Review ArticleOpen Access

Single-cell manipulation by two-dimensional micropatterning

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

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Haimei Zhang

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

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Shiyu Deng

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

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Qiushuo Sun

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

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Qingsong Hu

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

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Yuhang Pan

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

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Fen Hu

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

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Imshik Lee

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

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Fulin Xing

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

E-mail Address: xingfulin@nankai.edu.cn

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, and

Leiting Pan

The Key Laboratory of Weak-Light Nonlinear, Photonics of Education Ministry, School of Physics and TEDA Institute of Applied Physics, Nankai University, Tianjin 300071, P. R. China

State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, College of Life Sciences, Nankai University, Tianjin 300071, P. R. China

Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, P. R. China

Shenzhen Research Institute of Nankai University, Shenzhen, Guangdong 518083, P. R. China

E-mail Address: plt@nankai.edu.cn

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https://doi.org/10.1142/S1793545823300112Cited by:0 (Source: Crossref)

Abstract

Cells are highly sensitive to their geometrical and mechanical microenvironment that directly regulate cell shape, cytoskeleton and organelle, as well as the nucleus morphology and genetic expression. The emerging two-dimensional micropatterning techniques offer powerful tools to construct controllable and well-organized microenvironment for single-cell level investigations with qualitative analysis, cellular standardization, and in vivo environment mimicking. Here, we provide an overview of the basic principle and characteristics of the two most widely-used micropatterning techniques, including photolithographic micropatterning and soft lithography micropatterning. Moreover, we summarize the application of micropatterning technique in controlling cytoskeleton, cell migration, nucleus and gene expression, as well as intercellular communication.

Keywords:

1. Introduction

In multicellular organisms, extracellular microenvironments create diverse conditions that exert spatial extension/confinement on single cells in situ.^1,2,3 In recent years, growing evidence highlights that extracellular physical features, e.g., extracellular matrix (ECM) geometry,^4,5,6,7 adjacent cells extrusion,^8,9 substrate stiffness,^10,11,12 extracellular fluid viscosity,^13,14,15 and extracellular forces,^16,17 are determinants of cell behavior and fate, which are considered equally important as acknowledged biochemical factors. These extracellular physical cues act on the membrane receptors, mechanical sensing proteins and proximal-membrane cytoskeleton to trigger downstream signaling pathway.^{18,19,20,21,22} Conventional two-dimensional (2D) homogeneous substrates were generally applied for cell culture, on which cells spread and migrate without any confinement, which is distinct from the crowded and complicated microenvironment in vivo.

The emergence of 2D micropatterning technique has provided unique opportunities to create a specific ECM environment for standardization of cell morphology,^23,24,25,26 simulation of physiological environment,^27,28,29 and high-throughput analysis.^{30,31,32,33,34} A common principle of cell-patterning technique is the fabrication of adhesive region and non-adhesive region on the 2D substrate.^35,36 Cells cannot spread across the adhesive–non-adhesive boundaries, instead, they are confined in adhesive regions. Varieties of strategies have been developed to realize the adhesive-nonadhesive alternate substrates with 2D spatial confinement, which not only regulate the cell morphology but also affect the intracellular cytoskeleton, organelles and nucleus.^37,38

In this review, we first introduce the principle and properties of general 2D micropatterning techniques, including photolithography-based micropatterning and soft lithography micropatterning. We then focus on the application of 2D micropatterning techniques in single-cell manipulation, cytoskeleton targeting, cell migration, nucleus morphology and gene expression, and intercellular communication. Lastly, we summarize the current technical challenges and future progress in micropatterning technique in biological applications.

2. Micropatterning Technique

2.1. Photolithographic micropatterning

The principle of micropatterning in cell culture is to fabricate a substrate to which cells can adhere on specific areas. Photolithography, as the workhorse of microfabrication and semiconductor industry, plays a crucial role in 2D micropatterning for biological applications.^39,40,41 Photolithography is often carried out by projecting a pattern on a photomask onto a photoresist film spin on a substrate.^42,43 UV light is exposed through the mask to modify the photoresist on specific regions. Combined with the following surface modification process, i.e., surface passivation and protein coating, photolithographic micropatterning creates a surface where cells adhere to protein-coated regions and are repelled by non-adhesive regions. The fabrication process is shown in Fig. 1(a). Polyethylene glycol (PEG) and its derivatives (e.g., poly-lysine-PEG and fluorescence probe-labeled PEG), which are inherently cell repellant due to a low level of non-specific binding of proteins and other macromolecules,^{44,45,46,47,48} have been used from general non-fouling coatings to cell patterning, biosensing, microfluidics, etc. Photolithography micropatterning provides a fast, easy-to-use, and high-resolution 2D micropatterning strategy. To further extend the application of micropatterning from 2D to 3D, two-photon laser scanning (TPLS) lithography technology uses two-photon excitation to confine focus to a micron scale, while leaving the area outside the focus unchanged. Using this technology, 3D patterns of biochemical signals can be generated in PEG hydrogels, overcoming the limitation that traditional photolithography technology can only construct 2D micropatterns.⁴⁹ Indeed, photolithography is an intrinsically expensive process because of the equipment, such as the highly demanding processes required for the fabrication of microelectronic devices.^50,51

2.2. Soft lithography micropatterning

Soft lithography, also known as micro-contact print ( $μ$ $μ$ CP), is another widely used 2D micropatterning technique (Fig. 1(b)), which provides access to generate specific surface modification for single-cell manipulation.⁵² Vary from photolithography-based micropatterning, the key element of soft lithography is an elastomeric stamp with patterns as relief structures on its surface.⁵³ The stamp is typically fabricated by casting a liquid precursor, mostly using poly(dimethyl siloxane) (PDMS), against a master whose surface has been patterned with the complementary structures. This stamp is incubated with protein solution as the “ink”, followed by transferring protein layer from PDMS to substrates through press. Regions without a protein layer should be coated with protein repelling regents.^54,55 Soft lithography is low-cost, experimentally convenient and has emerged as an applicable technology for multiple applications that include cell biology, microfluidics, lab-on-a-chip, microelectromechanical systems and flexible electronics/photonics.⁵⁶ At present, the master molds of soft lithography have been manufactured by combining traditional photolithography with two-photon polymerization, achieving high-resolution complex three-dimensional (3D) structures by convenient operation.⁵⁷ Furthermore, additive manufacturing (AM), also known as 3D printing, is used to produce lab-on-a-chip master molds, which are combined with elastomeric stamps for soft lithography to simplify the process.⁵⁸

**Table 1. Advantages and disadvantages of the micropatterning technique.**
	Advantages	Disadvantages
Photolithography	–Simple-established process. –Easy to change the pattern through photomask design. –High resolution and precision for making ultrastructure.	Unfriendly to small batch production due to the high cost of the device and materials. Slow manufacturing speed. Cell manipulation is limited to 2D substrates. Used photoresists and organic solvents might be cytotoxic.
Soft lithography	–Low cost and simple make. –Suitable for planar or non-planar 2D or 3D structures. –The chemical inertness of polymeric stamps makes them reusable. –The strong projection ability of polymeric stamp is conducive to optical inspections.	The elasticity and thermal expansion of polymeric stamp limits pattern accuracy. The softly of polymeric stamp limits its aspect ratio. Polymeric stamp has shrinkage and deformation during curing.

3. Application of Micropatterning in Single Cell Control

Since the emergence of 2D micropatterning techniques, they have been widely used in biological research from tissue engineering to single-cell manipulation. The key point of its application in single cell-manipulation is the pattern designing according to target cellular components. The following content introduced some typical and elegant designs and applications of 2D micropatterning in single-cell manipulation.

