Determining the Efficiency of Single Molecule Quantum Dot Labeling of HER2 in Breast Cancer Cells

A Radical-Generating Probe to Release Free Fluorophores and Identify Artemisinin-Sensitive Cancer Cells

The smart light-up probes have been extensively developed to image various enzymes and other bioactive molecules. Upon activation, these probes result in light-up fluorophores that exist in a protein-bound or a free form. The difference between these two forms has not yet been reported. Here, we present a pair of smart light-up probes that generate a protein-bound fluorophore and a free fluorophore upon activation by heme. Probe 8 generated a radical-attached fluorophore that predominantly existed in the free form, while probe 10 generated an α,β-unsaturated ketone-attached fluorophore that showed extensive labeling of proteins. In live-cell imaging, probe 8 showed greater fluorescence intensity than probe 10 when low concentrations (0.1-5 μM) of the probes were used, but probe 8 was less fluorescent than probe 10 when the concentrations of the probes were high (10 μM). Finally, probe 8 was used to reflect the activation level of the endoperoxide bond in cancer cells and to effectively distinguish ART-sensitive cancer cells from ART-insensitive ones.

Quantum Dot Research in Breast Cancer: Challenges and Prospects

The multifaceted role of quantum dots (QDs) in breast cancer research highlights significant advancements in diagnostics, targeted therapy, and drug delivery systems. This comprehensive review addresses the development of precise imaging techniques for early cancer detection and the use of QDs in enhancing the specificity of therapeutic delivery, particularly in challenging cases like triple-negative breast cancer (TNBC). The paper also discusses the critical understanding of QDs' interactions with cancer cells, offering insights into their potential for inducing cytotoxic effects and facilitating gene therapy. Limitations such as biocompatibility, toxicity concerns, and the transition from laboratory to clinical practice are critically analyzed. Future directions emphasize safer, non-toxic QD development, improved targeting mechanisms, and the integration of QDs into personalized medicine, aiming to overcome the current challenges and enhance breast cancer management.

Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review

Purpose

Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine.

Materials and Methods

A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models.

Results

Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively.

Conclusion

The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine.

Quantification of EGFR-HER2 Heterodimers in HER2-Overexpressing Breast Cancer Cells Using Liquid-Phase Electron Microscopy

Currently, breast cancer patients are classified uniquely according to the expression level of hormone receptors, and human epidermal growth factor receptor 2 (HER2). This coarse classification is insufficient to capture the phenotypic complexity and heterogeneity of the disease. A methodology was developed for absolute quantification of receptor surface density ρ_R, and molecular interaction (dimerization), as well as the associated heterogeneities, of HER2 and its family member, the epidermal growth factor receptor (EGFR) in the plasma membrane of HER2 overexpressing breast cancer cells. Quantitative, correlative light microscopy (LM) and liquid-phase electron microscopy (LPEM) were combined with quantum dot (QD) labeling. Single-molecule position data of receptors were obtained from scanning transmission electron microscopy (STEM) images of intact cancer cells. Over 280,000 receptor positions were detected and statistically analyzed. An important finding was the subcellular heterogeneity in heterodimer shares with respect to plasma membrane regions with different dynamic properties. Deriving quantitative information about EGFR and HER2 ρ_R, as well as their dimer percentages, and the heterogeneities thereof, in single cancer cells, is potentially relevant for early identification of patients with HER2 overexpressing tumors comprising an enhanced share of EGFR dimers, likely increasing the risk for drug resistance, and thus requiring additional targeted therapeutic strategies.

