Abstract
Intercellular interaction between cell–cell and cell–ECM is critical to numerous biology and medical studies, such as stem cell differentiation, immunotherapy and tissue engineering. Traditional methods employed for delving into intercellular interaction are limited by expensive equipment and sophisticated procedures. Microfluidics technique is considered as one of the powerful measures capable of precisely capturing and manipulating cells and achieving low reagent consumption and high throughput with decidedly integrated functional components. Over the past few years, microfluidics-based systems for intercellular interaction study at a single-cell level have become frequently adopted. This review focuses on microfluidic single-cell studies for intercellular interaction in a 2D or 3D environment with a variety of cell manipulating techniques and applications. The challenges to be overcome are highlighted.
Introduction
Intercellular interaction, including cell–cell and cell–ECM communication, is pivotal to multicellular organisms. Communication errors can cause diseases like cancer metastasis, motor neuron disease, virus–host interaction, and diabetes (Schwager et al., 2019; Toda et al., 2019; ; Reyes-Ruiz et al., 2020). For this reason, the intercellular interaction study can improve the understanding of pathogenic mechanism and advance drug development. However, challenges remain in the analysis of the mechanisms of intercellular interaction, as impacted by the sophisticated intercellular interaction networks in microenvironments (You et al., 2002; ).
Traditional methods to study intercellular interaction are to maintain the native microenvironment in vivo, which are commonly limited by the availability of expensive equipment and the complicated processes (; ). A more effective method of intercellular interaction research is to employ in vitro tools that can significantly simplify the isolation and control of the microenvironment. Plenty of methods in vitro have been used for intercellular interaction studies. The Boyden chamber, which is also called the transwell chamber, consists of two compartments separated by a microporous membrane, has been used for intercellular intercation research, such as differentiation, secretion, and migration (; ). Because of the simplicity and stability, this method continues to be employed (). The defects of the Boyden chamber lie in the lack of physiological relevance and the limit of spatial control. Moreover, the Boyden chamber assay is difficult to study a small amount of cells or single cells, and to integrate with downstream analyses (e.g., protein-protein interactions, RNA-Seq, and ChIP-Seq). Alternative systems include Petri dishes and co-culture in gels or bioreactors. The shortcomings of the traditional methods are low flexibility and low compatibility with other analysis processes (Vu et al., 2017).
Microfluidics-based systems for cell–cell and cell–ECM communication studies have recently become practical. The advantages of the microfluidics-based systems are low reagent consumption, precise reagent manipulation, high throughput, and easy integration of functional components (Sackmann et al., 2014; ). The microfluidics-based system can delve into intercellular interaction both on a population basis and on a single-cell level. Over the past decades, microfluidics-based systems have been utilized to study intercellular interaction at population levels with demonstrated merits and demerits (Zervantonakis et al., 2011; ; ; Vu et al., 2017; Rothbauer et al., 2018). Recently, advanced microfluidics-based systems for cell–cell communication at a single-cell level have been adopted for biological and medical studies (; Sakthivel et al., 2019). In contrast to a group of cells, single-cell microfluidics-based systems exhibit numerous advantages. For instance, as cells are heterogeneous and varied in numerous aspects like mechanical characterization and protein expression, microfluidics-based systems can isolate and study individual cells, including circulating tumor cells (CTCs) and stem cells (; ; ). Intercellular interaction at a single-cell level is valuable in understanding communication pathways and commutating behaviors of special subpopulations of cells, which could be employed for the studies of secretion, differentiation, and migration (; ).
Generally, based on the way that cells interact with each other, microfluidics-based systems for intercellular interaction studies at a single-cell level could be discussed based on 2D (two-dimensional) and 3D (three-dimensional) methods as shown in Figure 1. 2D microfluidics-based systems usually focus on the communication of homotypic or heterotypic cells at an identical surface (; Tavakoli et al., 2019). Although many materials [e.g., poly(methyl methacrylate), polystyrene, and fluorinated thermoplastic polymers] have been used for microfluidics-based systems for cell–cell communication studies, the most commonly method is based on polydimethylsiloxane (PDMS) devices fabricated by soft lithography. The advantages of using PDMS devices are easy fabrication and good permeability to gas (e.g., O2 and CO2), allowing complicated and long-time 2D cell–cell communication studies (Vu et al., 2017). Though the 2D methods are favored for simple quantification of gene expression, physiology and cell morphology, 3D microfluidics-based systems could study more complex interactions on different dimensions. 3D microfluidics-based systems are able to perform interactions between cell and cell and between cell and extracellular matrix (ECM) (; ; ). ECM, a surrounding of a complex molecular composition and fibers, creates structural support and thereby allows cells to grow three-dimensionally (; Yamada and Cukierman, 2007). ECM mainly contains collagen, elastin, glycoproteins, and polysaccharides (). In the past decades, many natural biomaterials [e.g., gelatin hydrogel (GA), hyaluronic acid (HA), and matrigel] have been used for 3D cell-culture in vitro (; Perebikovsky et al., 2021). GA is a subtype of collagen, which can be isolated from bones, ligaments, and tendons. GA could exhibit different mechanical properties due to the sources and extraction processes. Due to the low cost and low antigenicity, GA has been widely used in the biomedical field. HA, which is present in connective tissues, could be used for the studies of cell migration, proliferation and inflammatory diseases. Matrigel is derived from the basement membrane (BM) of the Engelbreth–Holm–Swarm (EHS) mouse sarcoma; It is often crosslinked with collagen for intercellular intercation study. Additionally, there are many (semi)synthetic-based hydrogels [e.g., polyethylene glycol (PEG), polylactic acid (PLA), or poly(lactic-co-glycolic acid) (PLGA)] used for modeling the ECM (). Unlike native biomaterials, they do not exhibit functional ligands for cells and hence require crosslinking with native proteins or chemical insertion of matrix metalloproteinase (MMP)-sensitive peptides and integrin-binding domains [RGD (Arg-Gly-Asp) motifs]. In the present review, we categorize the microfluidic devices as 2D and 3D. Both 2D and 3D intercellar communication and their applications are demonstrated. The relationship between organ-on-a-chip and intercellular interactions at the single-cell level are described. Lastly, the challenges are addressed.
FIGURE 1
2D Microfluidic Systems
In the past decades, 2D microfluidics-based systems for studies at a single-cell level have been extensively applied. With 2D microfluidic cell–cell communication systems, two cells could be spatially paired near each other to record their interactions (; ). As the single-layer nature of numerous microfluidic devices, the 2D approach could be easier developed on a chip (; ). As shown in Figure 2, 2D microfluidic systems are classified based on different cell positioning methods: microwell, structure trap, electric field, droplet, acoustofluidics, magnetic force, and optical tweezers in this section. Table 1 compares the 2D approaches.
FIGURE 2
TABLE 1
| Method | Throughput | Cell pairing rate | Application | Cell type | Reference |
| Microwell | N/A | N/A | Heterotypic cell pair | Rat ventricular myocyte (NRVM) and human embryonic kidney 293 (HEK293) cell | |
| 1,000–5,000 wells | 25% | Heterotypic cell pair | Natural killer (NK) cell and K562 cell | Yamanaka et al., 2012 | |
| N/A | 70 % | Heterotypic cell pair | Kasumi-1 cell, NK-92 cell, CCRF-SB cell, and Ramos cell | ||
| 36,100 (190 × 190 array) wells | 40% | Heterotypic cell pair | Rat primary hepatocyte and PC-3 prostate cancer cell | ||
| 6,400 wells | N/A | Heterotypic cell pair | Dynamic CD8+ T cells (isolated from OT-1 mouse) and murine acute myeloid leukemia cells (C1498) | Tu et al., 2020 | |
| Structure trap | 150 traps | 50% | Heterotypic cell pair | Mouse embryonic fibroblast (MEF) and mouse embryonic stem cell (mESC) | |
| 6,000 traps | 70% | Heterotypic cell pair and fusion | NIH/3T3 fibroblasts, myeloma cells, B cells, mouse embryonic stem cell (mESCs) and mouse embryonic fibroblasts (mEFs) | Skelley et al., 2009 | |
| 750–900 traps in ∼2 × 3 mm2 | 80% | Homotypic/Heterotypic cell pair and fusion | eGFP-expressing NIH/3T3, DsRed-expressing NIH/3T3 and BA/F3 mouse leukocyte | ||
| 648 cell-enclosing units | 85% | Heterotypic cell pair | NK-92 cell and K562 human erythroleukemia cells | ||
| 500–850 traps mm–2 | 40–85% | Heterotypic cell pair | Dynamic CD8+ T cells (from OT-1 mice) and SIINFEKL-loaded MHCII-eGFP B cells | ||
| 440 traps | N/A | Hematopoietic cell pair | Primary T cell and dendritic cell (DC) | ||
| N/A | N/A | Hematopoietic cell pair | Normal CD34+ and CML CD34+ hematopoietic stem cells | ||
