MALDI-MS in first-line screening of newborns for sickle cell disease: results from a prospective study in comparison to HPLC.

OBJECTIVES: Newborn screening (NBS) for sickle cell disease (SCD) requires a robust, high-throughput method to detect hemoglobin S (HbS). Screening for SCD is performed by qualitative methods, such as isoelectric focusing (IEF), and both qualitative and quantitative methods such as high performance liquid chromatography (HPLC), capillary electrophoresis (CE), and tandem mass spectrometry (MS/MS). All these methods detect HbS, as well as low-level or absent HbA, and also other variants of hemoglobin. HPLC is considered as a reference method for NBS, because of its high sensitivity and specificity in detecting HbS. NeoSickle®, a fully automated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) platform, combined with automated sample processing, a laboratory information management system and NeoSickle® software for automatic data interpretation, has increased the throughput of SCD testing. The purpose of this study was to compare the performances of NeoSickle® and HPLC. METHODS: A prospective study was conducted including 9,571 samples from the NBS program to compare MALDI-MS using NeoSickle® with an HPLC method. Correlation between the two methods was studied. For the MALDI-MS method, sensitivity, specificity, NPV, and PPV were calculated. RESULTS: We found over 99.4 % correlation between the HPLC and MALDI-MS results. NeoSickle® showed 100 % of sensitivity and specificity in detecting SCD syndrome, leading to positive and negative predictive values of 100 %. CONCLUSIONS: NeoSickle® is adapted to NBS for SCD, and can be used in first-line high-throughput screening to detect HbS, and beta-thalassemia major warning. When HbS is detected, second-line use of another specific method as HPLC is necessary.

A Multicentre Pilot Study of a Two-Tier Newborn Sickle Cell Disease Screening Procedure with a First Tier Based on a Fully Automated MALDI-TOF MS Platform.

The reference methods used for sickle cell disease (SCD) screening usually include two analytical steps: a first tier for differentiating haemoglobin S (HbS) heterozygotes, HbS homozygotes and ß-thalassemia from other samples, and a confirmatory second tier. Here, we evaluated a first-tier approach based on a fully automated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) platform with automated sample processing, a laboratory information management system and NeoSickle® software for automatic data interpretation. A total of 6701 samples (with high proportions of phenotypes homozygous (FS) or heterozygous (FAS) for the inherited genes for sickle haemoglobin and samples from premature newborns) were screened. The NeoSickle® software correctly classified 98.8% of the samples. This specific blood sample collection was enriched in qualified difficult samples (premature newborns, FAS samples, late and very late samples, etc.). In this study, the sensitivity of FS sample detection was found to be 100% on the Lille MS facility and 99% on the Dijon MS facility, and the specificity of FS sample detection was found to be 100% on both MS facilities. The MALDI-MS platform appears to be a robust solution for first-tier use to detect the HbS variant: it is reproducible and sensitive, it has the power to analyze 600-1000 samples per day and it can reduce the unit cost of testing thanks to maximal automation, minimal intervention by the medical team and good overall practicability. The MALDI-MS approach meets today's criteria for the large-scale, cost-effective screening of newborns, children and adults.

The Reliable, Automatic Classification of Neonates in First-Tier MALDI-MS Screening for Sickle Cell Disease.

Previous research has shown that a MALDI-MS technique can be used to screen for sickle cell disease (SCD), and that a system combining automated sample preparation, MALDI-MS analysis and classification software is a relevant approach for first-line, high-throughput SCD screening. In order to achieve a high-throughput "plug and play" approach while detecting "non-standard" profiles that might prompt the misclassification of a sample, we have incorporated various sets of alerts into the decision support software. These included "biological alert" indicators of a newborn's clinical status (e. g., detecting samples with no or low HbA), and "technical alerts" indicators for the most common non-standard profiles, i.e., those which might otherwise lead to sample misclassification. We evaluated these alerts by applying them to two datasets (produced by different laboratories). Despite the random generation of abnormal spectra by one-off technical faults or due to the nature and quality of the samples, the use of alerts fully secured the process of automatic sample classification. Firstly, cases of ß-thalassemia were detected. Secondly, after a visual check on the tagged profiles and reanalysis of the corresponding biological samples, all the samples were correctly reclassified without prompting further alerts. All of the samples for which the results were not tagged were well classified (i.e., sensitivity and specificity = 1). The alerts were mainly designed for detecting false-negative classifications; all the FAS samples misclassified by the software as FA (a false negative) were marked with an alert. The implementation of alerts in the NeoScreening® Laboratory Information Management System's decision support software opens up perspectives for the safe, reliable, automated classification of samples, with a visual check solely on abnormal results or samples. It should now be possible to evaluate the combination of the NeoSickle® analytical solution and the NeoScreening® Laboratory Information Management System in a real-life, prospective study of first-line SCD screening.

eClims: An Extensible and Dynamic Integration Framework for Biomedical Information Systems.

Biomedical information systems (BIS) require consideration of three types of variability: data variability induced by new high throughput technologies, schema or model variability induced by large scale studies or new fields of research, and knowledge variability resulting from new discoveries. Beyond data heterogeneity, managing variabilities in the context of BIS requires extensible and dynamic integration process. In this paper, we focus on data and schema variabilities and we propose an integration framework based on ontologies, master data, and semantic annotations. The framework addresses issues related to: 1) collaborative work through a dynamic integration process; 2) variability among studies using an annotation mechanism; and 3) quality control over data and semantic annotations. Our approach relies on two levels of knowledge: BIS-related knowledge is modeled using an application ontology coupled with UML models that allow controlling data completeness and consistency, and domain knowledge is described by a domain ontology, which ensures data coherence. A system build with the eClims framework has been implemented and evaluated in the context of a proteomic platform.