1
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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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2
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Chen Y, De Spiegelaere W, Trypsteen W, Gleerup D, Vandesompele J, Lievens A, Vynck M, Thas O. Benchmarking digital PCR partition classification methods with empirical and simulated duplex data. Brief Bioinform 2024; 25:bbae120. [PMID: 38555473 PMCID: PMC10981767 DOI: 10.1093/bib/bbae120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/09/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
Digital PCR (dPCR) is a highly accurate technique for the quantification of target nucleic acid(s). It has shown great potential in clinical applications, like tumor liquid biopsy and validation of biomarkers. Accurate classification of partitions based on end-point fluorescence intensities is crucial to avoid biased estimators of the concentration of the target molecules. We have evaluated many clustering methods, from general-purpose methods to specific methods for dPCR and flowcytometry, on both simulated and real-life data. Clustering method performance was evaluated by simulating various scenarios. Based on our extensive comparison of clustering methods, we describe the limits of these methods, and formulate guidelines for choosing an appropriate method. In addition, we have developed a novel method for simulating realistic dPCR data. The method is based on a mixture distribution of a Poisson point process and a skew-$t$ distribution, which enables the generation of irregularities of cluster shapes and randomness of partitions between clusters ('rain') as commonly observed in dPCR data. Users can fine-tune the model parameters and generate labeled datasets, using their own data as a template. Besides, the database of experimental dPCR data augmented with the labeled simulated data can serve as training and testing data for new clustering methods. The simulation method is available as an R Shiny app.
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Affiliation(s)
- Yao Chen
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium
- Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Ghent University, Belgium
- Ghent University Digital PCR Consortium, Ghent University, Belgium
| | - Ward De Spiegelaere
- Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Ghent University, Belgium
- Ghent University Digital PCR Consortium, Ghent University, Belgium
| | - Wim Trypsteen
- Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Ghent University, Belgium
- Ghent University Digital PCR Consortium, Ghent University, Belgium
- Department of Internal Medicine, Ghent University and University Hospital, Belgium
| | - David Gleerup
- Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Ghent University, Belgium
- Ghent University Digital PCR Consortium, Ghent University, Belgium
| | - Jo Vandesompele
- Ghent University Digital PCR Consortium, Ghent University, Belgium
- Department of Biomolecular Medicine, Ghent University and University Hospital, Belgium
- Cancer Research Institute Ghent (CRIG), Ghent University and University Hospital, Belgium
- Pxlence, Belgium
| | - Antoon Lievens
- Ghent University Digital PCR Consortium, Ghent University, Belgium
| | - Matthijs Vynck
- Department of Morphology, Imaging, Orthopedics, Rehabilitation and Nutrition, Ghent University, Belgium
- Ghent University Digital PCR Consortium, Ghent University, Belgium
| | - Olivier Thas
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium
- Ghent University Digital PCR Consortium, Ghent University, Belgium
- I-BioStat, Data Science Institute, Hasselt University, Belgium
- National Institute for Applied Statistics Research Australia (NIASRA), University of Wollongong, Australia
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3
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Hauchamps P, Bayat B, Delandre S, Hamrouni M, Toussaint M, Temmerman S, Lin D, Gatto L. CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data. BMC Bioinformatics 2024; 25:80. [PMID: 38378440 PMCID: PMC10877884 DOI: 10.1186/s12859-024-05691-z] [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: 11/10/2023] [Accepted: 02/02/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND With the increase of the dimensionality in flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. A crucial part of these pipelines consists of pre-processing and applying quality control filtering to the raw data, in order to use high quality events in the downstream analyses. This part can in turn be split into a number of elementary steps: signal compensation or unmixing, scale transformation, debris, doublets and dead cells removal, batch effect correction, etc. However, assembling and assessing the pre-processing part can be challenging for a number of reasons. First, each of the involved elementary steps can be implemented using various methods and R packages. Second, the order of the steps can have an impact on the downstream analysis results. Finally, each method typically comes with its specific, non standardized diagnostic and visualizations, making objective comparison difficult for the end user. RESULTS Here, we present CytoPipeline and CytoPipelineGUI, two R packages to build, compare and assess pre-processing pipelines for flow cytometry data. To exemplify these new tools, we present the steps involved in designing a pre-processing pipeline on a real life dataset and demonstrate different visual assessment use cases. We also set up a benchmarking comparing two pre-processing pipelines differing by their quality control methods, and show how the package visualization utilities can provide crucial user insight into the obtained benchmark metrics. CONCLUSION CytoPipeline and CytoPipelineGUI are two Bioconductor R packages that help building, visualizing and assessing pre-processing pipelines for flow cytometry data. They increase productivity during pipeline development and testing, and complement benchmarking tools, by providing user intuitive insight into benchmarking results.
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Affiliation(s)
- Philippe Hauchamps
- Computational Biology and Bioinformatics, de duve Institute, UCLouvain, Brussels, Belgium
| | | | | | | | | | | | | | - Laurent Gatto
- Computational Biology and Bioinformatics, de duve Institute, UCLouvain, Brussels, Belgium.
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4
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Vande Zande P, Siddiq MA, Hodgins-Davis A, Kim L, Wittkopp PJ. Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae. PLoS Genet 2023; 19:e1011078. [PMID: 38091349 PMCID: PMC10752532 DOI: 10.1371/journal.pgen.1011078] [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: 11/10/2023] [Revised: 12/27/2023] [Accepted: 11/22/2023] [Indexed: 12/26/2023] Open
Abstract
Genetic networks are surprisingly robust to perturbations caused by new mutations. This robustness is conferred in part by compensation for loss of a gene's activity by genes with overlapping functions, such as paralogs. Compensation occurs passively when the normal activity of one paralog can compensate for the loss of the other, or actively when a change in one paralog's expression, localization, or activity is required to compensate for loss of the other. The mechanisms of active compensation remain poorly understood in most cases. Here we investigate active compensation for the loss or reduction in expression of the Saccharomyces cerevisiae gene TDH3 by its paralog TDH2. TDH2 is upregulated in a dose-dependent manner in response to reductions in TDH3 by a mechanism requiring the shared transcriptional regulators Gcr1p and Rap1p. TDH1, a second and more distantly related paralog of TDH3, has diverged in its regulation and is upregulated by another mechanism. Other glycolytic genes regulated by Rap1p and Gcr1p show changes in expression similar to TDH2, suggesting that the active compensation by TDH3 paralogs is part of a broader homeostatic response mediated by shared transcriptional regulators.
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Affiliation(s)
- Pétra Vande Zande
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Mohammad A. Siddiq
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Andrea Hodgins-Davis
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lisa Kim
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Patricia J. Wittkopp
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, United States of America
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5
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Dutta S, Box AC, Li Y, Sardiu ME. Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach. Proteomics 2023; 23:e2200290. [PMID: 36852539 DOI: 10.1002/pmic.202200290] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 01/30/2023] [Accepted: 02/17/2023] [Indexed: 03/01/2023]
Abstract
The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a "wisdom of the crowd" approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.
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Affiliation(s)
- Sreejata Dutta
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Andrew C Box
- Stowers Institute for Medical Research, Kansas City, Missouri, USA
| | - Yanming Li
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
- University of Kansas Cancer Center, Kansas City, Kansas, USA
| | - Mihaela E Sardiu
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
- University of Kansas Cancer Center, Kansas City, Kansas, USA
- Kansas Institute for Precision Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
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6
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Guldberg SM, Okholm TLH, McCarthy EE, Spitzer MH. Computational Methods for Single-Cell Proteomics. Annu Rev Biomed Data Sci 2023; 6:47-71. [PMID: 37040735 PMCID: PMC10621466 DOI: 10.1146/annurev-biodatasci-020422-050255] [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] [Indexed: 04/13/2023]
Abstract
Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.
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Affiliation(s)
- Sophia M Guldberg
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
| | - Trine Line Hauge Okholm
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
| | - Elizabeth E McCarthy
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA
- Institute for Human Genetics; Division of Rheumatology, Department of Medicine; Medical Scientist Training Program; and Biological and Medical Informatics Graduate Program, University of California, San Francisco, California, USA
| | - Matthew H Spitzer
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
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7
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Lui A, Lee J, Thall PF, Daher M, Rezvani K, Basar R. A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data. J R Stat Soc Ser C Appl Stat 2023; 72:718-738. [PMID: 37325776 PMCID: PMC10264057 DOI: 10.1093/jrsssc/qlad029] [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: 03/29/2023] [Accepted: 04/02/2023] [Indexed: 06/17/2023]
Abstract
A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A model-based method is used to construct cell clusters within each sample by modeling subpopulations as latent features, using a finite Indian buffet process. Non-ignorable missing data due to technical artifacts in mass cytometry instruments are accounted for by defining a static missingship mechanism. In contrast with conventional cell clustering methods, which cluster observed marker expression levels separately for each sample, the FAM-based method can be applied simultaneously to multiple samples, and also identify important cell subpopulations likely to be otherwise missed. The proposed FAM-based method is applied to jointly analyse three CyTOF datasets to study natural killer (NK) cells. Because the subpopulations identified by the FAM may define novel NK cell subsets, this statistical analysis may provide useful information about the biology of NK cells and their potential role in cancer immunotherapy which may lead, in turn, to development of improved NK cell therapies.
