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Proteomics and diagnostics: Let
’s Get Specific, againDom Zichi1, Bruce Eaton1,2, Britta Singer1 and Larry Gold1,3
DNA array technology has changed all discussions about

proteomics. Whole genome arrays allow unbiased

experimentation, and the surprises that flow from those

approaches. ‘Whole proteome’ proteomics is not possible

today, and might never be possible unless experiments are

guided by careful evaluation of reagent specificity. In this paper

we explore some possible ways to increase the content of

proteomic analysis.

Addresses1 SomaLogic, 1775 38th Street, Boulder, CO 80301, USA2 Department of Chemistry and Biochemistry, University of Colorado,

Boulder, CO 80309, USA3 Department of Molecular, Cellular and Developmental Biology,

University of Colorado, Boulder, CO 80309, USA

Corresponding author: Zichi, Dom ([email protected]), Eaton,

Bruce ([email protected]), Singer, Britta

([email protected]), and Gold, Larry ([email protected]),

Current Opinion in Chemical Biology 2008, 12:78–85

This review comes from a themed issue on

Proteomics and Genomics

Edited by Natalie Ahn and Andrew H.-J. Wang

Available online 7th March 2008

1367-5931/$ – see front matter

# 2008 Elsevier Ltd. All rights reserved.

DOI 10.1016/j.cbpa.2008.01.016

IntroductionSome years ago we published a short paper called ‘Let’s

Get Specific’ [1��] in which we tried to understand what we

thought to be the extraordinary binding specificity of

aptamers [2,3]. We thought that elements of aptamer

affinity and binding specificity were derived in part the

large libraries used to find aptamers [often as many as 1015

molecules for a SELEX experiment [4,5], and recent

developments [6��]], as well as the structural possibilities

explored by single-stranded oligonucleotides. More

recently we have focused our attention on the more general

nature of biochemical specificity, and we have wondered

about the connection between reagent specificity and

proteomics. When one considers proteomics, the reagents

of historical interest are antibodies; we shall try in this short

review to compare binding specificity for both antibodies

and aptamers and the ways in which proteomics might be

scaled to deliver on the promise of high analyte density and

extreme sensitivity and specificity.

A beautifully written (and referenced) recent review by

Borrebaeck and Wingren [7��] is aimed at a substantially


different question than we address in this article. Borre-

baeck and Wingren have focused attention on the

impressive (and growing) list of improvements to various

components of an antibody-based proteomic array. Our

focus is on what specificity is possible with various

reagents, and to raise the possibility that array formats

must solve any intrinsic limitations of those reagents.

High analyte density is often abbreviated as ‘content’ – in

the case of nucleic acid arrays, ‘content’ eventually

included probes for entire genomes of viruses, bacteria,

yeast, model organisms (flies, worms, and the mouse), and

humans. Using large arrays, scientists have utilized

mRNA and SNP profiling, along with epigenetic DNA

methylation, as genome-wide biomarker-discovery tech-

nologies. All three platforms are possible because gen-

ome-wide specific hybridization is possible – that is, by

judicious use of proper probe lengths and appropriate

base composition along with the right temperature, buffer

conditions, and hybridization time, specific sequences

can be recognized in the face of an entire genome.

DNA ‘chip’ technologies are remarkable as engineering

marvels, but their discovery power flows from the high

specificity of nucleic acid hybridization. In fact, not

surprisingly, this same high specificity is the hallmark

of how nucleic acids perform their functions in biochem-

istry. Remarkably, however, the array manufacturers have

reached elegant solutions even as they built their content

on to compromised platforms. Probes bound to surfaces,

be they beads (Luminex), slides (Agilent, Affymetrix,

NimbleGen), or things in the middle (Illumina) present

less than perfect kinetics and slow approaches to equi-

librium. These approaches are quite unlike hybridization

in solution as it was originally developed [8,9]. Only rarely

did platform builders include approaches that overcame

the slow kinetic approach to equilibrium (NanoGen,

MetriGenix, PamGene, etc.), and those sensible plat-

forms seem to have lost in the market place.

