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Automated Design of Probes for rRNA-targeted Fluorescence In Situ Hybridization (FISH) Reveals the Advantages of Dual Probes for Accurate Identification.

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Fluorescence in situ hybridization (FISH) is a common technique for identifying cells in their natural environment, and is often used to complement next-generation sequencing approaches as an integral part of the full-cycle rRNA approach. A major challenge in FISH is the design of oligonucleotide probes with high sensitivity and specificity to their target group. The rapidly expanding number of rRNA sequences has increased awareness of the number of potential non-targets for every FISH probe, making the design of new FISH probes challenging using traditional methods. In this study we conducted a systematic analysis of published probes revealing that many have insufficient coverage or specificity for their intended target group. Therefore, we developed an improved thermodynamic model of FISH that can be applied at any taxonomic level, used the model to systematically design probes for all recognized genera of bacteria and archaea, and identified potential cross-hybridizations for the selected probes. This analysis resulted in high specificity probes for 35.6% of the genera when a single probe was used in the absence of competitor probes and for 60.9% when up to two competitor probes were used. Requiring the hybridization of two independent probes for positive identification further increased specificity. In this case, we could design highly specific probe sets for up to 68.5% of the genera without the use of competitor probes and 87.7% when up to two competitor probes were used. The probes designed in this study, as well as tools for designing new probes, are available online (http://DECIPHER.cee.wisc.edu).
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Automated Design of Probes for rRNA-Targeted Fluorescence In Situ
Hybridization Reveals the Advantages of Using Dual Probes for
Accurate Identification
Erik S. Wright,
a,b
L. Safak Yilmaz,
e
Andrew M. Corcoran,
c
Hatice E. O
¨kten,
d
Daniel R. Noguera
c
Department of Bacteriology,
a
Systems Biology Theme, Wisconsin Institute for Discovery,
b
and Department of Civil and Environmental Engineering,
c
University of
Wisconsin—Madison, Wisconsin, USA; Department of Environmental Engineering, Bahcesehir University, Istanbul, Turkey
d
; Program in Systems Biology, University of
Massachusetts Medical School, Worcester, Massachusetts, USA
e
Fluorescence in situ hybridization (FISH) is a common technique for identifying cells in their natural environment and is often used to
complement next-generation sequencing approaches as an integral part of the full-cycle rRNA approach. A major challenge in FISH is
the design of oligonucleotide probes with high sensitivity and specificity to their target group. The rapidly expanding number of rRNA
sequences has increased awareness of the number of potential nontargets for every FISH probe, making the design of new FISH probes
challenging using traditional methods. In this study, we conducted a systematic analysis of published probes that revealed that many
have insufficient coverage or specificity for their intended target group. Therefore, we developed an improved thermodynamic model
of FISH that can be applied at any taxonomic level, used the model to systematically design probes for all recognized genera of bacteria
and archaea, and identified potential cross-hybridizations for the selected probes. This analysis resulted in high-specificity probes for
35.6% of the genera when a single probe was used in the absence of competitor probes and for 60.9% when up to two competitor
probes were used. Requiring the hybridization of two independent probes for positive identification further increased specificity. In
this case, we could design highly specific probe sets for up to 68.5% of the genera without the use of competitor probes and 87.7% when
up to two competitor probes were used. The probes designed in this study, as well as tools for designing new probes, are available on-
line (http://DECIPHER.cee.wisc.edu).
The use of the small subunit rRNA (SSU rRNA) as a phyloge-
netic marker for microbial classification, identification, and
quantification was readily embraced after its discovery as a useful
molecule to reconstruct microbial evolution (1). Fluorescence in
situ hybridization (FISH), introduced in the late 1980s (2,3), re-
mains the technique of choice for cultivation-independent quan-
tification of taxonomically relevant groups within microbial com-
munities (4). Since its introduction, the wealth of knowledge
surrounding microbial diversity has expanded tremendously,
aided by rapid advances in DNA sequencing techniques. As a re-
sult, the SSU rRNA databases used for the early design of FISH
probes were incomplete, and therefore, specificity and coverage
may not be the same as originally thought for many probes that are
still commonly used. For instance, Amann and Fuchs (4) reeval-
uated coverage and specificity of several group-specific probes
more than 15 years after they were originally designed. They found
that group coverage was generally smaller than the original expec-
tation (e.g., 38 to 94%), and in most cases, phylogenetic groups
outside the targeted group had the potential to cause false-positive
hybridizations.
Problems with probe specificity are exacerbated when consid-
ering the hybridization of mismatched targets, for which the loca-
tion and type of mismatch affect the strength of hybridization (5).
Thermodynamic models that describe the hybridization of FISH
probes to target sites with and without mismatches (6,7) are help-
ful in identifying cross-hybridizations with nontargets, which may
potentially be eliminated with competitor probes (8). However,
the manual application of such models may be impractical in view
of the large number of potential mismatches of concern when
using modern databases, making it more difficult to select a suffi-
cient set of competitor probes to use. These difficulties are ampli-
fied during the design of new probes when other factors such as
probe length, nucleotide permutations, and multiple potential
target sites are considered.
A catalog of FISH probes that have been designed and utilized
throughout the years can be found in probeBase (9), and in the
best scenario, a probe for the target group of interest may have
already been designed and documented in the literature. In
cases where de novo probe design is needed, a wide variety of
design approaches are employed using software such as ARB
(10), PRIMROSE (11), and mathFISH (6), as well as public
databases such as SILVA (12) and RDP (Ribosomal Database
Project) (13). These approaches often use a simplified subset of
sequences to evaluate probe specificity, have different ways of pre-
dicting cross-hybridizations, and in many cases, require extensive
experimental trial and error for probe optimization. Often poten-
tial target and nontarget organisms for a FISH probe are uncul-
turable, which requires difficult optimization using a mixed com-
munity or Clone-FISH (14). High-throughput design approaches
that minimize the amount of experimental optimization are
Received 21 May 2014 Accepted 6 June 2014
Published ahead of print 13 June 2014
Editor: A. M. Spormann
Address correspondence to Erik S. Wright, eswright@wisc.edu.
