FastEV & Small RNA sequencing

FastEV performs well in small RNA sequencing

RNA from FastEV was subjected to next generation smallRNA sequencing (smallRNAseq) at University of Helsinki. On average, FastEV performed similarly to EV controls, but some conditions excelled giving a higher number of reads and detected miRNAs than controls. The number of reads and robustly expressed miRNAs (≥5 counts) ranged between 2.5-4.2 M and 125-260, respectively, in FastEV samples .

Figures: Number of reads and detected miRNAs obtained from the FastEV samples in smallRNAseq. The read and miR numbers from FastEV conditions were comparable to EV controls (PEG, UC) and exceded the level in plasma controls. Data is average of fasting and non-fasting samples (n=2). Error bars show SD. PEG, polyethylene glycol, UC, ultracentrifugation.​

Methods: Analysis of FastEV conditions with smallRNAseq

FastEV conditions and control EV enrichment methods (UC, PEG) were used for two plasma samples, one fasting and one non-fasting, form two healthy donors. Then, total RNAs were extracted from the samples for smallRNA sequencing. For details, see basics/RNA. Equal RNA quantities were prepared for sequencing using Lexogen Small RNA-Seq Library Prep Kit without size selection, and sequenced with single-end 75 bp reads in NextSeq High Output flow cell (Illumina). Study included one technical replicate per sample per donor. Quality of sequencing and smallRNA species were analyzed with multiQC (Ewels et al., 2016) and with smallRNAtoolbox (Aparicio-Puerta et al., 2019), respectively.

Differential enrichments of miRNA by FastEV

FastEV conditions showed differences in miRNA abundancies compared to plasma, EV control and each other.

Figures: MicroRNAs sorted according to expression levels in raw plasma. Expression level categories according to expression in plasma were high (>1000 reads), mid (21-999 reads), Low (5-20 reads) and very low ( 0-4 reads). FastEV conditions differed from plasma in all categories, whereas EV control (PEG) resembled plasma in the high and mid categories. Differences and similarities in expression patterns were evident between FastEV conditions, e.g. compare conditions pointed with the arrows. MiR-486, the most abundant miR in plasma, was excluded from the high expression category. Data is from non-fasting samples, but similar results were obtained with fasting samples. PEG, polyethylene glycol.

FastEV offers different small RNA subpopulations for biomarker discovery

SmallRNAseq clearly showed that FastEV conditions yielded different proportions of many smallRNA subtypes. Particularly, miRNA, unassigned and yRNA categories varied widely between different FastEV conditions and controls.

Figure: Proportions of small RNA subtypes. SmallRNAseq data was analyzed for the proportions of reads (%) of different small RNA subtypes. Greatest differences were observed in miRNA (miRBase), unassigned and Y-RNA categories among FastEV conditions and controls. Data is average of fasting and non-fasting samples (n=2). PEG, polyethylene glycol, UC, ultracentrifugation.