Dynamics of cancerous tissue correlates with invasiveness

Two of the classical hallmarks of cancer are uncontrolled cell division and tissue invasion, which turn the disease into a systemic, life-threatening condition. Although both processes are studied, a clear correlation between cell division and motility of cancer cells has not been described previously. Here, we experimentally characterize the dynamics of invasive and non-invasive breast cancer tissues using human and murine model systems. The intrinsic tissue velocities, as well as the divergence and vorticity around a dividing cell correlate strongly with the invasive potential of the tissue, thus showing a distinct correlation between tissue dynamics and aggressiveness. We formulate a model which treats the tissue as a visco-elastic continuum. This model provides a valid reproduction of the cancerous tissue dynamics, thus, biological signaling is not needed to explain the observed tissue dynamics. The model returns the characteristic force exerted by an invading cell and reveals a strong correlation between force and invasiveness of breast cancer cells, thus pinpointing the importance of mechanics for cancer invasion

Interplay Between LOX Enzymes and Integrins in the Tumor Microenvironment

Members of the lysyl oxidase (LOX) family are secreted copper-dependent amine oxidases that catalyze the covalent crosslinking of collagens and elastin in the extracellular matrix (ECM), an essential process for the structural integrity of all tissues. LOX enzymes can also remodel the tumor microenvironment and have been implicated in all stages of tumor initiation and progression of many cancer types. Changes in the ECM can influence several cancer cell phenotypes. Integrin adhesion complexes (IACs) physically connect cells with their microenvironment. This review article summarizes the main findings on the role of LOX proteins in modulating the tumor microenvironment, with a particular focus on how ECM changes are integrated by IACs to modulate cells behavior. Finally, we discuss how the development of selective LOX inhibitors may lead to novel and effective therapies in cancer treatment

Matritecture:Mapping the extracellular matrix architecture during health and disease

All cells in multicellular organisms are housed in the extracellular matrix (ECM), an acellular edifice built up by more than a thousand proteins and glycans. Cells engage in a reciprocal relationship with the ECM; they build, inhabit, maintain, and remodel the ECM, while, in turn, the ECM regulates their behavior. The homeostatic balance of cell-ECM interactions can be lost, due to ageing, irritants or diseases, which results in aberrant cell behavior. The ECM can suppress or promote disease progression, depending on the information relayed to cells. Instructions come in the form of biochemical (e.g., composition), biophysical (e.g., stiffness), and topographical (e.g., structure) cues. While advances have been made in many areas, we only have a very limited grasp of ECM topography. A detailed atlas deciphering the spatiotemporal arrangement of all ECM proteins is lacking. We feel that such an extracellular matrix architecture (matritecture) atlas should be a priority goal for ECM research. In this commentary, we will discuss the need to resolve the spatiotemporal matritecture to identify potential disease triggers and therapeutic targets and present strategies to address this goal. Such a detailed matritecture atlas will not only identify disease-specific ECM structures but may also guide future strategies to restructure disease-related ECM patterns reverting to a normal pattern

Fiber finding algorithm using stepwise tracing to identify biopolymer fibers in noisy 3D images

We present a novel fiber finding algorithm (FFA) that will permit researchers to detect and return traces of individual biopolymers. Determining the biophysical properties and structural cues of biopolymers can permit researchers to assess the progression and severity of disease. Confocal microscopy images are a useful method for observing biopolymer structures in three dimensions, but their utility for identifying individual biopolymers is impaired by noise inherent in the acquisition process, including convolution from the point spread function (PSF). The new, iterative FFA we present here 1) measures a microscope’s PSF and uses it as a metric for identifying fibers against the background; 2) traces each fiber within a cone angle; and 3) blots out the identified trace before identifying another fiber. Blotting out the identified traces in each iteration allows the FFA to detect and return traces of single fibers accurately and efficiently—even within fiber bundles. We used the FFA to trace unlabeled collagen type I fibers—a biopolymer used to mimic the extracellular matrix in in vitro cancer assays—imaged by confocal reflectance microscopy in three dimensions, enabling quantification of fiber contour length, persistence length, and three-dimensional (3D) mesh size. Based on 3D confocal reflectance microscopy images and the PSF, we traced and measured the fibers to confirm that colder gelation temperatures increased fiber contour length, persistence length, and 3D mesh size—thereby demonstrating the FFA’s use in quantifying biopolymers’ structural and physical cues from noisy microscope images