Multiorgan-on-a-Chip: A Systemic Approach To Model and Decipher Inter-Organ Communication
2021; Elsevier BV; Volume: 39; Issue: 8 Linguagem: Inglês
10.1016/j.tibtech.2020.11.014
ISSN0167-9430
AutoresNathalie Picollet-D’hahan, Agnieszka Żuchowska, Iris Lemeunier, Séverine Le Gac,
Tópico(s)Neuroscience and Neural Engineering
ResumoMultiorgan-on-a-chip (multi-OoC) devices, by supporting cross-organ communication, allow the study of multiorgan processes and modeling of systemic diseases.Multi-OoC approaches provide new insights that would be lost using single-OoC models.Various coupling configurations have been proposed for building multi-OoC platforms, and these present different levels of user-friendliness.Multi-OoC platforms have the potential to transform medical research by opening new avenues for understanding multiorgan diseases and for developing personalized treatments.To further emulate the complexity of the human system in vivo, key elements of the immune, nervous, and vascular systems are being integrated into multi-OoC models.The next generation of multi-OoCs will incorporate multimodal and real-time readouts in the form of on-chip chemical, physical, and molecular sensors, as well as online multiomic analysis. Multiorgan-on-a-chip (multi-OoC) platforms have great potential to redefine the way in which human health research is conducted. After briefly reviewing the need for comprehensive multiorgan models with a systemic dimension, we highlight scenarios in which multiorgan models are advantageous. We next overview existing multi-OoC platforms, including integrated body-on-a-chip devices and modular approaches involving interconnected organ-specific modules. We highlight how multi-OoC models can provide unique information that is not accessible using single-OoC models. Finally, we discuss remaining challenges for the realization of multi-OoC platforms and their worldwide adoption. We anticipate that multi-OoC technology will metamorphose research in biology and medicine by providing holistic and personalized models for understanding and treating multisystem diseases. Multiorgan-on-a-chip (multi-OoC) platforms have great potential to redefine the way in which human health research is conducted. After briefly reviewing the need for comprehensive multiorgan models with a systemic dimension, we highlight scenarios in which multiorgan models are advantageous. We next overview existing multi-OoC platforms, including integrated body-on-a-chip devices and modular approaches involving interconnected organ-specific modules. We highlight how multi-OoC models can provide unique information that is not accessible using single-OoC models. Finally, we discuss remaining challenges for the realization of multi-OoC platforms and their worldwide adoption. We anticipate that multi-OoC technology will metamorphose research in biology and medicine by providing holistic and personalized models for understanding and treating multisystem diseases. Interactions between multiple organs are essential to ensure proper physiological functioning of the human body. Although organs are physically separated in vivo, their communication is mediated via the blood and lymph circulation by various signals (soluble factors, exosomes, cells, etc.) to maintain overall viability and homeostasis. For example, the journey of orally ingested substances (nutrients, chemicals, drugs, etc.) is well orchestrated and involves different organs through a specific sequence in which each organ has a specific function: the small intestine absorbs the (digested) substances, the liver metabolizes them, they are then delivered to target organs via the blood circulation, and the kidney excretes corresponding waste products. This complex process of absorption/distribution/metabolism/excretion/toxicity (ADMET; see Glossary) affects the fate, distribution, efficacy (if applicable), and possible toxicity of exogenous substances (e.g., food, drugs, additives, environmental pollutants) [1.Cheng F. et al.AdmetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties.J. Chem. Inf. Model. 2012; 52: 3099-3105Crossref PubMed Scopus (725) Google Scholar] through unwanted side-effects in secondary tissues. In addition, many functions and processes in the body depend on regulatory pathways and hormonal feedback loops that involve organs of the endocrine system. The reproductive system, which comprises multiple tissues, relies on endocrine loops that control peripheral tissues. Similarly, Langerhans islets in the pancreas secrete insulin that promotes glucose uptake by the liver. Together, this systemic and cross-organ communication is key to deciphering and emulating the temporal processes involved in physiological functions. As a direct consequence, many diseases such as sepsis, osteoarthritis, gout, infertility, and neurodegenerative diseases involve multiple organs, and systemic approaches must therefore be pursued to accurately model them. Similarly, deciphering this cross-organ communication is essential for identifying biomarkers in body fluids for diagnostic purposes. For instance, tumor tissues release various molecules (miRNA, circulating tumor DNA, peptides, etc.), tumor-derived extracellular vesicles (tdEVs), and circulating tumor cells (CTCs) which play a central role in cancer metastasis and are key for cancer patient management [2.Rikkert L.G. et al.Cancer-ID: toward identification of cancer by tumor-derived extracellular vesicles in blood.Front. 