Techniques for Studying Decoding of Single Cell Dynamics

Immune quorum sensing dictates IFN-I response dynamics in human plasmacytoid dendritic cells

Type I interferons (IFN-Is) are key in fighting viral infections, but also serve major roles beyond antiviral immunity. Crucial is the tight regulation of IFN-I responses, while excessive levels are harmful to the cells. In essence, immune responses are generated by single cells making their own decisions, which are based on the signals they perceive. Additionally, immune cells must anticipate the future state of their environment, thereby weighing the costs and benefits of each possible outcome, in the presence of other potentially competitive decision makers (i.e., IFN-I producing cells). A rather new cellular communication mechanism called quorum sensing describes the effect of cell density on cellular secretory behaviors, which fits well with matching the right amount of IFN-Is produced to fight an infection. More competitive decision makers must contribute relatively less and vice versa. Intrigued by this concept, we assessed the effects of immune quorum sensing in pDCs, specialized immune cells known for their ability to mass produce IFN-Is. Using conventional microwell assays and droplet-based microfluidics assays, we were able the characterize the effect of quorum sensing in human primary immune cells in vitro. These insights open new avenues to manipulate IFN-I response dynamics in pathological conditions affected by aberrant IFN-I signaling.

Advances in high throughput cell culture technologies for therapeutic screening and biological discovery applications

Cellular phenotypes and functional responses are modulated by the signals present in their microenvironment, including extracellular matrix (ECM) proteins, tissue mechanical properties, soluble signals and nutrients, and cell-cell interactions. To better recapitulate and analyze these complex signals within the framework of more physiologically relevant culture models, high throughput culture platforms can be transformative. High throughput methodologies enable scientists to extract increasingly robust and broad datasets from individual experiments, screen large numbers of conditions for potential hits, better qualify and predict responses for preclinical applications, and reduce reliance on animal studies. High throughput cell culture systems require uniformity, assay miniaturization, specific target identification, and process simplification. In this review, we detail the various techniques that researchers have used to face these challenges and explore cellular responses in a high throughput manner. We highlight several common approaches including two-dimensional multiwell microplates, microarrays, and microfluidic cell culture systems as well as unencapsulated and encapsulated three-dimensional high throughput cell culture systems, featuring multiwell microplates, micromolds, microwells, microarrays, granular hydrogels, and cell-encapsulated microgels. We also discuss current applications of these high throughput technologies, namely stem cell sourcing, drug discovery and predictive toxicology, and personalized medicine, along with emerging opportunities and future impact areas.

Unraveling IFN-I response dynamics and TNF crosstalk in the pathophysiology of systemic lupus erythematosus

Introduction

The innate immune system serves the crucial first line of defense against a wide variety of potential threats, during which the production of pro-inflammatory cytokines IFN-I and TNFα are key. This astonishing power to fight invaders, however, comes at the cost of risking IFN-I-related pathologies, such as observed during autoimmune diseases, during which IFN-I and TNFα response dynamics are dysregulated. Therefore, these response dynamics must be tightly regulated, and precisely matched with the potential threat. This regulation is currently far from understood.

Methods

Using droplet-based microfluidics and ODE modeling, we studied the fundamentals of single-cell decision-making upon TLR signaling in human primary immune cells (n = 23). Next, using biologicals used for treating autoimmune diseases [i.e., anti-TNFα, and JAK inhibitors], we unraveled the crosstalk between IFN-I and TNFα signaling dynamics. Finally, we studied primary immune cells isolated from SLE patients (n = 8) to provide insights into SLE pathophysiology.

Results

single-cell IFN-I and TNFα response dynamics display remarkable differences, yet both being highly heterogeneous. Blocking TNFα signaling increases the percentage of IFN-I-producing cells, while blocking IFN-I signaling decreases the percentage of TNFα-producing cells. Single-cell decision-making in SLE patients is dysregulated, pointing towards a dysregulated crosstalk between IFN-I and TNFα response dynamics.

Discussion

We provide a solid droplet-based microfluidic platform to study inherent immune secretory behaviors, substantiated by ODE modeling, which can challenge the conceptualization within and between different immune signaling systems. These insights will build towards an improved fundamental understanding on single-cell decision-making in health and disease.

