Clinical microfluidics for neutrophil genomics and proteomics

Journal name:
Nature Medicine
Year published:
Published online


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.

At a glance


  1. Summary of microfluidic device characterization.
    Figure 1: Summary of microfluidic device characterization.

    (a,b) Microfluidic chip design (a) and schematic of the surface functionalization of antibodies to the device (b). Green biotinylated CD66b-specific monoclonal antibodies bind red Neutravidin molecules that are covalently linked to the surface. Whole blood flows through each parallel capture channel and cells expressing CD66b antigen are specifically bound to the surface. (c,d) Chip loading for cells captured (c) and RNA (d) with a linear fit (solid line), 95% confidence limits (dashed line) and 95% prediction bands (dotted line). The R value for the fits for c and d are 0.95 and 0.98, respectively. Error bars for both graphs represent means ± s.d. determined from three or more separate, independent experiments. (e) Wright-Giemsa stain of isolated cells captured from a burn subject 10 d after injury, showing a mixture of fully segmented neutrophils and band forms. Scale bar, 20 μm. (f) Immunofluorescence of neutrophils isolated from a healthy volunteer stained with DAPI (blue), antibody to CD14 conjugated to FITC (green) and antibody to CD16b conjugated to phycoerythrin (red). Scale bar, 25 μm.

  2. Genomic and protemic characterization of neutrophil lysates.
    Figure 2: Genomic and protemic characterization of neutrophil lysates.

    (a,b) Unsupervised cluster analysis for neutrophil validation studies for microarray data (a) and LC-MS proteomics data (b). Red bars indicate upregulated genes, blue bars downregulated genes, orange bars upregulated proteins and green bars downregulated proteins. GM + I, GM-CSF plus IFN-γ. (c,d) Venn diagrams of significant (FDR < 0.01), gene expression changes (c) and protein abundance changes (d) after ex vivo stimulation. (e) Flow cytometry validation of ex vivo stimulation results, showing the mean fluorescence signal measured in CD66b+ granulocytes for unstimulated blood, LPS-stimulated blood and GM-CSF plus IFN-γ–stimulated blood. (f) Unsupervised hierarchical clustering of genes (1,690 probe sets with s.d. > 1) from five healthy subjects isolated by microfluidics (M) or bulk Ficoll-dextran (B) methods. There are no significant genes differentially expressed between the microfluidics and bulk isolation at FDR < 5%.

  3. Summary of RNA extractions from cell lysates collected at six different clinical sites.
    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 and burn samples. (b) Histogram of the RIN quality score from both groups in a; RNA is scored on a scale of one to ten (higher is better), and any sample that scored four or higher was processed for microarray expression analysis. (c) Correlation of the total extracted RNA with clinical polymorphonuclear leukocyte 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 (dashed line) and 95% prediction bands (dotted line). (d) Syringe pump unit used at the clinical sites for sample processing.

  4. Summary of the microarray results for a subset of the clinical samples from Figure 3.
    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 8,719 genes. (a) Unsupervised K-means clustering of these 8,719 genes identified from the 187 microarrays in the time-course clinical data, leading to five distinct clusters (from top to bottom): 1, early upregulation with resolution; 2, late upregulation with a peak signal at 7–21 d; 3, early downregulation with resolution at 14–21 d; 4, early downregulation without recovery; and 5, late downregulation without recovery. (b) Bar graph of the ten most statistically significant (all P < 0.05) upregulated pathways (red) and downregulated pathways from the genes in a.

Accession codes

Referenced accessions

Gene Expression Omnibus


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Author information


  1. Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, Massachusetts, USA.

    • Kenneth T Kotz,
    • Wenzong Xiao,
    • Aman Russom,
    • Alan E Rosenbach,
    • Jeremy Goverman,
    • Shawn P Fagan,
    • Daniel Irimia,
    • Ronald G Tompkins &
    • Mehmet Toner
  2. Stanford Genome Technology Center, Palo Alto, California, USA.

    • Wenzong Xiao,
    • Weihong Xu,
    • Julie Wilhelmy,
    • Michael N Mindrinos &
    • Ronald W Davis
  3. Department of Surgery, University of Rochester School of Medicine, Rochester, New York, USA.

    • Carol Miller-Graziano,
    • Asit De &
    • Paul E Bankey
  4. Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory (PNNL), Richland, Washington, USA.

    • Wei-Jun Qian,
    • Brianne O Petritis,
    • David G Camp II &
    • Richard D Smith
  5. Department of Surgery, University of Florida College of Medicine, Gainesville, Florida, USA.

    • Elizabeth A Warner &
    • Lyle L Moldawer
  6. Department of Radiation Oncology, Washington University, St. Louis, Missouri, USA.

    • Bernard H Brownstein
  7. University of Florida, Gainesville, Florida, USA.

    • Henry V Baker
  8. University of Michigan School of Medicine, Ann Arbor, Michigan, USA.

    • Ulysses G J Balis &
    • Stephen F Lowry
  9. University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.

    • Timothy R Billiar &
    • Jason Sperry
  10. University of Medicine and Dentistry of New Jersey, New Brunswick, New Jersey, USA.

    • Steven E Calvano
  11. Washington University School of Medicine, St. Louis, Missouri, USA.

    • J Perren Cobb
  12. University of Washington, Seattle, Washington, USA.

