A classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy

We combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established non-alcoholic steatohepatitis (NASH) mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression

Engineering Three Dimensional In Vitro Models of Bone Tumors for Drug Testing and Mechanistic Studies

Development of anti-cancer therapeutics has been traditionally reliant on two-dimensional (2D) systems and animal models, both of which have major limitations that contribute to the poor clinical translation of preclinical findings. The goal of this thesis work was to develop three-dimensional (3D) in vitro models of bone malignancies for accurate drug testing and mechanistic studies. To this end, I investigated the use of different 3D scaffolds to recreate the distinct in vivo bone niches relevant for these bone cancers in vitro. First, I evaluated the use of electrospun poly(ε-caprolactone) scaffolds to provide 3D architectural cues for the culture of Ewing sarcoma (EWS) cells. 3D-cultured EWS cells were remarkably different from the same cells cultured in 2D, and more similar to those grown in vivo with respect to morphology, growth kinetics, and protein expression. This work underscored the importance of providing a 3D context for tumor growth in vitro. The second part of this thesis investigated the use of 3D hyaluronan (HA) hydrogels to support the culture of bone metastatic prostate cancer (PCa) cells. Due to their high fidelity to the tumor of origin, there is an emerging interest in the use of patient-derived xenograft (PDX) models to overcome the limitations of cancer cell lines. However, existing PDX culture systems are few and limited. Hence, I sought to develop an in vitro PCa PDX model by first establishing a method to enrich for PCa PDX tumor cells, then evaluated the ability of 3D hyaluronan (HA) hydrogels to maintain the viability, morphology, growth and phenotype of the encapsulated tumor cells. This work demonstrated the feasibility of using a 3D scaffold-based approach to culture PDX tumor cells in vitro. Lastly, I incorporated integrin-binding and matrix metalloproteinase-degradable peptides to HA hydrogels to support osteoblast culture with PCa PDX cells in 3D. Through this 3D co-culture system, the in vivo structural organization, phenotype, as well as biochemical crosstalk between PCa and osteoblasts in bone was recapitulated. In this work, I demonstrate for the first time, the feasibility of co-culturing PDX tumor cells with stromal cells in vitro using a tunable 3D system for controlled mechanistic investigations

Magnetic force-based cell manipulation for in vitro tissue engineering

Cell manipulation techniques such as those based on three-dimensional (3D) bioprinting and microfluidic systems have recently been developed to reconstruct complex 3D tissue structures in vitro. Compared to these technologies, magnetic force-based cell manipulation is a simpler, scaffold- and label-free method that minimally affects cell viability and can rapidly manipulate cells into 3D tissue constructs. As such, there is increasing interest in leveraging this technology for cell assembly in tissue engineering. Cell manipulation using magnetic forces primarily involves two key approaches. The first method, positive magnetophoresis, uses magnetic nanoparticles (MNPs) which are either attached to the cell surface or integrated within the cell. These MNPs enable the deliberate positioning of cells into designated configurations when an external magnetic field is applied. The second method, known as negative magnetophoresis, manipulates diamagnetic entities, such as cells, in a paramagnetic environment using an external magnetic field. Unlike the first method, this technique does not require the use of MNPs for cell manipulation. Instead, it leverages the magnetic field and the motion of paramagnetic agents like paramagnetic salts (Gadobutrol, MnCl2, etc.) to propel cells toward the field minimum, resulting in the assembly of cells into the desired geometrical arrangement. In this Review, we will first describe the major approaches used to assemble cells in vitro—3D bioprinting and microfluidics-based platforms—and then discuss the use of magnetic forces for cell manipulation. Finally, we will highlight recent research in which these magnetic force-based approaches have been applied and outline challenges to mature this technology for in vitro tissue engineering