Supplemental material for this article may be found at http://dx.doi.org/10.1128
/AEM.01685-14.
Copyright © 2014, American Society for Microbiology. All Rights Reserved.
doi:10.1128/AEM.01685-14
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needed to keep FISH at the forefront of microbial ecology as our
awareness of microbial diversity continues to increase.
Group-specific FISH probes are typically hybridized under
stringent conditions to minimize cross-hybridization with mis-
matched nontargets. The stringency of hybridization during FISH
experiments is controlled by the concentration of formamide in
the hybridization buffer, where more formamide will result in
greater DNA/RNA denaturation. Increasing stringency during
hybridization is a common tactic employed to minimize mis-
matched hybridization at the expense of reducing signal intensity
from targeted organisms (i.e., reducing sensitivity). A practice that
mitigates this sensitivity reduction is to block mismatched non-
targets from hybridizing by using unlabeled competitor oligonu-
cleotides, which dim or completely eliminate signal from nontar-
gets (15). A third strategy is to require the hybridization of two
different probes labeled with distinct fluorophores, as has been
suggested previously (16–18). This strategy requires optimization
of two different probes for simultaneous hybridization but may
substantially reduce the number of nontargets. In this study, we
explored these three strategies for the elimination of nontargets
when using probes for genus-level identification.
Our first objective was to update the available mathematical
models of FISH to improve the predictions of equilibrium form-
amide melting profiles. We characterized different models using
cross-validation with several data sets of perfectly matched probes
and an independent data set of mismatched probes. The best
model was then used to systematically analyze the probe data set
available in probeBase (9) and to update coverage and specificity
for existing probes. Next, we developed a design tool that can
optimize probe sensitivity, target group coverage, and probe spec-
ificity by two different approaches. The first approach depends on
a single probe for identification, whereas the second approach
uses two probes and the true identification is obtained from the
hybridization overlap. The usefulness of this tool was demon-
strated by massively designing genus-specific probes targeting ev-
ery one of the 1,943 named genera in the RDP database. Our
findings demonstrate that thermodynamics-based probe design
can be automated to simultaneously optimize probe coverage,
sensitivity, and specificity, while allowing the use of comprehen-
sive rRNA databases to exhaustively evaluate perfect matches and
potential mismatched cross-hybridizations.
MATERIALS AND METHODS
Microbial strains and growth conditions. Xenorhabdus nematophila
(ATCC 19061), Photorhabdus asymbiotica (ATCC 43949), Serratia marc-
escens (ATCC 13880), Aquabacterium parvum (ATCC BAA208), and
Escherichia coli K-12 (ATCC MG1655) were used to form artificial com-
munities in this study. The respective GenBank accession numbers for the
16S rRNA sequences of these five strains are D78009,Z76752,M59160,
AF035052, and U00096 (gene rrsA). Strains grown aerobically in lysogeny
broth (LB) (Sigma-Aldrich, St. Louis, MO) included X. nematophila at
30°C, P. asymbiotica at 28°C, S. marcescens at 25°C, and E. coli at 37°C. A.
parvum was grown aerobically in R2A medium (Fisher Scientific, Wal-
tham, MA) at 20°C. All cultures were harvested during mid-exponential
growth phase (optical density at 600 nm of 0.3 to 0.4).
Flow cytometry FISH. Fixation, hybridization, and washing steps of
the flow cytometry protocol were carried out as described previously (19),
with minor modifications. In brief, cell cultures were fixed with 3 volumes
of 4% paraformaldehyde in phosphate-buffered saline (PBS) buffer (130
mM NaCl, 10 mM Na
2
HPO
4
) (pH 7.2) per volume cell culture. After 30
min, cells were centrifuged and resuspended in PBS to wash out fixative.
Fixed cultures were then centrifuged again and stored in 50% ethanol and
PBS solution at 20°C. Before hybridization, ethanol and PBS were re-
moved, and cells were resuspended in 800 l of hybridization buffer (1 M
NaCl, 0.05 M EDTA, 20 mM Tris HCl [pH 8], 0.1% SDS, variable form-
amide concentrations) with 250 nM 5=-labeled probe. The following
probes were used: Xeno-188 (Cy5 labeled, 5=-GCC ACC GTT TCC AGT
GG) and Xeno-1279 (fluorescein labeled, 5=-AGG TCG CTT CTC TTT
GTA TCY G). An unlabeled competitor oligonucleotide probe, with se-
quence (5=-AGG TCG CTT CAC TTT GTA TCY G) was used to block
hybridization of Xeno-1279 to P. asymbiotica. All probes were synthesized
by Integrated DNA Technologies (Coralville, IA). Samples were incu-
bated overnight at 46°C in hybridization buffer. Excess probe was washed
with hybridization buffer for 20 min at 46°C and then resuspended in PBS
with 0.01% Tween 20 (Fisher Scientific) at 4°C.
Measurement of cell brightness was performed as previously described
(20). In brief, three replicate hybridizations at each formamide concen-
tration were measured using a FACSCalibur flow cytometer (Becton,
Dickinson, San Jose, CA). A total of 10,000 events falling into the bacterial
gate were collected, and probe brightness was determined from the chan-
nel corresponding to the mode of the smoothed fluorescence histogram
(19). Negative controls were performed using the complement to the EUB
probe (nonEUB; 5=-ACT CCT ACG GGA GGC AGC). Cy5- and fluores-
cein-labeled versions of the nonEUB probe were separately hybridized
with the samples to determine the background for each dye. Net bright-
ness was calculated by subtracting background fluorescence from each
brightness measurement at the respective formamide concentration.
