Tools for Single-Cell Kinetic Analysis of Virus-Host Interactions.

Measures of cellular gene expression or behavior, when performed on individual cells, inevitably reveal a diversity of behaviors and outcomes that can correlate with normal or diseased states. For virus infections, the potential diversity of outcomes are pushed to an extreme, where measures of infection reflect features of the specific infecting virus particle, the individual host cell, as well as interactions between viral and cellular components. Single-cell measures, while revealing, still often rely on specialized fluid handling capabilities, employ end-point measures, and remain labor-intensive to perform. To address these limitations, we consider a new microwell-based device that uses simple pipette-based fluid handling to isolate individual cells. Our design allows different experimental conditions to be implemented in a single device, permitting easier and more standardized protocols. Further, we utilize a recently reported dual-color fluorescent reporter system that provides dynamic readouts of viral and cellular gene expression during single-cell infections by vesicular stomatitis virus. In addition, we develop and show how free, open-source software can enable streamlined data management and batch image analysis. Here we validate the integration of the device and software using the reporter system to demonstrate unique single-cell dynamic measures of cellular responses to viral infection

Movie frames of FC and microscopy PDC plots at 6, 12, and 18 hpi.

<p>The green and red fluorescence of each cell in VSV-rWT and VSV-M51R infections are shown in each scatter plot. All timepoints for the microscopy data are taken from the <i><b>same</b></i> sample whereas the FC data at each timepoint represents data from <i><b>separate</b></i> samples obtained in parallel. Furthermore, the microscope sample was sacrificed at 18 hpi and used for the FC 18 hpi timepoint. Thus, the 18 hpi timepoint is the same sample for both methods and is directly comparable. The summary plots on the right-hand side show gated population percentages over time, illustrating the similarity in sensitivity of the two methods but highlighting the improved temporal resolution of the microscopy approach. The gray cross-hair in each plot represents the overall population mean.</p

Microwell-array-based cytometry plots (VSV-M51R & VSV-rWT) show agreement with results from baseline cytometry experiments from Fig 6.

<p>Microwell-array-based cytometry plots (VSV-M51R & VSV-rWT) show agreement with results from baseline cytometry experiments from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145081#pone.0145081.g006" target="_blank">Fig 6</a>.</p

Sample VSV-rWT viral and innate immune reporter protein expression kinetics from six individual cells.

<p>Kinetic parameters have been extracted from the trajectories of both the viral (red) and host (green) trajectories and displayed in the top left corner of each figure. Maximum yields from individual cells vary greatly (a-b), host reporter expression can be greater or lesser than viral gene expression given similar starting conditions (c-d), and cells can lyse (d) or remain intact during imaging. Single-cell analysis can detect and quantify cells displaying rare behavior, such as those that appear to have a basal level of innate immune activation all the time (e) and cells infected with wt-VSV that express ZS-Green despite encoding a functional matrix gene.</p

Data acquisition and analysis.

<p>a) Images are acquired in multiple colors and locations over time. The resultant list of files is imported into JEX. JEX performs analysis on the imported data, and stores the outputs into the same database. All function parameters and data from intermediate steps can be recorded in the JEX database. b) The database of information is stored in a simply named and transparent folder structure for perusal and use outside of JEX, but is also easily accessed via the JEX user interface. c) The workflow for baseline microscopy experiments generally consists of: 1) an initial background and illumination correction, 2) stitching the image array for each color and timepoint within each well, 3) identification of cells based on Hoechst staining, 4) quantification of cell location and fluorescence intensity for each color, and 5) plotting of results. The workflow for MA-based experiments includes additional functions for identifying microwells, counting the number of cells within each microwell, and quantifying whole-microwell and single-cell fluorescence. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145081#pone.0145081.s001" target="_blank">S1 Text</a> for details of all steps.</p

IκBα Nuclear Export Enables 4-1BB–Induced cRel Activation and IL-2 Production to Promote CD8 T Cell Immunity

Optimal CD8 T cell immunity is orchestrated by signaling events initiated by TCR recognition of peptide Ag in concert with signals from molecules such as CD28 and 4-1BB. The molecular mechanisms underlying the temporal and spatial signaling dynamics in CD8 T cells remain incompletely understood. In this study, we show that stimulation of naive CD8 T cells with agonistic CD3 and CD28 Abs, mimicking TCR and costimulatory signals, coordinately induces 4-1BB and cRel to enable elevated cytosolic cRel:IκBα complex formation and subsequent 4-1BB-induced IκBα degradation, sustained cRel activation, heightened IL-2 production and T cell expansion. NfkbiaNES/NES CD8 T cells harboring a mutated IκBα nuclear export sequence abnormally accumulate inactive cRel:IκBα complexes in the nucleus following stimulation with agonistic anti-CD3 and anti-CD28 Abs, rendering them resistant to 4-1BB induced signaling and a disrupted chain of events necessary for efficient T cell expansion. Consequently, CD8 T cells in NfkbiaNES/NES mice poorly expand during viral infection, and this can be overcome by exogenous IL-2 administration. Consistent with cell-based data, adoptive transfer experiments demonstrated that the antiviral CD8 T cell defect in NfkbiaNES/NES mice was cell intrinsic. Thus, these results reveal that IκBα, via its unique nuclear export function, enables, rather than inhibits 4-1BB-induced cRel activation and IL-2 production to facilitate optimal CD8 T cell immunity

Data from: High specificity in circulating tumor cell identification is required for accurate evaluation of programmed death-ligand 1

Background: Expression of programmed-death ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC) is typically evaluated through invasive biopsies; however, recent advances in the identification of circulating tumor cells (CTCs) may be a less invasive method to assay tumor cells for these purposes. These liquid biopsies rely on accurate identification of CTCs from the diverse populations in the blood, where some tumor cells share characteristics with normal blood cells. While many blood cells can be excluded by their high expression of CD45, neutrophils and other immature myeloid subsets have low to absent expression of CD45 and also express PD-L1. Furthermore, cytokeratin is typically used to identify CTCs, but neutrophils may stain non-specifically for intracellular antibodies, including cytokeratin, thus preventing accurate evaluation of PD-L1 expression on tumor cells. This holds even greater significance when evaluating PD-L1 in epithelial cell adhesion molecule (EpCAM) positive and EpCAM negative CTCs (as in epithelial-mesenchymal transition (EMT)). Methods: To evaluate the impact of CTC misidentification on PD-L1 evaluation, we utilized CD11b to identify myeloid cells. CTCs were isolated from patients with metastatic NSCLC using EpCAM, MUC1 or Vimentin capture antibodies and exclusion-based sample preparation (ESP) technology. Results: Large populations of CD11b+CD45lo cells were identified in buffy coats and stained non-specifically for intracellular antibodies including cytokeratin. The amount of CD11b+ cells misidentified as CTCs varied among patients; accounting for 33–100% of traditionally identified CTCs. Cells captured with vimentin had a higher frequency of CD11b+ cells at 41%, compared to 20% and 18% with MUC1 or EpCAM, respectively. Cells misidentified as CTCs ultimately skewed PD-L1 expression to varying degrees across patient samples. Conclusions: Interfering myeloid populations can be differentiated from true CTCs with additional staining criteria, thus improving the specificity of CTC identification and the accuracy of biomarker evaluation