This study was supported by the National Natural Science Foundation of China (12202216), Ningbo Natural Science Foundation (2023J128), and the &#34;Mechanics+&#34; Interdisciplinary Top Innovative Youth Fund Project of Ningbo University (GC2024006).

The study received approval from the Ethics Committee of the Research Institute at Ningbo University (Approval Number: ty2022001). All participants provided written informed consent after being briefed on the purpose, requirements, and procedures of the experiment. Details of the consumables, equipment, and software used are listed in the Table of Materials.
1. Experimental preparation
<ol>
	<li>Equipment preparation
	<ol>
		<li>Position eight infrared cameras in appropriate locations during equipment calibration and check for any reflections in the experimental area (e.g., reflective markers on participants&#39; clothing). Remove or cover the reflections, and ensure sufficient lighting.</li>
		<li>Insert the paired encryption dongle into the PC&#39;s parallel port. Start the motion capture cameras, tracking software, force platform amplifier, and ADC.</li>
		<li>Open the tracking software and wait for the eight cameras to initialize. Confirm the process is complete when the camera lights turn from red to green.</li>
		<li>Switch to Camera mode, and expand the System Resources panel on the left. Select all eight cameras.</li>
		<li>Adjust the settings in the left panel under Properties. Set the flash intensity to 0.95-1, gain to 1x, and grayscale mode to Auto. Under Centroid Fitting, set the threshold to 0.2-0.4, minimum circularity ratio to 0.5, and maximum blob height to 50.</li>
		<li>Position the T-frame with markers at the center of the motion capture area. Re-select all eight cameras from the toolbar on the left.</li>
		<li>Perform calibration in the Tools panel on the right. Select the 5 marker Wand &#38; T-Frame calibration object from the T-frame list.</li>
		<li>Click on the Stop button under the Mask Camera option in the System Preparation Tools panel. After the screen turns blue, click on the button again to revert it to the Stop state.</li>
		<li>Click on the Start button under the Calibrate Cameras option. Move the T-frame back and forth within the capture range, ensuring the swinging height matches the camera&#39;s focal height. Stop when the blue lights on the cameras stop flashing.</li>
		<li>Switch the view to 3D Perspective, and place the T-frame back at the center of the motion capture area. Ensure alignment with the force platform boundaries.</li>
		<li>Click on the Start button under the Set Volume Origin option in the right panel.</li>
	</ol>
	</li>
	<li>Pressure platform preparation
	<ol>
		<li>Position two embedded force plates in the center of the motion capture area and synchronize them at 1000 Hz.</li>
		<li>Mark six step widths on the force platform using different colored tapes to represent each condition, including the preferred step width and five variations (13% and 6.5% reductions, and 25%, 13%, and 6.5% increases in leg length). Connect the platform to the PC for data collection. 
		NOTE: Ensure that the system records the foot&#39;s anterior-posterior (Y-axis) coordinates upon contact, with yellow tape marking the preferred step width and other colors indicating variations.</li>
	</ol>
	</li>
	<li>Timing system preparation
	<ol>
		<li>Place a single-beam electronic timing gate on a tripod to record the participants&#39; running speed as they pass over the force plates. 
		NOTE: Ensure the distance between the electronic timing gates is 3 m (as shown in Figure 1).</li>
	</ol>
	</li>
</ol>
2. Participants preparation
<ol>
	<li>Obtain written informed consent from participants, briefly explain the study&#39;s purpose and procedures, and encourage questions. Ensure all participants are right-leg dominant to minimize variability. 
	NOTE: The present study involves 13 healthy Chinese males, with an average age of 22.7 years, weight of 70.9 kg, and height of 1.75 m. Ensure no lower limb injuries or foot deformities in the past six months. This sample selection reduces the influence of age and sex on biomechanical parameters, ensuring statistical validity and reproducibility. The sample size is sufficient for analyzing the effects of step width on biomechanics.</li>
	<li>Instruct participants to wear tight-fitting athletic pants and standardized experimental shoes to minimize gait interference. Remove excess clothing.</li>
	<li>Collect and record anthropometric data: height, weight, leg length, shoulder width, and hip width. 
	NOTE: Measure leg length from the anterior superior iliac spine to the lateral malleolus. Measure shoulder width and hip width as the direct distances between respective anatomical landmarks.</li>
	<li>Keep participants&#39; skin dry and attach reflective markers to anatomical landmarks using double-sided tape. 
	NOTE: Use 38 reflective markers with specific positions and label names detailed in Table 1. Ensure precise placement and secure attachment of the markers. For areas with high motion or uneven surfaces, reinforce the markers with muscle tape or skin-friendly adhesive. After placing the markers, take photographs from the front, back, and sides for documentation.</li>
	<li>Instruct participants to jog for 5 min as a warm-up before starting the static and dynamic recordings.</li>
</ol>
3. Static calibration
<ol>
	<li>Launch the tracking software and create a new database by selecting New Database from the toolbar. In the Data Management section, choose New Patient Classification, then New Patient, and finally, click on New Session to set up a participant information database.</li>
	<li>Click on Go Live in the left toolbar, then use the Split horizontal button in the View interface. Select graphics to view trajectory counts.</li>
