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使用单细胞RNA测序数据预测肺衰老

Predicting lung aging using scRNA-Seq data.

作者信息

Song Qi, Singh Alex, McDonough John E, Adams Taylor S, Vos Robin, De Man Ruben, Myers Greg, Ceulemans Laurens J, Vanaudenaerde Bart M, Wuyts Wim A, Yan Xiting, Schupp Jonas, Hagood James S, Kaminski Naftali, Bar-Joseph Ziv

机构信息

Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

Faculty of Health Sciences, McMaster University, Ontario, Canada.

出版信息

PLoS Comput Biol. 2024 Dec 19;20(12):e1012632. doi: 10.1371/journal.pcbi.1012632. eCollection 2024 Dec.

DOI:10.1371/journal.pcbi.1012632
PMID:39700255
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11741621/
Abstract

Age prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we developed PolyEN, a new regression model which learns continuous representation for expression over time. These representations are then used by PolyEN to integrate genes to predict an age. Existing and new lung aging data we profiled demonstrated PolyEN's improved performance over existing methods for age prediction. Our results identified lung epithelial cells as the most significant predictors for non-smokers while lung endothelial cells led to the best chronological age prediction results for smokers.

摘要

基于单细胞RNA测序数据（scRNA-Seq）的年龄预测可为患者对各种疾病和状况的易感性提供信息。此外，这种分析可用于识别与衰老相关的基因和途径。为了基于scRNA-Seq数据进行年龄预测，我们开发了PolyEN，这是一种新的回归模型，它学习随时间变化的表达的连续表示。然后，PolyEN使用这些表示来整合基因以预测年龄。我们分析的现有和新的肺衰老数据表明，PolyEN在年龄预测方面比现有方法具有更好的性能。我们的结果表明，肺上皮细胞是非吸烟者最重要的预测因子，而肺内皮细胞则为吸烟者带来了最佳的实际年龄预测结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/570576ab0a6a/pcbi.1012632.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/f6b4043f1036/pcbi.1012632.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/5b992cfb4f27/pcbi.1012632.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/f911504baa03/pcbi.1012632.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/32a86bf727ce/pcbi.1012632.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/0a9262cf9f9a/pcbi.1012632.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/570576ab0a6a/pcbi.1012632.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/f6b4043f1036/pcbi.1012632.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/5b992cfb4f27/pcbi.1012632.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/f911504baa03/pcbi.1012632.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/32a86bf727ce/pcbi.1012632.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/0a9262cf9f9a/pcbi.1012632.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/76c8/11741621/570576ab0a6a/pcbi.1012632.g006.jpg

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本文引用的文献

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CellBiAge: Improved single-cell age classification using data binarization.CellBiAge：通过数据二值化提高单细胞年龄分类。
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Cell-type-specific aging clocks to quantify aging and rejuvenation in neurogenic regions of the brain.细胞类型特异性衰老时钟，用于量化大脑神经发生区域的衰老和年轻化。
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AgeAnno: a knowledgebase of single-cell annotation of aging in human.AgeAnno：人类细胞衰老注释知识库。
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Comorbidities and mortality risk in adults younger than 50 years of age with chronic obstructive pulmonary disease.50 岁以下慢性阻塞性肺疾病患者的合并症与死亡风险。
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A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues.一组新的基因集可识别衰老细胞，并预测跨组织的衰老相关途径。
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Cytokine-Induced Senescence in the Tumor Microenvironment and Its Effects on Anti-Tumor Immune Responses.肿瘤微环境中的细胞因子诱导衰老及其对抗肿瘤免疫反应的影响
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Mapping single-cell data to reference atlases by transfer learning.通过迁移学习将单细胞数据映射到参考图谱上。
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