18.6 Protein Expression Patterns—Temporal and Spatial

283

specifically to proteins—for protein capture (this might be especially useful for deter-

mining the expression levels of transcription factors).

An ingenious approach is to prepare an array of genes (which is much easier than

preparing an array of proteins) and then expose the microarray to a suitable mixture

of in vitro transcription and translation factors (e.g., from reticulocytes), such that

the proteins are synthesized in situ. ¹⁷

Polypeptide immobilization chemistries typically make use of covalently linking

peptide side chain amines or carboxyl groups with appropriately modified chip sur-

faces. Quite a variety of possible reactions exist, but usually several different residues

are able to react with the surface, making orientational specificity difficult to achieve.

Proteins recombinantly expressed with a terminal oligohistidine chain can be bound

to surface-immobilized nickel ions, but the binding is relatively unstable.

A significant problem with protein microarrays is the nonspecific adsorption of

proteins. Unfavourably oriented bound proteins, and exposed substratum, offer tar-

gets for nonspecific adsorption. Pretreatment with a so-called “blocking” protein

(seralbumin is a popular choice) is supposed to eliminate the nonspecific adsorption

sites, although some interference with specific binding may also result.

As with the transcriptome, statistical analyses of protein microarray data focuses

on either finding similarity of gene expression profiles (e.g., clustering) or calculating

the changes (ratios) between control and treated samples (differential expression).

18.6

Protein Expression Patterns—Temporal and Spatial

Whether the transcriptome or the proteome is measured, the result from each exper-

iment is a list of expressed objects (mRNAs or proteins) and their abundances or

net rates of synthesis. These abundances are usually normalized so that their sum

is unity. Each experiment is therefore represented by a point in protein (or mRNA)

space (whose dimension is the number of proteins; the distance along each axis is

proportional to abundance); each protein is represented by a point in expression space

(whose dimension is the number of experiments). The difficulty in making sense of

these data is their sheer extent: There are hundreds or thousands of proteins and there

may be dozens of experiments (which could, for example, be successive epochs in

a growth experiment, or a series of shocks). Hence, there is a great need for drastic

data reduction.

One approach has already been mentioned [Sect. 18.1; viz. to group proteins into

blocks whose expression tends to vary in the same way (increase, decrease, remain

unchanged)]. This is the foundation for understanding how genes are linked together

into networks, as will be discussed in the next chapter.

17 See Oh et al. (2007) for an example of this kind of approach.