A High-Throughput Image Cytometry Method for the Formation, Morphometric, and Viability Analysis of Drug-Treated Mammospheres

Coagulation factor V in breast cancer: a p53-regulated tumor suppressor and predictive marker for treatment response to chemotherapy

BACKGROUND

Patients with cancer are at an increased risk of developing coagulation complications, and chemotherapy treatment increases the risk. Tumor progression is closely linked to the hemostatic system. Breast cancer tumors express coagulation factor V (FV), an essential factor in blood coagulation. The functional role of FV during treatment with chemotherapy is poorly understood and was explored in this study.

OBJECTIVES

We aimed to investigate the role of FV in breast cancer progression by exploring associations with treatment response, gene regulation, and the functional effects of FV.

METHODS

The receiver operating characteristic plotter was used to explore the predictive value of FV mRNA (F5) expression for treatment with FEC (5-fluorouracil, anthracycline, and cyclophosphamide). Breast cancer cohorts were analyzed to study treatment response to FEC. The effect of chemotherapy on F5 expression, the regulation of F5, and the functional effects of FV dependent and independent of chemotherapy were studied in breast cancer cell lines.

RESULTS

F5 tumor expression was significantly higher in responders to FEC than in nonresponders. In vitro experiments revealed that anthracycline treatment increased the expression of F5. Inhibition and knockdown of p53 reduced the anthracycline-induced F5 expression. Mutation of a p53 half-site (c.158+1541/158+1564) in a luciferase plasmid reduced luciferase activity, suggesting that p53 plays a role in regulating F5. FV overexpression increased apoptosis and reduced proliferation slightly during anthracycline treatment.

CONCLUSION

Our study identified F5 as a p53-regulated tumor suppressor candidate and a promising marker for response to chemotherapy. FV may have functional effects that are therapeutically relevant in breast cancer.

Assessing the distribution of cancer stem cells in tumorspheres

Cancer Stem Cells presumably drive tumor growth and resistance to conventional cancer treatments. From a previous computational model, we inferred that these cells are not uniformly distributed in the bulk of a tumorsphere. To confirm this result, we cultivated tumorspheres enriched in stem cells, and performed immunofluorescent detection of the stemness marker SOX2 using confocal microscopy. In this article, we present an image processing method that reconstructs the amount and location of the Cancer Stem Cells in the spheroids. Its advantage is the use of a statistical criterion to classify the cells in Stem and Differentiated, instead of setting an arbitrary threshold. Moreover, the analysis of the experimental images presented in this work agrees with the results from our computational models, thus enforcing the notion that the distribution of Cancer Stem Cells in a tumorsphere is non-homogeneous. Additionally, the method presented here provides a useful tool for analyzing any image in which different kinds of cells are stained with different markers.

High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos

Three-dimensional (3D) organoid cultures are flexible systems to interrogate cellular growth, morphology, multicellular spatial architecture, and cellular interactions in response to treatment. However, computational methods for analysis of 3D organoids with sufficiently high-throughput and cellular resolution are needed. Here we report Cellos, an accurate, high-throughput pipeline for 3D organoid segmentation using classical algorithms and nuclear segmentation using a trained Stardist-3D convolutional neural network. To evaluate Cellos, we analyze ~100,000 organoids with ~2.35 million cells from multiple treatment experiments. Cellos segments dye-stained or fluorescently-labeled nuclei and accurately distinguishes distinct labeled cell populations within organoids. Cellos can recapitulate traditional luminescence-based drug response of cells with complex drug sensitivities, while also quantifying changes in organoid and nuclear morphologies caused by treatment as well as cell-cell spatial relationships that reflect ecological affinity. Cellos provides powerful tools to perform high-throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

Docetaxel-Loaded Methoxy poly(ethylene glycol)-poly (L-lactic Acid) Nanoparticles for Breast Cancer: Synthesis, Characterization, Method Validation, and Cytotoxicity

This study aimed to synthesize and characterize DTX-mPEG-PLA-NPs along with the development and validation of a simple, accurate, and reproducible method for the determination and quantification of DTX in mPEG-PLA-NPs. The prepared NPs were characterized using AFM, DLS, zetasizer, and drug release kinetic profiling. The RP-HPLC assay was developed for DTX detection. The cytotoxicity and anti-clonogenic effects were estimated using MTT and clonogenic assays, respectively, using both MCF-7 and MDA-MB-231 cell lines in a 2D and 3D culture system. The developed method showed a linear response, high precision, accuracy, RSD values of ≤2%, and a tailing factor ≤2, per ICH guidelines. The DTX-mPEG-PLA-NPs exhibited an average particle size of 264.3 nm with an encapsulation efficiency of 62.22%. The in vitro drug kinetic profile, as per the Krosmeyers-Peppas model, demonstrated Fickian diffusion, with initial biphasic release and a multistep sustained release over 190 h. The MTT assay revealed improved in vitro cytotoxicity against MCF-7 and MDA-MB-231 in the 2D cultures and MCF-7 3D mammosphere cultures. Significant inhibitions of the clonogenic potential of MDA-MB-231 were observed for all concentrations of DTX-mPEG-PLA-NPs. Our results highlight the feasibility of detecting DTX via the robust RP-HPLC method and using DTX-mPEG-PLA-NPs as a perceptible and biocompatible delivery vehicle with greater cytotoxic and anti-clonogenic potential, supporting improved outcomes in BC.