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Affiliation(s)
- Arthur Lui
- Department of Statistics, Baskin School of Engineering, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA, 95064, USA
| | - Juhee Lee
- Department of Statistics, University of California at Santa Cruz, Santa Cruz, CA, USA
| | - Peter F Thall
- Department of Biostatistics, M.D. Anderson Cancer Center, Houston, TX, USA
| | - May Daher
- Department of Stem Cell Transplantation and Cellular Therapy, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Katy Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Rafet Basar
- Department of Stem Cell Transplantation and Cellular Therapy, M.D. Anderson Cancer Center, Houston, TX, USA
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8
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Fuda F, Chen M, Chen W, Cox A. Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress. Semin Diagn Pathol 2023; 40:120-128. [PMID: 36894355 DOI: 10.1053/j.semdp.2023.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/07/2023]
Abstract
There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases. Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines.
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Affiliation(s)
- Franklin Fuda
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Mingyi Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Weina Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Andrew Cox
- Lyda Hill Department of Bioinformatics, University of Texas, Southwestern Medical Center, Dallas, Texas, USA; Department of Cell and Molecular Biology, University of Texas, Southwestern Medical Center, Dallas, Texas, USA.
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9
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Behle A, Dietsch M, Goldschmidt L, Murugathas W, Berwanger L, Burmester J, Yao L, Brandt D, Busche T, Kalinowski J, Hudson E, Ebenhöh O, Axmann I, Machné R. Manipulation of topoisomerase expression inhibits cell division but not growth and reveals a distinctive promoter structure in Synechocystis. Nucleic Acids Res 2022; 50:12790-12808. [PMID: 36533444 PMCID: PMC9825172 DOI: 10.1093/nar/gkac1132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 11/03/2022] [Accepted: 11/10/2022] [Indexed: 12/23/2022] Open
Abstract
In cyanobacteria DNA supercoiling varies over the diurnal cycle and is integrated with temporal programs of transcription and replication. We manipulated DNA supercoiling in Synechocystis sp. PCC 6803 by CRISPRi-based knockdown of gyrase subunits and overexpression of topoisomerase I (TopoI). Cell division was blocked but cell growth continued in all strains. The small endogenous plasmids were only transiently relaxed, then became strongly supercoiled in the TopoI overexpression strain. Transcript abundances showed a pronounced 5'/3' gradient along transcription units, incl. the rRNA genes, in the gyrase knockdown strains. These observations are consistent with the basic tenets of the homeostasis and twin-domain models of supercoiling in bacteria. TopoI induction initially led to downregulation of G+C-rich and upregulation of A+T-rich genes. The transcriptional response quickly bifurcated into six groups which overlap with diurnally co-expressed gene groups. Each group shows distinct deviations from a common core promoter structure, where helically phased A-tracts are in phase with the transcription start site. Together, our data show that major co-expression groups (regulons) in Synechocystis all respond differentially to DNA supercoiling, and suggest to re-evaluate the long-standing question of the role of A-tracts in bacterial promoters.
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Affiliation(s)
| | | | - Louis Goldschmidt
- Institut f. Quantitative u. Theoretische Biologie, Heinrich-Heine Universität Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany
| | - Wandana Murugathas
- Institut f. Synthetische Mikrobiologie, Heinrich-Heine Universität Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany
| | - Lutz C Berwanger
- Institut f. Synthetische Mikrobiologie, Heinrich-Heine Universität Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany
| | - Jonas Burmester
- Institut f. Synthetische Mikrobiologie, Heinrich-Heine Universität Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany
| | - Lun Yao
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm, Sweden
| | - David Brandt
- Centrum für Biotechnologie (CeBiTec), Universität Bielefeld, Universitätsstrasse 27, 33615 Bielefeld, Germany
| | - Tobias Busche
- Centrum für Biotechnologie (CeBiTec), Universität Bielefeld, Universitätsstrasse 27, 33615 Bielefeld, Germany
| | - Jörn Kalinowski
- Centrum für Biotechnologie (CeBiTec), Universität Bielefeld, Universitätsstrasse 27, 33615 Bielefeld, Germany
| | - Elton P Hudson
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm, Sweden
| | - Oliver Ebenhöh
- Institut f. Quantitative u. Theoretische Biologie, Heinrich-Heine Universität Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany,Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Ilka M Axmann
- Institut f. Synthetische Mikrobiologie, Heinrich-Heine Universität Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany
| | - Rainer Machné
- To whom correspondence should be addressed. Tel: +49 211 81 12923;
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10
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Agglomerative and divisive hierarchical Bayesian clustering. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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11
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Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity. Nat Commun 2022; 13:5455. [PMID: 36114209 PMCID: PMC9481560 DOI: 10.1038/s41467-022-33136-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 09/05/2022] [Indexed: 11/30/2022] Open
Abstract
Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. It is widely used in computer science, bioscience, geoscience, and economics. Although the state-of-the-art partition-based and connectivity-based clustering methods have been developed, weak connectivity and heterogeneous density in data impede their effectiveness. In this work, we propose a boundary-seeking Clustering algorithm using the local Direction Centrality (CDC). It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points, thereby preventing cross-cluster connections and separating weakly-connected clusters. We demonstrate the validity of CDC by detecting complex structured clusters in challenging synthetic datasets, identifying cell types from single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF) data, recognizing speakers on voice corpuses, and testifying on various types of real-world benchmarks. Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. Here the authors propose a local direction centrality clustering algorithm that copes with heterogeneous density and weak connectivity issues.
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12
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Transformation of the symbiotic alga Oophila amblystomatis: a new tool for animal-algae symbiosis studies. Symbiosis 2022. [DOI: 10.1007/s13199-022-00861-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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13
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Lin TC, Palei S, Summerer D. Optochemical Control of TET Dioxygenases Enables Kinetic Insights into the Domain-Dependent Interplay of TET1 and MBD1 while Oxidizing and Reading 5-Methylcytosine. ACS Chem Biol 2022; 17:1844-1852. [PMID: 35709470 PMCID: PMC9295125 DOI: 10.1021/acschembio.2c00245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Methyl-CpG binding
domain (MBD) proteins and ten-eleven-translocation
(TET) dioxygenases are the readers and erasers of 5-methylcytosine
(5mC), the central epigenetic mark of mammalian DNA. We employ light-activatable
human TET1 controlled by a genetically encoded photocaged serine to
enable in vivo kinetic studies of their interplay at the common substrate
methylated cytosine–guanine (mCpG). We identify the multidomain
reader MBD1 to negatively regulate TET1-catalyzed 5mC oxidation kinetics
via its mCpG-binding MBD domain. However, we also identify the third
Cys-x-x-Cys (CXXC3) domain of MBD1 to promote oxidation kinetics by
TET1, dependent on its ability to bind nonmethylated CpG, the final
product of TET-mediated mCpG oxidation and active demethylation. In
contrast, we do not observe differences in TET1 regulation for MBD1
variants with or without the transcriptional repressor domain. Our
approach reveals a complex, domain-dependent interplay of these readers
and erasers of 5mC with different domain-specific contributions of
MBD1 to the overall kinetics of TET1-catalyzed global 5mC oxidation
kinetics that contribute to a better understanding of dynamic methylome
shaping.
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Affiliation(s)
- Tzu-Chen Lin
- Department of Chemistry and Chemical Biology, Technical University of Dortmund, Otto-Hahn-Str. 4a, 44227 Dortmund, Germany
| | - Shubhendu Palei
- Department of Chemistry and Chemical Biology, Technical University of Dortmund, Otto-Hahn-Str. 4a, 44227 Dortmund, Germany
| | - Daniel Summerer
- Department of Chemistry and Chemical Biology, Technical University of Dortmund, Otto-Hahn-Str. 4a, 44227 Dortmund, Germany
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14
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An overview of skew distributions in model-based clustering. J MULTIVARIATE ANAL 2022. [DOI: 10.1016/j.jmva.2021.104853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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15
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López Hernández JF, Helston RM, Lange JJ, Billmyre RB, Schaffner SH, Eickbush MT, McCroskey S, Zanders SE. Diverse mating phenotypes impact the spread of wtf meiotic drivers in Schizosaccharomyces pombe. eLife 2021; 10:e70812. [PMID: 34895466 PMCID: PMC8789285 DOI: 10.7554/elife.70812] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 12/10/2021] [Indexed: 11/13/2022] Open
Abstract
Meiotic drivers are genetic elements that break Mendel's law of segregation to be transmitted into more than half of the offspring produced by a heterozygote. The success of a driver relies on outcrossing (mating between individuals from distinct lineages) because drivers gain their advantage in heterozygotes. It is, therefore, curious that Schizosaccharomyces pombe, a species reported to rarely outcross, harbors many meiotic drivers. To address this paradox, we measured mating phenotypes in S. pombe natural isolates. We found that the propensity for cells from distinct clonal lineages to mate varies between natural isolates and can be affected both by cell density and by the available sexual partners. Additionally, we found that the observed levels of preferential mating between cells from the same clonal lineage can slow, but not prevent, the spread of a wtf meiotic driver in the absence of additional fitness costs linked to the driver. These analyses reveal parameters critical to understanding the evolution of S. pombe and help explain the success of meiotic drivers in this species.
