RESEARCH ARTICLE
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A Lifetime Nanosensor for In Vivo pH Quantitative Imaging
and Monitoring
Yiwei Fan, Yuetian Pei, Donghao Hu,* Yukai Wu, Kuangshi Sun, Lei Chen, Jiamiao Yin,
Weigang Yan, Mei Shi, Wei Feng, Xin Liu, and Fuyou Li*
Non-invasive, in vivo quantitative imaging for long-term biomarker monitoring
is crucial for elucidating disease mechanisms, advancing precision medicine,
and transforming diagnostics and therapeutic strategies. However, developing
chemical sensors for sustained in vivo quantitative monitoring despite sensor
concentration fluctuations, excitation variability, and tissue interference
remains a major challenge. Here, a long-lifetime nanosensor based on a
lanthanide-dye nanocomposite is presented that overcomes these limitations,
enabling precise quantitative in vivo pH monitoring. Benefiting from a 64-fold
reversible change in the dye’s molar extinction coefficient, this nanosensor
enables the dynamic tuning of reversible non-radiative energy transfer (RNET)
efficiency (6.42%–35.23%) and luminescence lifetime (265–383 µs). This
nanosensor enables 4 h of monitoring of gastrointestinal pH dynamics in mice
following proton pump inhibitor (PPI) administration, offering new insights
into pharmacodynamic effects across different administration routes and
dosages and inter-individual variability in drug efficacy. Moreover, coordination
with lanthanide nanocrystals induces a significant shift in the dye’s
pKa , highlighting the importance of nanomaterial interface engineering. This
work establishes a versatile platform for in vivo diagnostics and therapeutic
monitoring, marking a significant step forward in precision medicine.
Y. Fan, Y. Pei, X. Liu
Academy for Engineering and Technology
Fudan University
Shanghai 200433, China
D. Hu, Y. Wu, F. Li
School of Chemistry and Chemical Engineering & Institute of
Translational Medicine
Shanghai Jiao Tong University
Shanghai 200240, China
E-mail: hudonghao@sjtu.edu.cn; lifuyou@sjtu.edu.cn
K. Sun, L. Chen, J. Yin, M. Shi, W. Feng
Department of Chemistry
Fudan University
Shanghai 200433, China
W. Yan
Department of Urology
Peking Union Medical College Hospital, Peking Union Medical College
Chinese Academy of Medical Sciences
Beijing 100730, China
The ORCID identification number(s) for the author(s) of this article
can be found under https://doi.org/10.1002/smll.202502806
DOI: 10.1002/smll.202502806
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1. Introduction
The dynamic changes in physiological parameters and biomarker concentrations
in situ within living organisms are crucial for comprehending disease progression and developing effective theranostic strategies.[1] Biomarker concentrations
exhibit significant variability across different organs. For example, biomarker
concentrations in blood may not accurately reflect levels in specific organ or
lesion sites, as seen in the distinct pH
differences between tumor tissues and
blood.[2] Additionally, the fluctuation in
biomarker concentrations mirrors ongoing biochemical processes within living
organisms and is closely linked to the
progression of diseases.[3] Consequently,
the development of non-invasive detection
methods for in situ biomarker analysis
is imperative. Optical imaging stands out
as a promising in vivo detection strategy due to its non-invasiveness, high sensitivity, and superior spatiotemporal resolution. Traditional intensity-based optical probes have enabled qualitative or
semi-quantitative biomarker detection in vivo, including protons, metal ions, glutathione (GSH), ROS, RNS, and proteins.[4]
However, achieving accurate quantitative and long-term monitoring biomarker concentrations continues to pose significant challenges due to unknown probe concentrations in vivo, excitation
conditions, and interference from biological tissue interactions
such as scattering, absorption, reflection, and autofluorescence[5]
(Figure S1, Supporting Information).
To overcome these challenges, developing luminescent probes
with signals that are unaffected by the aforementioned interfering factors is essential. Among various signal properties, luminescence lifetime, an intrinsic property of luminophores, remains unaffected by these variables, rendering it a “high-fidelity”
signal for lifetime imaging, that accurately reconstructs signals
in vivo.[6] Using materials with long luminescence lifetime, such
as lanthanide nanocrystals, further eliminates tissue autofluorescence interference.[7] Nonetheless, most existing long-lifetime
probes, which depend on irreversible chemical reactions,[8] only
permit unidirectional changes in luminescence (Figure 1a) and
fail to capture dynamic biomarker concentration shifts. Although
some reversible lifetime probes have been developed for pH
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Figure 1. Design and working principles of the lifetime nanosensor for in vivo quantitative monitoring. a) Two chemical reaction modes for biomarker
detection: irreversible reaction (IR) and reversible reaction (RR). Only chemical sensors based on RR can monitor biomarker concentration effectively.
