Extracellular vesicles (EVs) are intercellular communicators with key functions in physiological and pathological processes and have recently garnered interest because of their diagnostic and therapeutic potential. The past decade has brought about the development and commercialization of a wide array of methods to isolate EVs from serum. Which subpopulations of EVs are captured strongly depends on the isolation method, which in turn determines how suitable resulting samples are for various downstream applications. To help clinicians and scientists choose the most appropriate approach for their experiments, isolation methods need to be comparatively characterized. Few attempts have been made to comprehensively analyse vesicular microRNAs (miRNAs) in patient biofluids for biomarker studies. To address this discrepancy, we set out to benchmark the performance of several isolation principles for serum EVs in healthy individuals and critically ill patients. Here, we compared five different methods of EV isolation in combination with two RNA extraction methods regarding their suitability for biomarker discovery-focused miRNA sequencing as well as biological characteristics of captured vesicles. Our findings reveal striking method-specific differences in both the properties of isolated vesicles and the ability of associated miRNAs to serve in biomarker research. While isolation by precipitation and membrane affinity was highly suitable for miRNA-based biomarker discovery, methods based on size-exclusion chromatography failed to separate patients from healthy volunteers. Isolated vesicles differed in size, quantity, purity and composition, indicating that each method captured distinctive populations of EVs as well as additional contaminants. Even though the focus of this work was on transcriptomic profiling of EV-miRNAs, our insights also apply to additional areas of research. We provide guidance for navigating the multitude of EV isolation methods available today and help researchers and clinicians make an informed choice about which strategy to use for experiments involving critically ill patients.

Septic shock is a common medical condition with a mortality approaching 50% where early diagnosis and treatment are of particular importance for patient survival. Novel biomarkers that serve as prompt indicators of sepsis are urgently needed. High‐throughput technologies assessing circulating microRNAs represent an important tool for biomarker identification, but the blood‐compartment specificity of these miRNAs has not yet been investigated. We characterized miRNA profiles from serum exosomes, total serum and blood cells (leukocytes, erythrocytes, platelets) of sepsis patients by next‐generation sequencing and RT‐qPCR (n = 3 × 22) and established differences in miRNA expression between blood compartments. In silico analysis was used to identify compartment‐specific signalling functions of differentially regulated miRNAs in sepsis‐relevant pathways. In septic shock, a total of 77 and 103 miRNAs were down‐ and up‐regulated, respectively. A majority of these regulated miRNAs (14 in serum, 32 in exosomes and 73 in blood cells) had not been previously associated with sepsis. We found a distinctly compartment‐specific regulation of miRNAs between sepsis patients and healthy volunteers. Blood cellular miR‐199b‐5p was identified as a potential early indicator for sepsis and septic shock. miR‐125b‐5p and miR‐26b‐5p were uniquely regulated in exosomes and serum, respectively, while one miRNA (miR‐27b‐3p) was present in all three compartments. The expression of sepsis‐associated miRNAs is compartment‐specific. Exosome‐derived miRNAs contribute significant information regarding sepsis diagnosis and survival prediction and could serve as newly identified targets for the development of novel sepsis biomarkers.

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Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Extracellular vesicles and their microRNA cargo are crucial facilitators of malignant cell communication and could mediate effects of anesthetics on tumor biology during cancer resection. The authors performed a proof-of-concept study to demonstrate that propofol and sevoflurane have differential effects on vesicle-associated microRNAs that influence signaling pathways involved in tumor progression and metastasis. Methods Circulating vesicles were investigated in a prospective, matched-case pilot study in two cohorts of colorectal cancer patients receiving either propofol (n = 8) or sevoflurane (n = 9), matched for tumor stage and location. Serum was sampled before anesthesia and after tumor resection. Vesicular microRNA profiles were analyzed by next generation sequencing and confirmed by real-time polymerase chain reaction. Next, we assessed perioperative changes in microRNA expression induced by either anesthetic and compared their biologic effects on tumor-relevant pathways. Additionally, vesicles from pre- and postoperative sera were biologic characterized. Results Postoperative microRNA profiles were shifted in both groups with overlap in the perioperative response. A total of 64 (48 up, range of log2 fold change 1.07 to 3.76; 16 down, −1.00 to −1.55) and 33 (32 up, 1.02 to 2.98; 1 down, −1.36) microRNAs were significantly regulated (adjusted P value less than 0.05) by propofol and sevoflurane, respectively. Thirty-six (propofol) and five (sevoflurane) microRNAs were specifically responsive to either anesthetic agent. In silico target analyses of microRNA expression patterns indicated an inhibitory effect of propofol on crucial carcinoma-related pathways such as proliferation (z-score, −1.73) and migration (z-score, −1.97), as well as enhanced apoptosis (z-score, 1.19). While size distribution and protein markers of circulating vesicles were not affected by anesthesia, their concentration was reduced after surgery using both anesthetic procedures. Conclusions This proof-of-concept study provides preliminary evidence that anesthetic agents have specific effects on microRNA profiles in circulating vesicles. These findings could form the basis for larger and mechanistically oriented outcome studies in cancer patients.