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Problem The underlying cause of recurrent pregnancy loss (RPL) remains largely unknown. As successful implantation and placentation depends on a tightly regulated immune response to facilitate adequate interaction with trophoblast cells, dysregulation of immunity might account for idiopathic RPL. So far, most studies focused on peripheral blood immune cells and performed single immunological parameter analysis which up till now did not allow for clear classification of RPL versus healthy pregnancies. Therefore, we here defined immune profiles in menstrual blood (MB) derived endometrial cells as well as peripheral blood (PB) of RPL patients and compared them to those of women who had healthy pregnancies. Method of Study PB and MB was obtained from women who suffered from at least three consecutive unexplained RPL (n=18) and aged-matched women without known disorders of reproduction and analyzed using flowcytometry. The proportions of 63 immune cell types defined by deep immune phenotyping were included in the analysis, next to age and CMV status. Results By harnessing the combined value of 8 machine learning classifiers in an ensemble strategy and recursive feature selection, we were able to determine a combination of immune parameters that separated RPL from controls. In peripheral blood, the combination of four cell types (non-switched memory B cells, CD8+CD4-T cells, CD56bright CD16- Natural Killer (NKbright) cells, CD4+ effector T cells) classified samples correctly to their respective cohort. The identified cell types differed from the results observed in MB, where a combination of 6 cell types (Ki67+CD8+ T cells, (HLA-DR+) regulatory T cells, CD27+ B cells, NKbright cells, Treg cells, CD24HiCD38Hi B cells) plus age. Based on the combination of these features, the average area under the curve of a receiver operating characteristics curve and the associated accuracy was >0.8 for both sample sources. Conclusion A combination of immune subsets for cohort classif

OBJECTIVE: To compare the immunologic profiles of peripheral and menstrual blood (MB) of women who experience recurrent pregnancy loss and women without pregnancy complications.DESIGN: Explorative case-control study. Cross-sectional assessment of flow cytometry-derived immunologic profiles.SETTING: Academic medical center.PATIENT(S): Women who experienced more than 2 consecutive miscarriages.INTERVENTION(S): None.MAIN OUTCOME MEASURE(S): Flow cytometry-based immune profiles of uterine and systemic immunity (recurrent pregnancy loss, n = 18; control, n = 14) assessed by machine learning classifiers in an ensemble strategy, followed by recursive feature selection.RESULT(S): In peripheral blood, the combination of 4 cell types (nonswitched memory B cells, CD8+ T cells, CD56bright CD16- natural killer [NKbright] cells, and CD4+ effector T cells) classified samples correctly to their respective cohort. The identified classifying cell types in peripheral blood differed from the results observed in MB, where a combination of 6 cell types (Ki67+CD8+ T cells, (Human leukocyte antigen-DR+) regulatory T cells, CD27+ B cells, NKbright cells, regulatory T cells, and CD24HiCD38Hi B cells) plus age allowed for assigning samples correctly to their respective cohort. Based on the combination of these features, the average area under the curve of a receiver operating characteristic curve and the associated accuracy were >0.8 for both sample sources.CONCLUSION(S): A combination of immune subsets for cohort classification allows for robust identification of immune parameters with possible diagnostic value. The noninvasive source of MB holds several opportunities to assess and monitor reproductive health.

Innate myeloid cell (IMC) populations form an essential part of innate immunity. Flow cytometric (FCM) monitoring of IMCs in peripheral blood (PB) has great clinical potential for disease monitoring due to their role in maintenance of tissue homeostasis and ability to sense micro-environmental changes, such as inflammatory processes and tissue damage. However, the lack of standardized and validated approaches has hampered broad clinical implementation. For accurate identification and separation of IMC populations, 62 antibodies against 44 different proteins were evaluated. In multiple rounds of EuroFlow-based design-testing-evaluation-redesign, finally 16 antibodies were selected for their non-redundancy and separation power. Accordingly, two antibody combinations were designed for fast, sensitive, and reproducible FCM monitoring of IMC populations in PB in clinical settings (11-color; 13 antibodies) and translational research (14-color; 16 antibodies). Performance of pre-analytical and analytical variables among different instruments, together with optimized post-analytical data analysis and reference values were assessed. Overall, 265 blood samples were used for design and validation of the antibody combinations and in vitro functional assays, as well as for assessing the impact of sample preparation procedures and conditions. The two (11- and 14-color) antibody combinations allowed for robust and sensitive detection of 19 and 23 IMC populations, respectively. Highly reproducible identification and enumeration of IMC populations was achieved, independently of anticoagulant, type of FCM instrument and center, particularly when database/software-guided automated (vs. manual “expert-based”) gating was used. Whereas no significant changes were observed in identification of IMC populations for up to 24h delayed sample processing, a significant impact was observed in their absolute counts after >12h delay. Therefore, accurate identification and quantitation of IMC popul

Innate myeloid cell (IMC) populations form an essential part of innate immunity. Flow cytometric (FCM) monitoring of IMCs in peripheral blood (PB) has great clinical potential for disease monitoring due to their role in maintenance of tissue homeostasis and ability to sense micro-environmental changes, such as inflammatory processes and tissue damage. However, the lack of standardized and validated approaches has hampered broad clinical implementation. For accurate identification and separation of IMC populations, 62 antibodies against 44 different proteins were evaluated. In multiple rounds of EuroFlow-based design-testing-evaluation-redesign, finally 16 antibodies were selected for their non-redundancy and separation power. Accordingly, two antibody combinations were designed for fast, sensitive, and reproducible FCM monitoring of IMC populations in PB in clinical settings (11-color; 13 antibodies) and translational research (14-color; 16 antibodies). Performance of pre-analytical and analytical variables among different instruments, together with optimized post-analytical data analysis and reference values were assessed. Overall, 265 blood samples were used for design and validation of the antibody combinations and in vitro functional assays, as well as for assessing the impact of sample preparation procedures and conditions. The two (11- and 14-color) antibody combinations allowed for robust and sensitive detection of 19 and 23 IMC populations, respectively. Highly reproducible identification and enumeration of IMC populations was achieved, independently of anticoagulant, type of FCM instrument and center, particularly when database/software-guided automated (vs. manual “expert-based”) gating was used. Whereas no significant changes were observed in identification of IMC populations for up to 24h delayed sample processing, a significant impact was observed in their absolute counts after >12h delay. Therefore, accurate identification and quantitation of IMC popul

