1
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Fisch L, Heming M, Schulte-Mecklenbeck A, Gross CC, Zumdick S, Barkhau C, Emden D, Ernsting J, Leenings R, Sarink K, Winter NR, Dannlowski U, Wiendl H, Hörste GMZ, Hahn T. GateNet: A novel neural network architecture for automated flow cytometry gating. Comput Biol Med 2024; 179:108820. [PMID: 39002319 DOI: 10.1016/j.compbiomed.2024.108820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 06/12/2024] [Accepted: 06/25/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND AND OBJECTIVE Flow cytometry is a widely used technique for identifying cell populations in patient-derived fluids, such as peripheral blood (PB) or cerebrospinal fluid (CSF). Despite its ubiquity in research and clinical practice, the process of gating, i.e., manually identifying cell types, is labor-intensive and error-prone. The objective of this study is to address this challenge by introducing GateNet, a neural network architecture designed for fully end-to-end automated gating without the need for correcting batch effects. METHODS For this study a unique dataset is used which comprises over 8,000,000 events from N = 127 PB and CSF samples which were manually labeled independently by four experts. Applying cross-validation, the classification performance of GateNet is compared to the human experts performance. Additionally, GateNet is applied to a publicly available dataset to evaluate generalization. The classification performance is measured using the F1 score. RESULTS GateNet achieves F1 scores ranging from 0.910 to 0.997 demonstrating human-level performance on samples unseen during training. In the publicly available dataset, GateNet confirms its generalization capabilities with an F1 score of 0.936. Importantly, we also show that GateNet only requires ≈10 samples to reach human-level performance. Finally, gating with GateNet only takes 15 microseconds per event utilizing graphics processing units (GPU). CONCLUSIONS GateNet enables fully end-to-end automated gating in flow cytometry, overcoming the labor-intensive and error-prone nature of manual adjustments. The neural network achieves human-level performance on unseen samples and generalizes well to diverse datasets. Notably, its data efficiency, requiring only ∼10 samples to reach human-level performance, positions GateNet as a widely applicable tool across various domains of flow cytometry.
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Affiliation(s)
- Lukas Fisch
- University of Münster, Institute for Translational Psychiatry, Münster, Germany.
| | - Michael Heming
- Department of Neurology with Institute of Translational Neurology, University and University Hospital Münster, Münster, Germany
| | - Andreas Schulte-Mecklenbeck
- Department of Neurology with Institute of Translational Neurology, University and University Hospital Münster, Münster, Germany
| | - Catharina C Gross
- Department of Neurology with Institute of Translational Neurology, University and University Hospital Münster, Münster, Germany
| | - Stefan Zumdick
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Carlotta Barkhau
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Daniel Emden
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Jan Ernsting
- University of Münster, Institute for Translational Psychiatry, Münster, Germany; Institute for Geoinformatics, University of Münster, Germany; Faculty of Mathematics and Computer Science, University of Münster, Germany
| | - Ramona Leenings
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Kelvin Sarink
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Nils R Winter
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Udo Dannlowski
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Heinz Wiendl
- Department of Neurology with Institute of Translational Neurology, University and University Hospital Münster, Münster, Germany
| | - Gerd Meyer Zu Hörste
- Department of Neurology with Institute of Translational Neurology, University and University Hospital Münster, Münster, Germany
| | - Tim Hahn
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
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2
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Glehr G, Riquelme P, Kronenberg K, Lohmayer R, López-Madrona VJ, Kapinsky M, Schlitt HJ, Geissler EK, Spang R, Haferkamp S, Hutchinson JA. Restricting datasets to classifiable samples augments discovery of immune disease biomarkers. Nat Commun 2024; 15:5417. [PMID: 38926389 PMCID: PMC11208602 DOI: 10.1038/s41467-024-49094-3] [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] [Received: 05/04/2023] [Accepted: 05/14/2024] [Indexed: 06/28/2024] Open
Abstract
Immunological diseases are typically heterogeneous in clinical presentation, severity and response to therapy. Biomarkers of immune diseases often reflect this variability, especially compared to their regulated behaviour in health. This leads to a common difficulty that frustrates biomarker discovery and interpretation - namely, unequal dispersion of immune disease biomarker expression between patient classes necessarily limits a biomarker's informative range. To solve this problem, we introduce dataset restriction, a procedure that splits datasets into classifiable and unclassifiable samples. Applied to synthetic flow cytometry data, restriction identifies biomarkers that are otherwise disregarded. In advanced melanoma, restriction finds biomarkers of immune-related adverse event risk after immunotherapy and enables us to build multivariate models that accurately predict immunotherapy-related hepatitis. Hence, dataset restriction augments discovery of immune disease biomarkers, increases predictive certainty for classifiable samples and improves multivariate models incorporating biomarkers with a limited informative range. This principle can be directly extended to any classification task.
