A practical review on the measurement tools for cellular adhesion force

Cell cell and cell matrix adhesions are fundamental in all multicellular organisms. They play a key role in cellular growth, differentiation, pattern formation and migration. Cell-cell adhesion is substantial in the immune response, pathogen host interactions, and tumor development. The success of tissue engineering and stem cell implantations strongly depends on the fine control of live cell adhesion on the surface of natural or biomimetic scaffolds. Therefore, the quantitative and precise measurement of the adhesion strength of living cells is critical, not only in basic research but in modern technologies, too. Several techniques have been developed or are under development to quantify cell adhesion. All of them have their pros and cons, which has to be carefully considered before the experiments and interpretation of the recorded data. Current review provides a guide to choose the appropriate technique to answer a specific biological question or to complete a biomedical test by measuring cell adhesion

A Beginner’s Guide to the Characterization of Hydrogel Microarchitecture for Cellular Applications

The extracellular matrix (ECM) is a three-dimensional, acellular scaffold of living tissues. Incorporating the ECM into cell culture models is a goal of cell biology studies and requires biocompatible materials that can mimic the ECM. Among such materials are hydrogels: polymeric networks that derive most of their mass from water. With the tuning of their properties, these polymer networks can resemble living tissues. The microarchitectural properties of hydrogels, such as porosity, pore size, fiber length, and surface topology can determine cell plasticity. The adequate characterization of these parameters requires reliable and reproducible methods. However, most methods were historically standardized using other biological specimens, such as 2D cell cultures, biopsies, or even animal models. Therefore, their translation comes with technical limitations when applied to hydrogel-based cell culture systems. In our current work, we have reviewed the most common techniques employed in the characterization of hydrogel microarchitectures. Our review provides a concise description of the underlying principles of each method and summarizes the collective data obtained from cell-free and cell-loaded hydrogels. The advantages and limitations of each technique are discussed, and comparisons are made. The information presented in our current work will be of interest to researchers who employ hydrogels as platforms for cell culture, 3D bioprinting, and other fields within hydrogel-based research

MECHANICAL CHARACTERIZATION OF NORMAL AND CANCEROUS BREAST TISSUE SPECIMENS USING ATOMIC FORCE MICROSCOPY

Breast cancer is one of the most common malignancies among women worldwide. Conventional breast cancer diagnostic methods involve needle-core biopsy procedures, followed by careful histopathological inspection of the tissue specimen by a pathologist to identify the presence of cancerous lesions. However, such inspections are primarily qualitative and depend on the subjective impressions of observers. The goal of this research is to develop approaches for obtaining quantitative mechanical signatures that can accurately characterize malignancy in pathological breast tissue. The hypothesis of this research is that by using contact-mode Atomic Force Microscopy (AFM), it is possible to obtain differentiable measures of stiffness of normal and cancerous tissue specimens. This dissertation summarizes research carried out in addressing key experimental and computational challenges in performing mechanical characterization on breast tissue. Firstly, breast tissue specimens studied were 600 um in diameter, about six times larger than the range of travel of conventional AFM X-Y stages used for imaging applications. To scan tissue properties across large ranges, a semi automated image-guided positioning system was developed that can be used to perform AFM probe-tissue alignment across distances greater than 100 um at multiple magnifications. Initial tissue characterization results indicate that epithelial tissue in cancer specimens display increased deformability compared to epithelial tissue in normal specimens. Additionally, it was also observed that the tissue response depends on the patient from whom the specimens were acquired. Another key challenge addressed in this dissertation is accurate data analysis of raw AFM data for characterization purposes. Two sources of uncertainty typically influence data analysis of AFM force curves: the AFM probe's spring constant and the contact point of an AFM force curve. An error-in-variable based Bayesian Changepoint algorithm was developed to quantify estimation errors in the tissue's elastic properties due to these two error sources. Next, a parametric finite element modeling based approach was proposed in order to account for spatial heterogeneity in the tissue response. By using an exponential hyperelastic material model, it was shown that it is possible to obtain more accurate material properties of tissue specimens as opposed to existing analytical contact models. The experimental and computational strategies proposed in this dissertation could have a significant impact on high-throughput quantitative studies of biomaterials, which could elucidate various disease mechanisms that are phenotyped by their mechanical signatures

Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales

With the increasing interplay between experimental and computational approaches at multiple length scales, new research directions are emerging in materials science and computational mechanics. Such cooperative interactions find many applications in the development, characterization and design of complex material systems. This manuscript provides a broad and comprehensive overview of recent trends where predictive modeling capabilities are developed in conjunction with experiments and advanced characterization to gain a greater insight into structure-properties relationships and study various physical phenomena and mechanisms. The focus of this review is on the intersections of multiscale materials experiments and modeling relevant to the materials mechanics community. After a general discussion on the perspective from various communities, the article focuses on the latest experimental and theoretical opportunities. Emphasis is given to the role of experiments in multiscale models, including insights into how computations can be used as discovery tools for materials engineering, rather than to "simply" support experimental work. This is illustrated by examples from several application areas on structural materials. This manuscript ends with a discussion on some problems and open scientific questions that are being explored in order to advance this relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J. Mater. Sc

A communications system perspective for dynamic mode atomic force microscopy, with applications to high-density storage and nanoimaging

In recent times, the atomic force microscope (AFM) has been used in various fields like biology, chemistry, physics and medicine for obtaining atomic level images. The AFM is a high-resolution microscope which can provide the resolution on the order of fractions of a nanometer. It has applications in the field of material characterization, probe based data storage, nano-imaging etc. The prevalent mode of using the AFM is the static mode where the cantilever is in continuous contact with the sample. This is harsh on the probe and the sample. The problem of probe and sample wear can be partly addressed by using the dynamic mode operation with the high quality factor cantilevers. In the dynamic mode operation, the cantilever is forced sinusoidally using a dither piezo. The oscillating cantilever gently taps the sample which reduces the probe-sample wear. In this dissertation, we demonstrate that viewing the dynamic mode operation from a communication systems perspective can yield huge gains in nano-interrogation speed and fidelity. In the first part of the dissertation, we have considered a data storage system that operates by encoding information as topographic profiles on a polymer medium. A cantilever probe with a sharp tip (few nm radius) is used to create and sense the presence of topographic profiles, resulting in a density of few Tb per square inch. The usage of the static mode is harsh on the probe and the media. In this work, the high quality factor dynamic mode operation, which alleviates the probe-media wear, is analyzed. The read operation is modeled as a communication channel which incorporates system memory due to inter-symbol interference and the cantilever state. We demonstrate an appropriate level of abstraction of this complex nanoscale system that obviates the need for an involved physical model. Next, a solution to the maximum likelihood sequence detection problem based on the Viterbi algorithm is devised. Experimental and simulation results demonstrate that the performance of this detector is several orders of magnitude better than the performance of other existing schemes. In the second part of the dissertation, we have considered another interesting application of the dynamic mode AFM in the field of nano-imaging. Nano-imaging has played a vital role in biology, chemistry and physics as it enables interrogation of material with sub-nanometer resolution. However, current nano-imaging techniques are too slow to be useful in the high speed applications of interest such as studying the evolution of certain biological processes over time that involve very small time scales. In this work, we present a high speed one-bit imaging technique using the dynamic mode AFM with a high quality factor cantilever. We propose a communication channel model for the cantilever based nano-imaging system. Next, we devise an imaging algorithm that incorporates a learned prior from the previous scan line while detecting the features on the current scan line. Experimental results demonstrate that our proposed algorithm provides significantly better image resolution compared to current nano-imaging techniques at high scanning speed. While modeling the probe-based data storage system and the cantilever based nano-imaging system, it has been observed that the channel models exhibit the behavior similar to intersymbol-interference (ISI) channel with data dependent time-correlated noise. The Viterbi algorithm can be adapted for performing maximum likelihood sequence detection in such channels. However, the problem of finding an analytical upper bound on the bit error rate of the Viterbi detector in this case has not been fully investigated. In the third part of the dissertation, we have considered a subset of the class of ISI channels with data dependent Gauss-Markov noise. We derive an upper bound on the pairwise error probability (PEP) between the transmitted bit sequence and the decoded bit sequence that can be expressed as a product of functions depending on current and previous states in the (incorrect) decoded sequence and the (correct) transmitted sequence. In general, the PEP is asymmetric. The average BER over all possible bit sequences is then determined using a pairwise state diagram. Simulations results demonstrate that analytic bound on BER is tight in high SNR regime

