(*contributed equally to this work) Published: Vol 9, Iss 23, Dec 5, 2019 DOI: 10.21769/BioProtoc.3439 Views: 4936
Reviewed by: Ralph Thomas BoettcherSurabhi SonamAnonymous reviewer(s)
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Abstract
Mechanomics, the mechanics equivalent of genomics, is a burgeoning field studying mechanical modulation of stem cell behavior and lineage commitment. Analogous to mechanical testing of a living material as it adapts and evolves, mapping of the mechanome necessitates the development of new protocols to assess changes in structure and function in live stem cells as they adapt and differentiate. Previous techniques have relied on imaging of cellular structures in fixed cells and/or live cell imaging of single cells with separate studies of changes in mechanical and biological properties. Here we present two complementary protocols to study mechanobiology and mechanoadaptation of live stem cells in adherent and motile contexts. First, we developed and tested live imaging protocols for simultaneous visualization and tracking of actin and tubulin mechanoadaptation as well as shape and volume of cells and their nuclei in adherent model embryonic murine mesenchymal stem cells (C3H/10T1/2) and in a neuroblastoma cell line. Then we applied the protocol to enable quantitative study of primary human mesenchymal stem cells in a motile state, e.g., ingression in a three-dimensional, in vitro cell culture model. Together, these protocols enable study of emergent structural mechanoadaptation of the cell's own cytoskeletal machinery while tracking lineage commitment using phenotypic (quantitative morphology measures) and genotypic (e.g., reverse transcription Polymerase Chain Reaction, rtPCR) methods. These tools are expected to facilitate the mapping of the mechanome and incipient mechanistic understanding of stem cell mechanobiology, from the cellular to the tissue and organ length scales.
Keywords: Live cell imagingBackground
Collectively referred to as mechanomics, the study of how mechanical cues modulate stem cell behavior has grown rapidly in the past decade (Figure 1). Experimental and computational mechanomics studies have demonstrated the profound influence of mechanical environment on cell motility (Knothe Tate et al., 2008; Blanchoin et al., 2014; Aubry et al., 2015; Knothe Tate et al., 2016; De Pascalis and Etienne-Manneville, 2017; Ladoux and Mege, 2017), stem cell niche quiescence (Yu et al., 2017; Ni et al, 2019) and lineage commitment (Anderson et al., 2006; Anderson and Knothe Tate 2007a and 2007b; Knothe Tate et al.,2008; McBride and Knothe Tate, 2008; McBride et al., 2008; Song et al., 2010; 2012 and 2013; Zimmermann et al., 2011; Chang and Knothe Tate, 2011; Earls et al., 2013; Heo et al., 2015; Nimmo et al., 2015; Steward and Kelly, 2015; Le et al., 2016; Stumpf et al.,2017; Galarza et al., 2018). Mechanical testing of live cells, as they adapt and differentiate, necessitates the development and testing of mechanobiological tools incorporating imaging and quantitative measures of shape, volume, and architecture changes (e.g., of cells, as well as their nuclei and cytoskeletons) for controlled loading scenarios, with relevant spatial and temporal resolution (Anderson et al., 2006; Anderson and Knothe Tate 2007a and 2007b; McBride and Knothe Tate, 2008; McBride et al., 2008; Song et al., 2010; 2012 and 2013; Zimmermann and Knothe Tate, 2011; Chang and Knothe Tate, 2011).
During development and postnatal healing, the mechanical cues to which stem cells are subjected exert a profound role in the cells' capacity to self-assemble structure (Figure 1), which manifests in several ways. The cell's internal structures such as the cytoskeleton self-assemble and adapt in response to the mechanical environment. Cells themselves self-assemble into multicellular constructs. Cells also create tissue architectures through transcription, assembly and post-translational modification of extracellular matrix (Knothe Tate et al., 2016).