	<li>Click on the Subject Preparation in the toolbar. Instruct the participant to stand with feet shoulder-width apart, one foot on the force platform, arms parallel to the shoulders, and looking straight ahead. Click on Start to begin data collection, and hold the position for 10 s. Click on Stop to complete the static capture. 
	NOTE: Switch to &#34;3D Perspective&#34; mode to ensure the camera captures at least one frame showing the 38 reflective markers on the participant&#39;s body.</li>
</ol>
4. Dynamic trials
<ol>
	<li>Inform participants to prepare for the experiment and enter the experimental state.</li>
	<li>Software operations
	<ol>
		<li>Click on Go Live in the left Resources panel and Capture in the right Tools panel. Once participants are ready, start the trial by clicking on Start and end it by clicking on Stop after completion. 
		NOTE: Label different trials with varying step widths in the &#34;Trial Name&#34; section.</li>
	</ol>
	</li>
	<li>Conduct the preferred step width experiment, capturing motion at three different speeds.
	<ol>
		<li>Instruct participants to walk naturally along a straight pathway with two force platforms, placing the left foot on platform A (Kistler) and the right foot on platform B (AMTI).</li>
		<li>Consider the trial successful if the time to complete one run (passing over the force platforms) falls between 0.95-1.05 s at 3.0 m/s or between 0.76-0.86 s at 3.7 m/s. Repeat the trial if the timing does not meet these criteria to minimize the influence of speed fluctuations on running biomechanics. The primary objective is to investigate the influence of speed. 
		NOTE: These time ranges were calculated based on predefined running speeds (3.0 m/s and 3.7 m/s) and the known distance between the electronic timing gates. This ensures participants maintain a consistent running speed throughout the trial and accurately reflect the experimental conditions.</li>
		<li>Instruct participants to run at a speed of 3.7 m/s on a straight pathway with two force platforms. 
		NOTE: If the time passing over the force platform is between 0.76-0.86 s, consider the trial successful; otherwise, retry. Ensure participants consistently place their left foot on platform A and right foot on platform B. Prior studies have shown significant biomechanical differences at various speeds9,10,11,12. Instruct participants to practice multiple times before formal trials to ensure a natural gait. Monitor the trials closely, ensuring correct execution and that markers stay in place. Each condition requires at least five successful trials.</li>
	</ol>
	</li>
	<li>Measuring the preferred step width and setting different step widths
	<ol>
		<li>Based on the definition of step width and previous research, measure the participants&#39; preferred step width in three movement modes8,18,19,20. Using the motion capture system, ensure the Y-axis in the lab coordinate system represents the mediolateral (side-to-side) direction.
		<ol>
			<li>Record the Y-axis coordinates of the left and right heel markers during initial ground contact. Calculate the preferred step width as the difference in the Y-axis coordinates of the two heels, providing a direct measure of the lateral distance between the feet.</li>
		</ol>
		</li>
		<li>Mark the force platforms with different colored tapes corresponding to five step width conditions (step width reduced or increased by 13%, 6.5%, or 25% of leg length). The yellow tape indicates the preferred step width. 
		NOTE: Previous studies have demonstrated significant biomechanical differences when step width changes by 13% relative to leg length5,8,18,19. Based on this finding, introduce additional step width conditions.</li>
		<li>Instruct the participants to walk straight while focusing ahead. Ensure the left foot steps on the yellow tape on platform A and the right foot steps on a different colored tape on platform B. Randomly assign specific tape colors to alter step width. 
		NOTE: Collect at least five successful trials for each step width condition.</li>
		<li>Instruct participants to run at speeds of 3.0 m/s and 3.7 m/s with the five different step widths. 
		NOTE: Ensure participants follow instructions correctly and monitor for marker detachment. Collect at least five successful trials for each condition, and ensure adequate rest time after each test.</li>
	</ol>
	</li>
</ol>
5. Data processing
<ol>
	<li>After the experiment, manually process all collected data using compatible software. Sequentially connect the data according to the marker points set in Table 1.</li>
	<li>Check each static or dynamic trajectory for any missing or interrupted markers on the participants. Use the point connection/fill tool in the software to manually reconnect the missing markers, referencing nearby markers for accuracy.</li>
	<li>Analyze ground reaction forces (GRFs) during dynamic activities by setting a vertical threshold of 20 N to detect foot contact and toe-off, defining the stance phase of each gait cycle. 
	NOTE: GRF data from heel contact to toe-off defines one gait cycle and is saved as raw C3D files.</li>
	<li>Export the processed data in C3D, MOT, TRC, and CSV formats. Ensure each participant completes approximately 55 trials across static, walking, and running conditions with six different step widths. Use the data to compare step widths and limb characteristics. 
	NOTE: Despite strict protocol adherence, some participants may have incomplete data due to marker trajectory disappearance in the equipment system.</li>
</ol>
6. Statistical analysis
<ol>
	<li>Analyze the data using statistical software (e.g., SPSS, Python), focusing on the effects of step width and running speed on lower limb biomechanics.</li>
	<li>Use repeated measures ANOVA to analyze biomechanical parameters, applying Bonferroni correction for post-hoc tests if necessary.</li>
	<li>6.3. Use non-parametric methods like the Friedman test when normality and sphericity assumptions are not met. Set all significance levels at p&#160;&#60; 0.05.</li>
</ol>