Cellular heterogeneity in TNF/TNFR1 signalling: live cell imaging of cell fate decisions in single cells

Cellular responses to TNF are inherently heterogeneous within an isogenic cell population and across different cell types. TNF promotes cell survival by activating pro-inflammatory NF-κB and MAPK signalling pathways but may also trigger apoptosis and necroptosis. Following TNF stimulation, the fate of individual cells is governed by the balance of pro-survival and pro-apoptotic signalling pathways. To elucidate the molecular mechanisms driving heterogenous responses to TNF, quantifying TNF/TNFR1 signalling at the single-cell level is crucial. Fluorescence live-cell imaging techniques offer real-time, dynamic insights into molecular processes in single cells, allowing for detection of rapid and transient changes, as well as identification of subpopulations, that are likely to be missed with traditional endpoint assays. Whilst fluorescence live-cell imaging has been employed extensively to investigate TNF-induced inflammation and TNF-induced cell death, it has been underutilised in studying the role of TNF/TNFR1 signalling pathway crosstalk in guiding cell-fate decisions in single cells. Here, we outline the various opportunities for pathway crosstalk during TNF/TNFR1 signalling and how these interactions may govern heterogenous responses to TNF. We also advocate for the use of live-cell imaging techniques to elucidate the molecular processes driving cell-to-cell variability in single cells. Understanding and overcoming cellular heterogeneity in response to TNF and modulators of the TNF/TNFR1 signalling pathway could lead to the development of targeted therapies for various diseases associated with aberrant TNF/TNFR1 signalling, such as rheumatoid arthritis, metabolic syndrome, and cancer.

Reversing pathological cell states: the road less travelled can extend the therapeutic horizon

Acquisition of omics data advances at a formidable pace. Yet, our ability to utilize these data to control cell phenotypes and design interventions that reverse pathological states lags behind. Here, we posit that cell states are determined by core networks that control cell-wide networks. To steer cell fate decisions, core networks connecting genotype to phenotype must be reconstructed and understood. A recent method, cell state transition assessment and regulation (cSTAR), applies perturbation biology to quantify causal connections and mechanistically models how core networks influence cell phenotypes. cSTAR models are akin to digital cell twins enabling us to purposefully convert pathological states back to physiologically normal states. While this capability has a range of applications, here we discuss reverting oncogenic transformation.

Dynamic full-field optical coherence tomography module adapted to commercial microscopes allows longitudinal in vitro cell culture study

Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organization processes such as rosette formation and mitosis as well as cell shape and motility. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening.

Time-dependent cell-state selection identifies transiently expressed genes regulating ILC2 activation

The decision of whether cells are activated or not is controlled through dynamic intracellular molecular networks. However, the low population of cells during the transition state of activation renders the analysis of the transcriptome of this state technically challenging. To address this issue, we have developed the Time-Dependent Cell-State Selection (TDCSS) technique, which employs live-cell imaging of secretion activity to detect an index of the transition state, followed by the simultaneous recovery of indexed cells for subsequent transcriptome analysis. In this study, we used the TDCSS technique to investigate the transition state of group 2 innate lymphoid cells (ILC2s) activation, which is indexed by the onset of interleukin (IL)-13 secretion. The TDCSS approach allowed us to identify time-dependent genes, including transiently induced genes (TIGs). Our findings of IL4 and MIR155HG as TIGs have shown a regulatory function in ILC2s activation.

Interface self-referenced dynamic full-field optical coherence tomography

Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as an invaluable live label-free and non-invasive imaging modality able to image subcellular biological structures and their metabolic activity within complex 3D samples. However, D-FFOCT suffers from fringe artefacts when imaging near reflective surfaces and is highly sensitive to vibrations. Here, we present interface Self-Referenced (iSR) D-FFOCT, an alternative configuration to D-FFOCT that takes advantage of the presence of the sample coverslip in between the sample and the objective by using it as a defocused reference arm, thus avoiding the aforementioned artefacts. We demonstrate the ability of iSR D-FFOCT to image 2D fibroblast cell cultures, which are among the flattest mammalian cells.

An optimized reporter of the transcription factor hypoxia-inducible factor 1α reveals complex HIF-1α activation dynamics in single cells

Immune cells adopt a variety of metabolic states to support their many biological functions, which include fighting pathogens, removing tissue debris, and tissue remodeling. One of the key mediators of these metabolic changes is the transcription factor hypoxia-inducible factor 1α (HIF-1α). Single-cell dynamics have been shown to be an important determinant of cell behavior; however, despite the importance of HIF-1α, little is known about its single-cell dynamics or their effect on metabolism. To address this knowledge gap, here we optimized a HIF-1α fluorescent reporter and applied it to study single-cell dynamics. First, we showed that single cells are likely able to differentiate multiple levels of prolyl hydroxylase inhibition, a marker of metabolic change, via HIF-1α activity. We then applied a physiological stimulus known to trigger metabolic change, interferon-γ, and observed heterogeneous, oscillatory HIF-1α responses in single cells. Finally, we input these dynamics into a mathematical model of HIF-1α-regulated metabolism and discovered a profound difference between cells exhibiting high versus low HIF-1α activation. Specifically, we found cells with high HIF-1α activation are able to meaningfully reduce flux through the tricarboxylic acid cycle and show a notable increase in the NAD+/NADH ratio compared with cells displaying low HIF-1α activation. Altogether, this work demonstrates an optimized reporter for studying HIF-1α in single cells and reveals previously unknown principles of HIF-1α activation.

