dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells

Abstract

Motivation: Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here, we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images.

Results: The spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high-quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli. We conclude that dNEMO is an ideal user interface for rapid and accurate measurement of fluorescent molecular assemblies in biological imaging data.

Availability and implementation: The data and software are freely available online at https://github.com/recleelab.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

The à trous wavelet transform on simulated and experimental images. (a) The 3D representations of the convolution matrix (kernel) for levels 1 through 3 of the wavelet transform. (b) Images for simulated data (top), smFISH-labeled NFKBIA transcripts (middle) or GFP-NEMO (bottom), along with the associated L1, L2 and L3 wavelet maps. The L2 wavelet map enhances contrast for diffraction-limited puncta in fluorescence microscopy images. Scale bar 25 microns

Fig. 2.

Overview of the dNEMO workflow. (a) In the first operation performed by the application, the image undergoes the à trous wavelet transform, producing a wavelet map, which is subsequently segmented using by watershed to identify puncta. (b) Individual cells are separately identified through an interactive manual segmentation tool operated by the user (middle left). (c) Once identified by the wavelet transform, identified puncta can be curated based on features like intensity and size. Puncta intensities are corrected using local background pixels for each individual punctum. (d) Settings used to define valid puncta for a single image are propagated over sets of time-lapse images creating a keyframe. Combined with previous segmentation of individual cells, punctum features are quantified over time and associated to single cells

Fig. 3.

Identification of smFISH transcripts in fixed-cell images (a) NFKBIA transcripts in HeLa cells labeled by smFISH are identified using dNEMO and associated to single cells. Scale bar 50 microns. (b) Distribution of puncta identified in individual cells reported by intensity (top) or size in number of pixels (bottom). Fluorescence per spot was corrected using the local background about each spot (see Supplementary Fig. S3)

Fig. 4.

Quantification of EGFP-NEMO in live-cell time-lapse images. (a) Live-cell time-lapse images of U2OS cells expressing EGFP-NEMO from its endogenous locus exposed to 100 ng/ml IL-1. NEMO transiently localized to punctate structures and were identified with dNEMO and associated to individual cells. Scale bar 20 microns. (b) Analysis of puncta identified within single cells over time. The number of puncta (top), the distribution of puncta intensities (middle) and the distribution of puncta sizes (bottom) are shown per cell over time