Institute Affiliations


  • Member, Maternal & Child Health Research Institute (MCHRI)

Professional Education


  • Doctor of Philosophy, Universitat Hamburg (2016)
  • Master of Science, Humboldt Universitat Berlin (2011)
  • Bachelor of Science, Maastricht University (2009)

All Publications


  • Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset. Science translational medicine Stelzer, I. A., Ghaemi, M. S., Han, X., Ando, K., Hedou, J. J., Feyaerts, D., Peterson, L. S., Rumer, K. K., Tsai, E. S., Ganio, E. A., Gaudilliere, D. K., Tsai, A. S., Choisy, B., Gaigne, L. P., Verdonk, F., Jacobsen, D., Gavasso, S., Traber, G. M., Ellenberger, M., Stanley, N., Becker, M., Culos, A., Fallahzadeh, R., Wong, R. J., Darmstadt, G. L., Druzin, M. L., Winn, V. D., Gibbs, R. S., Ling, X. B., Sylvester, K., Carvalho, B., Snyder, M. P., Shaw, G. M., Stevenson, D. K., Contrepois, K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. 2021; 13 (592)

    Abstract

    Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 * 10-40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 * 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

    View details for DOI 10.1126/scitranslmed.abd9898

    View details for PubMedID 33952678

  • Data-Driven Modeling of Pregnancy-Related Complications. Trends in molecular medicine Espinosa, C. n., Becker, M. n., Marić, I. n., Wong, R. J., Shaw, G. M., Gaudilliere, B. n., Aghaeepour, N. n., Stevenson, D. K. 2021

    Abstract

    A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.

    View details for DOI 10.1016/j.molmed.2021.01.007

    View details for PubMedID 33573911

  • Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions NATURE MACHINE INTELLIGENCE Culos, A., Tsai, A. S., Stanley, N., Becker, M., Ghaemi, M. S., McIlwain, D. R., Fallahzadeh, R., Tanada, A., Nassar, H., Espinosa, C., Xenochristou, M., Ganio, E., Peterson, L., Han, X., Stelzer, I. A., Ando, K., Gaudilliere, D., Phongpreecha, T., Maric, I., Chang, A. L., Shaw, G. M., Stevenson, D. K., Bendall, S., Davis, K. L., Fantl, W., Nolan, G. P., Hastie, T., Tibshirani, R., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020
  • Multi-Omic, Longitudinal Profile of Third-Trimester Pregnancies Identifies a Molecular Switch That Predicts the Onset of Labor. Stelzer, I., Ghaemi, M., Han, X., Ando, K., Peterson, L., Contrepois, K., Ganio, E., Tsai, A., Tsai, E., Rumer, K., Stanley, N., Fallazadeh, R., Becker, M., Culos, A., Gaudilliere, D., Wong, R., Winn, V., Shaw, G., Stevenson, D., Snyder, M., Angst, M., Aghaeepour, N., Gaudilliere, B. SPRINGER HEIDELBERG. 2020: 89A
  • Maternal Microchimeric Cells Modulate Immune Development in Mice by Skewing Hematopoiesis Toward Myeloid Cell Lineages. Urbschat, C., Schepanski, S., Solano, E. M., Stelzer, I. A., Fischer, N., Alawi, M., Thiele, K., Arck, P. SPRINGER HEIDELBERG. 2020: 316A
  • Maternal Microchimeric Cells are Linked to Early Life Immunity In Children. Urbschat, C., Schepanski, S., Thiele, K., Wieczorek, A., Stelzer, I. A., Fehse, B., Diemert, A., Arck, P. C. SPRINGER HEIDELBERG. 2020: 148A
  • The Vertical Transfer of Maternal Immune Cells During Pregnancy Promotes Neonatal Immunity Against Viral Infections. Stelzer, I., Urbschat, C., Thiele, K., Triviai, I., Stahl, F., Solano, M., Arck, P. SPRINGER HEIDELBERG. 2020: 151A
  • Multiomic immune clockworks of pregnancy. Seminars in immunopathology Peterson, L. S., Stelzer, I. A., Tsai, A. S., Ghaemi, M. S., Han, X. n., Ando, K. n., Winn, V. D., Martinez, N. R., Contrepois, K. n., Moufarrej, M. N., Quake, S. n., Relman, D. A., Snyder, M. P., Shaw, G. M., Stevenson, D. K., Wong, R. J., Arck, P. n., Angst, M. S., Aghaeepour, N. n., Gaudilliere, B. n. 2020

    Abstract

    Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy "immune clock" is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth.

