Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Cancer stem cell(CSC)are the"seed"cells in the occurrence,development,metastasis and recurrence of colorectal cancer.Targeted killing of CSC provides a new target for anti-colorectal cancer therapy.Ferroptosis is an iron-dependent cell death mode due to the abnormal accumulation of intracellular i-ron ions,which results in the massive reactive oxygen species(ROS)and lipid peroxides,leading to cell death.Studies have shown that cancer stem cells are more enriched in iron ions than non-CSC,which provides a new perspective for targeting ferropto-sis in cancer stem cells against colorectal cancer.This article re-views the research progress of inducing CSC ferroptosis in the treatment of colorectal cancer,such as targeted regulation of SLC7A11 expression in CSC,chelating iron in CSC lysosomes,targeting CSC phenotypic plasticity,reversing CSC iron homeo-stasis,and targeting CSC lipid droplet metabolism induce CSC ferroptosis,which provides new ideas for anti-tumor therapy.

Aim To design the pyrimidoindole deriva-tive N-butyl-9H-pyrimido[4,5-b]indole-2-carboxamide(BFPI)and synthesize it to investigate whether it in-hibits macrophage pyroptosis and foaming effects through the NLRP3/Caspase-1 pathway.Methods BFPI was synthesized using 2,4,6-triethoxycarbonyl-l,3,5-triazine and 2-aminoindole as starting materials and structurally characterized by 1H NMR,13C NMR,and ESI-MS.The in vitro cultured mouse monocyte macro-phage cell line RAW264.7 was divided into blank,model(PA)and therapeutic(BFPI)groups,and the cells in each group were treated with the corresponding culture medium for 24 h.The proliferative viability was detected by MTT assay,and the formation of intracel-lular lipid droplets was detected by oil red O staining,and NLRP3 was detected by Western-blot and RT-qPCR,caspase-1 and MCP-1 mRNA and protein ex-pression levels by Western blot and RT-qPCR.Results Compared with the blank group,the proliferation vi-ability of cells in the model group significantly de-creased and the formation of lipid droplets significantly increased;compared with the model group,the prolif-eration viability of cells in the treatment group signifi-cantly increased and the formation of lipid droplets sig-nificantly decreased,and the differences were statisti-cally significant(P＜0.01);compared with the blank group,the cellular NLRP3,caspase-1 and MCP-1 mR-NA and protein expression levels of cells in the model group significantly increased;compared with the model group,the expression levels of the above indexes of the cells in the treatment group significantly decreased,and the difference was statistically significant(P＜0.01).Conclusions BFPI contributes to delaying macrophage-derived foam cell formation during athero-genesis by inhibiting macrophage NLRP3,caspase-1,and MCP-1 expression and thereby promoting their pro-liferation and inhibiting lipid phagocytosis.

Aim To investigate the effect of homocys-teine(Hcy)on the permeability of pulmonary micro-vascular endothelial cells(PMVECs)and the role and mechanism of RASAL1.Methods CBS+/-mice were fed a high methionine diet(HMD)for 16 weeks to replicate an animal model of hyperhomocysteinemia(HHcy).HE staining was used to observe the changes in lung tissue structure.qRT-PCR was used to detect the levels of RASAL1 and DNMT1 mRNA in lung tis-sue.Western blot was used to detect the expression of RASAL1,DNMT1,ZO-1,and VE cadherin proteins.Methylation specific PCR was used to detect methyla-tion in the RASAL1 promoter region.PMVECs were transfected with Ad-RASAL1 to detect the expression of ZO-1 and VE cadherin.The si-DNMT1 interference fragment was transfected into PMVECs,and the ex-pression of the RASAL1 was detected by qRT-PCR and Western blot.Results Serum Hcy level of HMD mice was significantly raised,and HE staining showed severe structural disorder in lung tissue.The expres-sion of RASAL1,ZO-1,and VE cadherin was de-creased,while the expression of DNMT1 was in-creased.The degree of methylation in the RASAL1 promoter region was raised.The expression of ZO-1 and VE cadherin increased after PMVECs were trans-fected with Ad-RASAL1.After knocking down DN-MT1,RASAL1 expression was increased.Conclusion Hcy can increase the permeability of PMVECs,and its mechanism is related to the upregulation of RASAL1 methylation level.

Aim To investigate the intracellular up-take and target protein binding characteristics of two Bruton's tyrosine kinase inhibitors(BTKi)with differ-ent selectivities to provide further insights into the mechanisms of drug off-target-related bleeding risk.Methods Ibrutinib(non-selective BTKi)and za-nubrutinib(selective BTKi)were used as study drugs.After incubation of MEC-1 cells and human platelets with drugs,the cellular thermal shift assay(CETSA)was combined with Western blot to obtain the melting curve and isothermal curve to analyze the binding char-acteristics of the two drugs with the target protein BTK.After incubation of MEC-1 cells and human platelets with drugs,the concentrations of the two drugs were detected by liquid chromatography-tandem mass spectrometry(LC-MS/MS)to analyze the intracellular uptake of the two drugs.Results CETSA analysis confirmed that zanubrutinib was more selective for the target protein BTK compared to ibrutinib.LC-MS/MS analysis showed that both drugs were uptaken intracel-lularly by MEC-1 cells and platelets in a concentration-dependent manner.Conclusions While BTKi targe-ting BTK to B lymphocytes exerts therapeutic effects,off-target effects on platelets due to differences in their intracellular uptake,and target-binding characteristics may be one of the reasons for the differences in bleed-ing risk across selective BTKi.