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Affiliation(s)
- Gunther Glehr
- Department of Surgery, University Hospital Regensburg, Regensburg, Germany
| | - Paloma Riquelme
- Department of Surgery, University Hospital Regensburg, Regensburg, Germany
| | | | - Robert Lohmayer
- Algorithmic Bioinformatics Research Group, Leibniz Institute for Immunotherapy, Regensburg, Germany
| | | | | | - Hans J Schlitt
- Department of Surgery, University Hospital Regensburg, Regensburg, Germany
| | - Edward K Geissler
- Department of Surgery, University Hospital Regensburg, Regensburg, Germany
| | - Rainer Spang
- Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany
| | - Sebastian Haferkamp
- Department of Dermatology, University Hospital Regensburg, Regensburg, Germany
| | - James A Hutchinson
- Department of Surgery, University Hospital Regensburg, Regensburg, Germany.
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3
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Zhang M, Zhang Y, Zhang J, Zhang J, Gao S, Li Z, Tao K, Liang X, Pan J, Zhu M. An automatic analysis and quality assurance method for lymphocyte subset identification. Clin Chem Lab Med 2024; 62:1411-1420. [PMID: 38217085 DOI: 10.1515/cclm-2023-1141] [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] [Received: 10/12/2023] [Accepted: 12/20/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVES Lymphocyte subsets are the predictors of disease diagnosis, treatment, and prognosis. Determination of lymphocyte subsets is usually carried out by flow cytometry. Despite recent advances in flow cytometry analysis, most flow cytometry data can be challenging with manual gating, which is labor-intensive, time-consuming, and error-prone. This study aimed to develop an automated method to identify lymphocyte subsets. METHODS We propose a knowledge-driven combined with data-driven method which can gate automatically to achieve subset identification. To improve accuracy and stability, we have implemented a Loop Adjustment Gating to optimize the gating result of the lymphocyte population. Furthermore, we have incorporated an anomaly detection mechanism to issue warnings for samples that might not have been successfully analyzed, ensuring the quality of the results. RESULTS The evaluation showed a 99.2 % correlation between our method results and manual analysis with a dataset of 2,000 individual cases from lymphocyte subset assays. Our proposed method attained 97.7 % accuracy for all cases and 100 % for the high-confidence cases. With our automated method, 99.1 % of manual labor can be saved when reviewing only the low-confidence cases, while the average turnaround time required is only 29 s, reducing by 83.7 %. CONCLUSIONS Our proposed method can achieve high accuracy in flow cytometry data from lymphocyte subset assays. Additionally, it can save manual labor and reduce the turnaround time, making it have the potential for application in the laboratory.
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Affiliation(s)
- MinYang Zhang
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - YaLi Zhang
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - JingWen Zhang
- Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - JiaLi Zhang
- Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - SiYuan Gao
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - ZeChao Li
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - KangPei Tao
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - XiaoDan Liang
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - JianHua Pan
- Department of Clinical Hematology and Flow Cytometry Lab, Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
| | - Min Zhu
- Department of Digital Management Center, Guangzhou KingMed Diagnostics Group Co., Ltd., Guangzhou Kingmed Center for Clinical Laboratory Co., Ltd., Guangzhou, Guandong, P.R. China
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4
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Beck A, Muhoberac M, Randolph CE, Beveridge CH, Wijewardhane PR, Kenttämaa HI, Chopra G. Recent Developments in Machine Learning for Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:233-246. [PMID: 38910862 PMCID: PMC11191731 DOI: 10.1021/acsmeasuresciau.3c00060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/27/2023] [Accepted: 01/22/2024] [Indexed: 06/25/2024]
Abstract
Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.
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Affiliation(s)
- Armen
G. Beck
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Matthew Muhoberac
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Caitlin E. Randolph
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Connor H. Beveridge
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Prageeth R. Wijewardhane
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Hilkka I. Kenttämaa
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Gaurav Chopra
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
- Department
of Computer Science (by courtesy), Purdue University, West Lafayette, Indiana 47907, United States
- Purdue
Institute for Drug Discovery, Purdue Institute for Cancer Research,
Regenstrief Center for Healthcare Engineering, Purdue Institute for
Inflammation, Immunology and Infectious Disease, Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana 47907 United States
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5
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Wagle MM, Long S, Chen C, Liu C, Yang P. Interpretable deep learning in single-cell omics. Bioinformatics 2024; 40:btae374. [PMID: 38889275 PMCID: PMC11211213 DOI: 10.1093/bioinformatics/btae374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 05/11/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them 'black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations. RESULTS In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions.
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Affiliation(s)
- Manoj M Wagle
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Siqu Long
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Carissa Chen
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Chunlei Liu
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Pengyi Yang
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia
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6
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Chen J, Ionita M, Feng Y, Lu Y, Orzechowski P, Garai S, Hassinger K, Bao J, Wen J, Duong-Tran D, Wagenaar J, McKeague ML, Painter MM, Mathew D, Pattekar A, Meyer NJ, Wherry EJ, Greenplate AR, Shen L. Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592739. [PMID: 38766268 PMCID: PMC11100732 DOI: 10.1101/2024.05.06.592739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.