Signal changes in IR-based probes are unidirectional, whereas those in sensors vary with biomarker concentration fluctuations. Intensity-based imaging
(II) results in signal distortion due to biological tissue shielding, whereas lifetime imaging (LI) can extract in vivo signals with high fidelity. Ka represents
the equilibrium constant and “Conc.” denotes concentration. b) Schematic illustration of the “AND” logic gate for achieving in vivo quantitative imaging
and monitoring, facilitated by the combination of reversible reactions with lifetime imaging. c) By integrating reversible reactions with lifetime imaging,
we developed a lifetime nanosensor for quantitative in vivo imaging and monitoring. This lifetime nanosensor utilized lanthanide nanocrystals (LnNP)
as energy donors, ensuring stability against external environmental interference, and a responsive cyanine dye as the energy acceptor to construct the
RNET pathway. 𝜏 represents the luminescence lifetime.
detection, their lifetime signal changing is often compromised by
concentration-dependent aggregation effects.[9] The inability to
determine probe concentration in vivo prevents these tools from
achieving quantitative monitoring, thus restricting the broader
application of in vivo quantitative imaging and long-term monitoring.
Herein, we propose an “AND” logic gate to achieve quantitative
imaging and long-term monitoring in vivo: By considering both
the reaction type and signal extraction methods, the combination
of a reversible chemical sensor with luminescence lifetime imaging can achieve in vivo quantitative imaging and long-term monitoring of analyte concentration changes (Figure 1b). Specifically,
we report the development of a novel long-lifetime nanosensor
based on lanthanide-dye nanocomposites, employing lanthanide
nanocrystals as the energy donor and a pH-responsive dye as the
acceptor (Figure 1c). By modulating reversible changes in the molar extinction coefficient (𝜀) across varying pH levels, we further
developed a strategy for reversible regulation of non-radiative energy transfer. This approach ensures that the lifetime signal is
independent of probe concentration, excitation conditions, and
biological tissue interference. Furthermore, we find that the surface charge of lanthanide nanocrystals shifts the pKa of the dye
upon coordination, compared to its free form in solution. Finally,
we demonstrated the capability of long-term pH monitoring in
the gastrointestinal tract over a period of 4 h, identifying variations in biodynamic responses influenced by different administration routes and dosages, including onset and duration time.
This achievement represents a significant advancement in noninvasive, quantitative imaging and long-term monitoring within
complex biological systems, facilitating dynamic biomarker concentration monitoring in situ without the typical constraints of
probe concentration or environmental interference.
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2. Results and Discussion
2.1. Design and Characterization of pH-Responsive Dye (FD-822)
We first designed and synthesized the cyanine dye FD-822
(Figure S2, Supporting Information), guided by a rational design strategy incorporating two functional units: a coordination
unit and a pH-responsive unit. The introduction of a benzoic
acid group enhances the dye’s affinity for coordination with the
nanocrystal surface. The pH response mechanism is based on
the protonation of the nitrogen atoms in the benzyl indole group,
inducing a change in the conjugated structure of the cyanine
dye (Figure 2a) that alters the excited state energy levels of FD822. Time-dependent density functional theory (TD-DFT) calculations indicate that the HOMO-LUMO energy gap of FD-822
decreases significantly from 2.12 to 1.89 eV after protonation
(Figure 2b), reflecting a significant redshift of FD-822′s absorption peak (Figure 2c). To further investigate its photophysical
properties, we measured the UV–vis absorption and steady-state
emission spectra of FD-822 in various solvents under protonated
conditions (Figure S3, Supporting Information). The absorption
peak was primarily located at 822 nm, while the emission peak
was mainly centered at 850 nm. Notably, changes in dye concentration were found to affect both the fluorescence intensity and
the emission peak position, revealing a concentration-dependent
behavior that could impact the reliability of luminescence-based
detection and imaging (Figure S4, Supporting Information). This
indicated that relying solely on dye fluorescence is insufficient
for quantitative in vivo detection and underscores the necessity
of developing lifetime sensors.
