"nMR chemosensing" using monolayer-protected nanoparticles as receptors

Article abstract of DOI:10.1021/ja406688a

A new sensing protocol based on NMR magnetization transfer sequences and the molecular recognition abilities of nanoparticles allows the detection and identification of organic molecules in complex mixtures.

Full text of DOI:10.1021/ja406688a

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Communication
“NMR chemosensing” using monolayer-
protected nanoparticles as receptors
Barbara Perrone, Sara Springhetti, Federico Ramadori, Federico Rastrelli, and Fabrizio Mancin
J. Am. Chem. Soc., Just Accepted Manuscript • DOI: 10.1021/ja406688a • Publication Date (Web): 26 Jul 2013
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“NMR chemosensing” using monolayer-protected nanoparticles as
receptors
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Barbara Perrone, Sara Springhetti, Federico Ramadori, Federico Rastrelli* and Fabrizio Mancin*
Dipartimento di Scienze Chimiche, Università di Padova, via Marzolo 1, 35131 Padova, Italy
Supporting Information Placeholder
ABSTRACT: A new sensing protocol, based on NMR mag‐
netization transfer sequences and on the molecular recogni‐
tion abilities of nanoparticles, allows the detection and iden‐
tification of organic molecules in complex mixtures.
According to its definition, a chemosensor is “a molecule of
abiotic origin that signals the presence of matter or energy”.1
This concept has been commonly translated into a supramo‐
lecular receptor that, upon selective binding (recognition) of
its target (analyte), undergoes a measurable change of its
properties.2 Such a change, properly monitored, reveals the
presence of the analyte in the sample under investigation.
Typical molecular properties used for signal generation in‐
clude luminescence,3 absorbance,4 redox potential,5 relaxiv‐
ity6 and many others. Albeit several advantages justify the
widespread interest in chemosensors, one intrinsic limitation
of such an approach is that the reliability of a chemosensor
response depends crucially on its selectivity. Indeed, the sig‐
nal produced arises from a property of the chemosensor itself
and, as such, it does not provide any direct information on
the identity of the analyte detected. The user must presume
he is measuring the presence of the desired target, and not of
a known or unknown interferent, because he trusts the
recognition ability of the chemosensor. Indeed, the design of
new sensing schemes that allow for a direct individuation of
the target in complex environments represent a major chal‐
lenge. Our interest in understanding the structure and prop‐
erties of monolayer protected gold nanoparticles, particularly
using new tools based on NMR spectroscopy,7 led us to the
development of a new sensing protocol that allows also ana‐
lyte identification and quantification in complex mixtures.
Moreover, the results here reported also address the more
classical, but still actual, problem of realizing systems capa‐
ble to detect organic molecules in water, where most recep‐
used in this article. Lower) Nanoparticle‐based NMR sens‐
tors fail in efficiently recognizing their targets.
Chart 1. Upper) Nanoparticles coating thiols and analytes
ing with NOE pumping experiments: working scheme.
As a test mixture, we selected a group of three water soluble
the case of complex mixtures. However, when we mixed the
same sample with gold nanoparticles (2 nm gold core diame‐
ter) protected with thiol 1 (Chart 1), we were able to extract
just those signals arising from salicylate (Figure 1b) by means
of diffusion‐assisted nuclear Overhauser effect experiments
(NOE pumping).8
aromatic compounds of similar size and features, namely so‐
dium salicylate (4, Chart 1), sodium p‐toluensulphonate (7)
and disodium benzene‐1,3‐disulfonate (8). The identification
1
of the single components from a H‐NMR spectrum (Figure
1a) of this fairly simple mixture is not trivial at all, and this is
a well‐known drawback that heavily limits its usefulness in
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Figure 1. a) H‐NMR subspectrum of a mixture of molecules
4, 7 and 8 (7 mM in D2O). b) NOE‐pumping subspectrum of
the same sample in the presence of 1‐coated gold
nanoparticles (70 M). c) 1H‐NMR spectrum of a mixture of
molecules 4‐8 (7 mM in carbonate buffer 100 mM, pD = 10)9.
d) NOE‐pumping subspectrum of the same sample in the
presence of 1‐coated gold nanoparticles (70 M).
