Multiplex gene expression analysis for low abundance intracellular mRNAs in individual cells by RNA flow cytometry (P3381)

Abstract A variety of RNA analysis technologies is available for the detection of RNA transcripts from bulk cell populations. However, the techniques for RNA detection from single cells have been limited due to lack of sensitivity in detecting specific mRNAs present at low abundance in individual cells. By designing novel target-specific probes and adapting an in situ signal amplification method based on the RNAscope® detection platform, we demonstrate the specific and sensitive detection of intracellular RNAs in single cells by flow cytometry (RNAFlow). This method had sufficient sensitivity to distinguish cells containing low abundance RNA transcript. Furthermore, multiple distinct RNA targets were simultaneously detected with a high specificity in single cells without interference. The method can quantify the frequency of cells expressing specific RNA as well as the number of RNA copies in each expression-positive cell. We will present quantitative data to characterize RNA target-specific signal detection, demonstrated in applications such as HIV infection and immune activation. The RNAFlow assay, independently or as combined assay with co-protein detection, represents a valuable research tool for the specific and sensitive detection of multiple RNA transcripts in individual cells in heterogeneous biological specimens. Overall, this method will be useful for the analysis of functionally important RNA species from single cells, even at very low copy numbers.</jats:p

Assessment of RNA flow cytometry probe multiplexing.

<p>(<b>a</b>) Flow cytometry plots from three-probe (bcr, abl, 18 s) RNA analysis in the K562 cell line: 18 s rRNA (left) and bcr vs. abl RNA in the 18 s+ events (right). (<b>b</b>) 60x pseudocolored images of sorted bcr+abl+18 s+ cells from K562 cells; Alexa Fluor® 647 labeled bcr (red), Alexa Fluor® 546 labeled abl (green). The bcr/abl fusion transcripts are shown in yellow due to the merging of both the Alexa Fluor® 546 and Alexa Fluor® 647 dyes. Cells were counterstained with DAPI. (<b>c</b>) Graph showing the effects of RNA staining by multiple probes (bcr+abl+18 s) compared to a single target probe on MFIs in RNA flow cytometry. Bars represent the standard deviation for duplicate samples run.</p

Tyrosine nitration in prostaglandin H\u3csub\u3e2\u3c/sub\u3e synthase

In this study, we investigated the effects of various nitrogen oxide (NOx) species on the extent of prostaglandin H2 synthase-1 (PGHS-1) nitration in purified protein and in vascular smooth muscle cells. We also examined PGHS-1 activity under these conditions and found the degree of nitration to correlate inversely with enzyme activity. In addition, since NOx species are thought to invoke damage during the pathogenesis of atherosclerosis, we examined human atheromatous tissue for PGHS-1 nitration. Both peroxynitrite and tetranitromethane induced Tyr nitration of purified PGHS-1, whereas 1-hydroxy-2-oxo-3-(N-methyl-aminopropyl)-3-methyl-1-triazene (NOC-7; a nitric oxide-releasing compound) did not. Smooth muscle cells treated with peroxynitrite showed PGHS-1 nitration. The extent of nitration by specific NOx species was determined by electrospray ionization mass spectrometry. Tetranitromethane was more effective than peroxynitrite, NOC-7, and nitrogen dioxide at nitrating a tyrosine-containing peptide (12%, 5%, 1%, and \u3c1% nitration, respectively). Nitrogen dioxide and, to a lesser extent, peroxynitrite, induced dityrosine formation. Using UV/Vis spectroscopy, it was estimated that the reaction of PGHS-1 with excess peroxynitrite yielded two nitrated tyrosines/PGHS-1 subunit. Finally, atherosclerotic tissue obtained from endarterectomy patients was shown to contain nitrated PGHS-1. Thus, prolonged exposure to elevated levels of peroxynitrite may cause oxidative damage through tyrosine nitration

Validation of the sensitivity of RNA flow cytometry.