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Affiliation(s)
| | | | - Jeffrey J Lange
- Stowers Institute for Medical ResearchKansas CityUnited States
| | | | - Samantha H Schaffner
- Stowers Institute for Medical ResearchKansas CityUnited States
- Kenyon CollegeGambierUnited States
| | | | - Scott McCroskey
- Stowers Institute for Medical ResearchKansas CityUnited States
| | - Sarah E Zanders
- Stowers Institute for Medical ResearchKansas CityUnited States
- Department of Molecular and Integrative Physiology, University of Kansas Medical CenterKansas CityUnited States
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16
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Aanei CM, Veyrat-Masson R, Rigollet L, Stagnara J, Tavernier Tardy E, Daguenet E, Guyotat D, Campos Catafal L. Advanced Flow Cytometry Analysis Algorithms for Optimizing the Detection of "Different From Normal" Immunophenotypes in Acute Myeloid Blasts. Front Cell Dev Biol 2021; 9:735518. [PMID: 34650981 PMCID: PMC8506133 DOI: 10.3389/fcell.2021.735518] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/30/2021] [Indexed: 11/17/2022] Open
Abstract
Acute myeloid leukemias (AMLs) are a group of hematologic malignancies that are heterogeneous in their molecular and immunophenotypic profiles. Identification of the immunophenotypic differences between AML blasts and normal myeloid hematopoietic precursors (myHPCs) is a prerequisite to achieving better performance in AML measurable residual disease follow-ups. In the present study, we applied high-dimensional analysis algorithms provided by the Infinicyt 2.0 and Cytobank software to evaluate the efficacy of antibody combinations of the EuroFlow AML/myelodysplastic syndrome panel to distinguish AML blasts with recurrent genetic abnormalities (n = 39 AML samples) from normal CD45low CD117+ myHPCs (n = 23 normal bone marrow samples). Two types of scores were established to evaluate the abilities of the various methods to identify the most useful parameters/markers for distinguishing between AML blasts and normal myHPCs, as well as to distinguish between different AML groups. The Infinicyt Compass database-guided analysis was found to be a more user-friendly tool than other analysis methods implemented in the Cytobank software. According to the developed scoring systems, the principal component analysis based algorithms resulted in better discrimination between AML blasts and myHPCs, as well as between blasts from different AML groups. The most informative markers for the discrimination between myHPCs and AML blasts were CD34, CD36, human leukocyte antigen-DR (HLA-DR), CD13, CD105, CD71, and SSC, which were highly rated by all evaluated analysis algorithms. The HLA-DR, CD34, CD13, CD64, CD33, CD117, CD71, CD36, CD11b, SSC, and FSC were found to be useful for the distinction between blasts from different AML groups associated with recurrent genetic abnormalities. This study identified both benefits and the drawbacks of integrating multiple high-dimensional algorithms to gain complementary insights into the flow-cytometry data.
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Affiliation(s)
- Carmen-Mariana Aanei
- Laboratoire d’Hématologie, Centre Hospitalier Universitaire de Saint-Étienne, Saint-Étienne, France
| | - Richard Veyrat-Masson
- Laboratoire d’Hématologie, Centre Hospitalier Universitaire de Clermont-Ferrand, Clermont-Ferrand, France
| | - Lauren Rigollet
- Laboratoire d’Hématologie, Centre Hospitalier Universitaire de Saint-Étienne, Saint-Étienne, France
| | - Jérémie Stagnara
- Laboratoire d’Hématologie, Centre Hospitalier Universitaire de Saint-Étienne, Saint-Étienne, France
| | | | | | - Denis Guyotat
- Institut de Cancérologie Lucien Neuwirth, Saint-Priest-en-Jarez, France
| | - Lydia Campos Catafal
- Laboratoire d’Hématologie, Centre Hospitalier Universitaire de Saint-Étienne, Saint-Étienne, France
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17
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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: 72] [Impact Index Per Article: 24.0] [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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18
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Horndler L, Delgado P, Abia D, Balabanov I, Martínez‐Fleta P, Cornish G, Llamas MA, Serrano‐Villar S, Sánchez‐Madrid F, Fresno M, van Santen HM, Alarcón B. Flow cytometry multiplexed method for the detection of neutralizing human antibodies to the native SARS-CoV-2 spike protein. EMBO Mol Med 2021; 13:e13549. [PMID: 33471406 PMCID: PMC7933943 DOI: 10.15252/emmm.202013549] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/13/2021] [Accepted: 01/15/2021] [Indexed: 12/15/2022] Open
Abstract
A correct identification of seropositive individuals for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is of paramount relevance to assess the degree of protection of a human population to present and future outbreaks of the COVID-19 pandemic. We describe here a sensitive and quantitative flow cytometry method using the cytometer-friendly non-adherent Jurkat T-cell line that stably expresses the full-length native spike "S" protein of SARS-CoV-2 and a truncated form of the human EGFR that serves a normalizing role. S protein and huEGFRt coding sequences are separated by a T2A self-cleaving sequence, allowing to accurately quantify the presence of anti-S immunoglobulins by calculating a score based on the ratio of fluorescence intensities obtained by double-staining with the test sera and anti-EGFR. The method allows to detect immune individuals regardless of the result of other serological tests or even repeated PCR monitoring. As examples of its use, we show that as much as 28% of the personnel working at the CBMSO in Madrid is already immune. Additionally, we show that anti-S antibodies with protective neutralizing activity are long-lasting and can be detected in sera 8 months after infection.
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Affiliation(s)
- Lydia Horndler
- Centro de Biología Molecular Severo OchoaConsejo Superior de Investigaciones Científicas (CSIC)Universidad Autónoma de MadridMadridSpain
| | - Pilar Delgado
- Centro de Biología Molecular Severo OchoaConsejo Superior de Investigaciones Científicas (CSIC)Universidad Autónoma de MadridMadridSpain
| | - David Abia
- Bioinformatics FacilityCentro de Biología Molecular Severo OchoaConsejo Superior de Investigaciones Científicas (CSIC)Universidad Autónoma de MadridMadridSpain
| | - Ivaylo Balabanov
- Centro de Biología Molecular Severo OchoaConsejo Superior de Investigaciones Científicas (CSIC)Universidad Autónoma de MadridMadridSpain
| | | | | | | | | | | | - Manuel Fresno
- Centro de Biología Molecular Severo OchoaConsejo Superior de Investigaciones Científicas (CSIC)Universidad Autónoma de MadridMadridSpain
| | - Hisse M van Santen
- Centro de Biología Molecular Severo OchoaConsejo Superior de Investigaciones Científicas (CSIC)Universidad Autónoma de MadridMadridSpain
| | - Balbino Alarcón
- Centro de Biología Molecular Severo OchoaConsejo Superior de Investigaciones Científicas (CSIC)Universidad Autónoma de MadridMadridSpain
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19
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Cheung M, Campbell JJ, Whitby L, Thomas RJ, Braybrook J, Petzing J. Current trends in flow cytometry automated data analysis software. Cytometry A 2021; 99:1007-1021. [PMID: 33606354 DOI: 10.1002/cyto.a.24320] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/21/2021] [Accepted: 01/28/2021] [Indexed: 12/16/2022]
Abstract
Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts. Dimensionality reduction methods ranked highly, such as generic t-distributed stochastic neighbor embedding (t-SNE) and its initial Matlab-based implementation for cytometry data viSNE. Software with graphical user interfaces also ranked highly, including PhenoGraph, SPADE1, FlowSOM, and Citrus, with unsupervised learning methods outnumbering supervised learning methods, and algorithm type popularity spread across K-Means, hierarchical, density-based, model-based, and other classes of clustering algorithms. Additionally, to illustrate the actual use typically within clinical spaces alongside frequent citations, a survey issued by UK NEQAS Leucocyte Immunophenotyping to identify software usage trends among clinical laboratories was completed. The survey revealed 53% of laboratories have not yet taken up automated cell population identification methods, though among those that have, Infinicyt software is the most frequently identified. Survey respondents considered data output quality to be the most important factor when using automated FC data analysis software, followed by software speed and level of technical support. This review found differences in software usage between biomedical institutions, with tools for discovery, data exploration, and visualization more popular in academia, whereas automated tools for specialized targeted analysis that apply supervised learning methods were more used in clinical settings.
