Dataset Information

High-throughput "read-on-ski" automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids.

ABSTRACT: Organoid models are used to study kidney physiology, such as the assessment of nephrotoxicity and underlying disease processes. Personalized human pluripotent stem cell-derived kidney organoids are ideal models for compound toxicity studies, but there is a need to accelerate basic and translational research in the field. Here, we developed an automated continuous imaging setup with the "read-on-ski" law of control to maximize temporal resolution with minimum culture plate vibration. High-accuracy performance was achieved: organoid screening and imaging were performed at a spatial resolution of 1.1 μm for the entire multi-well plate under 3 min. We used the in-house developed multi-well spinning device and cisplatin-induced nephrotoxicity model to evaluate the toxicity in kidney organoids using this system. The acquired images were processed via machine learning-based classification and segmentation algorithms, and the toxicity in kidney organoids was determined with 95% accuracy. The results obtained by the automated "read-on-ski" imaging device, combined with label-free and non-invasive algorithms for detection, were verified using conventional biological procedures. Taking advantage of the close-to-in vivo-kidney organoid model, this new development opens the door for further application of scaled-up screening using organoids in basic research and drug discovery.

SUBMITTER: Wang Q

PROVIDER: S-EPMC9264113 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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High-throughput "read-on-ski" automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids.

Wang Qizheng Q Lu Jun J Fan Ke K Xu Yiwei Y Xiong Yucui Y Sun Zhiyong Z Zhai Man M Zhang Zhizhong Z Zhang Sheng S Song Yan Y Luo Jianzhong J You Mingliang M Guo Meijin M Zhang Xiao X

Journal of Zhejiang University. Science. B 20220701 7

Organoid models are used to study kidney physiology, such as the assessment of nephrotoxicity and underlying disease processes. Personalized human pluripotent stem cell-derived kidney organoids are ideal models for compound toxicity studies, but there is a need to accelerate basic and translational research in the field. Here, we developed an automated continuous imaging setup with the "read-on-ski" law of control to maximize temporal resolution with minimum culture plate vibration. High-accurac ...[more]

PMID: 35794686

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Project description:BackgroundMacrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively.ResultsWe show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest.ConclusionsThe computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without compromise in image quality or information loss in associated spectra. These results motivate further use of label free microscopy techniques in real-time imaging of live immune cells.

Dataset Information

High-throughput "read-on-ski" automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids.

Publications

High-throughput "read-on-ski" automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids.

Similar Datasets

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