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Stevenson DK, Winn VD, Shaw GM, England SK, Wong RJ. Solving the Puzzle of Preterm Birth. Clin Perinatol 2024; 51:291-300. [PMID: 38705641 DOI: 10.1016/j.clp.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Solving the puzzle of preterm birth has been challenging and will require novel integrative solutions as preterm birth likely arises from many etiologies. It has been demonstrated that many sociodemographic and psychological determinants of preterm birth relate to its complex biology. It is this understanding that has enabled the development of a novel preventative strategy, which integrates the omics profile (genome, epigenome, transcriptome, proteome, metabolome, microbiome) with sociodemographic, environmental, and psychological determinants of individual pregnant people to solve the puzzle of preterm birth.
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Affiliation(s)
- David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Biomedical Innovations Building (BMI), 240 Pasteur Drive, Room 2652, Stanford, CA 94305, USA.
| | - Virginia D Winn
- Department of Obstetrics and Gynecology, Division of Reproductive, Stem Cell and Perinatal Biology, Stanford University of School of Medicine, Biomedical Innovations Building (BMI), 240 Pasteur Drive, Module 2700, Stanford, CA 94305, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Biomedical Innovations Building (BMI), 240 Pasteur Drive, Room 2652, Stanford, CA 94305, USA
| | - Sarah K England
- Department of Obstetrics and Gynecology, Center for Reproductive Health Sciences, Washington University School of Medicine, 425 S. Euclid Avenue, CB 8064, St. Louis, MO 63110, USA
| | - Ronald J Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Biomedical Innovations Building (BMI), 240 Pasteur Drive, Room 2652, Stanford, CA 94305, USA
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2
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Vedeneeva E, Gursky V, Samsonova M, Neganova I. Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods. Biomedicines 2023; 11:3005. [PMID: 38002005 PMCID: PMC10669716 DOI: 10.3390/biomedicines11113005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 10/30/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Human pluripotent stem cells have the potential for unlimited proliferation and controlled differentiation into various somatic cells, making them a unique tool for regenerative and personalized medicine. Determining the best clone selection is a challenging problem in this field and requires new sensing instruments and methods able to automatically assess the state of a growing colony ('phenotype') and make decisions about its destiny. One possible solution for such label-free, non-invasive assessment is to make phase-contrast images and/or videos of growing stem cell colonies, process the morphological parameters ('morphological portrait', or signal), link this information to the colony phenotype, and initiate an automated protocol for the colony selection. As a step in implementing this strategy, we used machine learning methods to find an effective model for classifying the human pluripotent stem cell colonies of three lines according to their morphological phenotype ('good' or 'bad'), using morphological parameters from the previously published data as predictors. We found that the model using cellular morphological parameters as predictors and artificial neural networks as the classification method produced the best average accuracy of phenotype prediction (67%). When morphological parameters of colonies were used as predictors, logistic regression was the most effective classification method (75% average accuracy). Combining the morphological parameters of cells and colonies resulted in the most effective model, with a 99% average accuracy of phenotype prediction. Random forest was the most efficient classification method for the combined data. We applied feature selection methods and showed that different morphological parameters were important for phenotype recognition via either cellular or colonial parameters. Our results indicate a necessity for retaining both cellular and colonial morphological information for predicting the phenotype and provide an optimal choice for the machine learning method. The classification models reported in this study could be used as a basis for developing and/or improving automated solutions to control the quality of human pluripotent stem cells for medical purposes.
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Affiliation(s)
- Ekaterina Vedeneeva
- Department of Physics and Mechanics & Mathematical Biology and Bioinformatics Laboratory, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia; (E.V.); (M.S.)
| | - Vitaly Gursky
- Laboratory of Molecular Medicine, Institute of Cytology, 194064 Saint Petersburg, Russia;
- Theoretical Department, Ioffe Institute, 194021 Saint Petersburg, Russia
| | - Maria Samsonova
- Department of Physics and Mechanics & Mathematical Biology and Bioinformatics Laboratory, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia; (E.V.); (M.S.)