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The impact of step width on human running is a multifaceted and significant issue. Step width refers to the lateral distance between the center of the heel and the ground upon initial contact of each foot3. Changing step width may affect stability, balance, biomechanics, and energy expenditure during running. Earlier research suggests that an increase in step width may reduce the hip adduction angle, knee abduction moment, and peak impulse. Conversely, narrowing the step width could potentially in...

Environmental factors, including spatial and temporal elements, directly influence human daily movement. Individuals may adopt different postures and movement patterns while running and walking in various environmental conditions. It is well-established that altering running techniques can impact body biomechanics, with step width being closely associated with stability and balance during human running1,2. Step width is defined as the mediolateral distance between the midfoot and the initial ground contact of each foot, representing a variable in the frontal plane3. During walking and running, short-term variations in step width can influence lower extremity biomechanics across three planes3,4,5.
Numerous studies have demonstrated that step width significantly affects the biomechanics of lower limb joints, kinematics, and kinetics during running. A wider step width reduces hip adduction angles, knee abduction moments, and rearfoot eversion angles, contributing to enhanced stability and potentially lowering injury risk6,7. Conversely, a narrower step width increases knee internal rotation and hip adduction angles, potentially elevating joint loads. Specifically, a narrow step width has been associated with increased variations in knee internal rotation and peak knee abduction torque compared to a normal step width6,7. Additionally, wider step widths have been shown to reduce tibial loading, thereby decreasing stress on the tibia during running8. These findings underscore the critical role of step width in influencing running biomechanics, highlighting its importance in optimizing performance and minimizing injury risk.
Studies have further demonstrated that walking and running speeds influence the biomechanical parameters of the lower limbs9,10,11,12. However, the effects of step width change on biomechanics at varying exercise speeds remain unclear, and limited scientific data regarding human movement under different speed and step width conditions are available. Therefore, this study aims to investigate the impact of step width changes on lower limb biomechanics at different speeds, focusing on key parameters such as hip adduction angle and knee abduction moment.
To address this, a dataset was established comprising 13 healthy male participants aged 20-24 years, including C3D files and ready-to-use kinematic data. Participants were instructed to run at speeds of 3.0 m/s and 3.7 m/s using six distinct step widths. The selection of these step widths and speeds was informed by existing research findings and the current state of open-source datasets on gait biomechanics in the literature13,14,15,16,17. This study aims to examine the acute effects of step width changes on the lower limb kinetic chain while expanding the dataset to provide valuable insights into the relationship between step width and lower limb biomechanics.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>AMTI Force Plate</td>
			<td>AMTI</td>
			<td>OR6-7</td>
			<td>Watertown, MA, USA</td>
		</tr>
		<tr>
			<td>Colored Tape for Floor Marking</td>
			<td>Generic</td>
			<td>-</td>
			<td>Used to mark different step widths on force plates, purchased online</td>
		</tr>
		<tr>
			<td>Kistler Force Plate</td>
			<td>Kistler</td>
			<td>9260AA</td>
			<td>Winterthur, Switzerland</td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>Version R2021b</td>
			<td>Data processing and modeling</td>
		</tr>
		<tr>
			<td>Reflective Markers</td>
			<td>Vicon Metrics Ltd.</td>
			<td>-</td>
			<td>Used for marking participant body points</td>
		</tr>
		<tr>
			<td>Single-Beam Electronic Timing Gate</td>
			<td>Brower Timing System</td>
			<td>-</td>
			<td>Draper, UT, USA, Used to record running speed</td>
		</tr>
		<tr>
			<td>Standard Experimental Shoes</td>
			<td>Designated Brand</td>
			<td>-</td>
			<td>Standardized shoes to minimize gait interference</td>
		</tr>
		<tr>
			<td>Vicon Motion Capture System</td>
			<td>Vicon Metrics Ltd.</td>
			<td>Vicon T-Series</td>
			<td>Oxford, UK, Used for motion capture</td>
		</tr>
		<tr>
			<td>Vicon Nexus Software</td>
			<td>Vicon Metrics Ltd.</td>
			<td>Version 1.8.5A</td>
			<td>Data collection and analysis</td>
		</tr>
	</tbody>
</table>

