Publications by authors named "Ryan R Brinkman"

81 Publications

Multi-Omic Data Integration Allows Baseline Immune Signatures to Predict Hepatitis B Vaccine Response in a Small Cohort.

Front Immunol 2020 30;11:578801. Epub 2020 Nov 30.

Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada.

Background: Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts.

Methods: We applied cutting edge multi-omics approaches to extensively characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final HBV antibody titres.

Results: Using both an unsupervised molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Biological associations were unravelled, with signalling pathways such as JAK-STAT and interleukin signalling, Toll-like receptor cascades, interferon signalling, and Th17 cell differentiation emerging as important pre-vaccination modulators of response.

Conclusion: This study provides further evidence that baseline cellular and molecular characteristics of an individual's immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.
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http://dx.doi.org/10.3389/fimmu.2020.578801DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734088PMC
November 2020

Systems Biology Methods Applied to Blood and Tissue for a Comprehensive Analysis of Immune Response to Hepatitis B Vaccine in Adults.

Front Immunol 2020 4;11:580373. Epub 2020 Nov 4.

Department of Radiology, BC Children's Hospital, Vancouver, BC, Canada.

Conventional vaccine design has been based on trial-and-error approaches, which have been generally successful. However, there have been some major failures in vaccine development and we still do not have highly effective licensed vaccines for tuberculosis, HIV, respiratory syncytial virus, and other major infections of global significance. Approaches at rational vaccine design have been limited by our understanding of the immune response to vaccination at the molecular level. Tools now exist to undertake in-depth analysis using systems biology approaches, but to be fully realized, studies are required in humans with intensive blood and tissue sampling. Methods that support this intensive sampling need to be developed and validated as feasible. To this end, we describe here a detailed approach that was applied in a study of 15 healthy adults, who were immunized with hepatitis B vaccine. Sampling included ~350 mL of blood, 12 microbiome samples, and lymph node fine needle aspirates obtained over a ~7-month period, enabling comprehensive analysis of the immune response at the molecular level, including single cell and tissue sample analysis. Samples were collected for analysis of immune phenotyping, whole blood and single cell gene expression, proteomics, lipidomics, epigenetics, whole blood response to key immune stimuli, cytokine responses, T cell responses, antibody repertoire analysis and the microbiome. Data integration was undertaken using different approaches-NetworkAnalyst and DIABLO. Our results demonstrate that such intensive sampling studies are feasible in healthy adults, and data integration tools exist to analyze the vast amount of data generated from a multi-omics systems biology approach. This will provide the basis for a better understanding of vaccine-induced immunity and accelerate future rational vaccine design.
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http://dx.doi.org/10.3389/fimmu.2020.580373DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7672042PMC
November 2020

"Age Related Differences in the Biology of Chronic Graft-Versus-Host Disease After Hematopoietic Stem Cell Transplantation".

Front Immunol 2020 16;11:571884. Epub 2020 Oct 16.

Michael Cuccione Childhood Cancer Research Program, British Columbia Children's Hospital, University of British Columbia, Vancouver, BC, Canada.

It is established that pediatric hematopoietic stem cell transplant (HSCT) recipients have a lower rate of chronic graft-versus-host disease (cGvHD) compared to adults. Our group has previously published immune profiles changes associated with cGvHD of clinically well-defined adult and pediatric HSCT cohorts. Since all analyses were performed by the same research group and analyzed using identical methodology, we first compared our previous immune profile analyses between adults and children. We then performed additional analyses comparing the T cell populations across age groups, and a sub-analysis of the impact of the estimated pubertal status at time of HSCT in our pediatric cohort. In all analyses, we corrected for clinical covariates including total body irradiation and time of onset of cGvHD. Three consistent findings were seen in both children and adults, including elevations of ST2 and naive helper T (Th) cells and depression of NK cells. However, significant differences exist between children and adults in certain cytokines, B cell, and T populations. In children, we saw a broad suppression of newly formed B (NF-B) cells, whereas adults exhibited an increase in T1-CD21 B cells and a decrease in T1-CD24CD38 B cells. Prepubertal children had elevations of aminopeptidase N (sCD13) and ICAM-1. T abnormalities in children appeared to be primarily in memory T cells, whereas in adults the abnormalities were in naïve T cells. In adults, the loss of PD1 expression in naïve T and naïve Th cells was associated with cGvHD. We discuss the possible mechanisms for these age-related differences, and how they might theoretically impact on different therapeutic approaches to cGvHD between children and adults.
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http://dx.doi.org/10.3389/fimmu.2020.571884DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641628PMC
May 2021