| | - Irina Neganova
- Laboratory of Molecular Medicine, Institute of Cytology, 194064 Saint Petersburg, Russia;
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3
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Berson E, Gajera CR, Phongpreecha T, Perna A, Bukhari SA, Becker M, Chang AL, De Francesco D, Espinosa C, Ravindra NG, Postupna N, Latimer CS, Shively CA, Register TC, Craft S, Montine KS, Fox EJ, Keene CD, Bendall SC, Aghaeepour N, Montine TJ. Cross-species comparative analysis of single presynapses. Sci Rep 2023; 13:13849. [PMID: 37620363 PMCID: PMC10449792 DOI: 10.1038/s41598-023-40683-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/16/2023] [Indexed: 08/26/2023] Open
Abstract
Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.
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Affiliation(s)
- Eloïse Berson
- Department of Pathology, Stanford University, 300 Pasteur Dr., Stanford, CA, 94304, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Chandresh R Gajera
- Department of Pathology, Stanford University, 300 Pasteur Dr., Stanford, CA, 94304, USA
| | - Thanaphong Phongpreecha
- Department of Pathology, Stanford University, 300 Pasteur Dr., Stanford, CA, 94304, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Amalia Perna
- Department of Pathology, Stanford University, 300 Pasteur Dr., Stanford, CA, 94304, USA
| | - Syed A Bukhari
- Department of Pathology, Stanford University, 300 Pasteur Dr., Stanford, CA, 94304, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Alan L Chang
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Davide De Francesco
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Neal G Ravindra
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Nadia Postupna
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Caitlin S Latimer
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Carol A Shively
- Department of Pathology/Comparative Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Thomas C Register
- Department of Pathology/Comparative Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Suzanne Craft
- Department of Internal Medicine-Geriatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Kathleen S Montine
- Department of Pathology, Stanford University, 300 Pasteur Dr., Stanford, CA, 94304, USA
| | - Edward J Fox
- Department of Pathology, Stanford University, 300 Pasteur Dr., Stanford, CA, 94304, USA
| | - C Dirk Keene
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Sean C Bendall
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University, 300 Pasteur Dr., Stanford, CA, 94304, USA.
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Wong S, Hamidi H, Costa LJ, Bekri S, Neparidze N, Vij R, Nielsen TG, Raval A, Sareen R, Wassner-Fritsch E, Cho HJ. Multi-omic analysis of the tumor microenvironment shows clinical correlations in Ph1 study of atezolizumab +/- SoC in MM. Front Immunol 2023; 14:1085893. [PMID: 37559718 PMCID: PMC10408441 DOI: 10.3389/fimmu.2023.1085893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 05/23/2023] [Indexed: 08/11/2023] Open
Abstract
Multiple myeloma (MM) remains incurable, and treatment of relapsed/refractory (R/R) disease is challenging. There is an unmet need for more targeted therapies in this setting; deep cellular and molecular phenotyping of the tumor and microenvironment in MM could help guide such therapies. This phase 1b study (NCT02431208) evaluated the safety and efficacy of the anti-programmed death-ligand 1 monoclonal antibody atezolizumab (Atezo) alone or in combination with the standard of care (SoC) treatments lenalidomide (Len) or pomalidomide (Pom) and/or daratumumab (Dara) in patients with R/R MM. Study endpoints included incidence of adverse events (AEs) and overall response rate (ORR). A novel unsupervised integrative multi-omic analysis was performed using RNA sequencing, mass cytometry immunophenotyping, and proteomic profiling of baseline and on-treatment bone marrow samples from patients receiving Atezo monotherapy or Atezo+Dara. A similarity network fusion (SNF) algorithm was applied to preprocessed data. Eighty-five patients were enrolled. Treatment-emergent deaths occurred in 2 patients; both deaths were considered unrelated to study treatment. ORRs ranged from 11.1% (Atezo+Len cohorts, n=18) to 83.3% (Atezo+Dara+Pom cohort, n=6). High-dimensional multi-omic profiling of the tumor microenvironment and integrative SNF analysis revealed novel correlations between cellular and molecular features of the tumor and immune microenvironment, patient selection criteria, and clinical outcome. Atezo monotherapy and SoC combinations were safe in this patient population and demonstrated some evidence of clinical efficacy. Integrative analysis of high dimensional genomics and immune data identified novel clinical correlations that may inform patient selection criteria and outcome assessment in future immunotherapy studies for myeloma.