<p>(<b>a</b>) Breakdown of spot numbers from image analysis (bcr+18 s+DAPI+) into bins (x-axis) with corresponding frequencies (y-axis). (<b>b</b>) RNA flow histogram of K562 cells split into groups using the percentages obtained in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057002#pone-0057002-g005" target="_blank">Figure 5A</a> with the corresponding MFIs (x-axis and table). Colors correspond to the spot bin numbers in the bar chart in 5a. The lowest bin (spot count 1–5) was further subdivided and is shown in the inset on the histogram with the corresponding spot count on each peak. (<b>c</b>) Comparison chart of mean integrated intensities (black bars) obtained from image analysis and MFIs (white bars) obtained from RNA flow cytometry analysis (5b). Standard deviations are shown for each group. The coefficient of determination (r²) is displayed on the graph (0.988). (<b>d</b>) Spot intensities for each spot bin with correlating standard deviations for each group. The lower bin (1–5) was further subdivided and is shown on the left side of the chart. (<b>e</b>) Histogram showing sort gates for the different bcr MFI subsets in the K562 cell line. Outline overlay images shown to the right of the histogram are representative of the sorted cells from each subset sorted showing bcr RNA (green), 18 s rRNA (red), and DAPI counterstaining (blue). The bcr spot count average per cell is listed below its respective image.</p

Detection of HIV gag RNA <i>in situ</i> in cell lines and PBMCs.

<p>(<b>a</b>) Representative color merged 60x images of the HIV+ cell line H9IIIB (left) and HIV negative cell line H9 (right); HIV gag RNA-Alexa Fluor® 647 (green), 18 s rRNA-FITC (red), and Nuclei-DAPI (blue). (<b>b</b>) The frequency of HIV gag RNA-positive cells within the 18 s+DAPI+ population was calculated after spiking HIV+ cells into the negative cell population at the stated percentages in the table. The image below the table is representative of data from image segmentation analysis. (<b>c</b>) 40x image of CD4 immunophenotyping overlaid with HIV gag RNA in HIV-infected PBMCs; Anti-CD4 antibody-Alexa Fluor® 488 (red), HIV gag RNA-Alexa Fluor® 546 (green), and DAPI (blue).</p

Validation of the RNA flow cytometry procedure.

<p>(<b>a</b>) A pseudocolor merged segmentation mask image obtained using Cell Profiler software analysis of a mixture of H9 and H9IIIB cells (left image); HIV gag-Alexa Fluor® 647, 18 s-FITC, and DAPI counterstain. The calculated frequency of HIV gag+ cells (within the 18 s+DAPI+ cells) is shown on the image. The RNA flow cytometry plots of the same mixture of H9 and H9IIIB cells. (<b>b</b>) Flow cytometry overlay histograms of HIV gag RNA in HIV-infected PBMCs (solid line) and mock-infected PBMCs (tinted with dashed line) in freshly acquired PBMCs (left plot) and the same PBMCs after cryopreservation (right plot).</p

Comparison of HIV gag RNA <i>in situ</i> detection by a slide-based method vs a suspension-based method.

<p>(<b>a</b>) Pseudocolor-merged 40x images of a 1:1 mixture of H9 and H9IIIB cells via the slide-based RNA detection method (left) and the suspension method (right) of HIV gag RNA-Alexa Fluor® 546, 18 s rRNA-FITC, and DAPI. The calculated frequencies for HIV gag+18 s+ RNA for each are depicted in each image. (<b>b</b>) Mean intensity comparison of HIV gag for different HIV gag spot count ranges (bins) with the suspension-based and slide-based methods. Error bars depict the standard deviation within each spot count bin. The correlation coefficient, r, and the coefficient of determination, r², are shown on the graph.</p

Detection of Low Abundance RNA Molecules in Individual Cells by Flow Cytometry

A variety of RNA analysis technologies are available for the detection of RNA transcripts from bulk cell populations. However, the techniques for RNA detection from individual cells have been limited. Here we adapt a novel in situ signal amplification method (the RNAScope® detection platform) for the analysis of intracellular RNAs in individual cells by flow cytometry. Using novel target-specific probes that were designed to suppress background signals, we demonstrate the specific detection of HIV gag RNAs in HIV-infected cellular samples, in addition to bcr and abl mRNAs in the K562 cell line. This method was capable of distinguishing cells expressing low abundance RNA transcripts and correlated well with quantitative imaging analysis. Furthermore, multiple distinct RNA targets were simultaneously detected with a high specificity without interference. Overall, the sensitivity and specificity of this method will be useful for the analysis of functionally important RNA species from individual cells, even at very low copy numbers