Researchers have investigated the plasma metabolite profile of patients with frontotemporal dementia and Alzheimer’s disease using an untargeted metabolomic approach in combination with GC–MS.
Researchers have investigated the plasma metabolite profile of patients with frontotemporal dementia (FTD) and Alzheimer’s disease (AD) using an untargeted metabolomic approach in combination with gas chromatography–mass spectrometry (GC–MS) (1).
Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by progressive impairment in behaviour, executive function, and language. It is one of the most common causes of neurocognitive disorder in people under 65 years old with around 10 in every 100,000 persons likely to be afflicted. The most common form of FTD is a behavioural variant (bvFTD), which is clinically different to atypical AD cases and which it can sometimes be mistaken for. Generally, survival is shorter and cognitive decline is faster than with typical AD cases. Therefore, a differential diagnosis between bvFTD and atypical AD cases is crucial.
Currently, to differentiate between bvFTD and AD cases requires an evaluation of cognition and cerebrospinal fluid (CSF) biomarkers as well as a neuroimaging investigation and genetic mutations screening; however, an accurate diagnosis still poses a clinical challenge in specific cases. Furthermore, the collection of CSF requires an invasive lumbar puncture procedure, access to molecular neuroimaging methods can be restricted, and no blood biomarkers have been identified.
An alternative to diagnosis through these methods could come with the investigation of metabolites by Tsuruoka et al. (2), who identified six serum metabolites that were significantly increased in patients with dementia as well as 45 additional metabolites identified as candidate markers that could discriminate patients with dementia from cognitively healthy controls. As such, researchers aimed to investigate the plasma metabolite profile of patients with bvFTD compared to AD patients and cognitively healthy individuals using an untargeted metabolomic approach, with hopes to identify biological mechanisms and possible biomarkers for bvFTD diagnosis.
Results found a reduction in the levels of palmitoleic, oleic, and lauric acids in the bvFTD group compared to the AD group, however, no significance after multiple comparison correction was observed. Reduced levels of creatinine, glycine, tryptophan, uric acid, hypoxanthine, serine, valine, threonine, isoleucine, homoserine,methionine, glutamic acid, capric acid, tartronic acid, fumaric acid, and myoinositol, metabolites related to glycine/serine/threonine, alanine/aspartate/glutamate pathways and aminoacyl-tRNA biosynthesis, were also found in the bvFTD group when compared to controls. This led researchers to theorize that bvFTD patients may present an impairment of amino acid metabolism and the translation process.
Researchers hope this study can encourage further and more robust studies to fully explore the possibility of biomarkers for the diagnosis of this dementia form.—L.B.
References
AI and GenAI Applications to Help Optimize Purification and Yield of Antibodies From Plasma
October 31st 2024Deriving antibodies from plasma products involves several steps, typically starting from the collection of plasma and ending with the purification of the desired antibodies. These are: plasma collection; plasma pooling; fractionation; antibody purification; concentration and formulation; quality control; and packaging and storage. This process results in a purified antibody product that can be used for therapeutic purposes, diagnostic tests, or research. Each step is critical to ensure the safety, efficacy, and quality of the final product. Applications of AI/GenAI in many of these steps can significantly help in the optimization of purification and yield of the desired antibodies. Some specific use-cases are: selecting and optimizing plasma units for optimized plasma pooling; GenAI solution for enterprise search on internal knowledge portal; analysing and optimizing production batch profitability, inventory, yields; monitoring production batch key performance indicators for outlier identification; monitoring production equipment to predict maintenance events; and reducing quality control laboratory testing turnaround time.
2024 EAS Awardees Showcase Innovative Research in Analytical Science
November 20th 2024Scientists from the Massachusetts Institute of Technology, the University of Washington, and other leading institutions took the stage at the Eastern Analytical Symposium to accept awards and share insights into their research.