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Affiliation(s)
- Jiong Chen
- Department of Bioengineering, University of Pennsylvania School of Engineering and Applied Science, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Matei Ionita
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Yanbo Feng
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Yinfeng Lu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Department of Mathematics, University of Pennsylvania School of Arts and Sciences, PA, USA
| | - Patryk Orzechowski
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Department of Automatics and Robotics, AGH University of Science and Technology, al. Mickiewicza 30, Krakow, 30-059, Poland
| | - Sumita Garai
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Kenneth Hassinger
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Junhao Wen
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, CA, USA
| | - Duy Duong-Tran
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Department of Mathematics, United States Naval Academy, Annapolis, MD, USA
| | - Joost Wagenaar
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Michelle L. McKeague
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Mark M. Painter
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Divij Mathew
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Ajinkya Pattekar
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Nuala J. Meyer
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - E. John Wherry
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Allison R. Greenplate
- Department of Systems Pharmacology & Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, PA, USA
- Institute for Immunology and Immune Health, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, PA, USA
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7
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Ali KA, Shah RD, Dhar A, Myers NM, Nguyen C, Paul A, Mancuso JE, Scott Patterson A, Brody JP, Heiser D. Ex vivo discovery of synergistic drug combinations for hematologic malignancies. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2024; 29:100129. [PMID: 38101570 DOI: 10.1016/j.slasd.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/13/2023] [Accepted: 12/09/2023] [Indexed: 12/17/2023]
Abstract
Combination therapies have improved outcomes for patients with acute myeloid leukemia (AML). However, these patients still have poor overall survival. Although many combination therapies are identified with high-throughput screening (HTS), these approaches are constrained to disease models that can be grown in large volumes (e.g., immortalized cell lines), which have limited translational utility. To identify more effective and personalized treatments, we need better strategies for screening and exploring potential combination therapies. Our objective was to develop an HTS platform for identifying effective combination therapies with highly translatable ex vivo disease models that use size-limited, primary samples from patients with leukemia (AML and myelodysplastic syndrome). We developed a system, ComboFlow, that comprises three main components: MiniFlow, ComboPooler, and AutoGater. MiniFlow conducts ex vivo drug screening with a miniaturized flow-cytometry assay that uses minimal amounts of patient sample to maximize throughput. ComboPooler incorporates computational methods to design efficient screens of pooled drug combinations. AutoGater is an automated gating classifier for flow cytometry that uses machine learning to rapidly analyze the large datasets generated by the assay. We used ComboFlow to efficiently screen more than 3000 drug combinations across 20 patient samples using only 6 million cells per patient sample. In this screen, ComboFlow identified the known synergistic combination of bortezomib and panobinostat. ComboFlow also identified a novel drug combination, dactinomycin and fludarabine, that synergistically killed leukemic cells in 35 % of AML samples. This combination also had limited effects in normal, hematopoietic progenitors. In conclusion, ComboFlow enables exploration of massive landscapes of drug combinations that were previously inaccessible in ex vivo models. We envision that ComboFlow can be used to discover more effective and personalized combination therapies for cancers amenable to ex vivo models.
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Affiliation(s)
- Kamran A Ali
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA; Department of Biomedical Engineering, University of California, Irvine, 3120 Natural Sciences II, Irvine, CA, 92697, USA.
| | - Reecha D Shah
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
| | - Anukriti Dhar
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
| | - Nina M Myers
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
| | | | - Arisa Paul
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
| | | | | | - James P Brody
- Department of Biomedical Engineering, University of California, Irvine, 3120 Natural Sciences II, Irvine, CA, 92697, USA
| | - Diane Heiser
- Notable Labs, 320 Hatch Dr, Foster City, CA, 94404, USA
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8
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Lewis JE, Cooper LAD, Jaye DL, Pozdnyakova O. Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry. Mod Pathol 2024; 37:100373. [PMID: 37925056 DOI: 10.1016/j.modpat.2023.100373] [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] [Received: 09/18/2023] [Revised: 10/23/2023] [Accepted: 10/28/2023] [Indexed: 11/06/2023]
Abstract
The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.
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Affiliation(s)
- Joshua E Lewis
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Lee A D Cooper
- Department of Pathology, Northwestern University, Chicago, Illinois
| | - David L Jaye
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Olga Pozdnyakova
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts.
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9
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Robles EE, Jin Y, Smyth P, Scheuermann RH, Bui JD, Wang HY, Oak J, Qian Y. A cell-level discriminative neural network model for diagnosis of blood cancers. Bioinformatics 2023; 39:btad585. [PMID: 37756695 PMCID: PMC10563151 DOI: 10.1093/bioinformatics/btad585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/12/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
MOTIVATION Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. RESULTS We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes sample-level training data and predicts the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. AVAILABILITY AND IMPLEMENTATION The source code of CSNN and datasets used in the experiments are publicly available on GitHub (http://github.com/erobl/csnn). Raw FCS files can be downloaded from FlowRepository (ID: FR-FCM-Z6YK).
