Quantitative Biophysical Metrics for Rapid Evaluation of Ovarian Cancer Metastatic Potential

Ovarian cancer is routinely diagnosed long after the disease has metastasized through the fibrous sub-mesothelium. Despite extensive research in the field linking ovarian cancer progression to increasingly poor prognosis, there are currently no validated cellular markers or hallmarks of ovarian cancer that can predict metastatic potential. To discern disease progression across a syngeneic mouse ovarian cancer progression model, here, we fabricated extracellular-matrix mimicking suspended fiber networks: crosshatches of mismatch diameters for studying protrusion dynamics, aligned same diameter networks of varying inter-fiber spacing for studying migration, and aligned nanonets for measuring cell forces. We found that migration correlated with disease, while force-disease biphasic relationship exhibited f-actin stress-fiber network dependence. However, unique to suspended fibers, coiling occurring at tips of protrusions and not the length or breadth of protrusions displayed strongest correlation with metastatic potential. To confirm that our findings were more broadly applicable beyond the mouse model, we repeated our studies in human ovarian cancer cell lines and found that the biophysical trends were consistent with our mouse model results. Altogether, we report complementary high throughput and high content biophysical metrics capable of identifying ovarian cancer metastatic potential on time scale of hours. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text]

Potential Applications of Fluorescence-Activated Cell Sorting (FACS) and Droplet-Based Microfluidics in Promoting the Discovery of Specific Antibodies for Characterizations of Fish Immune Cells

Akin to their mammalian counterparts, teleost fish possess a complex assortment of highly specialized immune cells that are capable of unleashing potent innate immune responses to eradicate or mitigate incoming pathogens, and also differentiate into memory lymphocytes to provide long-term protection. Investigations into specific roles and functions of fish immune cells depend on the precise separation of each cell type. Commonly used techniques, for example, density gradient centrifugation, rely on immune cells to have differing sizes or densities and thus fail to separate between similar cell types (e.g. T and B lymphocytes). Furthermore, a continuously growing database of teleost genomic information has revealed an inventory of cellular markers, indicating the possible presence of immune cell subsets in teleost fish. This further complicates the interpretation of results if subsets of immune cells are not properly separated. Consequently, monoclonal antibodies (mAbs) against specific cellular markers are required to precisely identify and separate novel subsets of immune cells in fish. In the field of fish immunology, mAbs are largely generated using the hybridoma technology, resulting in the development of mAbs against specific cellular markers in different fish species. Nevertheless, this technology suffers from being labour-intensive, time-consuming and most importantly, the inevitable loss of diversities of antibodies during the fusion of antibody-expressing B lymphocytes and myeloma cells. In light of this, the focus of this review is to discuss the potential applications of fluorescence-activated cell sorting and droplet-based microfluidics, two emerging technologies capable of screening and identifying antigen-specific B lymphocytes in a high-throughput manner, in promoting the development of valuable reagents for fish immunology studies. Our main goal is to encourage the incorporation of alternative technologies into the field of fish immunology to promote the production of specific antibodies in a high-throughput and cost-effective way, which could better allow for the precise separation of fish immune cells and also facilitate the identification of novel immune cell subsets in teleost fish.

Long wavelength light that improves aged mitochondrial function selectively increases cytokine expression in serum and the retina

Aged mitochondrial function can be improved with long wavelength light exposure. This reduces cellular markers of inflammation and can improve system function from fly though to human. Here, we ask what impact 670nm light has on cytokine expression using a 40 cytokine array in blood serum and retina in C57Bl6 mice. There was a relatively uniform increase in cytokine expression between 3 and 12 months of age in serum and retina. 670nm exposure was delivered daily for a week in 12 month old mice. This shifted patterns of cytokine expression in both serum and retina inducing a selective increase with some in serum increasing >5 fold. Changes in retina were smaller. In serum there were major increases in IL-7, 6, 13, 16 and 23, also in TNF-α and CXCL 9 and 10. In retina the increases were found mainly in some IL (interleukins) and CXCLs (chemokines). A few cytokines were reduced by light exposure. Changes in serum cytokines implies that long wavelengths impacts systemically even to unexposed tissues deep in the body. In the context of wider literature, increased cytokine expression may be protective. However, their upregulation by light merits further analysis as cytokines upregulation can also be negative.

Integrated Cells and Collagen Fibers Spatial Image Analysis

Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and mass spectrometry imaging (MSI) provide large amounts of quantitative and spatial information about cells and tissue structures like vessels, bronchioles etc. Many published reports have demonstrated that the structural features of cells and extracellular matrix (ECM) and their interactions strongly predict disease development and progression. Computational image analysis methods in combination with spatial analysis and machine learning can reveal novel structural patterns in normal and diseased tissue. Here, we have developed a Python package designed for integrated analysis of cells and ECM in a spatially dependent manner. The package performs segmentation, labeling and feature analysis of ECM fibers, combines this information with pre-generated single-cell based datasets and realizes cell-cell and cell-fiber spatial analysis. To demonstrate performance and compatibility of our computational tool, we integrated it with a pipeline designed for cell segmentation, classification, and feature analysis in the KNIME analytical platform. For validation, we used a set of mouse mammary gland tumors and human lung adenocarcinoma tissue samples stained for multiple cellular markers and collagen as the main ECM protein. The developed package provides sufficient performance and precision to be used as a novel method to investigate cell-ECM relationships in the tissue, as well as detect structural patterns correlated with specific disease outcomes.