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Affiliation(s)
- Melissa Cheung
- Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom
| | | | - Liam Whitby
- UK NEQAS for Leucocyte Immunophenotyping, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - Robert J Thomas
- Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom
| | - Julian Braybrook
- National Measurement Laboratory, LGC, Teddington, United Kingdom
| | - Jon Petzing
- Centre for Biological Engineering, Loughborough University, Loughborough, Leicestershire, United Kingdom
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20
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Cavallaro M, Walsh MD, Jones M, Teahan J, Tiberi S, Finkenstädt B, Hebenstreit D. 3 '-5 ' crosstalk contributes to transcriptional bursting. Genome Biol 2021; 22:56. [PMID: 33541397 PMCID: PMC7860045 DOI: 10.1186/s13059-020-02227-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Transcription in mammalian cells is a complex stochastic process involving shuttling of polymerase between genes and phase-separated liquid condensates. It occurs in bursts, which results in vastly different numbers of an mRNA species in isogenic cell populations. Several factors contributing to transcriptional bursting have been identified, usually classified as intrinsic, in other words local to single genes, or extrinsic, relating to the macroscopic state of the cell. However, some possible contributors have not been explored yet. Here, we focus on processes at the 3 ' and 5 ' ends of a gene that enable reinitiation of transcription upon termination. RESULTS Using Bayesian methodology, we measure the transcriptional bursting in inducible transgenes, showing that perturbation of polymerase shuttling typically reduces burst size, increases burst frequency, and thus limits transcriptional noise. Analysis based on paired-end tag sequencing (PolII ChIA-PET) suggests that this effect is genome wide. The observed noise patterns are also reproduced by a generative model that captures major characteristics of the polymerase flux between the ends of a gene and a phase-separated compartment. CONCLUSIONS Interactions between the 3 ' and 5 ' ends of a gene, which facilitate polymerase recycling, are major contributors to transcriptional noise.
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Affiliation(s)
- Massimo Cavallaro
- School of Life Sciences, University of Warwick, Coventry, UK.
- Mathematics Institute and Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK.
- Department of Statistics, University of Warwick, Coventry, UK.
| | - Mark D Walsh
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Matt Jones
- School of Life Sciences, University of Warwick, Coventry, UK
| | - James Teahan
- Department of Chemistry, University of Warwick, Coventry, UK
| | - Simone Tiberi
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
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21
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Abstract
Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.
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Affiliation(s)
| | - Ruben Props
- Center for Microbial Ecology & Technology (CMET), Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
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22
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Del Barrio E, Inouzhe H, Loubes JM, Matrán C, Mayo-Íscar A. optimalFlow: optimal transport approach to flow cytometry gating and population matching. BMC Bioinformatics 2020; 21:479. [PMID: 33109072 PMCID: PMC7590740 DOI: 10.1186/s12859-020-03795-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 10/01/2020] [Indexed: 11/12/2022] Open
Abstract
Background Data obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating. Results We propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow, a Bioconductor R package at https://bioconductor.org/packages/optimalFlow. Conclusions optimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.
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Affiliation(s)
- Eustasio Del Barrio
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Calle Paseo de Belén, Valladolid, Spain.,IMUVA, Calle Paseo de Belén, Valladolid, Spain
| | - Hristo Inouzhe
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Calle Paseo de Belén, Valladolid, Spain. .,IMUVA, Calle Paseo de Belén, Valladolid, Spain.
| | - Jean-Michel Loubes
- Université Paul Sabatier, Route de Narbonne, Toulouse, France.,IMT, Route de Narbonne, Toulouse, France
| | - Carlos Matrán
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Calle Paseo de Belén, Valladolid, Spain.,IMUVA, Calle Paseo de Belén, Valladolid, Spain
| | - Agustín Mayo-Íscar
- Departamento de Estadística e Investigación Operativa, Universidad de Valladolid, Calle Paseo de Belén, Valladolid, Spain.,IMUVA, Calle Paseo de Belén, Valladolid, Spain
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23
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Fedorec AJ, Robinson CM, Wen KY, Barnes CP. FlopR: An Open Source Software Package for Calibration and Normalization of Plate Reader and Flow Cytometry Data. ACS Synth Biol 2020; 9:2258-2266. [PMID: 32854500 PMCID: PMC7506944 DOI: 10.1021/acssynbio.0c00296] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Indexed: 01/03/2023]
Abstract
The measurement of gene expression using fluorescence markers has been a cornerstone of synthetic biology for the past two decades. However, the use of arbitrary units has limited the usefulness of these data for many quantitative purposes. Calibration of fluorescence measurements from flow cytometry and plate reader spectrophotometry has been implemented previously, but the tools are disjointed. Here we pull together, and in some cases improve, extant methods into a single software tool, written as a package in the R statistical framework. The workflow is validated using Escherichia coli engineered to express green fluorescent protein (GFP) from a set of commonly used constitutive promoters. We then demonstrate the package's power by identifying the time evolution of distinct subpopulations of bacteria from bulk plate reader data, a task previously reliant on laborious flow cytometry or colony counting experiments. Along with standardized parts and experimental methods, the development and dissemination of usable tools for quantitative measurement and data analysis will benefit the synthetic biology community by improving interoperability.
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Affiliation(s)
- Alex J.
H. Fedorec
- Department
of Cell and Developmental Biology, University
College London, London WC1E 6BT, U.K.
| | - Clare M. Robinson
- Department
of Cell and Developmental Biology, University
College London, London WC1E 6BT, U.K.
| | - Ke Yan Wen
- Department
of Cell and Developmental Biology, University
College London, London WC1E 6BT, U.K.
| | - Chris P. Barnes
- Department
of Cell and Developmental Biology, University
College London, London WC1E 6BT, U.K.
- UCL
Genetics Institute, University College London, London WC1E 6BT, U.K.
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24
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Sulen A, Islam S, Wolff ASB, Oftedal BE. The prospects of single-cell analysis in autoimmunity. Scand J Immunol 2020; 92:e12964. [PMID: 32869859 DOI: 10.1111/sji.12964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/18/2020] [Accepted: 08/21/2020] [Indexed: 12/29/2022]
Abstract
In the last decade, there has been a tremendous development of technologies focused on analysing various molecular attributes in single cells, with an ever-increasing number of parameters becoming available at the DNA, RNA and protein levels. Much of this progress has involved cells in suspension, but also in situ analysis of tissues has taken great leaps. Paralleling the development in the laboratory, and because of increasing complexity, the analysis of single-cell data is also constantly being updated with new algorithms and analysis platforms. Our immune system shares this complexity, and immunologists have therefore been in the forefront of this technological development. These technologies clearly open new avenues for immunology research, maybe particularly within autoimmunity where the interaction between the faulty immune system and the thymus or the target organ is important. However, the technologies currently available can seem overwhelming and daunting. The aim of this review is to remedy this by giving a balanced overview of the prospects of using single-cell analysis in basal and clinical autoimmunity research, with an emphasis on endocrine autoimmunity.
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Affiliation(s)
- André Sulen
- KG Jebsen Center for Autoimmune Disorders, University of Bergen, Bergen, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Shahinul Islam
- KG Jebsen Center for Autoimmune Disorders, University of Bergen, Bergen, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway.,Department of Medicine, Haukeland University Hospital, Bergen, Norway
| | - Anette S B Wolff
- KG Jebsen Center for Autoimmune Disorders, University of Bergen, Bergen, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway.,Department of Medicine, Haukeland University Hospital, Bergen, Norway
| | - Bergithe E Oftedal
- KG Jebsen Center for Autoimmune Disorders, University of Bergen, Bergen, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway
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25
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Keyes TJ, Domizi P, Lo YC, Nolan GP, Davis KL. A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry. Cytometry A 2020; 97:782-799. [PMID: 32602650 PMCID: PMC7416435 DOI: 10.1002/cyto.a.24158] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 03/10/2020] [Accepted: 05/12/2020] [Indexed: 12/11/2022]
Abstract
The application of machine learning and artificial intelligence to high-dimensional cytometry data sets has increasingly become a staple of bioinformatic data analysis over the past decade. This is especially true in the field of cancer biology, where protocols for collecting multiparameter single-cell data in a high-throughput fashion are rapidly developed. As the use of machine learning methodology in cytometry becomes increasingly common, there is a need for cancer biologists to understand the basic theory and applications of a variety of algorithmic tools for analyzing and interpreting cytometry data. We introduce the reader to several keystone machine learning-based analytic approaches with an emphasis on defining key terms and introducing a conceptual framework for making translational or clinically relevant discoveries. The target audience consists of cancer cell biologists and physician-scientists interested in applying these tools to their own data, but who may have limited training in bioinformatics. © 2020 International Society for Advancement of Cytometry.
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Affiliation(s)
- Timothy J Keyes
- Medical Scientist Training Program, Stanford University School of Medicine, Stanford, California
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Pablo Domizi
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Yu-Chen Lo
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Garry P Nolan
- Department of Microbiology and Immunology | Baxter Laboratory for Stem Cell Biology, Stanford University School of Medicine, Stanford, California
| | - Kara L Davis
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
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26
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Rab11b-mediated integrin recycling promotes brain metastatic adaptation and outgrowth. Nat Commun 2020; 11:3017. [PMID: 32541798 PMCID: PMC7295786 DOI: 10.1038/s41467-020-16832-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 05/27/2020] [Indexed: 12/11/2022] Open
Abstract
Breast cancer brain metastases (BCBM) have a 5-20 year latency and account for 30% of mortality; however, mechanisms governing adaptation to the brain microenvironment remain poorly defined. We combine time-course RNA-sequencing of BCBM development with a Drosophila melanogaster genetic screen, and identify Rab11b as a functional mediator of metastatic adaptation. Proteomic analysis reveals that Rab11b controls the cell surface proteome, recycling proteins required for successful interaction with the microenvironment, including integrin β1. Rab11b-mediated control of integrin β1 surface expression allows efficient engagement with the brain ECM, activating mechanotransduction signaling to promote survival. Lipophilic statins prevent membrane association and activity of Rab11b, and we provide proof-of principle that these drugs prevent breast cancer adaptation to the brain microenvironment. Our results identify Rab11b-mediated recycling of integrin β1 as regulating BCBM, and suggest that the recycleome, recycling-based control of the cell surface proteome, is a previously unknown driver of metastatic adaptation and outgrowth.