Figure 2. Relative area of the salicylate (4) signal in the NOE‐
pumping experiment as a function of its concentration. Solid
line: best fit of the data ( = 6.8 ppm,  = 7.4 ppm,  = 7.75
ppm). Conditions: [1‐nanoparticles]= 70 M, carbonate
buffer 100 mM, pD = 10.
Besides selective detection, it is important to note that the
method reported also allows the quantitative determination
of the analyte. In a series of experiments performed with dif‐
ferent concentrations of salicylate (Figure 2) we found that
the integrals of the analyte signals increase following a satu‐
ration profile as expected for a binding process. Fitting of the
relative intensity data with a 1:1 binding model provides an
apparent association constant (Kass) value of 120±5 M‐1. This
value is comparable with those reported by Pasquato and Lu‐
carini10 for the association of organic radicals to 1‐coated na‐
noparticles of similar size and confirms that an analyte quan‐
tification, upon construction of a calibration curve, is possi‐
ble with this method. In fact, when the concentration of sa‐
licylate in the independent experiment of figure 1d was recal‐
culated using the parameters obtained from the fitting of da‐
ta in figure 2 (See Supporting Information), the value of
6.8±0.4 mM was obtained, very close to the analytical value
(7.0 mM). In the condition used (4 hours acquisition, 0 M
nanoparticles concentration), the experiment reported in
figure 2 also allowed to set a detection limit of 2.5 mM for
sodium salicylate.
In designing this experiment, we had in mind two starting
points. First, it is well known that water‐soluble monolayer‐
protected gold nanoparticles can incorporate hydrophobic
molecules in the protecting monolayer.10 Second, several
NMR experiments have been devised to identify compounds
with binding affinity to proteins and to other bio‐
macromolecules, based on the transfer of net magnetization
(or saturation) from large systems to small bound mole‐
cules.8,11 Among these, the NOE‐pumping experiment origi‐
nally proposed by Shapiro8 looked particularly suitable to our
purposes. This experiment consists of two blocks: i) first, the
signals of the small molecules in the sample are cancelled
(dephased) with a diffusion filter, ii) second, a NOE experi‐
ment is started immediately after. In this way, only the sig‐
nals of the small molecules interacting with the macromole‐
cule in fast exchange regime can be detected, as they arise
from the magnetization transferred from the macromolecule,
which survives the diffusion filter. Hence, in our experiment
the nanoparticles can be considered as “self‐organized” su‐
pramolecular receptors that recognize their substrate and
label it by magnetization transfer, which in turn generates
the detected response (Chart 1). Note that the signal pro‐
duced in this way does not arise from the chemosensor but
from the analyte itself and, containing as much information
as a NMR spectrum does, it allows not only the detection but
also the unambiguous identification of the target.
The effective binding of salicylate to nanoparticles was con‐
firmed by selective NOE experiments (see Supporting Infor‐
mation). Upon selective inversion of the nanoparticles reso‐
nances we observed that the signals arising from salicylate
displayed clear NOEs, confirming the direct interaction of
the two species. NOE intensities appear to be larger when
the signals relative to the alkyl chains of thiol 1 are inverted.
This observation suggests that salicylate is probably localized
in the inner, hydrophobic, region of the monolayer.
The selectivity we found using such protocol is somehow
surprising. NOE‐pumping of a water solution containing 1‐
coated nanoparticles and the three isomers salicylate (4), 3‐
hydroxybenzoate (5), and 4‐hydroxybenzoate (6), which dif‐
fer only for the relative positions of the two functional
groups, revealed the presence of the sole salicylate signals
(see Supporting Information). Such a selectivity remained
unaffected even when all the compounds 4‐8 so far studied
were mixed together in a single tube with the nanoparticles.
Again, only salicylate signals emerge from the NOE‐pumping
experiment (Figure 1c‐d), with a substantial simplification of
the spectrum of the mixture and clear detection of the pres‐
ence of salicylate in the sample.
