the lack of prostate specificity and intercellular localiza-
tion of its gene product make this gene a poor serum
biomarker. FN1 (fibronectin 1) and VEGF (vascular endo-
thelial growth factor) are associated with prostate cancer
and encode secreted proteins; however, these genes lack
prostate-specific tissue specificity (15–18). The lack of
prostate tissue specificity in the expression of these 2
genes may be a major reason why they are not yet used
clinically as prostate cancer serum biomarkers.
We have developed a bioinformatics protocol for
screening candidate serum biomarker sets to identify
high-quality markers for experimental evaluation. The in
silico secreted protein pipeline provides a rapid screen for
identifying biomarkers found extracellularly and is likely
to be detectable by serum assays. Tissue specificity pro-
filing compliments secreted protein prediction by identi-
fying the originating tissue components of a biomarker’s
serum signal and by allowing investigators to select
candidate markers with a higher probability of having
distinguishable signals. We hope that the use of intelligent
bioinformatics analysis before costly experimental evalu-
ation will accelerate the selection of candidate biomarkers
that can be successfully translated into novel, clinically
This work was supported under a grant by Invitrogen.
1. Klee EW, Ellis LB. Evaluating eukaryotic secreted protein prediction. BMC
2. Bendtsen JD, Nielsen H, von Heijne G, Brunak S. Improved prediction of
signal peptides: SignalP 3.0. J Mol Biol 2004;340:783–95.
3. Emanuelsson O, Nielsen H, Brunak S, von Heijne G. Predicting subcellular
localization of proteins based on their N-terminal amino acid sequence. J
Mol Biol 2000;300:1005–16.
4. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmem-
brane protein topology with a hidden Markov model: application to complete
genomes. J Mol Biol 2001;305:567–80.
5. Kall L, Krogh A, Sonnhammer EL. A combined transmembrane topology and
signal peptide prediction method. J Mol Biol 2004;338:1027–36.
6. Finlay JA, Klee EW, McDonald C, Attewell JR, Hebrink D, Dyer R, et al. A
systematic method for selection of promising serum protein biomarkers to
improve prostate cancer (PCai) detection. Clin Chem2006. Page information
to be filled inXXXXXXX
7. Pruitt KD, Tatusova T, Maglott DR. NCBI Reference Sequence (RefSeq): A
curated non-redundant sequence database of genomes, transcripts and
proteins. Nucleic Acids Res 2005;33:D501–D504.
8. Bairoch A, Apweiler RT. HEQQSWISSQQQQ-Prot protein sequence database
and its supplement TrEMBL in 2000. Nucl Acids Res 2000;28:45–48.
9. Boon K, Osorio EC, Greenhut SF, Schaefer CF, Shoemaker J, Polyak K, et al.
An anatomy of normal and malignant gene expression. Proc Natl Acad Sci
U S A 2002;99:11287–92.
10. Jongeneel CV, Delorenzi M, Iseli C, Zhou D, Haudenschild CD, Khrebtukova
I, et al. An atlas of human gene expression from massively parallel signature
sequencing (MPSS). Genome Res 2005;15:1007–14.
11. Pontius JU, Wagner L, Schuler GD. UniGene: a unified view of the transcrip-
tome. In: The NCBI Handbook. Bethesda (MD): National Center for Biotech-
nology Information; 2003.
12. Troyer DA, Mubiru J, Leach RJ, Naylor SL. Promise and challenge: markers
of prostate cancer detection, diagnosis, and prognosis. Dis Markers 2004;
13. Nelson PS. Predicting prostate cancer behavior using transcript profiles.
J Urol 2004;172:S28–S32; discussion S33.