Prior to lineage commitment, stem cells act as sensors and actuators, transducing mechanical signals from the local environment to the nucleus, where gene transcription is up- and down-regulated, resulting in genesis of tissue templates that grow and mature over time (Knothe Tate et al., 2008; Knothe Tate et al., 2016; Ng et al., 2017). Embryonic mesenchymal stem cells exhibit intrinsic sensitivity to mechanical stress; these cells change their baseline gene expression in response to subtle mechanical cues three orders of magnitude smaller than those to which e.g., trigger changes in baseline gene expression of adult chondrocytes of the knee joint (McBride and Knothe Tate, 2008; McBride et al., 2008; Song et al., 2012). In addition, conditionally knocking out specific constituents of stem cells’ mechanosensing apparatus causes stem cells to lose their capacity to self-assemble structure and hence tissues (Knothe Tate et al., 2010).
The geometric arrangement of cells in space and time results in patterning of the organism’s template during prenatal development as well as the injured and/or missing tissue template during postnatal healing. The process is gated by cell motility and adherence (with adherence defined by the lack of capacity to move) (Knothe Tate et al., 2008; Evans et al., 2013). Until recently, mechanistic and dynamic study of unfolding cell fate and tissue template genesis has been stymied by the lack of methods to study these processes in situ, in live multicellular constructs. This protocol presents methods to study cell motility in model constructs as well as mechanoadaptation of the cytoskeleton within the cell itself. Pilot study data demonstrates the utility of the methods.
Figure 1. Mapping the mechanome at the interface of genomics and mechanics of materials. Cell behavior is modulated by biophysical cues including volume (dilatational–compression and tension) and shape changing (deviatoric–shear) stresses. A. Mechanical loading throughout life literally shapes the structure and function of cells. Cells sense mechano-chemical stimuli and prototype tissue templates via up and downregulation of structural protein transcription, secretion into the extracellular matrix, and post-translational modification. This figure depicts characteristic magnitudes and time domains of mechanical signals applied in studies of multipotent cell differentiation, with nascent lineage commitment depicted by the shape of the data points. Tissue genesis and adaptation represents a continuum in space and time, over the life cycle of the individual organism, from development of the body template in utero (depicted 11.5 days after fertilization at the first stages of skeletogenesis in the mouse) and in the adult human. Image after (Ng et al., 2017) as adapted from (Anderson and Knothe Tate 2017b; Song et al., 2013) and used with permission. B. Using paired, live imaging and computational modeling approaches from our consortium, previous published studies mapped the mechanome retrospectively, depicting real stress state data from the coupled computational modeling and imaging studies in relation to 95% confidence intervals (shaded ovals) of nascent lineage commitment data (yellow, pink, violet), measured using rtPCR (Song et al., 2010; 2012 and 2013; Ng et al., 2017; Anderson and Knothe Tate, 2017b). Current and future approaches implement stress states from non-overlapping fates to prospectively test fate guidance through delivery of mechanical cues (B, e.g., X indicates chondrogenesis, in yellow; +haematapoesis, in purple; +osteogenesis in fuschia) that induce volume (dilatational) and shape (deviatoric) changing stresses at cell surfaces. This is akin to conducting a mechanical test on a stem cell during the process of lineage commitment. Adapted from original with permission (Song et al., 2013). C. An equivalent classical mechanics Mohr’s Circle diagram graphically represents the stress tensor obtained by performing a stress analysis on a material body such as a cell. Classical continuum mechanics’ principal limitation in biology is that it cannot adequately address biological behavior of live materials that evolve over time, e.g., via motility, mechanoadaptation, and differentiation (Knothe Tate et al., 2011 and 2016).