3.1. Micropatterning regulates cytoskeletons

Intracellular cytoskeletons, including actin filaments, microtubules and intermedia filaments, play crucial roles in various cellular processes ^{59,60,61,62,63} Actin cytoskeleton, as the most highly-dynamic component, dominates the generation of intracellular force, cell-matrix/cell adhesion and cell locomotion, etc.^64,65,66 Microtubules, as the principal rigid construction in the cell, determine the maintenance of cell shape, intracellular transport and cell division.^67,68 Intermediate filaments, e.g., vimentin and keratin, that extend from the nuclear into the cytoplasm, not only support the rigidity of cells, but more importantly, are related to other cellular processes, such as cell differentiation and intracellular transport.^69,70 All these cytoskeleton components are popular targets of 2D micropatterning-mediated regulation.

Actin enriches at most near-edge structures of the cell,⁷¹ and choreographs a highly dynamic interplay with the matrix.^72,73 Extensive researches focused on the effect of spatial confinement on actin cytoskeleton and revealed various underlying mechanisms based on 2D micropatterning. A recently-found intriguing phenomenon is that on isotropous circular pattern, single cell actin cytoskeleton formed a chiral left–right asymmetry pattern, which arises from $α$ $α$ -actinin1-dependent self-organization of the actin cytoskeleton on the pattern (Fig. 2(a)).⁷⁴ Further, this single-cell left–right asymmetry was generalized to the multicellular system in which a group of actin assembly regulators on chirality of individual cells and collective cells were revealed, including mDia1, profilin1, CapZ $β$ $β$ and $α -$ $α -$ actinin1.⁷⁵ This actin-driven cell chirality may underlie tissue and organ asymmetry in development. Asymmetrical patterns induced asymmetrical actin dynamic. For instance, Xing et al. found a “secondary” ring of actin cytoskeleton distributed along the periphery of the circular pattern, which was induced by the actin retrograde flow and spatial confinement (Fig. 2(b)).⁷⁶ Besides isotropous patterns, asymmetrical patterns were also applied in the investigations of actin cytoskeleton characteristics. Elena et al. monitored the dynamics of the stress fiber network during cell spreading by seeding U2OS cells on rectangular micropatterns with distinctive opening positions, revealing that the geometry of the ECM and the initial adhesion position determine the process of cell spreading and stress fiber network “memory” (Fig. 2(c)).⁷⁷ Weißenbruch et al. observed distinct phenotypes of NM IIA-KO and NM IIB-KO cells on asymmetrical cross-shaped micropatterns (Fig. 2(d)).⁷⁸ As the principal components of the cytoskeleton, actin distributes at the most-edge regions of the cell.⁷⁹ They can directly sense the spatial confinement and activate subsequent signaling pathways, establishing actin as the most popular target for 2D micropatterning.

Fig. 2. Micropatterning regulates cytoskeleton. (a) Single cell actin cytoskeleton formed a chiral left–right asymmetry distribution on isotropous circular pattern.⁷⁴ Scale bar, 10 $μ$ $μ$ m. (b) A “secondary” ring of actin cytoskeleton distributed along the periphery of the circular pattern.⁷⁶ (c) On rectangular micropatterns with distinctive opening positions, the geometry of ECM and the initial adhesion position determined the process of cell spreading and stress fiber network.⁷⁷ Scale bar, 10 $μ$ $μ$ m. (d) Myosin-KO cells exhibited distinct actin phenotypes on asymmetric cross patterns.⁷⁸ Scale bar, 10 $μ$ $μ$ m. (e) Centrosome located at the subcellular zone center determined by the pulling forces from actomyosin network.⁷⁹ Scale bar, 10 $μ$ $μ$ m. (f) Two-cell systems on complex patterns of H and bowtie patterns, centrosome located at the subcellular zone center.⁸⁰ (g) Both square and circle-shaped MEF cells had similar vimentin distributions. On the contrary, in asymmetric and polarized-shaped cells vimentin avoids the sharp edges.⁸¹ Scale bar, 5 $μ$ $μ$ m. (h) On “crossbow”-shaped micropatterns, vimentin network reciprocally restricted the retrograde movement of arcs.⁸² Scale bar, 10 $μ$ $μ$ m.

For microtubules, most micropatterning-related researches chose centrosome, the microtubule organizing center (MTOC) and the geometrical center of the cell, to regulate microtubules. Using various patterns including triangular and circle, Ana et al. revealed that the centrosome is located at the subcellular zone center determined by the pulling forces from the actomyosin network (Fig. 2(e)).⁷⁹ Similar observations were obtained from two-cell systems on complex patterns, such as “H” and bowtie patterns (Fig. 2(f)).⁸⁰ Micropatterning techniques are also applied to investigate intermediate filament, especially vimentin, and its interaction with other cytoskeleton components. In mouse embryonic fibroblasts (MEFs), both square and circle-shaped cells have similar vimentin distributions, while asymmetric and polarized-shaped cells have distinct vimentin distributions that avoid the sharp edges of the cell (Fig. 2(g)).⁸¹ Experiments with vimentin-null MEFs adherent to polarized (teardrop) and un-polarized (dumbbell) patterns show that the absence of vimentin alters microtubule organization and perturbs cell polarity.⁸¹ Using “crossbow” shaped micropatterns to induce actomyosin arc, Jiu et al. revealed that the vimentin network reciprocally restricts retrograde movement of arcs and hence controls the width of flat lamella at the leading edge of the cell (Fig. 2(h)).⁸² Unlike the wide distribution of actin cytoskeleton, microtubules and intermediate filaments are located around the nucleus. Since limited effects on the microtubules and intermediate filaments were observed upon spatial confinement at present. Hence, further investigations may gain more insight into the regulatory mechanism of these two cytoskeleton components via 2D micropatterning techniques.

The designed micropatterns should be closely associated with the target cytoskeleton which acts as the first and principal sensors of extracellular geometry. For instance, in actin-related investigations, the pattern could contain some fine structures, e.g., hollow patterns,^77,78 due to high dynamic and mechanical functions. Differently, the designs should focus on the size and outlines of the pattern for relatively rigid components including microtubule and intermediate filament.

3.2. Micropatterning regulates cell migration

ECM geometry cues have long been recognized as crucial factors in cell migration.^83,84 Micropatterning creates certain tracks, e.g., straight stripes, for single migrating cells. This kind of one-dimensional confinement provides both a certain orientation and spatial confinement from vertical directions of migration. Using stripes of different widths to provide physical confinement like those encountered physiologically by cells, Mohammed et al. identified substrate adhesion region confinement as a key determinant of speed in cell migration, i.e., stronger confinement often leads to higher migrating speed (Fig. 3(a)).⁸⁵ Similarly, the migration of NIH3T3 cells and Hela cells were found to be significantly affected by the widths and arc radiuses of the guided micro-stripes that they migrated fastest on the straight micro-stripes with width of about 20 $μ$ $μ$ m (Fig. 3(b)).⁸⁶ Using micro-lanes with fields of alternating fibronectin densities, Schreiber et al. found a biphasic behavior with maximal velocity for intermediate fibronectin densities, i.e $.$ $.$ , the velocity of cells first increased and then decreased as the rising of fibronectin densities (Fig. 3(c)).⁸⁷

Fig. 3. Micropatterning regulates cell migration. (a) Substrate adhesion region was identified as a key determinant of cell migration speed using stripes of different widths to simulate the physiological limitation.⁸⁵ (b) NIH3T3 and Hela cells migrated fastest on the straight micro-stripes with width of 20 $μ$ $μ$ m.⁸⁶ (c) Cell migration at different speeds on micro-lanes with alternating fibronectin densities.⁸⁷ (d) MDA-MB-231 and MCF10A cell lines exhibit intricate nonlinear migratory dynamics in dumbbell-shaped micropatterns.⁸⁸ Scale bar, 25 $μ$ $μ$ m. (e) Dumbbell-shaped patterns controlled the separation of two daughter cells and induced long intercellular bridges between two daughter cells.⁸⁹ Scale bar, 25 $μ$ $μ$ m. (f) The polarization direction of the tear-drop-shaped cell will determine the direction of its movement.⁹⁰ (g) On triangular and teardrops array adhesive patterns, direction of cell migration was determined by the coupled dynamics of the focal adhesion collective behavior and the cellular environment.⁹² Scale bar, 15 $μ$ $μ$ m. (h) T-plastin synergized with focal contacts to strengthen the protrusive actin network at the leading edge, promoting membrane protrusions through ECM gap on ladder-type micropatterns.⁹³ (i) Macrophages migrated across alternate adhesive-nonadhesive substrates in mesenchymal mode with enriched podosomes on nonadhesive regions.⁹⁴ Scale bar, 10 $μ$ $μ$ m.

