An Automated Image Analysis Method for Segmenting Fluorescent Bacteria in Three Dimensions

Single-cell fluorescence imaging is a powerful technique for studying inherently heterogeneous biological processes. To correlate a genotype or phenotype to a specific cell, images containing a population of cells must first be properly segmented. However, a proper segmentation with minimal user input becomes challenging when cells are clustered or overlapping in three dimensions. We introduce a new analysis package, Seg-3D, for the segmentation of bacterial cells in three-dimensional (3D) images, based on local thresholding, shape analysis, concavity-based cluster splitting, and morphology-based 3D reconstruction. The reconstructed cell volumes allow us to directly quantify the fluorescent signals from biomolecules of interest within individual cells. We demonstrate the application of this analysis package in 3D segmentation of individual bacterial pathogens invading host cells. We believe Seg-3D can be an efficient and simple program that can be used to analyze a wide variety of single-cell images, especially for biological systems involving random 3D orientation and clustering behavior, such as bacterial infection or colonization

Robust nonparametric quantification of clustering density of molecules in single-molecule localization microscopy

<div><p>We report a robust nonparametric descriptor, <i>J</i>′(<i>r</i>), for quantifying the density of clustering molecules in single-molecule localization microscopy. <i>J</i>′(<i>r</i>), based on nearest neighbor distribution functions, does not require any parameter as an input for analyzing point patterns. We show that <i>J</i>′(<i>r</i>) displays a valley shape in the presence of clusters of molecules, and the characteristics of the valley reliably report the clustering features in the data. Most importantly, the position of the <i>J</i>′(<i>r</i>) valley () depends exclusively on the density of clustering molecules (<i>ρ</i>_<i>c</i>). Therefore, it is ideal for direct estimation of the clustering density of molecules in single-molecule localization microscopy. As an example, this descriptor was applied to estimate the clustering density of <i>ptsG</i> mRNA in <i>E. coli</i> bacteria.</p></div

The relation is independent on all the other cluster features, <i>R</i>_<i>c</i>, <i>ρ</i>_<i>r</i>, <i>N</i>_<i>c</i>, <i>W</i>, and <i>H</i>.

<p>All data points collapse onto a single power-law curve, . Least-square fitting gives <i>α</i> = 0.76 ± 0.03.</p

Dependence of on the clustering features.

<p>(A) <i>ρ</i>_<i>c</i>, (B) <i>ρ</i>_<i>r</i>, (C) <i>R</i>_<i>c</i>, (D) <i>N</i>_<i>c</i>, (E) <i>W</i>, and (F) <i>H</i>.</p

<i>G</i>(<i>r</i>), <i>F</i>(<i>r</i>) and <i>J</i>(<i>r</i>) functions, and their derivatives.

<p>(A) Simulated noise points. (B) Simulated points forming clusters with a radius of <i>R</i> = 30 nm, in the presence of noise points. (C, D) <i>G</i>(<i>r</i>), <i>F</i>(<i>r</i>) and <i>J</i>(<i>r</i>) functions calculated from the points in (A) and (B), respectively. (E, F) Derivatives, <i>G</i>′(<i>r</i>), <i>F</i>′(<i>r</i>) and <i>J</i>′(<i>r</i>), calculated from the points in (A) and (B), respectively.</p

Application of <i>J</i>′(<i>r</i>) to <i>ptsG</i> mRNA in <i>E. coli</i> bacteria.

<p>(A, B) Super-resolved images of <i>ptsG</i> mRNA labeled through FISH by (A) 7 or (B) 14 fluorescent oligonucleotide probes. Scale bar = 1 <i>μ</i>m. (C) Computed <i>J</i>′(<i>r</i>) functions from (A) and (B). (D) Estimated clustering densities from (C).</p

Changes in <i>G</i>′(<i>r</i>) and <i>J</i>′(<i>r</i>) by varying a cluster feature at a time.

<p>(A) <i>ρ</i>_<i>c</i>, (B) <i>ρ</i>_<i>r</i>, (C) <i>R</i>_<i>c</i>, (D) <i>N</i>_<i>c</i>, (E) <i>W</i>, and (F) <i>H</i>.</p

<i>G</i>′(<i>r</i>) and <i>J</i>′(<i>r</i>) for data with heterogeneous clusters with two different clustering densities.

<p><i>G</i>′(<i>r</i>) and <i>J</i>′(<i>r</i>) for data with heterogeneous clusters with two different clustering densities.</p

The dependence of the relative error <i>δ</i>_{<i>ρ</i>_<i>c</i>} on the ratio of the density of clustering points (<i>ρ</i>_<i>c</i>) to the density of random points (<i>ρ</i>_<i>r</i>), <i>ρ</i>_<i>c</i>/<i>ρ</i>_<i>r</i>, at various clustering densities.

<p>The dependence of the relative error <i>δ</i>_{<i>ρ</i>_<i>c</i>} on the ratio of the density of clustering points (<i>ρ</i>_<i>c</i>) to the density of random points (<i>ρ</i>_<i>r</i>), <i>ρ</i>_<i>c</i>/<i>ρ</i>_<i>r</i>, at various clustering densities.</p

Understanding the Photophysics of the Spinach–DFHBI RNA Aptamer–Fluorogen Complex To Improve Live-Cell RNA Imaging

The use of aptamer–fluorogen complexes is an emerging strategy for RNA imaging. Despite its promise for cellular imaging and sensing, the low fluorescence intensity of the Spinach–DFHBI RNA aptamer–fluorogen complex hampers its utility in quantitative live-cell and high-resolution imaging applications. Here we report that illumination of the Spinach–fluorogen complex induces photoconversion and subsequently fluorogen dissociation, leading to fast fluorescence decay and fluorogen-concentration-dependent recovery. The fluorescence lifetime of Spinach–DFHBI is 4.0 ± 0.1 ns irrespective of the extent of photoconversion. We detail a low-repetition-rate illumination scheme that enables us to maximize the potential of the Spinach–DFHBI RNA imaging tag in living cells