Stimulus-response signaling dynamics characterize macrophage polarization states.

The functional state of cells is dependent on their microenvironmental context. Prior studies described how polarizing cytokines alter macrophage transcriptomes and epigenomes. Here, we characterized the functional responses of 6 differentially polarized macrophage populations by measuring the dynamics of transcription factor nuclear factor κB (NF-κB) in response to 8 stimuli. The resulting dataset of single-cell NF-κB trajectories was analyzed by three approaches: (1) machine learning on time-series data revealed losses of stimulus distinguishability with polarization, reflecting canalized effector functions. (2) Informative trajectory features driving stimulus distinguishability ("signaling codons") were identified and used for mapping a cell state landscape that could then locate macrophages conditioned by an unrelated condition. (3) Kinetic parameters, inferred using a mechanistic NF-κB network model, provided an alternative mapping of cell states and correctly predicted biochemical findings. Together, this work demonstrates that a single analyte's dynamic trajectories may distinguish the functional states of single cells and molecular network states underlying them. A record of this paper's transparent peer review process is included in the supplemental information.

Dynamical and combinatorial coding by MAPK p38 and NFκB in the inflammatory response of macrophages.

Macrophages sense pathogens and orchestrate specific immune responses. Stimulus specificity is thought to be achieved through combinatorial and dynamical coding by signaling pathways. While NFκB dynamics are known to encode stimulus information, dynamical coding in other signaling pathways and their combinatorial coordination remain unclear. Here, we established live-cell microscopy to investigate how NFκB and p38 dynamics interface in stimulated macrophages. Information theory and machine learning revealed that p38 dynamics distinguish cytokine TNF from pathogen-associated molecular patterns and high doses from low, but contributed little to information-rich NFκB dynamics when both pathways are considered. This suggests that immune response genes benefit from decoding immune signaling dynamics or combinatorics, but not both. We found that the heterogeneity of the two pathways is surprisingly uncorrelated. Mathematical modeling revealed potential sources of uncorrelated heterogeneity in the branched pathway network topology and predicted it to drive gene expression variability. Indeed, genes dependent on both p38 and NFκB showed high scRNAseq variability and bimodality. These results identify combinatorial signaling as a mechanism to restrict NFκB-AND-p38-responsive inflammatory cytokine expression to few cells.

Co-imaging of RelA and c-Rel reveals features of NF-κB signaling for ligand discrimination.

Individual cell sensing of external cues has evolved through the temporal patterns in signaling. Since nuclear factor κB (NF-κB) signaling dynamics have been examined using a single subunit, RelA, it remains unclear whether more information might be transmitted via other subunits. Using NF-κB double-knockin reporter mice, we monitored both canonical NF-κB subunits, RelA and c-Rel, simultaneously in single macrophages by quantitative live-cell imaging. We show that signaling features of RelA and c-Rel convey more information about the stimuli than those of either subunit alone. Machine learning is used to predict the ligand identity accurately based on RelA and c-Rel signaling features without considering the co-activated factors. Ligand discrimination is achieved through selective non-redundancy of RelA and c-Rel signaling dynamics, as well as their temporal coordination. These results suggest a potential role of c-Rel in fine-tuning immune responses and highlight the need for approaches that will elucidate the mechanisms regulating NF-κB subunit specificity.

Four guiding principles for effective trainee-led STEM community engagement through high school outreach.

To address ongoing academic achievement gap, there is a need for more school-university partnerships promoting early access to STEM education. During summer 2020, members of our institute initiated QBio-EDGE (Quantitative Biology-Empowering Diversity and Growth in Education), an outreach program for high schools in Los Angeles. In the hope of contributing to increasing diversity in academia, QBio-EDGE aims to make STEM education more accessible for students from historically excluded communities by exposing them to scientific research and diverse scientist role models. This program is led by early career researchers (ECRs), i.e., undergraduate, graduate, and postdoctoral researchers. In our first year, the outreach activities took place during virtual learning, presenting challenges and opportunities within the program development. Here, we provide a practical guide outlining our outreach efforts, key factors we considered in the program development, and hurdles we overcame. Specifically, we describe how we assembled our diverse team, how we established trusting partnerships with participating schools, and how we designed engaging student-centered, problem-based classroom modules on quantitative biology and computational methods applications to understand living systems. We also discuss the importance of increased institutional support. We hope that this may inspire researchers at all career stages to engage with local schools by participating in science outreach, specifically in quantitative and computational fields. We challenge institutions to actively strengthen these efforts.

Tonic TNF conditioning of macrophages safeguards stimulus-specific inflammatory responses.

Tumor necrosis factor (TNF) is a key inflammatory cytokine that warns recipient cells of a nearby infection or tissue damage. Acute exposure to TNF activates characteristic oscillatory dynamics of the transcription factor NFκB and induces a characteristic gene expression program; these are distinct from the responses of cells directly exposed to pathogen-associated molecular patterns (PAMPs). Here, we report that tonic TNF exposure is critical for safeguarding TNF's specific functions. In the absence of tonic TNF conditioning, acute exposure to TNF causes (i) NFκB signaling dynamics that are less oscillatory and more like PAMP-responsive NFκB dynamics, (ii) immune gene expression that is more similar to the Pam3CSK4 response program, and (iii) broader epigenomic reprogramming that is characteristic of PAMP-responsive changes. We show that the absence of tonic TNF signaling effects subtle changes to TNF receptor availability and dynamics such that enhanced pathway activity results in non-oscillatory NFκB. Our results reveal tonic TNF as a key tissue determinant of the specific cellular responses to acute paracrine TNF exposure, and their distinction from responses to direct exposure to PAMPs.

Protons in small spaces: Discrete simulations of vesicle acidification.

The lumenal pH of an organelle is one of its defining characteristics and central to its biological function. Experiments have elucidated many of the key pH regulatory elements and how they vary from compartment-to-compartment, and continuum mathematical models have played an important role in understanding how these elements (proton pumps, counter-ion fluxes, membrane potential, buffering capacity, etc.) work together to achieve specific pH setpoints. While continuum models have proven successful in describing ion regulation at the cellular length scale, it is unknown if they are valid at the subcellular level where volumes are small, ion numbers may fluctuate wildly, and biochemical heterogeneity is large. Here, we create a discrete, stochastic (DS) model of vesicular acidification to answer this question. We used this simplified model to analyze pH measurements of isolated vesicles containing single proton pumps and compared these results to solutions from a continuum, ordinary differential equations (ODE)-based model. Both models predict similar parameter estimates for the mean proton pumping rate, membrane permeability, etc., but, as expected, the ODE model fails to report on the fluctuations in the system. The stochastic model predicts that pH fluctuations decrease during acidification, but noise analysis of single-vesicle data confirms our finding that the experimental noise is dominated by the fluorescent dye, and it reveals no insight into the true noise in the proton fluctuations. Finally, we again use the reduced DS model explore the acidification of large, lysosome-like vesicles to determine how stochastic elements, such as variations in proton-pump copy number and cycling between on and off states, impact the pH setpoint and fluctuations around this setpoint.