National external quality assessment scheme for lymphocyte immunophenotyping in Belgium

A Critical Review on the Standardization and Quality Assessment of Nonfunctional Laboratory Tests Frequently Used to Identify Inborn Errors of Immunity

Inborn errors of immunity (IEI), which were previously termed primary immunodeficiency diseases, represent a large and growing heterogeneous group of diseases that are mostly monogenic. In addition to increased susceptibility to infections, other clinical phenotypes have recently been associated with IEI, such as autoimmune disorders, severe allergies, autoinflammatory disorders, benign lymphoproliferative diseases, and malignant manifestations. The IUIS 2019 classification comprises 430 distinct defects that, although rare individually, represent a group affecting a significant number of patients, with an overall prevalence of 1:1,200-2,000 in the general population. Early IEI diagnosis is critical for appropriate therapy and genetic counseling, however, this process is deeply dependent on accurate laboratory tests. Despite the striking importance of laboratory data for clinical immunologists, several IEI-relevant immunoassays still lack standardization, including standardized protocols, reference materials, and external quality assessment programs. Moreover, well-established reference values mostly remain to be determined, especially for early ages, when the most severe conditions manifest and diagnosis is critical for patient survival. In this article, we intend to approach the issue of standardization and quality control of the nonfunctional diagnostic tests used for IEI, focusing on those frequently utilized in clinical practice. Herein, we will focus on discussing the issues of nonfunctional immunoassays (flow cytometry, enzyme-linked immunosorbent assays, and turbidimetry/nephelometry, among others), as defined by the pure quantification of proteins or cell subsets without cell activation or cell culture-based methods.

Rapid cell population identification in flow cytometry data

We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.

A new statistical method for evaluating long-term analytical performance of laboratories applied to an external quality assessment scheme for flow cytometry

BACKGROUND

The Belgian External Quality Assessment Scheme for Flow Cytometry evaluates the long-term analytical performance of participating laboratories by calculating a regression line between the target and reported values of each parameter for each laboratory during the past 3 years. This study aims to develop a method to find laboratories with aberrant variability or bias using robust techniques and to obtain robust estimates of the variability.

METHODS

A method is proposed to find outliers with respect to the individual regression line, followed by a step to find regression lines with excessive variability and finally a step to find regression lines with high bias.

RESULTS

The model was applied to the results obtained by 52 laboratories for CD4%. From the 1340 data points, 35 were determined to be regression outliers. The second step revealed one regression line with excessive variability; the third step detected three regression lines with exceeding bias.

CONCLUSIONS

The methodology allows assessment of the long-term performance of laboratories, taking into account samples with different target values. Outliers in the first step indicate accidental mistakes, outliers in the second and third step point to high analytical variability or bias.

Merging mixture components for cell population identification in flow cytometry

We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated flow cytometry data distributions. Our framework allows the automated selection of the number of distinct cell subpopulations and we are able to identify cases where the algorithm fails, thus making it suitable for application in a high throughput FCM analysis pipeline. Furthermore, we demonstrate a method for summarizing complex merged cell subpopulations in a simple manner that integrates with the existing flowClust framework and enables downstream data analysis. We demonstrate the performance of our framework on simulated and real FCM data. The software is available in the flowMerge package through the Bioconductor project.