Reproducibility in Cytometry: Signals Analysis and its Connection to Uncertainty Quantification

TL;DR

This paper introduces signal analysis techniques for cytometry that separate biological variability from instrument artifacts, quantify measurement uncertainty, and improve reproducibility using scale transformations and constrained optimization.

Contribution

It presents novel methods for modeling signal deformations due to operating conditions and quantifying uncertainty in cytometry measurements.

Findings

Residual uncertainty less than 2.5% in signal shape

Less than 1% uncertainty in integrated area

Effective separation of biological variability from instrument effects

Abstract

Signals analysis for cytometry remains a challenging task that has a significant impact on uncertainty. Conventional cytometers assume that individual measurements are well characterized by simple properties such as the signal area, width, and height. However, these approaches have difficulty distinguishing inherent biological variability from instrument artifacts and operating conditions. As a result, it is challenging to quantify uncertainty in the properties of individual cells and perform tasks such as doublet deconvolution. We address these problems via signals analysis techniques that use scale transformations to: (I) separate variation in biomarker expression from effects due to flow conditions and particle size; (II) quantify reproducibility associated with a given laser interrogation region; (III) estimate uncertainty in measurement values on a per-event basis; and (IV) extract the singlets that make up a multiplet. The key idea behind this approach is to model how variable operating conditions deform the signal shape and then use constrained optimization to "undo" these deformations for measured signals; residuals to this process characterize reproducibility. Using a recently developed microfluidic cytometer, we demonstrate that these techniques can account for instrument and measurand induced variability with a residual uncertainty of less than 2.5% in the signal shape and less than 1% in integrated area.