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Blanco, Y.

Publications and source records attributed to Blanco, Y..

2 recordsLinked to original sources

Ultrasensitive graphene FET aptasensor for direct attomolar detection of glutamate in human clinical samples

Glutamate, the principal excitatory neurotransmitter in the brain, is crucial for cognition and memory, and its dysregulation is implicated in several neurological disorders, including Alzheimers Disease and epilepsy. However, precise and high-throughput quantification of glutamate in physiological samples remains challenging. Here, we report an ultrasensitive and highly specific glutamate aptamer-based biosensor, or aptasensor, developed through in silico design and microfabrication, followed by in vitro and clinical validation. The biosensor consists of arrays of graphene field-effect transistors functionalized with a novel DNA aptamer, NG-Apt-Glu, designed and characterized computationally and biochemically, revealing two putative glutamate binding sites. The aptasensor detects glutamate in artificial cerebrospinal fluid with a 1 aM detection limit, a wide linear range (1 aM-10 pM), and 24 mV/decade sensitivity, showing strong selectivity against GABA, glutamine, dopamine, and serotonin. To evaluate clinical applicability, glutamate levels were measured in cerebrospinal fluid from patients with Alzheimers Disease, showing a significant increase relative to controls and correlating with neurofilament light chain concentrations, a biomarker of neuronal death. These findings underscore glutamates involvement in Alzheimers pathophysiology and its potential as a biomarker for neurodegeneration. This ultrasensitive graphene-based aptasensor enables point-of-care monitoring, paving the way for early diagnosis and the development of novel therapeutic strategies.

bioengineering↗

Multiscale networks in multiple sclerosis

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information, and Boolean simulations identified paths within and among all layers. The path more commonly found from the boolean simulations connects MP2K, with Th17 cells, the retinal nerve fiber layer (RNFL) thickness and the age related MS severity score (ARMSS). Combinations of several proteins (HSPB1, MP2K1, SR6, KS6B1, SRC, MK03, LCK and STAT6)) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/530153v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@7dd26org.highwire.dtl.DTLVardef@482239org.highwire.dtl.DTLVardef@1bef03borg.highwire.dtl.DTLVardef@8dc6e6_HPS_FORMAT_FIGEXP M_FIG C_FIG Author SummaryComplex diseases such as Multiple Sclerosis (MS) involve the contribution of a wide range of biological processes. We conducted a systems biology study of MS based on network analysis and deep phenotyping in a prospective cohort of patients with clinical, imaging, genetics, and omics assessments. The gene, proteins and cell paths explained variation in central nervous system damage, and in metrics of disease severity. Such multilayer paths explain the different phenotypes of the disease and can be developed as biomarkers of MS.

systems biology↗