Clinical Microfluidics for Neutrophil Genomics and
Kenneth T. Kotz1*, Wenzong Xiao2, Carol Miller-Graziano3, Wei-Jun Qian4, Alan E. Rosenbach1, Aman
Russom1, Lyle L. Moldawer5, Asit De3, Paul E. Bankey3, Brianne O. Petritis4, David G. Camp II4, Jeremy
Goverman1, Shawn P. Fagan1, Bernard H. Brownstein6, Daniel Irimia1, Julie Wilhelmy2, Michael N.
Mindrinos2, Richard D. Smith4, Ronald W. Davis2, Ronald G. Tompkins1, Mehmet Toner1*, and the
Inflammation and the Host Response to Injury Collaborative Research Program†.
1 Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Shriners Hospital for Children, Boston, MA
02114. 2 Stanford Genome Technology Center, Palo Alto, CA 94304. 3 Department of Surgery, University of Rochester School of
Medicine, Rochester, NY 14642. 4 Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific
Northwest National Laboratory, Richland, WA 99352. 5 Department of Surgery, University of Florida College of Medicine,
Gainesville, FL 32610. 6 Department of Radiation Oncology, Washington University, St. Louis, MO 63110. Correspondence should
be addressed to KTK (email@example.com) or MT (firstname.lastname@example.org).
† Lists of participants and affiliations appear in the Acknowledgements section of the paper.
Neutrophils play critical roles in modulating the immune response. However, neutrophils
have a short circulating half life, are easily stimulated in vitro, and have low levels of
cellular mRNA. All of these factors have made it difficult to isolate a biologically-relevant
population of neutrophils for molecular and functional studies. Here we present a robust
methodology for rapidly isolating neutrophils directly from whole blood and develop on
chip processing for mRNA and protein isolation for genomics and proteomics. We
validate this device with an ex vivo stimulation experiment and demonstrate the ability of
the device to discriminate subtle differences in the genomic and proteomic response of
peripheral blood neutrophils to direct and indirect stimulation. Lastly, 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. We demonstrate that this
technique is easy to use by nurses and technical staff and yields excellent quality and
quantity of mRNA sensitive genomic readout of the host response to injury.
Neutrophils, which are the most common type of leukocyte (white blood cell), are
important for protection against infections and modulation of inflammatory responses.
These are the first cells to respond to an injury site, where they perform a variety of
functions, including defense against invading microbes1, proteolytic granule release 2,
cytokine signaling3, and resolution of inflammation 4. It is well recognized that they play
an important role in both chronic and acute inflammation and are a critical part of a
complex temporal pattern of activation and/or depression of the immune system after
injury and as such, an expanded role for neutrophils in adaptive immunity is now being
In a clinical setting, the peripheral blood is an easily accessible tissue, and there is a
great deal of interest in using leukocyte transcript profiling to understand disease
processes6,7. Laudanski et al. recently demonstrated that differential genomic changes
can be observed in distinct leukocyte subpopulations in response to the same in vivo
stimulus. However, the genomic changes seen in a total leukocyte population were
blunted in comparison. This observation has led to the practice of fractionating
leukocytes into more homogeneous subpopulations prior to genomic analysis to allow
for clearer functional interpretation of gene expression patterns 8.
In contrast to the progress that has been made in leukocyte transcript profiling, the
investigation of neutrophils by genomic and proteomic technologies has been hampered
by three major challenges inherent in the current isolation methods. First, standard
methods for neutrophil isolation require a two-step density gradient separation method,
which involves 3 hours of processing time and uses milliliter volumes of blood (typically
4-8 ml)9. Neutrophil isolation, however, is extremely time-sensitive owing to their short
6-10 hour circulating half-life10. Second, neutrophils are especially sensitive to external
stimuli (pH, temperature, physical manipulation, storage) and can be easily activated
during the isolation process11,12. Third, on average, neutrophils contain 10-20 times less
mRNA per cell than do monocytes 13,14 thus requiring large cell numbers and a pure
population for adequate assay. As a result, the standard isolation methods often yield
RNA that is poor in quality when compared to other cell types. These technical
challenges have limited the widespread study of neutrophils in a clinical setting. We
thus sought to develop a cost-effective, facile method to isolate a pure population of
neutrophils from whole blood and that could be readily utilized in a clinical research
In our laboratory, we have previously created microfluidic tools that use affinity capture
to isolate cellular subpopulations directly from whole blood. Previously developed
applications include CD4+ cell capture and enumeration in HIV infected patients15, and
an extremely sensitive method for extracting very rare circulating epithelial cells
(“circulating tumor cells” or CTCs) directly from whole blood in metastatic cancer
patients16. Maheswaran et al. recently extended the utility of the CTC capture device by
employing sensitive detection of EGFR mutations by PCR in the DNA of the captured
cells17. These microfluidic devices work by carefully controlling the flow of blood
between surfaces coated with antibodies specific for the cell type of interest 18. While
these studies demonstrate the potential for microfluidic sample processing within a
specialized laboratory setting, the tools have not been applied in a multi-center clinical
program due to the high technical skills required to use the microfluidic devices.
