Sensitive and precise quantification of insulin-like mRNA expression in Caenorhabditis elegans

Abstract

Insulin-like signaling regulates developmental arrest, stress resistance and lifespan in the nematode Caenorhabditis elegans. However, the genome encodes 40 insulin-like peptides, and the regulation and function of individual peptides is largely uncharacterized. We used the nCounter platform to measure mRNA expression of all 40 insulin-like peptides as well as the insulin-like receptor daf-2, its transcriptional effector daf-16, and the daf-16 target gene sod-3. We validated the platform using 53 RNA samples previously characterized by high density oligonucleotide microarray analysis. For this set of genes and the standard nCounter protocol, sensitivity and precision were comparable between the two platforms. We optimized conditions of the nCounter assay by varying the mass of total RNA used for hybridization, thereby increasing sensitivity up to 50-fold and reducing the median coefficient of variation as much as 4-fold. We used deletion mutants to demonstrate specificity of the assay, and we used optimized conditions to assay insulin-like gene expression throughout the C. elegans life cycle. We detected expression for nearly all insulin-like genes and find that they are expressed in a variety of distinct patterns suggesting complexity of regulation and specificity of function. We identified insulin-like genes that are specifically expressed during developmental arrest, larval development, adulthood and embryogenesis. These results demonstrate that the nCounter platform provides a powerful approach to analyzing insulin-like gene expression dynamics, and they suggest hypotheses about the function of individual insulin-like genes.

Figure 1. Validation of the nCounter platform with Affymetrix microarray analysis.

0.1 µg total RNA from 53 independent RNA preparations was used for nCounter analysis. The 53 samples comprise 18 groups of biological replicates (all but one with 3 replicates), and the average of each group is plotted for 25 genes common to both platforms. Each axis is on a log scale.

Figure 2. Code set specific optimization of total RNA mass used in nCounter hybridization increases sensitivity and precision.

(A) Positive control standard curve is plotted for each RNA mass. Average transcript count (3 technical replicates) is plotted against the coefficient of variation for (B) no RNA, (C) 0.1 µg, (D) 1 µg, and (E) 10 µg. (F–H) Average transcript count is plotted in 3 pair-wise comparisons of the 3 RNA masses used. Legend in B applies to B–H. The y-axis of B–E is coefficient of variation. The vertical grey line in B–E represents background. Data are normalized by positive control counts. Transcript counts are plotted on a log scale. “CV” refers to coefficient of variation. One data point is omitted from B (a target with count of 57 and CV of 120%).

Figure 3. Deletion alleles demonstrate specificity of nCounter hybridization.

Average and standard deviation (3 biological replicates) of transcript counts detected during L1 arrest is plotted for ins-4 (A), ins-5 (B), and ins-6 (C) in 4 different strains.

Figure 4. Quantification of insulin-like gene expression during the C. elegans life cycle reveals distinct expression patterns.

A schematic of the C. elegans life cycle is presented along with plots of the average and standard deviation (3 biological replicates) of transcript counts for each gene. The ins-13 probe is not specific and reports expression of acdh-2. In the plots, “emb” refers to mid-grastrulation embryos, “L1” refers to L1 arrest, and “dau” refers to dauer arrest.

Figure 5. QPCR results in expression profiles similar to nCounter.

Average and standard deviation (3 biological replicates) of transcript abundance determined by QPCR is plotted for ins-8, -21, -25, -32, -36, -39, daf-28 and sod-3.