Microscopy FISH. Microscopy FISH was performed following estab-
lished protocols (21) on an artificial community composed of an equal
mixture of the cultures listed above. In an additional set of experiments,
FISH was performed with activated sludge collected from the Nine
Springs Wastewater Treatment Plant (Madison, WI) on 3 April 2013.
Microbial communities were filtered onto 0.22-m Nuclepore filters
(Millipore, Billerica, MA) and then hybridized in hybridization buffer
(35% formamide, 0.9 M NaCl, 0.05 M EDTA, 20 mM Tris HCl [pH 8.0],
0.01% SDS) overnight at 46°C. After hybridization, excess probe was re-
moved with wash buffer (80 mM NaCl, 0.05 M EDTA, 20 mM Tris HCl
[pH 8.0], 0.01% SDS) for 20 min at 48°C. Samples were incubated in PBS
for 15 min at room temperature and then mounted on a glass slide in
Vectashield (Vector Laboratories, Burlingame, CA).
Samples were viewed using a Zeiss Axio Imager 2 (Zeiss, Oberkochen,
Germany) with microscope settings (i.e., exposure time) held constant for
each fluorescence channel across all samples. Representative images with
the most even distribution and variety of cells were collected. Images were
analyzed with ImageJ (22) by using the “Subtract Background” and
“Color Balance” commands. Background was subtracted with a 500-pixel
rolling ball radius using a sliding paraboloid. Color balance was used to
normalize the three colors (Cy5, fluorescein, and 4=,6-diamidino-2-phe-
nylindole [DAPI]), while keeping the same settings across all images.
RESULTS
Improved thermodynamic models for predicting formamide
dissociation profiles. The design of optimal probes requires rea-
sonable predictions of probe sensitivity and specificity. To theo-
retically optimize probes, we have previously developed mecha-
nistic models that predict probe affinity (20), formamide
dissociation profiles for perfect-match targets (19), and offsets in
these profiles due to mismatches (5). In this study, while we main-
tained the definition of probe affinity, we updated the parameters
of the original formamide dissociation model, developed a new
model for perfect matches, and used this new model to conserva-
tively predict mismatch effects. These tools all feed into a probe
design scheme that is depicted in Fig. 1A, summarized below, and
described in detail in the supplemental material.
Our initial model of formamide dissociation calculated the hy-
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bridization efficiency of FISH probes based on the effect of form-
amide on three free energy values representing the reactions for
probe-target duplex formation, probe folding, and target folding
(19). This original model was calibrated and validated using 27
probes, all targeting E. coli. In the current study, we expanded the
model training set, with probes from another study (23), yielding
a total of 106 probes targeting five different organisms (see Table
S1 in the supplemental material). This extended data set was used
to retrain the original model and update the parameters that de-
scribe the linear relationship between free energy changes and
formamide concentration (19). The new parameters defined the
retrained mechanistic model (RMM) and were consistent with the
confidence intervals provided in the original model (Table S2),
thereby validating the approach. However, predicted curves were
still steeper than experimental profiles (Fig. 1B; see Fig. S1 in the
supplemental material), although formamide melting point
([FA]
m
) predictions were within 10% of experimental observa-
tions in most cases (Fig. 1C).
To improve predictions of formamide denaturation in FISH,
we adopted a modeling approach recently used for microarray
hybridizations (24). In this approach, hybridization is represented
by a single reaction. The duplex sequence determines the free en-
ergy change of this reaction based on specific nearest neighbor
rules that represent average values for all nucleic acid interactions
taking place, and these rules are to be determined as modeling
parameters. Although this approach provided better fits (see Table
S2 in the supplemental material), the addition of 17 different
DNA/RNA nearest neighbor parameters (Table S3) overparam-
eterized the model. We therefore developed a single-reaction
model (SRM) that converted the free energy change predicted
from regular DNA/RNA nearest neighbor rules to a FISH-specific
free energy using a linear relationship with only two parameters
FIG 1 Probe design algorithm and modeling. (A) Flow chart summary of design algorithm. The feedback loop on the right indicates the iterative procedure to
find all possible probe candidates for a target, all of which are used in the output decision. See Fig. S6 in the supplemental material for a more detailed
representation. [FA], concentration of formamide; HE, hybridization efficiency. (B) Example predictions (probe St796-813) based on leave-one-probe-out cross
validation (LOPOCV) (supplemental material) for the single-reaction model (SRM) (black) and retrained mechanistic model (RMM) (gray). Experimental data
are included as data points with standard deviations (error bars). See Fig. S1 for all plots. (C) Distribution of the melting prediction errors during LOPOCV for
SRM (black bars) and RMM (light gray bars). The majority of probes are associated with predictions of less than 10% formamide error for both models. SRM
errors are smaller by about 1.4% formamide on average, and they are more normally distributed due to improvements in the model’s goodness of fit. (D)
Correlation of experimental and theoretical offsets in melting points due to incorporation of a single mismatch. The equation indicates that SRM is a conservative
predictor of mismatch stability, with the experimental offset being higher than predicted.
Wright et al.
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(Table S2). This model captured the slope of experimental profiles
better than RMM (Fig. 1B and Fig. S1) and also had smaller errors
in the prediction of melting points (Fig. 1C). However, since this
model excluded probe and target folding free energies, it cannot
predict probe affinity, which is the overall free energy change of
hybridization obtained using probe-specific free energy values.
Technically, SRM can be applied only to probes that have been
designed a priori to achieve a reasonable level of probe affinity, as
was the case with all of the probes used in model training (Table
S1). Thus, our probe design scheme (Fig. 1A) uses both SRM and
RMM, with the former predicting hybridization efficiency of the
probe with the target and the latter providing a checkpoint based
on all three free energy changes related to FISH (19).
