The main function of skeletal muscle is to produce contractile force (CF) (Vandenburgh et al., 2008; Shadrin et al., 2016), which is necessary for locomotion, respiration, and metabolic processes (Kaji et al., 2010; Qazi et al., 2015; Wang et al., 2019; Yusuf and Brand-Saberi, 2012). CF may be compromised during aging or due to major injuries or genetic mutations, such as in muscular dystrophies. Calcium (Ca²⁺) homeostasis, which is usually dysregulated in these conditions, has a profound impact on force production (Vallejo-Illarramendi et al., 2014; Al Tanoury et al., 2021). Muscular contraction is initiated by motoneurons, which release acetylcholine (ACh) upon activation by action potentials. ACh causes sarcolemmal depolarization and opening of calcium channels at the sarcolemma and the sarcoplasmic reticulum. Ca²⁺ diffuses throughout the sarcomere and initiates contraction by a cross-bridge cycle between actin and myosin (Widmaier and Raff, 2008; Fernández-Costa et al., 2021; Santoso et al., 2021).

For years, standard 2D cellular cultures and animal models have been used to understand muscle physiopathology and to develop novel treatments for muscle diseases. Despite the many discoveries regarding those models, a comparison with native human muscle suggests that major challenges remain in order to obtain a fully representative or trustworthy model (Horvath et al., 2016; Santoso et al., 2021; Wang et al., 2019). Characteristics such as size, drug diffusion, or immune response are difficult to match with the ones occurring in the human body (Fernández-Costa et al., 2021; Khodabukus, 2021). For instance, the muscular dystrophy X-linked mouse (mdx) is a well-accepted preclinical model for Duchenne Muscular Dystrophy (DMD) (Bersini et al., 2018; Lasa-Fernandez et al., 2020; Al Tanoury et al., 2021). However, mdx mice present substantial differences compared to Duchenne patients regarding muscle regeneration, Ca²⁺ handling, and life expectancy (Raymackers et al., 2003; Toral-Ojeda et al., 2018; Yoshioka et al., 2021b). Other DMD animal models (dogs or pigs) seem to better recapitulate the human disease, but they entail substantial drawbacks or challenges such as higher variability in disease progression, longer experimental times, and, obviously, much higher costs (Smith et al., 2016; Kornegay, 2017).

In vitro models, as an alternative to animal models, are already providing promising results in different applications such as disease modeling (Shimizu et al., 2017; Nesmith et al., 2016; Badu-Mensah et al., 2020; Fernández-Garibay et al., 2021), drug discovery (Vandenburgh, 2010; Ikeda et al., 2017; Afshar et al., 2020; Alave Reyes-Furrer et al., 2021), gene therapy (Powell et al., 1999; Vandenburgh et al., 1996), reconstructive surgery (Brady et al., 2008; Kim et al., 2018; Gilbert-Honick et al., 2020), or even as bio-actuators in biohybrid systems (Yamamoto et al., 2011; Cvetkovic et al., 2014; Gao et al., 2021). Indeed, the latest technological advances in tissue engineering such as 3D bioprinting or the development of novel biomaterials and culture platforms (Capel et al., 2019; Denes et al., 2019; Costantini et al., 2021; Gupta et al., 2021; Peper et al., 2021) are facilitating the development of better in vitro skeletal muscle constructs. To this end, researchers have recapitulated many native skeletal muscle features, including contraction (Madden et al., 2015; Urciuolo et al., 2020; Akiyama et al., 2021; Alave Reyes-Furrer et al., 2021), vascularization (van der Schaft et al., 2011; Bersini et al., 2018; Osaki et al., 2018a; Gilbert-Honick et al., 2018), mechanical tension (Okano and Matsuda, 1998; Vandenburgh and Karlisch, 1989), neuromuscular junction (NMJ) (Afshar Bakooshli et al., 2019; Yoshioka et al., 2020; Vila et al., 2021), and muscle stiffness (Urciuolo et al., 2020). These features, represented in Figure 1 (top), are considered key requirements for generating functional and mature skeletal muscle constructs.

Figure 1

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The current gold standard for the characterization of in vitro muscle models includes expression analysis of myogenic markers, determination of the fusion index, and evaluation of striation pattern and ACh receptor aggregation (Bajaj et al., 2011; Toral-Ojeda et al., 2016; Toral-Ojeda et al., 2018). Whereas this information is helpful for understanding the basic differentiation status of the culture, CF provides further physiological information about the degree of tissue maturation and functionality (Madden et al., 2015; Ikeda et al., 2016; Juhas et al., 2016; Nagashima et al., 2020). Indeed, CF evaluation has already established its usefulness as a primary outcome measure in drug screening platforms (Ikeda et al., 2017; Shimizu et al., 2017; Vandenburgh et al., 2009), analyzing the performance of different cellular sources (Nagashima et al., 2020; Shimizu et al., 2020), assessing the effect of scaffold composition (Boontheekul et al., 2007; Hinds et al., 2011), or even evaluating the influence of heat stress (Takagi et al., 2018), and media supplements on muscle development (Martin et al., 2017; Mills et al., 2019; Xu et al., 2019).

