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      Clinical Microfluidics for Neutrophil Genomics and Proteomics

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          Abstract

          Neutrophils play critical roles in modulating the immune response. 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 by comparison with standard bulk isolation methodologies. 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. The preliminary results from a small cohort of patients 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.

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          Most cited references 32

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          Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

          Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.
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            Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast.

            Mass spectrometry is a powerful technology for the analysis of large numbers of endogenous proteins. However, the analytical challenges associated with comprehensive identification and relative quantification of cellular proteomes have so far appeared to be insurmountable. Here, using advances in computational proteomics, instrument performance and sample preparation strategies, we compare protein levels of essentially all endogenous proteins in haploid yeast cells to their diploid counterparts. Our analysis spans more than four orders of magnitude in protein abundance with no discrimination against membrane or low level regulatory proteins. Stable-isotope labelling by amino acids in cell culture (SILAC) quantification was very accurate across the proteome, as demonstrated by one-to-one ratios of most yeast proteins. Key members of the pheromone pathway were specific to haploid yeast but others were unaltered, suggesting an efficient control mechanism of the mating response. Several retrotransposon-associated proteins were specific to haploid yeast. Gene ontology analysis pinpointed a significant change for cell wall components in agreement with geometrical considerations: diploid cells have twice the volume but not twice the surface area of haploid cells. Transcriptome levels agreed poorly with proteome changes overall. However, after filtering out low confidence microarray measurements, messenger RNA changes and SILAC ratios correlated very well for pheromone pathway components. Systems-wide, precise quantification directly at the protein level opens up new perspectives in post-genomics and systems biology.
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              Achieving stability of lipopolysaccharide-induced NF-kappaB activation.

              The activation dynamics of the transcription factor NF-kappaB exhibit damped oscillatory behavior when cells are stimulated by tumor necrosis factor-alpha (TNFalpha) but stable behavior when stimulated by lipopolysaccharide (LPS). LPS binding to Toll-like receptor 4 (TLR4) causes activation of NF-kappaB that requires two downstream pathways, each of which when isolated exhibits damped oscillatory behavior. Computational modeling of the two TLR4-dependent signaling pathways suggests that one pathway requires a time delay to establish early anti-phase activation of NF-kappaB by the two pathways. The MyD88-independent pathway required Inferon regulatory factor 3-dependent expression of TNFalpha to activate NF-kappaB, and the time required for TNFalpha synthesis established the delay.
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                Author and article information

                Journal
                9502015
                8791
                Nat Med
                Nature medicine
                1078-8956
                1546-170X
                29 June 2010
                29 August 2010
                September 2010
                15 July 2011
                : 16
                : 9
                : 1042-1047
                Affiliations
                [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
                Author notes
                Correspondence should be addressed to KTK ( kkotz@ 123456partners.org ) or MT ( mtoner@ 123456hms.harvard.edu )
                [†]

                Lists of participants and affiliations appear in the Acknowledgements section of the paper.

                Article
                nihpa210118
                10.1038/nm.2205
                3136804
                20802500

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                Funding
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U54 GM062119-10 ||GM
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U54 GM062119-09 ||GM
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: T32 GM007035-32 ||GM
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: R01 GM036214-18 ||GM
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P41 RR018522-06 ||RR
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P41 RR018522-05 ||RR
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P41 EB002503-07 ||EB
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P41 EB002503-05 ||EB
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P01 HG000205-16 ||HG
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Center for Research Resources : NCRR
                Funded by: National Institute of Biomedical Imaging and Bioengineering : NIBIB
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P01 HG000205-14 ||HG
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                Medicine

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