Abstract
Adoptive T‐cell therapy (ACT) holds significant promise for treating
solid tumors but is often constrained by insufficient T‐cell
infiltration, survival, and functional persistence. To overcome these
obstacles, we developed DON‐loaded nanodrug‐T cell conjugates with
PD‐L1 blockade, forging a dynamic mutualistic relationship between T
cells and therapeutic agents. Sustained release of glutamine antagonist
6‐diazo‐5‐oxo‐L‐norleucine (DON) within these conjugates continuously
enhances T‐cell endurance and potency by promoting memory
differentiation and elevating crucial adhesion and motility genes.
Concurrently, PD‐L1 blocking peptides liberate T cells from
immunosuppression, assisting T cells with precision toward tumor sites.
This dual‐targeting strategy—T cells directed at tumor antigens and
peptides at PD‐L1— enriches the tumor microenvironment with potent
therapeutics, amplifying T cell‐driven tumor destruction. Our approach
effectively overcomes the critical barriers of ACT—infiltration,
persistence, and efficacy—unlocking the full therapeutic potential of
T‐cell therapy against complex solid tumors.
Keywords: Adoptive T‐cell therapy, Cancer immunotherapy, Glutamine
metabolism, Solid tumor, T cell‐nanodrug conjugate
__________________________________________________________________
This study establishes DON‐loaded T cell‐nanodrug conjugates with PD‐L1
blockade, creating a synergistic system. As a glutamine antagonist,
sustained DON release enhances T‐cell endurance and infiltration by
promoting memory differentiation and upregulating adhesion and motility
genes, while PD‐L1 blockade alleviates immunosuppression and directs T
cells to tumors, together unlocking the full potential of T‐cell
therapy for solid tumors.
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1. Introduction
Adoptive T‐cell therapy (ACT) has effectively treated hematologic
malignancies but faces significant challenges when applied to solid
tumors, underscoring the necessity for advanced therapeutic
strategies.^[ [50]^1 ^] Solid tumors are notoriously difficult to treat
due to their dense extracellular matrix and immunosuppressive
microenvironment, which impede T‐cell infiltration, survival, and
function, thereby reducing the effectiveness of traditional ACT.^[
[51]^2 ^] To overcome these challenges, the concept of mutualism—a
symbiotic relationship where both species benefit—has been ingeniously
applied in synthetic biology through the development of cell‐drug
conjugates. These conjugates exemplify mutualistic interactions between
T cells and therapeutic agents, with each component enhancing the
effectiveness of the other, creating a potent and synergistic
therapeutic platform specifically designed for the complexities of
solid tumors.^[ [52]^3 ^]
The foundation of this innovative approach lies in the application of
chemical engineering, which facilitates the integration of multiple
therapeutic agents within a single cellular platform.^[ [53]^4 ^] This
integration enables the design of combination therapies that
concurrently target a range of biological pathways, thereby modulating
T cell functionality and reconfiguring the tumor microenvironment.^[
[54]^5 ^] Chemically engineered cells serve as precise delivery
vehicles, ensuring that therapeutic agents are concentrated at the
tumor site, thereby minimizing off‐target effects.^[ [55]^6 ^] Unlike
genetic engineering which imposes lasting genome alterations, chemical
engineering offers the distinct advantage of transient modifications.^[
[56]^7 ^] These temporary adjustments are confined to individual cells,
thereby mitigating the long‐term risks associated with irreversible
genetic changes.^[ [57]^8 ^]
One approach involves engineering T cells with TCR signaling‐responsive
IL‐15 nanogels for treating B16F10 melanoma. These nanogels release
IL‐15 upon membrane reduction, enhancing T‐cell activity and
demonstrating superior efficacy compared to T cells with free IL‐15.^[
[58]^9 ^] However, since IL‐15 release is only triggered by T‐cell
activation, the time to fully induce beneficial T‐cell phenotype
changes is limited. Another strategy uses liposome‐based T
cell‐nanodrug conjugates to deliver avasimibe, which boosts T cell
vitality by increasing membrane cholesterol levels.^[ [59]^10 ^] Yet,
unclear drug release dynamics from the liposome create uncertainty in T
cell modulation. These previous designs highlight the substantial
potential of T cell‐nanodrug conjugates. Achieving effective solid
tumor treatment requires comprehensive strategies that integrate the
regulation of T cell phenotype, functionality, infiltration, and
motility. Furthermore, while current approaches primarily focus on
enhancing T cell functionality, they often neglect the direct
tumor‐killing potential of the therapeutic agents, which is crucial for
reducing tumor burden and improving overall outcomes.
6‐Diazo‐5‐oxo‐L‐norleucine (DON) is a glutamine antagonist that
inhibits glutamine metabolism. Recent studies have highlighted the
potential of disrupting glutamine metabolism with DON to promote the
differentiation of T cells into long‐lived, activated memory phenotypes
through oxidative metabolism.^[ [60]^11 ^] Ex vivo pre‐treatment of
CAR‐T cells with DON has shown promise in enhancing their preparation
efficiency and antitumor efficacy.^[ [61]^12 ^] Additionally, DON has
demonstrated efficacy in killing tumor cells by targeting glutamine
metabolism.^[ [62]^13 ^] However, its systemic toxicity limits its
clinical use, underscoring the need for targeted delivery systems that
maximize therapeutic benefits while minimizing risks.^[ [63]^14 ^]
To address these challenges, we aim to develop poly (lactic‐co‐glycolic
acid) (PLGA) nanoparticles incorporating DON and PD‐L1 blocking peptide
OPBP‐1, creating T Cell‐nanodrug conjugates (OPBP‐1‐PLGA‐DON‐T cells).
This approach involves a biorthogonal reaction between
dibenzocyclooctyne (DBCO)‐functionalized OPBP‐1‐PLGA‐DON nanodrugs and
metabolically labeled azido‐bearing T cells. Biocompatible PLGA
nanoparticles enable sustained DON release, continuously modulating T
cells. Surface‐conjugated PD‐L1 blocking peptides liberate T cells from
PD‐1/PD‐L1 interactions and promote T‐cell accumulation within
PD‐L1‐expressing tumors, amplifying efficacy and reducing off‐target
toxicity. The spatially coordinated deployment of DON, facilitated by
PD‐L1 blocking peptides and T‐cell targeting, culminates in the
targeted enrichment of this therapeutic combination within tumors. By
leveraging cell‐drug conjugates that combine T cells, DON‐loaded
nanodrugs, and PD‐L1 blocking peptides, this strategy aims to overcome
fundamental challenges of ACT—infiltration, persistence, efficacy, and
safety—thereby enhancing the effectiveness of T‐cell therapies against
solid tumors (Figure [64] 1 ).
Figure 1.
Figure 1
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Schematic illustration of T Cell‐nanodrug conjugates enhancing ACT
against solid tumors. DBCO‐functionalized OPBP‐1‐PLGA‐DON nanodrugs are
anchored to azido‐bearing OT‐1 T cells via click chemistry. Upon
intravenous administration in mice, these conjugates sustainably
release DON, driving memory T‐cell differentiation, infiltration, and
tumor eradication. The surface‐conjugated PD‐L1 blocking peptide OPBP‐1
disrupts PD‐1/PD‐L1 interactions between T cells, tumor cells, and
dendritic cells, thereby liberating T‐cell activity and enhancing
accumulation within PD‐L1‐expressing tumors. OT‐1 T cells, acting as
precise delivery vehicles for DON, execute tumor cell killing while
mitigating systemic toxicity. This synergistic combination
significantly boosts ACT efficacy against solid tumors. The figure was
generated using Figdraw.