To verify its responsiveness and reversibility, FD-822 was incorporated into nanomicelles via self-assembly with DSPE-PEG2000
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Figure 2. Design and characterization of pH-responsive cyanine dye FD-822 and nanomicelles. a) Design principle and pH response mechanism of
FD-822. b) The frontier molecular orbitals for FD-822 before and after protonation, were calculated using TD-DFT. HOMO: highest occupied molecular
orbital, LUMO: lowest unoccupied molecular orbital. c) The UV–vis absorption spectra of FD-822 nanomicelles in water at different pH levels, with
absorption peaks at 542 and 822 nm. d) The absorbance at 800 nm (corresponding to the Tm3+ emission peak) of FD-822 nanomicelles at different pH
levels, alongside the titration fitting curve (n = 3). e) The reversibility of FD-822 nanomicelles between pH 3 and 8 in aqueous solution. f) Normalized
time-dependent absorption changes at 800 nm of FD-822 upon the addition of base (NaOH, blue arrowhead) and acid (HCl, red arrowhead). The inset
displays the fitting curve, observed rate constants (k1 and k2 ), and calculated half-life (t1/2 ). g) The normalized absorbance at 800 nm of FD-822 during
acid and base cycles, with slight fluctuations attributable to inconsistencies in the stirring process. h) Photographs demonstrating the reversible color
change of FD-822 during three cycles of acid-base addition.
(Figure S5, Supporting Information). Subsequently, the pHdependent UV–vis absorption spectra were recorded, showing an
absorption peak at 542 nm under alkaline conditions, which diminished with decreasing pH, accompanied by the emergence of
a dominant peak at 822 nm in acidic environments (Figure 2c).
This remarkable 280 nm spectral shift and a corresponding
64-fold difference in 𝜀 (at 800 nm) provide significant spectral
evidence of FD-822′s high sensitivity to pH changes. The absorbance pH response curve fitting (detail derivation in Supporting Information) determined a mean pKa value of FD-822 to be
6.03 over three titration cycles (Figure S6, Supporting Informa-
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tion). The pH difference (ΔpH), ranging from 10% to 90% of
absorbance, was calculated to be 2.21 (Figure 2d). Moreover, FD822 demonstrated robust reversible pH-responsive capabilities
across five cycles (Figure 2e), with minor absorbance reductions
attributed to dilution effects from the addition of acid and base
(Figure S7, Supporting Information).
The reaction kinetics further demonstrated fast optical response rates, with reaction constants of 0.39 s−1 (base addition,
t1/2 : 1.77 s) and 0.60 s−1 (acid addition, t1/2 : 1.16 s) (Figure 2f).
Such rapid responses reflect FD-822′s suitability for fast pH sensing, and notably, this rapid responsiveness is fully compatible
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with the excellent reversibility (pink dashed line in Figure 2g)
demonstrated during repeated acid-base cycling tests, with only
minor variations due to uneven magnetic stirring. Real-time
optical monitoring and accompanying photographs during the
acid–base cycles further corroborate its remarkable reversible response characteristics (Figure 2h). Overall, FD-822′s significant
spectral shifts over physiologically relevant pH ranges, combined
with its rapid responsiveness and robust reversibility, make it a
promising candidate for long-term, quantitative pH monitoring.
2.2. Construction and Characterization of the Lifetime
Nanosensor
Mono-exponential decay arises when a single dominant radiative
relaxation pathway governs the emission process, described by
I = I0 ⋅ e−t∕𝜏 , where I0 is the initial intensity and 𝜏 is the luminescence lifetime. This behavior indicates uniform excited-state
dynamics, with minimal interference from non-radiative decay
or environmental heterogeneity. In contrast, multi-exponential
decay, often caused by varied local environments or multiple decay pathways, complicates lifetime measurements and reduces
reproducibility.
In this study, NaYF4 :1%Tm nanocrystals, synthesized via a
method previously reported by our group,[8a] exhibited a precise mono-exponential decay lifetime, reflecting their high optical uniformity and minimal non-radiative losses. This ensures
that the lifetime signal is independent of probe concentration and
initial intensity. Moreover, they are notable for exceptional brightness even under low excitation power densities, absorbing and
emitting photons at the same energy level (3 H6 → 3 H4 , 800 nm).
