Next Article in Journal
Mesoporous Silica Nanoparticles as a Potential Nanoplatform: Therapeutic Applications and Considerations
Previous Article in Journal
Aging-Related Mechanisms Contribute to Corticosteroid Insensitivity in Elderly Asthma
Previous Article in Special Issue
Microfluidic Organ-on-A-chip: A Guide to Biomaterial Choice and Fabrication
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 7
10.3390/ijms24076357
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

3D Bioprinting for Next-Generation Personalized Medicine

by
Ethan Hau Yin Lam
1,2,3,
Fengqing Yu
1,4,
Sabrina Zhu
1,5 and
Zongjie Wang
6,7,8,*
1
Faculty of Arts and Science, University of Toronto, Toronto, ON M5S 3G3, Canada
2
Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON M5S 1A8, Canada
3
Department of Nutritional Sciences, University of Toronto, Toronto, ON M5S 1A8, Canada
4
Department of Computer Science, University of Toronto, Toronto, ON M5S 1A4, Canada
5
Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A8, Canada
6
Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada
7
Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3E1, Canada
8
McCormick School of Engineering, Northwestern University, Chicago, IL 60611, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(7), 6357; https://doi.org/10.3390/ijms24076357
Submission received: 1 January 2023 / Revised: 20 March 2023 / Accepted: 22 March 2023 / Published: 28 March 2023
(This article belongs to the Special Issue Materials for Bioengineering and Biotechnology)
Browse Figures
Versions Notes

Abstract

:
In the past decade, immense progress has been made in advancing personalized medicine to effectively address patient-specific disease complexities in order to develop individualized treatment strategies. In particular, the emergence of 3D bioprinting for in vitro models of tissue and organ engineering presents novel opportunities to improve personalized medicine. However, the existing bioprinted constructs are not yet able to fulfill the ultimate goal: an anatomically realistic organ with mature biological functions. Current bioprinting approaches have technical challenges in terms of precise cell deposition, effective differentiation, proper vascularization, and innervation. This review introduces the principles and realizations of bioprinting with a strong focus on the predominant techniques, including extrusion printing and digital light processing (DLP). We further discussed the applications of bioprinted constructs, including the engraftment of stem cells as personalized implants for regenerative medicine and in vitro high-throughput drug development models for drug discovery. While no one-size-fits-all approach to bioprinting has emerged, the rapid progress and promising results of preliminary studies have demonstrated that bioprinting could serve as an empowering technology to resolve critical challenges in personalized medicine.

1. Introduction

Therapeutic strategies have traditionally been developed for a broad population of patients that have been primarily categorized using pathological observations and clinical features [1]. However, various therapeutic strategies, including medications, are currently being designed to recognize variable efficacy and adverse effects in patients depending on differing lifestyles, genomic signatures, and additional comorbidities [1,2]. The genetic variability of patients also leads to the risk of organ rejection in regenerative medicine and organ transplantation (notably, HLA and minor histocompatibility antigen mismatch between donor and recipient), which has slowed the translation from an in vitro disease model to further testing in the clinical patient-care setting [3]. Given that disease states are impacted by a complex combination of factors, there is a significant need to individualize patient treatments. Personalized medicine is a rapidly advancing field of health care that uses an individual’s unique genetic profile to direct prevention, diagnosis, and treatment [2]. It aims to tailor and individualize therapeutic treatment plans according to the patients’ physiology, genetic profile, and drug response [2]. As a result, patients may receive a treatment with optimal efficacy and dosage, which overall enhances patient safety and clinical outcomes by avoiding misdiagnosis and adverse drug effects. Industries are also likely to benefit from reduced healthcare costs and favorable patient outcomes [4].
Personalized medicine has undergone multiple breakthroughs in the last several decades. These major discoveries are summarized in Figure 1. For example, the discovery of stem cells, especially human-induced pluripotent stem cells (hiPSC) by the Yamanaka factors [5] revolutionized the field of disease treatments, as they provided a method to generate induced pluripotent stem cells (iPSCs) without the use of embryos, which eliminated ethical concerns [6]. Currently, there are numerous iPSC-derived cell products and grafts in the pipeline of clinical translation. In addition to biological discovery, engineering technologies have also played an important role in the breakthrough of personalized medicine. The world’s first 3D-printeYes, d drug, Spritam, was approved by the U.S. Food and Drug Administration (FDA) for the treatment of partial-onset seizures for epilepsy [7].
While major advancements and technologies in precision medicine have been accomplished within the last few decades that have identified more sensitive and specific biomarkers and have better mediated drug dosages and selection research, challenges restricting the potential of personalized medicine remain [2]. The difficulties mainly lie in the validation of experimental results, accessibility of patient data, and transferability to the clinical setting. While numerous of genetic markers have been discovered, it was reported in the 2008 PCAST that the clinical validation of these candidate markers has been proceeding at a slower pace [8]. Many selected therapeutic agents that were successful in models could not be readily adapted for the clinical context and have failed because of drug intolerance, highlighting the need for accurate and representative pre-clinical models [9]. As patient samples are often related to ethical, legal, and social issues regarding accessibility, there has been a focus on developing patient-derived models, such as immortalized cell lines, patient-derived xenografts (PDXs), and organoids. As compared to traditional monolayer cultures, PDXs and organoids are better at mimicking in vivo micro-environments and, thus, have improved the generalizability of experimental outcomes to clinical contexts [9,10]. However, they have been limited due to their time-intensive requirements and significant costs [10]. Moreover, there has been a lack of consistency in the components required and processes involved in organoid protocols among different researchers, which has resulted in unexpected variabilities when comparing their results [9]. Therefore, developing high-throughput, consistent, and representative experimental models is important to facilitate personalized therapy and maximize treatment efficacy.
Recently, 3D bioprinting has emerged as a potential method for resolving the aforementioned challenges. The fabrication of 3D biological structures with multiple functional, structural, and mechanical components and properties is the core of 3D bioprinting, and this is performed through the precise, layer-by-layer positioning of biological materials, biochemicals, and living cells [11]. As a result, 3D cultures created by bioprinting technologies have been used to facilitate drug development and toxicity, as well as the study of tissue and disease formation and progression [12]. In particular, its application for in vitro tissue and organ modeling that is capable of mimicking human physiology presents novel opportunities, such as personalized implants and drug development, to improve personalized medicine. While 3D bioprinting is an exciting potential tool for advancing personalized medicine, the existing bioprinted constructs are not yet able to fulfill the ultimate goal: an anatomically realistic organ with mature biological functions. Current bioprinting approaches still have technical challenges in terms of precise cell deposition, effective differentiation, proper vascularization, and innervation.
This review provides an overview of the current developments in bioprinting techniques and applications and assesses the potential of 3D bioprinting to advance personalized medicine. Established bioprinting techniques, with an emphasis on extrusion printing and digital light processing (DLP), and available bioprinting materials, such as induced pluripotent stem cells, were considered. Major applications for bioprinted constructs, including the engraftment of stem cells as personalized implants for regenerative medicine, and in vitro high-throughput drug development models for drug discovery were evaluated. Finally, we summarized the current state-of-the-art in bioprinting, as well as the key challenges, and concluded with a brief perspective of bioprinting in personalized medicine.

2. Bioprinting: Methods and Materials

Bioprinting consists of two important aspects: the printing methods and the corresponding materials. This section reviews the concepts and advancements within these aspects to achieve optimized performance. There are four main bioprinting technologies: inkjet, laser-assisted, extrusion, and stereolithography (Figure 2). While each bioprinting technology has both strengths and limitations, each may be utilized according to the intended application to further develop the field of personalized medicine. A detailed comparison of different bioprinting methods is provided in Table 1 and in reference [13].

2.1. Bioprinting Technology

2.1.1. Inkjet Printing

Inkjet printing is the first bioprinting technology with a cartridge that stores bio-ink. This method is generally known for its affordability and easy accessibility, as commercial printers can be easily modified to accommodate the technology [13,14,15,16]. It has also demonstrated high cell viability; approximately 80–90% of cells remain functional [16]. This process utilizes a thermal or piezoelectric actuator to generate droplets of the bio-ink consistently.
The thermal actuator utilizes heat-induced bubble nucleation that causes pressure to build-up for the expulsion of the droplet. Thermal-based inkjet bioprinters typically reach temperatures of 100–300 °C. As a result of this high heat, this method of inkjet printing can cause cellular stress in the bio-ink [17]. However, it may be possible to mitigate the cellular stress factor by localizing the high temperatures on the cell for a minimal amount of time [18,19]. Alternatively, piezoelectric inkjet bioprinters, which produce acoustic waves at low viscosities and concentrations, have been used [20].

2.1.2. Extrusion Printing

Extrusion-based bioprinting is a modification of inkjet printing that exerts a constant force on the bio-ink during output [21]. This results in a cylindrical printed stream that attaches to the intended surface as a continuous line [22,23]. This is a significantly different approach to the singular, high-cell-density, bio-ink droplets found in standard inkjet bioprinting [24]. The limitations of this technique include a reduced cell viability due to the exposure of bio-inks to further mechanical stress and having a limited resolution over 100 µm [25].

2.1.3. Laser-Assisted Printing

Laser-assisted printing is a form of bioprinting that propels bio-ink onto the printing surface using a high-pressure bubble [20,21]. The formation of this bubble begins with a “donor layer”, also known as a ribbon. It contains an energy-absorbing layer, which is subjected to a high-energy laser pulse that vaporizes the layer at the focal spot [24]. The donor layer, which is composed of a thin layer of glass, metal, and bio-ink, is ultimately vaporized into a high-pressure bubble that enables the bio-ink to be propelled onto the printing surface [26]. Koch and their colleagues demonstrated that the jet velocity has been shown to be higher for smaller focal spots and dependent on the laser intensity. More specifically, at a laser intensity of 1.4 J/cm2, the jet velocity on a spot size of 4000 μm2 was higher, as compared to the velocity at 3000 μm2 [27]. This bioprinting method has numerous advantages. It promotes excellent cell viability at a high resolution, between 10–50 µm [11], while also avoiding contact between the dispenser nozzle and the bio-ink. However, this printing method is expensive, and the long-term effects of the laser have yet to be fully explored [24].

