Modeling the Temperature-Dependent Size Change of Polydisperse Nano-objects using a Deep Generative Model.

Modern microscopy techniques can be used to investigate soft nano-objects at the nanometer scale. However, time-consuming microscopy measurements combined with low numbers of observable polydisperse objects often limit the statistics. We propose a method for identifying the most representative objects from their respective point clouds. These point cloud data are obtained, for example, through the localization of single emitters in super-resolution fluorescence microscopy. External stimuli, such as temperature, can cause changes in the shape and properties of adaptive objects. Due to the demanding and time-consuming nature of super-resolution microscopy experiments, only a limited number of temperature steps can be performed. Therefore, we propose a deep generative model that learns the underlying point distribution of temperature-dependent microgels, enabling the reliable generation of unlimited samples with an arbitrary number of localizations. Our method greatly cuts down the data collection effort across diverse experimental conditions, proving invaluable for soft condensed matter studies.

Establishment of patient-derived organoids for guiding personalized therapies in breast cancer patients.

Breast cancer has become the most commonly diagnosed cancer. The intra- and interpatient heterogeneity induced a considerable variation in treatment efficacy. There is an urgent requirement for preclinical models to anticipate the effectiveness of individualized drug responses. Patient-derived organoids (PDOs) can accurately recapitulate the architecture and biological characteristics of the origin tumor, making them a promising model that can overtake many limitations of cell lines and PDXs. However, it is still unclear whether PDOs-based drug testing can benefit breast cancer patients, particularly those with tumor recurrence or treatment resistance. Fresh tumor samples were surgically resected for organoid culture. Primary tumor samples and PDOs were subsequently subjected to H&E staining, immunohistochemical (IHC) analysis, and whole-exome sequencing (WES) to make comparisons. Drug sensitivity tests were performed to evaluate the feasibility of this model for predicting patient drug response in clinical practice. We established 75 patient-derived breast cancer organoid models. The results of H&E staining, IHC, and WES revealed that PDOs inherited the histologic and genetic characteristics of their parental tumor tissues. The PDOs successfully predicted the patient's drug response, and most cases exhibited consistency between PDOs' drug susceptibility test results and the clinical response of the matched patient. We conclude that the breast cancer organoids platform can be a potential preclinical tool used for the selection of effective drugs and guided personalized therapies for patients with advanced breast cancer.

Multi-omics analysis elucidates the relationship between intratumor microbiome and host immune heterogeneity in breast cancer.

Research has indicated that intratumor microbiomes affect the occurrence, progression, and therapeutic response in many cancer types by influencing the immune system. We aim to evaluate the characteristics of immune-related intratumor microbiomes (IRIMs) in breast cancer (BC) and search for potential prognosis prediction factors and treatment targets. The clinical information, microbiome data, transcriptomics data of The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) patients were obtained from Kraken-TCGA-Raw-Data and TCGA portal. The core tumor-infiltrating immune cell was identified using univariate Cox regression analysis. Based on consensus clustering analysis, BC patients were categorized into two immune subtypes, referred to as immune-enriched and immune-deficient subtypes. The immune-enriched subtype, characterized by higher levels of immune infiltration of CD8+ T and macrophage M1 cells, demonstrated a more favorable prognosis. Furthermore, significant differences in alpha-diversity and beta-diversity were observed between the two immune subtypes, and the least discriminant analysis effect size method identified 33 types of IRIMs. An intratumor microbiome-based prognostic signature consisting of four prognostic IRIMs (Acidibacillus, Succinimonas, Lachnoclostridium, and Pseudogulbenkiania) was constructed using the Cox proportional-hazard model, and it had great prognostic value. The prognostic IRIMs were correlated with immune gene expression and the sensitivity of chemotherapy drugs, specifically tamoxifen and docetaxel. In conclusion, our research has successfully identified two distinct immune subtypes in BC, which exhibit contrasting prognoses and possess unique epigenetic and intratumor microbiomes. The critical IRIMs were correlated with prognosis, tumor-infiltrating immune cell abundance, and immunotherapeutic efficacy in BC. Consequently, this study has identified potential IRIMs as biomarkers, providing a novel therapeutic approach for treating BC.IMPORTANCERecent research has substantiated the presence of the intratumor microbiome in tumor immune microenvironment, which could influence tumor occurrence and progression, as well as provide new opportunities for cancer diagnosis and treatment. This study identified the critical immune-related intratumor microbiome (Acidibacillus, Succinimonas, Lachnoclostridium, and Pseudogulbenkiania), which were correlated with prognosis, tumor-infiltrating immune cell abundance, and immunotherapeutic efficacy in breast cancer and might be the novel target to regulate immunotherapy in BC.