Notably, the emission at 800 nm overlaps with the absorption
band of FD-822, facilitating energy transfer processes and enhancing their overall performance. These properties make them
ideal candidates for in vivo time-resolved imaging. The synthesized NaYF4 :1%Tm displayed a uniform morphology,[10] with an
average diameter of 21.6 ± 1.0 nm (Figure 3a). High-resolution
transmission electron microscopy (HR-TEM), selected area electron diffraction (SEAD) images, and X-ray diffraction (XRD)
analysis confirmed the hexagonal (𝛽-) phase of the lanthanide
nanocrystals (Figure S8, Supporting Information). A time-gated
luminescence spectrum was acquired using a 785 nm pulsed
laser for excitation, thereby enabling the observation of the Tm3+
characteristic emission peak at 800 nm (Figure 3f). The luminescence decay curve, obtained through transient luminescence
spectroscopy, showed a clear mono-exponential decay with a luminescence lifetime of 660 μs in cyclohexane (Figure S9, Supporting Information).
To construct a nanocomposite lifetime nanosensor, FD-822
was then coordinated with lanthanide nanocrystals, followed by
the application of DSPE-PEG2000 to facilitate self-assembly into
aqueous lifetime nanosensor (NaYF4 :1%Tm@FD-822@DSPEPEG, Figure S10, Supporting Information). The size and morphology of the nanosensor were uniform in water (Figure S11,
Supporting Information), with a hydrated particle size of 61.4
± 6.1 nm (Figure S12, Supporting Information), where the
PEG layer could be identified (Figure S13, Supporting Information). To optimize the nanosensor’s performance, the dye loading
amount was systematically adjusted, with 100 μL of a 1 mmol L−1
pH-822 MeOH solution selected as the dye input for the nanosen-
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sor preparation. This ensures sufficient dye for energy transfer
while minimizing quenching effects on the nanocrystals, thereby
maintaining a high luminescence intensity (Figure S14, Supporting Information). Based on the 𝜀 (5.05 × 104 L·mol−1 ·cm−1 ,
Figure S15, Supporting Information) at 542 nm of FD-822 at
pH = 8, it can be calculated that approximately thirteen FD822 molecules coordinated to the surface of each lanthanide
nanocrystal.
Furthermore, the pH-responsive behavior of the nanosensor was comprehensively evaluated by measuring its absorption
across different pH levels. The nanosensor exhibited a prominent peak at 542 nm under high pH levels, as the pH decreased,
the absorption at 542 nm weakened while the 822 nm peak
emerged (Figure 3d). These observations were consistent with
the response observed in FD-822 nanomicelles, confirming that
the coordination of FD-822 with NaYF4 :1%Tm effectively preserves the pH-responsive behavior. The absorbance at 800 nm
across varying pH levels was fitted to determine the nanosensor’s mean pKa of 3.81. The difference in pKa between the dye
coordinated on the nanocrystal surface and the dye in nanomicelles (pKa = 6.03, Figure 3e) can be attributed to variations in the
local charge density following coordination on the nanocrystal
surface (Figure 3b). Lanthanide nanocrystals possess a positive
surface charge,[11] which hinders the interaction between proton
and FD-822 on the nanocrystal surface, thereby increasing the
Gibbs free energy (ΔG) change of the dissociation reaction. As a
result, a more acidic environment is required to induce changes
in dye absorption on the nanocrystal surface, resulting in a lower
pKa for the nanosensor (detail derivation in Supporting Information). To validate this hypothesis, a common pH indicator dye
methyl red was employed. The pKa of the methyl red nanomicelles was 5.15, whereas coordination with NaYF4 :1%Tm significantly reduced its pKa to 3.99, corroborating our findings (Figures
S17 and S18, Supporting Information).
The energy transfer efficiency between the lanthanide
nanocrystals and FD-822 was further evaluated. By comparing
the absorption spectra of FD-822 with the emission spectrum
of Tm3+ , minimal spectral overlap was observed at higher pH
levels (Figure 3f). According to nonradiative energy transfer
(NET) theory, including Förster and Dexter mechanisms, such
limited spectral overlap indicates that efficient energy transfer
cannot occur. Therefore, FD-822 is nearly unable to quench
the luminescence of the nanocrystals at high pH levels. In
contrast, at low pH levels, the significantly enhanced 𝜀 of FD822 in the near-infrared region enables effective NET between
NaYF4 :1%Tm and FD-822 (Figure 3c). This pH-dependent modulation of NET highlights the dynamic interaction between the
nanocrystals and the dye, governed by the protonation state of
FD-822.