Additionally, genomic analysis of the captured cell populations in these studies was
limited to a narrow set of known candidate genes due to a lack of cell purity and starting
genomic material. As a result, microfluidic cell capture has not yet been combined with
the types of genome-wide microarray or proteomic analyses that would be fundamental
to identifying biomarkers and understanding the biological basis of disease at a
In this article, we report the first microfluidic tool that captures cells directly from whole
blood with the quantity and purity necessary for genome-wide microarray and high-
throughput proteomic analysis. We have designed a device that can efficiently capture
neutrophils from 150 µL of whole blood in 5 minutes. These cells can then be further
processed and directly lysed on the microfluidic chips in another 15 minutes for
downstream nucleic acid and protein analyses. We validated the device by looking at
the transcriptional and proteomic response of neutrophils in response to two different ex
vivo stimuli, endotoxin and cytokine. We show that the genomic and proteomic samples
resulting from microfluidic cell isolation are sufficiently high quality to discriminate
between subtle differences in neutrophil activation states. Finally, we implemented the
use of this microfluidic neutrophil-capture cassette by non-specialized staff in a multi-
center clinical program and have obtained a comparable level of high quality,
reproducible data. We anticipate that this device will have wide range of applications for
furthering the biological and therapeutic investigations of neutrophils, and that this
approach will be transferrable to other cell types in the peripheral blood.
Characterization of Cell Capture
We set out to design a device that could capture neutrophils directly from 150 µL of
whole blood. From previous immunoaffinity capture studies19, we chose anti-CD66b as
the capture antibody, which captures all polymorphonuclear leukocytes bearing
CEACAM-8 (carcinoembryonic antigen-related cell adhesion molecule 8) an adhesion
molecule expressed on neutrophils and eosinophils. To maximize the yield of neutrophil
capture we determined the optimal shear stress for capturing neutrophils, and we found
that a shear stress of 0.45 dynes/cm2 results in the highest density of capture. A
chamber height of 50 µm was chosen based on previous studies which optimized
capture efficiency for lymphocytes18. We then determined the surface area of the
chambers such that would capture sufficient numbers of cells for carrying out
downstream genomic and proteomic studies under the constraint of optimized shear
stress. We finalized the design, shown in Figure 1, which maximizes the width of a
device on a 38x75 mm microscope slide. The final device consists of discrete chambers
that divide the flow in order to a more uniform flow distribution across the width of the
device. To optimize the purity of neutrophil capture on the chip we tested different
protocols, different antibodies, and different blood loading protocols (data not shown). At
an optimal flow rate of 30 µl/min, using antibodies against CD66b,the number of
neutrophils captured by the device is proportional to the volume of starting whole blood
over a range of 10-250 µl (Figure 1e-f),with a captured neutrophil purity of >94% as
evaluated by on device Wright-Giemsa and immunofluorescent staining (Figure 1c-d,
Table 1). The major contaminating cell population was eosinophils, a lineage-related
polymorphonuclear (PMN) leukocyte, which is expected based on capture antibody. To
test the effects of severe inflammation on the ability to capture cells, we examined 13
samples from 5 different severely burned patients and found no change in purity (Table
RNA and Protein Extraction
To verify compatibility of cell lysate with genomic analysis, we carefully optimized the
protocol of processing cassette-isolated neutrophils to reliably yield sufficient quantities
of high-quality RNA. Captured cells were lysed in situ on the device with guanadinium-
based chaotropes and total RNA was extracted using commercial spin columns and
analyzed for nucleic acid quantity and quality. Total RNA recovered was linear over the
range of capture (Figure 1f). RNA quantity (0.33 ± 0.15 pg/cell) was similar to other
studies20, and RNA quality as measured on an Agilent Bioanalyzer 2100 was excellent
with an RIN range of 7.4-9.9 (Table 1, Supplementary Figure 1). The correlations of
gene expression between multiple samples isolated by the microfluidic cassettes were
0.98 ± 0.02 (n=12).