Next, we sought a model to predict the effect of mismatches on
melting point. Due to the large number of different mismatch
conformations (24) compared to available data sets, it was not
possible to develop models that can predict dissociation profiles
with specific mismatch parameters for FISH. Instead, conservative
predictors of the offset in melting point upon mismatch insertion
([FA]
m
) are preferred (5). Using a previously established data set
of melting points for mismatched probes (5), we checked the pre-
dictive ability of SRM against other predictors (see supplemental
material). While the predictive power of SRM was not signifi-
cantly better than any other model, it offered the advantage of not
requiring the calculation of the free energy of target folding, which
made the computation of [FA]
m
orders of magnitude faster than
the equivalent computation with RMM. In addition, SRM is a
conservative predictor, since it systematically underestimates the
experimental offsets (Fig. 1D). Accordingly, we defined qualita-
tive thresholds based on [FA]
m
calculated by SRM ([FA]
m,SRM
)
as follows. Nontargets can be considered at very high risk of hy-
bridization if [FA]
m,SRM
is more than 5%, high risk if
[FA]
m,SRM
is between 10% and 5%, moderate risk if
[FA]
m,SRM
is between 15% and 10%, low risk if [FA]
m,SRM
is between 20% and 15%, and no risk if [FA]
m,SRM
is below
20%.
Overall, the mathematical modeling background of the new
probe design tool was established with improved and efficient
predictors that reflect the best of our knowledge. Since SRM
was selected as the predictor for perfect matched and mis-
matched probes, we integrated it into a program for computing free
energy, formamide melting point, hybridization efficiency, and
[FA]
m,SRM
. This tool, named ProbeMelt, has been made accessible
online on the DECIPHER website (http://DECIPHER.cee.wisc.edu
/ProbeMelt.html). Given a set of probe and target sequences, Probe-
Melt will return their hybridization efficiencies at different levels of
stringency, with mismatched target sites color-coded by their risk of
cross-hybridization. This output can then be downloaded and easily
used for plotting formamide denaturation curves. As we show in the
next section, ProbeMelt can be used in combination with compre-
hensive database searches to efficiently identify potential targets and
nontargets.
In silico analysis of FISH probes in probeBase. Having estab-
lished an updated model for perfect match hybridizations and risk
levels for cross-hybridization of mismatched hybrids based on
[FA]
m,SRM
, we next performed an in silico evaluation of pub-
lished FISH probes. For this, we downloaded (13 August 2013) the
set of all 816 probes that had been “tested for FISH” available from
probeBase (9). For a subset of probes that had no nucleotide per-
mutations and included the formamide concentration used ex-
perimentally (649 probes), we computed the differences between
the predicted melting point (using ProbeMelt with perfectly
matched targets) and the experimental formamide concentration.
The difference was approximately normally distributed with a
mean of 6.5% and a standard deviation of 14%. This indicated that
probes are generally hybridized about 7% formamide below the
melting point, where stringency is reasonably high and the target’s
brightness is between the maximum (100%) and half-maximum
(50%). The large standard deviation was expected, given the con-
siderable variability in experimental procedures and objectives, as
well as prediction error.
Next we asked whether probes available in literature had rea-
sonably high coverage (arbitrarily defined here as 75%) of their
intended target group. We identified 138 probes in probeBase that
were designed specifically for targeting named taxonomic groups
present in the RDP database (version 10.28). For each of these
probes and their target groups, we calculated the fraction of se-
quences containing the target site for which [FA]
m,SRM
was
10% to 0%. That is, we estimated coverage taking into account
perfect matches as well as stable mismatches within the target
group. Using this method, we identified 15 probes with low cov-
erage (50%) and 15 probes with moderate coverage (50 to 75%)
of their stated target group (see Table S4 in the supplemental
material). Thus, the in silico analysis showed that 22% of probes in
probeBase targeting the SSU rRNA of common taxonomic groups
have less than desirable coverage. Many of these probes were de-
signed more than 10 years ago when rRNA databases were sub-
stantially smaller and before many taxonomic redefinitions took
place. Other probes in this group have been designed more re-
cently, and therefore, the lack of sufficient coverage may represent
either an inaccurate annotation or a systematic bias resulting from
specific probe design approaches.
One of the main limitations in the design of FISH probes is the
impracticality of experimentally testing all possible nontargets
that may have the potential to cross-hybridize. We propose that
ProbeMelt can be used to programmatically test potential false-
positive results in silico, thus helping in the probe design process as
a tool for screening and detecting cross-hybridizations. To illus-
trate the benefit of the model in this regard, we created color-
coded phylogenetic trees to graphically represent the in silico pre-
dictions of probe hybridization to different genera (see Fig. S9 in
the supplemental material). In this analysis, we used the set of 677
SSU rRNA-targeting probes from probeBase having only a single
permutation (i.e., no degeneracy). For each probe, we scored the
predicted level of hybridization to members of each genus by first
identifying target sequences in the RDP database that had a chance
of forming either perfectly matched or mismatched hybrids. We
then multiplied the fraction of the genus represented by these
target sequences with the calculated chance of hybridization,
which was defined as a linear value from 0 ([FA]
m,SRM
of less
than or equal to 15%) to 1 ([FA]
m,SRM
0% [perfect match]).
The sum of these products for all the potential targets within a
genus became a weighted score for the chance that the probe
would hybridize with members of the genus, a metric that was
useful to graphically represent the potential hybridization of
probes to each genus in the phylogenetic tree (Fig. S9).
Out of the 677 probes analyzed, 178 probes had no in silico
prediction of hybridization with more than 1% coverage to any
named genus in the RDP database, which reflects the high number
of probes designed to target very specific subgroups within a genus
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or groups that are unclassified in the RDP database. For other
probes, an assessment of whether the probe is adequate for the
specific identification of the targeted group can be obtained by
comparing the graphical representations in Fig. S9 in the supple-
mental material with the intended targets. For instance, probe
Nso1225, originally designed to target ammonia-oxidizing bacte-
ria in the Nitrosomonas and Nitrosospira genera within the Beta-
proteobacteria (25), is predicted to have a low to moderate chance
of hybridization with many other genera within and outside the
proteobacteria, and a high chance of hybridization with members
of the Bacteroidetes phylum (Fig. S9). Examples of other probes for
which the in silico predictions indicated a significant discrepancy
between the targeted genera and the predicted hybridizations are
found in the supplemental material (Table S5).
