Array-based detection of persistent organic pollutants via cyclodextrin promoted energy transfer

Article abstract of DOI:10.1039/c5cc04153h

We report herein the selective array-based detection of 30 persistent organic pollutants via cyclodextrin-promoted energy transfer. The use of three fluorophores enabled the development of an array that classified 30 analytes with 100% accuracy and identified unknown analytes with 96% accuracy, as well as identifying 92% of analytes in urine.

Full text of DOI:10.1039/c5cc04153h

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Array-Based Detection of Persistent Organic Pollutants via
Cyclodextrin Promoted Energy Transfer
Nicole Serio,a Daniel F. Moyano,b Vincent M. Rotello,b and Mindy Levine*a
Received 00th January 20xx,
Accepted 00th January 20xx
DOI: 10.1039/x0xx00000x
www.rsc.org/
chemicals involved was not initially disclosed.4
We report herein the selective array-based detection of 30
persistent organic pollutants via cyclodextrin-promoted energy
transfer. The use of three fluorophores enabled the development
of an array that classified 30 analytes with 100% accuracy and
identified unknown analytes with 96% accuracy, as well as
identifying 92% of analytes in urine.
These four anthropogenic disasters highlight the need for a
sensing platform that can detect a wide variety of POPs with
sensitivity, selectivity, generality, and rapidity. Such a detection
scheme would fill a crucial knowledge gap for first responders,
who currently need to wait for time-consuming laboratory tests
to accurately classify the nature of the pollutants. It would work
in conjunction with current methods, by allowing first
responders to screen numerous samples to rapidly understand
the nature of the pollutants involved and the extent of the event
so that they can begin an effective response. Previous research
in our groups has demonstrated that cyclodextrin-promoted
energy transfer can be used for the detection of a wide range of
aromatic toxicants,5 and that array-based detection enables the
sensitive, selective, and accurate identification of a wide variety
of analytes.6 We present herein the design, execution, and
evaluation of an extremely accurate array-based detection
system for aromatic POPs based on cyclodextrin-promoted
energy transfer from the POPs to high quantum yield
fluorophores.
Many anthropogenic events, such as oil spills and chemical leaks,
release a diverse suite of organic chemicals en masse into the
environment. These persistent organic pollutants (POPs) remain in
the environment for extended periods of time, and have significant
environmental and health consequences both in the short- and long-
term, to humans, animals, and plants living in disaster-affected areas.
Widespread and long-term environmental consequences occur
because of the persistent nature of organic pollutants in the
environment, which enables many toxicants to affect areas beyond
the immediate contamination site.1 Health consequences from
pollution occur via the exposure of individuals to the complex
mixture of released toxicants. Both the unknown consequences of
individuals’ exposure to toxicant mixtures and the persistence and
mobility of such toxicants and toxicant metabolites in the
environment can make the effective monitoring and treatment of
individuals living in disaster areas particularly difficult.
The ability to rapidly, sensitively, and selectively identify the
compound(s) involved in an anthropogenic contamination event
is crucial information for first responders. In the case of an oil
spill, such as 1989’s Exxon Valdez and 2010’s Deepwater
Horizon spills, the compounds involved in the contamination
event included numerous polycyclic aromatic hydrocarbons
(PAHs and heterocyclic hydrocarbons.2 There are also
contamination events in which the pollutant(s) are not initially
known, including the Love Canal incident in 1978 (ultimately
determined to involve a complex mixture of pesticides and
organochlorines),3 and West Virginia’s Elk River chemical spill
in 2014 involving 4-methylcyclohexylmethanol and a mixture
of glycol ethers (PPH), in which the full extent of the spill and
γ-Cyclodextrin promoted energy transfer uses γ-cyclodextrin as
a
supramolecular scaffold that enforces close proximity
between the aromatic analyte energy donor and high quantum
yield fluorophore acceptor.7 Once bound in close proximity,
excitation of the donor results in energy transfer to and
emission from the fluorophore, generating a unique highly
emissive fluorophore signal (Figure 1). Because each
fluorophore-analyte combination yields
a distinct signal,
statistical analyses of the response patterns of multiple
fluorophores in cyclodextrin to a single analyte identifies a
unique “fingerprint” for each analyte of interest.
a. Department of Chemistry, University of Rhode Island, 51 Lower College Road,
Kingston, RI 02881
Fig. 1 Illustration of γ-cyclodextrin promoted energy transfer, wherein the analyte
acts as an energy donor to a high quantum yield fluorophore acceptor.
b. Department of Chemistry, University of Massachusetts Amherst, 379A LGRT, 710
Nt. Pleasant St, Amherst, MA 01003
The thirty analytes targeted for this study were chosen to cover
a wide range of compound classes (Chart 1) that are highly
toxic and identified as hazardous by multiple monitoring
agencies, including the Stockholm Convention,8 the
† Electronic Supplementary Informaꢀon (ESI) available: Synthesis of fluorophore
31, detailed procedures for array detection, unknown classifications experiments,
all control experiments, graphs and images of all LDA results and all JCA results.