2.1.4. Stereolithography

Stereolithography utilizes visible or ultraviolet (UV) light via DLP to solidify bio-ink in a layer-by-layer process [24,25,26,27]. This process is known for eliminating shear pressure during nozzle-based printing due to its rapid, highly precise resolution, with a resolution range of 5–300 µm [26]. The exposure of light ultimately results in the solidification of the layers via photopolymerization [28]. For example, Gauvin et al. fabricated 3D structures utilizing a gelatin-based prepolymer solution under UV light [29]. However, the UV light exposure placed the bio-ink cells at risk of cytotoxic damage from photo-initiators, which could negatively impact cell viability [26,30]. Researchers have attempted to solve this problem by adding two monomers of alkene or thiol groups that could react spontaneously under UV irradiation at 266 nm. After 3 days, researchers observed that the cell viability was much higher, than the control variable, at approximately 95% [31].
Advancements in personalized medicine have been limited primarily by the expense of using 3D bioprinters. Furthermore, better technology and more precise bioprinting is likely to result in even higher costs. This ultimately makes more costly options, such as laser-assisted printers, inaccessible to medical laboratories and institutes, which in turn limits the advanced research on laser-assisted printing in personalized medicine.
Table
Table 1. Comparison of four major types of bioprinting techniques.
Table 1. Comparison of four major types of bioprinting techniques.
Bioprinting MethodKey AspectsAdvantagesDisadvantagesReferences
InkjetFirst bioprinting technology that has a bio-ink cartridge. Minimum droplet volume of 20 nL
  • Low cost
  • Easy accessibility
  • High cell viability (>85%)
  • Thermal actuator is potentially prone to high-temperature stress
[13,14,15,16]
ExtrusionA modification of inkjet-based bioprinting that prints a cylindrical stream onto a printing surface in a continuous line
  • Highly controlled printing structure
  • Limited resolution (100–500 µm)
  • Reduced cell viability
[22,23,32]
Laser-assistedPropels bio-ink onto the printing surface
  • High cell viability (>95%)
  • Fast printing speed
  • High resolution (10–50 µm)
  • High cost
  • Long-term effects of laser unclear
[11,20,21,33]
StereolithographyUses UV light to solidify bio-ink layer-by-layer
  • Fast printing speed
  • High resolution
  • Excellent cell adhesion
  • High cost
  • UV light exposure could reduce cell viability via cytotoxic damage
[24,25,27,34]
Note: More information is provided in Table 1 of reference [13].

2.2. Cell Source and Bio-Inks

In addition to printing technologies, the formulation of bio-inks, usually in the combination of supportive biomaterials and specific cell types, is critical for the success of bioprinting. The biomaterials used in bioprinting can be roughly divided into two subtypes: (1) natural biomaterials, such as alginate [14,35,36,37], agarose [35,38], collagen [39,40], and nanocellulose [41]; and (2) synthetic biomaterials, such as polyethylene glycol diacrylate (PEDGA) [42], and Pluoronic® [43,44]. Each material has unique mechanical (e.g., printability) and biological properties (e.g., the ability to support long-term cell adhesion and growth). A detailed comparison of the different bioprinting methods is provided in Table 2 and in reference [13].
The cell source for bioprinting is an important factor. To fabricate complex tissues and organs that mimic their natural counterparts, the cells for bioprinting need to not only reproduce the desired biological function but also to expand as required, as overexpansion could result in hyperplasia or cell death [45]. The difficulty of culturing many primary cells and their limited lifespans has made reproducing them via bioprinting infeasible [45], and the intrinsic abnormalities and mutations found in immortalized cell lines of tissues has limited their application as well [46]. Therefore, there has been increased interest using stem cells in 3D bioprinting.
Stem cells have excellent therapeutic potential because of their unique self-renewal and pluripotency. Due to the ethical concerns regarding embryonic stem cells, researchers have identified methods to induce pluripotency by reprogramming human somatic cells [5,47]. Bioprinting with these hiPSCs has unique advantages for regenerative and personalized medicine applications, such as patient-specific disease modeling, personalized implants, and drug development. This section introduces the current progress in bioprinting using stem cells and the application for personalized implants.
Human pluripotent stem cells are sensitive to environmental parameters [48], and thus, maintaining cell viability, pluripotency, and differentiation during and after the printing process is of high interest (Figure 3). Many researchers have studied different types of bioprinting and their effects on bio-inks to identify optimal printing methods. For example, both extrusion printing and laser printing did not affect the survival and pluripotency of hiPSCs [48,49]. There have also been novel technologies, such as a “microscopic painting device using a painting needle method” that improved the printing resolution while maintaining the high viscosity of the bio-ink and cell viability [50].
The cell-laden matrix, or the bio-ink, is another factor that influences stem cells [51,52,53]. Several studies confirmed that the use of Matrigel as a hydrogel could maintain pluripotency [48,54]. There have also been advancements in bio-ink formulations. Crook et al. used a clinically amenable bio-ink that was cross-linked to a 3D construct in order to maintain the multilineage cell-induction potential of iPSCs [55]. Cofiño et al. created a new formulation of bio-ink involving a methylcellulose and RAD16-I-based biomaterial that had high printability and biocompatibility with embedded human mesenchymal stem cells, resulting in high viability and differentiation [56]. Researchers proposed a core-sheath multilayer cell-laden structure that combined a cell-laden collagen-based bio-ink for the core and pure alginate for the sheath, and it protected cells and achieved efficient differentiation [57]. Additionally, there has been increasing interest in decellularized extracellular matrix (dECM) bio-inks [58,59], which create a more natural micro-environment and are likely enhance cellular function. Jang et al. showed that heart-tissue-derived dECM bio-ink provided benefits for the maturation of cardiac progenitor cells (CPC) and improved the epicardial activation, as compared with collagen bio-ink [60].

3. Applications in the Discovery of Personalized Medicine

With the aid of advanced bio-inks and 3D bioprinting technology that can maintain the viability and potency of cellular materials, such as stem cells, there have been successful applications in tissue regeneration and drug-development models. This section presents examples of bioprinting used to regenerate tissue for bones, livers, hearts, corneas, and other tissues required by the central neural system, highlighting factors such as cell viability, functionality, proliferation, maturation, and in vivo transplantation. In addition, the application of bioprinting for the development of biomimetic platforms for drug development is introduced. Examples of 3D-bioprinted cardiac tissue models, hepatic models, and glioblastoma models are presented to illustrate the potential of 3D bioprinting in advancing drug development in personalized medicine. The selected major discoveries are summarized in Table 3.

3.1. Printing of Stem-Cell Differentiated Organs for Tissue Regeneration

3.1.1. Bone

Currently, 3D bioprinting is a promising approach for bone and cartilage regeneration [61]. In bone tissue engineering, a scaffold has been constructed as a bone substitute that provided mechanical support and facilitated cellular activities, such as migration, proliferation, and differentiation. Poly(lactide) (PLA) has become one of the most used biomaterials for scaffolds, as it is biocompatible and biodegradable. Researchers combined 3D PLA scaffolds with human gingival mesenchymal stem cells (hGMSCs) and extracellular vesicles (EVs) to test their cytotoxicity and regeneration effects [62]. The PLA degradation of byproducts did not induce a cytotoxic response. After six weeks of in vivo implantation in rats subjected to cortical calvaria bone tissue damage, new bone nodules and blood vessels were observed in the calvariae [62]. Additionally, Teixeira et al. further showed an improvement in the osteo-inductivity of 3D-printed PLA scaffolds by incorporating polydopamine (PDA) and type-I collagen as surface coatings [63].