However, it is quite challenging to compare CF magnitudes obtained under different experimental conditions in which crucial key factors differ such as culture platforms (Anene-Nzelu et al., 2013; Khodabukus and Baar, 2016), culturing methods, cell lines, scaffold materials (Zhuang et al., 2020; Gilbert-Honick et al., 2020; Perez-Puyana et al., 2021) or even construct size. Consequently, there is a wide range of results from different research laboratories worldwide that are difficult to compare or interpret. This fact raises the need to define homogeneous units and to standardize the methodology for CF assessment in order to facilitate proper comparison among studies.

Several stimulation mechanisms may be used to trigger contraction of in vitro muscle constructs at specific paces: CF may be induced by chemical (Osaki et al., 2018b; Afshar et al., 2020; Yoshioka et al., 2020; Hofemeier et al., 2021), or physical stimuli (Figure 1, bottom). As for physical stimuli, they comprise electrical (Juhas and Bursac, 2014; Madden et al., 2015; Santoso et al., 2021), mechanical (Kim et al., 2019), and optical stimulation (Neal et al., 2015; Mills et al., 2019; Uchimura et al., 2021; Hofemeier et al., 2021).

Electrical stimulation is the most frequently used mechanism to induce CF in engineered muscles by mimicking motoneuron activity on these tissues (Cheng et al., 2014b; Rangarajan et al., 2014; Ikeda et al., 2016; Akiyama et al., 2021). It has also been used during muscle development to improve muscle size, sarcomere formation, Ca²⁺ transients, and CF production (Park et al., 2008; Langelaan et al., 2011; Khodabukus et al., 2019). Electrical stimulation causes depolarization of the muscle cell membrane, which triggers excitation-contraction coupling (Merrill et al., 2005; Uchimura et al., 2021). It is essential to optimize the electrical parameters for each experimental setup and culture stage, since inadequate stimulation can result in culture damage or untoward responses, including fatigue, electrochemical damage, or electroporation effect, leading to increased membrane permeability and impaired cell function (Nikolić et al., 2017; Khodabukus and Baar, 2012; Mills et al., 2019; Ruzgys et al., 2019; Nagamine et al., 2011; Khodabukus, 2021).

Optical stimulation or photo-stimulation entails the incorporation of light-sensitive proteins such as Channelrhodopsin-2 (ChR2) by gene delivery. ChR2 is a blue-light-sensitive opsin that triggers membrane depolarization and downstream signaling cascades (Mahmoudi et al., 2017). This approach allows for an accurate spatial and temporal control of the contractile activity in a non-invasive manner, with minimal side effects to the constructs (Bruegmann et al., 2010; Asano et al., 2012; Neal et al., 2015; Vila et al., 2021). The performance of this technique depends on efficient gene delivery into the cells (Zhang et al., 2006). In particular, gene transduction with lentiviral particles has been shown to effectively generate mature 2D and 3D muscle constructs with the capability to trigger contractions optogenetically (Osaki et al., 2018b; Hofemeier et al., 2021; Cheesbrough et al., 2022).

Mechanical stimulation aims to improve muscular development by evoking the stimuli given by the skeletal system during human embryogenesis (Powell et al., 2002; Moon et al., 2008; Candiani et al., 2010). Its mechanism is based on the generation of tensile stress on the tissue construct, which triggers cellular responses mediated by mechanotransduction pathways. Depending on the parameters used, mechanical stimulation may lead to tissue enhancement and rescue of atrophy, with increased myotube number, myofiber diameter, and fusion index (Vandenburgh et al., 1991; Aguilar-Agon et al., 2019; Kim et al., 2019). However, inadequate mechanical stimulation may also induce pathological changes in muscle constructs, such as poor tissue maturation as revealed by decreased sarcomeric proteins (Akimoto et al., 2001; Boonen et al., 2010).

Finally, chemical stimulation may induce contractility of the muscle construct by direct exposure to certain biochemical compounds such as KCl, caffeine, glutamic acid, and acetylcholine (Osaki et al., 2018b; Yoshioka et al., 2020; Hofemeier et al., 2021). In addition to contraction, chemical stimulation is commonly used to induce other cellular responses, including changes in Ca²⁺ fluxes and cell size (Madden et al., 2015; Shimizu et al., 2017).

Hereafter, the most relevant techniques used to determine and measure CF in engineered skeletal muscle tissues, either for 2D or 3D configurations, are described: (1) cantilever deflection, (2) post deflection, or (3) force transducers. We also discuss alternative or emerging technologies for CF assessment, and provide a detailed comparison of the three main platforms currently in use.