Machine learning predicts the risk of osteoporosis in patients with breast cancer and healthy women.

OBJECTIVE: In this study, we investigated the effects of endocrine therapy and related drugs on the body composition and bone metabolism of patients with breast cancer. Additionally, using body composition-related indicators in machine learning algorithms, the risks of osteoporosis in patients with breast cancer and healthy women were predicted. METHODS: We enrolled postmenopausal patients with breast cancer who were hospitalized in a tertiary hospital and postmenopausal women undergoing health checkups in our hospital between 2019 and 2021. The basic information, body composition, bone density-related indicators, and bone metabolism-related indicators of all the study subjects were recorded. Machine learning models were constructed using cross-validation. RESULTS: Compared with a healthy population, the body composition of patients with breast cancer was low in bone mass, protein, body fat percentage, muscle, and basal metabolism, whereas total water, intracellular fluid, extracellular fluid, and waist-to-hip ratio were high. In patients with breast cancer, the bone mineral density (BMD), Z value, and T value were low and the proportion of bone loss and osteoporosis was high. BMD in patients with breast cancer was negatively correlated with age, endocrine therapy status, duration of medication, and duration of menopause, and it was positively correlated with body mass index (BMI) and basal metabolism. The parameters including body composition, age, hormone receptor status, and medication type were used for developing the machine learning model to predict osteoporosis risk in patients with breast cancer and healthy populations. The model showed a high accuracy in predicting osteoporosis, reflecting the predictive value of the model. CONCLUSIONS: Patients with breast cancer may have changed body composition and BMD. Compared with the healthy population, the main indicators of osteoporosis in patients with breast cancer were reduced nonadipose tissue, increased risk of edema, altered fat distribution, and reduced BMD. In addition to age, duration of treatment, and duration of menopause, body composition-related indicators such as BMI and basal metabolism may be considerably associated with BMD of patients with breast cancer, suggesting that BMD status can be monitored in clinical practice by focusing on changes in the aforementioned indexes, which may provide a way to prevent preclinical osteoporosis.

Advances in medical image analysis with vision Transformers: A comprehensive review.

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://github.com/mindflow-institue/Awesome-Transformer.

Lymph node and bone metastasis of pulmonary intestinal adenocarcinoma: A case report.

Pulmonary enteric adenocarcinoma (PEAC) is a rare pathological type of lung adenocarcinoma, accounting for ~0.6% of primary lung adenocarcinoma, which has similar morphological and immunohistochemical characteristics to colorectal adenocarcinoma. Making a certain differential diagnosis of PEAC based on morphological and immunohistochemical results is difficult. It is known that PEAC may metastasize to the pancreas, skin, soleus muscle and intestine, but no bone metastasis has been reported. At our department, a rare case of PEAC with bone and lymph node metastasis was previously diagnosed. The present case study reports on a 58-year-old male patient encountered at our hospital with pain in the lumbar, back and right iliac with no obvious cause. Chest CT indicated a space-occupying lesion in the left upper lung lobe, enlarged lymph nodes in the mediastinum and left lung, and partial vertebral bone destruction. Enhanced CT results indicated multiple foci of active bone metabolism in the body, while rectal colonoscopy showed no obvious abnormalities. Histopathological and immunohistochemical results after right iliac bone puncture suggested stage IV PEAC with secondary malignancies in bones, mediastinal lymph node, hilar lymph node and left supraclavicular lymph node.

Causal associations of Sjögren's syndrome with cancers: a two-sample Mendelian randomization study.