Human diagnostics is better served by protein measure-

ments than by nucleic acid measurements, and served

best by protein measurements in blood samples (a matrix

with a vast number of proteins – see below). Human blood

is an integrator of much of what happens in the body, and

an effecter of much of biology, often because of secreted

proteins (such as growth factors) whose job is to move

through the blood to a nearby or distant site. In addition, a

variety of proteins make their way into blood when some

pathology causes localized cell death and the uninten-

tional release of proteins. Since blood equilibrates quickly

with all human tissues, including brain, panels of protein

biomarkers should become the earliest warnings one has

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http://dx.doi.org/10.1016/j.cbpa.2008.01.016

Proteomics and diagnostics: Let’s Get Specific, again Zichi et al. 79

for the early stages of disease, even when a person is

asymptomatic. Of course these same biomarkers can be

present at vanishingly low concentrations and measuring

these low abundant proteins is the major hurdle proteo-

mics must overcome.

Of the approximately 23,000 human genes and their

>100,000 encoded proteins (comprising splice variants,

post-translationally modified proteins, and even more rare

events [10�], we do not know how many proteins are found

in blood. Probably every human protein is present in blood

at a very low level (if only from cell death), and perhaps

several thousand are present between the concentrations of

the most abundant blood proteins (albumin at just under

1 mM) down to protein concentrations at about 1 fM – a

dynamic range of 12 logs or more! The problems in human

proteomics and diagnostics are to scale proteomics to high

content to discover useful biomarkers and to make avail-

able diagnostic products that utilize (smaller) panels of

proteins for specific medical purposes. That is, in the same

spirit as made possible by nucleic acid array technologies,

one must survey large fractions of the proteome content inan unbiased manner for novel biomarker discovery.

One might think this is a simple task, given the stunning

success with nucleic acid arrays. The problem of course is

that typing nucleic acid complement sequences (probes) is

a lot easier than understanding the biophysics of protein

recognition biochemistry. Indeed, the allure of typing

enthralled the antisense, ribozymes, and siRNA thera-peutic researchers with the hope that typing would be a

great way to identify new drugs. For proteomics the idea

has been to replace typing of nucleic acid complements

with typing orders to the antibody suppliers. However,

when commercial antibodies are printed (as though they

were analogues of nucleic acid probes) and then tested

with various protein mixtures, performance (meaning

specificity) probably is not adequate. The purpose of this

review is to discuss these attempts and the aptamer-based

alternatives, and to note the intrinsic kinetic problems

that must be solved by proteomics.

Reagent-free proteomicsWe mention briefly that reagent-free proteomics would

be a wonderful development, although it appears diffi-

cult. Since 1975, with the publication of Pat O’Farrell’s

extraordinary work on 2D gels [11], such reagent-free

proteomics has been possible. The quality of the first 2D

gels was amazingly high (something like 1100 proteins

were visible), major and minor proteins seemed to differ

quantitatively by three logs or so, and differently charged

species of the same molecular weight were not a major

source of additional spots that cluttered the patterns. In

Pat’s thesis seminar he showed wild-type Escherichia coligels versus ‘lactose-operon deletion’ gels, and the missing

spots were a powerful demonstration of what might be

done, at least qualitatively.


Blasting through many samples (tissue extracts or plasma

or serum, or urine, or whatever) with 2D gels had a

moment (‘back in the day’ as our children say . . .).However, the problems with reproducibility and quanti-

fication were serious, and the cost per analysis was high –

eventually people added MS to the methodology, and it is

now common for an entire 2D gel to be extracted (feature

by feature) for MS analysis. Furthermore, the limitations

in protein number (content again) have never been solved

– Pat O’Farrell’s number from E. coli really has remained,

after a lot of work, about the number of protein spots one

can visualize, and the proteins observed are thus inevi-

tably the most abundant proteins in the sample.