We then validated the NET efficiency between NaYF4 :1%Tm
and FD-822, specifically on the luminescence lifetime of the
nanosensor. Luminescence decay curves revealed a lifetime of
409.70 μs (mono-exponential fitting) for the aqueous nanocrystals without dye coordination, with the lifetime remaining constant across different pH levels (Figure S19, Supporting Information). Upon integration with FD-822, the lifetime significantly
decreased from 383.37 μs at pH = 9 to 265.35 μs at pH = 2
(Figure 3g). This indicates effective NET, with efficiency improving from 6.42% under alkaline conditions to 35.23% in acidic
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Figure 3. Investigation of the mechanism underlying the reversible lifetime response. a) Morphology and particle size distribution of NaYF4 :1%Tm
nanocrystals (n = 100); scale bar: 100 nm. b) Schematic illustration of the mechanism of the pKa shift and c) reversible luminescence lifetime. d) Normalized UV–vis absorption spectra of the lifetime nanosensor in water at different pH levels, showing absorption peaks at 542 and 822 nm. e) Absorption
shows the pKa shift of FD-822 in water and when coordinated with lanthanide nanocrystals. (n = 3) f) Overlap between absorption spectra of FD-822
before and after protonation and the luminescence emission spectra of lanthanide nanocrystal. g) Luminescence decay curves of NaYF4 :1%Tm@DSPEPEG and nanosensor before and after protonation. h) Luminescence lifetimes at various pH levels with the corresponding fitting curve. i) Reversibility
of luminescence lifetimes between pH 3 and 8 in aqueous solution (n = 3).
environments—representing a notable 5.5-fold enhancement.
Subsequently, we dispersed the nanosensor in water at different pH levels and monitored the corresponding luminescence
intensity and lifetime changes (Figures S20 and S21, Supporting Information). The mean pKa value of 3.77 was calculated
using the luminescence lifetime curve fitting (detail derivation
in Supporting Information, Figure S22, Supporting Information), with a ΔpH of 2.14 for a 10%–90% lifetime change range
(Figure 3h). Further reversible response explorations were performed: over five acid–base titration cycles, the luminescence
lifetime of the sensor demonstrated excellent reversible respon-
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siveness (Figure 3i; Figure S23, Supporting Information). To the
best of our knowledge, this work represents a pioneering effort
in constructing RNET at the lanthanide–dye interface. The observed reversible changes in luminescence lifetime open new avenues for advancing non-invasive quantitative imaging and in
vivo monitoring. Notably, absorbance was influenced by the concentration and failed to achieve fully reversible changes in titration experiments, whereas luminescence lifetime exhibits complete reversibility due to its independence from luminophore
concentration. This indirectly highlights the accuracy of our designed “AND” logic gate strategy in achieving quantitative in vivo
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imaging. These findings demonstrate the reliability and practicality of luminescence lifetime imaging, offering promising potential for precise pH sensing in complex environments.
2.3. Phantom Imaging of the Lifetime Nanosensor
After confirming the feasibility of the lifetime nanosensor,
we further validated the accuracy of signal extraction in lifetime imaging. First, we verified that the nanosensor’s lifetime remains consistent across a broad range of excitation power densities (0.6–69 mW cm−2 ), and concentrations
(3–50 mg mL−1 , Figure S24, Supporting Information) using
the lab-built time-resolved imaging system. Further, we demonstrated its long-term stability over seven days (Figure S25, Supporting Information).
To simulate tissue scattering of varying thicknesses, this
nanosensor was enclosed in a quartz tube and placed beneath a
Petri dish, with varying volumes of 1% intralipid emulsion solution added (Figure 4a). A series of time-gated intensity images at
varying delay times were obtained (Figure S26 and Video S1, Supporting Information), and subsequently processed by performing pixel-wise analysis in MATLAB to reconstruct final lifetime
images.
As the thickness of the intralipid emulsion increases, the luminescence intensity signal decreases markedly, exhibiting spatial non-uniformity even without obstruction from the intralipid
emulsion (Figure 4b). In contrast, lifetime imaging remains unaffected. The luminescence lifetime shows consistent spatial uniformity and remains unchanged (Figure 4c). For example, at
pH = 6.51 without intralipid emulsion shielding, the lifetime
along the region of interest (ROI) varied by only 6% compared
to a 33% variation in intensity due to uneven laser excitation
(Figure 4d), because lifetime is determined by the decay rate of
emission in the time domain and is independent of the initial
luminescence intensity. Overall, with increasing pH, the timegated intensity increased and the lifetime was extended (Figure
S27, Supporting Information). Although increasing the thickness of the intralipid emulsion leads to a sharp decrease in luminescence intensity, resulting in a slightly higher standard deviation in the lifetime fitting (Figure 4k), the mean lifetime remains
unchanged. These results underscore the robustness of lifetime
imaging, demonstrating its resilience to variations in excitation
conditions.