Self-Assembling Peptide Nanomaterials: Molecular Dynamics Studies, Computational Designs And Crystal Structure Characterizations

Peptides present complicated three-dimensional folds encoded in primary amino acid sequences of no more than 50 residues, providing cost-effective routes to the development of self-assembling nanomaterials.� The complexity and subtlety of the molecular interactions in such systems make it interesting to study and to understand the fundamental principles that determine the self-assembly of nanostructures and morphologies in solution. Such principles can then be applied to design novel self-assembling nanomaterials of precisely defined local structures and to controllably engineer new advanced functions into the materials. We first report the rational engineering of complementary hydrophobic interactions to control β-fibril type peptide self-assemblies that form hydrogel networks. Complementary to the experimental observations of the two distinct branching morphologies present in the two β-fibril systems that share a similar sequence pattern, we investigated on network branching, hydrogel properties by molecular dynamics simulations to provide a molecular picture of the assemblies. Next, we present the theory-guided computational design of novel peptides that adopt predetermined local nanostructures and symmetries upon solution assembly. Using such an approach, we discovered a non-natural, single peptide tetra-helical motif that can be used as a common building block for distinct predefined material nanostructures. The crystal structure of one designed peptide assembly demonstrates the atomistic match of the motif structure to the prediction, as well as provides fundamental feedback to the methods used to design and evaluate the computationally designed peptide candidates. This study could potentially improve the success rate of future designs of peptide-based self-assembling nanomaterials

Quantitative analysis of the mechanics of fibrillar fribronectin

Thesis (Ph.D.)--Boston UniversityThe information exchange between cells and their environment is a key mediator of cell behavior that will result in disease or dysfunction if disrupted. A thorough understanding of the in vivo cell environment is critical to relating cell behaviors observed in vitro to cell behaviors in pathogenesis and homeostasis. In addition to neighboring cells, the extracellular matrix (ECM) defines the local cell environment in the body. The protein fibronectin (Fn) is a prominent component of the ECM and a key cell adhesive ligand. Fn is assembled by cells into an extremely extensible, fibrous network through which cells migrate. Fn is also an integral part of the signaling machinery that instructs cell behavior. Cells may bind to Fn through a large number of receptors, in addition Fn binds and presents growth factors to cells, regulating their proliferative and migratory behavior. Stretch, applied to Fn fibers has been demonstrated to alter properties like binding site availability and fiber stiffness. In order to understand how molecular conformations and mechanical stretch regulate these cell instructive properties of Fn fibers, one must build a quantitative understanding of the intermolecular architecture of Fn fibers. In this study we have characterized the physical characteristics of fibronectin, its density, stiffness, extensibility, and viscoelasticity with respect to the extension of the Fn fiber. We have quantified conformational changes within the molecule that regulate both its mechanical properties and the availability of binding sites. In addition, we determined that the configuration of the molecular crosslinks strongly influences the fiber's physical properties. By taking measurements of the Fn fibers under constant tension we have shown that fibronectin is a highly viscoelastic material with extremely slow response times, indicating that in the slow pulling regime of cell tractions Fn material properties may deviate significantly from measurements made at higher pulling rates. A strong quantitative understanding of fibronectin's properties opens the door to new insights into disease and new approaches to creating engineered tissue constructs

