Materials and Methods

 

Reagents and Cells

 

Human foreskin fibroblasts were gifts from J. Brooks (Stanford University). Other primary human fibroblasts were obtained from Coriell Cell Repository or derived from autopsy skin samples after removal of keratinocytes and endothelial cells as described (1). The demographic information of fibroblasts is summarized in supplemental Table 1. Antibodies to pan-cytokeratin (C-11, Sigma), desmin (Ab-1, NeoMarkers), glial fibrillary acid protein (ab-7, NeoMarkers), vimentin (V9, Sigma), CD11b (H5A4, DSHB at University of Iowa), PECAM (Ab-2, NeoMarkers) and HoxD9 (Santa Cruz) were obtained from the indicated sources.

 

In vitro propagation of Fibroblasts

Fibroblasts were propagated in Dulbecco’s Modified Eagle medium supplemented with 10% fetal bovine serum (Hyclone), glutamine, and 100U penicillin-streptomycin (Gibco). Cells were passaged for at least 10 population doublings in vitro before mRNA harvest.  5 x 10⁶ cells were harvested 48 hours after the last passage for asynchronously growing cells or after 48 hours in DMEM with 0.1% fetal bovine serum for serum starved samples as previously described (2).

 

Immunofluorescence

10⁴ cells were plated in 8-well chamber slides (Lab-Tek II, Nalge Nunc). Cells were fixed in 4% paraformaldehyde and stained with the indicated antibodies and countered stained with DAPI as described (3).

 

Microarray Procedures

Human cDNA microarray construction and hybridization were as previously described (4). mRNA was purified using FastTrack according to manufacturer’s instructions (Invitrogen). A standard reference mixture of mRNAs derived from 11 cell lines was used in all experiments as an internal standard for quantitative measurement (5). Microarrays from two print runs were used in the experiments, and we observed a small number of genes which had artifactually varied hybridization signals between two print runs. This set of genes was determined by hybridizing the same mRNA sample to arrays from different print runs and was removed from further analysis.

 

Statistical Analysis

Hierarchical clustering with array-weighted average linkage clustering (6) and SAM (7) were performed as described. For SAM, 14 classes (fetal lung, fetal skin, abdomen, arm, foreskin, toe, and gum in either asynchronous or serum-starved condition) where replicate samples were available were used for multi-class analysis (7). The genes identified by SAM were then analyzed from all samples. The similarity score among clustering results is calculated as follows. The known sites of origin identify k classes.  Fibroblast samples were clustered based on the expression levels of varying sets of genes using the Partitioning Around Medoids algorithm (8), implemented in the R function pam from the cluster package. For n samples and k clusters, each application of the clustering algorithm produces a vector of n integer labels ranging from 1 through k. The similarity score for comparing two clusterings is defined as the maximum overlap of the two vectors of labels. More precisely, consider all possible permutations of the integers 1 through k for one of the vectors of cluster labels. For each such permutation, compute the number of entries at which the two vectors agree, and then take the maximum over permutations.

References

1.         Normand, J. & Karasek, M. A. (1995) In Vitro Cell Dev Biol Anim 31, 447-55.

2.         Iyer, V. R., Eisen, M. B., Ross, D. T., Schuler, G., Moore, T., Lee, J. C., Trent, J. M., Staudt, L. M., Hudson, J., Jr., Boguski, M. S., Lashkari, D., Shalon, D., Botstein, D. & Brown, P. O. (1999) Science 283, 83-7.

3.         Scott, M. L., Fujita, T., Liou, H. C., Nolan, G. P. & Baltimore, D. (1993) Genes Dev 7, 1266-76.

4.         Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., Powell, J. I., Yang, L., Marti, G. E., Moore, T., Hudson, J., Jr., Lu, L., Lewis, D. B., Tibshirani, R., Sherlock, G., Chan, W. C., Greiner, T. C., Weisenburger, D. D., Armitage, J. O., Warnke, R., Staudt, L. M. & et al. (2000) Nature 403, 503-11.

5.         Perou, C. M., Sorlie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., Rees, C. A., Pollack, J. R., Ross, D. T., Johnsen, H., Akslen, L. A., Fluge, O., Pergamenschikov, A., Williams, C., Zhu, S. X., Lonning, P. E., Borresen-Dale, A. L., Brown, P. O. & Botstein, D. (2000) Nature 406, 747-52.

6.         Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. (1998) Proc Natl Acad Sci U S A 95, 14863-8.

7.         Tusher, V. G., Tibshirani, R. & Chu, G. (2001) Proc Natl Acad Sci U S A 98, 5116-21.

8.         Kaufman, L. & Rousseuw, P. J. (1990) Finding groups in data: an introduction to cluster analysis. (Wiley, New York).