14. LaTulippe E, Satagopan J, Smith A, Scher H, Scardino P, Reuter V, et al.
Comprehensive gene expression analysis of prostate cancer reveals distinct
transcriptional programs associated with metastatic disease. Cancer Res
15. Chakrabarty S, Fritsche HA. Fibronectin and prostate cancer. J Clin Ligand
16. Albrecht M, Renneberg H, Wennemuth G, Moschler O, Janssen M, Aumuller
G, et al. Fibronectin in human prostatic cells in vivo and in vitro: expression,
distribution, and pathological significance. Histochem Cell Biol 1999;112:
17. Chakravarti A, Zhai GG. Molecular and genetic prognostic factors of prostate
cancer. World J Urol 2003;21:265–74.
18. Lin CC, Wu HC, Tsai FJ, Chen HY, Chen WC. Vascular endothelial growth
factor gene-460 C/T polymorphism is a biomarker for prostate cancer.
Representational Fragment Amplification: Exponential
Amplification of Fragmented cDNA Enables Multimil-
lion-Fold Expression Testing Gregory D. Sgarlato and
Howard H. Sussman*(Department of Pathology, Stanford
University, Stanford, CA; * address correspondence to
this author at: Stanford University, Lane Building, L217,
Microarray analysis, which enables the comprehensive
examination of many thousands of genes in a single
experiment, is a promising method for furthering under-
standing of disease states. Because of the large amounts of
probe required, however, microarray analysis has not
Table 1. Localization predictions and annotations of prostate cancer-associated proteins.
Type II membrane
We analyzed 7 genes by the secretion and specificity prediction methods described above. KLK3 and ACPP (positive controls) possess the secretion and prostate
specificity characteristics that we describe as key for serum biomarkers. Negative controls include ZWINT, AMACR, and HPN, which fail to encode secreted proteins,
and FN1 and VEGF, which lack prostate specific tissue expression.
aModerate refers to genes expressed in prostate and several other tissues.
Abstracts of Oak Ridge Posters
been possible for small excision biopsies, fine needle
aspirates, and microdissected tissue samples. Linear am-
plification of target cDNA with T7 RNA polymerase (1) is
currently the most common method for the amplification
of RNA for microarray analysis and has been validated
(2) and optimized (3, 4). Other linear RNA amplification
strategies have been developed (5, 6), but these do not
generate sufficient amounts of probe for microarray anal-
ysis. DNA fragments have been used for enriching pop-
ulations (7), cloning differences (8), and subtractive
screening (9). Representational fragment amplification
(RFA) is a method that we have developed for global
amplification of cDNA as universally primed fragments.
The product of RFA is double-stranded DNA, which can
be directly labeled for microarray analysis, screened for
genetic variation with traditional probes, analyzed with
PCR-based protocols, or stored for future analysis.
To perform RNA isolation, we processed paired non-
diseased and diseased cervical biopsy samples from pa-
tients diagnosed with squamous cell carcinoma of the
cervix as previously described (10). The specimens were
anonymized by ILS Bio or Genomics Collaborative and
collected with patient consent in compliance with the
company Institutional Review Boards and with the Code
of Federal Regulations (CFR) 45CFR46.101B. Briefly, tis-
sue samples frozen with liquid nitrogen were ground to a
fine powder, transferred to 6-mol/L guanidine-HCL at
room temperature, and homogenized by multiple passes
through a syringe equipped with an 18-gauge needle. We
isolated RNA with a Qiagen RNeasy Midi Kit.
For RFA cDNA synthesis, we used 5 ?g of RNA from
nondiseased and diseased tissues as templates in the
Roche cDNA Synthesis System, according to manufactur-
er’s instructions, substituting 2 mmol/L PolyT18_DpnII/
NlaIII-V (5?-GAG AGT GAG TGA TCA TGT TTT TTT
TTT TTT TTT TTV-3?) as the primer.
