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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. Synopsis: Dual reporter live macrophage imaging reveals that MAPK p38 and NFκB dynamics encode stimulus information but decoding both does not increase stimulus-specificity further. Their poorly correlated single-cell activities render AND-gate genes, encoding cytokines, particularly variable. A workflow is established for simultaneous MAPK p38 and NFκB live cell imaging in primary mouse macrophages. While MAPK p38 dynamics encode stimulus information, they contribute little to information-rich NFκB dynamics. Stimulus-specificity of innate immune response genes may thus be achieved by decoding NFκB dynamics or NFκB and p38 combinatorics, but no further gain is achieved from decoding both. NFκB and p38 activities are poorly correlated across single cells, rendering AND gate genes, often encoding cytokines, particularly variable. Dual reporter live macrophage imaging reveals that MAPK p38 and NFκB dynamics encode stimulus information but decoding both does not increase stimulus-specificity further. Their poorly correlated single-cell activities render AND-gate genes, encoding cytokines, particularly variable. [ABSTRACT FROM AUTHOR]

We developed an on-slide decellularization approach to generate acellular extracellular matrix (ECM) myoscaffolds that can be repopulated with various cell types to interrogate cell-ECM interactions. Using this platform, we investigated whether fibrotic ECM scarring affected human skeletal muscle progenitor cell (SMPC) functions that are essential for myoregeneration. SMPCs exhibited robust adhesion, motility, and differentiation on healthy muscle-derived myoscaffolds. All SPMC interactions with fibrotic myoscaffolds from dystrophic muscle were severely blunted including reduced motility rate and migration. Furthermore, SMPCs were unable to remodel laminin dense fibrotic scars within diseased myoscaffolds. Proteomics and structural analysis revealed that excessive collagen deposition alone is not pathological, and can be compensatory, as revealed by overexpression of sarcospan and its associated ECM receptors in dystrophic muscle. Our in vivo data also supported that ECM remodeling is important for SMPC engraftment and that fibrotic scars may represent one barrier to efficient cell therapy. [ABSTRACT FROM AUTHOR]

Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single‐cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca2+ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca2+ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements. Synopsis: The dependency of cellular signaling dynamics variability on transcriptional state was estimated by applying information theory estimation to paired multimodal measurements of mRNA abundances and cellular cytosolic Ca2+ dynamics response to ATP.Overall expression levels of 83 genes related to Ca2+ signaling explain 60% of the observed variability in Ca2+ signaling dynamics.Most of the information provided by any single gene is redundant with other genes.54% of information about Ca2+ dynamics exists in the most informative set of 12 genes or a random set of 53 genes. [ABSTRACT FROM AUTHOR]

Isogenic populations of mammalian cells exhibit significant gene expression variability. This variability can be separated into two components. Variance arises from events specific to the transcribed gene (i.e., cis or allele-specific sources) and variance from events that impact many genes at once (i.e., trans and global processes). Furthermore, the activity of the different regulatory factors that influence gene expression fluctuates at different timescales. Fast timescales will result in rapid fluctuation of gene expression, whereas slow timescales will result in longer persistence of gene expression levels over time. Here, we investigated sources of gene expression that are intrinsic, i.e., coming from cis-regulatory factors and follow slow timescales. To do so, we developed a reporter system that isolates allele-specific variability and measures its persistence in imaging and long-term fluctuation analysis experiments. Our results identify a new source of gene expression variability that is allele-specific but that fluctuates on timescales of days. We hypothesized that allele-specific fluctuations of epigenetic regulatory factors are responsible for the newly discovered allele-specific and slow source of gene expression variability. Using mathematical modeling, we showed that adding this effect to the two-state model is sufficient to account for all empirical observations. Furthermore, using direct assays of chromatin markers, we find fluctuation in H3K4me3 levels that match the observed changes in gene expression levels providing direct experimental support of our model. Collectively, our work shows that slow fluctuations of regulatory chromatin modifications contribute to the variability in gene expression. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Results Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and the cell-type annotation on targeted gene profiling data. Availability and implementation The R package is open-access and available at https://github.com/JSB-UCLA/scPNMF. The data used in this work are available at Zenodo: https://doi.org/10.5281/zenodo.4797997. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

