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Article ; Online: In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Fallahzadeh, Ramin / Bidoki, Neda H / Stelzer, Ina A / Becker, Martin / Marić, Ivana / Chang, Alan L / Culos, Anthony / Phongpreecha, Thanaphong / Xenochristou, Maria / De Francesco, Davide / Espinosa, Camilo / Berson, Eloise / Verdonk, Franck / Angst, Martin S / Gaudilliere, Brice / Aghaeepour, Nima

Cytometry. Part A : the journal of the International Society for Analytical Cytology

2022 Volume 103, Issue 5, Page(s) 392–404

Abstract: Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to ... ...

Abstract	Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness.
MeSH term(s)	Humans ; Neural Networks, Computer ; Machine Learning ; Proteomics
Language	English
Publishing date	2022-12-27
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2099868-5
ISSN	1552-4930 ; 0196-4763 ; 1552-4922
ISSN (online)	1552-4930
ISSN	0196-4763 ; 1552-4922
DOI	10.1002/cyto.a.24709
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Article ; Online: Systematic Immunophenotyping Reveals Sex-Specific Responses After Painful Injury in Mice.

Tawfik, Vivianne L / Huck, Nolan A / Baca, Quentin J / Ganio, Edward A / Haight, Elena S / Culos, Anthony / Ghaemi, Sajjad / Phongpreecha, Thanaphong / Angst, Martin S / Clark, J David / Aghaeepour, Nima / Gaudilliere, Brice

Frontiers in immunology

2020 Volume 11, Page(s) 1652

Abstract: Many diseases display unequal prevalence between sexes. The sex-specific immune response to both injury and persistent pain remains underexplored and would inform treatment paradigms. We utilized high-dimensional mass cytometry to perform a comprehensive ...

Abstract	Many diseases display unequal prevalence between sexes. The sex-specific immune response to both injury and persistent pain remains underexplored and would inform treatment paradigms. We utilized high-dimensional mass cytometry to perform a comprehensive analysis of phenotypic and functional immune system differences between male and female mice after orthopedic injury. Multivariate modeling of innate and adaptive immune cell responses after injury using an elastic net algorithm, a regularized regression method, revealed sex-specific divergence at 12 h and 7 days after injury with a stronger immune response to injury in females. At 12 h, females upregulated STAT3 signaling in neutrophils but downregulated STAT1 and STAT6 signals in T regulatory cells, suggesting a lack of engagement of immune suppression pathways by females. Furthermore, at 7 days females upregulated MAPK pathways (p38, ERK, NFkB) in CD4T memory cells, setting up a possible heightened immune memory of painful injury. Taken together, our findings provide the first comprehensive and functional analysis of sex-differences in the immune response to painful injury.
MeSH term(s)	Adaptive Immunity ; Animals ; Behavior, Animal ; CD4-Positive T-Lymphocytes/immunology ; CD4-Positive T-Lymphocytes/metabolism ; Cytokines/metabolism ; Disease Models, Animal ; Female ; Immunity, Innate ; Immunologic Memory ; Immunophenotyping ; Male ; Mice, Inbred C57BL ; Mitogen-Activated Protein Kinases/metabolism ; Neutrophils/immunology ; Neutrophils/metabolism ; Pain/immunology ; Pain/metabolism ; Pain/physiopathology ; Pain Threshold ; Phenotype ; STAT Transcription Factors/metabolism ; Sex Factors ; T-Lymphocytes, Regulatory/immunology ; T-Lymphocytes, Regulatory/metabolism ; Tibial Fractures/immunology ; Tibial Fractures/metabolism ; Tibial Fractures/physiopathology ; Time Factors
Chemical Substances	Cytokines ; STAT Transcription Factors ; Mitogen-Activated Protein Kinases (EC 2.7.11.24)
Language	English
Publishing date	2020-07-29
Publishing country	Switzerland
Document type	Comparative Study ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2020.01652
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Article ; Online: Single-cell peripheral immunoprofiling of Alzheimer's and Parkinson's diseases.

