Initially, the multi-class classification problem is attempted by taking into account cell morphologies across all the fabricated substrates Fig.
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It is important to mention that among all four different classes, Class D has a higher classification accuracy since the features of Class D have a separable distribution with tighter variance from the features of classes A, B, and C also evident in Fig. By removing Class B, the classification accuracy increases to Thus, it is demonstrated that the 3D microscale precision-stacked substrates promote a confined and suspended state that morphologically stands out both at the cellular as well as the sub-cellular FA level. To provide a quantitative estimate of MEW heterogeneity vs. D Fig.
Reporting only the most important features might exclude other features that did not play an important role during the multi-class classification task A vs. D but whose heterogeneity is of significant biological importance. The results demonstrate that the MEW substrates provide tighter control over both the most and the least significant features that the SVM machine learning algorithm used for the classification task. Univariate score US is equal to the ratio of the variance across the two different classes to the variance within class. The feature demonstrating the highest and the lowest US values are the most and the least significant for the classification task performed by the SVM machine learning algorithm.
Although the modulation of cellular phenotype with biochemical regulatory factors is well-known, structural and mechanical inputs from the ECM have been identified as key regulators of measurable cell phenotypic attributes. To investigate the effects of the physical properties of the matrix microenvironment on cellular phenotype, microfabrication technologies and 3D cell encapsulation technologies have enabled the identification of previously ignored structural and dimensional parameters, respectively, that are crucial for precisely engineered biomaterial substrates.
In order to independently modulate these substrate parameters within a coherent experimental model, we have demonstrated the marriage between electrospinning and additive manufacturing towards the 3D fabrication of high-fidelity biomaterial fibrous substrates with geometrical feature sizes at cell operating length scales.
Furthermore, we have advanced a machine learning-based metrology framework that can quantitatively assess and classify the effect of geometrical confinement on human adherent cells across different fibrous substrates dimensionalities and architectures. To measure this effect, we have demonstrated a quantitative confocal imaging workflow that reveals distinct confinement states both at the cellular and subcellular FA protein level. The classification results demonstrate that cells assume distinct confinement states that are enforced by the prescribed substrate dimensionalities and porous microarchitectures.
It is noteworthy to mention that the reproducibility and biological relevance of the advanced system may be further augmented by coating the fibrous substrates with ECM proteins fibronectin, vitronectin, collagen 46 , 47 , 48 or a conjugated RGD-peptide used in PEG-hydrogels To be sure, the poly-l-lysine PLL prescribed in this study to promote cell attachment in an integrin-independent manner could affect the overall metrology described herein. Therefore, further experimentation would serve to validate whether the programming of downstream cell morphology with precision substrate geometry design parameterization may be similarly observed using native ECMs that promote integrin-dependent attachment to the substrates.
Lastly, de novo production of ECM proteins may also play a role in adhesion organization and possibly diverge from the metrology results reported here. In the context of our study, the demonstrated PCL substrate material system is not advanced with the intention to replace biological gels for studying mechanosensing in an in vivo context. Based on the specific aim of this paper to fabricate substrates where precision geometries can be reproduced and isolated as an independent variable and tested with seeded cells, the MEW substrate offers a highly controllable 3D system with respect to porous microarchitecture at cellular-relevant length scales.
The metrology and classification results show that there is a tight link between the porous architecture and the induced cell shape phenotypes. Using these substrates, cell biologists could study mechanotransduction phenomena for different cell shapes that are induced geometrically in a 3D environment where ECM remodeling fibrous architecture variation as cells migrate is decoupled from resultant local stiffness variations. The use of simpler biomaterial systems with tight control over certain characteristics may help understand which characteristics of more complex systems such as biological gels are important for proper mechanosensing in vivo.