RNA hybridization‐based spatial transcriptomics provides unparalleled detection sensitivity. However, inaccuracies in segmentation of image volumes into cells cause misassignment of mRNAs which is a major source of errors. Here, we develop JSTA, a computational framework for joint cell segmentation and cell type annotation that utilizes prior knowledge of cell type‐specific gene expression. Simulation results show that leveraging existing cell type taxonomy increases RNA assignment accuracy by more than 45%. Using JSTA, we were able to classify cells in the mouse hippocampus into 133 (sub)types revealing the spatial organization of CA1, CA3, and Sst neuron subtypes. Analysis of within cell subtype spatial differential gene expression of 80 candidate genes identified 63 with statistically significant spatial differential gene expression across 61 (sub)types. Overall, our work demonstrates that known cell type expression patterns can be leveraged to improve the accuracy of RNA hybridization‐based spatial transcriptomics while providing highly granular cell (sub)type information. The large number of newly discovered spatial gene expression patterns substantiates the need for accurate spatial transcriptomic measurements that can provide information beyond cell (sub)type labels. SYNOPSIS: JSTA is a new computational method for joint cell segmentation and cell type annotation using spatial transcriptomics data and scRNAseq reference data. Integrating spatial transcriptomics with scRNAseq reference data leads to high resolution cell segmentation mapping and cell type annotation.JSTA identifies non‐random spatial distribution of cell (sub‐)types for various brain regions.JSTA reveals spatially differentially expressed genes within cell (sub‐)types. [ABSTRACT FROM AUTHOR]

Rapid death of infected cells is an important antiviral strategy. However, fast decisions that are based on limited evidence can be erroneous and cause unnecessary cell death and subsequent tissue damage. How cells optimize their death decision making strategy to maximize both speed and accuracy is unclear. Here, we show that exposure to TNF, which is secreted by macrophages during viral infection, causes cells to change their decision strategy from "slow and accurate" to "fast and error-prone". Mathematical modeling combined with experiments in cell culture and whole organ culture show that the regulation of the cell death decision strategy is critical to prevent HSV-1 spread. These findings demonstrate that immune regulation of cellular cognitive processes dynamically changes a tissues' tolerance for self-damage, which is required to protect against viral spread. Controlled cell death can be an efficient anti-viral strategy, but also leads to tissue damage and needs to be balanced. Oyler-Yaniv et al. combine mathematical modelling and microscopy to show that exposure to TNF in response to viral infection causes cells to tune their speed-vs-accuracy trade-off in cell death decision to limit HSV-1 spread. [ABSTRACT FROM AUTHOR]

Cellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved challenge. This is, in part, due to the combinatorial explosion of possible configurations that need to be evaluated for information in time-course measurements. Here, we develop a quantitative framework, based on inferred trajectory probabilities, to calculate the mutual information encoded in signaling dynamics while accounting for cell-cell variability. We use it to understand NFκB transcriptional dynamics in response to different immune threats, and reveal that some threats are distinguished faster than others. Our analyses also suggest specific temporal phases during which information distinguishing threats becomes available to immune response genes; one specific phase could be mapped to the functionality of the IκBα negative feedback circuit. The framework is generally applicable to single-cell time series measurements, and enables understanding how temporal regulatory codes transmit information over time. Understanding how cells discriminate between stimuli is an ongoing challenge. Here, the authors propose a mathematical framework for inferring the mutual information encoded in temporal signaling dynamics and use it to study how information is transmitted over time in response to different stimuli in NFκB, MAPK and p53 signaling pathways. [ABSTRACT FROM AUTHOR]

Balancing cell death is essential to maintain healthy tissue homeostasis and prevent disease. Tumor necrosis factor (TNF) not only activates nuclear factor κB (NFκB), which coordinates the cellular response to inflammation, but may also trigger necroptosis, a pro‐inflammatory form of cell death. Whether TNF‐induced NFκB affects the fate decision to undergo TNF‐induced necroptosis is unclear. Live‐cell microscopy and model‐aided analysis of death kinetics identified a molecular circuit that interprets TNF‐induced NFκB/RelA dynamics to control necroptosis decisions. Inducible expression of TNFAIP3/A20 forms an incoherent feedforward loop to interfere with the RIPK3‐containing necrosome complex and protect a fraction of cells from transient, but not long‐term TNF exposure. Furthermore, dysregulated NFκB dynamics often associated with disease diminish TNF‐induced necroptosis. Our results suggest that TNF's dual roles in either coordinating cellular responses to inflammation, or further amplifying inflammation are determined by a dynamic NFκB‐A20‐RIPK3 circuit, that could be targeted to treat inflammation and cancer. SYNOPSIS: Tumor necrosis factor (TNF) activates NFκB to counteract necrotic cell death. This study reveals that inducible A20 expression provides an incoherent feedforward loop that protects cells from transient, but not long‐lasting TNF exposure. Live‐cell imaging reveals two‐phased TNF‐induced necroptotic death kinetics.NFκB inducible A20 constitutes a noisy incoherent feedforward loop.A20 provides transient protection such that prolonged TNF still kills cells.Dysregulated NFκB dynamics may thus diminish pro‐inflammatory cell death. [ABSTRACT FROM AUTHOR]