2. Results and Discussion
2.1. Effects of DON on T‐cell Function and Migration
We investigated DON's role as a metabolic checkpoint inhibitor in
regulating T‐cell functions. Similar to what Rober and others found,^[
[66]^15 ^] our initial findings revealed a robust suppression of cell
proliferation in the B16‐OVA tumor cell line following DON‐induced
inhibition of glutamine metabolism, while murine‐derived T cells
remained unaffected, suggesting a selective cytotoxic effect
(Figure [67]S1A,B). To further investigate this phenomenon, flow
cytometry was employed to analyze the expression of the memory markers
CD44 and CD62L, elucidating the effects of the glutamine inhibitor DON
on T cell phenotypes. Our results indicate that when glutamine
metabolism is inhibited, T cells undergo metabolic compensation to
develop into memory T cells (Figure [68] 2A), accompanied by a
noticeable decrease in apoptotic susceptibility (Figure [69]2B).
Moreover, dye dilution experiments using carboxyfluorescein
succinimidyl ester (CFSE) demonstrated that DON‐mediated glutamine
inhibition maintained a stronger proliferation capacity in T cells
(Figure [70]2C).
Figure 2.
Figure 2
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In vitro functional validation of DON. Effects of DON on the
differentiation (A) and the apoptosis of CD8^+ T cells (B). T cell
memory differentiation was analyzed by flow cytometry using CD44 and
CD62L markers following 0.3 µM DON treatment for 72 h. Apoptosis was
assessed after 5 d of DON treatment using Annexin V and 7‐AAD staining
and analyzed by flow cytometry. (C) Proliferative activity of CD8^+ T
cells treated with DON assessed via CFSE assay. The orange peak
indicates the initial generation, with peaks to the left representing
subsequent proliferative generations. Untreated cells show five
generations of proliferation (left), while DON‐treated cells display
six generations (right), indicating enhanced proliferation. (D)
Infiltration of CFSE‐stained CD8^+ T cells into 3D tumor spheroids
after 0.3 µM DON treatment for 24 h (green indicates CFSE‐stained CD8^+
T cells; scale bar: 200 µm). (E) The number of CD8^+ T cells migrating
from the upper chamber to the lower chamber after treating with DON.
(F) Heatmap showing differentially expressed genes in CD8^+ T cells
treated with DON in vitro for 3 days. (G) Pathway enrichment analysis
of differentially expressed genes in CD8^+ T cells treated with DON in
vitro for 3 days. (H) qPCR detection of the effect of DON on the
expression of mouse CD8^+ T cell motility genes. n = 3, statistical
significance was calculated using unpaired, one‐sided Mann‐Whitney
test. ^* P < 0.05.
Following our initial investigations, we explored the impact of DON
treatment on T cell migration and infiltration capabilities, which had
not been previously illustrated. Co‐incubation of T cells with 3D tumor
spheroids demonstrated a significant enhancement in the infiltration of
CFSE‐labeled T cells into the interior of tumor spheroids, indicating
an augmented migratory response (Figure [72]2D and [73]S2). To further
validate the migratory response of T cells to DON, a transwell assay
was conducted. T cells were placed in the upper wells, while DON, with
or without tumor cells, was placed in the lower wells. The results
showed a marked increase in T cell migration to the lower well in the
presence of DON, regardless of tumor cell presence. These findings
underscore DON's role in enhancing T cell motility (Figure [74]2E).
Transcriptomic analysis of DON‐treated T cells reveals significant
increases in the expression of migration‐related genes like Ccl3 and
Cxcr5, as well as effector genes in T cells such as Gzmb and Ifng, and
stemness‐related genes like Tcf‐7 and Ccr7. Conversely,
exhaustion‐related genes like Tox and Lag3 exhibited significant
decreases (Figure [75]2F). Additionally, the pathway enrichment result
reveals significant enrichment of genes associated with cell adhesion,
migration, and differentiation (Figure [76]2G). The volcano plot of
differentially expressed genes also indicated significant upregulation
of genes involved in regulating cell adhesion, such as Cdh1 and Rgs16
(Figure [77]S3A). Moreover, qPCR examination of T cells treated with
DON revealed significant changes in the expression levels of
motility‐related genes such as Cxcr4 and Vla‐4. Notably, stemness genes
including Tcf1 and Ccr7 displayed increased expression levels, while
apoptosis and exhaustion‐related genes like Tox and Bcl6 exhibited
significant decreases (Figure [78]2H, [79]S3B). These cumulative
findings suggest that DON serves as a pivotal metabolic regulator,
fostering the differentiation of T cells into a memory phenotype
characterized by augmented self‐renewal capacities and prolonged
longevity. Furthermore, DON enhances T cell motility and adhesion,
facilitating their infiltration into solid tumors, a critical aspect of
effective solid tumor therapy. Therefore, through a thorough
investigation of DON‐T cell interactions, we confirm that DON is an
ideal candidate for constructing T cell‐drug conjugates against tumors.
2.2. Construction and Characterization of OPBP‐1‐PLGA‐DON‐T Cells
The T Cell‐nanodrug conjugates, designated as OPBP‐1‐PLGA‐DON‐T cells,
were prepared as shown in Figure [80] 3A. First, PLGA nanoparticles
loaded with DON were prepared to be approximately 200 ± 1.98 nm in size
with a negative surface charge (Figure [81]3B‐D). Additionally, we
assessed the DON content in the supernatant of the nanoparticle
preparation using high‐performance liquid chromatography (HPLC) and
calculated the drug loading and encapsulation efficiency based on the
total amounts of DON and PLGA added. The calculated drug loading rate
of PLGA was 4.1 ± 0.31%, and the encapsulation efficiency was 40
± 2.13%. The drug release profile of the PLGA nanoparticles was
evaluated under pH 7.4 and pH 6.5 conditions to mimic physiological and
tumor microenvironments (Figure [82]3E). At pH 6.5, approximately 68.13
± 5.77% of DON was released by day 10, confirming the sustained release
capability of the nanoparticles, consistent with other studies on
PLGA‐based drug delivery systems. The release results indicate that the
nanoparticles exhibit a biphasic release profile: the initial phase for
the first five days is characterized by diffusion‐dominated release
that is pH‐independent. Subsequently, under pH 6.5 conditions, the
release rate significantly increases due to the accelerated hydrolysis
of PLGA. This pH‐dependent long‐term release variation is consistent
with existing studies.^[ [83]^16 ^] Furthermore,
OPBP‐1‐PLGA‐DON‐conjugated T cells were found to efficiently accumulate
at tumor sites within 12 h (Figure [84]5D), during which approximately
3.8 ± 0.5% and 10.6 ± 0.5% of DON at both pH 7.4 and pH 6.5 was
released, as estimated from the ex vivo release profile. This
controlled release aligns with the timeline for T‐cell homing, reducing
premature drug loss and ensuring targeted and effective drug delivery.
Figure 3.
Figure 3
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Construction and characterization of OPBP‐1‐PLGA‐DON‐T cells. The
figure was generated using Figdraw. (A) Schematic representation of the
design and the preparation of OPBP‐1‐PLGA‐DON. (B) Transmission
electron microscopy (TEM) images displaying the morphology of
OPBP‐1‐PLGA‐DON. (C) The size distribution of OPBP‐1‐PLGA‐DON was
measured using dynamic light scattering (DLS) with a Malvern particle
size analyzer. (D) The surface potential of OPBP‐1‐PLGA‐DON illustrates
their electrostatic properties. (E) Drug release profiles of
OPBP‐1‐PLGA‐DON under physiological and tumor‐mimicking conditions (pH
7.4 and 6.5), demonstrating sustained release behavior over 10 days.
