Cellular Status Research Articles

Neurotrophic factor Neuritin modulates T cell electrical and metabolic state for the balance of tolerance and immunity.

The adaptive T cell response is accompanied by continuous rewiring of the T cell's electric and metabolic state. Ion channels and nutrient transporters integrate bioelectric and biochemical signals from the environment, setting cellular electric and metabolic states. Divergent electric and metabolic states contribute to T cell immunity or tolerance. Here, we report that neuritin (Nrn1) contributes to tolerance development by modulating regulatory and effector T cell function. Nrn1 expression in regulatory T cells promotes its expansion and suppression function, while expression in the T effector cell dampens its inflammatory response. Nrn1 deficiency causes dysregulation of ion channel and nutrient transporter expression in Treg and effector T cells, resulting in divergent metabolic outcomes and impacting autoimmune disease progression and recovery. These findings identify a novel immune function of the neurotrophic factor Nrn1 in regulating the T cell metabolic state in a cell context-dependent manner and modulating the outcome of an immune response.

Single-cell RNA sequencing in tuberculosis: Application and future perspectives.

Tuberculosis (TB) has one of the highest mortality rates among infectious diseases worldwide. The immune response in the host after infection is proposed to contribute significantly to the progression of TB, but the specific mechanisms involved remain to be elucidated. Single-cell RNA sequencing (scRNA-seq) provides unbiased transcriptome sequencing of large quantities of individual cells, thereby defining biological comprehension of cellular heterogeneity and dynamic transcriptome state of cell populations in the field of immunology and is therefore increasingly applied to lung disease research. Here, we first briefly introduce the concept of scRNA-seq, followed by a summarization on the application of scRNA-seq to TB. Furthermore, we underscore the potential of scRNA-seq for clinical biomarker exploration, host-directed therapy, and precision therapy research in TB and discuss the bottlenecks that need to be overcome for the broad application of scRNA-seq to TB-related research.

A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection.

Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.

Time-resolved interactome profiling deconvolutes secretory protein quality control dynamics

Many cellular processes are governed by protein–protein interactions that require tight spatial and temporal regulation. Accordingly, it is necessary to understand the dynamics of these interactions to fully comprehend and elucidate cellular processes and pathological disease states. To map de novo protein–protein interactions with time resolution at an organelle-wide scale, we developed a quantitative mass spectrometry method, time-resolved interactome profiling (TRIP). We apply TRIP to elucidate aberrant protein interaction dynamics that lead to the protein misfolding disease congenital hypothyroidism. We deconvolute altered temporal interactions of the thyroid hormone precursor thyroglobulin with pathways implicated in hypothyroidism pathophysiology, such as Hsp70-/90-assisted folding, disulfide/redox processing, and N-glycosylation. Functional siRNA screening identified VCP and TEX264 as key protein degradation components whose inhibition selectively rescues mutant prohormone secretion. Ultimately, our results provide novel insight into the temporal coordination of protein homeostasis, and our TRIP method should find broad applications in investigating protein-folding diseases and cellular processes.

OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells

Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, plays a pivotal role in inflammatory responses associated with both chronic diseases and acute injuries. In this study, OXidative Stress PREDictor (OxSpred), a supervised learning model tailored to accurately annotate the oxidative stress state of innate immune cells at the single‐cell level, is introduced. Compared to the traditional gene‐set‐variation‐analysis‐based enrichment method, OxSpred demonstrates superior accuracy with an area under the receiver operating characteristic curve of 0.89 and offers interpretable embeddings with significant biological relevance. Using the predicted ROS states, precise elucidation and interpretation of the roles of novel innate immune cell subtypes can be achieved. Overall, OxSpred enhances the utility of single‐cell transcriptomic datasets by providing a robust in silico method for determining intracellular oxidative stress states, thereby enriching the understanding of innate immune cell functions during inflammation.

Unraveling the Peroxidase Activity in Peroxiredoxins: A Comprehensive Review of Mechanisms, Functions, and Biological Significance.

