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Decellularized extracellular matrix (dECM)‐based hydrogels are widely applied to additive biomanufacturing strategies for relevant applications. The extracellular matrix components and growth factors of dECM play crucial roles in cell adhesion, growth, and differentiation. However, the generally poor mechanical properties and printability have remained as major limitations for dECM‐based materials. In this study, heart‐derived dECM (h‐dECM) and meniscus‐derived dECM (Ms‐dECM) bioinks in their pristine, unmodified state supplemented with the photoinitiator system of tris(2,2‐bipyridyl) dichlororuthenium(II) hexahydrate and sodium persulfate, demonstrate cytocompatibility with volumetric bioprinting processes. This recently developed bioprinting modality illuminates a dynamically evolving light pattern into a rotating volume of the bioink, and thus decouples the requirement of mechanical strengths of bioprinted hydrogel constructs with printability, allowing for the fabrication of sophisticated shapes and architectures with low‐concentration dECM materials that set within tens of seconds. As exemplary applications, cardiac tissues are volumetrically bioprinted using the cardiomyocyte‐laden h‐dECM bioink showing favorable cell proliferation, expansion, spreading, biomarker expressions, and synchronized contractions; whereas the volumetrically bioprinted Ms‐dECM meniscus structures embedded with human mesenchymal stem cells present appropriate chondrogenic differentiation outcomes. This study supplies expanded bioink libraries for volumetric bioprinting and broadens utilities of dECM toward tissue engineering and regenerative medicine.

 

With the rapid development and popularization of additive manufacturing, different technologies, including, but not limited to, extrusion-, droplet-, and vat-photopolymerization-based fabrication techniques, have emerged that have allowed tremendous progress in three-dimensional (3D) printing in the past decades. Bioprinting, typically using living cells and/or biomaterials conformed by different printing modalities, has produced functional tissues. As a subclass of vat-photopolymerization bioprinting, digital light processing (DLP) uses digitally controlled photomasks to selectively solidify liquid photocurable bioinks to construct complex physical objects in a layer-by-layer manner. DLP bioprinting presents unique advantages, including short printing times, relatively low manufacturing costs, and decently high resolutions, allowing users to achieve significant progress in the bioprinting of tissue-like complex structures. Nevertheless, the need to accommodate different materials while bioprinting and improve the printing performance has driven the rapid progress in DLP bioprinters, which requires multiple pieces of knowledge ranging from optics, electronics, software, and materials beyond the biological aspects. This raises the need for a comprehensive review to recapitulate the most important considerations in the design and assembly of DLP bioprinters. This review begins with analyzing unique considerations and specific examples in the hardware, including the resin vat, optical system, and electronics. In the software, the workflow is analyzed, including the parameters to be considered for the control of the bioprinter and the voxelizing/slicing algorithm. In addition, we briefly discuss the material requirements for DLP bioprinting. Then, we provide a section with best practices and maintenance of a do-it-yourself DLP bioprinter. Finally, we highlight the future outlooks of the DLP technology and their critical role in directing the future of bioprinting. The state-of-the-art progress in DLP bioprinter in this review will provide a set of knowledge for innovative DLP bioprinter designs. 

Droplet‐based bioprinting has shown remarkable potential in tissue engineering and regenerative medicine. However, it requires bioinks with low viscosities, which makes it challenging to create complex 3D structures and spatially pattern them with different materials. This study introduces a novel approach to bioprinting sophisticated volumetric objects by merging droplet‐based bioprinting and cryobioprinting techniques. By leveraging the benefits of cryopreservation, we fabricated, for the first time, intricate, self‐supporting cell‐free or cell‐laden structures with single or multiple materials in a simple droplet‐based bioprinting process that is facilitated by depositing the droplets onto a cryoplate followed by crosslinking during revival. The feasibility of this approach is demonstrated by bioprinting several cell types, with cell viability increasing to 80%–90% after up to 2 or 3 weeks of culture. Furthermore, the applicational capabilities of this approach are showcased by bioprinting an endothelialized breast cancer model. The results indicate that merging droplet and cryogenic bioprinting complements current droplet‐based bioprinting techniques and opens new avenues for the fabrication of volumetric objects with enhanced complexity and functionality, presenting exciting potential for biomedical applications. 

Volumetric printing, an emerging additive manufacturing technique, builds objects with enhanced printing speed and surface quality by forgoing the stepwise ink-renewal step. Existing volumetric printing techniques almost exclusively rely on light energy to trigger photopolymerization in transparent inks, limiting material choices and build sizes. We report a self-enhancing sonicated ink (or sono-ink) design and corresponding focused-ultrasound writing technique for deep-penetration acoustic volumetric printing (DAVP). We used experiments and acoustic modeling to study the frequency and scanning rate–dependent acoustic printing behaviors. DAVP achieves the key features of low acoustic streaming, rapid sonothermal polymerization, and large printing depth, enabling the printing of volumetric hydrogels and nanocomposites with various shapes regardless of their optical properties. DAVP also allows printing at centimeter depths through biological tissues, paving the way toward minimally invasive medicine.

 

Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the input data. Recently, these two areas have become integrated to achieve synergy for accelerating drug screening. This review provides a brief description of the basic concepts of deep learning used in OoCs and exemplifies the successful use cases for different types of OoCs. These microfluidic chips are of potential to be assembled as highly potent human-on-chips with complex physiological or pathological functions. Finally, we discuss the future supply with perspectives and potential challenges in terms of combining OoCs and deep learning for image processing and automation designs.