Cantilever deflection

This technique is based on the deflection of a cantilever beam, which is fixed to one end and free for vertical movement on the other. The cantilever bends proportionally to the CF, and the variation of the height of the free end, or deflection, is measured by a laser and photodetector or image analysis (Wilson et al., 2007; Wilson et al., 2010; Fujita et al., 2010; Sun et al., 2013). Schematics in Figure 3 depict the cantilever setup with its three main states: resting, right after attachment of myoblasts, which are at a pre-differentiation state; initial deflection, due to the passive force exerted by the differentiated myotubes; and increased bending, caused by the CF that myotubes exert under stimuli. This force results in contraction of the cantilever’s upper side and a decrease in its length, while the lower surface of the beam remains relaxed with no change in its length. The differential strain distribution along cantilever thickness causes an upwards bending. Since one end of the cantilever is fixed, the entire bending effect is transformed into an uplift of its free end. Thus, myoblast’s horizontal contraction is transformed into a vertical displacement of the cantilever end.

Figure 3

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A backward analysis relating the measured displacement and the compression state of the cantilever allows for CF calculation. This computation can be resolved using Stoney’s equation (Wilson et al., 2007; Smith et al., 2014; Santoso et al., 2021), Finite Elements Analysis (FEA) (Pirozzi et al., 2013), or analytical solutions for the curvature radius (Wilson et al., 2007; Nesmith et al., 2016; Al Tanoury et al., 2021). The first two methods are best suited for a silicon cantilever setup, and according to Pirozzi and colleagues, Stoney’s equation provides enough accuracy for most applications. The FEA analysis is more mechanically rigorous but also computationally more complex. In this case, a laser-photodetector system is used to measure the vertical displacements, which are relatively small. The third approach is most appropriate for a polymeric film-like substrate in which image analysis will provide a change in the radius of curvature of the film. These materials allow larger deformations of the substrates (almost rolling) than the stiffer cantilevers. In either case, a thorough mechanical characterization of the system (e.g. thickness, flexibility) is required in order to feed the previous models.

These calculations assume that myotubes and substrates behave as one continuous solid element rather than two independent ones. Thus, myotubes need to be perfectly attached to the substrate. Special care needs to be taken to identify potential inhomogeneities within the culture or misalignments from the deflection axis since any slight deviation could render significant differences in CF assessment (Wilson et al., 2010). Techniques such as microcontact printing have been proposed to address these challenges, providing alignment and direct cell adhesion to the cantilever by using Collagen I (Guo et al., 2020), DETA (Wilson et al., 2010), or fibronectin (Nesmith et al., 2016; Sun et al., 2013). The use of coatings like DETA (Wilson et al., 2010) promotes long-term cell attachment (up to 21 days), which allows higher maturation of muscle constructs. Furthermore, the use of laminin and cell-tak, a tissue adhesive, enables evaluation of tetanic contractions in C2C12 myotubes with a reported tetanic-to-twitch ratio >1.5 (Fujita et al., 2010).

As a representative example, Figure 3B shows a top view (bright field) and a side view (fluorescence) image of a silicon cantilever with one single human myotube atop, and Figure 3C shows a representative image of a polymeric film-like structure with healthy and dystrophic myotubes under stimulation.

One important advantage of this technique is that there is no manipulation of the tissue at any time; hence, we consider this technique to be non-invasive and with high throughput potential. This 2D platform has been used to test different cell sources such as C2C12 (Fujita et al., 2010; Shimizu et al., 2010; Yamamoto et al., 2011; Pirozzi et al., 2013; Sun et al., 2013), myocytes from rats (McAleer et al., 2014; Wilson et al., 2010) or chicken (Santoso et al., 2021), human-induced pluripotent stem cells (hiPSCs) (Badu-Mensah et al., 2020; Guo et al., 2020), or primary human myoblast (Smith et al., 2014; Nesmith et al., 2016). Moreover, muscle disease models such as DMD (Nesmith et al., 2016; Al Tanoury et al., 2021) and amyotrophic lateral sclerosis (ALS) Badu-Mensah et al., 2020 have been evaluated on this platform, providing relevant information about the pathological mechanisms, and even testing novel treatments.

Post deflection

This technique is based on the quantification of the deflection of a pair of vertical microposts (µposts), due to contraction of the myobundle (Vandenburgh et al., 2008). The tissue construct, which is attached to the top part of the µposts, pulls them together during contraction, causing a horizontal displacement. This effect enables the calculation of the CF from equation (2), where R is the µpost pillar radius, L the length (height), E the elastic modulus, δ the µpost top displacement, and F is the CF of the muscle (Vandenburgh et al., 2008; Shimizu et al., 2017; Nagashima et al., 2020).

