Optimized procedure for high-throughput transcriptome profiling of small extracellular vesicles isolated from low volume serum samples
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | Clinical Chemistry and Laboratory Medicine |
| MU Faculty or unit | |
| Citation | |
| web | https://www.degruyter.com/document/doi/10.1515/cclm-2023-0610/html#MLA |
| Doi | http://dx.doi.org/10.1515/cclm-2023-0610 |
| Keywords | colorectal cancer; high-throughput expression profiling; long non-coding RNAs; size exclusion chromatography; small extracellular vesicles; transcriptome |
| Attached files | |
| Description | Objectives: Small extracellular vesicles (EVs) contain various signalingmolecules, thus playing a crucial role in cellto-cell communication and emerging as a promising source of biomarkers. However, the lack of standardized procedures impedes their translation to clinical practice. Thus, we compared different approaches for high-throughput analysis of small EVs transcriptome. Methods: Small EVs were isolated from 150 mu L of serum. Quality and quantity were assessed by dynamic light scattering, transmission electron microscopy, and Western blot. Comparison of RNA extraction efficiency was performed, and expression of selected genes was analyzed by RT-qPCR. Whole transcriptome analysis was done using microarrays. Results: Obtained data confirmed the suitability of size exclusion chromatography for isolation of small EVs. Analyses of gene expression showed the best results in case of samples isolated by Monarch Total RNA Miniprep Kit. Totally, 7,182 transcripts were identified to be deregulated between colorectal cancer patients and healthy controls. The majority of them were non-coding RNAs with more than 70 % being lncRNAs, while protein-coding genes represented the second most common gene biotype. Conclusions: We have optimized the protocol for isolation of small EVs and their RNA from low volume of sera and confirmed the suitability of Clariom D Pico Assays for transcriptome profiling. |
| Related projects: |