For in vitro transcription synthesis, we used 10 ?g of
total RNA and followed the protocols for the Affymetrix
in vitro transcription (IVT) Kit. For microscale cDNA
synthesis, we used 10 ng total RNA from Human Univer-
sal Reference RNA (UHRR) (Stratagene, Inc.) and Human
Breast Carcinoma T-47 Cell Line Total RNA (Ambion,
Inc.). We established a template for cDNA synthesis
through dilution in 10 mmol/L Tris pH 8.0 containing 30
mg/L polyinosinic acid carrier (11). One of the volumes
of the Roche reagent set were used. For microscale cDNA
synthesis, we added 20 ?g of T4gp32 (12) immediately
before the addition of reverse transcriptase. Microscale
cDNA samples were heat killed and were not treated with
RNase or proteinase K.
For RFA amplicon synthesis, cDNA fractions (1/6 to
1/12) were digested with DpnII or NlaIII for 90 min at
37 °C, heat killed at 65 °C for 90 min, and ligated to 5 ?g
of the appropriate preassembled linker (3 to 16 hrs). The
DpnII linker was assembled with R-BGL-24, sequence
5?-AGC ACT CTC CAG CCT CTC ACC GCA-3?, and
R-BGL-12, sequence 5?-GAT CTG CGG TGA-3?) (9) and
the NlaIII linker is assembled with: R-BGL-28_NlaIII,
sequence 5?-AGC ACT CTC CAG CCT CTC ACC GCA
CAT G-3? and R-Bgl-08_NlaIII, sequence 5?-TGC GTGA-
3?). Linker-ligated cDNA dilutions were the templates for
amplifications. Amplification was performed on 4 to 6
identical 100-?L tubes containing diluted template, 100
pmol/L R-BGL-24 primer, and (final concentration) 66
mmol/L Tris-HCl pH 8.8 at 25 °C, 16 mmol/L (NH4)2SO4,
4 mmol/L MgCl2, and 0.2 mmol/L each dNTP. The
amplification tubes were incubated at 72 °C for 3 min
before the addition of 5 units of Taq polymerase. The
72 °C incubation continued for 10 min before 20–28 cycles
at 95 °C for 15 s and 72 °C for 3 min. The DpnII and NlaIII
amplicons were pooled, phenol/chloroform extracted,
and isopropanol precipitated and resuspended in 100 ?L
TE?1(1 mM Tris pH 8.0, 0.1 mmol/L EDTA). The RFA
amplicons were diluted in water and quantitated by A260
and checked for purity by A260/A280ratio.
For microscale RFA amplicon synthesis, linker ligations
contained 1 ?g of linker in a 25 ?L volume. We calculated
the target for 25 cycles of RFA to be 15.8 pg of mRNA. The
yield of double-stranded probe was 25 ?g: net 1.5 million–
Our improved precipitation protocols were performed
with equal-volume isopropanol precipitations with 0.3
mol/L sodium acetate, pH 5.3, incubated at ?80 °C for at
least 2 h. Ethanol washes were 85% ethanol.
The primer designs and protocols for real-time quanti-
tative reverse transcriptase (RT)-PCR were taken from
previously published experiments (10), and the amplified
segments were free of DpnII and NlaIII restriction sites
(CCNB1 primers; 3–2 PRIME-195F: TGG TCT GGG TCG
GCC TC, 3–2 PRIME-263R: TCG ACA TCA ACC TCT
CCA ATC TT, 3–2 213FT: ACC TTT GCA CTT CCT TCG
GAG AGC ATC). We used specific fluorescein/tetrameth-
ylrhodamine probes, and cycling was 95 °C, 15 s and
65 °C, 3 min for 40 cycles. For each independent gene
assay, we used actin diseased/nondiseased (D/N) ratios
from cDNA to normalize the gene D/N ratios of the
amplicon. The mean normalization factor was 30%.
For random primer biotinylation, we biotinylated RFA
amplicons in 4–6 independent, replicate BioPrime®DNA-
labeling reactions (0.5 ?g target) (13), following the man-
ufacture’s protocols (Invitrogen, Inc), and obtained a
mean 10-fold yield.
