Abstract
BACKGROUND: The immune system is a network of numerous cells that communicate both directly and indirectly with each other. The system is very sensitive to antigenic stimuli, which are memorised, and is closely connected with the endocrine and nervous systems. Therefore, in order to study the immune system correctly, it must be considered in all its complexity by analysing its components with multiparametric tools that take its dynamic characteristic into account. METHODS: We analysed lymphocyte subpopulations by using monoclonal antibodies with six different fluorochromes; the monoclonal panel employed included CD45, CD3, CD4, CD8, CD16, CD56, CD57, CD19, CD23, CD27, CD5, and HLA-DR. This panel has enabled us to measure many lymphocyte subsets in different states and with different functions: helper, suppressor, activated, effector, naïve, memory, and regulatory. A database was created to collect the values of immunological parameters of approximately 8,000 subjects who have undergone testing since 2000. When the distributions of the values for these parameters were compared with the medians of reference values published in the literature, we found that most of the values from the subjects included in the database were close to the medians in the literature. To process the data we used a comparative method that calculates the percentile rank of the values of a subject by comparing them with the values for others subjects of the same age. RESULTS: From this data processing we obtained a set of percentile ranks that represent the positions of the various parameters with regard to the data for other age-matched subjects included in the database. These positions, relative to both the absolute values and percentages, are plotted in a graph. We have called the final plot, which can be likened to that subject's immunological fingerprint, an "Immunogram". In order to perform the necessary calculations automatically, we developed dedicated software (Immunogramma) which provides at least two different "pictures" for each subject: the first is based on a comparison of the individual's data with those from all age-related subjects, while the second provides a comparison with only age and disease-related subjects. In addition, we can superimpose two fingerprints from the same subject, calculated at different times, in order to produce a dynamic picture, for instance before and after treatment. Finally, with the aim of interpreting the clinical and diagnostic meaning of a set of positions for the values of the measured parameters, we can also search the database to determine whether it contains other subjects who have a similar pattern for some selected immune parameters. CONCLUSIONS: This method helps to study and follow-up immune parameters over time. The software enables automation of the process and data sharing with other departments and laboratories, so the database can grow rapidly, thus expanding its informational capacity.