Other patterns, such as dumbbell shapes, were used to provide limited confinement to direct migration. In such two-state micropatterns, cells exhibit intricate nonlinear migratory dynamics for a cancerous (MDA-MB-231) and a non-cancerous (MCF10A) cell line (Fig. 3(d)).⁸⁸ Besides, Xing et al applied dumbbell-shaped patterns to control the separation of two daughter cells during cell division, and further induce long and regular intercellular bridges between two daughter cells (Fig. 3(e)).⁸⁹

Micropatterning can also manipulate the initial state of cell migration. Jiang et al. restricted the initial shape of individual cells to asymmetrical geometry through micropatterns, e.g., teardrop shapes, in which cells have polarized morphology, proving that the polarization direction of the cell will determine the direction of its movement (Fig. 3(f)).⁹⁰ It was also found that pre-confinement by teardrop shape caused the polarized distribution of MTOC and nucleus-Golgi complex, and this polarity was weakened when the cells were released from the confinement.⁹¹

Interestingly, some noncontinuity patterns were applied in the mechanism investigation of cell migration. For instance, Vecchio et al. designed triangular and teardrops array adhesive patterns and found that cell directionality is determined by the coupled dynamics of the focal adhesion collective behavior and the cellular environment (Fig. 3(g)).⁹² Using ladder-type micropatterns, Garbett et al. demonstrated that T-plastin, an actin-crosslinking protein, synergizes with focal adhesions to strengthen the protrusive actin network at the leading edge, thus promoting membrane protrusions through ECM gap (Fig. 3(h)).⁹³ Using alternate stripe and circular patterns, a novel migratory phenomenon was observed that macrophages can migrate on alternate adhesive-nonadhesive substrates in a classical mesenchymal mode, due to highly-enriched podosomes formed on nonadhesive regions (Fig. 3(i)).⁹⁴ Notably, 2D micropatterning has also been widely applied in the manipulation and quantitative study of collective cell migration,^95,96,97 showing its powerful ability in cell migration investigations.

Cells cannot fulfill their functions without migration. In living organisms, the highly complex microenvironment forces cells to evolve unexpected mobility to squeeze through tissue space. Micropatterning can help quantize environmental parameters, and therefore paving a way for better insight into the intrinsic properties of cell migration. In the future, more ingenious patterns should be designed considering cell types and specific biological questions.

3.3. Micropatterning regulate nucleus and gene expression

2D spatial confinement shapes the nucleus through the force transferred from cytoskeleton.^98,99,100 It has long been known that altering the shape and size of cells by single-cell micropatterning could determine the growth and death of individual cells, indicating that spatial confinement via 2D micropatterning can regulate cellular gene expression and cell fate.¹⁰¹ This top-down regulation was also found in macrophages that elongated macrophage shape leading to higher pro-healing (M2) expression and reduced pro-inflammatory (M1) secretion, which was associated with the modulation of the cytoskeleton (Fig. 4(a)).¹⁰² Similarly, spatial confinement imposed by micropatterning suppressed actin polymerization, and thereby reduced lipopolysaccharide-activated transcriptional programs (biomarkers IL-6, CXCL9, IL-1 $β$ $β$ , and iNOS) by mechano-modulating chromatin compaction and epigenetic alterations (HDAC3 levels and H3K36-dimethylation) (Fig. 4(b)).¹⁰³

Fig. 4. Micropatterning regulated nucleus and gene expression. (a) Elongation of macrophage shape leads to higher pro-healing (M2) expression and reduced pro-inflammatory (M1) secretion.¹⁰² Scale bar, 50 $μ$ $μ$ m. (b) Spatial confinement imposed by micropatterning suppressed actin polymerization, and reduced lipopolysaccharide-activated transcriptional programs by mechanomodulating chromatin compaction and epigenetic alterations.¹⁰³ (c) The alignment and differentiation of neurons and astrocytes were controlled on reproducible laminin-polylysine substrates.¹⁰⁶ (d) On stripe patterns, human mesenchymal stem cells (hMSCs) had an up-regulation of several hallmark markers associated to neurogenesis and myogenesis. Osteogenic markers were specifically down-regulated or remained at its nominal level.¹⁰⁷ Scale bar, 100 $μ$ $μ$ m. (e) On micropatterns with different numbers of microdomains, rat mesenchymal stem cells on single-cell patterns exhibited less significant differentiation than paired or aggregated cells.¹⁰⁸ Scale bar, 50 $μ$ $μ$ m. (f) Limited adhesion on small micropatterns promoted fusion of nucleoli, alongside a reduction in nuclear volume and condensation of heterochromatin.¹⁰⁹ Scale bar, 10 $μ$ $μ$ m. (g) Nuclear deformation leads to inner nuclear membrane unfolding, allowing for the deformation-dependent activation of cPLA2 signaling, and thus regulating myosin II activity to facilitate.¹¹¹

Micropatterning creates a specific extracellular environment, e.g., physical confinement, cell-cell contact, to induce cell differentiation.^104,105 Joo et al. designed reproducible laminin (LN)-polylysine cell culture substrates and induced the differentiation of neurons and astrocytes (Fig. 4(c)).¹⁰⁶ On similar stripe patterns, human mesenchymal stem cells (hMSCs) had an up-regulation of several hallmark markers associated to neurogenesis and myogenesis while osteogenic markers were specifically down-regulated or remained at their normal level (Fig. 4(d)).¹⁰⁷ Another study using micropatterns with different numbers of microdomains revealed that rat mesenchymal stem cells on single-cell patterns exhibited less significant differentiation than paired or aggregated cells. For those stem cells in contact, the extent of differentiation was fairly linearly related to the extent of contact characterized by coordination number (Fig. 4(e)).¹⁰⁸

Limited adhesion on small micropatterns also promotes the fusion of nucleoli, alongside a reduction in nuclear volume and condensation of heterochromatin. These changes in nucleolar architecture are mediated by altered chromatin biomechanics and depend on the integration of the nucleus with the actin cytoskeleton. Functionally, nucleolar remodeling regulates ribogenesis and protein synthesis, demonstrating that nucleolus is another key intracellular mechano-sensing element (Fig. 4(f)).¹⁰⁹ Since nucleus morphology is affected by 2D micropatterning, it is easy to figure out that 3D spatial confinement could also regulate nucleus and gene expression. For instance, it has been found to facilitate cell migration and mesenchymal-to-amoeboid transition through variation in actomyosin activity.¹¹⁰ Recently, it was revealed by two independent research groups that the nucleus can act as an elastic deformation gauge allowing the cell to measure the deformation of cell shape by spatially 3D confinement of the physical microenvironment.^111,112 Specifically, nuclear shape deformation leads to inner nuclear membrane unfolding, allowing for the deformation-dependent activation of cPLA2 signaling, and thus regulates myosin II activity to facilitate cell migration (Fig. 4(g)).¹¹¹ Therefore, the nucleus can act as a sensor that adapts to the extracellular environment and regulates cell phenotype. Spatial confinement combining 2D and 3D micropatterning may be a more effective approach in the regulation of nucleus morphology, gene expression, as well as cell fate. Considering cells residing on the surface of mucosa or lumen interior, 2D spatial confinement may simulate the in vivo environment for them. For those types of cells tightly in contact with other cells from all directions, 3D spatial confinement should be the proper approach. Furthermore, it still remains to clarify whether there is an underlying difference between 2D and 3D confinement on nucleus and gene expression.

