Quantitative Proteome Profiling of Normal Human Circulating Microparticles

Circulating microparticles (MPs) are produced as part of normal physiology. Their numbers, origin, and composition change in pathology. Despite this, the normal MP proteome has not yet been characterized with standardized high-resolution methods. We here quantitatively profile the normal MP proteome using nano-LC–MS/MS on an LTQ-Orbitrap with optimized sample collection, preparation, and analysis of 12 different normal samples. Analytical and procedural variation were estimated in triply processed samples analyzed in triplicate from two different donors. Label-free quantitation was validated by the correlation of cytoskeletal protein intensities with MP numbers obtained by flow cytometry. Finally, the validity of using pooled samples was evaluated using overlap protein identification numbers and multivariate data analysis. Using conservative parameters, 536 different unique proteins were quantitated. Of these, 334 (63%) were present in all samples and represent an MP core proteome. Technical triplicates showed <10% variation in intensity within a dynamic range of almost 5 decades. Differences due to variable MP numbers and losses during preparative steps could be normalized using cytoskeletal MP protein intensities. Our results establish a reproducible LC–MS/MS procedure, provide a simple and robust MP preparation method, and yield a baseline MP proteome for future studies of MPs in health and disease

Quantitative Proteome Profiling of Normal Human Circulating Microparticles

Circulating microparticles (MPs) are produced as part of normal physiology. Their numbers, origin, and composition change in pathology. Despite this, the normal MP proteome has not yet been characterized with standardized high-resolution methods. We here quantitatively profile the normal MP proteome using nano-LC–MS/MS on an LTQ-Orbitrap with optimized sample collection, preparation, and analysis of 12 different normal samples. Analytical and procedural variation were estimated in triply processed samples analyzed in triplicate from two different donors. Label-free quantitation was validated by the correlation of cytoskeletal protein intensities with MP numbers obtained by flow cytometry. Finally, the validity of using pooled samples was evaluated using overlap protein identification numbers and multivariate data analysis. Using conservative parameters, 536 different unique proteins were quantitated. Of these, 334 (63%) were present in all samples and represent an MP core proteome. Technical triplicates showed <10% variation in intensity within a dynamic range of almost 5 decades. Differences due to variable MP numbers and losses during preparative steps could be normalized using cytoskeletal MP protein intensities. Our results establish a reproducible LC–MS/MS procedure, provide a simple and robust MP preparation method, and yield a baseline MP proteome for future studies of MPs in health and disease

Relationship between ANA-staining intensity and histological grade of tumor (high, moderate, and poorly differentiated).

<p>While grading had no significant relationship with ANA-positivity of any specificity the presence of a speckled ANA pattern was significantly correlated with moderate-poor differentiation grade when compared with highly differentiated tumors (p = 0.04).</p

No correlation between serum-CA-125 values and the presence and intensity of ANA.

<p>The immunofluorescence score is depicted as a function of CA-125 values for the samples from benign (red symbols) and malignant cases (green symbols). Cut-off for positivity in the CA-125 test is 35 U/mL. All patients with benign ovarian tumors and epithelial ovarian cancers are included in this figure.</p

Study demographics in patients diagnosed with borderline ovarian tumor, ovarian cancer or a benign ovarian tumor.

<p>*endometrioid adenocarcinoma N = 11, Clear cell neoplasms N = 6 and carcinosarcoma N = 4.</p><p>**No significant difference for the subset of 127 matched patients with benign conditions: Median age: 64 (range 54–90).</p

Antinuclear Antibodies (ANA) specificities.

<p>Examples of ANA-patterns of strongly positive sera staining in a cytoplasmic (A) and a speckled (B) pattern. In (C) is shown the distribution of the main types of ANA patterns as well as the signal strength in the benign and the malignant group. Strongly positive samples are almost only seen in the group with malignancy.</p

The frequency of different Antinuclear Antibodies (ANA)-patterns in sera from patients with epithelial ovarian cancer and benign pelvic conditions.

<p>Fisher's exact test was used to obtain <i>p</i>-values. Some sera contain ANAs of more than one specificity. Centr., centromere; Nuclear membr., nuclear membrane; Homog., homogeneous; cytopl., cytoplasmic; NS, non significant.</p

Odds ratio for association between polymorphisms in <i>IL18</i>, <i>NLRP3</i>, <i>TLR1</i> and <i>TLR5</i> and EULAR good/moderate vs. none and good vs. moderate/non-response, respectively.

<p>For <i>TLR5</i> rs5744174, patients were also stratified on diagnosis based on IgM-RF (seropositive-/seronegative RA). Log scale, 95% confidence interval.</p

Linkage disequilibrium-maps for A) <i>NLRP3</i>, B) <i>TLR5</i>, and C) <i>TLR10/1</i>.

<p>Numbers in squares represent r². Darker red indicates stronger linkage disequilibrium. Maps were made using Haploview software version 4.2 and CEPH/CEU HapMap dataset (Phase II+III merged, release 28/ August10). HapMap data were downloaded by respective genes (<i>TLR5</i> and <i>NLRP3</i>) and for <i>TLR10/1</i> data spanning both genes. To simplify LD-maps, SNPs were selected in the following way: NLRP3: minor allele frequency (MAF) >0.1, Hardy-Weinberg equilibrium (HW) p-value >0.01, genotype>50%, force include: rs10754558, force exclude #4–29; TLR5: MAF >0.1, HW p-value >0.01, genotype>50%, force include rs5744176, force exclude # 17–25; TLR10/1: MAF >0.1, HW p-value >0.01, genotype>50%, force include rs11096957, force exclude #4–58, 98–111.</p

Baseline clinical characteristics and treatment response of the study population.

<p>SD: standard deviation; DMARD: disease modifying anti-rheumatic drugs; VAS: visual analogue scale; ΔVAS: baseline VAS minus follow-up VAS; TJC: tender joint count; SJC: swollen joint count; HAQ: health assessment questionnaire; CRP: C-reactive protein; DAS28: disease activity score (28-joints); EULAR: European League Against Rheumatism; ACR50: American College of Rheumatology, 50% improvement; RelDAS28: relative change in DAS28;</p><p>^#: Two-sided t-test p-value of difference in means/proportions between seropositive and seronegative patients; Seropositive: Positive for IgM-RF</p><p>Baseline clinical characteristics and treatment response of the study population.</p