We have established a technology platform that serves as a major step towards the development of bioinformatics-guided additive manufacturing systems, one that promises insight into cellular interactions beyond the reach of current phenotypic control and analysis. The combination of advanced fabrication and metrology tools paves a new avenue for the systematic engineering of functional biomaterial systems that can reliably guide distinct, uniform, and predictable cell responses for a wide range of biomedical applications. The need for tighter control over cell function is a major roadblock for getting tissue engineering products to the clinic Currently, the noise in cell phenotype makes it harder to detect positive outcomes during a clinical trial.
Therefore, any measures taken to tighten specifications on the substrate, and thereby also tighten the variance in cell phenotype, is much needed by this industry For example, we have preliminarily shown that there exists an operational window of geometrical parameters attributed to an ordered fiber-based material matrix substrate that map to unique states of biophysical cell confinement characterized by homogeneous cell shape phenotypes. Therefore, we expect that there exists granularity in the geometric confinement states that will yield the phenotypic spectrum of whole cell and subcellular morphometric features, along with different functional outcomes for various model cell types, including differentiation in stem cells.
The range and sensitivity of this operational parameter space will determine the extent to which cellular phenotype can be controlled. Advancing a technology platform that leverages a shape-driven control pathway to create and maintain a desired phenotype at the single cell and population level is potentially far-reaching for fundamental cell biology and regenerative medicine, respectively.
UK is the biomaterial substrate that was used for the experimental study in this paper. The loaded syringe is clamped on a programmable syringe pump Harvard Apparatus. PLL coated glass coverslips are taped on the grounded aluminum collector. The design and experimental modeling of the established MEW system configuration has been previously described in detail Prior to printing, PCL pellets are loaded in a glass Luer-lock syringe that is vertically placed into a vacuum convective heat oven overnight to remove any bubbles that may affect the process stability and downstream structural formability of the melt electrospun fibers.
Custom translational patterns are written in Python 2. The structural formability of all the fibrous substrates is quantitatively characterized with respect to fiber diameter and effective pore size area. The apparent pore size distributions were measured directly from the acquired micrographs using the MIPAR image processing software A custom semi-automatic segmentation recipe was developed based on contrast and brightness preprocessing steps, the application of classical thresholding algorithms based on the grayscale intensity difference between the background and the printed fibers , and the subsequent manual correction of erroneous segmented areas.
The mean fiber diameter and mean effective pore size are reported along with their standard deviation for each type of fibrous substrate under investigation. Both substrates were placed inside sterilized, non-treated 6-well plates for all biological studies. Specifically, 0. The coated substrates are then exposed to UV light overnight for sterilization. Right before cell seeding the substrates are thoroughly rinsed with final media formulation. To examine actin cytoskeletal organization, the samples are stained with Texas Red-X phalloidin , Invitrogen.
Excess medium is then removed by touching the edges of the slide against a paper towel. The edges of the coverslip are sealed with nail polish to avoid the formation of bubbles over time. The analysis followed for detecting and quantifying the features of interest is performed with the open source software Fiji 53 and MIPAR software The samples are scanned across their thickness with a step size of 0. Z-stack images with , , and nm laser wavelengths were acquired corresponding to the green, red, and blue channels, respectively.
Raw Z-stack images are post-processed using the ImageJ software and, unless otherwise specified, are presented as maximum intensity projections. The analysis followed for detecting and quantifying the features of interest is performed with the open source software Fiji 53 and the beta version of the MIPAR software A fully automated procedure was developed to determine cell body and nuclei contours using the red and blue fluorescent channel images, respectively.
In this procedure, fluorescent images are transformed to 8-bit grayscale images and pre-processed to ease the automated segmentation procedure, explained hereafter. Initially, brightness and contrast are equalized across the image by performing a uniform histogram scaling using the Contrast Limited Adaptive Histogram Equalization CLAHE algorithm This step is crucial for the accuracy and objectivity of the segmentation since it allows for noise reduction while preserving the edges of the features of interest. The phalloidin and DAPI signals are mostly present on the actin microfilaments and on the border of the nuclei, respectively.
