Single-cell mechanical assay unveils viscoelastic similarities in normal and neoplastic brain cells.

Understanding cancer cell mechanics allows for the identification of novel disease mechanisms, diagnostic biomarkers, and targeted therapies. In this study, we utilized our previously established fluid shear stress assay to investigate and compare the viscoelastic properties of normal immortalized human astrocytes and invasive human glioblastoma (GBM) cells when subjected to physiological levels of shear stress that are present in the brain microenvironment. We used a parallel-flow microfluidic shear system and a camera-coupled optical microscope to expose single cells to fluid shear stress and monitor the resulting deformation in real time, respectively. From the video-rate imaging, we fed cell deformation information from digital image correlation into a three-parameter generalized Maxwell model to quantify the nuclear and cytoplasmic viscoelastic properties of single cells. We further quantified actin cytoskeleton density and alignment in immortalized human astrocytes and GBM cells via fluorescence microscopy and image analysis techniques. Results from our study show that contrary to the behavior of many extracranial cells, normal and cancerous brain cells do not exhibit significant differences in their viscoelastic properties. Moreover, we also found that the viscoelastic properties of the nucleus and cytoplasm as well as the actin cytoskeletal densities of both brain cell types are similar. Our work suggests that malignant GBM cells exhibit unique mechanical behaviors not seen in other cancer cell types. These results warrant future studies to elucidate the distinct biophysical characteristics of the brain and reveal novel mechanical attributes of GBM and other primary brain tumors.

Normalizing granuloma vasculature and matrix improves drug delivery and reduces bacterial burden in tuberculosis-infected rabbits.

Host-directed therapies (HDTs) represent an emerging approach for bacterial clearance during tuberculosis (TB) infection. While most HDTs are designed and implemented for immuno-modulation, other host targets-such as nonimmune stromal components found in pulmonary granulomas-may prove equally viable. Building on our previous work characterizing and normalizing the aberrant granuloma-associated vasculature, here we demonstrate that FDA-approved therapies (bevacizumab and losartan, respectively) can be repurposed as HDTs to normalize blood vessels and extracellular matrix (ECM), improve drug delivery, and reduce bacterial loads in TB granulomas. Granulomas feature an overabundance of ECM and compressed blood vessels, both of which are effectively reduced by losartan treatment in the rabbit model of TB. Combining both HDTs promotes secretion of proinflammatory cytokines and improves anti-TB drug delivery. Finally, alone and in combination with second-line antitubercular agents (moxifloxacin or bedaquiline), these HDTs significantly reduce bacterial burden. RNA sequencing analysis of HDT-treated lung and granuloma tissues implicates up-regulated antimicrobial peptide and proinflammatory gene expression by ciliated epithelial airway cells as a putative mechanism of the observed antitubercular benefits in the absence of chemotherapy. These findings demonstrate that bevacizumab and losartan are well-tolerated stroma-targeting HDTs, normalize the granuloma microenvironment, and improve TB outcomes, providing the rationale to clinically test this combination in TB patients.

CNS tumor stroma transcriptomics identify perivascular fibroblasts as predictors of immunotherapy resistance in glioblastoma patients.

Excessive deposition of extracellular matrix (ECM) is a hallmark of solid tumors; however, it remains poorly understood which cellular and molecular components contribute to the formation of ECM stroma in central nervous system (CNS) tumors. Here, we undertake a pan-CNS analysis of retrospective gene expression datasets to characterize inter- and intra-tumoral heterogeneity of ECM remodeling signatures in both adult and pediatric CNS disease. We find that CNS lesions - glioblastoma in particular - can be divided into two ECM-based subtypes (ECMhi and ECMlo) that are influenced by the presence of perivascular stromal cells resembling cancer-associated fibroblasts (CAFs). Ligand-receptor network analysis predicts that perivascular fibroblasts activate signaling pathways responsible for recruitment of tumor-associated macrophages and promotion of cancer stemness. Our analysis reveals that perivascular fibroblasts are correlated with unfavorable response to immune checkpoint blockade in glioblastoma and poor patient survival across a subset of CNS tumors. We provide insights into new stroma-driven mechanisms underlying immune evasion and immunotherapy resistance in CNS tumors like glioblastoma, and discuss how targeting these perivascular fibroblasts may prove an effective approach to improving treatment response and patient survival in a variety of CNS tumors.

Single-cell mechanical analysis reveals viscoelastic similarities between normal and neoplastic brain cells.