| 4,000 traps | >70% | Heterotypic cell pair | CD16–KHYG-1 cells (KHYG-1 human NK cell expressing CD16) and the K562 myelogenous leukemia cell | ||
| 200 traps | 70% | Heterotypic and homotypic cell pair and co-culture | Human SW480 epithelial cell, HT29 colon carcinoma and MCF-7 epithelial-like breast cancer cells | ||
| N/A | 71.1% | Homotypic cell pair and co-culture | HeLa cell | ||
| N/A | 94% | Heterotypic cell pair and co-culture | MDA-MB-231/GFP, MDA-MB-436/RFP, and MCF-7/GFP cells | Zhang et al., 2014 | |
| N/A | 67-100% | Homotypic cell pair and co-culture | Untreated MCF-7 cell and thapsigargin-treated MCF-7 cell | ||
| N/A | 25% | Heterotypic cell pair and co-culture | UM-SCC-1 cell and endothelial cell | ||
| 80 traps | 84% | Heterotypic and homotypic cell pair | HUVEC (Human Umbilical Vein Endothelial Cell), HeLa cell and MCF-7 cell | Zhu et al., 2019 | |
| N/A | >50% | Multiple cells pair and co-culture | Human Oral squamous cell carcinoma (OSCC) TW2.6 expressing WNT5B-specific shRNA, OSCC TW2.6 pLKO-GFP cell, and lymphatic endothelial cell (LECs) | ||
| N/A | >50% | Multiple cells pair | HeLa cell, HT-29 cell, and NIH/3T3 fibroblast | Tang et al., 2020 | |
| N/A | N/A | Particle pair | 20.3 and 10.1 μm particles | ||
| 30 valve mm–2 | 92.1% | Particle pair | 30 or 100 μm particles | ||
| N/A | N/A | Homotypic cell pair | 10 μm polystyrene beads, HEK cell | ||
| Electric field | N/A | 20% | Heterotypic cell fusion | Jurkat, NG 108-15, PC-12, and Cos-7 cell | Strömberg et al., 2000 |
| 384-well plate | N/A | Cell microenvironment | Immortalized human umbilical vein cells (iHUVEC) | Yin et al., 2010 | |
| 384-well plate | N/A | Homotypic cell pair and co-culture | K562 leukemia cells | ||
| (>2,400 pairs) in a 1×1.5 cm2 | 74.2% | Homotypic cell pair | HeLa cell | Wu et al., 2017 | |
| N/A | 80% | Heterotypic cell pair and co-culture | Prostate cancer (PC-3) cell and myoblast (C2C12) cell | ||
| Droplet | N/A | 13% | Heterotypic cell pair and co-culture | Mating-type minus (mt-) and mating-type plus (mt+) vegetative C. reinhardtii cell | |
| 103 trapping sites | N/A | Homotypic cell pair | Primary T cell and dendritic cell (DC) | Sarkar et al., 2015 | |
| N/A | N/A | Heterotypic cell pair | CD8+ T cell, dendritic cell (DC), RPMI-8226 cell [multiple myeloma (MM) cell] | Sarkar et al., 2016 | |
| 1152 trapping sites | 88.1% | Particle pair | 15 μm fluorescent particle and 30 μm non-fluorescent particle | ||
| N/A | 16.2% | Heterotypic cell pair | Jurkat E6.1 cell and K562 cell | Segaliny et al., 2018 | |
| 4,000 trapping sites | N/A | Heterotypic cell pair | CD8+ T cell, MDA-MB-231 cell and SKOV3 cells | Sullivan et al., 2020 | |
| Acousto-fluidics | N/A | 73% | Heterotypic cell pair and co-culture | HEK 293T cell, hTERT-HMVEC (human microvascular endothelial cells, CRL-4205) and HeLa S3 (CCL-2.2) cell | |
| N/A | 30% | Heterotypic cell pair cell with different size | P. falciparum parasite, red blood cell (RBC) and lymphocytes | ||
| Magnetic force | N/A | N/A | Heterotypic cell pair | B cell and T cell isolated from the spleens of C57BL6 mice | |
| Optical tweezers | 77 pairs | N/A | Homotypic cell pair | Normal and system lupus erythematosus (SLE) red blood cells (RBCs) | |
| N/A | N/A | Homotypic cell pair | Human pluripotent stem cell (hPSC) | ||
| N/A | N/A | Heterotypic cell pair | Human embryonic stem cell (hESC) and primary human dermal fibroblasts (HDFns) |
Microfluidic devices and their applications in 2D cell–cell communication.
Microwell
A simplest method for 2D cell–cell communication systems is to adopt microwells. Microwells could control the number of cells by the size of each wells. With microwells, one can rely on the probability to capture a pair of desired cells together inside a well (Rettig and Folch, 2005; ). reported a platform integrating microwell and microcontact printing methods to couple unexcitable donor cells with host cardiomyocytes under functional consequences. With such a platform, the pairing of a neonatal rat ventricular myocyte (NRVM) with an engineered human embryonic kidney 293 (HEK293) cell was carried out. Interactions of natural killer (NK) and cancer cells are critical to immunological control of cancer (). Yamanaka et al. (2012) employed arrays of sub-nanoliter wells (nano-wells) to monitor single NK cell–K562 cell [human immortalized myelogenous leukemia cell line, histocompatibility complex (MHC) class I-deficient] interactions. With this platform, the relationship between the secretion of interferon-γ (IFN-γ) from NK cell and target cell (K562) cytolysis was analyzed. Moreover, used microwells to accomplish an array of immobilized single hematological cancer cells; microwell size and surface coating were enhanced to maximize loading of single hematologic cells. On the demonstrated microwell array, quantitative study of lymphocyte cytotoxicity at the single-cell level was carried out with NK-92 cells against leukemic cells (CCRF-SB cells). Except for controlling the size of the microwells, presented an L-microwell for trapping single-cell in a respective branch via stretching/releasing of a PDMS substrate. The pair of single PC3 cancer cell and macrophage was obtained to monitor the diffusion of cell secreted molecules over 5,000 cell pairs on a 2.25 cm2 array. Recently, Tu et al. developed a device to establish a cell–cell interaction assay for profiling dynamic CD8+ T cells (isolated from OT-1 mouse) and murine acute myeloid leukemia cells (C1498) interactions at the single-cell level. This device could be used to test different cancer immunotherapy by comparing single T cells’ responses to different treatments (Figure 3A; Tu et al., 2020). This device was reported to hold great potential in testing clinical treatment for acute myeloid leukemia (e.g., CAR-T therapy and immune checkpoint blockade therapies). The heterogeneous cytotoxicity of T cells under immune checkpoint therapy was investigated, and the result confirmed that anti-PD1 (programmed cell death protein-1) had a positive influence on the cell killing ability of T cells.
FIGURE 3
Structure Trap
Another frequently employed method of 2D microfluidics-based cell–cell communication systems is to exploit structure traps. The traps could capture the single-cell pairs based on cell size or/and deformability (; ). Specifically, cells flowed along a path of least fluidic resistance to be trapped or bypass along the channels (Tan and Takeuchi, 2007). Once a trap was taken up by one cell, fluidic resistance increased, and the following cells would be directed to the next traps. Structure traps were extensively employed in stem cell, immune cell, and cancer cell microenvironment studies.
A simulated microenvironment in vitro is pervasive in stem cell studies. presented a platform for studies of dynamic cellular interactions between single mouse embryonic fibroblasts (MEF) and mouse embryonic stem cells (mESC). MEF and mESC pairs were performed via sequential cell trapping and dynamic variation of fluidic resistance. Cell fusion step is critical to initiate the stem cell reprogramming. Skelley et al. (2009) adopted a structure trap-based method to pair thousands of cells for cell fusion. Their device performed more than 50% properly paired and fused cells, which could observe reprogramming in hybrids between mESCs and MEF. Additionally, fused mESC-MEF hybrids could adopt an ESC-like morphology. The staining of alkaline phosphatase showed evidence of reprograming as judged by reactivation of endogenous Oct4-GFP reporters. Subsequently, they developed a method based on cell deformability with a fusion yield up to 95% with electro-fusion (); NIH/3T3 fibroblasts and BA/F3 mouse leukocytes, eGFP- and DsRed-expressing NIH/3T3 mouse fibroblasts were paired, then eGFP- and DsRed-expressing NIH/3T3 mouse fibroblasts were fused based on various biological, chemical, and physical stimuli.
Moreover, many structure trap methods have been applied to the observation of immune cells. demonstrated a microfluidic cell loading-dock system (Cell-Dock) which presented precise and dynamic cell packings to study immune cell cytolysis reactions with reported efficiencies of 85 and 74% for three- and five-cell capture, respectively. On this platform, the dynamics of immune cell cytolysis reactions using NK-92 cells as effector cells and K562 human erythroleukemia cells as target cells. The result indicated that the NK cells, which had the stronger cytolysis capabilities, overexpressed cytotoxicity and adhesion molecules (ICAM1 and B4GALT1). presented a trap-based method to enable pairwise-correlated multiparametric profiling of lymphocyte interactions (dynamic CD8+ T cells and SIINFEKL-loaded MHCII-eGFP B cells) over hundreds of pairs. The heterogeneity in early activation dynamics of CD8 T cells [OT-1 and TRP1 transnuclear (TN)] was also explored. demonstrated a trap-based method to delve into hematopoietic cell pairs. They investigated in the real time contact- and non-contact-based interactions between primary T cells and dendritic cells (DCs) originated from human monocytes. The same group then studied stem cells damage and chronic myeloid leukaemia (CML) (). They investigated the responses of normal and CML CD34+ hematopoietic stem cell to the tyrosine kinase inhibitor, dasatinib, a drug approved for the treatment of CML. Integrating microwells and single-cell trap arrays, reported a platform allowing visualization of the immunological synapse (IS) in vertically “stacked” cells to investigate the interaction between single NK cell (KHYG-1 human NK cell expressing CD16) and tumor cell (K562) for the IS in a high-throughput manner. They found novel distribution of F-actin and cytolytic granules at the IS, PD1 microclusters at the NK IS, and kinetics of cytotoxicity.