See DOI: 10.1039/x0xx00000x
This journal is © The Royal Society of Chemistry 20xx
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Environmental Protection Agency (EPA),9 and the International yield fluorophores were chosen as energy acceptors (31-33).11
Agency for Research on Cancer (IARC).10 Three high quantum
Chart 1. Structures of all analytes (1-30) and fluorophores (31-33) under investigation.
Analytes 1-14 are PAH and PAH metabolites, and have been provide
a better qualitative measure of the degree of
found in the blood12 and breast milk13 of individuals living in differentiation.
polluted areas, with many of them known or suspected LDA was successful in classifying all 30 analytes with 100%
carcinogens. PCBs (15-18) cause neurotoxicity and endocrine accuracy via jackknifed classification analysis (JCA), which
disruption,14 and many of them are known or suspected eliminates any potential bias in the array.22 The array was also
carcinogens. Many aromatic pesticides (19-22) are suspected 96% successful in identifying unknown samples from the
carcinogens,15 and others are designated as EPA Priority training set correctly (115/120 correct identifications). These
Pollutants. Compounds 23 and 24 are known carcinogens and results represent a substantially larger substrate scope than
endocrine disruptors,16 and compound 25 is a widely used many literature-reported arrays,23 and a success rate in line with
additive with suspected endocrine disrupting effects.17 or better than literature reports of analogous systems.24
Brominated flame retardants (26 and 27) are a class of The array was divided into two sections to more clearly analyze
pollutants that has been investigated for possible toxicity.18 the relationships between the analytes: (1) PAHs and PAH
Compound 28 is classified by the IARC as Group 1 carcinogen, metabolites; and (2) PCBs, endocrine disruptors, pesticides,
has been linked to bladder and lung cancer,19 and is an EPA biphenyls and flame retardants (Figure 3).
Priority Pollutant. Compound 29 is an amine derivative of Figure S1 demonstrates that all but five of the PAHs are
biphenyl and has been linked to bladder cancer.20 Compound 30 clustered together. The five outliers are compounds
was chosen for its structural similarity to 28, to assay the and 13; many of these are structurally related to benzo[
array’s ability to distinguish such structural variations.
5
,
7
a
,
9
,
10
]pyrene
and are highly fluorescent analytes (which leads to a stronger
emission signal). Figure 3A shows the remaining PAHs, and
highlights other key structural relationships: Anthracene and
two of its metabolites, compounds and , cluster together in
the array but generate well-separated signals. Fluorene 11 and
three derivatives, 12 13, and 14 also appear in the same region,
,
1
2
3
,
but again demonstrate good separation. Similarly, carbazole 12
and partly saturated analogue 13 are close together but still well
separated.
Figure 3B shows the LDA plot with biphenyl-type analytes.
Structural relationships can clearly be seen, for example:
chlorinated compounds with similar structures cluster together,
Fig. 2 General illustration of LDA analysis to identify unknowns. By comparing
the unique signals generated by unknowns and comparing them to known
samples, LDA can correctly identify the analyte(s) present.
For each analyte-fluorophore pair, the integrated emission of
the fluorophore from excitation near the analyte’s absorption
maximum was quantified and defined as the “fluorescence
response.” These responses were then evaluated using linear
discriminant analysis (LDA), a well-established statistical
analysis tool for array-based detection systems (Figure 2).21 It is
important to note that LDA identifies the axis of greatest
differentiation. A low score for one of the axes does not directly
translate into "small feature changes" dictating differentiation,
including compounds 19 and 20, and compounds 15-18
,
although within each cluster each compound generates a unique
signal; benzidine 28 and its derivative 30 are grouped together,
although structurally related 29 is not; brominated compounds
21, 26, and 27 are closely related on the LDA plot; and
bisphenol A 25 and its brominated derivative 26 appear in the
same region on the LDA plot.
Overall, every one of the 30 analytes generates a unique signal
on the LDA plot, with analytes with structural similarities
grouped in a similar area. For those analytes that appear to have
but can instead be
a reflection of particularly strong
differentiation across other axes. For our studies the ellipsoids
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overlap in the Figure 3 plots, their successful differentiation These results indicate that there is a relatively weak response
occurs in the third score, along the Z-axis (details shown in the between these chlorinated compounds and the sensor platform.