(2) $F=\frac{3\pi E{R}^4\delta }{4L^3}$

This equation corresponds to the deflection of a beam with a circular section. According to the classical principles of material science, the application of this expression assumes slender µpost profiles, exertion of myobundle force at the top of µposts (instead of a larger region as depicted in Figure 3), and a linear cantilever deflection in response to the exerted force. This is true if the stiffness of the µpost is at certain ranges, according to Sakar et al., 2012. They reported that an overly flexible cantilever might result in non-linear responses. Also, the effect of the top cap on the bending behavior of the µpost is negligible. However, if one would require higher fidelity on these calculations, finite element analysis could be used to include the thickness of the myobundle at the attachment site as well as any other structural feature. For example, the spring constant of the µposts was calculated with this methodology by including its exact geometry and the material properties (Hofemeier et al., 2021). In any case, a thorough characterization of the µposts is required prior to establishing the cell culture (i.e. µposts stiffness and shape [Vandenburgh et al., 2008; Christensen et al., 2020]).

Commonly, 3D skeletal muscle constructs are generated by embedding myoblasts in hydrogels (i.e. collagen, matrigel, fibrinogen) that simulate the native extracellular matrix (ECM) structure (Pollot et al., 2018) and provide the appropriate environment to develop the muscle structure. A casting process of the hydrogel and myoblasts allow the formation of the myobundle around the integrated µposts. Figure 4A (right) shows a graphical representation of this system, where the post deflection is assessed by a video camera. Figure 4A (Left) provide detailed representation of the three main states of the µposts: (1) resting, before the gelation or polymerization of the hydrogel, (2) initial deflection, due to the passive force exerted by the newly formed construct, and (3) increased deflection, caused by a contraction of the construct. The uniaxial passive force within the hydrogel (started at step 2) will promote myoblast alignment (Vandenburgh et al., 1996; Ruedinger et al., 2015; Thorrez et al., 2018) and myotube formation. Figure 4B shows a representative image of this displacement recorded from a top view before and after stimulation, and Figure 4C shows a typical sequence of the myobundle formation and an immunostaining image below, to show the alignment of the fibers and the formation of myotubes.

Figure 4

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Vandenburgh and colleagues pioneered the development of this type of experimental setup in 2008 (Vandenburgh et al., 2008). Since then, many studies have been published using this platform, ranging from analysis of µpost design (Christensen et al., 2020), cellular sources (Yamamoto et al., 2009; Shimizu et al., 2020; Nagashima et al., 2020; Yoshioka et al., 2021a), drug screening assays (Afshar et al., 2020; Nagashima et al., 2020; Yoshioka et al., 2020) and passive forces (Agrawal et al., 2017), to the evaluation of contractility recovery following atrophy (Shimizu et al., 2017) or in a ALS-motor unit (Osaki et al., 2018b, Osaki et al., 2018a).

One advantage of this setup is that CF can be evaluated throughout the development of the muscle construct without causing disturbance, since information may be collected from the same sample as many times as required. Interestingly and perhaps due to its simplicity, this technique has encouraged the development of commercial platforms currently used to develop bioengineering cardiac tissues (Hansen et al., 2010). The major challenge is to track the deflection of µPosts accurately. In this line, fluorescent microbeads have been proposed as a means to easily follow µPost displacements (Sakar et al., 2012). For the above-mentioned reasons, we consider the post deflection method to be non-invasive, with high-throughput potential.

Many authors have adopted this experimental setup in combination with different stimulation protocols in order to enhance tissue maturation and induce contraction. For example, by chronical optical stimulation with ChR2, an increase of twitch and tetanic CF was observed in C2C12 (Sakar et al., 2012) and in human skeletal micro muscles (hµMs) (Mills et al., 2019). Alternatively, Kim and colleagues have used co-stimulation (mechanical and electrical) in a fascicle-like muscle model (eSMT), aiming ECM remodeling and thus CF improvement (Kim et al., 2019).

Force transducers

This technique is based on the use of a high-resolution force transducer coupled to the end of an engineered muscle (the other end is fixed to the substrate, as illustrated in Figure 5A). As the tissue contracts, it pulls from the transducer, allowing CF to be measured. The transducer converts the CF into a digital signal that can be recorded by a computer with the appropriate instrumentation.

Figure 5

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Unlike the previous techniques, this setup only allows to perform endpoint measurements and requires manipulation of the 3D construct (invasive and low throughput), which may affect tissue structure and CF performance. Figure 5A (left) represents a step-by-step process of this setup. In short, the engineered muscle is grown in a specific bioreactor and then transferred to another setup where it is coupled to the force transducer (Alave Reyes-Furrer et al., 2021). This procedure is quite delicate as the muscle is prone to breakage. A linear actuator is then used to locate and set the construct to its original length and stretch the tissue above its baseline length (L₀). Before taking any measurement, the construct may require time to stabilize and reacquire its resting state. Otherwise, tissue performance would be affected by the stress generated by the exchange of platforms.