Cellos: High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology

Three-dimensional (3D) culture models, such as organoids, are flexible systems to interrogate cellular growth and morphology, multicellular spatial architecture, and cell interactions in response to drug treatment. However, new computational methods to segment and analyze 3D models at cellular resolution with sufficiently high throughput are needed to realize these possibilities. Here we report Cellos (Cell and Organoid Segmentation), an accurate, high throughput image analysis pipeline for 3D organoid and nuclear segmentation analysis. Cellos segments organoids in 3D using classical algorithms and segments nuclei using a Stardist-3D convolutional neural network which we trained on a manually annotated dataset of 3,862 cells from 36 organoids confocally imaged at 5 μm z-resolution. To evaluate the capabilities of Cellos we then analyzed 74,450 organoids with 1.65 million cells, from multiple experiments on triple negative breast cancer organoids containing clonal mixtures with complex cisplatin sensitivities. Cellos was able to accurately distinguish ratios of distinct fluorescently labelled cell populations in organoids, with ≤3% deviation from the seeding ratios in each well and was effective for both fluorescently labelled nuclei and independent DAPI stained datasets. Cellos was able to recapitulate traditional luminescence-based drug response quantifications by analyzing 3D images, including parallel analysis of multiple cancer clones in the same well. Moreover, Cellos was able to identify organoid and nuclear morphology feature changes associated with treatment. Finally, Cellos enables 3D analysis of cell spatial relationships, which we used to detect ecological affinity between cancer cells beyond what arises from local cell division or organoid composition. Cellos provides powerful tools to perform high throughput analysis for pharmacological testing and biological investigation of organoids based on 3D imaging.

Three-dimensional cell cultures as preclinical models to assess the biological activity of phytochemicals in breast cancer

The demand for the development of three-dimensional (3D) cell culture models in both/either drug screening and/or toxicology is gradually magnified. Natural Products derived from plants are known as phytochemicals and serve as resources for novel drugs and cancer therapy. Typical examples include taxol analogs (i.e., paclitaxel and docetaxel), vinca alkaloids (i.e., vincristine, vinblastine), and camptothecin analogs (topotecan, irinotecan). Breast cancer is the most frequent malignancy in women, with a 70% chance of patients being cured; however, metastatic disease is not considered curable using currently available chemotherapeutic options. In addition, phytochemicals present promising options for overcoming chemotherapy-related problems, such as drug resistance and toxic effects on non-target tissues. In the toxicological evaluation of these natural compounds, 3D cell culture models are a powerful tool for studying their effects on different tissues and organs in similar environments and behave as if they are in vivo conditions. Considering that 3D cell cultures represent a valuable platform for identifying the biological features of tumor cells as well as for screening natural products with antitumoral activity, the present review aims to summarize the most common 3D cell culture methods, focusing on multicellular tumor spheroids (MCTS) of breast cancer cell lines used in the discovery of phytochemicals with anticancer properties in the last ten years.

Anticancer Activity and Mechanism of Action Evaluation of an Acylhydrazone Cu(II) Complex toward Breast Cancer Cells, Spheroids, and Mammospheres

The purpose of this work was to screen the anticancer activity and mechanisms of action of Cu(II)-acylhydrazone complex [Cu(HL)(H₂ O)](NO₃ )⋅H₂ O, (CuHL), to find a potential novel agent for breast chemotherapies. Cytotoxicity studies on MCF7 cells demonstrated that CuHL has stronger anticancer properties than cisplatin over breast cancer cell models. Computational simulations showed that CuHL could interact in the minor groove of the DNA dodecamer, inducing a significant genotoxic effect on both cancer cells from 0.5 to 1 μM. In this sense, molecular docking and molecular dynamics simulations showed that the compound could interact with 20S proteasome subunits. Also, cell proteasome experiments using breast cancer cells revealed that the complex can inhibit proteasomal activity. Moreover, CuHL induced apoptosis in breast cancer cells at very low micromolar concentrations (0.5-2.5 μM) and displayed relevant anticancer activity over spheroids derived from MCF7 cells. Ultimately, CuHL diminished the number of mammospheres formed, disturbing their morphology and size.

Development of an Image-Based HCS-Compatible Method for Endothelial Barrier Function Assessment

The recent renascence of phenotypic drug discovery (PDD) is catalyzed by its ability to identify first-in-class drugs and deliver results when the exact molecular mechanism is partially obscure. Acute respiratory distress syndrome (ARDS) is a severe, life-threatening condition with a high mortality rate that has increased in frequency due to the COVID-19 pandemic. Despite decades of laboratory and clinical study, no efficient pharmacological therapy for ARDS has been found. An increase in endothelial permeability is the primary event in ARDS onset, causing the development of pulmonary edema that leads to respiratory failure. Currently, the detailed molecular mechanisms regulating endothelial permeability are poorly understood. Therefore, the use of the PDD approach in the search for efficient ARDS treatment can be more productive than classic target-based drug discovery (TDD), but its use requires a new cell-based assay compatible with high-throughput (HTS) and high-content (HCS) screening. Here we report the development of a new plate-based image cytometry method to measure endothelial barrier function. The incorporation of image cytometry in combination with digital image analysis substantially decreases assay variability and increases the signal window. This new method simultaneously allows for rapid measurement of cell monolayer permeability and cytological analysis. The time-course of permeability increase in human pulmonary artery endothelial cells (HPAECs) in response to the thrombin and tumor necrosis factor α treatment correlates with previously published data obtained by transendothelial resistance (TER) measurements. Furthermore, the proposed image cytometry method can be easily adapted for HTS/HCS applications.