ESI). The array also successfully identified 115 out of 120 A second control experiment was performed where the array
cases of unknowns for a 96% accuracy.
was generated without γ-cyclodextrin. Ten analytes (
6, 8, 11,
14 17 18 19 20 28 30) were used for this experiment and the
,
,
,
,
,
,
results are reported in Table S11 of the Supporting Information.
LDA was able to differentiate between the analytes with 53%
accuracy via JCA, in stark contrast to the results achieved with
a 10 mM γ-cyclodextrin (100% differentiation). Additionally,
the scale of responses in this control array is vastly different,
with benzo[a]pyrene showing much less differentiation from
the other analytes in the absence of γ-cyclodextrin compared to
its response in the presence of cyclodextrin. This experiment
highlights the integral role the γ-cyclodextrin has in
successfully differentiating between analytes, by acting as a
supramolecular scaffold that enforces close proximity and the
necessary intermolecular orientations to enable efficient POP-
fluorophore interactions.
The potential utility of this array-based detection scheme was
demonstrated through detection of POPs in a complex matrix,
human urine. This array was generated in a 1:1 v/v mixture of
urine and γ-cyclodextrin, and fifteen analytes were used (
1, 2,
3,
5,
6
,
7,
8,
12 13 16 17 18 19 20 22). The array was able
,
,
,
,
,
,
,
to successfully classify the analytes with 93% accuracy via JCA
(Figure 5). Furthermore, the array was also able to correctly
identify 55 out of 60 unknown analytes.
Fig. 1 LDA score plots of (A) PAHs; and (B) All biphenyl-type analytes.
This sensor platform uses γ-cyclodextrin as a supramolecular
host that promotes proximity-induced non-covalent interactions
between the POP of interest and a high quantum yield
fluorophore. For most of the POPs, this interaction occurs via
energy transfer, in which excitation of the analyte results in
energy transfer to and emission from the fluorophore. However
even weakly photoactive analytes (i.e. compounds 21, 22, and
27) modulate the fluorescence emission of the acceptor via
proximity-induced fluorescence modulation, and these changes
in fluorescence are sufficient to enable accurate array-based
detection. In all cases, these proximity-induced interactions rely
on a multitude of non-covalent interactions to bring the
molecules in close proximity, including π-π stacking,25 Van der
Waals forces,26 hydrophobic binding, and electrostatic
interactions.27 These interactions guide the response of each
analyte when paired with three fluorophores, and give rise to a
distinct pattern that can be deciphered via LDA analysis (Figure
4).
Fig. 5 LDA score plots of analytes in a urine matrix.
Notably, many of the general trends that were observed in the
buffer array were also observed in urine. For example,
benzo[
a
]pyrene
6, pyrene
5
, 9,10-dihydrobenzo[
a]pyrene-7,8H-
one , and 7-methylbenzo[
8
a
]pyrene are all well-separated
7
from the other analytes and are plotted in the same general area
in both arrays (compare to Figure 3A). Similarly, compounds
19 and 20 are also well separated from the other analytes and
score in the same general region in both matrices. Lastly, the
Fig. 4 Proximity-induced interactions between the analyte and fluorophore give
rise to a new fluorescence signal via energy transfer or fluorescence modulation.
other structurally similar analytes cluster together: PCBs 16
and 18 carbazole 12 and tetrahydrocarbazole 13
,
17
and
,
;
;
compounds 1-3. The fact that similar trends can be seen in both
matrices clearly indicates that the association that occurs
between the γ-cyclodextrin host and guest molecules is specific
for each analyte-fluorophore combination and occurs similarly
in both matrices.
Two critical control experiments were performed. In the first
experiment, an array was generated in the absence of any
analyte, using γ-cyclodextrin and the three fluorophores. The
blank samples excited at 300 nm and 360 nm were correctly
classified as blank samples, whereas samples excited at 250 nm
and 400 nm were misclassified as PCBs or DDT, respectively.
In conclusion, we have developed an array-based strategy to
detect a wide variety of POPs in both simple (phosphate-
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buffered saline) and complex (urine) environments. This work
has shown that individual analytes can be identified with
exceptional accuracy, highlighting the ability of this detection
scheme to provide specific information that will be useful for
first responders. The success of this array relies on strong non-
covalent interactions between a toxicant donor, fluorophore
acceptor, and cyclodextrin host to achieve efficient proximity-
induced energy transfer, and the cyclodextrin host is crucial to
ensure association between the toxicant and fluorophore. This
method is expected to be generally applicable for multiple
classes of aromatic analytes in
a range of complex
environments. Applications of this array-based sensor for POP
detection in real-world matrices is currently underway, and
results of these and other investigations in our laboratories will
be reported in due course.
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Gulf of Mexico Research Initiative (GOMRI) and a grant from
the National Cancer Institute (CA185435), and in the Rotello
group by the National Institutes of Health (GM077173).
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