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

LaLone, Vernon; Aizenshtadt, Aleksandra; Goertz, John; Skottvoll, Frøydis Sved; Mota, Marco Barbero; You, Junji; Zhao, Xiaoyu; Berg, Henriette Engen; Stokowiec, Justyna; Yu, Minzhi; Schwendeman, Anna; Scholz, Hanne; Wilson, Steven Ray; Krauß, Stefan; Stevens, Molly M.

doi:10.1016/j.crmeth.2023.100440

Cited by 11 publications

(12 citation statements)

References 53 publications

(78 reference statements)

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“…Although tissue models based on human donor material still represent the gold standard for functional and/or metabolic studies, hPSC-derived organoids start to resemble adult functionality and metabolic features thus rendering them suitable for disease modeling while offering better scalability and long-term stability in culture. [105] Throughout a series of improvements, we and others have obtained pluripotent stem cell-derived sc-islets [49,106,107] and sc-liver organoids [54,62,[108][109][110] that approach adult metabolic Figure 7. Anti-diabetic drug testing on-chip.…”

Section: Discussionmentioning

confidence: 99%

“…Sc-liver organoids were generated using a previously published 10-step protocol, [53,54] which includes an aggregation step at the stage of hepatic progenitors, ensuring the formation of uniform-size organoids of 150-200 μm in diameter (Figure 4a). Sc-liver organoids were benchmarked to freshly thawed primary human hepatocytes (PHH) characterized by the expression of hepatocytes-specific markers including hepatocyte nuclear factor 4 alpha (HNF4a), genes involved in glucose metabolism including glucose transporter 2 (GLUT2), glucose-6-phosphatase, catalytic subunit (G6PC), and cytochrome P450 genes including CYP3A4, CYP2B6 and CYP2C9 (Figure 4b).…”

Section: Generation Of Sc-islet Organoids and Sc-liver Organoidsmentioning

confidence: 99%

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

Aizenshtadt,

Wang,

Abadpour

et al. 2024

Adv Healthcare Materials

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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 (dual‐rOoC) 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.This article is protected by copyright. All rights reserved

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Section: Discussionmentioning

confidence: 99%

Section: Generation Of Sc-islet Organoids and Sc-liver Organoidsmentioning

confidence: 99%

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

Aizenshtadt,

Wang,

Abadpour

et al. 2024

Adv Healthcare Materials

Self Cite

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“…Later, they developed a quantitative Ramanomics (qRamanomics) approach for detailed analysis of 3D liver models in response to drug exposure. 152 Their approach involves a robust dataprocessing and calibration pipeline, allowing the label-free, multifactorial, chemometric phenotyping of biospecimens through confocal Raman imaging. With qRamanomics, they compared drug response of 3D primary human hepatocytes (3D PHH) and induced hepatocyte-like cells (3D iHLC), revealing distinct differences in lipid distribution and colocalization of other major biomolecules across drug treatment (Figure 5a).…”

Section: ■ Imaging Drug Response Through Biochemical and Metabolic Ch...mentioning

confidence: 99%

“…Using an inkjet printer to deposit biomolecular components to make microarrays across the surface of silicon chips, the authors were able to monitor the phospholipidosis of cells treated with amiodarone. Later, they developed a quantitative Ramanomics (qRamanomics) approach for detailed analysis of 3D liver models in response to drug exposure . Their approach involves a robust data-processing and calibration pipeline, allowing the label-free, multifactorial, chemometric phenotyping of biospecimens through confocal Raman imaging.…”

Section: Imaging Drug Response Through Biochemical and Metabolic Changesmentioning

confidence: 99%

“…Reprinted from Cell Rep. Methods , Vol. 3 (4), LaLone, V.; Aizenshtadt, A.; Goertz, J.; Skottvoll, F. S.; Mota, M. B.; You, J.; Zhao, X.; Berg, H. E.; Stokowiec, J.; Yu, M.; Schwendeman, A.; Scholz, H.; Wilson, S. R.; Krauss, S.; Stevens, M. M. Quantitative Chemometric Phenotyping of Three-Dimensional Liver Organoids by Raman Spectral Imaging, 100440 (ref ). Copyright 2023, with permission from Elsevier.…”

Section: Imaging Drug Response Through Biochemical and Metabolic Changesmentioning

confidence: 99%

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Innovative Approaches for Drug Discovery: Quantifying Drug Distribution and Response with Raman Imaging

Dunnington,

Wong,

2024

Anal. Chem.

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

Sheikh,

Agrawal,

Roy

et al. 2023

Adv Healthcare Materials

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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, we demonstrate the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody‐functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. Our multimodal imaging techniques include surface‐enhanced Raman spectroscopy (SERS), immunohistochemistry, 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.This article is protected by copyright. All rights reserved

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

Cited by 11 publications

References 53 publications

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

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

Innovative Approaches for Drug Discovery: Quantifying Drug Distribution and Response with Raman Imaging

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

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