For all microarray analysis experiments, we followed
the manufacturer’s hybridization and processing proto-
cols for the HU133 plus 2.0 chips. We used Affymetrix
Microarray Analysis Suite (GCOS v1.0) and ArrayAssist
(3.3) (Stratagene, Inc) to import Affymetrix CEL files and
to generate intensity values based on the robust multiar-
ray average method (14). The relationships between the
different platforms were assessed by Pearson correlation
coefficients (15, 16).
The RFA method uses 2 enzymes to fragment the cDNA
in a known, reproducible manner (Fig. 1A). To give every
cDNA fragment a common priming site at both ends, the
cDNA fragments are ligated to compatible adaptors con-
taining the R-BGL-24 universal primer sequence. Ampli-
fication of these smaller cDNA fragments proceeds expo-
nentially and can be used to generate expression
Clinical Chemistry 52, No. 11, 2006
signatures. We used the synthesis rate definition for 5
units of Taq DNA polymerase to establish the goal of 5 ?g
of DNA per 100 ?L (40 ?g/8 tubes).
Complete real-time quantitative RT-PCR analysis of 6
gene transcripts for 3 paired cervical biopsy samples (10)
are presented in Table 1. These quantitative results dem-
onstrate that RFA is a robust methodology that can
produce accurate DNA signatures from limited amounts
of starting material. For each patient, we compared the
gene-fragment concentrations from the DpnII and NlaIII
amplicons with gene concentrations in the original cDNA
to determine whether these amplicons maintained the
relative D/N expression ratios seen in the cDNA. For all
patients the RFA amplification was ?100 000-fold, and
the mean CV for the all analyses was 11.8%.
Yields of RFA amplicon were determined by harvesting
identical tubes at different cycles of amplification. The net
yield data from 5 tubes of pooled amplification are plotted
in Fig. 1B. For statistical comparison, we established
replicate hybridization experiments, generating 6-tube
and 24-tube pools from 24 cycles of amplification from
identical target. The R2values were 0.9971 for the dupli-
cate hybridizations to Affymetrix U133A plus chips and
0.9934 for the 6-tube pool vs the 24-tube pool. Scatterplot
representation of values ?100 for the 6-tube vs 24-tube
pools are shown in Fig. 1C. Agilent 2100 Bioanalyzer
analysis showed that most of the products of the RFA
protocols were 100 to 700 bases in length.
To validate the independent repeatability of small-scale
amplifications, we established microscale cDNA synthesis
protocols (10 ng total RNA) that used independent IVT
(T7) results to compare and contrast the independent
15-million–fold RFA results. We used UHRR as the con-
trol sample for the Breast Carcinoma Cell line (T-47)
microarray experiments designed to compare the RFA
and T7 platforms (17). Amplification was 200-fold for the
duplicate pair of IVT samples, with a mean duplicate
array correlation of 0.9822, and 15–million-fold for the
Fig. 1. (A), schematic of RFA.
Double stranded cDNA is synthesized from total RNA using the unique PolyT primer containing DpnII and NlaIII recognition sequences. Small fractions of the cDNA are
digested with DpnII or NlaIII (4 base cutters). The digests are heat killed and ligated to specific linkers containing the universal primer sequence. Small fractions of
the cDNA ligations are amplified in 8 tubes for 20–28 cycles depending on the concentration of cDNA target. (B), RFA amplicon yields through increasing cycles: 40
tubes of replicate RFA were cycled and at the end of selected cycles groups of 5 tubes were transferred to a 72 °C block. The DNA present in the 5-tube pools was
isolated using the improved precipitation protocols described in Materials and Methods. Net yields are calculated to account for the precipitation of the R-BGL-24
primer. The mean yield from cycles 16 and 17 (5.15) was used as control primer only values. Fold amplification is calculated by dividing net yields by the starting known
concentration of cDNA template in the 5 tubes of amplification. (C), repeat RFA: the 6-tube pool vs 24-tube pool of RFA products after 24 cycles of amplification. Aliquots
of 6-tube and 24-tube pools were biotinylated and hybridized to Affymetrix U133A plus chips. The Affymetrix CEL files were converted to RMA intensity, and values ?100
were removed from both the 6-tube and 24-tube data sets. The resulting 19 063 paired values were plotted.