influence of step-width manipulation on running biomechanics

Step width is a critical factor influencing lower limb biomechanics during running, significantly affecting stability, performance, and injury risk. Understanding these effects is essential for optimizing running performance and minimizing injury risk. This study evaluated the effects of varying step widths on lower limb biomechanics at different running speeds. Thirteen healthy Chinese males (aged 20-24) participated in the study, running at speeds of 3.0 m/s and 3.7 m/s using six distinct step widths: the preferred step width and five variations (reductions of 13% and 6.5%, and increases of 6.5%, 13%, and 25%, based on leg length). Data were collected using a motion capture system and force plates and analyzed through repeated measures ANOVA and correlation tests. The results indicated that wider step widths significantly reduced peak knee abduction moments and hip adduction angles, whereas narrower step widths increased knee joint loading. These findings have important implications for clinicians and runners, suggesting that careful step width selection can help reduce injury risk and enhance running efficiency. This study contributes a new dataset that lays the foundation for future research into the relationship between step width and running biomechanics and serves as a reference for training and rehabilitation practices.

After the experiment and data processing, the processed marker trajectory and simulated ground reaction force (GRF) data were saved in C3D files, as outlined in Table 1. The C3D.zip folder contains the raw dataset obtained from the motion capture experiment. All data records are listed in Table 2. The dataset is organized into folders, each representing one of the 13 participants. Within these subfolders, the experimental files are named NNN_CV_TT.XXX, where NNN denotes the part...

The present protocol outlines an experimental setup designed to investigate the influence of step width manipulation on running biomechanics using a motion capture system. The objective is to expand relevant datasets and examine the effects of varying step widths on the kinematic chain of the human lower limb.

Influence of Step-Width Manipulation on Running Biomechanics

Step width is a critical factor influencing lower limb biomechanics during running, significantly affecting stability, performance, and injury risk. ...

influence-of-step-width-manipulation-on-running-biomechanics

Research

JoVE Journal

Bioengineering

306 Views.  Ningbo University. The present protocol outlines an experimental setup designed to investigate the influence of step width manipulation on running biomechanics using a motion capture system. The objective is to expand relevant datasets and examine the effects of varying step widths on the kinematic chain of the human lower limb.

Video: Influence of Step-Width Manipulation on Running Biomechanics

Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation

A method to manipulate the position and orientation of submicron particles nondestructively would be an incredibly useful tool for basic biological research. Perhaps the most widely used physical force to achieve noninvasive manipulation of small particles has been dielectrophoresis(DEP).1 However, DEP on its own lacks the versatility and precision that are desired when manipulating cells since it is traditionally done with stationary electrodes. Optical tweezers, which utilize a three dimensional electromagnetic field gradient to exert forces on small particles, achieve this desired versatility and precision.2 However, a major drawback of this approach is the high radiation intensity required to achieve the necessary force to trap a particle which can damage biological samples.3 A solution that allows trapping and sorting with lower optical intensities are optoelectronic tweezers (OET) but OET's have limitations with fine manipulation of small particles; being DEP-based technology also puts constraint on the property of the solution.4,5
This video article will describe two methods that decrease the intensity of the radiation needed for optical manipulation of living cells and also describe a method for orientation control. The first method is plasmonic tweezers which use a random gold nanoparticle (AuNP) array as a substrate for the sample as shown in Figure 1. The AuNP array converts the incident photons into localized surface plasmons (LSP) which consist of resonant dipole moments that radiate and generate a patterned radiation field with a large gradient in the cell solution. Initial work on surface plasmon enhanced trapping by Righini et al and our own modeling have shown the fields generated by the plasmonic substrate reduce the initial intensity required by enhancing the gradient field that traps the particle.6,7,8 The plasmonic approach allows for fine orientation control of ellipsoidal particles and cells with low optical intensities because of more efficient optical energy conversion into mechanical energy and a dipole-dependent radiation field. These fields are shown in figure 2 and the low trapping intensities are detailed in figures 4 and 5. The main problems with plasmonic tweezers are that the LSP's generate a considerable amount of heat and the trapping is only two dimensional. This heat generates convective flows and thermophoresis which can be powerful enough to expel submicron particles from the trap.9,10 The second approach that we will describe is utilizing periodic dielectric nanostructures to scatter incident light very efficiently into diffraction modes, as shown in figure 6.11 Ideally, one would make this structure out of a dielectric material to avoid the same heating problems experienced with the plasmonic tweezers but in our approach an aluminum-coated diffraction grating is used as a one-dimensional periodic dielectric nanostructure. Although it is not a semiconductor, it did not experience significant heating and effectively trapped small particles with low trapping intensities, as shown in figure 7. Alignment of particles with the grating substrate conceptually validates the proposition that a 2-D photonic crystal could allow precise rotation of non-spherical micron sized particles.10 The efficiencies of these optical traps are increased due to the enhanced fields produced by the nanostructures described in this paper.