Modelling the molecular mechanisms of biocompatibility of artifical materials

One of the most common reasons for implant failure is immune rejection. Implant rejection leads to additional surgical intervention and, ultimately, increases health cost as well as recovery time. Within a few hours after implantation, the implant surface is covered with host proteins. Adsorption of fibrinogen, a soluble plasma glycoprotein, is responsible in triggering the immune response to a given material and, subsequently, in determining its biocompatibility. The work presented here is focused on modeling the interaction between artificial surfaces and plasma proteins at the microscopic level by taking into account the physico-chemical properties of the surfaces. Carbon-based nanomaterials are chosen as a model system due to their unique bioadhesive and contradictory biocompatible properties as well as the possibility of functionalization for specific applications. Graphene and its derivatives, such as graphene oxide and reduced graphene oxide, demonstrate controversial toxicity properties in vitro as well as in vivo. In this study, by covalently adding chemical groups, the wettability of graphene surfaces and the subsequent changes in its biocompatibility are being examined. An empirical force field potential (AMBER03) molecular dynamic simulation code implemented in the YASARA software package was utilized to model graphene/biomolecule interactions. The accuracy of the force field choice was verified by modeling the adsorption of individual amino acids to graphene surface in a vacuum. The obtained results are in excellent agreement with previously published ab initio findings. In order to mimic the natural protein environment, the interaction of several amino acids with graphene in an explicit solvent was modeled. The results show that the behaviour of amino acids in aqueous conditions is drastically different from that in vacuum. This finding highlights the importance of the host environment when biomaterial-biomolecule interfaces are modeled. The surface of Graphene Oxide (GO) has been shown to exhibit properties that are useful in applications such as biomedical imaging, biological sensors and drug delivery. An assessment of the intrinsic affinity of amino acids to GO by simulating their adsorption onto a GO surface was performed. The emphasis was placed on developing an atomic charge model for GO that was not defined before. Next, the simulation of a fibrinogen fragment (D-domain) at the graphene surface in an explicit solvent with physiological conditions was performed. This D-domain contains the hidden (not expressed to the solvent) motifs (PI 7190-202 and P2 7377-395, and specifically P2-C portion 7383-395) that were experimentally found to be responsible for attracting inflammatory cells through CDllb/CD18 (Mac-1) leukocyte integrin and, consequently, promoting the cascade of immune reactions. It was hypothesized that the hydrophobic nature of graphene would cause critical changes in the fibrinogen D-domain structure, thus exposing the sequences and result in the foreign body reaction. To further study this issue, molecular mechanics was used to stimulate the interactions between fibrinogen and a graphene surface. The atomistic details of the interactions that determine plasma protein affinity modes on surfaces with high hydrophobicity were studied. The results of this work suggest that graphene is potentially pro-inflammatory surface, and cannot be used directly (without alterations) for biomedical purposes. A better understanding of the molecular mechanisms underlying the interaction between synthetic materials and biological systems will further the ultimate goal of understanding the biocompatibility of existing materials as well as design of new materials with improved biocompatibility

Morpho-Rheological Fingerprinting of Rod Photoreceptors Using Real-Time Deformability Cytometry

Distinct cell-types within the retina are mainly specified by morphological and molecular parameters, however, physical properties are increasingly recognized as a valuable tool to characterize and distinguish cells in diverse tissues. High-throughput analysis of morpho-rheological features has recently been introduced using real-time deformability cytometry (RT-DC) providing new insights into the properties of different cell-types. Rod photoreceptors represent the main light sensing cells in the mouse retina that during development forms apically the densely packed outer nuclear layer. Currently, enrichment and isolation of photoreceptors from retinal primary tissue or pluripotent stem cell-derived organoids for analysis, molecular profiling, or transplantation is achieved using flow cytometry or magnetic activated cell sorting approaches. However, such purification methods require genetic modification or identification of cell surface binding antibody panels. Using primary retina and embryonic stem cell-derived retinal organoids, we characterized the inherent morpho-mechanical properties of mouse rod photoreceptors during development based on RT-DC. We demonstrate that rods become smaller and more compliant throughout development and that these features are suitable to distinguish rods within heterogenous retinal tissues. Hence, physical properties should be considered as additional factors that might affect photoreceptor differentiation and retinal development besides representing potential parameters for label-free sorting of photoreceptors