Biomechanical cues within tissue microenvironments are critical for maintaining homeostasis, and their disruption can contribute to malignant transformation and metastasis. Once transformed, metastatic cancer cells can migrate persistently by adapting (plasticity) to changes in the local fibrous extracellular matrix, and current strategies to recapitulate persistent migration rely exclusively on the use of aligned geometries. Here, the controlled interfiber spacing in suspended Crosshatch networks of nanofibers induces cells to exhibit plasticity in migratory behavior (persistent and random) and the associated cytoskeletal arrangement. At dense spacing (3 and 6 µm), unexpectedly, elongated cells migrate persistently (in 1 dimension) at high speeds in 3‐dimensional shapes with thick nuclei, and short focal adhesion cluster (FAC) lengths. With increased spacing (18 and 36 µm), cells attain 2‐dimensional morphologies, have flattened nuclei and longer FACs, and migrate randomly by rapidly detaching their trailing edges that strain the nuclei by ∼35%. At 54‐µm spacing, kite‐shaped cells become near stationary. Poorly developed filamentous actin stress fibers are found only in cells on 3‐µm networks. Gene‐expression profiling shows a decrease in transcriptional potential and a differential up‐regulation of metabolic pathways. The consistency in observed phenotypes across cell lines supports using this platform to dissect hallmarks of plasticity in migration in vitro.—Jana, A., Nookaew, I., Singh, J., Behkam, B., Franco, A. T., Nain, A. S. Crosshatch nanofiber networks of tunable interfiber spacing induce plasticity in cell migration and cytoskeletal response. FA8EB J. 33, 10618–10632 (2019). http://www.fasebj.org

Contact inhibition of locomotion (CIL), in which cells repolarize and move away from contact, is now established as a fundamental driving force in development, repair, and disease biology. Much of what we know of CIL stems from studies on two-dimensional (2D) substrates that do not provide an essential biophysical cue—the curvature of extracellular matrix fibers. We discover rules controlling outcomes of cell–cell collisions on suspended nanofibers and show them to be profoundly different from the stereotyped CIL behavior on 2D substrates. Two approaching cells attached to a single fiber do not repolarize upon contact but rather usually migrate past one another. Fiber geometry modulates this behavior; when cells attach to two fibers, reducing their freedom to reorient, only one cell repolarizes on contact, leading to the cell pair migrating as a single unit. CIL outcomes also change when one cell has recently divided and moves with high speed—cells more frequently walk past each other. Our computational model of CIL in fiber geometries reproduces the core qualitative results of the experiments robustly to model parameters. Our model shows that the increased speed of postdivision cells may be sufficient to explain their increased walk-past rate. We also identify cell–cell adhesion as a key mediator of collision outcomes. Our results suggest that characterizing cell–cell interactions on flat substrates, channels, or micropatterns is not sufficient to predict interactions in a matrix—the geometry of the fiber can generate entirely new behaviors.

Significance:When cells heal a wound or invade a new area, they coordinate their motion. Coordination is often studied by looking at what happens after pairs of cells collide. Post-collision, cells often exhibit contact inhibition of locomotion-they turn around and crawl away from the point where they touched. Our knowledge of repolarization on contact comes from studies on flat surfaces, unlike cells in the body, which crawl along fibers. We discover that cells on single fibers walk past one another-but that cells in contact with multiple fibers stick to one another and move as pairs. This outcome changes to walk-past after cell division. Our experiments and models reveal how the environment regulates cell-cell coordination after contact. Abstract:Contact inhibition of locomotion (CIL), in which cells repolarize and move away from contact, is now established as a fundamental driving force in development, repair, and disease biology. Much of what we know of CIL stems from studies on 2D substrates that fail to provide an essential biophysical cuethe curvature of extracellular matrix fibers. We discover rules controlling outcomes of cell-cell collisions on suspended nanofibers, and show them to be profoundly different from the stereotyped CIL behavior known on 2D substrates. Two approaching cells attached to a single fiber do not repolarize upon contact but rather usually migrate past one another. Fiber geometry modulates this behavior: when cells are attached to two fibers, reducing their freedom to reorient, only one of a pair of colliding cells repolarizes on contact, leading to the cell pair migrating as a single unit. CIL outcomes also change when one cell has recently divided and moves with high speed-cells more frequently walk past each other. In collisions with division in the twofiber geometry, we also capture rare events where a daughter cell pushes the non-dividing cell along the fibers. Our computational model of CIL in fiber geometries reproduces the core qualitative results of the experiments robustly to model parameters. Our model shows that the increased speed of post-division cells may be sufficient to explain their increased walk-past rate. Our results suggest that characterizing cell-cell interactions on flat substrates, channels, or micropatterns is not sufficient to predict interactions in a matrix the geometry of the fiber can generate entirely new behaviors.

BackgroundOrganisms are subject to various stress conditions, which affect both the organism’s gene expression and phenotype. It is critical to understand microbial responses to stress conditions and uncover the underlying molecular mechanisms. To this end, it is necessary to build a database that collects transcriptomics and phenotypic data of microbes growing under various stress factors for in-depth systems biology analysis. Despite of numerous databases that collect gene expression profiles, to our best knowledge, there are few, if any, databases that collect both transcriptomics and phenotype data simultaneously. In light of this, we have developed an open source, web-based database, namely integrated transcriptomics and phenotype (iTAP) database, that records and links the transcriptomics and phenotype data for two model microorganisms, Escherichia coli and Saccharomyces cerevisiae in response to exposure of various stress conditions.ResultsTo collect the data, we chose relevant research papers from the PubMed database containing all the necessary information for data curation including experimental conditions, transcriptomics data, and phenotype data. The transcriptomics data, including the p value and fold change, were obtained through the comparison of test strains against control strains using Gene Expression Omnibus’s GEO2R analyzer. The phenotype data, including the cell growth rate and the productivity, volumetric rate, and mass-based yield of byproducts, were calculated independently from charts or graphs within the reference papers. Since the phenotype data was never reported in a standardized format, the curation of correlated transcriptomics–phenotype datasets became extremely tedious and time-consuming. Despite the challenges, till now, we successfully correlated 57 and 143 datasets of transcriptomics and phenotype for E. coli and S. cerevisiae, respectively, and applied a regression model within the iTAP database to accurately predict over 93 and 73 % of the growth rates of E. coli and S. cerevisiae, respectively, directly from the transcriptomics data.ConclusionThis is the first time that transcriptomics and phenotype data are categorized and correlated in an open-source database. This allows biologists to access the database and utilize it to predict the phenotype of microorganisms from their transcriptomics data. The iTAP database is freely available at https://sites.google.com/a/vt.edu/biomolecular-engineering-lab/software.

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