3.1.2. Kidney

The bioengineering of kidneys has been challenging due to the organs’ complex development, spatial organization, and lineage specifications [64]. Studies have shown that in vitro generated 3D cellular aggregates have superior long-term stability, as compared to 2D differentiation [65]. This agreed with findings by Goulart et al. [66], where bioprinting hepatic tissues using human liver iPS-derived parenchymal cells as 3D spheroids enhanced their cellular survival and function, as compared with in vitro single-cell dispersions [67].
Table
Table 3. Summary of selected major discoveries in bioprinting applications.
Table 3. Summary of selected major discoveries in bioprinting applications.
Target TissueBioprinting MethodCell TypeBiomaterialCellular ResponseReferences
BoneCommercial fused-filament fabrication 3D printer (DeltaWASP 2040; CSP srl, Massa Lombarda, Italy)Human gingival mesenchymal stem cells (hGMSCs)Poly(lactide) (PLA), extracellular vesicles (EVs), polyethyleneimine (PEI)-engineered EVs (PEI-EVs)(1) Both 3D-PLA + EVs + hGMSCs and 3D-PLA + PEI-EVs + hGMSCs showed no cytotoxicity
(2) Better osteogenic properties were observed in 3D-PLA + PEI-EVs + hGMSCs. New bone nodules and blood vessels were observed in calvariae after in vivo implantation in rats subjected to cortical calvaria bone tissue damage.		[62]
3D Cloning FDM printer (Microbras, Brazil), PLA white commercial filament
(1.75 mm in diameter, produced by E-Sun, China)		Porcine bone marrow stem cells (MSCs)Poly(lactic acid) (PLA), polydopamine (PDA), type-I colla-gen (COL I)PDA combined with COL coating increased cell adhesion and the metabolic activity of MSCs in the early stage (<7 days) of cell culture and facilitated the deposition of extracellular matrix by day 14, and produced much higher amounts of alkaline phosphatase than un-coated PLA by day 21.
Kidney and LiverExtrusion bioprinting (Cellink INKREDIBLE + 3D bioprinter)iPS-derived parenchymal (hepatocyte-like) cells, iPS-derived
hepato-cyte-like cells spheroids		MatrigelLiver constructs from 3D printing with hepatic spheroids showed prolonged survival, reduced cell death, increased urea production, and prolonged secretion of albumin and A1AT, as compared to printed constructs using single-cell dispersion.[66]
Extrusion bioprinting (Novogen 3D bio-printer)iPSCSTEMdiff APEL and TESR-E6 mediumBioprinted line conformation increased nephron numbers, as measured by an increase in MAFB+ glomerular area, as compared to manual organoids.[68]
HeartSpheroid bioprinting with microfluidic-chip-based 3D cell-culturing system (Regenova, Cyfuse Bio-medical K.K., Tokyo, Japan)Human-induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs), human adult ventricular cardiac fibroblasts (FBs), and human umbilical vein endothelial cells (ECs)Free of biomaterialsIn vivo implantation of the 3D-bioprinted cardiac patches onto nude rat hearts showed viable cells in the patch along with erythrocytes (evidence of vascularization), and the presence of human nucleic acid (HNA)-positive cells in rat myocardium (evidence of engraftment).[69]
Spheroid bioprinting with microfluidic-chip-based 3D cell-culturing system (Regenova, Cyfuse Biomedical K.K., Tokyo, Japan)Human IPSC-derived cardiomyocytes, fibroblasts, and endothelial cellsFree of biomaterialsIn vivo implantation of the bioprinted cardiac patches onto rat myocardial infarction model showed lower scar area, higher vessel count, and higher cardiac output than the control group without the implantation. The survival rates were 100% and 83% in the experimental and the control groups, respectively, after 4 weeks of surgery[70]
NerveMicro-extrusion bioprintingFrontal cortical human neural stem cells (hNSCs)Polysaccharides alginate (Al), carboxymethyl-chitosan (CMC), and agarose (Ag)Co-printing of cells with bio-ink allowed the formation of a porous 3D-scaffold encapsulation of stem cells for in situ expansion and differentiation. Differentiated neurons formed synaptic contacts and showed spontaneous calcium spikes and bicuculline-induced bursting activity.[71]
Extrusion bioprintingCortical neurons and glial cells de-rived from human iPSCsMatrigel and alginateLong-term survival of neurons, up to 70 days post-printing, was observed. Functional analysis showed calcium activity and a small degree of synchronous activity.[49]
Lab-on-a-printer (LOP) technology (Aspect Biosystems’ RX1 printer)hiPSC-derived neural progenitor cellsFibrinogen base with alginate, cross-linked with a mixture of chitosan, calcium chloride, thrombin, and genipinCell viability was 91.65 ± 6.85% by day 6 of the culture period, and 64.12 ± 21.27% by day 15. The printed neural tissues showed neurite extension and the expression of neuronal marker TUJ1 and nucleated cell marker[72,73]
Extrusion bioprintingInduced pluripotent stem cell (iPSC)-derived spinal neuronal progenitor cells (sNPCs) and oligodendrocyte progenitor cells (OPCs)MatrigelCell viability was >75% for both iPSC-derived sNPCs and OPCs printed in 50% Matrigel after 4 days in culture. The bioprinted sNPCs differentiated and showed progressive axon propagation in the micro-scale scaffold channels. Functionality was verified by cellular response signaling molecules, potassium and glutamate.[54]
PancreasMicro-extrusion bioprintingHuman umbilical vein endothelial cellsPancreatic tissue-derived dECM (pdECM)PdECM increased the insulin secretion over the conventionally applied biomaterials, alginate and collagen. Co-culturing with human umbilical vein-derived endothelial cells decreased the central necrosis of islets. Culturing in both 3D gels (without printing) and the printed construct showed similar viability on days 1 and 5.[74]
CorneaLaser-assisted bioprinting (LaBP)Human embryonic stem-cell-derived limbal epithelial stem cells (hESC-LESC), human adipose-tissue-derived stem cells (hASCs)Recombinant human laminin and human sourced collagen IThe printed hESC-LESCs retained an epithelium-like structure and showed apical expression of CK3 and basal expression of the progenitor markers. After 7 days in vivo transplantation in the porcine organ, the 3D-bioprinted stromal structures showed interaction and attachment to the host tissue.[75]
One of the major challenges in the transplantation of stem-cell derived kidney tissue has been the limited number of nephron structures. Takasato et al. reported the presence of approximately 100 nephrons within a transwell-cultured micro-mass kidney organoid that was initiated with 5 × 105 cells [76]. The human kidney, however, is estimated to contain approximately 1 × 106 nephrons [77]. Lawlor et al. compared extrusion-based 3D cellular bioprinting of kidney organoids with manual organoid generation [68]. In the manual approach, the organoids were generated by centrifuging cells to form an aggregate, and then the cells placed onto a transwell filter (Figure 4A). In the bioprinting approach, the organoids were generated by the automated deposition of cell pastes using a NovoGen MMX extrusion-based 3D cellular bioprinter. The researchers also varied the deposition ratio, that is, the ratio of the tip movement along the transwell surface to the volume of the deposited cell suspension, to obtain 2 different bioprinting conformations: (1) a single-point deposition (ratio 0, no tip movement at extrusion), and (2) a line of cells ~12 mm long (ratio 40, movement of 12 mm during extrusion) (Figure 4A). They used an MAFBmTagBFP2 reporter line for the cell paste and the MAFB-positive area from the fluorescence imaging of the resultant viable organoids as a surrogate for the nephron number [78,79]. They found that despite starting with a smaller cell number of 1.1 × 105, the bioprinted R40 organoids contained a greater area of glomeruli, as compared to the manual organoids that had been initiated with 2.3 × 105 cells (Figure 4B). In addition, they further examined the distribution and functionality of the patch organoid. With a deposition ratio = 30, they extruded the cell paste, containing approximately 4 × 105 cells, across a total field of approximately 4.8 × 6 mm. They observed the uniform distribution of the epithelial structures (Figure 4C) and the correctly patterned nephrons (Figure 4D) in the resulting patch. They also showed evidence for the functionality of the nephrons, by the TRITC-albumin uptake into the YFP-positive proximal tubules (Figure 4E). Given the potential of adapting the bioprinting deposition ratio to enable additional nephron formation and larger fields of kidney tissue, further studies could investigate the bioprinting parameters for maximizing the nephron numbers to align with their human counterpart, the long-term formation of the nephron in the resulting organoid, and the functionality of the patches in vivo.

3.1.3. Heart

There have been many efforts in the regeneration of heart tissue through cardiac stem-cell therapy. Many studies have printed cardiac tissue with a scaffold for implantation. The inclusion of a scaffold, however, has resulted in challenges related to mechanical properties, immunogenicity, and degradation. As previously mentioned, researches have been searching for the optimal scaffold. Moreover, Ong et al. proposed a novel method for bioprinting cardiac patches without a scaffold [69]. This scaffold-free approach involved printing cardiac spheroids containing human-induced pluripotent stem-cell-derived cardiomyocytes (hiPSC-CMs), fibroblasts (FB), and endothelial cells (EC). The cardiac tissue exhibited spontaneous beating and desired ventricular myocyte-like electrophysiological properties. Another in vivo implantation study examined the regenerative potential of this approach. Female Lewis nude rats were subjected to myocardial infarction (Figure 5a), and bioprinted cardiac patches were implanted and evaluated on the scar areas, in addition to their vascularization and cardiac functions (Figure 5b,c). The implantation group exhibited less scar area, higher vessel counts (Figure 5e,f), and a higher cardiac output than the control group (Figure 5g). However, since the heart rate of rats was different from the contraction rate of a human iPSC cardiac patch (CP) and the integration of the CP with a natural heart was not shown, further studies should investigate the long-term regeneration potential and the implant–host integration [70].

3.1.4. Neurons and Central Nervous System

Neurons and glial cells are fragile and, thus, challenging for 3D printing. Previous studies have printed undifferentiated hiPSCs first and then allowed the cells to differentiate and self-assemble in brain organoids. It was reported that co-printing biomaterials and human neural stem cells (hNSCs) could encapsulate the stem cells, followed by their in situ expansion and differentiation into functional neurons and neuroglia [71]. As this method lacked control over the differentiation and the formation of brain constructs post-printing, there have been efforts to print neurons directly. Salaris et al. demonstrated another approach based on the extrusion printing of cortical neurons and glial cells, with Matrigel and alginate as the bio-ink. They showed the long-term survival of neurons, up to 70 days post-printing. Their functional analysis showed early and immature network activity [49]. Other researchers have also developed worked on the development of novel printing technology with fibrin-based bio-ink to print hiPSC-derived neural progenitor cells and observed early neuronal expression markers [72,73]. In addition, there have been advancements in the implantation of biocompatible scaffolds with neural stem cells (NSCs) and neural progenitor cells (NPCs). Hsieh et al. embedded NSCs in polyurethane (PU)-based thermos-responsive and biodegradable hydrogels, which repaired the nervous system function in zebrafish with brain injury, as demonstrated by a significant increase in hatching rate [80]. Recently, Joung et al. embedded spinal neuronal progenitor cells (sNPCs) and oligodendrocyte progenitor cells (OPCs) in a bioprinted scaffold, and as a result of their multicellular approach, they observed the axon propagation of printed sNPCs in the scaffold [54].

3.1.5. Others Approaches for Pancreatic and Corneal Applications

Furthermore, bioprinting with stem cells has other applications, including in pancreatic and corneal diseases. Kim et al. used a pancreatic-tissue-derived extracellular matrix to recreate a natural tissue micro-environment and increase islet function in the 3D constructs [74]. They also observed a decrease in the necrosis of the islets when adding the human umbilical-vein-derived endothelial cells to the culture [74]. Sorkio et al. used human embryonic stem-cell-derived limbal epithelial stem cells (hESC-LESC) and human adipose-tissue-derived stem cells (hASCs) to print multilayer structures that resembled natural corneal tissue [75]. The implantation in a porcine corneal organ demonstrated an interaction with the host tissue and possible hASC migration [75].

3.2. Printing of In Vitro Models for Drug Development

The efficacy of personalized medicine is largely governed by the efficacy of the drug-development process, defined as the process through which potential drugs are identified, assessed, and optimized, prior to clinical trials [81]. Recent papers have explored the application of bioprinting technologies for drug development, especially in the development of 3D human cell organoids, organ-on-a-chip, and 3D-printed human cell assays. The development of such anatomically relevant 3D-bioprinted models for various tissues and disease states emphasizes the current status and future potential of bioprinting technologies in advancing drug development and resolving a key challenge in personalized medicine.
The primary contribution of bioprinting to drug development in personalized medicine has been through the development of more physiologically relevant models, which can expedite the drug development process, improve model reproducibility, and facilitate model customization. In comparison to conventional 2D models, 3D models have better capabilities for modeling cell–cell/matrix interactions and the spatial distributions of cells, thus improving the in vitro and in vivo correlations in clinical drug trials [82]. Based on this increased physiological accuracy, 3D-bioprinted models have been predicted to reduce resource demands and time intensity, as compared to the approximately USD 2.6 billion and 15 years required by current models and drug development systems to introduce a new drug to the market [83]. Furthermore, the bioprinting process provides significant control in the development of models (e.g., organoids) and, thus, is likely to improve model reproducibility and customizability in personalized medicine. This section highlights examples of 3D-bioprinted cardiac tissue and liver constructs and glioblastoma models.