3.4. Micropatterning-based intercellular communication assay

Intercellular communication allows cells to share information and facilitate various functions in multicellular organisms.^113,114In vivo, some cells contact tightly with other cells (e.g., epithelial cells), while other cells are distributed discretely in space (e.g., macrophages and neutrophils), resulting in a diversity of intercellular communication, such as paracrine,^115,116,117 gap junction,^118,119,120 tunneling nanotubes.^121,122,123 On traditional substrates, cells spread and migrate randomly and disorderly, which brings inconvenience to intercellular communication assay. Therefore, micropatterning technique, which is capable of arranging cells as needed, provides a unique opportunity to investigate intercellular communication in a controllable and quantitative manner.

Intercellular calcium waves (ICWs), as a principal form of intercellular communication, are mediated by multiple pathways.^{124,125,126,127,128} For tightly-contacted multicellular system, lane patterns are widely used to quantify contact-dependent ICW. Specifically, ICWs were observed in HeLa cells expressing Cx43gap junction channels (Fig. 5(a)).¹²⁹ Besides, using a large number of lanes with widths ranging from 20mm to 110mm, there is no clear link between pattern width and calcium propagation rate for the human ESC-derived cardiomyocytes cells (Fig. 5(b)).¹³⁰

Fig. 5. Micropatterning-based intercellular communication assay. (a) Intercellular Ca $^{2 +}$ $^{2 +}$ waves in HeLa cells expressing Cx43gap junction channels.¹²⁹ (b) Intercellular Ca $^{2 +}$ $^{2 +}$ waves propagate in the lane-shaped collective human ESC-derived cardiomyocytes cells.¹³⁰ (c) In microglial cells arranged in concentric circles, ICWs propagated on account of the regenerative transmitter release from the relay station cells located in the propagating path.¹³¹ (d) On dumbbell-shaped micropatterns, Ca $^{2 +}$ $^{2 +}$ signal transferred over the intercellular bridges mediated by both passive Ca $^{2 +}$ $^{2 +}$ diffusion and IP₃-mediated endogenous Ca $^{2 +}$ $^{2 +}$ response.⁸⁹ Scale bar, 10 $μ$ $μ$ m. (e) Ca $^{2 +}$ $^{2 +}$ response in micropatterned osteoblastic networks mediated by gap junctions.¹³²

Applying spatial-discrete patterns, e.g., concentric circles, Xing et al. revealed that in microglial cells, ICWs propagated on account of the regenerative transmitter release from the relay station cells located in the propagating path (Fig. 5(c)).¹³¹ Further on, they designed dumbbell-shaped micropatterns to resolve spatiotemporal characteristics of ICW signal transfer over the intercellular bridges, a plasma continuity formed between the two daughter cells.⁸⁹ It was revealed that both passive Ca $^{2 +}$ $^{2 +}$ diffusion and IP₃-mediated endogenous Ca $^{2 +}$ $^{2 +}$ response contribute to the ICW propagation between intercellular-bridge-connected cells (Fig. 5(d)).⁸⁹ Similarly, using micropatterned assemblies, osteocytic networks showed more sensitive and dynamic than osteoblastic networks based on their Ca $^{2 +}$ $^{2 +}$ response mediated by gap junctions, especially under low-level physiological-related fluid shear stress stimulations (Fig. 5(e)).^132,133 Taken together, different micropatterns, e.g., discrete patterns and interconnected micropatterns, would work for distinct types of intercellular communication.

4. Outlook

Since the birth of micropatterning, this advanced technique has presented significant contributions to biological and biomedical researches, especially in the field of cell biology. Here, we summarized current mainstream methods, i.e., photolithographic micropatterning and soft lithography micropatterning techniques, for single-cell level manipulation, as well as their applications in cell biology research, including cytoskeleton, cell migration, nucleus and gene expression, and intercellular communication. Besides, 2D micropatterning offers a platform to manipulate the behavior of collective cells. However, challenges still exist in developing easy-to-use and hypotoxicity craft. More imaginative and diverse micro-patterns need to be designed for complex cell biology problems due to the imperfect simulation of the physiological environment. We hope that, with the progress of various micropatterning techniques, more mysteries of life can be observed and resolved.

Acknowledgments

Xuehe Ma, Haimei Zhang and Shiyu Deng contributed equally to this paper. This work was supported by the National Natural Science Foundation of China (Nos. 12174208, 32227802), National Key Research and Development Program of China (No. 2022YFC3400600), Guangdong Major Project of Basic and Applied Basic Research (No. 2020B0301030009), China Postdoctoral Science Foundation (No. 2020 M680032), Fundamental Research Funds for the Central Universities (Nos. 2122021337, 2122021405) and the 111 Project (No. B23045).

Conflicts of Interest

The authors declare that there are no conflicts of interest relevant to this article.