Mass spectrometry (MS) has had a similar fate, so far.

Even though the sensitivity of a great mass spectrometer

might enable analysis of samples quite a bit lower than

nmolar in a few microliters, when complex matrices are

explored the noise obscures all but the most abundant

proteins. In fact, it appears that 2D gels and MS query

more or less the same most abundant proteins from within

a complex sample. Clearly, the resolving power of 2D gels

coupled to MS in proteomics has yet to match what is

capable in DNA hybridization micro arrays.

Reagent-dependent proteomics: antibodiesNo one really knows what fraction of the human pro-

teome has been used to generate high quality antibodies

[see the Human Protein Atlas (http://www.proteinatla-

s.org/), [12��]] or protein reagents with alternative frame-

works [13�]. The assumption has been that antibody

production could be scaled up to meet the need, and

that people would be able to print antibodies the same

way that people print nucleic acid probes.

Just how specific are antibody reagents, and (thus) would

an array of printed antibodies allow quantitative proteo-

mics? Sadly we do not have a literature quite like the

definitive literature for nucleic acid hybridization. The

‘Turner’ rules [14] and hundreds of other careful papers

(e.g. [15,16,17] and many, many others) allow one to

calculate the likely interactions between two single-

stranded oligonucleotides. Proteins are likely to interact

with other proteins with low affinity (comprising diffusion

limited association rates along with fast dissociation

rates), but we have no rules. However, three independent

lines of evidence suggest that proteins are sloppy in their

intermolecular interactions and that one ought to expect

the equivalent of ‘error-prone DNA hybridization.’

First, phage display has been used to identify short

peptides (many experiments were done with peptides

10 amino acids in length) that will bind to a target protein

[18,19]. Usually binding peptides are found, with Kds

between 1 mM and about 10 nM. The classic phage

display libraries in the early days contained roughly

107 peptides, about the number present in the human


http://www.proteinatlas.org/


80 Proteomics and Genomics

proteome. Many peptides are found to bind weakly to

almost any target protein. Binding interactions of low

affinity should be expected as ‘noise’ in human biology.

Second, intracellular protein-interaction maps have been

made (and continue to be made) using some variant of the

yeast two-hybrid system [20,21]. Again one fights noise –

whatever bait protein is used, many candidate proteins

emerge that interact weakly with the bait and are either

noise or subtle reflections of meaningful biology – it is

very difficult to tell which. Probably the ways that the

two-hybrid system is tuned matters greatly – if both

proteins are expressed at high intracellular levels, weak

interactions (probably reflecting mM Kds or even greater)

are sufficient to activate transcription.

Third, antibodies found in healthy people (these obser-

vations are not about people who have known auto-

immune disorders) react with roughly 28% of the

human proteins [13�]. This is an astonishing observation,

and almost certainly most often reflects weak and unin-

tended binding. That is, in the format employed, anti-

bodies against other targets react with human proteins.

But how could this be? Did not we learn that antibodies

are magic bullets? In fact, this platform is shown

(Figure 1B – below) and represents a particularly com-

promised platform because of very slow effective dis-

sociation rate constants for unintended non-target

Figure 1

Specificity: A function of rebinding rates. In the binding reactions shown, the c

green or blue. Intrinsic on and off rates are identical in the examples. (A) An

mixture of antigens. (B) Reverse arrays with homogenous antigen spots and m

example tissue homogenate, probed with a single antibody. Intrinsic on and

limited on rates, with solution off rates). Rebinding rates (for cognate and inc

very slow in (C).


proteins. The slow effective dissociation rate results from

multivalent binding of antibodies to the ‘wrong’ human

proteins.

Antibody arrays: printed antibodies, reversearrays, and sandwich formatsThree kinds of protein-based proteomic arrays have been

tried. The easy approach was to simply print antibodies

and see what would happen, and then to make even

better antibodies to see if performance was improved.

