There are two types of EVs, namely exosomes, that originate from late endosome/multivesicular bodies, and microvesicles that bud directly from the plasma membrane. Their detection in biofluids corresponds to damage that has probably started many years before.Įxtracellular vesicles (EVs) are nano- or micro-sized membranous particles released by any cells and can be found in biological fluids. Moreover, neurofilaments are the end-product of a process of degradation in the axons. However, a drawback is that neurofilaments increase not only in ALS but in other neurodegenerative conditions too. Neurofilaments are promising biochemical biomarkers to diagnose ALS even before the onset of clinical symptoms. A promising assay to detect pathological species of TDP-43 in cerebrospinal fluid (CSF) has been recently developed, however methods for plasma/serum are lacking.
In ALS, TDP-43 is a clear hallmark of the disease it has been observed as highly post-translationally modified (cleaved and hyperphosphorylated) in protein inclusions in 97% of post mortem specimens from patients, but its analysis in biofluids is still hard to reproduce. For example, the prodromal stage of Alzheimer’s disease is monitored well by detecting the increase of amyloid protein peptides in the brain up to 10 years before the onset of symptoms.
These biomarkers should reveal an adaptive response to a toxic stimulus before the degeneration starts. ALS’s biggest challenge is identifying reproducible biochemical biomarkers to predict the disease in the early phase and that change during its progression. About 50% of patients also develop non-motor symptoms with cognitive and behavioral changes that may appear before or after motor impairment. ALS leads to selective loss of upper and lower motor neurons resulting in progressive paralysis and death within a few years from onset. We conceived an innovative mathematical model based on machine learning which, by integrating EV size distribution data with protein cargoes, gave very high prediction rates for disease diagnosis and prognosis.Īmyotrophic lateral sclerosis (ALS) is a rare and fatal neurodegenerative disorder with an incidence of 3.03 cases per 100,000 persons. Our analysis unmasked features in plasma EVs of ALS patients with potential straightforward clinical application. Immuno-electron microscopy also suggested that phosphorylated TDP-43 is not an intravesicular cargo of plasma-derived EVs. In terms of disease progression, the levels of cyclophilin A with the EV size distribution distinguished fast and slow disease progressors, a possibly new means for patient stratification. EVs in the plasma of ALS patients and the two mouse models of ALS had a distinctive size distribution and lower HSP90 levels compared to the controls. Our procedure resulted in high-yield isolation of intact and polydisperse plasma EVs, with minimal lipoprotein contamination. We used machine learning techniques to predict diagnosis and prognosis.
We purified plasma EVs by nickel-based isolation, characterized their EV size distribution and morphology respectively by nanotracking analysis and transmission electron microscopy, and analyzed EV markers and protein cargos by Western blot and proteomics. We analyzed plasma-derived EVs of ALS patients ( n = 106) and controls ( n = 96), and SOD1 G93A and TDP-43 Q331K mouse models of ALS. Their biophysical and biochemical characteristics vary with the parent cell’s physiological and pathological state and make them an attractive source of multidimensional data for patient classification and stratification. Extracellular vesicles (EVs) are nanostructures released by any cell type into body fluids. There is an urgent need to identify biomarkers to tackle the disease’s complexity and help in early diagnosis, prognosis, and therapy. Amyotrophic lateral sclerosis (ALS) is a multifactorial, multisystem motor neuron disease for which currently there is no effective treatment.
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