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Verdonk F, Cambriel A, Hedou J, Ganio E, Bellan G, Gaudilliere D, Einhaus J, Sabayev M, Stelzer IA, Feyaerts D, Bonham AT, Ando K, Choisy B, Drover D, Heifets B, Chretien F, Aghaeepour N, Angst MS, Molliex S, Sharshar T, Gaillard R, Gaudilliere B. An immune signature of postoperative cognitive decline in elderly patients. bioRxiv 2024:2024.03.02.582845. [PMID: 38496400 PMCID: PMC10942349 DOI: 10.1101/2024.03.02.582845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Postoperative cognitive decline (POCD) is the predominant complication affecting elderly patients following major surgery, yet its prediction and prevention remain challenging. Understanding biological processes underlying the pathogenesis of POCD is essential for identifying mechanistic biomarkers to advance diagnostics and therapeutics. This longitudinal study involving 26 elderly patients undergoing orthopedic surgery aimed to characterize the impact of peripheral immune cell responses to surgical trauma on POCD. Trajectory analyses of single-cell mass cytometry data highlighted early JAK/STAT signaling exacerbation and diminished MyD88 signaling post-surgery in patients who developed POCD. Further analyses integrating single-cell and plasma proteomic data collected before surgery with clinical variables yielded a sparse predictive model that accurately identified patients who would develop POCD (AUC = 0.80). The resulting POCD immune signature included one plasma protein and ten immune cell features, offering a concise list of biomarker candidates for developing point-of-care prognostic tests to personalize perioperative management of at-risk patients. The code and the data are documented and available at https://github.com/gregbellan/POCD . Teaser Modeling immune cell responses and plasma proteomic data predicts postoperative cognitive decline.
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Gaudilliere D, Gaudilliere B. Harnessing the n+1 dimensions of single-cell omics data for the prediction and prevention of human diseases. Semin Immunopathol 2023; 45:1-2. [PMID: 36853420 PMCID: PMC10047610 DOI: 10.1007/s00281-023-00985-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Affiliation(s)
- Dyani Gaudilliere
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA.
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Rumer KK, Hedou J, Tsai A, Einhaus J, Verdonk F, Stanley N, Choisy B, Ganio E, Bonham A, Jacobsen D, Warrington B, Gao X, Tingle M, McAllister TN, Fallahzadeh R, Feyaerts D, Stelzer I, Gaudilliere D, Ando K, Shelton A, Morris A, Kebebew E, Aghaeepour N, Kin C, Angst MS, Gaudilliere B. Integrated Single-cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study. Ann Surg 2022; 275:582-590. [PMID: 34954754 PMCID: PMC8816871 DOI: 10.1097/sla.0000000000005348] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery. SUMMARY BACKGROUND DATA SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs. METHODS Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery. RESULTS A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82). CONCLUSIONS The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.
