3D Cellular Model

3D Cellular Model

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I'm learning biology on my own and it's pretty hard to understand all processes inside a cell from the side of chemistry and physics and in some cases pointless to bring a complexity instead of abstraction.

I just curious to know if we have some open-source projects that provide 3D program models of a cell? I mean not just like study model that shows cell organelles and their names but I want something that shows real cellular processes like (1x1 or close to it) real model of a living (in program) cell with some types of abstraction levels.

Not of a complete cell, that would seriously overload even most powerful animation software/hardware you could build, not to mention there are gaps in our understanding of the cell. Understanding a cell purely through chemistry is possible if a bit masochistic, sooner or later you are going to need to approach it from a different direction to get the whole picture. There is so much complexity in a modern cell that sooner or later you have to start looking at the emergent characteristics. Don't believe me check out this chart of just the common metabolic pathways in a human cell, you are looking a 3.5 billions years of kluges, breathtaking elegance, rube goldberg monstrosities, and duck tape solutions all layered and tangled into each other, messy doesn't begin to describe it.

But there is several places creating models of individual processes, modeling each individual atom. These will let you at least understand hte chemistry of the components. is focused on nucleic acids and their interactions.

Their work is related to Harvard's biovisions program which uses XVIVO. which is a much rougher but larger scale approach and may be the closest to what you want.

Cambridge MRC has a more diverse selection with better visualization of the actual chemical interactions.

Swissmodel by the Swiss Institute of bioinformatics focuses on protein modeling and has a free to use program and database.

There is even a wikipedia database Protepedia

Image from animation

I'm sure others on the site could list a dozen other such programs/databases.

The complete function of a cell is something which is still quite beyond our current knowledge and understanding. Many gaps remain that prevent us from building such a complete model, and also from engineering an artificial object that would reproduce some basic function of a living cell.

3D cell culture models: Drug pharmacokinetics, safety assessment, and regulatory consideration

Nonclinical testing has served as a foundation for evaluating potential risks and effectiveness of investigational new drugs in humans. However, the current two-dimensional (2D) in vitro cell culture systems cannot accurately depict and simulate the rich environment and complex processes observed in vivo, whereas animal studies present significant drawbacks with inherited species-specific differences and low throughput for increased demands. To improve the nonclinical prediction of drug safety and efficacy, researchers continue to develop novel models to evaluate and promote the use of improved cell- and organ-based assays for more accurate representation of human susceptibility to drug response. Among others, the three-dimensional (3D) cell culture models present physiologically relevant cellular microenvironment and offer great promise for assessing drug disposition and pharmacokinetics (PKs) that influence drug safety and efficacy from an early stage of drug development. Currently, there are numerous different types of 3D culture systems, from simple spheroids to more complicated organoids and organs-on-chips, and from single-cell type static 3D models to cell co-culture 3D models equipped with microfluidic flow control as well as hybrid 3D systems that combine 2D culture with biomedical microelectromechanical systems. This article reviews the current application and challenges of 3D culture systems in drug PKs, safety, and efficacy assessment, and provides a focused discussion and regulatory perspectives on the liver-, intestine-, kidney-, and neuron-based 3D cellular models.

© 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

3D organotypic culture models

The ability to measure the outcome of a drug or therapeutic compound in an in vitro model system that recapitulates the disease state or organ function from an in vivo environment is the ‘holy-grail’ for scientists to study cellular responses ex vivo. In attempts to accomplish this over the past few years, innovative approaches, newer techniques and processes have created methodologies to generate organotypic culture models (OCM) ex vivo that are viable in culture for extended periods of time and express both phenotypic and genotypic characteristics found in vivo.

OCMs, often referred to as spheroids, have the keen ability to self-assemble in the presence of other cell types (eg fibroblasts) and microenvironment components such as extracellular matrix (ECM) (2-4). The challenges with many OCM models used with HCI is their placement and location within the well of the microplate.

Typically, OCMs are loosely attached to the surface of the microplate or in suspension, and therefore have a tendency to move around in the well before or during image acquisition and analysis. When cultured imbedded within an ECM, OCMs typically form in non-uniform size and shapes from well to well in most microplate formats, creating additional challenges when acquiring and analysing images.

Regardless of the 3D cell model system used, it will be important during assay development and validation to show reproducibility performance of the experiment using data analysis and informatics tools to generate statistical outcomes that demonstrate repeatability and robustness (5).


Explore Tissue Environments with Solid Synthetic Scaffold Models.

Explore Tissue Environments with Solid Synthetic Scaffold Models.

Learn about Spheroid Model Environments.

Learn about Spheroid Model Environments.

Learn more about Organoid Model Environments.

Learn more about Organoid Model Environments.

Benefits of Using 3D Cell Models in Drug Discovery

As researchers explore therapeutic strategies for more complex and heterogeneous diseases the need for more physiologically relevant models has never been greater. Researchers are increasingly looking to 3D cell cultures, spheroids, organoids and microtissues to bridge the gap between 2D cell cultures and in vivo animal models.

We recently caught up with Dr. Karin Boettcher, Associate Product Manager Cellular Imaging & Analysis at PerkinElmer to discuss how 3D models can be applied to drug discovery. Karin highlights the impact these models are having on the field and provides tips on how to ensure your 3D cell models are consistent and reproducible.

Laura Mason (LM): What is 3D cell culture and how can 3D models be applied to the drug discovery field?

Karin Boettcher (KB): Cell culture is an indispensable technique for generating large numbers of cells for a wide range of in vitro applications, such as high-throughput screening. Classically, cells are used in microplates where they grow as adherent monolayers, or 2D cell cultures. In 3D cell culture, rather than growing as a layer, cells are cultured in a three-dimensional shape in a gel matrix, or in the form of spheroids or organoids. 3D cultures can be used as model systems in a multitude of applications across the drug discovery workflow, including target identification and validation, lead optimization, candidate selection, or during assay development to make informed decisions on high-throughput screening strategies. 3D cell models may also be used in a research context, e.g. in regenerative medicine, developmental biology, cancer research, and toxicology.

LM: What are the key benefits of using 3D models compared to other available models? Are there any particular examples you could share that demonstrate these benefits?

KB: Researchers are increasingly looking to 3D cell cultures, spheroids, organoids and microtissues to bridge the gap between 2D cell cultures and in vivo animal models. 3D cell models provide more physiologically relevant conditions than 2D cell cultures, as they closely mimic the microenvironments, cell-to-cell interactions and biological processes that occur in vivo. Plus, they show a higher degree of morphological and functional differentiation – again, similar to in vivo cell characteristics.

LM: Could you tell us about some of the challenges associated with using 3D models?

KB: Reproducible cell seeding and reliable formation of similar-sized 3D microtissues is essential for robust and repeatable results, especially when you’re integrating 3D models into high-throughput workflows. Generating consistent, reproducible 3D cell models can be problematic.

If you’re analyzing your 3D cell model using a high-content imaging approach, high-quality images are a crucial prerequisite for success, and capturing these images can be particularly challenging for large, thick 3D structures like spheroids.

Also, in a high-content assay, you’re aiming to capture very high-resolution images in order to analyze fine sub-cellular detail, but acquiring all of this information when you’re working with 3D cell samples may slow you down and undoubtedly increase the amount of imaging data that you generate. It can be a challenge to handle the huge volumes of data that imaging and analysis of 3D cell models produces.

LM: How are researchers able to overcome these challenges?

#1: Growing consistent 3D cell models

There are several means by which consistent 3D cell models can be produced.

Specialized microplates with advanced surface coatings in which to grow spheroids are commercially available. For example, the use of ultra-low attachment (ULA) microplates is a popular method. It’s ideal if you need a simple and economical method to grow uniform spheroids. The synthetic plate coating ensures reduced cell-to-plate adhesion, which typically promotes uniform, single-spheroid formation.

Other methods to generate 3D spheroids or support the structures of 3D cell model systems include agarose, hydrogels, scaffolds or even a combination of these substrates with growth factors. There are also physical methods to form 3D spheroids that include gravity or “hanging drop” systems, bioprinting and magnetic nanoparticles.

Seeding of cells, transfer of microtissues and medium exchange can be performed reliably using automated hand-held pipetting devices, but this is not practical when large numbers of spheroids are needed for high-throughput studies. In this case, automated liquid handling can provide greater efficiency and reproducible results.

#2: Obtaining high quality images

For high-content analysis approaches, confocal spinning disk imaging systems yield the best signal-to-noise ratios and highest X, Y, and Z resolution, while maintaining high-throughput acquisition. In addition, with a confocal high-content imaging system that combines laser-based excitation with two or four cameras, images are acquired at very high frame rates with minimal sample illumination. This reduces photodamage, so it’s ideal for imaging 3D cell models when multiple frames and fluorescent channels are required.

To image deep into 3D structures, you can use water immersion objectives, which have higher numerical apertures allowing capture of up to four times more light and providing higher resolution in X, Y, and Z than comparable air objective lenses. Furthermore, water immersion objectives have a smaller focal depth and therefore reduce the amount of contaminating light and background compared to air objectives.

#3: Minimizing imaging time and data volumes

Often, in imaging assays using 3D models or microtissues, only part of the total well area (where the 3D model is situated) will be of interest to you. Ideally, you only want to acquire high resolution data from that region and not spend time capturing data from the rest of the well.

With PerkinElmer’s PreciScan intelligent image acquisition technology (component of our Harmony high-content imaging and analysis software), you can accurately target regions of interest at low resolution and then image only the 3D cell model at high-resolution. This significantly reduces acquisition and analysis times, as well as reducing the amount of data you need to analyze and manage.

LM: How can researchers get the most out of using 3D cell cultures?

KB: As an investigator, you might have spent months or years developing the appropriate cell model for your research. To get the most out of your 3D cell cultures, we suggest a high-content imaging and analysis approach to maximize the return on your investment.

Analyzing images in 3D, rather than 2D or 2.5D (maximum intensity projection), is recommended as these methods miss out on things like cell type distribution within 3D cell cultures, spatial differences between outer and inner spheroid layers and differences in subcellular morphology features. Investigators tend to avoid 3D volumetric analysis because it is difficult, cumbersome and there have been no seamless, integrated image analysis solutions available. Now, however, with solutions like PerkinElmer’s Harmony 4.8 software, you can segment and quantitate volume and morphology in 3D, better visualize and understand spatial relationships, plus accelerate 3D image acquisition and analysis.

To best understand your 3D cell model in drug discovery applications, full 3D volumetric analysis for validating and identifying targets, lead optimization, candidate selection or selective screening approaches is key to making informed decisions on screening strategies.

Karin Boettcher was speaking to Laura Elizabeth Mason, Science Writer for Technology Networks

A 3D cell culture approach for studying neuroinflammation

Background: Neurodegenerative diseases are highly complex making them challenging to model in cell culture. All cell types of the brain have been implicated as exerting an effect on pathogenesis, and disease progression is likely influenced by the cross-talk between the different cell types. Sophisticated investigation of the cellular level consequences of cross-talk between different cells types requires three-dimensional (3D) co-culture systems.

New method: Murine neural stem cells were differentiated into mixed-neuronal lineage populations in 3D culture. By seeding these differentiated cultures with microglia from adult brain, we have generated a 3D ex-vivo model of murine brain tissue populated with microglia.

Results: Monitoring the infiltration of GFP-expressing microglia into the 3D neuronal lineage cultures showed population throughout the tissue and assumption of ramified homeostatic morphology by the microglia. The co-cultures showed good longevity and were functionally responsive to external stimuli.

