Accurately quantifying low-abundant targets amid similar sequences by revealing hidden correlations in oligonucleotide microarray data

Abstract

Microarrays have enabled the determination of how thousands of genes are expressed to coordinate function within single organisms. Yet applications to natural or engineered communities where different organisms interact to produce complex properties are hampered by theoretical and technological limitations. Here we describe a general method to accurately identify low-abundant targets in systems containing complex mixtures of homologous targets. We combined an analytical predictor of nonspecific probe-target interactions (cross-hybridization) with an optimization algorithm that iteratively deconvolutes true probe-target signal from raw signal affected by spurious contributions (cross-hybridization, noise, background, and unequal specific hybridization response). The method was capable of quantifying, with unprecedented specificity and accuracy, ribosomal RNA (rRNA) sequences in artificial and natural communities. Controlled experiments with spiked rRNA into artificial and natural communities demonstrated the accuracy of identification and quantitative behavior over different concentration ranges. Finally, we illustrated the power of this methodology for accurate detection of low-abundant targets in natural communities. We accurately identified Vibrio taxa in coastal marine samples at their natural concentrations (<0.05% of total bacteria), despite the high potential for cross-hybridization by hundreds of different coexisting rRNAs, suggesting this methodology should be expandable to any microarray platform and system requiring accurate identification of low-abundant targets amid pools of similar sequences.

Fig. 1.

Performance of the analytical cross-hybridization predictor. (a) Comparison of cross-hybridization probability obtained by analytical predictor (Eq. 4) with experimental data obtained by spike-in microarray experiments where total RNA of five different bacteria (V. cholerae, Vibrio anguillarum, Vibrio vulnificus, Vibrio alginolyticus, and Escherichia coli) were mixed individually with artificial RNA community samples at different amounts. (b) Comparison of ΔG_jk―/ΔG_jj ratios calculated by the analytical predictor (Eq. 5) with the ΔG_jk―/ΔG_jj ratios calculated by mfold, as a function of sequence identity. Error bars are standard errors.

Fig. 2.

V. cholerae (V.C.) signal within artificial communities before (a–c) and after (d–f) application of the algorithm (Eqs. 1 and 2). Artificial RNA communities contained 12.5, 1.25, and 0.5 μg of eukaryal RNA with 6.25, 1.25, and 0.25 ng of V. cholerae (black bar) RNA (between 0.1% and 0.05% of the total RNA). RNA abundances were determined by either averaging normalized background-subtracted intensities over all of the probe sets targeting bacteria-specific RNA after outlier removal (a–c) or by application of Eqs. 1 and 2 (d–f).

Fig. 3.

Quantification of spiked V. cholerae RNA in artificial and natural RNA communities (Eqs. 1–5). RNA abundances returned by the algorithm (Eqs. 1 and 2) show Langmuir dependence on true abundances (Eq. 3) with linear (0.25 ng ≤ true RNA abundance ≤20 ng) (a), nonlinear (20 ng < true RNA abundance ≤60 ng) (b), and plateau regimes (>60 ng).

Fig. 4.

Identification of seven naturally occurring closely related Vibrio taxa (16S rRNA sequence identity >95%) in coastal seawater samples (Eqs. 1–5). Relative RNA abundances (percent of total RNA), ranging from 0.005% to 0.15%, and absolute RNA abundances (nanograms of RNA in hybridized sample), ranging from 0.12 to 22.50 ng, are indicated for some Vibrio populations.