These arrays are called ‘antibody capture’ arrays and are

commercially available. When the difficulties of sensi-

tivity and specificity with these arrays became clear,

people tried ‘reverse arrays’ in which, for example, plasma

or tissue homogenates were spotted directly on to a

surface and probed with antibodies. For reasons visible

in Figure 1 reverse arrays may in fact be more specific

than antibody-capture arrays. Finally, people have tried

very hard to deploy antibody sandwiches onto an array

format.

First, arrays of single antibodies are now available com-

mercially (reviewed in Borrebaeck and Wingren, [7��]).The content is impressive – hundreds of antibodies are

probed (often) with a standard mixture of proteins and an

unknown but similar mixture, with one mixture labeled

with one color dye and the other with another. While the

Brown lab at Stanford was an early proponent of such

orrect interaction is blue with green. The wrong interactions are pink with

tibody capture arrays, with single antibody in each spot, probed with

ixture of antibodies, for example plasma. (C) Mixed antigens spotted, for

off rates are identical in the examples (A), (B), and (C) (usually diffusion

orrect interactions) are extremely fast in (B), somewhat slower in (A), and



experiments, using commercial antibodies [22], the stron-

gest work thus far comes from Borrebaeck’s lab in Sweden

[7��]. Over several years that lab has developed surfaces

for printing antibodies, discovered which antibody frame-

work was best at obtaining high quality antibodies with

stable structures and good analyte recognition, and so on.

The work is lovely. The use of such arrays becomes

straightforward. If, for example, a large number of plasma

samples from healthy women were mixed together and

another set of plasma samples from similar women with

stage II ovarian cancer were compared on a large antibody

array, any differences in the average protein concen-

tration in the two sample sets would show up as a change

in the ratio of dye #1 to dye #2 on a specific antibody

feature. This now-standard experiment (patterned after

mRNA profiling arrays) has been reported several times

[23,24�,25], and soon will become a cottage industry. We

hope that data from antibody arrays will be confirmed by

high quality ELISAs, used to probe the same plasma

mixtures for that analyte (or, even better, each individual

sample from the plasmas that made up each mixture [26]).

The formalism is obvious – oligonucleotide array (hybrid-

ization) data are confirmed by something precise (QPCR)

because even hybridization arrays (as specific as they are)

do not guarantee that the measured oligonucleotide is

the intended oligonucleotide. The proteomics com-

munity will learn about this problem, although slowly,

exactly as the DNA-array community learned about the

problem.

Figure 1A pictures the problem and suggests a key metric

for evaluating even very good antibody arrays. Put

simply, the burden of proof lies with the manufacturers,

distributors, and academic scientists who claim high

specificity in array formats. The question is at least

partially answered by spiking plasma samples with

non-human proteins at low concentrations to determine

the limit of detection and to measure ‘background’ in

unspiked plasma. We imagine that a set of (ten or so?)

non-human proteins will become a standard ‘specificity

metric’ so that various platforms and reagents may be

compared. The severity of the problem goes beyond

limits of detection, of course. If an abundant protein in

plasma binds inappropriately to an antibody selected for

a non-abundant protein, the measured protein will not be

the intended analyte. This is a real issue – if several

plasma proteins are 106 to 1012 times more abundant than

the analyte of interest and those abundant proteins have

even submicromolar Kds for the capture antibodies aimed

at the intended analyte, a significant fraction of the signal

on that capture antibody will result from the wrong

proteins, due only to equilibrium binding. Moreover,

avidity/rebinding components can increase the noise

on an antibody array. Equilibrium binding for target

analytes and all other proteins in a mixture is quite a

hurdle to overcome in printed antibody arrays (and see

next paragraph).