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Affiliation(s)
- Kristen K. Rumer
- Division of General Surgery, Department of Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Julien Hedou
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Amy Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Jakob Einhaus
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University of Tuebingen, Tuebingen, Germany
| | - Franck Verdonk
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
- Sorbonne University, GRC 29, DMU DREAM, Assistance Publique-Hôpitaux de Paris, France
| | - Natalie Stanley
- Department of Computer Science and Computational Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Benjamin Choisy
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Edward Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Adam Bonham
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Danielle Jacobsen
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Beata Warrington
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Xiaoxiao Gao
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Martha Tingle
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Tiffany N. McAllister
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Ina Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Dyani Gaudilliere
- Division of Plastic and Reconstructive Surgery, Department of Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Kazuo Ando
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Andrew Shelton
- Division of General Surgery, Department of Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Arden Morris
- Division of General Surgery, Department of Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Electron Kebebew
- Division of General Surgery, Department of Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA
- Department of Pediatrics, Stanford University, Stanford, CA
| | - Cindy Kin
- Division of General Surgery, Department of Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Martin S. Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
- Department of Pediatrics, Stanford University, Stanford, CA
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Verdonk F, Einhaus J, Tsai AS, Hedou J, Choisy B, Gaudilliere D, Kin C, Aghaeepour N, Angst MS, Gaudilliere B. Measuring the human immune response to surgery: multiomics for the prediction of postoperative outcomes. Curr Opin Crit Care 2021; 27:717-725. [PMID: 34545029 PMCID: PMC8585713 DOI: 10.1097/mcc.0000000000000883] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE OF REVIEW Postoperative complications including infections, cognitive impairment, and protracted recovery occur in one-third of the 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on our healthcare system. However, the accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain as major clinical challenges. RECENT FINDINGS Although multifactorial in origin, the dysregulation of immunological mechanisms that occur in response to surgical trauma is a key determinant of postoperative complications. Prior research, primarily focusing on inflammatory plasma markers, has provided important clues regarding their pathogenesis. However, the recent advent of high-content, single-cell transcriptomic, and proteomic technologies has considerably improved our ability to characterize the immune response to surgery, thereby providing new means to understand the immunological basis of postoperative complications and to identify prognostic biological signatures. SUMMARY The comprehensive and single-cell characterization of the human immune response to surgery has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers, ultimately providing patients and surgeons with actionable information to improve surgical outcomes. Although recent studies have generated a wealth of knowledge, laying the foundation for a single-cell atlas of the human immune response to surgery, larger-scale multiomic studies are required to derive robust, scalable, and sufficiently powerful models to accurately predict the risk of postoperative complications in individual patients.
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Affiliation(s)
- Franck Verdonk
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine
| | - Jakob Einhaus
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine
| | - Amy S Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine
| | - Julien Hedou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine
| | - Benjamin Choisy
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine
| | | | - Cindy Kin
- Department of Surgery, Stanford University School of Medicine
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine
- Department of Biomedical Data Science, Stanford University
- Department of Pediatrics, Stanford University, Stanford, California, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine
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Spatz JM, Fulford MH, Tsai A, Gaudilliere D, Hedou J, Ganio E, Angst M, Aghaeepour N, Gaudilliere B. Human immune system adaptations to simulated microgravity revealed by single-cell mass cytometry. Sci Rep 2021; 11:11872. [PMID: 34099760 PMCID: PMC8184772 DOI: 10.1038/s41598-021-90458-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 05/11/2021] [Indexed: 12/19/2022] Open
Abstract
Exposure to microgravity (µG) during space flights produces a state of immunosuppression, leading to increased viral shedding, which could interfere with long term missions. However, the cellular mechanisms that underlie the immunosuppressive effects of µG are ill-defined. A deep understanding of human immune adaptations to µG is a necessary first step to design data-driven interventions aimed at preserving astronauts' immune defense during short- and long-term spaceflights. We employed a high-dimensional mass cytometry approach to characterize over 250 cell-specific functional responses in 18 innate and adaptive immune cell subsets exposed to 1G or simulated (s)µG using the Rotating Wall Vessel. A statistically stringent elastic net method produced a multivariate model that accurately stratified immune responses observed in 1G and sµG (p value 2E-4, cross-validation). Aspects of our analysis resonated with prior knowledge of human immune adaptations to µG, including the dampening of Natural Killer, CD4+ and CD8+ T cell responses. Remarkably, we found that sµG enhanced STAT5 signaling responses of immunosuppressive Tregs. Our results suggest µG exerts a dual effect on the human immune system, simultaneously dampening cytotoxic responses while enhancing Treg function. Our study provides a single-cell readout of sµG-induced immune dysfunctions and an analytical framework for future studies of human immune adaptations to human long-term spaceflights.