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Affiliation(s)
- Sandy Wong
- University of California San Francisco (UCSF) Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, United States
| | - Habib Hamidi
- Genentech Inc., South San Francisco, CA, United States
| | - Luciano J. Costa
- O’Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Selma Bekri
- Tisch Cancer Institute, Icahn School of Medicine at Mt. Sinai, New York, NY, United States
| | | | - Ravi Vij
- Division of Oncology, Washington University, St. Louis, MO, United States
| | | | - Aparna Raval
- Genentech Inc., South San Francisco, CA, United States
| | - Rajan Sareen
- Genentech Inc., South San Francisco, CA, United States
| | | | - Hearn J. Cho
- Tisch Cancer Institute, Icahn School of Medicine at Mt. Sinai, New York, NY, United States
- The Multiple Myeloma Research Foundation (MMRF), Norwalk, CT, United States
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Fallahzadeh R, Bidoki NH, Stelzer IA, Becker M, Marić I, Chang AL, Culos A, Phongpreecha T, Xenochristou M, Francesco DD, Espinosa C, Berson E, Verdonk F, Angst MS, Gaudilliere B, Aghaeepour N. In-silico generation of high-dimensional immune response data in patients using a deep neural network. Cytometry A 2023; 103:392-404. [PMID: 36507780 PMCID: PMC10182197 DOI: 10.1002/cyto.a.24709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/14/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022]
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.
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Affiliation(s)
- Ramin Fallahzadeh
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Neda H. Bidoki
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Ina A. Stelzer
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
| | - Martin Becker
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Ivana Marić
- Department of Pediatrics, Stanford University, Stanford, California, USA
| | - Alan L. Chang
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Anthony Culos
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Thanaphong Phongpreecha
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Maria Xenochristou
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Davide De Francesco
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Eloise Berson
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Franck Verdonk
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
| | - Martin S. Angst
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Pediatrics, Stanford University, Stanford, California, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Pain and Perioperative Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
- Department of Pediatrics, Stanford University, Stanford, California, USA
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6
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Giles ML, Sing Way S, Marchant A, Aghaepour N, James T, Schaltz-Buchholzer F, Zazara D, Arck P, Kollmann TR. Maternal vaccination to prevent adverse pregnancy outcomes: An underutilized molecular immunological intervention? J Mol Biol 2023; 435:168097. [PMID: 37080422 DOI: 10.1016/j.jmb.2023.168097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/27/2023] [Accepted: 04/10/2023] [Indexed: 04/22/2023]
Abstract
Adverse pregnancy outcomes including maternal mortality, stillbirth, preterm birth, intrauterine growth restriction cause millions of deaths each year. More effective interventions are urgently needed. Maternal immunization could be one such intervention protecting the mother and newborn from infection through its pathogen-specific effects. However, many adverse pregnancy outcomes are not directly linked to the infectious pathogens targeted by existing maternal vaccines but rather are linked to pathological inflammation unfolding during pregnancy. The underlying pathogenesis driving such unfavourable outcomes have only partially been elucidated but appear to relate to altered immune regulation by innate as well as adaptive immune responses, ultimately leading to aberrant maternal immune activation. Maternal immunization, like all immunization, impacts the immune system beyond pathogen-specific immunity. This raises the possibility that maternal vaccination could potentially be utilised as a pathogen-agnostic immune modulatory intervention to redirect abnormal immune trajectories towards a more favourable phenotype providing pregnancy protection. In this review we describe the epidemiological evidence surrounding this hypothesis, along with the mechanistic plausibility and present a possible path forward to accelerate addressing the urgent need of adverse pregnancy outcomes.
