Image-based cell subpopulation identification through automated cell tracking, principal component analysis, and partitioning around medoids clustering.

In vitro cell culture model systems often employ monocultures, despite the fact that cells generally exist in a diverse, heterogeneous microenvironment in vivo. In response, heterogeneous cultures are increasingly being used to study how cell phenotypes interact. However, the ability to accurately identify and characterize distinct phenotypic subpopulations within heterogeneous systems remains a major challenge. Here, we present the use of a computational, image analysis-based approach-comprising automated contour-based cell tracking for feature identification, principal component analysis for feature reduction, and partitioning around medoids for subpopulation characterization-to non-destructively and non-invasively identify functionally distinct cell phenotypic subpopulations from live-cell microscopy image data. Using a heterogeneous model system of endothelial and smooth muscle cells, we demonstrate that this approach can be applied to both mono and co-culture nuclear morphometric and motility data to discern cell phenotypic subpopulations. Morphometric clustering identified minimal difference in mono- versus co-culture, while motility clustering revealed that a portion of endothelial cells and smooth muscle cells adopt increased motility rates in co-culture that are not observed in monoculture. We anticipate that this approach using non-destructive and non-invasive imaging can be applied broadly to heterogeneous cell culture model systems to advance understanding of how heterogeneity alters cell phenotype. This work presents a computational, image-analysis-based approach-comprising automated contour-based cell tracking for feature identification, principle component analysis for feature reduction, and partitioning around medoids for subpopulation characterization-to non-destructively and non-invasively identify functionally distinct cell phenotypic subpopulations from live-cell microscopy image data.

Nuclear position relative to the Golgi body and nuclear orientation are differentially responsive indicators of cell polarized motility.

Cell motility is critical to biological processes from wound healing to cancer metastasis to embryonic development. The involvement of organelles in cell motility is well established, but the role of organelle positional reorganization in cell motility remains poorly understood. Here we present an automated image analysis technique for tracking the shape and motion of Golgi bodies and cell nuclei. We quantify the relationship between nuclear orientation and the orientation of the Golgi body relative to the nucleus before, during, and after exposure of mouse fibroblasts to a controlled change in cell substrate topography, from flat to wrinkles, designed to trigger polarized motility. We find that the cells alter their mean nuclei orientation, in terms of the nuclear major axis, to increasingly align with the wrinkle direction once the wrinkles form on the substrate surface. This change in alignment occurs within 8 hours of completion of the topographical transition. In contrast, the position of the Golgi body relative to the nucleus remains aligned with the pre-programmed wrinkle direction, regardless of whether it has been fully established. These findings indicate that intracellular positioning of the Golgi body precedes nuclear reorientation during mouse fibroblast directed migration on patterned substrates. We further show that both processes are Rho-associated kinase (ROCK) mediated as they are abolished by pharmacologic ROCK inhibition whereas mouse fibroblast motility is unaffected. The automated image analysis technique introduced could be broadly employed in the study of polarization and other cellular processes in diverse cell types and micro-environments. In addition, having found that the nuclei Golgi vector may be a more sensitive indicator of substrate features than the nuclei orientation, we anticipate the nuclei Golgi vector to be a useful metric for researchers studying the dynamics of cell polarity in response to different micro-environments.

Identifying the mechanism for superdiffusivity in mouse fibroblast motility.

We seek to characterize the motility of mouse fibroblasts on 2D substrates. Utilizing automated tracking techniques, we find that cell trajectories are super-diffusive, where displacements scale faster than t1/2 in all directions. Two mechanisms have been proposed to explain such statistics in other cell types: run and tumble behavior with Lévy-distributed run times, and ensembles of cells with heterogeneous speed and rotational noise. We develop an automated toolkit that directly compares cell trajectories to the predictions of each model and demonstrate that ensemble-averaged quantities such as the mean-squared displacements and velocity autocorrelation functions are equally well-fit by either model. However, neither model correctly captures the short-timescale behavior quantified by the displacement probability distribution or the turning angle distribution. We develop a hybrid model that includes both run and tumble behavior and heterogeneous noise during the runs, which correctly matches the short-timescale behaviors and indicates that the run times are not Lévy distributed. The analysis tools developed here should be broadly useful for distinguishing between mechanisms for superdiffusivity in other cells types and environments.

Shape memory activation can affect cell seeding of shape memory polymer scaffolds designed for tissue engineering and regenerative medicine.

The ability of a three-dimensional scaffold to support cell seeding prior to implantation is a critical criterion for many scaffold-based tissue engineering and regenerative medicine strategies. Shape memory polymer functionality may present important new opportunities and challenges in cell seeding, but the extent to which shape memory activation can positively or negatively affect cell seeding has yet to be reported. The goal of this study was to determine whether shape memory activation can affect cell seeding. The hypothesis was that shape memory activation of porous scaffolds during cell seeding can affect both the number of cells seeded in a scaffold and the distribution (in terms of average infiltration distance) of cells following seeding. Here, we used a porous shape memory foam scaffold programmed to expand when triggered to study cell number and average cell infiltration distance following shape memory activation. We found that shape memory activation can affect both the number of cells and the average cell infiltration distance. The effect was found to be a function of rate of shape change and scaffold pore interconnectivity. Magnitude of shape change had no effect. Only reductions in cell number and infiltration distance (relative to control and benchmark) were observed. The findings suggest that strategies for tissue engineering and regenerative medicine that involve shape memory activation in the presence of a cell-containing medium in vitro or in vivo should consider how recovery rate and scaffold pore interconnectivity may ultimately impact cell seeding.