(F) Blocking activity of the PD‐L1 OPBP‐1 on PLGA nanoparticles
assessed by flow cytometry. Fluorescence intensity reflects PD‐L1
binding to PD‐1. Blocking efficacy was quantified by comparing
fluorescence reduction in nanoparticle‐treated samples relative to
untreated controls. (G) Efficiency evaluation of azido‐glucose
modification of T cell. (H) Visualization of OPBP‐1‐PLGA‐DON‐T cells
using confocal microscopy, with blue representing cell nuclei, red
indicating cell membranes, and green depicting FITC‐labeled PLGA
nanoparticles. After incubating ordinary T cells or azide‐modified T
cells with DBCO‐PLGA‐FITC at 37 °C for 1 h, the cells were examined
using a fluorescence confocal microscope. In the conjugation group, the
green nanoparticles were observed to co‐localize with red cell
membranes, indicating successful conjugation of PLGA nanoparticles to
the T cells. Scale bar: 15 µm. (I) The loading of OPBP‐1‐PLGA‐DON on T
cells was characterized by flow cytometry. (J) Morphological features
of T cell‐PLGA conjugates characterized by SEM. Scale bar: 2 µm, n = 3.
Statistical significance was calculated using unpaired, one‐sided
Mann‐Whitney test. ^*** P < 0.001.
Figure 5.
Figure 5
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Targeting and in vivo distribution of OPBP‐1‐PLGA‐DON‐T cells. (A)
Phagocytosis of free or T‐cell‐carried PLGA‐FITC nanoparticles (green)
by RAW264.7 macrophage cell line. Scale bar: 20 µm. (B) Quantification
of intracellular FITC fluorescence in RAW264.7 cells. (C) FITC‐labeled
OPBP‐1, OPBP‐1‐PLGA‐FITC, and OPBP‐1‐PLGA‐FITC‐T cells were
co‐incubated with CHOK1 cells (left) and PD‐L1 overexpressing
CHOK1‐mPD‐L1 cells (right) for 30 min, followed by confocal imaging.
The cell membrane is shown in red, the nucleus in blue, and OPBP‐1,
OPBP‐1‐PLGA‐FITC, and OPBP‐1‐PLGA‐FITC‐T cell in green. Scale bar:
30 µm. (D) In vivo distribution of NS (G1), T cell alone (G2), T cells
with free OPBP‐1‐PLGA‐DON nanodrugs (G3) and OPBP‐1‐PLGA‐DON conjugated
T cells (G4) in B16‐OVA tumor‐bearing mice after intravenous
administration for 12 h and 24 h. T cells were modified with DBCO‐Cy5
for fluorescence evaluation. (E) Quantification of Cy5 fluorescence
intensity in various organ tissues. n = 3, Statistical significance was
calculated using a one‐way ANOVA followed by Tukey's post hoc test for
multiple comparisons. ^* P < 0.05, ^*** P < 0.001.
Secondly, PLGA nanoparticles were further modified with the PD‐L1
blocking peptide OPBP‐1 (Figure [87]S4). Using fluorescence correlation
spectroscopy (FCS), we quantified the number of peptides per
nanoparticle by dividing the counts per molecule of nanoparticles by
the counts per molecule of free Atto488‐labeled peptides, yielding an
average of 202 ± 53 peptides per nanoparticle (Figure [88]S5). To
determine whether drug loss during the surface modification process was
substantial, we performed quantitative analysis of DON content in the
collected supernatants throughout the conjugation and washing
processes. The results indicate that the DON loss ratio was 4.47
± 0.3%, demonstrating that while a small amount of drug was lost, it
remained minimal relative to the total drug loading. To evaluate the
ability of OPBP‐1‐modified PLGA nanoparticles to inhibit the PD‐1/PD‐L1
interaction, CHO‐K1‐PD1 cells were treated with free PD‐L1 proteins in
the presence or absence of the nanoparticles. Flow cytometry was used
to measure the fluorescence intensity, reflecting the extent of PD‐L1
binding to PD‐1. Untreated cells served as a positive control for
maximum PD‐L1 binding, while cells without PD‐L1 treatment were used to
determine background fluorescence. The blocking efficacy of the
OPBP‐1‐modified PLGA nanoparticles was quantified to be 42.17 ± 2.48%
by comparing the reduction in fluorescence intensity in
nanoparticle‐treated samples relative to the positive control,
providing a clear measure of their ability to disrupt the PD‐1/PD‐L1
interaction (Figure [89]3F).
For conjugating OPBP‐1‐PLGA‐DON onto T cells, we isolated lymphocytes
from the spleens and lymph nodes of C57BL/6 mice, and CD8^+ T cells
were purified using magnetic bead separation. Metabolic cell‐labeling
technology was employed to label T cells with azido groups using 50 µM
tetraacetyl‐N‐azidoacetylmannosamine (AC[4]ManNAz). Experimental
results confirmed that this approach successfully modified
approximately 95% of cells with azido groups (Figure [90]3G and
Figure [91]S6). Subsequently, OPBP‐1‐PLGA‐DON nanoparticles were
anchored to the cell membrane through click chemistry between the
modified DBCO on the surface of PLGA nanoparticles and the azido groups
on T cells. As shown in the confocal results in Figure [92]3H and the
flow cytometry results in Figure [93]3I, the nanoparticles were stably
anchored to the cell surface through this method, verifying the
successful construction of OPBP‐1‐PLGA‐DON‐T cells. Scanning electron
microscope (SEM) examination estimated that 110–150 nanoparticles are
loaded onto a single T cell (Figure [94]3J), assuring effective
nanodrug anchoring and T cell modification.
2.3. Enhanced Functionality of OPBP‐1‐PLGA‐DON‐T Cells
To robustly investigate the impact of constructing T cell‐drug
conjugates, we established five experimental groups (Figure [95] 4 ). T
cells covalently conjugated with DON‐loaded PLGA nanoparticles (G4)
demonstrated superior effects on CD44^+CD62L^− memory effector T cell
(TEM) differentiation (Figure [96]4A) and exhibited a marked reduction
in apoptosis (Figure [97]4B). Additionally, T cell‐mediated tumor cell
killing was significantly enhanced in G4, as shown in Figure [98]4C,
with 7‐AAD staining indicating a higher percentage of B16‐OVA tumor
cell death. This was further supported by increased IFN‐γ secretion
(Figure [99]4D) compared to DON‐treated T cells in the medium (G3),
highlighting the enhanced modulatory efficiency provided by nanodrug
conjugation on T cells through localized DON release.
Figure 4.
Figure 4
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In vitro functional validation of OPBP‐1‐PLGA‐DON‐T cells. (A) T cell
memory differentiation was analyzed by flow cytometry using CD44 and
CD62L markers after 72 h of treatment. (B) Apoptosis of T cells were
analyzed using Annexin V and 7‐AAD staining via flow cytometry after 5
days of treatment. (C) Killing efficiency of T cells against B16‐OVA
cells was determined by identifying dead tumor cells using 7‐AAD
staining analyzed via flow cytometry. (D) IFN‐γ secretion quantified by
ELISA after 48 h of co‐culture with tumor cells. (E) CFSE‐stained CD8^+
T cells from Groups G1 (T cells), G2 (T cells with PLGA nanoparticles),
G3 (T cells with free DON), G4 (T cells with DON‐loaded PLGA), and G5
(T cells with OPBP‐1‐PLGA‐DON) were co‐cultured with B16‐OVA tumor
spheroids. Confocal microscopy visualized T‐cell infiltration (green,
CFSE‐stained; scale bar: 200 µm). (F) Quantitative analysis of green
fluorescence intensity within tumor spheroids. All the results shown
are compared with the control group. (G) T‐cell proliferation analyzed
by flow cytometry after co‐culture with antigen‐presenting dendritic
cells. Dendritic cells (DCs) pre‐treated with antigens were co‐cultured
with CFSE‐labeled T cells under various conditions for 48 h, and
changes in CFSE fluorescence of the T cells were analyzed by flow
cytometry.Green indicates CFSE‐stained CD8^+ T cells. Scale bar:
200 µm. n = 3, Statistical significance was calculated using a one‐way
analysis of variance (ANOVA) followed by Tukey's post hoc test for
multiple comparisons. ^* P < 0.05, ^** P < 0.01, ^*** P < 0.001.