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Affiliation(s)
| | - Sing Sing Way
- Center for Inflammation and Tolerance; Cincinnati Children's Hospital, Cincinnati USA
| | | | - Nima Aghaepour
- Stanford University School of Medicine, Stanford, CA, USA
| | - Tomin James
- Stanford University School of Medicine, Stanford, CA, USA
| | | | - Dimitra Zazara
- Division of Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg, Hamburg, Germany
| | - Petra Arck
- Division of Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg, Hamburg, Germany
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Zhang X, Hong S, Yu C, Shen X, Sun F, Yang J. Comparative analysis between high -grade serous ovarian cancer and healthy ovarian tissues using single-cell RNA sequencing. Front Oncol 2023; 13:1148628. [PMID: 37124501 PMCID: PMC10140397 DOI: 10.3389/fonc.2023.1148628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/31/2023] [Indexed: 05/02/2023] Open
Abstract
Introduction High-grade serous ovarian cancer (HGSOC) is the most common histological subtype of ovarian cancer, and is associated with high mortality rates. Methods In this study, we analyzed specific cell subpopulations and compared different gene functions between healthy ovarian and ovarian cancer cells using single-cell RNA sequencing (ScRNA-seq). We delved deeper into the differences between healthy ovarian and ovarian cancer cells at different levels, and performed specific analysis on endothelial cells. Results We obtained scRNA-seq data of 6867 and 17056 cells from healthy ovarian samples and ovarian cancer samples, respectively. The transcriptional profiles of the groups differed at various stages of ovarian cell development. A detailed comparison of the cell cycle, and cell communication of different groups, revealed significant differences between healthy ovarian and ovarian cancer cells. We also found that apoptosis-related genes, URI1, PAK2, PARP1, CLU and TIMP3, were highly expressed, while immune-related genes, UBB, RPL11, CAV1, NUPR1 and Hsp90ab1, were lowly expressed in ovarian cancer cells. The results of the ScRNA-seq were verified using qPCR. Discussion Our findings revealed differences in function, gene expression and cell interaction patterns between ovarian cancer and healthy ovarian cell populations. These findings provide key insights on further research into the treatment of ovarian cancer.
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Affiliation(s)
- Xiao Zhang
- Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province, Hangzhou, China
| | - Shihao Hong
- Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province, Hangzhou, China
| | - Chengying Yu
- Department of Obstetrics and Gynecology, Longyou People’s Hospital, Quzhou, China
| | | | - Fangying Sun
- Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province, Hangzhou, China
| | - Jianhua Yang
- Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province, Hangzhou, China
- *Correspondence: Jianhua Yang,
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Verma V, Drury GL, Parisien M, Özdağ Acarli AN, Al-Aubodah TA, Nijnik A, wen X, Tugarinov N, Verner M, Klares R, Linton A, Krock E, Morado Urbina CE, Winsvold B, Fritsche LG, Fors EA, Piccirillo C, Khoutorsky A, Svensson CI, Fitzcharles MA, Ingelmo PM, Bernard NF, Dupuy FP, Üçeyler N, Sommer C, King IL, Meloto CB, Diatchenko L. Unbiased immune profiling reveals a natural killer cell-peripheral nerve axis in fibromyalgia. Pain 2022; 163:e821-e836. [PMID: 34913882 PMCID: PMC8942876 DOI: 10.1097/j.pain.0000000000002498] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/13/2021] [Indexed: 11/25/2022]
Abstract
ABSTRACT The pathophysiology of fibromyalgia syndrome (FMS) remains elusive, leading to a lack of objective diagnostic criteria and targeted treatment. We globally evaluated immune system changes in FMS by conducting multiparametric flow cytometry analyses of peripheral blood mononuclear cells and identified a natural killer (NK) cell decrease in patients with FMS. Circulating NK cells in FMS were exhausted yet activated, evidenced by lower surface expression of CD16, CD96, and CD226 and more CD107a and TIGIT. These NK cells were hyperresponsive, with increased CCL4 production and expression of CD107a when co-cultured with human leukocyte antigen null target cells. Genetic and transcriptomic pathway analyses identified significant enrichment of cell activation pathways in FMS driven by NK cells. Skin biopsies showed increased expression of NK activation ligand, unique long 16-binding protein, on subepidermal nerves of patients FMS and the presence of NK cells near peripheral nerves. Collectively, our results suggest that chronic activation and redistribution of circulating NK cells to the peripheral nerves contribute to the immunopathology associated with FMS.