Peroxiredoxins (Prxs) are members of the antioxidant enzymes necessary for every living object in the three domains of life and play critical roles in controlling peroxide levels in cells. This comprehensive literature review aims to elucidate the peroxidase activity of Prxs, examining their roles and significance for organisms across various taxa. Ironically, the primary role of the Prxs is the peroxidase activity, which comprises the reduction of hydrogen peroxide and other organic hydroperoxides and decreases the risk of oxidative damage in the cells. The above enzymatic activity occurs through the reversible oxidation-reduction catalyzed by cysteine residues in the active site by forming sulfenic acid and reduction by intracellular reductants. Structurally and functionally, Prxs function as dimers or decamers and show different catalytic patterns according to their subfamilies or cellular compartments. Compared to the mechanisms of the other two subgroups of Prxs, including 2-Cys Prxs and atypical Prxs, the 1-Cys Prxs have monomer-dimer switch folding coupled with catalytic activity. In addition to their peroxidase activity, which is widely known, Prxs are becoming acknowledged to be involved in other signaling processes, including redox signaling and apoptosis. This aversion to oxidative stress and regulation by the cellular redox state places them at the heart of adaptive cellular responses to changes in the environment or manifestations of diseases. In conclusion, based on the data obtained and on furthering the knowledge of Prxs' structure and function, these enzymes may be classified as a diverse yet essential family of proteins that can effectively protect cells from the adverse effects of oxidative stress due to peroxidase activity. This indicates secondary interactions, summarized as peroxide detoxification or regulatory signaling, and identifies their applicability in multiple biological pathways. Such knowledge is valuable for enhancing the general comprehension of essential cellular functions and disclosing further therapeutic approaches to the diseases caused by the increased production of reactive oxygen species.

Proximity labeling reveals dynamic changes in the SQSTM1 protein network

Sequestosome1 (SQSTM1) is an autophagy receptor that mediates the degradation of intracellular cargo, including protein aggregates, through multiple protein interactions. These interactions form the SQSTM1 protein network, and these interactions are mediated by SQSTM1 functional interaction domains, which include LIR, PB1, UBA, and KIR. Technological advances in cell biology continue to expand our knowledge of the SQSTM1 protein network and the relationship between the actions of the SQSTM1 protein network in cellular physiology and disease states. Here we apply proximity profile labeling to investigate the SQSTM1 protein interaction network by fusing TurboID with the human protein SQSTM1 (TurboID::SQSTM1). This chimeric protein displayed well-established SQSTM1 features including production of SQSTM1 intracellular bodies, binding to known SQSTM1 interacting partners, and capture of novel SQSTM1 protein interactors. Strikingly, aggregated tau protein altered the protein interaction network of SQSTM1 to include many stress-associated proteins. We demonstrate the importance of the PB1 and/or UBA domains for binding network members, including the K18 domain of tau. Overall, our work reveals the dynamic landscape of the SQSTM1 protein network and offers a resource to study SQSTM1 function in cellular physiology and disease state.

Huntington’s disease cellular phenotypes are rescued non-cell autonomously by healthy cells in mosaic telencephalic organoids

Huntington’s disease (HD) causes selective degeneration of striatal and cortical neurons, resulting in cell mosaicism of coexisting still functional and dysfunctional cells. The impact of non-cell autonomous mechanisms between these cellular states is poorly understood. Here we generated telencephalic organoids with healthy or HD cells, grown separately or as mosaics of the two genotypes. Single-cell RNA sequencing revealed neurodevelopmental abnormalities in the ventral fate acquisition of HD organoids, confirmed by cytoarchitectural and transcriptional defects leading to fewer GABAergic neurons, while dorsal populations showed milder phenotypes mainly in maturation trajectory. Healthy cells in mosaic organoids restored HD cell identity, trajectories, synaptic density, and communication pathways upon cell-cell contact, while showing no significant alterations when grown with HD cells. These findings highlight cell-type-specific alterations in HD and beneficial non-cell autonomous effects of healthy cells, emphasizing the therapeutic potential of modulating cell-cell communication in disease progression and treatment.