Soluble cyclase-mediated nuclear cAMP synthesis is sufficient for cell proliferation

cAMP, a key player in many physiological processes, was classically considered to originate solely from the plasma membrane (PM). This view was recently challenged by observations showing that upon internalization GsPCRs can sustain signaling from endosomes and/or the trans-Golgi network (TGN). In this new view, after the first PM-generated cAMP wave, the internalization of GsPCRs and ACs generates a second wave that was strictly associated with nuclear transcriptional events responsible for triggering specific biological responses. Here, we report that the endogenously expressed TSHR, a canonical GsPCR, triggers an internalization-dependent, calcium-mediated nuclear sAC activation that drives PKA activation and CREB phosphorylation. Both pharmacological and genetic sAC inhibition, which did not affect the cytosolic cAMP levels, blunted nuclear cAMP accumulation, PKA activation, and cell proliferation, while an increase in nuclear sAC expression significantly enhanced cell proliferation. Furthermore, using novel nuclear-targeted optogenetic actuators, we show that light-stimulated nuclear cAMP synthesis can mimic the proliferative action of TSH by activating PKA and CREB. Therefore, based on our results, we propose a novel three-wave model in which the "third" wave of cAMP is generated by nuclear sAC. Despite being downstream of events occurring at the PM (first wave) and endosomes/TGN (second wave), the nuclear sAC-generated cAMP (third wave) is sufficient and rate-limiting for thyroid cell proliferation.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Live imaging of apoptotic signaling flow using tunable combinatorial FRET-based bioprobes for cell population analysis of caspase cascades

Understanding cellular signaling flow is required to comprehend living organisms. Various live cell imaging tools have been developed but challenges remain due to complex cross-talk between pathways and response heterogeneities among cells. We have focused on multiplex live cell imaging for statistical analysis to address the difficulties and developed simple multiple fluorescence imaging system to quantify cell signaling at single-cell resolution using Förster Resonance Energy Transfer (FRET)-based chimeric molecular sensors comprised of fluorescent proteins and dyes. The dye-fluorescent protein conjugate is robust for a wide selection of combinations, facilitating rearrangement for coordinating emission profile of molecular sensors to adjust for visualization conditions, target phenomena, and simultaneous use. As the molecular sensor could exhibit highly sensitive in detection for protease activity, we customized molecular sensor of caspase-9 and combine the established sensor for caspase-3 to validate the system by observation of caspase-9 and -3 dynamics simultaneously, key signaling flow of apoptosis. We found cumulative caspase-9 activity rather than reaction rate inversely regulated caspase-3 execution times for apoptotic cell death. Imaging-derived statistics were thus applied to discern the dominating aspects of apoptotic signaling unavailable by common live cell imaging and proteomics protein analysis. Adopted to various visualization targets, the technique can discriminate between rivalling explanations and should help unravel other protease involved signaling pathways.

Revising immune cell coordination: Origins and importance of single-cell variation

Moving from the optimalization of single-cell technologies to the interpretation of the multi-complex single-cell data, the field of immunoengineering is granted with numerous important insights into the coordination of immune cell activation and how to modulate it for therapeutic purposes. However, insights come with additional follow-up questions that challenge our perception on how immune responses are generated and fine-tuned to fight a wide array of pathogens in ever-changing and often unpredictable microenvironments. Are immune responses really either being tightly regulated by molecular determinants, or highly flexible attributed to stochasticity? What exactly makes up the basic rules by which single cells cooperate to establish tissue-level immunity? Taking the type I IFN system and its newest insights as a main example throughout this review, we revise the basic concepts of (single) immune cell coordination, redefine the concepts of noise, stochasticity and determinism, and highlight the importance of single-cell variation in immunology and beyond.

