Single-cell sequencing reveals an important role of SPP1 and microglial activation in age-related macular degeneration DOI Creative Commons
Shizhen Lei, Mang Hu,

Zhongtao Wei

et al.

Frontiers in Cellular Neuroscience, Journal Year: 2024, Volume and Issue: 17

Published: Jan. 8, 2024

Purpose To investigate the role of senescence-related cytokines (SRCs) in pathophysiology age-related macular degeneration (AMD). Design The whole study is based on single-cell and bulk tissue transcriptomic analysis human neuroretinas with or without AMD. data was obtained from Gene-Expression Omnibus (GEO) database. Methods For analysis, gene expression matrix goes through quality control (QC) filtering, being normalized, scaled integrated for downstream analysis. further analyses were performed using Seurat R package CellChat package. After cell type annotation, phenotype functional markers microglia investigated cell-cell communication performed. GSE29801 dataset contains neuroretina ( n = 118) group AMD patients. SPP1 subtypes compared by Student’s t -test. In addition, classified into SPP1-low SPP1-high groups according to level SPP1. differentially expressed genes between these two subsequently identified pathway enrichment conducted. Results Secreted phosphoprotein 1, as an SRC, revealed be highly SPP1-receptor signaling activated neuroretina. associated pro-inflammatory phagocytic state microglia. elevated late dry wet inflammatory pathways found Conclusion Our findings indicated that microglial activation might play important Therefore, serve a potential therapeutic target More vitro vivo studies are required confirm results effect SPP1-targeting strategy.

Language: Английский

Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information DOI Creative Commons
Zhaoyang Liu, Dongqing Sun, Chenfei Wang

et al.

Genome biology, Journal Year: 2022, Volume and Issue: 23(1)

Published: Oct. 17, 2022

Abstract Background Cell-cell interactions are important for information exchange between different cells, which the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization cell-cell using computational methods. However, it is hard to evaluate these methods since no ground truth provided. Spatial transcriptomics (ST) data profiles relative position cells. We propose that spatial distance suggests interaction tendency cell types, thus could be used evaluating tools. Results benchmark 16 by integrating scRNA-seq with ST data. characterize into short-range and long-range distributions ligands receptors. Based on this classification, we define enrichment score apply an evaluation workflow tools 15 simulated 5 real datasets. also compare consistency results from single commonly identified interactions. Our suggest predicted highly dynamic, statistical-based show overall better performance than network-based ST-based Conclusions study presents a comprehensive scRNA-seq. CellChat, CellPhoneDB, NicheNet, ICELLNET other terms software scalability. recommend at least two ensure accuracy have packaged detailed documentation GitHub ( https://github.com/wanglabtongji/CCI ).

Language: Английский

Citations

92
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
Deep learning applications in single-cell genomics and transcriptomics data analysis DOI Creative Commons
Nafiseh Erfanian, A. Ali Heydari, Adib Miraki Feriz

et al.

Biomedicine & Pharmacotherapy, Journal Year: 2023, Volume and Issue: 165, P. 115077 - 115077

Published: July 1, 2023

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding complex biological systems diseases, such as cancer, immune system, chronic diseases. However, technologies generate massive amounts data that often high-dimensional, sparse, complex, thus making analysis with traditional computational approaches difficult unfeasible. To tackle these challenges, many turning deep learning (DL) potential alternatives conventional machine (ML) algorithms for studies. DL is branch ML capable extracting high-level features from raw inputs multiple stages. Compared ML, models have provided significant improvements across domains applications. In this work, we examine applications genomics, transcriptomics, spatial multi-omics integration, address whether techniques will prove be advantageous or if omics domain poses unique challenges. Through systematic literature review, found has not yet revolutionized most pressing challenges field. using shown promising results (in cases outperforming previous state-of-the-art models) preprocessing downstream analysis. Although developments generally been gradual, recent advances reveal can offer valuable resources fast-tracking advancing research single-cell.

Language: Английский

Citations

58
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
LIANA+ provides an all-in-one framework for cell–cell communication inference DOI Creative Commons
Daniel Dimitrov, Philipp Schäfer, Elias Farr

et al.

Nature Cell Biology, Journal Year: 2024, Volume and Issue: 26(9), P. 1613 - 1622

Published: Sept. 1, 2024

The growing availability of single-cell and spatially resolved transcriptomics has led to the development many approaches infer cell-cell communication, each capturing only a partial view complex landscape intercellular signalling. Here we present LIANA+, scalable framework built around rich knowledge base decode coordinated inter- intracellular signalling events from single- multi-condition datasets in both data. By extending unifying established methodologies, LIANA+ provides comprehensive set synergistic components study communication via diverse molecular mediators, including those measured multi-omics is accessible at https://github.com/saezlab/liana-py with extensive vignettes ( https://liana-py.readthedocs.io/ ) an all-in-one solution inference.

Language: Английский

Citations

25
Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system DOI
Philipp Schäfer, Daniel Dimitrov, Eduardo J. Villablanca

et al.

Nature Immunology, Journal Year: 2024, Volume and Issue: 25(3), P. 405 - 417

Published: Feb. 27, 2024

Language: Английский

Citations

18
Challenges and best practices in omics benchmarking DOI
Thomas G. Brooks, Nicholas F. Lahens,

Antonijo Mrčela

et al.

Nature Reviews Genetics, Journal Year: 2024, Volume and Issue: 25(5), P. 326 - 339

Published: Jan. 12, 2024

Language: Английский

Citations

17
Multimodal transcriptomics reveal neurogenic aging trajectories and age-related regional inflammation in the dentate gyrus DOI Creative Commons
Yi‐Cheng Wu,

Vladyslav I. Korobeynyk,

Margherita Zamboni

et al.

Nature Neuroscience, Journal Year: 2025, Volume and Issue: 28(2), P. 415 - 430

Published: Jan. 6, 2025

Abstract The mammalian dentate gyrus (DG) is involved in certain forms of learning and memory, DG dysfunction has been implicated age-related diseases. Although neurogenic potential maintained throughout life the as neural stem cells (NSCs) continue to generate new neurons, neurogenesis decreases with advancing age, implications for cognitive decline disease. In this study, we used single-cell RNA sequencing characterize transcriptomic signatures their surrounding niche, identifying molecular changes associated aging from activation quiescent NSCs maturation fate-committed progeny. By integrating spatial transcriptomics data, identified regional invasion inflammatory into hippocampus age show here that early-onset neuroinflammation activity. Our data reveal lifelong dynamics niche provide a powerful resource understand alterations hippocampus.

Language: Английский

Citations

2