Metastatic cancer cells for many cancers are known to have altered cytoskeletal properties, in particular to be more deformable and contractile. Consequently, shape characteristics of more metastatic cancer cells may be expected to have diverged from those of their parental cells. To examine this hypothesis we study shape characteristics of paired osteosarcoma cell lines, each consisting of a less metastatic parental line and a more metastatic line, derived from the former by in vivo selection. Two-dimensional images of four pairs of lines were processed. Statistical analysis of morphometric characteristics shows that shape characteristics of the metastatic cell line are partly overlapping and partly diverged from the parental line. Significantly, the shape changes fall into two categories, with three paired cell lines displaying a more mesenchymal-like morphology, while the fourth displaying a change towards a more rounded morphology. A neural network algorithm could distinguish between samples of the less metastatic cells from the more metastatic cells with near perfect accuracy. Thus, subtle changes in shape carry information about the genetic changes that lead to invasiveness and metastasis of osteosarcoma cancer cells.

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We study the shape characteristics of osteosarcoma cancer cell lines on surfaces of differing hydrophobicity using Zernike moments to represent cell shape. We compare the shape characteristics of four invasive cell lines with a corresponding less-invasive parental line on three substrates. Cell shapes of each pair of cell lines are quite close and display overlapping characteristics. To quantitatively study shape changes in high-dimensional parameter space we project down to principal component space and define a vector that summarizes average shape differences. Using this vector we find that three of the four pairs of cell lines show similar changes in shape, while the fourth pair shows a very different pattern of changes. We find that shape differences are sufficient to enable a neural network to classify cells accurately as belonging to the highly invasive or the less invasive phenotype. The patterns of shape changes were also reproducible for repetitions of the experiment. We also find that shape changes on different substrates show similarities between the eight cells studied, but the differences were typically not enough to permit classification. Our paper strongly suggests that shape may provide a means to read out the phenotypic state of some cell types, and shape analysis can be usefully performed using a Zernike moment representation.

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A number of recent studies have shown that cell shape and cytoskeletal texture can be used as sensitive readouts of the physiological state of the cell. However, utilization of this information requires the development of quantitative measures that can describe relevant aspects of cell shape. In this paper we develop a toolbox, TISMorph, that calculates a set of quantitative measures to address this need. Some of the measures introduced here have been used previously, while others are new and have desirable properties for shape and texture quantification of cells. These measures, broadly classifiable into the categories of textural, irregularity and spreading measures, are tested by using them to discriminate between osteosarcoma cell lines treated with different cytoskeletal drugs. We find that even though specific classification tasks often rely on a few measures, these are not the same between all classification tasks, thus requiring the use of the entire suite of measures for classification and discrimination. We provide detailed descriptions of the measures, as well as the TISMorph package to implement them. Quantitative morphological measures that capture different aspects of cell morphology will help enhance large-scale image-based quantitative analysis, which is emerging as a new field of biological data.

Summary statement 13We develop a set of quantitative measures for studying changes in the shape and 14 We find that even though specific classification tasks often rely on a few measures, these 26 are not the same between all classification tasks, thus requiring the use of the entire suite 27 of measures for classification and discrimination. We provide detailed descriptions of the 28 measures, as well as codes to implement them. Image based quantitative analysis has the 29 . CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx

Although most cancer drugs target the proliferation of cancer cells, it is metastasis, the complex process by which cancer cells spread from the primary tumor to other tissues and organs of the body where they form new tumors, that leads to over 90% of all cancer deaths. Thus, there is an urgent need for anti-metastasis therapy. Surprisingly, emerging evidence suggests that certain anti-cancer drugs such as paclitaxel and doxorubicin can actually promote metastasis, but the mechanism(s) behind their pro-metastatic effects are still unclear. We used a microfluidic microcirculation platform which mimics the capillary constrictions of the pulmonary and peripheral microcirculation, to determine if in-vivo-like mechanical stimuli can evoke different responses from cells subjected to various cancer drugs. We found that leukemic cancer cells treated with doxorubicin and daunorubicin, commonly used anti-cancer drugs, have over 100% longer transit times through the device, compared to untreated leukemic cells. Such delays in the microcirculation are known to promote extravasation of cells, a key step in the metastatic cascade. Furthermore, there was a significant (p < 0.01) increase in the chemotactic migration of the doxorubicin treated leukemic cells. Both enhanced retention in the microcirculation and enhanced migration following chemotherapy, are pro-metastatic effects which can serve as new targets for anti-metastatic drugs.

Cells move differently on substrates with different elasticities. In particular, the persistence of their directionality is greater on substrates with a higher elastic modulus. We show that this behavior -without any further assumptionswill result in a net transport of cells directed up a soft-to-stiff gradient. Using simple random walk models with controlled persistence and stochastic simulations, we characterize this propensity to move in terms of the durotactic index measured in experiments. A one-dimensional model captures the essential features of this motion and highlights the competition between diffusive spreading and linear, wavelike propagation. Since the directed motion is rooted in a nondirectional change in the behavior of individual cells, the motility is a kinesis rather than a taxis. Persistence-driven durokinesis is generic and may be of use in the design of instructive environments for cells and other motile, mechanosensitive objects.