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Affiliation(s)
- J M Spatz
- Department of Medicine, Metabolism Division, San Francisco Department of Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Medicine and Department of Surgery, University of California, San Francisco, CA, USA
| | - M Hughes Fulford
- Department of Medicine, Metabolism Division, San Francisco Department of Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Medicine and Department of Surgery, University of California, San Francisco, CA, USA
| | - A Tsai
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Dr. Rm S238, Grant Bldg, Stanford, CA, 94305, USA
| | - D Gaudilliere
- Department of Surgery, Plastic Surgery Division, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - J Hedou
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Dr. Rm S238, Grant Bldg, Stanford, CA, 94305, USA
| | - E Ganio
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Dr. Rm S238, Grant Bldg, Stanford, CA, 94305, USA
| | - M Angst
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Dr. Rm S238, Grant Bldg, Stanford, CA, 94305, USA
| | - N Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Dr. Rm S238, Grant Bldg, Stanford, CA, 94305, USA
- Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Dr. Rm S238, Grant Bldg, Stanford, CA, 94305, USA.
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6
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Jehan F, Sazawal S, Baqui AH, Nisar MI, Dhingra U, Khanam R, Ilyas M, Dutta A, Mitra DK, Mehmood U, Deb S, Mahmud A, Hotwani A, Ali SM, Rahman S, Nizar A, Ame SM, Moin MI, Muhammad S, Chauhan A, Begum N, Khan W, Das S, Ahmed S, Hasan T, Khalid J, Rizvi SJR, Juma MH, Chowdhury NH, Kabir F, Aftab F, Quaiyum A, Manu A, Yoshida S, Bahl R, Rahman A, Pervin J, Winston J, Musonda P, Stringer JSA, Litch JA, Ghaemi MS, Moufarrej MN, Contrepois K, Chen S, Stelzer IA, Stanley N, Chang AL, Hammad GB, Wong RJ, Liu C, Quaintance CC, Culos A, Espinosa C, Xenochristou M, Becker M, Fallahzadeh R, Ganio E, Tsai AS, Gaudilliere D, Tsai ES, Han X, Ando K, Tingle M, Marić I, Wise PH, Winn VD, Druzin ML, Gibbs RS, Darmstadt GL, Murray JC, Shaw GM, Stevenson DK, Snyder MP, Quake SR, Angst MS, Gaudilliere B, Aghaeepour N. Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries. JAMA Netw Open 2020; 3:e2029655. [PMID: 33337494 PMCID: PMC7749442 DOI: 10.1001/jamanetworkopen.2020.29655] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
IMPORTANCE 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. OBJECTIVE To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. DESIGN, SETTING, AND PARTICIPANTS 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. EXPOSURES 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. MAIN OUTCOMES AND MEASURES The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. RESULTS 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. CONCLUSIONS AND RELEVANCE 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.
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Affiliation(s)
- Fyezah Jehan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Sunil Sazawal
- Centre for Public Health Kinetics, New Delhi, Delhi, India
| | - Abdullah H. Baqui
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Muhammad Imran Nisar
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Usha Dhingra
- Centre for Public Health Kinetics, New Delhi, Delhi, India
| | - Rasheda Khanam
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Muhammad Ilyas
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Arup Dutta
- Centre for Public Health Kinetics, New Delhi, Delhi, India
| | - Dipak K. Mitra
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Usma Mehmood
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Saikat Deb
- Centre for Public Health Kinetics, New Delhi, Delhi, India
- Public Health Laboratory-Ivo de Carneri, Pemba Island, Zanzibar
| | - Arif Mahmud
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Aneeta Hotwani
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | | | - Sayedur Rahman
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Ambreen Nizar
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | | | - Mamun Ibne Moin
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Sajid Muhammad
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | | | - Nazma Begum
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Waqasuddin Khan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Sayan Das
- Centre for Public Health Kinetics, New Delhi, Delhi, India