3.2.1. Cardiovascular Models for Drug Development

Cardiovascular diseases represent the leading cause of death in the United States, and cardiovascular drugs have clinical trial failure rates as high as 80%, which has established cardiovascular drug development and discovery as a key area for advancement [19,20]. This high failure/market-retraction rate has been attributed to the lack of physiologically natural 3D micro-environments in the cell cultures used to evaluate cardiotoxicity, as well as to the disproportionate effects of these drugs on various populations (e.g., ethnicities, the elderly, etc.) [82,84]. Therefore, representative and personalized biomimetic screening platforms for cardiovascular drugs are urgently needed for advances in 3D bioprinting. Lind et al. introduced multi-material inkjet 3D-bioprinting to advance existing microphysiological systems, such as organs-on-chips, by condensing the multi-step lithographic development of these systems into a single manufacturing step (Figure 6a). [85]. The authors of this study developed multiple functional bio-inks based on factors, such as piezoresistance, to construct micro-architectures that directed the self-assembly of laminar rat-derived cardiac tissues [85]. Researchers have similarly integrated non-invasive contractile stress sensors that enabled the electronic communication of contractile stress data and eliminated the laborious microscopy-focused designs involved in optical data communication, allowing data acquisition in cell-incubator environments [85]. Researchers observed that a 3D-bioprinted microphysiological device demonstrated inotropic responses to verapamil (L-type calcium channel blocker, which were similar to data from isolated whole postnatal rat hearts, demonstrating the model’s potential as a drug development platform [85]. The results of these preliminary studies and the continuous refinement of these engineered cardiac tissues point to bioprinting as a promising technology to advance personalized medicine.

3.2.2. Liver Models for Drug Development

Liver models are especially relevant for drug development since drug responses can vary between individuals, and drug-induced liver injury is one of the most common causes for discontinuing clinical trials [82]. Furthermore, conventional human models are often costly and unreliable in terms of their translation to human studies due to differences in the hepatocellular functions of different species [87,88,89]. A recent study applied DLP technology to produce an in vitro 3D model of human-induced pluripotent stem-cell derived hepatic progenitor cells (hiPSC-HPCs) and nonparenchymal cells with micro-scale resolution (Figure 6b) [86]. The authors observed that the 3D-bioprinted triculture model demonstrated an increase in the anabolic and catabolic functions of the hiPSC-HPCs and key cytochrome P450 enzyme expression levels, as well as an improved drug induction/metabolism potential, as compared to conventional 2D-monolayer and 3D single-culture (HPC only) models [86]. This in vitro hepatic model and its facilitation of the hiPSC-derived hepatic cell maturation and functional maintenance in a biomimetic micro-environment signaled the significant potential for patient-specific drug development [86].

3.2.3. Kidney Models for Drug Development

Traditionally, 2D cell cultures used to assess nephrotoxicity in drug development have been unable to accommodate the 3D micro-environment of the adult human kidney, thus directing attention to the development of 3D models for the in vitro study of the organ, as well as for drug development and nephrotoxicity screening [90]. While bioprinted renal constructs have been challenged by the selection of appropriate cells and materials for bio-inks and by the development of complex renal structures, bio-inks such as a kidney-derived extracellular matrix and gelatin-fibrin hydrogels have demonstrated significant potential in recreating the in vivo kidney micro-environment [90]. Pluripotent stem-cell-derived renal progenitors have likewise been used to construct rudimentary multicellular structures, similar to those in vivo [91]. For example, Lawlor et al. utilized extrusion-based 3D bioprinting to deliver the rapid, high-throughput generation of kidney organoids with improved reproducibility and viability [68]. These organoids rivaled the morphology, the component cell-type, and the gene expression of organoids produced manually [68]. The automatic bioprinting of iPSCs into a 96-well-plate platform with a high number of viable and reproducibly patterned organoids was similarly assessed for high-throughput, drug-induced nephrotoxicity testing [68]. The authors treated the organoids with a series of nephrotoxic aminoglycosides and observed a concentration-dependent decrease in cell viability, which was consistent with observations of kidney injury in patients treated with aminoglycoside therapy (Figure 7a) [68]. The authors ultimately concluded that bioprinting was a feasible strategy for drug-testing in kidneys [68].

3.2.4. Brain Models for Drug Development

Multiple groups have similarly applied 3D bioprinting to develop disease models to assay potential drugs. A recent study utilized DLP to develop an in vitro biomimetic tissue model for the glioblastoma tissue and micro-environment and to simulate immune interactions in the neural environment (Figure 7b). [92]. The authors identified that their bioprinted constructs with integrated macrophages were highly similar to patient-derived transcriptional profiles that were predictive of patient survival and the maintenance of stemness, invasion, and drug resistance [92]. Furthermore, the authors compared the gene expression data from the 3D tetra-culture model with the gene expression and drug sensitivity data from the Cancer Cell Line Encyclopedia and the Cancer Therapeutic Response Platform to derive predictions of drug sensitivity and resistance in the bioprinted construct based on its transcriptional signatures [92]. While these models were unable to completely recreate the disease state, more current models have been highly promising [92].
Preliminary studies indicated a significant potential for 3D bioprinting to advance personalized medicine. However, current constructs have been limited by the need for appropriate and accurate bio-ink materials and by the lack of consistency in differentiation and maturation protocols for induced pluripotent stem cells [24]. Furthermore, while a bioprinting resolution of nearly 200 µm has been achieved and demonstrated to be suitable for blood vessel models and organoids, higher printing resolution is required to produce fine capillary networks [32]. The density–viability–resolution trilemma has acknowledged the difficulty of producing a 3D-bioprinted structure that has a high cellular density (≥20 million cell/mL), a high cell viability (≥80%), and a high fabrication resolution (≤50 µm), simultaneously [93]. As 3D bioprinting advances in these sectors (as it undoubtedly will, given the field’s rapid progress and current attention), this novel technology could improve its capacity to model human tissues and advance personalized medicine.

4. Conclusions

Bioprinting enables the fabrication of tissue-like 3D constructs with tissue-level integrity by patterning living cells and biocompatible materials in a well-defined, layer-by-layer manner. There is no one-size-fits -all technique or material for bioprinting currently. The most impactful techniques have included inkjet printing, extrusion printing, laser-assisted printing, and stereolithography. The predominant materials have included natural (e.g., collagen) and synthetic (e.g., PEG) hydrogels.
Bioprinting stem cells is a promising method for personalized tissue regeneration. Most studies have been able to print hiPSCs or hiPSC-derived cells with high viability, differentiation, and proliferation, as well as subsequent tissue constructs with certain functionalities at the tissue level. Despite its promising future, bioprinting tissue has several key challenges to solve before it can be further evolved for regenerative implants. For example, it is still difficult to directly print small tissues (e.g., capillary vessel, 8–10 µm in diameter). Therefore, to fully reconstruct the cellular micro-environment, bioprinting techniques with higher resolution and better structural integrity need to be developed in order to maintain highly detailed, microscopic features during printing. In addition, while the short-term integration and vascularization were demonstrated in the preliminary in vivo studies on implantation, the long-term immunogenicity, integration, and maturation of implanted bioprinted tissues has yet to be determined. As allogeneic transplants often cause graft-versus-host disease (GvHD), we suspect there is a demand to comprehensively understand the host-immune response to the bioprinted grafts in future preclinical and clinical trials.
In addition to the in vivo applications, multiple groups have similarly developed 3D-bioprinted models to recreate natural tissue activity in vitro to expedite drug development. Such efforts have attracted significant attention from the pharmaceutical industry and regulatory agencies (i.e., FDA). Indeed, in early 2023, the FDA lifted the mandatory requirement of animal studies before human trials in the drug discovery pipeline. This reveals the immense potential for bioprinting-mediated in vitro drug development. With more research into reproducibility, quality control, and automation, we predict that bioprinting can be standardized for next-generation preclinical drug testing in the near future. It may also be possible to fluidically or physically connect bioprinted tissues to simulate body-level drug responses.
In conclusion, after two decades of development, 3D bioprinting has become a powerful yet versatile technique to generate tissue-like 3D constructs. With further technical development and additional biological validation, 3D bioprinting has the potential to transform the design of next-generation personalized medicine.