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Liu P, Liu S, Fang Y, Xue X, Zou J, Tseng G, Konnikova L. Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data. Front Cell Dev Biol 2020; 8:234. [PMID: 32411698 PMCID: PMC7198724 DOI: 10.3389/fcell.2020.00234] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 03/20/2020] [Indexed: 11/13/2022] Open
Abstract
The progress in the field of high-dimensional cytometry has greatly increased the number of markers that can be simultaneously analyzed producing datasets with large numbers of parameters. Traditional biaxial manual gating might not be optimal for such datasets. To overcome this, a large number of automated tools have been developed to aid with cellular clustering of multi-dimensional datasets. Here were review two large categories of such tools; unsupervised and supervised clustering tools. After a thorough review of the popularity and use of each of the available unsupervised clustering tools, we focus on the top six tools to discuss their advantages and limitations. Furthermore, we employ a publicly available dataset to directly compare the usability, speed, and relative effectiveness of the available unsupervised and supervised tools. Finally, we discuss the current challenges for existing methods and future direction for the new generation of cell type identification approaches.
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Affiliation(s)
- Peng Liu
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Silvia Liu
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Yusi Fang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xiangning Xue
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jian Zou
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - George Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Liza Konnikova
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Immunology, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, United States
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Rybakowska P, Alarcón-Riquelme ME, Marañón C. Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry. Comput Struct Biotechnol J 2020; 18:874-886. [PMID: 32322369 PMCID: PMC7163213 DOI: 10.1016/j.csbj.2020.03.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 03/18/2020] [Accepted: 03/25/2020] [Indexed: 01/05/2023] Open
Abstract
High-dimensional, single-cell cell technologies revolutionized the way to study biological systems, and polychromatic flow cytometry (FC) and mass cytometry (MC) are two of the drivers of this revolution. As up to 30-50 dimensions respectively can be measured per single-cell, they allow deep phenotyping combined with cellular functions studies, like cytokine production or protein phosphorylation. In parallel, the bioinformatics field develops algorithms that are able to process incoming data and extract the most useful and meaningful biological information. However, the success of automated analysis tools depends on the generation of high-quality data. In this review we present the most recent FC and MC computational approaches that are used to prepare, process and interpret high-content cytometry data. We also underscore proper experimental design as a key step for obtaining good quality data.
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Affiliation(s)
- Paulina Rybakowska
- GENYO, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Spain
| | - Marta E. Alarcón-Riquelme
- GENYO, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Spain
- Institute for Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Concepción Marañón
- GENYO, Centre for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, Spain
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Lucchesi S, Furini S, Medaglini D, Ciabattini A. From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies. Vaccines (Basel) 2020; 8:E138. [PMID: 32244919 PMCID: PMC7157606 DOI: 10.3390/vaccines8010138] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 03/17/2020] [Accepted: 03/18/2020] [Indexed: 12/15/2022] Open
Abstract
Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.
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Affiliation(s)
- Simone Lucchesi
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (S.L.); (D.M.)
| | - Simone Furini
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy;
| | - Donata Medaglini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (S.L.); (D.M.)
| | - Annalisa Ciabattini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (S.L.); (D.M.)
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Ludwig J, Zu Siederdissen CH, Liu Z, Stadler PF, Müller S. flowEMMi: an automated model-based clustering tool for microbial cytometric data. BMC Bioinformatics 2019; 20:643. [PMID: 31815609 PMCID: PMC6902487 DOI: 10.1186/s12859-019-3152-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 10/10/2019] [Indexed: 12/17/2022] Open
Abstract
Background Flow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM. Results We bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further. Conclusions flowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.
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Affiliation(s)
- Joachim Ludwig
- Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
| | | | - Zishu Liu
- Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
| | - Peter F Stadler
- Department of Computer Science, University Leipzig, Härtelstr. 16-18, Leipzig, 04107, Germany
| | - Susann Müller
- Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany
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Lucchesi S, Nolfi E, Pettini E, Pastore G, Fiorino F, Pozzi G, Medaglini D, Ciabattini A. Computational Analysis of Multiparametric Flow Cytometric Data to Dissect B Cell Subsets in Vaccine Studies. Cytometry A 2019; 97:259-267. [PMID: 31710181 PMCID: PMC7079172 DOI: 10.1002/cyto.a.23922] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 09/11/2019] [Accepted: 10/07/2019] [Indexed: 01/03/2023]
Abstract
The generation of the B cell response upon vaccination is characterized by the induction of different functional and phenotypic subpopulations and is strongly dependent on the vaccine formulation, including the adjuvant used. Here, we have profiled the different B cell subsets elicited upon vaccination, using machine learning methods for interpreting high‐dimensional flow cytometry data sets. The B cell response elicited by an adjuvanted vaccine formulation, compared to the antigen alone, was characterized using two automated methods based on clustering (FlowSOM) and dimensional reduction (t‐SNE) approaches. The clustering method identified, based on multiple marker expression, different B cell populations, including plasmablasts, plasma cells, germinal center B cells and their subsets, while this profiling was more difficult with t‐SNE analysis. When undefined phenotypes were detected, their characterization could be improved by integrating the t‐SNE spatial visualization of cells with the FlowSOM clusters. The frequency of some cellular subsets, in particular plasma cells, was significantly higher in lymph nodes of mice primed with the adjuvanted formulation compared to antigen alone. Thanks to this automatic data analysis it was possible to identify, in an unbiased way, different B cell populations and also intermediate stages of cell differentiation elicited by immunization, thus providing a signature of B cell recall response that can be hardly obtained with the classical bidimensional gating analysis. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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Affiliation(s)
- Simone Lucchesi
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Emanuele Nolfi
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Elena Pettini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Gabiria Pastore
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Fabio Fiorino
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Gianni Pozzi
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Donata Medaglini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
| | - Annalisa Ciabattini
- Laboratory of Molecular Microbiology and Biotechnology (LA.M.M.B.), Department of Medical BiotechnologiesUniversity of SienaSienaItaly
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High throughput pSTAT signaling profiling by fluorescent cell barcoding and computational analysis. J Immunol Methods 2019; 477:112667. [PMID: 31726053 DOI: 10.1016/j.jim.2019.112667] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 07/08/2019] [Accepted: 09/12/2019] [Indexed: 12/31/2022]
Abstract
Fluorescent cell barcoding (FCB) is a multiplexing technique for high-throughput flow cytometry (FCM). Although powerful in minimizing staining variability, it remains a subjective FCM technique because of inter-operator variability and differences in data analysis. FCB was implemented by combining two-dye barcoding (DyLight 350 plus Pacific Orange) with five-color surface marker antibody and intracellular staining for phosphoprotein signaling analysis. We proposed a robust method to measure intra- and inter-assay variability of FCB in T/B cells and monocytes by combining range and ratio of variability to standard statistical analyses. Data analysis was carried out by conventional and semi-automated workflows and built with R software. Results obtained from both analyses were compared to assess feasibility and reproducibility of FCB data analysis by machine-learning methods. Our results showed efficient FCB using DyLight 350 and Pacific Orange at concentrations of 0, 15 or 30, and 250 μg/mL, and a high reproducibility of FCB in combination with surface marker and intracellular antibodies. Inter-operator variability was minimized by adding an internal control bridged across matrices used as rejection criterion if significant differences were present between runs. Computational workflows showed comparable results to conventional gating strategies. FCB can be used to study phosphoprotein signaling in T/B cells and monocytes with high reproducibility across operators, and the addition of bridge internal controls can further minimize inter-operator variability. This FCB protocol, which has high throughput analysis and low intra- and inter-assay variability, can be a powerful tool for clinical trial studies. Moreover, FCB data can be reliably analyzed using computational software.
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Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A 2019; 116:21085-21093. [PMID: 31570626 DOI: 10.1073/pnas.1902823116] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Understanding how phenotypes evolve requires disentangling the effects of mutation generating new variation from the effects of selection filtering it. Tests for selection frequently assume that mutation introduces phenotypic variation symmetrically around the population mean, yet few studies have tested this assumption by deeply sampling the distributions of mutational effects for particular traits. Here, we examine distributions of mutational effects for gene expression in the budding yeast Saccharomyces cerevisiae by measuring the effects of thousands of point mutations introduced randomly throughout the genome. We find that the distributions of mutational effects differ for the 10 genes surveyed and are inconsistent with normality. For example, all 10 distributions of mutational effects included more mutations with large effects than expected for normally distributed phenotypes. In addition, some genes also showed asymmetries in their distribution of mutational effects, with new mutations more likely to increase than decrease the gene's expression or vice versa. Neutral models of regulatory evolution that take these empirically determined distributions into account suggest that neutral processes may explain more expression variation within natural populations than currently appreciated.