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Affiliation(s)
- Vivek Verma
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Integrated Program in Neuroscience, Faculty of Medicine, McGill University, Montréal, Canada
| | - Gillian L. Drury
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Marc Parisien
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Ayşe N. Özdağ Acarli
- Department of Neurology, Faculty of Medicine, Istanbul University, Istanbul, Turkey
- Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Tho-Alfakar Al-Aubodah
- Department of Microbiology and Immunology, Faculty of Medicine, McGill University, Montréal, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Anastasia Nijnik
- Department of Physiology, Faculty of Medicine, McGill University, Montréal, Canada
- McGill Research Centre on Complex Traits, McGill University, Montréal, Canada
| | - Xia wen
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Nicol Tugarinov
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Maria Verner
- Faculty of Dentistry, McGill University, Montréal, Canada
| | - Richie Klares
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Alexander Linton
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
| | - Emerson Krock
- Department of Physiology and Pharmacology, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Carlos E. Morado Urbina
- Department of Physiology and Pharmacology, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Bendik Winsvold
- Department of Research and Innovation, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Lars G. Fritsche
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, USA
| | - Egil A. Fors
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ciriaco Piccirillo
- Department of Microbiology and Immunology, Faculty of Medicine, McGill University, Montréal, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Arkady Khoutorsky
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Faculty of Dentistry, McGill University, Montréal, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montréal, Canada
| | - Camilla I. Svensson
- Department of Physiology and Pharmacology, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mary A. Fitzcharles
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Division of Rheumatology, Faculty of Medicine, McGill University, Montréal, Canada
| | - Pablo M. Ingelmo
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montréal, Canada
| | - Nicole F. Bernard
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
- Division of Experimental Medicine, Faculty of Medicine, McGill University, Montréal, Canada
| | - Franck P. Dupuy
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Nurcan Üçeyler
- Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Claudia Sommer
- Department of Neurology, University Hospital of Würzburg, Würzburg, Germany
| | - Irah L. King
- Department of Microbiology and Immunology, Faculty of Medicine, McGill University, Montréal, Canada
- Meakins-Christie Laboratories, Research Institute of the McGill University Health Centre, Montréal, Canada
| | - Carolina B. Meloto
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Faculty of Dentistry, McGill University, Montréal, Canada
| | - Luda Diatchenko
- Alan Edwards Centre for Research on Pain, McGill University, Montréal, Canada
- Faculty of Dentistry, McGill University, Montréal, Canada
- Department of Anesthesia, Faculty of Medicine, McGill University, Montréal, Canada
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Simons L, Moayedi M, Coghill RC, Stinson J, Angst MS, Aghaeepour N, Gaudilliere B, King CD, López-Solà M, Hoeppli ME, Biggs E, Ganio E, Williams SE, Goldschneider KR, Campbell F, Ruskin D, Krane EJ, Walker S, Rush G, Heirich M. Signature for Pain Recovery IN Teens (SPRINT): protocol for a multisite prospective signature study in chronic musculoskeletal pain. BMJ Open 2022; 12:e061548. [PMID: 35676017 PMCID: PMC9185591 DOI: 10.1136/bmjopen-2022-061548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Current treatments for chronic musculoskeletal (MSK) pain are suboptimal. Discovery of robust prognostic markers separating patients who recover from patients with persistent pain and disability is critical for developing patient-specific treatment strategies and conceiving novel approaches that benefit all patients. Given that chronic pain is a biopsychosocial process, this study aims to discover and validate a robust prognostic signature that measures across multiple dimensions in the same adolescent patient cohort with a computational analysis pipeline. This will facilitate risk stratification in adolescent patients with chronic MSK pain and more resourceful allocation of patients to costly and potentially burdensome multidisciplinary pain treatment approaches. METHODS AND ANALYSIS Here we describe a multi-institutional effort to collect, curate and analyse a high dimensional data set including epidemiological, psychometric, quantitative sensory, brain imaging and biological information collected over the course of 12 months. The aim of this effort is to derive a multivariate model with strong prognostic power regarding the clinical course of adolescent MSK pain and function. ETHICS AND DISSEMINATION The study complies with the National Institutes of Health policy on the use of a single internal review board (sIRB) for multisite research, with Cincinnati Children's Hospital Medical Center Review Board as the reviewing IRB. Stanford's IRB is a relying IRB within the sIRB. As foreign institutions, the University of Toronto and The Hospital for Sick Children (SickKids) are overseen by their respective ethics boards. All participants provide signed informed consent. We are committed to open-access publication, so that patients, clinicians and scientists have access to the study data and the signature(s) derived. After findings are published, we will upload a limited data set for sharing with other investigators on applicable repositories. TRIAL REGISTRATION NUMBER NCT04285112.