Single molecule detection; from microscopy to sensors

Single molecule detection is necessary to find out physical, chemical properties and their mechanism involved in the normal functioning of body cells. In this way, they can provide a new direction to the healthcare system. Various techniques have been developed and employed for their successful detection. Herein, we have emphasized various traditional methods as well as biosensing technology which offer single molecule sensitivity. The various methods including plasmonic resonance, nanopores, whispering gallery mode, Simoa assay and recognition tunneling are discussed in the initial part which has been followed by a discussion about biosensor-based detection. Plasmonic, SERS, CRISPR/Cas, and other types of biosensors are focused in this review and found to be highly sensitive for single molecule detection. This review provides an overview of progression in different techniques employed for single molecule detection.

Systems approaches to investigate the role of NF-κB signaling in aging

The nuclear factor-κB (NF-κB) signaling pathway is one of the most well-studied pathways related to inflammation, and its involvement in aging has attracted considerable attention. As aging is a complex phenomenon and is the result of a multi-step process, the involvement of the NF-κB pathway in aging remains unclear. To elucidate the role of NF-κB in the regulation of aging, different systems biology approaches have been employed. A multi-omics data-driven approach can be used to interpret and clarify unknown mechanisms but cannot generate mechanistic regulatory structures alone. In contrast, combining this approach with a mathematical modeling approach can identify the mechanistics of the phenomena of interest. The development of single-cell technologies has also helped clarify the heterogeneity of the NF-κB response and underlying mechanisms. Here, we review advances in the understanding of the regulation of aging by NF-κB by focusing on omics approaches, single-cell analysis, and mathematical modeling of the NF-κB network.

A transcriptional biosensor to monitor single cancer cell therapeutic responses by bioluminescence microscopy

When several life-prolonging drugs are indicated for cancer treatment, predictive drug-response tumor biomarkers are essential to guide management. Most conventional biomarkers are based on bulk tissue analysis, which cannot address the complexity of single-cell heterogeneity responsible for drug resistance. Therefore, there is a need to develop alternative drug response predictive biomarker approaches that could directly interrogate single-cell and whole population cancer cell drug sensitivity. In this study, we report a novel method exploiting bioluminescence microscopy to detect single prostate cancer (PCa) cell response to androgen receptor (AR)-axis-targeted therapies (ARAT) and predict cell population sensitivity.

Methods: We have generated a new adenovirus-delivered biosensor, PCA3-Cre-PSEBC-ITSTA, which combines an integrated two-step transcriptional amplification system (ITSTA) and the activities of the prostate cancer antigen 3 (PCA3) and modified prostate-specific antigen (PSEBC) gene promoters as a single output driving the firefly luciferase reporter gene. This system was tested on PCa cell lines and on primary PCa cells. Single cells, exposed or not to ARAT, were dynamically imaged by bioluminescence microscopy. A linear discriminant analysis (LDA)-based method was used to determine cell population sensitivities to ARAT.

Results: We show that the PCA3-Cre-PSEBC-ITSTA biosensor is PCa-specific and can dynamically monitor single-cell AR transcriptional activity before and after ARAT by bioluminescence microscopy. After biosensor transduction and bioluminescence microscopy single-cell luminescence dynamic quantification, LDA analysis could discriminate the cell populations overall ARAT sensitivity despite heterogeneous single-cell responses. Indeed, the biosensor could detect a significant decrease in AR activity following exposure to conventional ARAT in hormone-naive primary PCa cells, while in castration-resistant PCa patients, treatment response correlated with the observed clinical ARAT resistance.

Conclusion: The exploitation of bioluminescence microscopy and multi-promoter transcriptionally-regulated biosensors can aptly define the overall treatment response of patients by monitoring live single cell drug response from primary cancer tissue. This approach can be used to develop predictive biomarkers for drug response in order to help clinicians select the best drug combinations or sequences for each patient.

Cell-Cell Communication Networks in Tissue: Toward Quantitatively Linking Structure with Function

Forefront techniques for molecular interrogation of mammalian tissues, such as multiplexed tissue imaging, intravital microscopy, and single-cell RNA sequencing (scRNAseq), can combine to quantify cell-type abundance, co-localization, and global levels of receptors and their ligands. Nonetheless, it remains challenging to translate these various quantities into a more comprehensive understanding of how cell-cell communication networks dynamically operate. Therefore, construction of computational models for network-level functions - including niche-dependent actions, homeostasis, and multi-scale coordination - will be valuable for productively integrating the battery of experimental approaches. Here, we review recent progress in understanding cell-cell communication networks in tissue. Featured examples include ligand-receptor dissection of immunosuppressive and mitogenic signaling in the tumor microenvironment. As a future direction, we highlight an unmet potential to bridge high-level statistical approaches with low-level physicochemical mechanisms.