    • Joseph Cuschieri,
    • Nicole S Gibran,
    • Laura Hennessy,
    • Matthew B Klein,
    • Ronald V Maier &
    • Grant E O′Keefe
  13. University of Texas Medical Branch, Galveston, Texas, USA.

    • Celeste C Finnerty,
    • David N Herndon &
    • Marc G Jeschke
  14. Loyola University School of Medicine, Maywood, Illinois, USA.

    • Richard L Gamelli
  15. University of Louisville, Louisville, Kentucky, USA.

    • Brian G Harbrecht
  16. Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Douglas L Hayden,
    • Philip H Mason,
    • Grace P McDonald-Smith,
    • Laurence G Rahme,
    • David A Schoenfeld,
    • H Shaw Warren &
    • Bram Wispelwey
  17. University of Colorado Health Sciences Center, Denver, Colorado, USA.

    • Jeffrey L Johnson &
    • Ernest E Moore
  18. University of Texas Southwestern Medical School, Dallas, Texas, USA.

    • Joseph P Minei
  19. St. Michael's Hospital, Toronto, Ontario, Canada.

    • Avery B Nathens
  20. Boston University School of Medicine, Boston, Massachusetts, USA.

    • Daniel G Remick
  21. Northwestern University, Chicago, Illinois, USA.

    • Michael B Shapiro
  22. Princeton University, Princeton, New Jersey, USA.

    • John D Storey
  23. Stanford University, Palo Alto, California, USA.

    • Robert Tibshirani &
    • Wing H Wong
  24. San Francisco General Hospital, San Francisco, California, USA.

    • Michael A West
  25. Stanford University, Palo Alto, California, USA.

  26. San Francisco General Hospital, San Francisco, California, USA.


  1. the Inflammation and the Host Response to Injury Collaborative Research Program

    • Henry V Baker,
    • Ulysses G J Balis,
    • Timothy R Billiar,
    • Steven E Calvano,
    • J Perren Cobb,
    • Joseph Cuschieri,
    • Celeste C Finnerty,
    • Richard L Gamelli,
    • Nicole S Gibran,
    • Brian G Harbrecht,
    • Douglas L Hayden,
    • Laura Hennessy,
    • David N Herndon,
    • Marc G Jeschke,
    • Jeffrey L Johnson,
    • Matthew B Klein,
    • Stephen F Lowry,
    • Ronald V Maier,
    • Philip H Mason,
    • Grace P McDonald-Smith,
    • Joseph P Minei,
    • Ernest E Moore,
    • Avery B Nathens,
    • Grant E O′Keefe,
    • Laurence G Rahme,
    • Daniel G Remick,
    • David A Schoenfeld,
    • Michael B Shapiro,
    • Jason Sperry,
    • John D Storey,
    • Robert Tibshirani,
    • H Shaw Warren,
    • Michael A West,
    • Bram Wispelwey &
    • Wing H Wong


K.T.K. performed and analyzed experiments. W. Xiao and W.-J.Q. preformed microarray and proteomics analyses. K.T.K., W. Xu, J.W., M.N.M., W. Xu, A.R., E.A.W., L.L.M., D.I., B.H.B., R.W.D. & M.T. designed genomic experiments. K.T.K., W.-J.Q., D.G.C. II and R.D.S. designed proteomic experiments. J.G., S.P.F., A.E.R. and R.G.T. aided in clinical sample studies at Massachusetts General Hospital. K.T.K., C.M.-G., A.D., L.L.M., W. Xiao, M.N.M., J.W., W.-J.Q., B.O.P., D.G.C. II, A.E.R., P.E.B. and M.T. designed, conducted and analyzed the ex vivo stimulation experiment. K.T.K., C.M.-G., W. Xiao, M.N.M. and L.L.M. wrote the manuscript. All authors contributed to the final editing of the manuscript.

Henry V Baker8, Ulysses G J Balis9, Timothy R Billiar10, Steven E Calvano11, J Perren Cobb12, Joseph Cuschieri13, Celeste C Finnerty14, Richard L Gamelli15, Nicole S Gibran13, Brian G Harbrecht16, Douglas L Hayden17, Laura Hennessy13, David N Herndon14, Marc G Jeschke14, Jeffrey L Johnson18, Matthew B Klein13, Stephen F Lowry9, Ronald V Maier13, Philip H Mason17, Grace P McDonald-Smith17, Joseph P Minei19, Ernest E Moore18, Avery B Nathens20, Grant E O′Keefe13, Laurence G Rahme17, Daniel G Remick21, David A Schoenfeld17, Michael B Shapiro22, Jason Sperry10, John D Storey23, Robert Tibshirani24, H Shaw Warren17, Michael A West25, Bram Wispelwey17 & Wing H Wong24

Competing financial interests

The authors declare no competing financial interests.

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Supplementary information

PDF files

  1. Supplementary Text and Figures (860K)

    Supplementary Figures 1–3, Supplementary Tables 1–2,4,6-7,9-12 and Supplementary Methods

  2. Supplementary Table 3 (2M)

    Significantly perturbed genes and proteins following ex vivo stimulation of whole blood by LPS or GM+I.

  3. Supplementary Table 5 (608K)

    Gene expression across all subjects for the genes in Figure 2f comparing microfluidic isolation with Ficoll-dextran isolation

  4. Supplementary Table 8 (6M)

    Significantly perturbed genes after severe trauma injury

Additional data