Cell systems最新文献

Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.

One snapshot of the peer review process for "Transcriptome data are insufficient to control false discoveries in regulatory network inference" (Kernfeld et al., 2024).¹.

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the "healthy" state of the cell and the "dynamic stabilization" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.

Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.

Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.

Here, we present a method for expressing multiple open reading frames (ORFs) from single transcripts using the leaky scanning model of translation initiation. In this approach termed "stoichiometric expression of mRNA polycistrons by eukaryotic ribosomes" (SEMPER), adjacent ORFs are translated from a single mRNA at tunable ratios determined by their order in the sequence and the strength of their translation initiation sites. We validate this approach by expressing up to three fluorescent proteins from one plasmid in two different cell lines. We then use it to encode a stoichiometrically tuned polycistronic construct encoding gas vesicle acoustic reporter genes that enables efficient formation of the multi-protein complex while minimizing cellular toxicity. We also demonstrate that SEMPER enables polycistronic expression of recombinant monoclonal antibodies from plasmid DNA and of two fluorescent proteins from single mRNAs made through in vitro transcription. Finally, we provide a probabilistic model to elucidate the mechanisms underlying SEMPER. A record of this paper's transparent peer review process is included in the supplemental information.

Organoids derived from human stem cells are a promising approach for disease modeling, regenerative medicine, and fundamental research. However, organoid variability and limited control over morphological outcomes remain as challenges. One open question is the extent to which engineering control over culture conditions can guide organoids to specific compositions. Here, we extend a DNA "velcro" cell patterning approach, precisely controlling the number and ratio of human induced pluripotent stem cell-derived progenitors contributing to nephron progenitor (NP) organoids and mosaic NP/ureteric bud (UB) tip cell organoids within arrays of microwells. We demonstrate long-term control over organoid size and morphology, decoupled from geometric constraints. We then show emergent trends in organoid tissue proportions that depend on initial progenitor cell composition. These include higher nephron and stromal cell representation in mosaic NP/UB organoids vs. NP-only organoids and a "goldilocks" initial cell ratio in mosaic organoids that optimizes the formation of proximal tubule structures.

In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal's ambient concentration increases. However, among competitive ligands, the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.

Systems like the prototypical lac operon can reliably hold repression of transcription upon DNA replication across cell cycles with just 10 repressor molecules per cell and behave as if they were at equilibrium. The origin of this phenomenology is still an unresolved question. Here, we develop a general theory to analyze strong perturbations in quasi-equilibrium systems and use it to quantify the effects of DNA replication in gene regulation. We find a scaling law linking actual with predicted equilibrium transcription via a single kinetic parameter. We show that even the lac operon functions beyond the physical limits of naive regulation through compensatory mechanisms that suppress non-equilibrium effects. Synthetic systems without adjuvant activators, such as the cAMP receptor protein (CRP), lack this reliability. Our results provide a rationale for the function of CRP, beyond just being a tunable activator, as a mitigator of cell cycle perturbations.