Improving FoRe: A New Inlet Design for Filtering Samples through Individual Microarray Spots

In this publication we present an improvement to our previously introduced vertical flow microarray, the FoRe array, which capitalizes on the fusion of immunofiltration and densely packed micron test sites. Filtering samples through individual microarray spots allows us to rapidly analyze dilute samples with high-throughput and high signal-to-noise. Unlike other flowthrough microarrays, in the FoRe design samples are injected into micron channels and sequentially exposed to different targets. This arrangement makes it possible to increase the sensitivity of the microarray by simply increasing the sample volume or to rapidly reconcentrate samples after preprocessing steps dilute the analyte. Here we present a new inlet system which allows us to increase the analyzed sample volume without compromising the micron spot size and dense layout. We combined this with a model assay to demonstrate that the device is sensitive to the amount of antigen, and as a result, sample volume directly correlates to sensitivity. We introduced a simple technique for analysis of blood, which previously clogged the nanometer-sized pores, requiring only microliter volumes expected from an infant heel prick. A drop of blood is mixed with buffer to separate the plasma before reconcentrating the sample on the microarray spot. We demonstrated the success of this procedure by spiking TNF-α into blood and achieved a limit of detection of 18 pM. Compared to traditional protein microarrays, the FoRe array is still inexpensive, customizable, and simple to use, and thanks to these improvements has a broad range of applications from small animal studies to environmental monitoring

MOESM4 of Climatic differentiation in polyploid apomictic Ranunculus auricomus complex in Europe

Additional file 4. Correlation coefficients between 19 climatic variables extracted for the Ranunculus auricomus complex. Bold font highlights the absolute values greater than 0.8. Variables in bold were removed in the PCA analysis and for the distribution modelling

MOESM3 of Climatic differentiation in polyploid apomictic Ranunculus auricomus complex in Europe

Additional file 3. List of previously published chromosome counts from the Ranunculus auricomus complex in Europe. Spreadsheet “Data” lists extracted taxa, localities and chromosome numbers. Group: aur—“auricomus” group, cass—“cassubicus” group, fal—“fallax” group, ?—could not have been determined. Remaining captions are self-explanatory. Spreadsheet “References” lists reviewed literature

MOESM1 of Climatic differentiation in polyploid apomictic Ranunculus auricomus complex in Europe

Additional file 1. List of studied accessions. “Sample/standard ratio” refers to relative genome size as described in “Methods”. Group: aur—“auricomus” group, cass—“cassubicus” group, fal—“fallax” group. Sample number is identical with the collection number of the herbarium specimen. Herbarium codes follow Index Herbariorum ( http://sweetgum.nybg.org/science/ih/ )

MOESM5 of Climatic differentiation in polyploid apomictic Ranunculus auricomus complex in Europe

Additional file 5. Loadings of variables, proportion and cumulative proportion of the variance of the first 5 PCA axes on a set of 10 climatic variables extracted for Ranunculus auricomus complex. Bold font highlights three most extreme values of loadings for particular axes

Current and historical species distribution models for <i>Chasmanthera dependens</i> in West Africa and tropical Africa, respectively.

<p>Probability of occurrence is represented by different colors from low (blue) to high (red). Results are based on the data from CCSM4 and MPI ESM-P paleoclimatic models representing the Last Glacial Maximum (LGM, ca. 21 kyr BP) and Holocene Climate Optimum (HCO, ca. 6 kyr BP), as well as current climate observations. (a) Model of current distribution; (b) red dots indicate current occurrence points, which served as a basis for modelling, (c) HCO, CCSM4; (d) HCO, MPI ESM-P; (e) LGM, CCSM4; (f) LGM, MPI ESM-P.</p

Sampling localities of studied <i>Chasmanthera dependens</i> populations.

<p>Sampling localities of studied <i>Chasmanthera dependens</i> populations.</p

Analyses of molecular variance (AMOVAs) for cpDNA and AFLP data in <i>Chasmanthera dependens</i>.

<p>Analyses of molecular variance (AMOVAs) for cpDNA and AFLP data in <i>Chasmanthera dependens</i>.</p

Indices of haplotypic (cpDNA) and genotypic (AFLP) diversity of <i>Chasmanthera dependens</i> populations.

<p>Indices of haplotypic (cpDNA) and genotypic (AFLP) diversity of <i>Chasmanthera dependens</i> populations.</p

Principal co-ordinate analysis (PCoA) of AFLP genotypes of 54 samples of <i>Chasmanthera dependens</i> using Jaccard distances.

<p>The first two axes explained 9.27% and 7.33% of the total variation. Color-coding differentiates a) the populations and b) the haplotypes revealed by the statistical parsimony network analysis.</p