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Traction Force Microscopy Reveals Stiffness Dependent Response to

Disruptions in Actin Cytoskeletal Organization

By

Max Hockenberry

Senior Honors Thesis

Department of Physics and Astronomy

University of North Carolina at Chapel Hill

10/8/2020

Approved:

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Acknowledgements:

I would like to thank the numerous individuals who have enabled me to complete this

project and encourage me throughout the process. For advising my research and constantly

engaging in my learning I would like to thank Dr. Michael Falvo and Dr. Sreeja Asokan. For

providing mentorship, lab resources, and countless critical insights I would like to thank Dr.

James E. Bear and Dr. Richard Superfine. And for fostering a welcoming and encouraging

environment as well as fielding endless questions I would like to thank all of the members of

both the Superfine and Bear labs.

Table of Contents

Chapter 1 : Traction Force Microscopy and its Use in Cell Biology...3

Section 1.1: The Cytoskeleton; its Organization, Regulation, and Relevance...3

Section 1.2: Traction Force Microscopy; Theory, Implementation, and Purpose...9

Section 1.3 Traction Force Microscopy Methods; Generation, Analysis, and Interpretation...18

Generation of Poly-Acrylamide Hydrogels with Varying Uniform Stiffness’s:...18

Functionalization of ECM Proteins to Hydrogel:...20

Optimizing of TFM Imaging with Hydrogels:...20

Traction Force Reconstruction and Analysis:...22

Automated Cell Tracking and Analysis...25

Section 1.4: Traction Force Pipeline Recapitulates Landmark Results...25

Section 1.5: Conclusions and Future Directions...26

Chapter 2 : Traction Forces in Arp2/3 and Fascin Null cells...30

Section 2.1: The Arp2/3 Complex and Fascin Function as Actin Organizing Proteins...30

Section 2.2: Disruption of the Arp2/3 Complex and Fascin Results in Disruption to Cytoskeletal Structure and Force Generation...33

JR20 Fibroblast Conditional Arp2/3 Knockout System:...33

Arp2/3 KO cells demonstrate varying traction response dependent on stiffness:...37

IA32 siRNA Fascin Knockdown System:...39

IA32 KD cells demonstrate increased traction strength on 2 kPa substrates:...41

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Section 2.3: Perturbance of the Arp2/3 Complex Results in Stiffness Dependent Ablation of

Periodic Force Generation...42

Section 2.4: Conclusions and Future Directions...47

Chapter 3 : Automated Cell Segmentation and Tracking...60

Section 3.1: Cell Segmentation is an Old and Challenging Bioinformatics Problem...60

Section 3.2: Semi-Automated Segmentation Pipeline with Accessible GUI Provides Platform for Generation of Cellular Masks...62

Section 3.3: Cell Segmentation Tool Generates Accurate Masks Compared to Hand Outlined Images...65

Section 3.4: Conclusions and Future Directions...66

Chapter 4 Concluding Remarks...68

Chapter 5 Appendix...71

Appendix A: Traction Force and Durotaxis...71

Appendix B: Additional Statistics of Arp2/3 KO Traction Metrics...71

Appendix C: Traction Force Code Packages...74

Appendix D: Table of Results...76

Appendix E: Protocols...76

Appendix F: Code Availability:...77

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Chapter 1 : Traction Force Microscopy and its Use in Cell

Biology

Section 1.1: The Cytoskeleton; its Organization, Regulation, and Relevance

Cells form the fundamental building blocks of all life and their organization, activity, and

ubiquity are responsible for the complicated structures and behaviors present throughout the

natural world. Billions of years of evolution has shaped enormously intricate cellular phenomena

such as cell motility, phagocytosis, development and myriad other examples. While fascinating

to examine on physiological merit alone, dysregulation of these phenomena often results in

debilitating disease states and other maladies. Understanding these convoluted yet delicate

systems and the events that lead to their disruption is essential for treating disease, promoting

new technologies, and even understanding the organismal effects of climate change. Traditional

approaches to studying the biology of cells has yielded enormous progress since the days of

Leeuwenhoek however, it has become increasingly apparent in recent years of the need for

interdisciplinary techniques to parse cellular complexity. In particular, the study of forces

generated and distributed during cellular events has provided crucial advances in our

understanding of various phenomena. Cells not only generate forces on their microenvironment

during their movement, but also respond to the mechanical properties of their environment in

diverse and controlled ways. Finally, it is well understood that many disease states such as

cancer are driven by not just biochemical signaling, but also by cellular response to the physical

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Figure 1-1. Common cellular structures involved with biophysical stimuli. Shown center (A) is a typical fibroblast with a DNA containing nucleus, Golgi Apparatus packaging center, mitochondria, and cytoskeletal components. Arrayed around are examples of filopodia (B), membrane receptors (C), molecular clutch integration (D), flagella (E), ribosomal translation

(F), focal complexes (G), DNA organization (H), and vesicular transport (I).

A variety of cellular events generate notable forces that are important for their proper

functioning. Many cell types depend upon both the generation and distribution of forces such as

fibroblast generation of traction forces to participate in efficient motility (Oakes, 2018),

macrophages engagement of phagocytosis which is dependent on cytoskeleton rearrangement

caused by cellular forces (Herant et al., 2006), or even specific differentiation of stem cells in

response their environments elasticity (A. J. Engler et al., 2006). These activities are often

regulated by force generation which largely depends on the organization of the cytoskeleton; a

network of coordinated proteins that give rise to cell morphology (Fletcher & Mullins, 2010).

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intermediate filaments, and actin networks. Microtubules, formed from the dimer of alpha and

beta tubulin in a tube structure, provide mechanical rigidity to the cell, facilitate scaffolding for

intracellular trafficking, make up cilia and flagella, and are the major constitutive component of

mitotic spindle used during cell division (Desai & Mitchison, 1997). Intermediate filaments form

a large group of cytoskeleton filaments that share similar sequences and are thought to directly

support the plasma membrane at cell junctions as well as maintain nuclear structure (Chang &

Goldman, 2004). Finally, actin structures cover a range of structures from the dense dendritic

networks of cortical actin to the bundled filaments found in filopodial extensions (Hall, 1998).

Of particular interest, the organization of actin and its associated proteins is essential for many

cellular phenomena including movement (Ananthakrishnan & Ehrlicher, 2007), macrophage

phagocytosis (Rougerie et al., 2013), and even development (Rauzi et al., 2008). Further, it is

widely understood that the actin cytoskeleton and its associated proteins such as myosin are

largely responsible for the generation of traction forces through focal adhesions (Beningo et al.,

2001; Zielinski et al., 2013). This actin cytoskeleton and its functions are controlled in a number

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Figure 1-2. Key cytoskeleton components include microtubules (A), intermediate filaments (B), and actin (C). Microtubules are made up of alpha and beta dimers of tubulin which combine in filaments and then into larger hollow microtubule structures. Intermediate filaments combine

through dimerization, and then in more complicated structures due to protein-protein interactions. Actin structures are organized by a number of molecular regulators into a variety

of shapes with attachment sites for numerous different proteins.