    View details for DOI 10.1007/s00281-019-00772-1

    View details for PubMedID 32020337

  • Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries. JAMA network open Jehan, F. n., Sazawal, S. n., Baqui, A. H., Nisar, M. I., Dhingra, U. n., Khanam, R. n., Ilyas, M. n., Dutta, A. n., Mitra, D. K., Mehmood, U. n., Deb, S. n., Mahmud, A. n., Hotwani, A. n., Ali, S. M., Rahman, S. n., Nizar, A. n., Ame, S. M., Moin, M. I., Muhammad, S. n., Chauhan, A. n., Begum, N. n., Khan, W. n., Das, S. n., Ahmed, S. n., Hasan, T. n., Khalid, J. n., Rizvi, S. J., Juma, M. H., Chowdhury, N. H., Kabir, F. n., Aftab, F. n., Quaiyum, A. n., Manu, A. n., Yoshida, S. n., Bahl, R. n., Rahman, A. n., Pervin, J. n., Winston, J. n., Musonda, P. n., Stringer, J. S., Litch, J. A., Ghaemi, M. S., Moufarrej, M. N., Contrepois, K. n., Chen, S. n., Stelzer, I. A., Stanley, N. n., Chang, A. L., Hammad, G. B., Wong, R. J., Liu, C. n., Quaintance, C. C., Culos, A. n., Espinosa, C. n., Xenochristou, M. n., Becker, M. n., Fallahzadeh, R. n., Ganio, E. n., Tsai, A. S., Gaudilliere, D. n., Tsai, E. S., Han, X. n., Ando, K. n., Tingle, M. n., Maric, I. n., Wise, P. H., Winn, V. D., Druzin, M. L., Gibbs, R. S., Darmstadt, G. L., Murray, J. C., Shaw, G. M., Stevenson, D. K., Snyder, M. P., Quake, S. R., Angst, M. S., Gaudilliere, B. n., Aghaeepour, N. n. 2020; 3 (12): e2029655

    Abstract

    Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies.To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB.This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019.Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites.The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation.Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways.This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

    View details for DOI 10.1001/jamanetworkopen.2020.29655

    View details for PubMedID 33337494

  • Author Correction: Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nature communications Ganio, E. A., Stanley, N. n., Lindberg-Larsen, V. n., Einhaus, J. n., Tsai, A. S., Verdonk, F. n., Culos, A. n., Ghaemi, S. n., Rumer, K. K., Stelzer, I. A., Gaudilliere, D. n., Tsai, E. n., Fallahzadeh, R. n., Choisy, B. n., Kehlet, H. n., Aghaeepour, N. n., Angst, M. S., Gaudilliere, B. n. 2020; 11 (1): 4495

    Abstract

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

    View details for DOI 10.1038/s41467-020-18410-y

    View details for PubMedID 32883978

  • Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nature communications Ganio, E. A., Stanley, N. n., Lindberg-Larsen, V. n., Einhaus, J. n., Tsai, A. S., Verdonk, F. n., Culos, A. n., Gahemi, S. n., Rumer, K. K., Stelzer, I. A., Gaudilliere, D. n., Tsai, E. n., Fallahzadeh, R. n., Choisy, B. n., Kehlet, H. n., Aghaeepour, N. n., Angst, M. S., Gaudilliere, B. n. 2020; 11 (1): 3737