Author Contributions

E.H.Y.L., F.Y. and S.Z. contributed equally to the conceptualization, methodology, writing and revision of the manuscript. The order listed in the publication is determined by the alphabetical order of individual’s surname. Z.W. provided supervision and project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research is supported by the Indicium research competition hosted by STEM Fellowship (https://stemfellowship.org/).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gorshkov, K.; Chen, C.Z.; Marshall, R.E.; Mihatov, N.; Choi, Y.; Nguyen, D.-T.; Southall, N.; Chen, K.; Park, J.K.; Zheng, W. Advancing Precision Medicine with Personalized Drug Screening. Drug Discov. Today 2019, 24, 272–278. [Google Scholar] [CrossRef] [PubMed]
  2. Ho, D.; Quake, S.R.; McCabe, E.R.B.; Chng, W.J.; Chow, E.K.; Ding, X.; Gelb, B.D.; Ginsburg, G.S.; Hassenstab, J.; Ho, C.-M.; et al. Enabling Technologies for Personalized and Precision Medicine. Trends Biotechnol. 2020, 38, 497–518. [Google Scholar] [CrossRef] [PubMed]
  3. Li, Y.; Nieuwenhuis, L.M.; Keating, B.J.; Festen, E.A.M.; de Meijer, V.E. The Impact of Donor and Recipient Genetic Variation on Outcomes After Solid Organ Transplantation: A Scoping Review and Future Perspectives. Transplantation 2022, 106, 1548–1557. [Google Scholar] [CrossRef]
  4. Mathur, S.; Sutton, J. Personalized Medicine Could Transform Healthcare. Biomed. Rep. 2017, 7, 3–5. [Google Scholar] [CrossRef] [Green Version]
  5. Takahashi, K.; Tanabe, K.; Ohnuki, M.; Narita, M.; Ichisaka, T.; Tomoda, K.; Yamanaka, S. Induction of Pluripotent Stem Cells from Adult Human Fibroblasts by Defined Factors. Cell 2007, 131, 861–872. [Google Scholar] [CrossRef] [Green Version]
  6. Robertson, J.A. Human Embryonic Stem Cell Research: Ethical and Legal Issues. Nat. Rev. Genet. 2001, 2, 74–78. [Google Scholar] [CrossRef]
  7. Reddy, C.V.; Balamuralidhara, V.; Venkatesh, M.P.; Pramod Kumar, T.M. First FDA Approved 3D Printed Drug Paved New Path for Increased Precision in Patient Care. Appl. Clin. Res. Clin. Trials Regul. Aff. 2020, 7, 93–103. [Google Scholar] [CrossRef]
  8. Overby, C.L.; Tarczy-Hornoch, P. Personalized Medicine: Challenges and Opportunities for Translational Bioinformatics. Pers. Med. 2013, 10, 453–462. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Yang, J.; Huang, S.; Cheng, S.; Jin, Y.; Zhang, N.; Wang, Y. Application of Ovarian Cancer Organoids in Precision Medicine: Key Challenges and Current Opportunities. Front. Cell Dev. Biol. 2021, 9, 701429. [Google Scholar] [CrossRef]
  10. Tang, Y.-C.; Powell, R.T.; Gottlieb, A. Molecular Pathways Enhance Drug Response Prediction Using Transfer Learning from Cell Lines to Tumors and Patient-Derived Xenografts. Sci. Rep. 2022, 12, 16109. [Google Scholar] [CrossRef]
  11. Murphy, S.V.; Atala, A. 3D Bioprinting of Tissues and Organs. Nat. Biotechnol. 2014, 32, 773–785. [Google Scholar] [CrossRef] [PubMed]
  12. Mazzocchi, A.; Soker, S.; Skardal, A. 3D Bioprinting for High-Throughput Screening: Drug Screening, Disease Modeling, and Precision Medicine Applications. Appl. Phys. Rev. 2019, 6, 011302. [Google Scholar] [CrossRef] [PubMed]
  13. Huang, Z.; Shao, G.; Li, L. Micro/Nano Functional Devices Fabricated by Additive Manufacturing. Prog. Mater. Sci. 2023, 131, 101020. [Google Scholar] [CrossRef]
  14. Yu, Y.; Zhang, Y.; Martin, J.A.; Ozbolat, I.T. Evaluation of Cell Viability and Functionality in Vessel-like Bioprintable Cell-Laden Tubular Channels. J. Biomech. Eng. 2013, 135, 091011. [Google Scholar] [CrossRef] [Green Version]
  15. Ng, W.L.; Huang, X.; Shkolnikov, V.; Goh, G.L.; Suntornnond, R.; Yeong, W.Y. Controlling Droplet Impact Velocity and Droplet Volume: Key Factors to Achieving High Cell Viability in Sub-Nanoliter Droplet-Based Bioprinting. Int. J. Bioprint. 2021, 8, 424. [Google Scholar] [CrossRef]
  16. Cui, X.; Gao, G.; Qiu, Y. Accelerated Myotube Formation Using Bioprinting Technology for Biosensor Applications. Biotechnol. Lett. 2013, 35, 315–321. [Google Scholar] [CrossRef] [PubMed]
  17. Jammalamadaka, U.; Tappa, K. Recent Advances in Biomaterials for 3D Printing and Tissue Engineering. J. Funct. Biomater. 2018, 9, 22. [Google Scholar] [CrossRef] [Green Version]
  18. Li, J.; Chen, M.; Fan, X.; Zhou, H. Recent Advances in Bioprinting Techniques: Approaches, Applications and Future Prospects. J. Transl. Med. 2016, 14, 271. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Cui, X.; Dean, D.; Ruggeri, Z.M.; Boland, T. Cell Damage Evaluation of Thermal Inkjet Printed Chinese Hamster Ovary Cells. Biotechnol. Bioeng. 2010, 106, 963–969. [Google Scholar] [CrossRef]
  20. Xia, Z.; Jin, S.; Ye, K. Tissue and Organ 3D Bioprinting. SLAS Technol. 2018, 23, 301–314. [Google Scholar] [CrossRef] [Green Version]
  21. Ozbolat, I.T.; Hospodiuk, M. Current Advances and Future Perspectives in Extrusion-Based Bioprinting. Biomaterials 2016, 76, 321–343. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Duong, V.T.; Dang, T.T.; Hwang, C.H.; Back, S.H.; Koo, K. Coaxial Printing of Double-Layered and Free-Standing Blood Vessel Analogues without Ultraviolet Illumination for High-Volume Vascularised Tissue. Biofabrication 2020, 12, 045033. [Google Scholar] [CrossRef] [PubMed]
  23. Nguyen, C.T.; Duong, V.T.; Hwang, C.H.; Koo, K. Angiogenesis in Free-Standing Two-Vasculature-Embedded Scaffold Extruded by Two-Core Laminar Flow Device. Int. J. Bioprint. 2022, 8, 557. [Google Scholar] [CrossRef] [PubMed]
  24. Mandrycky, C.; Wang, Z.; Kim, K.; Kim, D.-H. 3D Bioprinting for Engineering Complex Tissues. Biotechnol. Adv. 2016, 34, 422–434. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Leberfinger, A.N.; Ravnic, D.J.; Dhawan, A.; Ozbolat, I.T. Concise Review: Bioprinting of Stem Cells for Transplantable Tissue Fabrication. Stem Cells Transl. Med. 2017, 6, 1940–1948. [Google Scholar] [CrossRef] [Green Version]
  26. Kačarević, Ž.P.; Rider, P.M.; Alkildani, S.; Retnasingh, S.; Smeets, R.; Jung, O.; Ivanišević, Z.; Barbeck, M. An Introduction to 3D Bioprinting: Possibilities, Challenges and Future Aspects. Materials 2018, 11, 2199. [Google Scholar] [CrossRef] [Green Version]
  27. Koch, L.; Brandt, O.; Deiwick, A.; Chichkov, B. Laser-Assisted Bioprinting at Different Wavelengths and Pulse Durations with a Metal Dynamic Release Layer: A Parametric Study. Int. J. Bioprint. 2017, 3, 96. [Google Scholar] [CrossRef] [Green Version]
  28. Wang, Z.; Jin, X.; Dai, R.; Holzman, J.F.; Kim, K. An Ultrafast Hydrogel Photocrosslinking Method for Direct Laser Bioprinting. RSC Adv. 2016, 6, 21099–21104. [Google Scholar] [CrossRef] [Green Version]
  29. Gauvin, R.; Chen, Y.-C.; Lee, J.W.; Soman, P.; Zorlutuna, P.; Nichol, J.W.; Bae, H.; Chen, S.; Khademhosseini, A. Microfabrication of Complex Porous Tissue Engineering Scaffolds Using 3D Projection Stereolithography. Biomaterials 2012, 33, 3824–3834. [Google Scholar] [CrossRef] [Green Version]
  30. Wang, X.; Yao, C.; Weng, W.; Cheng, K.; Wang, Q. Visible-Light-Responsive Surfaces for Efficient, Noninvasive Cell Sheet Harvesting. ACS Appl. Mater. Interfaces 2017, 9, 28250–28259. [Google Scholar] [CrossRef]
  31. Hoffmann, A.; Leonards, H.; Tobies, N.; Pongratz, L.; Kreuels, K.; Kreimendahl, F.; Apel, C.; Wehner, M.; Nottrodt, N. New Stereolithographic Resin Providing Functional Surfaces for Biocompatible Three-Dimensional Printing. J. Tissue Eng. 2017, 8, 2041731417744485. [Google Scholar] [CrossRef] [Green Version]
  32. Miri, A.K.; Mirzaee, I.; Hassan, S.; Oskui, S.M.; Nieto, D.; Khademhosseini, A.; Zhang, Y.S. Effective Bioprinting Resolution in Tissue Model Fabrication. Lab. Chip 2019, 19, 2019–2037. [Google Scholar] [CrossRef] [PubMed]
  33. Ozbolat, I.T.; Yu, Y. Bioprinting Toward Organ Fabrication: Challenges and Future Trends. IEEE Trans. Biomed. Eng. 2013, 60, 691–699. [Google Scholar] [CrossRef] [PubMed]
  34. Wang, Z.; Abdulla, R.; Parker, B.; Samanipour, R.; Ghosh, S.; Kim, K. A Simple and High-Resolution Stereolithography-Based 3D Bioprinting System Using Visible Light Crosslinkable Bioinks. Biofabrication 2015, 7, 045009. [Google Scholar] [CrossRef] [Green Version]
  35. Gopinathan, J.; Noh, I. Recent Trends in Bioinks for 3D Printing. Biomater. Res. 2018, 22, 11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Ahlfeld, T.; Cidonio, G.; Kilian, D.; Duin, S.; Akkineni, A.R.; Dawson, J.I.; Yang, S.; Lode, A.; Oreffo, R.O.C.; Gelinsky, M. Development of a Clay Based Bioink for 3D Cell Printing for Skeletal Application. Biofabrication 2017, 9, 034103. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Gao, Q.; Kim, B.-S.; Gao, G. Advanced Strategies for 3D Bioprinting of Tissue and Organ Analogs Using Alginate Hydrogel Bioinks. Mar. Drugs 2021, 19, 708. [Google Scholar] [CrossRef] [PubMed]
  38. Xiong, J.; Narayanan, J.; Liu, X.; Chong, T.; Chen, S.; Chung, T.-S. Topology Evolution and Gelation Mechanism of Agarose Gel. Available online: https://pubs.acs.org/doi/pdf/10.1021/jp044473u (accessed on 23 February 2023).