SnRNA sequencing defines signaling by RBC-derived extracellular vesicles in the murine heart

Extracellular vesicles (EVs) mediate intercellular signaling by transferring their cargo to recipient cells, but the functional consequences of signaling are not fully appreciated. RBC-derived EVs are abundant in circulation and have been implicated in regulating immune responses. Here, we use a transgenic mouse model for fluorescence-based mapping of RBC-EV recipient cells to assess the role of this intercellular signaling mechanism in heart disease. Using fluorescent-based mapping, we detected an increase in RBC-EV–targeted cardiomyocytes in a murine model of ischemic heart failure. Single cell nuclear RNA sequencing of the heart revealed a complex landscape of cardiac cells targeted by RBC-EVs, with enrichment of genes implicated in cell proliferation and stress signaling pathways compared with non-targeted cells. Correspondingly, cardiomyocytes targeted by RBC-EVs more frequently express cellular markers of DNA synthesis, suggesting the functional significance of EV-mediated signaling. In conclusion, our mouse model for mapping of EV-recipient cells reveals a complex cellular network of RBC-EV–mediated intercellular communication in ischemic heart failure and suggests a functional role for this mode of intercellular signaling.

CyTOF-Enabled Analysis Identifies Class-Switched B Cells as the Main Lymphocyte Subset Associated With Disease Relapse in Children With Idiopathic Nephrotic Syndrome

B cell depleting therapies permit immunosuppressive drug withdrawal and maintain remission in patients with frequently relapsing nephrotic syndrome (FRNS) or steroid–dependent nephrotic syndrome (SDNS), but lack of biomarkers for treatment failure. Post-depletion immune cell reconstitution may identify relapsing patients, but previous characterizations suffered from methodological limitations of flow cytometry. Time-of-flight mass cytometry (CyTOF) is a comprehensive analytic modality that simultaneously quantifies over 40 cellular markers. Herein, we report CyTOF-enabled immune cell comparisons over a 12-month period from 30 children with SDNS receiving B cell depleting therapy who either relapsed (n = 17) or remained stable (n = 13). Anti-CD20 treatment depleted all B cells subsets and CD20 depleting agent choice (rituximab vs ofatumumab) did not affect B cell subset recovery. Despite equal total numbers of B cells, 5 subsets of B cells were significantly higher in relapsing individuals; all identified subsets of B cells were class-switched. T cell subsets (including T follicular helper cells and regulatory T cells) and other major immune compartments were largely unaffected by B cell depletion, and similar between relapsing and stable children. In conclusion, CyTOF analysis of immune cells from anti-CD20 antibody treated patients identifies class-switched B cells as the main subset whose expansion associates with disease relapse. Our findings set the basis for future studies exploring how identified subsets can be used to monitor treatment response and improve our understanding of the pathogenesis of the disease.

Impact of Glucocorticoid on a Cellular Model of Parkinson’s Disease: Oxidative Stress and Mitochondrial Function

Stress seems to contribute to the neuropathology of Parkinson’s disease (PD), possibly by dysregulation of the hypothalamic–pituitary–adrenal axis. Oxidative distress and mitochondrial dysfunction are key factors involved in the pathophysiology of PD and neuronal glucocorticoid-induced toxicity. Animal PD models have been generated to study the effects of hormonal stress, but no in vitro model has yet been developed. Our aim was to examine the impact of corticosterone (CORT) administration on a dopaminergic neuronal cell model of PD induced by the neurotoxin MPP+, as a new combined PD model based on the marker of endocrine response to stress, CORT, and oxidative-mitochondrial damage. We determined the impact of CORT, MPP+ and their co-incubation on reactive oxygen species production (O2−•), oxidative stress cellular markers (advanced-oxidation protein products and total antioxidant status), mitochondrial function (mitochondrial membrane potential and mitochondrial oxygen consumption rate) and neurodegeneration (Fluoro-Jade staining). Accordingly, the administration of MPP+ or CORT individually led to cell damage compared to controls (p < 0.05), as determined by several methods, whereas their co-incubation produced strong cell damage (p < 0.05). The combined model described here could be appropriate for investigating neuropathological hallmarks and for evaluating potential new therapeutic tools for PD patients suffering mild to moderate emotional stress.

Proteomic landscape of Alzheimer’s Disease: novel insights into pathogenesis and biomarker discovery

Mass spectrometry-based proteomics empowers deep profiling of proteome and protein posttranslational modifications (PTMs) in Alzheimer’s disease (AD). Here we review the advances and limitations in historic and recent AD proteomic research. Complementary to genetic mapping, proteomic studies not only validate canonical amyloid and tau pathways, but also uncover novel components in broad protein networks, such as RNA splicing, development, immunity, membrane transport, lipid metabolism, synaptic function, and mitochondrial activity. Meta-analysis of seven deep datasets reveals 2,698 differentially expressed (DE) proteins in the landscape of AD brain proteome (n = 12,017 proteins/genes), covering 35 reported AD genes and risk loci. The DE proteins contain cellular markers enriched in neurons, microglia, astrocytes, oligodendrocytes, and epithelial cells, supporting the involvement of diverse cell types in AD pathology. We discuss the hypothesized protective or detrimental roles of selected DE proteins, emphasizing top proteins in “amyloidome” (all biomolecules in amyloid plaques) and disease progression. Comprehensive PTM analysis represents another layer of molecular events in AD. In particular, tau PTMs are correlated with disease stages and indicate the heterogeneity of individual AD patients. Moreover, the unprecedented proteomic coverage of biofluids, such as cerebrospinal fluid and serum, procures novel putative AD biomarkers through meta-analysis. Thus, proteomics-driven systems biology presents a new frontier to link genotype, proteotype, and phenotype, accelerating the development of improved AD models and treatment strategies.