Moreover, we found that the lifetime imaging fitting curves
at different penetration depths overlapped well (Figure 4e), with
minimal pKa differences and a low coefficient of variation (CV =
0.82%, Figure 4g). At pH = pKa , the calculated lifetimes in the
titration curves were nearly identical (Figure 4j). In contrast, intensity imaging fitting curves did not overlap (Figure 4f), with
significant pKa differences and a higher CV of 12.82%. Additionally, lifetime ratios at different penetration depths were consistent (Figure 4h), while luminescence intensity showed a 10-fold
attenuation between 0 and 6 mm depths (Figure 4i). Therefore,
intensity-based imaging struggles to accurately extract luminescence signals from within the body due to biological tissue interference. In contrast, lifetime imaging reliably captures in situ
luminescence signals and accurately translates them into analyte concentration information, enabling precise determination
of pH values.
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2.4. In Vivo pH Quantitative Imaging and Long-Term Monitoring
After verifying the accuracy of lifetime imaging and confirming
the reliable pH responsiveness of the nanosensor, in vivo, pH
imaging and dynamic monitoring were conducted. Proton pump
inhibitors (PPIs), long-acting drugs that suppress gastric acid secretion, are used to treat Helicobacter pylori infections and various hyperacidic conditions.[12] Consequently, understanding the
biodynamics of PPIs, including their onset time and duration, is
essential for the development of new PPIs and for studying their
interactions with bodily organs.[13] Conventional research methods rely on collecting gastric juice from multiple mice for pH
measurement at different time intervals, which not only fails to
capture individual variations but also makes long-term, quantitative, and in situ monitoring in a single mouse challenging. As
proof of concept, the lifetime nanosensor was administered via
gavage into the stomachs of mice. We then assessed the effects
and durations of various administration routes and dosages of
PPI (rabeprazole sodium), comparing their biodynamic monitoring. Mouse 1 and mouse 2 were injected intravenously with 200
and 100 μL of PPI (1.5 mg mL−1 ), respectively, while mouse 3 received 200 μL of PPI via gavage. The control mouse was injected
with 200 μL of saline intravenously. Subsequently, time-resolved
imaging and monitoring were performed at different time points
(0, 0.5, 1, 2, and 4 h) post-administration (Figure 5a).
Time-gated intensity imaging effectively filtered out the excitation light, enabling high signal-to-background ratio (SBR) images due to the excellent luminescence of nanocrystals combined
with the lab-built time-resolved imaging system. For example, an
SBR of 2106 was achieved in Mouse 1, 1 h after PPI administration (Figure 5g). The results enable clear observation of the timegated intensity in the mouse stomach (Figure 5b). Overall, following PPI administration, the time-gated intensity in the mouse
stomach initially increased significantly and then decreased over
time (Figure 5c), reflecting the dynamic changes in stomach pH.
This was driven by a PPI-induced increase in pH, which caused a
blueshift in FD-822 absorption, reducing NET and enhancing luminescence intensity (as mentioned above). However, intensity
signals are influenced by penetration depth and excitation conditions, thus factors such as mouse posture, individual differences,
and slight variations in excitation light positioning can introduce
deviations. For example, the decrease in luminescence intensity
at 2 and 4 h in Mouse 2 was attributed to a deliberate change
in the mouse’s posture. Additionally, the nanosensor’s migration
from the stomach to the intestine blurred stomach contours, further complicating interpretation. Consequently, relying solely on
luminescence intensity complicates the accurate assessment of
stomach pH differences.
The limited volume of gastric juice obtainable from a single
mouse makes conventional pH electrodes unfeasible. This limitation highlights the necessity of employing our nanosensors
for single-mouse monitoring. The luminescence lifetime in the
mouse’s stomachs exhibited a trend of initial increase followed
by a subsequent decrease (Figure 5d), demonstrating the capability of the nanosensor to monitor local dynamic pH changes
in vivo over time. The lifetime distribution shows uniform variations (Figure S28, Supporting Information), highlighting the
nanosensor’s consistent performance and stable signals for accurate in vivo pH imaging and dynamic monitoring.