References

1. C. T. Leung, J. S. Brugge , “Outgrowth of single oncogene-expressing cells from suppressive epithelial environments,” Nature 482(7385), 410–413 (2012). Crossref, Web of Science, Google Scholar
2. S. L. Schuster, F. J. Segerer, F. A. Gegenfurtner et al., “Contractility as a global regulator of cellular morphology, velocity, and directionality in low-adhesive fibrillary micro-environments,” Biomaterials 102, 137–147 (2016). Crossref, Web of Science, Google Scholar
3. L. Shang, F. Ye, M. Li et al., “Spatial confinement toward creating artificial living systems,” Chem. Soc. Rev. 51(10), 4075–4093 (2022). Crossref, Web of Science, Google Scholar
4. M. Ahmed, C. Ffrench-Constant , “Extracellular matrix regulation of stem cell behavior,” Curr. Stem Cell Rep. 2, 197–206 (2016). Crossref, Google Scholar
5. T. Wang, S. S. Nanda, G. C. Papaefthymiou et al., “Mechanophysical cues in extracellular matrix regulation of cell behavior,” ChemBioChem 21(9), 1254–1264 (2020). Crossref, Web of Science, Google Scholar
6. M. C. Cramer, S. F. Badylak , “Extracellular matrix-based biomaterials and their influence upon cell behavior,” Ann. Biomed. Eng. 48, 2132–2153 (2020). Crossref, Web of Science, Google Scholar
7. C. Walker, E. Mojares, A. del Río Hernández , “Role of extracellular matrix in development and cancer progression,” Int. J. Mol. Sci. 19(10), 3028 (2018). Crossref, Web of Science, Google Scholar
8. S. A. Gudipaty, J. Rosenblatt , “Epithelial cell extrusion: Pathways and pathologies,” Sem. Cell Develop. Biol. 67, 132–140 (2017). Crossref, Web of Science, Google Scholar
9. S. Okuda, K. Fujimoto , “A mechanical instability in planar epithelial monolayers leads to cell extrusion,” Biophys. J. 118(10), 2549–2560 (2020). Crossref, Web of Science, Google Scholar
10. Y. Yang, K. Wang, X. Gu et al., “Biophysical regulation of cell behavior—Cross talk between substrate stiffness and nanotopography,” Engineering 3(1), 36–54 (2017). Crossref, Web of Science, Google Scholar
11. R. G. Wells , “The role of matrix stiffness in regulating cell behavior,” Hepatology 47(4), 1394–1400 (2008). Crossref, Web of Science, Google Scholar
12. N. D. Leipzig, M. S. Shoichet , “The effect of substrate stiffness on adult neural stem cell behavior,” Biomaterials 30(36), 6867–6878 (2009). Crossref, Web of Science, Google Scholar
13. K. Bera, A. Kiepas, I. Godet et al., “Extracellular fluid viscosity enhances cell migration and cancer dissemination,” Nature 611(7935), 365–373 (2022). Crossref, Web of Science, Google Scholar
14. J. Gonzalez-Molina, X. Zhang, M. Borghesan et al., “Extracellular fluid viscosity enhances liver cancer cell mechanosensing and migration,” Biomaterials 177, 113–124 (2018). Crossref, Web of Science, Google Scholar
15. M. Pittman, E. Iu, K. Li et al., “Membrane ruffling is a mechanosensor of extracellular fluid viscosity,” Nat. Phys. 18(9), 1112–1121 (2022). Crossref, Web of Science, Google Scholar
16. A. Kumar, J. K. Placone, A. J. Engler , “Understanding the extracellular forces that determine cell fate and maintenance,” Development 144(23), 4261–4270 (2017). Crossref, Web of Science, Google Scholar
17. K. Goodwin, E. E. Lostchuck, K. M. L. Cramb et al., “Cell–cell and cell–extracellular matrix adhesions cooperate to organize actomyosin networks and maintain force transmission during dorsal closure,” Mol. Biol. Cell 28(10), 1301–1310 (2017). Crossref, Web of Science, Google Scholar
18. I. A. Janson, A. J. Putnam , “Extracellular matrix elasticity and topography: Material-based cues that affect cell function via conserved mechanisms,” J. Biomed. Mater. Res. Part A 103(3), 1246–1258 (2015). Crossref, Web of Science, Google Scholar
19. K. H. Nakayama, L. Hou, N. F. Huang , “Role of extracellular matrix signaling cues in modulating cell fate commitment for cardiovascular tissue engineering,” Adv. Healthcare Mater. 3(5), 628–641 (2014). Crossref, Web of Science, Google Scholar
20. H. J. Lee, N. Li, S. M. Evans et al., “Biomechanical force in blood development: Extrinsic physical cues drive pro-hematopoietic signaling,” Differentiation 86(3), 92–103 (2013). Crossref, Web of Science, Google Scholar
21. Y. Li, M. Chen, J. Hu et al., “Volumetric compression induces intracellular crowding to control intestinal organoid growth via Wnt/ $β$ $β$ -catenin signaling,” Cell Stem Cell 28(1), 63–78.e7 (2021). Crossref, Web of Science, Google Scholar
22. E. Gruber, C. Heyward, J. Cameron et al., “Toll-like receptor signaling in macrophages is regulated by extracellular substrate stiffness and Rho-associated coiled-coil kinase (ROCK1/2),” Int. Immunol. 30(6), 267–278 (2018). Crossref, Web of Science, Google Scholar
23. A. P. Quist, S. Oscarsson , “Micropatterned surfaces: Techniques and applications in cell biology,” Exp. Opin. Drug Discov. 5(6), 569–581 (2010). Crossref, Web of Science, Google Scholar
24. G. Blin , “Quantitative developmental biology in vitro using micropatterning,” Development 148(15), dev186387 (2021). Crossref, Web of Science, Google Scholar
25. R. Link, K. Weißenbruch, M. Tanaka et al., “Cell shape and forces in elastic and structured environments: From single cells to organoids,” Adv. Funct. Mater. 2302145 (2023). Crossref, Web of Science, Google Scholar
26. I. Batalov, Q. Jallerat, S. Kim et al., “Engineering aligned human cardiac muscle using developmentally inspired fibronectin micropatterns,” Sci. Rep. 11(1), 1–14 (2021). Crossref, Web of Science, Google Scholar
27. N. R. M. Beijer, Z. M. Nauryzgaliyeva, E. M. Arteaga et al., “Dynamic adaptation of mesenchymal stem cell physiology upon exposure to surface micropatterns,” Sci. Rep. 9(1), 1–14 (2019). Crossref, Web of Science, Google Scholar
28. S. Bang, K. S. Hwang, S. Jeong et al., “Engineered neural circuits for modeling brain physiology and neuropathology,” Acta Biomaterialia 132, 379–400 (2021). Crossref, Web of Science, Google Scholar
29. E. Cimetta, S. Pizzato, S. Bollini et al., “Production of arrays of cardiac and skeletal muscle myofibers by micropatterning techniques on a soft substrate,” Biomed. Microdev. 11, 389–400 (2009). Crossref, Web of Science, Google Scholar
30. S. Kobel, M. P. Lutolf , “High-throughput methods to define complex stem cell niches,” Biotechniques 48(4), ix–xxii (2010). Crossref, Web of Science, Google Scholar
31. C. Moraes, G. H. Wang, Y. Sun et al., “A microfabricated platform for high-throughput unconfined compression of micropatterned biomaterial arrays,” Biomaterials 31(3), 577–584 (2010). Crossref, Web of Science, Google Scholar
32. U. Tuvshindorj, V. Trouillet, A. Vasilevich et al., “The galapagos chip platform for high-throughput screening of cell adhesive chemical micropatterns,” Small 18(10), 2105704 (2022). Crossref, Web of Science, Google Scholar
33. P. Zapata, J. Su, A. J. García et al., “Quantitative high-throughput screening of osteoblast attachment, spreading, and proliferation on demixed polymer blend micropatterns,” Biomacromolecules 8(6), 1907–1917 (2007). Crossref, Web of Science, Google Scholar