Remarkably, anchoring PLGA nanoparticles alone on T cells (G2)
significantly enhanced T cell function and proliferation in various
aspects, suggesting an additional, unidentified mechanism at play.
Incorporating DON into PLGA nanocarriers (G4) further amplified these
beneficial effects. The modification with the PD‐L1 blocking peptide
OPBP‐1 (G5) extended these benefits even further, showcasing a potent
synergy between DON and OPBP‐1 for a more robust anti‐tumor effect.
We also evaluated the infiltration capability of OPBP‐1‐PLGA‐DON‐T
cells using 3D tumor spheroids. Tumor spheroids were generated by
centrifugation to achieve a compact structure. CFSE‐labeled T cells
were co‐cultured with these spheroids, and their infiltration was
assessed using confocal microscopy by examining the distribution of
green fluorescence within the spheroids. The results indicated that
these T cells penetrated deeper into tumor spheroids
(Figures [101]4E,F) with the combined aid of both DON and OPBP‐1. While
DON alone promoted T cell migration and adhesion, the modification with
OPBP‐1 further enhanced these processes by PD‐1/PD‐L1 interactions,
significantly improving T cell penetration into the 3D tumor spheroids.
Furthermore, OPBP‐1 liberated T cells from PD‐1/PD‐L1 interactions with
dendritic cells (DCs), enhancing DC antigen cross‐presentation and
stimulating T‐cell proliferation (Figure [102]4G). All these findings
underscore the transformative potential of T cell‐drug conjugates in
improving ACT outcomes, offering a powerful approach against solid
tumors.
2.4. Enhanced Tumor Targeting of OPBP‐1‐PLGA‐DON‐T Cells
Due to their nanostructure and physicochemical properties,
nanoparticles are easily cleared by the immune phagocytic system of
macrophages, neutrophils, and other effector cells in the body.^[
[103]^17 ^] Approximately 40% of particles around 200 nm in size are
cleared within three days after entering the body.^[ [104]^18 ^] We
hypothesize that attaching nanodrugs to T cells could circumvent this
issue.
To test this, we loaded FITC into PLGA nanoparticles (PLGA‐FITC) and
examined the phagocytosis of fluorescent nanoparticles by macrophages
under different conditions in vitro. The results demonstrated a
significant reduction in the phagocytosis of nanoparticles when
attached to T cells compared to freely dispersed nanoparticles
(Figures [105] 5A,B). This suggests that the T‐cell conjugation
strategy can markedly improve the in vivo circulation and therapeutic
efficacy of nanodrugs compared to conventional nanoparticle
administration.
We further refined this approach by modifying the surface of the
nanoparticles with the PD‐L1 blocking peptide OPBP‐1 and tested its
co‐localization using affinity experiments with CHOK1 and CHOK1‐mPD‐L1
cells (PD‐L1 overexpressing cells). The results showed that both OPBP‐1
alone and OPBP‐1‐PLGA‐FITC co‐localized with CHOK1‐mPD‐L1 cells.
Furthermore, T cells conjugated with OPBP‐1‐PLGA‐FITC were
significantly enriched around PD‐L1 overexpressing cells
(Figure [106]5C), confirming the enhanced targeting of T cells to
tumors.
We investigated the in vivo distribution of OPBP‐1‐PLGA‐DON‐T cells
following transfusion, labeling the T cells with Cy5 fluorescence via
bioorthogonal reactions. The fluorescence intensity in various organs
was analyzed at 12 and 24 h post‐reinfusion to assess T cell
distribution. At 12 h, higher fluorescence intensity was observed in
the liver and kidneys for all groups, likely reflecting the initial
biodistribution of infused cells to major filtering and circulation
organs. Notably, the OPBP‐1‐PLGA‐DON‐T cell group (G4), where
OPBP‐1‐PLGA‐DON nanodrugs were covalently conjugated to T cells,
exhibited significantly higher fluorescence intensity within the tumor
compared to the T cell alone group (G2) and the group with free
OPBP‐1‐PLGA‐DON nanodrugs (G3). This indicates that T‐cell‐nanodrug
conjugation enhanced T‐cell accumulation in tumor tissues
(Figure [107]5D,E). By 24 h, fluorescence intensity in non‐tumor
organs, such as the liver and kidneys, decreased, whereas tumor
retention for the conjugate group (G4) remained substantial and
consistent (Figure [108]5E). This enhanced tumor enrichment underscores
the dual advantage of the conjugate system: it not only reduces the
phagocytic clearance of nanodrugs by the immune system but also
improves the targeting of both T cells and nanodrugs to tumors. The
mutualistic relationship between T cells and nanodrugs enables each to
enhance the other's effectiveness, creating a collaborative and potent
therapeutic platform.
2.5. Enhanced Antitumor Effects of OPBP‐1‐PLGA‐DON‐T Cells In Vivo
To elucidate the antitumor efficacy of OPBP‐1‐PLGA‐DON‐T cells
following T‐cell reinfusion, we established a subcutaneous melanoma
B16‐OVA tumor model in C57BL/6J mice (Figure [109] 6A). The
experimental cohorts included normal saline (G1), CD8^+ T cells derived
from OT‐1 mice (G2), free OPBP‐1‐PLGA‐DON and T cells (G3), and
OPBP‐1‐PLGA‐DON‐T cells at dose of 0.5 mg k^−1g (G4) and 1.5 mg k^−1g
of DON (G5), administered twice for ACT. Throughout the treatment
regimen, the body weights of the mice remained stable (Figure [110]6B).
Tumor volume analysis revealed that the T‐cell‐nanodrug conjugates (G4
and G5) markedly inhibited tumor growth compared to pure T cells (G2,
Figures [111]6C‐D and [112]S7). We conducted a survival study using the
B16‐OVA tumor model (Figure [113]S8), where the conjugate groups with
0.5 mg k^−1g and 1.5 mg k^−1g DON exhibited significantly improved
survival rates compared to T cells alone and T cells with free
nanodrugs, highlighting their enhanced therapeutic potential.
Figure 6.
Figure 6
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In vivo anti‐tumor activity of OPBP‐1‐PLGA‐DON‐T Cells. (A) Schematic
representation of administration with C57BL/6J mice inoculated with
B16‐OVA cells. On day 9, mice were randomly divided into 5 groups and
underwent ACT on day 9 and 17. Body weight (B) and tumor growth curve
(C) after ACT, n = 5. (D) Tumor growth curves for each group of mice,
n = 5. Proportion of DBCO^+ CD8^+ T cells in tumor (E), blood (F),
spleen (G) and lymph node (H) after ACT, n = 3. Data for T‐cell
analysis were limited to three mice per group due to insufficient cell
numbers from smaller tumors in some treatment groups. (I) HE staining
of the heart, liver, spleen, lung, kidney, and small intestine after
administration. Scale bar: 200 µm. (J) Ratio of AST/ALT after
treatment, n = 3. ALT/AST analysis was performed on the same three mice
used for T cell analysis to maintain consistency. statistical
significance was calculated using a one‐way ANOVA followed by Tukey's
post hoc test for multiple comparisons. ^* P < 0.05, ^** P < 0.01, ^***
P < 0.001.
