scholarly journals Acquisition of High‐Quality Spectral Flow Cytometry Data

2020 ◽  
Vol 93 (1) ◽  
Author(s):  
Amy Fox ◽  
Taru S. Dutt ◽  
Burton Karger ◽  
Andrés Obregón‐Henao ◽  
G. Brooke Anderson ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Spectral Flow ◽  
High Quality ◽  
Flow Cytometry Data

scholarly journals Design and Optimization Protocol for High-Dimensional Immunophenotyping Assays using Spectral Flow Cytometry

2019 ◽  
Author(s):  
L Ferrer-Font ◽  
C Pellefigues ◽  
JU Mayer ◽  
S Small ◽  
MC Jaimes ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Immune System ◽  
Selection Process ◽  
High Dimensional ◽  
Spectral Flow ◽  
High Quality ◽  
Design And Optimization ◽  
Technological Advances ◽  
Large Numbers ◽  
Recent Advancement

ABSTRACTTechnological advances in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system has led to the development of large 20+ flow cytometry panels. Yet, as panel complexity and size increases, so does the difficulty involved in designing a high-quality panel, accessing the instrumentation capable of accommodating large numbers of parameters, and in analysing such high-dimensional data.A recent advancement is spectral flow cytometry, which in contrast to conventional flow cytometry distinguishes the full emission spectrum of each fluorochrome across all lasers, rather than identifying only the peak of emission. Fluorochromes with a similar emission maximum but distinct off-peak signatures can therefore be accommodated within the same flow cytometry panel, allowing greater flexibility in terms of panel design and fluorophore detection.Here, we highlight the specific characteristics regarding spectral flow cytometry and aim to guide users through the process of building, designing and optimising high-dimensional spectral flow cytometry panels using a comprehensive step-by-step protocol. Special considerations are also given for using highly-overlapping dyes and a logical selection process an optimal marker-fluorophore assignment is provided.

scholarly journals High‐Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data

2020 ◽  
Vol 97 (8) ◽  
pp. 824-831 ◽  
Author(s):  
Laura Ferrer‐Font ◽  
Johannes U. Mayer ◽  
Samuel Old ◽  
Ian F. Hermans ◽  
Jonathan Irish ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Data Analysis ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Spectral Flow ◽  
Mass Cytometry ◽  
Flow Cytometry Data ◽  
High Dimensional Data Analysis

scholarly journals Managing Multi-center Flow Cytometry Data for Immune Monitoring

2014 ◽  
Vol 13s7 ◽  
pp. CIN.S16346 ◽  
Author(s):  
Scott White ◽  
Karoline Laske ◽  
Marij J.P. Welters ◽  
Nicole Bidmon ◽  
Sjoerd H. Van Der Burg ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Large Scale ◽  
Single Cells ◽  
Large Data ◽  
Automated Analysis ◽  
Information Management System ◽  
Immune Monitoring ◽  
Quality Data ◽  
High Quality ◽  
Flow Cytometry Data

With the recent results of promising cancer vaccines and immunotherapy 1 – 5 , immune monitoring has become increasingly relevant for measuring treatment-induced effects on T cells, and an essential tool for shedding light on the mechanisms responsible for a successful treatment. Flow cytometry is the canonical multi-parameter assay for the fine characterization of single cells in solution, and is ubiquitously used in pre-clinical tumor immunology and in cancer immunotherapy trials. Current state-of-the-art polychromatic flow cytometry involves multi-step, multi-reagent assays followed by sample acquisition on sophisticated instruments capable of capturing up to 20 parameters per cell at a rate of tens of thousands of cells per second. Given the complexity of flow cytometry assays, reproducibility is a major concern, especially for multi-center studies. A promising approach for improving reproducibility is the use of automated analysis borrowing from statistics, machine learning and information visualization 21 – 23 , as these methods directly address the subjectivity, operator-dependence, labor-intensive and low fidelity of manual analysis. However, it is quite time-consuming to investigate and test new automated analysis techniques on large data sets without some centralized information management system. For large-scale automated analysis to be practical, the presence of consistent and high-quality data linked to the raw FCS files is indispensable. In particular, the use of machine-readable standard vocabularies to characterize channel metadata is essential when constructing analytic pipelines to avoid errors in processing, analysis and interpretation of results. For automation, this high-quality metadata needs to be programmatically accessible, implying the need for a consistent Application Programming Interface (API). In this manuscript, we propose that upfront time spent normalizing flow cytometry data to conform to carefully designed data models enables automated analysis, potentially saving time in the long run. The ReFlow informatics framework was developed to address these data management challenges.

scholarly journals How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

2021 ◽  
Vol 12 ◽  
Author(s):  
Hannah den Braanker ◽  
Margot Bongenaar ◽  
Erik Lubberts
Keyword(s):  
Flow Cytometry ◽  
Dimensional Analysis ◽  
High Dimensional ◽  
Spectral Flow ◽  
Control Data ◽  
Single Experiment ◽  
Flow Cytometry Data ◽  
Healthy Control ◽  
The Common ◽  
Quality Control Data

Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. This methods article provides an R-based pipeline based on previously published packages, that are readily available to use. Application of our workflow will aid spectral flow cytometry users to obtain valid and reproducible results.

scholarly journals Recent Bioinformatics Advances in the Analysis of High Throughput Flow Cytometry Data

2009 ◽  
Vol 2009 ◽  
pp. 1-2
Author(s):  
Raphael Gottardo ◽  
Ryan R. Brinkman ◽  
George Luta ◽  
Matt P. Wand
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Flow Cytometry Data

scholarly journals Automated gating of flow cytometry data via robust model-based clustering

2008 ◽  
Vol 73A (4) ◽  
pp. 321-332 ◽  
Author(s):  
Kenneth Lo ◽  
Ryan Remy Brinkman ◽  
Raphael Gottardo
Keyword(s):  
Flow Cytometry ◽  
Model Based Clustering ◽  
Flow Cytometry Data ◽  
Model Based ◽  
Robust Model

Natural polyploidy in Vanilla planifolia (Orchidaceae)

Genome ◽  
2008 ◽  
Vol 51 (10) ◽  
pp. 816-826 ◽  
Author(s):  
Séverine Bory ◽  
Olivier Catrice ◽  
Spencer Brown ◽  
Ilia J. Leitch ◽  
Rodolphe Gigant ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Phenotypic Variation ◽  
Chromosome Numbers ◽  
Reunion Island ◽  
Vanilla Planifolia ◽  
Root Cells ◽  
Flow Cytometry Data ◽  
Important Variation ◽  
Réunion Island ◽  
Stomatal Length

Vanilla planifolia accessions cultivated in Reunion Island display important phenotypic variation, but little genetic diversity is demonstrated by AFLP and SSR markers. This study, based on analyses of flow cytometry data, Feulgen microdensitometry data, chromosome counts, and stomatal length measurements, was performed to determine whether polyploidy could be responsible for some of the intraspecific phenotypic variation observed. Vanilla planifolia exhibited an important variation in somatic chromosome number in root cells, as well as endoreplication as revealed by flow cytometry. Nevertheless, the 2C-values of the 50 accessions studied segregated into three distinct groups averaging 5.03 pg (for most accessions), 7.67 pg (for the ‘Stérile’ phenotypes), and 10.00 pg (for the ‘Grosse Vanille’ phenotypes). For the three groups, chromosome numbers varied from 16 to 32, 16 to 38, and 22 to 54 chromosomes per cell, respectively. The stomatal length showed a significant variation from 37.75 µm to 48.25 µm. Given that 2C-values, mean chromosome numbers, and stomatal lengths were positively correlated and that ‘Stérile’ and ‘Grosse Vanille’ accessions were indistinguishable from ‘Classique’ accessions using molecular markers, the occurrence of recent autotriploid and autotetraploid types in Reunion Island is supported. This is the first report showing evidence of a recent autopolyploidy in V. planifolia contributing to the phenotypic variation observed in this species.

scholarly journals Normative data for flow cytometry immunophenotyping of benign lymph nodes sampled by surgical biopsy

2017 ◽  
Vol 71 (2) ◽  
pp. 174-179 ◽  
Author(s):  
Gregory David Scott ◽  
Susan K Atwater ◽  
Dita A Gratzinger
Keyword(s):  
Flow Cytometry ◽  
Lymph Node ◽  
T Cell ◽  
Lymph Nodes ◽  
Inflammatory Disease ◽  
Clinical History ◽  
Surgical Biopsy ◽  
Data Set ◽  
Flow Cytometry Data ◽  
Benign Lymph Node

AimsTo create clinically relevant normative flow cytometry data for understudied benign lymph nodes and characterise outliers.MethodsClinical, histological and flow cytometry data were collected and distributions summarised for 380 benign lymph node excisional biopsies. Outliers for kappa:lambda light chain ratio, CD10:CD19 coexpression, CD5:CD19 coexpression, CD4:CD8 ratios and CD7 loss were summarised for histological pattern, concomitant diseases and follow-up course.ResultsWe generated the largest data set of benign lymph node immunophenotypes by an order of magnitude. B and T cell antigen outliers often had background immunosuppression or inflammatory disease but did not subsequently develop lymphoma.ConclusionsDiagnostic immunophenotyping data from benign lymph nodes provide normative ranges for clinical use. Outliers raising suspicion for B or T cell lymphoma are not infrequent (26% of benign lymph nodes). Caution is indicated when interpreting outliers in the absence of excisional biopsy or clinical history, particularly in patients with concomitant immunosuppression or inflammatory disease.

Spectral Flow Cytometry Makes Debut

2013 ◽  
Vol 33 (21) ◽  
pp. 1, 20-21
Author(s):  
Kathy Liszewski
Keyword(s):  
Flow Cytometry ◽  
Spectral Flow
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