Overview of Approach
Live imaging methods were first developed for simultaneous tracking of actin and tubulin transcription and dynamic cytoskeletal mechanoadaptation, as well as changes in nucleus shape and volume, in adherent cells (Figure 2). This model system was designed to enable study of emergent structural mechanoadaptation of the cell's own cytoskeletal machinery while measuring emergent lineage commitment using phenotypic (morphology) and genotypic (rtPCR) methods. The C3H/10T1/2 cell line was used for the intracellular mechanoadaptation studies to insure cell-to-cell standardization. Derived from the mesenchyme, the C3H/10T1/2 murine pluripotent embryonic cells do not show phenotypic drift and are capable of differentiating along osteogenic, chrondogenic, adipogenic, smooth muscle, and endothelial lineage paths. This established cell line was used previously by our research consortium in a series of mechanoadaptation studies (McBride and Knothe Tate, 2008; McBride et al., 2008; Song et al., 2010; 2012 and 2013; Chang and Knothe Tate, 2011; Zimmermann and Knothe Tate, 2011). In addition, we tested the protocol on the rat neuroblastoma B35 cell line (Tpm3.1 neuroblastoma), an immortalized and well characterized cell line that displays prominent actin stress fibers (Bryce et al., 2003). Finally, use of these two different cell lines enabled us to optimize the protocol, accurately and efficiently, for use in primary cell cultures as well as cell lines.
Thereafter, an in vitro model was developed for dynamic, live imaging and tracking of stem cells ingressing from a seeded surface to the interior of an idealized tissue template, mimicking an epithelial to mesenchymal transition (EMT) (Li et al. 2014) in a model tissue template (Anlage) during development or postnatal healing (Figure 2). To maximize clinical relevance, we tested the tissue ingression protocol using primary mesenchymal stem cells isolated from adult human periosteum (periosteum-derived stem cells, PDCs; Human ethics protocol approved) (Chang and Knothe Tate, 2011; Zimmermann and Knothe Tate, 2011) as well as bone marrow derived stem cells (BMSCs, adult human) from a commercial vendor.
Figure 2. Protocol for mapping the mechanome at the interface of genomics and mechanics of materials, on cells adherent to tissue template scaffolds or functionalized coverslips (Part 1) or ingressing into idealized tissue templates (Part 2). Protocols steps are depicted in context of the aim to map the mechanome throughout cell and tissue genesis, in developmental and postnatal healing contexts where cells exist in adherent or motile states. Part 1. Mapping the mechanome by tracking changes in cell/nucleus shape & volume and actin and tubulin as a function of gene transcription and mechanoadaptation, similar to a mechanical test of a living material as it evolves and adapts. Part 2. Energy of Mechanomics by live imaging and tracking of stem cell ingression from a seeded surface to the interior of an idealized tissue template as a function of cell metabolism, enabling assessment of the energy of mechanomics. The approach measures metabolic energy expended by the cell over time (power) as a function of distance covered by the motile cell.
Materials and Reagents
Note: +Examples of items available from multiple vendors.
Equipment
Software
Part 1–Tracking change in cell/nucleus shape & volume, cytoskeletal transcription & mechanoadaptation
Here we implemented multiplexed imaging and tracking of changes in the nucleus, actin filaments and microtubules, in live cells and at high resolution using a viral gene delivery system to insert the desired gene into the host's genome (Shoji et al., 1997; Airenne et al., 2003; Ho et al., 2005; Kost et al., 2005; Salminen et al., 2005). The main advantage of the technique is that both actin and tubulin monomers are fluorescently tagged through the cellular transcriptional system and then individual monomers get incorporated into cytoskeletal structures by cells. This method offers rapid, safe, easy and convenient steps for delivery of desired gene to different mammalian cell lines but may not be efficient in certain cell types, including primary ovine Periosteum Derived Stem Cells, PDSCs (Chang and McBride, MechBio Team unpublished data). Here we optimized the method for the aforementioned C3H/10T1/2 murine embryonic stem cell line and the immortalized Tpm3.1 neuroblastoma cell lines. These two cell lines were chosen specifically because the transduction of two different cell types let us measure the amount of transduction efficiency and protein expression in a quantitative and robust way.