| | - Salahuddin Ahmed
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Tarik Hasan
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Javairia Khalid
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Syed Jafar Raza Rizvi
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Nabidul Haque Chowdhury
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Furqan Kabir
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Fahad Aftab
- Centre for Public Health Kinetics, New Delhi, Delhi, India
| | - Abdul Quaiyum
- International Center for Maternal and Newborn Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alexander Manu
- Maternal, Newborn, Child and Adolescent Health Research, World Health Organization, Geneva, Switzerland
| | - Sachiyo Yoshida
- Maternal, Newborn, Child and Adolescent Health Research, World Health Organization, Geneva, Switzerland
| | - Rajiv Bahl
- Maternal, Newborn, Child and Adolescent Health Research, World Health Organization, Geneva, Switzerland
| | - Anisur Rahman
- Matlab Health Research Centre, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Jesmin Pervin
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Jennifer Winston
- Department of Obstetrics and Gynecology, University of North Carolina at Chapel Hill, Chapel Hill
| | - Patrick Musonda
- School of Public Health, University of Zambia, Lusaka, Zambia
| | - Jeffrey S. A. Stringer
- Department of Obstetrics and Gynecology, University of North Carolina at Chapel Hill, Chapel Hill
| | - James A. Litch
- Global Alliance to Prevent Prematurity and Stillbirth, Seattle, Washington
| | - Mohammad Sajjad Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Ontario, Canada
| | - Mira N. Moufarrej
- Department of Bioengineering, Stanford University, Stanford, California
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, California
| | - Songjie Chen
- Department of Genetics, Stanford University School of Medicine, Stanford, California
| | - Ina A. Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Alan L. Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Ghaith Bany Hammad
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Ronald J. Wong
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Candace Liu
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | | | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Maria Xenochristou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Edward Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Amy S. Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Dyani Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Eileen S. Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Xiaoyuan Han
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Kazuo Ando
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Martha Tingle
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Ivana Marić
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Paul H. Wise
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Virginia D. Winn
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, California
| | - Maurice L. Druzin
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, California
| | - Ronald S. Gibbs
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, California
| | - Gary L. Darmstadt
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | | | - Gary M. Shaw
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - David K. Stevenson
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California
| | - Stephen R. Quake
- Department of Bioengineering, Stanford University, Stanford, California
| | - Martin S. Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California
- Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
- Department of Biomedical Informatics, Stanford University School of Medicine, Stanford, California
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7
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Culos A, Tsai AS, Stanley N, Becker M, Ghaemi MS, McIlwain DR, Fallahzadeh R, Tanada A, Nassar H, Espinosa C, Xenochristou M, Ganio E, Peterson L, Han X, Stelzer IA, Ando K, Gaudilliere D, Phongpreecha T, Marić I, Chang AL, Shaw GM, Stevenson DK, Bendall S, Davis KL, Fantl W, Nolan GP, Hastie T, Tibshirani R, Angst MS, Gaudilliere B, Aghaeepour N. Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. NAT MACH INTELL 2020; 2:619-628. [PMID: 33294774 PMCID: PMC7720904 DOI: 10.1038/s42256-020-00232-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 08/26/2020] [Indexed: 12/17/2022]
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.
