Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment

Molecular Cancer, Год журнала: 2023, Номер 22(1)

Опубликована: Окт. 13, 2023

Cancer-associated fibroblasts (CAFs) are a heterogeneous cell population that plays crucial role in remodeling the tumor microenvironment (TME). Here, through integrated analysis of spatial and single-cell transcriptomics data across six common cancer types, we identified four distinct functional subgroups CAFs described their distribution characteristics. Additionally, RNA sequencing (scRNA-seq) from three additional types two newly generated scRNA-seq datasets rare namely epithelial-myoepithelial carcinoma (EMC) mucoepidermoid (MEC), expanded our understanding CAF heterogeneity. Cell-cell interaction conducted within context highlighted pivotal roles matrix (mCAFs) angiogenesis inflammatory (iCAFs) shaping immunosuppressive microenvironment. In patients with breast (BRCA) undergoing anti-PD-1 immunotherapy, iCAFs demonstrated heightened capacity facilitating proliferation, promoting epithelial-mesenchymal transition (EMT), contributing to establishment an Furthermore, scoring system based on showed significant correlation immune therapy response melanoma patients. Lastly, provided web interface ( https://chenxisd.shinyapps.io/pancaf/ ) for research community investigate pan-cancer.

Язык: Английский

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CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

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

Опубликована: Ноя. 2, 2023

Abstract Hundreds of millions single cells have been analyzed to date using high throughput transcriptomic methods, thanks technological advances driving the increasingly rapid generation single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent in large language models other machine learning approaches. Despite promise these emerging analytical tools analyzing amounts data, a major challenge remains sheer number inconsistent format, data accessibility. Many are available via unique portals platforms often lack interoperability. Here, we present CZ CellxGene Discover ( cellxgene.cziscience.com ), platform curated interoperable resource, free-to-use online portal, hosts growing corpus community contributed spans more than 50 million cells. Curated, standardized, associated with consistent cell-level metadata, this collection is largest its kind. A suite features enables accessibility reusability both computational visual interfaces allow researchers rapidly explore individual perform cross-corpus analysis. functionality enabling meta-analyses tens across studies tissues providing global views human at resolution

Язык: Английский

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Antimicrobial resistance crisis: could artificial intelligence be the solution?

Military Medical Research, Год журнала: 2024, Номер 11(1)

Опубликована: Янв. 23, 2024

Abstract Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced priority list of most threatening pathogens against which novel antibiotics need to be developed. The discovery introduction are time-consuming expensive. According WHO’s report antibacterial agents in clinical development, only 18 have been approved since 2014. Therefore, critically needed. Artificial intelligence (AI) rapidly applied drug development its recent technical breakthrough dramatically improved efficiency antibiotics. Here, we first summarized recently marketed antibiotics, antibiotic candidates development. In addition, systematically reviewed involvement AI utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well mechanism prediction, stewardship.

Язык: Английский

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Machine learning in preclinical drug discovery

Nature Chemical Biology, Год журнала: 2024, Номер 20(8), С. 960 - 973

Опубликована: Июль 19, 2024

Язык: Английский

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CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Nucleic Acids Research, Год журнала: 2024, Номер 53(D1), С. D886 - D900

Опубликована: Ноя. 28, 2024

Hundreds of millions single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level cells. Meta-analyses that span diverse building on recent advances in large language models other machine-learning approaches pose new directions to model extract insight from single-cell data. Despite promise emerging analytical tools analyzing amounts data, sheer number datasets, data accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), platform curated interoperable Available via free-to-use online portal, hosts growing corpus community-contributed over 93 million unique Curated, standardized associated with consistent cell-level metadata, this collection is largest its kind rapidly community contributions. A suite features enables reusability both computational visual interfaces allow researchers explore individual perform cross-corpus analysis, run meta-analyses tens across studies tissues resolution

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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Macrophage profiling in atherosclerosis: understanding the unstable plaque

Basic Research in Cardiology, Год журнала: 2024, Номер 119(1), С. 35 - 56

Опубликована: Янв. 20, 2024

Язык: Английский

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Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery

Theranostics, Год журнала: 2024, Номер 14(7), С. 2946 - 2968

Опубликована: Янв. 1, 2024

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy accelerating discovery process. With evolution spatial (ST) technology emerged as a derivative approach. Spatial hot topic field omics research recent years; it not only provides information on gene expression levels but also offers expression. This shown tremendous potential disease understanding In this article, we introduce analytical strategies review its applications novel target mechanism unravelling. Moreover, discuss current challenges issues that need be addressed. conclusion, new perspective

Язык: Английский

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Nucleic acid-based drugs for patients with solid tumours

Nature Reviews Clinical Oncology, Год журнала: 2024, Номер 21(6), С. 407 - 427

Опубликована: Апрель 8, 2024

Язык: Английский

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Targets of tumor microenvironment for potential drug development

MedComm – Oncology, Год журнала: 2024, Номер 3(1)

Опубликована: Март 1, 2024

Abstract The tumor microenvironment (TME) is the ecosystem surrounding a tumor, which usually consists of nontumoral cells or components, and molecules they produce release. frequent continuous interplay between TME strongly affects development, disease progression, metastasis, responses to therapeutic interventions. As hub potential targets, has gained appreciable momentum in cancer research. Here we systematically review progress targeting as strategy develop novel antitumor drugs from immunological, stromal extracellular matrix components TME, shedding light on its complex synergies with cells. This exploration highlights transformative these elements hold refining treatment approaches. thorough examination not only accentuates TME's multifaceted nature but also positions it formidable avenue for propelling forward paradigms therapy. aims foster deeper understanding role oncogenesis exploitation advancing targeted, efficacious treatments, marking significant stride realm

Язык: Английский

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