Procedure
Data analysis
Representative data and protocol validation
Given the ubiquitous role of the cytoskeleton in cellular behavior, we first examined transduction efficiency for each of the cell lines as well as the impact of BacMam viral particle on their respective growth rates, implemented simultaneously previously tested protocols (Song et al., 2010; 2012 and 2013; Chang and Knothe Tate, 2011; Zimmermann and Knothe Tate, 2011). The cytoskeleton, and in particular actin filaments, control the stiffness and hence modulate the mechanoadaptation of the cells. Since the baculovirus uses the actin filaments for intracellular transport, it was important to check for potential effects of viral transduction on the cell stiffness. To assess the effect of the viral transduction on the mechanical properties of cells, the stiffness of the cells was examined using atomic force microscopy (AFM) per our previous methods (Jalilian et al., 2015).
The transduction of the cytoskeletal tagging agents was qualitatively apparent (Figure 3). The efficiency of transduction was proportional to growth rate, indicating that incubation time for efficient transduction should be increased in cells that have lower growth rates. For example, under the same conditions, the efficiency of viral transduction is higher in neuroblatoma cells that the C3H cells due to their higher growth rate (48 h for neuroblatoma cells compared to 72 h for C3H cells) (Figure 4).
Figure 3. C3H/10T1/2 and neuroblastoma cells were transduced with Actin- and Tubulin-BacMam particles (20-30 particles per cell). (A) Merged, high resolution confocal image of live cells enlarged from (B), showing distinct actin and microtubule structures in both cell lines, with nuclei in blue.
Figure 4. Representative growth curves for transduced C3H/10T1/2 (left) and neuroblastoma cells (right). Transduced cells were cultured for 7 days and their proliferation rate was measured against their relative controls; n = 3 independent experiments.
No significant differences were observed between the growth rate of transduced cells and their controls, indicating no significant effect of BacMam viral transduction on the physiological growth rate of the transduced cells (Figure 4). Furthermore, no significant differences were observed in the Young’s modulus of the transduced cells compared to their controls, indicating that BacMam viral transduction had no significant effect on the mechanical properties of the cells in the time periods studied (Figure 5).
Figure 5. Young's modulus for transduced C3H/10T1/2 (left) and neuroblastoma (right) cells and baseline controls. Each point represents a measurement from a unique single cell. Between 15 and 20 cells for each cell line were examined from n = 3 independent experiments.
Part 2: Live imaging and tracking of stem cell ingression from a seeded surface to the interior of an idealized tissue template
Whereas Part 1 allows the equivalent of mechanical testing of cells and cellular constructs as they adapt in space and time, Part 2 of the protocol focuses on cell movement in space and time. This provides a means to measure the energy or power (energy use over time) of cell motility. The protocol describes live imaging and tracking of stem cell ingression from a seeded surface to the interior of an idealized tissue template as a function of cell metabolism, enabling assessment of the energy of mechanomics. The approach measures metabolic energy expended by the cell over time (power) as a function of distance covered by the motile cell.
Procedure
Data analysis
Representative data and protocol validation
In the model tissue template, PDCs were observed to migrate significantly faster than BMSCs, covering a 1.4-fold and 1.5-fold greater respective distance on Day 3 and Day 7 of culture (P ≤ 0.01) (Figure 7A). Relative differences in migrating PDCs' and BMSCs' patterns, as well as cell morphologies and numbers were qualitatively apparent as well. While PDCs exhibited long cellular protrusions to form extensive networks within the Matrigel®, migrating BMSCs tended to form defined clusters of cells dispersed throughout the Matrigel. The number of PDCs migrating and forming networks increased visibly from Day 3 to 7 (Figures 7B PDCB, PDCC and BMSCB, BMSCC) compared to BMSCs, while BMSC cluster size increased (Figures 7B BMSCB and BMSCC) in the same period. PDCs and BMSCs exhibited similar metabolic activity (Figure 8) after 3 and 7 days in culture.