Figure 5B shows a representative graph where the muscular construct has been subjected to a series of stimuli in order to determine its response to pulse frequency from twitch (1 Hz) to fused tetanic response (40 Hz). And Figure 5C presents a descriptive photograph of 2-week differentiated induced skeletal muscle bundles (iSKM) anchored within a nylon frame and a typical representative immunostaining of dense, uniformly distributed myotubes in bundle-CSA.

Researchers have used this platform to evaluate several conditions on 3D muscle constructs, such as cell sources (Dennis et al., 2001; Madden et al., 2015; Maffioletti et al., 2018; Rao et al., 2018), co-cultures (Larkin et al., 2006; Juhas et al., 2018) and media supplements (Fujita et al., 2009; Xu et al., 2019), as well as to perform drug screening assays (Ikeda et al., 2017; Zhang et al., 2018; Khodabukus et al., 2020). For example, Dennis and Kosnik generated three-dimensional skeletal muscle constructs from adult rats (myooids), to assess different CF parameters such as rheobase and chronaxie (Dennis and Kosnik, Ii, 2000), while Larkin et al., and Dhawan et al. evaluated the effect of muscle innervation on CF. Here, as expected the nerve-muscle constructs generated greated tetanic CF in vitro (212 µm vs 90 µN) and in vivo (649 ± 228µN vs 124 ± 31µN), respectively (Larkin et al., 2006; Dhawan et al., 2007).

CF platform comparison

There are a number of significant differences among the three main CF assessment methodologies, which are summarized in Figure 6. Generally, cantilevers are best suited for 2D models, myotubes, or muscular films, while µposts and force transducers are designed only for 3D tissue constructs, also known as myobundles. Besides the size, 2D cultures are usually kept in culture for a shorter time and present a risk of delamination, which increases with contractile activity. This partially explains the lower percentage of studies that have performed tetanic stimulation in 2D muscle models. In contrast, 3D models are cultured over longer periods, and present a lower risk of detachment from the µposts or anchoring structures. These 3D constructs are more resilient to chronic stimulation and better withstand tetanic stimulation (up to 90% of studies present this analysis). All three setups have been combined with electrical stimulation, typically by introducing platinum, gold, or carbon electrodes. However, optical and biochemical stimulation was only reported in cantilever and post deflection.

Figure 6

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The measured force range in Newtons for each platform is also significantly different, being 0.02–3.31 µN for the cantilever, 1.4 µN – 1.1 mN for µposts, and 18 µN – 30 mN for the transducer. These values are consistent with the sizes of the constructs each platform can accommodate. Figure 6 also presents the ratio of studies that presented the values of passive force (the contractile force at a resting state without stimulation) and twitch kinetics (PA, TTP, RT₅₀). In addition, there are a number of contractile functional measurements that have been implemented in each platform: that is calcium handling, force-frequency relationship, fatigue, or force-length relationship. However, insufficient data is available to perform a fair comparison between studies (less than 20% of the studies evaluated these characteristics).

Regarding other functional outcomes, Figure 6 includes information about sarcomere evaluation, immunofluorescent imaging of α-actinin, and molecular expression (including genomic and proteomic). Most of these analyses are covered in all three setups, but the availability of biological material greatly differs depending on the setup. 3D muscle constructs provide larger amounts of tissue for analysis, enabling more extensive evaluations and assessments of scarcely expressed molecules.

Other characteristics unique to each setup need to be considered: Cantilever and posts deflection are potentially considered high throughput techniques, while force transducer platforms are not yet ready to handle many samples in parallel. Similarly, the first two are also non-invasive since there is no manipulation of the tissues to obtain the CF and allow for continuous monitoring of the evolution of the culture. The force transducer setup requires getting the myobundle or muscle construct attached to the sensor, and therefore, it becomes an end-point measurement. However, handling the myobundle allows control of the initial length of the myobundle prior to stimulation, enabling the analysis of additional variables. This feature is of most relevance as it can directly impact the force generated by the construct can generate. Another important feature is the compatibility of these techniques with high-resolution microscopy. While a 2D setup is easily integrated, 3D constructs have major limitations and require modification of the culturing setup. One should finally consider the complexity of the culture setup and the measuring platform, its cost, the access to appropriate facilities, as well as the required training/experience of the personnel operating these measurements. For instance, cantilever or µposts fabrication is quite complex since it requires microfabrication facilities or specific fabrication equipment, while no specific culturing protocols are required. On the contrary, the force transducer platform requires straightforward instrumentation, but it requires expert skills to manipulate the tissue construct correctly.

