Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging

Cisplatin-Induced Metabolic Responses Measured with Raman Spectroscopy in Cancer Cells, Spheroids, and Canine-Derived Organoids

Ex vivo assessment of drug response with conventional cell viability assays remains the standard practice for guiding initial therapeutic choices. However, such ensemble approaches fail to capture heterogeneities in treatment response and cannot identify early markers of response. Here, we leverage Raman spectroscopy (RS) as an accurate, low-cost, extraction-free, and label-free approach to track metabolic changes in cancer cells, spheroids, and organoids in response to cisplatin treatment. We identified 12 statistically significant metabolites in cells and 19 metabolites in spheroids and organoids as a function of depth. We show that the cisplatin treatment of 4T1 cells and spheroids results in a shift in metabolite levels; metabolites including nucleic acids such as DNA, 783 cm-1 with p = 0.00021 for cells; p = 0.02173 for spheroids, major amino acids such as threonine, 1338 cm-1 with p = 0.00045 for cells; p = 0.01022 for spheroids, proteins such as amide III, 1248 cm-1 with p = 0.00606 for cells; p = 0.00511 for spheroids serve as early predictors of response. Our RS findings were also applicable to canine-derived organoids, showing spatial variations in metabolic changes as a function of organoid depth in response to cisplatin. Further, the metabolic pathways such as tricarboxylic acid (TCA)/citric acid cycle and glyoxylate and dicarboxylate metabolism that drive drug response showed significant differences based on organoid depth, replicating the heterogeneous treatment response seen in solid tumors where there is a difference from the periphery to the tumor core. Our study showcases the versatility of RS as a predictive tool for treatment response applicable from cells to organotypic cultures, that has the potential to decrease animal burden and readout time for preclinical drug efficacy.

Tunable Hybrid Hydrogels of Alginate and Cell-Derived dECM to Study the Impact of Matrix Alterations on Epithelial-to-Mesenchymal Transition

Epithelial-to-mesenchymal transition (EMT) is crucial for tumor progression, being linked to alterations in the extracellular matrix (ECM). Understanding the ECM's role in EMT can uncover new therapeutic targets, yet replicating these interactions in vitro remains challenging. It is shown that hybrid hydrogels of alginate (ALG) and cell-derived decellularized ECM (dECM), with independently tunable composition and stiffness, are useful 3D-models to explore the impact of the breast tumor matrix on EMT. Soft RGD-ALG hydrogels (200 Pa), used as neutral bulk material, supported mammary epithelial cells morphogenesis without spontaneous EMT, allowing to define the gene, protein, and biochemical profiles of cells at different TGFβ1-induced EMT states. To mimic the breast tumor composition, dECM from TGFβ1-activated fibroblasts (adECM) are generated, which shows upregulation of tumor-associated proteins compared to ndECM from normal fibroblasts. Using hybrid adECM-ALG hydrogels, it is shown that the presence of adECM induces partial EMT in normal epithelial cells, and amplifes TGF-β1 effects compared to ALG and ndECM-ALG. Increasing the hydrogel stiffness to tumor-like levels (2.5 kPa) have a synergistic effect, promoting a more evident EMT. These findings shed light on the complex interplay between matrix composition and stiffness in EMT, underscoring the utility of dECM-ALG hydrogels as a valuable in vitro platform for cancer research.

An FDA-Validated, Self-Cleaning Liquid Chromatography-Mass Spectrometry System for Determining Small-Molecule Drugs and Metabolites in Organoid/Organ-on-Chip Medium

As organoids and organ-on-chip (OoC) systems move toward preclinical and clinical applications, there is an increased need for method validation. Using a liquid chromatography-mass spectrometry (LC-MS)-based approach, we developed a method for measuring small-molecule drugs and metabolites in the cell medium directly sampled from liver organoids/OoC systems. The LC-MS setup was coupled to an automatic filtration and filter flush system with online solid-phase extraction (SPE), allowing for robust and automated sample cleanup/analysis. For the matrix, rich in, e.g., protein, salts, and amino acids, no preinjection sample preparation steps (protein precipitation, SPE, etc.) were necessary. The approach was demonstrated with tolbutamide and its liver metabolite, 4-hydroxytolbutamide (4HT). The method was validated for analysis of cell media of human stem cell-derived liver organoids cultured in static conditions and on a microfluidic platform according to Food and Drug Administration (FDA) guidelines with regards to selectivity, matrix effects, accuracy, precision, etc. The system allows for hundreds of injections without replacing chromatography hardware. In summary, drug/metabolite analysis of organoids/OoCs can be performed robustly with minimal sample preparation.

Pump-Less, Recirculating Organ-on-Chip (rOoC) Platform to Model the Metabolic Crosstalk between Islets and Liver

Type 2 diabetes mellitus (T2DM), obesity, and metabolic dysfunction-associated steatotic liver disease (MASLD) are epidemiologically correlated disorders with a worldwide growing prevalence. While the mechanisms leading to the onset and development of these conditions are not fully understood, predictive tissue representations for studying the coordinated interactions between central organs that regulate energy metabolism, particularly the liver and pancreatic islets, are needed. Here, a dual pump-less recirculating organ-on-chip platform that combines human pluripotent stem cell (sc)-derived sc-liver and sc-islet organoids is presented. The platform reproduces key aspects of the metabolic cross-talk between both organs, including glucose levels and selected hormones, and supports the viability and functionality of both sc-islet and sc-liver organoids while preserving a reduced release of pro-inflammatory cytokines. In a model of metabolic disruption in response to treatment with high lipids and fructose, sc-liver organoids exhibit hallmarks of steatosis and insulin resistance, while sc-islets produce pro-inflammatory cytokines on-chip. Finally, the platform reproduces known effects of anti-diabetic drugs on-chip. Taken together, the platform provides a basis for functional studies of obesity, T2DM, and MASLD on-chip, as well as for testing potential therapeutic interventions.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Multimodal Imaging of Pancreatic Cancer Microenvironment in Response to an Antiglycolytic Drug

Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3-bromopyruvate (3-BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor-associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody-functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface-enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC-MS) analysis. The study reveals the efficacy of 3-BP in treating pancreatic cancer and identifies drug treatment-induced lipid species remodeling and associated pathways through bioinformatics analysis.