Clinical Protocol for a Longitudinal Cohort Study Employing Systems Biology to Identify Markers of Vaccine Immunogenicity in Newborn Infants in The Gambia and Papua New Guinea.

Front Pediatr 2020 30;8:197. Epub 2020 Apr 30.

Vaccines and Immunity Theme, Medical Research Council Unit the Gambia at London School of Hygiene and Tropical Medicine, Fajara, Gambia.

Infection contributes to significant morbidity and mortality particularly in the very young and in low- and middle-income countries. While vaccines are a highly cost-effective tool against infectious disease little is known regarding the cellular and molecular pathways by which vaccines induce protection at an early age. Immunity is distinct in early life and greater precision is required in our understanding of mechanisms of early life protection to inform development of new pediatric vaccines. We will apply transcriptomic, proteomic, metabolomic, multiplex cytokine/chemokine, adenosine deaminase, and flow cytometry immune cell phenotyping to delineate early cellular and molecular signatures that correspond to vaccine immunogenicity. This approach will be applied to a neonatal cohort in The Gambia ( ~ 720) receiving at birth: (1) Hepatitis B (HepB) vaccine alone, (2) Bacille Calmette Guerin (BCG) vaccine alone, or (3) HepB and BCG vaccines, (4) HepB and BCG vaccines delayed till day 10 at the latest. Each study participant will have a baseline peripheral blood sample drawn at DOL0 and a second blood sample at DOL1,-3, or-7 as well as late timepoints to assess HepB vaccine immunogenicity. Blood will be fractionated via a "small sample big data" standard operating procedure that enables multiple downstream systems biology assays. We will apply both univariate and multivariate frameworks and multi-OMIC data integration to identify features associated with anti-Hepatitis B (anti-HB) titer, an established correlate of protection. Cord blood sample collection from a subset of participants will enable human modeling to test mechanistic hypotheses identified regarding vaccine action. Maternal anti-HB titer and the infant microbiome will also be correlated with our findings which will be validated in a smaller cohort in Papua New Guinea ( ~ 80). The study has been approved by The Gambia Government/MRCG Joint Ethics Committee and The Boston Children's Hospital Institutional Review Board. Ethics review is ongoing with the Papua New Guinea Medical Research Advisory Committee. All de-identified data will be uploaded to public repositories following submission of study output for publication. Feedback meetings will be organized to disseminate output to the study communities. : Clinicaltrials.gov Registration Number: NCT03246230.
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http://dx.doi.org/10.3389/fped.2020.00197DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7205022PMC
April 2020

Occurrence of T-cell and NK-cell subsets with less well-recognized phenotypes in peripheral blood submitted for routine flow cytometry analysis.

Cytometry B Clin Cytom 2021 Mar 28;100(2):235-239. Epub 2020 Mar 28.

Department of Pathology and Lab Medicine, University of British Columbia, Vancouver, British Columbia, Canada.

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http://dx.doi.org/10.1002/cyto.b.21876DOI Listing
March 2021

Single Cell Phenotypic Profiling of 27 DLBCL Cases Reveals Marked Intertumoral and Intratumoral Heterogeneity.

Cytometry A 2020 06 22;97(6):620-629. Epub 2019 Oct 22.

Terry Fox Laboratory, BC Cancer Agency, Vancouver, Canada.