BACKGROUND: Several observational studies have explored the associations between Sjögren's syndrome (SS) and certain cancers. Nevertheless, the causal relationships remain unclear. Mendelian randomization (MR) method was used to investigate the causality between SS and different types of cancers. METHODS: We conducted the two-sample Mendelian randomization with the public genome-wide association studies (GWASs) summary statistics in European population to evaluate the causality between SS and nine types of cancers. The sample size varies from 1080 to 372,373. The inverse variance weighted (IVW) method was used to estimate the causal effects. A Bonferroni-corrected threshold of P < 0.0031 was considered significant, and P value between 0.0031 and 0.05 was considered to be suggestive of an association. Sensitivity analysis was performed to validate the causality. Moreover, additional analysis was used to assess the associations between SS and well-accepted risk factors of cancers. RESULTS: After correcting the heterogeneity and horizontal pleiotropy, the results indicated that patients with SS were significantly associated with an increased risk of lymphomas (odds ratio [OR] = 1.0010, 95% confidence interval [CI]: 1.0005-1.0015, P = 0.0002) and reduced risks of prostate cancer (OR = 0.9972, 95% CI: 0.9960-0.9985, P = 2.45 × 10-5) and endometrial cancer (OR = 0.9414, 95% CI: 0.9158-0.9676, P = 1.65 × 10-5). Suggestive associations were found in liver and bile duct cancer (OR = 0.9999, 95% CI: 0.9997-1.0000, P = 0.0291) and cancer of urinary tract (OR = 0.9996, 95% CI: 0.9992-1.0000, P = 0.0281). No causal effect of SS on other cancer types was detected. Additional MR analysis indicated that causal effects between SS and cancers were not mediated by the well-accepted risk factors of cancers. No evidence of the causal relationship was observed for cancers on SS. CONCLUSIONS: SS had significant causal relationships with lymphomas, prostate cancer, and endometrial cancer, and suggestive evidence of association was found in liver and bile duct cancer and cancer of urinary tract, indicating that SS may play a vital role in the incidence of these malignancies.

Visualized machine learning models combined with propensity score matching analysis in single PR-positive breast cancer prognosis: a multicenter population-based study.

The characteristics of single PR-positive (ER-PR+, sPR+) breast cancer (BC) and its prognosis are not well elucidated due to its rarity and conflicting evidence. There is a lack of an accurate and efficient model for predicting survival, thereby rendering treatment challenging for clinicians. Whether endocrine therapy should be intensified in sPR+ BC patients was another controversial clinical topic. We constructed and cross-validated XGBoost models that showed high precision and accuracy in predicting the survival of patients with sPR+ BC cases (1-year: AUC=0.904; 3-year: AUC=0.847; 5-year: AUC=0.824). The F1 score for the 1-, 3-, and 5-year models were 0.91, 0.88, and 0.85, respectively. The models exhibited superior performance in an external, independent dataset (1-year: AUC=0.889; 3-year: AUC=0.846; 5-year: AUC=0.821). Further, intensified endocrine therapy did not provide a significant overall survival benefit compared to initial or no endocrine therapy (P=0.600, HR: 1.46; 95% CI: 0.35-6.17). Propensity-score matching (PSM)-adjusted data showed that there was no statistically significant difference in the prognosis between ER-PR+HER2+ and ER-PR-HER2+ BC. Patients having the ER-PR+HER2- subtype had a slightly worse prognosis than those with the ER-PR-HER2- subtype. In conclusion, XGBoost models can be highly reproducible and effective in predicting survival in patients with sPR+ BC. Our findings revealed that patients with sPR-positive BC may not benefit from endocrine therapy. Patients with sPR+ BC may benefit from intensive adjuvant chemotherapy compared to endocrine therapy.

Novel models by machine learning to predict prognosis of breast cancer brain metastases.