Comparison with existing methods: We have previously used 2-dimensional adhered cultures to model cell-cell interactions between microglia and neuronal lineage cells. While the microglia integrate well into these cultures and demonstrate inter-cellular cross-talk, it is known that adhered culture can change their activation state and therefore a 3D system better represents communication throughout a network of neuronal and support cells.

Conclusions: Our system offers a straight-forward and time effective way to model 3D mouse brain tissue that is responsive to external neuroinflammatory stimulus. It not only allows inter-cellular interactions to be studied in live tissue but additionally permits study of changes within any available mouse genotype.

Keywords: 3D Co-culture Microglia Neural stem cell Neurodegeneration Neuroinflammation Neurosphere Organoid.

3D Cell Culture: A Review of Current Techniques

Over the last decade, a central focus of drug discovery efforts has been the incorporation of in vitro testing models that better mimic in vivo conditions found within the target patient. An initial step saw a move away from biochemical assays using purified drug target, in favor of a cell-based approach which utilized over-expression of drug target in common host cell lines, such as CHO and HEK-293. The quest for greater physiological relevance proceeded to the use of primary cells, preferably human if supply was adequate, and the reliance on endogenous expression of drug target should detection technology be sensitive enough. A large percentage of these cell types, being naturally adherent, allowed simple culturing workflows that seeded cells in a coated microplate well, incubating the microplate to encourage the cells to attach in a two dimensional (2D) monolayer before performing the prescribed assay. While providing initial improvements over biochemical and immortalized cell lines, an abundance of evidence now supports the reality that culturing cells in this 2D manner is often problematic and is a relatively poor model for in vivo conditions and behaviors. Using a 2D model, attrition rates of drug candidates for cancer were approximately 95% 1 , stemming from in vitro drug efficacy values that did not translate to the clinic, as well as unforeseen toxicity issues. In 2011 alone, out of approximately 900 anti-cancer therapies in clinical trials or under Federal Drug Administration review 2 , only twelve achieved approval 3 resulting in the loss of hundreds of millions of dollars that were spent on pre-clinical and clinical trials. The reason for these shortfalls can be traced to using conventional 2D conditions, where extracellular matrix (ECM) components, cell-to-cell and cell-to-matrix interactions, important for differentiation, proliferation and cellular functions in vivo, are lost 4 .

Figure 1. Tumor Microenvironment 5 .

Parallel research also indicates that traditional 2D cell culture methods may not accurately mimic the 3D in vivo environment in which cancer cells reside (Figure 1), as the 2D environment does not allow for areas of hypoxia, heterogeneous cell populations (including stromal cells), varying cell proliferation zones (quiescent vs. replicating), ECM influences, soluble signal gradients, and differential nutrient and metabolic waste transport 6 (Figure 2). As a result, the unnatural 2D environment may provide inaccurate data regarding the predicted response of cancer cells to chemotherapeutics 7 .

Figure 2. Schematic of three microenvironmental regions in a centrally necrotic tumor. A spontaneous tumor may consist of many such necrotic foci. Decreasing magnitude of various physiological parameters is indicated as +++, ++, +, +/-, and - 6 .

Additional studies demonstrate that individual drug targets may not be expressed, or the level of cell signaling may not be equivalent to that found in vivo, thereby impacting experimental results. Indeed, a research study demonstrated that in melanoma cells, 106 genes were up-regulated and 73 genes down-regulated using tumor-like models compared to baseline expression of 2D monolayer cell cultures of the same cells 8 . What is interesting is the fact that the genes found to be up-regulated in the 3D model were also found to be up-regulated in tumors.

Many of the same concerns with using 2D cell culture to create accurate tumor models extend to liver toxicity studies as well. While the gold standard for xenobiotic toxicity testing involves in vivo animal studies, increasing animal welfare concerns, as well as the poor concordance of animal study results to disease phenotypes observed in heterogeneous human populations, make incorporation of a workable, predictive in vitro testing method a priority 9,10 . While immortalized liver-derived cell lines simplify procedures and eliminate the need for whole animal testing, the expression profile of genes involved in phase I and phase II metabolism do not correlate well to that observed in liver tissue 11 . Primary hepatocyte cultures provide levels of functionality much closer to that seen in vivo, but these cells are problematic when used in vitro. Under traditional 2D culture conditions, the cells de-differentiate, rapidly decrease expression of cytochrome P450 enzymes, and eventually lose viability 12 .

The wealth of research highlighting the limitations of 2D cell culture, both as in vivo tumor and liver models, highlights the need for new cell models in research methods. This demand can be met through the adoption of 3D cell models, as 3D cultured cells exhibit features that are closer to complex in vivo conditions 13 . Advantages of incorporating 3D cultured cells, compared to 2D culture models, for evaluation of drug candidates can include: (1) oxygen and nutrient gradients, (2) increased cell-to-cell and cell-to-ECM interactions, (3) non-uniform exposure of cells within a 3D structure to the test molecule, (4) varying cell proliferation zones, and (5) the impact of site specific stromal cells in the tumor microenvironment 5 . Studies show that tumor cells of specific cell lines, when evaluated in a 3D format, are less sensitive to anti-cancer agents than when the same cells are cultured in 2D formats 14 . However, other research shows that different cell lines, using a different 3D technology, demonstrate the opposite effect 15 . These findings highlight how the use of 3D cell culture in cancer research may provide key insights into drug activity in vivo that may be overlooked if limited to 2D cell culture models only. In addition, the mechanisms involved to create these differences can be elucidated, such as signaling pathway variations, or a shift in the dependence on the target in a 3D system compared to cells cultured using 2D methods.

When cells are grown in basement membrane-like gels, there is a mutual integration of the signaling pathways16. A549 3D spheroids demonstrate constantly high levels of IL-6 and IL-8 secretion when compared with their monolayer counterparts. Enhanced extracellular matrix deposition for better biomarker expression was reported using 3D culture systems 17 . The differentiation of mesenchymal stromal cells to chondrocytes using hyaluronic acid (HA) 3D model was also analyzed. It was found that the cell receptors could interact with HA better and influence cell differentiation. Various factors, including biologically functional microenvironment, material chemistry, cellular interactions, and mechanical property enhanced chondrogenesis 18 .

Similar findings to those seen with 3D tumor models are also observed when incorporating 3D cell cultures in hepatotoxicity studies. Wu et al. observed that 3D cultured rat hepatocytes maintain a more differentiated state as compared to a monolayer culture 19 . Rapid decreases in CYP1A2 and -1A1 expression using traditional 2D monolayer culture of mouse hepatocytes, compared to consistently high levels using a 3D model were also detected over a five day incubation period 20 . Longer-term evaluations were performed by Kratschmar et al., with the evaluation of rat hepatocytes maintained for 25 days using either a 2D sandwich culture or 3D co-culture method. High expression of Nrf2-, as well as glucocorticoid-dependent genes, were seen using the 3D culture method, while 2D hepatocytes exhibited rapid decline, followed by consistent low expression of both gene sets 21 .

3D Cell Culture Models

A wide variety of techniques currently exist to culture cells into 3D structures. These can be grouped into two main categories scaffold and non-scaffold based, and include the following individual technologies:

Scaffold Based

  • Polymeric Hard Scaffolds
  • Biologic Scaffolds
  • Micropatterned Surface Microplates

Non-Scaffold Based

  • Hanging Drop Microplates
  • Spheroid Microplates containing Ultra-Low Attachment (ULA) coating
  • Microfluidic 3D Cell Culture

Scaffold Based 3D Cell Culture

3D tumor and tissue models can be created by culturing cells on pre-fabricated scaffolds, or matrices, designed to mimic the in vivo ECM. Cells attach, migrate, and fill the interstices within the scaffold to form 3D cultures 22 . The scaffolds are used as a physical support system for in vitro cell culture, and have also shown promise for use with in vivo tissue regeneration, as they have the potential to recreate the natural physical and structural environment of living tissue 23 . Multiple geometric configurations of the commonly incorporated polymers, including polystyrene (PS) and polycaprolactone (PCL), exist. As seen in Figure 3, these include porous disc (3A), electrospun (3B), and orthogonal layering (3C).

Figure 3. (A) Porous disc (Image courtesy of James Weaver and Mooney lab, HSEAS and Wyss Institute) (B) Electrospun (Image courtesy of The Electrospinning Company, Ltd.) and (C) Orthogonal layering (Image courtesy of 3D Biotek, LLC) geometric configurations of polymeric 3D scaffolds.

Polymeric hard scaffolds are incorporated into two distinct research areas regenerative medicine and preclinical in vitro testing. In the former, cells are grown on the scaffold with the goal of eventual in vivo transplantation to replace degenerative or altered tissue. Currently, scaffolds are used for engineering bone, cartilage, ligament, skin, vascular, neural, and skeletal muscle tissues 24 . Ge et al., in particular, developed a 3D printing method using a lactide and glycolide copolymer which supported proliferation and osteogenic differentiation of osteoblasts 25 . Evaluation of bone regeneration efficacy demonstrated that new bone tissue formation and maturation occurred within the scaffold over a 24 week period.

For preclinical in vitro testing, cells are grown on the scaffold for the sole purpose of modeling tumors or tissue in a laboratory setting. Once the scaffolds are formed, they are cut to a diameter that fits into the appropriate test vessel typically Petri dishes, or a microplate well (Figure 4). Typical thickness of the final scaffold is 150-200 &mum each containing consistent pore size.

Figure 4. Preparation of Orthogonal Layering polymeric 3D scaffolds for insertion into test vessel (Image courtesy of NIST).

Once scaffolds are inserted (Figure 5), cell treatment and assay component dispensing procedures are then performed in a manner akin to that used with 2D cell culture. The arrangement of fibers and pores allow cells to remain close to nutrient sources, enabling exchange of nutrients, waste material, and gases similar to that seen in vivo. Using this knowledge, Bergenstock et al. illustrated that MCF-7 and HepG2 cancer cell lines, cultured in 3D using PS scaffolds as well as in a traditional 2D format, exhibited greater levels of proliferation and metabolic activity, as witnessed by higher A570 absorbance values across incubation periods up to two weeks. A reduced cytotoxic effect from treatment with the anticancer drugs tamoxifen and methotrexate was also seen during the same timeframe from 3D cultured cells 26 .

Figure 5. Final joint configuration of polymeric 3D scaffold in microplate format (Image courtesy of The Electrospinning Company, Ltd.)

Biological Scaffolds

Scaffolds can also be created from components of a more natural or biological origin, such as proteins commonly found in the in vivo ECM. These commonly include, but are not limited to, fibronectin, collagen, laminin, and gelatin. Biological scaffolds not only provide a matrix to which the cells can attach and reorganize into 3D structures, but more importantly, they provide the correct microenvironment of soluble growth factors, hormones, and other molecules that cells interact with in an in vivo environment, which can alter gene and protein expression 27 . Current methods call for cells to either be mixed with scaffold proteins in a liquid state (hydrogel) prior to plating in a microplate well (Figure 6A), or added to previously formed scaffolds, or to have the protein mixture overlaid onto cells already aggregated into 3D spheroids. Cells can then restructure the surrounding environment to release signaling molecules, allow migration, or accommodate other cellular functions (Figure 6B). The end result is the creation of a proper homeostatic state.

Figure 6. (A) Representation of collagen fibers suspended in medium following dispensing into a microplate well. (Image courtesy of Lonza, Inc.) (B) HT-1080 cell invasion into a 3D collagen matrix 28 .