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Czech E, Aksoy BA, Aksoy P, Hammerbacher J. Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging. BMC Bioinformatics 2019; 20:448. [PMID: 31477013 PMCID: PMC6720861 DOI: 10.1186/s12859-019-3055-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 08/26/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind. RESULTS Cytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset. CONCLUSION Cytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit .
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Affiliation(s)
- Eric Czech
- Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA.
| | - Bulent Arman Aksoy
- Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA
| | - Pinar Aksoy
- Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA
| | - Jeff Hammerbacher
- Microbiology and Immunology Department at Medical University of South Carolina, Charleston, USA
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Beal J, Overney C, Adler A, Yaman F, Tiberio L, Samineni M. TASBE Flow Analytics: A Package for Calibrated Flow Cytometry Analysis. ACS Synth Biol 2019; 8:1524-1529. [PMID: 31053031 DOI: 10.1021/acssynbio.8b00533] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Flow cytometry is a powerful method for high-throughput precision measurement of cell fluorescence and size. Effective use of this tool for quantification of synthetic biology devices and circuits, however, generally requires careful application of complex multistage workflows for calibration, filtering, and analysis with appropriate statistics. The TASBE Flow Analytics package provides a free, open, and accessible implementation of such workflows in a form designed for high-throughput analysis of large synthetic biology data sets. Given a set of experimental samples and controls, this package can process them to output calibrated data, quantitative analyses and comparisons, automatically generated figures, and detailed debugging and diagnostic reports in both human-readable and machine-readable forms. TASBE Flow Analytics can be used through a simple user-friendly interactive Excel interface, as a library supporting Matlab, Octave, or Python interactive sessions, or as a component integrated into automated workflows.
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Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Cassandra Overney
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
- Olin College, Needham, Massachusetts 02492, United States
| | - Aaron Adler
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Fusun Yaman
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Lisa Tiberio
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Meher Samineni
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
- University of Utah, Salt Lake City, Utah 84112, United States
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Lee H, Sun Y, Patti-Diaz L, Hedrick M, Ehrhardt AG. High-Throughput Analysis of Clinical Flow Cytometry Data by Automated Gating. Bioinform Biol Insights 2019; 13:1177932219838851. [PMID: 30983860 PMCID: PMC6448119 DOI: 10.1177/1177932219838851] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 02/13/2019] [Indexed: 11/30/2022] Open
Abstract
Advancements in flow cytometers with capability to measure 15 or more parameters have enabled us to characterize cell populations at unprecedented levels of detail. Beyond discovery research, there is now a growing demand to dive deeper into evaluating the immune response in clinical trials for immune modulating compounds. However, for high-volume, complex flow cytometry data generated in clinical trials, conventional manual gating remains the standard of practice. Traditional manual gating is resource intense and becomes a bottleneck and an impractical method to complete high volumes of flow cytometry data analysis. Current efforts to automate “manual gating” have shown that computational algorithms can facilitate the analysis of daunting multi-parameter data; however, a greater degree of precision in comparison with traditional manual gating is needed for wide-scale adoption of automated gating methods. In an effort to more closely follow the manual gating process, our automated gating pipeline was created to include negative controls (Fluorescence Minus One [FMO]) to enhance the reliability of gate placement. We demonstrate that use of an automated pipeline, heavily relying on FMO controls for population discrimination, can analyze multi-parameter, large-scale clinical datasets with comparable precision and accuracy to traditional manual gating.
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Affiliation(s)
- Hunjoong Lee
- Hunjoong Lee, Clinical Flow Cytometry, Translational Medicine, Bristol-Myers Squibb, Pennington, NJ 08534, USA.
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Zhang X, Li B, Xu H, Wells M, Tefsen B, Qin B. Effect of micronutrients on algae in different regions of Taihu, a large, spatially diverse, hypereutrophic lake. WATER RESEARCH 2019; 151:500-514. [PMID: 30641465 DOI: 10.1016/j.watres.2018.12.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 12/04/2018] [Accepted: 12/05/2018] [Indexed: 05/03/2023]
Abstract
Eutrophication or excessive nutrient richness is an impairment of many freshwater ecosystems and a prominent cause of harmful algal blooms. It is generally accepted that nitrogen and phosphorus nutrients are the primary causative factor, however, for systems subject to large anthropogenic perturbation, this may no longer be true, and the role of micronutrients is often overlooked. Here we report a study on Lake Tai (Taihu), a large, spatially diverse and hypereutrophic lake in China. We performed small-scale mesocosm nutrient limitation bioassays using boron, iron, cobalt, copper, molybdenum, nitrogen and phosphorus on phytoplankton communities sampled from different locations in Taihu to test the relative effects of micronutrients on in situ algal assemblages. In addition to commonly-used methods of chemical and biological analysis (including algal phytoplankton counting), we used flow cytometry coupled with data-driven analysis to monitor changes to algal assemblages. We found statistically significant effects of limitation or co-limitation for boron, cobalt, copper and iron. For copper at one location chlorophyll-a was over four times higher for amendment with copper, nitrogen and phosphorous than for the latter two alone. Since copper is often proposed as amendment for the environmental management of harmful algal blooms, this result is significant. We have three primary conclusions: first, the strong effects for Cu that we report here are mutually consistent across chlorophyll-a results, count data, and results determined from a data-driven approach to flow cytometry. Given that we cannot rule out a role for a Fe-Cu homeostatic link in causing these effects, future research into MNs and how they interact with N, P, and other MNs should be pursued to explore new interventions for effective management of HABs. Second, in view of the stimulatory effect that Cu exhibited, management of HABs with Cu as an algal biocide may not always be advisable. Third, our approach to flow cytometry offers data confirming our results from chemical and biological analysis, however also holds promise for future development as a high-throughput tool for use in understanding changes in algal assemblages. The results from this study concur with a small and emerging body of literature suggesting that the potential role of micronutrients in eutrophication requires further consideration in environmental management.
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Affiliation(s)
- Xiaokai Zhang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, PR China; Department of Environmental Science, University of Liverpool, Brownlow Hill, Liverpool, L69 7ZX, United Kingdom
| | - Boling Li
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, PR China; Department of Environmental Science, University of Liverpool, Brownlow Hill, Liverpool, L69 7ZX, United Kingdom
| | - Hai Xu
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, PR China
| | - Mona Wells
- Freshwater Ecology Group, National Institute of Water and Atmospheric Research, Dunedin, 9016, New Zealand.
| | - Boris Tefsen
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215123, PR China
| | - Boqiang Qin
- Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, PR China
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Theorell A, Bryceson YT, Theorell J. Determination of essential phenotypic elements of clusters in high-dimensional entities-DEPECHE. PLoS One 2019; 14:e0203247. [PMID: 30845234 PMCID: PMC6405191 DOI: 10.1371/journal.pone.0203247] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Accepted: 01/11/2019] [Indexed: 01/17/2023] Open
Abstract
Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make such highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi- and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better than the currently available best performing clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. DEPECHE is implemented in the open source R package DepecheR currently available at github.com/Theorell/DepecheR.
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Affiliation(s)
- Axel Theorell
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, Jülich, North Rhine-Westphalia, Germany
| | - Yenan Troi Bryceson
- Center for Hematology and Regenerative Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
- Broegelmann Research Laboratory, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Jakob Theorell
- Center for Hematology and Regenerative Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- * E-mail:
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CytoBinning: Immunological insights from multi-dimensional data. PLoS One 2018; 13:e0205291. [PMID: 30379838 PMCID: PMC6209166 DOI: 10.1371/journal.pone.0205291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/22/2018] [Indexed: 01/25/2023] Open
Abstract
New cytometric techniques continue to push the boundaries of multi-parameter quantitative data acquisition at the single-cell level particularly in immunology and medicine. Sophisticated analysis methods for such ever higher dimensional datasets are rapidly emerging, with advanced data representations and dimensional reduction approaches. However, these are not yet standardized and clinical scientists and cell biologists are not yet experienced in their interpretation. More fundamentally their range of statistical validity is not yet fully established. We therefore propose a new method for the automated and unbiased analysis of high-dimensional single cell datasets that is simple and robust, with the goal of reducing this complex information into a familiar 2D scatter plot representation that is of immediate utility to a range of biomedical and clinical settings. Using publicly available flow cytometry and mass cytometry datasets we demonstrate that this method (termed CytoBinning), recapitulates the results of traditional manual cytometric analyses and leads to new and testable hypotheses.
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40
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Duveau F, Hodgins-Davis A, Metzger BP, Yang B, Tryban S, Walker EA, Lybrook T, Wittkopp PJ. Fitness effects of altering gene expression noise in Saccharomyces cerevisiae. eLife 2018; 7:37272. [PMID: 30124429 PMCID: PMC6133559 DOI: 10.7554/elife.37272] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 08/17/2018] [Indexed: 01/22/2023] Open
Abstract
Gene expression noise is an evolvable property of biological systems that describes differences in expression among genetically identical cells in the same environment. Prior work has shown that expression noise is heritable and can be shaped by selection, but the impact of variation in expression noise on organismal fitness has proven difficult to measure. Here, we quantify the fitness effects of altering expression noise for the TDH3 gene in Saccharomyces cerevisiae. We show that increases in expression noise can be deleterious or beneficial depending on the difference between the average expression level of a genotype and the expression level maximizing fitness. We also show that a simple model relating single-cell expression levels to population growth produces patterns consistent with our empirical data. We use this model to explore a broad range of average expression levels and expression noise, providing additional insight into the fitness effects of variation in expression noise.