BACKGROUND: Breast cancer brain metastases (BCBM) are the most fatal, with limited survival in all breast cancer distant metastases. These patients are deemed to be incurable. Thus, survival time is their foremost concern. However, there is a lack of accurate prediction models in the clinic. What's more, primary surgery for BCBM patients is still controversial. METHODS: The data used for analysis in this study was obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of BCBM patients. Through cross-validation, we constructed XGBoost models to predict survival in patients with BCBM. Meanwhile, a BCBM cohort from our hospital was used to validate our models. We also investigated the prognosis of patients treated with surgery or not, using propensity score matching and K-M survival analysis. Our results were further validated by subgroup COX analysis in patients with different molecular subtypes. RESULTS: The XGBoost models we created had high precision and correctness, and they were the most accurate models to predict the survival of BCBM patients (6-month AUC = 0.824, 1-year AUC = 0.813, 2-year AUC = 0.800 and 3-year survival AUC = 0.803). Moreover, the models still exhibited good performance in an externally independent dataset (6-month: AUC = 0.820; 1-year: AUC = 0.732; 2-year: AUC = 0.795; 3-year: AUC = 0.936). Then we used Shiny-Web tool to make our models be easily used from website. Interestingly, we found that the BCBM patients with an annual income of over USD$70,000 had better BCSS (HR = 0.523, 95%CI 0.273-0.999, P < 0.05) than those with less than USD$40,000. The results showed that in all distant metastasis sites, only lung metastasis was an independent poor prognostic factor for patients with BCBM (OS: HR = 1.606, 95%CI 1.157-2.230, P < 0.01; BCSS: HR = 1.698, 95%CI 1.219-2.365, P < 0.01), while bone, liver, distant lymph nodes and other metastases were not. We also found that surgical treatment significantly improved both OS and BCSS in BCBM patients with the HER2 + molecular subtypes and was beneficial to OS of the HR-/HER2- subtype. In contrast, surgery could not help BCBM patients with HR + /HER2- subtype improve their prognosis (OS: HR = 0.887, 95%CI 0.608-1.293, P = 0.510; BCSS: HR = 0.909, 95%CI 0.604-1.368, P = 0.630). CONCLUSION: We analyzed the clinical features of BCBM patients and constructed 4 machine-learning prognostic models to predict their survival. Our validation results indicate that these models should be highly reproducible in patients with BCBM. We also identified potential prognostic factors for BCBM patients and suggested that primary surgery might improve the survival of BCBM patients with HER2 + and triple-negative subtypes.

Causal effects of COVID-19 on cancer risk: A Mendelian randomization study.

In contemporary literature, little attention has been paid to the association between coronavirus disease-2019 (COVID-19) and cancer risk. We performed the Mendelian randomization (MR) to investigate the causal associations between the three types of COVID-19 exposures (critically ill COVID-19, hospitalized COVID-19, and respiratory syndrome coronavirus 2 (SARS-CoV-2) infection) and 33 different types of cancers of the European population. The results of the inverse-variance-weighted model indicated that genetic liabilities to critically ill COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (odds ratio [OR] = 1.0924; p-value = 0.0116), esophageal cancer (OR = 1.0004; p-value = 0.0226), colorectal cancer (OR = 1.0010; p-value = 0.0242), stomach cancer (OR = 1.2394; p-value = 0.0331), and colon cancer (OR = 1.0006; p-value = 0.0453). The genetic liabilities to hospitalized COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (OR = 1.1096; p-value = 0.0458), esophageal cancer (OR = 1.0005; p-value = 0.0440) as well as stomach cancer (OR = 1.3043; p-value = 0.0476). The genetic liabilities to SARS-CoV-2 infection had suggestive causal associations with the increased risk for stomach cancer (OR = 2.8563; p-value = 0.0019) but with the decreasing risk for head and neck cancer (OR = 0.9986, p-value = 0.0426). The causal associations of the above combinations were robust through the test of heterogeneity and pleiotropy. Together, our study indicated that COVID-19 had causal effects on cancer risk.

Comprehensive analysis of NT5DC family prognostic and immune significance in breast cancer.