Since these hydrogels are derived from natural sources, they promote many cellular functions, leading to increased viability, and proliferation of numerous cell types. They can also be advantageous to use over polymeric scaffolds, as the latter lack endogenous factors that promote appropriate cell behavior and act mainly to permit cell function 29 .

Hydrogels also offer the caveat of using multi-layer formats to form tissue-like structures. Individual cell types are embedded into separate hydrogel suspensions and layered on top of each other. Cells then organize within the hydrogel to form the layers found within in vivo tissues. Permeable supports can also be incorporated to simulate various air-liquid interfaces in an in vitro manner. Examples include bilayers resembling the dermis and epidermis of human skin 30 and human corneal limbal crypts 31 .

Micropatterned Surface Microplates

Micropatterned surface plates take advantage of recent progress made in micro-fabrication technology. Each plate contains micrometer sized compartments, regularly arrayed on the bottom of each well. Wells can be various shapes, including square, round, or square with slits between the barriers of adjacent wells (Figure 7A-C). The different configurations are optimized for either spheroid or cell networking formation depending on the cell type being used.

Figure 7. Micropatterned microplates containing a (A) round, (B) square, or (C) slit patterning within the plate well (Images courtesy of Kuraray Co., Ltd.)

Wells are coated to create a low adhesion surface within each micro-space. In this way, cells added to the well initially attach to the bottom of the micro-space, then aggregate together to form spheroid-like structures in the compartment over subsequent days of culture. Or in the case of the slit pattern, form contiguous cell networks along the bottom of the well (Figure 8).

Figure 8. Slit-type micropatterned microplates containing cell network (Image courtesy of Kuraray Co., Ltd.)

The bottom of the plate is made of a thin, transparent film that is suitable for microscopic imaging of the cellular structures. Recent studies confirm that cells cultured using micropatterned plates exhibit different enzyme expression levels and drug reactivity compared to culturing in traditional 2D format. Kobayashi et al.demonstrated that the hepatocellular carcinoma cell line, FLC-4, exhibited increased expression levels of drug metabolizing enzymes, including CYP3A4, CYP2C9 and UGT1A1 when cultured on micropatterned plates in comparison to the same cells cultured in traditional 2D format 32 . Results were consistent with rat primary hepatocytes cultured as spheroids, confirming that the morphological change and cell-cell interactions were the cause of the increased enzyme expression 33 .

Non-Scaffold Based 3D Cell Culture

Hanging drop plates (HDP) take advantage of the fact that cells, in the absence of a surface with which to attach, will self assemble into a 3D spheroid structure. Each plate conforms to SBS standards, but instead of containing normal wells with a traditional bottom, HDP well bottoms contain an opening. The top of HDP wells resemble a conventional microplate where cells in media can be dispensed (Figure 9A), while the aperture of the bottom opening is carefully designed to form a discrete droplet of media sufficient for cellular aggregation, but also small enough that surface tension prevents the droplets from being dislodged during manipulation (Figure 9B). Cells in the suspended media droplet aggregate over the course of hours to days creating the final spheroid structure.

Figure 9. Visualization of HDP top and bottom openings (Images courtesy of 3D Biomatrix).

Spheroid size is controlled by the number of cells dispensed into each drop. Co-cultured spheroids can also be created by adding multiple cell types either at the time of initial dispensing, or sequentially, allowing each set of cells to aggregate into separate layers.

For long-term culturing of spheroids, and to conduct assays, spheroids are typically transferred from the hanging drop plate to a second plate capable of containing larger media or buffer volumes (Figure 10). The larger volume ensures that the aggregated cells are in the presence of suitable conditions, such as nutrient levels and pH, for propagation periods reaching days or even weeks.

Figure 10. Hanging drop spheroid aggregation plate and secondary assay/propagation plate. (Images courtesy of 3D Biomatrix).

Due to the round configuration of the 3D cellular structure, spheroids are well-suited for use as primary cell-based tissue models and tumor models that include immortalized cancer cell lines. This is confirmed by work performed by Kermanizadeh, et al. using a 3D human liver microtissue model to examine potential chronic hepatotoxic effects of nanomaterials34. Ovarian cancer spheroids, created using a 384-well hanging drop platform, were also used incorporated by Raghavan, et al. for use with preclinical drug sensitivity assays 35 . Spheroids formed using hanging drop methods can also be embedded into biological scaffolds to mimic the ECM surrounding cancerous tumors. The combination allows for an in vivo-like examination of tumor metastasis using an in vitro 3D model 36 .

Spheroid Microplates containing Ultra-Low Attachment (ULA) coating

Spheroid microplates can also be incorporated to create the same round multi-cell tissue or tumor models generated using hanging drop plates. Plates have the typical well shape and depth of an SBS 96- or 384-well microplate. Because of the larger media and reagent volume capacity within the well, spheroid aggregation, propagation, and experimental procedures can be carried out in the same plate without the need for transfer to a second microplate.

Figure 11. (A) Clear (Image courtesy of PerkinElmer, Inc.) and (B) black walled, clear bottom ULA spheroid microplates in 96- and 384-well configurations (Image courtesy of Corning, Inc.)

The ULA surface coating added to the well bottom minimizes cell adherence to allow spheroid formation. Well bottoms also possess either a round, tapered (Figure 12) or v-shaped geometry that both ensures the creation of consistent, single spheroids and also helps to position the spheroids in the middle of the well.

Figure 12. ULA spheroid microplates possessing (A) round (Image courtesy of Corning, Inc.) and (B) tapered well bottom configurations (Image courtesy of InSphero).

ULA spheroid microplate-based cell aggregation and assay performance is also incorporated into current research methods. Ivanov, et al. demonstrated how neural stem cells could be added to ULA spheroid microplates. Aggregation then took place creating 3D neurospheres that were used to monitor growth kinetics and drug toxicity over time37. Similar work was also performed by Vinci, et al. who tested a variety of immortalized cell lines to make tumor spheroids, or tumoroids, in spheroid microplates 38 . Propagation and cell viability after exposure to cytotoxic agents was once again examined over a multi-day incubation period. Testing then extended to the addition of an ECM to previously formed spheroids, such that the spheroid was embedded completely in the matrix. Experiments then confirmed that, using this configuration, 3D tumor invasion assays could be performed with spheroid microplates.

Microfluidic 3D Cell Culture

As previously mentioned, 3D cell culture techniques strive to recreate the three-dimensional architecture of in vivo tissues and tumors, as well as the interactions between cells and ECM. Microfluidic platforms can also be used to create similar heterogeneous models, while contributing an additional level of complexity by introducing a perfusive flow aspect to the cellular environment allowing for continuous nutrition and oxygen introduction as well as waste removal through culture medium. Cells are maintained within a compartment by disparate physical or non-physical barriers. Media containing nutrients, chemicals, treatment molecules, or staining reagents is then perfused past the cells (Figure 13). Predefined gaps in the barriers allow interactions between compartments.

Figure 13. Representation of perfusion in a microfluidic 3D culture system. Cells are maintained in a predefined compartment by micropillars, allowing interplay with perfused media (Image courtesy of EMD Millipore Corporation).

Physical barriers incorporated into microfluidic devices are traditionally composed of glass or silicon, polymers including polydimethylsiloxane (PDMS), polymethylmethacrylate (PMMA), polycarbonate (PC) and polystyrene (PS), in addition to chromatographic or filter paper 39 . Cells can also be combined with a supporting matrix, such as a collagen or Matrigel®, to encourage cell-ECM interaction and encourage assembly into 3D structures. Introduction of an ECM also allows for the creation of microfluidic devices that do not incorporate physical barriers. Matrix polymerization maintains the cells in the pre-defined culture area and also acts as a filter during the perfusive flow 40 .

Microfluidic systems are also used for varied 3D cell culture applications including stem, primary, and cancer cells. Yu, et al. developed a 3D microfluidic cell culture system and applied this system to the study of the differentiation of rat bone marrow mesenchymal stem cells (BMSCs) in vitro 41 . A PDMS device was also developed by Wan, et al. to investigate the differentiation of murine embryonic stem cells into cardiomyocytes 42 . Finally, Liu, et al. incorporated a microfluidic device to investigate the effect of carcinoma-associated fibroblasts on cancer cell invasion in a 3D matrix 43 .

3D Cell Culture 2021: Models, Applications & Translation

5 - 7 May 2021 Online Event

Due to the current development of the COVID-19 pandemic the conference will take place online. We are very sorry not to meet you in Konzerthaus Freiburg as originally planned, but very much look forward to welcoming you to an interactive virtual event.

As soon as registered you may get to the virtual platform here:

Link to Virtual Venue (for registered participants only)

The online conference programme has been published below. Authors can prepare their lecture/poster presentation according to the instructions.

Keynote Speakers

We are pleased to announce renowned keynote speakers (as of February 2021):

Application of microphysiological systems to drug safety assessment: progress and challenges
Rhiannon David, AstraZeneca, Cambridge/UK

Advancing regulatory science through innovation in vitro microphysiological systems
Suzanne C. Fitzpatrick, CFSAN/FDA, College Park Maryland, MD/USA

Toward a world of ElectroGenetics
Martin Fussenegger, ETH Zurich/CH

Organotypic 3D models to characterize the molecular requirements of immune cell infiltration and activation
Wolfgang Sommergruber, University of Applied Sciences, Vienna/A

Lecture Programme

(subject to change, as of 30 April 2021)

Wednesday, 5 May 2021

Opening and Technical Remarks
Synthetic Biology, Screening Platforms and Metabolomics

Chair: H. Hauser, Helmholtz Centre for Infection Research, Braunschweig/D

Keynote Lecture 1

Label-free measurements of the metabolic activity within 3D cell culture model via automated 3D microphysiometry
S. Eggert ¹ M. Gutbrod² G. Liebsch² R. Meier² D. Hutmacher³ P. Mela¹
¹ Technical University of Munich, Garching/D ² PreSens Precision Sensing GmbH, Regensburg/D ³ Queensland University of Technology, Brisbane/AUS

Hypoxia or normoxia: mesenchymal stem cells and chondrocytes with a genetically integrated hypoxia sensor in different 3D cell culture systems
C. Schmitz¹ T. Fleischhammer¹ I. Pepelanova¹ E. Potekhina² V. Belousov³
T. Scheper¹ A. Lavrentieva ¹
¹ Leibniz University Hanover/D ² Shemyakin‐Ovchinnikov Institute of Bioorganic Chemistry, Moscow/RUS ³ Federal Center of Brain Research and Neurotechnologies, Federal Medical Biological Agency, Moscow/RUS

EU-OPENSCREEN: A novel collaborative model for accelerating early phase drug discovery
A. Silvestri ¹ O. Genilloud² P. Gribbon³ J. Kolanowski⁴ Z. Leśnikowski⁵
C. Steinhauer⁶ M. Vicent⁷ W. Fecke¹
¹ EU-OPENSCREEN ERIC, Berlin/D ² Fundación Medina, Granada/E
³ Fraunhofer Institute for Molecular Biology and Applied Ecology, Hamburg/D
⁴ Polish Academy of Sciences, Poznan/PL ⁵ Polish Academy of Sciences, Lodz/PL
⁶ Copenhagen University/DK ⁷ Principe Felipe Research Center, Valencia/E

Advanced 3D Models for Tumor Research

Chair: J.M. Kelm, PreComb Therapeutics AG, Wädenswil/CH

Keynote Lecture 2

Development of a 4D-in vitro-test system for specificity and potency of 3rd generation chimeric antigen receptor-T-cells (CAR-T-cells) based on microcavity array-bioreactors
E. Gottwald ¹ L. Werner¹ C. Nies¹ S. Wang² M. Schmitt²
¹ Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen/D

