SQUARE SAMPLING - AN EASY METHOD OF ESTIMATING NUMERICAL DENSITIES OFCELLS OU PARTICLES WITHIN A TISSUE

OBJECTIVE: A new parametric method is presented, called ''square sampl ing,'' which speeds up the estimate of the number of cells or particle s that are randomly distributed within a tissue. STUDY DESIGN: The pri nciple of square sampling is subdivision of a biopsy into at least 100 squares of the same size using a measuring ocular or computer-based m orphometric system and estimating the cell number by counting ''positi ve'' squares, squares with at least one cell of interest, assuming a b inomial distribution of positive squares, depending on numerical densi ty. RESULTS: The derived estimate yielded almost identical results whe n compared with the exact count of pseudo-Gaucher cells within bone ma rrow biopsies from untreated patients with chronic myeloid leukemia (r = .97, examined area = 94 x 2 mm(2), with 400 squares/2 mm(2)), but ( 1) the total time of investigation could be halved by square sampling (25.1 versus 55.3 hours, P <.00005), and (2) the estimated number of c ells did not vary more widely around the mean exact count than the cel l numbers exactly counted (P > .05). CONCLUSION: Square sampling is an easy, fast and effective alternative to nonparametric approaches in o rder to quantify the numerical density of cells randomly distributed w ithin a tissue. The method can also be applied to test hypotheses of r andom distribution as well as to quantify a clustering of cells in cas es of nonrandom cell distribution.