Multi-omics in exploring the pathophysiology of diabetic retinopathy DOI Creative Commons
Xinlu Li, Xifeng Dong, Wen Zhang

et al.

Frontiers in Cell and Developmental Biology, Journal Year: 2024, Volume and Issue: 12

Published: Dec. 11, 2024

Diabetic retinopathy (DR) is a leading global cause of vision impairment, with its prevalence increasing alongside the rising rates diabetes mellitus (DM). Despite retina's complex structure, underlying pathology DR remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) and recent advancements in multi-omics analyses have revolutionized molecular profiling, enabling high-throughput analysis comprehensive characterization biological systems. This review highlights significant contributions scRNA-seq, conjunction other technologies, to research. Integrated scRNA-seq transcriptomic revealed novel insights into pathogenesis, including alternative transcription start site events, fluctuations cell populations, altered gene expression profiles, critical signaling pathways within retinal cells. Furthermore, by integrating genetic association studies analyses, researchers identified biomarkers, susceptibility genes, potential therapeutic targets for DR, emphasizing importance specific types disease progression. The integration metabolomics has also been instrumental identifying metabolites dysregulated associated DR. It highly conceivable that continued synergy between approaches will accelerate discovery mechanisms development interventions

Language: Английский

Comparative proteomic landscapes elucidate human preimplantation development and failure DOI
Wencheng Zhu, Juan Meng, Yán Li

et al.

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

Published: Jan. 1, 2025

Language: Английский

Citations

2
Identification of cancer cell-intrinsic biomarkers associated with tumor progression and characterization of SFTA3 as a tumor suppressor in lung adenocarcinomas DOI Creative Commons
Yu Zhao, Chengcheng Zhou, Ling Zuo

et al.

BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 8, 2025

Recent advancements in contemporary therapeutic approaches have increased the survival rates of lung cancer patients; however, long-term benefits remain constrained, underscoring pressing need for novel biomarkers. Surfactant-associated 3 (SFTA3), a long non-coding RNA predominantly expressed normal epithelial cells, plays crucial role development. Nevertheless, its function adenocarcinoma (LUAD) remains inadequately understood. Single-cell sequencing data were utilized to identify cell-intrinsic gene signatures associated with progression LUAD, and their roles LUAD comprehensively analyzed. Serum samples collected quantify expression levels SFTA3 patients. Furthermore, series biological experiments, including cell viability assays, scratch wound healing colony formation conducted demonstrate tumor-suppressive effects SFTA3. was performed elucidate molecular mechanisms underlying cells. We constructed prognostic model comprising eight genes: ALDOA, ATP5MD, SERPINH1, SFTA3, SLK, U2SURP, SCGB1A1, SCGB1A3. The effectively stratified patients into high- low-risk categories, revealing that experienced superior clinical outcomes, exhibited an immunologically hot tumor microenvironment (TME), had greater probability responding immunotherapy. In contrast, high-risk group cold TME may benefit more from chemotherapy. our study revealed progressive decrease cells correlated advancement. Notably, serum significantly decreased suggesting potential utility liquid biopsy diagnosis. Additionally, knockdown enhances proliferation migration whereas overexpression inhibits these phenotypes. epithelial-mesenchymal transition pathway enriched following silencing, impact by modulating this process. also identified key transcription factors epigenetic implicated downregulation LUAD. developed robust as biomarker applications diagnosis, prognosis, personalized treatment findings offer new insights tumorigenesis immune evasion.

Language: Английский

Citations

0
Enhancing Biomedicine: Proteomics and Metabolomics in Action DOI Creative Commons
Michele Costanzo, Marianna Caterino, Lucia Santorelli

et al.

Proteomes, Journal Year: 2025, Volume and Issue: 13(1), P. 5 - 5

Published: Jan. 16, 2025

The rapid and substantial advancements in proteomic metabolomic technologies have revolutionized our ability to investigate biological systems [...].

Language: Английский

Citations

0
Advancements in proteogenomics for preclinical targeted cancer therapy research DOI Creative Commons

Yuying Suo,

Yuanli Song, Yuqiu Wang

et al.

Biophysics Reports, Journal Year: 2025, Volume and Issue: 11(1), P. 56 - 56

Published: Jan. 1, 2025

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic profiling of a tumor, proteogenomics, marks transformative milestone for preclinical research. In this paper, we initially provided an overview proteogenomics research, spanning genomics, transcriptomics, proteomics. Subsequently, the applications were introduced examined from different perspectives, including but not limited to genetic alterations, quantifications, single-cell patterns, post-translational modification levels, subtype signatures, immune landscape. We also paid attention combined multi-omics data analysis pan-cancer analysis. This paper highlights crucial role elucidating mechanisms tumorigenesis, discovering effective therapeutic targets promising biomarkers, developing subtype-specific therapies.

Language: Английский

Citations

0
Graph neural networks for single-cell omics data: a review of approaches and applications DOI Creative Commons

Shiming Li,

Heyang Hua, Shengquan Chen

et al.