The dynamic signal encoding paradigm suggests that information flows from the extracellular environment into specific signaling patterns (encoding) that are then read by downstream effectors to control cellular behavior. Previous work empirically quantified the information content of dynamic signaling patterns. However, whether this information can be faithfully transmitted to the gene expression level is unclear. Here we used NFkB signaling as a model to understand the accuracy of information transmission from signaling dynamics into gene expression. Using a detailed mathematical model, we simulated realistic NFkB signaling patterns with different degrees of variability. The NFkB patterns were used as an input to a simple gene expression model. Analysis of information transmission between ligand and NFkB and ligand and gene expression allows us to determine information loss in transmission between receptors to dynamic signaling patterns and between signaling dynamics to gene expression. Information loss could occur due to biochemical noise or due to a lack of specificity. We found that noise-free gene expression has very little information loss suggesting that gene expression can preserve specificity in NFkB patterns. As expected, the addition of noise to the gene expression model results in information loss. Interestingly, this effect can be mitigated by a specific choice of parameters that can substantially reduce information loss due to biochemical noise during gene expression. Overall our results show that the cellular capacity for information transmission from dynamic signaling patterns to gene expression can be high enough to preserve ligand specificity and thereby the accuracy of cellular response to environmental cues. Author summary: The fidelity of signal transduction depends on the accurate encoding of ligand information in specific signaling patterns and the reliable transmission of these patterns by downstream gene expression machinery. We present an analysis of the accuracy of information transmission from signaling dynamics into gene expression in the case of the transcription factor NFkB. We show that noiseless gene expression can preserve ligand identity with minimal information loss. The addition of noise to gene expression model results in information loss, an effect that can be largely mitigated by choice of parameter values. [ABSTRACT FROM AUTHOR]

Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele‐specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal. Synopsis: Single‐cell coupled measurements of Ca2+ signaling dynamics, cell size, cell cycle, and expression of Ca2+ signaling‐related genes show that most of the gene expression variability is not gene‐specific. The remaining gene‐specific variability approaches Poisson limit for most genes. Cell state information, i.e. cell size, cell cycle stage, and Ca2+ signaling dynamic response to ligand was measured alongside single‐cell measurements of gene expression for 150 genes related to Ca2+ signaling using MERFISH.The majority of observed gene expression variance can be explained by 13 features related to cell state.The remaining unexplained variance approaches the Poisson limit for most genes. [ABSTRACT FROM AUTHOR]

The heterogeneity in mammalian cells signaling response is largely a result of pre-existing cell-to-cell variability. It is unknown whether cell-to-cell variability rises from biochemical stochastic fluctuations or distinct cellular states. Here, we utilize calcium response to adenosine trisphosphate as a model for investigating the structure of heterogeneity within a population of cells and analyze whether distinct cellular response states coexist. We use a functional definition of cellular state that is based on a mechanistic dynamical systems model of calcium signaling. Using Bayesian parameter inference, we obtain high confidence parameter value distributions for several hundred cells, each fitted individually. Clustering the inferred parameter distributions revealed three major distinct cellular states within the population. The existence of distinct cellular states raises the possibility that the observed variability in response is a result of structured heterogeneity between cells. The inferred parameter distribution predicts, and experiments confirm that variability in IP3R response explains the majority of calcium heterogeneity. Our work shows how mechanistic models and single-cell parameter fitting can uncover hidden population structure and demonstrate the need for parameter inference at the single-cell level. [ABSTRACT FROM AUTHOR]

Collective behavior emerges from interactions among group members who often vary in their behavior. The presence of just one or a few keystone individuals, such as leaders or tutors, may have a large effect on collective outcomes. These individuals can catalyze behavioral changes in other group members, thus altering group composition and collective behavior. The influence of keystone individuals on group function may lead to trade-offs between ecological situations, because the behavioral composition they facilitate may be suitable in one situation but not another. We use computer simulations to examine various mechanisms that allow keystone individuals to exert their influence on group members. We further discuss a trade-off between two potentially conflicting collective outcomes, cooperative prey attack and disease dynamics. Our simulations match empirical data from a social spider system and produce testable predictions for the causes and consequences of the influence of keystone individuals on group composition and collective outcomes. We find that a group's behavioral composition can be impacted by the keystone individual through changes to interaction patterns or behavioral persistence over time. Group behavioral composition and the mechanisms that drive the distribution of phenotypes influence collective outcomes and lead to trade-offs between disease dynamics and cooperative prey attack. [ABSTRACT FROM AUTHOR]