Plasmonic tweezers and photonic crystal nanostructures are shown to produce useful enhancements in the efficiency and orientation control of optically trapping micro- and nano-particles.

A method to manipulate the position and orientation of submicron particles nondestructively would be an incredibly useful tool for basic biological ...

Spatio-Temporal Manipulation of Small GTPase Activity at Subcellular Level and on Timescale of Seconds in Living Cells

Dynamic regulation of the Rho family of small guanosine triphosphatases (GTPases) with great spatiotemporal precision is essential for various cellular functions and events1, 2. Their spatiotemporally dynamic nature has been revealed by visualization of their activity and localization in real time3. In order to gain deeper understanding of their roles in diverse cellular functions at the molecular level, the next step should be perturbation of protein activities at a precise subcellular location and timing. 
To achieve this goal, we have developed a method for light-induced, spatio-temporally controlled activation of small GTPases by combining two techniques: (1) rapamycin-induced FKBP-FRB heterodimerization and (2) a photo-caging method of rapamycin. With the use of rapamycin-mediated FKBP-FRB heterodimerization, we have developed a method for rapidly inducible activation or inactivation of small GTPases including Rac4, Cdc424, RhoA4 and Ras5, in which rapamycin induces translocation of FKBP-fused GTPases, or their activators, to the plasma membrane where FRB is anchored. For coupling with this heterodimerization system, we have also developed a photo-caging system of rapamycin analogs. A photo-caged compound is a small molecule whose activity is suppressed with a photocleavable protecting group known as a caging group. To suppress heterodimerization activity completely, we designed a caged rapamycin that is tethered to a macromolecule such that the resulting large complex cannot cross the plasma membrane, leading to virtually no background activity as a chemical dimerizer inside cells6. Figure 1 illustrates a scheme of our system. With the combination of these two systems, we locally recruited a Rac activator to the plasma membrane on a timescale of seconds and achieved light-induced Rac activation at the subcellular level6.

A method for spatio-temporal control of small GTPase activity by light is described. This method is based on rapamycin-induced FKBP-FRB heterodimerization and photo-caging systems. Optimization of light-irradiation enables the spatio-temporally controlled activation of small GTPases at the subcellular level.

Dynamic regulation of the Rho family of small guanosine triphosphatases (GTPases) with great spatiotemporal precision is essential for various cellular ...

Engineering Skeletal Muscle Tissues from Murine Myoblast Progenitor Cells and Application of Electrical Stimulation

Engineered muscle tissues can be used for several different purposes, which include the production of tissues for use as a disease model in vitro, e.g. to study pressure ulcers, for regenerative medicine and as a meat alternative 1. The first reported 3D muscle constructs have been made many years ago and pioneers in the field are Vandenburgh and colleagues 2,3. Advances made in muscle tissue engineering are not only the result from the vast gain in knowledge of biochemical factors, stem cells and progenitor cells, but are in particular based on insights gained by researchers that physical factors play essential roles in the control of cell behavior and tissue development. State-of-the-art engineered muscle constructs currently consist of cell-populated hydrogel constructs. In our lab these generally consist of murine myoblast progenitor cells, isolated from murine hind limb muscles or a murine myoblast cell line C2C12, mixed with a mixture of collagen/Matrigel and plated between two anchoring points, mimicking the muscle ligaments. Other cells may be considered as well, e.g. alternative cell lines such as L6 rat myoblasts 4, neonatal muscle derived progenitor cells 5, cells derived from adult muscle tissues from other species such as human 6 or even induced pluripotent stem cells (iPS cells) 7. Cell contractility causes alignment of the cells along the long axis of the construct 8,9 and differentiation of the muscle progenitor cells after approximately one week of culture. Moreover, the application of electrical stimulation can enhance the process of differentiation to some extent 8. Because of its limited size (8 x 2 x 0.5 mm) the complete tissue can be analyzed using confocal microscopy to monitor e.g. viability, differentiation and cell alignment. Depending on the specific application the requirements for the engineered muscle tissue will vary; e.g. use for regenerative medicine requires the up scaling of tissue size and vascularization, while to serve as a meat alternative translation to other species is necessary.

