Benchmarking transcriptome deconvolution methods for estimating tissue- and cell-type-specific extracellular vesicle abundances

IF 15.5 1区 医学 Q1 CELL BIOLOGY Journal of Extracellular Vesicles Pub Date : 2024-09-25 DOI:10.1002/jev2.12511
Jannik Hjortshøj Larsen, Iben Skov Jensen, Per Svenningsen
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Abstract

Extracellular vesicles (EVs) contain cell-derived lipids, proteins and RNAs; however, determining the tissue- and cell-type-specific EV abundances in body fluids remains a significant hurdle for our understanding of EV biology. While tissue- and cell-type-specific EV abundances can be estimated by matching the EV's transcriptome to a tissue's/cell type's expression signature using deconvolutional methods, a comparative assessment of deconvolution methods' performance on EV transcriptome data is currently lacking. We benchmarked 11 deconvolution methods using data from four cell lines and their EVs, in silico mixtures, 118 human plasma and 88 urine EVs. We identified deconvolution methods that estimated cell type-specific abundances of pure and in silico mixed cell line-derived EV samples with high accuracy. Using data from two urine EV cohorts with different EV isolation procedures, four deconvolution methods produced highly similar results. The three methods were also concordant in their tissue- and cell-type-specific plasma EV abundance estimates. We identified driving factors for deconvolution accuracy and highlighted the importance of implementing biological knowledge in creating the tissue/cell type signature. Overall, our analyses demonstrate that the deconvolution algorithms DWLS and CIBERSORTx produce highly similar and accurate estimates of tissue- and cell-type-specific EV abundances in biological fluids.

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用于估算组织和细胞类型特异性细胞外囊泡丰度的转录组去卷积方法基准测试
细胞外囊泡(EV)含有细胞衍生的脂质、蛋白质和 RNA;然而,确定体液中组织和细胞类型特异的 EV 丰度仍然是我们了解 EV 生物学的一个重大障碍。虽然组织和细胞类型特异性的 EV 丰度可以通过使用去卷积方法将 EV 的转录组与组织/细胞类型的表达特征相匹配来估算,但目前还缺乏对去卷积方法在 EV 转录组数据上的性能的比较评估。我们使用来自四种细胞系及其 EV、硅学混合物、118 人血浆和 88 尿液 EV 的数据,对 11 种去卷积方法进行了基准测试。我们确定了一些去卷积方法,这些方法能高精度地估算纯细胞系和硅学混合细胞系衍生 EV 样本的细胞类型特异性丰度。使用来自两个尿液EV队列的数据和不同的EV分离程序,四种解卷积方法得出了高度相似的结果。这三种方法对组织和细胞类型特异性血浆 EV 丰度的估计也是一致的。我们确定了去卷积准确性的驱动因素,并强调了在创建组织/细胞类型特征时应用生物学知识的重要性。总之,我们的分析表明,解卷积算法 DWLS 和 CIBERSORTx 对生物液体中组织和细胞类型特异性 EV 丰度的估计高度相似且准确。
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来源期刊
Journal of Extracellular Vesicles
Journal of Extracellular Vesicles Biochemistry, Genetics and Molecular Biology-Cell Biology
CiteScore
27.30
自引率
4.40%
发文量
115
审稿时长
12 weeks
期刊介绍: The Journal of Extracellular Vesicles is an open access research publication that focuses on extracellular vesicles, including microvesicles, exosomes, ectosomes, and apoptotic bodies. It serves as the official journal of the International Society for Extracellular Vesicles and aims to facilitate the exchange of data, ideas, and information pertaining to the chemistry, biology, and applications of extracellular vesicles. The journal covers various aspects such as the cellular and molecular mechanisms of extracellular vesicles biogenesis, technological advancements in their isolation, quantification, and characterization, the role and function of extracellular vesicles in biology, stem cell-derived extracellular vesicles and their biology, as well as the application of extracellular vesicles for pharmacological, immunological, or genetic therapies. The Journal of Extracellular Vesicles is widely recognized and indexed by numerous services, including Biological Abstracts, BIOSIS Previews, Chemical Abstracts Service (CAS), Current Contents/Life Sciences, Directory of Open Access Journals (DOAJ), Journal Citation Reports/Science Edition, Google Scholar, ProQuest Natural Science Collection, ProQuest SciTech Collection, SciTech Premium Collection, PubMed Central/PubMed, Science Citation Index Expanded, ScienceOpen, and Scopus.
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