Integrated automatic probe design. To facilitate the design of
new probes consistent with modern databases, we integrated
SRM into a program that can optimize probes’ coverage, specific-
ity, and sensitivity, as well as evaluate a large number of nontarget
groups for potential cross-hybridizations (Fig. 1A). This program
for the design of FISH probes, described in detail in the supple-
mental material, has been made available as part (DesignProbes
function) of the DECIPHER package (26)forR(
27) and also
online as the Design Probes tool (http://DECIPHER.cee.wisc.edu
/DesignProbes.html). The objectives of the probe design program
are to (i) use the thermodynamic principles formulated in SRM
and RMM to design probes with high affinity to the target se-
quences, (ii) use multiple permutations per probe to maximize
coverage of the targeted group, (iii) use the thermodynamic prin-
ciples of formamide denaturation embedded in SRM to compre-
hensively detect nontarget sequences with the potential for cross-
hybridization, and (iv) evaluate the use of two probes targeting the
same group as a way to further maximize specificity.
The program accepts user-defined target and nontarget
groups, and it can also search for potential nontarget cross-
hybridizations in a comprehensive rRNA database (available
online at http://DECIPHER.cee.wisc.edu/Download.html).
Both 16S and 23S rRNA comprehensive databases are available
for use in finding additional nontargets during probe design. In
cases where a single probe is insufficient to achieve the desired
level of specificity, the program is able to search the space of all
combinations of dual probes to find the probe set with minimal
cross-hybridization overlap. In a dual-probe experiment, the
two probes would be labeled with different fluorophores and
the overlap in fluorescence signal would be considered a posi-
tive identification.
For an example, we applied the program to design genus-spe-
cific probes for all 1,943 named genera in the RDP database (ver-
sion 10.30) encompassing 1,696,150 SSU rRNA sequences (13).
We designed probes specifically targeting each genus, with all
1,942 other genera declared as nontarget sequences. For input
parameters, we chose to design probes (with up to 4 permutations
allowed) that represent at least 90% of sequences classified as be-
longing to each genus. The lengths of the probes were adjusted to
achieve at least 50% hybridization efficiency at standard FISH
conditions: 46°C and 35% formamide. Initially, to estimate an
upper bound on the number of genera for which it may be possible
to completely prevent cross-hybridization, we performed a hypo-
thetical simulation that assumed any mismatch completely
blocked hybridization. By using a single probe, potential false-
positive results could be prevented for 55.6% of genera, and with
dual probes, the number increased to 76.3% of genera. This hy-
pothetical demonstration epitomized the difficulty of designing
genus-specific probes due to strong conservation of the 16S rRNA
gene between closely related genera and the presence of polyphyl-
etic genera in the database.
We next asked whether the actual probe design would result in
probes near the hypothetical maximum and whether it was gen-
erally possible to design adequate probes for genus-level identifi-
cation. Using a [FA]
m,SRM
of greater than or equal to 20% as
the threshold for potential cross-hybridizations, with a single
probe it was possible to find probes with no false-positive results
for only 13.4% of genera, and 35.4% of genera if up to 5 false-
positive genera were permitted. With the dual-probe approach,
the fraction rose substantially, with the ideal probe set having no
cross-hybridizations for 35.5% of genera, and 64.1% of genera
when 5 false-positive genera were allowed. Since a [FA]
m,SRM
of
greater than or equal to 20% is a conservative estimate of spec-
ificity, we repeated this analysis using [FA]
m,SRM
of greater than
or equal to 10% (only high-risk nontargets) as qualification for
a potential false-positive result. With the relaxed definition of
specificity, 25.6% of single probes had no predicted false-positive
results and 58.5% were usable if 5 cross-hybridizations are al-
lowed. For dual probes, these numbers increased to 50.1% and
81.5%, respectively.
Next, we incorporated the possibility of using two competitor
oligonucleotides to block hybridization of mismatched nontargets
with a high risk of cross-hybridization (defined as mismatches
having [FA]
m,SRM
of greater than or equal to 10%). Using the
competitors, it was possible to design probes with no predicted
false-positive genera for 35.6% of genera with a single probe and
60.9% of genera with dual probes. If up to 5 cross-hybridizations
were allowed, adequate single probes could be identified for 68.5%
of genera with one probe and 87.7% of genera with dual probes.
The fraction of genera with adequate dual probes was comparable
to that obtained with recently published in silico designs of genus-
specific 16S primers for PCR (28). These results demonstrated the
challenges associated with designing probes at the genus level, the
advantage of using two probes over a single probe for identifica-
tion, and the considerable benefit obtained from the use of com-
petitor oligonucleotides. All of these predesigned genus-specific
probes and the recommended competitor probes to achieve max-
imum specificity are available in the DECIPHER 16S Oligos data-
base online (http://DECIPHER.cee.wisc.edu/16SOligos.html).
As a final analysis, we constructed networks representing the
cross-hybridization of probes targeting each genus with other
nontarget genera. Figure 2 shows the dual-probe network, where
each node represents probes targeting a single genus, and the
edges represent the cross-hybridization between the 1,943 genera.
The network was visualized by using the magnitude of [FA]
m
along each edge to guide a force-directed layout. Although the
network’s layout is based solely on its connectivity, the network
structure is clearly organized by phylogenetic relationship as
shown by like colors grouping together. To measure the net-
work density, we calculated the average number of neighbors
per node, which is the sum of all outgoing and incoming cross-
hybridizations with other genera. The dual-probe network was
substantially sparser (P1e15 by the Mann-Whitney U test)
than the network representing single-probe designs (not
shown), having an average number of neighbors equal to 44
versus 101 for the single-probe network. This analysis illus-
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trated the increased probability of cross-hybridization between
related organisms and the large increase in specificity obtained
by using two probes.