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Affiliation(s)
- Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- These authors contributed equally: Anthony Culos, Amy S. Tsai
| | - Amy S Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Anthony Culos, Amy S. Tsai
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Mohammad S Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Ontario, Canada
| | - David R McIlwain
- Department of Microbiology and Immunology, Baxter Laboratory in Stem Cell Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Athena Tanada
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Huda Nassar
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Maria Xenochristou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Edward Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Laura Peterson
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Xiaoyuan Han
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ina A Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Kazuo Ando
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Dyani Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ivana Marić
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Alan L Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sean Bendall
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kara L Davis
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Wendy Fantl
- Department of Microbiology and Immunology, Baxter Laboratory in Stem Cell Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Trevor Hastie
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Robert Tibshirani
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors jointly supervised this work: Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors jointly supervised this work: Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors jointly supervised this work: Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour
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8
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Ganio EA, Stanley N, Lindberg-Larsen V, Einhaus J, Tsai AS, Verdonk F, Culos A, Ghaemi S, Rumer KK, Stelzer IA, Gaudilliere D, Tsai E, Fallahzadeh R, Choisy B, Kehlet H, Aghaeepour N, Angst MS, Gaudilliere B. Author Correction: Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nat Commun 2020; 11:4495. [PMID: 32883978 PMCID: PMC7471263 DOI: 10.1038/s41467-020-18410-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Edward A Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Jakob Einhaus
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Amy S Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Franck Verdonk
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sajjad Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA.,Digital Technologies Research Centre, National Research Council Canada, Toronto, ON, Canada
| | - Kristen K Rumer
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Ina A Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Dyani Gaudilliere
- Division of Plastic and Reconstructive Surgery, Department of Surgery, School of Medicine, Stanford University, Stanford, CA, USA
| | - Eileen Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Benjamin Choisy
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Henrik Kehlet
- Section of Surgical Pathophysiology 7621, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen, Denmark
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
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9
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Ganio EA, Stanley N, Lindberg-Larsen V, Einhaus J, Tsai AS, Verdonk F, Culos A, Ghaemi S, Rumer KK, Stelzer IA, Gaudilliere D, Tsai E, Fallahzadeh R, Choisy B, Kehlet H, Aghaeepour N, Angst MS, Gaudilliere B. Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nat Commun 2020; 11:3737. [PMID: 32719355 PMCID: PMC7385146 DOI: 10.1038/s41467-020-17565-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 07/03/2020] [Indexed: 02/08/2023] Open
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.
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Affiliation(s)
- Edward A Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Jakob Einhaus
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Amy S Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Franck Verdonk
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sajjad Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
- Digital Technologies Research Centre, National Research Council Canada, Toronto, ON, Canada
| | - Kristen K Rumer
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Ina A Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Dyani Gaudilliere
- Division of Plastic and Reconstructive Surgery, Department of Surgery, School of Medicine, Stanford University, Stanford, CA, USA
| | - Eileen Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Benjamin Choisy
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Henrik Kehlet
- Section of Surgical Pathophysiology 7621, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen, Denmark
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA, USA.
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10
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Stanley N, Stelzer IA, Tsai AS, Fallahzadeh R, Ganio E, Becker M, Phongpreecha T, Nassar H, Ghaemi S, Maric I, Culos A, Chang AL, Xenochristou M, Han X, Espinosa C, Rumer K, Peterson L, Verdonk F, Gaudilliere D, Tsai E, Feyaerts D, Einhaus J, Ando K, Wong RJ, Obermoser G, Shaw GM, Stevenson DK, Angst MS, Gaudilliere B, Aghaeepour N. VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nat Commun 2020; 11:3738. [PMID: 32719375 PMCID: PMC7385162 DOI: 10.1038/s41467-020-17569-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 07/03/2020] [Indexed: 12/29/2022] Open
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.
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Affiliation(s)
- Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Ina A Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Amy S Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Edward Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pathology, Stanford University, Stanford, USA
| | - Huda Nassar
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Sajjad Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
- Digital Technologies Research Centre, National Research Council Canada, Toronto, ON, Canada
| | - Ivana Maric
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Alan L Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Maria Xenochristou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Xiaoyuan Han
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Biomedical Data Science, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Kristen Rumer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Laura Peterson
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Franck Verdonk
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Dyani Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
- Department of Plastic Surgery, Stanford University, Stanford, USA
| | - Eileen Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Jakob Einhaus
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Kazuo Ando
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Ronald J Wong
- Department of Pediatrics, Stanford University, Stanford, USA
| | | | - Gary M Shaw
- Department of Pediatrics, Stanford University, Stanford, USA
| | | | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA
- Department of Pediatrics, Stanford University, Stanford, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, USA.
- Department of Pediatrics, Stanford University, Stanford, USA.