Furthermore, the quantity of infused T cells labeled with Cy5‐DBCO in
tumors, blood, spleen, and lymph nodes was significantly higher in all
conjugate groups (G4 and G5) than in the non‐conjugate group (G3,
Figures [115]6E‐H). This suggests that sustained glutamine inhibition
enhances T cell persistence and infiltration into solid tumors by
promoting migration and altering T cell metabolism.
Given that DON nanodrug conjugates are anchored to T cells for enhanced
modulation, we were able to use a lower dose of DON compared to other
studies.^[ [116]^19 ^] Safety assessments, including HE staining of
various tissues and liver function tests (Figures [117]6I,J),
demonstrated that this reduced‐dose T‐cell‐nanodrug conjugate system
did not induce organ damage and maintained normal physiological levels
of AST and ALT post‐treatment.
In clinical contexts, DON's off‐target effects have limited its use due
to adverse reactions. Our findings indicate that the T‐cell‐nanodrug
conjugate approach significantly improves DON targeting to tumor
tissues via OPBP‐1‐mediated binding to PD‐L1 and TCR‐mediated binding
to the OVA antigen on tumor cells. Consequently, our system achieves
targeted, temporal, and spatial regulation of T‐cell glutamine
metabolism, significantly amplifying its antitumor effects while
enabling the precise delivery of DON for synergistic tumor eradication,
thereby mitigating its systemic toxic effects.
2.6. Enhanced Systemic Immune Response and Antitumor Efficacy of
OPBP‐1‐PLGA‐DON‐T Cells
We also assessed the systemic effects of OPBP‐1‐PLGA‐DON‐T cells on
overall CD8^+ T cell function and antitumor activity. Treatment with
OPBP‐1‐PLGA‐DON‐T cells significantly increased the total CD8^+ T cell
population within tumors, with a dose‐dependent enhancement observed as
DON concentration increased (Figure [118] 7A).
Figure 7.
Figure 7
[119]Open in a new tab
Enhanced systemic immune response and antitumor efficacy of
OPBP‐1‐PLGA‐DON‐T Cells. (A) Proportion of CD8^+ T cells within tumor
tissue determined by flow cytometry after incubating tumor single‐cell
suspensions with fluorescent antibodies. Proportion of CD8^+ IFN‐γ^+ T
cells isolated from tumor tissue (B), spleen (C) and draining lymph
node (D) of tumor‐bearing mice after stimulation and activation. (E)
Schematic representation of the mechanism by which OPBP‐1‐PLGA‐DON
conjugation enhances antitumor effects of T cells. The figure was
generated using Figdraw. (F) Expression of Ki67 in T cells isolated
from various tissues after administration. (G) The proportion of CD44
and CD62L, which represent memory‐like cell expression in T cells
isolated from the spleen determined by flow cytometry. n = 3,
statistical significance was calculated using a one‐way ANOVA followed
by Tukey's post hoc test for multiple comparisons. ^* P < 0.05, ^**
P < 0.01, ^*** P < 0.001.
Intracellular cytokine staining revealed that OPBP‐1‐PLGA‐DON‐T cells
significantly boosted the secretion of IFN‐γ by tumor‐infiltrating
CD8^+ T cells (Figure [120]7B). Although the overall percentage of
IFN‐γ positive cells in tumors was low due to the limitations of the ex
vivo stimulation method,^[ [121]^20 ^] which involved 1 µg/mL OVA
peptide for 6 h, the conjugated group (G5) still showed a significant
improvement. IFN‐γ positive cells increased approximately fourfold
compared to the NS group (G2) and doubled compared to the free nanodrug
and T cell group (G3). Additionally, this increase in IFN‐γ secretion
was also observed in CD8^+ T cells within the spleen (Figure [122]7C)
and dLN (Figure [123]7D), thereby enhancing the proportion of activated
T cells across these key immune sites. These findings suggest that the
transferred T cells are not only effective locally within the tumor
microenvironment but also contribute to a systemic antitumor response,
amplifying the overall immune attack on the cancer.
Given the potential for T cell exhaustion following reinfusion, we
further evaluated the proliferation and memory‐like characteristics of
T cells across various tissues. The results demonstrated that
OPBP‐1‐PLGA‐DON‐equipped CD8^+ T cells exhibited enhanced proliferation
(Figure [124]7E‐F), while both CD8^+ and CD4^+ T cells exhibited
significantly enhanced memory‐like features (Figure [125]7G). These
enhanced properties are crucial for sustaining long‐term antitumor
immunity and preventing relapse.
2.7. Antitumor Efficacy of OPBP‐1‐PLGA‐DON‐T Cells in Colorectal Cancer and
Lymphoma Tumor Models
To comprehensively evaluate the therapeutic potential of
OPBP‐1‐PLGA‐DON‐T cells, subcutaneous colorectal cancer (MC38‐OVA) and
lymphoma (EG7‐OVA) tumor models were established in C57BL/6J mice
(Figure [126] 8A). Following two infusions, this treatment did not
affect the body weight of the mice (Figure [127]S9), indicating good
safety. Importantly, the OPBP‐1‐PLGA‐DON‐T cell conjugate group
exhibited significantly inhibited tumor growth compared to the control
groups in both colorectal cancer‐bearing mice (Figures [128]8B‐D) and
lymphoma‐bearing mice (Figures [129]8E‐G).
Figure 8.
Figure 8
[130]Open in a new tab
Antitumor efficacy of OPBP‐1‐PLGA‐DON‐T Cells in colorectal cancer and
lymphoma models. (A) Schematic of the administration protocol: C57BL/6J
mice were inoculated with MC38‐OVA or EG7‐OVA cells on the right dorsal
side. After 7 days, mice were randomly divided into four groups, and
adoptive transfer therapy was performed on days 7 and 14. (B) Tumor
growth curve for MC38‐OVA tumor‐bearing mice. (C) Representative images
of MC38‐OVA tumor size following adoptive T cell therapy. (D)
Statistical analysis of tumor size shown in panel (C). (E) Tumor growth
curve for EG7‐OVA tumor‐bearing mice. (F) Representative images of
EG7‐OVA tumor size following adoptive T cell therapy. (G) Statistical
analysis of tumor weight shown in panel (F). Flow cytometry analysis of
CD44 and CD62L expression on CD8^+ T cell subsets within the tumor (H),
the spleen (J) and the DLN (L). Data for T‐cell analysis (H) were
limited to three mice per group due to insufficient cell numbers from
smaller tumors in some treatment groups. Statistical analysis of the
proportion of TEM (CD44^+ CD62L^−) among CD8^+ T cells in the tumor
(I), the spleen (K) and the DLN (M). n = 5 for all panels, with
statistical significance determined using a one‐way ANOVA followed by
Tukey's post hoc test for multiple comparisons. ^* P < 0.05, ^**
P < 0.01, ^*** P < 0.001.
Previously, we demonstrated that both free DON and the conjugation of
OPBP‐1‐PLGA‐DON on T cells promote the differentiation of T cells into
TEM. Further analysis within the tumor microenvironment
(Figure [131]8H‐I) revealed that the proportion of TEM in the
OPBP‐1‐PLGA‐DON‐T cells (G4) was slightly higher than in T cells with
free nanodrugs (G3), but this difference was not statistically
significant, indicating that both treatments similarly enhance T cell
memory differentiation within tumors. In contrast, in the spleen
(Figure [132]8J,K) and lymph nodes (Figure [133]8L,M), the
OPBP‐1‐PLGA‐DON‐T cells (G4) exhibited a significantly higher
proportion of TEM compared to T cells with free nanodrugs (G3).