To verify compatibility of cell lysate with mass spectrometry (MS)-based proteomic
analysis we optimized the reagent and lysis conditions (Supplementary Table 1) to
maximize the number of peptides and proteins identified for samples with low total
protein content 21. Under identical isolation conditions, we observed that the chips can
consistently capture ~4 x 105 neutrophils, which are sufficient for proteomic analysis
with ~500 proteins detectable using a single reverse-phase LC separation coupled with
tandem mass spectrometry (LC-MS/MS) (Table 2). Importantly, chip-to-chip correlations
based on either peptide intensities or protein spectral counts were extremely high (0.88
± 0.06 and 0.96 ± 0.02, respectively) for multiple samples, confirming the reproducibility
of this method. Protein distribution based on cellular components was nearly identical to
bulk isolated neutrophils, suggesting that there was no differential adsorption of cellular
proteins to the surface of the device (Supplementary Figure 2). Protein distribution was
also compared with the microarray data above, and again there was no apparent
systemic bias that could be attributed to processing in the microfluidic device
(Supplementary Figure 2-3).
To demonstrate the ease and utility of this new technology in a clinical setting, we
supervised the implementation of microfluidic neutrophil isolation and processing
methods in the genomic sampling arm of a multi-center study. After providing hands-on
training for processing neutrophils using the microfluidic device, we supplied
preassembled microfluidic devices as part of single use kits to clinical staff and research
technicians at six clinical sites listed in Supplementary Table 2. Protocols were refined
to capture a sufficient number of cells in a specific clinical sampling population to yield a
minimum of 20 ng total RNA, a quantity of RNA that is more than ample to achieve
reliable amplification for microarray expression studies using currently available
amplification systems22,23. Using the device loading data in Figure 1, and complete
blood counts from severely burned and trauma patients, we calculated the blood
requirements (150 µl) sufficient to capture >20 ng RNA in 99% of the clinical samples.
In order to reduce the effects of sample processing on protein signaling and gene
transcription, we developed a protocol to achieve 20 minutes total processing time for
obtaining cell lysate from whole blood 24. This processing time is shorter than the 30
minute transcriptional time lag seen in vitro following TLR activation with LPS 25 and in
vivo with LPS-stimulated volunteers where gene expression showed marked differences
within a 2 hour period7,26.
To demonstrate the utility of the new device as a robust tool for neutrophil analysis, we
provided over 1000 cassettes to date to be used to process blood of both burn and
trauma patients. The total RNA extracted from the samples processed at the clinical
sites meets targets in quantity and quality in 98% of the samples. Total RNA extraction
quantities correlated in a linear fashion with clinical neutrophil counts based on CBC
analysis (Figure 2), essentially a recapitulation of the data from Figure 1. Additionally,
there were no significant effects in the quantity and quality data based on cassette
production batches (Supplementary Figure 4). This reliably uniform performance
demonstrates potential device applicability for both on-site and near-site clinical
Ex vivo Stimulation Studies
We next assessed whether the relatively small number of isolated neutrophils captured
by the microfluidics cassettes would impact the resulting genomic sensitivity and
potential discriminatory genomic capabilities in response to various stimuli. To do this,
we compared the genome-wide expression profile in neutrophils following both adaptive
and innate neutrophil activation protocols. One activation protocol was gram negative
lipopolysaccharide (LPS) activation (TLR4 pathway), as a model for bacterial-induced
phenotypic changes in neutrophils. The second protocol consisted of lymphokine
stimulation using granulocyte-macrophage colony-simulating factor (GM-CSF) and
interferon-gamma (INF-γ) (referred to as GM+I). In both protocols, whole blood was
stimulated ex vivo27 to allow leukocyte and plasma protein interactions. Stimulating
whole blood also allowed us to assess the specificity of capture in a background of
highly activated leukocytes. Multiple cassette isolations were performed for each
experiment arm for phenotypic enumeration, genomics and proteomic analysis. Neither
capture purity nor the membrane integrity (Trypan blue exclusion) of the isolated cells
was affected by the stimulation (Table 1).