The tumor microenvironment in which cancer cells, endothelial cells, and macrophages coexist could examine tumor progression (). presented a device to study tumor–stromal cell interactions. For single human epithelial cell SW480 and breast cancer cell (MCF-7)/colon carcinoma cell (HT29) co-culture, the device integrated a differential fluidic resistance trapping method with a novel cellular valving principle. The single-cell co-culture was in proximity for the formation of connexon structures and the study on contact modes of communication. presented a platform with hydrodynamic sieve-like traps to position cells on protein (native fibronectin) micropatterns. Moreover, Zhang et al. (2014) reported a technique called “Block-Cell-Printing” (BloC-Printing) for heterotypic breast cancer cell pairing (Figure 3B). The technique was the ability to induce cell synapse formation and directed migration. reported a platform that enabled the organization of MIN6 β-cell and three MCF-7 cells into precise cell clusters in a flow chamber compatible under a high-resolution microscopy. They found that the changes of the concentration of cytosolic Ca2+ in the cancer cells were proportional to the distance from the ATP-releasing β-cell. demonstrated a 3D microsystem for cancer–stromal cell interaction by co-culturing single UM-SCC-1 (head and neck squamous cell carcinoma) cell and human dermal microvascular endothelial cell. The microsystem was capable of connecting the cell culture chamber to the media exchange layer. Zhu et al. (2019) proposed a novel platform to perform cell pairing for Human Umbilical Vein Endothelial Cell (HUVEC) and cancer cell (HeLa and MCF-7) communication. On such a platform, the HUVEC could enhance HeLa cell proliferation. Moreover, demonstrated a multi-cell co-culture device to study the interactions of immune and cancer cells. Using this device, triple single-cells were performed with lymphatic endothelial cells and the human oral squamous cell carcinoma (OSCC) TW2.6 cells with the expression of WNT5B-specific shRNA (WNT5B sh4) and vector control OSCC TW2.6 pLKO-GFP cell. Tang et al. (2020) reported a microfluidic device to monitor cell–cell interaction between tumor cells (HeLa and HT 29-cell) and NIH/3T3 fibroblast cells; serpentine-like channel and traps were used for the immobilization of adjustable quantities of cells based on passive hydrodynamics. To investigate cell–cell interaction, calcein-AM transfer between multiple cells under different patterns had been quantified with local fluorescent intensity. Except for the presented application, structure traps could also be employed for other cell–cell interaction analyses, such as asymmetric trap for obstacle/particle steric interactions (), pneumatic valve for parallel and dynamic monitoring processes of particle clusters interactions (), and hydrodynamic trap for cell–cell interaction ().
Electric Field
Electric-field-based cell–cell communication studies at a single-cell level have been extensively applied (Samiei et al., 2015; ; Yao et al., 2019). Integrated electrodes employed dielectrophoresis (DEP) to trap single cells at the electrode interface (Wu et al., 2018). Strömberg et al. (2000) developed a single cell-pair electrofusion technique suitable for fusion between individual vesicles and proteoliposomes cells. Yin et al. (2010) presented a platform integrating microfluidic channel with DEP for single immortalized human umbilical vein cells (iHUVEC) pairing and co-culture. On the platform, single cells, cell pairs, and a small group of cell pairs were performed with DEP. The signaling output of the NF-κB (nuclear factor-k-gene binding) pathway in response to combinations of IGF1 (insulin-like growth factor 1) and TNF (tumor necrosis factor) was investigated. Activation of NF-κB with immobilized TNF and IGF1, the cell response could be abolished to different degrees by variable dose of the pathway inhibitor IκB kinase (IKK).
designed a device for homotypic cell pairs (K562 leukemia cells) integrating the inverted open microwell with DEP to regulate cell loading to the microwell and the formation of cell aggregates for cell–cell interaction studies (Figure 3C). Wu et al. (2017) developed a platform to achieve high-throughput homotypic cell pairing (more than 2,400 single cell pairs) within a 1 × 1.5 cm2 area by positive dielectrophoresis (p-DEP) in several minutes. Cell communication and precise cell pairing steps in cell fusion were combined. developed a device that could control the co-culture microenvironment with electrolytic valving. They achieved cell–cell interaction assays between prostate cancer (PC3) cells and myoblast (C2C12) cells.
Droplet
Microfluidics-based droplets isolate single cells and reagents in monodisperse picoliter liquid droplets (Teshima et al., 2010; ; ) that can be manipulated with various cells (). presented a device integrating droplet microfluidics with inertial microfluidics for single mating-type minus (mt–) and mating-type plus (mt+) C. reinhardtii cell pairs in droplets. With the reported device, about 13% of the droplets contained the correct one-to-one pairing of two separate strains of C. reinhardtii for long-culture analyses. Sarkar et al. (2015) put forward a droplet microfluidics-based platform to encapsulate primary T cell and DC pairs in nanoliter-volume droplets for cell–cell interaction and dynamic calcium signaling study (Figure 3D). The platform could generate and dock monodisperse nanoliter (volume 0.523 nL) droplets, capable of monitoring a thousand droplets per experiment. To assess the interaction of single T cells with dendritic cells (DCs), they reported an integrated single-cell localization, activation, and dynamic analysis platform, on which they also accessed Ag-loaded DCs activate the antitumor of CD8+ T cells (Sarkar et al., 2016). designed a promising method to enrich droplets exactly encapsulating a single particle via fluorescence or scattering-light activated sorting. With their method, two droplets, each having a remarkable particle, were precisely paired and merged in a microwell device yielding a 90% of post-sorting particles capturing rate and a total 88.1% co-encapsulation ratio. Segaliny et al. (2018) employed a droplet platform to form heterotypic cell pairs for the T cell heterogeneity study and functional TCR T cell (T cells expressing engineered T cell receptor) screening. With such a platform, single MART-1 and NY-ESO-1 engineered TCR T cell (generated by transducing Jurkat E6.1 T cells) could be activated upon recognition of target tumor cells (NY-ESO-1+K562 cell) were screened out and then monitored in the real time; it also included a system for respective clone with a 100% specificity verified by downstream single cell reverse-transcription PCR and sequencing of the TCR chains. Recently, Sullivan et al. (2020) designed a platform for immunotherapeutic applications via single-cell interactions; single CD8+ T cells and MDA-MB-231 breast cancer cells, and single CD8+ T cells and SKOV3 ovarian cancer cells were paired in 4,000 trapping sites for subsequent analyses. On this platform, two antibodies (TSR-042 and TSR-033) for the inhibition of the PD1 and LAG3 pathways were investigated for combination therapy. The results indicated that the combination of TSR-042 and TSR-033 could increase tumor cell killing at the single-cell level.
Acoustofluidics
Another method of manipulating cells is based on acoustofluidics. Surface acoustic wave (SAW)-based methods could precisely position cells and fluids (). employed a SAW-based method to regulate spatial arrangements and the distance of suspended cells to conduct quantitative investigation of the gap junctional intercellular interaction in homotypic and heterotypic groups through the visualization of fluorescent dyes transferred between cells (Figure 3E). The SAW-based method was also applied to monitor how single lymphocytes and red blood cell (RBC) were affected by the malarial parasite Plasmodium falciparum ().
Magnetic Force
Magnetic manipulation, in which magnetic beads are selectively attached to cells, is a commonly used method for single cells separation or purification in microfluidic devices (). The magnetic field gradients can capture the magnetic beads and the attached cells from samples with large volume (Yaman et al., 2018; Pei et al., 2020). demonstrated scalable integrated circuits for transporting single cells along programmable trajectorys to place single lymphocytes (B cell and T cell) pairs into large arrays for the downstream analyses experiments.
Optical Tweezers
Optical tweezers are contact-free and easily implemented in microfluidic devices for single cell research (; ; Zhu et al., 2018). Optical tweezers precisely control single cells to perform high-throughput analysis. employed optical tweezers to trap and study the interaction of normal and system lupus erythematosus (SLE) red blood cells (RBCs). Quantitative determination of force parameters of normal and pathological RBC pair aggregation utilizing double-trap optical tweezers was performed. Direct measurements of aggregation speed for pairs of RBCs showed a strong difference between normal and SLE blood samples: the aggregation speed of the normal RBCs was about half of that of SLE ones. Optical tweezers were demonstrated a sensitive tool for monitoring the SLE disease and its response to drug therapies on the single cell level.
adopted optical tweezers to arrange a pair of human pluripotent stem cells (hPSCs) expressing negatively charged podocalyxin near each other. Using parylene-C surfaces treated with oxygen plasma, the patterned hPSCs were cultured after optical manipulations. Optical tweezers have successfully been used for unveiling protein expression information by detecting hyperosmotic stress of trapped single cells (). As the advantages of simple microfluidic device architecture, fast cellular operation, and flexibility toward cell types and applications, optical tweezers have potential in single cell studies. In addition, other optical functions can be integrated. For example, employed laser-induced fusion of human embryonic stem cells (hESCs) with primary human dermal fibroblasts (HDFns) on a chip by using optical tweezers.
Summary
The microwell method is the simplest for intercellular interaction studies at the single-cell level. Compared with the structure trap method, the main advantage of the microwell method is the high throughput capability without complex hydrodynamic channel design and accurate fluid operation. The structure trap method is one of the most common method. However, the precise control of the trap as well as the accurate fluid operation are critical to single-cell pairs. Cell clogging needs to be awared in the structure trap method when processing a large number of cells. The electric field method can capture cells with wide properties. However, proper electric fields should be applied to maintain high cell vitality. Just like the structure trap method, the droplet method needs accurate fluid operation. The merit of droplet method is the encapsulation of single cells and the processing reactions which benefits to the molecular biology research (e.g., single DNA or RNA strand study). The acoustofluidics method has the merit of causing less physiological damage to cells during the process, and it is easy to add acoustic transducers on to conventional microfluidic systems. The magnetic force method generally requires magnetic labeling of cells that may influence the biological property of the captured cells and the subsequent studies. However, the magnetic field usually covers a large area, and thus, this method is advantageous for capture specific single cells from samples of large volume. The optical tweezers method provides higher precision (down to 10 nm) than other methods. However, its applications in microfluidics for cell-based assay are still limited due to the complex operation and expensive instrumentation.
3D Microfluidic Systems
3D microfluidic systems deliver in vivo-like 3D tissue- and organ-specific microarchitectures (Sart et al., 2017; Yoshida et al., 2017). The 3D method recapitulates the cell–ECM and cell–cell interactions for 3D cell culture, biochemical signal study and drug screening (; ). In this section, we mainly focus on microfluidic cell–cell communication systems by the 3D method at a single-cell level. The 3D method is classified based on the interactions of single cells with ECM or with single cells. Table 2 summarizes the 3D approaches.