The previous section gathers a summary of the most relevant techniques used to quantify CF in vitro. There is a distinctive approach for 2D and 3D tissue models and the different methodologies used to assess CF in each of them. This section provides a uniform analysis of published data using the three methods described, enabling comparison of contractility across different models. It also provides insights into the development of disease models and evaluation of the effect of several pharmacological treatments.

Analysis criteria

It is relatively common to find CF values with different metrics or without proper clarification of morphological parameters such as sample width/thickness or CSA. This indetermination hinders a straightforward comparison between data collected using the different methodologies. Table 1 compares the morphological information (diameter and CSA) and the CF and sF of different healthy muscle constructs. CF data was directly extracted from the studies, time-average measurements were selected for both twitch and tetanus contraction unless these data were not provided in the study (noted with ^#). Maximum or instantaneous CF values were used as a necessary replacement in those cases. Additionally, it was often necessary to recalculate other parameters from the information provided by the authors (graphs or images), since it was either missing or provided in different units. Values that were recalculated are noted with an asterisk (*) in the table. To calculate the CF values, we have assumed the following premises:

1. Determination of the CSA (mm²): For 2D models we used the width (diameter) and thickness of the tissue, assuming an elliptical shape ($CSA=\pi {(width/2)}^{}{(thickness/2)}^{}$). Calculations were based on diameter measurement assuming that myotube thickness is 1.5 times lower than diameter. For the 3D models, we worked with the circular cross-section of the sample ($CSA$ = $\pi R^2$ , where $R$ is the radius of the construct). Note that for the comparison among studies, all data have been normalized to the whole CSA (Figure 2).

2. If the authors present the sF directly rather than CF from twitch or tetanus, a backward calculation was made to ensure the comparison for either the CSA or CF.

3. Tetanic-to-Twitch Ratio was calculated from sF data within the same study (sF_Te / sF_Tw), except for the post deflection experiments with C2C12 myotubes, where data unavailability forced the use of sF_Te and sF_Tw data from different studies.

Muscle construct size and composition

The size of myotubes in vitro is smaller than the size of muscle cells in the native muscle tissue, commonly known as muscle fibers (Figure 1). While in the muscle fibers size ranges from 10 to 100 µm (King et al., 2004; Powell et al., 2002), the size of the myotubes in the cantilever deflection was 33.15 ± 23.831 µm and 13.29 ± 4.810 µm for C2C12 and human, respectively. This in vitro data is clearly within the lower range of the scale and could be related to the lack of maturation of the tissue.

In 3D muscle constructs in vitro, the size is limited by gas diffusion and, thus, it is difficult to scale up. Diameters of the engineered tissues range between 0.14–2 mm for post deflection and 0.17–3 mm for force transducers. While as shown in Figure 7A, the average-CSA ranges between 0.001–1 mm² for post deflection, and 0.0001–5 mm² for force transducers. Of note, the length of muscle constructs, which is independent of the diameter, is substantially higher in the force transducer platform (12–20 mm) compared to the one in µposts (around 4 mm) (Vandenburgh et al., 2008; Gholobova et al., 2020b).

Figure 7

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The dimensions of these constructs are affected by several variables such as the size of the culture platform (i.e. mold dimensions), hydrogel composition, and compaction (collagen vs. fibrinogen and matrigel) (Hinds et al., 2011; Pollot et al., 2018), culture protocols (i.e. stimulation during myogenesis), and cell culture density (Cvetkovic et al., 2014; Yoshioka et al., 2021a).

The scaffold composition, the type of proteins and their concentration, are also essential for tissue development and contraction performance. For example, time-to-tetanic peak was found almost two-times higher in 4 mg/ml fibrin-based hydrogels than in 2 mg/ml hydrogels. Additionally, when comparing fibrin with collagen I hydrogels, the latter exhibited lower twitch CF, tetanic CF, and tetanic-to-twitch ratio compared to fibrin hydrogels (Hinds et al., 2011). This can be partially explained by the elastic modulus of the hydrogel and the pore size. Collagen presents higher stiffness than fibrin, thus affecting the mechanical response of the tissue. The muscular cells need to overcome the elastic resistance provided by the hydrogel to exert proper contraction. On top of that collagen hydrogels present the smallest pore size and a higher degree of crosslinking fibers. Although this characteristic promotes differentiation, it also hinders cell migration (Pollot et al., 2018).

However, the core common issue in these 3D models is the number of myotubes and their localization within the scaffold. The cross-sectional area of native skeletal muscle presents a high and homogeneously distributed density of muscle fibers per area area (King et al., 2004). In contrast, histological studies of in vitro models have shown that myotubes are mainly in the outer ring of the constructs rather than homogeneously distributed along the cross-section (Nakamura et al., 2017). Thus, up to 90% of the CSA could be constituted by non-contractile tissue (Juhas and Bursac, 2014; Sakar et al., 2012; Akiyama et al., 2021; Ebrahimi et al., 2021), likely due to the lack of proper diffusion of nutrients and oxygen within the in vitro tissue (van der Schaft et al., 2011).