34. Q. Tseng, I. Wang, E. Duchemin-Pelletier et al., “A new micropatterning method of soft substrates reveals that different tumorigenic signals can promote or reduce cell contraction levels,” Lab on a Chip 11(13), 2231–2240 (2011). Crossref, Web of Science, Google Scholar
35. W. Yang, Z. Wang, T. Yu et al., “Recent advance in cell patterning techniques: Approaches, applications and future prospects,” Sens. Actuat. A: Phys. 333, 113229 (2022). Crossref, Web of Science, Google Scholar
36. D. Falconnet, G. Csucs, H. M. Grandin et al., “Surface engineering approaches to micropattern surfaces for cell-based assays,” Biomaterials 27(16), 3044–3063 (2006). Crossref, Web of Science, Google Scholar
37. G. Lee, S. B. Han, D. H. Kim , “Cell-ECM contact-guided intracellular polarization is mediated via lamin A/C dependent nucleus-cytoskeletal connection,” Biomaterials 268, 120548 (2021). Crossref, Web of Science, Google Scholar
38. K. M. Yamada, M. Sixt , “Mechanisms of 3D cell migration,” Nat. Rev. Mol. Cell Biol. 20(12), 738–752 (2019). Crossref, Web of Science, Google Scholar
39. L. I. U. Wen-Wen, C. Zhen-Ling, X. Y. Jiang , “Methods for cell micropatterning on two-dimensional surfaces and their applications in biology,” Chin. J. Anal. Chem. 37(7), 943–949 (2009). Crossref, Web of Science, Google Scholar
40. E. D’Arcangelo, A. P. McGuigan , “Micropatterning strategies to engineer controlled cell and tissue architecture in vitro,” Biotechniques 58(1), 13–23 (2015). Crossref, Web of Science, Google Scholar
41. F. Brétagnol, O. Kylián, M. Hasiwa et al., “Micro-patterned surfaces based on plasma modification of PEO-like coating for biological applications,” Sens. Actuat. B: Chem. 123(1), 283–292 (2007). Crossref, Web of Science, Google Scholar
42. H. Wu, T. W. Odom, G. M. Whitesides , “Reduction photolithography using microlens arrays: applications in gray scale photolithography,” Anal. Chem. 74(14), 3267–3273 (2002). Crossref, Web of Science, Google Scholar
43. C. Y. Wu, H. Hsieh, Y. C. Lee , “Contact photolithography at sub-micrometer scale using a soft photomask,” Micromachines 10(8), 547 (2019). Crossref, Web of Science, Google Scholar
44. D. H. Kang, H. N. Kim, P. Kim et al., “Poly (ethylene glycol)(PEG) microwells in microfluidics: Fabrication methods and applications,” Biochip J. 8, 241–253 (2014). Crossref, Web of Science, Google Scholar
45. L. Xu, J. Yang, B. Xue et al., “Molecular insights for the biological interactions between polyethylene glycol and cells,” Biomaterials 147, 1–13 (2017). Crossref, Web of Science, Google Scholar
46. J. Zhu , “Bioactive modification of poly (ethylene glycol) hydrogels for tissue engineering,” Biomaterials 31(17), 4639–4656 (2010). Crossref, Web of Science, Google Scholar
47. P. Vermette, L. Meagher , “Interactions of phospholipid-and poly (ethylene glycol)-modified surfaces with biological systems: relation to physico-chemical properties and mechanisms,” Colloids Surfaces B: Biointerfaces 28(2–3), 153–198 (2003). Crossref, Web of Science, Google Scholar
48. T. W. Herling, A. Levin, K. L. Saar et al., “Microfluidic approaches for probing amyloid assembly and behaviour,” Lab on a Chip 18(7), 999–1016 (2018). Crossref, Web of Science, Google Scholar
49. A. Jaiswal, C. K. Rastogi, S. Rani et al., “Two decades of two-photon lithography: Materials science perspective for additive manufacturing of 2D/3D nano-microstructures,” Iscience 26, 106374 (2023). Crossref, Web of Science, Google Scholar
50. G. Manessis, A. I. Gelasakis, I. Bossis , “Point-of-care diagnostics for farm animal diseases: From biosensors to integrated lab-on-chip devices,” Biosensors 12(7), 455 (2022). Crossref, Google Scholar
51. D. Qin, Y. Xia, G. M. Whitesides , “Soft lithography for micro-and nanoscale patterning,” Nat. Protocols 5(3), 491–503 (2010). Crossref, Web of Science, Google Scholar
52. E. Ferrari, F. Nebuloni, M. Rasponi et al., “Photo and soft lithography for organ-on-chip applications,” Organ-on-a-Chip: Meth. Protocols 2373, 1–19 (2022). Crossref, Google Scholar
53. Y. Xia, G. M. Whitesides , “Soft lithography,” Ann. Rev. Mater. Sci. 28(1), 153–184 (1998). Crossref, Web of Science, Google Scholar
54. G. M. Whitesides, E. Ostuni, S. Takayama et al., “Soft lithography in biology and biochemistry,” Ann. Rev. Biomed. Eng. 3(1), 335–373 (2001). Crossref, Web of Science, Google Scholar
55. R. S. Kane, S. Takayama, E. Ostuni et al., “Patterning proteins and cells using soft lithography,” Biomaterials 20(23–24), 2363–2376 (1999). Crossref, Web of Science, Google Scholar
56. M. Abdelgawad, M. W. L. Watson, E. W. K. Young et al., “Soft lithography: masters on demand,” Lab on a Chip 8(8), 1379–1385 (2008). Crossref, Web of Science, Google Scholar
57. Y. Lin, C. Gao, R. Zhou et al., “Soft lithography based on photolithography and two-photon polymerization,” Microfluid. Nanofluid. 22, 1–11 (2018). Crossref, Web of Science, Google Scholar
58. N. Mohd Fuad, M. Carve, J. Kaslin et al., “Characterization of 3D-printed moulds for soft lithography of millifluidic devices,” Micromachines 9, 116 (2018). Crossref, Web of Science, Google Scholar
59. L. Chang, R. D. Goldman , “Intermediate filaments mediate cytoskeletal crosstalk,” Nat. Rev. Mol. Cell Biol. 5(8), 601–613 (2004). Crossref, Web of Science, Google Scholar
60. H. Y. G. Lim, N. Plachta , “Cytoskeletal control of early mammalian development,” Nat. Rev. Mol. Cell Biol. 22(8), 548–562 (2021). Crossref, Web of Science, Google Scholar
61. R. Pinto-Costa, M. M. Sousa , “Microtubules, actin and cytolinkers: How to connect cytoskeletons in the neuronal growth cone,” Neurosci. Lett. 747, 135693 (2021). Crossref, Web of Science, Google Scholar
62. D. D. Tang, B. D. Gerlach , “The roles and regulation of the actin cytoskeleton, intermediate filaments and microtubules in smooth muscle cell migration,” Resp. Res. 18, 1–12 (2017). Web of Science, Google Scholar
63. W. Ning, Y. Yu, H. Xu et al., “The CAMSAP3-ACF7 complex couples noncentrosomal microtubules with actin filaments to coordinate their dynamics,” Develop. Cell 39(1), 61–74 (2016). Crossref, Web of Science, Google Scholar
64. R. Dominguez, K. C. Holmes , “Actin structure and function,” Ann. Rev. Biophys. 40, 169–186 (2011). Crossref, Web of Science, Google Scholar
65. T. Svitkina , “The actin cytoskeleton and actin-based motility,” Cold Spring Harbor Persp. Biol. 10(1), a018267 (2018). Web of Science, Google Scholar
66. L. Blanchoin, R. Boujemaa-Paterski, C. Sykes et al., “Actin dynamics, architecture, and mechanics in cell motility,” Physiol. Rev. 94(1), 235–263 (2014). Crossref, Web of Science, Google Scholar
67. L. Huang, Y. Peng, X. Tao et al., “Microtubule organization is essential for maintaining cellular morphology and function,” Oxid. Med. Cell. Longevity 2022, 1623181 (2022). Web of Science, Google Scholar
68. A. Akhmanova, L. C. Kapitein , “Mechanisms of microtubule organization in differentiated animal cells,” Nat. Rev. Mol. Cell Biol. 23(8), 541–558 (2022). Crossref, Web of Science, Google Scholar