Population averaging due to paracrine communication can arbitrarily reduce cellular response variability. Yet, variability is ubiquitously observed, suggesting limits to paracrine averaging. It remains unclear whether and how biological systems may be affected by such limits of paracrine signaling. To address this question, we quantify the signal and noise of Ca2+ and ERK spatial gradients in response to an in vitro wound within a novel microfluidics-based device. We find that while paracrine communication reduces gradient noise, it also reduces the gradient magnitude. Accordingly we predict the existence of a maximum gradient signal to noise ratio. Direct in vitro measurement of paracrine communication verifies these predictions and reveals that cells utilize optimal levels of paracrine signaling to maximize the accuracy of gradient-based positional information. Our results demonstrate the limits of population averaging and show the inherent tradeoff in utilizing paracrine communication to regulate cellular response fidelity. [ABSTRACT FROM AUTHOR]

The mitotic spindle self-assembles in prometaphase by a combination of centrosomal pathway, in which dynamically unstable microtubules search in space until chromosomes are captured, and a chromosomal pathway, in which microtubules grow from chromosomes and focus to the spindle poles. Quantitative mechanistic understanding of how spindle assembly can be both fast and accurate is lacking. Specifically, it is unclear how, if at all, chromosome movements and combining the centrosomal and chromosomal pathways affect the assembly speed and accuracy. We used computer simulations and high-resolution microscopy to test plausible pathways of spindle assembly in realistic geometry. Our results suggest that an optimal combination of centrosomal and chromosomal pathways, spatially biased microtubule growth, and chromosome movements and rotations is needed to complete prometaphase in 10-20 mm while keeping erroneous merotelic attachments down to a few percent. The simulations also provide kinetic constraints for alternative error correction mechanisms, shed light on the dual role of chromosome arm volume, and compare well with experimental data for bipolar and multipolar HT-29 colorectal cancer cells. [ABSTRACT FROM AUTHOR]

The mitotic spindle is a complex macromolecular machine that coordinates accurate chromosome segregation. The spindle accomplishes its function using forces generated by microtubules (MTs) and multiple molecular motors, but how these forces are integrated remains unclear, since the temporal activation profiles and the mechanical characteristics of the relevant motors are largely unknown. Here, we developed a computational search algorithm that uses experimental measurements to ‘reverse engineer’ molecular mechanical machines. Our algorithm uses measurements of length time series for wild-type and experimentally perturbed spindles to identify mechanistic models for coordination of the mitotic force generators in Drosophila embryo spindles. The search eliminated thousands of possible models and identified six distinct strategies for MT–motor integration that agree with available data. Many features of these six predicted strategies are conserved, including a persistent kinesin-5-driven sliding filament mechanism combined with the anaphase B-specific inhibition of a kinesin-13 MT depolymerase on spindle poles. Such conserved features allow predictions of force–velocity characteristics and activation–deactivation profiles of key mitotic motors. Identified differences among the six predicted strategies regarding the mechanisms of prometaphase and anaphase spindle elongation suggest future experiments. [ABSTRACT FROM AUTHOR]

Gene expression variability, differences in the number of mRNA per cell across a population of cells, is ubiquitous across diverse organisms with broad impacts on cellular phenotypes. The role of chromatin in regulating average gene expression has been extensively studied. However, what aspects of the chromatin contribute to gene expression variability is still underexplored. Here we addressed this problem by leveraging chromatin diversity and using a systematic investigation of randomly integrated expression reporters to identify what aspects of chromatin microenvironment contribute to gene expression variability. Using DNA barcoding and split-pool decoding, we created a large library of isogenic reporter clones and identified reporter integration sites in a massive and parallel manner. By mapping our measurements of reporter expression at different genomic loci with multiple epigenetic profiles including the enrichment of transcription factors and the distance to different chromatin states, we identified new factors that impact the regulation of gene expression distributions. [ABSTRACT FROM AUTHOR]

Technological advances in automated microscopy now allow rapid acquisition of many images without human intervention, images that can be used for large-scale screens. The main challenge in such screens is the conversion of the raw images into interpretable information and hence discoveries. This post-acquisition component of image-based screens requires computational steps to identify cells, choose the cells of interest, assess their phenotype, and identify statistically significant 'hits'. Designing such an analysis pipeline requires careful consideration of the necessary hardware and software components, image analysis, statistical analysis and data presentation tools. Given the increasing availability of such hardware and software, these types of experiments have come within the reach of individual labs, heralding many interesting new ways of acquiring biological knowledge. [ABSTRACT FROM AUTHOR]