TABLE 2
| Type of device | ECM type | Application | Cell type | Reference |
| Cell-ECM | Dex-TA, Dex-HA-TA, PEGDA | Long-term 3D cell culture | Human MSCs | |
| TG-PEG | Osteogenic differentiation study | D1 cell | ||
| RGD-functionalized alginate | 3D cell culture | Mesenchymal stem cells (MSCs) | Utech et al., 2015 | |
| Matrigel | Clonal acinar formation | Human prostate cell (RWPE1) | ||
| Type-I collagen | Long-term 3D cell culture | Human gastric carcinoma cell (Kato III) | ||
| Type-I collagen | Long-term 3D cell culture | MCF-7 breast carcinoma cells | ||
| Agarose gel | Long-term 3D cell culture | The breast adenocarcinoma MCF-7 human cell, human embryonic kidney (HEK) cell line 293FT | ||
| Ca-alginate hydrogel | Formation of 3D tissue constructs | 10 μm green and red fluorescent microspheres | ||
| PAH, PSS, PEG | High-throughput sub-cellular toxicity assay | CEM cell and Hela cell | Xia et al., 2018 | |
| Cell-cell | PFPE-b-(PPG-PEGPPG)- b-PFPE | 3D cell culture | Single fibroblast cell (NIH 3T3) and non-adherent T cell (EL4) | Wang et al., 2016 |
| PEGDA 3400 | 3D cell culture and drug screening | Human primary renal epithelial (HRE) cell and MCF-7 cell | ||
| Polyacrylamide (PAA) hydrogels | Cell microenvironment | Mammary epithelial cell (MCF10A) | Tseng et al., 2012 | |
| Polyacrylamide (PAA) hydrogels | E-cadherin molecular tension in cell pairs | Madin–Darby canine kidney (MDCK) cells | Sim et al., 2015 | |
| NuSil | Cell-cell coupling for human airway smooth muscle study | Primary human airway smooth muscle cells (HASMCs) | Polio et al., 2019 | |
| Ca-alginate hydrogel | Cell-cell communication | NIH/3T3 fibroblast cells, Human bone marrow-derived mesenchymal stem cells (MSCs), human umbilical vein endothelial cells (HUVECs) | Zhang et al., 2018 |
Types of microfluidic devices and their applications in 3D intercellular interaction.
Cell–ECM
Extracellular matrix allows cells to grow in a 3D environment with structural support. Hydrogel is a prioritized material to develop artificial ECM in vitro because hydrogels often consist of the materials found in the ECM in vivo. developed a device for in situ enzymatic crosslinking of a stream of tyramine-conjugated hydrogel precursor droplets in oil through the regulated diffusing process of small crosslinker molecules. Single cell study of mesenchymal stem cells (MSCs) in crosslinked hydrogels exhibited great cell activity (>90%), metabolic activity (>70%), and multilineage differentiation capacity (>60%) for 28 days. reported a platform for selective crosslinking of cell laden pre-hydrogel droplets (TG-PEG) with synthetic microniches. As shown in Figure 4A, TG-PEG is based on two polyethylene glycol precursors that are crosslinked by the transglutaminase factor XIII (FXIII) resulting in a biocompatible nanoporous matrix. The solution of cells loaded with CaCO3 nanoparticles was introduced into the microfluidic chip. The TG-PEG hydrogel matrix precursor solution supplemented with hydrochloric acid (HCl)/ethylenediaminetetraacetic acid (EDTA) and the unactivated FXIII solution were also separately injected. Reagents joined in a laminar flow and were sheared by oil at a cross-junction, resulting in numerous droplets. HCl dissolved CaCO3 nanoparticles on the cell, leading to Ca2+-induced activation of FXIII and thereby on-demand crosslinking for microniche formation. EDTA could prevent background gelation occurring without a cell. Single MSCs (D1 cells) were encapsulated in TG-PEG hydrogel microniches and cultured in differentiation media for osteogenic differentiation analyses (Figure 4A). On this platform, alkaline phosphatase (ALP) expression was assessed for studying the human MSCs differentiating down the osteogenic path. To conduct single-cell–ECM communication studies with homogeneous hydrogel droplets, Utech et al. (2015) presented a method that encapsulated single MSCs in alginate hydrogels by a highly regulated manner: acetic acid was introduced in a continuing oil phase for dissociating Ca2+-EDTA into Ca2+, which released Ca2+ for the reaction of alginate chains. RGD-functionalized alginate was employed for encapsulating single MSCs, as RGD presented integrin binding sites for cell attachment. On this platform, single MSCs could be cultured inside the generated microenvironments for 15 days with stable encapsulation, cell growth, and proliferation. demonstrated a platform for clonal acinar formation, where single cells were encapsulated in matrigel beads. In contrast to traditional bulky 3D clonal acinar formation, such a platform led to a more uniform acini population that facilitated recording the acinar developing process from the initial division to the ensuing steps.
FIGURE 4
It has long been considered that tumor cells in 3D culture can address some limitations encountered by traditional 2D monolayer cultures. For instance, 3D multicellular tumor spheroids mostly show poor sensitivity to cytotoxic drugs in contrast to cells grown on 2D substrates. used a micro-collagen gel array (μCGA) for 3D single-cell culture (Figure 4B). With type-I collagen, a 2 × 2 cm2 PDMS chip with 10,000 μCGA units was demonstrated to encapsulate considerable single tumor cells (human gastric carcinoma cells) in less than 15 min. The interacting process between tumor cell and the ECM was studied on drug responses. employed a platform to investigate the cancer cell–ECM communication. In contrast to traditional 2D protocols of cell culture and drug screening, this chip could efficiently proliferate single-cell clones. Moreover, an accurate assessment of drug effects under a lower drug concentration was presented, which did not immediately take the life of the tumor cells and avoided excess doses causing side effects. Vasileios et al. designed a device with deep neural networks accurately classifying single droplet images in real time based on the occurrence and number of micro-objects (e.g., single mammalian cells and multicellular spheroids) (). It could identify specific objects in mixtures of objects of different types and sizes; Hek293FT cells were encapsulated in agarose gel beads for 3D cell culture with a high diversity of visual appearances. developed a platform integrating cell classifying and high-throughput production of 3D Ca-alginate hydrogel microstructures. The platform was produced in a smooth and efficient manner with light-addressable electro-deposition based on photoconductive material Titanyl phthalocyanine (TiOPc). This method of shaping regulated 3D gel structures did not require pre-fabrication electrodes or a 3D mold, enhancing 3D gel producing efficiency. Besides, Xia et al. (2018) reported a single-cell array for a reactive oxygen species (ROS) assay. On such an array, cell responses at the sub-cellular level, single-cell level, and population level could be overall obtained with a high throughput. As shown in Table 2, with two different cancer cells (CEM and Hela cells) and three materials {PAH [poly(allylamine hydrochloride)], PSS [poly(sodium 4-styrene sulfonate)], and PEG}, this study showed differences in responses at a single-cell level and molecular heterogeneity at sub-cellular level in considerable cells radiated.
Cell–Cell
The cellular heterogeneity are commonly identified at the phenotypic, transcriptomic or genomic levels (Treutlein et al., 2014; Pang et al., 2015, 2016; ; ). Populations of multicellular constructs exhibit heterogeneities, total organizational and morphology changes have been demonstrated to simulate tumors or organs complexity (; ). Microfluidics-based single-cell analysis methods have become a powerful tool to delve into the cell microenvironment, including immune cell studies. Wang et al. (2016) reported a microfluidic approach to develop cell-laden hydrogel modules (HMs) for single-cell encapsulation and culture. Single fibroblast cells (NIH/3T3) and non-adherent T cells (EL4) encapsulated in HMs achieved significant cell activity and proliferation (Figure 4C). The influences of spatial constraints and structure- and mechanics-related characteristics of HMs were examined during cell growth at a single-cell level. The interacting process between tumor cell and the extracellular matrix was studied on drug responses.
Besides, intercellular intercation regulate cell shape variations in the embryonic developing process and tissue homeostasis. reported a modular bioink approach by the high-throughput fabrication of hydrogels (diameter 35 μm) which completely encapsulated single cells. Two distinctive major types of mammalian cell (multipotent human MSCs and bovine chondrocytes) and polyethylene glycol diacrylate (PEGDA) as a model hydrogel were tested. High-throughput microfluidics and flow cytometry-based classifying methods produced small (<40 μm) single-cell-laden hydrogels that exhibited high (>90%) encapsulation yield. Single cell PEGDA hydrogels, endothelial cells, MSCs, and proangiogenic fibrinogen macromaterial solution were used for the modular bioink. Within 1 week of culture, the angiogenic cells assembled into a CD31+ prevascular network throughout the construct. Tseng et al. (2012) designed a platform to study how ECM impacted the spatial organization of intercellular junctions. Fibronectin micropatterns were employed to constrain the location of mammary epithelial cell (MCF10A)-ECM adhesion. Deformations of polyacrylamide (PAA) hydrogels were used to measure the forces exerted by cell doublets on the substrate and to indirectly derive the forces they exerted on each other. By using various ECM micropatterns, they found that ECM impacted the stability of intercellular junction positioning and the magnitude of intracellular (cell–ECM) and intercellular (cell–cell) forces. Traction Force Microscopy (TFM) was used for getting images of fluorescent beads with and without cells, then the displacement field was subsequently calculated by a particle image velocimetry (PIV) program implemented as an ImageJ plugin. Specially, ECM could participate in similar morphogenetic processes. In response to heterogeneous distribution of ECM, cell doublets developed anisotropic force fields and adopted stable positions along the axis of low tension. According to this mechanism, cells tend to stabilize the position of their intercellular junctions away from the ECM.
Then, Sim et al. (2015) employed a 3D method to determine how the force balance between cell–cell and cell–ECM with varied aspect ratios and cell spread areas using pairs of Madin-Darby canine kidney (MDCK) cells (Figure 4D). By patterning ECM (collagen I/gelatin) on PAA hydrogels with micrometer resolution, various cytoskeleton strain energy states were generated. As shown in Figure 4D, E-cadherin–DsRed MDCK cell pairs were patterned on squares or I-shaped ECM structures (green). TFM was also used to test the green fluorescent beads mixed in the PAA hydrogels. Continuous peripheral ECM adhesions resulted in increased cell–cell and cell–ECM forces with a growing spread area. Specially, cell pairs maintained constant E-cadherin molecular tension and regulated total forces relative to cell spread area and shape but independent of total focal adhesion area. Recently, Polio et al. (2019) employed NuSil gel micropatterning for delving into force transmission in a two-cell ensemble of primary human airway smooth muscle cells (HASMCs). The ECM stiffness could be a switch regulating whether forces were transmitted via the cell–cell or cell–ECM contacts. Connectivity variation could significantly alter the total contractile strength of the ensemble as well.