² University Hospital Heidelberg/D

3D modelling of the chronic lymphocytic leukaemia microenvironment
F. Sbrana¹ R. Pinos¹ D. Ribezzi¹ F. Scagnoli¹ F. Barbaglio¹ D. Belloni¹
L. Scarfò¹ C. Scielzo ¹
¹ IRCCS, Ospedale San Raffaele, Milan/I

Generation of multicellular spheroid culture models of pediatric vital tumor samples obtained through the INFORM registry study for functional studies
H. Peterziel¹ A. Mangang¹ P. Fiesel² S. Oppermann³ L. Turunen⁴ J. Saarela⁴
O. Witt⁵ I. Oehme ¹
¹ Hopp Children&rsquos Cancer Center Heidelberg (KiTZ), German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg/D ² German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK) Heidelberg/D ³ Hopp Children&rsquos Cancer Center Heidelberg (KiTZ), German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg/D ⁴ University of Helsinki, Finland (FIMM-UH), Helsinki/FIN ⁵ Hopp Children&rsquos Cancer Center Heidelberg (KiTZ), German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK) and Heidelberg University Hospital/D

Poster Session 1 / Exhibition

End of Day 1

DECHEMA Sections "Cell Culture Technology" and "Medical Biotechnology"

Thursday, 6 May 2021

Complex and Multi-Celltype Models

Chair: C. Kasper, University of Natural Resources and Life Sciences, Vienna/A

Image based quantification of myeloid cell repolarization and their interplay within the tumor microenvironment in 3D
G. Goverse ¹ N. Beztsinna¹ B. Visser¹ M. van de Merbel¹ E. Spanjaard¹ K. Yan¹
L. Price¹ L. Daszkiewicz¹
¹ OcellO B.V., Leiden/NL

Manufacturing of functional beta cell-mesenchymal stem cell-spheroids for transplantation or drug testing
F. Petry ¹ P. Czermak¹ , ² D. Salzig¹
¹ University of Applied Sciences Mittelhessen, Giessen/D

Development of a new human organotypic 3D urinary bladder epithelium model in complex whole blood co-culture with immune cells
M. Willig¹ R. Kehlbach¹ G. Stein¹ M. Schmolz ¹
¹ HOT Screen GmbH, Reutlingen/D

Discussion with all speakers of this session

Coffee Break

Innovative Microphysiological Systems I

Chair: U. Marx, TissUse GmbH, Berlin/D

Keynote Lecture 3

Applying a microphysiological 3D human liver – islet microtissue platform to study drug – drug interaction
L. Hoelting ¹ I. Karakoc¹ B. Yesildag¹ W. Moritz¹ O. Frey¹
¹ InSphero AG, Schlieren/CH

Evaluating drug-induced liver toxicity of acetaminophen, trovafloxacin and levofloxacin in a triple-cell microphysiological liver sinusoidal model

T. Kaden ¹ R. Li² A. Mosig³ K. Graf¹ M. Raasch¹ K. Rennert¹
¹ Dynamic42 GmbH, Jena/D ² Biopredic sarl, St Gregoire/F ³ University Hospital Jena/D

Discussion with all speakers of this session

Lunch Break

Poster Session 2 / exhibition
Innovative microphysiological systems II

Chair: J. Hansmann, Universitätsklinikum Würzburg/D

Keynote Lecture 4


Development of vascularized melanoma skin equivalents for studying metastasis and anti-melanoma therapies
A. Leikeim ¹ J. Kliche¹ M. Komma¹ F. Schmidt² F. Groeber-Becker²
¹ Uniklinikum Würzburg/D ² Fraunhofer ISC - Translational Center Regenerative Therapies TLC-RT, Würzburg/D

Discussion with all speakers of this session

Award Ceremony
Virtual Get Together at " wonder "

End of Day 2

Friday, 7 May 2021

Investigating Host-Microbial Interactions

Chair: I. Prade, Forschungsinstitut für Leder und Kunststoffbahnen (FILK) gGmbH, Freiberg/D

Trends in 3D Cell Culture: Organoids and Spheroids

Amanda Linkous, PhD, research associate professor, Department of Biochemistry and scientific center manager, NCI Center for Systems Biology of Small Cell Lung Cancer at Vanderbilt University, talks to contributing editor Tanuja Koppal, PhD, about new scientific and technological advancements in 3D cell culture. She discusses some of the emerging applications for the use of 3D organoids and spheroids that have been made possible due to these innovations.

Q: Can you offer some details about your work and expertise?

A: I completed my postdoctoral training in the Neuro-Oncology Branch at the National Cancer Institute (NCI). It was during this time that I developed a passion for studying cancer stem cell biology and the molecular signaling that promotes glioblastoma (GBM) tumor progression. Over the years, I continued to pursue this devastating disease as my primary research focus. As the former director of the Starr Foundation Cerebral Organoid Translational Core at Weill Cornell Medicine, I established a novel, ex vivo 3D system to study the interactions and molecular crosstalk between brain tumor cells and a miniature model of the human brain. We named this cerebral organoid glioma model the &ldquoGLICO&rdquo model and published our findings in Cell Reports.

Q: What is the difference between 3D spheroids and organoids?

A: Spheroids and organoids are both multi-cellular 3D structures. Spheroids, however, are cell aggregates typically composed of cancer cells cultured under scaffold-free, non-adherent conditions. The complexity of spheroid cultures is limited these cultures are also difficult to maintain long-term due to factors such as hypoxia, necrosis, and loss of key genetic features. Organoids, termed &ldquomini-organs&rdquo by many, are comprised of organ-specific cell types (from stem cells or progenitor cells) that exhibit lineage-specific differentiation and self-assembly. A scaffolding matrix such as Matrigel is often used to support the architecture of the developing microstructure. The level of self-organization that occurs in organoids is quite remarkable and highly similar to normal organ development in vivo.

Q: Can you discuss some key technical and experimental challenges in culturing and using 3D cellular models?

A: There is a bit of an art-form to working with 3D cell models. In the case of cerebral organoids, each stage of the differentiation process is dependent on timing. If one administers the appropriate growth factor or biological cue, but at the inappropriate time, then there can be wide variation in the resulting cell types that are generated. Therefore, it is critical to monitor the cultures daily and learn to follow the morphological clues that the cultures are giving you. In addition, depending on the size of the organoid, it is easy to shear or damage an organoid when attempting to transfer or physically manipulate the sample. Thus, as with any model system, one must carefully consider the limitations of 3D cellular models when designing experiments.

Q: You mentioned sample size and the importance of timing, but have you encountered other issues when designing experiments with 3D cellular models?

A: Absolutely! As I mentioned, organoids are frequently referred to as &ldquomini-organs&rdquo and often resemble small pieces of tissue. This tissue can be incredibly dense, so imaging analysis with standard fluorescence or confocal microscopy can be quite difficult, if not impossible. We were very interested in imaging tumor volume within our mini-brains, so we utilized multi-photon microscopy and even light sheet microscopy for some experiments. The imaging resolution was fantastic however, these imaging modalities presented challenges of their own. Multi-photon microscopy required the live sample to be immobilized for long periods of time. Since our 3D models required gentle agitation within an incubator in order to remain viable, there was a lot of trial and error to determine the best way to effectively image the tumor and return the organoid to a shaking culture environment, without affecting the viability of the sample itself. Alternatively, light sheet microscopy allowed imaging on fixed 3D samples, but the preparation and labeling of the sample could take weeks.

Another experimental challenge that we faced included isolation of tumor cells from the normal 3D mini-brain. We tried multiple reagents and methods of tissue dissociation, but we finally designed the optimal protocol for our specific needs. With any 3D model, researchers must consider what biological questions they want to ask, and what readout or analysis metric makes the most sense.

Q: What are some of the emerging applications for 3D cell models and how well do they represent in vivo processes and results?

A: 3D cell models have become increasingly more sophisticated in recent years and are used to study a multitude of disease states including neurogenerative diseases, cancer, cardiac disease, cystic fibrosis, and even drug addiction. Moreover, the utilization of 3D models in regenerative medicine is advancing rapidly. One of the reasons that 3D models are so heavily sought after is that they recapitulate the biology and pathophysiology of many in vivo processes. Coupled with their scalability for high-throughput drug screening, 3D models such as organoids offer an unprecedented way to approach personalized medicine. For example, our GLICO model enables the generation of hundreds of patient-specific miniature brain tumors in a way that is not currently possible for any in vivo glioblastoma model.

Q: What advice would you give to lab managers who are looking to get started in 3D cell culture work?

A: 3D cell culture is expensive, but it is extremely important to work with high-quality reagents and never take shortcuts, no matter how tempting it might be. The initial building phase of a 3D culture program is also incredibly time consuming. The amount of training time required to achieve 3D culture competency for new personnel can range from six months to one year. If you struggle with patience, you might want to re-evaluate your decision to enter the world of 3D model systems. If you are willing to make the commitment, however, you will find it to be one of the coolest and most rewarding platforms for studying developmental and/or disease biology.

Amanda Linkous previously served as the director of the Starr Foundation Cerebral Organoid Translational Core at Weill Cornell Medicine (New York, NY). She completed her postdoctoral training in the Neuro-Oncology Branch at the National Cancer Institute (Bethesda, MD). She has extensive expertise in cancer stem cell biology and the molecular signaling that promotes tumor progression. She established a novel, ex vivo 3D system to study the interactions and molecular cross-talk between tumor cells and a miniature model of the human brain&mdasha finding that was featured on CNN Pioneers and in a special edition of Science

2D, Two-dimensional 3D, Three-dimensional bFGF, Basic fibroblast growth factor CNS, Central nervous system ECM, Extracellular matrix EGF, Epidermal growth factor HA, Hyaluronan/hyaluronic acid HCS, High-content screening HDP, Hanging drop plate HGF, Hepatocyte growth factor HTS, High-throughput screening IGF-1, Insulin-like growth factor 1 iPSC, Induced pluripotent stem cell MMP, Matrix metalloproteinase NGF, Nerve growth factor PDGF, platelet-derived growth factor PEG, Polyethylene glycol TGF-β, Transforming growth factor-β TIMP, Tissue inhibitor of metalloproteinase VEGF, Vascular endothelial growth factor.

