[A new remedy in pathology practice: molecular solution to sample mix-up].

Objective: Although very rare, suspicious situations about the identity of diagnostic tissue material have been encountering in pathology practice. Such situations undoubtedly have the potential to create undesirable results. In the present study, an application targeting getting rid of any doubts about the identity of the diagnostic tissue samples is described.

Material And Method: A combination of short tandem repeats (STR) of the human genome consisting of CSF1PO, TH01, TPOX, D3S1358, D5S818, D7S820, D8S1179, D13S317, D16S539 and Penta E were selected on the basis of ease of application and bioinformatic discrimination power. Possible forms of diagnostic tissue mix up were set in 3 different models with 3 diagnostic tissue samples of 2 different cases. Of the tissue samples selected, A (salivary gland) and B (striated muscle) belonged to the same case and C (uterus wall) belonged to another case. In the first model, there was no problem about tissue identity (M1: A/B). In the second model, two different diagnostic material were mixed up (M2: B/C). In the last model, there were 3 diagnostic material obtained from 2 different cases (M3: A/B/C). DNA was extracted from all tissue samples and all of the selected 10 STR were amplified with specially designed primers by PCR. After chemical denaturation, amplicons were submitted to polyacrylamide gel electrophoresis for discrimination of single DNA strands according to their conformation polymorphism (SSCP). Special patterns of each STR in the gel matrix obtained from M1, M2 and M3 models, were evaluated on the principle of being 'same or different' to determine the diagnostic material identity.

Results: Each of the salivary gland, striated muscle and uterus wall samples were correctly identified (matched with the right source cases) after evaluating 10 different STR SSCP patterns designed under M1, M2 and M3 models.

Conclusion: This application targeting to solve diagnostic tissue identity problems is a simple and cheap application of SSCP and its efficacy was proven on the designed models.