Understanding cancer cell mechanics allows for the identification of novel disease mechanisms, diagnostic biomarkers, and targeted therapies. In this study, we utilized our previously established fluid shear stress assay to investigate and compare the viscoelastic properties of normal immortalized human astrocytes (IHAs) and invasive human glioblastoma (GBM) cells when subjected to physiological levels of shear stress that are present in the brain microenvironment. We used a parallel-flow microfluidic shear system and a camera-coupled optical microscope to expose single cells to fluid shear stress and monitor the resulting deformation in real-time, respectively. From the video-rate imaging, we fed cell deformation information from digital image correlation into a three-parameter generalized Maxwell model to quantify the nuclear and cytoplasmic viscoelastic properties of single cells. We further quantified actin cytoskeleton density and alignment in IHAs and GBM cells via immunofluorescence microscopy and image analysis techniques. Results from our study show that contrary to the behavior of many extracranial cells, normal and cancerous brain cells do not exhibit significant differences in their viscoelastic behavior. Moreover, we also found that the viscoelastic properties of the nucleus and cytoplasm as well as the actin cytoskeletal densities of both brain cell types are similar. Our work suggests that malignant GBM cells exhibit unique mechanical behaviors not seen in other cancer cell types. These results warrant future study to elucidate the distinct biophysical characteristics of the brain and reveal novel mechanical attributes of GBM and other primary brain tumors.

Shear Assay Protocol for the Determination of Single-Cell Material Properties.

Irregular biomechanics are a hallmark of cancer biology subject to extensive study. The mechanical properties of a cell are similar to those of a material. A cell's resistance to stress and strain, its relaxation time, and its elasticity are all properties that can be derived and compared to other types of cells. Quantifying the mechanical properties of cancerous (malignant) versus normal (non-malignant) cells allows researchers to further uncover the biophysical fundamentals of this disease. While the mechanical properties of cancer cells are known to consistently differ from the mechanical properties of normal cells, a standard experimental procedure to deduce these properties from cells in culture is lacking. This paper outlines a procedure to quantify the mechanical properties of single cells in vitro using a fluid shear assay. The principle behind this assay involves applying fluid shear stress onto a single cell and optically monitoring the resulting cellular deformation over time. Cell mechanical properties are subsequently characterized using digital image correlation (DIC) analysis and fitting an appropriate viscoelastic model to the experimental data generated from the DIC analysis. Overall, the protocol outlined here aims to provide a more effective and targeted method for the diagnosis of difficult-to-treat cancers.

Pan-cancer transcriptomic analysis of CNS tumor stroma identifies a population of perivascular fibroblasts that predict poor immunotherapy response in glioblastoma patients.

Excessive deposition of extracellular matrix (ECM) is a hallmark of solid tumors; however, it remains poorly understood which cellular and molecular components contribute to the formation of ECM stroma in central nervous system (CNS) tumors. Here, we undertook a pan-CNS analysis of retrospective gene expression datasets to characterize inter- and intra-tumoral heterogeneity of ECM remodeling signatures in both adult and pediatric CNS disease. We found that CNS lesions - glioblastoma in particular - can be divided into two ECM-based subtypes (ECMhi and ECMlo) that are influenced by the presence of perivascular cells resembling cancer-associated fibroblasts (CAFs). We show that perivascular fibroblasts activate chemoattractant signaling pathways to recruit tumor-associated macrophages, and promote an immune-evasive, stem-like cancer cell phenotype. Our analysis reveals that perivascular fibroblasts are correlated with unfavorable response to immune checkpoint blockade in glioblastoma and poor patient survival across a subset of CNS tumors. We provide insights into novel stroma-driven mechanisms underlying immune evasion and immunotherapy resistance in CNS tumors like glioblastoma, and discuss how targeting these perivascular fibroblasts may prove an effective approach to improving treatment response and patient survival in a variety of CNS tumors.

Prioritizing autoimmunity risk variants for functional analyses by fine-mapping mutations under natural selection.

Pathogen-driven selection shaped adaptive mutations in immunity genes, including those contributing to inflammatory disorders. Functional characterization of such adaptive variants can shed light on disease biology and past adaptations. This popular idea, however, was difficult to test due to challenges in pinpointing adaptive mutations in selection footprints. In this study, using a local-tree-based approach, we show that 28% of risk loci (153/535) in 21 inflammatory disorders bear footprints of moderate and weak selection, and part of them are population specific. Weak selection footprints allow partial fine-mapping, and we show that in 19% (29/153) of the risk loci under selection, candidate disease variants are hitchhikers, and only in 39% of cases they are likely selection targets. We predict function for a subset of these selected SNPs and highlight examples of antagonistic pleiotropy. We conclude by offering disease variants under selection that can be tested functionally using infectious agents and other stressors to decipher the poorly understood link between environmental stressors and genetic risk in inflammatory conditions.