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Overall, animals do not allow analysis of inter-organ crosstalk, determination of quantitative pharmacokinetics (PK), or prediction of ADMET parameters, as recently highlighted [5.Ingber D.E. Is it time for reviewer 3 to request human organ chip experiments instead of animal validation studies?.Adv. Sci. 2020; 72002030Crossref Scopus (22) Google Scholar]. Therefore, advanced in vitro approaches incorporating a systemic dimension and multiple organs must be developed to faithfully emulate human health and pathophysiology. Previous efforts to study organ communication in vitro have employed either conditioned medium or cocultures in Transwell platforms. However, Transwell devices use large volumes of liquid, and communication is therefore slow and low-concentration signaling factors are diluted, which altogether hampers studying cellular communication. Furthermore, the culture is entirely static, which precludes emulation of dynamic processes and the application of controlled cell biochemical and/or physical stimuli. Using a microfluidic format can solve some of these issues by offering sub-milliliter volumes, dynamic culture, and exquisite spatiotemporal control over physical and chemical parameters in the cell/tissue vicinity. For instance, cell–cell communication has been studied in microdevices under continuous flow by using chambers separated by porous membranes [6.Chung H.H. et al.Use of porous membranes in tissue barrier and co-culture models.Lab Chip. 2018; 18: 1671-1689Crossref PubMed Google Scholar], pillar arrays [7.Lembong J. et al.A fluidic culture platform for spatially patterned cell growth, differentiation, and cocultures.Tissue Eng. 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Eng. 2020; 13: 94-102Crossref PubMed Scopus (3) Google Scholar], and possibly active stimulation (electrical, biochemical, or mechanical) [22.Kaarj K. Yoon J.-Y. Methods of delivering mechanical stimuli to organ-on-a-chip.Micromachines (Basel). 2019; 10: 700Crossref Scopus (22) Google Scholar, 23.Gaio N. et al.Cytostretch, an organ-on-chip platform.Micromachines (Basel). 2016; 7: 120Crossref Scopus (16) Google Scholar, 24.Visone R. et al.A microscale biomimetic platform for generation and electro-mechanical stimulation of 3D cardiac microtissues.APL Bioeng. 2018; 2046102Crossref PubMed Scopus (7) Google Scholar]. The OoC field has been blossoming for a decade, and models have been proposed for virtually all organs and physiological barriers in the human body [21.Hinman S.S. et al.Microphysiological system design: simplicity Is elegance.Curr. Opin. Biomed. Eng. 2020; 13: 94-102Crossref PubMed Scopus (3) Google Scholar,25.Mastrangeli M. et al.Building blocks for a European organ-on-chip roadmap.ALTEX. 2019; 36: 481-492Crossref PubMed Scopus (4) Google Scholar,26.Mastrangeli M. et al.Organ-on-chip in development: towards a roadmap for organs-on-chip.ALTEX. 2019; 36: 650-668Crossref PubMed Scopus (7) Google Scholar]. These OoC platforms are revolutionizing the field of in vitro experimentation and hold great promise for reducing animal testing. Nevertheless, most OoC models are based on a single cell type or tissue, and lack both a systemic dimension and cross-organ communication. In a major recent breakthrough, multiple organs have been modeled in a single device as a multiorgan platform [13.Rajan S.A.P. et al.Probing prodrug metabolism and reciprocal toxicity with an integrated and humanized multi-tissue organ-on-a-chip platform.Acta Biomater. 