Figure 7. Human PDCs and BMSCs exhibit distinct migration behavior and dynamics in an EMT model. A. In pilot testing of the model, a two-way ANOVA (n = 6) showed that PDCs from a single patient migrated significantly further and exhibited higher migration rate than that of BMSCs from a single donor on Days 3 and 7. Error bars represent standard error of the mean (SEM) and asterisks (*) indicate significant differences at P ≤ 0.01. B and C. Representative stack images of PDCs and BMSCs on (B) Day 3 and (C) on Day 7. The individual image represents the different 3D views of the distance covered by both cell types, as well as the cell behavior and distribution throughout the thickness of the Matrigel®.
Figure 8. Proliferation of PDCs and BMSCs quantified as MTT absorbance and normalized to that of the control. PDCs and BMSCs show a comparable increase in cell number over ten days in culture, suggesting the comparable proliferation rate between the two cell lines. No statistically significant differences are observed between MTT absorbance of PDCs or BMSCs at Days 0, 3, 7, or 10 (significance defined as P < 0.05).
Discussion
To probe the mechanome, the mechanics equivalent to the genome, and thereby unravel relationships between the local mechanical environment of the stem cell (SC), SC mechanoadaptation and lineage commitment, we developed and tested two integrated cell and tissue model culture and imaging platforms. Our objective was to develop and test the two model platforms to enable quantitative study of mechanoadaptations and mechanomics at the tissue and cellular length scales, and in contexts emulating physiological cell and tissue environments (Sorkin et al., 2004). First, through simultaneous implementation of nuclear and cytoskeletal tagging, we integrated methods used previously in separate applications, successfully tracking cell and nucleus volume and shape changes as well as cytoskeletal actin and tubulin architectural adaptation within individual, adherent model embryonic murine mesenchymal stem cells. Then, a tissue template (Anlage) model was developed on a platform enabling ingression of cells seeded in a monolayer into a three-dimensional extracellular matrix protein comprised tissue template. The model was tested successfully using human mesenchymal stem cells including PDCs and BMSCs and showed promising initial results, enabling comparative measures of motility rates and directions. The model lends itself for refinement and expansion to emulate EMT-METs tissue and cell contexts of interest to a variety of research groups. While the two model systems have not yet been tested together in a relevant cell model, doing so would present an elegant platform for cross scale imaging of emergent structure and function in mechanobiology.
Given the spatial and temporal complexity of mechanobiology of single cells, there is an acute need to quantify effects of biomechanical stimuli. There is an imperative to measure effects of physical forces on the nucleus shape, cell volume, spatiotemporal organization of the actin filaments and tubulins, as well as gene expression associated with unfolding lineage commitment; previous measures have been carried out in both fixed and live cells (McBride and Knothe Tate, 2008; McBride et al., 2008; Song et al., 2010, 2012 and 2013; Chang and Knothe Tate, 2011). Although different protocols have been optimized and used for imaging of actin and microtubules separately in live cells (Wang et al., 2003; Suresh, 2007; Na et al., 2008; Mendez et al., 2014), this is the first protocol to our knowledge that has demonstrated simultaneous, multiplexed imaging of actin, tubulin and nucleus in live cells. Application of this protocol to live stem cells enabled simultaneous multi-color time lapse imaging of actin and tubulin, cytoskeletal adaptation, as well as changes in nucleus volume and shape. The protocol lends itself for time lapse studies of cellular mechanoadaptation during application of controlled mechanical stimuli, like a mechanical test of a living material that evolves (adapts structure and/or mechanical properties and/or biological phenotype) during the testing procedure. The protocol was tested and optimized for both stem cells and neuroblastoma cells, demonstrating its utility for different cell types. In addition, this protocol can be used to image the organization of both actin and tubulin in migrating cells in real time and in a way that has not been possible before.