  39. Stepanovska, J.; Supova, M.; Hanzalek, K.; Broz, A.; Matejka, R. Collagen Bioinks for Bioprinting: A Systematic Review of Hydrogel Properties, Bioprinting Parameters, Protocols, and Bioprinted Structure Characteristics. Biomedicines 2021, 9, 1137. [Google Scholar] [CrossRef]
  40. Yang, Y.-L.; Leone, L.M.; Kaufman, L.J. Elastic Moduli of Collagen Gels Can Be Predicted from Two-Dimensional Confocal Microscopy. Biophys. J. 2009, 97, 2051–2060. [Google Scholar] [CrossRef] [Green Version]
  41. Chinga-Carrasco, G.; Rosendahl, J.; Catalán, J. Nanocelluloses—Nanotoxicology, Safety Aspects and 3D Bioprinting. In Nanotoxicology in Safety Assessment of Nanomaterials; Louro, H., Silva, M.J., Eds.; Advances in Experimental Medicine and Biology; Springer International Publishing: Cham, Switzerland, 2022; pp. 155–177. ISBN 978-3-030-88071-2. [Google Scholar]
  42. Bandyopadhyay, A.; Mandal, B.B.; Bhardwaj, N. 3D Bioprinting of Photo-Crosslinkable Silk Methacrylate (SilMA)-Polyethylene Glycol Diacrylate (PEGDA) Bioink for Cartilage Tissue Engineering. J. Biomed. Mater. Res. A 2022, 110, 884–898. [Google Scholar] [CrossRef]
  43. Chang, C.C.; Boland, E.D.; Williams, S.K.; Hoying, J.B. Direct-Write Bioprinting Three-Dimensional Biohybrid Systems for Future Regenerative Therapies. J. Biomed. Mater. Res. B Appl. Biomater. 2011, 98B, 160–170. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Kolesky, D.B.; Truby, R.L.; Gladman, A.S.; Busbee, T.A.; Homan, K.A.; Lewis, J.A. 3D Bioprinting of Vascularized, Heterogeneous Cell-Laden Tissue Constructs. Adv. Mater. 2014, 26, 3124–3130. [Google Scholar] [CrossRef] [PubMed]
  45. Ong, C.S.; Yesantharao, P.; Huang, C.Y.; Mattson, G.; Boktor, J.; Fukunishi, T.; Zhang, H.; Hibino, N. 3D Bioprinting Using Stem Cells. Pediatr. Res. 2018, 83, 223–231. [Google Scholar] [CrossRef] [Green Version]
  46. Skeldon, G.; Lucendo-Villarin, B.; Shu, W. Three-Dimensional Bioprinting of Stem-Cell Derived Tissues for Human Regenerative Medicine. Philos. Trans. R. Soc. B Biol. Sci. 2018, 373, 20170224. [Google Scholar] [CrossRef] [Green Version]
  47. Yamanaka, S. Strategies and New Developments in the Generation of Patient-Specific Pluripotent Stem Cells. Cell Stem Cell 2007, 1, 39–49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Koch, L.; Deiwick, A.; Franke, A.; Schwanke, K.; Haverich, A.; Zweigerdt, R.; Chichkov, B. Laser Bioprinting of Human Induced Pluripotent Stem Cells-the Effect of Printing and Biomaterials on Cell Survival, Pluripotency, and Differentiation. Biofabrication 2018, 10, 035005. [Google Scholar] [CrossRef] [PubMed]
  49. Salaris, F.; Colosi, C.; Brighi, C.; Soloperto, A.; de Turris, V.; Benedetti, M.C.; Ghirga, S.; Rosito, M.; Di Angelantonio, S.; Rosa, A. 3D Bioprinted Human Cortical Neural Constructs Derived from Induced Pluripotent Stem Cells. J. Clin. Med. 2019, 8, 1595. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Chikae, S.; Kubota, A.; Nakamura, H.; Oda, A.; Yamanaka, A.; Akagi, T.; Akashi, M. Three-Dimensional Bioprinting Human Cardiac Tissue Chips of Using a Painting Needle Method. Biotechnol. Bioeng. 2019, 116, 3136–3142. [Google Scholar] [CrossRef]
  51. Dai, R.; Wang, Z.; Samanipour, R.; Koo, K.; Kim, K. Adipose-Derived Stem Cells for Tissue Engineering and Regenerative Medicine Applications. Stem Cells Int. 2016, 2016, 6737345. [Google Scholar] [CrossRef] [Green Version]
  52. Wang, Z.; Tian, Z.; Menard, F.; Kim, K. Comparative Study of Gelatin Methacrylate Hydrogels from Different Sources for Biofabrication Applications. Biofabrication 2017, 9, 044101. [Google Scholar] [CrossRef]
  53. Sakthivel, K.; Kumar, H.; Mohamed, M.G.A.; Talebjedi, B.; Shim, J.; Najjaran, H.; Hoorfar, M.; Kim, K. High Throughput Screening of Cell Mechanical Response Using a Stretchable 3D Cellular Microarray Platform. Small 2020, 16, 2000941. [Google Scholar] [CrossRef] [PubMed]
  54. Joung, D.; Truong, V.; Neitzke, C.C.; Guo, S.-Z.; Walsh, P.J.; Monat, J.R.; Meng, F.; Park, S.H.; Dutton, J.R.; Parr, A.M.; et al. 3D Printed Stem-Cell Derived Neural Progenitors Generate Spinal Cord Scaffolds. Adv. Funct. Mater. 2018, 28, 1801850. [Google Scholar] [CrossRef] [PubMed]
  55. Crook, J.M.; Tomaskovic-Crook, E. Bioprinting 3D Human Induced Pluripotent Stem Cell Constructs for Multilineage Tissue Engineering and Modeling. In 3D Bioprinting: Methods in Molecular Biology; Humana: New York, NY, USA, 2020; Volume 2140, pp. 251–258. [Google Scholar] [CrossRef]
  56. Cofiño, C.; Perez-Amodio, S.; Semino, C.E.; Engel, E.; Mateos-Timoneda, M.A. Development of a Self-Assembled Peptide/Methylcellulose-Based Bioink for 3D Bioprinting. Macromol. Mater. Eng. 2019, 304, 1900353. [Google Scholar] [CrossRef]
  57. Yeo, M.; Lee, J.-S.; Chun, W.; Kim, G.H. An Innovative Collagen-Based Cell-Printing Method for Obtaining Human Adipose Stem Cell-Laden Structures Consisting of Core-Sheath Structures for Tissue Engineering. Biomacromolecules 2016, 17, 1365–1375. [Google Scholar] [CrossRef] [PubMed]
  58. Pati, F.; Jang, J.; Ha, D.-H.; Won Kim, S.; Rhie, J.-W.; Shim, J.-H.; Kim, D.-H.; Cho, D.-W. Printing Three-Dimensional Tissue Analogues with Decellularized Extracellular Matrix Bioink. Nat. Commun. 2014, 5, 3935. [Google Scholar] [CrossRef] [Green Version]
  59. Shin, Y.J.; Shafranek, R.T.; Tsui, J.H.; Walcott, J.; Nelson, A.; Kim, D.-H. 3D Bioprinting of Mechanically Tuned Bioinks Derived from Cardiac Decellularized Extracellular Matrix. Acta Biomater. 2021, 119, 75–88. [Google Scholar] [CrossRef]
  60. Jang, J.; Park, H.-J.; Kim, S.-W.; Kim, H.; Park, J.Y.; Na, S.J.; Kim, H.J.; Park, M.N.; Choi, S.H.; Park, S.H.; et al. 3D Printed Complex Tissue Construct Using Stem Cell-Laden Decellularized Extracellular Matrix Bioinks for Cardiac Repair. Biomaterials 2017, 112, 264–274. [Google Scholar] [CrossRef]
  61. Guo, X.; Ma, Y.; Min, Y.; Sun, J.; Shi, X.; Gao, G.; Sun, L.; Wang, J. Progress and Prospect of Technical and Regulatory Challenges on Tissue-Engineered Cartilage as Therapeutic Combination Product. Bioact. Mater. 2023, 20, 501–518. [Google Scholar] [CrossRef]
  62. Diomede, F.; Gugliandolo, A.; Cardelli, P.; Merciaro, I.; Ettorre, V.; Traini, T.; Bedini, R.; Scionti, D.; Bramanti, A.; Nanci, A.; et al. Three-Dimensional Printed PLA Scaffold and Human Gingival Stem Cell-Derived Extracellular Vesicles: A New Tool for Bone Defect Repair. Stem Cell Res. Ther. 2018, 9, 104. [Google Scholar] [CrossRef] [Green Version]
  63. Teixeira, B.N.; Aprile, P.; Mendonça, R.H.; Kelly, D.J.; Thiré, R.M.d.S.M. Evaluation of Bone Marrow Stem Cell Response to PLA Scaffolds Manufactured by 3D Printing and Coated with Polydopamine and Type I Collagen. J. Biomed. Mater. Res. B Appl. Biomater. 2019, 107, 37–49. [Google Scholar] [CrossRef] [Green Version]
  64. Montserrat, N.; Garreta, E.; Izpisua Belmonte, J.C. Regenerative Strategies for Kidney Engineering. FEBS J. 2016, 283, 3303–3324. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Rashidi, H.; Luu, N.-T.; Alwahsh, S.M.; Ginai, M.; Alhaque, S.; Dong, H.; Tomaz, R.A.; Vernay, B.; Vigneswara, V.; Hallett, J.M.; et al. 3D Human Liver Tissue from Pluripotent Stem Cells Displays Stable Phenotype in Vitro and Supports Compromised Liver Function in Vivo. Arch. Toxicol. 2018, 92, 3117–3129. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Goulart, E.; de Caires-Junior, L.C.; Telles-Silva, K.A.; Araujo, B.H.S.; Rocco, S.A.; Sforca, M.; de Sousa, I.L.; Kobayashi, G.S.; Musso, C.M.; Assoni, A.F.; et al. 3D Bioprinting of Liver Spheroids Derived from Human Induced Pluripotent Stem Cells Sustain Liver Function and Viability in Vitro. Biofabrication 2019, 12, 015010. [Google Scholar] [CrossRef] [PubMed]
  67. Zhang, D.; Kilian, K.A. The Effect of Mesenchymal Stem Cell Shape on the Maintenance of Multipotency. Biomaterials 2013, 34, 3962–3969. [Google Scholar] [CrossRef] [PubMed]
  68. Lawlor, K.T.; Vanslambrouck, J.M.; Higgins, J.W.; Chambon, A.; Bishard, K.; Arndt, D.; Er, P.X.; Wilson, S.B.; Howden, S.E.; Tan, K.S.; et al. Cellular Extrusion Bioprinting Improves Kidney Organoid Reproducibility and Conformation. Nat. Mater. 2021, 20, 260–271. [Google Scholar] [CrossRef] [PubMed]
  69. Ong, C.S.; Fukunishi, T.; Zhang, H.; Huang, C.Y.; Nashed, A.; Blazeski, A.; DiSilvestre, D.; Vricella, L.; Conte, J.; Tung, L.; et al. Biomaterial-Free Three-Dimensional Bioprinting of Cardiac Tissue Using Human Induced Pluripotent Stem Cell Derived Cardiomyocytes. Sci. Rep. 2017, 7, 4566. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Yeung, E.; Fukunishi, T.; Bai, Y.; Bedja, D.; Pitaktong, I.; Mattson, G.; Jeyaram, A.; Lui, C.; Ong, C.S.; Inoue, T.; et al. Cardiac Regeneration Using Human-Induced Pluripotent Stem Cell-Derived Biomaterial-Free 3D-Bioprinted Cardiac Patch in Vivo. J. Tissue Eng. Regen. Med. 2019, 13, 2031–2039. [Google Scholar] [CrossRef]