Dual probes designed by the algorithm successfully distin-
guish targets from potential nontargets. We experimentally eval-
uated with flow cytometry and microscopy one set of probes de-
signed by the algorithm. We chose the proteobacterium X.
nematophila as the target, because it is a model organism and had
easily cultured nontargets for each of the dual probes. The dual-
probe design output (see Fig. S7 in the supplemental material) for
the genus Xenorhabdus was the Xeno-188 (5=-GCC ACC GTT
TCC AGT GG) and Xeno-1279 (5=-AGG TCG CTT CTC TTT
GTA TCY G) probes, which were labeled with Cy5 and fluores-
cein, respectively. This dual-probe set was predicted to have 10
potential overlapping cross-hybridizations, in contrast to the
most specific single probe available for Xenorhabdus, which was
predicted to have 30 false-positive genera (Fig. S8).
We constructed an artificial community of nontargets by mixing
four different proteobacteria: A. parvum,E. coli,P. asymbiotica, and
S. marcescens.Table 1 shows that both E. coli and S. marcescens had
two mismatches to the Xeno-188 probe ([FA]
m,SRM
of 23% for
both; no risk of cross-hybridization predicted) and only one mis-
match to the Xeno-1279 probe ([FA]
m,SRM
of 10% and 11%,
respectively; moderate cross-hybridization risk). In contrast, P.
asymbiotica had no mismatches to Xeno-188 and only a single
mismatch to Xeno-1279 ([FA]
m,SRM
of 6%; high risk), which
made it a candidate false-positive result even when both probes
were used together. The distantly related A. parvum was added to
FIG 2 Dual-probe cross-hybridization network visualized with Cytoscape (v2.8.3). Nodes of the network represent the probes designed to target each genusin
the RDP database. Nodes are colored by their phylogenetic group and sized according to their number of potential cross-hybridizations with other genera. Edges
represent cross-hybridizations with a high risk of causing a false-positive identification ([FA]
m,SRM
of more than 10%). Labeled genera have more than 5,000
sequences in the RDP database (version 10.28).
TABLE 1 Target sites for probes targeting the artificial community
Species
Sequence of the Xeno-
188 target site (5=to 3=)
a,c
Xeno-188 probe
Sequence of the Xeno-1279
target site (5=to 3=)
b,c
Xeno-1279 probe
[FA]
m,SRM
RFU (%)
(35% FA)
d
[FA]
m,SRM
RFU (%)
(35% FA)
d
X. nematophila CCACTGGAAACGGTGGC 079CRGATACAAAGAGAAGCGACCT 071
A. parvum A...GTC.T..AA.... 66 N/A .C.G....G..G.CT..C.A.C 60 N/A
E. coli .T............A.. 23 0.4 ..C................... 10 1.2
P. asymbiotica ................. 037...........T.......... 615
S. marcescens .T............A.. 23 0.3 ..T................... 11 4.9
a
Probe labeled with 5=-Cy5.
b
Probe labeled with 5=-fluorescein. R stands for A or G.
c
The nucleotides that are different from those in the X.nematophila sequence are shown; nucleotides that are identical to those in the X.nematophila sequence are indicated by
dots.
d
Relative fluorescence units (RFU) at 35% formamide (FA) relative to the maximum X. nematophila fluorescence on the entire formamide curve (Fig. 3). N/A, not available.
Automatic Design of Dual Probes for FISH
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the community as a negative control with 7 mismatches to Xeno-
188 and 9 mismatches to Xeno-1279, resulting in very large
[FA]
m,SRM
magnitudes. The observed formamide melting curves
were in agreement with the model’s predictions (Fig. 3). Further-
more, use of an unlabeled competitor oligonucleotide probe al-
most completely blocked hybridization of Xeno-1279 with P.
asymbiotica (Fig. 3B).
Figure 4A and Bshow images of cells hybridized with both
probes and counterstained with 4=,6-diamidino-2-phenylindole
(DAPI). X. nematophila (target) cells are easily identifiable by the
white color resulting from superimposition of strong signals from
DAPI and the two probes. Cells hybridized only with the Xeno-
188 probe and DAPI appear purple (P. asymbiotica), whereas cells
with signals from the Xeno-1279 probe and DAPI appear green (E.
coli and S. marcescens), and cells not hybridizing to either probe
appear blue (A. parvum). These results were expected based on the
model predictions (Table 1) and formamide curves (Fig. 3), ex-
cept for P. asymbiotica, which did not require an unlabeled com-
petitor oligonucleotide to block hybridization of Xeno-1279. Here
P. asymbiotica could hybridize to both probes, but the fluores-
cence signal from the perfect match Xeno-188 outweighed the
signal from the mismatched Xeno-1279, resulting in purple cells.
For this reason, the competitor probe tested with flow cytometry
was not required for microscopy FISH.
Additional tests with and without spiking X. nematophila into
an activated sludge sample were also conducted. Activated sludge
is an ideal negative control for the dual probes, because it contains
a wide range of organisms, including many members of the family
Enterobacteriaceae, which is the family of the target genus, yet
likely has a negligible abundance of Xenorhabdus cells, which are
obligate symbionts of the nematode Steinernema (29). In the ab-
sence of X. nematophila (Fig. 4D), we detected cells that separately
hybridized to each of the two probes and cells that were not hy-
bridized, but there were no cells that simultaneously hybridized
with both probes, indicating that Xenorhabdus cells were not pres-
ent in the activated sludge sample. In the spiked samples (Fig. 4C),
the X. nematophila cells were clearly seen as hybridized with both
probes, further demonstrating the advantage of using dual probes
for the specific detection of organisms at the genus level.