Unsupervised hierarchical clustering of the genomic data from the cassette extractions
showed patterns of neutrophil RNA expression distinct between the two stimulation
protocols and distinct from control, inactive neutrophils (Figure 3). Samples cluster
according to stimulation condition thus verifying the ability of the cassette to isolate cells
with varying activation states and subsequently resolve details of the distinct activation
pathways. Correlations between multiple extractions within a single sample were
excellent for both the genomic data (0.98 ± 0.02) and the proteomics data (0.96 ± 0.02)
demonstrating a high level of reproducibility of the method. Overall 12-14% of the
twenty-one thousand genes measured on the microarray were significantly perturbed
(fold changes >2, and false discovery rate <0.01) following either stimulation, and 12-
15% among the six hundred proteins detected were also significantly changed (fold
changes >2, and false discovery rate <0.05) (Supplementary Table 3). As summarized
in Figure 3c, 62% of the significant genes overlapped between LPS and GM+I; among
these most genes (1684 of 1690) showed the same trend of changes after either
stimulation. Similarly 45% of the significant proteins overlap and all showed the same
trend of changes between the two stimulations (Figure 3d).
We next extracted gene and protein ontology as well as the metabolic and cell signaling
pathways derived from the gene expression and protein identification data in Figure 3
using the Ingenuity Pathway Analysis system. A complete list of the statistically
significant functional pathways identified using this computational tool is provided in
Supplementary Table 4. As expected, cytokine signaling pathways were upregulated in
the stimulated samples as compared to the unstimulated samples. Also, antigen
presentation was significantly upregulated for the GM+I stimulation when compared to
both unstimulated and LPS stimulated samples. Thus microfluidic cassette isolated
neutrophils yield genomic data that can be discriminated at the functional level as well
as the gene level.
To further validate the stimulation study, we examined a number of the genes found in
the microarray studies using flow cytometry, which is capable of very sensitive and
specific protein abundance changes in subpopulations of cells. While neutrophils are
traditionally considered phagocytic cells, recent studies have shown their capability for
antigen presentation with HLA-DR, and chemokine signaling with CCL20 associated
with activation and migration of regulatory T cells and Th17 lymphocytes 28,29. Because
gene transcripts corresponding to these two proteins were differentially altered in our ex
vivo stimulation experiment, we validated the relative changes of these two proteins
using multicolor flow cytometry. As shown in Figure 3, CCL20 is upregulated in the LPS
stimulated sample with respect to the GM+I sample while HLA-DR shows the opposite
trend. These results examining surface proteins are consistent with the microarray
results, confirming that the changes in HLA-DR and CCL20 are not due to artifact or a
contaminating cell population.
The difficulty in isolating neutrophils may have contributed to small numbers of
comprehensive genomic and proteomic studies of neutrophils in the literature. Several
key studies have focused on aspects of neutrophil biology, such as development 30,
apoptosis31, and response to pathogens32-34. Of these basic studies, our data compare
well with previous microarray data looking at LPS stimulation of purified neutrophils 33-35.
We identify all of the genes in the study by Fessler et al. and Zhang et al., with 87% of
the relative change in expression the same as with our study. We see 78% of the genes
in the study by Malcom et al., with 84% of the genes changing in the same direction as
our data. There is another microarray study that examined the response of purified
neutrophils to GM-CSF at 18 hours 36. Our data identifies 99.9% of the gene transcripts
in the Kobayashi study, with 65% of the genes showing the same trend in relative gene
expression. These are remarkable observations given that we are using different
microarray platforms, different stimulation conditions (purified cells vs. ex vivo
stimulated whole blood), different cell numbers, and different stimulation times (4 vs.
18 hours in the LPS studies). This verifies the expression changes that we observe are
originating from neutrophil signals, and cross-signals from possible contaminants are
generally small if not absent 28. Further, with the microfluidic device described here, we
observe measurably different responses between model adaptive and innate stimuli in a
small population of cells, which is important given the ease, speed, and limited sample
necessary for downstream “-omics” analyses.
