Proposal Number

:PPM 04-0131-200019

Program Cycle

: 

PPM 04

Submitting Institution Name

: 

Texas A&M University at Qatar

Project Status

: 

Award Active

Start Date

: 

22/02/2021

Lead Investigator

: 

Prof. Milivoj Belic

Project Duration

: 

3 Year(s)

End Date

: 

1/1/2024

Submission Type

: 

New

Proposal Title

: 

Early diagnosis of proteinopathies using massively parallel nano-spectroscopy with single-molecule sensitivity. Advanced clinical diagnostics for the development of personalized treatments

Proposal Description:

A number of common diseases, neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington disease (HD), transmissible spongiform encephalopathy (TSE) and amyotrophic lateral sclerosis (ALS), and metabolic disorders such as familial transthyretin amyloidosis (FTA) and type 2 diabetes (T2D), are associated with defective protein turnover. Hence the name proteinopathies. The hallmark of these diseases is misfolding, aggregation and tissue-specific accumulation of aggregated protein deposits composed of specific peptides/proteins, such as amyloid-β (Aβ) and tau in AD, α-synuclein in PD, poly-Q extended huntingtin in HD, prion protein (PrP) in TSE, superoxide dismutase 1 (SOD1) in ALS, transthyretin (TTR) in FTA, and islet amyloid polypeptide (IAPP) in T2D. While there is a considerable need – the number of individuals with AD and T2D is increasing worldwide, methods for early detection of pathological peptide/protein aggregates and continuous monitoring of the efficacy of disease-modifying drugs aiming to reduce the amyloidogenic load are still underdeveloped. This research proposal brings together two innovative approaches, in the realm of methodology development and medical diagnostics, to enable early, reliable, fast and cost-effective diagnosis of proteinopathies and continuous monitoring of the effect of personalized treatment on disease progression. Our vision is to achieve clinical diagnosis with the ultimate, single-molecule sensitivity using spatiotemporally resolved massively parallel Fluorescence Correlation Spectroscopy (mpFCS) to characterize the concentration and size of pathologic protein aggregates in biological fluids, most notably in blood serum but also in the cerebrospinal fluid (CSF). To achieve this goal, we will work on the following specific aims. Aim 1: Software development for seamless acquisition, processing, and representation of large data sets. To enable effective data acquisition, computationally efficient and robust data analysis and intuitively comprehensible graphical representation of results, the Compute Unified Device Architecture (CUDA) parallel computing platform will be used to harness the computing power of graphical processing units (GPUs). Dedicated software will be developed for: temporal autocorrelation, Maximum Entropy Method based fitting routine for analysis of FCS data, and the phasor approach based analysis of time-gated fluorescence lifetime measurements. This will enable us to determine the concentration and size of nanoplaques, and the fluorescence lifetime in complex biological systems using bias-free fitting. Aim 2: Refinement of instrument parameters and integration of massively parallel FCS with Fluorescence Lifetime Imaging Microscopy (mpFCS-FLIM). Implementation of fluorescence lifetime measurements will allow us to acquire information on the pH, viscosity, ionic strength, oxygen saturation, Ca2+ levels, etc. in the analyzed biological fluid, in addition to determining the concentration and size of pathologic protein aggregates. In this way, valuable additional information will be obtained that can aid in improving disease diagnosis reliability. Aim 3: Development of massively parallel dual-color Fluorescence Cross-Correlation Spectroscopy (FCCS). This will allow us to implement immune-based assays to probe the chemical composition of the protein aggregates, thus aiding disease diagnosis.

Research Area Keywords:

Proteinopathy; Clinical diagnostics; Alzheimer‘s disease; Nano-spectroscopy; CUDA computing

Research Type

Applied Research