Thus, segmentation using thresholding results in incomplete cell body and nucleus mask, in which the center is not filled and the border is not continuous. Following the NML step, segmentation is performed using thresholding during which the image is binarized based on a certain pixel threshold value. Despite the effectiveness of the NLM step into preserving the borders of the segmented feature, high gradient values in the cytoskeleton or the nucleus caused by non-homogeneous content, require an additional erosion step.
During that step, black pixels are removed if they are surrounded by white pixels, whose number is greater than or equal of a user-specified value. It was determined that a value of 5 was suitable for all the images. The binarized image is inverted resulting in an image with white background and a black mask of the feature of interest. The detection and segmentation of FAs is performed using the same algorithmic workflow with the addition of some extras filtering steps that allowed the removal of noisy signal due to cytoplasmic background and the isolation of the mature FAs with respect to nascent adhesions.
The former one is achieved by adding an extra dilation step before the erosion step. The latter one is achieved by adding an extra filtering step that removes all the black pixel features with an area equal or smaller to 0. The image processing workflow is described in detail Supplemental Document with critical settings used for each filtering step along with the image outcome after each filtering step. Metrics of the segmented features of interest are defined hereafter. The moment invariants are directly obtained from the MIPAR software after the image-based cell feature extraction procedure is completed.
The lengths in both directions are directly obtained from the MIPAR software after the image-based cell feature extraction procedure is completed. It takes values between 0 and 1 with the ratio approaching to 1 as the cell area increases to match the fitted convex hull. The Cartesian data of the nuclei and FA masks are leveraged to extract the centroids of the detected nuclei and individual FAs, respectively. Using these data, two functions are defined: a the E-function and b the G-function. The E-function is defined as the cumulative frequency distribution of the radial Euclidean distance of each FA centroid from the nuclear centroid within each cell.
Straight lines constrained on the origin of the Cartesian axes are fitted on the E-function curves using linear regression. Moreover, the G-function is defined as the distance of each detected FA to its nearest detected FA neighbor. A 7-D Cartesian coordinate system of cell shape phenotypes, in which each axis represents each feature metric, is developed for the 7-metrics computed from the various measures of cell shapes, i.
Within this representation, each point represents one single-cell feature-vector with 7 elements corresponding to the computed metrics for the specific cell. All metrics are normalized using a Z -score function, which centers and scales all metric values to have zero mean and unit standard deviation, respectively The transformed metric vectors for each cell population are multidimensional data sets to train a support vector machine SVM with a linear kernel using the classification learner package in Matlab The linear-kernel SVM is a supervised machine learning algorithm that can classify the data by determining the best hyperplane that distinguishes all data points into the defined classes The best hyperplane for the SVM algorithm is considered the one with the largest margin between the two classes with the margin being the maximum width of the slab parallel to the hyperplane that has no interior data points.
The predictive accuracy of the linear-kernel SVM is assessed using a 5-fold cross-validation scheme to protect against overfitting and to assure the generalization performance of the classifier 61 , Here, the data are randomly partitioned in 5 folds where, for each fold, the scheme trains the linear SVM using the out-of-fold observations and assesses the model performance using the in-fold data. Folkman, J. Role of cell shape in growth control. Nature , — Watt, F. Cell shape controls terminal differentiation of human epidermal keratinocytes.
Natl Acad. USA 85 , — Stevens, M.
Exploring and engineering the cell surface interface. Science , — Chen, C. Geometric control of cell life and death. Fraley, S. A distinctive role for focal adhesion proteins in three-dimensional cell motility. Cell Biol.
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Doyle, A. Local 3D matrix microenvironment regulates cell migration through spatiotemporal dynamics of contractility-dependent adhesions. Tutak, W. Nanofiber scaffolds influence organelle structure and function in bone marrow stromal cells. B Appl.
Farooque, T. Measuring stem cell dimensionality in tissue scaffolds. Biomaterials 35 , — Jeon, H. A mini-review: cell response to microscale, nanoscale, and hierarchical patterning of surface structure. Kumar, G. Freeform fabricated scaffolds with roughened struts that enhance both stem cell proliferation and differentiation by controlling cell shape. Biomaterials 33 , — The determination of stem cell fate by 3D scaffold structures through the control of cell shape. Biomaterials 32 , — Kirschner, C. Hydrogels in healthcare: from static to dynamic material microenvironments.