Engineered muscle tissue has great potential in regenerative medicine, as disease model and also as an alternative source for meat. Here we describe the engineering of a muscle construct, in this case from mouse myoblast progenitor cells, and the stimulation by electrical pulses.

Engineered muscle tissues can be used for several different purposes, which include the production of tissues for use as a disease model in vitro, e.g. to ...

Time-lapse Fluorescence Imaging of Arabidopsis Root Growth with Rapid Manipulation of The Root Environment Using The RootChip

The root functions as the physical anchor of the plant and is the organ responsible for uptake of water and mineral nutrients such as nitrogen, phosphorus, sulfate and trace elements that plants acquire from the soil. If we want to develop sustainable approaches to producing high crop yield, we need to better understand how the root develops, takes up a wide spectrum of nutrients, and interacts with symbiotic and pathogenic organisms. To accomplish these goals, we need to be able to explore roots in microscopic detail over time periods ranging from minutes to days.
We developed the RootChip, a polydimethylsiloxane (PDMS)- based microfluidic device, which allows us to grow and image roots from Arabidopsis seedlings while avoiding any physical stress to roots during preparation for imaging1 (Figure 1). The device contains a bifurcated channel structure featuring micromechanical valves to guide the fluid flow from solution inlets to each of the eight observation chambers2. This perfusion system allows the root microenvironment to be controlled and modified with precision and speed. The volume of the chambers is approximately 400 nl, thus requiring only minimal amounts of test solution.
Here we provide a detailed protocol for studying root biology on the RootChip using imaging-based approaches with real time resolution. Roots can be analyzed over several days using time lapse microscopy. Roots can be perfused with nutrient solutions or inhibitors, and up to eight seedlings can be analyzed in parallel. This system has the potential for a wide range of applications, including analysis of root growth in the presence or absence of chemicals, fluorescence-based analysis of gene expression, and the analysis of biosensors, e.g. FRET nanosensors3.

This article provides a protocol for cultivation of Arabidopsis seedlings in the RootChip, a microfluidic imaging platform that combines automated control of growth conditions with microscopic root monitoring and FRET-based measurement of intracellular metabolite levels.

The root functions as the physical anchor of the plant and is the organ responsible for uptake of water and mineral nutrients such as nitrogen, ...

Mass Cytometry: Protocol for Daily Tuning and Running Cell Samples on a CyTOF Mass Cytometer

In recent years, the rapid analysis of single cells has commonly been performed using flow cytometry and fluorescently-labeled antibodies. However, the issue of spectral overlap of fluorophore emissions has limited the number of simultaneous probes. In contrast, the new CyTOF mass cytometer by DVS Sciences couples a liquid single-cell introduction system to an ICP-MS.1 Rather than fluorophores, chelating polymers containing highly-enriched metal isotopes are coupled to antibodies or other specific probes.2-5 Because of the metal purity and mass resolution of the mass cytometer, there is no "spectral overlap" from neighboring isotopes, and therefore no need for compensation matrices. Additionally, due to the use of lanthanide metals, there is no biological background and therefore no equivalent of autofluorescence. With a mass window spanning atomic mass 103-203, theoretically up to 100 labels could be distinguished simultaneously. Currently, more than 35 channels are available using the chelating reagents available from DVS Sciences, allowing unprecedented dissection of the immunological profile of samples.6-7
Disadvantages to mass cytometry include the strict requirement for a separate metal isotope per probe (no equivalent of forward or side scatter), and the fact that it is a destructive technique (no possibility of sorting recovery). The current configuration of the mass cytometer also has a cell transmission rate of only ~25%, thus requiring a higher input number of cells.
Optimal daily performance of the mass cytometer requires several steps. The basic goal of the optimization is to maximize the measured signal intensity of the desired metal isotopes (M) while minimizing the formation of oxides (M+16) that will decrease the M signal intensity and interfere with any desired signal at M+16. The first step is to warm up the machine so a hot, stable ICP plasma has been established. Second, the settings for current and make-up gas flow rate must be optimized on a daily basis. During sample collection, the maximum cell event rate is limited by detector efficiency and processing speed to 1000 cells/sec. However, depending on the sample quality, a slower cell event rate (300-500 cells/sec) is usually desirable to allow better resolution between cells events and thus maximize intact singlets over doublets and debris. Finally, adequate cleaning of the machine at the end of the day helps minimize background signal due to free metal.

