Gate to the Future: Computational Analysis of Immunophenotyping Data

Mair, Florian

doi:10.1002/cyto.a.23700

Cited by 9 publications

(12 citation statements)

References 18 publications

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“…Sequential bivariate gating : Once data preprocessing steps are complete, users can identify cell populations using manual analysis or one or more of more than 50+ automated gating algorithms currently available . Sequential gating in 2D plots is the standard method for manual analysis.…”

Section: Data Handling Evaluation Storage and Repositoriesmentioning

confidence: 99%

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

et al. 2019

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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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Section: Data Handling Evaluation Storage and Repositoriesmentioning

confidence: 99%

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

et al. 2019

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“…1) (10). A nonexhaustive overview of the main features of typical supervised and unsupervised analysis tools relative to manual gating was recently conducted by Mair ( Fig.…”

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confidence: 99%

“…A nonexhaustive overview of the main features of typical supervised and unsupervised analysis tools relative to manual gating was recently conducted by Mair ( Fig. 1) (10). Supervised methods tend to outperform unsupervised methods when the goal is to replace manual gating for generating cell population statistics, as evaluated through open peer-reviewed comparative studies (3,(11)(12)(13)(14).…”

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confidence: 99%

“…Data-driven analysis pipelines based on supervised gating methods can result in improved reproducibility and reliability and reduced effort in large clinical cohorts where the goal is to assess phenotypically well-known and biologically relevant populations (10). However, adoption of such methods is in its nascency.…”

mentioning

confidence: 99%

“…Adding to the inevitability of increased adoption over time, each new generation of scientists will also on average be more computationally inclined than the last. More extensive dissemination of basic experimental guidelines to ensure high quality of raw data is pivotal for the success of any analysis pipeline, whether manual or automated (10,25). Finally, as methods continue to progress in their capabilities and performance, reasons to maintain rooted in an era of manual analysis will continue to become less compelling than they already are.…”

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confidence: 99%

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

Brinkman

2019

Cytometry Pt A

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The steps that characterize data analysis for flow cytometrybased clinical trials can be grouped into quality assessment, compensation, normalization, transformation, cell population identification, cross-sample comparison (population mapping or matching), feature extraction, visualization, and interpretation. Of these steps, cell population identification (i.e., gating), which generates reportables such as cell population counts/percentages and MFI, is the focus of significant efforts to improve the rigor and reproducibility of data analysis. Here, rigor is the application of the scientific method to ensure unbiased and well-controlled analysis, interpretation, and reporting of results. Reproducibility is important as analyses are only validated when they can be duplicated by multiple scientists. It is especially important for clinical studies due to what has been deemed a reproducibility crisis in medicine, as only 11% of a series of preclinical cancer studies could be replicated (1).While manual gating is the gold standard and current practice for cell population identification, assessments of its reproducibility have recognized it as a significant contributor of variation in flow cytometry studies, with interlaboratory C.V.s up to 30% (2). While having a single operator analyze all the data can significantly reduce variability, the chosen individual is still influenced by the same personal biases that result in inconsistencies within and across individuals, centers, and time. Resorting to a single operator to improve reproducibility does not scale for large studies as manual gating can take from 45 to 90 min for one clinical sample (3). With clinical trials now involving thousands of patients assayed with 18 parameters, the challenges associated with the rigor and reproducibility of manual analysis of flow cytometry-based clinical data have only become more apparent and pressing.The first algorithm for automated flow cytometry cell population identification was published in 1985, where it was noted that: "Unfortunately, the use of three or more independent fluorescent parameters complicates the analysis of the resulting data significantly" (4). While data complexity has increased significantly since then, automated analysis approaches for flow cytometry data have also matured. The state of the art is at a stage where results from automated data analysis algorithms have reached a level of maturity that enables them to match, and in many cases exceed, the results produced by human experts. The maturity and acceptance has gone as far as being included as part of the documentation for the FDA approval of a first in class CAR-T therapy (5-7).The overwhelming majority of automated gating methods are unsupervised. Unsupervised algorithms are those approaches that work on data that do not come with predefined labels. With respect to FCM data, these algorithms

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Barcoding of live peripheral blood mononuclear cells to assess immune cell phenotypes using full spectrum flow cytometry

Junker

Teixeira

2022

Cytometry Pt A

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Barcoded flow cytometry is a multiplexing technique allowing for the simultaneous acquisition of cells from different donors or experimental conditions in a highthroughput manner. This approach allows to synchronize acquisition of samples and reduce variance introduced through the operator or technical platform. However, to date, only very few flow cytometry barcoding protocols have been developed, which often suffer from technical limitations. Here, we developed a novel barcoding protocol for a full-spectrum flow cytometry platform. We developed a 21-color immunophenotyping assay for up to 20 different samples analyzed simultaneously with comparable variance between repeated single-tube acquisition and postde-multiplexing. Barcoding offers great potential in parallelizing the analysis of complex cell populations such as peripheral blood mononuclear cells (PBMCs). Consequently, we assessed the performance of our method in situations where PBMCs were challenged with phytohaemagglutinin (PHA), a strong mitogen and broad activator of B cells and T cells, and superantigen Staphylococcus enterotoxin B (SEB) that has been reported to induce polyclonal T cell activation. PBMCs were either barcoded before pooled challenge or challenged individually pre-barcoding. Our final workflow included pooled immunophenotyping followed by machine learning aided single-cell data analysis and enabled us to identify robust PHA and SEB mode of action related phenotypic changes in PBMC immune cell lineages. Conclusively, we present a novel technique allowing the barcoded acquisition and analysis of PBMCs from up to 20 different donors and present a valid basis for the future development of complex immunophenotyping protocols.

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Gate to the Future: Computational Analysis of Immunophenotyping Data

Cited by 9 publications

References 18 publications

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

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

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

Barcoding of live peripheral blood mononuclear cells to assess immune cell phenotypes using full spectrum flow cytometry

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