Abstract Mo084: Stem Cell-Based Functional Genomics Unravel A Novel Vascular Smooth Muscle Cell State Induced By The 9p21 Coronary Artery Disease Risk Locus

Background: Genome-wide association studies have identified common genetic variants (Single Nucleotide Polymorphisms – SNPs) at ~300 human genomic loci linked to coronary artery disease (CAD) susceptibility. Among these genomic regions, the most impactful is the 9p21.3 CAD risk locus, which spans a 60 kb gene desert and encompasses ~80 SNPs in high linkage disequilibrium. We have previously generated isogenic induced Pluripotent Stem Cells (iPSCs) lines from risk and non-risk donors at 9p21.3 and performed a complete deletion of the locus. By using iPSC-derived vascular smooth muscle cells (iPSC-VSMCs) we demonstrated that the risk haplotype at 9p21.3 causes altered expression of numerous genes essential for muscle function, including contraction and adhesion. Notably, deletion of the risk haplotype restores the non-risk phenotype, suggesting a gain of function effect. Here, we aimed to identify the transcriptomic signature and state trajectories of VSMCs induced by the 9p21.3 risk haplotype and validate them through functional assays and ex-vivo analysis of human coronary arteries. Methods: We have used iPSCs from individuals carrying the risk and non-risk haplotype at 9p21.3 and isogenic knockout lines. After VSMCs differentiation induction, mature VSMCs were used for single cell RNA sequencing using 10X Chromium platform and functional assays. Results: Our analysis revealed that the 9p21.3 risk haplotype prompts VSMCs to acquire a novel cellular state showing reduced plasticity and divergent from other VSMC states previously linked to CAD. We found markers of alternative lineages, including osteogenic and neuronal, coupled with altered expression of genes within other CAD loci, and functional defects. We identified a set of new molecular markers crucial to define risk-associated VSMCs and uncovered their upstream regulators. Conclusions: Our study provides insights into CAD pathogenesis driven by the 9p21.3 risk locus and identified new gene regulatory networks essential for maintaining the normal functionality of the muscle layer of the arteries. Leveraging the power of iPSCs we present novel concepts about the biology of VSMCs and shed light on the impact of the strongest CAD genetic risk factor in preserving a healthy cellular state within the vasculature.

Purinosomes spatially co-localize with mitochondrial transporters

In this study, we advance our understanding of the spatial relationship between the purinosome, a liquid condensate consisting of six enzymes involved in de novo purine biosynthesis, and mitochondria. Previous research has shown that purinosomes move along tubulin toward mitochondria, suggesting a direct uptake of glycine from mitochondria. Here, we propose that the purinosome is located proximally to the mitochondrial transporters SLC25A13 and SLC25A38, facilitating the uptake of glycine, aspartate, and glutamate, essential factors for purine synthesis. We utilized the proximity ligation assay (PLA) and APEX proximity labeling to investigate the association between purinosome proteins and mitochondrial transporters. Our results indicate that purinosome assembly occurs close to the mitochondrial membrane under purine-deficient conditions, with the transporters migrating to be adjacent to the purinosome. Furthermore, both targeted and non-targeted analyses suggest that the SLC25A13-APEX2-V5 probe accurately reflects endogenous cellular status. These findings provide insights into the spatial organization of purine biosynthesis and lay the groundwork for further investigations into additional proteins involved in this pathway.

Comparison of Multiple Machine Learning Models for the Classification of Cell States Based on Impedance Features

Abstract Microfluidic impedance flow cytometry (IFC) enables high-throughput single-cell analysis in label-free manner. Tens of thousands of cells can be measured in several minutes under multiple frequencies, which give rise to impedance features with rich information ideal for machine learning (ML)-based cell classification. Conventional data processing approach for IFC typically exploits the scattered distribution of the measured cells which correlates the impedance features (e.g., the impedance amplitude and phase at different frequencies, the amplitude ratio between high to low frequencies) and exhibits resolved cell clusters in scatter plot. By manually gating on the distributed dots plot, the cell subgroups get mapped to different cell type or cellular states. ML-based data processing for IFC not only reduces the human workload, and more importantly, it also eliminates the human interference to manual gating strategy, and thus potentially leading to more concise and accurate cell classification results. Here, we demonstrate the ML-based classification of different cell states for tumor cells subject to anticancer drug treatment. IFC-measured impedance data of H1650 cells and Hela cells under drug-induced mitosis block state and apoptosis state have been applied for ML-based cell state identification. Three machine learning models, including the random forest (RF), support vector machine (SVM) and K-nearest neighbours (KNN) have been trained for impedance features extracted from cell signals at both 500 kHz and 10 MHz. In comparison, the RF model give rise to the highest classification accuracies among all trained models here. For H1650 cells, 84.01% and 85.96% accuracies have been respectively achieved for G1/S state vs. apoptosis and G2/M vs. apoptosis. For the classification between G2/M vs. apoptosis for the paclitaxel-treated Hela cells, the RF model produces high accuracy of 98.70%.