Abaci, H. E., Guo, Z., Doucet, Y., Jackow, J., and Christiano, A. (2017). Next generation human skin constructs as advanced tools for drug development. Exp. Biol. Med. 242, 1657�. doi: 10.1177/1535370217712690

Ahmed, T. A., Dare, E. V., and Hincke, M. (2008). Fibrin: a versatile scaffold for tissue engineering applications. Tissue Eng. B Rev. 14, 199�. doi: 10.1089/ten.teb.2007.0435

Alcaraz, J., Otero, J., Jorba, I., and Navajas, D. (2017). Bidirectional mechanobiology between cells and their local extracellular matrix probed by atomic force microscopy. Semin. Cell Dev. Biol. 73, 71�. doi: 10.1016/j.semcdb.2017.07.020

Alépພ, N., Bahinski, A., Daneshian, M., De Wever, B., Fritsche, E., Goldberg, A., et al. (2014). State-of-the-art of 3D cultures (organs-on-a-chip) in safety testing and pathophysiology. ALTEX 31, 441�. doi: 10.14573/altex1406111

Alsaab, H. O., Sau, S., Alzhrani, R., Tatiparti, K., Bhise, K., Kashaw, S. K., et al. (2017). PD-1 and PD-L1 Checkpoint signaling inhibition for cancer immunotherapy: mechanism, combinations, and clinical outcome. Front. Pharmacol. 8:561. doi: 10.3389/fphar.2017.00561

Anitua, E., Prado, R., and Orive, G. (2013). Endogenous morphogens and fibrin bioscaffolds for stem cell therapeutics. Trends Biotechnol. 31, 364�. doi: 10.1016/j.tibtech.2013.04.003

Aparicio, S., Hidalgo, M., and Kung, A. L. (2015). Examining the utility of patient-derived xenograft mouse models. Nat. Rev. Cancer 15, 311�. doi: 10.1038/nrc3944

Arrowsmith, J., and Miller, P. (2013). Trial watch: phase II and phase III attrition rates 2011-2012. Nat. Rev. Drug Discov. 12:569. doi: 10.1038/nrd4090

Aw Yong, K. M., Li, Z., Merajver, S. D., and Fu, J. (2017). Tracking the tumor invasion front using long-term fluidic tumoroid culture. Sci. Rep. 7:10784. doi: 10.1038/s41598-017-10874-1

Axpe, E., and Oyen, M. L. (2016). Applications of alginate-based bioinks in 3D bioprinting. Int. J. Mol. Sci. 17:E1976. doi: 10.3390/ijms17121976

Baeva, L. F., Lyle, D. B., Rios, M., Langone, J. J., and Lightfoote, M. M. (2014). Different molecular weight hyaluronic acid effects on human macrophage interleukin 1beta production. J. Biomed. Mater. Res. A 102, 305�. doi: 10.1002/jbm.a.34704

Banks, J. M., Harley, B. A. C., and Bailey, R. C. (2015). Tunable, photoreactive hydrogel system to probe synergies between mechanical and biomolecular cues on adipose-derived mesenchymal stem cell differentiation. ACS Biomater. Sci. Engineer. 1, 718�. doi: 10.1021/acsbiomaterials.5b00196

Barnes, J. M., Przybyla, L., and Weaver, V. M. (2017). Tissue mechanics regulate brain development, homeostasis and disease. J. Cell Sci. 130, 71�. doi: 10.1242/jcs.191742

Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., et al. (2012). The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603�. doi: 10.1038/nature11003

Barros, C. S., Franco, S. J., and Müller, U. (2011). Extracellular matrix: functions in the nervous system. Cold Spring Harb. Perspect. Biol. 3:a005108. doi: 10.1101/cshperspect.a005108

Bokhari, M., Carnachan, R. J., Cameron, N. R., and Przyborski, S. A. (2007). Culture of HepG2 liver cells on three dimensional polystyrene scaffolds enhances cell structure and function during toxicological challenge. J. Anat. 211, 567�. doi: 10.1111/j.1469-7580.2007.00778.x

Bonnans, C., Chou, J., and Werb, Z. (2014). Remodelling the extracellular matrix in development and disease. Nat. Rev. Mol. Cell Biol. 15, 786�. doi: 10.1038/nrm3904

Bordeleau, F., Mason, B. N., Lollis, E. M., Mazzola, M., Zanotelli, M. R., Somasegar, S., et al. (2017). Matrix stiffening promotes a tumor vasculature phenotype. Proc. Natl. Acad. Sci. U.S.A. 114, 492�. doi: 10.1073/pnas.1613855114

Borlak, J., Singh, P. K., and Rittelmeyer, I. (2015). Regulation of liver enriched transcription factors in rat hepatocytes cultures on collagen and EHS sarcoma matrices. PLoS ONE 10:e0124867. doi: 10.1371/journal.pone.0124867

Bourguignon, L. Y. (2016). Matrix hyaluronan promotes specific MicroRNA upregulation leading to drug resistance and tumor progression. Int. J. Mol. Sci. 17:517. doi: 10.3390/ijms17040517

Branco, M. C., Sigano, D. M., and Schneider, J. P. (2011). Materials from peptide assembly: towards the treatment of cancer and transmittable disease. Curr. Opin. Chem. Biol. 15, 427�. doi: 10.1016/j.cbpa.2011.03.021

Breslin, S., and Oɽriscoll, L. (2013). Three-dimensional cell culture: the missing link in drug discovery. Drug Discov. To. 18, 240�. doi: 10.1016/j.drudis.2012.10.003

Brown, A. C., and Barker, T. H. (2014). Fibrin-based biomaterials: modulation of macroscopic properties through rational design at the molecular level. Acta Biomater. 10, 1502�. doi: 10.1016/j.actbio.2013.09.008

Burdick, J. A., and Prestwich, G. D. (2011). Hyaluronic acid hydrogels for biomedical applications. Adv. Mater. Weinheim. 23, H41–H56. doi: 10.1002/adma.201003963

Caiazzo, M., Okawa, Y., Ranga, A., Piersigilli, A., Tabata, Y., and Lutolf, M. P. (2016). Defined three-dimensional microenvironments boost induction of pluripotency. Nat. Mater. 15, 344�. doi: 10.1038/nmat4536

Caliari, S. R., and Burdick, J. A. (2016). A practical guide to hydrogels for cell culture. Nat. Methods 13, 405�. doi: 10.1038/nmeth.3839

Campos, L. S. (2004). Neurospheres: insights into neural stem cell biology. J. Neurosci. Res. 78, 761�. doi: 10.1002/jnr.20333

Candido, S., Abrams, S. L., Steelman, L. S., Lertpiriyapong, K., Fitzgerald, T. L., Martelli, A. M., et al. (2016). Roles of NGAL and MMP-9 in the tumor microenvironment and sensitivity to targeted therapy. Biochim. Biophys. Acta 1863, 438�. doi: 10.1016/j.bbamcr.2015.08.010

Carrera, S., de Verdier, P. J., Khan, Z., Zhao, B., Mahale, A., Bowman, K. J., et al. (2010). Protection of cells in physiological oxygen tensions against DNA damage-induced apoptosis. J. Biol. Chem. 285, 13658�. doi: 10.1074/jbc.M109.062562

Chitnis, T., and Weiner, H. L. (2017). CNS inflammation and neurodegeneration. J. Clin. Invest. 127, 3577�. doi: 10.1172/JCI90609

Chouaib, S., Noman, M. Z., Kosmatopoulos, K., and Curran, M. A. (2017). Hypoxic stress: obstacles and opportunities for innovative immunotherapy of cancer. Oncogene 36, 439�. doi: 10.1038/onc.2016.225

Clevers, H. (2016). Modeling development and disease with organoids. Cell 165, 1586�. doi: 10.1016/j.cell.2016.05.082

Crawford, Y., and Ferrara, N. (2009). Tumor and stromal pathways mediating refractoriness/resistance to anti-angiogenic therapies. Trends Pharmacol. Sci. 30, 624�. doi: 10.1016/

Cukierman, E., Pankov, R., Stevens, D. R., and Yamada, K. M. (2001). Taking cell-matrix adhesions to the third dimension. Science 294, 1708�. doi: 10.1126/science.1064829

DeClerck, Y. A. (2000). Interactions between tumour cells and stromal cells and proteolytic modification of the extracellular matrix by metalloproteinases in cancer. Eur. J. Cancer 36, 1258�. doi: 10.1016/S0959-8049(00)00094-0

Dekkers, J. F., Wiegerinck, C. L., de Jonge, H. R., Bronsveld, I., Janssens, H. M., de Winter-de Groot, K. M., et al. (2013). A functional CFTR assay using primary cystic fibrosis intestinal organoids. Nat. Med. 19, 939�. doi: 10.1038/nm.3201

De Palma, M., Biziato, D., and Petrova, T. V. (2017). Microenvironmental regulation of tumour angiogenesis. Nat. Rev. Cancer 17, 457�. doi: 10.1038/nrc.2017.51

Dickreuter, E., and Cordes, N. (2017). The cancer cell adhesion resistome: mechanisms, targeting and translational approaches. Biol. Chem. 398, 721�. doi: 10.1515/hsz-2016-0326

Doublier, S., Belisario, D. C., Polimeni, M., Annaratone, L., Riganti, C., Allia, E., et al. (2012). HIF-1 activation induces doxorubicin resistance in MCF7 3-D spheroids via P-glycoprotein expression: a potential model of the chemo-resistance of invasive micropapillary carcinoma of the breast. BMC Cancer 12:4. doi: 10.1186/1471-2407-12-4

Doyle, A. D., Carvajal, N., Jin, A., Matsumoto, K., and Yamada, K. M. (2015). Local 3D matrix microenvironment regulates cell migration through spatiotemporal dynamics of contractility-dependent adhesions. Nat. Commun. 6:8720. doi: 10.1038/ncomms9720

Dutta, D., Heo, I., and Clevers, H. (2017). Disease modeling in stem cell-derived 3D organoid systems. Trends Mol. Med. 23, 393�. doi: 10.1016/j.molmed.2017.02.007

Edmondson, R., Broglie, J. J., Adcock, A. F., and Yang, L. (2014). Three-dimensional cell culture systems and their applications in drug discovery and cell-based biosensors. Assay Drug Dev. Technol. 12, 207�. doi: 10.1089/adt.2014.573

Egeblad, M., Nakasone, E. S., and Werb, Z. (2010). Tumors as organs: complex tissues that interface with the entire organism. Dev. Cell 18, 884�. doi: 10.1016/j.devcel.2010.05.012

Ekert, J. E., Johnson, K., Strake, B., Pardinas, J., Jarantow, S., Perkinson, R., et al. (2014). Three-dimensional lung tumor microenvironment modulates therapeutic compound responsiveness in vitro–implication for drug development. PLoS ONE 9:e92248. doi: 10.1371/journal.pone.0092248

Eyckmans, J., Boudou, T., Yu, X., and Chen, C. S. (2011). A hitchhiker's guide to mechanobiology. Dev. Cell 21, 35�. doi: 10.1016/j.devcel.2011.06.015

Fatehullah, A., Tan, S. H., and Barker, N. (2016). Organoids as an in vitro model of human development and disease. Nat. Cell Biol. 18, 246�. doi: 10.1038/ncb3312

Finnberg, N. K., Gokare, P., Lev, A., Grivennikov, S. I., MacFarlane, A. W., Campbell, K. S., et al. (2017). Application of 3D tumoroid systems to define immune and cytotoxic therapeutic responses based on tumoroid and tissue slice culture molecular signatures. Oncotarget 8, 66747�. doi: 10.18632/oncotarget.19965

Friedrich, J., Seidel, C., Ebner, R., and Kunz-Schughart, L. A. (2009). Spheroid-based drug screen: considerations and practical approach. Nat. Protoc. 4, 309�. doi: 10.1038/nprot.2008.226

Frischknecht, R., and Gundelfinger, E. D. (2012). The brain's extracellular matrix and its role in synaptic plasticity. Adv. Exp. Med. Biol. 970, 153�. doi: 10.1007/978-3-7091-0932-8_7

Gacche, R. N. (2015). Compensatory angiogenesis and tumor refractoriness. Oncogenesis 4:e153. doi: 10.1038/oncsis.2015.14

Gill, B. J., and West, J. L. (2014). Modeling the tumor extracellular matrix: Tissue engineering tools repurposed towards new frontiers in cancer biology. J. Biomech. 47, 1969�. doi: 10.1016/j.jbiomech.2013.09.029

Girard, Y. K., Wang, C., Ravi, S., Howell, M. C., Mallela, J., Alibrahim, M., et al. (2013). A 3D fibrous scaffold inducing tumoroids: a platform for anticancer drug development. PLoS ONE 8:e75345. doi: 10.1371/journal.pone.0075345