How Bacteria Respond to Material Stiffness during Attachment: A Role of Escherichia coli Flagellar Motility.

Material stiffness has been shown to have potent effects on bacterial attachment and biofilm formation, but the mechanism is still unknown. In this study, response to material stiffness by Escherichia coli during attachment was investigated with biofilm assays and cell tracking using the Automated Contour-base Tracking for in Vitro Environments (ACTIVE) computational algorithm. By comparing the movement of E. coli cells attached on poly(dimethylsiloxane) (PDMS) surfaces of different Young's moduli (0.1 and 2.6 MPa, prepared by controlling the degree of cross-linking) using ACTIVE, attached cells on stiff surfaces were found more motile during early stage biofilm formation than those on soft surfaces. To investigate if motility is important to bacterial response to material stiffness, we compared E. coli RP437 and its isogenic mutants of flagellar motor (motB) and synthesis of flagella (fliC) and type I fimbriae (fimA) for attachment on 0.1 and 2.6 MPa PDMS surfaces. The motB mutant exhibited defects in response to PDMS stiffness (based on cell counting and tracking with ACTIVE), which was recovered by complementing the motB gene. Unlike motB results, mutants of fliC and fimA did not show significant defects on both face-up and face-down surfaces. Collectively, these findings suggest that E. coli cells can actively respond to material stiffness during biofilm formation, and motB is involved in this response.

On-command on/off switching of progenitor cell and cancer cell polarized motility and aligned morphology via a cytocompatible shape memory polymer scaffold.

In vitro biomaterial models have enabled advances in understanding the role of extracellular matrix (ECM) architecture in the control of cell motility and polarity. Most models are, however, static and cannot mimic dynamic aspects of in vivo ECM remodeling and function. To address this limitation, we present an electrospun shape memory polymer scaffold that can change fiber alignment on command under cytocompatible conditions. Cellular response was studied using the human fibrosarcoma cell line HT-1080 and the murine mesenchymal stem cell line C3H/10T1/2. The results demonstrate successful on-command on/off switching of cell polarized motility and alignment. Decrease in fiber alignment causes a change from polarized motility along the direction of fiber alignment to non-polarized motility and from aligned to unaligned morphology, while increase in fiber alignment causes a change from non-polarized to polarized motility along the direction of fiber alignment and from unaligned to aligned morphology. In addition, the findings are consistent with the hypothesis that increased fiber alignment causes increased cell velocity, while decreased fiber alignment causes decreased cell velocity. On-command on/off switching of cell polarized motility and alignment is anticipated to enable new study of directed cell motility in tumor metastasis, in cell homing, and in tissue engineering.

How Escherichia coli lands and forms cell clusters on a surface: a new role of surface topography.

Bacterial response to surface topography during biofilm formation was studied using 5 µm tall line patterns of poly (dimethylsiloxane) (PDMS). Escherichia coli cells attached on top of protruding line patterns were found to align more perpendicularly to the orientation of line patterns when the pattern narrowed. Consistently, cell cluster formation per unit area on 5 µm wide line patterns was reduced by 14-fold compared to flat PDMS. Contrasting the reduced colony formation, cells attached on narrow patterns were longer and had higher transcriptional activities, suggesting that such unfavorable topography may present a stress to attached cells. Results of mutant studies indicate that flagellar motility is involved in the observed preference in cell orientation on narrow patterns, which was corroborated by the changes in cell rotation pattern before settling on different surface topographies. These findings led to a set of new design principles for creating antifouling topographies, which was validated using 10 µm tall hexagonal patterns.

Automated, contour-based tracking and analysis of cell behaviour over long time scales in environments of varying complexity and cell density.

Understanding single and collective cell motility in model environments is foundational to many current research efforts in biology and bioengineering. To elucidate subtle differences in cell behaviour despite cell-to-cell variability, we introduce an algorithm for tracking large numbers of cells for long time periods and present a set of physics-based metrics that quantify differences in cell trajectories. Our algorithm, termed automated contour-based tracking for in vitro environments (ACTIVE), was designed for adherent cell populations subject to nuclear staining or transfection. ACTIVE is distinct from existing tracking software because it accommodates both variability in image intensity and multi-cell interactions, such as divisions and occlusions. When applied to low-contrast images from live-cell experiments, ACTIVE reduced error in analysing cell occlusion events by as much as 43% compared with a benchmark-tracking program while simultaneously tracking cell divisions and resulting daughter-daughter cell relationships. The large dataset generated by ACTIVE allowed us to develop metrics that capture subtle differences between cell trajectories on different substrates. We present cell motility data for thousands of cells studied at varying densities on shape-memory-polymer-based nanotopographies and identify several quantitative differences, including an unanticipated difference between two 'control' substrates. We expect that ACTIVE will be immediately useful to researchers who require accurate, long-time-scale motility data for many cells.