The actin cytoskeleton is controlled by several biochemical pathways which operates

through a variety of physical interactions. In general, proteins interact biochemically through a

number of macromolecular interactions including electrostatic forces, hydrogen bonding, and

Van der Waals forces. Protein organization gives rise to complex three-dimensional structure

which allows for interactions between proteins to accomplish numerous functions. These

physical interactions give rise to changes in protein structure and organization which then effects

their function and ability to do biochemical work. In the cytoskeleton, the Rho-family GTPases

including Rho, Rac, and Cdc42 work as regulators of actin dynamics (Lee & Dominguez, 2010).

These GTPases cycle between active guanosine triphosphate (GTP) and inactive guanosine

diphosphate (GDP) states. When in the active GTP form, the Rho-family GTPases interact with

actin binding proteins (ABPs) which function to form physical networks of actin filaments

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hydrolyzes turning into GDP which inactivates the GTPase. This cycle of activity is further

modulated by guanine nucleotide exchange factors (GEFs) and GTPase activating proteins

(GAPs) which function to promote hydrolysis or cycle GDP to GTP respectively. Briefly, Cdc42

controls emergence of filopodia, Rac1 the spreading of lamellipodia, and RhoA the

reinforcement of stress fibers at focal adhesions (Sit & Manser, 2011). The complexity of these

biochemical interactions allows for tight control of the formation and degradation of actin

structures which ultimately allows for cells to control these structures in response to any number

of cues.

Figure 1-3. Simple GTPase regulation by GEFs and GAPs. In general, a GTPase cycles between its on (GTP bound) and off (GDP bound) state. GAPs facilitate GTP hydrolysis to GDP while

GEFs assist nucleotide exchange of GDP to GTP.

Another way in which the actin cytoskeleton is regulated is through mechanotransduction.

Research in recent decades has demonstrated conclusively that mechanical stimuli such as stress

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direct cytoskeleton reorganization and gene expression. Forces transmitted physically through

protein connections on the cells exterior to its interior rather than biochemical cues results in

changes in genetic expression and ultimately cellular behavior. One example is the Yap/Taz

pathway which has been heavily implicated in actin regulation by mechanosensitive means

(Aragona et al., 2013). This pathway sits at the intersection of a critical cellular processes

including development, tissue homeostasis, and contact inhibition and frequent consequences of

aberrations in the pathway are the formation and proliferation of tumor cells. Interesting,

Yap/Taz activity is necessary for the growth of organs through the promotion of tissue progenitor

cell growth. Mechanosensitive pathways continue to be uncovered in relation to many cell

functions. One prominent example includes the discovery that by simply altering the matrix

elasticity of cellular substrates, one could direct stem cell differentiation into a wide range of

tissue types (A. J. Engler et al., 2006). Not surprisingly mechanosensitive pathways are

suggested to regulate cell motility where studies have demonstrated ECM stiffness strongly

impacts migration through actin and non-muscle myosin II (NMII) activity (Ulrich et al., 2009).

These examples serve to highlight the importance of mechanical cues in cellular functions but

additionally the difficulty in disentangling mechanical action from cellular biochemical response

which has serious implications for many disease states.

A wide variety of human maladies are associated with dysregulation of the cytoskeleton. A

canonical example includes disruption to the mechanosensitive hair bundle of the inner ear

resulting in deafness (Vollrath et al., 2007). In the cells that make up the hair bundle, arrays of

sensitive actin-based cilia convey forces through stretch activated channels as a response to

auditory input in the form of sound vibrations. Breaking the mechanical linkage between these

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the importance of these mechanosensitive channels in proper physiological function. Another

example includes the neural disease glaucoma which develops as a poor response to increased

pressure on cells in the eye (J. C. H. Tan et al., 2006). Even cancer metastasis is known to have

mechanotransduction components where cellular response to matrix elasticity, cell shape, and

cytoskeletal tension has been demonstrated to control the cell-cycle and can promote tumor

invasion (Huang & Ingber, 1999). The importance of considering cellular forces in the study of

abnormal cell physiology is abundantly clear.

Cells are complex biological units that engage in a slew of behaviors mediated by protein

interactions. The cell contains many important organelles, with the cytoskeleton providing a cell

shape, rigidity, and the ability to mechanically respond to its local environment. Cells also use

their cytoskeleton to generate forces on their environment during events including motility and

phagocytosis. The mechanical linkage a cell maintains to its outside world is controlled tightly

by a host of biochemical players which are in turn controlled by intricate feedback systems.

Further, the disruption of a cell’s feedback systems or ability to generate and distribute forces

effectively is linked to many disease states. Research concerning these disruptions to

mechanotransduction has increased dramatically in recent decades which has served to only

illuminate how little we understand about cellular forces. In order to examine the forces a cell

exerts, a variety of advanced interdisciplinary techniques have been developed including

molecular tension sensors, cutting edge imaging, atomic force probes, and traction force

microscopy.

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A variety of techniques exist to study cell-generated forces including collagen contraction

assays, micropillar arrays, molecular sensors, and traction force microscopy (TFM). Each of

these tools comes with its own set of considerations but almost all require extensive

interdisciplinary knowledge which serves to hinder their implementation in the wider biological

sciences field (Polacheck & Chen, 2016). Of these methods, traction force microscopy in both

two and three dimensions has been readily implemented due to its low barrier of entry as it does

not require intensive fabrication or specialized tools (Style et al., 2014). The tradeoff for relative

ease of use is computationally expensive operations and some difficulty in directly interpreting

results. Despite this, traction force microscopy continues to be utilized to answer a variety of

biological questions ranging from probing forces generated by individual organelles, to forces

generated within large tissue structures and will likely remain relevant for many years

(Colin-York & Fritzsche, 2018).

Traction force microscopy has undergone many evolutions from the initial efforts using

deformable silicone rubber sheets (Harris et al., 1980), to more modern techniques involving

poly-acrylamide (PAA) gels containing fiduciary fluorescent beads which displace as a result of

cellular stresses on their microenvironment (Schwarz & Soiné, 2015). These PAA hydrogels

have a tunable stiffness in physiologically relevant ranges and can be manufactured using

common lab reagents and equipment. Briefly, PAA hydrogels are generated with embedded

fluorescent report beads (usually of sub-micron size) and functionalized with an ECM protein

such as fibronectin or collagen. Then cells are seeded on the gel and then imaged. The cells will

exert traction forces on their external environment which will be detected by movement of the

reporter beads. After imaging, a detergent such as Triton X-100 or sodium lauryl sulfate (SDS)

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Then the images of the beads under traction stress are compared with the reference image to

obtain a displacement field which can be further used to obtain a stress field. Many techniques

exist to convert a displacement field to a stress field and include techniques like boundary

element modelling (BEM) or Fourier Transform Traction Cytometry (FTTC) which make use of

the Boussinesq equation. Finally, regularization is often implemented to eliminate non-physical

solutions as the Boussinesq equation is famously ill-conditioned meaning that there are many

non-physical solutions. This computationally intensive method results in maps of cellular

traction stresses which can be analyzed for any number of properties including average traction,

strain energy, net contractile moments, or patterns of strain distribution. TFM provides a method

of comparing contractile forces between cells, analyzing changes in the distribution of those

forces, and measure the mechanical energy a cell exerts. These properties are particularly

important when examining cellular phenomenon such as cell motility.