Diffuse large B-cell lymphoma (DLBCL) is the most common histologic subtype of non-Hodgkin lymphoma and is notorious for its clinical heterogeneity. Patient outcomes can be predicted by cell-of-origin (COO) classification, demonstrating that the underlying transcriptional signature of malignant B-cells informs biological behavior in the context of standard combination chemotherapy regimens. In the current study, we used mass cytometry (CyTOF) to examine tumor phenotypes at the protein level with single cell resolution in a collection of 27 diagnostic DLBCL biopsy specimens from treatment naïve patients. We found that malignant B-cells from each patient occupied unique regions in 37-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Interestingly, variable MHC class II expression was found to be the greatest contributor to phenotypic diversity. Within individual tumors, a subset of cases showed multiple phenotypic subpopulations, and in one case, we were able to demonstrate direct correspondence between protein-level phenotypic subsets and DNA mutation-defined subclones. In summary, CyTOF analysis can resolve both intertumoral and intratumoral heterogeneity among primary samples and reveals that each case of DLBCL is unique and may be comprised of multiple, genetically distinct subclones. © 2019 International Society for Advancement of Cytometry.
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http://dx.doi.org/10.1002/cyto.a.23919DOI Listing
June 2020

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).

Eur J Immunol 2019 Oct;49(10):1457-1973

Flow Cytometry Laboratory, Institute of Molecular Toxicology and Pharmacology, Helmholtz Zentrum München, German Research Center for Environmental Health, München, Germany.

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.
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http://dx.doi.org/10.1002/eji.201970107DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350392PMC
October 2019

Improving the Rigor and Reproducibility of Flow Cytometry-Based Clinical Research and Trials Through Automated Data Analysis.

Authors:
Ryan R Brinkman

Cytometry A 2020 02 13;97(2):107-112. Epub 2019 Sep 13.

Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada.

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http://dx.doi.org/10.1002/cyto.a.23883DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043840PMC
February 2020

Improving the Quality and Reproducibility of Flow Cytometry in the Lung. An Official American Thoracic Society Workshop Report.

Am J Respir Cell Mol Biol 2019 08;61(2):150-161

Defining responses of the structural and immune cells in biologic systems is critically important to understanding disease states and responses to injury. This requires accurate and sensitive methods to define cell types in organ systems. The principal method to delineate the cell populations involved in these processes is flow cytometry. Although researchers increasingly use flow cytometry, technical challenges can affect its accuracy and reproducibility, thus significantly limiting scientific advancements. This challenge is particularly critical to lung immunology, as the lung is readily accessible and therefore used in preclinical and clinical studies to define potential therapeutics. Given the importance of flow cytometry in pulmonary research, the American Thoracic Society convened a working group to highlight issues and technical challenges to the performance of high-quality pulmonary flow cytometry, with a goal of improving its quality and reproducibility.
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http://dx.doi.org/10.1165/rcmb.2019-0191STDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6670040PMC
August 2019

Data-Driven Flow Cytometry Analysis.

Methods Mol Biol 2019 ;1989:245-265

Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada.

The emergence of flow and mass cytometry technologies capable of generating 40-dimensional data has spurred research into automated methodologies that address bottlenecks across the entire analysis process from quality checking, data transformation, and cell population identification, to biomarker identification and visualizations. We review these approaches in the context of the stepwise progression through the different steps, including normalization, automated gating, outlier detection, and graphical presentation of results.
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http://dx.doi.org/10.1007/978-1-4939-9454-0_16DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8043852PMC
March 2020

Flow cytometry data analysis: Recent tools and algorithms.

Int J Lab Hematol 2019 May;41 Suppl 1:56-62

Terry Fox Laboratory, BC Cancer, Vancouver, British Columbia, Canada.

Flow cytometry (FCM) allows scientists to rapidly quantify up to 50 parameters for millions of cells per sample. The bottleneck in the application of the technology is data analysis, and the high number of parameters measured by the current generation of instruments requires the use of advanced computational algorithms to make full use of their capabilities. This review summarizes the main steps of FCM data analysis, focusing on the use of the most recent bioinformatic tools developed for an R-based programming environment. In particular, for each stage of the data analysis, libraries and packages currently available are listed, and a brief description of their functioning is included.
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http://dx.doi.org/10.1111/ijlh.13016DOI Listing
May 2019

Dynamic molecular changes during the first week of human life follow a robust developmental trajectory.