Among the most common malignancies, breast cancer has a high incidence and mortality rate. NT5DC family is a highly well-conserved 5'-nucleotidase. Previous studies showed that the progression of tumors was associated with some NT5DC family members. However, there are no studies about the comprehensive analysis such as expression, prognosis, and immune properties of NT5DC family in breast cancer. Based on the data from The Cancer Genome Atlas database, we used UALCAN, Tumor Immune Estimation Resource, Breast cancer gene-expression miner (Bc-GenExMiner), Kaplan-Meier Plotter, TISIDB, cBioPortal, GeneMANIA, Search Tool for the Retrieval of Interacting Genes, Metascape, Tumor Immune Single-cell Hub, The Database for Annotation, Visualization and Integrated Discovery, and Gene Set Cancer Analysis databases to explore expression, prognostic and diagnostic value, genetic alterations, biological function, immune value and drug sensitivity of NT5DC family in breast cancer patients. There was a downregulation of NT5C2, NT5DC1, and NT5DC3 in breast cancer compared to normal tissues, and NT5DC2 instead. All NT5DC family members were associated with the clinicopathological parameters of breast cancer patients. Survival and ROC analysis revealed that NT5DC family genes were related to the prognosis and diagnosis of breast cancer. NT5DC family were mainly involved in nucleotide metabolism. Moreover, NT5DC family were significantly associated with tumor immune microenvironment, diverse immune cells, and immune checkpoints in breast cancer. This research showed that NT5DC family might be novel prognostic biomarkers and immunotherapeutic targets of breast cancer.

The cuproptosis-related signature predicts prognosis and indicates immune microenvironment in breast cancer.

Breast cancer (BC) is the most diagnosed cancer in women. Cuproptosis is new regulated cell death, distinct from known death mechanisms and dependent on copper and mitochondrial respiration. However, the comprehensive relationship between cuproptosis and BC is still blank until now. In the present study, we acquired 13 cuproptosis-related regulators (CRRs) from the previous research and downloaded the RNA sequencing data of TCGA-BRCA from the UCSC XENA database. The 13 CRRs were all differently expressed between BC and normal samples. Using consensus clustering based on the five prognostic CRRs, BC patients were classified into two cuproptosis-clusters (C1 and C2). C2 had a significant survival advantage and higher immune infiltration levels than C1. According to the Cox and LASSO regression analyses, a novel cuproptosis-related prognostic signature was developed to predict the prognosis of BC effectively. The high- and low-risk groups were divided based on the risk scores. Kaplan-Meier survival analysis indicated that the high-risk group had shorter overall survival (OS) than the low-risk group in the training, test and entire cohorts. GSEA indicated that the immune-related pathways were significantly enriched in the low-risk group. According to the CIBERSORT and ESTIMATE analyses, patients in the high-risk group had higher infiltrating levels of antitumor lymphocyte cell subpopulations and higher immune score than the low-risk group. The typical immune checkpoints were all elevated in the high-risk group. Furthermore, the high-risk group showed a better immunotherapy response than the low-risk group based on the Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS). In conclusion, we identified two cuproptosis-clusters with different prognoses using consensus clustering in BC. We also developed a cuproptosis-related prognostic signature and nomogram, which could indicate the outcome, the tumor immune microenvironment, as well as the response to immunotherapy.

A lactate-related LncRNA model for predicting prognosis, immune landscape and therapeutic response in breast cancer.

Breast cancer (BC) has the highest incidence rate of all cancers globally, with high heterogeneity. Increasing evidence shows that lactate and long non-coding RNA (lncRNA) play a critical role in tumor occurrence, maintenance, therapeutic response, and immune microenvironment. We aimed to construct a lactate-related lncRNAs prognostic signature (LRLPS) for BC patients to predict prognosis, tumor microenvironment, and treatment responses. The BC data download from the Cancer Genome Atlas (TCGA) database was the entire cohort, and it was randomly assigned to the training and test cohorts at a 1:1 ratio. Difference analysis and Pearson correlation analysis identified 196 differentially expressed lactate-related lncRNAs (LRLs). The univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were used to construct the LRLPS, which consisted of 7 LRLs. Patients could be assigned into high-risk and low-risk groups based on the medium-risk sore in the training cohort. Then, we performed the Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. The results indicated that the prognosis prediction ability of the LRLPS was excellent, robust, and independent. Furthermore, a nomogram was constructed based on the LRLPS risk score and clinical factors to predict the 3-, 5-, and 10-year survival probability. The GO/KEGG and GSEA indicated that immune-related pathways differed between the two-risk group. CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS) showed that low-risk patients had higher levels of immune infiltration and better immunotherapeutic response. The pRRophetic and CellMiner databases indicated that many common chemotherapeutic drugs were more effective for low-risk patients. In conclusion, we developed a novel LRLPS for BC that could predict the prognosis, immune landscape, and treatment response.