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Acta Mater. Tibbitt, M. Hydrogel as extracellular matrix mimics for 3D cell culture. Kubow, K. Matrix microarchitecture and myosin II determine adhesion in 3D matrices. Hakkinen, K. Direct comparisons of the morphology, migration, cell adhesions, and actin cytoskeleton of fibroblasts in four different three-dimensional extracellular matrices.
Tissue Eng. A 17 , — Ochsner, M. Dimensionality controls cytoskeleton assembly and metabolism of fibroblast cells in response to rigidity and shape. Cukierman, E. Taking cell-matrix adhesions to the third dimension.
Cell interactions with three-dimensional matrices. Mechanosensing via cell-matrix adhesions in 3D microenvironments. Cell Res. Baker, B. Cell-mediated fibre recruitment drives extracellular matrix mechanosensing in engineered fibrillar microenvironments. Sun, D. Near-field electrospinning. Nano Lett. Luo, G. Direct-write, self-aligned electrospinning on paper for controllable fabrication of three-dimensional structures. ACS Appl. Interfaces 7 , — Brown, T. Direct writing by way of melt electrospinning. Melt electrospinning today: an opportune time for an emerging polymer process.
Dalton, P. Melt electrowriting with additive manufacturing principles. Erisken, C. Functionally graded electrospun polycaprolactone and beta-tricalcium phosphate nanocomposites for tissue engineering applications. Biomaterials 29 , — Senturk-Ozer, S. Dynamics of electrospinning of poly caprolactone via a multi-nozzle spinneret connected to a twin screw extruder and properties of electrospun fibers.
Ergun, A. A 18 , — Osteochondral tissue formation through adipose-derived stromal cell differentiation on biomimetic polycaprolactone nanofibrous scaffolds with graded insulin and Beta-glycerophosphate concentrations. Chen, X. Shell—core bi-layered scaffolds for engineering of vascularized osteon-like structures.
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Fabrication and characterization of electrospun poly e-caprolactone fibrous membrane with antibacterial functionality. Open Sci. Ladoux, B. Physically based principles of cell adhesion mechanosensitivity in tissues. Kuo, J. Mechanotransduction at focal adhesions: integrating cytoskeletal mechanics in migrating cells.
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According to a recent study , more than a third of people wish there had been lessons in school on how to budget, a further third would have appreciated classes on the importance of insurance. I wish that before I left school somebody had told me to stop worrying about what the future held and to make the most of the present time. One tip I would give to school leavers is to use summer holidays and spare time wisely. Research has shown that students who have a gap year achieve more highly at university than students who enter university straight after school and mature age students.
Having worked in a number of different countries it is true, travel really does broaden the mind. Do something that will enrich your life and that will take you out of your comfort zone. Employers will always look favourably on the efforts taken by go getters who have gone out and done work experience.
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Work experience or volunteering is a great way to network and exposes you to a range of core workplace activities, including teamwork, communication skills and how to use your initiative. If I could give my year-old self any piece of advice over and over again it would be not to be scared of rejection. Getting job rejections can be emotionally difficult and frustrating but it can also be a useful springboard to reassess your goals. There are many different pathways to get to the same destination. You can look at alternative pathways.
When I left school I thought that was the end of homework. How wrong I was. And with all that in mind, I wish you good luck for the rest of your adventures in this little thing we call life! A contemporary Robinsonade — York, York. Edition: Available editions United Kingdom. Tim Whalley , University of Stirling.
Doing the exam jump. Dear class of , Finishing school can be a daunting experience but you are young, bright and have your future ahead of you — easy for me to say, you might think. Yeh, I got this. Make the most of your time I wish that before I left school somebody had told me to stop worrying about what the future held and to make the most of the present time.