    Abstract

    Glucocorticoids (GC) are a controversial yet commonly used intervention in the clinical management of acute inflammatory conditions, including sepsis or traumatic injury. In the context of major trauma such as surgery, concerns have been raised regarding adverse effects from GC, thereby necessitating a better understanding of how GCs modulate the immune response. Here we report the results of a randomized controlled trial (NCT02542592) in which we employ a high-dimensional mass cytometry approach to characterize innate and adaptive cell signaling dynamics after a major surgery (primary outcome) in patients treated with placebo or methylprednisolone (MP). A robust, unsupervised bootstrap clustering of immune cell subsets coupled with random forest analysis shows profound (AUC = 0.92, p-value = 3.16E-8) MP-induced alterations of immune cell signaling trajectories, particularly in the adaptive compartments. By contrast, key innate signaling responses previously associated with pain and functional recovery after surgery, including STAT3 and CREB phosphorylation, are not affected by MP. These results imply cell-specific and pathway-specific effects of GCs, and also prompt future studies to examine GCs' effects on clinical outcomes likely dependent on functional adaptive immune responses.

    View details for DOI 10.1038/s41467-020-17565-y

    View details for PubMedID 32719355

  • VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nature communications Stanley, N. n., Stelzer, I. A., Tsai, A. S., Fallahzadeh, R. n., Ganio, E. n., Becker, M. n., Phongpreecha, T. n., Nassar, H. n., Ghaemi, S. n., Maric, I. n., Culos, A. n., Chang, A. L., Xenochristou, M. n., Han, X. n., Espinosa, C. n., Rumer, K. n., Peterson, L. n., Verdonk, F. n., Gaudilliere, D. n., Tsai, E. n., Feyaerts, D. n., Einhaus, J. n., Ando, K. n., Wong, R. J., Obermoser, G. n., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B. n., Aghaeepour, N. n. 2020; 11 (1): 3738

    Abstract

    High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

    View details for DOI 10.1038/s41467-020-17569-8

    View details for PubMedID 32719375

  • Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. Nature machine intelligence Culos, A. n., Tsai, A. S., Stanley, N. n., Becker, M. n., Ghaemi, M. S., McIlwain, D. R., Fallahzadeh, R. n., Tanada, A. n., Nassar, H. n., Espinosa, C. n., Xenochristou, M. n., Ganio, E. n., Peterson, L. n., Han, X. n., Stelzer, I. A., Ando, K. n., Gaudilliere, D. n., Phongpreecha, T. n., Marić, I. n., Chang, A. L., Shaw, G. M., Stevenson, D. K., Bendall, S. n., Davis, K. L., Fantl, W. n., Nolan, G. P., Hastie, T. n., Tibshirani, R. n., Angst, M. S., Gaudilliere, B. n., Aghaeepour, N. n. 2020; 2 (10): 619–28

    Abstract

    The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

    View details for DOI 10.1038/s42256-020-00232-8

    View details for PubMedID 33294774

    View details for PubMedCentralID PMC7720904

  • Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia. Frontiers in immunology Han, X. n., Ghaemi, M. S., Ando, K. n., Peterson, L. S., Ganio, E. A., Tsai, A. S., Gaudilliere, D. K., Stelzer, I. A., Einhaus, J. n., Bertrand, B. n., Stanley, N. n., Culos, A. n., Tanada, A. n., Hedou, J. n., Tsai, E. S., Fallahzadeh, R. n., Wong, R. J., Judy, A. E., Winn, V. D., Druzin, M. L., Blumenfeld, Y. J., Hlatky, M. A., Quaintance, C. C., Gibbs, R. S., Carvalho, B. n., Shaw, G. M., Stevenson, D. K., Angst, M. S., Aghaeepour, N. n., Gaudilliere, B. n. 2019; 10: 1305