Hydrogels having separately regulated compartments encapsulating cells would accurately regulate the path of pairing single cells. Zhang et al. (2018) employed a single-step microfluidic platform for generating monodisperse multicompartment hydrogels which could serve as a 3D matrix for pairing single cells with a high biocompatibility. Stem cells (MSCs) and niche cells (HUVECs and NIH/3T3) were entrapped in separate but adjacent hydrogel droplet compartments, capable of facilitating the study on cell–cell interactions. The method represented an essential step toward high-throughput single cell encapsulation and pairing for the study on intercellular interactions.
Organ-On-A-Chip
The intercellular interaction is also important for organ-on-a-chip, whereas organ-on-a-chip technology can contribute to the intercellular interaction studies. In the past decade, the development of microfluidics enabled the construction of organ-on-a-chip (Zhang et al., 2018; Wu et al., 2020). For example, reported lung-on-a-chip where a reciprocating mechanical motion was implemented to mimic the lung alveolar motion. Ren et al. (2012) described a microfluidic device with parallel channels interconnected by micropillar arrays to mimic the capillary-myocardial tissue interface for studying hypoxia-induced myocardial injury. Fresh medium with/without oxygen consumption blocking reagent was infused into the respective adjunct channel to produce a hypoxia gradient in the middle channel to mimic the hypoperfusion/hypoxia condition during myocardial infarction. There are also other studies mimicking kidney (Wang et al., 2017), liver (), brain (), and intestine () by using the microfluidic chips.
However, the reported organ-on-a-chip platforms could partly mimic the organs in vivo (Tian et al., 2019). They simulated some physiological functions or anatomic structures, but could hardly recapitulate all the necessary environmental conditions including gas (O2 and CO2), pH, and growth factors (Wang et al., 2020). In addition to reconstruct the structures and functions in vivo, critical tissue interfaces, spatiotemporal cell–cell and cell–ECM interactions, and biochemical concentration gradients are desirable for further advancement of the fields of regenerative and precision medicine. Cell–cell and cell–ECM interaction at the single-cell level provide a simple and easy solution. Some tumorigenesis (e.g., breast cancer and glioma) is also closely related to single tumor stem cells and tumor microenvironment interaction (). The single-cell intercellular interaction plays a key role in organ-on-a-chip.
Conclusion and Outlook
Based on the advantages of microfluidics, such as low reagent consumption, precise fluid manipulation at the microliter scale and easy integration of functional components, microfluidics for the intercellular interaction study at the single-cell level has been significantly developed over the past decade. In this review, based on the way that cells interact with each other, the microfluidics-based systems are categorized into 2D and 3D methods. Considerable achievements and applications have been reported for immunology, 3D niche microenvironment, cell secretion and others. With various applied scenarios, reviewed microfluidic tools for 2D/3D cell–cell communications are listed in Table 3. Generally, 2D microfluidics-based systems are easy in operation with potential high-throughputs of cell pairs. The main advantage of 3D methods is the better controllability of cell interactions and recapitulation of the tissue architectures and extracellular microenvironments in vivo.
TABLE 3
| Type of device | Type | Application | Advantage | Disadvantage | |
| 2D | Microwell | ∙Immunology | ∙Fusion-capable | ∙Limited cell confinement in some cases | |
| Structure trap | ∙Tumor immunology | ∙High throughput | |||
| Electric field | ∙Stem cell differentiation | ∙Cell movement could be confined | ∙Neglecting cell-ECM communication | ||
| Droplet | ∙Cell secretions | ∙Simple to track and image | |||
| Acoustofluidics | ∙Microenvironment | ∙Cell communication | ∙Easy to operate | ∙Difficult to stimulate one single-cell without afflicting the other single cells | |
| Magnetic force | ∙P. falciparum parasite and RBC | ||||
| Optical tweezers | |||||
| 3D | Cell-ECM | ∙Stem cell culture and differentiation | ∙3D cell culture | ∙Complex operate | |
| Cell-cell | ∙Tumor-initiating and tumorigenesis | ∙More in vivo-like microenvironment | ∙Low throughput | ||
| ∙Immunology | ∙Communication combining cell-ECM and cell-cell | ∙Difficult to image | |||
| ∙Microenvironment | |||||
| ∙Cell communication |
Overview of microfluidics techniques for cell-cell communication study at a single-cell level.
The resolutions noted are nominal and may subject to change under specific operation conditions.
Despite the exciting progress in microfluidics-based systems for intercellular interaction studies at the single-cell level, there are still challenges in its applications. First, with cell pairs, the study on cell–cell communication focuses on several important signals. However, a specific signal is difficult to be isolated precisely because of the complex signaling pathways between single cells. With several methods, e.g., cell protrusions (Zhang et al., 2019) and extracellular vesicles (), isolating and analyzing a specific signal from the large communication information could be presented. Second, challenges remain in the studies of communication between rare cells (e.g., CTCs and tumor-initiating cells) and microenvironments (e.g., endothelial cells, macrophages, and ECM). Each single rare cell is capable of communicating with others via electrical signal transmission, soluble factors diffusion, physical contact, and ECM. The communication information is critical to tumor occurrence and metastasis. Lastly, innovative and user-friendly devices and designs need to be implemented by integrating various novel methods (e.g., digital microfluidics and 3D-printer technology) with high precision, robustness, throughput and reproducibility for research scientists in varied disciplines. Overall, with the advances in new fabrication techniques and materials, we anticipate the field would rapidly expand and be widely applied in biology and medicine.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Statements
Author contributions
LP, JD, ZK, LG, and X-XL wrote the manuscript. LP, XX, and S-KF revised the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by the National Science Foundation of China (81702955), the Natural Science Foundation of Shaanxi Province (2019JZ-38, 2019JQ-885), the Natural Science Foundation of Shaanxi Provincial Department of Education (19JK0771), the Project of Shaanxi Key Laboratory of Brain Disorders (18NBZD03), the Fundamental Research Foundation of Xi’an Medical University (2018PT16) and the startup funding from Kansas State University.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1
AhmadiF.SamlaliK.VoP. Q. N.ShihS. C. C. (2019). An integrated droplet-digital microfluidic system for on-demand droplet creation, mixing, incubation, and sorting.Lab Chip19524–535. 10.1039/c8lc01170b
2
AliA.AbouleilaY.ShimizuY.HiyamaE.EmaraS.MashaghiA.et al (2019). Single-cell metabolomics by mass spectrometry: advances, challenges, and future applications.TrAC Trends Anal. Chem.120:115436. 10.1016/j.trac.2019.02.033
3
AlonsoD. G.YuM.QuH.MaL.ShenF. (2019). Advances in microfluidics-based technologies for single cell culture.Adv. Biosyst.3:1900003. 10.1002/adbi.201900003
4
AnagnostidisV.SherlockB.MetzJ.MairP.HollfelderF.GielenF. (2020). Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures.Lab Chip20889–900. 10.1039/d0lc00055h
5
BocchiM.RambelliL.FaenzaA.GiulianelliL.PecorariN.DuqiE.et al (2012). Inverted open microwells for cell trapping, cell aggregate formation and parallel recovery of live cells.Lab Chip123168–3176. 10.1039/c2lc40124j
6
BoothR.KimH. (2012). Characterization of a microfluidic in vitro model of the blood-brain barrier (μBBB).Lab Chip121784–1792. 10.1039/c2lc40094d
7
ChenJ.XuY.WangX.LiuD.YangF.ZhuX.et al (2019). Rapid and efficient isolation and detection of extracellular vesicles from plasma for lung cancer diagnosis.Lab Chip19432–443. 10.1039/c8lc01193a
8
ChenL.KimJ. J.DoyleP. S. (2018). Microfluidic platform for selective microparticle parking and paired particle isolation in droplet arrays.Biomicrofluidics12:024102. 10.1063/1.5011342
9
ChenS.ChengJ.KongC. W.WangX.ChengS. H.LiR. A. (2013). Laser-induced fusion of human embryonic stem cells with optical tweezers.Appl. Phys. Lett.103:033701. 10.1063/1.4813822