RNA-based logic for selective protein expression in senescent cells

Cellular senescence is a cellular state characterized by irreversible growth arrest, resistance to apoptosis and secretion of inflammatory molecules, which is causally linked to the pathogenesis of many age-related diseases. Besides, there is accumulating evidence that selective removal of senescent cells can benefit therapies for cancer and fibrosis by modulating the inflammatory microenvironment. While the field of so-called senolytics has spawned promising small molecules and peptides for the selective removal of senescent cells, there is still no effective means to detect senescent cells in vivo, a prerequisite for understanding the role of senescence in pathophysiology and to assess the effectiveness of treatments aimed at removing senescent cells. Here, we present a strategy based on an mRNA logic circuit, that yields mRNA-dependent protein expression only when a senescence-specific miRNA signature is present. Following a validation of radiation-induced senescence induction in primary human fibroblasts, we identify miRNAs up- and downregulated in association with cellular senescence using RT-qPCR. Incorporating binding sites to these miRNAs into the 3’ untranslated regions of the mRNA logic circuit, we demonstrate the senescence-specific expression of EGFP for detection of senescent cells and of a constitutively active caspase-3 for selective removal. Altogether, our results pave the way for a novel approach to execute an mRNA-based programme specifically in senescent cells aimed at their detection or selective removal.

ScCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.

Accidental Encounter of Repair Intermediates in Alkylated DNA May Lead to Double-Strand Breaks in Resting Cells.

In clinics, chemotherapy is often combined with surgery and radiation to increase the chances of curing cancers. In the case of glioblastoma (GBM), patients are treated with a combination of radiotherapy and TMZ over several weeks. Despite its common use, the mechanism of action of the alkylating agent TMZ has not been well understood when it comes to its cytotoxic effects in tumor cells that are mostly non-dividing. The cellular response to alkylating DNA damage is operated by an intricate protein network involving multiple DNA repair pathways and numerous checkpoint proteins that are dependent on the type of DNA lesion, the cell type, and the cellular proliferation state. Among the various alkylating damages, researchers have placed a special on O6-methylguanine (O6-mG). Indeed, this lesion is efficiently removed via direct reversal by O6-methylguanine-DNA methyltransferase (MGMT). As the level of MGMT expression was found to be directly correlated with TMZ efficiency, O6-mG was identified as the critical lesion for TMZ mode of action. Initially, the mode of action of TMZ was proposed as follows: when left on the genome, O6-mG lesions form O6-mG: T mispairs during replication as T is preferentially mis-inserted across O6-mG. These O6-mG: T mispairs are recognized and tentatively repaired by a post-replicative mismatched DNA correction system (i.e., the MMR system). There are two models (futile cycle and direct signaling models) to account for the cytotoxic effects of the O6-mG lesions, both depending upon the functional MMR system in replicating cells. Alternatively, to explain the cytotoxic effects of alkylating agents in non-replicating cells, we have proposed a "repair accident model" whose molecular mechanism is dependent upon crosstalk between the MMR and the base excision repair (BER) systems. The accidental encounter between these two repair systems will cause the formation of cytotoxic DNA double-strand breaks (DSBs). In this review, we summarize these non-exclusive models to explain the cytotoxic effects of alkylating agents and discuss potential strategies to improve the clinical use of alkylating agents.

Heterogeneous Evolution of Breast Cancer Cells-An Endogenous Molecular-Cellular Network Study.