Briefings in Bioinformatics, Journal Year: 2025, Volume and Issue: 26(2)

Published: March 1, 2025

Abstract Rapid advancement of sequencing technologies now allows for the utilization precise signals at single-cell resolution in various omics studies. However, massive volume, ultra-high dimensionality, and high sparsity nature data have introduced substantial difficulties to traditional computational methods. The intricate non-Euclidean networks intracellular intercellular signaling molecules within datasets, coupled with complex, multimodal structures arising from multi-omics joint analysis, pose significant challenges conventional deep learning operations reliant on Euclidean geometries. Graph neural (GNNs) extended data, allowing cells their features datasets be modeled as nodes a graph structure. GNNs been successfully applied across broad range tasks analysis. In this survey, we systematically review 107 successful applications six variants tasks. We begin by outlining fundamental principles variants, followed systematic GNN-based models epigenomics, transcriptomics, spatial proteomics, multi-omics. each section dedicated specific type, summarized publicly available commonly utilized articles reviewed that section, totaling 77 datasets. Finally, summarize potential shortcomings current research explore directions future anticipate will serve guiding resource researchers deepen application omics.

Language: Английский

Citations

0
Machine Learning‐Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma DOI Creative Commons
Lin Zhu, Liang Feng, Xue Han

et al.

Journal of Cellular and Molecular Medicine, Journal Year: 2025, Volume and Issue: 29(6)

Published: March 1, 2025

ABSTRACT Using machine learning approaches, we developed and validated a novel prognostic model for oesophageal squamous cell carcinoma (ESCC) based on glycolipid metabolism‐related genes. Through integrated analysis of TCGA GEO datasets, established robust 15‐gene signature that effectively stratified patients into distinct risk groups. This demonstrated superior value revealed significant associations with immune infiltration patterns. High‐risk exhibited reduced infiltration, particularly in B cells NK cells, alongside increased tumour purity. Single‐cell RNA sequencing uncovered unique cellular composition patterns enhanced interaction intensities the high‐risk group, especially within epithelial smooth muscle cells. Functional validation confirmed MECP2 as promising therapeutic target, its knockdown significantly inhibiting progression both vitro vivo. Drug sensitivity identified specific agents showing potential efficacy patients. Our study provides practical tool insights relationship between metabolism immunity ESCC, offering strategies personalised treatment.

Language: Английский

Citations

0
Glial changes and gene expression in Alzheimer's disease from snRNA-Seq and spatial transcriptomics DOI
Songren Wei, Chenyang Li,

W.Q. Li

et al.

Journal of Alzheimer s Disease, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Background Alzheimer's disease (AD) is characterized by cortical atrophy, glutamatergic neuron loss, and cognitive decline. However, large-scale quantitative assessments of cellular changes during AD pathology remain scarce. Objective This study aims to integrate single-nuclei sequencing data from the Seattle Disease Cortical Atlas (SEA-AD) with spatial transcriptomics quantify in prefrontal cortex temporal gyrus, regions vulnerable neuropathological (ADNC). Methods We mapped differentially expressed genes (DEGs) analyzed their interactions pathological factors such as APOE expression Lewy bodies. Cellular proportions were assessed, focusing on neurons, glial cells, immune cells. Results RORB-expressing L4-like though ADNC, exhibited stable cell numbers throughout progression. In contrast, astrocytes displayed increased reactivity, upregulated cytokine signaling oxidative stress responses, suggesting a role neuroinflammation. A reduction synaptic maintenance pathways indicated decline astrocytic support functions. Microglia showed heightened surveillance phagocytic activity, indicating maintaining homeostasis. Conclusions The underscores critical roles particularly microglia, These findings contribute better understanding dynamics may inform therapeutic strategies targeting function AD.

Language: Английский

Citations

0
Investigation of Human Aging at the Single-Cell Level DOI
Yunjin Li, Qixia Wang,

Yuan Xuan

et al.

Ageing Research Reviews, Journal Year: 2024, Volume and Issue: 101, P. 102530 - 102530

Published: Oct. 11, 2024

Language: Английский

Citations

2
Single-cell technologies: current and near future DOI Creative Commons

Chenfei Wang,

Qi Liu, Xiaohui Fan

et al.

Science China Life Sciences, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 12, 2024

Language: Английский

Citations

1
scCompass: An integrated cross-species scRNA-seq database for AI-ready DOI Open Access
Pengfei Wang, Wenhao Liu, Jiajia Wang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 15, 2024

Abstract Emerging single-cell sequencing technology has generated large amounts of data, allowing analysis cellular dynamics and gene regulation at the resolution. Advances in artificial intelligence enhance life sciences research by delivering critical insights optimizing data processes. However, inconsistent processing quality standards remain to be a major challenge. Here we propose scCompass, which provides solution build large-scale, cross-species model-friendly collection. By applying standardized pre-processing, scCompass integrates curates transcriptomic from 13 species nearly 105 million single cells. Using this extensive dataset, are able archieve stable expression genes (SEGs) organ-specific (OSGs) human mouse. We provide different scalable datasets that can easily adapted for AI model training pretrained checkpoints with state-of-the-art (SOTA) foundataion models. In summary, AI-readiness combined user-friendly sharing, visualization online analysis, greatly simplifies access exploitation researchers cell biology( http://www.bdbe.cn/kun ).

Language: Английский

Citations

0