Second, reverse-phase protein microarrays (RPAs) have

become popular. A key reference is from the Petricoin

and Liotta labs [27��]. As shown in Figure 1C, multivalent

recognition in an RPA is unlikely – a spotted tissue extract

or plasma sample will have thousands of proteins, and

statistically it is unlikely that identical proteins will fall

near each other, and thus provide opportunities for multi-

valent binding. However, in the review cited above, the

authors write that ‘Currently the two biggest technical

challenges facing this techniques are the need for specific

antibodies and . . .’ We agree, but in fact this is just the

equilibrium binding problem mentioned above, but noth-

ing worse than that. Again, as previously mentioned,

antibody specificity almost never is good enough to yield

binding only to the intended analyte in the presence of

many other vastly more abundant proteins. We believe

this is inherent in the biochemistry of antibody CDRs

(complementarity-determining regions) and cannot be

solved easily even with recombinant antibody technol-

ogy.

There is, of course, an additional issue facing RPAs. The

spotted analytes are of uncertain protein state, from

native to denatured and everything in between. Anti-

bodies often are not well characterized with respect to the

preferred analyte structure, which could be an important

attribute in biology, and this must confound the use of

RPAs. The good news (and the bad) is that RPAs are easy

to use, and thus another cottage industry has been born,

but getting the right assay for the serious study of human

health is unlikely to be easy.

Third, an enormous amount of time and money has been

spent trying to build arrays of antibody sandwiches. We

show (Figure 2a) why sandwich assays won the battle for

single analyte measurements for diagnostics – specificity is

the product of the two specificities of the two antibody

reagents in a sandwich. If a capture antibody is bound by

proteins other than the intended analyte, the second

antibody (which recognizes a different analyte epitope)

will NOT also bind to the inappropriately captured

unwanted protein. One problem with adapting this sand-

wich format for protein arrays is that any nonspecifically

bound protein with a secondary antibody in the assay will

now signal for the wrong analyte (Figure 2b). As the

sandwich array content grows, these nonspecific inter-

actions will grow, limiting the practical size of this format

for arrays of antibody sandwiches. The content limita-

tions to sandwich arrays are depicted (Figure 2b) and

argue powerfully that sandwich arrays will not be used for

novel biomarker discovery because they cannot scale to

high content.

So formats matter here (Figures 1 and 2). Forcing protein–

protein interactions as required to drive high content

proteomics arrays is in conflict with the rather delicate

structural integrity of these biopolymers. When proteins



Figure 2

Antibody sandwich assays. (a) As shown, antibody sandwiches help with specificity. The correct interaction is blue with green. The wrong interactions

are pink with green. The second antibody (dark green) provides additional specificity and the incorrect interaction with the capture antibody is not

recognized by the second antibody. (b) The specificity is lost when many proteins are probed in the same assay. When antibodies to the pink antigen

(red is the capture antibody, and brown is the secondary antibody) are added to the array, the wrong interaction in (a) will now generate signal and

specificity is lost.

within a printed feature are crowded they may denature

and further lose specificity. In addition, when the soluble

partner is bivalent, weak binding might be sufficient for

kinetic entrapment, which really is nothing more than

avidity (which is nothing more than artificially increased

rebinding rates or even multivalent binding caused by

packed proteins attached to the surface with close pack-

ing). So these features– substantial weak interactions

between many abundant proteins, along with bivalent

probes, must lead to measurements that are obscured by

noise.

The take home message is clear: antibodies will bind

more specifically to protein analytes in solution or even in


cells or in blood than they will if they are used as capture

antibodies or as antibody probes against printed human

proteins. Surprisingly, reverse arrays offer some advan-

tages over antibody capture arrays, which we did not

appreciate when we started writing this review.

Reagent-dependent proteomics: aptamersOur colleagues and we have been working on aptamer

arrays for almost two decades. We collectively come to

proteomics from backgrounds in genetics, molecular

biology, biochemistry, and physical chemistry. For at least

ten years we worked to format arrays of aptamers to

measure proteins – we even published a paper on a mixed

sandwich protocol (using an aptamer and an antibody –



[28]). Equilibrium binding assays led to comparable per-

formance (and limitations) as those achieved by anti-

bodies in any of the formats discussed above. Recently,

we have been using kinetic manipulations so that mere

equilibrium binding will not need to do the impossible –

to distinguish between the intended analyte and to not

bind significantly to any abundant and inappropriate

protein.