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Affiliation(s)
- Edgar E Robles
- Department of Computer Science, University of California, Irvine, CA 92697, United States
| | - Ye Jin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, United States
| | - Padhraic Smyth
- Department of Computer Science, University of California, Irvine, CA 92697, United States
| | - Richard H Scheuermann
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, United States
- Department of Pathology, University of California, San Diego, CA 92093, United States
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA 92037, United States
| | - Jack D Bui
- Department of Pathology, University of California, San Diego, CA 92093, United States
| | - Huan-You Wang
- Department of Pathology, University of California, San Diego, CA 92093, United States
| | - Jean Oak
- Department of Pathology, Stanford University, Stanford, CA 94305, United States
| | - Yu Qian
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA 92037, United States
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10
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Timonen VA, Kerkelä E, Impola U, Penna L, Partanen J, Kilpivaara O, Arvas M, Pitkänen E. DeepIFC: Virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning. Cytometry A 2023; 103:807-817. [PMID: 37276178 DOI: 10.1002/cyto.a.24770] [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: 09/30/2022] [Revised: 05/16/2023] [Accepted: 06/02/2023] [Indexed: 06/07/2023]
Abstract
Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.
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Affiliation(s)
- Veera A Timonen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Erja Kerkelä
- Advanced Cell Therapy Centre, Finnish Red Cross Blood Service, Vantaa, Finland
| | - Ulla Impola
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Leena Penna
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Jukka Partanen
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Outi Kilpivaara
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Medical and Clinical Genetics, Medicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- HUSLAB Laboratory of Genetics, HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Mikko Arvas
- Research and Development, Finnish Red Cross Blood Service, Helsinki, Finland
| | - Esa Pitkänen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
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11
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Lewis JE, Cooper LA, Jaye DL, Pozdnyakova O. Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.18.558289. [PMID: 37808719 PMCID: PMC10557578 DOI: 10.1101/2023.09.18.558289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models which make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1,820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia [AUROC 0.961] and accurately differentiates AML versus B- and T-lymphoblastic leukemia [AUROC 0.965]. Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) [AUROC 0.814], and NPM1 variants [AUROC 0.807]. Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.
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Affiliation(s)
- Joshua E. Lewis
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Lee A.D. Cooper
- Department of Pathology, Northwestern University, Chicago, IL, USA
| | - David L. Jaye
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Olga Pozdnyakova
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
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12
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Zhang J, Li J, Lin L. Statistical and machine learning methods for immunoprofiling based on single-cell data. Hum Vaccin Immunother 2023:2234792. [PMID: 37485833 PMCID: PMC10373621 DOI: 10.1080/21645515.2023.2234792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/30/2023] [Accepted: 07/04/2023] [Indexed: 07/25/2023] Open
Abstract
Immunoprofiling has become a crucial tool for understanding the complex interactions between the immune system and diseases or interventions, such as therapies and vaccinations. Immune response biomarkers are critical for understanding those relationships and potentially developing personalized intervention strategies. Single-cell data have emerged as a promising source for identifying immune response biomarkers. In this review, we discuss the current state-of-the-art methods for immunoprofiling, including those for reducing the dimensionality of high-dimensional single-cell data and methods for clustering, classification, and prediction. We also draw attention to recent developments in data integration.
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Affiliation(s)
- Jingxuan Zhang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Jia Li
- Department of Statistics, Pennsylvania State University, University Park, PA, USA
| | - Lin Lin
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
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13
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Robinson JP, Ostafe R, Iyengar SN, Rajwa B, Fischer R. Flow Cytometry: The Next Revolution. Cells 2023; 12:1875. [PMID: 37508539 PMCID: PMC10378642 DOI: 10.3390/cells12141875] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.
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Affiliation(s)
- J Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Raluca Ostafe
- Molecular Evolution, Protein Engineering and Production Facility (PI4D), Purdue University, West Lafayette, IN 47907, USA
| | | | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - Rainer Fischer
- Department of Comparative Pathobiology, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
- Purdue Institute of Inflammation, Immunology and Infectious Diseases, Purdue University, West Lafayette, IN 47907, USA
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14
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Penhaskashi J, Sekimoto O, Chiappelli F. Permafrost viremia and immune tweening. Bioinformation 2023; 19:685-691. [PMID: 37885785 PMCID: PMC10598357 DOI: 10.6026/97320630019685] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 10/28/2023] Open
Abstract
The immune system, an exquisitely regulated physiological system, utilizes a wide spectrum of soluble factors and multiple cell populations and subpopulations at diverse states of maturation to monitor and protect the organism against foreign organisms. Immune surveillance is ensured by distinguishing self-antigens from self-associated with non-self (e.g., viral) peptides presented by major histocompatibility complexes (MHC). Pathology is often identified as unregulated inflammatory responses (e.g., cytokine storm), or recognizing self as a non-self entity (i.e., auto-immunity). Artificial intelligence (AI), and in particular specific machine learning (ML) paradigms (e.g., Deep Learning [DL]) proffer powerful algorithms to better understand and more accurately predict immune responses, immune regulation and homeostasis, and immune reactivity to challenges (i.e., immune allostasis) by their intrinsic ability to interpret immune parameters, pathways and events by analyzing large amounts of complex data and drawing predictive inferences (i.e., immune tweening). We propose here that DL models play an increasingly significant role in better defining and characterizing immunological surveillance to ancient and novel virus species released by thawing permafrost.