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Figure 4. Time-resolved phantom imaging of the nanosensor in response to varying pH levels. a) Schematic of the lab-built time-resolved imaging
system and experimental setup, featuring a quartz tube filled with nanosensors submerged at varying depths of 1% intralipid. b) Time-gated intensity
images and c) lifetime images of the nanosensor across various pH levels and penetration depths. d) Distributions of time-gated intensity (blue dashed
line) and luminescence lifetime (red dashed line) along the ROI at pH = 6.51. Titration fitting curves illustrate the relationship between (e) lifetime
and (f) time-gated intensity as a function of pH at different penetration depths. g) pKa values obtained from lifetime imaging and time-gated intensity
imaging results across various 1% intralipid penetration depths. CV: the coefficient of variation. h) Lifetime and i) intensity ratios at the same pH across
different penetration depths. j) The normalized lifetime and intensity calculated at pH equal to the pKa across various 1% intralipid penetration depths.
k) Histograms of the lifetime distribution for the nanosensor across various pH levels and penetration depths.
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Figure 5. In vivo, pH time-resolved imaging and monitoring following administration of proton pump inhibitors (PPIs). a) Schematic illustration of the
experimental setup, including the timeline and imaging diagram of the experiment and the administration routes for each mouse b) Time-gated intensity
imaging of the stomach and intestine in mice at different time points with various administration routes. c) The time-dependent curve of the average
luminescence time-gated intensity in the mouse stomach. d) Lifetime imaging of the stomach and intestine in mice at different time points across
various administration routes. e) The time-dependent curve of the average luminescence lifetime in the stomach shows lifetime distribution. f) Timedependent pH curves of the mouse stomach monitored over 4 h, derived from lifetime imaging results and fitted using an in vitro standard calibration
curve. The gray area indicates regions where measurements were not accurate (Lifetime changing over 95%). g) Time-gated intensity distribution along
the white dotted line of Mouse 1 at 1 h. h) Luminescence lifetime distribution in the stomach and intestine of control mouse. The inset displays the
calculated average pH values for the stomach and intestines.
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Lifetime imaging was distinctly captured in the stomach region, without interference from intestinal signals, likely due to
continuous peristalsis causing the intestinal signal to fluctuate
(Mouse 1 at 4 h, Video S2, Supporting Information). These fluctuations were subsequently filtered out during lifetime fitting, isolating only the stomach signal. The nanosensor’s ability to provide accurate luminescence lifetime imaging offers a clear advantage over conventional intensity-based imaging methods, as it is
less susceptible to fluctuations caused by factors such as body
posture or peristalsis.
The time-dependent lifetime curves (Figure 5e) provided critical insights into the pharmacodynamics of PPI administration. In the control mouse, little change in pH was observed
(Figure 5f), confirming the stability of the nanosensor and the
absence of external perturbations. In the experimental groups,
however, significant increases in lifetime were observed following PPI administration, reflecting a rise in stomach pH. By correlating these lifetime changes with the calibration curve established in vitro (Figure S29, Supporting Information), the timedependent stomach pH of each mouse was quantified. Before
PPI administration, all mice exhibited a normal stomach pH
of ≈3.2, with minimal variation. Following PPI administration,
distinct changes in stomach pH were observed depending on
the administration route. Mouse 1 and 2, which received intravenous injections, demonstrated a noticeable pH increase
within 0.5 h, peaking at 2 h. Mouse 3, which was administered PPI via oral gavage, exhibited a delayed onset time of 1 h.
Both Mouse 2 and 3 returned to baseline pH by 4 h, whereas
Mouse 1 maintained a higher pH of 4.5 at 4 h, indicating a
prolonged effect of high-dose administration. This observation
is consistent with the pharmacokinetics of PPI administration,
in which higher doses and intravenous administration of PPI
are associated with a more rapid onset and a longer duration
of action.[14] Notably, although the dose administered to Mouse2
was lower than that of Mouse1, its onset of action was even faster,
indicating inter-individual variability in drug efficacy and further underscoring the superiority of our nanosensor in enabling
continuous long-term quantitative monitoring in individual
mice.