Innate myeloid cell (IMC) populations form an essential part of innate immunity. Flow cytometric (FCM) monitoring of IMCs in peripheral blood (PB) has great clinical potential for disease monitoring due to their role in maintenance of tissue homeostasis and ability to sense micro-environmental changes, such as inflammatory processes and tissue damage. However, the lack of standardized and validated approaches has hampered broad clinical implementation. For accurate identification and separation of IMC populations, 62 antibodies against 44 different proteins were evaluated. In multiple rounds of EuroFlow-based design-testing-evaluation-redesign, finally 16 antibodies were selected for their non-redundancy and separation power. Accordingly, two antibody combinations were designed for fast, sensitive, and reproducible FCM monitoring of IMC populations in PB in clinical settings (11-color; 13 antibodies) and translational research (14-color; 16 antibodies). Performance of pre-analytical and analytical variables among different instruments, together with optimized post-analytical data analysis and reference values were assessed. Overall, 265 blood samples were used for design and validation of the antibody combinations and in vitro functional assays, as well as for assessing the impact of sample preparation procedures and conditions. The two (11- and 14-color) antibody combinations allowed for robust and sensitive detection of 19 and 23 IMC populations, respectively. Highly reproducible identification and enumeration of IMC populations was achieved, independently of anticoagulant, type of FCM instrument and center, particularly when database/software-guided automated (vs. manual “expert-based”) gating was used. Whereas no significant changes were observed in identification of IMC populations for up to 24h delayed sample processing, a significant impact was observed in their absolute counts after >12h delay. Therefore, accurate identification and quantitation of IMC

To compare cell adhesion molecules levels in cerebrospinal fluid (CSF) between Zika virus (ZIKV)-exposed neonates with/without microcephaly (cases) and controls, 16 neonates (cases), 8 (50%) with and 8 (50%) without microcephaly, who underwent lumbar puncture (LP) during the ZIKV epidemic (2015–2016) were included. All mothers reported ZIKV clinical symptoms during gestation, all neonates presented with congenital infection findings, and other congenital infections were ruled out. Fourteen control neonates underwent LP in the same laboratory (2017–2018). Five cell adhesion proteins were measured in the CSF using mass spectrometry. Neurexin-1 (3.50 [2.00–4.00] vs. 7.5 [5.00–10.25], P = 0.001), neurexin-3 (0.00 [0.00–0.00] vs. 3.00 [1.50–4.00], P = 0.001) and neural cell adhesion molecule 2 (NCAM2) (0.00 [0.00–0.75] vs. 1.00 [1.00–2.00], P = 0.001) were significantly lower in microcephalic and non-microcephalic cases than in controls. When these two sub-groups of prenatally ZIKA-exposed children were compared to controls separately, the same results were found. When cases with and without microcephaly were compared, no difference was found. Neurexin-3 (18.8% vs. 78.6%, P = 0.001) and NCAM2 (25.0% vs. 85.7%, P = 0.001) were less frequently found among the cases. A positive correlation was found between cephalic perimeter and levels of these two proteins. Neurexin-2 and neurexin-2b presented no significant differences. Levels of three cell adhesion proteins were significantly lower in CSF of neonates exposed to ZIKV before birth than in controls, irrespective of presence of congenital microcephaly. Moreover, the smaller the cephalic perimeter, the lower CSF cell adhesion protein levels. These findings suggest that low CSF levels of neurexin-1, neurexin-3 and NCAM2 may reflect the effects of ZIKV on foetal brain development.

Background: Different clinical manifestations of invasive pneumococcal disease (IPD) have thus far mainly been explained by patient characteristics. Here we studied the contribution of pneumococcal genetic variation to IPD phenotype.Methods: The index cohort consisted of 349 patients admitted to two Dutch hospitals between 2000-2011 with pneumococcal bacteraemia. We performed genome-wide association studies to identify pneumococcal lineages, genes and allelic variants associated with 23 clinical IPD phenotypes. The identified associations were validated in a nationwide (n=482) and a post-pneumococcal vaccination cohort (n=121). The contribution of confirmed pneumococcal genotypes to the clinical IPD phenotype, relative to known clinical predictors, was tested by regression analysis.Results: Among IPD patients, the presence of pneumococcal gene slaA was a nationwide confirmed independent predictor of meningitis (OR=10.5, p=0.001, 5% presence), as was sequence cluster 9 (predominant serotype 7F, OR=3.68, p=0.057, 11% presence). A set of 4 pneumococcal genes co-located on a prophage was a confirmed independent predictor of 30-day mortality (OR=3.4, p=0.003, 48% presence). We could detect the pneumococcal variants of concern in these patients' blood samples.Conclusions: In this study, knowledge of pneumococcal genotypic variants improved the clinical risk assessment for detrimental manifestations of IPD. This provides us with novel opportunities to target, anticipate or avert the pathogenic effects related to particular pneumococcal variants, and indicates that information on pneumococcal genotype is important for the diagnostic and treatment strategy in IPD. Ongoing surveillance is warranted to monitor the clinical value of information on pneumococcal variants in dynamic microbial and susceptible host populations.