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Affiliation(s)
- Fabien Duveau
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Laboratoire Matière et Systèmes Complexes, CNRS UMR 7057, Université Paris Diderot, Paris, France
| | - Andrea Hodgins-Davis
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Brian Ph Metzger
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Department of Ecology and Evolution, University of Chicago, Chicago, United States
| | - Bing Yang
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, United States
| | - Stephen Tryban
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Elizabeth A Walker
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Tricia Lybrook
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States
| | - Patricia J Wittkopp
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, United States.,Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, United States
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41
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Lee AJ, Chang I, Burel JG, Lindestam Arlehamn CS, Mandava A, Weiskopf D, Peters B, Sette A, Scheuermann RH, Qian Y. DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data. Cytometry A 2018; 93:597-610. [PMID: 29665244 PMCID: PMC6030426 DOI: 10.1002/cyto.a.23371] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 02/05/2018] [Accepted: 03/15/2018] [Indexed: 11/10/2022]
Abstract
Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.
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Affiliation(s)
| | - Ivan Chang
- J. Craig Venter Institute, La Jolla, California
| | - Julie G. Burel
- La Jolla Institute for Allergy and Immunology, La Jolla, California
| | | | | | - Daniela Weiskopf
- La Jolla Institute for Allergy and Immunology, La Jolla, California
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, California
| | - Alessandro Sette
- La Jolla Institute for Allergy and Immunology, La Jolla, California
- Department of Medicine, University of California, San Diego, California
| | - Richard H. Scheuermann
- J. Craig Venter Institute, La Jolla, California
- Department of Pathology, University of California, San Diego, California
| | - Yu Qian
- J. Craig Venter Institute, La Jolla, California
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42
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Rahim A, Meskas J, Drissler S, Yue A, Lorenc A, Laing A, Saran N, White J, Abeler-Dörner L, Hayday A, Brinkman RR. High throughput automated analysis of big flow cytometry data. Methods 2018; 134-135:164-176. [PMID: 29287915 PMCID: PMC5815930 DOI: 10.1016/j.ymeth.2017.12.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 12/07/2017] [Accepted: 12/15/2017] [Indexed: 11/20/2022] Open
Abstract
The rapid expansion of flow cytometry applications has outpaced the functionality of traditional manual analysis tools used to interpret flow cytometry data. Scientists are faced with the daunting prospect of manually identifying interesting cell populations in 50-dimensional datasets, equalling the complexity previously only reached in mass cytometry. Data can no longer be analyzed or interpreted fully by manual approaches. While automated gating has been the focus of intense efforts, there are many significant additional steps to the analytical pipeline (e.g., cleaning the raw files, event outlier detection, extracting immunophenotypes). We review the components of a customized automated analysis pipeline that can be generally applied to large scale flow cytometry data. We demonstrate these methodologies on data collected by the International Mouse Phenotyping Consortium (IMPC).
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Affiliation(s)
- Albina Rahim
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada; Department of Bioinformatics, University of British Columbia, Vancouver, BC, Canada
| | - Justin Meskas
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Sibyl Drissler
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Alice Yue
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada; School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Anna Lorenc
- Department of Immunobiology, King's College London, United Kingdom
| | - Adam Laing
- Department of Immunobiology, King's College London, United Kingdom
| | - Namita Saran
- Department of Immunobiology, King's College London, United Kingdom
| | - Jacqui White
- Wellcome Trust Sanger Institute, Hinxton, United Kingdom
| | | | - Adrian Hayday
- Department of Immunobiology, King's College London, United Kingdom; The Francis Crick Institute, London, United Kingdom
| | - Ryan R Brinkman
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
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43
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Effects of mutation and selection on plasticity of a promoter activity in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A 2017; 114:E11218-E11227. [PMID: 29259117 DOI: 10.1073/pnas.1713960115] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Phenotypic plasticity is an evolvable property of biological systems that can arise from environment-specific regulation of gene expression. To better understand the evolutionary and molecular mechanisms that give rise to plasticity in gene expression, we quantified the effects of 235 single-nucleotide mutations in the Saccharomyces cerevisiae TDH3 promoter (PTDH3 ) on the activity of this promoter in media containing glucose, galactose, or glycerol as a carbon source. We found that the distributions of mutational effects differed among environments because many mutations altered the plastic response exhibited by the wild-type allele. Comparing the effects of these mutations with the effects of 30 PTDH3 polymorphisms on expression plasticity in the same environments provided evidence of natural selection acting to prevent the plastic response in PTDH3 activity between glucose and galactose from becoming larger. The largest changes in expression plasticity were observed between fermentable (glucose or galactose) and nonfermentable (glycerol) carbon sources and were caused by mutations located in the RAP1 and GCR1 transcription factor binding sites. Mutations altered expression plasticity most frequently between the two fermentable environments, with mutations causing significant changes in plasticity between glucose and galactose distributed throughout the promoter, suggesting they might affect chromatin structure. Taken together, these results provide insight into the molecular mechanisms underlying gene-by-environment interactions affecting gene expression as well as the evolutionary dynamics affecting natural variation in plasticity of gene expression.
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44
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Cosa A, Moreno A, Pacheco-Torres J, Ciccocioppo R, Hyytiä P, Sommer WH, Moratal D, Canals S. Multi-modal MRI classifiers identify excessive alcohol consumption and treatment effects in the brain. Addict Biol 2017; 22:1459-1472. [PMID: 27273582 DOI: 10.1111/adb.12418] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 04/22/2016] [Accepted: 05/13/2016] [Indexed: 12/14/2022]
Abstract
Robust neuroimaging markers of neuropsychiatric disorders have proven difficult to obtain. In alcohol use disorders, profound brain structural deficits can be found in severe alcoholic patients, but the heterogeneity of unimodal MRI measurements has so far precluded the identification of selective biomarkers, especially for early diagnosis. In the present work we used a combination of multiple MRI modalities to provide comprehensive and insightful descriptions of brain tissue microstructure. We performed a longitudinal experiment using Marchigian-Sardinian (msP) rats, an established model of chronic excessive alcohol consumption, and acquired multi-modal images before and after 1 month of alcohol consumption (6.8 ± 1.4 g/kg/day, mean ± SD), as well as after 1 week of abstinence with or without concomitant treatment with the antirelapse opioid antagonist naltrexone (2.5 mg/kg/day). We found remarkable sensitivity and selectivity to accurately classify brains affected by alcohol even after the relative short exposure period. One month drinking was enough to imprint a highly specific signature of alcohol consumption. Brain alterations were regionally specific and affected both gray and white matter and persisted into the early abstinence state without any detectable recovery. Interestingly, naltrexone treatment during early abstinence resulted in subtle brain changes that could be distinguished from non-treated abstinent brains, suggesting the existence of an intermediate state associated with brain recovery from alcohol exposure induced by medication. The presented framework is a promising tool for the development of biomarkers for clinical diagnosis of alcohol use disorders, with capacity to further inform about its progression and response to treatment.
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Affiliation(s)
- Alejandro Cosa
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
- Center for Biomaterials and Tissue Engineering; Universitat Politècnica de València; Valencia Spain
| | - Andrea Moreno
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
| | - Jesús Pacheco-Torres
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
| | | | - Petri Hyytiä
- Department of Pharmacology, Faculty of Medicine; University of Helsinki; Helsinki Finland
| | - Wolfgang H. Sommer
- Department of Psychopharmacology, Central Institute of Mental Health; University of Heidelberg; Mannheim Germany
| | - David Moratal
- Center for Biomaterials and Tissue Engineering; Universitat Politècnica de València; Valencia Spain
| | - Santiago Canals
- Instituto de Neurociencias; Consejo Superior de Investigaciones Científicas and Universidad Miguel Hernández; Sant Joan d'Alacant Spain
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45
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Gondois-Rey F, Chéret A, Granjeaud S, Mallet F, Bidaut G, Lécuroux C, Ploquin M, Müller-Trutwin M, Rouzioux C, Avettand-Fenoël V, Moretta A, Pialoux G, Goujard C, Meyer L, Olive D. NKG2C + memory-like NK cells contribute to the control of HIV viremia during primary infection: Optiprim-ANRS 147. Clin Transl Immunology 2017; 6:e150. [PMID: 28791125 PMCID: PMC5539415 DOI: 10.1038/cti.2017.22] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 04/06/2017] [Accepted: 05/04/2017] [Indexed: 12/30/2022] Open
Abstract
Natural-killer (NK) cells are important immune effectors during a viral infection. Latent CMV infection is widely spread and was demonstrated to shape the NK cell repertoire through the NKG2C receptor. An expansion of NKG2C+ NK cells has been reported during primary HIV infection (PHI), but their role is not known. We previously found a correlation between the maturation state of the NK cell compartment and a lower viral load by studying patients from the ANRS 147 Optiprim trial. We investigated here extensively the NKG2C+ NK cells at the time of PHI and its evolution after 3 months of early antiretroviral therapy (combination antiretroviral therapy (cART)). Multiparametric cytometry combined with bioinformatics was used to determine subsets. NKbright NKG2C+ progenitor, NKdim NKG2C+ effector and NKdim NKG2C+CD57+ memory-like populations were identified. Two groups of patients were unraveled according to the distribution of the NKG2C+ subsets skewed toward either progenitor/effector or memory-like phenotype. Patients with high NKG2C+CD57+ NK cell frequencies showed lower HIV-RNA, lower immune activation, higher pDC counts and reached more rapidly undetectable levels of HIV-RNA at M1 under cART. NKG2C+CD57+ NK cell frequency was the only factor strongly correlated to low viral load among other clinical features. While the patients were cytomegalovirus (CMV) infected, there was no sign of reactivation of CMV during PHI suggesting that memory-like NK cells were already present at the time of HIV infection and constituted a preexisting immune response able to contribute to natural control of HIV. This parameter appears to be a good candidate in the search of predictive markers to monitor HIV remission.