Interestingly, in the control mouse, some nanosensors remained in the mouse stomach and intestine after 4 h, enabling
the capture of their luminescence lifetime. The lifetime imaging
and distribution histograms (Figure 5h) showed a significantly
lower lifetime in the stomach compared to the intestine, corresponding to mean pH values of 3.1 and over 6, respectively, consistent with previous reports in the literature.[9b,15] This demonstrates the ability of lifetime imaging to provide detailed, organspecific information by distinguishing the heterogeneous microenvironments of different gastrointestinal regions. This indicates a much lower pH in the stomach compared to the intestine.
While time-gated intensity imaging (Figure S29, Supporting Information) could visually indicate differences, it lacked the precision to accurately quantify pH or resolve the distinct physiological characteristics of the stomach and intestine. These findings
underscore the potential of lifetime imaging as a powerful tool
for mapping and analyzing the functional heterogeneity of complex organ systems.
Overall, lifetime imaging is unaffected by excitation conditions or tissue obstruction. This enabled accurate monitoring
Small 2025, 21, 2502806
of luminescence signals, thereby facilitating the determination
of stomach pH and key kinetic parameters such as onset time
and duration of PPI effects. In contrast, intensity-based imaging could not reliably capture these patterns, which limited its
exploration of biological phenomena. Although this work represents a significant step forward in achieving quantitative pH
monitoring within the gastrointestinal tract, further advancements may be realized by employing organic dyes with higher
pKa values and other reversible biomarker-responsive energy
acceptors.
3. Conclusion
Quantitative, long-term in vivo monitoring of biomarkers continues to pose significant challenges due to unknown probe concentrations in vivo, excitation power interference, and effects from
biological tissue interactions. By developing reversible chemical
sensors combined with lifetime imaging for precise in vivo luminescence signal extraction, we proposed an “AND” logic gate to
achieve non-invasive in vivo quantitative pH imaging and longterm monitoring. This study presents a long-lifetime nanosensor
based on the lanthanide-dye nanocomposites featuring a responsive cyanine dye FD-822, which undergoes a significant reversible
64-fold molar extinction coefficient change. Leveraging efficient
inorganic-organic interface interaction, we developed a strategy
for reversible regulation of non-radiative energy transfer, thereby
enabling reversible luminescence lifetime changing. Moreover,
the observed shift in the dye’s pKa upon coordination with lanthanide nanocrystals highlights the fundamental role of nanomaterial interfaces, providing critical insights and a framework for
next-generation nanosensor design. Such interfacial interactions
can enhance nanosensor performance and influence parameters
critical to controlled drug delivery, underscoring the importance
of nanomaterial interface engineering.
Building on in vitro experiments that demonstrated the
nanosensor’s rapid responsiveness, reversibility, and resistance
to biological tissue interference, in situ pH quantitative lifetime imaging and long-term monitoring in individual mice were
achieved in vivo for up to 4 h, allowing us to observe interindividual variability in drug efficacy. Observations indicated a
substantial increase in stomach pH following PPI administration, with intravenous administration demonstrating a faster onset and prolonged effect, especially at higher doses. Notably, we
also observed individual differences in onset time, underscoring
the advantage of our lifetime nanosensor in enabling pH monitoring in individual mice. In addition, the observed gastrointestinal pH variations align with literature reports, validating the
quantitative accuracy of our nanosensor.
However, several limitations merit consideration. The positive
charge of the nanocrystals results in a relatively low pKa value,
which consequently confines the responsive range to relatively
acidic environments, limiting applications in other disease models. Furthermore, improving the sensor’s brightness would reduce acquisition times, thereby enabling real-time lifetime imaging with enhanced temporal resolution for continuous monitoring. The RNET strategy demonstrated here shows promise for
extension to other analytes, establishing a foundation for developing versatile probes for diverse biomarkers in biological and
pharmacological research.
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Supporting Information
Supporting Information is available from the Wiley Online Library or from
the author.
Acknowledgements
This work was supported by the National Key R&D Program of China
(2022YFF0710000), the National Natural Science Foundation of China
(21937003, 22405171), Shanghai Jiao Tong University 2030 Initiative, and
China Postdoctoral Science Foundation (2024M751953).
Conflict of Interest
The authors declare no conflict of interest.
Code Availability
The lifetime imaging data processing MATLAB code are openly available
on GitHub at https://github.com/FanYiwei98/Lifetime_Imaging.
Data Availability Statement
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
Keywords
in vivo, lanthanide, lifetime imaging, nanosensor, quantitative monitoring
Received: March 4, 2025
Revised: April 1, 2025
Published online: April 23, 2025
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