Integrative Biology最新文献

Macrophages are white blood cells that play disparate roles in homeostasis and immune responses. They can reprogram their phenotypes to pro-inflammatory (M1) or anti-inflammatory (M2) states in response to their environment. About 8-15% of the macrophage transcriptome has circadian oscillations, including genes closely related to their functioning. As circadian rhythms are associated with cellular phenotypes, we hypothesized that polarization of macrophages to opposing subtypes might differently affect their circadian rhythms. We tracked circadian rhythms in RAW 264.7 macrophages using luminescent reporters. Cells were stably transfected with Bmal1:luc and Per2:luc reporters, representing positive and negative components of the molecular clock. Strength of rhythmicity, periods and amplitudes of time series were assessed using multiple approaches. M1 polarization decreased amplitudes and rhythmicities of Bmal1:luc and Per2:luc, but did not significantly affect periods, while M2 polarization increased periods but caused no substantial alterations to amplitudes or rhythmicity. As macrophage phenotypes are also altered in the presence of cancer cells, we tested circadian effects of conditioned media from mouse breast cancer cells. Media from highly aggressive 4T1 cells caused loss of rhythmicity, while media from less aggressive EMT6 cells yielded no changes. As macrophages play roles in tumors, and oncogenic features are associated with circadian rhythms, we tested whether conditioned media from macrophages could alter circadian rhythms of cancer cells. Conditioned media from RAW 264.7 cells resulted in lower rhythmicities and periods, but higher amplitudes in human osteosarcoma, U2OS-Per2:luc cells. We show that phenotypic changes in macrophages result in altered circadian characteristics and suggest that there is an association between circadian rhythms and macrophage polarization state. Additionally, our data demonstrate that macrophages treated with breast cancer-conditioned media have circadian phenotypes similar to those of the M1 subtype, and cancer cells treated with macrophage-conditioned media have circadian alterations, providing insight to another level of cross-talk between macrophages and cancer.

Heterogeneity among cancer cells and in the tumor microenvironment (TME) is thought to be a significant contributor to the heterogeneity of clinical therapy response observed between patients and can evolve over time. A primary example of this is multiple myeloma (MM), a generally incurable cancer where such heterogeneity contributes to the persistent evolution of drug resistance. However, there is a paucity of functional assays for studying this heterogeneity in patient samples or for assessing the influence of the patient TME on therapy response. Indeed, the population-averaged data provided by traditional drug response assays and the large number of cells required for screening remain significant hurdles to advancement. To address these hurdles, we developed a suite of accessible technologies for quantifying functional drug response to a panel of therapies in ex vivo three-dimensional culture using small quantities of a patient's own cancer and TME components. This suite includes tools for label-free single-cell identification and quantification of both cell division and death events with a standard brightfield microscope, an open-source software package for objective image analysis and feasible data management of multi-day timelapse experiments, and a new approach to fluorescent detection of cell death that is compatible with long-term imaging of primary cells. These new tools and capabilities are used to enable sensitive, objective, functional characterization of primary MM cell therapy response in the presence of TME components, laying the foundation for future studies and efforts to enable predictive assessment drug efficacy for individual patients.

This paper describes a microscale fibroplasia and contraction model that is based on fibrin-embedded lung fibroblasts and provides a convenient visual readout of fibrosis. Cell-laden fibrin microgel drops are formed by aqueous two-phase microprinting. The cells deposit extracellular matrix (ECM) molecules such as collagen while fibrin is gradually degraded. Ultimately, the cells contract the collagen-rich matrix to form a compact cell-ECM spheroid. The size of the spheroid provides the visual readout of the extent of fibroplasia. Stimulation of this wound-healing model with the profibrotic cytokine TGF-β1 leads to an excessive scar formation response that manifests as increased collagen production and larger cell-ECM spheroids. Addition of drugs also shifted the scarring profile: the FDA-approved fibrosis drugs (nintedanib and pirfenidone) and a PAI-1 inhibitor (TM5275) significantly reduced cell-ECM spheroid size. Not only is the assay useful for evaluation of antifibrotic drug effects, it is relatively sensitive; one of the few in vitro fibroplasia assays that can detect pirfenidone effects at submillimolar concentrations. Although this paper focuses on lung fibrosis, the approach opens opportunities for studying a broad range of fibrotic diseases and for evaluating antifibrotic therapeutics.

Mechanotransduction arises from information encoded in the shape of materials such as curvature. It induces activation of small GTPase signaling affecting cell phenotypes including differentiation. We carried out a set of preliminary experiments to test the hypothesis that curvature (1/radius) would also affect cell motility due to signal pathway crosstalk. High molecular weight poly (methyl methacrylate) straight nanofibers were electrospun with curvature ranging from 41 to 1 μm-1 and collected on a passivated glass substrate. The fiber curvature increased mouse mesenchymal stem cell aspect ratio (P < 0.02) and decreased cell area (P < 0.01). Despite little effect on some motility patterns such as polarity and persistence, we found selected fiber curvatures can increase normalized random fibroblastic mouse embryonic cell (MEF) migration velocity close to 2.5 times compared with a flat surface (P < 0.001). A maximum in the velocity curve occurred near 2.5 μm-1 and may vary with the time since initiation of attachment to the surface (range of 0-20 h). In the middle range of fiber curvatures, the relative relationship to curvature was similar regardless of treatment with Rho-kinase inhibitor (Y27632) or cdc42 inhibitor (ML141), although it was decreased on most curvatures (P < 0.05). However, below a critical curvature threshold MEFs may not be able to distinguish shallow curvature from a flat surface, while still being affected by contact guidance. The preliminary data in this manuscript suggested the large low curvature fibers were interpreted in a manner similar to a non-curved surface. Thus, curvature is a biomaterial construct design parameter that should be considered when specific biological responses are desired. Statement of integration, innovation, and insight  Replacement of damaged or diseased tissues that cannot otherwise regenerate is transforming modern medicine. However, the extent to which we can rationally design materials to affect cellular outcomes remains low. Knowing the effect of material stiffness and diameter on stem cell differentiation, we investigated cell migration and signaling on fibrous scaffolds. By investigating diameters across orders of magnitude (50-2000 nm), we identified a velocity maximum of ~800 nm. Furthermore, the results suggest large fibers may not be interpreted by single cells as a curved surface. This work presents insight into the design of constructs for engineering tissues.

A critical property of many therapies is their selective binding to target populations. Exceptional specificity can arise from high-affinity binding to surface targets expressed exclusively on target cell types. In many cases, however, therapeutic targets are only expressed at subtly different levels relative to off-target cells. More complex binding strategies have been developed to overcome this limitation, including multi-specific and multivalent molecules, creating a combinatorial explosion of design possibilities. Guiding strategies for developing cell-specific binding are critical to employ these tools. Here, we employ a uniquely general multivalent binding model to dissect multi-ligand and multi-receptor interactions. This model allows us to analyze and explore a series of mechanisms to engineer cell selectivity, including mixtures of molecules, affinity adjustments, valency changes, multi-specific molecules and ligand competition. Each of these strategies can optimize selectivity in distinct cases, leading to enhanced selectivity when employed together. The proposed model, therefore, provides a comprehensive toolkit for the model-driven design of selectively binding therapies.