Dataset Information

Automated, Point-of-Care mobile flow cytometry: Bringing the laboratory to the sample.

ABSTRACT:

Background

Innate effector cells are very responsive to infectious and inflammatory cues found in damaged and inflamed tissues. Their activation is a potential target to assess the state of the immune system. Unfortunately, these cells are very susceptible for ex-vivo activation, hampering accurate interpretation of flow cytometry data. Whether a brief window exists before ex-vivo activation starts to occur is currently unknown.

Aims

1) This study extensively investigated ex-vivo activation of innate effector cells over time. 2) We tested the feasibility of applying a mobile, automated, flow cytometry laboratory for out-of-hospital Point-of-Care analyses to minimize ex-vivo activation bias.

Methods

1) Ex-vivo neutrophil, eosinophil and monocyte activation in a blood collection tube over time and the reactivity to a formyl-peptide was investigated in a healthy cohort. 2) To facilitate fast, out-of-hospital analysis, application of the mobile flow cytometry was tested by placing an automated flow cytometer into a van. The stability of the setup was assessed by repetitively measuring laser alignment and fluorescence verification beads.

Findings

1) Immediately after venipuncture activation marker expression on neutrophils, eosinophils and monocyte subsets started to change in a time-dependent manner. 2) The mobile flow cytometry laboratory travelled over 3000 km, performing measurements at 19 locations with a median single-person-set-up time of 14 min. The laser alignment and fluorescence were stable during all experiments.

Conclusions

Accurate flow data of innate immune cells are only obtained when ex-vivo activation is kept to minimum. The use of a mobile, fast, automated, flow cytometry laboratory for out-of-hospital Point-of-Care analyses provides new investigational and diagnostic possibilities outside major hospital flow cytometry laboratories.

SUBMITTER: Jukema BN

PROVIDER: S-EPMC11019183 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Publications

Automated, Point-of-Care mobile flow cytometry: Bringing the laboratory to the sample.

Jukema B N BN Pelgrim T C TC Spoelder M M Bongers C C W G CCWG Hopman M T E MTE Smit K K Rijk M H MH Venekamp R P RP Vrisekoop N N Koenderman L L

Heliyon 20240403 8

<h4>Background</h4>Innate effector cells are very responsive to infectious and inflammatory cues found in damaged and inflamed tissues. Their activation is a potential target to assess the state of the immune system. Unfortunately, these cells are very susceptible for ex-vivo activation, hampering accurate interpretation of flow cytometry data. Whether a brief window exists before ex-vivo activation starts to occur is currently unknown.<h4>Aims</h4>1) This study extensively investigated ex-vivo ...[more]

PMID: 38628748

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Project description:BackgroundIn a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.ResultsWe compare the performance of parameter-optimized and default-parameter (in flowCore) data transformations on real and simulated data by measuring the variation in the locations of cell populations across samples, discovered via automated gating in both the scatter and fluorescence channels. We find that parameter-optimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to default-parameter counterparts.ConclusionsOur results indicate that the preferred transformation for fluorescence channels is a parameter- optimized biexponential or generalized Box-Cox, in accordance with current best practices. Interestingly, for populations in the scatter channels, we find that the optimized hyperbolic arcsine may be a better choice in a high-throughput setting than current standard practice of no transformation. However, generally speaking, the choice of transformation remains data-dependent. We have implemented our algorithm in the BioConductor package, flowTrans, which is publicly available.

Dataset Information

Automated, Point-of-Care mobile flow cytometry: Bringing the laboratory to the sample.

Background

Aims

Methods

Findings

Conclusions

Publications

Automated, Point-of-Care mobile flow cytometry: Bringing the laboratory to the sample.

Similar Datasets

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