Decoding the dynamics of multilayered stochastic antiviral IFN-I responses

Type I Interferon (IFN-I) responses were first recognized for their role in antiviral immunity, but it is now widely appreciated that IFN-Is have many immunomodulatory functions, influencing antitumor responses, autoimmune manifestations, and antimicrobial defenses. Given these pivotal roles, it may be surprising that multilayered stochastic events create highly heterogeneous, but tightly regulated, all-or-nothing cellular decisions. Recently, mathematical models have provided crucial insights into the stochastic nature of antiviral IFN-I responses, which we critically evaluate in this review. In this context, we emphasize the need for innovative single-cell technologies combined with mathematical models to further reveal, understand, and predict the complexity of the IFN-I system in physiological and pathological conditions that may be relevant to a plethora of diseases.

A forecast for large-scale, predictive biology: Lessons from meteorology

Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.

NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy

One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs.

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Monitoring of cell phenotype changes by fluorescence label-free time-lapse microscopy

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Adaptive semantic segmentation for the robust detection of cell shape

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TE to correlate morphological and textural soma descriptors along time

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Directed TE graph for the representation of mutual relationship among descriptors

One of the most challenging frontiers for the automatic understanding of biological systems is fluorescent label-free imaging in which the behavior changes of living being are characterized without cell staining. To this aim, we present here the NeuriTES platform that revisits standard paradigms of video analysis to detect unlabeled objects and correlate the analysis to phenotype evolution of the mechanisms under observation. Through the exploitation of adaptive algorithms and of transfer entropy measures, the platform assures regular cell detection and the possibility to extract reliable parameters related to the evolving cell system. As a proof-of-concept, NeuriTES is applied to two fascinating phenotype investigation scenarios, amyotrophic lateral sclerosis (ALS) disease mechanism and the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Directed graphs assist the biologists with a visual understanding of the mechanisms identified.

Evolution of biophysical tools for quantitative protein interactions and drug discovery

With millions of signalling events occurring simultaneously, cells process a continuous flux of information. The genesis, processing, and regulation of information are dictated by a huge network of protein interactions. This is proven by the fact that alterations in the levels of proteins, single amino acid changes, post-translational modifications, protein products arising out of gene fusions alter the interaction landscape leading to diseases such as congenital disorders, deleterious syndromes like cancer, and crippling diseases like the neurodegenerative disorders which are often fatal. Needless to say, there is an immense effort to understand the biophysical basis of such direct interactions between any two proteins, the structure, domains, and sequence motifs involved in tethering them, their spatio-temporal regulation in cells, the structure of the network, and their eventual manipulation for intervention in diseases. In this chapter, we will deliberate on a few techniques that allow us to dissect the thermodynamic and kinetic aspects of protein interaction, how innovation has rendered some of the traditional techniques applicable for rapid analysis of multiple samples using small amounts of material. These advances coupled with automation are catching up with the genome-wide or proteome-wide studies aimed at identifying new therapeutic targets. The chapter will also summarize how some of these techniques are suited either in the standalone mode or in combination with other biophysical techniques for the drug discovery process.

Memorizing environmental signals through feedback and feedforward loops

Cells in diverse organisms can store the information of previous environmental conditions for long periods of time. This form of cellular memory adjusts the cell's responses to future challenges, providing fitness advantages in fluctuating environments. Many biological functions, including cellular memory, are mediated by specific recurring patterns of interactions among proteins and genes, known as 'network motifs.' In this review, we focus on three well-characterized network motifs - negative feedback loops, positive feedback loops, and feedforward loops, which underlie different types of cellular memories. We describe the latest studies identifying these motifs in various molecular processes and discuss how the topologies and dynamics of these motifs can enable memory encoding and storage.

Cell-cycle-gated feedback control mediates desensitization to interferon stimulation

Cells use molecular circuits to interpret and respond to extracellular cues, such as hormones and cytokines, which are often released in a temporally varying fashion. In this study, we combine microfluidics, time-lapse microscopy, and computational modeling to investigate how the type I interferon (IFN)-responsive regulatory network operates in single human cells to process repetitive IFN stimulation. We found that IFN-α pretreatments lead to opposite effects, priming versus desensitization, depending on input durations. These effects are governed by a regulatory network composed of a fast-acting positive feedback loop and a delayed negative feedback loop, mediated by upregulation of ubiquitin-specific peptidase 18 (USP18). We further revealed that USP18 upregulation can only be initiated at the G1/early S phases of cell cycle upon the treatment onset, resulting in heterogeneous and delayed induction kinetics in single cells. This cell cycle gating provides a temporal compartmentalization of feedback loops, enabling duration-dependent desensitization to repetitive stimulations.