While microarray technology for gene expression is well established, mass
spectrometry based high throughput proteomics is being rapidly developed to
quantitatively examine a large number of proteins in cells37, providing important new
information on post-transcriptional and post-translational regulations of cells 38,39in
health and diseases. We first compared gene expression with protein abundance
changes on 15 genes that were known to interact with LPS. We observed a much
stronger concordance for 15 genes that were known to interact with LPS. Among these
15 genes, 12 genes show the same direction of changes between mRNA and protein
abundances (Supplementary Table 5). This observation is consistent with recent report
that mRNA and protein data often agree well on specific biological pathways 39. In
addition, we examined the gene expression profile of the list of proteins significantly
perturbed by LPS, to identify genes that have changes of opposite directions at gene
level vs. protein level (Supplementary Table 6). As examples, nucleolin (NCL), a protein
involved in the synthesis and maturation of ribosomes, is 2.4 fold increased at mRNA
level but 29 times decreased at protein level, which corresponds to a trend observed
previously 40, suggesting a selective degradation of this protein after LPS stimulation. In
addition, the endogenous amount of lipocalin 2 (LCN2), a secretive protein important to
antibacterial innate immune response 41 and known to be induced by LPS 42, was
measured as decreased by 3 fold, despite of a 6 fold increase of mRNA. The integrative
analysis of genes and proteins of neutrophils isolated from patient samples using the
microfluidics device will likely provide new insights in many clinical studies.
In this article, we have described the design and application of a new microfluidic device
that rapidly and reproducibly separates neutrophils from whole blood. The fundamental
design principles for the device build upon previous studies of positive immunoaffinity
capture of cells under well-defined shear stress. Here we developed a new point of care
technology, applied it to the capture of neutrophils, demonstrated the scalability of the
device, its applicability in a clinical setting, and its versatility in processing samples for
multiple downstream analyses. Furthermore, this study is the first to employ a
microfluidic device to rapidly capture cells directly from whole blood for both microarray
and high-throughput proteomic analysis. We applied it to neutrophils, an extremely
challenging cell type to isolate rapidly with high purity, and we believe that the device is
in general, scalable to any cell type that can be positively selected with an antibody.
This neutrophil separation device should be useful for monitoring diseases which are in
some part characterized by chronic inflammation, particularly autoimmune disorders.
With minor modifications the device can be used to capture cells from other clinical
samples, such as bronchoalveolar lavage (BAL), urine, and cerebrospinal fluid (CSF).
This will facilitate genomic studies on cells at the site of different infections (respiratory,
urinary, nervous system, etc.) leading to a better understanding of localized disease
pathogenesis. With a better understanding of such processes, this device should open
up new applications of clinical genomics to disease and therapeutic monitoring.
All studies involving human samples were approved by the appropriate human use
committees at all institutions within the NIH-funded “Inflammation and host response to
injury” glue grant. Patient enrollment criteria are given in the supplementary information.
Unless otherwise stated, blood was collected into EDTA Vacutainer collection tubes
(Becton Dickinson). All samples were run on the microfluidic device within 3 hours of
Microfluidic device design and fabrication
Microfluidic devices in this study were fabricated following standard rapid-prototyping
methods 43,44. Devices were produced and functionalized with antibody (AbD Serotec
CB66b, clone 80H3) in batches of ~100, and an internal quality control program was put
in place for devices used for clinical sampling. Blood was pumped through the devices
using a standard syringe pump (Harvard Apparatus), followed by a washing step using
nuclease-free PBS (Ambion). Cell purity was assessed by immunofluorescent staining
and Wright-Giemsa staining. Cell lysis for either genomics or proteomics was performed
in situ. Guanadinium isothiocyanate buffer and buffered 2,2,2-trifluoroethanol were the
lytic reagents for genomics and proteomics respectively. All lysates were stored at -
80° C and shipped on dry ice for analysis according to protocols within the Inflammation
and Host Response to Injury program. Full details for device production and blood
processing are given in the supplementary information.
Ex vivo stimulation
Freshly drawn peripheral blood was anti-coagulated with sodium Heparin and was
directly processed for cell isolation by microfluidics (designated as unstimulated) or
stimulated with LPS (50 ng/ml) or GM-CSF (20 ng/ml) + IFN-γ (100 IU/ml) (abbreviated
as GM+I) for 16 hours on a rocker at 37 ° C in 5% CO2 atmosphere before cell isolation.
In addition to microfluidic processing, leukocytes were isolated using density gradient
techniques from both unstimulated and stimulated whole blood and then tested for
expression of surface HLA-DR and intra-cellular CCL20 in CD66b identified PMNs by
flow cytometry. Data are expressed as median fluorescence intensity.
