Brain Proteomics

263

10.7

Conclusion and Remarks

Recently, methodological, instrumental and software developments have been

reflected in proteomics applications and have given researchers a deeper un-

derstanding of the molecular basis of diseases. In this chapter, we focused on

studies using proteomic technology to investigate Alzheimer’s disease, Parkin-

son’s disease, and schizophrenia, three neurodegenerative diseases that seri-

ously affect human life. Today’s proteomics technology has the potential to

reveal large-scale protein profiles of single types of cells or organelles from bi-

ological material. As of 2024, information on a total of 204229 human proteins

is stored in the UniProt Knowledgebase, 20428 of which have been manually

annotated and reviewed (Access date: 23.01.2024). Considering the enormous

number and diversity of the cells that make up the human brain, the im-

portance of data obtained from high-resolution mass spectrometers becomes

increasingly important.

On the other hand, it should not be forgotten that the reflection of the neu-

ropathological status on the proteome data depends on the process of combin-

ing and interpreting robust and reliable results. A meticulous study should be

carried out in proteomic studies for sufficient sample size, good determination

of sample inclusion and exclusion criteria, a good sample preparation work-

flow that requires minimal manipulation of the sample, and powerful statistical

and bioinformatic analyses. Widely used and constantly updated applications

such as STRING, Perseus and other informatics tools mentioned above, as

well as new applications or modules such as STRINGApp and PerseusNet,

continue to make significant contributions to the field of proteomics in gen-

eral. However, the fact that proteomic data obtained on neurodegenerative

diseases have not yet been strongly implicated into clinical routine suggests

that more efforts are needed in wet laboratory studies and the development

of more comprehensive informatics tools.

In summary, while MS-based proteomic studies provide rich findings that

advance our general knowledge of neurodegenerative diseases, difficulties in

presenting, interpreting, and integrating the large volumes of data collected

slow the process of generating specific information about neurodegeneration

that can be applied to the clinic. An overview of recent studies reveals the

need for new proteomic analyses that will show protein-protein interactions,

post-translational modifications, and alterations in the protein expression, es-

pecially for the determination of new diagnostic criteria and for better classi-

fication of disease subtypes in schizophrenia. Multidisciplinary collaborations

involving researchers such as molecular biologists, chemists, and computer sci-

entists are of great importance in overcoming the challenges in evaluating pro-

teomic data analysis. In particular, the increased prevalence of user-friendly

software and the easier applicability of these to proteomics will enable the

data to be interpreted and presented rapidly.