Figure 1-4. Generic Traction Force Microscopy analysis pipeline. Typically, numerous correction and regularization steps occur between these steps to decrease high frequency noise

from experiments.

The mathematical modeling of 2-D traction force microscopy relies on elastic half plane

theory and the inversion of a Green’s function converting between displacements and forces.

This theoretical framework considers forces applied to the free surface of an elastic medium

whereby the medium takes the form of an infinite half plane where all deformations due to forces Bead Displacement Tracking

Bead

Displacement

Tracking

Displacement Field Calculation

Displacement

Field

Calculation

Force Field

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vanish at infinity (Figure 1-4). The derivation of this model results in the following set of

equations (Equations 1-1, 1-2, 1-3) describing the displacement vectors as a result of a point

force in the elastic half plane (Landau & Liftshitz, 1970).

Figure 1-5. Infinite elastic half plane which deforms (μ) as a result of a force (F) applied on the plane of known elastic modulus.

ux=1+ν

2πE

{

[

xz r3−

(1−2ν)x r(r+z)

]

Tz+

2(1−ν)r+z

r(r+z) Tx+

[

2r(νr+z)+z2

]

x

r3(r+z)2

(

x TEq. 1-1x+y Ty

)

}

uy=1+ν

2πE

{

[

zy r3−

(1−2ν)y r(r+z)

]

Tz+

2(1−ν)r+z

r(r+z) Ty+

[

2r(νr+z)+z2

]

y

r3(r

+z)2

(

x TEq. 1-2x+y Ty

)

}

uz=1+ν

2πE

{

[

z2 r3−

2(1−ν) r

]

Tz+[

1−2ν

r(r+z)+ z

r3]

(

x Tx+y Ty

)

}

Eq. 1-3

Where the Poisson ratio is σ, E is the Young’s Modulus of the elastic half plane, r is the distance

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While useful, this formulation is the inverse of the data obtained from TFM; that is, Equations

1-1, 1-2, and 1-3 describes displacement as a function of force as a function of displacement. Bead

displacements must then be converted into traction forces through the use of an inverse Green’s

function as follows:

u(⃗x)=(G(⃗x)∗⃗T(⃗x)) Eq. 1-4

Where G( ⃗x) is the Green’s function (tensor that maps a traction field to the displacement field),

T(⃗x) is the traction field, and u⃗( ⃗x) is the displacement field. Then by rearrangement and

substitution from above in the x/y dimensions:

G(⃗x)=1+ν

πE 1 x3

(

(1−ν)x2+ν x12 ν x1x2

ν x1x2 (1−ν)x2+ν x22

)

Eq. 1-5

|x|=

x12+x22 Eq. 1-6

The inversion operation produced by this formulation presents a difficulty in that the inversion is

not diagonal and thus nontrivial to invert. This is because tractions at one spatial position are not

necessarily mapped to displacements at the same position in real space. Interestingly, the

Boussinesq solution can be transformed into Fourier space through convolution which results in

matrix that is diagonal and thus trivial to invert (Butler et al., 2002). We can apply a Fourier

transform to G( ⃗x) and ⃗T(⃗x) with the formulation now depending on a spatial wave vector k

(Equations 1-7 through 1-10) whereas we can rewrite the equations as before.

^⃗

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^

G

(

k

)

=1+ν

πE 1 k3(

(1−ν)k2

+ν k12 −ν k1k2

ν k1k2 (1−ν)k2+ν k22) Eq. 1-8

|

k

|

=

k12+k22 Eq. 1-9

^ ⃗

T(⃗k)= ^G

(

k

)

−1u^⃗(⃗k) Eq. 1-10

In the simplest case, inversion of this solution would allow for the traction field to be obtained

from the displacement field. Unfortunately, this solution is ill-conditioned meaning small

variations in initial conditions can lead to wildly varying solutions; otherwise known as an

unstable solution. Because experimentally obtained displacement fields unavoidably contain

noise, the solution traction field is likely to be divergent and nonphysical. In order to resolve this,

a technique called regularization is often applied. Regularization allows all problems related to

real phenomena to have stable solutions and thus convert and ill posed problem’s solutions into

physical solutions (A.N Tikhonov & Arsenin, 1977). This powerful technique is used throughout

a variety of mathematical and physical fields. Commonly the Tikhonov regularization method is

utilized as demonstrated below (Alexander N. Tikhonov, 1963). This method states that the

minimum of the residual and solution norms results in a solution that appropriately filters and

minimizes noise as compared to an unregularized solution.

min

{

G^⃗T^λ− ^⃗u

2+λ2

T^λ

2

}

Eq. 1-11

With the residual norm being

G^⃗T^λ−^⃗u

2 the solution norm

T^λ

2and the regularization

parameter λ which controls the smoothing operation on the solution. Finally, by varying λ we

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smooth curve in the traction field from points of high stress and consequently we can write our

full equation in Fourier space as a function of regularization parameter λ.

^ ⃗

¿

(

G^ T^

G+λ2I

)

−1G^Tu^⃗ Eq. 1-12

With G^T being the matrix transpose of the Greens function and I the identity matrix. The

selection of the regularization parameter becomes critical for obtaining reasonable solutions as

choosing a parameter that is too large results in an over-smoothed traction field while one that is

too small results in an under-smoothed traction field (Figure 1-7). To determine the optimal

value for the regularization parameter the residual norm is blotted against the solution norm

resulting in an L-shaped curve (Figure 1-6). The curve’s corner contains both the lowest residual

norm and the lowest solution norm which strikes a balance between over and under smoothing

when fitting the data (Han et al., 2015) and allows for reproducible regularization and ultimately

the ability to compare samples in a data set.

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Figure 1-7. Varying Regularization parameters are applied to the same frame of a traction force movie showing how choice of regularization can wildly impact obtained average traction force

values and perceived results.

Recent technological advances have allowed for dramatically increased resolution in traction

force microscopy techniques. Because stress field resolution is related to sampling of the

displacement field and ultimately fiduciary bead density, usage of multiple colored beads and

correlation statistics yields dramatic increases in traction resolution (Sabass et al., 2008). By

introducing additional chromatically separated fluorescent beads and combining their separate

image channels after imaging, one can obtain increased bead field density without sacrificing

resolution which results in smaller mesh size for computation. Additionally, some success with

incorporating super resolution microscopy techniques such as simulated emission depletion

λ = 0.01

λ = 0.0001

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microscopy (STED) has further pushed the boundary of measuring minute cellular forces on the

sub-micron level (Colin-York et al., 2016). This technique functions by selectively

photo-bleaching fluorescent beads in a sample while leaving some available to fluoresce in a given

area. Resolutions in the tens of nanometers are commonly reported for many applications.

