Professional Education

  • Doctor of Philosophy, University of North Carolina, Chapel Hill (2018)
  • Bachelor of Science, Dickinson College (2013)


All Publications

  • Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia FRONTIERS IN IMMUNOLOGY Han, X., Ghaemi, M. S., Ando, K., Peterson, L. S., Ganio, E. A., Tsai, A. S., Gaudilliere, D. K., Stelzer, I. A., Einhaus, J., Bertrand, B., Stanley, N., Culos, A., Tanada, A., Hedou, J., Tsai, E. S., Fallahzadeh, R., Wong, R. J., Judy, A. E., Winn, V. D., Druzins, M. L., Blumenfeld, Y. J., Hlatky, M. A., Quaintance, C. C., Gibbs, R. S., Carvalho, B., Shaw, G. M., Stevenson, D. K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. 2019; 10
  • A year-long immune profile of the systemic response in acute stroke survivors. Brain : a journal of neurology Tsai, A. S., Berry, K., Beneyto, M. M., Gaudilliere, D., Ganio, E. A., Culos, A., Ghaemi, M. S., Choisy, B., Djebali, K., Einhaus, J. F., Bertrand, B., Tanada, A., Stanley, N., Fallahzadeh, R., Baca, Q., Quach, L. N., Osborn, E., Drag, L., Lansberg, M. G., Angst, M. S., Gaudilliere, B., Buckwalter, M. S., Aghaeepour, N. 2019


    Stroke is a leading cause of cognitive impairment and dementia, but the mechanisms that underlie post-stroke cognitive decline are not well understood. Stroke produces profound local and systemic immune responses that engage all major innate and adaptive immune compartments. However, whether the systemic immune response to stroke contributes to long-term disability remains ill-defined. We used a single-cell mass cytometry approach to comprehensively and functionally characterize the systemic immune response to stroke in longitudinal blood samples from 24 patients over the course of 1 year and correlated the immune response with changes in cognitive functioning between 90 and 365 days post-stroke. Using elastic net regularized regression modelling, we identified key elements of a robust and prolonged systemic immune response to ischaemic stroke that occurs in three phases: an acute phase (Day 2) characterized by increased signal transducer and activator of transcription 3 (STAT3) signalling responses in innate immune cell types, an intermediate phase (Day 5) characterized by increased cAMP response element-binding protein (CREB) signalling responses in adaptive immune cell types, and a late phase (Day 90) by persistent elevation of neutrophils, and immunoglobulin M+ (IgM+) B cells. By Day 365 there was no detectable difference between these samples and those from an age- and gender-matched patient cohort without stroke. When regressed against the change in the Montreal Cognitive Assessment scores between Days 90 and 365 after stroke, the acute inflammatory phase Elastic Net model correlated with post-stroke cognitive trajectories (r = -0.692, Bonferroni-corrected P = 0.039). The results demonstrate the utility of a deep immune profiling approach with mass cytometry for the identification of clinically relevant immune correlates of long-term cognitive trajectories.

    View details for DOI 10.1093/brain/awz022

    View details for PubMedID 30860258

  • Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia. Han, X., Ghaemi, M. S., Ando, K., Peterson, L., Ganio, E. A., Tsai, A. S., Gaudilliere, D., Einhaus, J., Tsai, E. S., Stanley, N. M., Culos, A., Taneda, A. H., Fallahzadeh, R., Wong, R. J., Winn, V. D., Stevenson, D. K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. SAGE PUBLICATIONS INC. 2019: 271A
  • Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics (Oxford, England) Ghaemi, M. S., DiGiulio, D. B., Contrepois, K., Callahan, B., Ngo, T. T., Lee-McMullen, B., Lehallier, B., Robaczewska, A., Mcilwain, D., Rosenberg-Hasson, Y., Wong, R. J., Quaintance, C., Culos, A., Stanley, N., Tanada, A., Tsai, A., Gaudilliere, D., Ganio, E., Han, X., Ando, K., McNeil, L., Tingle, M., Wise, P., Maric, I., Sirota, M., Wyss-Coray, T., Winn, V. D., Druzin, M. L., Gibbs, R., Darmstadt, G. L., Lewis, D. B., Partovi Nia, V., Agard, B., Tibshirani, R., Nolan, G., Snyder, M. P., Relman, D. A., Quake, S. R., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2019; 35 (1): 95–103


    Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.Results: We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementation: Datasets and scripts for reproduction of results are available through: information: Supplementary data are available at Bioinformatics online.