RNA extraction followed a modified commercial protocol (QIAGEN RNeasy Plus). To
remove genomic DNA, 350 µl of guanadinium isothiocyanate with detergent (Qiagen
RLT Plus Buffer) was added to the thawed, original lysate along with 2-mercaptoethaol
(Sigma) at 20 µl/ml. This combined lysate was then processed according to
manufacturer protocols (Qiagen RNeasy Plus Kit) yielding purified total RNA analyzed
on an Agilent Bioanalyzer 2100 system.
cDNA was synthesized using Ovation Biotin RNA Amplification and Labeling System
(NuGEN Technologies) from 20 ng of total RNA as starting material, and hybridized onto
GeneChip Human Genome U133 Plus 2.0 Array (Affymetrix). Chips were washed and
scanned as recommended by the Ovation System User Guide (version 1.0).
Low level analysis was performed using dChip using the perfect match only option 45.
Probesets significantly perturbed by LPS and GM+I were identified using significance
analysis of microarrays 46 with false discovery rate of <0.01 on the basis of 1,000
permutations. Unique genes were identified from the probe sets with mapped Entrez
GeneIDs (http://www.ncbi.nih.gov/Entrez/). Ingenuity Pathways Analysis was used to
identify pathways and functional module as previously described 7.
Enriched neutrophil lysates were digested using trypsin and aliquots of peptide samples
were analyzed by reversed phase capillary LC-MS using a LTQ-Orbitrap instrument
(Thermo Scientific; ThermoElectron). The LC-MS datasets were automatically analyzed
using an in-house developed software package that includes tools such as ICR2LS and
VIPER47. Peptides were identified based on the accurate mass and time (AMT) tag
strategy. Peptide sequences of detected LC-MS features were assigned when the
measured masses and normalized elution time (NET) for each feature matched the
calculated mass and NET of a peptide in the AMT tag database within a 2 ppm mass
error and 2% NET error. The final processed data contain the peptide MS intensity
values for each identified feature.
The raw peptide intensity data was then normalized, re-scaled and rolled up to protein
level abundance by taking the mean abundances for those peptides mapping to each
protein using the tool DAnTE48. p-values from ANOVA test comparing proteins under
different conditions were then adjusted for false discovery rate <0.05 using Q-value 49.
We thank O. Hurtado, K. Eken, and A. Gupta for microfabrication support. We thank E. A. Warner for
contrubtions in editing the manuscript. We thank UF technical staff for managing clinical implementation
of the microfluidic devices. K.T.K. was supported with a NIH training grant T32 GM-007035-32. These
studies were supported by the NIH, “Inflammation and the Host Response to Injury Large Scale
Collaborative Project”, U54 GM-062119, and BioMEMS Resource Center, P41 EB-002503, and
Proteomics Research Resource for Integrative Biology, RR018522. The proteomics work was performed
in the Environmental Molecular Sciences Laboratory, a U.S. Department of Energy (DOE) Office of
Biological and Environmental Research national scientific user facility on the PNNL campus. PNNL is
multi-program national laboratory operated by Battelle for the DOE under Contract No. DE-AC05-76RLO
The magnitude of the clinical and genomic and proteomic data reported here required the efforts of many
individuals at participating institutions. In particular, we wish to acknowledge the supportive research
environment created and sustained by the participants in the Glue Grant Program:
Henry V. Baker, Ph.D.1, Ulysses G.J. Balis, M.D.2, Timothy R. Billiar, M.D.3, Steven E. Calvano, Ph.D.5,
Irshad H. Chaudry, Ph.D.6, J. Perren Cobb, M.D.4, Joseph Cuschieri, M.D.7, Celeste C. Finnerty, Ph.D.9,
Richard L. Gamelli10, M.D., Nicole S. Gibran, M.D.7, Brian G. Harbrecht, M.D.11, Douglas L. Hayden,
M.A.12, Laura Hennessy, R.N.7, David N. Herndon, M.D.9, Marc G. Jeschke, M.D., Ph.D. 9, Jeffrey L.