Finally, advanced computational methods have allowed for resolving diffraction limited features

in much the same manner as super-resolution microscopy (Han et al., 2015). By carefully

choosing one’s regularization scheme, it is possible to reconstruct cellular tractions from

structures like nascent adhesions previously below the ability to observe. This process works by

taking advantage of the sparsity of solutions generated using L1 regularization (L1 norm) where

an incomplete sampling of frequencies occurs as with experimental data (Candès et al., 2006).

When using L1 regularization, a regularization parameter can be selected such that it is the

inflection point of the L-curve (deemed λ-optimal). This value has been demonstrated to not only

accurately reconstruct tractions from large and small stresses, but also to minimize noise spikes

which tend to appear as small tractions (Han et al., 2015). Each of these methods has pushed the

boundaries of detection for traction force microscopy and promise to allow exploration of

cellular traction forces on ever smaller scales.

Traction force microscopy is well suited to answer a variety of cellular biology questions

ranging from the mechanical mechanism of cell motility, to quantifying the forces macrophages

exert during phagocytosis. Significant work has used traction force microscopy to examine the

biophysical nature of basic cellular structure and behavior. For example, multipole analysis of

traction stresses helped develop a mechanical model of cellular migration (Tanimoto & Sano,

2014) while traction force on confined cells highlighted the dependence of traction localization

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proteins such as vinculin has also been probed where traction force formed the basis of analyzing

how vinculin functions to generate and dissipate mechanical energy (Rosowski et al., 2018). Our

understanding of frustrated phagocytosis; the process by which macrophages struggle to engulf

substrate bound antibody markers, has seen remarkable advances through quantifying sub

cellular force patterns (Kovari et al., 2016; Vorselen et al., 2020). Even cell biology has found

numerous uses for traction force where both mechanisms of mechanical memory in collective

migration forces and the role of matrix elasticity in cancer metastasis has been described

(Nasrollahi et al., 2017; Wisdom et al., 2018). Together, traction force microscopy has greatly

contributed to our appreciation of the mechanical nature of cell behavior and the usage of

cellular forces for a variety of cellular phenomena.

Section 1.3 Traction Force Microscopy Methods; Generation, Analysis, and Interpretation

Generation of Poly-Acrylamide Hydrogels with Varying Uniform Stiffness’s:

PAA hydrogels were fabricated using the method published by Knoll et. Al. Briefly, both

a 12mm coverslip and a 35mm glass bottomed cell culturing dish are plasma cleaned for three

minutes. The coverslip is coated in 50 μL of 0.1% Poly-D-Lysine (PDL, Gibco catalog number

A3890401) for one hour and then dried off with compressed air. The coverslip is then coated

with a 1/5000 dilution of 200 nm diameter carboxylate fluorescent dyed Styrofoam beads (Fisher

FluoSpheres catalog number F8810) for ten minutes and dried off with compressed air. This step

binds the traction force beads on the coverslip surface, which are later to be transferred onto the

gel. The next step involves activating the glass surface in the 35 mm dish, to introduce the proper

binding groups required to covalently link the polyacrylamide gel onto the glass. To do so, the

plasma cleaned 35mm glass bottom cell culturing dish is coated with 250 μL of 0.5%

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drying off with compressed air. Then the glass bottom dish is coated with 400 μL of 0.5%

Glutaraldehyde (Sigma, SKU 354400) and incubated for 30 minutes followed by drying off with

compressed air. Now, the polyacrylamide gel can covalently bind to the glass surface.

Table 1-1. Tabulated Recipes for generating polyacrylamide hydrogels of desired stiffness as described. Adapted from Tse, Engler 2010. Additionally, 5 μL of acrylic acid is added to each 10mL gel premix.

Acrylamide Percent (%) Bis-Acrylamide Percent (%) Amount of Acrylamide from 40% stock (mL) Amount of Bis-Acrylamide from 2% stock (mL) Amount of ddH2O (mL)

Nominal Stiffness (kPa)

4 0.1 1 0.5 8.5 2.01 ± 0.75

5 0.3 1.25 1.5 7.25 8.73 ± 0.79

8 0.48 2 2.4 5.6 40.40 ± 2.39

Figure 1-8. Basic chemistry for attaching PAA gels covalently to glass bottom dishes. As described above, the glass is first treated with 3-APTES which presents a primary amine for

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Functionalization of ECM Proteins to Hydrogel:

ECM proteins like fibronectin are functionalized to the surface of the PAA hydrogels by

a NHS/EDC reaction (Knoll et al., 2014). Briefly, hydrogels prepared as previously described are

washed 5x with DPBS (Gibco Catalog Number: 14190250) in a biological hood before being

incubated at room temperature with soak solution (137 mM NaCl, 5% (v/v) glycerol) for one

hour. Then the soak solution is aspirated in a biological hood and the gels are incubated with 2x

conjugation solution (0.2 M MES, 10% (v/v) glycerol, pH 4.5), 10x

1-ethyl-3-[3-dimethylaminopropyl]carbodiimide hydrochloride (EDC,150 mM in DI water), and 10x

N-hydroxysulfosuccinimide (NHS, 250 mM in DI water) for thirty minutes in the dark at room

temperature. Again, the conjugation solution is aspirated in a biological hood before 250 μL of

50 µg/mL fibronectin is incubated with the hydrogel at 4°C overnight. The following day the gel

is washed 3x with DPBS before being ready for experiments. Functionalized gels are stored in

DPBS at 4°C and used within three days of preparation.

Optimizing of TFM Imaging with Hydrogels:

Imaging cellular movements in PAA hydrogels requires special considerations. In most

microscope setups, the objective is below the hydrogel requiring imaging through the height of

the gel prior to visualizing the cell and the fluorescent beads. Because hydrogels have an index

of refraction differing from both glass and the solution immersing cells, often distortions can be

observed at higher magnifications. This effect can be mitigated through using objectives with

long working distances, silicon or water immersion objectives, or using a lower magnification.

Additionally, the quality of displacements recovered from imaging can be affected by cell height.

In some cases, the nucleus of a cell can act as an optical lens causing beads underneath to appear

heavily distorted and thus introducing dramatic error into traction force measurements (Figure

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effect, but in the case of fibroblasts in our study it is often easier to select cells that are well

spread which do not display this effect. Rounded cells in the case of fibroblasts are often

indicative of ‘sick’ or potentially dying cells.

Figure 1-9. Rounded cells heavily distort underlying traction force beads leading to errors in computing bead displacements. Left shows a phase contrast image of a rounded cell and right

shows the traction beads where under the cell a heavy distortion is observed.