Several approaches such as tissue vascularization are being explored to overcome this hindrance (Gholobova et al., 2020a). For example, the use of porous biodegradable polymers promotes muscle differentiation and development of pre-vascular vessel-like structures (Levenberg et al., 2005). Other approaches include the use of sacrificial templates with thermo-responsive materials mimicking the vessel system (Wan et al., 2020), the development of aligned prevascularized muscles (van der Schaft et al., 2011), and the stimulation of angiogenesis and myogenesis through the exposure of myobundles to close-by engineered capillary networks (Osaki et al., 2018a).

Regardless of the experimental platform used or the size of the construct, in vitro muscle constructs present a significantly smaller CF compared to native tissue (0.2–2.9% of native muscle Tw-sF). Gene expression analyses performed on engineered constructs have shown that they are similar to fetal muscles and far removed from adult native muscle (Nagashima et al., 2020). The general conclusion is that engineered tissues are still at an immature stage of development. Indeed, some of the latest experimental approaches consider using co-culture with neural cells to stimulate NMJ formation, muscle innervation (Demestre et al., 2015; Zahavi et al., 2015; Rimington et al., 2021), and tissue maturation.

Among the main experimental platforms, the force transducer studies are the ones that have generated the highest amount of CF data. Moreover, this platform reports the highest CF mean values among the in vitro muscle constructs. Since constructs evaluated by the force transducer platforms are also the ones with the largest muscle mass, a proper normalization is necessary to compare the CF of the engineered muscles between the different platforms. When normalizing CF to total CSA, we found a very high data variability for all the parameters analyzed (twitch, tetanus), both in constructs generated by C2C12 cells and human cells. However, the tetanic-to-twitch ratio presented a low data variability, enabling comparison between different studies. Tetanic-to-Twitch ratio is a parameter independent of the CSA, which indicates that the total CSA used is not adequate for CF data normalization among studies, even those using the same platform.

There are several aspects influencing contractile function that largely differ across studies, that is, assessment time, tissue maturation, contractile area, and length and mass of the muscle construct. For instance, the percentage of myotubes with a cross-striated pattern, which accounts for the muscle maturation level, appears to be higher in muscle constructs assessed by force transducers ( > 95%) compared to µpost (73.52%) and cantilever platforms (12%) (Hinds et al., 2011; Ebrahimi et al., 2021; Al Tanoury et al., 2021). Importantly, none of these variables are normalized by total CSA, and many are rarely reported in the literature. One possibility for future analyses would be to use the effective CSA, considering the contractile area. This can be achieved with immunofluorescence analysis of sarcomeric proteins (Sakar et al., 2012; Juhas and Bursac, 2014). In addition, this analysis could also provide further valuable data such as muscle maturity level (cross-striation) and sarcomere length, which relates to optimal muscle length.

Our comparative analysis across the experimental platforms shows that sF values reported with the post deflection platform are substantially lower than those with force transducers. This may be due to several factors, including a lower contractile area, lower maturation level, and/or a suboptimal sarcomeric length. In the force transducer, the initial length, L₀, in the resting state before contraction stimulation is adjustable, allowing pre-tension of the construct and maximization of CF. However, it is unattainable to control muscle length in the post deflection method or the cantilever system. In µposts, there is a shortening of L₀ caused by the passive tension of the construct, that is, a progressive contraction of the cells and hydrogel during maturation. Muscle construct shortening in µposts may vary depending on the elastic modulus and would be more pronounced in hydrogels presenting lower elastic properties, such as fibrin, than collagen (Hinds et al., 2011; Cvetkovic et al., 2014). Due to limited available data, it is not possible to determine to what extent this factor affects the sF readout of µposts experiments. However, data extracted from few selected studies suggest that the sarcomere length does not differ substantially between cantilever deflection (1.58 µm, Santoso et al., 2021), post-deflection (2.76 µm, Ebrahimi et al., 2021) and force transducer platforms (1.5–2.7 µm, Hinds et al., 2011; Rao et al., 2018). Moreover, these data are in line with the optimal sarcomere length (about 2 µm) that produces the maximum tension in native muscles (Moo et al., 2020).

Another factor that may account for the lower sF values in µpost muscle constructs compared to force transducers is the muscle mass. Thus, in the force transducer platform, the muscle construct is undoubtedly longer, about 12–20 mm (Gholobova et al., 2020b) compared to 4 mm, in the case of µposts (Vandenburgh et al., 2008). The total CSA would not fully normalize for longer muscle constructs with similar CSA, where more muscle mass contributes to muscle contraction. Hence, an inappropriate CF normalization may partly account for higher sF values observed on average in force transducer constructs compared to post-deflection.