The remarkable selectivity obtained with 1‐coated nanoparti‐
cles should hence arise from the different hydrophobicity of
the five mixture components. As a matter of fact, computa‐
tionally predicted n‐octanol/water partition coefficients at
pH 7.4 (log D) follow the order 4 (‐1.14) > 6 (‐1.35) ~ 5 (‐1.47)
> 7 (‐2.57) >> 8 (‐7.14),12 with salicylate 4 being the most hy‐
drophobic of the series. A similar correlation was obtained by
submitting the mixture of the five compounds to HPLC sepa‐
ration using a C18 reversed‐phase column (Supporting In‐
formation). Here, the order of elution times was: 8 << 7 < 6 <
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Figure 4. a) 1H‐NMR subspectrum of human urine
containing mM sodium salycilate. b) NOE‐pumping
subspectrum of the same sample in the presence of 1‐coated
Figure 3. a) H‐NMR subspectrum of a mixture of molecules
4, 7 and 8 (7 mM in D2O). b) NOE‐pumping subspectrum of
the same sample in the presence of 2‐coated gold
nanoparticles (70 M). c) NOE‐pumping subspectrum of the
same sample in the presence of 3‐coated gold nanoparticles
(70 M). The asterix identify the singals of an impurity in
compound 8.
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gold nanoparticles (70 M).
Since there are no limitations on the chemical structure both
of the analytes and of the nanoparticles’ coating thiols, the
NOE‐pumping experiment has the advantage of a very gen‐
eral applicability. However we found that, in some cases, the
experimental times and detection limits of the protocol can
be substantially improved by using Saturation Transfer Dif‐
ference (STD) experiments.11 While conceptually similar to a
NOE experiment, STD provides stronger signals because sat‐
uration can be driven for longer periods as compared with
typical NOE mixing times. The final result is that the intensi‐
ty of the signals stemming from interacting molecules de‐
crease and are revealed by subtraction from a reference spec‐
trum. The use of STD is however limited by the requirement
that no overlap must occur between the signals of the analyte
and the macromolecule. Salicylate and 1‐coated nanoparti‐
cles meet such conditions and, using STD experiments, we
were able to cut the acquisition times down to 30 min and
increase the detection limit down to 250 M (see Supporting
Information).
5 << 4. This observations indicate that the 1‐coated nanopar‐
ticles act as selective receptors for hydrophobic molecules,
with a threshold than can be set at calculated log D (pH 7.4)
values around ‐1.2 or HPLC retention times around 9‐10
minutes in the adopted conditions.
However, other structural parameters influence the recogni‐
tion process. One of the main advantages brought about by
the use of nanoparticles as detection agents is the ease of
their modification. Indeed, the use of nanoparticles coated
with a different thiol, namely the phosphorylcholine deriva‐
tive 2, led to a different selectivity. When used to analyze the
mixture of molecules 4 , 7 and 8, these nanoparticles were
able to reveal, by NOE‐pumping, the presence not only of
salicylate but also of p‐toluensulphonate 7 (Figure 3b), ben‐
zenedisulphonate 8 and, with remarkable sensitivity, also of
an impurity contained in the latter. Hence, a change of the
features of the nanoparticles‐coating monolayer led in this
case to a broadening of the affinity toward different mole‐
cules. In order to identify the features of the coating thiol 2
that are responsible for such an affinity modification (as thiol
2 features both a longer, 11 carbon atoms alkyl chain, and a
different, phosphoryl choline, headgroup), we synthesized
thiol 3, featuring an 11 carbon‐long alkyl chain, same as 2, and
a tri(ethylene)glycol headgroup, same as 1. Results of NOE‐
pumping experiments performed with 3‐coated nanoparticles
(Figure 3c) revealed a selectivity in‐between that of 1‐ and 2‐
coated nanoparticles, indicating that both the size of the hy‐
drophobic region in the coating monolayer and specific in‐
teraction with the thiols headgroups may affect the binding
affinity, a well‐known phenomenon for HPLC stationary
phases. Such observations also indicate that hydrophobicity
is not the only source of recognition. In fact, hydroxybenzo‐
Finally, we decided to test our nanoparticle‐based NMR sens‐
ing protocol in a situation as challenging as the analysis of
drug metabolites in urines. Indeed, NMR‐based metabolom‐
ics is an area of utmost interest, which could substantially
benefit from any protocols that expand the amount of infor‐
mation attainable.13 In this framework, we added 1‐coated
nanoparticles to a human urine sample containing 5 mM so‐
dium salicylate, a concentration similar to that found in
urines after medium‐dose administration of acetylsalicylic
1
acid.14 Clean detection of salicylate in the H NMR spectrum
of urine samples (Figure 4a) is quite difficult, since its sig‐
nals are mixed with many others of similar intensities and
chemical shifts in the aromatic region (e.g. those of hippuric
acid). On the other hand, a NOE‐pumping experiment can‐
cels out the whole bunch of matrix signals, leaving only those
of salicylate (figure 4b) and allowing for a clear and unam‐
biguous detection of the target molecule. A similar experi‐
ment (see Supporting Information) also allowed us to reveal
and identify another metabolite of acetylsalicylic acid
(salyciluric acid), excreted in urines in different physiological
conditions.