The model tissue Anlage can be further tuned to include extracellular matrix constituents typical for specific tissues, from tissue templates during development to postnatal tissue healing. Experimental design can be honed to include molecular gradients specific to mechanistic pathways of interest. Of particular interest in this first feasibility study, we wanted to determine whether the rate of ingression could be measured in a standardized context as a currently underappreciated factor in regenerative medicine and intrinsic healing capacities of resident stem cell populations (Yu et al., 2017; Ni et al., 2019). The model system also lends itself well for future studies aiming to elucidate regulation of stem cell niche quiescence (Yu et al., 2017; Ni et al., 2019).
Increasingly, systems biology approaches combined with engineering innovations are leading to quantification and mechanistic elucidation of common paradigms across disparate tissues, organs, organisms and even kingdoms. In mechanomics, cell shape and fate are intrinsic expressions of form and function (Song et al., 2012 and 2013; Wang et al., 2014; Knothe Tate et al., 2011 and 2016). Through coupling of multiscale imaging and mechanical modeling, and performing mechanical tests on SCs as they adapt and differentiate, it may be possible to begin to formulate a first Law of Biology, establishing a quantitative basis and a predictive model for the relationship between stress distribution in a cell and the unfolding of cell fate.
Comparison with other methods
A number of studies have used different protocols to visualize cytoskeletal de-/polymerization, especially of actin filaments, in live cells. Some inject fluorescently labeled actin directly into living cells (Riedl et al., 2008 and 2010). In addition, direct observation of the actin dynamics through actin-GFP fusions or through fluorescent-tagged actin-associated protein had limited success mainly due to interference of the tagged protein in actin polymerization and depolymerization processes which impacts natural behavior of the filaments. Excessive fluorescent background because of the unbound G-actin has also been reported as a major problem (Riedl et al., 2008 and 2010). Despite these limitations, methods have been developed to successfully tag actin filaments without any significant effects on actin kinetics and assembly (Riedl et al., 2008; Lukinavičius et al., 2014). New techniques enable imaging of actin filaments and tubulins in live cells and at high resolution. However, the imaging of the actin filaments and tubulins simultaneously has not been demonstrated previously to our knowledge. In our previous studies, we have developed a live cell imaging technique to observe cytoskeleton spatiotemporal organization and cell mechanoadaptation during stem cell differentiation and in near real time (Zimmermann and Knothe Tate, 2011; Chang and Knothe Tate, 2011). However, these experiments were performed separately on actin and tubulin as well as the cell and its nucleus.
Limitations and Context
The BacMam protocol was shown to be efficient and efficacious for two cell lines (model embryonic murine mesenchymal stem cell line and immortalized neuroblastoma cells) but was not effective for transduction in ovine PDCs [unpublished data, MechBio Team]. Similarly, while the tissue template model platform worked well with human PDCs and BMSCs, it would need to be validated for other cell types and extracellular matrix constituents. While each refinement will require additional time investment, the presentation of the methods in their current form is intended to facilitate the process. Furthermore, the model systems require additional validation for different systems of interest and/or cell types.
Both model systems presented in this manuscript represent idealizations of actual stem cell biology in situ in living organisms. Nonetheless, they provide valuable tools for quantifying intracellular adaptation in response to controlled mechanical cues and live imaging of stem cell ingression. From an engineering perspective, the model platforms provide defined control volumes in which the boundary and initial conditions are set and hence known by the researcher, enabling quantitative spatial and temporal study of mechanomics using traditional engineering problem solving rubrics, as well as implementation of governing equations for outcome measures of interest. The systems are designed to intersect between engineering, systems biology and cell biology approaches to enable mechanistic study of mechanical properties and biological behaviors in the same system and at the same time. The platforms can be specialized for various systems of biological or physiological interest similar to microfluidics (Shemesh et al., 2015) and organ-on-a-chip approaches but are specifically designed to emulate fundamental events such as ingression towards or away from tissue templates, like MET-EMTs, that are postulated to play a key role in stem cell fate decisions.