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Affiliation(s)
- Laura Simons
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, University of Toronto Faculty of Dentistry, Toronto, Ontario, Canada
- Centre for the Study of Pain, University of Toronto, Toronto, Ontario, Canada
| | - Robert C Coghill
- Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, USA
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Jennifer Stinson
- Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- The Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Christopher D King
- Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, USA
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Marina López-Solà
- Serra Hunter Programme, Department of Medicine, University of Barcelona, Barcelona, Spain
| | - Marie-Eve Hoeppli
- Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, USA
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Emma Biggs
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Ed Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Sara E Williams
- Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio, USA
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Kenneth R Goldschneider
- Pediatric Pain Research Center (PPRC), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Anesthesiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Fiona Campbell
- Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Danielle Ruskin
- Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Elliot J Krane
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Suellen Walker
- Developmental Neurosciences Department, UCL GOS Institute of Child Health, UCL, London, UK
| | - Gillian Rush
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Marissa Heirich
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
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10
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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] [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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11
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Phongpreecha T, Gajera CR, Liu CC, Vijayaragavan K, Chang AL, Becker M, Fallahzadeh R, Fernandez R, Postupna N, Sherfield E, Tebaykin D, Latimer C, Shively CA, Register TC, Craft S, Montine KS, Fox EJ, Poston KL, Keene CD, Angelo M, Bendall SC, Aghaeepour N, Montine TJ. Single-synapse analyses of Alzheimer's disease implicate pathologic tau, DJ1, CD47, and ApoE. SCIENCE ADVANCES 2021; 7:eabk0473. [PMID: 34910503 PMCID: PMC8673771 DOI: 10.1126/sciadv.abk0473] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Synaptic molecular characterization is limited for Alzheimer’s disease (AD). Our newly invented mass cytometry–based method, synaptometry by time of flight (SynTOF), was used to measure 38 antibody probes in approximately 17 million single-synapse events from human brains without pathologic change or with pure AD or Lewy body disease (LBD), nonhuman primates (NHPs), and PS/APP mice. Synaptic molecular integrity in humans and NHP was similar. Although not detected in human synapses, Aβ was in PS/APP mice single-synapse events. Clustering and pattern identification of human synapses showed expected disease-specific differences, like increased hippocampal pathologic tau in AD and reduced caudate dopamine transporter in LBD, and revealed previously unidentified findings including increased hippocampal CD47 and lowered DJ1 in AD and higher ApoE in AD with dementia. Our results were independently supported by multiplex ion beam imaging of intact tissue. This highlights the higher depth and breadth of insight on neurodegenerative diseases obtainable through SynTOF.
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Affiliation(s)
- Thanaphong Phongpreecha
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | | | - Candace C. Liu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Alan L. Chang
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Nadia Postupna
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Emily Sherfield
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Dmitry Tebaykin
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Caitlin Latimer
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Carol A. Shively
- Department of Pathology/Comparative Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Thomas C. Register
- Department of Pathology/Comparative Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Suzanne Craft
- Department of Internal Medicine–Geriatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Edward J. Fox
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Kathleen L. Poston
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - C. Dirk Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Sean C. Bendall
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Thomas J. Montine
- Department of Pathology, Stanford University, Stanford, CA, USA
- Corresponding author.