2020; 106: 124-135Crossref PubMed Scopus (8) Google Scholar] (Figure 2). As detailed in Box 1, two major approaches are being pursued to realize multi-OoC platforms: coupling of single-OoC units and integration of multiple organs into one plate (multi-OoC plates).Box 1Multi-OoC Typology and ApplicationsMulti-OoC devices can be classified into two main distinct types, this typology referring to the engineering approach used for their realization, namely through connection of single OoC units or by using a multi-OoC plate.First, single OoC units are connected via capillary tubing or a microfluidic motherboard to reproduce the systemic interactions between two or more organ models (Figure IA). This modular approach allows reconfiguration of the multi-OoC platform and supports the use of individual vascularized organs by using organ-specific microvasculature endothelial cells. Furthermore, the single OoC modules can first be established and matured using specific medium before they are connected to each other. By contrast, multi-OoC devices (Figure IB) integrate in a single-plate format all different organ models at different locations, where channels in the plate act as a vascular-like system to support inter-organ communication. This second approach is much akin to the human-on-a-chip or body-on-a-chip paradigm in which virtually all organs are modeled (Figure IC). Multi-OoC plates are more compact and user-friendly, they do not require manual and cumbersome connection, they limit the risks for leakage, and, in some cases, they can integrate a liquid actuation system. They are also advantageous for minimizing the total recirculation volume (see section on 'Circulation of Medium' in the main text). However, organ-specific vascularization is less trivial, and combining different organs modeled following various approaches see (section on .Organ Models. in the main text) may be more challenging.These two different multi-OoC approaches are arguably better suited for specific purposes. The former 'Lego-like' approach is likely to be preferred for more fundamental research in an academic setting. However, they offer only low-to-moderate throughput, which is not ideal for preclinical, toxicity, or drug efficacy tests. By contrast, the more integrated and turnkey plate-based platforms offer higher throughput, and are hence more appropriate for the identification of biomarkers and therapeutic targets, and for the selection and optimization of drug candidates.Figure ISchematic Representation of the Two Main Approaches for Developing Multi-OoC Systems.Show full caption(A) Through coupling of single OoC devices, each modeling a different organ, via capillary connection or a microfluidic motherboard (B); and (C) by integrating different organ models in a single plate, an approach that is more in line with the body-on-a-chip philosophy.View Large Image Figure ViewerDownload Hi-res image Download (PPT) Multi-OoC devices can be classified into two main distinct types, this typology referring to the engineering approach used for their realization, namely through connection of single OoC units or by using a multi-OoC plate. First, single OoC units are connected via capillary tubing or a microfluidic motherboard to reproduce the systemic interactions between two or more organ models (Figure IA). This modular approach allows reconfiguration of the multi-OoC platform and supports the use of individual vascularized organs by using organ-specific microvasculature endothelial cells. Furthermore, the single OoC modules can first be established and matured using specific medium before they are connected to each other. By contrast, multi-OoC devices (Figure IB) integrate in a single-plate format all different organ models at different locations, where channels in the plate act as a vascular-like system to support inter-organ communication. This second approach is much akin to the human-on-a-chip or body-on-a-chip paradigm in which virtually all organs are modeled (Figure IC). Multi-OoC plates are more compact and user-friendly, they do not require manual and cumbersome connection, they limit the risks for leakage, and, in some cases, they can integrate a liquid actuation system. They are also advantageous for minimizing the total recirculation volume (see section on 'Circulation of Medium' in the main text). However, organ-specific vascularization is less trivial, and combining different organs modeled following various approaches see (section on .Organ Models. in the main text) may be more challenging. These two different multi-OoC approaches are arguably better suited for specific purposes. The former 'Lego-like' approach is likely to be preferred for more fundamental research in an academic setting. However, they offer only low-to-moderate throughput, which is not ideal for preclinical, toxicity, or drug efficacy tests. By contrast, the more integrated and turnkey plate-based platforms offer higher throughput, and are hence more appropriate for the identification of biomarkers and therapeutic targets, and for the selection and optimization of drug candidates. In this review we first provide an overview of existing multi-OoC platforms and discuss combinations of organs that are best suited for particular applications. Specific areas of research are highlighted in which a multi-OoC approach brings superior information compared with single-OoC models. Finally, we discuss essential challenges remaining for the realization of multi-OoC platforms. In the following section we review various multi-OoC applications. In