Glycosylation, oxidation and other post-translational modifications of membrane and transmembrane proteins can alter lipid density, packing and interactions, and are considered an important factor that affects fluidity variation in membranes. Red blood cells (RBC) membrane physical state, showing pronounced alterations in Type 1 diabetes mellitus (T1DM), could be the ideal candidate for monitoring the disease progression and the effects of therapies. On these grounds, the measurement of RBC membrane fluidity alterations can furnish a more sensitive index in T1DM diagnosis and disease progression than Glycosylated hemoglobin (HbA1c), which reflects only the information related to glycosylation processes. Here, through a functional two-photon microscopy approach we retrieved fluidity maps at submicrometric scale in RBC of T1DM patients with and without complications, detecting an altered membrane equilibrium. We found that a phase separation between fluid and rigid domains occurs, triggered by systemic effects on membranes fluidity of glycation and oxidation. The phase separation patterns are different among healthy, T1DM and T1DM with complications patients. Blood cholesterol and LDL content are positively correlated with the extent of the phase separation patterns. To quantify this extent a machine learning approach is employed to develop a Decision-Support-System (DSS) able to recognize different fluidity patterns in RBC. Preliminary analysis shows significant differences(p<0.001) among healthy, T1DM and T1DM with complications patients. The development of an assay based on Phase separation of the plasma membrane of the Red Blood cells is a potential tool for diagnosis and progression monitoring of type 1 diabetes mellitus, and could allow customization and the selection of medical treatments in T1DM in clinical settings, and enable the early detection of complications.
Maulucci, G., Cordelli, E., Rizzi, A., De Leva, F., Papi, M., Ciasca, G., Samengo, D. M., Pani, G., Pitocco, D., Soda, P., Ghirlanda, G., Iannello, G., De Spirito, M., Phase separation of the plasma membrane in human red blood cells as a potential tool for diagnosis and progression monitoring of type 1 diabetes mellitus, <<PLOS ONE>>, 2017; 12 (9): N/A-N/A. [doi:10.1371/journal.pone.0184109] [http://hdl.handle.net/10807/122410]
Phase separation of the plasma membrane in human red blood cells as a potential tool for diagnosis and progression monitoring of type 1 diabetes mellitus
Maulucci, Giuseppe
Membro del Collaboration Group
;Rizzi, AlessandroMembro del Collaboration Group
;De Leva, FrancescaMembro del Collaboration Group
;Papi, MassimilianoMembro del Collaboration Group
;Ciasca, GabrieleMembro del Collaboration Group
;Samengo, Daniela MariaMembro del Collaboration Group
;Pani, GiovambattistaMembro del Collaboration Group
;Pitocco, DarioMembro del Collaboration Group
;Ghirlanda, Giovanni;De Spirito, Marco
2017
Abstract
Glycosylation, oxidation and other post-translational modifications of membrane and transmembrane proteins can alter lipid density, packing and interactions, and are considered an important factor that affects fluidity variation in membranes. Red blood cells (RBC) membrane physical state, showing pronounced alterations in Type 1 diabetes mellitus (T1DM), could be the ideal candidate for monitoring the disease progression and the effects of therapies. On these grounds, the measurement of RBC membrane fluidity alterations can furnish a more sensitive index in T1DM diagnosis and disease progression than Glycosylated hemoglobin (HbA1c), which reflects only the information related to glycosylation processes. Here, through a functional two-photon microscopy approach we retrieved fluidity maps at submicrometric scale in RBC of T1DM patients with and without complications, detecting an altered membrane equilibrium. We found that a phase separation between fluid and rigid domains occurs, triggered by systemic effects on membranes fluidity of glycation and oxidation. The phase separation patterns are different among healthy, T1DM and T1DM with complications patients. Blood cholesterol and LDL content are positively correlated with the extent of the phase separation patterns. To quantify this extent a machine learning approach is employed to develop a Decision-Support-System (DSS) able to recognize different fluidity patterns in RBC. Preliminary analysis shows significant differences(p<0.001) among healthy, T1DM and T1DM with complications patients. The development of an assay based on Phase separation of the plasma membrane of the Red Blood cells is a potential tool for diagnosis and progression monitoring of type 1 diabetes mellitus, and could allow customization and the selection of medical treatments in T1DM in clinical settings, and enable the early detection of complications.
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