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12
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McIlwain DR, Chen H, Rahil Z, Bidoki NH, Jiang S, Bjornson Z, Kolhatkar NS, Martinez CJ, Gaudillière B, Hedou J, Mukherjee N, Schürch CM, Trejo A, Affrime M, Bock B, Kim K, Liebowitz D, Aghaeepour N, Tucker SN, Nolan GP. Human influenza virus challenge identifies cellular correlates of protection for oral vaccination. Cell Host Microbe 2021; 29:1828-1837.e5. [PMID: 34784508 PMCID: PMC8665113 DOI: 10.1016/j.chom.2021.10.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 09/16/2021] [Accepted: 10/21/2021] [Indexed: 01/23/2023]
Abstract
Developing new influenza vaccines with improved performance and easier administration routes hinges on defining correlates of protection. Vaccine-elicited cellular correlates of protection for influenza in humans have not yet been demonstrated. A phase-2 double-blind randomized placebo and active (inactivated influenza vaccine) controlled study provides evidence that a human-adenovirus-5-based oral influenza vaccine tablet (VXA-A1.1) can protect from H1N1 virus challenge in humans. Mass cytometry characterization of vaccine-elicited cellular immune responses identified shared and vaccine-type-specific responses across B and T cells. For VXA-A1.1, the abundance of hemagglutinin-specific plasmablasts and plasmablasts positive for integrin α4β7, phosphorylated STAT5, or lacking expression of CD62L at day 8 were significantly correlated with protection from developing viral shedding following virus challenge at day 90 and contributed to an effective machine learning model of protection. These findings reveal the characteristics of vaccine-elicited cellular correlates of protection for an oral influenza vaccine.
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Affiliation(s)
- David R McIlwain
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA; WCCT Global, Cypress, CA, USA.
| | - Han Chen
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Zainab Rahil
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Neda Hajiakhoond Bidoki
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA; Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Informatics, Stanford University School of Medicine, Stanford, CA, USA
| | - Sizun Jiang
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA; Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zach Bjornson
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Brice Gaudillière
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Julien Hedou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Mukherjee
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian M Schürch
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA; Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Angelica Trejo
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Kenneth Kim
- Ark Clinical Research, LLC, Long Beach, CA, USA
| | | | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Informatics, Stanford University School of Medicine, Stanford, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
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13
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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] [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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14
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Quintelier K, Couckuyt A, Emmaneel A, Aerts J, Saeys Y, Van Gassen S. Analyzing high-dimensional cytometry data using FlowSOM. Nat Protoc 2021; 16:3775-3801. [PMID: 34172973 DOI: 10.1038/s41596-021-00550-0] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/31/2021] [Indexed: 02/06/2023]
Abstract
The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology. Since the original FlowSOM publication (2015), we have validated the tool on a wide variety of datasets, and to write this protocol, we made use of this experience to improve the user-friendliness of the package (e.g., comprehensive functions replacing commonly required scripts). Where the original paper focused mainly on the algorithm description, this protocol offers user guidelines on how to implement the procedure, detailed parameter descriptions and troubleshooting recommendations. The protocol provides clearly annotated R code, and is therefore relevant for all scientists interested in computational high-dimensional analyses without requiring a strong bioinformatics background. We demonstrate the complete workflow, starting from data preparation (such as compensation, transformation and quality control), including detailed discussion of the different FlowSOM parameters and visualization options, and concluding with how the results can be further used to answer biological questions, such as statistical comparison between groups of interest. An average FlowSOM analysis takes 1-3 h to complete, though quality issues can increase this time considerably.
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Affiliation(s)
- Katrien Quintelier
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.,Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium.,Department of Pulmonary Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Artuur Couckuyt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.,Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium
| | - Annelies Emmaneel
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.,Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium
| | - Joachim Aerts
- Department of Pulmonary Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Yvan Saeys
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.,Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium
| | - Sofie Van Gassen
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium. .,Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium.