Glowacki, J., and Mizuno, S. (2008). Collagen scaffolds for tissue engineering. Biopolymers 89, 338�. doi: 10.1002/bip.20871

Goodwin, T. J., Prewett, T. L., Wolf, D. A., and Spaulding, G. F. (1993). Reduced shear stress: a major component in the ability of mammalian tissues to form three-dimensional assemblies in simulated microgravity. J. Cell. Biochem. 51, 301�. doi: 10.1002/jcb.240510309

Goubko, C. A., Basak, A., Majumdar, S., and Cao, X. (2014). Dynamic cell patterning of photoresponsive hyaluronic acid hydrogels. J. Biomed. Mater. Res. A 102, 381�. doi: 10.1002/jbm.a.34712

Gracz, A. D., Williamson, I. A., Roche, K. C., Johnston, M. J., Wang, F., Wang, Y., et al. (2015). A high-throughput platform for stem cell niche co-cultures and downstream gene expression analysis. Nat. Cell Biol. 17:340. doi: 10.1038/ncb3104

Griffith, L. G., and Swartz, M. A. (2006). Capturing complex 3D tissue physiology in vitro. Nat. Rev. Mol. Cell Biol. 7, 211�. doi: 10.1038/nrm1858

Guilbaud, J. B., Vey, E., Boothroyd, S., Smith, A. M., Ulijn, R. V., Saiani, A., et al. (2010). Enzymatic catalyzed synthesis and triggered gelation of ionic peptides. Langmuir 26, 11297�. doi: 10.1021/la100623y

Haines-Butterick, L., Rajagopal, K., Branco, M., Salick, D., Rughani, R., Pilarz, M., et al. (2007). Controlling hydrogelation kinetics by peptide design for three-dimensional encapsulation and injectable delivery of cells. Proc. Natl. Acad. Sci. U.S.A. 104, 7791�. doi: 10.1073/pnas.0701980104

Haisler, W. L., Timm, D. M., Gage, J. A., Tseng, H., Killian, T. C., and Souza, G. R. (2013). Three-dimensional cell culturing by magnetic levitation. Nat. Protoc. 8, 1940�. doi: 10.1038/nprot.2013.125

Hamill, O. P., and Martinac, B. (2001). Molecular basis of mechanotransduction in living cells. Physiol. Rev. 81, 685�. doi: 10.1152/physrev.2001.81.2.685

Handorf, A. M., Zhou, Y., Halanski, M. A., and Li, W. J. (2015). Tissue stiffness dictates development, homeostasis, and disease progression. Organogenesis 11, 1�. doi: 10.1080/15476278.2015.1019687

Happel, M. F. K., and Frischknecht, R. (2016). “Neuronal plasticity in the juvenile and adult brain regulated by the extracellular matrix,” in Composition and Function of the Extracellular Matrix in the Human Body [Internet], 143�.

Herrmann, R., Fayad, W., Schwarz, S., Berndtsson, M., and Linder, S. (2008). Screening for compounds that induce apoptosis of cancer cells grown as multicellular spheroids. J. Biomol. Screen. 13, 1𠄸. doi: 10.1177/1087057107310442

Ho, W. J., Pham, E. A., Kim, J. W., Ng, C. W., Kim, J. H., Kamei, D. T., et al. (2010). Incorporation of multicellular spheroids into 3-D polymeric scaffolds provides an improved tumor model for screening anticancer drugs. Cancer Sci. 101, 2637�. doi: 10.1111/j.1349-7006.2010.01723.x

Holle, A. W., Young, J. L., and Spatz, J. P. (2016). In vitro cancer cell-ECM interactions inform in vivo cancer treatment. Adv. Drug Deliv. Rev. 97:270�. doi: 10.1016/j.addr.2015.10.007

Holohan, C., Van Schaeybroeck, S., Longley, D. B., and Johnston, P. G. (2013). Cancer drug resistance: an evolving paradigm. Nat. Rev. Cancer 13, 714�. doi: 10.1038/nrc3599

Huang, H., Ding, Y., Sun, X. S., and Nguyen, T. A. (2013). Peptide hydrogelation and cell encapsulation for 3D culture of MCF-7 breast cancer cells. PLoS ONE 8:e59482. doi: 10.1371/journal.pone.0059482

Huang, H., Shi, J., Laskin, J., Liu, Z., McVey, D. S., and Sun, X. S. (2011). Design of a shear-thinning recoverable peptide hydrogel from native sequences and application for influenza H1N1 vaccine adjuvant. Soft Matter 7, 8905�. doi: 10.1039/c1sm05157a

Huang, H., and Sun, X. S. (2010). Rational design of responsive self-assembling peptides from native protein sequences. Biomacromolecules 11, 3390�. doi: 10.1021/bm100894j

Huang, Y. J., and Hsu, S. H. (2014). Acquisition of epithelial-mesenchymal transition and cancer stem-like phenotypes within chitosan-hyaluronan membrane-derived 3D tumor spheroids. Biomaterials 35, 10070�. doi: 10.1016/j.biomaterials.2014.09.010

Hughes, C. S., Postovit, L. M., and Lajoie, G. A. (2010). Matrigel: a complex protein mixture required for optimal growth of cell culture. Proteomics 10, 1886�. doi: 10.1002/pmic.200900758

Hynes, R. O. (2014). Stretching the boundaries of extracellular matrix research. Nat. Rev. Mol. Cell Biol. 15, 761�. doi: 10.1038/nrm3908

Hynes, R. O., and Naba, A. (2012). Overview of the matrisome𠄺n inventory of extracellular matrix constituents and functions. Cold Spring Harb. Perspect. Biol. 4:a004903. doi: 10.1101/cshperspect.a004903

Imamura, Y., Mukohara, T., Shimono, Y., Funakoshi, Y., Chayahara, N., Toyoda, M., et al. (2015). Comparison of 2D- and 3D-culture models as drug-testing platforms in breast cancer. Oncol. Rep. 33, 1837�. doi: 10.3892/or.2015.3767

Iskandar, A. R., Xiang, Y., Frentzel, S., Talikka, M., Leroy, P., Kuehn, D., et al. (2015). Impact assessment of cigarette smoke exposure on organotypic bronchial epithelial tissue cultures: a comparison of mono-culture and coculture model containing fibroblasts. Toxicol. Sci. 147, 207�. doi: 10.1093/toxsci/kfv122

Ivascu, A., and Kubbies, M. (2006). Rapid generation of single-tumor spheroids for high-throughput cell function and toxicity analysis. J. Biomol. Screen. 11, 922�. doi: 10.1177/1087057106292763

Jakubikova, J., Cholujova, D., Hideshima, T., Gronesova, P., Soltysova, A., Harada, T., et al. (2016). A novel 3D mesenchymal stem cell model of the multiple myeloma bone marrow niche: biologic and clinical applications. Oncotarget 7, 77326�. doi: 10.18632/oncotarget.12643

Janzen, W. P. (2014). Screening technologies for small molecule discovery: the state of the art. Chem. Biol. 21, 1162�. doi: 10.1016/j.chembiol.2014.07.015

Jayawarna, V., Ali, M., Jowitt, T. A., Miller, A. F., Saiani, A., Gough, J. E., et al. (2006). Nanostructured hydrogels for three-dimensional cell culture through self-assembly of fluorenylmethoxycarbonyl𠄽ipeptides. Adv. Mater. 18, 611�. doi: 10.1002/adma.200501522

Jiang, H., Hegde, S., and DeNardo, D. G. (2017). Tumor-associated fibrosis as a regulator of tumor immunity and response to immunotherapy. Cancer Immunol. Immunother. 66, 1037�. doi: 10.1007/s00262-017-2003-1

Joddar, B., Garcia, E., Casas, A., and Stewart, C. M. (2016). Development of functionalized multi-walled carbon-nanotube-based alginate hydrogels for enabling biomimetic technologies. Sci. Rep. 6:32456. doi: 10.1038/srep32456

Johansson, A., Hamzah, J., and Ganss, R. (2016). More than a scaffold: stromal modulation of tumor immunity. Biochim. Biophys. Acta 1865, 3�. doi: 10.1016/j.bbcan.2015.06.001

Jones, V. S., Huang, R. Y., Chen, L. P., Chen, Z. S., Fu, L., and Huang, R. P. (2016). Cytokines in cancer drug resistance: cues to new therapeutic strategies. Biochim. Biophys. Acta 1865, 255�. doi: 10.1016/j.bbcan.2016.03.005

Junttila, M. R., and de Sauvage, F. J. (2013). Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 501, 346�. doi: 10.1038/nature12626

Kabba, J. A., Xu, Y., Christian, H., Ruan, W., Chenai, K., Xiang, Y., et al. (2017). Microglia: housekeeper of the central nervous system. Cell. Mol. Neurobiol. doi: 10.1007/s10571-017-0504-2. [Epub ahead of print].

Kersh, A. E., Ng, S., Chang, Y. M., Sasaki, M., Thomas, S. N., Kissick, H. T., et al. (2018). Targeted therapies: immunologic effects and potential applications outside of cancer. J. Clin. Pharmacol. 58, 7�. doi: 10.1002/jcph.1028

Kirshner, J., Thulien, K. J., Martin, L. D., Debes Marun, C., Reiman, T., Belch, A. R., et al. (2008). A unique three-dimensional model for evaluating the impact of therapy on multiple myeloma. Blood 112, 2935�. doi: 10.1182/blood-2008-02-142430

Kleinman, H. K., and Martin, G. R. (2005). Matrigel: basement membrane matrix with biological activity. Semin. Cancer Biol. 15, 378�. doi: 10.1016/j.semcancer.2005.05.004

Knight, E., Murray, B., Carnachan, R., and Przyborski, S. (2011). Alvetex(R): polystyrene scaffold technology for routine three dimensional cell culture. Methods Mol. Biol. 695, 323�. doi: 10.1007/978-1-60761-984-0_20

Kraehenbuehl, T. P., Zammaretti, P., Van der Vlies, A. J., Schoenmakers, R. G., Lutolf, M. P., Jaconi, M. E., et al. (2008). Three-dimensional extracellular matrix-directed cardioprogenitor differentiation: systematic modulation of a synthetic cell-responsive PEG-hydrogel. Biomaterials 29, 2757�. doi: 10.1016/j.biomaterials.2008.03.016

Kretzschmar, K., and Clevers, H. (2016). Organoids: modeling development and the stem cell niche in a dish. Dev. Cell 38, 590�. doi: 10.1016/j.devcel.2016.08.014

Kural, M. H., and Billiar, K. L. (2013). Regulating tension in three-dimensional culture environments. Exp. Cell Res. 319, 2447�. doi: 10.1016/j.yexcr.2013.06.019

Kutschka, I., Chen, I. Y., Kofidis, T., Arai, T., von Degenfeld, G., Sheikh, A. Y., et al. (2006). Collagen matrices enhance survival of transplanted cardiomyoblasts and contribute to functional improvement of ischemic rat hearts. Circulation 114(1 Suppl.), I167–I173. doi: 10.1161/CIRCULATIONAHA.105.001297

Lancaster, M. A., and Knoblich, J. A. (2014). Organogenesis in a dish: modeling development and disease using organoid technologies. Science 345:1247125. doi: 10.1126/science.1247125

Lanzi, C., Zaffaroni, N., and Cassinelli, G. (2017). Targeting heparan sulfate proteoglycans and their modifying enzymes to enhance anticancer chemotherapy efficacy and overcome drug resistance. Curr. Med. Chem. 24, 2860�. doi: 10.2174/0929867324666170216114248