A variety of transmitted light techniques exist to capture images of cells. Among them,

differential interference contrast (DIC) and phase contrast are two of the most popular. In the

case of imaging cells migrating along PAA gels, both DIC and phase contrast strongly illuminate

heterogeneities in the gel substrates such as contaminants or inconsistencies in the substrate

(Figure 1-10). While this does not affect the fluorescent channel or cell behavior, this can often

complicate automatic computational analysis as used for thresholding and segmenting cells. This

effect is variable depending on the quality of the gel and reagents used in its preparation, but

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Figure 1-10. Phase contrast and DIC images of MEF cells on soft PAA substrates. Phase contrast is demonstrated on the left and DIC is shown on the right. In both images a number of image artifacts are present which can be subsequently identified and removed during the image

analysis. Traction Force Reconstruction and Analysis:

Traction forces are recapitulated using open software developed and shared by the Danuser

lab (Danuser et al., 2020). Briefly, processed images with fluorescent fiduciary beads are opened

and inputted along with their associated properties such as pixel size and time interval between

frames. Then the images are drift corrected using a sub-pixel detection algorithm followed by

bead displacement detection. The bead displacements are built into a displacement map that is

then used to calculate the stress field using the previously described methods. Finally, various

values are calculated for the image field of view and outputted as a traction map to be further

analyzed. A standard 200 frame, 16-hour cell motility movie takes on the order of three hours to

completely process on a dedicated Windows desktop machine (Intel Xeon CPU E5-1650 v4

@3.60 GHz, 96.0 GB RAM). After processing of traction force movies is completed, a number

of associated MATLAB based scripts can be run to compute various traction force metrics.

Traditionally, a number of metrics are used to examine traction forces and include average

traction force and strain energy (Equations 1-13, 1-14). Average traction force is a measure of

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energy is representative of the total elastic energy stored by the cell in its substrate and is a

measure of mechanical output typically on the order of femtojoules.

σ=⃗F

A Eq. 1-13

U=1

2

σ•⃗μ dA Eq. 1-14 Strain energy is an expression of the total mechanical output of a cell which has been

demonstrated by many to be a function of cellular spread area (Califano & Reinhart-King, 2010).

In order to make meaningful comparisons between different conditions on unconstrained cell

movements, cell spread area is taken into account by first computing the cell’s spread area and

then dividing the total strain energy by the area to obtain a density of strain energy (Equation

1-15). For unconstrained cells of the same cell line, we find that strain energy generally increases

strongly with cell area (Figure 1-11) and normalizing using cell spread area generates a

consistent metric across cells of various sizes.

U/A= 1

(25)

Spread Area Vs. Strain

0 500 1000 1500

0 10 20 30

Spread Area (um^2)

S tr ai n E n er g y (f J)

Figure 1-11. Plot of cell spread area versus strain energy demonstrates a positive relationship between spread area and strain energy. Single cell on 8 kPa PAA traction force substrate over

16 hours.

Net contractile moment is a scalar value that is representative of the contractile strength

of a given cell (Zielinski et al., 2013). It considers the magnitude, direction, and distance from

the cell centroid to compute contractile moments of a cell (Equation 1-16).

Mij=1

2

[

xi~Tj(x , y)+xj~Ti(x , y)

]

dA Mnet=Mxx+Myy

Eq. 1-16

While the metrics of strain energy, average traction force, strain energy density, and net

contractile moment are typically sufficient to probe complex cellular force response to

treatments, additional correlative analysis is also leveled on time series data as necessary to

(26)

Automated Cell Tracking and Analysis

In order to examine cell motility, an automated image analysis pipeline was constructed

to take advantage of high-throughput imaging. While various automated cell tracking has been

attempted previously, little success has been achieved without fluorescent labeling of cells.

Because traction force microscopy necessitates sparse seeding of cells, automated segmentation

and thresholding is possible with relatively simple algorithms. Briefly, a set of contrast filters are

applied to a set of images to first obtain a thresholding value which is then applied to the image

set with another set of contrast filters. This process is further discussed at length in Chapter 3.

Section 1.4: Traction Force Pipeline Recapitulates Landmark Results

In order to assess the accuracy of our traction force pipeline we first sought to recover

trends in cell average traction force and strain energy as stiffness was varied. Variable stiffness

polyacrylamide substrates were generated as described at 2, 8, and 40 kPa. These substrates were

functionalized with fibronectin and then seeded with mouse embryonic fibroblasts. Motile cells

were imaged for 18 hours in both transmitted light and fluorescent imaging. The obtained images

were processed as described previously and analyzed for trends in average area, average traction

force, and average strain energy density (Figure 1-12). Previous work by a number of labs has

identified a number of consistent trends among traction force reconstructions including that as

substrate stiffness increases; average traction force increases, and strain energy remains fairly

constant. Further, treating fibroblasts with the drug blebbistatin is known to decrease contractile

forces by inhibiting myosin motor activity. Blebbistatin treatment of JR20 fibroblasts and

subsequent analysis with our traction force pipeline demonstrates a significant drop in strain

energy after treatment in line with our expectations (Figure 1-13). Our results in JR20 fibroblasts

(27)

Figure 1-12. Strain energy density and average traction force versus stiffness in JR20 fibroblasts. Strain energy density shows no significant variation at increasing stiffness while average traction increases significantly (* p < 0.05, ** p < 0.01, *** p < 0.001, n ≥ 15 cells per

condition, time averaged over 16 hours for each).

0 10 20 30 40 50 60

0 0.2 0.4 0.6 0.8 1 1.2 Time (minutes) N o rm a liz e d S tr a in e n e rg y

Figure 1-13. Normalized strain energy during 10 μM blebbistatin wash in experiment. Blebbistatin washed in at 20 minutes.

(28)

Traction force microscopy provides a valuable tool to examine cellular force generation. Our

traction force pipeline takes advantage of existing open source software while building a

surrounding computational framework to better handle large amounts of data while probing

dynamic cellular behavior. While able to recapitulate average single time measurements of

traction forces such as average traction force and strain energy, and by increasing computational

throughput we can examine large scale dynamic cellular behavior which is particularly relevant

for questions of cell motility. We have demonstrated the ability to examine traction forces on the

scale of many hours with appropriate resolution to target the mechanical nature of a cellular

phenomenon as we have further enhanced an existing traction force pipeline with significant

automation in both pre and post processing. Our approach returns internally consistent results

and is able to repeat observations noted by numerous groups using traction force microscopy to

study cellular behavior. We have further defined and utilized metrics such as strain energy

density, net contractile moment, and correlation analysis to define the phenomena of long-term

traction forces during cell motility. This platform allows for both a quantitative and critical

method of examining cellular forces during biological events such as motility or phagocytosis

where previous efforts were restricted to largely small-time scales.

A number of key components will be expanded upon in future studies. While we followed

established protocols for generation of our PAA hydrogels, it would be prudent to more closely

examine their mechanical properties through the use of confocal imaging and atomic force

microscopy. In particular, confocal imaging of the hydrogels would allow for accurate

volumetric scans of the fluorescent bead distribution within the gel to confirm their localization

to the topmost surface. If necessary, scanning electron microscopy could also be utilized to get a

(29)

fibronectin on the gel’s surface. Atomic force microscopy can be used to obtain measurements of

the gels elastic modulus which is an essential variable in traction force analysis. Validation is

also an important element of future work. While our obtained results match literature and are

well within acceptable limits, it is important to fully characterize the system with test cases such

as point forces. Modeling a known displacement due to a known traction force and then testing if

the pipeline can accurately recapitulate the known force is a critically important experiment.