We have tried to solve the problems identified in this

review article without using sandwich arrays (which prob-

ably would not scale with aptamers any better than they

scale with antibodies, as in Figure 2). The principle

obstacle we needed to overcome was to identify a second

specificity element (beyond equilibrium binding) that

could be built into the assay; at one point we worked

very hard on photo-crosslinking as the second specificity

element [29�].

We have had some success (Figure 3) at quantifying many

human proteins with aptamers that have both low Kds for

their cognate proteins and higher Kds for the abundant

proteins in plasma. In addition, we have been able to

Figure 3

Multiplexed proteomics with aptamers work well. (a) Multiplexed readout of

(blue) and 5% plasma (red) and measured on arrays.


select aptamers with very slow dissociation rates, some-

thing we had tried to do unsuccessfully many times in the

past. These slow dissociation rates allow us to remove the

(abundant) non-target proteins that would otherwise con-

tribute to noise. Most importantly, we have been able to

format assays so that binding discrimination occurs in

solution before array read-out.

In Figure 3 we show some proteomic data, both for an

aptamer-array with many proteins measured simul-

taneously (Figure 3a), and spike-and-recovery exper-

iments (Figure 3b) in 5% plasma (with an expected

neurotrophin-3 concentration of about 1–5 pM) and buf-

fer. The significance of these data is that broad and

quantitative measurements of (low) protein concen-

trations are now available.

The details of these experiments will be submitted

shortly (personal communication from Dan Schneider,

Sheri Wilcox, Jeff Carter, Marty Stanton, and many others

at SomaLogic). We continue to be instructed, intellec-

tually, by the kinetic descriptions of John Hopfield from

decades ago; his descriptions of the intrinsic problems of

serum proteins with aptamers. (b) Neurotrophin-3 was spiked into buffer



specific binding in a complex sample (cells or plasma or invitro) have much to say about sound experimentation

[30��,31].

ConclusionsThis short review was intended at first to highlight the

obvious: if one measures enough proteins in plasma in

people with and without a variety of diseases, one ought

to be able to identify novel biomarkers that could be used

in small panels to facilitate appropriate evidence-based

medical decisions for specific indications. Ultimately one

expects that small specific proteomic panels will be

aggregated into larger panels that enable more compre-

hensive medical decisions to be made. This appears to us

to be within reach [32�].

But as we wrote we came to the conclusion that novel

biomarker discovery is constrained by reagent limitations

(with respect to limits of detection and content/scale) –

that is, old-fashioned biochemistry remains important.

We find the work reviewed here (and also a lot we did

not review) to be stunning in its medical implications if

(and only if) the reagents and/or platforms are up to the

task. We have tried to make clear what these old or new

reagents must do for the dream to be realized. We are

optimists about the diagnostic and medical future through

proteomics.

Conflicts of interest statementDr Eaton consults for SomaLogic. Drs Singer, Zichi, and

Gold are employees of SomaLogic.

AcknowledgementsWe thank our colleagues at both SomaLogic and the University of Coloradofor hundreds of serious conversations.

References and recommended readingPapers of particular interest, published within the period of review,have been highlighted as:

� of special interest�� of outstanding interest

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One of our favorite papers of all time – a clear exposition about thelimitations of equilibrium binding when searching for a needle in a hay-stack.

31. Hopfield JJ, Yamane T, Yue V, Coutts SM: Directexperimental evidence for kinetic proofreading in aminoacylation of tRNAIle. Proc Natl Acad Sci U S A 1976,73:1164-1168.

32.�

Kessler A: The End of Medicine: How Silicon Valley (and NakedMice) will Reboot your Doctor New York: Collins; 2006.

An imaginative description of a better future for healthcare that dependson proteomic arrays and in vivo imaging.