  71. Gu, Q.; Tomaskovic-Crook, E.; Lozano, R.; Chen, Y.; Kapsa, R.M.; Zhou, Q.; Wallace, G.G.; Crook, J.M. Functional 3D Neural Mini-Tissues from Printed Gel-Based Bioink and Human Neural Stem Cells. Adv. Healthc. Mater. 2016, 5, 1429–1438. [Google Scholar] [CrossRef] [Green Version]
  72. Abelseth, E.; Abelseth, L.; De la Vega, L.; Beyer, S.T.; Wadsworth, S.J.; Willerth, S.M. 3D Printing of Neural Tissues Derived from Human Induced Pluripotent Stem Cells Using a Fibrin-Based Bioink. ACS Biomater. Sci. Eng. 2019, 5, 234–243. [Google Scholar] [CrossRef]
  73. De la Vega, L.; Rosas Gómez, D.A.; Abelseth, E.; Abelseth, L.; Allisson da Silva, V.; Willerth, S.M. 3D Bioprinting Human Induced Pluripotent Stem Cell-Derived Neural Tissues Using a Novel Lab-on-a-Printer Technology. Appl. Sci. 2018, 8, 2414. [Google Scholar] [CrossRef] [Green Version]
  74. Kim, J.; Shim, I.K.; Hwang, D.G.; Lee, Y.N.; Kim, M.; Kim, H.; Kim, S.-W.; Lee, S.; Kim, S.C.; Cho, D.-W.; et al. 3D Cell Printing of Islet-Laden Pancreatic Tissue-Derived Extracellular Matrix Bioink Constructs for Enhancing Pancreatic Functions. J. Mater. Chem. B 2019, 7, 1773–1781. [Google Scholar] [CrossRef] [PubMed]
  75. Sorkio, A.; Koch, L.; Koivusalo, L.; Deiwick, A.; Miettinen, S.; Chichkov, B.; Skottman, H. Human Stem Cell Based Corneal Tissue Mimicking Structures Using Laser-Assisted 3D Bioprinting and Functional Bioinks. Biomaterials 2018, 171, 57–71. [Google Scholar] [CrossRef] [PubMed]
  76. Takasato, M.; Er, P.X.; Chiu, H.S.; Little, M.H. Generating Kidney Organoids from Human Pluripotent Stem Cells. Nat. Protoc. 2016, 11, 1681–1692. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  77. Hughson, M.; Farris, A.B.; Douglas-Denton, R.; Hoy, W.E.; Bertram, J.F. Glomerular Number and Size in Autopsy Kidneys: The Relationship to Birth Weight. Kidney Int. 2003, 63, 2113–2122. [Google Scholar] [CrossRef] [Green Version]
  78. Hale, L.J.; Howden, S.E.; Phipson, B.; Lonsdale, A.; Er, P.X.; Ghobrial, I.; Hosawi, S.; Wilson, S.; Lawlor, K.T.; Khan, S.; et al. 3D Organoid-Derived Human Glomeruli for Personalised Podocyte Disease Modelling and Drug Screening. Nat. Commun. 2018, 9, 5167. [Google Scholar] [CrossRef] [Green Version]
  79. Vanslambrouck, J.M.; Wilson, S.B.; Tan, K.S.; Soo, J.Y.-C.; Scurr, M.; Spijker, H.S.; Starks, L.T.; Neilson, A.; Cui, X.; Jain, S.; et al. A Toolbox to Characterize Human Induced Pluripotent Stem Cell-Derived Kidney Cell Types and Organoids. J. Am. Soc. Nephrol. JASN 2019, 30, 1811–1823. [Google Scholar] [CrossRef]
  80. Hsieh, F.-Y.; Lin, H.-H.; Hsu, S.-H. 3D Bioprinting of Neural Stem Cell-Laden Thermoresponsive Biodegradable Polyurethane Hydrogel and Potential in Central Nervous System Repair. Biomaterials 2015, 71, 48–57. [Google Scholar] [CrossRef]
  81. Drug Screening—Latest Research and News|Nature. Available online: https://www.nature.com/subjects/drug-screening (accessed on 21 December 2022).
  82. Ma, X.; Liu, J.; Zhu, W.; Tang, M.; Lawrence, N.; Yu, C.; Gou, M.; Chen, S. 3D Bioprinting of Functional Tissue Models for Personalized Drug Screening and in Vitro Disease Modeling. Adv. Drug Deliv. Rev. 2018, 132, 235–251. [Google Scholar] [CrossRef]
  83. Mullard, A. New Drugs Cost US$2.6 Billion to Develop. Nat. Rev. Drug Discov. 2014, 13, 877. [Google Scholar] [CrossRef]
  84. Ribas, J.; Sadeghi, H.; Manbachi, A.; Leijten, J.; Brinegar, K.; Zhang, Y.S.; Ferreira, L.; Khademhosseini, A. Cardiovascular Organ-on-a-Chip Platforms for Drug Discovery and Development. Appl. Vitro Toxicol. 2016, 2, 82–96. [Google Scholar] [CrossRef] [Green Version]
  85. Lind, J.U.; Busbee, T.A.; Valentine, A.D.; Pasqualini, F.S.; Yuan, H.; Yadid, M.; Park, S.-J.; Kotikian, A.; Nesmith, A.P.; Campbell, P.H.; et al. Instrumented Cardiac Microphysiological Devices via Multimaterial Three-Dimensional Printing. Nat. Mater. 2017, 16, 303–308. [Google Scholar] [CrossRef]
  86. Ma, X.; Qu, X.; Zhu, W.; Li, Y.-S.; Yuan, S.; Zhang, H.; Liu, J.; Wang, P.; Lai, C.S.E.; Zanella, F.; et al. Deterministically Patterned Biomimetic Human IPSC-Derived Hepatic Model via Rapid 3D Bioprinting. Proc. Natl. Acad. Sci. USA 2016, 113, 2206–2211. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Liu Tsang, V.; Chen, A.A.; Cho, L.M.; Jadin, K.D.; Sah, R.L.; DeLong, S.; West, J.L.; Bhatia, S.N. Fabrication of 3D Hepatic Tissues by Additive Photopatterning of Cellular Hydrogels. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 2007, 21, 790–801. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Khetani, S.R.; Bhatia, S.N. Microscale Culture of Human Liver Cells for Drug Development. Nat. Biotechnol. 2008, 26, 120–126. [Google Scholar] [CrossRef]
  89. Hewitt, N.J.; Lechón, M.J.G.; Houston, J.B.; Hallifax, D.; Brown, H.S.; Maurel, P.; Kenna, J.G.; Gustavsson, L.; Lohmann, C.; Skonberg, C.; et al. Primary Hepatocytes: Current Understanding of the Regulation of Metabolic Enzymes and Transporter Proteins, and Pharmaceutical Practice for the Use of Hepatocytes in Metabolism, Enzyme Induction, Transporter, Clearance, and Hepatotoxicity Studies. Drug Metab. Rev. 2007, 39, 159–234. [Google Scholar] [CrossRef]
  90. Fransen, M.F.J.; Addario, G.; Bouten, C.V.C.; Halary, F.; Moroni, L.; Mota, C. Bioprinting of Kidney in Vitro Models: Cells, Biomaterials, and Manufacturing Techniques. Essays Biochem. 2021, 65, 587–602. [Google Scholar] [CrossRef] [PubMed]
  91. Chuah, J.K.C.; Zink, D. Stem Cell-Derived Kidney Cells and Organoids: Recent Breakthroughs and Emerging Applications. Biotechnol. Adv. 2017, 35, 150–167. [Google Scholar] [CrossRef]
  92. Tang, M.; Xie, Q.; Gimple, R.C.; Zhong, Z.; Tam, T.; Tian, J.; Kidwell, R.L.; Wu, Q.; Prager, B.C.; Qiu, Z.; et al. Three-Dimensional Bioprinted Glioblastoma Microenvironments Model Cellular Dependencies and Immune Interactions. Cell Res. 2020, 30, 833–853. [Google Scholar] [CrossRef]
  93. You, S.; Xiang, Y.; Hwang, H.H.; Berry, D.B.; Kiratitanaporn, W.; Guan, J.; Yao, E.; Tang, M.; Zhong, Z.; Ma, X.; et al. High Cell Density and High-Resolution 3D Bioprinting for Fabricating Vascularized Tissues. Sci. Adv. 2023, 9, eade7923. [Google Scholar] [CrossRef]
Ijms 24 06357 g001 550
Figure 1. A brief timeline of major scientific discoveries and events [5,6,7,8,9,10,11,12,13,14,15,16,17].
Figure 1. A brief timeline of major scientific discoveries and events [5,6,7,8,9,10,11,12,13,14,15,16,17].
Ijms 24 06357 g001
Ijms 24 06357 g002 550
Figure 2. Four main bioprinting methods. (a) Inkjet Bioprinting. (b) Extrusion Bioprinting. (c) Laser-Assisted Bioprinting. (d) Stereolithography Bioprinting were reprinted with permission from Ref. [14]. 2020, Yu. This figure is modified, and obtained from an open-access journal article.
Figure 2. Four main bioprinting methods. (a) Inkjet Bioprinting. (b) Extrusion Bioprinting. (c) Laser-Assisted Bioprinting. (d) Stereolithography Bioprinting were reprinted with permission from Ref. [14]. 2020, Yu. This figure is modified, and obtained from an open-access journal article.
Ijms 24 06357 g002
Ijms 24 06357 g003 550
Figure 3. A schematic of factors under consideration for the bioprinting of stem cells.
Figure 3. A schematic of factors under consideration for the bioprinting of stem cells.
Ijms 24 06357 g003
Ijms 24 06357 g004 550
Figure 4. Cellular extrusion bioprinting improved nephron formation in kidney organoid, as compared to manual generation. (A) A schematic of the protocol for manual versus bioprinted kidney organoid formation (R40, R0). R40 and R0 were generated from 1.1 × 105 differentiated iPSC (MAFBmTAGBFP2GATA3mCherry) cells, and manual organoids from 2.3 × 105 cells. (B) Comparison of MAFB reporter area in manual and bioprinted kidney organoids. Larger area was observed in R40 organoids, suggesting greater nephron number formation. Bars indicate mean. R40-Man, p = 2.1 × 10−5, R40-R0, p = 2 × 10−16. (C) Uniform formation of nephron structures in the bioprinted kidney organoid patch, analyzed by brightfield imaging. (D) Nephrons showed expression of markers of proximal tubules (LTL (left panel; green) and HNF4A (right panel; red)), podocytes (mTagBFP2 (left panel; blue)), nephron epithelium (EPCAM (left panel; red)), distal tubule/loop of Henle TAL (SLC12A1 (right panel; green)), surrounded by interstitial endothelial cells (SOX17 (right panel; grey)). Analyzed by confocal immunofluorescence imaging. (E) Patch organoid was generated from proximal tubule-specific iPSC reporter line (where yellow fluorescent protein (YFP) was inserted under the control of the HNF4A promoter), following incubation in TRITC-albumin substrate. Live confocal imaging shows uptake of TRITC-albumin (red) into YFP-positive proximal tubules (yellow). Small panels below show higher magnification of the outlined areas, with and without phase-contrast overlays. Scale bars = 100 μm. Reproduced with permission from [68], copyright 2020 Springer Nature Ltd.