Development of a classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy

Non-alcoholic fatty liver disease (NAFLD) is the most common liver disorder in developed countries [1]. A subset of individuals with NAFLD progress to non-alcoholic steatohepatitis (NASH), an advanced form of NAFLD which predisposes individuals to cirrhosis, liver failure and hepatocellular carcinoma. The current gold standard for NASH diagnosis and staging is based on histological evaluation, which is largely semi-quantitative and subjective. To address the need for an automated and objective approach to NASH detection, we combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established NASH mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression. By employing a selected pool of biochemical components, we identified biochemical changes specific to NASH and show that the classification model is capable of accurately detecting NASH (AUC=0.85–0.87) in mice. The unique biochemical fingerprint generated in this study may serve as a useful criterion to be leveraged for further validation in clinical samples.Singapore. National Research Foundation (under its CREATE programme)Singapore-MIT Alliance. BioSystems and Micromechanics (BioSyM) Inter-Disciplinary Research GroupSingapore. Agency for Science, Technology and Research (Project Number 1334i00051)Singapore. National Medical Research Council (R-185-000-294-511)National University of Singapore. Mechanobiology Institute (R-714-001-003-271)National Institutes of Health (U.S.) (9P41EB015871-28)Samsung Advanced Institute of TechnologySingapore. National Medical Research Council (Open Fund Individual Research Grant scheme (OFIRG15nov062

Datasets describing the growth and molecular features of hepatocellular carcinoma patient-derived xenograft cells grown in a three-dimensional macroporous hydrogel

This data article presents datasets associated with the research article entitled “Generation of matched patient-derived xenograft in vitro–in vivo models using 3D macroporous hydrogels for the study of liver cancer” (Fong et al., 2018) [1]. A three-dimensional macroporous sponge system was used to generate in vitro counterparts to various hepatocellular carcinoma patient-derived xenograft (HCC-PDX) lines. This article describes the viability, proliferative capacity and molecular features (genomic and transcriptomic profiles) of the cultured HCC-PDX cells. The sequencing datasets are made publicly available to enable critical or further analyzes

Organoids

Organoids have attracted increasing attention because they are simple tissue-engineered cell-based in vitro models that recapitulate many aspects of the complex structure and function of the corresponding in vivo tissue. They can be dissected and interrogated for fundamental mechanistic studies on development, regeneration, and repair in human tissues. Organoids can also be used in diagnostics, disease modeling, drug discovery, and personalized medicine. Organoids are derived from either pluripotent or tissue-resident stem (embryonic or adult) or progenitor or differentiated cells from healthy or diseased tissues, such as tumors. To date, numerous organoid engineering strategies that support organoid culture and growth, proliferation, differentiation and maturation have been reported. This Primer serves to highlight the rationale underlying the selection and development of these materials and methods to control the cellular/tissue niche; and therefore, structure and function of the engineered organoid. We also discuss key considerations for generating robust organoids, such as those related to cell isolation and seeding, matrix and soluble factor selection, physical cues and integration. The general standards for data quality, reproducibility and deposition within the organoid community is also outlined. Lastly, we conclude by elaborating on the limitations of organoids in different applications, and key priorities in organoid engineering for the coming years

Deep learning enables automated scoring of liver fibrosis stages

Current liver fibrosis scoring by computer-assisted image analytics is not fully automated as it requires manual preprocessing (segmentation and feature extraction) typically based on domain knowledge in liver pathology. Deep learning-based algorithms can potentially classify these images without the need for preprocessing through learning from a large dataset of images. We investigated the performance of classification models built using a deep learning-based algorithm pre-trained using multiple sources of images to score liver fibrosis and compared them against conventional non-deep learning-based algorithms - artificial neural networks (ANN), multinomial logistic regression (MLR), support vector machines (SVM) and random forests (RF). Automated feature classification and fibrosis scoring were achieved by using a transfer learning-based deep learning network, AlexNet-Convolutional Neural Networks (CNN), with balanced area under receiver operating characteristic (AUROC) values of up to 0.85–0.95 versus ANN (AUROC of up to 0.87–1.00), MLR (AUROC of up to 0.73–1.00), SVM (AUROC of up to 0.69–0.99) and RF (AUROC of up to 0.94–0.99). Results indicate that a deep learning-based algorithm with transfer learning enables the construction of a fully automated and accurate prediction model for scoring liver fibrosis stages that is comparable to other conventional non-deep learning-based algorithms that are not fully automated

Actomyosin contractility drives bile regurgitation as an early response during obstructive cholestasis

10.1016/j.jhep.2017.01.026JOURNAL OF HEPATOLOGY6661231-124