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

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.

Label and quantify mRNA molecules in live cell experiments using SunRISER and dNEMO.

Visualization of mRNA molecules in single cells has revealed their core mechanisms as well as sources of cell-to-cell and spatiotemporal heterogeneity. Here, we describe a protocol to label, visualize, and quantify mRNA molecules by time-lapse imaging with the capability of resolving mRNA molecules over durations of hours to days. We provide links to mRNA-labeling plasmids as well as free software for a semi-automated image analysis pipeline. For complete details on the use and execution of this protocol, please refer to Guo and Lee (2022) and Kowalczyk et al. (2021).

A variable-gain stochastic pooling motif mediates information transfer from receptor assemblies into NF-κB.

A myriad of inflammatory cytokines regulate signaling pathways to maintain cellular homeostasis. The IκB kinase (IKK) complex is an integration hub for cytokines that govern nuclear factor κB (NF-κB) signaling. In response to inflammation, IKK is activated through recruitment to receptor-associated protein assemblies. How and what information IKK complexes transmit about the milieu are open questions. Here, we track dynamics of IKK complexes and nuclear NF-κB to identify upstream signaling features that determine same-cell responses. Experiments and modeling of single complexes reveal their size, number, and timing relays cytokine-specific control over shared signaling mechanisms with feedback regulation that is independent of transcription. Our results provide evidence for variable-gain stochastic pooling, a noise-reducing motif that enables cytokine-specific regulation and parsimonious information transfer. We propose that emergent properties of stochastic pooling are general principles of receptor signaling that have evolved for constructive information transmission in noisy molecular environments.

NF-κB Dynamics Discriminate between TNF Doses in Single Cells.

Although cytokine-dependent dynamics of nuclear factor κB (NF-κB) are known to encode information that regulates cell fate decisions, it is unclear whether single-cell responses are switch-like or encode more information about cytokine dose. Here, we measure the dynamic subcellular localization of NF-κB in response to a range of tumor necrosis factor (TNF) stimulation conditions to determine the prevailing mechanism of single-cell dose discrimination. Using an information theory formalism that accounts for signaling dynamics and non-responsive cell subpopulations, we find that the information transmission capacity of single cells exceeds that predicted from a switch-like response. Instead, we observe that NF-κB dynamics within single cells contain sufficient information to encode multiple, TNF-dependent cellular states, and have an activation threshold that varies across the population. By comparing single-cell responses to an internal, experimentally observed reference, we demonstrate that cells can grade responses to TNF across several orders of magnitude in concentration. This suggests that cells contain additional control points to fine-tune their cytokine responses beyond the decision to activate.