    View details for PubMedID 30561547

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


    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

  • Compressing Networks with Super Nodes. Scientific reports Stanley, N., Kwitt, R., Niethammer, M., Mucha, P. J. 2018; 8 (1): 10892


    Community detection is a commonly used technique for identifying groups in a network based on similarities in connectivity patterns. To facilitate community detection in large networks, we recast the network as a smaller network of 'super nodes', where each super node comprises one or more nodes of the original network. We can then use this super node representation as the input into standard community detection algorithms. To define the seeds, or centers, of our super nodes, we apply the 'CoreHD' ranking, a technique applied in network dismantling and decycling problems. We test our approach through the analysis of two common methods for community detection: modularity maximization with the Louvain algorithm and maximum likelihood optimization for fitting a stochastic block model. Our results highlight that applying community detection to the compressed network of super nodes is significantly faster while successfully producing partitions that are more aligned with the local network connectivity and more stable across multiple (stochastic) runs within and between community detection algorithms, yet still overlap well with the results obtained using the full network.

    View details for DOI 10.1038/s41598-018-29174-3

    View details for PubMedID 30022035

  • Cezanne/OTUD7B is a cell cycle-regulated deubiquitinase that antagonizes the degradation of APC/C substrates. The EMBO journal Bonacci, T., Suzuki, A., Grant, G. D., Stanley, N., Cook, J. G., Brown, N. G., Emanuele, M. J. 2018


    The anaphase-promoting complex/cyclosome (APC/C) is an E3 ubiquitin ligase and key regulator of cell cycle progression. Since APC/C promotes the degradation of mitotic cyclins, it controls cell cycle-dependent oscillations in cyclin-dependent kinase (CDK) activity. Both CDKs and APC/C control a large number of substrates and are regulated by analogous mechanisms, including cofactor-dependent activation. However, whereas substrate dephosphorylation is known to counteract CDK, it remains largely unknown whether deubiquitinating enzymes (DUBs) antagonize APC/C substrate ubiquitination during mitosis. Here, we demonstrate that Cezanne/OTUD7B is a cell cycle-regulated DUB that opposes the ubiquitination of APC/C targets. Cezanne is remarkably specific for K11-linked ubiquitin chains, which are formed by APC/C in mitosis. Accordingly, Cezanne binds established APC/C substrates and reverses their APC/C-mediated ubiquitination. Cezanne depletion accelerates APC/C substrate degradation and causes errors in mitotic progression and formation of micronuclei. These data highlight the importance of tempered APC/C substrate destruction in maintaining chromosome stability. Furthermore, Cezanne is recurrently amplified and overexpressed in numerous malignancies, suggesting a potential role in genome maintenance and cancer cell proliferation.

    View details for DOI 10.15252/embj.201798701

    View details for PubMedID 29973362

  • Testing Alignment of Node Attributes with Network Structure through Label Propagation Stanley, N., Niethammer, M., Mucha, P. 2018
  • Identifying Security Critical Properties for the Dynamic Verification of a Processor Zhang, R., Stanley, N., Griggs, C., Chi, A., Sturton, C. ASSOC COMPUTING MACHINERY. 2017: 541–54
  • Enhanced Detectability of Community Structure in Multilayer Networks through Layer Aggregation PHYSICAL REVIEW LETTERS Taylor, D., Shai, S., Stanley, N., Mucha, P. J. 2016; 116 (22): 228301


    Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different, but potentially related, types of interactions, and it is important to understand limitations on the detectability of community structure in these networks. Using random matrix theory, we analyze detectability limitations for multilayer (specifically, multiplex) stochastic block models (SBMs) in which L layers are derived from a common SBM. We study the effect of layer aggregation on detectability for several aggregation methods, including summation of the layers' adjacency matrices for which we show the detectability limit vanishes as O(L^{-1/2}) with increasing number of layers, L. Importantly, we find a similar scaling behavior when the summation is thresholded at an optimal value, providing insight into the common-but not well understood-practice of thresholding pairwise-interaction data to obtain sparse network representations.

    View details for DOI 10.1103/PhysRevLett.116.228301

    View details for Web of Science ID 000377018100018

    View details for PubMedID 27314740

    View details for PubMedCentralID PMC5125641

  • Clustering Network Layers with the Strata Multilayer Stochastic Block Model IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING Stanley, N., Shai, S., Taylor, D., Mucha, P. J. 2016; 3 (2): 95–105


    Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisely extract information from a multilayer network, we propose to identify and combine sets of layers with meaningful similarities in community structure. In this paper, we describe the "strata multilayer stochastic block model" (sMLSBM), a probabilistic model for multilayer community structure. The central extension of the model is that there exist groups of layers, called "strata", which are defined such that all layers in a given stratum have community structure described by a common stochastic block model (SBM). That is, layers in a stratum exhibit similar node-to-community assignments and SBM probability parameters. Fitting the sMLSBM to a multilayer network provides a joint clustering that yields node-to-community and layer-to-stratum assignments, which cooperatively aid one another during inference. We describe an algorithm for separating layers into their appropriate strata and an inference technique for estimating the SBM parameters for each stratum. We demonstrate our method using synthetic networks and a multilayer network inferred from data collected in the Human Microbiome Project.

    View details for DOI 10.1109/TNSE.2016.2537545

    View details for Web of Science ID 000409671000002

    View details for PubMedID 28435844

    View details for PubMedCentralID PMC5400296