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.

Keywords: autoimmunity; diabetes; transcriptomics.

© 2017 British Society for Immunology.

Figures

Figure 1

Protocol development and quality control. (a) All blood volumes tested yielded sufficient RNA for cDNA preparation and subsequent quantitative polymerase chain reaction (qPCR) (> 100 ng). Duplicate samples of four different blood volumes were extracted for three subjects; samples are coloured by subject. Red, subject 1; blue, subject 2; green, subject 3. (b) Using improper blood volumes impacts qPCR results negatively. qPCR data for 176 genes from fingerstick samples with the correct ratio of blood:Tempus solution (purple) correlate more closely with data from phlebotomy Tempus tube samples (black) than from samples processed with the wrong ratio of blood:Tempus solution. Orange: too much blood and too little Tempus solution; green: too little blood per standard quantity Tempus solution; n = 4 subjects.

Figure 2

Compliance and protocol testing in type 1 diabetes (T1D) and healthy cohorts. (a) High compliance with fingerstick protocol. Samples returned by healthy (orange) and T1D (blue) subjects are depicted. Median sample count returned was 25; expected maximum was 26 samples per subject in this 6‐month study. Darker coloration denotes samples that yielded no RNA at processing (n = 5 of 645 samples received). (b) Gene expression signatures of 176 genes from 15 μL fingerstick samples are sufficiently similar to 3 mL tubes drawn by a phlebotomist. Left: correlations between systemic lupus erythematosus (SLE) and healthy or T1D samples establish minimum expected correlation of gene expression data between unrelated subjects; n = 3 SLE versus 3 healthy controls (HC) and 3 T1D for 18 total comparisons. Middle, 3 mL phlebotomy tube versus mock fingerstick: 15 μL aliquot taken from phlebotomist‐drawn tempus tube versus the same full 3 mL tube, n = 6 subjects. Shows maximum achievable correlation between small and full sample volumes. Right, phlebotomy sample versus fingerstick: correlations between fingerstick and full‐volume phlebotomy‐drawn tempus tube obtained on the same day. Shows actual correlations to gold standard tempus tube for first six subjects enrolled in this cohort; n = 6 subjects.

Figure 3

Variability within any given subject is similar for type 1 diabetes (T1D) and healthy individuals, but variance across subjects is higher for healthy subjects. (a) Quantitative polymerase chain reaction (qPCR) data from longitudinal samples for all subjects correlate more strongly with each other (black dots) than with all other subjects (grey dots). Individual subject IDs listed on the x‐axis. Black dots: mean correlation of time‐points across all genes within an individual. Grey dots: mean correlation of longitudinal time‐points across all genes between individuals. For example, the grey point for subject 1001 is the correlation of data from all visits for subject 1001 to data from all visits for all other individuals; the black dot is the correlation of gene expression between all longitudinal samples from subject 1001 only. (b) As a group, transcriptional signatures from healthy subjects were more variable than those from T1D subjects. Mean variance for all subjects in a group was calculated for each module of four genes. Each point represents 1 module. In 39 of 44 modules, healthy control (HC) individuals show higher variance than T1D individuals. Hashed line indicates equivalence (equal variance in both subject types).

Figure 4

Subjects with infections tend to have concurrent transcriptional increase of interferon‐stimulated genes. Each panel depicts one transcriptional module of four interferon‐stimulated genes. Log fold change [log2(FC)] between the subject and the mean FC of healthy subjects for that module of four genes is on the y‐axis; the x‐axis is time in weeks. Orange lines indicate healthy subjects who reported infection at any point in the study; blue lines indicate type 1 diabetes (T1D) subjects who reported infection. Closed circles denote samples from subject‐reported infection events. Hashed lines designate 95% confidence interval limits for each module; points outside hashed lines are transcriptional outliers. Association between transcriptional outliers and infections (dots) was used to assess significance. In the logistic regression model, patient was a random effect and infection was an independent variable. P‐values have been adjusted for multiple testing.