Abstracts of Oak Ridge Posters
duplicate microscale RFA samples, with a mean duplicate
array correlation of 0.9798. Analysis of duplicate UHRR
microarray results from both platforms revealed a mean
correlation of 0.8296 for the 4 independent RFA vs T7
analyses (RFA-1/T7–1:0.8295; RFA-1/T7–2:0.8119; RFA-
There were 4305 T7 and 7560 RFA paired mean D/N
ratios ?2.0, with 2280 confirmed D/N values between the
T7 and RFA platforms. We compared the D/N results
from the 2 platforms using only the genes that were
determined to be present in the T7 analysis by the
Affymetrix software (19 460 D/N values). The correlation
for mean log D/N values between the 2 platforms for the
genes was 0.5583. The correlations remained high for T7
log D/N duplicate (0.8478) and RFA log D/N duplicate
(0.7418). Analysis of mean D/N values within probe sets
that were confirmed by both platforms revealed that 1454
D/N values were up-regulated more than 2.0-fold, 275
D/N values were down-regulated more than 2.0-fold, and
10 326 D/N values were within the bounds of 2.0-fold
up-regulated to 2.0-fold down-regulated. Summarizing
these results gives 62% agreement for differential expres-
sion calls between the 2 platforms for the 19 460 genes
with confirmed present calls.
The 15–million-fold RFA D/N results are comparable
to the 200-fold T7 D/N results and demonstrate good
correlations for such high degrees of amplification. These
results show that RFA robustly amplifies cDNA as small
double-stranded fragments that are universally primed.
Fragment cDNA retains accurate gene expression ratios
for individual fragments from 2 populations, as demon-
strated by real-time quantitative RT-PCR data, while
enabling exponential amplification.
1. Van Gelder RN, von Zastrow ME, Yool A, Dement WC, Barchas JD, Eberwine
JH. Amplified RNA synthesized from limited quantities of heterogeneous
cDNA. Proc Natl Acad Sci U S A 1990;87:1663–7.
2. Wang E, Miller LD, Ohnmacht GA, Liu ET, Marincola FM. High-fidelity mRNA
amplification for gene profiling. Nat Biotechnol 2000;18:457–9.
3. Zhao H, Hastie T, Whitfield ML, Børresen-Dale AL, Jeffrey SS. Optimization
and evaluation of T7 based RNA linear amplification protocols for cDNA
microarray analysis. BMC Genomics 2002;3:31.
4. Kenzelmann M, Kle `aren R, Hergenhahn M, Bonrouhi M, Gre `one HJ, Schmid
W, et al. High-accuracy amplification of nanogram total RNA amounts for
gene profiling. Genomics 2004;83:550–8.
5. Dafforn A, Chen P, Deng G, Herrler M, Iglehart D, Koritala S, et al. Linear
mRNA amplification from as little as 5 ng total RNA for global gene
expression analysis. BioTechniques 2004;37:854–7.
6. Schumacher JA, Jenson SD, Elenitoba-Johnson KS, Lim MS. Utility of linearly
amplified RNA for RT-PCR detection of chromosomal translocations: valida-
tion using the t(2;5)(p23;q35) NPM-ALK chromosomal translocation. J Mol
7. Lisitsyn N, Lisitsyn N, Wigler M. Cloning the differences between two
complex genomes. Science 1993;259:946–51.
8. Wieland I, Bolger G, Asouline G, Wigler M. A method for difference cloning:
gene amplification following subtractive hybridization. Proc Natl Acad Sci
U S A 1990;87:2720–4.