The steps necessary for daily tuning and optimization of the performance of a CyTOF mass cytometer are described. Comments on optimal sample preparation and flow rate are discussed

In recent years, the rapid analysis of single cells has commonly been performed using flow cytometry and fluorescently-labeled antibodies. However, the ...

Easy and Accurate Mechano-profiling on Micropost Arrays

Cell culture substrates with integrated flexible microposts enable a user to study the mechanical interactions between cells and their immediate surroundings. Particularly, cell-substrate interactions are the main interest. Today micropost arrays are a well-characterized and established method with a broad range of applications that have been published over the last decade. However, there seems to be a reservation among biologists to adapt the technique due to the lengthy and challenging process of micropost manufacture along with the lack of easily approachable software for analyzing images of cells interacting with microposts. 
The force read-out from microposts is surprisingly easy. A micropost acts like a spring with the cell ideally attached at its tip. Depending on size a cell applies force from its cytoskeleton through one or multiple focal adhesion points to the micropost, thus deflecting the micropost. The amount of deflection correlates directly to the applied force in direction and in magnitude. The number of microposts covered by a cell and the post deflection patterns are characteristic and allow determination of values like force per post and many biologically relevant parameters that allow &#8220;mechano-profiling&#8221; of cell phenotypes.
A convenient method for mechano-profiling is described here combining the first generation of ready-to-use commercially available microposts with an in-house developed software package that is now accessible to all researchers. As a demonstration of typical application, single images of bone cancer cells were taken in bright-field microscopy for mechano-profiling of cell line models of metastasis. This combination of commercial traction force sensors and open source software for analysis allows for the first time a rapid implementation of the micropost array technique into routine lab work done by non-expert users. Furthermore, a robust and streamlined analysis process enables a user to analyze a large number of micropost images in a highly time-efficient manner.

Described are protocols for quantifying mechanical interactions between adherent cells and microstructured substrates. These interactions are closely linked to essential cell behaviors including migration, proliferation, differentiation, and apoptosis. The protocols present an open-source image analysis software called MechProfiler, which enables determination of involved forces for each micropost.

Cell culture substrates with integrated flexible microposts enable a user to study the mechanical interactions between cells and their immediate ...

Rod-based Fabrication of Customizable Soft Robotic Pneumatic Gripper Devices for Delicate Tissue Manipulation

Soft compliant gripping is essential in delicate surgical manipulation for minimizing the risk of tissue grip damage caused by high stress concentrations at the point of contact. It can be achieved by complementing traditional rigid grippers with soft robotic pneumatic gripper devices. This manuscript describes a rod-based approach that combined both 3D-printing and a modified soft lithography technique to fabricate the soft pneumatic gripper. In brief, the pneumatic featureless mold with chamber component is 3D-printed and the rods were used to create the pneumatic channels that connect to the chamber. This protocol eliminates the risk of channels occluding during the sealing process and the need for external air source or related control circuit. The soft gripper consists of a chamber filled with air, and one or more gripper arms with a pneumatic channel in each arm connected to the chamber. The pneumatic channel is positioned close to the outer wall to create different stiffness in the gripper arm. Upon compression of the chamber which generates pressure on the pneumatic channel, the gripper arm will bend inward to form a close grip posture because the outer wall area is more compliant. The soft gripper can be inserted into a 3D-printed handling tool with two different control modes for chamber compression: manual gripper mode with a movable piston, and robotic gripper mode with a linear actuator. The double-arm gripper with two actuatable arms was able to pick up objects of sizes up to 2 mm and yet generate lower compressive forces as compared to elastomer-coated and non-coated rigid grippers. The feasibility of having other designs, such as single-arm or hook gripper, was also demonstrated, which further highlighted the customizability of the soft gripper device, and it's potential to be used in delicate surgical manipulation to reduce the risk of tissue grip damage.

This protocol describes a rod-based approach, combining 3D-printing and soft lithography techniques for fabricating the soft gripper devices. This approach eliminates the need for an external air source by incorporating a chamber component and reduces the chance of occlusion during the sealing process, particularly for miniaturized pneumatic channels.

Soft compliant gripping is essential in delicate surgical manipulation for minimizing the risk of tissue grip damage caused by high stress concentrations ...