69. S. A. Eldirany, I. B. Lomakin, M. Ho et al., “Recent insight into intermediate filament structure,” Curr. Opin. Cell Biol. 68, 132–143 (2021). Crossref, Web of Science, Google Scholar
70. S. Roy, A. Kapoor, F. Zhu et al., “Artemisinins target the intermediate filament protein vimentin for human cytomegalovirus inhibition,” J. Biol. Chem. 295(44), 15013–15028 (2020). Crossref, Web of Science, Google Scholar
71. D. T. Burnette, S. Manley, P. Sengupta et al., “A role for actin arcs in the leading-edge advance of migrating cells,” Nat. Cell Biol. 13(4), 371–382 (2011). Crossref, Web of Science, Google Scholar
72. M. Mavrakis, M. A. Juanes , “The compass to follow: Focal adhesion turnover,” Curr. Opin. Cell Biol. 80, 102152 (2023). Crossref, Web of Science, Google Scholar
73. S. Linder, P. Cervero, R. Eddy et al., “Mechanisms and roles of podosomes and invadopodia,” Nat. Rev. Mol. Cell Biol. 24(2), 86–106 (2023). Crossref, Web of Science, Google Scholar
74. Y. H. Tee, T. Shemesh, V. Thiagarajan et al., “Cellular chirality arising from the self-organization of the actin cytoskeleton,” Nat. Cell Biol. 17(4), 445–457 (2015). Crossref, Web of Science, Google Scholar
75. Y. H. Tee, W. J. Goh, X. Yong et al., “Actin polymerisation and crosslinking drive left-right asymmetry in single cell and cell collectives,” Nat. Commun. 14(1), 776 (2023). Crossref, Web of Science, Google Scholar
76. F. Xing, H. Zhang, M. Li et al., “Regulation of actin cytoskeleton via photolithographic micropatterning,” J. Innov. Opt. Health Sci. 16(2), 2244005 (2023). Link, Web of Science, Google Scholar
77. E. Kassianidou, D. Probst, J. Jäger et al., “Extracellular matrix geometry and initial adhesive position determine stress fiber network organization during cell spreading,” Cell Rep. 27(6), 1897–1909.e4 (2019). Crossref, Web of Science, Google Scholar
78. K. Weißenbruch, J. Grewe, M. Hippler et al., “Distinct roles of nonmuscle myosin II isoforms for establishing tension and elasticity during cell morphodynamics,” Elife 10, e71888 (2021). Crossref, Web of Science, Google Scholar
79. A. J. Jimenez, A. Schaeffer, C. De Pascalis et al., “Acto-myosin network geometry defines centrosome position,” Curr. Biol. 31(6), 1206–1220.e5 (2021). Crossref, Web of Science, Google Scholar
80. M. Burute, M. Prioux, G. Blin et al., “Polarity reversal by centrosome repositioning primes cell scattering during epithelial-to-mesenchymal transition,” Develop. Cell 40(2), 168–184 (2017). Crossref, Web of Science, Google Scholar
81. S. H. Shabbir, M. M. Cleland, R. D. Goldman et al., “Geometric control of vimentin intermediate filaments,” Biomaterials 35(5), 1359–1366 (2014). Crossref, Web of Science, Google Scholar
82. Y. Jiu, J. Lehtimäki, S. Tojkander et al., “Bidirectional interplay between vimentin intermediate filaments and contractile actin stress fibers,” Cell Rep. 11(10), 1511–1518 (2015). Crossref, Web of Science, Google Scholar
83. S. Park, W. H. Jung, M. Pittman et al., “The effects of stiffness, fluid viscosity, and geometry of microenvironment in homeostasis, aging, and diseases: A brief review,” J. Biomech. Eng. 142(10), 100804 (2020). Crossref, Web of Science, Google Scholar
84. E. J. Campbell, P. Bagchi , “A computational study of amoeboid motility in 3D: The role of extracellular matrix geometry, cell deformability, and cell–matrix adhesion,” Biomech. Model. Mechanobiol. 20, 167–191 (2021). Crossref, Web of Science, Google Scholar
85. D. Mohammed, G. Charras, E. Vercruysse et al., “Substrate area confinement is a key determinant of cell velocity in collective migration,” Nat. Phys. 15(8), 858–866 (2019). Crossref, Web of Science, Google Scholar
86. X. Yao, J. Ding , “Effects of microstripe geometry on guided cell migration,” ACS Appl. Mater. Interfaces 12(25), 27971–27983 (2020). Crossref, Web of Science, Google Scholar
87. C. Schreiber, B. Amiri, J. C. J. Heyn et al., “On the adhesion–velocity relation and length adaptation of motile cells on stepped fibronectin lanes,” Proc. Natl. Acad. Sci. 118(4), e2009959118 (2021). Crossref, Web of Science, Google Scholar
88. D. B. Brückner, A. Fink, C. Schreiber et al., “Stochastic nonlinear dynamics of confined cell migration in two-state systems,” Nat. Phys. 15(6), 595–601 (2019). Crossref, Web of Science, Google Scholar
89. F. Xing, S. Qu, J. Liu et al., “Intercellular bridge mediates Ca $^{2 +}$ $^{2 +}$ signals between micropatterned cells via IP3 and Ca $^{2 +}$ $^{2 +}$ diffusion,” Biophys. J. 118(5), 1196–1204 (2020). Crossref, Web of Science, Google Scholar
90. X. Jiang, D. A. Bruzewicz, A. P. Wong et al., “Directing cell migration with asymmetric micropatterns,” Proc. Natl. Acad. Sci. 102(4), 975–978 (2005). Crossref, Web of Science, Google Scholar
91. W. Zheng, Y. Xie, K. Sun et al., “An on-chip study on the influence of geometrical confinement and chemical gradient on cell polarity,” Biomicrofluidics 8(5), 052010 (2014). Crossref, Web of Science, Google Scholar
92. S. L. Vecchio, R. Thiagarajan, D. Caballero et al., “Collective dynamics of focal adhesions regulate direction of cell motion,” Cell Syst. 10(6), 535–542.e4 (2020). Crossref, Web of Science, Google Scholar
93. D. Garbett, A. Bisaria, C. Yang et al., “T-Plastin reinforces membrane protrusions to bridge matrix gaps during cell migration,” Nat. Commun. 11(1), 4818 (2020). Crossref, Web of Science, Google Scholar
94. F. Xing, H. Dong, J. Yang et al., “Mesenchymal migration on adhesive–nonadhesive alternate surfaces in macrophages,” Adv. Sci. 10, 2301337 (2023). Crossref, Google Scholar
95. F. J. Segerer, F. Thüroff, A. P. Alberola et al., “Emergence and persistence of collective cell migration on small circular micropatterns,” Phys. Rev. Lett. 114(22), 228102 (2015). Crossref, Web of Science, Google Scholar
96. S. Jain, V. M. L. Cachoux, G. H. N. S. Narayana et al., “The role of single-cell mechanical behaviour and polarity in driving collective cell migration,” Nat. Phys. 16(7), 802–809 (2020). Crossref, Web of Science, Google Scholar
97. T. Chen, A. Callan-Jones, E. Fedorov et al., “Large-scale curvature sensing by directional actin flow drives cellular migration mode switching,” Nat. Phys. 15(4), 393–402 (2019). Crossref, Web of Science, Google Scholar
98. M. A. P. Bray, W. J. Adams, N. A. Geisse et al., “Nuclear morphology and deformation in engineered cardiac myocytes and tissues,” Biomaterials 31(19), 5143–5150 (2010). Crossref, Web of Science, Google Scholar
99. M. Versaevel, M. Riaz, T. Grevesse et al., “Cell confinement: Putting the squeeze on the nucleus,” Soft Matter 9(29), 6665–6676 (2013). Crossref, Web of Science, Google Scholar
100. M. Ochsner, M. Textor, V. Vogel et al., “Dimensionality controls cytoskeleton assembly and metabolism of fibroblast cells in response to rigidity and shape,” PloS One 5(3), e9445 (2010). Crossref, Web of Science, Google Scholar
101. C. S. Chen, M. Mrksich, S. Huang et al., “Geometric control of cell life and death,” Science 276(5317), 1425–1428 (1997). Crossref, Web of Science, Google Scholar
102. F. Y. McWhorter, T. Wang, P. Nguyen et al., “Modulation of macrophage phenotype by cell shape,” Proc. Natl. Acad. Sci. 110(43), 17253–17258 (2013). Crossref, Web of Science, Google Scholar