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Affiliation(s)
- Jaden Penhaskashi
- />Division of West Valley Dental Implant Center, Encino, CA 91316, USA
| | | | - Francesco Chiappelli
- />Dental Group of Sherman Oaks, CA 91403 , USA
- />Center for the Health Sciences, UCLA, Los Angeles, CA, USA
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15
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Zhang Y, Yu L, Jing R, Han B, Luo J. Fast and Efficient Design of Deep Neural Networks for Predicting N 7-Methylguanosine Sites Using autoBioSeqpy. ACS OMEGA 2023; 8:19728-19740. [PMID: 37305295 PMCID: PMC10249100 DOI: 10.1021/acsomega.3c01371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/10/2023] [Indexed: 06/13/2023]
Abstract
N7-Methylguanosine (m7G) is a crucial post-transcriptional RNA modification that plays a pivotal role in regulating gene expression. Accurately identifying m7G sites is a fundamental step in understanding the biological functions and regulatory mechanisms associated with this modification. While whole-genome sequencing is the gold standard for RNA modification site detection, it is a time-consuming, expensive, and intricate process. Recently, computational approaches, especially deep learning (DL) techniques, have gained popularity in achieving this objective. Convolutional neural networks and recurrent neural networks are examples of DL algorithms that have emerged as versatile tools for modeling biological sequence data. However, developing an efficient network architecture with superior performance remains a challenging task, requiring significant expertise, time, and effort. To address this, we previously introduced a tool called autoBioSeqpy, which streamlines the design and implementation of DL networks for biological sequence classification. In this study, we utilized autoBioSeqpy to develop, train, evaluate, and fine-tune sequence-level DL models for predicting m7G sites. We provided detailed descriptions of these models, along with a step-by-step guide on their execution. The same methodology can be applied to other systems dealing with similar biological questions. The benchmark data and code utilized in this study can be accessed for free at http://github.com/jingry/autoBioSeeqpy/tree/2.0/examples/m7G.
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Affiliation(s)
- Yonglin Zhang
- Department
of Pharmacy, Affiliated Hospital of North
Sichuan Medical College, Nanchong 637000, China
| | - Lezheng Yu
- School
of Chemistry and Materials Science, Guizhou
Education University, Guiyang 550024, China
| | - Runyu Jing
- School
of Cyber Science and Engineering, Sichuan
University, Chengdu 610017, China
| | - Bin Han
- GCP
Center/Institute of Drug Clinical Trials, Affiliated Hospital of North Sichuan Medical College, Nanchong 637503, China
| | - Jiesi Luo
- Basic
Medical College, Southwest Medical University, Luzhou 646099, Sichuan, China
- Key
Medical
Laboratory of New Drug Discovery and Druggability Evaluation, Luzhou
Key Laboratory of Activity Screening and Druggability Evaluation for
Chinese Materia Medica, Southwest Medical
University, Luzhou 646099, China
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16
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Yan X, Yue T, Winkler DA, Yin Y, Zhu H, Jiang G, Yan B. Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation. Chem Rev 2023. [PMID: 37262026 DOI: 10.1021/acs.chemrev.3c00070] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.
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Affiliation(s)
- Xiliang Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
| | - Tongtao Yue
- Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia
| | - Yongguang Yin
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Bing Yan
- Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China
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17
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Fang K, Chen X, Dong Z, Xu P. Developing and validating a highly sensitive platelet clump detection model for the Sysmex haematology analyser. Ann Clin Biochem 2023; 60:126-135. [PMID: 36653307 DOI: 10.1177/00045632231154782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Mainstream haematology analysers (HAs) are reported to have low detection sensitivity for platelet clumps. In this study, a deep learning (DL) algorithm, convolutional neural network (CNN), was implemented to detect platelet clumps. METHODS Adenosine diphosphate (ADP) was used to induce platelet aggregation to mimic platelet clumps detected (PCD) samples. Six types of leukocyte scattergrams were collected from the Sysmex XN-10. Then, multiple CNNs were trained and validated by scattergrams in a fivefold cross-validation (CV) method. Finally, the CNN model with the best CV accuracy was tested with practical routine work samples. RESULTS A total of 386 samples (190 PCD and 196 negative samples) and 4253 samples (150 PCD and 4103 negative samples) were eligible for CNN training and practical test, respectively. The CNN with the highest CV accuracy was trained by using scattergrams of side scatter (SSC) vs. forward scatter (FSC) from the white count and nucleated red blood cells (WNR) channel, whose mean area under the curve (AUC), accuracy, specificity and sensitivity were 0.968, 0.940, 0.937 and 0.942, respectively, in the CV. In the practical test, the AUC, accuracy, specificity and sensitivity of the CNN were 0.916, 0.961, 0.860 and 0.965, respectively. The dispersed spots presenting around the leucocytes in the WNR channel may be a sign of platelet clumping. CONCLUSIONS This study demonstrates that the CNN algorithms can identify platelet clumps based on optical information from dedicated leukocyte channels and has a higher ability to detect platelet clumps than the XN-10 device's internal algorithm under practical circumstances.