Fluorescence and visual immunoassay of HIV-1 p24 antigen in clinical samples via multiple selective recognitions of CdTe QDs

Human immunodeficiency virus (HIV) infection inflicts significant economic and social burdens on many countries worldwide. Given the substantial morbidity and mortality from HIV infection, there is an urgent need for accurate and early detection of the virus. In this study, immunofluorescence and visual techniques are described that detect the HIV-1 p24 antigen, which relied on selective recognition of Ag⁺/Ag nanoparticles (Ag NPs) and Cu²⁺/Cu⁺ using cadmium telluride quantum dots (CdTe QDs). After the sandwich immunoreactions were accomplished, the alkaline phosphatase (ALP) hydrolyzed L-ascorbic acid 2-phosphate (AAP) to form ascorbic acid (AA) that further reduces Ag⁺ and Cu²⁺ to Ag NPs and Cu⁺, respectively. This method was highly sensitive and selective and could detect as low as 1 pg/mL of p24 antigen by naked eyes and had a good linearity in the concentration range 1-100 pg/mL. When using Ag⁺ and Cu²⁺ as media, the limit of detection (LOD) of the new method was 0.3 pg/mL and 0.2 pg/mL, respectively. Compared with clinical electrochemiluminescence immunoassay (ECLIA) results and clinical data, this method demonstrated good consistency for the quantification of HIV-1 p24 antigen in 34 clinical serum samples. In addition, this method could accurately distinguish HIV from other viruses and infections such as hepatitis B virus, systemic lupus erythematosus, hepatitis C virus, Epstein-Barr virus, cytomegalovirus, lipemia, and hemolysis. Therefore, our dual-mode analysis method may provide additional solutions to identify clinical HIV infection. An immunofluorescence and visualization dual-mode strategy for the detection of p24 antigen was constructed based on immune recognition reaction and a phenomenon that cadmium telluride quantum dots (CdTe QDs) can selectively recognize Ag⁺/Ag nanoparticles (Ag NPs) and Cu²⁺/Cu⁺.

Supra-Molecular Assemblies of ORAI1 at Rest Precede Local Accumulation into Puncta after Activation

The Ca²⁺ selective channel ORAI1 and endoplasmic reticulum (ER)-resident STIM proteins form the core of the channel complex mediating store operated Ca²⁺ entry (SOCE). Using liquid phase electron microscopy (LPEM), the distribution of ORAI1 proteins was examined at rest and after SOCE-activation at nanoscale resolution. The analysis of over seven hundred thousand ORAI1 positions revealed a number of ORAI1 channels had formed STIM-independent distinct supra-molecular clusters. Upon SOCE activation and in the presence of STIM proteins, a fraction of ORAI1 assembled in micron-sized two-dimensional structures, such as the known puncta at the ER plasma membrane contact zones, but also in divergent structures such as strands, and ring-like shapes. Our results thus question the hypothesis that stochastically migrating single ORAI1 channels are trapped at regions containing activated STIM, and we propose instead that supra-molecular ORAI1 clusters fulfill an amplifying function for creating dense ORAI1 accumulations upon SOCE-activation.

EGFR Expression in HER2-Driven Breast Cancer Cells

The epidermal growth factor receptor HER2 is overexpressed in 20% of breast cancer cases. HER2 is an orphan receptor that is activated ligand-independently by homodimerization. In addition, HER2 is able to heterodimerize with EGFR, HER3, and HER4. Heterodimerization has been proposed as a mechanism of resistance to therapy for HER2 overexpressing breast cancer. Here, a method is presented for the simultaneous detection of individual EGFR and HER2 receptors in the plasma membrane of breast cancer cells via specific labeling with quantum dot nanoparticles (QDs). Correlative fluorescence microscopy and liquid phase electron microscopy were used to analyze the plasma membrane expression levels of both receptors in individual intact cells. Fluorescent single-cell analysis of SKBR3 breast cancer cells dual-labeled for EGFR and HER2 revealed a heterogeneous expression for receptors within both the cell population as well as within individual cells. Subsequent electron microscopy of individual cells allowed the determination of individual receptors label distributions. QD-labeled EGFR was observed with a surface density of (0.5–5) × 10¹ QDs/µm², whereas labeled HER2 expression was higher ranging from (2–10) × 10² QDs/µm². Although most SKBR3 cells expressed low levels of EGFR, an enrichment was observed at large plasma membrane protrusions, and amongst a newly discovered cellular subpopulation termed EGFR-enriched cells.