Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Abstract

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

Keywords: artificial intelligence (AI); machine learning (ML); neuroproteomics; neurotrauma; personalized medicine (PM); proteomics; traumatic brain injuries (TBI).

Figure 1

Neuroproteomics quantification methodologies. Neuroproteomics utilizes many molecular techniques to quantify and characterize protein concentration and quantity. SDS-Page Gel Separation (Western Blot), ELISA, Mass Spectrometry, Bioinformatic data, and neuroimaging correlations utilize quantitative data collection methods within proteomics.

Figure 2

General neuroproteomics workflow for discovery of disease biomarkers.

Figure 3

The general workflow of the MS-based glycoproteomics in neurotrauma samples.

Figure 4

Different techniques used in sample preparation and cell isolation for further analysis of single-cell proteomics (SCP) by using (A) Micromanipulator, (B) Fluorescence-activated cell sorting (FACS), (C) Limiting dilution, (D) Microfluidic chip, (E) Magnetic activated cell sorting, (F) Laser capture microdissection (LCM).

Figure 5

Label-free quantitation proteomics is performed by determining the area under the curve (AUC) of peptides eluted from the liquid chromatography and conjugated to MS/MS. (A) The label-free method can be applied to a single sample containing a mixture of peptides. (B) The method can also be applied to different samples (such as healthy and disease-representing cells) to quantify a specific peptide in the two cell populations. Abbreviations: Tandem mass spectrometry (MS/MS).

Figure 6

Artificial intelligence incorporated proteomics in personalized medicine. Once molecular diagnostic, nanoproteomics, pharmacoproteomic, and genomic data is collected correlations, AUC comparison, and significant tests are utilized to identify potential relationships between data. Data storage through AI and ML technology analyze large data sets to create faster and more accurate predictions with prognostic, diagnostic, and theragnostic value, all essential for effective personalized medicine.

Figure 7

Proteomics, machine learning, and artificial intelligence in personalized medicine. In human clinical trials, neuroscientist study the proteomics of primary injury brain samples. After a primary injury, homeostatic conditions within the brain lead to kinase enzyme activation, PTM addition, and NFT formation, leading to plaque and malformation. Evidence of the damage was once primarily collected through neuroimaging, however faster and more effective technique have been developed through proteomics data analysis, computer data processing, AI and ML. Today, the collection of different data from a variety of tests provides significant data for special cases of TBI and can even provide better prognostic and diagnostic outputs for personalized medicine.