Nat Commun 2019 03 12;10(1):1092. Epub 2019 Mar 12.

BC Cancer Agency, 686 West Broadway, Suite 500, Vancouver, BC, V5Z 1G1, Canada.

Systems biology can unravel complex biology but has not been extensively applied to human newborns, a group highly vulnerable to a wide range of diseases. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1 ml of blood, a volume readily obtained from newborns. Indexing to baseline and applying innovative integrative computational methods reveals dramatic changes along a remarkably stable developmental trajectory over the first week of life. This is most evident in changes of interferon and complement pathways, as well as neutrophil-associated signaling. Validated across two independent cohorts of newborns from West Africa and Australasia, a robust and common trajectory emerges, suggesting a purposeful rather than random developmental path. Systems biology and innovative data integration can provide fresh insights into the molecular ontogeny of the first week of life, a dynamic developmental phase that is key for health and disease.
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http://dx.doi.org/10.1038/s41467-019-08794-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6414553PMC
March 2019

Implementation and Validation of an Automated Flow Cytometry Analysis Pipeline for Human Immune Profiling.

Cytometry A 2019 02 20;95(2):183-191. Epub 2018 Dec 20.

Earle A. Chiles Research Institute, Providence Portland Medical Center, Portland, Oregon.

Automated reagent preparation, sample processing, and data acquisition have increased the rate at which flow cytometry data can be generated. Furthermore, advances in technology and flow cytometry instrumentation continually increase the complexity and dimensionality of this data. Together, this leads to increased pressure on manual data analysis, which has inherent limitations including subjectivity of the analyst and the length of time needed for data processing. These issues can create bottlenecks in the data processing workflow and potentially compromise data quality. To address these issues, as well as the challenges associated with manual gating in a high-volume human immune profiling laboratory, we sought to implement an automated analysis pipeline. In this report, we discuss considerations for selecting an automated analysis method, the process of implementing an automated pipeline, and detail our successful incorporation of an automated gating strategy with flowDensity into our analysis workflow. This validated pipeline augments our laboratory's ability to provide rapid high-throughput immune profiling for patients participating in cancer immunotherapy clinical trials. © International Society for Advancement of Cytometry.
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http://dx.doi.org/10.1002/cyto.a.23664DOI Listing
February 2019

A standardized immune phenotyping and automated data analysis platform for multicenter biomarker studies.

JCI Insight 2018 12 6;3(23). Epub 2018 Dec 6.

Department of Surgery, University of British Columbia, Vancouver, British Columbia, Canada.

The analysis and validation of flow cytometry-based biomarkers in clinical studies are limited by the lack of standardized protocols that are reproducible across multiple centers and suitable for use with either unfractionated blood or cryopreserved PBMCs. Here we report the development of a platform that standardizes a set of flow cytometry panels across multiple centers, with high reproducibility in blood or PBMCs from either healthy subjects or patients 100 days after hematopoietic stem cell transplantation. Inter-center comparisons of replicate samples showed low variation, with interindividual variation exceeding inter-center variation for most populations (coefficients of variability <20% and interclass correlation coefficients >0.75). Exceptions included low-abundance populations defined by markers with indistinct expression boundaries (e.g., plasmablasts, monocyte subsets) or populations defined by markers sensitive to cryopreservation, such as CD62L and CD45RA. Automated gating pipelines were developed and validated on an independent data set, revealing high Spearman's correlations (rs >0.9) with manual analyses. This workflow, which includes pre-formatted antibody cocktails, standardized protocols for acquisition, and validated automated analysis pipelines, can be readily implemented in multicenter clinical trials. This approach facilitates the collection of robust immune phenotyping data and comparison of data from independent studies.
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http://dx.doi.org/10.1172/jci.insight.121867DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328091PMC
December 2018

Methodology for evaluating and comparing flow cytometers: A multisite study of 23 instruments.

