Phospholipidomics of peripheral blood mononuclear cells (PBMCs): the tricky case of children with autism spectrum disorder (ASD) and their healthy siblings

Autism spectrum disorder (ASD) is a broad and heterogeneous group of neurological developmental disorders characterized by impaired social interaction and communication, restricted and repetitive behavioural patterns, and altered sensory processing. Currently, no reliable ASD molecular biomarkers are available. Since immune dysregulation has been supposed to be related with ASD onset and dyslipidaemia has been recognized as an early symptom of biological perturbation, lipid extracts from peripheral blood mononuclear cells (PBMCs), consisting primarily of lymphocytes (T cells, B cells, and NK cells) and monocytes, of 38 children with ASD and their non-autistic siblings were investigated by hydrophilic interaction liquid chromatography (HILIC) coupled with electrospray ionization and Fourier-transform mass spectrometry (ESI-FTMS). Performances of two freeware software for data extraction and processing were compared with acquired reliable data regardless of the used informatics. A reduction of variables from 1460 by the untargeted XCMS to 324 by the semi-untargeted Alex 123 software was attained. All-ion fragmentation (AIF) MS scans along with Alex 123 software were successfully applied to obtain information related to fatty acyl chain composition of six glycerophospholipid classes occurring in PBMC. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were explored to verify the occurrence of significant differences in the lipid pool composition of ASD children compared with 36 healthy siblings. After rigorous statistical validation, we conclude that phospholipids extracted from PBMC of children affected by ASD do not exhibit diagnostic biomarkers. Yet interindividual variability comes forth from this study as the dominant effect in keeping with the existing phenotypic and etiological heterogeneity among ASD individuals. Graphical abstract