Issue: 12(15), P. 1970 - 1970

Published: July 30, 2023

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of heterogeneity, identification rare but significant cell types, and exploration cell-cell communications interactions. Its broad applications span both basic clinical research domains. In this comprehensive review, we survey current landscape scRNA-seq analysis methods tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, inference communication. We review challenges encountered in analysis, issues sparsity or low expression, reliability assumptions discuss potential impact suboptimal clustering differential expression tools downstream analyses, particularly identifying subpopulations. Finally, recent advancements future directions enhancing analysis. Specifically, highlight development novel annotating single-cell data, integrating interpreting multimodal datasets covering epigenomics, proteomics, inferring communication networks. By elucidating latest progress innovation, provide overview rapidly advancing field

Language: Английский

Citations

65
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
The diversification of methods for studying cell–cell interactions and communication DOI
Erick Armingol, Hratch Baghdassarian, Nathan E. Lewis

et al.

Nature Reviews Genetics, Journal Year: 2024, Volume and Issue: 25(6), P. 381 - 400

Published: Jan. 18, 2024

Language: Английский

Citations

52
Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy DOI
Jonathan H. Chen, Linda T. Nieman, Maxwell Spurrell

et al.

Nature Immunology, Journal Year: 2024, Volume and Issue: 25(4), P. 644 - 658

Published: March 19, 2024

Language: Английский

Citations

51
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

42
Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology DOI Creative Commons
Petra Kukanja, Christoffer Mattsson Langseth, Leslie A. Kirby

et al.

Cell, Journal Year: 2024, Volume and Issue: 187(8), P. 1990 - 2009.e19

Published: March 20, 2024

Multiple sclerosis (MS) is a neurological disease characterized by multifocal lesions and smoldering pathology. Although single-cell analyses provided insights into cytopathology, evolving cellular processes underlying MS remain poorly understood. We investigated the dynamics of modeling temporal regional rates progression in mouse experimental autoimmune encephalomyelitis (EAE). By performing spatial expression profiling using situ sequencing (ISS), we annotated neighborhoods found centrifugal evolution active lesions. demonstrated that disease-associated (DA)-glia arise independently are dynamically induced resolved over course. Single-cell mapping human archival spinal cords confirmed differential distribution homeostatic DA-glia, enabled deconvolution inactive sub-compartments, identified new lesion areas. establishing resource neuropathology at resolution, our study unveils intricate MS.

Language: Английский

Citations

35