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Affiliation(s)
- Kui Fang
- 232834The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Xiling Chen
- 232834The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Zheqing Dong
- 232834The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Peng Xu
- 232834The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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18
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Paproski RJ, Pink D, Sosnowski DL, Vasquez C, Lewis JD. Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learning. Mol Oncol 2023; 17:407-421. [PMID: 36520580 PMCID: PMC9980304 DOI: 10.1002/1878-0261.13362] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/08/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Extracellular vesicles (EVs) are highly abundant in human biofluids, containing a repertoire of macromolecules and biomarkers representative of the tissue of origin. EVs released by tumours can communicate key signals both locally and to distant sites to promote growth and survival or impact invasive and metastatic progression. Microscale flow cytometry of circulating EVs is an emerging technology that is a promising alternative to biopsy for disease diagnosis. However, biofluid-derived EVs are highly heterogeneous in size and composition, making their analysis complex. To address this, we developed a machine learning approach combined with EV microscale cytometry using tissue- and disease-specific biomarkers to generate predictive models. We demonstrate the utility of this novel extracellular vesicle machine learning analysis platform (EVMAP) to predict disease from patient samples by developing a blood test to identify high-grade prostate cancer and validate its performance in a prospective 215 patient cohort. Models generated using the EVMAP approach significantly improved the prediction of high-risk prostate cancer, highlighting the clinical utility of this diagnostic platform for improved cancer prediction from a blood test.
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Affiliation(s)
- Robert J Paproski
- Department of Oncology, University of Alberta, Edmonton, AB, Canada.,Nanostics Inc., Edmonton, AB, Canada
| | - Desmond Pink
- Department of Oncology, University of Alberta, Edmonton, AB, Canada.,Nanostics Inc., Edmonton, AB, Canada
| | | | - Catalina Vasquez
- Department of Oncology, University of Alberta, Edmonton, AB, Canada.,Nanostics Inc., Edmonton, AB, Canada
| | - John D Lewis
- Department of Oncology, University of Alberta, Edmonton, AB, Canada.,Nanostics Inc., Edmonton, AB, Canada
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19
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Robles EE, Jin Y, Smyth P, Scheuermann RH, Bui JD, Wang HY, Oak J, Qian Y. A cell-level discriminative neural network model for diagnosis of blood cancers. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.07.23285606. [PMID: 36798344 PMCID: PMC9934808 DOI: 10.1101/2023.02.07.23285606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Motivation Precise identification of cancer cells in patient samples is essential for accurate diagnosis and clinical monitoring but has been a significant challenge in machine learning approaches for cancer precision medicine. In most scenarios, training data are only available with disease annotation at the subject or sample level. Traditional approaches separate the classification process into multiple steps that are optimized independently. Recent methods either focus on predicting sample-level diagnosis without identifying individual pathologic cells or are less effective for identifying heterogeneous cancer cell phenotypes. Results We developed a generalized end-to-end differentiable model, the Cell Scoring Neural Network (CSNN), which takes the available sample-level training data and predicts both the diagnosis of the testing samples and the identity of the diagnostic cells in the sample, simultaneously. The cell-level density differences between samples are linked to the sample diagnosis, which allows the probabilities of individual cells being diagnostic to be calculated using backpropagation. We applied CSNN to two independent clinical flow cytometry datasets for leukemia diagnosis. In both qualitative and quantitative assessments, CSNN outperformed preexisting neural network modeling approaches for both cancer diagnosis and cell-level classification. Post hoc decision trees and 2D dot plots were generated for interpretation of the identified cancer cells, showing that the identified cell phenotypes match the cancer endotypes observed clinically in patient cohorts. Independent data clustering analysis confirmed the identified cancer cell populations. Availability The source code of CSNN and datasets used in the experiments are publicly available on GitHub and FlowRepository. Contact Edgar E. Robles: roblesee@uci.edu and Yu Qian: mqian@jcvi.org. Supplementary information Supplementary data are available on GitHub and at Bioinformatics online.
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20
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Thirumal S, Jamzad A, Cotechini T, Siemens DR, Mousavi P. Automated Cell Phenotyping for Imaging Mass Cytometry. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:426-429. [PMID: 36085862 DOI: 10.1109/embc48229.2022.9871071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Imaging mass cytometry (IMC) is a new advancement in tissue imaging that is quickly gaining wider usage since its recent launch. It improves upon current tissue imaging methods by allowing for a significantly higher number of proteins to be imaged at once on a single tissue slide. For most analyses of IMC data, determining the phenotype of each cell is a crucial step. Current methods of phenotyping require sufficient biological knowledge regarding the protein expression profile of the various cell types. Here, we develop a deep convolutional autoencoder-classifier to automate the cell phenotyping process into four basic cell types. Biopsy tissue from bladder cancer patients is used to evaluate the efficacy of the classification. The model is evaluated and validated through feature importance, confirming that the significant features are biologically relevant. Our results demonstrate the potential of deep learning to automate the task of cell phenotyping for high-dimensional IMC data.