    Abstract

    Preeclampsia is one of the most severe pregnancy complications and a leading cause of maternal death. However, early diagnosis of preeclampsia remains a clinical challenge. Alterations in the normal immune adaptations necessary for the maintenance of a healthy pregnancy are central features of preeclampsia. However, prior analyses primarily focused on the static assessment of select immune cell subsets have provided limited information for the prediction of preeclampsia. Here, we used a high-dimensional mass cytometry immunoassay to characterize the dynamic changes of over 370 immune cell features (including cell distribution and functional responses) in maternal blood during healthy and preeclamptic pregnancies. We found a set of eight cell-specific immune features that accurately identified patients well before the clinical diagnosis of preeclampsia (median area under the curve (AUC) 0.91, interquartile range [0.82-0.92]). Several features recapitulated previously known immune dysfunctions in preeclampsia, such as elevated pro-inflammatory innate immune responses early in pregnancy and impaired regulatory T (Treg) cell signaling. The analysis revealed additional novel immune responses that were strongly associated with, and preceded the onset of preeclampsia, notably abnormal STAT5ab signaling dynamics in CD4+T cell subsets (AUC 0.92, p = 8.0E-5). These results provide a global readout of the dynamics of the maternal immune system early in pregnancy and lay the groundwork for identifying clinically-relevant immune dysfunctions for the prediction and prevention of preeclampsia.

    View details for DOI 10.3389/fimmu.2019.01305

    View details for PubMedID 31263463

    View details for PubMedCentralID PMC6584811

  • Reply: Breastfeeding-related maternal microchimerism NATURE REVIEWS IMMUNOLOGY Kinder, J. M., Stelzer, I. A., Arck, P. C., Way, S. 2017; 17 (11): 730

    View details for DOI 10.1038/nri.2017.117

    View details for Web of Science ID 000413976600012

    View details for PubMedID 28972207

  • Immunological implications of pregnancy-induced microchimerism NATURE REVIEWS IMMUNOLOGY Kinder, J. M., Stelzer, I. A., Arck, P. C., Way, S. 2017; 17 (8): 483–94

    Abstract

    Immunological identity is traditionally defined by genetically encoded antigens, with equal maternal and paternal contributions as a result of Mendelian inheritance. However, vertically transferred maternal cells also persist in individuals at very low levels throughout postnatal development. Reciprocally, mothers are seeded during pregnancy with genetically foreign fetal cells that persist long after parturition. Recent findings suggest that these microchimeric cells expressing non-inherited, familially relevant antigenic traits are not accidental 'souvenirs' of pregnancy, but are purposefully retained within mothers and their offspring to promote genetic fitness by improving the outcome of future pregnancies. In this Review, we discuss the immunological implications, benefits and potential consequences of individuals being constitutively chimeric with a biologically active 'microchiome' of genetically foreign cells.

    View details for DOI 10.1038/nri.2017.38

    View details for Web of Science ID 000406426200008

    View details for PubMedID 28480895

    View details for PubMedCentralID PMC5532073

  • Maternal microchimeric CD3+T cells promote fetal hematopoiesis in fetal bone marrow in mice Stelzer, I. A., Triviai, L., Solano, M. E., Arck, P. C. ELSEVIER IRELAND LTD. 2016: 81–82
  • Junctional Adhesion Molecule (JAM)-B protein is modulated by progesterone signaling and differentially expressed in mouse placentae of different mating combinations Stelzer, I. A., Mori, M., DeMayo, F., Lydon, J., Arck, P. C., Solano, M. E. ELSEVIER IRELAND LTD. 2016: 81
  • Immunity and the Endocrine System ENCYCLOPEDIA OF IMMUNOBIOLOGY, VOL 5: PHYSIOLOGY AND IMMUNE SYSTEM DYSFUNCTION Stelzer, I., Arck, P., Ratcliffe, M. J., Cavazzana, M., Cooke, A. 2016: 73–85
  • Differential mouse-strain specific expression of Junctional Adhesion Molecule (JAM)-B in placental structures CELL ADHESION & MIGRATION Stelzer, I., Mori, M., DeMayo, F., Lydon, J., Arck, P., Solano, M. 2016; 10 (1-2): 2–17