each application we discuss the set of organs considered and highlight unique information provided by this multi-OoC approach. Selected examples over the past 5 years are summarized in Table 1 (Key Table).Table 1Key Table. Overview of Recently Reported Multi-OoC PlatformsaAbbreviations: A549, human non-small cell lung cancer cells; AA, amino acid; ALI, air–liquid Interface; AMSCs, airway stromal mesenchymal cells (donor derived); APCs, antigen-presenting cells; BCA, bicinchoninic acid; BF, bovine fetuin; BSA, bovine serum albumin; Caco-2, heterogeneous human epithelial colorectal adenocarcinoma cells; ECM, extracellular matrix; FBS, fetal bovine serum; Fob1.19, human osteoblast cells; hA, human astrocytes; HA, hyaluronic acid; HA-1800, human astrocyte cells; 16HBE, human bronchial epithelial cells; HBMECs, human brain microvascular endothelial cells; HBVPs, human brain vascular pericytes; HCT-116, human colon cancer cells; HepaRG, human hepatic stem cells; HEPES, (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) buffer; HepG2/C3a, human hepatocellular carcinoma cells; hHSteC, human hepatic stellate cells; HL60, human leukemia cells; hLSMECs, human liver sinusoidal microvascular endothelial cells; HM, human microglial; HMVEC-L, human lung microvasculature endothelial cells; HNC, human neural cells; hPCF, human primary cardiac fibroblasts; hRPTECs, human renal proximal tubule epithelial cells; HUVECs, human umbilical vein endothelial cells; Hw36, human primary hepatocytes; Kupffer cells, stellate macrophages; IFN, interferon; IL, interleukin; L-02, human hepatocyte cells; LC, liquid chromatography; LDH, lactate dehydrogenase; MBA-MD-231, human breast cancer cells; MCF-7, human breast cancer cells; MDCK, human Madin–Darby canine kidney cells; MEA, measurements of neurons using the Maestro™ MEA (Multi Electrode Arrays) system; NHBE, normal human bronchial/tracheal epithelial cells; NPCs, neural progenitor cells; NTera2/cl.D1, pluripotent human testicular embryonal carcinoma cells; PEEK, polyetheretherketone; PEGDA, poly(ethyleneglycol) diacrylate; PET, polyethylene terephthalate; PI, propidium iodide; PSF, polysulfone; RPTECs, human primary renal proximal tubule epithelial cells; RPTEC/TERT-1, human immortalized renal proximal tubule cells; SSCs, spermatogonial stem cells; THP-1, human monocyte cells; TIG-121, normal human diploid fibroblast cells; Treg, regulatory T cell; UC, ulcerative colitis. Cell lines preceded by (GFP) or (RFP) indicate that they have been engineered to express GFP/RFP.,bReferences [13,18,27–35,37,39,40,43–47,50,51,53,54,88,93,94] can be found in the reference list at the end of the paper.a Abbreviations: A549, human non-small cell lung cancer cells; AA, amino acid; ALI, air–liquid Interface; AMSCs, airway stromal mesenchymal cells (donor derived); APCs, antigen-presenting cells; BCA, bicinchoninic acid; BF, bovine fetuin; BSA, bovine serum albumin; Caco-2, heterogeneous human epithelial colorectal adenocarcinoma cells; ECM, extracellular matrix; FBS, fetal bovine serum; Fob1.19, human osteoblast cells; hA, human astrocytes; HA, hyaluronic acid; HA-1800, human astrocyte cells; 16HBE, human bronchial epithelial cells; HBMECs, human brain microvascular endothelial cells; HBVPs, human brain vascular pericytes; HCT-116, human colon cancer cells; HepaRG, human hepatic stem cells; HEPES, (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) buffer; HepG2/C3a, human hepatocellular carcinoma cells; hHSteC, human hepatic stellate cells; HL60, human leukemia cells; hLSMECs, human liver sinusoidal microvascular endothelial cells; HM, human microglial; HMVEC-L, human lung microvasculature endothelial cells; HNC, human neural cells; hPCF, human primary cardiac fibroblasts; hRPTECs, human renal proximal tubule epithelial cells; HUVECs, human umbilical vein endothelial cells; Hw36, human primary hepatocytes; Kupffer cells, stellate macrophages; IFN, interferon; IL, interleukin; L-02, human hepatocyte cells; LC, liquid chromatography; LDH, lactate dehydrogenase; MBA-MD-231, human breast cancer cells; MCF-7, human breast cancer cells; MDCK, human Madin–Darby canine kidney cells; MEA, measurements of neurons using the Maestro™ MEA (Multi Electrode Arrays) system; NHBE, normal human bronchial/tracheal epithelial cells; NPCs, neural progenitor cells; NTera2/cl.D1, pluripotent human testicular embryonal carcinoma cells; PEEK, polyetheretherketone; PEGDA, poly(ethyleneglycol) diacrylate; PET, polyethylene terephthalate; PI, propidium iodide; PSF, polysulfone; RPTECs, human primary renal proximal tubule epithelial cells; RPTEC/TERT-1, human immortalized renal proximal tubule cells; SSCs, spermatogonial stem cells; THP-1, human monocyte cells; TIG-121, normal human diploid fibroblast cells; Treg, regulatory T cell; UC, ulcerative colitis. 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