Background
Chemotherapy has become the first-line treatment for cancer since 1940s. However, it is almost inevitably challenged by the occurrence of drug resistance, which occurs when cancer cells evade the antitumor effects of drugs and has become a major challenge in clinical cancer treatment. For efficient cancer therapy, it is crucial to develop materials and technologies for accurate diagnosis of cancer drug resistance, to reflect the drug-resistant cell subtypes in a heterogenous tumor, and monitor the progress of cancer drug resistance, so as to establish an effective therapeutic schedule. However, the existing drug resistance detection methods as well as some molecular biological techniques, such as quantitative polymerase chain reaction (q-PCR), can only reveal the overall average level of drug resistance. There is no conventional method to detect cancer drug resistance at the single-cell level.
Introduction
Prof. Hongjing Dou, from Shanghai Jiao Tong University, revealed that the specific interactions between nanoparticles and cancer cells can be used to mark and code different cancer cells and thus be employed in the diagnosis of cancer drug resistance. They synthesized a library of fluorescent nanoparticles with various sizes, surface charges, and compositions (SiO2 nanoparticles (SNPs), organic PS-co-PAA nanoparticles (ONPs), and ZIF-8 nanoparticles (ZNPs)), and explored the interactions between these nanoparticles with various drug-resistant cancer cells, where the fluorescent signals were used to mark and code drug-resistant cancer cells, and systematically investigate the applications of these nanoparticles in the single-cell diagnosis of cancer drug resistance in chemotherapy. They found that ZNPs displayed a distinct low accumulation in drug-resistant cancer cells, which was 15-50% to that in drug-sensitive cancer cells. Therefore, the distinct low accumulation of ZIF-8 in drug-resistant cancer cells can be used to mark and code the subpopulations of drug-resistant cancer cells. Furthermore, they revealed that the clathrin/caveolae-independent endocytosis of ZNPs together with the P-glycoprotein-related decreased cell membrane fluidity resulted in a lower cellular accumulation of ZNPs in drug-resistant cancer cells, consequently causing the distinct cellular accumulation of ZNPs between the drug-resistant and drug-sensitive cancer cells. Their findings provide another insight into the nanoparticle-cell interactions and offer a promising platform for the diagnosis of cancer drug resistance of various cancer cells and clinical cancer samples at the single-cell level. The relevant article, entitled “Single-Cell Diagnosis of Cancer Drug Resistance through the Differential Endocytosis of Nanoparticles between Drug-Resistant and Drug-Sensitive Cancer Cells” was published in ACS Nano.
Paper link:
https://doi.org/10.1021/acsnano.3c07030
Research Content
Scheme 1. (A) Schematic illustration of the nanoparticle library employed in this study (including nanoparticles with varying compositions and sizes). (B) Schematic depiction of the endocytosis process of nanoparticles in drug-sensitive and drugresistant cancer cells and the different degrees of nanoparticles accumulating in cells after incubation, which in turn generated different fluorescence intensities in the corresponding flow cytometry profiles.
Figure 1. Characterization of the library of fluorescent nanoparticles with different sizes, shapes, and surface charges. (A) TEM images of ZNPs, SNPs, and ONPs of different sizes. Scale bars = 200 nm. (B) The sizes that were calculated from TEM images and (C) zeta potentials of the fluorescent nanoparticles.
Figure 2. Nanoparticle-cell interactions between the nine nanoparticles and SKOV3-PTX/SKOV3 cells. (A) Flow cytometry data for SKOV3-PTX and SKOV3 cells that were incubated with ZNPs, SNPs, and ONPs for 2 h. (B) Quantified analysis of the mean fluorescence intensities (MFIs) of three sets of nanoparticles in SKOV3-PTX and SKOV3 cells. The MFI of nanoparticles in SKOV3 cells was set as 100%. Statistical analysis was performed via a two-tailed Student’s t-test (n = 3, *p < 0.05, **p < 0.01, and ***p < 0.001). (C) Confocal microscopy images of SKOV3-PTX and SKOV3 cells that had been incubated with ZNP-1, SNP-1, and ONP-1 for 2 h. Scale bar = 50 μm.
Figure 3. Effects of MDR1 gene expression and P-gp functions on the accumulation of ZNP-1 in drug-resistant cancer cells. (A) The flow cytometry profile of ZNP-1 in SKOV3-PTX cells, with both SKOV3-PTX-LF and SKOV3-PTX-HF cells being selected. (B) IC50 of PTX and (C) MDR1 gene expression in SKOV3, SKOV3-PTX, SKOV3-PTX-LF, and SKOV3-PTX-HF cells. (D) Flow cytometry data for K562-ADR cells that had been treated with 1 and 8 μg/mL of ADR and subsequently incubated with ZNP-1 for 2 h. (E) Flow cytometry data and (F) quantified analysis of MFI for SKOV3-PTX cells that had been incubated with ZNP-1 for 2 h after these cells were untreated (+ Blank) or pretreated with the P-gp inhibitors VPL (10 μM), CsA (10 μM), and RAPA (10 μM) for 15 min. The MFI of ZNP-1 in SKOV3 cells (+ Blank) was set as 100%. Statistical analysis was performed via a two-tailed Student’s t-test (n = 3, *p < 0.05, **p < 0.01, and ***p < 0.001)
Figure 4. Entry of nanoparticles into cancer cells. Flow cytometry data for the cellular uptake of (A) ZNP-1 and (C) SNP-1 with the pretreatment of several kinds of endocytosis inhibitors. All of the groups (SKOV3 or SKOV3-PTX cells) treated with endocytosis inhibitors were compared with the blank group (SKOV3 or SKOV3-PTX cells). TEM images displaying the entry of (B) ZNP-1 and (D) SNP-1 into drug-resistant cancer cells. Scale bar = 0.5 μm. Schematic illustrations displaying the cellular uptake and intracellular traffic of (E) ZNPs and (F) SNPs in drug-resistant cancer cells.
Figure 5. (A) Schematic illustrations of the treatment of clinical samples. (B) Flow cytometry data and (C) quantified analysis of MFI for clinical samples that were incubated with ZNP-1 for 1 h. t-SNE overview of the cells that passed quality control are presented, and 26 clusters that were identified through graph-based clustering are additionally indicated by (D) the different sample and (E) the expression of ABCB1. (F) Quantified analysis of the mean ABCB1 expression in each cluster.
Conclusion
This work has studied the interactions between well-designed nanoparticles and drug-resistant cancer cells and thus built a nanoparticle-based detection platform for cancer drug resistance. It is worth pointing out that prof. Dou group has conducted a series of studies on the interaction between polysaccharide nanogels and drug-resistant cancer cells, and it has been found that polysaccharide nanogels with nearly neutral surface charge have the similar performance and cell-sorting functions to ZIF-8 nanoparticles (Nano Research, 2020, 11(13), 3110; Advanced Biosystems, 2020, 4(2), 1900213). And in this work, the relevant mechanisms are discussed in depth, which revealed that endocytic pathways and P-gp-related decreased cell membrane fluidity directed the cellular accumulation of the nanoparticles in drug-resistant cancer cells. Overall, these findings offer a promising platform for the diagnosis of cancer drug resistance at the single-cell level, which exhibits the potential of single-cell diagnosis of chemotherapy resistance of clinical samples.
Corresponding Authors
Hongjing Dou - The State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Zhangjiang Institute for Advanced Study (ZIAS), Shanghai Jiao Tong University, Shanghai 201203, China; orcid.org/0000-0001-5850-9174;
Min Zhou - Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
Xiaoyan Chen - Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.