10
ChenY. C.ChengY. H.KimH. S.IngramP. N.NorJ. E.YoonE. (2014a). Paired single cell co-culture microenvironments isolated by two-phase flow with continuous nutrient renewal.Lab Chip142941–2947. 10.1039/c4lc00391h
11
ChenY. C.IngramP.YoonE. (2014b). Electrolytic valving isolation of cell co-culture microenvironment with controlled cell pairing ratios.Analyst1396371–6378. 10.1039/c4an01282h
12
ChengY. H.ChenY. C.LinE.BrienR.JungS.ChenY. T.et al (2018). Hydro-Seq enables contamination-free highthroughput single-cell RNA-sequencing for circulating tumor cells.Nat. Commun.10:2163. 10.1038/s41467-019-10122-2
13
ChungM. T.NúñezD.CaiD.KurabayashiK. (2017). Deterministic droplet-based co-encapsulation and pairing of microparticles via active sorting and downstream merging.Lab Chip173664–3671. 10.1039/c7lc00745k
14
CollinsD. J.MorahanB.Garcia-BustosJ.DoerigC.PlebanskiM.NeildA. (2015). Two-dimensional single-cell patterning with one cell per well driven by surface acoustic waves.Nat. Commun.6:8686. 10.1038/ncomms9686
15
CukiermanE.PankovR.YamadaK. M. (2002). Cell interactions with three-dimensional matrices.Curr. Opin. Cell Biol.14633–640. 10.1016/S0955-0674(02).00364-2
16
DaneshpourH.YoukH. (2019). Modeling cell–cell communication for immune systems across space and time.Curr. Opin. Chem. Biol.1844–52. 10.1016/j.coisb.2019.10.008
17
DolegaM. E.AbeilleF.Picollet-D’hahanN.GidrolX. (2015). Controlled 3D culture in Matrigel microbeads to analyze clonal acinar development.Biomaterials52347–357. 10.1016/j.biomaterials.2015.02.042
18
DongeunH.HamiltonG. A.IngberD. E. (2011). From 3D cell culture to organs-on-chips.Trends Cell Biol.21745–754. 10.1016/j.tcb.2011.09.005
19
DuchampM.DahounT.VaillierC.ArnaudM.BobisseS.CoukosG.et al (2019). Microfluidic device performing on flow study of serial cell–cell interactions of two cell populations.RSC Adv.941066–41073. 10.1039/c9ra09504g
20
DuraB.DouganS. K.BarisaM.HoehlM. M.LoC. T.PloeghH. L.et al (2015). Profiling lymphocyte interactions at the single-cell level by microfluidic cell pairing.Nat. Commun.6:5940. 10.1038/ncomms6940
21
DuraB.LiuY.VoldmanJ. (2014). Deformability-based microfluidic cell pairing and fusion.Lab Chip142783–2790. 10.1039/c4lc00303a
22
DuttaR. C.DuttaA. K. (2009). Cell-interactive 3D-scaffold, advances and applications.Biotechnol. Adv.27334–339. 10.1016/j.biotechadv.2009.02.002
23
ErtlP.StickerD.CharwatV.KasperC.LepperdingerG. (2014). Lab-on-a-chip technologies for stem cell analysis.Trends Biotechnol.32245–253. 10.1016/j.tibtech.2014.03.004
24
FakhfouriA.DevendranC.CollinsD. J.AiY.NeildA. (2016). Virtual membrane for filtration of particles using surface acoustic waves (SAW).Lab Chip163515–3523. 10.1039/c6lc00590j
25
FaleyS. L.CoplandM.WlodkowicD.KolchW.SealeK. T.WikswoJ. P.et al (2009). Microfluidic single cell arrays to interrogate signalling dynamics of individual, patient-derived hematopoietic stem cells.Lab Chip182659–2664. 10.1039/b902083g
26
FaleyS. L.SealeK.HugheyJ.SchafferD. K.CompernolleS. V.McKinneyB.et al (2008). Microfluidic platform for real-time signaling analysis of multiple single T cells in parallel.Lab Chip81700–1712. 10.1039/b719799c
27
FaraghatS. A.HoettgesK. F.SteinbachM. K.van der VeenD. R.BrackenburyW. J.HensleeE. A.et al (2017). High-throughput, low-loss, low-cost, and label-free cell separation using electrophysiology-activated cell enrichment.Proc. Natl. Acad. Sci. U.S.A.1144591–4596. 10.1073/pnas.1700773114
28
Fatsis-KavalopoulosN.CallaghanP. O.XieB.VeraR. H.Idevall-HagrenO.KreugerJ. (2019). Formation of precisely composed cancer cell clusters using a cell assembly generator (CAGE). for studying paracrine signaling at single-cell resolution.Lab Chip191071–1081. 10.1039/c8lc01153b
29
FrimatJ. P.BeckerM.ChiangY. Y.MarggrafU.JanasekD.HengstlerJ. G.et al (2011). A microfluidic array with cellular valving for single cell co-culture.Lab Chip11231–237. 10.1039/c0lc00172d
30
GoersL.FreemontP.PolizziK. M. (2014). Co-culture systems and technologies: taking synthetic biology to the next level.J. R. Soc. Interface11:20140065. 10.1098/rsif.2014.0065
31
GromovaA.SpadaA. R. L. (2020). Harmony lost: cell–cell communication at the neuromuscular junction in motor neuron disease.Trends Neurosci.43709–724. 10.1016/j.tins.2020.07.002
32
GuanZ.JiaS.ZhuZ.ZhangM.YangC. J. (2014). Facile and rapid generation of large-scale microcollagen gel array for long-term single-cell 3D culture and cell proliferation heterogeneity analysis.Anal. Chem.862789–2797. 10.1021/ac500088m
33
GuoF.FrenchJ. B.LiP.ZhaoH.ChanC. Y.FickJ. R.et al (2013). Probing cell-cell communication with microfluidic devices.Lab Chip133152–3162. 10.1039/c3lc90067c
34
GuoF.LiP.FrenchJ. B.MaoZ.ZhaoH.LiS.et al (2015). Controlling cell–cell interactions using surface acoustic waves.Proc. Natl. Acad. Sci. U.S.A.11243–48. 10.1073/pnas.1422068112
35
GuptaK.KimD.-H.EllisonD.SmithC.KunduA.TuanJ.et al (2010). Lab-on-a-chip devices as an emerging platform for stem cell biology.Lab Chip102019–2031. 10.1039/c004689b
36
HåkansonM.TextorM.CharnleyM. (2011). Engineered 3D environments to elucidate the effect of environmental parameters on drug response in cancer.Integr. Biol. 331–38. 10.1039/c0ib00074d
37
HeC.-K.ChenW.WangS.-H.HsuC.-H. (2019). Hydrodynamic shuttling for deterministic high efficiency multiple single-cell capture in a microfluidic chip.Lab Chip191370–1377. 10.1039/c9lc00036d
38
HongS.PanQ.LeeL. P. (2012). Single-cell level co-culture platform for intercellular communication.Integr. Biol.4374–380. 10.1039/c2ib00166g
39
HuangJ.LinF.XiongC. (2019). Mechanical characterization of single cells based on microfluidic techniques.TrAC Trends Anal. Chem.120115436. 10.1016/j.trac.2019.07.015
40
HuangN. T.ZhangH.ChungM. T.SeoJ. H.KurabayashiK. (2014). Recent advancements in optofluidics-based single-cell analysis: optical on-chip cellular manipulation, treatment, and property detection.Lab Chip141230–1245. 10.1039/c3lc51211h
41
HuangQ.MaoS.KhanM.LinJ.-M. (2019). Single-cell assay on microfluidics.Analyst144808–823. 10.1039/c8an01079j
42
HuberD.OskooeiA.SolvasX. C.deMelloA.KaigalaG. V. (2018). Hydrodynamics in cell studies.Chem. Rev.1182042–2079. 10.1021/acs.chemrev.7b00317
43
HuhD.MatthewsB. D.MammotoA.Montoya-ZavalaM.HsinH. Y.IngberD. E. (2010). Reconstituting organ-level lung functions on a chip.Science3281662–1668. 10.1126/science.1188302
44
HwangJ. W.KimM. J.KimH. J.HwangY. H.YoonS.ZahidM. D. A.et al (2012). Optimization of pancreatic islet spheroid using various concave patterned-films.Macromol. Res.201264–1270. 10.1007/s13233-012-0189-9
45
IrimiaD.WangX. (2018). Inflammation-on-a-chip: probing the immune system ex vivo.Trends Biotechnol.36923–937. 10.1016/j.tibtech.2018.03.011
46
JangJ. H.HuangY.ZhengP.JoM. C.BertoletG.ZhuM. X.et al (2015). Imaging of cell-cell communication in a vertical orientation reveals high-resolution structure of immunological synapse and novel PD-1 dynamics.J. Immunol.1951320–1330. 10.4049/jimmunol.1403143
47
JinC.McFaulS. M.DuffyS. P.DengX.TavassoliP.BlackP. C.et al (2014). Technologies for label-free separation of circulating tumor cells: from historical foundations to recent developments.Lab Chip1432–44. 10.1039/c3lc50625h
48
JingP.LiuY.KeelerE. G.CruzN. M.FreedmanB. S.LinL. Y. (2018). Optical tweezers system for live stem cell organization at the single-cell level.Biomed. Opt. Express9771–779. 10.1364/BOE.9.000771
49
JoenssonH. N.SvahnH. A. (2012). Droplet microfluidics-a tool for single-cell analysis.Angew. Chem. Int. Ed.5112176–12191. 10.1002/anie.201200460
50
KampermanT.HenkeS.van den BergA.ShinS. R.TamayolA.KhademhosseiniA.et al (2017a). Single cell microgel based modular bioinks for uncoupled cellular micro- and macroenvironments.Adv. Healthc. Mater.6:1600913. 10.1002/adhm.201600913
51
KampermanT.HenkeS.VisserC. W.KarperienM.LeijtenJ. (2017b). Centering single cells in microgels via delayed crosslinking supports long-term 3D culture by preventing cell escape.Small13:1603711. 10.1002/smll.201603711
52
KanedaS.KawadaJ.ShinoharaM.KumemuraM.UenoR.KawamotoT.et al (2019). Boyden chamber-based compartmentalized tumor spheroid culture system to implement localized anticancer drug treatment.Biomicrofluidics13:054111. 10.1063/1.5125650
53
KhokhlovaM. D.LyubinE. V.ZhdanovA. G.RykovaS. Y.SokolovaI. A.FedyaninA. A. (2012). Normal and system lupus erythematosus red blood cell interactions studied by double trap optical tweezers: direct measurements of aggregation forces.J. Biomed. Opt.17:025001. 10.1117/1.JBO.17.2.025001
54
KhooB. L.GrenciG.LimY. B.LeeS. C.HanJ.LimC. T. (2018). Expansion of patient-derived circulating tumor cells from liquid biopsies using a CTC microfluidic culture device.Nat. Protoc.1334–58. 10.1038/nprot.2017.125