DISCUSSION
While the use of single probes to detect specific microorganisms
with FISH is commonplace, our analysis of genus-level probes
demonstrates that significant increases in specificity can be ob-
tained by using dual probes. This concept is not novel, as the use of
multiple probes without a nested hierarchy has previously been
proposed and used as a strategy to improve confidence levels of
target detection (16–18). However, to our knowledge, this is the
first study that systematically compares specificity when using sin-
gle or dual probes. Furthermore, we present here the first compu-
tational tool for automated design of FISH probes that effortlessly
incorporates target identification using dual probes.
Also, in this study we improved the prediction of formamide
curves for perfectly matched hybrids in comparison to earlier
models (19) by using new thermodynamic calculations and data
sets with multiple organisms. We also used the computationally
efficient model (SRM) to evaluate and classify cross-hybridiza-
tions according to thermodynamically based calculations of mis-
FIG 3 Denaturation curves illustrating the relationship between model pre-
dictions and experimental data. The Cy5-labeled Xeno-188 probe (A) and the
fluorescein-labeled Xeno-1279 probe (B) were hybridized separately with four
different organisms. Points show the median cell fluorescence values deter-
mined with a flow cytometer and then normalized to the maximum value
observed in each channel. Error bars show the standard deviations from three
replicate hybridizations. The model predictions (lines) adequately describe the
melting points but do not reflect the plateau of each curve. Model predictions
for X. nematophila and P. asymbiotica are superimposed in panel A, because
[FA]
m,SRM
is zero. S. marcescens and E. coli are superimposed in panel A,
because they have the same [FA]
m,SRM
. The addition of a competitor oligo-
nucleotide probe effectively blocked hybridization of P. asymbiotica with
Xeno-1279 in panel B.
FIG 4 Dual probes successfully distinguish the target X. nematophila from
predicted nontargets and wastewater sludge. Separate probes labeled with Cy5
(red channel) and fluorescein (green channel) were hybridized using an arti-
ficial community with four nontargets with (A) or without (B) the target
organism (X. nematophila). The target appears white, while nontargets appear
purple if they hybridized to only the Xeno-188 probe, green if they hybridize to
only the Xeno-1279 probe, and blue (DAPI) if they hybridize to neither. To
confirm the specificity of the probes with a wide array of organisms, the exper-
iment was repeated using activated sludge from a wastewater treatment plant
with (C) and without (D) adding in the target organism.
Wright et al.
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match effects. This model offers a departure from other probe
design programs that estimate potential cross-hybridizations
based on the number of mismatches (11) or qualitative weighted
scores (10). Thus, our model allows, for the first time, the system-
atic application of thermodynamic principles to evaluate the spec-
ificity of existing probes or to optimize the design of new probes.
Our analysis predicted a much larger space of potential false-pos-
itive hybridizations for existing probes, an observation that was
expected given the rapid expansion of the rRNA databases (30)
and has been confirmed in specific situations (7), but to our
knowledge has never been systematically evaluated.
As specificity can be improved with the application of dual
probes, a desired condition is for both probes to have similar
[FA]
m
values so that the benefits of increased specificity can be
achieved with a single hybridization. If the probes have [FA]
m
values that are too different from each other, then successive hy-
bridizations would be required (31). Thus, one of the advantages
of mathematical modeling is to maximize the probability that a
single hybridization will work, as the dual probes are designed to
have similar melting points. More importantly, perfect match pre-
dictions should be accurate enough to identify true positive results
with high confidence. To evaluate the confidence in modeling
predictions, we performed statistical analyses (see supplemental
material), which revealed that dual probes designed with SRM are
expected to provide reasonable confidence in the identification of
target organisms in more than two-thirds of all cases. Nonetheless,
in silico predictions cannot completely substitute experimental
evaluations of formamide curves. Thus, in agreement with com-
mon FISH practice (4,32), experimental formamide curves
should be obtained for both probes to determine the difference in
experimental [FA]
m
values and establish the [FA]
m
errors of each
probe. The final advantage of mathematical modeling is the deter-
mination of potential false-positive results based on mismatch
thermodynamics. This not only minimizes the chance of cross-
hybridizations but also provides a list of organisms (i.e., likely
false-positive results) to use competitors against or to choose from
during experimental optimization with pure cultures.
The overall probe design strategy that results from our model
development and subsequent analyses is summarized in Fig. 5,
which depicts the design and verification of new probes from the
user’s perspective. The user must first select target and nontarget
sequences to consider during design. Here the user may take a
phylogeny-based or a taxonomy-based approach to define the tar-
get and nontarget groups. Optionally, the user may consider
letting the program find the nontarget groups of concern in a
comprehensive reference database, which will employ a taxonomy-
based grouping of nontargets, as we used here in the design of the
genus-specific 16S Oligos. The user also specifies the desired form-
amide concentration at which the hybridizations will be carried
out, as well as the minimum hybridization efficiency desired at the
selected formamide concentration. This is important, since users
may choose to hybridize with nonstringent buffers in order to
maximize probe signal when the targeted organisms are expected
to have low ribosomal content. After submission, the program will
return lists of single and dual probes ranked by specificity. The
user should carefully consider the outputs to decide whether sin-
gle or dual probes will be necessary to achieve the desired level of
specificity.
After the selected probes are synthesized, it is necessary to gen-
erate experimental formamide curves for perfectly matched tar-
gets with the samples of interest. In some cases, pure cultures
would be available, but in other cases, formamide curves need to
be obtained using the mixed culture that contains the organisms
of interest. Formamide curves are useful in determining the melt-
ing point, comparing model predictions with experimental obser-
vations, and deciding on an appropriate hybridization stringency
for targets that may have low maximal brightness. Target organ-
isms with low brightness should be hybridized at a formamide
concentration closer to the point of maximum brightness,
whereas very bright nontargets can be hybridized close to their
melting point. Low-risk nontargets ([FA]
m,SRM
between 15%
and 20%) should be more carefully considered when choosing
to hybridize significantly below the melting point. In the case of
dual probes, if the experimental [FA]
m
values of the two probes
are within 10% of each other, then the probe set is adequate for
hybridization near the lower [FA]
m
. In other cases, the user has the
option to go back to the design step, select a different set from
already designed probes, or use two different formamide concen-
trations in successive hybridizations (31).
