Reproducibility in cytometry: Signals analysis and its connection to uncertainty quantification
Paul N Patrone1, Matthew DiSalvo1, Anthony J Kearsley1
1National Institute of Standards and Technology, Gaithersburg, MD, United States of America.
Plos One
|December 22, 2023
Summary
New signal analysis techniques for flow cytometry improve data accuracy by separating biological variation from instrument noise. This allows for more reliable cell measurement and doublet deconvolution, reducing uncertainty in cytometry data.
Area of Science:
- Quantitative Biology
- Biophysical Measurement
- Analytical Chemistry
Background:
- Conventional cytometry struggles to differentiate biological variability from instrument artifacts, leading to uncertainty in cell property quantification.
- Existing methods face challenges in tasks like doublet deconvolution due to difficulties in assessing measurement uncertainty.
- Instrumental factors like flow conditions and particle size complicate accurate signal interpretation in cytometry.
Purpose of the Study:
- To develop advanced signal analysis techniques for cytometry to address challenges in uncertainty quantification and data interpretation.
- To improve the ability to distinguish biological variation from technical variability in cytometry measurements.
- To enable accurate doublet deconvolution and per-event uncertainty estimation.
Main Methods:
- Utilized signal analysis techniques employing scale transformations to model and correct for signal deformations caused by operating conditions.
- Applied constrained optimization to 'undo' signal shape deformations, with residuals quantifying reproducibility.
- Demonstrated the approach on a microfluidic cytometer platform.
Main Results:
- Successfully separated variations in biomarker expression from flow conditions and particle size effects.
- Quantified reproducibility associated with laser interrogation regions.
- Achieved residual uncertainty of less than 2.5% in signal shape and less than 1% in integrated area, accounting for instrument and measurand variability.
Conclusions:
- The developed signal analysis methods effectively account for instrument-induced variability in cytometry.
- This approach enables precise per-event uncertainty estimation and improves the reliability of cytometry data.
- The techniques facilitate accurate singlet extraction from multiplets and enhance overall cytometry data quality.
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