科研之友
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摘要
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 CD 16, CD56, CD57, CD 19, 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, naive, 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
- 出版日期2010-4
