During mitosis, cells round up and utilize the interphase adhesion sites within the fibrous extracellular matrix (ECM) as guidance cues to orient the mitotic spindles. Here, using suspended ECM-mimicking nanofiber networks, we explore mitotic outcomes and error distribution for various interphase cell shapes. Elongated cells attached to single fibers through two focal adhesion clusters (FACs) at their extremities result in perfect spherical mitotic cell bodies that undergo significant 3-dimensional (3D) displacement while being held by retraction fibers (RFs). Increasing the number of parallel fibers increases FACs and retraction fiber-driven stability, leading to reduced 3D cell body movement, metaphase plate rotations, increased interkinetochore distances, and significantly faster division times. Interestingly, interphase kite shapes on a crosshatch pattern of four fibers undergo mitosis resembling single-fiber outcomes due to rounded bodies being primarily held in position by RFs from two perpendicular suspended fibers. We develop a cortex–astral microtubule analytical model to capture the retraction fiber dependence of the metaphase plate rotations. We observe that reduced orientational stability, on single fibers, results in increased monopolar mitotic defects, while multipolar defects become dominant as the number of adhered fibers increases. We use a stochastic Monte Carlo simulation of centrosome, chromosome, and membrane interactions to explain the relationship between the observed propensity of monopolar and multipolar defects and the geometry of RFs. Overall, we establish that while bipolar mitosis is robust in fibrous environments, the nature of division errors in fibrous microenvironments is governed by interphase cell shapes and adhesion geometries.

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Through force exertion, cells actively engage with their immediate fibrous extracellular matrix (ECM) environment, causing dynamic remodeling of the environment and influencing cellular shape and contractility changes in a feedforward loop. Controlling cell shapes and quantifying the force-driven dynamic reciprocal interactions in a label-free setting is vital to understand cell behavior in fibrous environments but currently unavailable. Here, we introduce a force measurement platform termed crosshatch nanonet force microscopy (cNFM) that reveals new insights into cell shape-force coupling. Using a suspended crosshatch network of fibers capable of recovering in vivo cell shapes, we utilize deep learning methods to circumvent the fiduciary fluorescent markers required to recognize fiber intersections. Our method provides high fidelity computer reconstruction of different fiber architectures by automatically translating phase-contrast time-lapse images into synthetic fluorescent images. An inverse problem based on the nonlinear mechanics of fiber networks is formulated to match the network deformation and deformed fiber shapes to estimate the forces. We reveal an order-of-magnitude force changes associated with cell shape changes during migration, forces during cell-cell interactions and force changes as single mesenchymal stem cells undergo differentiation. Overall, deep learning methods are employed in detecting and tracking highly compliant backgrounds to develop an automatic and label-free force measurement platform to describe cell shape-force coupling in fibrous environments that cells would likely interact with in vivo.

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