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21
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Prybutok AN, Cain JY, Leonard JN, Bagheri N. Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems. Curr Opin Biotechnol 2022; 75:102704. [DOI: 10.1016/j.copbio.2022.102704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 01/27/2022] [Accepted: 02/06/2022] [Indexed: 11/03/2022]
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22
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Swarm immunology: harnessing blockchain technology and artificial intelligence in human immunology. Nat Rev Immunol 2022; 22:401-403. [PMID: 35624333 PMCID: PMC9136788 DOI: 10.1038/s41577-022-00740-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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23
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Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer 2022; 22:114-126. [PMID: 34663944 PMCID: PMC8810682 DOI: 10.1038/s41568-021-00408-3] [Citation(s) in RCA: 148] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/08/2021] [Indexed: 02/07/2023]
Abstract
Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches are limited to a single mode of data, leaving integrated approaches across modalities relatively underdeveloped. Multimodal integration of advanced molecular diagnostics, radiological and histological imaging, and codified clinical data presents opportunities to advance precision oncology beyond genomics and standard molecular techniques. However, most medical datasets are still too sparse to be useful for the training of modern machine learning techniques, and significant challenges remain before this is remedied. Combined efforts of data engineering, computational methods for analysis of heterogeneous data and instantiation of synergistic data models in biomedical research are required for success. In this Perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods. Advancing along this direction will result in a reimagined class of multimodal biomarkers to propel the field of precision oncology in the coming decade.
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Affiliation(s)
- Kevin M Boehm
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pegah Khosravi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rami Vanguri
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jianjiong Gao
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Hu Z, Bhattacharya S, Butte AJ. Application of Machine Learning for Cytometry Data. Front Immunol 2022; 12:787574. [PMID: 35046945 PMCID: PMC8761933 DOI: 10.3389/fimmu.2021.787574] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/14/2021] [Indexed: 01/23/2023] Open
Abstract
Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample classification. Here, we review the existing machine learning applications for analyzing cytometry data and highlight the importance of publicly available cytometry data that enable researchers to develop and validate machine learning methods.
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Affiliation(s)
- Zicheng Hu
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, United States
| | - Sanchita Bhattacharya
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
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25
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Artificial Intelligence in Clinical Immunology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Khosravi P, Lysandrou M, Eljalby M, Li Q, Kazemi E, Zisimopoulos P, Sigaras A, Brendel M, Barnes J, Ricketts C, Meleshko D, Yat A, McClure TD, Robinson BD, Sboner A, Elemento O, Chughtai B, Hajirasouliha I. A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion. J Magn Reson Imaging 2021; 54:462-471. [PMID: 33719168 PMCID: PMC8360022 DOI: 10.1002/jmri.27599] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 02/22/2021] [Accepted: 02/23/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. PURPOSE To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. STUDY TYPE Retrospective. POPULATION Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases). FIELD STRENGTH/SEQUENCE 1.5 to 3.0 Tesla, T2-weighted image pulse sequences. ASSESSMENT MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high-risk tumor from low-risk tumor. STATISTICAL TESTS To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa. RESULTS Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86-0.92]) and 0.78 (95% CI: [0.74-0.82]) to classify cancer vs. benign and high- vs. low-risk of prostate disease, respectively. DATA CONCLUSION AI-biopsy provided a data-driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI-biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag-and-drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI-assessed MR images in real time. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Pegah Khosravi
- Computational Oncology, Department of Epidemiology and BiostatisticsMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Caryl and Israel Englander Institute for Precision MedicineThe Meyer Cancer Center, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Maria Lysandrou
- Neuroscience InstituteThe University of ChicagoChicagoIllinoisUSA
| | - Mahmoud Eljalby
- Department of UrologyWeill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
| | - Qianzi Li
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Mathematics and Statistics DepartmentCarleton CollegeNorthfieldMinnesotaUSA
| | - Ehsan Kazemi
- Yale University, Department of Electrical Engineering
| | - Pantelis Zisimopoulos
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Caryl and Israel Englander Institute for Precision MedicineThe Meyer Cancer Center, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Alexandros Sigaras
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Caryl and Israel Englander Institute for Precision MedicineThe Meyer Cancer Center, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Matthew Brendel
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
| | - Josue Barnes
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Caryl and Israel Englander Institute for Precision MedicineThe Meyer Cancer Center, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Camir Ricketts
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Caryl and Israel Englander Institute for Precision MedicineThe Meyer Cancer Center, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Dmitry Meleshko
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Caryl and Israel Englander Institute for Precision MedicineThe Meyer Cancer Center, Weill Cornell MedicineNew YorkNew YorkUSA
| | - Andy Yat
- Department of RadiologyNew York‐Presbyterian HospitalNew YorkNew YorkUSA
| | - Timothy D. McClure
- Department of UrologyWeill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
| | - Brian D. Robinson
- Department of PathologyNew York Presbyterian Hospital‐Weill Cornell Medical CollegeNew YorkNew YorkUSA
| | - Andrea Sboner
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Caryl and Israel Englander Institute for Precision MedicineThe Meyer Cancer Center, Weill Cornell MedicineNew YorkNew YorkUSA
- Department of PathologyNew York Presbyterian Hospital‐Weill Cornell Medical CollegeNew YorkNew YorkUSA
| | - Olivier Elemento
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Caryl and Israel Englander Institute for Precision MedicineThe Meyer Cancer Center, Weill Cornell MedicineNew YorkNew YorkUSA
- WorldQuant Initiative for Quantitative PredictionWeill Cornell MedicineNew YorkNew YorkUSA
| | - Bilal Chughtai
- Department of UrologyWeill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
| | - Iman Hajirasouliha
- Department of Physiology and BiophysicsInstitute for Computational Biomedicine, Weill Cornell Medicine of Cornell UniversityNew YorkNew YorkUSA
- Caryl and Israel Englander Institute for Precision MedicineThe Meyer Cancer Center, Weill Cornell MedicineNew YorkNew YorkUSA
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Burton RJ, Ahmed R, Cuff SM, Baker S, Artemiou A, Eberl M. CytoPy: An autonomous cytometry analysis framework. PLoS Comput Biol 2021; 17:e1009071. [PMID: 34101722 PMCID: PMC8213167 DOI: 10.1371/journal.pcbi.1009071] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 06/18/2021] [Accepted: 05/12/2021] [Indexed: 12/24/2022] Open
Abstract
Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm-agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.