Cytometry A 2018 11 23;93(11):1087-1091. Epub 2018 Sep 23.

Wake Forest University Baptist Medical Center, Comprehensive Cancer Center and Department of Cancer Biology, Winston-Salem, North Carolina.

We demonstrate improved methods for making valid and accurate comparisons of fluorescence measurement capabilities among instruments tested at different sites and times. We designed a suite of measurements and automated data processing methods to obtain consistent objective results and applied them to a selection of 23 instruments at nine sites to provide a range of instruments as well as multiple instances of similar instruments. As far as we know, this study represents the most accurate methods and results so far demonstrated for this purpose. The first component of the study reporting improved methods for photoelectron scale (Spe) evaluations, which was published previously (Parks, El Khettabi, Chase, Hoffman, Perfetto, Spidlen, Wood, Moore, and Brinkman: Cytometry A 91 (2017) 232-249). Those results which were within themselves are not sufficient for instrument comparisons, so here, we use the Spe scale results for the 23 cytometers and combine them with additional information from the analysis suite to obtain the metrics actually needed for instrument evaluations and comparisons. We adopted what we call the 2+2SD limit of resolution as a maximally informative metric, for evaluating and comparing dye measurement sensitivity among different instruments and measurement channels. Our results demonstrate substantial differences among different classes of instruments in both dye response and detection sensitivity and some surprisingly large differences among similar instruments, even among instruments with nominally identical configurations. On some instruments, we detected defective measurement channels needing service. The system can be applied in shared resource laboratories and other facilities as an aspect of quality assurance, and accurate instrument comparisons can be valuable for selecting instruments for particular purposes and for making informed instrument acquisition decisions. An institutionally supported program could serve the cytometry community by facilitating access to materials, and analysis and maintaining an archive of results. © 2018 International Society for Advancement of Cytometry.
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http://dx.doi.org/10.1002/cyto.a.23605DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901711PMC
November 2018

ddPCRclust: an R package and Shiny app for automated analysis of multiplexed ddPCR data.

Bioinformatics 2018 08;34(15):2687-2689

Terry Fox Laboratory, BC Cancer Agency, Vancouver, Canada.

Motivation: Droplet digital PCR (ddPCR) is an emerging technology for quantifying DNA. By partitioning the target DNA into ∼20 000 droplets, each serving as its own PCR reaction compartment, a very high sensitivity of DNA quantification can be achieved. However, manual analysis of the data is time consuming and algorithms for automated analysis of non-orthogonal, multiplexed ddPCR data are unavailable, presenting a major bottleneck for the advancement of ddPCR transitioning from low-throughput to high-throughput.

Results: ddPCRclust is an R package for automated analysis of data from Bio-Rad's droplet digital PCR systems (QX100 and QX200). It can automatically analyze and visualize multiplexed ddPCR experiments with up to four targets per reaction. Results are on par with manual analysis, but only take minutes to compute instead of hours. The accompanying Shiny app ddPCRvis provides easy access to the functionalities of ddPCRclust through a web-browser based GUI.

Availability And Implementation: R package: https://github.com/bgbrink/ddPCRclust; Interface: https://github.com/bgbrink/ddPCRvis/; Web: https://bibiserv.cebitec.uni-bielefeld.de/ddPCRvis/.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty136DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061851PMC
August 2018

High throughput automated analysis of big flow cytometry data.

Methods 2018 02 27;134-135:164-176. Epub 2017 Dec 27.

Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC, Canada; Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada. Electronic address:

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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http://dx.doi.org/10.1016/j.ymeth.2017.12.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5815930PMC
February 2018

Evaluating flow cytometer performance with weighted quadratic least squares analysis of LED and multi-level bead data.

Cytometry A 2017 03 3;91(3):232-249. Epub 2017 Feb 3.

Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia.