Phongpreecha, Thanaphong / Fernandez, Rosemary / Mrdjen, Dunja / Culos, Anthony / Gajera, Chandresh R / Wawro, Adam M / Stanley, Natalie / Gaudilliere, Brice / Poston, Kathleen L / Aghaeepour, Nima / Montine, Thomas J

Science advances

2020 Volume 6, Issue 48

Abstract: Peripheral blood mononuclear cells (PBMCs) may provide insight into the pathogenesis of Alzheimer's disease (AD) or Parkinson's disease (PD). We investigated PBMC samples from 132 well-characterized research participants using seven canonical immune ... ...

Abstract	Peripheral blood mononuclear cells (PBMCs) may provide insight into the pathogenesis of Alzheimer's disease (AD) or Parkinson's disease (PD). We investigated PBMC samples from 132 well-characterized research participants using seven canonical immune stimulants, mass cytometric identification of 35 PBMC subsets, and single-cell quantification of 15 intracellular signaling markers, followed by machine learning model development to increase predictive power. From these, three main intracellular signaling pathways were identified specifically in PBMC subsets from people with AD versus controls: reduced activation of PLCγ2 across many cell types and stimulations and selectively variable activation of STAT1 and STAT5, depending on stimulant and cell type. Our findings functionally buttress the now multiply-validated observation that a rare coding variant in
MeSH term(s)	Alzheimer Disease/drug therapy ; Biomarkers ; Humans ; Leukocytes, Mononuclear ; Parkinson Disease ; Phospholipase C gamma
Chemical Substances	Biomarkers ; Phospholipase C gamma (EC 3.1.4.3)
Language	English
Publishing date	2020-11-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2810933-8
ISSN	2375-2548 ; 2375-2548
ISSN (online)	2375-2548
ISSN	2375-2548
DOI	10.1126/sciadv.abd5575
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Article ; Online: A data-driven health index for neonatal morbidities.

iScience

2022 Volume 25, Issue 4, Page(s) 104143

Abstract: Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep ... ...

Abstract	Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.
Language	English
Publishing date	2022-03-22
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2022.104143
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Article ; Online: Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Becker, Martin / Nassar, Huda / Espinosa, Camilo / Stelzer, Ina A / Feyaerts, Dorien / Berson, Eloise / Bidoki, Neda H / Chang, Alan L / Saarunya, Geetha / Culos, Anthony / De Francesco, Davide / Fallahzadeh, Ramin / Liu, Qun / Kim, Yeasul / Marić, Ivana / Mataraso, Samson J / Payrovnaziri, Seyedeh Neelufar / Phongpreecha, Thanaphong / Ravindra, Neal G /

Stanley, Natalie / Shome, Sayane / Tan, Yuqi / Thuraiappah, Melan / Xenochristou, Maria / Xue, Lei / Shaw, Gary / Stevenson, David / Angst, Martin S / Gaudilliere, Brice / Aghaeepour, Nima

Nature computational science

2023 Volume 3, Issue 4, Page(s) 346–359

Abstract: Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new ...

Abstract	Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.
Language	English
Publishing date	2023-04-13
Publishing country	United States
Document type	Journal Article
ISSN	2662-8457
ISSN (online)	2662-8457
DOI	10.1038/s43588-023-00429-y
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Article ; Online: Author Correction: Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma.

Ganio, Edward A / Stanley, Natalie / Lindberg-Larsen, Viktoria / Einhaus, Jakob / Tsai, Amy S / Verdonk, Franck / Culos, Anthony / Ghaemi, Sajjad / Rumer, Kristen K / Stelzer, Ina A / Gaudilliere, Dyani / Tsai, Eileen / Fallahzadeh, Ramin / Choisy, Benjamin / Kehlet, Henrik / Aghaeepour, Nima / Angst, Martin S / Gaudilliere, Brice

Nature communications

2020 Volume 11, Issue 1, Page(s) 4495

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

Abstract	An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Language	English
Publishing date	2020-09-03
Publishing country	England
Document type	Journal Article ; Published Erratum
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-020-18410-y
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Article ; Online: Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma.