Conclusions
The mechanisms underpinning the stem cells' innate capacity to adapt to mechanical stimuli and the role of mechanoadaptation in lineage commitment are unknown. An understanding of SC mechanoadaptation is key to deciphering lineage commitment, during prenatal development, postnatal wound healing, and the engineering of tissues. Cell shape and fate are intrinsic expressions of form and function in the most basic building element of tissues. Just as the development of experimental and theoretical mechanics, including tools to visualize and measure displacements and forces on surfaces and interiors of structural elements, led to a fundamental understanding of mechanics of materials, the development of mechanomics tools for live imaging of cell motility cell mechanoadaptation are expected to define the equivalent of a Mohr’s circle of lineage commitment (referred to herein as mapping the mechanome, Figure 1).
Acknowledgments
The authors would like to acknowledge the collaborative efforts of the MechBio Team in the Graduate School of Biomedical Engineering and infrastructure and collaboration of the Mark Wainwright Analytical Centre, in particular the Biomedical Imaging Facility at the University of New South Wales.
Author contributions
IJ refined and tested the multiplexed live cytoskeletal tracking approach with MLKT and carried out related experiments with imaging advice from RW. VP and MC developed under mentorship of MLKT and KP and RW the tissue template model system and carried out related experiments with imaging expertise from FT. We acknowledge with gratitude the extensive previous methods and protocols developed by MechBio Team members and collaborators and their publications, upon which the current protocol has been developed and tested (Sorkin et al., 2004; Anderson et al., 2006; 2007a and 2007b; Knothe Tate et al., 2008; 2010 and 2016; McBride and Knothe Tate, 2008; McBride et al., 2008; Song et al., 2010; 2012 and 2013; Chang and Knothe Tate, 2011; Zimmerman and Knothe Tate, 2011; Evans et al., 2013; Jalilian et al., 2015; Putra et al., 2019).
As noted above, parts of the protocol were used in previous publications (Putra et al., 2019; Ng et al., 2019). The actin and tubulin tagging protocols with controlled loading were also used separately in previous publications (Zimmerman and Knothe Tate, 2011; Chang and Knothe Tate, 2011).
Competing interests
In context of full disclosure, the live imaging studies of nucleus shape and volume changes and cytoskeletal adaptation were carried out using an imaging and perfusion chamber developed by Professor Knothe Tate's MechBio Team and later commercialized through a nonexclusive license agreement with Harvard Apparatus, Warner Instruments (https://www.warneronline.com/proflow-shear-flow-chamber-pfc-1). The study design and outcomes were conducted without consultation or involvement by Warner Instruments and other perfusion chamber and/or microfluidics-based platforms could be implemented with these protocols (Shemesh et al., 2015).
The studies were made possible through the generous support of the U.S. National Institutes of Health (DD and MLKT), U.S. National Institutes of Health Training Grant (recipients: SM-G, MJS), U.S. National Science Foundation (MLKT), Australian National Health and Medical Research Council (MLKT), the Paul Trainor Foundation (MLKT), and Gold and Silver Star grants from UNSW for "near miss funding" of Australian Research Council grant proposals.
Ethics
The University of New South Wales human ethics committee approved the described experiment. Informed consent was obtained from all subjects.
References
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© 2019 The Authors; exclusive licensee Bio-protocol LLC.
How to cite
Putra, V. D. L., Jalilian, I., Campbell, M., Poole, K., Whan, R., Tomasetig, F. and Knothe Tate, M. L. (2019). Mapping the Mechanome–A Protocol for Simultaneous Live Imaging and Quantitative Analysis of Cell Mechanoadaptation and Ingression. Bio-protocol 9(23): e3439. DOI: 10.21769/BioProtoc.3439.
Category
Systems Biology > Mechanomics > Mechanoadaptation
Stem Cell > Pluripotent stem cell > Cell differentiation
Cell Biology > Cell imaging > Live-cell imaging
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