scholarly journals Automated analysis of flow cytometry data comes of age

2016 ◽  
Vol 89 (1) ◽  
pp. 13-15 ◽  
Author(s):  
Ryan R. Brinkman ◽  
Nima Aghaeepour ◽  
Greg Finak ◽  
Raphael Gottardo ◽  
Tim Mosmann ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Automated Analysis ◽  
Flow Cytometry Data

A Novel Automated Analysis Of Flow Cytometry Data Identifies Distinct Cell Signatures In The Immune System Of CLL Patients

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2864-2864
Author(s):  
Jens Rueter ◽  
Vivek Philip ◽  
Krishna Karuturi ◽  
Zaher Oueida ◽  
Margaret Chavaree ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Computational Methods ◽  
Peripheral Blood ◽  
Automated Analysis ◽  
Cell Types ◽  
Analysis Procedure ◽  
Individual Sample ◽  
Flow Cytometry Data ◽  
Distinct Cell ◽  
Common Cell

Abstract Introduction Recent developments of novel immunotherapeutic drugs have shown promising results for patients with hematologic malignancies, however, an unmet need for accurate and specific biomarkers persists. To address this need, we developed a novel integrative analysis procedure for the automated analysis of multidimensional flow cytometry data obtained from the peripheral blood of patients with chronic lymphocytic leukemia (CLL). State of the art flow cytometry analysis is accomplished by manual sequential segmentation, or gating, of cell populations based on similarities in fluorescence and light scatter characteristics through visualization of the data in one- or two-dimensional plots. This approach has a number of limitations, including the subjective nature of the gating and the inability to fully utilize the high-dimensional data. Recent efforts have produced sophisticated computational methods that overcome many of these limitations; however, these newer computational methods have not been rigorously tested in a clinical context and have focused on the rigorous and automated analysis of samples from individual patients, with substantially less effort towards the analysis of patient populations. The ultimate goal of our analysis is to develop computational approaches that will enable an identification of subsets of patients with distinct immunological markers. Methods We developed a novel analysis framework that facilitates automated identification of both common cell types and patient population subgroups, based on post-processing of individual sample analysis with the FLOCK program. FLOCK identifies clusters of putatively similar cells in an individual sample by multidimensional clustering of the fluorescence marker and light-scattering measurements. We developed a rigorous hierarchical clustering approach to identify common “cell signatures” across multiple patients. The cell signatures were then mapped back onto the individual patient samples and used in a second clustering that identified patient subgroups based on similar abundances of specific cell types. Results We used our analytic framework to analyze multidimensional flow cytometry data (26 cell surface markers in 4 different antibody cocktails) from peripheral blood specimens of a heterogeneous group of 55 CLL patients and 13 healthy controls. Our analysis revealed distinct differences between controls and CLL patients. Analyzing the non-malignant peripheral blood cell types, we were furthermore able to differentiate between distinct clinical subpopulations of patients (e.g. identify treatment-naïve patients from those that had previously undergone chemotherapy). Conclusion/Discussion Using a novel integrative analysis procedure to analyze complex flow cytometry data of the peripheral blood from CLL patients, we are able to identify distinct cell type distributions. We propose that this information is a marker for the overall health/disease status of the corresponding patient, and could ultimately be used for diagnosis, prognosis, and selection of optimal treatment. In the context of multiple novel treatment options for CLL patients, such a tool will be crucial for defining individual patient prognosis, and defining an accurately matched treatment plan. Disclosures: No relevant conflicts of interest to declare.

scholarly journals High throughput automated analysis of big flow cytometry data

Methods ◽  
2018 ◽  
Vol 134-135 ◽  
pp. 164-176 ◽  
Author(s):  
Albina Rahim ◽  
Justin Meskas ◽  
Sibyl Drissler ◽  
Alice Yue ◽  
Anna Lorenc ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Automated Analysis ◽  
Flow Cytometry Data

scholarly journals Automated analysis of bacterial flow cytometry data with FlowGateNIST

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0250753 ◽  
Author(s):  
David Ross
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Automated Analysis ◽  
Experimental Methods ◽  
Automatic Calibration ◽  
Flow Cytometry Data ◽  
Background Events ◽  
Main Components ◽  
Engineered Bacteria ◽  
Python Package

Flow cytometry is commonly used to evaluate the performance of engineered bacteria. With increasing use of high-throughput experimental methods, there is a need for automated analysis methods for flow cytometry data. Here, we describe FlowGateNIST, a Python package for automated analysis of bacterial flow cytometry data. The main components of FlowGateNIST perform automatic gating to differentiate between cells and background events and then between singlet and multiplet events. FlowGateNIST also includes a method for automatic calibration of fluorescence signals using fluorescence calibration beads. FlowGateNIST is open source and freely available with tutorials and example data to facilitate adoption by users with minimal programming experience.

scholarly journals Automated Analysis of Bacterial Flow Cytometry Data with FlowGateNIST

2021 ◽  
Author(s):  
David Ross
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Automated Analysis ◽  
Experimental Methods ◽  
Automatic Calibration ◽  
Flow Cytometry Data ◽  
Background Events ◽  
Main Components ◽  
Engineered Bacteria ◽  
Python Package

AbstractFlow cytometry is commonly used to evaluate the performance of engineered bacteria. With increasing use of high-throughput experimental methods, there is a need for automated analysis methods for flow cytometry data. Here, we describe FlowGateNIST, a Python package for automated analysis of bacterial flow cytometry data. The main components of FlowGateNIST perform automatic gating to differentiate between cells and background events and then between singlet and multiplet events. FlowGateNIST also includes a method for automatic calibration of fluorescence signals using fluorescence calibration beads. FlowGateNIST is open source and freely available with tutorials and example data to facilitate adoption by users with minimal programming experience.

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 Automated Analysis of Clinical Flow Cytometry Data

2017 ◽  
Vol 37 (4) ◽  
pp. 931-944 ◽  
Author(s):  
Richard H. Scheuermann ◽  
Jack Bui ◽  
Huan-You Wang ◽  
Yu Qian
Keyword(s):  
Flow Cytometry ◽  
Automated Analysis ◽  
Flow Cytometry Data
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