9. Hubank M, Schatz DG. Identifying differences in mRNA expression by
representational difference analysis of cDNA. Nucleic Acids Res 1994;22:
10. Sgarlato GD, Eastman CL, Sussman HH. Panel of genes transcriptionally
up-regulated in squamous cell carcinoma of the cervix identified by repre-
sentational difference analysis, confirmed by macroarray, and validated by
real-time quantitative reverse transcription-PCR. Clin Chem 2005;51:27–
11. Winslow SG, Henkart PA. Polyinosinic acid as a carrier in the microscale
purification of total RNA. Nucleic Acids Res 1991;19:3251–3.
12. Chandler DP, Wagnon CA, Bolton H Jr. Reverse transcriptase (RT) inhibition
of PCR at low concentrations of template and its implications for quantita-
tive RT-PCR. Appl Environ Microbiol 1998;64:669–77.
13. Mackey J, Darfler M, Nisson P, Rashtchian A. Use of random primer
extension for concurrent amplification and nonradioactive labeling of nucleic
acids. Anal Biochem 1993;212:428–35.
14. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et
al. Exploration, normalization, and summaries of high density oligonucleo-
tide array probe level data. Biostatistics 2003;4:249–64.
15. Barczak A, Rodriguez MW, Hanspers K, Koth LL, Tai YC, Bolstad BM, et al.
Table 1. RFA D/N expression ratios as compared with original cDNA.
DpnII D/N ratio2.42
NlaII D/N ratio2.94
cDNA direct D/N ratio2.39
DpnII D/N ratio 16.83
NlaII D/N ratio24.51
cDNA direct D/N ratio 20.35
DpnII D/N ratio6.66
NlaII D/N ratio 6.03
cDNA direct D/N ratio6.48
PatientST DEVSPINT2 st dev ZWINTst dev
Average D/N expression ratios were established for three genes from duplicate real-time quantitative RT-PCR results. D/N expression ratio means are derived from
mean copy numbers for fragments using real-time quantitative RT-PCR as described. The summarized table represents corrected D/N values of DpnII amplicon and NlaIII
amplicon for comparison to D/N values of the cDNA for patients 1, 2 and 3. Coefficient of variation values are calculated from the D/N values of DpnII amplicon, NlaIII
amplicons and cDNA taken as a group.
and, not derived. Fragments were below the level required for accurate detection by RT-PCR
Clinical Chemistry 52, No. 11, 2006
Spotted long oligonucleotide arrays for human gene expression analysis.
Genome Res 2003;13:1775–85.
16. Park PJ, Cao YA, Lee SY, Kim JW, Chang MS, Hart R, et al. Current issues
for DNA microarrays: platform comparison, double linear amplification, and
universal RNA reference. J Biotechnol 2004;112:225–45.
17. Novoradovskaya N, Whitfield ML, Basehore LS, Novoradovsky A, Pesich R,
Usary J, et al. Universal reference RNA as a standard for microarray
experiments. BMC Genomics 2004;5:20.
Diffractive Optics Technology: A Novel Detection
Technology for Immunoassays, Vitali Borisenko, Wei Hu,
Pui Tam, Irene Chen, Jean-Franc ¸ois Houle*and Walter Aus-
serer (Axela Biosensors Inc., Toronto, Ontario, Canada;
* address correspondence to this author at: Axela Biosen-
sors Inc., 480 University Avenue, Suite 910, Toronto,
Ontario, M5G 1V2, Canada; fax 416-260-9255; e-mail
There is an increasing need for high-sensitivity immuno-
assays that can be used in point-of-care patient testing of
complex media. For example, analytes such as the natri-
uretic peptides and recently discovered sepsis markers
are found in blood in very low picomolar concentrations
(1, 2). Although advances have been made in the use of
fluorescent, chemiluminescent, and other labels to mea-
sure markers at lower detection limits, background inter-
ference from biological samples and detection instrumen-
tation remains problematic. Optical biosensors offer the
promise of label-free real-time measurements, but their
application to quantification of analytes in complex media
is impaired by higher detection limits and is susceptible to
changes in refractive indices or nonspecific surface bind-
ing. Moreover, the costliness of these devices has largely
prohibited bedside implementation. In this report, we
detail the use of a novel diffractive optics technology
(dot™) that takes advantage of the inherent properties of
diffractive optics to deliver a cost-effective, portable,
robust, optical biosensor that detects analytes at picomo-
lar concentrations in complex media.