103. N. Jain, V. Vogel , “Spatial confinement downsizes the inflammatory response of macrophages,” Nat. Mater. 17(12), 1134–1144 (2018). Crossref, Web of Science, Google Scholar
104. M. P. Prabhakaran, E. Vatankhah, D. Kai et al., “Methods for nano/micropatterning of substrates: Toward stem cells differentiation,” Int. J. Polym. Mater. Polym. Biomater. 64(7), 338–353 (2015). Crossref, Web of Science, Google Scholar
105. T. Nakamoto, X. Wang, N. Kawazoe et al., “Influence of micropattern width on differentiation of human mesenchymal stem cells to vascular smooth muscle cells,” Colloids Surfaces B: Biointerfaces 122, 316–323 (2014). Crossref, Web of Science, Google Scholar
106. S. Joo, J. Yeon Kim, E. Lee et al., “Effects of ECM protein micropatterns on the migration and differentiation of adult neural stem cells,” Sci. Rep. 5(1), 13043 (2015). Crossref, Web of Science, Google Scholar
107. C. Y. Tay, H. Yu, M. Pal et al., “Micropatterned matrix directs differentiation of human mesenchymal stem cells towards myocardial lineage,” Exp. Cell Res. 316(7), 1159–1168 (2010). Crossref, Web of Science, Google Scholar
108. J. Tang, R. Peng, J. Ding , “The regulation of stem cell differentiation by cell-cell contact on micropatterned material surfaces,” Biomaterials 31(9), 2470–2476 (2010). Crossref, Web of Science, Google Scholar
109. O. J. Pundel, L. M. Blowes, J. T. Connelly , “Extracellular adhesive cues physically define nucleolar structure and function,” Adv. Sci. 9(10), 2105545 (2022). Crossref, Google Scholar
110. Y. J. Liu, M. Le Berre, F. Lautenschlaeger et al., “Confinement and low adhesion induce fast amoeboid migration of slow mesenchymal cells,” Cell 160(4), 659–672 (2015). Crossref, Web of Science, Google Scholar
111. A. J. Lomakin, C. J. Cattin, D. Cuvelier et al., “The nucleus acts as a ruler tailoring cell responses to spatial constraints,” Science 370(6514), eaba2894 (2020). Crossref, Web of Science, Google Scholar
112. V. Venturini, F. Pezzano, F. Catala Castro et al., “The nucleus measures shape changes for cellular proprioception to control dynamic cell behavior,” Science 370(6514), eaba2644 (2020). Crossref, Web of Science, Google Scholar
113. M. Mittelbrunn, F. Sánchez-Madrid , “Intercellular communication: Diverse structures for exchange of genetic information,” Nat. Rev. Mol. Cell Biol. 13(5), 328–335 (2012). Crossref, Web of Science, Google Scholar
114. S. F. Mause, C. Weber , “Microparticles: Protagonists of a novel communication network for intercellular information exchange,” Circul. Res. 107(9), 1047–1057 (2010). Crossref, Web of Science, Google Scholar
115. X. W. Ng, Y. H. Chung, D. W. Piston , “Intercellular communication in the islet of langerhans in health and disease,” Compreh. Physiol. 11(3), 2191 (2021). Crossref, Web of Science, Google Scholar
116. R. Isaac, F. C. G. Reis, W. Ying et al., “Exosomes as mediators of intercellular crosstalk in metabolism,” Cell Metabol. 33(9), 1744–1762 (2021). Crossref, Web of Science, Google Scholar
117. G. Camussi, M. C. Deregibus, V. Cantaluppi , “Role of stem-cell-derived microvesicles in the paracrine action of stem cells,” Biochem. Soc. Trans. 41(1), 283–287 (2013). Crossref, Web of Science, Google Scholar
118. M. Z. Totland, N. L. Rasmussen, L. M. Knudsen et al., “Regulation of gap junction intercellular communication by connexin ubiquitination: Physiological and pathophysiological implications,” Cell. Mol. Life Sci. 77, 573–591 (2020). Crossref, Web of Science, Google Scholar
119. H. T. Le, W. C. Sin, S. Lozinsky et al., “Gap junction intercellular communication mediated by connexin43 in astrocytes is essential for their resistance to oxidative stress,” J. Biol. Chem. 289(3), 1345–1354 (2014). Crossref, Web of Science, Google Scholar
120. C. Georgikou, L. Yin, J. Gladkich et al., “Inhibition of miR30a-3p by sulforaphane enhances gap junction intercellular communication in pancreatic cancer,” Cancer Lett. 469, 238–245 (2020). Crossref, Web of Science, Google Scholar
121. C. Zurzolo , “Tunneling nanotubes: Reshaping connectivity,” Curr. Opin. Cell Biol. 71, 139–147 (2021). Crossref, Web of Science, Google Scholar
122. M. Guescini, G. Leo, S. Genedani et al., “Microvesicle and tunneling nanotube mediated intercellular transfer of g-protein coupled receptors in cell cultures,” Exp. Cell Res. 318(5), 603–613 (2012). Crossref, Web of Science, Google Scholar
123. S. Kimura, K. Hase, H. Ohno , “Tunneling nanotubes: Emerging view of their molecular components and formation mechanisms,” Exp. Cell Res. 318(14), 1699–1706 (2012). Crossref, Web of Science, Google Scholar
124. L. Leybaert, M. J. Sanderson , “Intercellular Ca $^{2 +}$ $^{2 +}$ waves: mechanisms and function,” Physiol. Rev. 92(3), 1359–1392 (2012). Crossref, Web of Science, Google Scholar
125. C. M. Wang, C. Ploia, F. Anselmi et al., “Adenosine triphosphate acts as a paracrine signaling molecule to reduce the motility of T cells,” The EMBO J. 33(12), 1354–1364 (2014). Crossref, Web of Science, Google Scholar
126. C. B. Tabi, I. Maïna, A. Mohamadou et al., “Long-range intercellular Ca2+ wave patterns,” Physica A: Statist. Mech. Appl. 435, 1–14 (2015). Crossref, Web of Science, Google Scholar
127. S. E. Stasiak, R. R. Jamieson, J. Bouffard et al., “Intercellular communication controls agonist-induced calcium oscillations independently of gap junctions in smooth muscle cells,” Sci. Adv. 6(32), eaba1149 (2020). Crossref, Web of Science, Google Scholar
128. E. Decrock, M. Vinken, M. Bol et al., “Calcium and connexin-based intercellular communication, a deadly catch?,” Cell Calcium 50(3), 310–321 (2011). Crossref, Web of Science, Google Scholar
129. T. Nakano, Y. H. Hsu, W. C. Tang et al., “Microplatform for intercellular communication,” 2008 3rd IEEE Int. Conf. Nano/Micro Engineered and Molecular Systems, pp. 476–479, IEEE (2008). Crossref, Google Scholar
130. M. R. Salick, B. N. Napiwocki, J. Sha et al., “Micropattern width dependent sarcomere development in human ESC-derived cardiomyocytes,” Biomaterials 35(15), 4454–4464 (2014). Crossref, Web of Science, Google Scholar
131. F. Xing, P. Zhang, P. Jiang et al., “Spatiotemporal characteristics of intercellular calcium wave communication in micropatterned assemblies of single cells,” ACS Appl. Mater. Interfaces 10(3), 2937–2945 (2018). Crossref, Web of Science, Google Scholar
132. X. E. Guo, E. Takai, X. Jiang et al., “Intracellular calcium waves in bone cell networks under single cell nanoindentation,” Mol. Cell. Biomech. 3(3), 95 (2006). Google Scholar
133. X. L. Lu, B. Huo, V. Chiang et al., “Osteocytic network is more responsive in calcium signaling than osteoblastic network under fluid flow,” J. Bone Min. Res. 27(3), 563–574 (2012). Crossref, Web of Science, Google Scholar

Vol. 17, No. 01

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Received 26 August 2023

Revised 15 November 2023

Accepted 29 November 2023

Published: 24 January 2024

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