55
KimH.LeeS.LeeW.KimJ. (2017). Particle clustering: high-density microfluidic particle-cluster-array device for parallel and dynamic study of interaction between engineered particles.Adv. Biomater.29:1701351. 10.1002/adma.201701351
56
KimH. J.LiH.CollinsJ. J.IngberD. E. (2016). Contributions of microbiome and mechanical deformation to intestinal bacterial overgrowth and inflammation in a human gut-on-a-chip.Proc. Natl. Acad. Sci. U.S.A.113E7–E15. 10.1073/pnas.1522193112
57
KimS. E.KimH. M.DohJ. (2019). Single cell arrays of hematological cancer cells for assessment of lymphocyte cytotoxicity dynamics, serial killing, and extracellular molecules.Lab Chip192009–2018. 10.1039/c9lc00133f
58
KlepárníkK.ForetF. (2013). Recent advances in the development of single cell analysis—a review.Anal. Chim. Acta80012–21. 10.1016/j.aca.2013.09.004
59
KonryT.SarkarS.SabhachandaniP.CohenN. (2016). Innovative tools and technology for analysis of single cells and cell–cell interaction.Annu. Rev. Biomed. Eng.18259–284. 10.1146/annurev-bioeng-090215-112735
60
KumarR.Harris-HookerS.KumarR.SanfordG. (2011). Co-culture of retinal and endothelial cells results in the modulation of genes critical to retinal neovascularization.Vasc. Cell3:27. 10.1186/2045-824x-4-6
61
LagusT. P.EddJ. F. (2013). High-throughput co-encapsulation of self-ordered cell trains: cell pair interactions in microdroplets.RSC Adv.320512–20522. 10.1039/c3ra43624a
62
LawsonD. A.KessenbrockK.DavisR. T.PervolarakisN.WerbZ. (2018). Tumour heterogeneity and metastasis at single-cell resolution.Nat. Cell Biol.201349–1360. 10.1038/s41556-018-0236-7
63
LecaultV.InsbergheM. V.SekulovicS.KnappD. J. H. F.WohrerS.BowdenW.et al (2011). High-throughput analysis of single hematopoietic stem cell proliferation in microfluidic cell culture arrays.Nat. Methods8581–586. 10.1038/nmeth.1614
64
LeeJ.BurnsM. A. (2015). Asymmetric traps array for particle transport.RSC Adv.53358–3364. 10.1039/c4ra14501a
65
LeeG. H.KimS. H.AhnK.LeeS. H.ParkJ. Y. (2016). Separation and sorting of cells in microsystems using physical principles.J. Micromech. Microeng.26:013003. 10.1088/0960-1317/26/1/013003
66
LeeG. H.KimS. H.KangA.TakayamaS.LeeS. H.ParkJ. Y. (2015). Deformable L-shaped microwell array for trapping pairs of heterogeneous cells.J. Micromech. Microeng.25:035005. 10.1088/0960-1317/25/3/035005
67
LeeY. B.KimE. M.ByunH.ChangH.JeongK.AmanZ. M.et al (2018). Engineering spheroids potentiating cell-cell and cell-ECM interactions by self-assembly of stem cell microlayer.Biomaterials104810–4826. 10.1016/j.biomaterials.2018.02.049
68
LiN.ZhangW.LiY.LinJ.-M. (2019). Analysis of cellular biomolecules and behaviors using microfluidic chip and fluorescence method.TrAC Trends Anal. Chem.117200–214. 10.1016/j.trac.2019.05.029
69
LiY.JangJ. H.WangC.HeB.ZhangK.ZhangP.et al (2017). Microfluidics cell loading-dock system: ordered cellular array for dynamic lymphocyte-communication study.Adv. Biosyst.1:1700085. 10.1002/adbi.201700085
70
LienemannP. S.RossowT.MaoA. S.Vallmajo-MartinQ.EhrbarM.MooneyD. J. (2017). Single cell-laden protease-sensitive microniches for long-term culture in 3D.Lab Chip17727–737. 10.1039/c6lc01444e
71
LimB.ReddyV.HuX. H.KimK. W.JadhavM.Abedini-NassabR.et al (2014). Magnetophoretic circuits for digital control of single particles and cells.Nat. Commun.5:3846. 10.1038/ncomms4846
72
LinL.ChuY. S.ThieryJ. P.LimC. T.RodriguezI. (2013). Microfluidic cell trap array for controlled positioning of single cells on adhesive micropatterns.Lab Chip13714–721. 10.1039/c2lc41070b
73
LiuY.WuC.LaiH. S. S.LiuY. T.LiW. J.ShenY. T. (2017). Three-dimensional calcium alginate hydrogel assembly via TiOPc-based light-induced controllable electrodeposition.Micromachines8:192. 10.3390/mi8060192
74
LuY.YangL.WeiW.ShiQ. (2017). Microchip-based single-cell functional proteomics for biomedical applications.Lab Chip171250–1263. 10.1039/c7lc00037e
75
LuoT.LeiF.ZhuR.SunD. (2019). Microfluidic single-cell manipulation and analysis: methods and applications.Micromachines10:104. 10.3390/mi10020104
76
MaC.ZhaoL.ZhouE. M.XuJ.ShenS.WangJ. (2016). On-chip construction of liver lobule-like microtissue and its application for adverse drug reaction assay.Anal. Chem.881719–1727. 10.1021/acs.analchem.5b03869
77
McSpaddenL. C.NguyenH.BursacN. (2012). Size and ionic currents of unexcitable cells coupled to cardiomyocytes distinctly modulate cardiac action potential shape and pacemaking activity in micropatterned cell pairs.Circ. Arrhythm. Electrophysiol.5821–830. 10.1161/circep.111.969329
78
MoralesX.Cortés-DomínguezI.Ortiz-de-SolorzanoC. (2021). Modeling the mechanobiology of cancer cell migration using 3D biomimetic hydrogels. Gels71710.3390/gels7010017
79
MuX.ZhengW.SunJ.ZhangW.JiangX. (2013). Microfluidics for manipulating cells.Small99–21. 10.1002/smll.201200996
80
MuirA.DanaiL. V.HeidenM. G. V. (2018). Microenvironmental regulation of cancer cell metabolism: implications for experimental design and translational studies.Dis. Models Mech.11:dmm035758. 10.1242/dmm.035758
81
MurphyT. W.ZhangQ.NalerL. B.MaS.LuC. (2018). Recent advances in the use of microfluidic technologies for single cell analysis.Analyst14360–80. 10.1039/c7an01346a
82
NahavandiS.TangS. Y.BaratchiS.SoffeR.NahavandiS.Kalantar-zadehK.et al (2014). Microfluidic platforms for the investigation of intercellular signalling mechanisms.Small104810–4826. 10.1002/smll.201401444
83
PangL.DingJ.GeY.FanJ.FanS.-K. (2019a). Single-cell-derived tumor-sphere formation and drug-resistance assay using an integrated microfluidics.Anal. Chem.18318–8325. 10.1021/acs.analchem.9b01084
84
PangL.DingJ.LiuX. X.FanS.-K. (2019b). Digital microfluidics for cell manipulation.TrAC Trends Anal. Chem.117291–299. 10.1016/j.trac.2019.06.008
85
PangL.DingJ.LiuX. X.YuanH.GeY.FanJ.et al (2020). Microstructure-based techniques for single-cell manipulation and analysis.TrAC Trends Anal. Chem.129:115940. 10.1016/j.trac.2020.115940
86
PangL.LiuW.TianC.XuJ.LiT.ChenS. W.et al (2016). Construction of single-cell arrays and assay of cell drug-resistance in an integrated microfluidics.Lab Chip164612–4620. 10.1039/c6lc01000h
87
PangL.ShenS.MaC.MaT.ZhangR.TianC.et al (2015). Deformability and size-based cancer cell separation using an integrated microfluidic device.Analyst1407335–7346. 10.1039/c5an00799b
88
PeiH.LiL.HanZ.WangY.TangB. (2020). Recent advance in microfluidic technologies for circulating tumor cells: from enrichment, single cell analysis to liquid biopsy for clinical applications.Lab Chip203854–3875. 10.1039/d0lc00577k
89
PerebikovskyA.LiuY.HwuA.KidoH.ShamlooE.SongD.et al (2021). Rapid sample preparation for detection of antibiotic resistance on a microfluidic disc platform.Lab Chip21534–545. 10.1039/d0lc00838a
90
PolioS. R.StasiakS. E.JamiesonR. R.BalestriniJ. L.KrishnanR.ParameswaranH. (2019). Extracellular matrix stiffness regulates human airway smooth muscle contraction by altering the cell-cell coupling.Sci. Rep.9:9564. 10.1038/s41598-019-45716-9
91
RenL.LiuW.WangY.WangJ. C.TuQ.XuJ.et al (2012). Investigation of hypoxia-induced myocardial injury dynamics in a tissue interface mimicking microfluidic device.Anal. Chem.85235–244. 10.1021/ac3025812
92
RettigJ. R.FolchA. (2005). Large-scale single-cell trapping and imaging using microwell arrays.Anal. Chem.775628–5634. 10.1021/ac0505977
93
Reyes-RuizJ. M.Osuna-RamosJ. F.Jesús-GonzálezL. A. D.Cordero-RiveraC. D.Farfan-MoralesC. N.Hurtado-MonzónA. M.et al (2020). The regulation of flavivirus infection by hijacking exosome-mediated cell–cell communication: new insights on virus–host interactions.Viruses12:765. 10.3390/v12070765
94
RothbauerM.ZirathaH.ErtlP. (2018). Recent advances in microfluidic technologies for cell-to-cell interaction studies.Lab Chip18249–270. 10.1039/c7lc00815e
95
SackmannE. K.FultonA. L.BeebeD. J. (2014). The present and future role of microfluidics in biomedical research.Nature13181–189. 10.1038/nature13118
96
SakthivelK.O’BrienA.KimK.HoorfarM. (2019). Microfluidic analysis of heterotypic cellular interactions: a review of techniques and applications.TrAC Trends Anal. Chem.117166–185. 10.1016/j.trac.2019.03.026
97
SamieiE.NejadH. R.HoorfarM. (2015). A dielectrophoretic-gravity driven particle focusing technique for digital microfluidic systems.Appl. Phys. Lett.106:204101. 10.1063/1.4921630
98
SarkarS.MotwaniV.SabhachandaniP.CohenN.KonryT. (2015). T cell dynamic activation and functional analysis in nanoliter droplet microarray.J. Clin. Cell. Immunol.6:334. 10.4172/2155-9899.1000334
99