Finally, an unexpected finding derived from our analysis is the overall higher tetanic-to-twitch ratio values of muscle constructs in µpost (1.5–5.6) compared to constructs assessed by force transducers (1-3). Native rat and human muscles present tetanic-to-twitch ratios in the range of 4–10 (Cheng et al., 2014a), so constructs in µposts appear to recapitulate more closely this feature of native muscles. We hypothesize that constructs assessed using force transducers may suffer undue stress due to the additional manipulation required for FC measurement, which may cause malfunction or fatigue, limiting the tetanic response.

Multiple physiological conditions still need to be adjusted and accounted for in order to achieve higher and more reliable contractile performance data in engineered muscles. In light of the CF and tetanic-to-twitch ratio data, it appears that the use of the ratio would limit the variability of CF data, rendering it a useful parameter for CF data comparison across studies.

CF is a key parameter used to assess the actual functionality of in vitro muscle models. In this review, we have discussed the three main methodologies commonly used to measure CF in muscle in vitro systems: cantilever deflection, post deflection, and force transducers. These platforms cover the evaluation of 2D and 3D in vitro skeletal muscle models, from myotubes to muscle engineered tissue in healthy and disease models.

Currently, there is no standard parameter to express CF for in vitro muscle systems. Thus, we have used specific contractile force (sF), by normalizing either twitch or tetanic contraction with the calculated whole cross-sectional area of the engineered tissue in order to compare all the relevant studies. We believe, however, that a more accurate way to calculate sF would be to use the effective-CSA to normalize CF. This would require analyzing the specific number of myotubes in the CSA, which is unfortunately not available in many studies up to date. Therefore, for future studies, we propose that contractility studies in skeletal muscle constructs should include information related to the construct size (length and diameter), contractile area (i.e. myotube coverage in CSA), maturity level (i.e. % of myotubes showing cross-striations), and sarcomere length to complement the information on contractile performance. In any case, we highly recommend including the tetanic-to-twitch ratio since this parameter provides unique information about muscle contractile performance. Furthermore, our analysis suggests that it may enable proper comparison among different studies. Future studies will help us understand the relative impact of these variables on the force performance of muscle constructs and, consequently, the best methodology for measuring and normalizing CF data.

To date, 3D models have evidenced the highest CF; however sF values present a high variability, with no significant differences among the platforms, and they are still far from the native sF range. There is a certain consensus that the cause of this reduced contractile capacity is a maturation deficit of the tissue constructs. In this line, different approaches are being developed to obtain more physiologically relevant muscle models by implementing vascularization, innervation, and mechanical or electromechanical stimulation. Nevertheless, how these new experimental developments would influence the CF of engineered muscle constructs remains unresolved.

Even though CF is a practical, distinctive parameter for assessing muscle function, it is highly influenced by intrinsic properties of the muscle constructs, so the assessment should be complemented alongside other methodologies, such as immunofluorescence, Ca²⁺ handling, and protein quantification. Future investigations should provide insights into integrating this information with contractile values reported in the literature. Moreover, we expect the emerging CF assessment technologies to provide unique insights into intracellular contractile mechanisms.

Given the growing interest in 3D models and the advances in this field, we anticipate a new generation of in vitro systems that will become the driving force behind subsequent research discoveries. Thus, there are still many opportunities for cooperation in these developments and a need for creative and technological researchers to make it possible.

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Author details

1. Camila Vesga-Castro

   1. University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal, San Sebastian, Spain
   2. University of Navarra, Biomedical Engineering Center, Campus Universitario, Pamplona, Spain
   3. Group of Neurosciences, Department of Pediatrics, UPV/EHU, Hospital Donostia - IIS Biodonostia, San Sebastian, Spain

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7234-8650

2. Javier Aldazabal

   1. University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal, San Sebastian, Spain
   2. University of Navarra, Biomedical Engineering Center, Campus Universitario, Pamplona, Spain

   Contribution

   Formal analysis, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2246-5242

3. Ainara Vallejo-Illarramendi

   1. Group of Neurosciences, Department of Pediatrics, UPV/EHU, Hospital Donostia - IIS Biodonostia, San Sebastian, Spain
   2. CIBERNED, Instituto de Salud Carlos III, Ministry of Science, Innovation, and Universities, Madrid, Spain

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review and editing

   For correspondence

   ainara.vallejo@ehu.eus

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2409-8263

4. Jacobo Paredes

   1. University of Navarra, Tecnun School of Engineering, Manuel de Lardizábal, San Sebastian, Spain
   2. University of Navarra, Biomedical Engineering Center, Campus Universitario, Pamplona, Spain

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review and editing

   For correspondence

   jparedes@tecnun.es

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0620-0770

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