Lewis, D. M., Blatchley, M. R., Park, K. M., and Gerecht, S. (2017). O2-controllable hydrogels for studying cellular responses to hypoxic gradients in three dimensions in vitro and in vivo. Nat. Protoc. 12, 1620�. doi: 10.1038/nprot.2017.059

Lewis, E. E., Wheadon, H., Lewis, N., Yang, J., Mullin, M., Hursthouse, A., et al. (2016). A quiescent, regeneration-responsive tissue engineered mesenchymal stem cell bone marrow niche model via magnetic levitation. ACS Nano 10, 8346�. doi: 10.1021/acsnano.6b02841

Lewis, N. S., Lewis, E. E., Mullin, M., Wheadon, H., Dalby, M. J., and Berry, C. C. (2017). Magnetically levitated mesenchymal stem cell spheroids cultured with a collagen gel maintain phenotype and quiescence. J. Tissue Eng. 8:2041731417704428. doi: 10.1177/2041731417704428

Li, J., Gao, Y., Kuang, Y., Shi, J., Du, X., Zhou, J., et al. (2013). Dephosphorylation of D-peptide derivatives to form biofunctional, supramolecular nanofibers/hydrogels and their potential applications for intracellular imaging and intratumoral chemotherapy. J. Am. Chem. Soc. 135, 9907�. doi: 10.1021/ja404215g

Li, Q., Chen, C., Kapadia, A., Zhou, Q., Harper, M. K., Schaack, J., et al. (2011). 3D models of epithelial-mesenchymal transition in breast cancer metastasis: high-throughput screening assay development, validation, and pilot screen. J. Biomol. Screen. 16, 141�. doi: 10.1177/1087057110392995

Li, Z., and Deming, T. J. (2010). Tunable hydrogel morphology via self-assembly of amphiphilic pentablock copolypeptides. Soft Matter 6, 2546�. doi: 10.1039/b927137f

Lin, C. H., Jokela, T., Gray, J., and LaBarge, M. A. (2017). Combinatorial microenvironments impose a continuum of cellular responses to a single pathway-targeted anti-cancer compound. Cell Rep. 21, 533�. doi: 10.1016/j.celrep.2017.09.058

Lin, Chun, T. H., and Kang, L. (2016). Adipose extracellular matrix remodelling in obesity and insulin resistance. Biochem. Pharmacol. 119, 8�. doi: 10.1016/j.bcp.2016.05.005

Liu, F., Huang, J., Ning, B., Liu, Z., Chen, S., and Zhao, W. (2016). Drug discovery via human-derived stem cell organoids. Front. Pharmacol. 7:334. doi: 10.3389/fphar.2016.00334

Lopes-Bastos, B. M., Jiang, W. G., and Cai, J. (2016). Tumour-endothelial cell communications: important and indispensable mediators of tumour angiogenesis. Anticancer Res. 36, 1119�.

Lutolf, M. P., Lauer-Fields, J. L., Schmoekel, H. G., Metters, A. T., Weber, F. E., Fields, G. B., et al. (2003). Synthetic matrix metalloproteinase-sensitive hydrogels for the conduction of tissue regeneration: engineering cell-invasion characteristics. Proc. Natl. Acad. Sci. U.S.A. 100, 5413�. doi: 10.1073/pnas.0737381100

Ma, W. Y., Hsiung, L. C., Wang, C. H., Chiang, C. L., Lin, C. H., Huang, C. S., et al. (2015). A novel 96well-formatted micro-gap plate enabling drug response profiling on primary tumour samples. Sci. Rep. 5:9656. doi: 10.1038/srep09656

Mahler, A., Reches, M., Rechter, M., Cohen, S., and Gazit, E. (2006). Rigid, self-assembled hydrogel composed of a modified aromatic dipeptide. Adv. Mater. 18, 1365�. doi: 10.1002/adma.200501765

McMillin, D. W., Negri, J. M., and Mitsiades, C. S. (2013). The role of tumour-stromal interactions in modifying drug response: challenges and opportunities. Nat. Rev. Drug Discov. 12, 217�. doi: 10.1038/nrd3870

Montanez-Sauri, S. I., Beebe, D. J., and Sung, K. E. (2015). Microscale screening systems for 3D cellular microenvironments: platforms, advances, and challenges. Cell. Mol. Life Sci. 72, 237�. doi: 10.1007/s00018-014-1738-5

Montanez-Sauri, S. I., Sung, K. E., Berthier, E., and Beebe, D. J. (2013). Enabling screening in 3D microenvironments: probing matrix and stromal effects on the morphology and proliferation of T47D breast carcinoma cells. Integr. Biol. 5, 631�. doi: 10.1039/c3ib20225a

Moon, J. J., Saik, J. E., Poche, R. A., Leslie-Barbick, J. E., Lee, S. H., Smith, A. A., et al. (2010). Biomimetic hydrogels with pro-angiogenic properties. Biomaterials 31, 3840�. doi: 10.1016/j.biomaterials.2010.01.104

Moors, M., Rockel, T. D., Abel, J., Cline, J. E., Gassmann, K., Schreiber, T., et al. (2009). Human neurospheres as three-dimensional cellular systems for developmental neurotoxicity testing. Environ. Health Perspect. 117, 1131�. doi: 10.1289/ehp.0800207

Mouw, J. K., Ou, G., and Weaver, V. M. (2014). Extracellular matrix assembly: a multiscale deconstruction. Nat. Rev. Mol. Cell Biol. 15, 771�. doi: 10.1038/nrm3902

Mueller-Klieser, W. (1987). Multicellular spheroids. A review on cellular aggregates in cancer research. J. Cancer Res. Clin. Oncol. 113, 101�. doi: 10.1007/BF00391431

Muranen, T., Selfors, L. M., Worster, D. T., Iwanicki, M. P., Song, L., Morales, F. C., et al. (2012). Inhibition of PI3K/mTOR leads to adaptive resistance in matrix-attached cancer cells. Cancer Cell 21, 227�. doi: 10.1016/j.ccr.2011.12.024

Murphy, M. C., Jones, D. T., Jack, C. R. Jr., Glaser, K. J., Senjem, M. L., Manduca, A., et al. (2016). Regional brain stiffness changes across the Alzheimer's disease spectrum. Neuroimage Clin. 10, 283�. doi: 10.1016/j.nicl.2015.12.007

Nath, S., and Devi, G. R. (2016). Three-dimensional culture systems in cancer research: focus on tumor spheroid model. Pharmacol. Ther. 163, 94�. doi: 10.1016/j.pharmthera.2016.03.013

Orbach, R., Adler-Abramovich, L., Zigerson, S., Mironi-Harpaz, I., Seliktar, D., and Gazit, E. (2009). Self-assembled Fmoc-peptides as a platform for the formation of nanostructures and hydrogels. Biomacromolecules 10, 2646�. doi: 10.1021/bm900584m

Orgel, J. P., Persikov, A. V., and Antipova, O. (2014). Variation in the helical structure of native collagen. PLoS ONE 9:e89519. doi: 10.1371/journal.pone.0089519

Pamies, D., Hartung, T., and Hogberg, H. T. (2014). Biological and medical applications of a brain-on-a-chip. Exp. Biol. Med. 239, 1096�. doi: 10.1177/1535370214537738

Pampaloni, F., Reynaud, E. G., and Stelzer, E. H. (2007). The third dimension bridges the gap between cell culture and live tissue. Nat. Rev. Mol. Cell Biol. 8, 839�. doi: 10.1038/nrm2236

Pathak, A., and Kumar, S. (2011). Biophysical regulation of tumor cell invasion: moving beyond matrix stiffness. Integr. Biol. 3, 267�. doi: 10.1039/c0ib00095g

Pauli, C., Hopkins, B. D., Prandi, D., Shaw, R., Fedrizzi, T., Sboner, A., et al. (2017). Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7, 462�. doi: 10.1158/2159-8290.CD-16-1154

Pitt, J. M., Marabelle, A., Eggermont, A., Soria, J. C., Kroemer, G., and Zitvogel, L. (2016). Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy. Ann. Oncol. 27, 1482�. doi: 10.1093/annonc/mdw168

Pogge von Strandmann, E., Reinartz, S., Wager, U., and Muller, R. (2017). Tumor-host cell interactions in ovarian cancer: pathways to therapy failure. Trends Cancer 3, 137�. doi: 10.1016/j.trecan.2016.12.005

Poincloux, R., Collin, O., Lizarraga, F., Romao, M., Debray, M., Piel, M., et al. (2011). Contractility of the cell rear drives invasion of breast tumor cells in 3D Matrigel. Proc. Natl. Acad. Sci. U.S.A. 108, 1943�. doi: 10.1073/pnas.1010396108

Polonchuk, L., Chabria, M., Badi, L., Hoflack, J. C., Figtree, G., Davies, M. J., et al. (2017). Cardiac spheroids as promising in vitro models to study the human heart microenvironment. Sci. Rep. 7:7005. doi: 10.1038/s41598-017-06385-8

Prieto-Vila, M., Takahashi, R. U., Usuba, W., Kohama, I., and Ochiya, T. (2017). Drug resistance driven by cancer stem cells and their niche. Int. J. Mol. Sci. 18:E2574. doi: 10.3390/ijms18122574

Puls, T. J., Tan, X., Whittington, C. F., and Voytik-Harbin, S. L. (2017). 3D collagen fibrillar microstructure guides pancreatic cancer cell phenotype and serves as a critical design parameter for phenotypic models of EMT. PLoS ONE 12:e0188870. doi: 10.1371/journal.pone.0188870

Raeber, G. P., Lutolf, M. P., and Hubbell, J. A. (2005). Molecularly engineered PEG hydrogels: a novel model system for proteolytically mediated cell migration. Biophys. J. 89, 1374�. doi: 10.1529/biophysj.104.050682

Ravi, M., Paramesh, V., Kaviya, S. R., Anuradha, E., and Solomon, F. D. (2015). 3D cell culture systems: advantages and applications. J. Cell. Physiol. 230, 16�. doi: 10.1002/jcp.24683

Rimann, M., and Graf-Hausner, U. (2012). Synthetic 3D multicellular systems for drug development. Curr. Opin. Biotechnol. 23, 803�. doi: 10.1016/j.copbio.2012.01.011

Ryan, S. L., Baird, A. M., Vaz, G., Urquhart, A. J., Senge, M., Richard, D. J., et al. (2016). Drug discovery approaches utilizing three-dimensional cell culture. Assay Drug Dev. Technol. 14, 19�. doi: 10.1089/adt.2015.670

Sathaye, S., Mbi, A., Sonmez, C., Chen, Y., Blair, D. L., Schneider, J. P., et al. (2015). Rheology of peptide- and protein-based physical hydrogels: are everyday measurements just scratching the surface? Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 7, 34�. doi: 10.1002/wnan.1299

Schneider, J. P., Pochan, D. J., Ozbas, B., Rajagopal, K., Pakstis, L., and Kretsinger, J. (2002). Responsive hydrogels from the intramolecular folding and self-assembly of a designed peptide. J. Am. Chem. Soc. 124, 15030�. doi: 10.1021/ja027993g

Sebens, S., and Schafer, H. (2012). The tumor stroma as mediator of drug resistance𠄺 potential target to improve cancer therapy? Curr. Pharm. Biotechnol. 13, 2259�. doi: 10.2174/138920112802501999

Shamir, E. R., and Ewald, A. J. (2014). Three-dimensional organotypic culture: experimental models of mammalian biology and disease. Nat. Rev. Mol. Cell Biol. 15, 647�. doi: 10.1038/nrm3873