Nature communications

2020 Volume 11, Issue 1, Page(s) 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 ...

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.
MeSH term(s)	Acute Disease ; Adaptive Immunity/drug effects ; Aged ; Arthroplasty, Replacement, Hip/adverse effects ; Case-Control Studies ; Double-Blind Method ; Fatigue/drug therapy ; Female ; Glucocorticoids/pharmacology ; Humans ; Male ; Methylprednisolone/pharmacology ; Methylprednisolone/therapeutic use ; NF-KappaB Inhibitor alpha/metabolism ; Pain/drug therapy ; Phenotype ; Phosphorylation ; STAT3 Transcription Factor/metabolism ; Treatment Outcome ; Wounds and Injuries/etiology ; Wounds and Injuries/immunology
Chemical Substances	Glucocorticoids ; STAT3 Transcription Factor ; STAT3 protein, human ; NF-KappaB Inhibitor alpha (139874-52-5) ; Methylprednisolone (X4W7ZR7023)
Language	English
Publishing date	2020-07-27
Publishing country	England
Document type	Journal Article ; Randomized Controlled Trial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-020-17565-y
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Article ; Online: Objective Activity Parameters Track Patient-specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States.

Annals of surgery

2021 Volume 277, Issue 3, Page(s) e503–e512

Abstract: Objective: The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical ...

Abstract	Objective: The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established. Background: Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. Although phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity. Methods: Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state. Results: The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, 6 distinct domains of physical function and sleep are identified to represent the objective temporal patterns: ''activity capacity'' and ''moderate and overall activity (declined immediately after surgery); ''sleep disruption and sedentary activity (increased after surgery); ''overall sleep'', ''sleep onset'', and ''light activity'' (no clear changes were observed after surgery). These patterns can be linked to individual patients preopera-tive immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in monocytic myeloid-derived suppressor cells predicted a slower recovery. Conclusions: Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.
MeSH term(s)	Humans ; Benchmarking ; Exercise ; Monocytes ; Physical Examination ; Postoperative Period
Language	English
Publishing date	2021-10-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	340-2
ISSN	1528-1140 ; 0003-4932
ISSN (online)	1528-1140
ISSN	0003-4932
DOI	10.1097/SLA.0000000000005250
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Article ; Online: Proteomic signatures predict preeclampsia in individual cohorts but not across cohorts - implications for clinical biomarker studies.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians

2021 Volume 35, Issue 25, Page(s) 5621–5628

Abstract: Background: Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular ... ...

Abstract	Background: Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. Objective: The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. Study design: Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. Results: The model derived in the Stanford cohort was highly significant ( Conclusions: Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the "same" clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.
MeSH term(s)	Female ; Humans ; Pregnancy ; Proteomics/methods ; Pre-Eclampsia/diagnosis ; Proteome/metabolism ; Biomarkers ; Blood Proteins
Chemical Substances	Proteome ; Biomarkers ; Blood Proteins
Language	English
Publishing date	2021-03-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2077261-0
ISSN	1476-4954 ; 1057-0802 ; 1476-7058
ISSN (online)	1476-4954
ISSN	1057-0802 ; 1476-7058
DOI	10.1080/14767058.2021.1888915
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Article ; Online: Early prediction and longitudinal modeling of preeclampsia from multiomics.

Marić, Ivana / Contrepois, Kévin / Moufarrej, Mira N / Stelzer, Ina A / Feyaerts, Dorien / Han, Xiaoyuan / Tang, Andy / Stanley, Natalie / Wong, Ronald J / Traber, Gavin M / Ellenberger, Mathew / Chang, Alan L / Fallahzadeh, Ramin / Nassar, Huda / Becker, Martin / Xenochristou, Maria / Espinosa, Camilo / De Francesco, Davide / Ghaemi, Mohammad S /

Costello, Elizabeth K / Culos, Anthony / Ling, Xuefeng B / Sylvester, Karl G / Darmstadt, Gary L / Winn, Virginia D / Shaw, Gary M / Relman, David A / Quake, Stephen R / Angst, Martin S / Snyder, Michael P / Stevenson, David K / Gaudilliere, Brice / Aghaeepour, Nima

Patterns (New York, N.Y.)

2022 Volume 3, Issue 12, Page(s) 100655

Abstract: Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a ... ...

Abstract	Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.
Language	English
Publishing date	2022-12-09
Publishing country	United States
Document type	Journal Article
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2022.100655
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