Cancer metabolic features allow discrimination of tumor from white blood cells by label-free multimodal optical imaging

Hybrid Biosilica Nanoparticles for in-vivo Targeted Inhibition of Colorectal Cancer Growth and Label-Free Imaging

Background

Metastasis-initiating cells are key players in progression, resistance, and relapse of colorectal cancer (CRC), by leveraging the regulatory relationship between Transforming Growth Factor-beta (TGF-β) signaling and anti-L1 cell adhesion molecule (L1CAM).

Methods

This study introduces a novel strategy for CRC targeted therapy and imaging based on the use of a hybrid nanosystem made of gold nanoparticles-covered porous biosilica further modified with the (L1CAM) antibody.

Results

The nanosystem intracellularly delivers galunisertib (LY), a TGF-β inhibitor, aiming to inhibit epithelial-mesenchymal transition (EMT), a process pivotal for metastasis. Anti-L1CAM antibody-functionalized nanoparticles (NPs) target tumor-initiating cells expressing L1CAM, inhibiting cancer growth. The number of antibody molecules conjugated to the single NP is precisely quantified, revealing a high surface coverage that facilitates the tumor targeting. The therapeutic efficacy of the nanosystem is investigated in organoid-like cultures of CRC cells and in vivo mouse models, showing a significant reduction in tumor growth. The spatial distribution of NPs within CRC tumors from mice is investigated using a label-free optical approach based on Raman micro-spectroscopy.

Conclusion

This research highlights the multifunctional capabilities of engineered biosilica NPs, which offer new insights in targeted CRC therapy and imaging, improving patient outcomes and paving the way for personalized therapies.

Correlative Raman Imaging: Development and Cancer Applications

Despite extensive research efforts, cancer continues to stand as one of the leading causes of death on a global scale. To gain profound insights into the intricate mechanisms underlying cancer onset and progression, it is imperative to possess methodologies that allow the study of cancer cells at the single-cell level, focusing on critical parameters such as cell morphology, metabolism, and molecular characteristics. These insights are essential for effectively discerning between healthy and cancerous cells and comprehending tumoral progression. Recent advancements in microscopy techniques have significantly advanced the study of cancer cells, with Raman microspectroscopy (RM) emerging as a particularly powerful tool. Indeed, RM can provide both biochemical and spatial details at the single-cell level without the need for labels or causing disruptions to cell integrity. Moreover, RM can be correlated with other microscopy techniques, creating a synergy that offers a spectrum of complementary insights into cancer cell morphology and biology. This review aims to explore the correlation between RM and other microscopy techniques such as confocal fluoresce microscopy (CFM), atomic force microscopy (AFM), digital holography microscopy (DHM), and mass spectrometry imaging (MSI). Each of these techniques has their own strengths, providing different perspectives and parameters about cancer cell features. The correlation between information from these various analysis methods is a valuable tool for physicians and researchers, aiding in the comprehension of cancer cell morphology and biology, unraveling mechanisms underlying cancer progression, and facilitating the development of early diagnosis and/or monitoring cancer progression.

Application of Raman spectroscopy to the evaluation of F-actin changes in sea urchin eggs at fertilization

The actin filaments on the surface of echinoderm oocytes and eggs readily undergo massive reorganization during meiotic maturation and fertilization. In sea urchin eggs, the actin cytoskeletal response to the fertilizing sperm is fast enough to accompany Ca2+ signals and to guide sperm's entry into the egg. Although recent work using live cell imaging technology confirmed changes in the actin polymerization status in fertilized eggs, as was previously shown using light and electron microscopy, it failed to provide experimental evidence of F-actin depolymerization a few seconds after insemination, which is concurrent with the sperm-induced Ca2+ release. In the present study, we applied Raman microspectroscopy to tackle this issue by examining the spectral profiles of the egg's subplasmalemmal regions before and after treating the eggs with actin drugs or fertilizing sperm. At both early (15 s) and late (15 min) time points after fertilization, specific peak shifts in the Raman spectra revealed change in the actin structure, and Raman imaging detected the cytoskeletal changes corresponding to the F-actin reorganization visualized with LifeAct-GFP in confocal microscopy. Our observation suggests that the application of Raman spectroscopy, which does not require microinjection of fluorescent probes and exogenous gene expression, may serve as an alternative or even advantageous method in disclosing rapid subtle changes in the subplasmalemmal actin cytoskeleton that are difficult to resolve.

Raman microscopy reveals how cell inflammation activates glucose and lipid metabolism

Metabolism of endothelial cells (ECs) depends on the availability of the energy substrates. Since the endothelium is the first line of defence against inflammation in the cardiovascular system and its dysfunction can lead to the development of cardiovascular diseases, it is important to understand how glucose metabolism changes during inflammation. In this work, glucose uptake was studied in human microvascular endothelial cells (HMEC-1) in high glucose (HG), and additionally in an inflammatory state, using Raman imaging. HG state was induced by incubation of ECs with a deuterated glucose analogue, while the EC inflammation was caused by TNF-α pre-treatment. Spontaneous and stimulated Raman scattering spectroscopy provided comprehensive information on biochemical changes, including lipids and the extent of unsaturation induced by excess glucose in ECs., induced by excess glucose in ECs. In this work, we indicated spectroscopic markers of metabolic changes in ECs as a strong increase in the ratio of the intensity of lipids / (proteins + lipids) bands and an increase in the level of lipid unsaturation and mitochondrial changes. Inflamed ECs treated with HG, revealed enhanced glucose uptake, and intensified lipid production i.a. of unsaturated lipids. Additionally, increased cytochrome c signal in the mitochondrial region indicated higher mitochondrial activity and biogenesis. Raman spectroscopy is a powerful method for determining the metabolic markers of ED which will better inform understanding of disease onset, development, and treatment.

AI-aided holographic flow cytometry for label-free identification of ovarian cancer cells in the presence of unbalanced datasets

Liquid biopsy is a valuable emerging alternative to tissue biopsy with great potential in the noninvasive early diagnostics of cancer. Liquid biopsy based on single cell analysis can be a powerful approach to identify circulating tumor cells (CTCs) in the bloodstream and could provide new opportunities to be implemented in routine screening programs. Since CTCs are very rare, the accurate classification based on high-throughput and highly informative microscopy methods should minimize the false negative rates. Here, we show that holographic flow cytometry is a valuable instrument to obtain quantitative phase-contrast maps as input data for artificial intelligence (AI)-based classifiers. We tackle the problem of discriminating between A2780 ovarian cancer cells and THP1 monocyte cells based on the phase-contrast images obtained in flow cytometry mode. We compare conventional machine learning analysis and deep learning architectures in the non-ideal case of having a dataset with unbalanced populations for the AI training step. The results show the capacity of AI-aided holographic flow cytometry to discriminate between the two cell lines and highlight the important role played by the phase-contrast signature of the cells to guarantee accurate classification.