SarkarS.SabhachandaniP.StroopinskyD.PalmerK.CohenN.RosenblattJ.et al (2016). Dynamic analysis of immune and cancer cell interactions at single cell level in microfluidic droplets.Biomicrofluidics10:054115. 10.1063/1.4964716
100
SartS.TomasiR. F.-X.AmselemG.BaroudC. N. (2017). Multiscale cytometry and regulation of 3D cell cultures on a chip.Nat. Commun.8:469. 10.1038/s41467-017-00475-x
101
SchwagerS. C.TaufaleleP. V.Reinhart-KingC. A. (2019). Cell–cell mechanical communication in cancer.Cell. Mol. Bioeng.121–14. 10.1007/s12195-018-00564-x
102
SegalinyA. I.LiG.KongL.RenC.ChenX.WangJ. K.et al (2018). Functional TCR T cell screening using single-cell droplet microfluidics.Lab Chip183733–3749. 10.1039/c8lc00818c
103
SimJ. Y.MoelleraJ.HartK. C.RamalloD.VogelV.DunnA. R.et al (2015). Spatial distribution of cell–cell and cell–ECM adhesions regulates force balance while maintaining E-cadherin molecular tension in cell pairs.Mol. Biol. Cell262456–2465. 10.1091/mbc.E14-12-1618
104
SkelleyA. M.KirakO.SuhH.JaenischR.VoldmanJ. (2009). Microfluidic control of cell pairing and fusion.Nat. Methods6147–152. 10.1038/nmeth.1290
105
StrömbergA.RyttsénF.ChiuD. T.DavidsonM.ErikssonP. S.WilsonC. F. (2000). Manipulating the genetic identity and biochemical surface properties of individual cells with electric-field-induced fusion.Proc. Natl. Acad. Sci. U.S.A.977–11. 10.1073/pnas.97.1.7
106
SullivanM. R.UgoliniG. S.SarkarS.KangW.SmithE. C.MckenneyS.et al (2020). Quantifying the efficacy of checkpoint inhibitors on CD8+ cytotoxic T cells for immunotherapeutic applications via single-cell interaction.Cell Death Dis.11:979. 10.1038/s41419-020-03173-7
107
TanW. H.TakeuchiS. A. (2007). Trap-and-release integrated microfluidic system for dynamic microarray applications.Proc. Natl. Acad. Sci. U.S.A.1041146–1151. 10.1073/pnas.0606625104
108
TangX.LiuX.LiP.LiuF.KojimaM.HuangQ.et al (2020). On-chip cell-cell interaction monitoring at single cell level by efficient immobilization of multiple cells in adjustable quantities.Anal. Chem.9211607–11616. 10.1021/acs.analchem.0c01148
109
TavakoliH.ZhouW.MaL.PerezS.IbarraA.XuF.et al (2019). Recent advances in microfluidic platforms for single-cell analysis in cancer biology, diagnosis and therapy.TrAC Trends Anal. Chem.11713–26. 10.1016/j.trac.2019.05.010
110
TeshimaT.IshiharaH.IwaiK.AdachiA.TakeuchiS. (2010). A dynamic microarray device for paired bead-based analysis.Lab Chip102443–2448. 10.1039/c004986g
111
TianC.TuQ.LiuW.WangJ. (2019). Recent advances in microfluidic technologies for organ-on-a-chip, TrAC.Trends Anal. Chem.117146–156. 10.1016/j.trac.2019.06.005
112
TodaS.FrankelN. A.LimW. A. (2019). Engineering cell–cell communication networks: programming multicellular behaviors.Curr. Opin. Chem. Biol.5231–38. 10.1016/j.cbpa.2019.04.020
113
TreutleinB.BrownfieldD. G.WuA. R.NeffN. F.MantalasG. L.EspinozaF. H.et al (2014). Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq.Nature509371–375. 10.1038/nature13173
114
TsengQ.Duchemin-PelletierE.DeshiereA.BallandM.GuilloudH.FilholO.et al (2012). Spatial organization of the extracellular matrix regulates cell–cell junction positioning.Proc. Natl. Acad. Sci. U.S.A.1091506–1511. 10.1073/pnas.1106377109
115
TuH.WuZ.XiaY.ChenH.HuH.DingZ.et al (2020). Profiling of immune-cancer interactions at the single cell level using microfluidic well array.Analyst1454138–4147. 10.1039/d0an00110d
116
UtechS.ProdanovicR.MaoA. S.OstafeR.MooneyD. J.WeitzD. A. (2015). Microfluidic generation of monodisperse, structurally homogeneous alginate microgels for cell encapsulation and 3D cell culture.Adv. Healthc. Mater.41628–1633. 10.1002/adhm.201500021
117
VuT. Q.de CastroR. M. B.QinL. (2017). Bridging the gap: microfluidic devices for short and long distance cell–cell communication.Lab Chip171009–1023. 10.1039/c6lc01367h
118
WangL.TaoT.SuW.YuH.YuY.QinJ. (2017). A disease model of diabetic nephropathy in a glomerulus-on-a-chip microdevice.Lab Chip171749–1760. 10.1039/c7lc00134g
119
WangY.LiY.Thérien-AubinH.MaJ.ZandstraP. W.KumachevaE. (2016). Two-dimensional arrays of cell-laden polymer hydrogel modules.Biomicrofluidics10:014110. 10.1063/1.4940430
120
WangZ.HeX.QiaoH.ChenP. (2020). Global trends of organoid and organ-on-a-chip in the past decade: a bibliometric and comparative study.Tissue Eng. Pt. A26656–671. 10.1089/ten.tea.2019.0251
121
WuC.ChenR.LiuY.YuZ.JiangY.ChengX. (2017). A planar dielectrophoresis-based chip for high throughput cell pairing.Lab Chip174008–4014. 10.1039/c7lc01082f
122
WuQ.LiuJ.WangX.FengL.WuJ.ZhuX.et al (2020). Organ-on-a-chip: recent breakthroughs and future prospects.BioMed. Eng. OnLine9:19. 11461–11469. 10.1186/s12938-020-0752-0
123
WuY.RenY.TaoY.HouL.JiangH. (2018). High-throughput separation, trapping and manipulation of single cells and particles using combined dielectrophoresis at a bipolar electrode array.Anal. Chem.9011461–11469. 10.1021/acs.analchem.8b02628
124
XiaJ.QiuY.XunX.MaL.GuanJ.SuM. (2018). Single cell patterning for high throughput sub-cellular toxicity assay.Anal. Chim. Acta100726–32. 10.1016/j.aca.2017.11.044
125
YamadaK. M.CukiermanE. (2007). Modeling tissue morphogenesis and cancer in 3D.Cell130601–610. 10.1016/j.cell.2007.08.006
126
YamanS.Anil-IneviM.OzciviciE.TekinH. C. (2018). Magnetic force-based microfluidic techniques for cellular and tissue bioengineering.Front. Bioeng. Biotechnol.6:192. 10.3389/fbioe.2018.00192
127
YamanakaY. J.BergerC. T.SipsM.CheneyP. C.AlterG.LoveJ. C. (2012). Single-cell analysis of the dynamics and functional outcomes of interactions between human natural killer cells and target cells.Integr. Biol.41175–1184. 10.1039/c2ib20167d
128
YaoJ.ZhuG.ZhaoT.TakeiM. (2019). Microfluidic device embedding electrodes for dielectrophoretic manipulation of cells-a review.Electrophoresis401166–1177. 10.1002/elps.201800440
129
YinZ.TaoS. C.CheongR.ZhuH.LevchenkoA. (2010). An integrated micro-electro-fluidic and protein arraying system for parallel analysis of cell responses to controlled microenvironments.Integr. Biol.2416–423. 10.1039/c0ib00017e
130
YoshidaA.TsujiS.TaniguchiH.KenmotsuT.SadakaneK.YoshikawaK. (2017). Manipulating living cells to construct a 3D single-cell assembly without an artificial scaffold.Polymers9:319. 10.3390/polym9080319
131
YouL.CoxR. S.WeissR.ArnoldF. H. (2002). Programmed population control by cell–cell communication and regulated killing.Nature428868–871. 10.1038/nature02491
132
ZervantonakisI. K.KothapalliC. R.ChungS.SudoR.KammR. D. (2011). Microfluidic devices for studying heterotypic cell-cell interactions and tissue specimen cultures under controlled microenvironments.Biomicrofluidics5:013406. 10.1063/1.3553237
133
ZhangK.ChouC. K.XiaX.HungM. C.QinL. (2014). Block-cell-printing for live single-cell printing.Proc. Natl. Acad. Sci. U.S.A.1112948–2953. 10.1073/pnas.1313661111
134
ZhangL.ChenK.ZhangH.PangB.ChoiC. H.MaoA. S.et al (2018). Microfluidic templated multicompartment microgels for 3D encapsulation and pairing of single cells.Small14:1702955. 10.1002/smll.201702955
135
ZhangP.HanX.YaoJ.ShaoN.ZhangK.ZhouY.et al (2019). High-throughput isolation of cell protrusions with single-cell precision for profiling subcellular gene expression.Angew. Chem. Int. Ed.5813700-13705. 10.1002/anie.201903694
136
ZhuJ.WanY.ChenP.SuH.DuW.LiuB. F. (2019). Highly efficient microfluidic device for cell trapping and pairing towards cell-cell communication analysis.Sens. Actuators B283685–692. 10.1016/j.snb.2018.12.078
137
ZhuX. D.ChuJ.WangY. H. (2018). Advances in microfluidics applied to single cell operation.Biotechnol. J.131860–1868. 10.1002/biot.201700416
Summary
Keywords
cell-cell communication, cell culture, single-cell manipulation, single-cell analysis, microfluidic technology
Citation
Pang L, Ding J, Liu X-X, Kou Z, Guo L, Xu X and Fan S-K (2021) Microfluidics-Based Single-Cell Research for Intercellular Interaction. Front. Cell Dev. Biol. 9:680307. doi: 10.3389/fcell.2021.680307
Received
14 March 2021
Accepted
20 July 2021
Published
12 August 2021
Volume
9 - 2021
Edited by
Jiangxin Wang, Shenzhen University, China
Reviewed by
Christian Hiepen, Freie Universität Berlin, Germany; Inmaculada Navarro-Lérida, Autonomous University of Madrid, Spain
Updates
Copyright
© 2021 Pang, Ding, Liu, Kou, Guo, Xu and Fan.
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) and the copyright owner(s) 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.
*Correspondence: Xi Xu, xuxi@xiyi.edu.cnShih-Kang Fan, skfan@ksu.edu
This article was submitted to Signaling, a section of the journal Frontiers in Cell and Developmental Biology
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