Mammalian Genome

, Volume 3, Issue 11, pp 609–619 | Cite as

Hybridization fingerprinting of high-density cDNA-library arrays with cDNA pools derived from whole tissues

  • Thomas M. Gress
  • Jörg D. Hoheisel
  • Gregory G. Lennon
  • Günther Zehetner
  • Hans Lehrach
Original Contributions


As part of an integrated mapping and sequencing analysis of genomes, we have developed an approach allowing the characterization of large numbers of cDNA library clones with a minimal number of experiments. Three basic elements used in the analysis of cDNA libraries are responsible for the high efficiency of this new approach: (1) high-density library arrays allowing thousands of clones to be screened simultaneously; (2) hybridization fingerprinting techniques to identify clones abundantly expressed in specific tissues (by hybridizations with labeled tissue cDNA pools) and to avoid the repeated selection of identical clones and of clones containing noncoding inserts; and (3) a computerized system for the evaluation of hybridization data. To demonstrate the feasibility of this approach, we hybridized high-density cDNA library arrays of human fetal brain and embryonal Drosophila with radiolabeled cDNA pools derived from whole mouse tissues. Fingerprints of the library arrays were generated, localizing clones containing cDNA sequences from mRNAs expressed at middle to high abundance (>0.1–0.15%) in the respective tissue. Partial sequencing data from a number of clones abundantly expressed in several tissues were generated to demonstrate the value of the approach, especially for the selection of cDNA clones for the analyses of genomes based on expressed sequence tagged sites. Data obtained by the technique described will ultimately be correlated with additional transcriptional and sequence information for the same library clones and with genomic mapping information in a relational database.


cDNA Library Library Clone Relational Database Fetal Brain Computerize System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York Inc 1992

Authors and Affiliations

  • Thomas M. Gress
    • 1
  • Jörg D. Hoheisel
    • 1
  • Gregory G. Lennon
    • 1
  • Günther Zehetner
    • 1
  • Hans Lehrach
    • 1
  1. 1.Imperial Cancer Research FundLondonUK

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