We developed a fully automated procedure for analyzing data from LED pulses and multilevel bead sets to evaluate backgrounds and photoelectron scales of cytometer fluorescence channels. The method improves on previous formulations by fitting a full quadratic model with appropriate weighting and by providing standard errors and peak residuals as well as the fitted parameters themselves. Here we describe the details of the methods and procedures involved and present a set of illustrations and test cases that demonstrate the consistency and reliability of the results. The automated analysis and fitting procedure is generally quite successful in providing good estimates of the Spe (statistical photoelectron) scales and backgrounds for all the fluorescence channels on instruments with good linearity. The precision of the results obtained from LED data is almost always better than that from multilevel bead data, but the bead procedure is easy to carry out and provides results good enough for most purposes. Including standard errors on the fitted parameters is important for understanding the uncertainty in the values of interest. The weighted residuals give information about how well the data fits the model, and particularly high residuals indicate bad data points. Known photoelectron scales and measurement channel backgrounds make it possible to estimate the precision of measurements at different signal levels and the effects of compensated spectral overlap on measurement quality. Combining this information with measurements of standard samples carrying dyes of biological interest, we can make accurate comparisons of dye sensitivity among different instruments. Our method is freely available through the R/Bioconductor package flowQB. © 2017 International Society for Advancement of Cytometry.
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http://dx.doi.org/10.1002/cyto.a.23052DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5483398PMC
March 2017

T-Cell Phenotypes Predictive of Frailty and Mortality in Elderly Nursing Home Residents.

J Am Geriatr Soc 2017 01 24;65(1):153-159. Epub 2016 Oct 24.

Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada.

Objectives: To determine whether immune phenotypes associated with immunosenescence are predictive of frailty and mortality within 1-year in elderly nursing home residents.

Design: Cross sectional study of frailty; prospective cohort study of mortality.

Setting: Thirty-two nursing homes in four Canadian cities between September 2009 and October 2011.

Participants: Nursing home residents aged 65 and older (N = 1,072, median age 86, 72% female).

Measurements: After enrollment, peripheral blood mononuclear cells were obtained and analyzed using flow cytometry for CD4 and CD8 T-cell subsets (naïve, memory (central, effector, terminally differentiated, senescent), and regulatory T-cells) and cytomegalovirus (CMV)-reactive CD4 and CD8 T-cells. Multilevel linear regression analysis was performed to determine the relationship between immune phenotypes and frailty; frailty was measured at the time of enrollment using the Frailty Index. A Cox proportional hazards model was used to determine the relationship between immune phenotypes and time to death (within 1 year).

Results: Mean Frailty Index was 0.44 ± 0.13. Multilevel regression analysis showed that higher percentages of naïve CD4 T-cells (P = .001) and effector memory CD8 T-cells (P = .02) were associated with a lower mean Frailty Index, whereas a higher percentage of CD8 central memory T-cells was associated with a higher mean Frailty Index score (P = .02). One hundred fifty one (14%) members of the cohort died within 1 year. Multivariable analysis showed a significant negative multiplicative interaction between age and percentage of CMV-reactive CD4 T-cells (hazard ratio = 0.87, 95% confidence interval = 0.79-0.96). No other significant factors were identified.

Conclusion: Immune phenotypes found to be predictive of frailty and mortality in this study can help further understanding of immunosenescence and may provide a rationale for future intervention studies designed to modulate immunity.
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http://dx.doi.org/10.1111/jgs.14507DOI Listing
January 2017

Use FlowRepository to share your clinical data upon study publication.

Cytometry B Clin Cytom 2018 01 8;94(1):196-198. Epub 2016 Jul 8.

Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada.