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Affiliation(s)
- Ross J. Burton
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Raya Ahmed
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Simone M. Cuff
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Sarah Baker
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Andreas Artemiou
- School of Mathematics, Cardiff University, Cardiff, United Kingdom
| | - Matthias Eberl
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom
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Bhattacharya S, Hu Z, Butte AJ. Opportunities and Challenges in Democratizing Immunology Datasets. Front Immunol 2021; 12:647536. [PMID: 33936065 PMCID: PMC8086961 DOI: 10.3389/fimmu.2021.647536] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/04/2021] [Indexed: 11/26/2022] Open
Abstract
The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.
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Affiliation(s)
- Sanchita Bhattacharya
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Zicheng Hu
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States
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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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Chen L, Liu Y, Xu H, Ma L, Wang Y, Wang F, Zhu J, Hu X, Yi K, Yang Y, Shen H, Zhou F, Gao X, Cheng Y, Bai L, Duan Y, Wang F, Zhu Y. Touchable cell biophysics property recognition platforms enable multifunctional blood smart health care. MICROSYSTEMS & NANOENGINEERING 2021; 7:103. [PMID: 34963817 PMCID: PMC8651774 DOI: 10.1038/s41378-021-00329-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/25/2021] [Accepted: 11/06/2021] [Indexed: 05/10/2023]
Abstract
As a crucial biophysical property, red blood cell (RBC) deformability is pathologically altered in numerous disease states, and biochemical and structural changes occur over time in stored samples of otherwise normal RBCs. However, there is still a gap in applying it further to point-of-care blood devices due to the large external equipment (high-resolution microscope and microfluidic pump), associated operational difficulties, and professional analysis. Herein, we revolutionarily propose a smart optofluidic system to provide a differential diagnosis for blood testing via precise cell biophysics property recognition both mechanically and morphologically. Deformation of the RBC population is caused by pressing the hydrogel via an integrated mechanical transfer device. The biophysical properties of the cell population are obtained by the designed smartphone algorithm. Artificial intelligence-based modeling of cell biophysics properties related to blood diseases and quality was developed for online testing. We currently achieve 100% diagnostic accuracy for five typical clinical blood diseases (90 megaloblastic anemia, 78 myelofibrosis, 84 iron deficiency anemia, 48 thrombotic thrombocytopenic purpura, and 48 thalassemias) via real-world prospective implementation; furthermore, personalized blood quality (for transfusion in cardiac surgery) monitoring is achieved with an accuracy of 96.9%. This work suggests a potential basis for next-generation blood smart health care devices.
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Affiliation(s)
- Longfei Chen
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
- Shenzhen Research Institute, Wuhan University, Shenzhen, 518000 China
| | - Yantong Liu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
- Shenzhen Research Institute, Wuhan University, Shenzhen, 518000 China
| | - Hongshan Xu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Linlu Ma
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Yifan Wang
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Fang Wang
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Jiaomeng Zhu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Xuejia Hu
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Kezhen Yi
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Yi Yang
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
- Shenzhen Research Institute, Wuhan University, Shenzhen, 518000 China
| | - Hui Shen
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Xiaoqi Gao
- Affiliations School of Physics & Technology, Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, Wuhan University, Wuhan, 430072 China
| | - Yanxiang Cheng
- Remin Hospital of Wuhan University, Wuhan University, Wuhan, 430060 China
| | - Long Bai
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310002 China
| | - Yongwei Duan
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Fubing Wang
- Department of Laboratory Medicine, Zhongnan Hospital, Wuhan University, Wuhan, 430071 China
| | - Yimin Zhu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310002 China
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Artificial Intelligence in Clinical Immunology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_83-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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