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15
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Kaushik A, Dunham D, He Z, Manohar M, Desai M, Nadeau KC, Andorf S. CyAnno: A semi-automated approach for cell type annotation of mass cytometry datasets. Bioinformatics 2021; 37:4164-4171. [PMID: 34037686 PMCID: PMC9502137 DOI: 10.1093/bioinformatics/btab409] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 04/04/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be highly enriched with undefined heterogeneous cells, i.e., 'ungated' cells, and that current semi-automated approaches ignore their modeling resulting in misclassified annotations. RESULT We introduce 'CyAnno', a novel semi-automated approach for deconvoluting the unlabeled cytometry dataset based on a machine learning framework utilizing manually gated training data that allows the integrative modeling of 'gated' cell types and the 'ungated' cells. By applying this framework on several CyTOF datasets, we demonstrated that including the 'ungated' cells can lead to a significant increase in the precision of the 'gated' cell types prediction. CyAnno can be used to identify even a single cell type, including rare cells, with higher efficacy than current state-of-the-art semi-automated approaches. AVAILABILITY The CyAnno is available as a python script with a user-manual and sample dataset at https://github.com/abbioinfo/CyAnno. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Abhinav Kaushik
- Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA
| | - Diane Dunham
- Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA
| | - Ziyuan He
- Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA
| | - Monali Manohar
- Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA
| | - Manisha Desai
- Quantitative Sciences Unit, Stanford University, Stanford, CA 94305-5101, USA
| | - Kari C Nadeau
- Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA
| | - Sandra Andorf
- Sean N Parker Center for Allergy and Asthma Research at Stanford University, Stanford University, Stanford, CA 94305-5101, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Divisions of Biomedical Informatics and Allergy & Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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16
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Gerber R, Robinson MD. Censcyt: censored covariates in differential abundance analysis in cytometry. BMC Bioinformatics 2021; 22:235. [PMID: 33971812 PMCID: PMC8108359 DOI: 10.1186/s12859-021-04125-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Innovations in single cell technologies have lead to a flurry of datasets and computational tools to process and interpret them, including analyses of cell composition changes and transition in cell states. The diffcyt workflow for differential discovery in cytometry data consist of several steps, including preprocessing, cell population identification and differential testing for an association with a binary or continuous covariate. However, the commonly measured quantity of survival time in clinical studies often results in a censored covariate where classical differential testing is inapplicable. RESULTS To overcome this limitation, multiple methods to directly include censored covariates in differential abundance analysis were examined with the use of simulation studies and a case study. Results show that multiple imputation based methods offer on-par performance with the Cox proportional hazards model in terms of sensitivity and error control, while offering flexibility to account for covariates. The tested methods are implemented in the R package censcyt as an extension of diffcyt and are available at https://bioconductor.org/packages/censcyt . CONCLUSION Methods for the direct inclusion of a censored variable as a predictor in GLMMs are a valid alternative to classical survival analysis methods, such as the Cox proportional hazard model, while allowing for more flexibility in the differential analysis.
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Affiliation(s)
- Reto Gerber
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland.
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17
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Espinosa C, Becker M, Marić I, Wong RJ, Shaw GM, Gaudilliere B, Aghaeepour N, Stevenson DK. Data-Driven Modeling of Pregnancy-Related Complications. Trends Mol Med 2021; 27:762-776. [PMID: 33573911 DOI: 10.1016/j.molmed.2021.01.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/01/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022]
Abstract
A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.
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Affiliation(s)
- 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
| | - 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
| | - Ivana Marić
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ronald J Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Division of Neonatal and Developmental 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; Department of Pediatrics, Division of Neonatal and Developmental 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; 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
| | - David K Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA, USA.
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18
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Phongpreecha T, Fernandez R, Mrdjen D, Culos A, Gajera CR, Wawro AM, Stanley N, Gaudilliere B, Poston KL, Aghaeepour N, Montine TJ. Single-cell peripheral immunoprofiling of Alzheimer's and Parkinson's diseases. SCIENCE ADVANCES 2020; 6:eabd5575. [PMID: 33239300 PMCID: PMC7688332 DOI: 10.1126/sciadv.abd5575] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/09/2020] [Indexed: 05/05/2023]
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 PLCG2 is associated with a decreased risk of AD. Together, these data suggest enhanced PLCγ2 activity as a potential new therapeutic target for AD with a readily accessible pharmacodynamic biomarker.
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Affiliation(s)
- Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Dunja Mrdjen
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Adam M Wawro
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University, Stanford, CA, USA.
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19
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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] [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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20
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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] [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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