Isolation of single circulating trophoblasts from maternal circulation for noninvasive fetal copy number variant profiling.

OBJECTIVE: To develop a multi-step workflow for the isolation of circulating extravillous trophoblasts (cEVTs) by describing the key steps enabling a semi-automated process, including a proprietary algorithm for fetal cell origin genetic confirmation and copy number variant (CNV) detection. METHODS: Determination of the limit of detection (LoD) for submicroscopic CNV was performed by serial experiments with genomic DNA and single cells from Coriell cell line biobank with known imbalances of different sizes. A pregnancy population of 372 women was prospectively enrolled and blindly analyzed to evaluate the current workflow. RESULTS: An LoD of 800 Kb was demonstrated with Coriell cell lines. This level of resolution was confirmed in the clinical cohort with the identification of a pathogenic CNV of 800 Kb, also detected by chromosomal microarray. The mean number of recovered cEVTs was 3.5 cells per sample with a significant reverse linear trend between gestational age and cEVT recovery rate and number of recovered cEVTs. In twin pregnanices, evaluation of zygosity, fetal sex and copy number profiling was performed in each individual cell. CONCLUSION: Our semi-automated methodology for the isolation and single-cell analysis of cEVTS supports the feasibility of a cell-based noninvasive prenatal test for fetal genomic profiling.

Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.

Alkaptonuria (AKU) is an ultrarare autosomal recessive disorder (MIM 203500) that is caused byby a complex set of mutations in homogentisate 1,2-dioxygenasegene and consequent accumulation of homogentisic acid (HGA), causing a significant protein oxidation. A secondary form of amyloidosis was identified in AKU and related to high circulating serum amyloid A (SAA) levels, which are linked with inflammation and oxidative stress and might contribute to disease progression and patients' poor quality of life. Recently, we reported that inflammatory markers (SAA and chitotriosidase) and oxidative stress markers (protein thiolation index) might be disease activity markers in AKU. Thanks to an international network, we collected genotypic, phenotypic, and clinical data from more than 200 patients with AKU. These data are currently stored in our AKU database, named ApreciseKUre. In this work, we developed an algorithm able to make predictions about the oxidative status trend of each patient with AKU based on 55 predictors, namely circulating HGA, body mass index, total cholesterol, SAA, and chitotriosidase. Our general aim is to integrate the data of apparently heterogeneous patients with AKUAKU by using specific bioinformatics tools, in order to identify pivotal mechanisms involved in AKU for a preventive, predictive, and personalized medicine approach to AKU.-Cicaloni, V., Spiga, O., Dimitri, G. M., Maiocchi, R., Millucci, L., Giustarini, D., Bernardini, G., Bernini, A., Marzocchi, B., Braconi, D., Santucci, A. Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.