In the dotLabTMSystem, coherent light striking a non-
random pattern of capture molecules on the dotLab
Sensor creates constructive and destructive interferences
that produce a well-defined diffraction image. As mole-
cules bind to the capture molecules, the height of the
diffraction pattern is increased, which in turn increases
the diffraction efficiency and the diffractive order inten-
sity. A photodiode monitors the intensity of the diffrac-
tive order, which is correlated to analyte concentrations.
Because diffraction is inherently self-referencing, the
transduction of binding events is dependent on the initial
pattern, and an increase in diffractive order intensity will
occur only if molecules bind exclusively to the patterned
capture reagents. Therefore, nonspecific binding to both
the patterned and nonpatterned regions will not affect the
signal, a characteristic that offers an important advantage
over other optical biosensor systems in which any surface-
binding event will cause an increase in signal.
Previous diffraction-based immunosensors have used
silicon wafer chips that were in contact with analyte-
containing solutions and required washing and drying
before analysis by a simple reader (3). The dotLab system
uses a plastic consumable, the dotLab Sensor, with an
integrated prism situated below the flow channel so that
the light source interrogates the diffraction grating with-
out passing through the bulk solution. Previous studies
suggest that this total internal reflection scheme allows for
95% of the laser intensity to be measured, whereas in a
nontotal internal reflection set-up only 5% is measured
(4). Moreover, with this configuration, our system moni-
tors biomolecule binding in real time and in complex
We tested the beta prototype of the dotLab instrument,
which incorporates the core dot technology in an inte-
grated package intended for in-house and controlled
external development work. The instrument includes pre-
cision fluidic control of reagents, buffers, and samples—a
proprietary integrated optical assembly designed to func-
tion with the dotLab Sensor; and software for acquisition,
control, and user-interface. A movable stage allows mon-
itoring and potential patterning with different capture
reagents for 8 discrete diffraction spots. In addition, each
individual spot can be cross-patterned to perform in-
traspot multiplexing and assays, as we have previously
demonstrated (5, 6).
We used a single-spot and a streptavidin-patterned
sensor with reagents for the detection of N-terminal
probrain natriuretic peptide (NT-proBNP) in various ma-
trices at reference concentrations, as summarized in Table
1. For direct detection of recombinant NT-proBNP, we
used a biotinylated monoclonal antibody directed against
NT-proBNP immobilized on a streptavidin-patterned dot-
Lab Sensor (Fig. 1A). The binding event can be observed
in real time in the trace. The binding of recombinant
NT-proBNP is also detected, but at a lower intensity. The
recombinant protein has a low relative molecular mass
(Mr) of ?8000, which generates very little signal on its
own. At lower concentrations, we used a different format
to enhance the diffraction signal. For example, to detect
NT-proBNP at nanomolar concentrations, we used a
combination of capture and detector antibodies (Fig. 1B),
with which the binding of the biotinylated mouse capture
antibody was readily detected, whereas the introduction
of the recombinant protein did not produce a detectable
We carried out rinse steps with pulses of phosphate
buffered saline (PBS) (137 mmol/L NaCl; 2.7 mmol/L
KCl; 10 mmol/L Na2HPO4/KH2PO4, pH 7.4; OmniPur)
and PBS-Tween (10 mmol/L phosphate buffer, pH 7.4;
140 mmol/L NaCl; 3 mmol/L KCl; 0.025% (w/v) Tween-
20; Calbiochem). When we introduced a polyclonal goat
antibody directed against NT-proBNP, the diffraction
signal increased. For internal calibration purposes and to
Abstracts of Oak Ridge Posters