Further, a number of additional drug treatments can be used to modify cell contractility and thus

their traction forces. A more detailed study probing the effects of these drug treatments would

provide a better understanding of the dynamic range and sensitivity of our method while putting

results into important context with other studies within the field. Finally, several advanced

metrics and approaches such as super resolution traction force and three-dimensional traction

force could be developed to push the boundary of our understanding of the role traction forces

play during dynamic cellular activity.

As biological questions build in complexity, research has become increasingly dependent on

interdisciplinary methods to understand biological systems. The method by which cells control

and respond to forces in their environment during biologically relevant activities is only

beginning to be understood through the usage of tools like traction force microscopy. Future

endeavors will continue to rely on combining approaches to more holistically discern underlying

details in mechanobiology over physiologically relevant time scales. As complex systems

biology and computational advancements increase, it has become possible to ask and even

(30)
(31)

Chapter 2 : Traction Forces in Arp2/3 and Fascin Null cells

Section 2.1: The Arp2/3 Complex and Fascin Function as Actin Organizing Proteins

The actin cytoskeleton performs a number of key cellular functions related to maintaining

morphology, generating protrusive forces, and regulating transcription. Actin itself is a

monomeric protein with a number of isoforms and a large number of binding partners which

facilitates its diverse range of functions (Dominguez & Holmes, 2011). Of particular interest to

traction generation, actin monomers can be polymerized into complicated structural elements

through the action of actin bundling proteins of which the Arp2/3 complex and Fascin play

important roles (Goley & Welch, 2006; Vignjevic et al., 2006). Further, these structures interact

with the molecular motor myosin II to drive contractility in many cell types in a highly organized

manner (Murrell et al., 2015). Disruption of the delicate arrangement of the actin cytoskeleton

through a variety of pharmacological treatments that target the Arp2/3 complex or stabilize actin

filament polymerization has been demonstrated to alter cellular traction forces and their patterns

of generation (Hui et al., 2015; Lee & Dominguez, 2010). The connection between actin

cytoskeletal organization and resulting downstream effects on traction generation is readily

apparent, however the exact role of actin organizing proteins like the Arp2/3 complex and Fascin

during force generation is not yet clear.

A key structural component of force generating structures in fibroblasts is the Arp2/3

complex which is responsible for the formation of the lamellipodia in motile cells (Wu et al.,

2012). The Arp2/3 complex is a grouping of seven protein subunits which like other actin

(32)

mother filament at an approximately 78° angle while in an activated state (Swaney & Li, 2016).

Arp2/3 nucleation is also further regulated by WASPs and SCAR/WAVEs proteins which

function to control polymerization by driving the Arp2/3 complex into its activated state in a

localized manner (Bear et al., 1998; Davidson & Insall, 2013; Pollitt & Insall, 2009). In

fibroblasts, Arp2/3 is largely localized to leading edge protrusions and plays an active role in not

only generation of lamellipodia, but also directed migration in the form of response to surface

bound cues but is not required for response to chemical gradients (Mingle et al., 2005; Wu et al.,

2012).

Figure 2-14. The Arp2/3 complex nucleates actin at approximately 78° angles from the mother filament. This creates a dense branched actin network at the leading edge of many cell types and

is crucial for the formation of the force generating lamellipodia.

As another key actin bundling protein, Fascin has been demonstrated to be critical for the

formation of bundled actin in the context of filopodia: small fingerlike membrane protrusions

which have been suggested to play a role in directional probing during cell motility (Vignjevic

(33)

places and thus bands of Fascin connect fibrils of actin into larger bundles which serves to not

only mechanically support filopodia, but interestingly is found in lamellipodia (Jansen et al.,

2011; Yang et al., 2013). Fascin bundling is regulated by protein kinase C (PKC) which binds to

Fascin and phosphorylates it which inactivates is bundling activity (Yamakita et al., 1996).

Recent research has suggested that not only is Fascin relevant to filopodial extensions, but

lamellipodial Fascin serves a critical role in aligning actin filaments against the leading edge and

further is a major contributing factor to lamellipodial elasticity (Tanaka et al., 2019). Fascin’s

role as an actin organizing protein and its importance in structuring force generating actin at

cellular leading edges leaves Fascin as an attractive molecule for study with traction force

microscopy.

Figure 2-15. Fascin is a monomeric actin bundling protein that binds actin filaments to generate bundled actin in long straight fibrils which functions as a crucial component of filopodia.

The actin organizing proteins Fascin and the Arp2/3 complex have a rich history of

relevance to not only cell motility, but also in the study of various disease states including cancer

metastasis, platelet abnormalities, inflammatory disease, and blood disorders (Hashimoto et al.,

(34)

particular note, disruption of Arp2/3 results in decreased cell motility and lack of response to

certain directed migration cues while increased Fascin expression is thought to promote motility

and response to haptotactic gradients (Adams, 2004; Jawhari et al., 2003; Wu et al., 2012). While

it is understood that both Arp2/3 and Fascin are critical actin bundling proteins responsible for

aiding in cellular motility in an important disease context, little is known about cellular force

response to their disruption on physiologically relevant soft substrates. In order to better

understand how this facet of actin bundling proteins and organization impacts cellular force

generation, we turned to traction force microscopy to characterize traction forces exerted by

fibroblasts with disruptions to the Arp2/3 complex and Fascin mediated actin bundling.

Section 2.2: Disruption of the Arp2/3 Complex and Fascin Results in Disruption to Cytoskeletal Structure and Force Generation

JR20 Fibroblast Conditional Arp2/3 Knockout System:

Previous work in the Bear lab identified a novel method of generating a genetic knock

out of the Arp2/3 complex without sacrificing cell line stability; once thought impossible due to

the central importance of the Arp2/3 complex to a variety of cellular processes (Wu et al., 2012).

Briefly, a lentiviral shRNA knockdown-rescue system was used to deplete the p34Arc or Arp2

subunit of Arp2/3 while expressing a GFP tagged subunit in a cell line lacking both p16INK4a and

Arf. While these cells were depleted of Arp2/3, regular culturing over a few passages would

result in re-expression of the original gene product. A more robust Arp2/3 knockdown was

achieved by usage of a Cre-LoxP system where the Arpc2 gene can be conditionally excised

from the genome (Figure 2-3). Briefly, LoxP sites are genetically introduced onto either side of

the gene of interest (Arpc2) and when treated with Tamoxifen, genetic recombination with the

Cre recombinase results in gene activation, repression, or in our system excised based on

(35)

depletion of the Arp2/3 complex as demonstrated by quantitative western blotting and

immunofluorescence. It is also reported that the loss of the Arp2/3 complex in this context had

no significant effect on cell proliferation.