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11
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Hung KS, Sheckter CC, Gaudilliere D, Suarez P, Curtin C. Surgical Treatment of Osteonecrosis of the Jaw: An Emerging Problem in the Era of Bisphosphonates. J Oral Maxillofac Surg 2020; 78:682-683. [PMID: 32004467 DOI: 10.1016/j.joms.2019.12.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/10/2019] [Indexed: 11/26/2022]
Affiliation(s)
- Kay Su Hung
- Medical Student, Division of Plastic & Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA.
| | - Clifford Charles Sheckter
- Resident, Division of Plastic & Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA; and Clinical Excellence Research Center, Department of Medicine, Stanford University, Stanford, CA
| | - Dyani Gaudilliere
- Clinical Assistant Professor, Division of Plastic & Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA
| | - Paola Suarez
- Data Analyst, Division of Neurosurgery, Department of Surgery, Stanford University, Stanford, CA
| | - Catherine Curtin
- Professor, Division of Plastic & Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA; and Division of Plastic Surgery, Palo Alto Veterans Affairs, Palo Alto, CA
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12
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Tsai AS, Berry K, Beneyto MM, Gaudilliere D, Ganio EA, Culos A, Ghaemi MS, Choisy B, Djebali K, Einhaus JF, Bertrand B, Tanada A, Stanley N, Fallahzadeh R, Baca Q, Quach LN, Osborn E, Drag L, Lansberg MG, Angst MS, Gaudilliere B, Buckwalter MS, Aghaeepour N. A year-long immune profile of the systemic response in acute stroke survivors. Brain 2019; 142:978-991. [PMID: 30860258 DOI: 10.1093/brain/awz022] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 11/18/2018] [Accepted: 12/14/2018] [Indexed: 02/07/2023] Open
Abstract
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.
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Affiliation(s)
- Amy S Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Kacey Berry
- Stanford Stroke Center, Stanford School of Medicine, CA, USA.,Department of Neurology and Neurological Sciences, Stanford School of Medicine, CA, USA
| | - Maxime M Beneyto
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Dyani Gaudilliere
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford School of Medicine, CA, USA
| | - Edward A Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Mohammad S Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Benjamin Choisy
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Karim Djebali
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Jakob F Einhaus
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Basile Bertrand
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Athena Tanada
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Quentin Baca
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Lisa N Quach
- Stanford Stroke Center, Stanford School of Medicine, CA, USA.,Department of Neurology and Neurological Sciences, Stanford School of Medicine, CA, USA
| | - Elizabeth Osborn
- Stanford Stroke Center, Stanford School of Medicine, CA, USA.,Department of Neurology and Neurological Sciences, Stanford School of Medicine, CA, USA
| | - Lauren Drag
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, CA, USA
| | - Maarten G Lansberg
- Stanford Stroke Center, Stanford School of Medicine, CA, USA.,Department of Neurology and Neurological Sciences, Stanford School of Medicine, CA, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
| | - Marion S Buckwalter
- Stanford Stroke Center, Stanford School of Medicine, CA, USA.,Department of Neurology and Neurological Sciences, Stanford School of Medicine, CA, USA.,Department of Neurosurgery, Stanford School of Medicine, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, CA, USA
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13
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Tsai AS, Berry K, Beneyto MM, Gaudilliere D, Ganio EA, Choisy B, Djebali K, Baca Q, Quach L, Drag L, Lansberg MG, Angst MS, Gaudilliere B, Buckwalter MS, Aghaeepour N. Abstract WP564: Deep Immune Profiling of the Post-Stroke Peripheral Immune Response Reveals Tri-phasic Response and Correlations With Long-Term Cognitive Outcomes. Stroke 2019. [DOI: 10.1161/str.50.suppl_1.wp564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Introduction:
Stroke produces profound local and systemic immune responses that engage all major innate and adaptive immune compartments. The aim of this study was to characterize the systemic immune response to stroke and to determine if it contributes to long-term cognitive disability.
