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Blood, Vol. 109, Issue 7, 2968-2977, April 1, 2007
Physiologic and aberrant regulation of memory T-cell trafficking by the costimulatory molecule CD28
Blood Mirenda et al.
109: 2968
Supplemental materials for: Mirenda et al, Vol 109, Issue 7, 2968-2977
Files in this Data Supplement:
- Document 1. Supplemental methods (PDF, 65.9 KB)
- Figure S1. Automatic cell-counting algorithm (PDF, 589 KB) -
The automatic cell-counting algorithm is an intensity-profile–based approach for the calculation of single- and double-fluorescence–labeled cell population. As illustrated in this figure, cells in an image can be represented as groups of spatially clustered glowing pixels with certain intensity distribution. Assuming that each cell has a single-peak fluorescence profile, the brightest pixel in each cell may be used as a countable feature of the cell population. The approach consists of 3 major steps, including image preprocessing, region masking, and cell counting. Image preprocessing uses an auto-level technique to eliminate uniform background and normalize the intensity scale of the image. Region masking is used to define the counting area. In a dual-color (fluorophore) application, 1 color is usually used for nonspecifically cell labeling, while the other color labels the positive areas with its fluorescence profile–defining cell clusters. The positive indicator is used to mask the counting area in the nonspecifically labeled channel and count cells within the unmasked area by their maximum pixels. To find the maximum pixel in a cell, the pixel values of a counting area are segmented into a number of fluorescence levels (a), through which locations of the brightest spots are searched reiteratively. In detail, all intensity pixels above a certain threshold are classified into groups according to their spatial distribution, from which the maximum pixel of each group is located. Repeating the search level by level, all cell profiles will be examined, and all maximums will be included. The number and location of the maximum pixels should be exactly the same between 2 adjacent loops, unless lowering threshold includes new cells (b and d). Finally, cell counts from each level are combined, and a filter is applied to remove all repeatedly counted maximum pixels. The number of maximums left represents the number of cells. The algorithm is robust to uneven cell expression level and poor-contrast tissue images with a nonuniform background. It is not sensitive to the variation of cell size and shape or critical to the extent of cell overlapping. The algorithm can identify and ignore holes in a cell and eliminate small artifacts and noise influences, making the counting more accurate. However, it is less capable of identifying a cell with a multi-peak profile, although adjusting the number of thresholding levels will reduce the rate of miscount. A schematic representation of the cell counting algorithm is shown in this figure:
a. No cell is above Level 1,
b. Cell 1 is above Level 2 from which its peak at Posmax1(x,y) can be detected,
c. Cell 1 is above Level 3 with the calculated peak at the same position as B,
d. Cell 1 and Cell 2 are above Level n, and a 2nd peak at Posmax2(x,y) is detected.
e-g. An example of image preprocessing, region masking, and cell counting. Criostat sections were laid onto Polysine Microscope slides (VWR International Lutterworth, Leicestershire, UK), left to dry overnight, and then mounted in Vectorshield mounting medium for fluorescence with DAPI (Vector Laboratories, Peterborough, UK). Slides were visualized with a Coolview 12-cooled CCD camera (Photonic Science, Newbury, UK) mounted over a Zeiss Axiovert S100 microscope equipped with Metamorph software (Zeiss, Welwyn Garden City, UK). A x10 NA 0.6 objective and standard epi-illuminating fluorescence filter cubes were used and 12 bit image data sets were generated. Quantification of T cell infiltrates observed by wide field fluorescence microscopy was performed using a specifically designed software to run in the LabView (V7.1, National Instruments, Newbury, UK) environment.
- Figure S2. Both CD28 ligands CD80 and CD86 can enhance T-cell migration (PDF, 188 KB) -
To assess the relative contribution of CD80 and CD86 molecules to CD28-induced transmigration, human T-cell blasts (5 × 105/well) were added to monolayers of ICAM-1–expressing human M1 fibroblasts (5 × 104/well), (M1), or derivatives coexpressing human CD80 (M1-CD80; a) or CD86 (M1-CD86; b) molecules. In some transwells, M1-CD80 and M1-CD86 monolayers were preincubated with anti-CD80 (5 µg/mL; ■) or anti-CD86 mAbs (5 µg/mL; ◯), respectively, with an isotype-matched control antibody (□) or medium alone (●) for 30 minutes at room temperature. Expression of CD80 and CD86 by transfected M1 cells was similar and comparable to that of CD86 on mature dendritic cells (data not shown). T cells migrated more efficiently through M1 cells expressing CD80 or CD86 compared with nontransfected M1 fibroblasts. In addition, antibody-mediated blockade of B7-mediated interactions led to a less efficient migration. These results suggest that both CD28 ligands can promote T-lymphocyte transmigration. The results are expressed as a percentage of migrated T cells at the given time points and reported as the average of 3 experiments of identical design. The bars show the standard deviations (SDs). *Statistically significant versus control cultures: panel A, M1-CD80 *P < .04 vs M1 at all time points except 3 and 4 hours (P < .06); M1-CD80 + anti-CD80 *P < .03 vs M1-CD80 at all time points except 3 and 4 hours (P < .08); panel B, M1-CD86 *P < .05 vs M1 at all time points except 3 and 4 hours (P < .09); M1-CD86 + anti-CD86 *P < .04 vs M1-CD86 at all time points. **Lower P value; higher significance.
- Figure S3. Phenotype of HY-specific T cells at the time of injection (PDF, 200 KB) -
Expression of the indicated molecules was assessed by flow cytometry at the time of T-cell injection (ie, 7-10 days following stimulation in vitro; panel a, indicated as Res) or following activation for 24 hours with plastic-bound anti-CD3 (1 µg/mL) and anti-CD28 (5 µg/mL) (panel b, Act). Triggering of CD28 alone did not induce changes in the expression of the above indicated molecules (data not shown).
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