Johnson, M.D.13, Matthew B. Klein, M.D.7, James A. Lederer, Ph.D.14, Stephen F. Lowry, M.D.2, Ronald
V. Maier, M.D.7, John A. Mannick, M.D.14, Philip H. Mason, Ph.D.12, Grace P. McDonald-Smith, M.Ed.12,
Joseph P. Minei, M.D.15, Ernest E. Moore, M.D.13, Avery B. Nathens, M.D., Ph.D., M.P.H.16, Grant E.
O'Keefe, M.D., M.P.H.7, Laurence G. Rahme, Ph.D.12, Daniel G. Remick, M.D.17, David A. Schoenfeld,
Ph.D.12, Michael B. Shapiro, M.D.19, Geoffrey M. Silver, M.D.10, Jason Sperry, M.D., Ph.D.3, John D.
Storey, Ph.D.20, Robert Tibshirani, Ph.D.8, H. Shaw Warren, M.D.12, Michael A. West, M.D., PhD.18, and
Bram Wispelwey, M.S.12
1 University of Florida, Gainesville, FL 32610, 2 University of Michigan School of Medicine, Ann Arbor, MI
48109, 3 University of Pittsburgh Medical Center, Pittsburgh, PA 15213, 4 Washington University School
of Medicine, St. Louis, MO 63110, 5 University of Medicine and Dentistry of New Jersey, New Brunswick,
NJ 08903, 6 University of Alabama Medical School, Birmingham, AL 35294, 7 University of Washington,
Seattle, WA 98195, 8 Stanford University, Palo Alto, CA 94304, 9 University of Texas Medical Branch,
Galveston, TX 77550, 10 Loyola University School of Medicine, Maywood, IL 60153, 11 University of
Louisville, Louisville, KY 40292, 12 Massachusetts General Hospital, Boston, MA 02114,13 University of
Colorado Health Sciences Center, Denver, CO 80204, 14 Brigham & Women’s Hospital, Boston, MA
02115, 15 University of Texas Southwestern Medical School, Dallas, TX 75390, 16 St. Michael’s Hospital,
Toronto, Ontario, CA M5B 1W8, 17 Boston University School of Medicine, Boston, MA 02118, 18 San
Francisco General Hospital, San Francisco, CA 04110,19 Northwestern University, Chicago, IL 60611,
20 Princeton University, Princeton, NJ 08544
KTK, performed and analyzed experiments. KTK, WX, JW, MNM, AR, LLM, DI, BHB, RWD, MT, designed
genomic experiments. KTK, WJQ, DGC, RDS designed proteomic experiments. JG, SPF, AER, RGT,
aided in clinical sample studies at MGH. KTK, CMG, LMM, WX, MNM, JW, WJQ, BOP, DGC, AER, PEB,
MT designed, conducted, and analyzed the ex vivo stimulation experiment. KTK, CMG, WX, MNM, LLM
wrote the manuscript. All authors contributed to the final editing of the manuscript.
1. A. W. Segal, Annu Rev Immunol 23, 197 (2005).
2. N. Borregaard and J. B. Cowland, Blood 89 (10), 3503 (1997).
3. M. A. Cassatella, Adv Immunol 73, 369 (1999).
4. C. N. Serhan, S. Yacoubian, and R. Yang, Annu Rev Pathol 3, 279 (2008).
5. C. Nathan, Nat Rev Immunol 6 (3), 173 (2006).
6. M. E. Burczynski and A. J. Dorner, Pharmacogenomics 7 (2), 187 (2006).
7. S. E. Calvano, W. Xiao, D. R. Richards et al., Nature 437 (7061), 1032 (2005).
8. K. Laudanski, C. Miller-Graziano, W. Xiao et al., Proc Natl Acad Sci U S A 103 (42),
9. W. M. Nauseef, Methods Mol Biol 412, 15 (2007).
10. Maxwell Myer Wintrobe and John P. Greer, Wintrobe's clinical hematology, 11th ed.
(Lippincott Williams & Wilkins, Philadelphia, 2003).
11. M. T. Elghetany and B. H. Davis, Cytometry B Clin Cytom 65 (1), 1 (2005).
12. P. P. Youssef, B. X. Mantzioris, P. J. Roberts-Thomson et al., J Immunol Methods 186
(2), 217 (1995).