A fundamental tenet of scientific research is that published results including underlying data should be open to independent validation and refutation. Data sharing encourages collaboration, facilitates quality and reduces redundancy in data production. Authors submitting manuscripts to several journals have already adopted the habit of sharing their underlying flow cytometry data by deposition to FlowRepository-a data repository that is jointly supported by the International Society for Advancement of Cytometry, the International Clinical Cytometry Society and the European Society for Clinical Cell Analysis. De-identification is required for publishing data from clinical studies and we discuss ways to satisfy data sharing requirements and patient privacy requirements simultaneously. Scientific communities in the fields of microarray, proteomics, and sequencing have been benefiting from reuse and re-exploration of data in public repositories for over decade. We believe it is time that clinicians follow suit and that de-identified clinical data also become routinely available along with published cytometry-based findings. © 2016 International Clinical Cytometry Society.
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http://dx.doi.org/10.1002/cyto.b.21393DOI Listing
January 2018

flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data.

Cytometry A 2016 05 18;89(5):461-71. Epub 2016 Mar 18.

Vaccine Research Center, National Institutes of Allergy and Infectious Disease, National Institutes of Health, Baltimore, Maryland.

Modern flow cytometry systems can be coupled to plate readers for high-throughput acquisition. These systems allow hundreds of samples to be analyzed in a single day. Quality control of the data remains challenging, however, and is further complicated when a large number of parameters is measured in an experiment. Our examination of 29,228 publicly available FCS files from laboratories worldwide indicates 13.7% have a fluorescence anomaly. In particular, fluorescence measurements for a sample over the collection time may not remain stable due to fluctuations in fluid dynamics; the impact of instabilities may differ between samples and among parameters. Therefore, we hypothesized that tracking cell populations (which represent a summary of all parameters) in centered log ratio space would provide a sensitive and consistent method of quality control. Here, we present flowClean, an algorithm to track subset frequency changes within a sample during acquisition, and flag time periods with fluorescence perturbations leading to the emergence of false populations. Aberrant time periods are reported as a new parameter and added to a revised data file, allowing users to easily review and exclude those events from further analysis. We apply this method to proof-of-concept datasets and also to a subset of data from a recent vaccine trial. The algorithm flags events that are suspicious by visual inspection, as well as those showing more subtle effects that might not be consistently flagged by investigators reviewing the data manually, and out-performs the current state-of-the-art. flowClean is available as an R package on Bioconductor, as a module on the free-to-use GenePattern web server, and as a plugin for FlowJo X. © 2016 International Society for Advancement of Cytometry.
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http://dx.doi.org/10.1002/cyto.a.22837DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5522377PMC
May 2016

Automated analysis of flow cytometry data comes of age.

Cytometry A 2016 Jan;89(1):13-5

J. Craig Venter Institute, La Jolla, California.

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http://dx.doi.org/10.1002/cyto.a.22810DOI Listing
January 2016

Publishing code is essential for reproducible flow cytometry bioinformatics.

Cytometry A 2016 Jan;89(1):10-1

Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, Canada.

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http://dx.doi.org/10.1002/cyto.a.22805DOI Listing
January 2016

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.

Cytometry A 2016 Jan 8;89(1):16-21. Epub 2015 Oct 8.

Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, Canada.

The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.
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http://dx.doi.org/10.1002/cyto.a.22732DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874734PMC
January 2016

ISAC's Gating-ML 2.0 data exchange standard for gating description.

Cytometry A 2015 Jul 14;87(7):683-7. Epub 2015 May 14.

Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada.

The lack of software interoperability with respect to gating has traditionally been a bottleneck preventing the use of multiple analytical tools and reproducibility of flow cytometry data analysis by independent parties. To address this issue, ISAC developed Gating-ML, a computer file format to encode and interchange gates. Gating-ML 1.5 was adopted and published as an ISAC Candidate Recommendation in 2008. Feedback during the probationary period from implementors, including major commercial software companies, instrument vendors, and the wider community, has led to a streamlined Gating-ML 2.0. Gating-ML has been significantly simplified and therefore easier to support by software tools. To aid developers, free, open source reference implementations, compliance tests, and detailed examples are provided to stimulate further commercial adoption. ISAC has approved Gating-ML as a standard ready for deployment in the public domain and encourages its support within the community as it is at a mature stage of development having undergone extensive review and testing, under both theoretical and practical conditions.
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Source
http://dx.doi.org/10.1002/cyto.a.22690DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874733PMC
July 2015