Specifically, in the spleen, TEM levels were 16.06 ± 4.93% in the G4
group compared to 8.92 ± 3.72% in the G3 group, and in the lymph nodes,
TEM levels reached 7.75 ± 0.92% in the G4 group compared to 0.76
± 0.32% in the G3 group. A similar trend was observed among CD4^+ T
cells, with significantly elevated TEM proportions in the spleen
(Figures [134]S10A,B) and lymph nodes (Figures [135]S10C,D). The
increased presence of TEM across multiple tissues demonstrates that
OPBP‐1‐PLGA‐DON conjugation effectively promotes T cell differentiation
into memory phenotypes. This adoptive transfer therapy with T
cell‐nanodrug conjugates enhances the generation of immune memory cell
populations, leading to a more robust and sustained anti‐tumor
response.
3. Conclusion
We have developed a novel T‐Cell‐PD‐L1‐DON‐nanodrug conjugate system
that significantly enhances T‐cell functionality and anti‐tumor
efficacy. This innovative approach enables precise in situ regulation
of T‐cell metabolism, promoting differentiation into memory phenotypes
and improving tumor infiltration. The incorporation of PD‐L1 blocking
peptides on the surface alleviates immune suppression, amplifying
tumor‐targeting capabilities and boosting overall anti‐tumor responses.
By dual‐targeting tumors through the TCR and OPBP‐1, these conjugates
effectively concentrate DON within tumors, synergistically enhancing
T‐cell‐mediated tumor cell elimination while minimizing off‐target
effects.
Crucially, our nanodrug conjugates offer a comprehensive solution by
modulating T‐cell phenotype, proliferation, function, motility, and
infiltration, tackling the formidable challenges of ACT in solid tumor
treatment with a single, cohesive strategy. Given the established
clinical use of PLGA nanoparticles, our design holds the potential for
future clinical translation.
This T Cell‐nanodrug conjugate system represents a versatile and
powerful platform, capable of integrating diverse therapeutic agents
for highly personalized treatments tailored to the unique molecular and
immunological landscapes of individual tumors. Future research will be
pivotal in validating its efficacy across various tumor types and in
exploring synergistic combinations with other therapies, such as innate
immunity activators, to unlock even greater therapeutic potential.
4. Experimental Section
Cell Lines and Mice
MC38‐OVA cells were kindly provided by Prof. Changzheng Lu (Shenzhen
Bay Laboratory, China). EG7‐OVA cells were kindly provided by Prof.
Shengdian Wang (Institute of Biophysics, Chinese Academy of Sciences,
China). Cell line stably expressed human PD‐1 (CHO‐K1‐mPD‐L1) on the
cell membrane was established from CHO‐K1 cells transfected with
lentiviral vector pLVX‐Puro. B16‐OVA cells and CHOK1 cells were
cultured in RPMI 1640 (Gibco, Grand Island, USA) consisting of 10%
fetal bovine serum (Sigma, USA), 100 µg/mL streptomycin (Solarbio,
China) and 100 U/mL penicillin (Solarbio, China), in an incubator with
98% humidity and 5% CO[2] at 37 °C.
C57BL/6J mice were maintained in the specific pathogen‐free facility at
Sun Yat‐sen University. OT‐1 mice (OVA[257–264] TCR transgenic mice)
were kindly provided by Prof. Xuanming Yang (Shanghai Jiao Tong
University, China). Animal experimental procedures were carried out
following the national and institutional guidelines and were approved
by the Ethics Committee of Sun Yat‐sen University (SYSU‐YXYSZ20230605).
Cytotoxicity Assays
The cytotoxicity of DON (MedChemExpress, USA) on B16‐OVA cells was
assessed using a standard MTT assay. Initially, freshly trypsinized
cells were seeded into flat‐bottom 96‐well plates at a density of
1.5 × 10^4 cells per well. Subsequently, varying concentrations of DON
ranging from 0.3 µM to 100 µM were added to the wells, followed by
incubation at 37 °C for 24, 48, and 72 h. After the respective
incubation periods, 20 µL of MTT solution was added to each well, and
the plates were further incubated at 37 °C for an additional 4 h.
Following this incubation, the supernatant was carefully removed, and
150 µL of dimethyl sulfoxide (DMSO) was added to dissolve the purple
formazan crystals formed by viable cells. The plates were then shaken
in the dark for 15–20 mins to ensure complete dissolution of the
crystals. The absorbance was measured at 490 nm using the microplate
photometer (Thermo Scientific, USA) within 15 min.
The cytotoxicity of DON on T cells was evaluated using a Cell Counting
Kit‐8 (CCK‐8) assay. Initially, T cells were isolated from the spleen
and lymph nodes of mice and prepared as a single‐cell suspension at a
concentration of 5 × 10^4 cells per well. Subsequently, 200 µL of the
cell suspension was evenly distributed into flat‐bottom 96‐well plates,
and varying concentrations of DON ranging from 0.3 µM to 100 µM were
added to the wells. The plates were then incubated at 37 °C for 24, 48,
and 72 h to allow for exposure to DON. Following the respective
incubation periods, 20 µL of CCK‐8 solution was added to each well and
the plates were further incubated at 37 °C for 2 h to allow for cell
staining. After incubation with CCK‐8, the absorbance of each well was
measured at 450 nm using the microplate photometer (Thermo Scientific,
USA) within 15 min.
T Cell Differentiation and Apoptosis
T cells isolated from mouse spleen and lymph node were prepared as a
single‐cell suspension at a concentration of 1 × 10^6 cells. Then,
200 µL of cell suspension was evenly seeded in round‐bottom 96‐well
(SORFA, China) plates and supplemented with 100 U/mL of IL‐2
(Peprotech, USA), 1 µg/mL of CD3 (145‐2C11, Biolegend, USA) and CD28
(37.51, Biolegend, USA) antibodies, and 0.3 µM DON. The plates were
then incubated at 37 °C for 72 h.^[ [136]^21 ^] After the incubation
period, cells were collected and stained with anti‐CD8α‐PE (53‐6.7,
eBioscience, USA), anti‐CD44‐APC (IIM7, eBioscience, USA), and
anti‐CD62L‐Cy5.5 (L‐selectin, eBioscience, USA) fluorescent antibodies
for flow cytometry analysis to determine the ratio of T cell effector
memory (TEM) to central memory (TCM) cells.
For assessing T‐cell apoptosis, 200 µL of the cell suspension was
distributed into round‐bottom wells of a 96‐well plate. 100 U/mL of
IL‐2 (Peprotech, USA), 1 µg/mL of CD3 (145‐2C11, Biolegend, USA) and
CD28 (37.51, Biolegend, USA) antibodies, and 0.3 µM DON were added to
the wells. The cells were then co‐incubated at 37 °C for 5 days in a
cell culture incubator. After the co‐incubation period, the cells were
harvested, resuspended in 1 × Binding buffer, and stained with 7‐AAD
and Annexin V dyes. The stained cells were then incubated at room
temperature in the dark for 15 min and subjected to flow cytometry
within 1 h.
T Cell Infiltration and Migration
B16‐OVA cells were added to a 96‐well plate pre‐treated with agarose at
a concentration of 8000 cells per well in 100 µL of medium. The plate
was centrifuged at 2000 g for 10 min to aggregate the cells into
spheres, which were then cultured at 37 °C for 5 days to form tightly
packed 3D cell spheres.^[ [137]^22 ^] On the fifth day, 0.3 µM DON were
added to the culture medium and incubated for 24 h. Then, T cells were
stained with CFSE and added to each well at a concentration of 1 × 10^6
cells per well, followed by 48 h of incubation for cell infiltration.
