Comparison of PING results to previous high-throughput studies.

<p>For each prey, the corresponding Venn diagram gives the number of baits in our pool of 90 detected as interacting by Arifuzzaman <i>et al.</i>, Butland <i>et al.</i>, and by this study (p≤0.01 level in at least one trial).</p

Mapping of Protein-Protein Interactions of <i>E. coli</i> RNA Polymerase with Microfluidic Mechanical Trapping

<div><p>The biophysical details of how transcription factors and other proteins interact with RNA polymerase are of great interest as they represent the nexus of how structure and function interact to regulate gene expression in the cell. We used an <i>in vitro</i> microfluidic approach to map interactions between a set of ninety proteins, over a third of which are transcription factors, and each of the four subunits of <i>E. coli</i> RNA polymerase, and we compared our results to those of previous large-scale studies. We detected interactions between RNA polymerase and transcription factors that earlier high-throughput screens missed; our results suggest that such interactions can occur without DNA mediation more commonly than previously appreciated.</p></div

PING results across three screening experiments.

<p>For each prey, baits are ranked in ascending order of the average logarithm of p values, as an approximate metric of confidence in the interaction. (For each prey, the second column of baits continues from the first column, in increasing order of average log p.) Interactions with baits nearest the beginning of each list can be considered the best-established. The heat map represents the individual p values for each bait-prey combination across three experimental trials. Green indicates p≤0.01, with dark green indicating p<2×10⁻⁵, the limit of the computational hypothesis test (these were assigned an ad hoc p = 10⁻⁶ for ranking purposes). Yellow indicates 0.01Table S2. Baits listed in bold are the set of twenty-two with no previously reported RNAP interactions, plus the five—crp, fnr, rhaR, rhaS, and soxS—which had previously reported interactions, but none identified in either of the two high-throughput AP-MS screens.</p

Illustration of the procedures used to remove GC content dependent artifact in shotgun sequencing data using one of the patient samples as example.

<p>(A) The number of sequence tag per 20 kb bin is plotted against GC content of the bin. (B) The average number of sequence tag per 20 kb is calculated for every 0.1% GC content. (C) A weight is calculated for a particular value of GC content, such that sequence tags falling within a 20 kb bin having such GC content would receive the same calculated weight.</p

Two versions of the RNAP interaction network.

<p>(A) Previously-reported interactions. Green edges indicate interactions reported in the high-throughput study by Arifuzzaman et al., blue edges indicate interactions reported by Butland et al., and orange edges indicate interactions deposited in DIP or SwissProt with another reference <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091542#pone.0091542-Shah1" target="_blank">[18]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091542#pone.0091542-Westblade1" target="_blank">[20]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091542#pone.0091542-Arifuzzaman2" target="_blank">[28]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091542#pone.0091542-Weber1" target="_blank">[37]</a>. Node shapes indicate the functional annotation for each protein. Squares correspond to RNAP subunits, with round-cornered squares being σ factors. Triangles correspond to ribosomal proteins, parallelograms to chaperones, and diamonds to transcription factors. All other proteins are represented by circles. (B) PING-generated map including interactions established at the p≤0.01 level in at least one trial. Though this map has approximately the same number of orphans (19) as the previously-reported map (22), they are not biased to transcription factors.</p

Principles of PING.

<p>(A) A single chamber of the PING chip. The sandwich valves are used to segregate adjacent chambers during ITT and equilibration after introduction of the labeling antibody. The DNA chamber contains the linear templates for the bait and prey to be expressed, and is closed off by the neck valve during all surface chemistry steps, prior to the ITT reaction. The button is used for mechanical trapping. (B) Cartoon of a mechanically-trapped interacting bait-prey pair after labeling, showing the layers of surface chemistry holding the bait in place.</p

Comparing the distribution of sequence tag density of each chromosome among 19 patients after correcting for GC bias.

<p>(A) Distributions of normalized sequence tag density for each chromosome (excluding chromosome Y) within each patient sample. Red: chromosome 21; blue: chromosome X; magenta: chromosome 18; green: chromosome 13; black: all other autosomes. For all but one (P19) male pregnancy sample, it is obvious that the distribution of chromosome X shifts towards the left, while the distributions of chromosomes 21, 18, and 13 for the respective cases of trisomy 21, 18, and 13 shift towards the right, relative to the distributions of all other chromosomes that are present in two copies. (B) The sequence tag distribution of each chromosome is compared to all other chromosomes (except chromosome Y) by calculating the <i>z</i>-statistic. If we require that the copy number of a chromosome to be significantly different from that of all other chromosomes at level <i>α</i><0.001 to be flagged as abnormal, chromosome X is under-represented as compared to a normal female genome in all but one male pregnancy (P19), while chromosomes 21, 18, and 13 are over-represented in the respective cases of trisomy 21, 18, and 13. Plotted here is the minimum <i>z</i>-statistic for each chromosome when it is compared against 22 other chromosomes. The horizontal dashed line corresponds to the statistic associated with <i>α</i><0.001.</p

Estimation of the requirement of sequencing depth for the detection of fetal aneuploidy in cell-free plasma as a function of fetal DNA fraction.

<p>The estimates are based on level of confidence α<0.001 for chromosomes 13, 18, 21, and X, each having different length. As fetal DNA fraction decreases, the total number of shotgun sequences required increases. With a sequencing throughput of ∼10 million sequence reads per channel on the flowcell, trisomy 21 can be detected if >3.9% of the cell-free DNA is fetal (dashed lines). The total number of sequence tags and the estimated fetal DNA fraction from our set of 19 patient samples are also plotted. For one of the normal male samples (P19, indicated by the solid arrow), chromosome X was not detected as under-represented. This was probably due to insufficient sampling, as the total number of sequence obtained for this sample was close to the limit of detection given its fetal DNA fraction.</p

Fetal DNA fraction estimated from under-representation of chromosome X in male pregnancies and over-representation of chromosomes 13, 18, and 21 from the respective cases of trisomy 13, 18 and 21.

<p>Fetal DNA fraction estimated from under-representation of chromosome X in male pregnancies and over-representation of chromosomes 13, 18, and 21 from the respective cases of trisomy 13, 18 and 21.</p

High-Performance Binary Protein Interaction Screening in a Microfluidic Format

The standard procedure to increase microfluidic chip performance is to grow the number of parallel test systems on the chip. This process is accompanied by miniaturizing biochemical workflows and micromechanical elements, which is often a major challenge for both engineering fields. In this work, we show that it is possible to substantially increase the runtime performance of a microfluidic affinity assay for protein interactions by simultaneously engineering fluid logics and assay chemistry. For this, synergistic effects between the micro- and chemical architecture of the chip are exploited. The presented strategy of reducing the runtime rather than size and volume of the mechanical elements and biological reagent compartments will, in general, be of importance for future analytical test systems on microfluidic chips to overcome performance barriers