Figure 2-16. Genetic diagram of Cre-LoxP excision of the Arpc2 gene in JR20 fibroblasts. Addition of Tamoxifen results in the Arpc2 gene being excised and subsequent depletion of the

Arp2/3 complex over the course of three days.

Depletion of Arp2/3 in MEF cells on functionalized glass substrates is known to disrupt

lamellipodial formation and defects in focal adhesions and cell spreading (Wu et al., 2012). Little

however was known about how disruption of the Arp2/3 complex effected morphological

features of fibroblasts on soft (1-40 kPa) substrates which more closely mimicked physiological

conditions. In order to address this, soft polyacrylamide traction force substrates were generated

with uniform elastic moduli of 2, 8, and 40 kPa to probe how cell morphology changes with

depletion of the Arp2/3 complex on soft substrates. Time averaged cell spread area, cell

perimeter, and average cell velocity were computed for both parental and Arp2/3 KO cells

(36)

Figure 2-17. DIC and phase contrast imaging of JR20 parental and Arp2/3 KO cells on PAA substrates of varying stiffness demonstrates morphological changes. While both cells struggle to

spread and polarize efficiently on 2 kPa substrates, Arp2/3 KO fibroblasts do not generate lamellipodia on either 8 or 40 kPa substrates. Further, leading edge protrusions for Arp2/3 KO

cells appeared fibular and elongated on soft substrates as compared to parentals.

Parental

Arp2/3 KO

2 kPa

8 kPa

(37)

Figure 2-18. Quantification of cellular spread area and perimeter as stiffness increases in both Parental and Arp2/3 KO cells. For both perimeter and spread area, no significant difference was

observed at 2 kPa however as stiffness increased to 8 and 40 kPa, Arp2/3 KO cells displayed significantly increased spread area and perimeter (* p < 0.05, ** p < 0.01, *** p < 0.001, N =

2, n ≥ 10 per condition).

Figure 2-19. Quantification of perimeter/surface area ratio and average cellular velocity as stiffness increases for Parental and Arp2/3 KO cells. While perimeter to surface area ratio did not significantly change between all values tested, velocity of Arp2/3 KO cells was significantly

Spread Area vs. Stiffness

0 5000 10000 15000

n.s ** *

S p re ad A re a (u m ^ 2)

Perimeter vs. Stiffness

0 2000 4000 6000 8000

n.s ** ***

(38)

higher at both 8 and 40 kPa (* p < 0.05, ** p < 0.01, *** p < 0.001, N = 2, n ≥ 10 per condition).

We note a number of interesting disruptions to cellular morphology on soft substrates

after depletion of the Arp2/3 complex. While Arp2/3 KO and parental cells did not display

dramatic differences in cell shape at 2 kPa, the loss of the ability to form a lamellipodia becomes

increasingly apparent at both 8 and 40 kPa where Arp2/3 KO cells adopt an elongated shape

(Figure 2-4). While still capable of moving, Arp2/3 KO cells do so through a more filamentous

motion that backfills as compared to parental cells which demonstrate a creeping motion with its

lamellipodia (Supplemental 2). As stiffness increases, Arp2/3 KO cells have an increased

average spread area, perimeter, and velocity as compared to parental cells. Interestingly, this

effect is exasperated as stiffness increases from 8 to 40 kPa while at 2 kPa each of these metrics

is not significantly different between Arp2/3 KO and parental cells.

Arp2/3 KO cells demonstrate varying traction response dependent on

stiffness:

Traction force microscopy was performed on Arp2/3 KO cells on PAA substrates of

elastic moduli 2, 8, and 40 kPa as described previously. Resulting traction maps were analyzed

for average strain energy, strain energy density, net contractile moment, and traction force

(Figure 2-7). We note that Arp2/3 depleted cells on 2 kPa substrates do not deviate from parental

cells but as stiffness increases to 8 kPa, Arp2/3 KO cells exert greater strain energy density and

have increased contractility. On 40 kPa substrates, Arp2/3 KO cells show decreased traction

(39)
(40)

IA32 siRNA Fascin Knockdown System:

Fascin was perturbed using a small interfering RNA (siRNA) in IA32 MEFs; an

established fibroblast cell line used in previous studies in the Bear lab (Andrade et al., 2015;

Rotty et al., 2015). Briefly, an approximately 20 BP complementary sequence of RNA is

transfected into cultured cells which through the action of the Dicer enzyme results in the

complementary mRNA to be degraded and thus preventing gene products from being expressed.

A short complementary sequence to Fascin-1 is generated and transfected into IA32 cells

resulting in degradation of Fascin mRNA and eventual depletion of Fascin in cells. Previous

work with this system determined that not only was Fascin mediated f-actin bundles critically

important for directing lamellipodia formation and orientation, but also that depletion of Fascin

eliminated fibronectin directed haptotaxis but not PDGF chemotaxis (Johnson et al., 2015).

(41)

Figure 2-22. Fascin KD results in an increase in average cell perimeter and cell velocity compared to parental cells at 2 kPa while spread area and perimeter to spread area ratio is not

significantly different (* p < 0.05, ** p < 0.01, *** p < 0.001, N = 1, n ≥ 10 per condition)..

When compared to parental cells, IA32 Fascin KD cells display both motility and

polarization on 2 kPa hydrogels. It is immediately obvious however that Fascin KD cells show a

clear lack of defined structure within the leading edge as apparent in parental cells. This

disruption of structure is demarcated by sharp contrast that runs along the length of the cell

approximately 10 μm from the leading edge of lamellipodia and closer in regions absent of

lamellipodia (Supplemental 2). On 2 kPa PAA substrates, knockdown of Fascin in IA32 MEFs

results in an increased average cell perimeter and velocity compared to parental lines while not

(42)

IA32 KD cells demonstrate increased traction strength on 2 kPa substrates:

IA32 parental and Fascin KD cells were analyzed using traction force microscopy on 2

kPa PAA substrates as described previously.

Figure 2-23. IA32 Fascin KD cells demonstrate increased average traction, strain energy, and strain energy density (* p < 0.05, ** p < 0.01, *** p < 0.001, N = 1, n ≥ 10 per condition).

In our study of Fascin depletion on soft 2 kPa gels we noted a significant increase in

average traction, strain energy, and strain energy density. We also observed a small increase in

average net contractile moment; however, the sample size did not provide enough resolution to

(43)

Cell Culturing:

Cells were cultured with DMEM supplemented by 10% FBS and 292 μg/mL

L-glutamine. Parental JR20 cells were treated with 50 uM Tamoxifen and allowed to incubate for

three days before exchanging DMEM. IA32 parental and Fascin knockdowns were thawed from

stored cell stocks and used immediately after verifying cell integrity.

