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We developed a subtractive manufacturing with swelling induced stochastic folding of sacrificial materials for fabricating complex perfusable tissues in high-throughput multi-well plate formats.

Stem cell-derived organoids are a promising tool to model native human tissues as they resemble human organs functionally and structurally compared to traditional monolayer cell-based assays. For instance, colon organoids can spontaneously develop crypt-like structures similar to those found in the native colon. While analyzing the structural development of organoids can be a valuable readout, using traditional image analysis tools makes it challenging because of the heterogeneities and the abstract nature of organoid morphologies. To address this limitation, we developed and validated a deep learning-based image analysis tool, named D-CryptO, for the classification of organoid morphology. D-CryptO can automatically assess the crypt formation and opacity of colorectal organoids from brightfield images to determine the extent of organoid structural maturity. To validate this tool, changes in organoid morphology were analyzed during organoid passaging and short-term forskolin stimulation. To further demonstrate the potential of D-CryptO for drug testing, organoid structures were analyzed following treatments with a panel of chemotherapeutic drugs. With D-CryptO, subtle variations in how colon organoids responded to the different chemotherapeutic drugs were detected, which suggest potentially distinct mechanisms of action. This tool could be expanded to other organoid types, like intestinal organoids, to facilitate 3D tissue morphological analysis., Comment: Lab on a Chip (2022)

Organ-on-a-chip systems that recapitulate tissue-level functions have been proposed to improve in vitro–in vivo correlation in drug development. Significant progress has been made to control the cellular microenvironment with mechanical stimulation and fluid flow. However, it has been challenging to introduce complex 3D tissue structures due to the physical constraints of microfluidic channels or membranes in organ-on-a-chip systems. Although this problem could be addressed with the integration of 3D bioprinting, it is not an easy task because the two technologies have fundamentally different fabrication processes. Inspired by 4D bioprinting, we develop a 4D subtractive manufacturing technique where a flexible sacrificial material can be patterned on a 2D surface, change shape when exposed to aqueous hydrogel, and subsequently degrade to produce perfusable networks in a natural hydrogel matrix that can be populated with cells. The technique is applied to fabricate organ-specific vascular networks, vascularized kidney proximal tubules, and terminal lung alveoli in a customized 384-well plate and then further scaled to a 24-well plate format to make a large vascular network, vascularized liver tissues, and for integration with ultrasound imaging. This biofabrication method eliminates the physical constraints in organ-on-a-chip systems to incorporate complex ready-to-perfuse tissue structures in an open-well design.

Bioprinting offers unprecedented control in the 3D deposition of cells and biomaterials, but reproducing tissue microarchitecture and cell diversity remains challenging. Brassard et al. now overcome these limitations by bioprinting organoid-forming stem cells at high densities. This study opens new possibilities for controlling tissue structural complexities across multiple length scales.

Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. However, different from 2D monolayer culture, the characterization of 3D tissue models from non-invasive brightfield images is a significant challenge. To address this issue, here we report a Deep-Learning Uncovered Measurement of Epithelial Networks (Deep-LUMEN) assay. Deep-LUMEN is an object detection algorithm that has been fine-tuned to automatically uncover subtle differences in epithelial spheroid morphology from brightfield images. This algorithm can track changes in the luminal structure of tissue spheroids and distinguish between polarized and non-polarized lung epithelial spheroids. The Deep-LUMEN assay was validated by screening for changes in spheroid epithelial architecture in response to different extracellular matrices and drug treatments. Specifically, we found the dose-dependent toxicity of Cyclosporin can be underestimated if the effect of the drug on tissue morphology is not considered. Hence, Deep-LUMEN could be used to assess drug effects and capture morphological changes in 3D spheroid models in a non-invasive manner.Significance of the workDeep learning has been applied for the first time to autonomously detect subtle morphological changes in 3D multi-cellular spheroids, such as spheroid polarity, from brightfield images in a label-free manner. The technique has been validated by detecting changes in spheroid morphology in response to changes in extracellular matrices and drug treatments.

The wet strength of cellulose-cellulose joints, reinforced with PAE-loaded microgels, was decreased by nearly a factor of two when the labile disulfide crosslinks on the supporting microgels were exposed to a reducing agent. The supporting microgels were temperature and pH sensitive poly(N-isopropylmethacrylamide-co-acrylic acid) microgels, prepared with a disulfide crosslinker. The level of PAE loading increased with the microgel carboxyl content. This work illustrates a new approach to increasing the recyclability and compostability of wet-strength papers made with PAE wet-strength resin.

Correction for ‘Deep-LUMEN assay – human lung epithelial spheroid classification from brightfield images using deep learning’ by Lyan Abdul et al., Lab Chip, 2020, DOI: 10.1039/d0lc01010c.

Despite the complexity and structural sophistication that 3D organoid models provide, their lack of vascularization and perfusion limit the capability of these models to recapitulate organ physiology effectively. A microfluidic platform named IFlowPlate is engineered, which can be used to culture up to 128 independently perfused and vascularized colon organoids in vitro. Unlike traditional microfluidic devices, the vascularized organoid-on-chip device with an "open-well" design does not require any external pumping systems and allows tissue extraction for downstream analyses, such as histochemistry or even in vivo transplantation. By optimizing both the extracellular matrix (ECM) and the culture media formulation, patient-derived colon organoids are co-cultured successfully within a self-assembled vascular network, and it is found that the colon organoids grow significantly better in the platform under constant perfusion versus conventional static condition. Furthermore, a colon inflammation model with an innate immune function where circulating monocytes can be recruited from the vasculature, differentiate into macrophage, and infiltrate the colon organoids in response to tumor necrosis factor (TNF)- inflammatory cytokine stimulation is developed using the platform. With the ability to grow vascularized colon organoids under intravascular perfusion, the IFlowPlate platform could unlock new possibilities for screening potential therapeutic targets or modeling relevant diseases.