ReviewProteomics2026
Deconvolution Methods to Link Multi-Omics Data to Cell Type-Specific Extracellular Vesicle Abundances.
Review in Proteomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Extracellular vesicles (EVs) provide non-invasive information on cellular health and disease. Yet, with the small size of EVs and more than 200 cell types contributing EVs to the extracellular fluids, it is challenging to determine whether changes in EV-associated lipids, RNAs, and proteins occur because of differences in expression or cell type-specific EV abundances. This limits our use of EV-based biomarkers and our understanding of how EVs contribute to health and diseases. In recent decades, next-generation RNA sequencing methods have fueled the development of transcriptome deconvolution methods to determine cell type proportions in tissue RNA samples. These methods can also estimate cell type-specific EV abundances using the EV's RNA "fingerprint"; however, differences between cell and EV RNA composition can significantly bias the estimates. Based on a recent benchmarking study of transcriptome deconvolution methods, we will review technical and biological factors that drive the most accurate deconvolution, focusing on mRNA sequencing data from EVs. Moreover, we will describe biological factors that can affect the interpretation of the deconvolution methods of cell type-specific EV abundance estimates in acute and chronic conditions and give a perspective on how deconvolution can be used to monitor physiological and disease processes in the human body.
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