    Abstract

    The junctional adhesion molecule (JAM)-B, a member of the immunoglobulin superfamily, is involved in stabilization of interendothelial cell-cell contacts, formation of vascular tubes, homeostasis of stem cell niches and promotion of leukocyte adhesion and transmigration. In the human placenta, JAM-B protein is abundant and mRNA transcripts are enriched in first-trimester extravillous trophoblast in comparison to the villous trophoblast. We here aimed to elucidate the yet unexplored spatio-temporal expression of JAM-B in the mouse placenta. We investigated and semi-quantified JAM-B protein expression by immunohistochemistry in early post-implantation si tes and in mid- to late gestation placentae of various murine mating combinations. Surprisingly, the endothelium of the placental labyrinth was devoid of JAM-B expression. JAM-B was mainly present in spongiotrophoblast cells of the junctional zone, as well as in the fetal vessels of the chorionic plate, the umbilical cord and in maternal myometrial smooth muscle. We observed a strain-specific placental increase of JAM-B protein expression from mid- to late gestation in Balb/c-mated C57BL/6 females, which was absent in DBA/2J-mated Balb/c females. Due to the essential role of progesterone during gestation, we further assessed a possible modulation of JAM-B in mid-gestational placentae deficient in the progesterone receptor (Pgr(-/-)) and observed an increased expression of JAM-B in Pgr(-/-) placentae, compared to Pgr(+/+) tissue samples. We propose that JAM-B is an as yet underappreciated trophoblast lineage-specific protein, which is modulated via the progesterone receptor and shows unique strain-specific kinetics. Future work is needed to elucidate its possible contribution to placental processes necessary to ensuring its integrity, ultimately facilitating placental development and fetal growth.

    View details for DOI 10.1080/19336918.2015.1118605

    View details for Web of Science ID 000374999400002

    View details for PubMedID 26914234

    View details for PubMedCentralID PMC4853043

  • Maternal microchimerism: lessons learned from murine models Stelzer, I., Thiele, K., Solano, M. ELSEVIER IRELAND LTD. 2015: 12–25

    Abstract

    The presence of maternal cells in the organs of the offspring is referred to as maternal microchimerism (MMc). MMc is physiologically acquired during pregnancy and lactation and can persist until adulthood. The detection of MMc in a variety of human diseases has raised interest in the short- and long-term functional consequences for the offspring. Owing to limited availability and access to human tissue, mouse models have become an essential tool in elucidating the functional role of MMc. This review compiles the detection techniques and experimental settings used in murine MMc research. It aims to summarize the potential mechanisms of migration of MMc, pre- and postnatal tissue distribution, phenotype and concatenated function, as well as factors modulating its occurrence. In this context, we propose MMc to be a materno-fetal messenger with the capacity to critically shape the development of the offspring's immunity.

    View details for DOI 10.1016/j.jri.2014.12.007

    View details for Web of Science ID 000353602500004

    View details for PubMedID 25638482

  • Fetal origin of immune diseases: prenatal stress challenge modulates the phenotype of maternal microchimerism in murine pregnancies Stelzer, I., O'Rourke, G., Mittruecker, H., Solano, M., Arck, P. ELSEVIER IRELAND LTD. 2014: 30
  • Advancing the detection of maternal haematopoietic microchimeric cells in fetal immune organs in mice by flow cytometry. Chimerism Solano, M. E., Thiele, K., Stelzer, I. A., Mittrucker, H., Arck, P. C. 2014; 5 (3-4): 99–102

    Abstract

    Maternal microchimerism, which occurs naturally during gestation in hemochorial placental mammals upon transplacental migration of maternal cells into the fetus, is suggested to significantly influence the fetal immune system. In our previous publication, we explored the sensitivity of quantitative polymerase chain reaction and flow cytometry to detect cellular microchimerism. With that purpose, we created mixed cells suspensions in vitro containing reciprocal frequencies of wild type cells and cells positive for enhanced green fluorescent protein or CD45.1(+), respectively. Here, we now introduce the H-2 complex, which defines the major histocompatibility complex in mice and is homologous to HLA in human, as an additional target to detect maternal microchimerism among fetal haploidentical cells. We envision that this advanced approach to detect maternal microchimeric cells by flow cytometry facilitates the pursuit of phenotypic, gene expression and functional analysis of microchimeric cells in future studies.

    View details for DOI 10.4161/19381956.2014.959827

    View details for PubMedID 25483743