Running formamide curves for all potential cross-hybridiza-
tions is usually prohibitive, either because representatives are not
available in pure culture, there are too many potential cross-hy-
bridizations, or it is nearly impossible to obtain adequate mis-
matched formamide curves for nontargets in mixed cultures (ex-
cept if there are substantial differences in morphology). Here the
design outputs can be helpful in two ways. First, cross-hybridiza-
tions are classified according to risk, while taking into account the
effects of mismatches, insertions, and deletions. Second, the de-
sign outputs include a dual-probe option that drastically reduces
the list of potential cross-hybridizations so that the user can have
a reduced set of cross-hybridizations to focus their attention on
and potentially design competitor probes against. One aspect that
is not included in the model but that works to the benefit of the
user is that in many mismatched hybrids there is a reduction in the
maximum signal obtained, and therefore, some predicted cross-
hybridizations will have low maximum fluorescence in practice
(Fig. 3).
Our high-throughput application in this study focused on design-
ing probes at the genus level, but the same approach could be applied
at lower or higher taxonomic levels. Inherent challenges at higher
taxonomic levels may include needing a larger number of permuta-
tions to achieve reasonable coverage of the target group and main-
taining high sensitivity across a wide variety of organisms, since probe
signal at the same target site can vary significantly among organisms
(33). To demonstrate probe design at higher taxonomic levels, we
designed probes for all phyla in the RDP database while maintaining
the same constraints used in genus-level probe design. The program
was able to design probes for all 39 phyla except Euryarchaeota, OD1,
and OP11, which contained too much sequence diversity to
achieve 90% coverage with a maximum of four probe permuta-
tions. However, specificity was considerably lower than for genus-
level probes, with many nontarget genera detected as potential
cross-hybridizations in all cases. Reassuringly, four of the probes
designed by the algorithm closely corresponded to the small num-
ber of phylum-specific probes already available in probeBase (see
Table S6 in the supplemental material).
In conclusion, using the approach provided by this new model
and the advantages of dual probes, FISH protocols can now be
systematically designed with high sensitivity to the targeted group
and higher specificity than previously obtainable. To this end, we
Automatic Design of Dual Probes for FISH
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have provided three online tools: (i) ProbeMelt for generating
denaturation curves and quickly identifying potential nontargets
of previously designed probes, (ii) 16S Oligos for FISH, which is a
preprocessed database of genus-specific single and dual probes,
and (iii) Design Probes for designing new probes targeting a user-
defined set of sequences while minimizing cross-hybridization
with other user-provided sequences and/or a comprehensive se-
quence database. For those users that prefer to use the stand-alone
DECIPHER program for R, the functions CalculateEfficiency-
FISH and DesignProbes are described in the downloadable docu-
mentation, including an extensive example of probe design in the
vignette “Designing Group-Specific FISH Probes” available on-
line. The main difference between the website and stand-alone
program for probe design is that the latter assumes some experi-
ence with R, allows design of probes for multiple target groups at
one time, and allows greater flexibility in the number and defini-
tion of nontarget groups in a comprehensive sequence database. It
is our hope that these tools will enable new research in environ-
mental microbiology by simplifying the accurate design of highly
sensitive and specific probes.
ACKNOWLEDGMENTS
This research was partially supported by National Science Foundation
grant CBET-0606894.
FIG 5 Flow chart depicting how to design and validate probes step by step with the help of the Design Probes tool online. LSU, large subunit; FAM, 6-car-
boxyfluorescein.
Wright et al.
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We thank Rowan Meara for conducting some of the preliminary ex-
periments that were not shown herein, Heidi Goodrich-Blair for provid-
ing X. nematophila and P. asymbiotica isolates, Sri Ram for constructive
feedback on the manuscript, and the Center for High Throughput Com-
puting for providing computing resources.
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... Hybridization and probe washing steps were performed at a lower stringency than previously reported methods for environmental microbial samples (Cardinale et al., 2008). The motivation behind this decision was based on previous work which suggested this alteration may aid in maximizing fluorescent signal intensity (Wright et al., 2014). The solution was replaced with 1 μg•ml −1 DAPI in 6.5× SSC for 10 min at room temperature. ...
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... A FISH probe catalog of previously designed and used probes is openly available in probeBase (Greuter et al., 2016). For de novo probe design, a variety of approaches can be used through different software (Wright et al., 2014), with mathFISH being one of the most used . Each de novo synthesized probe needs to be extensively optimized through an experimental approach using mocked mixed communities to ensure specificity and sensibility of taxonomical identification within complex microbial communities. ...
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... T. taylori in gill sections of L. orbiculatus (Mauritania, 2018) and C. costata (Panama, 2019). Probes were designed to target the 16S rRNA gene sequence of Ca. T. taylori using DECIPHER's design probes web tool (SI Appendix, SI Methods) (68). A formamide series from 0 to 60%, in 10% steps, was carried out to optimize the probe hybridization conditions (SI Appendix, SI Methods; probe sequences used are in SI Appendix, Table S6). ...
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... Using a 16S rRNA gene sequence obtained by Sanger sequencing that showed >98% identity with Thiodictyon species (Chromatiaceae, Gammaproteobacteria), we retrieved potential FISH probes with the "Design Probes" web tool of the software toolset DECIPHER under default hybridization conditions (59). One of the probe sequences with maximum specificity (score of 0), reasonable hybridization efficiency, and no reported cross-hybridizations with nontarget sequences was analyzed with the online tool "mathFISH" (60) and manually modified to optimize binding to the target sequence. ...
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