13. M. A. Cassatella, Immunol Today 16 (1), 21 (1995).
14. L. Xing and D. G. Remick, Shock 28 (2), 178 (2007).
15. X. Cheng, D. Irimia, M. Dixon et al., J Acquir Immune Defic Syndr 45 (3), 257 (2007).
16. S. Nagrath, L. V. Sequist, S. Maheswaran et al., Nature 450 (7173), 1235 (2007).
17. S. Maheswaran, L. V. Sequist, S. Nagrath et al., N Engl J Med 359 (4), 366 (2008).
18. X. Cheng, D. Irimia, M. Dixon et al., Lab Chip 7 (2), 170 (2007).
19. K. Sekine, A. Revzin, R. G. Tompkins et al., J Immunol Methods 313 (1-2), 96 (2006).
20. P. A. Lyons, M. Koukoulaki, A. Hatton et al., BMC Genomics 8, 64 (2007).
21. H. Wang, W. J. Qian, H. M. Mottaz et al., J Proteome Res 4 (6), 2397 (2005).
22. N. Kurn, P. Chen, J. D. Heath et al., Clin Chem 51 (10), 1973 (2005).
23. R. Singh, R. J. Maganti, S. V. Jabba et al., Am J Physiol Cell Physiol 288 (5), C1179
24. K. Kotz, X. Cheng, and M. Toner, J Vis Exp (8), 320 (2007).
25. M. W. Covert, T. H. Leung, J. E. Gaston et al., Science 309 (5742), 1854 (2005).
26. A. Russom, P. Sethu, D. Irimia et al., Clin Chem (2008).
27. L.E. DeForge, J.S. Kenney, M.L. Jones et al., J Immunol 148 (7), 2133 (1992).
28. A. K. De, C. L. Miller-Graziano, S. E. Calvano et al., J Immunol 175 (9), 6155 (2005).
29. E. J. Gosselin, K. Wardwell, W. F. Rigby et al., J Immunol 151 (3), 1482 (1993).
30. K. Theilgaard-Monch, L. C. Jacobsen, R. Borup et al., Blood 105 (4), 1785 (2005).
31. S. D. Kobayashi, J. M. Voyich, K. R. Braughton et al., J Immunol 170 (6), 3357 (2003).
32. D. L. Borjesson, S. D. Kobayashi, A. R. Whitney et al., J Immunol 174 (10), 6364 (2005).
33. M. B. Fessler, K. C. Malcolm, M. W. Duncan et al., J Biol Chem 277 (35), 31291 (2002). Download full-text
34. X. Zhang, Y. Kluger, Y. Nakayama et al., J Leukoc Biol 75 (2), 358 (2004).
35. K. C. Malcolm, P. G. Arndt, E. J. Manos et al., Am J Physiol Lung Cell Mol Physiol 284
(4), L663 (2003).
36. S. D. Kobayashi, J. M. Voyich, A. R. Whitney et al., J Leukoc Biol 78 (6), 1408 (2005).
37. S. E. Ong and M. Mann, Nat Chem Biol 1 (5), 252 (2005).
38. T. J. Griffin, S. P. Gygi, T. Ideker et al., Mol Cell Proteomics 1 (4), 323 (2002).
39. L. M. de Godoy, J. V. Olsen, J. Cox et al., Nature 455 (7217), 1251 (2008).
40. X. Zhang, Y. Kuramitsu, M. Fujimoto et al., Electrophoresis 27 (8), 1659 (2006).
41. T. Berger, A. Togawa, G. S. Duncan et al., Proc Natl Acad Sci U S A 103 (6), 1834
42. L. A. Meheus, L. M. Fransen, J. G. Raymackers et al., J Immunol 151 (3), 1535 (1993).
43. J. C. McDonald, D. C. Duffy, J. R. Anderson et al., Electrophoresis 21 (1), 27 (2000).
44. K. Kotz, X. Cheng, and M. Toner, J Vis Exp (8), 319 (2007).
45. C. Li and W. H. Wong, Proc Natl Acad Sci U S A 98 (1), 31 (2001).
46. V. G. Tusher, R. Tibshirani, and G. Chu, Proc Natl Acad Sci U S A 98 (9), 5116 (2001).
47. J. S. Zimmer, M. E. Monroe, W. J. Qian et al., Mass Spectrom Rev 25 (3), 450 (2006).
48. A. D. Polpitiya, W. J. Qian, N. Jaitly et al., Bioinformatics 24 (13), 1556 (2008).
49. John D. Storey, Journal Of The Royal Statistical Society Series B 64 (3), 479 (2002).