Figure 4. Cellular extrusion bioprinting improved nephron formation in kidney organoid, as compared to manual generation. (A) A schematic of the protocol for manual versus bioprinted kidney organoid formation (R40, R0). R40 and R0 were generated from 1.1 × 105 differentiated iPSC (MAFBmTAGBFP2GATA3mCherry) cells, and manual organoids from 2.3 × 105 cells. (B) Comparison of MAFB reporter area in manual and bioprinted kidney organoids. Larger area was observed in R40 organoids, suggesting greater nephron number formation. Bars indicate mean. R40-Man, p = 2.1 × 10−5, R40-R0, p = 2 × 10−16. (C) Uniform formation of nephron structures in the bioprinted kidney organoid patch, analyzed by brightfield imaging. (D) Nephrons showed expression of markers of proximal tubules (LTL (left panel; green) and HNF4A (right panel; red)), podocytes (mTagBFP2 (left panel; blue)), nephron epithelium (EPCAM (left panel; red)), distal tubule/loop of Henle TAL (SLC12A1 (right panel; green)), surrounded by interstitial endothelial cells (SOX17 (right panel; grey)). Analyzed by confocal immunofluorescence imaging. (E) Patch organoid was generated from proximal tubule-specific iPSC reporter line (where yellow fluorescent protein (YFP) was inserted under the control of the HNF4A promoter), following incubation in TRITC-albumin substrate. Live confocal imaging shows uptake of TRITC-albumin (red) into YFP-positive proximal tubules (yellow). Small panels below show higher magnification of the outlined areas, with and without phase-contrast overlays. Scale bars = 100 μm. Reproduced with permission from [68], copyright 2020 Springer Nature Ltd.
Ijms 24 06357 g004
Ijms 24 06357 g005 550
Figure 5. A scaffold-free approach to heart regeneration through printing cardiac spheroids containing human-induced pluripotent stem-cell-derived cardiomyocytes. (a) Surgical procedures of mouse myocardial infarction model. (b) Patch group: one cardiac patch (CP) and a natural omentum patch (OP) were sutured over the site of infarction. (c) One OP was sutured over the site of infarction. (d) Schematics of experimental setup. (e,f) vascularization of the infarcted area. Patch group vs. control group. Green: positive staining of the endothelial cell. Scale bar = 100 μm. (g) Cardiac output (mL/min). Patch group vs. control group. (CO: 104.6 ± 45.5 vs. 68.6 ± 16.4, p = 0.1). Reproduced with permission from [70], copyright 2019 John Wiley & Sons, Ltd.
Figure 5. A scaffold-free approach to heart regeneration through printing cardiac spheroids containing human-induced pluripotent stem-cell-derived cardiomyocytes. (a) Surgical procedures of mouse myocardial infarction model. (b) Patch group: one cardiac patch (CP) and a natural omentum patch (OP) were sutured over the site of infarction. (c) One OP was sutured over the site of infarction. (d) Schematics of experimental setup. (e,f) vascularization of the infarcted area. Patch group vs. control group. Green: positive staining of the endothelial cell. Scale bar = 100 μm. (g) Cardiac output (mL/min). Patch group vs. control group. (CO: 104.6 ± 45.5 vs. 68.6 ± 16.4, p = 0.1). Reproduced with permission from [70], copyright 2019 John Wiley & Sons, Ltd.
Ijms 24 06357 g005
Ijms 24 06357 g006 550
Figure 6. Bioprinted cardiovascular and liver models for drug development. (a) Schematic diagram of cardiac tissue model design with stress sensors (scale bars are 10 mm) and verapamil and isoproterenol dose-response plots below. Adapted with permission from [85]. 2016, Nature Publishing Group. (b) DLP-bioprinted hepatic model on left, fluorescence and bright field images of 3D-printed construct on top-right, and CYP induction plots on lower-right with asterisks indicating statistical significance with threshold of p < 0.05 (scale bars are 500 mm). Adapted with permission from [86]. 2015, S. Chen.
Figure 6. Bioprinted cardiovascular and liver models for drug development. (a) Schematic diagram of cardiac tissue model design with stress sensors (scale bars are 10 mm) and verapamil and isoproterenol dose-response plots below. Adapted with permission from [85]. 2016, Nature Publishing Group. (b) DLP-bioprinted hepatic model on left, fluorescence and bright field images of 3D-printed construct on top-right, and CYP induction plots on lower-right with asterisks indicating statistical significance with threshold of p < 0.05 (scale bars are 500 mm). Adapted with permission from [86]. 2015, S. Chen.
Ijms 24 06357 g006
Ijms 24 06357 g007 550
Figure 7. Bioprinted kidney and glioblastoma models for drug development. (a) Application of 96-well bioprinted organoids for testing viability in response to aminoglycoside antibiotics. The curves represent a non-linear fit for each compound, with n = 19 (amikacin), n = 24 (tobramycin), n = 30 (gentamycin), n = 30 (neomycin), n = 22 (streptomycin). Adapted from [68]. 2020, Nature Publishing Group. (b) Development of 3D glioblastoma model involving glioblastoma stem cells (GSCs), macrophages, astrocytes, and neural stem cells (NSCs) on left, methodology of evaluating drug sensitivity based on 3D tetra-culture gene expression signature from Cancer Cell Line Encyclopedia (CCLE) and the Cancer Therapeutic Response Platform (CTRP) datasets in middle, therapeutic efficacy prediction of drugs in CTRP dataset cancer cells using differentially expressed genes (as determined by RNA-sequencing) between 3D tetra-culture model and GSCs grown in sphere culture. Adapted from [92]. 2020, Nature Publishing Group.
Figure 7. Bioprinted kidney and glioblastoma models for drug development. (a) Application of 96-well bioprinted organoids for testing viability in response to aminoglycoside antibiotics. The curves represent a non-linear fit for each compound, with n = 19 (amikacin), n = 24 (tobramycin), n = 30 (gentamycin), n = 30 (neomycin), n = 22 (streptomycin). Adapted from [68]. 2020, Nature Publishing Group. (b) Development of 3D glioblastoma model involving glioblastoma stem cells (GSCs), macrophages, astrocytes, and neural stem cells (NSCs) on left, methodology of evaluating drug sensitivity based on 3D tetra-culture gene expression signature from Cancer Cell Line Encyclopedia (CCLE) and the Cancer Therapeutic Response Platform (CTRP) datasets in middle, therapeutic efficacy prediction of drugs in CTRP dataset cancer cells using differentially expressed genes (as determined by RNA-sequencing) between 3D tetra-culture model and GSCs grown in sphere culture. Adapted from [92]. 2020, Nature Publishing Group.
Ijms 24 06357 g007
Table
Table 2. Summary of available natural and synthetic bio-inks.
Table 2. Summary of available natural and synthetic bio-inks.
Bio-ink
Material		DescriptionAdvantagesDisadvantagesReferences
AlginateNatural negatively charged polysaccharides from brown algae
  • Non-immunogenic when implanted in vivo
  • High biocompatibility
  • Capable of transporting oxygen, nutrients, etc.
  • Lack of cell adhesion
  • Poor printability
  • Unpredictable biodegradability
[14,35,36,37]
AgarosePolysaccharide obtained from seaweedHigh cell viabilityPoor support and limited cell growth[35,38]
CollagenStructural protein in the extracellular matrixEasily obtainable from skin and connective tissues of organisms
Relatively strong 3D structures
  • Poor mechanical properties, unless cross-linked
  • Low mechanical strength
  • Unpredictable viscosity and elastic modulus
[39,40]
NanocelluloseCellulose that can be derived from biomass, bacteria, and marine sources
  • Non-cytotoxicity
  • High-aspect-ratio
  • Strong mechanical properties
May not be an accurate model for human cells as we do not produce cellulase to be biodegraded[41]
PEGDASynthetic polymer used for hydrogel fabrication and UV curing
  • Highly biocompatible
  • Non-toxic and non-immunogenic
  • Capable of photopolymerization
  • High mechanical strength
Material can be brittle and rigid[42]
Pluronic®Synthetic polymer-poloxamer
  • Excellent printability
  • Temperature-responsive gelation
Biocompatibility is not sufficient for long-term cell survival[43,44]
Note: More information is provided in Table 2 in reference [13].
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lam, E.H.Y.; Yu, F.; Zhu, S.; Wang, Z. 3D Bioprinting for Next-Generation Personalized Medicine. Int. J. Mol. Sci. 2023, 24, 6357. https://doi.org/10.3390/ijms24076357

AMA Style

Lam EHY, Yu F, Zhu S, Wang Z. 3D Bioprinting for Next-Generation Personalized Medicine. International Journal of Molecular Sciences. 2023; 24(7):6357. https://doi.org/10.3390/ijms24076357

Chicago/Turabian Style

Lam, Ethan Hau Yin, Fengqing Yu, Sabrina Zhu, and Zongjie Wang. 2023. "3D Bioprinting for Next-Generation Personalized Medicine" International Journal of Molecular Sciences 24, no. 7: 6357. https://doi.org/10.3390/ijms24076357

APA Style

Lam, E. H. Y., Yu, F., Zhu, S., & Wang, Z. (2023). 3D Bioprinting for Next-Generation Personalized Medicine. International Journal of Molecular Sciences, 24(7), 6357. https://doi.org/10.3390/ijms24076357

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop