Protein Profile Patterns for Universal Screening

In a recent review article published in the Journal of Chromatography B, scientists from the Manipal Academy of Higher Education in Manipa, India, discussed different protein profile pattern analysis techniques allow for universal screening (1).

Death of neurons in the aging brain or Proteins in neurons | Image Credit: © Design Cells - stock.adobe.com

Universal screening is important to help detect diseases like cancer and coronary disease early in patients. For this to be possible, scientists need techniques to be available for use at point-of-care, minimally-/non-invasive, cost-effective, and operable by trained technicians, among other factors. Protein profile pattern analysis is one way that scientists can detect disease.

“By generating standard profiles for normal and various disease conditions, using clinically certified samples, one can then do a Match/No Match of a test sample profile to any other standard profile, to diagnose its actual condition,” the scientists wrote in the study (1).

Many biochemical processes precede the various changes involved in transforming a living system from its normal state to any abnormal condition, with events such as induction, progression, and regression occurring as byproducts of diseases, geriatric health problems, malnutrition, and more. Though many genomic markers have been identified and used for routine diagnostic applications, it can only provide indirect information on cellular states, rather than details of protein changes like post-translational modifications or protein degradation. Further, it cannot provide information on complex reactions like protein-signaling and folding, which control many life activities, including those connected with various disease processes. Proteins participate in nearly all body processes, with new proteins typically being produced from cellular signaling reactions. They can also serve as suitable markers for screening and early detection; proteins can help in understanding the steps involved during any transition of the living system from a normal to an abnormal condition.

Present technologies are not amenable for applications to screen for different diseases with a single examination. This is because multiple-marker identification, even for a single class of markers, like proteins, often require individual separation processes and estimation; this itself requires well-equipped lab facilities, multi-specialty hospitals with multiple, different, facilities, and professionals like pathologists and biochemists in very large numbers. One approach that may be cost-effective and accessible is liquid biopsy, which looks for markers in readily available samples like body fluids which can be accessed in a non- or minimally invasive manner.

The scientists used two approaches are being tried to achieve this objective. The first involves identifying suitable and specific markers for each condition, using established methods like various mass spectroscopy techniques (surface-enhanced laser desorption/ionization mass spectroscopy (SELDI-MS), matrix-assisted laser desorption/ionization (MALDI-MS), etc., immunoassays (enzyme-linked immunoassay (ELISA), proximity extension assays, etc.) and separation methods like 2-dimensional polyacrylamide gel electrophoresis (2-D PAGE), sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE), capillary electrophoresis (CE), and more. The second approach does not involve the identification of specific markers; rather, an efficient method, such as high-performance liquid chromatography or ultra-high-performance liquid chromatography (HPLC/UHPLC), is used to separate the protein markers. From there, a profile of the protein pattern is recorded, which is analyzed using artificial intelligence (AI)/machine learning (ML) methods to derive characteristic patterns and use them for identifying the disease condition.

With universal healthcare applications, especially in developing countries, scientists must have techniques that are highly cost-effective, methods that can be used by trained technicians, and diagnostic results that are “observer-independent” and can be arrived at easily. The techniques mentioned above show the utility of HPLC-separation-high sensitivity fluorescence detection, in addition to pattern analysis of the recorded protein profile of any clinical sample. This can be used for universal health care applications, regular periodic screening, and early detection of various conditions, such as cancers and coronary diseases.

Reference

(1) Barik, A. K.; Mathew, C.; Sanoop, P. M.; John, R. V.; Adigal, S. S.; et al. Protein Profile Pattern Analysis: A Multifarious, In Vitro Diagnosis Technique for Universal Screening. J. Chromatogr. B. 2024, 1232, 123944. DOI: 10.1016/j.jchromb.2023.123944