Longitudinal monitoring of gene expression in ultra-low-volume blood samples self-collected at home

Abstract

Blood transcriptional profiles could serve as biomarkers of clinical changes in subjects at-risk for or diagnosed with diabetes. However, transcriptional variation over time is poorly understood due to the impracticality of frequent longitudinal phlebotomy in large patient cohorts. We have developed a novel transcriptome assessment method that could be applied to fingerstick blood samples self-collected by study volunteers. Fifteen μL of blood from a fingerstick yielded sufficient RNA to analyse > 176 transcripts by high-throughput quantitative polymerase chain reaction (PCR). We enrolled 13 subjects with type 1 diabetes and 14 controls to perform weekly collections at home for a period of 6 months. Subjects returned an average of 24 of 26 total weekly samples, and transcript data were obtained successfully for > 99% of samples returned. A high degree of correlation between fingerstick data and data from a standard 3 mL venipuncture sample was observed. Increases in interferon-stimulated gene expression were associated with self-reported respiratory infections, indicating that real-world transcriptional changes can be detected using this assay. In summary, we show that longitudinal monitoring of gene expression is feasible using ultra-low-volume blood samples self-collected by study participants at home, and can be used to monitor changes in gene expression frequently over extended periods.

A Transcriptome Fingerprinting Assay for Clinical Immune Monitoring

“…From this approach we have developed a transcriptome fingerprint of the immune system based on a non-systems scale assay, which is applicable to investigation of immunopathogenesis, longitudinal monitoring, and biomarker discovery. Sample acquisition for this assay is straightforward as blood can be collected by venipuncture or finger stick [40] and requires no onsite processing. The TFA assay is cost-effective, generates a manageable volume of data, and does not require sophisticated bioinformatics infrastructure and pipelines for analysis.…”

Influence of storage conditions of small volumes of blood on immune transcriptomic profiles

“…We next sought to determine whether any transcriptional differences existed between the different storage conditions. We employed Fluidigm-based transcriptome profiling for immune-related genes as previously described [22]. We expected to see transcriptional similarity across the different conditions.…”

“…Gene expression was performed by parallel quantitative PCR using the high-throughput BioMark HD platform (Fluidigm Co., San Francisco, CA, USA), according to the manufacturer's instructions. Transcripts were selected according to previous literature[22]. Eight genes were used as housekeeping, namely: DOCK2, EEF1A1, FAM105B, FTL, MYL6, MYL12B, RPS10, and RPS25.…”

A modular framework for the development of targeted Covid-19 blood transcript profiling panels

“…The immune profiling of asymptomatic or pre-symptomatic patients (e.g quarantined) would be another setting where implementation of such an assay could prove useful. For this, it would for instance be possible to use protocols that we have previously developed for home-based, self-sampling and blood RNA stabilization (37,38).…”