Methods:
We included 24 consecutive subjects with ischemic stroke and excluded patients for autoimmune disorders, use of immunosuppressant drugs, or life expectancy <90 days. Blood samples were collected for up to 9 timepoints after stroke (days 1, 2, 3, 5, 7, 14, 30, 90, and 365). Change in cognitive function between days 90 and 365 was assessed using the Montreal Cognitive Assessment (MoCA). Control samples were from a cohort of 24 sex- and age-matched patients prior to hip replacement surgery. We used mass cytometry to acquire 240 immune features from each sample, representing 20 immune cell subtypes, their frequency, cell surface markers, and activation states. Elastic Net (EN) regularized regression modeling was used to characterize phases of the immune response and to correlate stages of the immune response with change in cognitive function.
Results:
The EN model identified three distinct phases of the systemic immune response to ischemic stroke: The acute phase (day 2) was characterized by increased STAT3 (signal transducer and activator of transcription 3) signaling responses in innate immune cell types. The intermediate phase (day 5) was characterized by increased CREB (cAMP response element-binding protein) signaling responses in adaptive immune cell types. The late phase (day 90) was characterized by persistent elevation of neutrophils and IgM+ B cells. By day 365 there was a return to baseline immune responses, comparable to the controls. A decline in MoCA scores between day 90 and day 365 after stroke correlated with a stronger inflammatory response in the acute phase (r = -0.692, Bonferroni-corrected p = 0.04).
Conclusions:
The results demonstrate three distinct phases of the peripheral immune response that occur after stroke, spanning from days 2 to day 90. The acute phase immune response predicts post-stroke cognitive decline, suggesting that therapies aimed at optimizing this response could lead to preservation of cognitive functioning post-stroke.
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Affiliation(s)
- Amy S Tsai
- Anesthesiology, Stanford Med Sch, Stanford, CA
| | - Ketura Berry
- Neurology and Neurological Sciences, and Neurosurgery, Stanford Med Sch, Stanford, CA
| | | | | | | | | | | | | | - Lisa Quach
- Neurology and Neurological Sciences, and Neurosurgery, Stanford Med Sch, Stanford, CA
| | - Lauren Drag
- Neurology and Neurological Sciences, and Neurosurgery, Stanford Med Sch, Stanford, CA
| | - Maarten G Lansberg
- Neurology and Neurological Sciences, and Neurosurgery, Stanford Med Sch, Stanford, CA
| | | | | | - Marion S Buckwalter
- Neurology and Neurological Sciences, and Neurosurgery, Stanford Med Sch, Stanford, CA
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14
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Ghaemi MS, DiGiulio DB, Contrepois K, Callahan B, Ngo TTM, Lee-McMullen B, Lehallier B, Robaczewska A, Mcilwain D, Rosenberg-Hasson Y, Wong RJ, 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 VD, Druzin ML, Gibbs R, Darmstadt GL, Lewis DB, Partovi Nia V, Agard B, Tibshirani R, Nolan G, Snyder MP, Relman DA, Quake SR, Shaw GM, Stevenson DK, Angst MS, Gaudilliere B, Aghaeepour N. Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics 2019; 35:95-103. [PMID: 30561547 PMCID: PMC6298056 DOI: 10.1093/bioinformatics/bty537] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 06/22/2018] [Accepted: 07/02/2018] [Indexed: 12/12/2022] Open
Abstract
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: https://nalab.stanford.edu/multiomics-pregnancy/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mohammad Sajjad Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Département de Mathématiques et de Génie Industriel, École Polytechnique de Montréal, QC, Canada
- Groupe d’Études et de Recherche en Analyse des Décision (GERAD), Montréal, QC, Canada
- Centre Interuniversitaire de Recherche sur les Réseaux d’Entreprise, la Logistique et le Transport (CIRRELT), Montréal, QC, Canada
| | - Daniel B DiGiulio
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Benjamin Callahan
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA
| | - Thuy T M Ngo
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute and Department of Molecular and Medical Genetics, Oregon Health Sciences University, Portland, OR, USA
| | | | - Benoit Lehallier
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Anna Robaczewska
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - David Mcilwain
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Yael Rosenberg-Hasson
- Institute for Immunity, Transplantation and Infection, Human Immune Monitoring Center Stanford, CA, USA
| | - Ronald J Wong
- Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Cecele Quaintance
- Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Athena Tanada