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Affiliation(s)
- Françoise Gondois-Rey
- Aix Marseille University, CNRS, Inserm, Institut Paoli-Calmettes, CRCM, Department of Immunity and Cancer, Marseille, France
| | - Antoine Chéret
- Department of Internal Medecine, Hôpital Bicêtre, APHP, Le Kremlin-Bicêtre, France.,EA 7327 Paris Descartes University, Paris, France
| | - Samuel Granjeaud
- Aix Marseille University, CNRS, Inserm, Institut Paoli-Calmettes, CRCM, CiBi Platform, Marseille, France
| | - Françoise Mallet
- Aix Marseille University, CNRS, Inserm, Institut Paoli-Calmettes, CRCM, Department of Immunity and Cancer, Marseille, France
| | - Ghislain Bidaut
- Aix Marseille University, CNRS, Inserm, Institut Paoli-Calmettes, CRCM, CiBi Platform, Marseille, France
| | - Camille Lécuroux
- Paris-Sud University, U1184, Le Kremlin-Bicêtre, France.,CEA, Department of DSV/iMETI, IDMIT, Fontenay-aux-Roses, France.,Inserm U1184, Department of ImVA 'Immunology of chronic Viral infections and Autoimmune diseases', Le Kremlin-Bicêtre, France
| | - Mickaël Ploquin
- Institut Pasteur, HIV, Inflammation and Persistance Unit, Virology Department, Paris, France
| | - Michaela Müller-Trutwin
- Institut Pasteur, HIV, Inflammation and Persistance Unit, Virology Department, Paris, France
| | - Christine Rouzioux
- EA 7327 Paris Descartes University, Paris, France.,Virology Laboratory, APHP CHU Necker-Enfants Malades, Paris, France
| | - Véronique Avettand-Fenoël
- EA 7327 Paris Descartes University, Paris, France.,Virology Laboratory, APHP CHU Necker-Enfants Malades, Paris, France
| | - Alessandro Moretta
- Dipartimento di medecina Sperimentale and Centro di Eccellenza per la Ricerca Biomedica, Università di Genova, Genova, Italy
| | - Gilles Pialoux
- Department of Infectious Diseases APHP, Hôpital Tenon, Paris, France
| | - Cécile Goujard
- Department of Internal Medecine, Hôpital Bicêtre, APHP, Le Kremlin-Bicêtre, France
| | - Laurence Meyer
- Inserm, CESP U1018, Univ Paris-Sud, Department of Epidemiology and Population Health, APHP, Hôpital Bicêtre, Le Kremlin-Bicêtre, France
| | - Daniel Olive
- Aix Marseille University, CNRS, Inserm, Institut Paoli-Calmettes, CRCM, Department of Immunity and Cancer, Marseille, France
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46
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Yu JS, Pertusi DA, Adeniran AV, Tyo KEJ. CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort. Bioinformatics 2017; 33:909-916. [PMID: 27998936 PMCID: PMC5860017 DOI: 10.1093/bioinformatics/btw710] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 10/19/2016] [Accepted: 11/08/2016] [Indexed: 02/05/2023] Open
Abstract
Motivation High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required. Results We have developed CellSort, a support vector machine (SVM) algorithm that identifies optimal sorting gates based on machine learning using positive and negative control populations. CellSort can take advantage of more than two dimensions to enhance the ability to distinguish between populations. We also present a Bayesian approach to predict the number of sorting rounds required to enrich a population from a given library size. This Bayesian approach allowed us to determine strategies for biasing the sorting gates in order to reduce the required number of enrichment rounds. This algorithm should be generally useful for improve sorting outcomes and reducing effort when using FACS. Availability and Implementation Source code available at http://tyolab.northwestern.edu/tools/ . k-tyo@northwestern.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jessica S Yu
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
| | - Dante A Pertusi
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
| | - Adebola V Adeniran
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
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47
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Diggins KE, Greenplate AR, Leelatian N, Wogsland CE, Irish JM. Characterizing cell subsets using marker enrichment modeling. Nat Methods 2017; 14:275-278. [PMID: 28135256 PMCID: PMC5330853 DOI: 10.1038/nmeth.4149] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 12/22/2016] [Indexed: 12/21/2022]
Abstract
Learning cell identity from high-content single-cell data presently relies on human experts. We present marker enrichment modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human- and machine-readable text label. MEM outperforms traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.
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Affiliation(s)
- K. E. Diggins
- Department of Cancer Biology and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - A. R. Greenplate
- Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - N. Leelatian
- Department of Cancer Biology and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - C. E. Wogsland
- Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - J. M. Irish
- Department of Cancer Biology and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
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48
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Éliás S, Schmidt A, Kannan V, Andersson J, Tegnér J. TGF-β Affects the Differentiation of Human GM-CSF + CD4 + T Cells in an Activation- and Sodium-Dependent Manner. Front Immunol 2016; 7:603. [PMID: 28066414 PMCID: PMC5179518 DOI: 10.3389/fimmu.2016.00603] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2016] [Accepted: 12/01/2016] [Indexed: 12/19/2022] Open
Abstract
The cytokine granulocyte-macrophage colony-stimulating factor (GM-CSF) is involved in the pathogenesis of chronic inflammatory diseases such as multiple sclerosis. However, the environmental cues promoting differentiation of GM-CSF producing T cells are unclear. Herein, we performed a broad experimental screening of cytokines and data-driven analysis assessing their ability to induce human GM-CSF+ CD4+ T cells and their subpopulations. TGF-β was discovered to induce GM-CSF production independently of proliferation and IL-2 signaling including STAT5. In contrast, IL-6 and IL-23 decreased GM-CSF production. On the population level, GM-CSF induction was highly correlated with expression of FOXP3 across cytokine stimulations but not with that of IL-17. However, on single-cell level GM-CSF and IFN-γ expression were most correlated, independently of the cytokine environment. Importantly, under low sodium conditions in the medium or upon stimulation with plate-bound instead of bead-bound anti-CD3 and anti-CD28 antibodies, the effects of TGF-β on GM-CSF, but not on FOXP3, were reversed. Our analysis indicates a novel role for TGF-β in generating GM-CSF+ subsets of human CD4+ T cells. These results are important for understanding of autoimmune disease and therapeutic considerations.
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Affiliation(s)
- Szabolcs Éliás
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet , Stockholm , Sweden
| | - Angelika Schmidt
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet , Stockholm , Sweden
| | - Venkateshan Kannan
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet , Stockholm , Sweden
| | - John Andersson
- Immunology and Allergy Unit, Department of Medicine Solna, Karolinska Institutet , Stockholm , Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska University Hospital and Science for Life Laboratory, Karolinska Institutet , Stockholm , Sweden
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49
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Weber LM, Robinson MD. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A 2016; 89:1084-1096. [DOI: 10.1002/cyto.a.23030] [Citation(s) in RCA: 251] [Impact Index Per Article: 31.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 09/05/2016] [Accepted: 11/06/2016] [Indexed: 01/19/2023]
Affiliation(s)
- Lukas M. Weber
- Institute of Molecular Life Sciences, University of Zurich; Zurich Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich; Zurich Switzerland
| | - Mark D. Robinson
- Institute of Molecular Life Sciences, University of Zurich; Zurich Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich; Zurich Switzerland
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50
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Abstract
High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, we describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based algorithm for visualizing single-cell data and enabling cellular hierarchy inference among subpopulations of similar cells. It was initially developed for flow and mass cytometry single-cell data. We describe SPADE's implementation and application using an open-source R package that runs on Mac OS X, Linux and Windows systems. A typical SPADE analysis on a 2.27-GHz processor laptop takes ∼5 min. We demonstrate the applicability of SPADE to single-cell RNA-seq data. We compare SPADE with recently developed single-cell visualization approaches based on the t-distribution stochastic neighborhood embedding (t-SNE) algorithm. We contrast the implementation and outputs of these methods for normal and malignant hematopoietic cells analyzed by mass cytometry and provide recommendations for appropriate use. Finally, we provide an integrative strategy that combines the strengths of t-SNE and SPADE to infer cellular hierarchy from high-dimensional single-cell data.
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