Long-term Live Imaging Device for Improved Experimental Manipulation of Zebrafish Larvae

The zebrafish larva is an important model organism for both developmental biology and wound healing. Further, the zebrafish larva is a valuable system for live high-resolution microscopic imaging of dynamic biological phenomena in space and time with cellular resolution. However, the traditional method of agarose encapsulation for live imaging can impede larval development and tissue regrowth. Therefore, this manuscript describes the zWEDGI (zebrafish Wounding and Entrapment Device for Growth and Imaging), which was designed and fabricated as a functionally compartmentalized device to orient larvae for high-resolution microscopy while permitting caudal fin transection within the device and subsequent unrestrained tail development and re-growth. This device allows for wounding and long-term imaging while maintaining viability. Given that the zWEDGI mold is 3D printed, the customizability of its geometries make it easily modified for diverse zebrafish imaging applications. Furthermore, the zWEDGI offers numerous benefits, such as access to the larva during experimentation for wounding or for the application of reagents, paralleled orientation of multiple larvae for streamlined imaging, and reusability of the device.

This manuscript describes the zWEDGI (zebrafish Wounding and Entrapment Device for Growth and Imaging), which is a compartmentalized device designed to orient and restrain zebrafish larvae. The design permits tail transection and long-term collection of high-resolution fluorescent microscopy images of wound healing and regeneration.

The zebrafish larva is an important model organism for both developmental biology and wound healing. Further, the zebrafish larva is a valuable system for ...

Biaxial Mechanical Characterizations of Atrioventricular Heart Valves

Extensive biaxial mechanical testing of the atrioventricular heart valve leaflets can be utilized to derive&#160;optimal parameters used in constitutive models, which provide a mathematical representation of the mechanical function of those structures. This presented biaxial mechanical testing protocol involves (i) tissue acquisition, (ii) the preparation of tissue specimens, (iii) biaxial mechanical testing, and (iv) postprocessing of the acquired data. First, tissue acquisition requires obtaining porcine or ovine hearts from a local Food and Drug Administration-approved abattoir for later dissection to retrieve the valve leaflets. Second, tissue preparation requires using tissue specimen cutters on the leaflet tissue to extract a clear zone for testing. Third, biaxial mechanical testing of the leaflet specimen requires the use of a commercial biaxial mechanical tester, which consists of force-controlled, displacement-controlled, and stress-relaxation testing protocols to characterize the leaflet tissue&#39;s mechanical properties. Finally, post-processing requires the use of data image correlation techniques and force and displacement readings to summarize the tissue&#39;s mechanical behaviors in response to external loading. In general, results from biaxial testing demonstrate that the leaflet tissues yield a nonlinear, anisotropic mechanical response. The presented biaxial testing procedure is advantageous to other methods since the method presented here allows for a more comprehensive characterization of the valve leaflet tissue under one unified testing scheme, as opposed to separate testing protocols on different tissue specimens. The proposed testing method has its limitations in that shear stress is potentially present in the tissue sample. However, any potential shear is presumed negligible.

This protocol involves characterizations of atrioventricular valve leaflets with force-controlled, displacement-controlled, and stress-relaxation biaxial mechanical testing procedures. Results acquired with this protocol can be used for constitutive model development to simulate the mechanical behavior of functioning valves under a finite element simulation framework.

Extensive biaxial mechanical testing of the atrioventricular heart valve leaflets can be utilized to derive&#160;optimal parameters used in constitutive ...

Manipulation of Single Neural Stem Cells and Neurons in Brain Slices using Robotic Microinjection

A central question in developmental neurobiology is how neural stem and progenitor cells form the brain. To answer this question, one needs to label, manipulate, and follow single cells in the brain tissue with high resolution over time. This task is extremely challenging due to the complexity of tissues in the brain. We have recently developed a robot, that guide a microinjection needle into brain tissue upon utilizing images acquired from a microscope to deliver femtoliter volumes of solution into single cells. The robotic operation increases resulting an overall yield that is an order of magnitude greater than manual microinjection and allows for precise labeling and flexible manipulation of single cells in living tissue. With this, one can microinject hundreds of cells within a single organotypic slice. This article demonstrates the use of the microinjection robot for automated microinjection of neural progenitor cells and neurons in the brain tissue slices. More broadly, it can be used on any epithelial tissue featuring a surface that can be reached by the pipette. Once set up, the microinjection robot can execute 15 or more microinjections per minute. The microinjection robot because of its throughput and versality will make microinjection a broadly straightforward high-performance cell manipulation technique to be used in bioengineering, biotechnology, and biophysics for performing single-cell analyses in organotypic brain slices.

This protocol demonstrates the use of a robotic platform for microinjection into single neural stem cells and neurons in brain slices. This technique is versatile and offers a method of tracking cells in tissue with high spatial resolution.

A central question in developmental neurobiology is how neural stem and progenitor cells form the brain. To answer this question, one needs to label, ...