Optimally chirped multimodal CARS microscopy based on a single Ti:sapphire oscillator.

We demonstrate high performance coherent anti-Stokes Raman scattering (CARS) microscopy of live cells and tissues with user-variable spectral resolution and broad Raman tunability (2500 - 4100 cm(-1)), using a femtosecond Ti:Sapphire pump and photonic crystal fiber output for the broadband synchronized Stokes pulse. Spectral chirp of the fs laser pulses was a user-variable parameter for optimization in a spectral focusing implementation of multimodal CARS microscopy. High signal-to-noise, high contrast multimodal imaging of live cells and tissues was achieved with pixel dwell times of 2-8 micros and low laser powers (< 30 mW total).

Preassembly and ligand-induced restructuring of the chains of the IFN-gamma receptor complex: the roles of Jak kinases, Stat1 and the receptor chains.

We previously demonstrated using noninvasive technologies that the interferon-gamma (IFN-gamma) receptor complex is preassembled (1). In this report we determined how the receptor complex is preassembled and how the ligand-mediated conformational changes occur. The interaction of Stat1 with IFN-gammaR1 results in a conformational change localized to IFN-gammaR1. Jak1 but not Jak2 is required for the two chains of the IFN-gamma receptor complex (IFN-gammaR1 and IFN-gammaR2) to interact; however, the presence of both Jak1 and Jak2 is required to see any ligand-dependant conformational change. Two IFN-gammaR2 chains interact through species-specific determinants in their extracellular domains. Finally, these determinants also participate in the interaction of IFN-gammaR2 with IFN-gammaR1. These results agree with a detailed model of the IFN-gamma receptor that requires the receptor chains to be pre-associated constitutively for the receptor to be active.

Interactions among the components of the interleukin-10 receptor complex.

We used fluorescence resonance energy transfer previously to show that the interferon-gamma (IFN-gamma) receptor complex is a preformed entity mediated by constitutive interactions between the IFN-gammaR2 and IFN-gammaR1 chains, and that this preassembled entity changes its structure after the treatment of cells with IFN-gamma. We applied this technique to determine the structure of the interleukin-10 (IL-10) receptor complex and whether it undergoes a similar conformational change after treatment of cells with IL-10. We report that, like the IFN-gamma receptor complex, the IL-10 receptor complex is preassembled: constitutive but weaker interactions occur between the IL-10R1 and IL-10R2 chains, and between two IL-10R2 chains. The IL-10 receptor complex undergoes no major conformational changes when cells are treated with cellular or Epstein-Barr viral IL-10. Receptor complex preassembly may be an inherent feature of Class 2 cytokine receptor complexes.

Seeing the light: preassembly and ligand-induced changes of the interferon gamma receptor complex in cells.

Our experiments were designed to test the hypothesis that the cell surface interferon gamma receptor chains are preassembled rather than associated by ligand and to assess the molecular changes on ligand binding. To accomplish this, we used fluorescence resonance energy transfer, a powerful spectroscopic technique that has been used to determine molecular interactions and distances between the donor and acceptor. However, current commercial instruments do not provide sufficient sensitivity or the full spectra to provide decisive results of interactions between proteins labeled with blue and green fluorescent proteins in living cells. In our experiments, we used the blue fluorescent protein and green fluorescent protein pair, attached a monochrometer and charge-coupled device camera to a modified confocal microscope, reduced background fluorescence with the use of two-photon excitation, and focused on regions of single cells to provide clear spectra of fluorescence resonance energy transfer. In contrast to the prevailing view, the results demonstrate that the receptor chains are preassociated and that the intracellular domains move apart on binding the ligand interferon gamma. Application of this technology should lead to new rapid methods for high throughput screening and delineation of the interactome of cells.