Section 2.3: Perturbance of the Arp2/3 Complex Results in Stiffness

Dependent Ablation of Periodic Force Generation

Analysis of fibroblast traction forces over longer time periods reveals a dramatic

periodicity associated with several traction metrics (Figure 2-11). Given the nature of the

oscillations, autocorrelative analysis was performed to more closely examine the length of

periodicity and quantify the observed phenomena. Oscillations on a time scale of approximately

30 minutes were noted for morphological changes such as cell spread area, perimeter, and cell

velocity which is consistent with expectations of lamellipodial protrusion rate (Figure 2-12).

Surprisingly however, traction force metrics such as strain energy density and traction force

fluctuate on a time scale of 4-8 hours (Figure 2-11). Fascinatingly, depletion of the Arp2/3

complex resulted in dramatically decreasing the periodicity of strain energy density and average

traction force in specifically the stiffest substrates tested (Figure 2-14). No significant difference

in periodicity was detected on 2 or 8 kPa substrates as a result of depletion of the Arp2/3

(44)

Figure 2-24. Average traction force and strain energy density display long term periodic behavior as demonstrated by autocorrelation analysis.

(45)

In order to further validate the observed periodicity was as a result of actual oscillations

in tractions we performed cross correlation analysis on aberrations within the microscope path

which correlate with perceived stage jittering but not with information used to determine traction

force measurements. First gaussian random noise was cross correlated to obtain plots of

uncorrelated data (Figure 2-13, panel A). Next, we performed cross correlation on two metrics

known to be strongly correlated: strain energy density and net contractile moment (Figure 2-13,

panel B). This allowed us to gauge the scale of the correlations we might expect to find if

random noise or stage jittering was responsible for the observed oscillatory behavior. Finally, we

performed cross correlation analysis of the centroid positions of light path aberrations with the

strain energy density time series from the same traction force images in both parental and Arp2/3

KO cells (Figure 2-13, Panel C). We do not observe notable correlation with a similar fluctuation

periodicity between the perceived oscillations in traction force metrics and oscillations found

from stage jitter or random noise. As a final check, we also performed auto correlation analysis

(46)
(47)

Figure 2-27. Strain energy density and average traction periodic behavior is ablated at specifically the stiffest regime examined (* p < 0.05, ** p < 0.01, *** p < 0.001, N = 2, n ≥ 10

per condition).

During analysis of these periodic fluctuations in strain energy density and average

traction, we noted an interesting correlation between lengths of periodicity and if the cell divided

during the time course of the experiment. By splitting our data sets into sets where cells divided

or did not, we note a significant difference in periodicity in cells that divide and those that did

not potentially hinting at a connection between the cell cycle and force generation (Figure 2-16).

Cells that divided during the experimental time course were noted to have significantly

decreased periodicity in strain energy density at both 8 and 40 kPa and nearly significant in the

case of 2 kPa cells with a small sample size. The Arp2/3 KO cells did not display sufficient

division events to allow statistical quantification of this phenomenon in the absence of the

(48)

Figure 2-15. Strain energy density oscillates with a longer period during experiments where observed cell does not divide during the experiments time course (* p < 0.05, ** p < 0.01, *** p

< 0.001, N = 1, n ≥ 5 per condition).

Section 2.4: Conclusions and Future Directions

The actin bundling protein Fascin and the Arp2/3 complex play a pivotal role in the

generation of bundled and branched actin respectively in the lamellipodia which itself is the site

of major traction generation in motile fibroblasts. Several experiments were performed to better

understand how disturbance of the actin organizing proteins effected both morphological

properties and traction generation on specifically soft substrates and further how this effected

fibroblast motility in physiological contexts. These experiments, while initially conceived to

probe a wide variety of questions related to motility and traction generation, remarkably suggest

an overarching theme: organization of the actin cytoskeleton at the leading edge is not only

(49)

mechanical stimuli. While mechanotransduction (the conversion of mechanical stimuli to

biochemical signals and often gene expression) has been well defined in the context of many

cytoskeletal players, the explicit role of actin organization and substrate stiffness in actively

guiding both dynamic processes such as motility and controlling gene expression is not well

appreciated.

Fascin is traditionally considered a key actin bundler in filopodia, however recent

publications suggest that Fascin is also an important structural component of lamellipodia and

actively contributes to its mechanical properties (Tanaka et al., 2019). Lamellipodial Fascin has

often been ignored in part due to difficulty of observation but also that disturbance of Fascin did

not dramatically impact the structure of lamellipodia on glass substrates (Vignjevic et al., 2006).

In the case of soft elastic substrates, we noticed dramatic loss of structural elements within a few

microns of the cell’s leading edge suggesting that while depletion of Fascin might not affect

lamellipodial structure significantly on glass, it has a profound effect on soft substrates. Given

Fascin’s apparent localization to bundled F-actin in lamellipodia and its known role as an

important actin bundler in filopodia, it is not surprising its depletion would result in a loss of

structuring at the cell’s leading edge. It is possible that this behavior is particularly noticeable on

soft substrates rather than on glass as a consequence of rebalancing of the actin cytoskeleton as it

responds to its particular mechanical environment. Cell mechanical properties and actin

distribution has been noted to modulate as a function of stiffness up to a particular range where

its response becomes indistinguishable from glass (A. Engler et al., 2004; Solon et al., 2007).

Particularly, bundled actin in stress fibers is known to increase with increasing stiffness which

has important consequences in cell behavior. While our study only examined depletion of Fascin

(50)

morphology and velocity along with changes to the ability to generate tractions. Further

experimentation on Fascin depleted fibroblasts at various stiffnesses may more clearly show a

dependence on substrate stiffness with traction generation. In the absence of Fascin, fibroblasts

on soft substrates not only moved with greater velocity but further demonstrated greater average

traction and strain energy. Our results did not demonstrate a significantly increased contractility

over parental cells which suggests the increased traction forces the cell generates in the absence

of Fascin is not a result of changes to contractility but rather another mechanism. This is

consistent with findings from other groups which determined that increased contractility often

resulted in decreased average velocity in cells (Dokukina & Gracheva, 2010). As it does not

appear this phenomenon is directly related to changes in contractility, the role of actin

organization in traction generation came under more scrutiny.

Perturbance of the Arp2/3 complex serves as an interesting foil for our observations in

Fascin depleted cells as disruption of the Arp2/3 complex results in ablation of lamellipodia

while leaving linearly bundled F-actin largely intact. Previous work in this cell line identified the

role of Arp2/3 in lamellipodial formation and further that disruption resulted in significant

alterations to focal adhesion alignment, morphology, and dynamics (Wu et al., 2012). The

importance of Arp2/3 in regulating branched actin and focal adhesions suggests an importance in

generating tractions on a variety of stiffness conditions and thus basic characterization of Arp2/3

KO tractions on various substrate stiffnesses was completed. Interestingly, traction forces

generated by Arp2/3 KO cells were not significantly different on 2 kPa gels, increased on 8 kPa

gels and decreased on 40 kPa gels pointing at a stiffness dependent response to disruption of the

Arp2/3 complex. Further, average cell velocity increased slightly at 8 kPa and dramatically at 40

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