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Amy Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Dyani Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Edward Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Xiaoyuan Han
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Kazuo Ando
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Leslie McNeil
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Martha Tingle
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Paul Wise
- Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ivana Maric
- Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Marina Sirota
- Institute for Computational Health Sciences, University of California San Francisco, San Francisco, CA, USA
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Tony Wyss-Coray
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Virginia D Winn
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | - Maurice L Druzin
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald Gibbs
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary L Darmstadt
- Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - David B Lewis
- Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Vahid Partovi Nia
- Département de Mathématiques et de Génie Industriel, École Polytechnique de Montréal, QC, Canada
- Groupe d’Études et de Recherche en Analyse des Décision (GERAD), Montréal, QC, Canada
| | - Bruno Agard
- Département de Mathématiques et de Génie Industriel, École Polytechnique de Montréal, QC, Canada
- Centre Interuniversitaire de Recherche sur les Réseaux d’Entreprise, la Logistique et le Transport (CIRRELT), Montréal, QC, Canada
| | - Robert Tibshirani
- Departments of Biomedical Data Sciences and Statistics, Stanford University, Stanford, CA, USA
- Department of Statistics, Stanford University School of Medicine, Stanford, CA, USA
| | - Garry Nolan
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - David A Relman
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Stephen R Quake
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Gary M Shaw
- Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - David K Stevenson
- Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
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Girod S, Schvartzman SC, Gaudilliere D, Salisbury K, Silva R. Haptic feedback improves surgeons' user experience and fracture reduction in facial trauma simulation. ACTA ACUST UNITED AC 2018; 53:561-570. [PMID: 27898160 DOI: 10.1682/jrrd.2015.03.0043] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2015] [Revised: 10/29/2015] [Indexed: 11/05/2022]
Abstract
Computer-assisted surgical (CAS) planning tools are available for craniofacial surgery, but are usually based on computer-aided design (CAD) tools that lack the ability to detect the collision of virtual objects (i.e., fractured bone segments). We developed a CAS system featuring a sense of touch (haptic) that enables surgeons to physically interact with individual, patient-specific anatomy and immerse in a three-dimensional virtual environment. In this study, we evaluated initial user experience with our novel system compared to an existing CAD system. Ten surgery resident trainees received a brief verbal introduction to both the haptic and CAD systems. Users simulated mandibular fracture reduction in three clinical cases within a 15 min time limit for each system and completed a questionnaire to assess their subjective experience. We compared standard landmarks and linear and angular measurements between the simulated results and the actual surgical outcome and found that haptic simulation results were not significantly different from actual postoperative outcomes. In contrast, CAD results significantly differed from both the haptic simulation and actual postoperative results. In addition to enabling a more accurate fracture repair, the haptic system provided a better user experience than the CAD system in terms of intuitiveness and self-reported quality of repair.
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Affiliation(s)
- Sabine Girod
- Departments of Surgery, Oral Medicine & Maxillofacial Surgery Section, and
| | | | - Dyani Gaudilliere
- Departments of Surgery, Oral Medicine & Maxillofacial Surgery Section, and
| | | | - Rebeka Silva
- San Francisco Department of Veterans Affairs Health Care System, Dental Service, San Francisco, CA
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16
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Collins B, Gonzalez D, Gaudilliere D, Girod S. Body Dysmorphic Disorder and Comorbidity Among Orthognathic Surgery Patients Purpose. J Oral Maxillofac Surg 2013. [DOI: 10.1016/j.joms.2013.06.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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