ates
5
and 6, albeit more hydrophobic than p‐
toluensulphonate 7, are not associated to the nanoparticles
and hence are not revealed by the NOE‐pumping experiment
(see Supporting Information). Likely, an amphyphylic struc‐
ture, with quite well defined hydrophilic and hydrophobic
regions, is required for the inclusion of the analyte in the
monolayer.
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To our knowledge, all the NMR‐based chemosensors report‐
ed so far follow the “classic” approach were the recognition
causes modifications of a chemosensor property, such as the
ability to affect water relaxivity6 or the chemical shift of a re‐
ceptor’s heteronucleus.15 In this way, most of the meaningful
information associated with NMR is lost. More similar to our
approach is the “chromatographic NMR spectroscopy”,
where interactions with a stationary phase are used to per‐
turb the diffusion coefficients of the sample components in
such way that the NMR signals can be separated by means of
a DOSY experiment.16 However, besides the intrinsic difficul‐
ty of spreading the diffusion rates with a chromatographic
medium, this approach suffers from limitations such as the
need of high resolutions in both the frequency and the diffu‐
sion domains, along with non‐trivial spectral inversion prob‐
lems. The “NMR chemosensing” approach introduced here
retains all the structural information provided by NMR spec‐
troscopy, can be very easily implemented on standard in‐
struments and can benefit from the features of monolayer
protected nanoparticles, which can be easily tailored to meet
the recognition requirements of different classes of analytes.
5
6
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This experiment was performed using a carbonate buffer to
prove that the particles selectivity is not affected at high ionic
strength conditions.
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ASSOCIATED CONTENT
Supporting Information
Synthesis and characterization of the organic compounds
and nanoparticles; additional NMR experiments. This mate‐
rial is available free of charge via the Internet at
http://pubs.acs.org.
10 a) Lucarini, M.; Franchi, P.; Pedulli, G. F.; Pengo, P.; Scrimin, P.;
Pasquato, L. J. Am. Chem. Soc. 2004, 126, 9326‐9329; b) Lucarini,
M.; Franchi, P.; Pedulli, G. F.; Gentilini, C.; Polizzi, S.; Pengo, P.;
Scrimin, P.; Pasquato, L. J. Am. Chem. Soc. 2005, 127, 16384‐
16385; c) Pezzato, C.; Lee, B.; Severin, K.; Prins, L. J. Chem.
Commun. 2013, 49, 469‐471.
11 Mayer, M.; Meyer, B. Angew. Chem. Int. Ed. 1999, 38, 1784‐1788.
12 Data from the Chemspider® database.
13 DeFeo, E. M.; Wu, C. L.; McDougal, W. S.; Cheng, L.L. Nature
Rev. Urol., 2011, 8, 301‐311.
14 MacPherson, C. R.; Milne, M.D.; Evans, B. M. Brit. J. Pharmacol.
1955, 10, 484‐489.
15 Kitamura, M.; Suzuki, T.; Abe, R.; Ueno, T.; Aoki, S. Inorg. Chem.
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16 a) Pages, G.; Delaurent, C.; Caldarelli, S. Angew. Chem.‐Int. Edit.
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3258.
AUTHOR INFORMATION
Corresponding Author
fabrizio.mancin@unipd.it; federico.rastrelli@unipd.it
Notes
The authors declare no competing financial interests.
ACKNOWLEDGMENT
Funded by the ERC Starting Grants Project MOSAIC (grant
259014). The authors acknowledge Davide Zaramella and
Massimo Bellanda for assistance with HPLC and STD meas‐
urements, respectively, and Moreno Lelli and Giulio Tognin
for helpful discussions.
REFERENCES
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ACS7Pa.r6agon Plus En7vir.o4nment
1H (ppm)
7.2
7.0
6.8
6.6
45
46
47

Journal of the American Chemical Society
Page 12 of 12
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162x68mm (96 x 96 DPI)
ACS Paragon Plus Environment