Clinical microfluidics for neutrophil genomics and proteomics

Abstract

Neutrophils have key roles in modulating the immune response. We present a robust methodology for rapidly isolating neutrophils directly from whole blood with 'on-chip' processing for mRNA and protein isolation for genomics and proteomics. We validate this device with an ex vivo stimulation experiment and by comparison with standard bulk isolation methodologies. Last, we implement this tool as part of a near-patient blood processing system within a multi-center clinical study of the immune response to severe trauma and burn injury. The preliminary results from a small cohort of subjects in our study and healthy controls show a unique time-dependent gene expression pattern clearly demonstrating the ability of this tool to discriminate temporal transcriptional events of neutrophils within a clinical setting.

Figures

Figure 1

Summary of microfluidic device characterization. (a) Microfluidic chip design and (b) schematic of the surface functionalization of antibodies to the device. Green biotinylated α-CD66b monoclonal antibodies bind to red Neutravidin molecules that are covalently linked to the surface. Whole blood flows through each parallel capture channel and cells with CD66b antigen are specifically bound to the surface. Chip loading for cells captured (c) and RNA (d) with a linear fit (grey solid line), 95% confidence limits(grey dashed line), and 95% prediction bands (grey dotted line). The R value for the fits for (e) and (f) are 0.95 and 0.98, respectively. (e) Wright-Giemsa stain of burn blood captured from burn patient 10 days post injury showing mixture of fully segmented neutrophils and band forms (scale bar 20 μm).(f) Immunofluorescence of healthy volunteer stained with DAPI (blue), CD14-FITC (green), and CD16b-PE (red) (scale bar 25 μm);

Figure 2

Genomic and protemic characterization of neutrophil lysates. Unsupervised cluster analysis for PMN validation studies for (a) microarray data and (b) LC-MS proteomics data. Red bars indicate upregulated genes, blue bars downregulated genes, orange bars upregulated proteins, and green bars downregulated proteins. Venn diagram of significant gene expression changes (c) and protein abundance changes (d) following ex vivo stimulation. 1684 genes overlapped between the two lists and showed the same directions of changes, while 6 genes showed opposite changes. For the proteins 37 proteins overlapped between the two lists and showed the same directions of changes, and none showed opposite changes. (e) Flow cytometry validation of ex vivo stimulation results, showing the mean fluorescence signal measured in CD66b+ granulocytes for unstimulated blood (white bars), LPS stimulated blood (blue checks), and GM+I stimulated blood (red stripes) (f) Unsupervised hierarchical clustering of genes (1690 probesets with SD>1) from five different healthy subjects isolated using microfluidics (M) or bulk Ficoll-dextran (B) methods. Note that there are no significant genes differentially expressed between the microfluidics and bulk isolation at FDR < 5%.

Figure 3

Summary of RNA extractions from cell lysates collected at six different clinical sites. (a) Histogram of the total RNA isolated from the trauma samples (black) and burn samples (gray). (b) Histogram of the RNA RIN quality score from both groups in panel a; RNA is scored on a scale of one to ten (higher is better), and any sample that scores four or higher is processed for microarray expression analysis. (c) Correlation of the total extracted RNA with clinical PMN counts taken from a complete blood count with five part differential; the solid line is a linear fit (R=0.23) through the origin with 95% confidence limits (grey dashed line), and 95% prediction bands (grey dotted line). (d) Syringe pump unit used at the clinical sites for sample processing.

Figure 4

Summary of the microarray results for a subset of the clinical samples from Figure 3. For the preliminary analysis shown here, we chose transcripts with a statistical significance of ≤ 0.001 (Q-value) corresponding to 8719 genes. (a) Unsupervised K-means clustering of these 8719 genes identified from the 187 microarrays in the time-course clinical data leads to five distinct clusters (from top to bottom): (1) Early up-regulation with resolution; (2) late up-regulation with a peak signal at 7–21 days; (3) Early down-regulation with resolution at 14–21 days; (4) Early down-regulation without recovery; and (5) late down-regulation without recovery. (b) Bar graph of the ten most statistically significant up-regulated pathways (red) and down-regulated pathways from the genes in (a).