Shri, M., Agrawal, H., Rani, P., Singh, D., and Onteru, S. K. (2017). Hanging drop, a best three-dimensional (3D) culture method for primary buffalo and sheep hepatocytes. Sci. Rep. 7:1203. doi: 10.1038/s41598-017-01355-6

Silva, J. M., Garc໚, J. R., Reis, R. L., Garc໚, A. J., and Mano, J. F. (2017). Tuning cell adhesive properties via layer-by-layer assembly of chitosan and alginate. Acta Biomater. 51, 279�. doi: 10.1016/j.actbio.2017.01.058

Simian, M., and Bissell, M. J. (2017). Organoids: a historical perspective of thinking in three dimensions. J. Cell Biol. 216, 31�. doi: 10.1083/jcb.201610056

Sittampalam, S., Eglen, R., Ferguson, S., Maynes, J. T., Olden, K., Schrader, L., et al. (2015). Three-dimensional cell culture assays: are they more predictive of in vivo efficacy than 2D monolayer cell-based assays? Assay Drug Dev. Technol. 13, 254�. doi: 10.1089/adt.2015.29001.rtd

Sivaraman, A., Leach, J. K., Townsend, S., Iida, T., Hogan, B. J., Stolz, D. B., et al. (2005). A microscale in vitro physiological model of the liver: predictive screens for drug metabolism and enzyme induction. Curr. Drug Metab. 6, 569�. doi: 10.2174/138920005774832632

Sleeman, J. P. (2012). The metastatic niche and stromal progression. Cancer Metastasis Rev. 31, 429�. doi: 10.1007/s10555-012-9373-9

Smith, A. M., Williams, R. J., Tang, C., Coppo, P., Collins, R. F., Turner, M. L., et al. (2008). Fmoc-diphenylalanine self assembles to a hydrogel via a novel architecture based on π-π interlocked β-sheets. Adv. Mater. 20, 37�. doi: 10.1002/adma.200701221

Smith, S. C., Baras, A. S., Lee, J. K., and Theodorescu, D. (2010). The COXEN principle: translating signatures of in vitro chemosensitivity into tools for clinical outcome prediction and drug discovery in cancer. Cancer Res. 70, 1753�. doi: 10.1158/0008-5472.CAN-09-3562

Souza, G. R., Molina, J. R., Raphael, R. M., Ozawa, M. G., Stark, D. J., Levin, C. S., et al. (2010). Three-dimensional tissue culture based on magnetic cell levitation. Nat. Nanotechnol. 5, 291�. doi: 10.1038/nnano.2010.23

Stadler, M., Walter, S., Walzl, A., Kramer, N., Unger, C., Scherzer, M., et al. (2015). Increased complexity in carcinomas: analyzing and modeling the interaction of human cancer cells with their microenvironment. Semin. Cancer Biol. 35:107�. doi: 10.1016/j.semcancer.2015.08.007

Stock, K., Estrada, M. F., Vidic, S., Gjerde, K., Rudisch, A., Santo, V. E., et al. (2016). Capturing tumor complexity in vitro: comparative analysis of 2D and 3D tumor models for drug discovery. Sci. Rep. 6:28951. doi: 10.1038/srep28951

Sutherland, R. M. (1988). Cell and environment interactions in tumor microregions: the multicell spheroid model. Science 240, 177�. doi: 10.1126/science.2451290

Sutherland, R. M., Inch, W. R., McCredie, J. A., and Kruuv, J. (1970). A multi-component radiation survival curve using an in vitro tumour model. Int. J. Radiat. Biol. Relat. Stud. Phys. Chem. Med. 18, 491�. doi: 10.1080/09553007014551401

Takasato, M., Er, P. X., Chiu, H. S., Maier, B., Baillie, G. J., Ferguson, C., et al. (2015). Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis. Nature 526, 564�. doi: 10.1038/nature15695

Tibbitt, M. W., and Anseth, K. S. (2009). Hydrogels as extracellular matrix mimics for 3D cell culture. Biotechnol. Bioeng. 103, 655�. doi: 10.1002/bit.22361

Timm, D. M., Chen, J., Sing, D., Gage, J. A., Haisler, W. L., Neeley, S. K., et al. (2013). A high-throughput three-dimensional cell migration assay for toxicity screening with mobile device-based macroscopic image analysis. Sci. Rep. 3:3000. doi: 10.1038/srep03000

Toledano, S., Williams, R. J., Jayawarna, V., and Ulijn, R. V. (2006). Enzyme-triggered self-assembly of peptide hydrogels via reversed hydrolysis. J. Am. Chem. Soc. 128, 1070�. doi: 10.1021/ja056549l

Tseng, H., Gage, J. A., Raphael, R. M., Moore, R. H., Killian, T. C., Grande-Allen, K. J., et al. (2013). Assembly of a three-dimensional multitype bronchiole coculture model using magnetic levitation. Tissue Eng. C Methods 19, 665�. doi: 10.1089/ten.tec.2012.0157

Turley, S. J., Cremasco, V., and Astarita, J. L. (2015). Immunological hallmarks of stromal cells in the tumour microenvironment. Nat. Rev. Immunol. 15, 669�. doi: 10.1038/nri3902

Tyler, W. J. (2012). The mechanobiology of brain function. Nat. Rev. Neurosci. 13, 867�. doi: 10.1038/nrn3383

Valastyan, S., and Weinberg, R. A. (2011). Tumor metastasis: molecular insights and evolving paradigms. Cell 147, 275�. doi: 10.1016/j.cell.2011.09.024

van de Wetering, M., Francies, H. E., Francis, J. M., Bounova, G., Iorio, F., Pronk, A., et al. (2015). Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933�. doi: 10.1016/j.cell.2015.03.053

Villoslada, P., Moreno, B., Melero, I., Pablos, J. L., Martino, G., Uccelli, A., et al. (2008). Immunotherapy for neurological diseases. Clin. Immunol. 128, 294�. doi: 10.1016/j.clim.2008.04.003

Wallace, D. G., and Rosenblatt, J. (2003). Collagen gel systems for sustained delivery and tissue engineering. Adv. Drug Deliv. Rev. 55, 1631�. doi: 10.1016/j.addr.2003.08.004

Wang, Y., Zhang, Z., Xu, L., Li, X., and Chen, H. (2013). Hydrogels of halogenated Fmoc-short peptides for potential application in tissue engineering. Colloids Surf. B Biointerfaces 104, 163�. doi: 10.1016/j.colsurfb.2012.11.038

Weber, L. M., Hayda, K. N., Haskins, K., and Anseth, K. S. (2007). The effects of cell-matrix interactions on encapsulated beta-cell function within hydrogels functionalized with matrix-derived adhesive peptides. Biomaterials 28, 3004�. doi: 10.1016/j.biomaterials.2007.03.005

Weeber, F., Ooft, S. N., Dijkstra, K. K., and Voest, E. E. (2017). Tumor organoids as a pre-clinical cancer model for drug discovery. Cell Chem. Biol. 24, 1092�. doi: 10.1016/j.chembiol.2017.06.012

Williams, A. S., Kang, L., and Wasserman, D. H. (2015). The extracellular matrix and insulin resistance. Trends Endocrinol. Metab. 26, 357�. doi: 10.1016/j.tem.2015.05.006

Wong, C. C., Gilkes, D. M., Zhang, H., Chen, J., Wei, H., Chaturvedi, P., et al. (2011). Hypoxia-inducible factor 1 is a master regulator of breast cancer metastatic niche formation. Proc. Natl. Acad. Sci. U.S.A. 108, 16369�. doi: 10.1073/pnas.1113483108

Worthington, P., Drake, K. M., Li, Z., Napper, A. D., Pochan, D. J., and Langhans, S. A. (2017). Beta-hairpin hydrogels as scaffolds for high-throughput drug discovery in three-dimensional cell culture. Anal. Biochem. 535, 25�. doi: 10.1016/j.ab.2017.07.024

Worthington, P., Pochan, D. J., and Langhans, S. A. (2015). Peptide hydrogels - versatile matrices for 3D cell culture in cancer medicine. Front. Oncol. 5:92. doi: 10.3389/fonc.2015.00092

Wynn, T. A., Chawla, A., and Pollard, J. W. (2013). Macrophage biology in development, homeostasis and disease. Nature 496, 445�. doi: 10.1038/nature12034.

Wynn, T. A., and Ramalingam, T. R. (2012). Mechanisms of fibrosis: therapeutic translation for fibrotic disease. Nat. Med. 18, 1028�. doi: 10.1038/nm.2807

Xu, X., Farach-Carson, M. C., and Jia, X. (2014). Three-dimensional in vitro tumor models for cancer research and drug evaluation. Biotechnol. Adv. 32, 1256�. doi: 10.1016/j.biotechadv.2014.07.009

Yan, C., and Pochan, D. J. (2010). Rheological properties of peptide-based hydrogels for biomedical and other applications. Chem. Soc. Rev. 39, 3528�. doi: 10.1039/b919449p

Yang, X. B., Bhatnagar, R. S., Li, S., and Oreffo, R. O. (2004). Biomimetic collagen scaffolds for human bone cell growth and differentiation. Tissue Eng. 10, 1148�. doi: 10.1089/ten.2004.10.1148

Yang, Z., and Zhao, X. (2011). A 3D model of ovarian cancer cell lines on peptide nanofiber scaffold to explore the cell-scaffold interaction and chemotherapeutic resistance of anticancer drugs. Int. J. Nanomed. 6, 303�. doi: 10.2147/IJN.S15279

Zhang, S., Gelain, F., and Zhao, X. (2005). Designer self-assembling peptide nanofiber scaffolds for 3D tissue cell cultures. Semin. Cancer Biol. 15, 413�. doi: 10.1016/j.semcancer.2005.05.007

Zhang, S., Lockshin, C., Herbert, A., Winter, E., and Rich, A. (1992). Zuotin, a putative Z-DNA binding protein in Saccharomyces cerevisiae. EMBO J. 11, 3787�.

Zhang, Y. S., and Khademhosseini, A. (2017). Advances in engineering hydrogels. Science 356:eaaf3627. doi: 10.1126/science.aaf3627

Zhou, M., Smith, A. M., Das, A. K., Hodson, N. W., Collins, R. F., Ulijn, R. V., et al. (2009). Self-assembled peptide-based hydrogels as scaffolds for anchorage-dependent cells. Biomaterials 30, 2523�. doi: 10.1016/j.biomaterials.2009.01.010

Zhu, J. (2010). Bioactive modification of poly(ethylene glycol) hydrogels for tissue engineering. Biomaterials 31, 4639�. doi: 10.1016/j.biomaterials.2010.02.044

Zollinger, A. J., and Smith, M. L. (2017). Fibronectin, the extracellular glue. Matrix Biol. 60�, 27�. doi: 10.1016/j.matbio.2016.07.011

Keywords: three-dimensional cell culture, hydrogel, spheroid, high-throughput screening, extracellular matrix

Citation: Langhans SA (2018) Three-Dimensional in Vitro Cell Culture Models in Drug Discovery and Drug Repositioning. Front. Pharmacol. 9:6. doi: 10.3389/fphar.2018.00006

Received: 27 November 2017 Accepted: 03 January 2018
Published: 23 January 2018.

Yuhei Nishimura, Mie University Graduate School of Medicine, Japan

Franz Rl, Universitätsklinikum Frankfurt, Germany
Johannes F. W. Greiner, Bielefeld University, Germany

Copyright © 2018 Langhans. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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