The tumor spheres were then washed twice with PBS and subjected to
confocal microscopy for imaging. In a separate set of experiments, T
cells stained with CFSE were added directly to the tumor spheres after
they formed, followed by 24 h of incubation with 0.3 µM DON. After 48 h
of coincubation, the tumor spheres were collected and subjected to
confocal microscopy for imaging.
For assessing T‐cell migration, T cells isolated from mouse spleen and
lymph nodes were stimulated with 100 U/mL IL‐2 and 1 µg/mL of CD3 and
CD28 antibodies for three days. Following stimulation, 200 µL of cell
suspension containing 1 × 10^6 cells was added to the upper chamber of
a 6.5 mm transwell with a 5.0 µm pore polycarbonate membrane insert. In
the lower chamber, 400 µL of culture medium containing DON or the
supernatant from B16‐OVA cells treated with DON for 24 h was added. The
transwell plates were then incubated at 37 °C for 48 h to allow for
cell migration. After incubation, cells were collected and the number
of CD8^+ T cells was quantified using flow cytometry.^[ [138]^23 ^]
RNA Isolation and Library Preparation
Total RNA was extracted using the TRIzol reagent (Invitrogen, CA, USA)
according to the manufacturer's protocol. RNA purity and quantification
were evaluated using the NanoDrop 2000 spectrophotometer (Thermo
Scientific, USA). RNA integrity was assessed using the Agilent 2100
Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Then the
libraries were constructed using the VAHTS Universal V6 RNA‐seq Library
Prep Kit according to the manufacturer's instructions. The
transcriptome sequencing and analysis were conducted by OE Biotech Co.,
Ltd. (Shanghai, China).
RNA Sequencing and Analysis
The libraries were sequenced on an Illumina Novaseq 6000 platform and
150 bp paired‐end reads were generated. About 55 raw reads for each
sample were generated. Raw reads in fastq format were first processed
using fastp, and the low‐quality reads were removed to obtain clean
reads. Then, about 49 clean reads for each sample were retained for
subsequent analyses. The clean reads were mapped to the reference
genome using HISAT2. The FPKM of each gene was calculated, and the read
counts of each gene were obtained by HTSeq‐count. PCA analyses were
performed using R (v 3.2.0) to evaluate the biological duplication of
samples.
Differential expression analysis was performed using DESeq2. Q
value < 0.05 and foldchange > 2 or foldchange < 0.5 were set as the
thresholds for significantly differential expression genes (DEGs).
Hierarchical cluster analysis of DEGs was performed using R (v 3.2.0)
to demonstrate the expression pattern of genes in different groups and
samples. The radar map of the top 30 genes was drawn to show the
expression of up‐regulated or down‐regulated DEGs using R packet
gradar.
Based on the hypergeometric distribution, GO, KEGG pathway, Reactome,
and Wiki Pathways enrichment analyses of DEGs were performed to screen
the significant enriched term using R (v 3.2.0), respectively. R (v
3.2.0) was used to draw the column diagram, the chord diagram, and the
bubble diagram of the significant enrichment term.
Gene Set Enrichment Analysis (GSEA) was performed using GSEA software.
The analysis used a predefined gene set, and the genes were ranked
according to the degree of differential expression in the two types of
samples. Then it is tested whether the predefined gene set was enriched
at the top or bottom of the ranking list.
Preparation and Characterization of OPBP‐1‐PLGA‐DON Nanodrugs
PLGA (50:50 lactic acid: glycolic acid ratio, MW 7500–17000 Da,
Sigma‐Aldrich, USA) nanoparticles were prepared using ultrasonic
emulsification, with DON loaded into their structure. The DON content
in the supernatant of the nanoparticle preparation using HPLC and
calculated the drug loading and encapsulation efficiency according to
the following formulas based on the total amounts of DON and PLGA
added:
[MATH:
Loadingratio=Weigh
tofloadedDON
TotalweightofPLGAandDON :MATH]
;
[MATH:
Encapsulationefficiency=<
mrow>WeightofloadedDON
WeightofinitiallyaddedDON :MATH]
. Carboxyl groups were introduced onto the surface of PLGA
nanoparticles via 30 min EDC/NHS activation in MES buffer (pH 6.0),
followed by three centrifugation washes (15000 rpm, 5 min). The PD‐L1
antagonistic peptide OPBP‐1 and DBCO‐PEG‐NH₂ were sequentially
conjugated to the carboxyl groups of PLGA nanoparticles through their
amino groups, with each incubation lasting 2 h at 25 °C. Post‐reaction,
the nanoparticles were washed three times with PBS (15000 rpm, 5 min)
to remove unreacted components. During this process, supernatants were
collected to quantify DON content and assess drug loss during surface
modification. This conjugation allowed for the anchoring of the DBCO
group and the PD‐L1 peptide OPBP‐1 onto the surface of the
nanoparticles. The particle size, zeta potential, morphology, and
stability of the PLGA nanoparticles were analyzed using techniques
including Malvern zeta sizer (ZS90, Malvern Analytical, Malvern, UK)
and transmission electron microscopy (H‐7650, Hitachi, Japan).
High‐performance liquid chromatography and UV spectrophotometry were
employed to investigate the encapsulation and loading efficiency of the
drug by PLGA nanoparticles, enabling the optimization of preparation
methods to obtain ideal OPBP‐1‐PLGA‐DON nanodrugs. One mL of
OPBP‐1‐PLGA‐DON and an equivalent concentration of DON aqueous solution
were added into a dialysis bag (8000‐14000 M), and placed in 100 mL of
PBS at 37 °C and kept gently stirring to simulate drug release. At
0.5 h, 1 h, 2 h, 4 h, 6 h, 8 h, 10 h, 12 h, 1d, 2 d, 3 d, 4 d, 5 d, 6
d, 7 d, 8 d, 9 d and 10 d, 1 mL of release medium samples were
withdrawn and replaced with 1 mL of fresh release medium. After
filtration, the content of the samples was detected by an RP‐HPLC
(Alliance E2695, Waters, USA), and the cumulative drug release of DON
in vitro was calculated.^[ [139]^24 ^]
Quantification of peptides on PLGA nanoparticles
FCS was performed using a Zeiss LSM 510 microscope (LSM
510‐META/Confocor2, Carl Zeiss, Jena, Germany) with settings adjusted
as follows: emission signals for FCS were recorded without the beam
splitter, using the appropriate filter sets. The pinholes were
optimized to maximize the count rate, using free dye in PBS, with
sample volumes typically set to 5 µL. Fluorescent fluctuations over
time were recorded for 30 intervals of 10 seconds each. The raw data
were processed and analyzed using ConfoCor3 software. Autocorrelation
curves were fitted using a two‐component model (equation 3). Diffusion
times for free Atto488‐labeled OPBP‐1 and its conjugated PLGA
nanoparticles were fixed during the fitting process. The number of
OPBP‐1 peptides per PLGA nanoparticle was determined by dividing the
molecular brightness of Atto488‐OPBP‐1‐PLGA (in counts per molecule,
CPM) by the CPM of freely diffusing Atto488‐OPBP‐1. The fraction of
free dye or free Atto488‐OPBP‐1 was below 1% and was excluded from the
analysis.
[MATH: G2compτ=1+1N·1+Ttrip1−Ttri<
mi>pe−ττ
trip<
mtd>·f11+ττD11+τS2τD11/2
+f21+ττD21+τS2τD21/2
:MATH]
(1)
where G [2comp ](τ) is the two‐component autocorrelation function, N is
the number of particles, S the structural parameter, T[trip] is the
fraction of fluorophores in the triplet state, τ[ trip ]is the
corresponding triplet time, f1 and f2 are the fraction of the particles
of the corresponding component 1 or 2,
[MATH:
τD1
:MATH]
and
[MATH:
τD2
:MATH]
are the diffusion times of the corresponding component 1 or 2.
Blocking Activity of OPBP‐1‐PLGA
The PD‐1 overexpressing cells CHO‐K1‐PD‐1 previously established by our
group, and the PD‐L1 proteins with Fc tag of IgG1 (Sino Biological,
Beijing, China) were used. Peptides or OPBP‐1‐PLGA were diluted to a
concentration of 100 µM using normal saline and incubated with 50 ng of
PD‐L1‐Fc on ice for 30 minutes. This mixture was then incubated with
CHO‐K1‐PD‐1 cells on ice for an additional 30 mins, followed by
treatment with phycoerythrin (PE)‐conjugated goat anti‐human IgG1
antibodies (anti‐Fc‐PE) (eBioscience). Cells incubated only with the
anti‐Fc‐PE antibody were used as negative controls to account for
non‐specific fluorescence. For positive controls, cells were incubated
with PD‐1‐Fc and anti‐Fc‐PE in the absence of peptides to represent the
strongest PD‐1/PD‐L1 protein binding. The mean fluorescent intensity
(MFI) of the cells was measured using a BD FACSLSRForte (BD
Biosciences, USA) flow cytometer and used to calculate blocking
efficacy. The blocking efficacy (%) was calculated using the formula:
the blocking efficacy (%) = (MFI of the positive control – MFI of the
tested peptides)/ MFI of the positive control × 100%.
Preparation and Characterization of OPBP‐1‐PLGA‐DON‐T Cells
T cells isolated from mouse spleen and lymph nodes were co‐incubated
with varying concentrations of acetylated N‐azidoacetylgalactosamine
(AC[4]ManNAz) for 3 days. DBCO‐Cy5 was then added to assess azido
modification efficiency. T cells with different azido densities (N[3]‐T
cells) were obtained using the aforementioned method. OPBP‐1‐PLGA‐DON
nanoparticles were modified with DBCO through the amide reaction and
incubated with N[3]‐T cells at 37 °C for 1 hour to allow the attachment
of different quantities of OPBP‐1‐PLGA‐DON nanoparticles to the T cell
surface. Thiourea fluorescein was used as a model drug encapsulated in
the nanoparticles. The binding efficiency and stability of the
nanoparticles on the T cell surface were examined using flow cytometry
and laser confocal fluorescence microscopy.
Phagocytosis by Macrophages
Physiological saline, free PLGA‐FITC nanoparticles, and PLGA‐FITC
nanoparticles loaded onto T cells were separately co‐incubated with the
macrophage cell line RAW264.7. Following the co‐incubation period, the
phagocytosis of both nanoparticles and T cell “backpacks” by RAW264.7
cells was visualized and analyzed using confocal microscopy.
Functions of OPBP‐1‐PLGA‐DON‐T Cells
The effects of OPBP‐1‐PLGA‐DON conjugates on T cell proliferation,
differentiation, function, and infiltration capacity into 3D tumor
models were evaluated using CFSE staining, immunophenotypic analysis,
flow cytometry, and confocal microscopy.
In Vivo Distribution T‐cell‐nanodrug conjugates
OPBP‐1‐PLGA nanoparticles were constructed and loaded onto CD8^+ T
cells isolated from OT‐1 mice. The distribution of fluorescently
labeled T cell “backpacks” in tumor‐bearing mice was detected using an
in vivo imaging system, and dynamic distribution in organs such as
tumors, hearts, and livers was evaluated.
In Vivo Antitumor Efficacy of OPBP‐1‐PLGA‐DON T‐Cells
B16‐OVA cells were cultured and passaged, and single‐cell suspensions
were prepared. These cells were then subcutaneously injected into the
right dorsal region of C57BL/6J mice aged 5–8 weeks. When tumor volume
reached 30–40 mm^3 and was relatively uniform, mice were grouped for
antitumor therapy. Therapy included physiological saline, T cells,
OPBP‐1‐PLGA‐DON + T cells, and high and low doses of OPBP‐1‐PLGA‐DON
loaded T cells (OPBP‐1‐PLGA‐DON‐T cells), with tail vein injections of
3 × 10^6 T cells on days 9 and 17. The MC38‐OVA and EG7‐OVA models were
established with four treatment groups: saline, T cells,
OPBP‐1‐PLGA‐DON + T cells, and OPBP‐1‐PLGA‐DON (1.5 mg k^−1g) + T
cells, with tail vein injections of 3 × 10^6 T cells on days 7 and 14.
Tumor size, mouse weight, survival period, and antitumor effects were
recorded. Tumor tissues were dissected, digested, and processed for
single‐cell suspensions to evaluate CD8^+ T cell infiltration using
flow cytometry. To determine the intracellular IFN‐γ and Ki67 of CD8^+
T cells from tumor tissue, lymphocytes were separated by Percoll (GE
Healthcare, USA). The isolated lymphocytes were carefully collected,
washed twice with PBS (pH 7.2), and plated in a 24‐well plate (1 × 10^6
cells/well). After adding 1 µL of protein transport inhibitor cocktail
(BD, USA), the cells were stimulated by 1µg/mL OVA peptide for 6 h.
Cells were then collected and incubated with anti‐CD3‐eFlour710 (17A2,
eBioscience, USA), anti‐CD8α‐PE (53‐6.7, eBioscience, USA),
anti‐CD44‐APC (IIM7, eBioscience, USA) and anti‐CD62L‐Cy5.5
(L‐selectin, eBioscience, USA). After 30 mins of incubation, cells were
washed and adhered for another 30 min at room temperature. Then 800 µL
of permeabilization buffer was added to the cells. Anti‐IFN‐γ‐APC
(XMG1.2, eBioscience, USA) and anti‐Ki67‐Pc7 (SolA15, eBioscience, USA)
were added for intracellular staining on ice for 30 min. The proportion
of lymphocytes in the tumor tissue was measured by flow cytometry after
being washed twice with PBS (pH 7.2). We first gated on CD3^+CD8^+
cells and then calculated the proportion of CD44^+CD62L^− or IFN‐γ^+
cells, comparing this with the isotype control to identify the target
cell population.
Cells from lymph nodes or spleens (2 × 10^6 cells/well) were cultured
with RPMI‐1640 (Gibco, Grand Island, USA) containing 10% FBS. As
described above, cells were treated by a protein transport inhibitor
cocktail and stimulated by 1µg/mL OVA peptide for 6 h. Cells were then
collected and incubated with anti‐CD3‐eFlour710 (17A2, eBioscience,
USA) and anti‐CD8α‐PE (53‐6.7, eBioscience, USA). After 30 mins of
incubation, cells were washed and fixed for another 30 min at room
temperature. Then 800 µL of permeabilization buffer was added to the
cells. Anti‐IFN‐γ‐APC (XMG1.2, eBioscience, USA) and anti‐Ki67‐Pc7
(SolA15, eBioscience, USA) were added for intracellular staining on ice
for 30 min. The proportion was measured by flow cytometry (Cytoflex,
Beckman Coulter, USA) after being washed twice with PBS (pH 7.2).
Statistical analysis
Differences between the two groups were statistically analyzed using
Graphpad 8.0.2 software with one‐sided Mann–Whitney test. Comparisons
of more than two groups were done with one‐way ANOVA analysis followed
by Tukey's multiple comparisons test. All data were shown as means ±
SD. ^* P < 0.05, ^** P < 0.01, and ^*** P < 0.001.
Conflict of Interest
The authors declare no conflict of interest.
Supporting information
Supporting Information
[140]ADVS-12-2501815-s001.docx^ (2.8MB, docx)
Acknowledgements