Breast cancer heterogeneity presents a significant challenge in clinical therapy, such as over-treatment and drug resistance. These challenges are largely due to its obscure normal epithelial origins, evolutionary stability, and transitions on the cancer subtypes. This study aims to elucidate the cellular emergence and maintenance of heterogeneous breast cancer via quantitative bio-process modeling, with potential benefit to therapeutic strategies for the disease. An endogenous molecular-cellular hypothesis posits that both pathological and physiological states are phenotypes evolved from and shaped by interactions among a number of conserved modules and cellular factors within a biological network. We hereby developed a model of core endogenous network for breast cancer in accordance with the theory, quantifying its intrinsic dynamic properties with dynamic modeling. The model spontaneously generates cell states that align with molecular classifications at both the molecular and modular level, replicating four widely recognized molecular subtypes of the cancer and validating against data extracted from the TCGA database. Further analysis shows that topologically, a singular progression gateway from normal breast cells to cancerous states is identified as the Luminal A-type breast cancer. Activated positive feedback loops are found to stabilize cellular states, while negative feedback loops facilitate state transitions. Overall, more routes are revealed on the cellular transition between stable states, and a traceable count explains the origin of breast cancer heterogeneity. Ultimately, the research intended to strength the search for therapeutic targets.

A dual-scale fused hypergraph convolution-based hyperedge prediction model for predicting missing reactions in genome-scale metabolic networks.

Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular metabolic and physiological states. However, there are still missing reactions in GEMs due to incomplete knowledge. Recent gaps filling methods suggest directly predicting missing responses without relying on phenotypic data. However, they do not differentiate between substrates and products when constructing the prediction models, which affects the predictive performance of the models. In this paper, we propose a hyperedge prediction model that distinguishes substrates and products based on dual-scale fused hypergraph convolution, DSHCNet, for inferring the missing reactions to effectively fill gaps in the GEM. First, we model each hyperedge as a heterogeneous complete graph and then decompose it into three subgraphs at both homogeneous and heterogeneous scales. Then we design two graph convolution-based models to, respectively, extract features of the vertices in two scales, which are then fused via the attention mechanism. Finally, the features of all vertices are further pooled to generate the representative feature of the hyperedge. The strategy of graph decomposition in DSHCNet enables the vertices to engage in message passing independently at both scales, thereby enhancing the capability of information propagation and making the obtained product and substrate features more distinguishable. The experimental results show that the average recovery rate of missing reactions obtained by DSHCNet is at least 11.7% higher than that of the state-of-the-art methods, and that the gap-filled GEMs based on our DSHCNet model achieve the best prediction performance, demonstrating the superiority of our method.

Preclinical spheroid models identify BMX as a therapeutic target for metastatic MYCN nonamplified neuroblastoma.

The development of targeted therapies offers new hope for patients affected by incurable cancer. However, multiple challenges persist, notably in controlling tumor cell plasticity in patients with refractory and metastatic illness. Neuroblastoma (NB) is an aggressive pediatric malignancy originating from defective differentiation of neural crest-derived progenitors with oncogenic activity due to genetic and epigenetic alterations and remains a clinical challenge for high-risk patients. To identify critical genes driving NB aggressiveness, we performed combined chromatin and transcriptome analyses on matched patient-derived xenografts (PDXs), spheroids, and differentiated adherent cultures derived from metastatic MYCN nonamplified tumors. Bone marrow kinase on chromosome X (BMX) was identified among the most differentially regulated genes in PDXs and spheroids versus adherent models. BMX expression correlated with high tumor stage and poor patient survival and was crucial to the maintenance of the self-renewal and tumorigenic potential of NB spheroids. Moreover, BMX expression positively correlated with the mesenchymal NB cell phenotype, previously associated with increased chemoresistance. Finally, BMX inhibitors readily reversed this cellular state, increased the sensitivity of NB spheroids toward chemotherapy, and partially reduced tumor growth in a preclinical NB model. Altogether, our study identifies BMX as a promising innovative therapeutic target for patients with high-risk MYCN nonamplified NB.

Transforming Cell-Drug Interaction through Granular Hydrogel-Mediated Delivery of Polyplex Nanoparticles for Enhanced Safety and Extended Efficacy in Gene Therapy.

The utilization of hydrogels for DNA/cationic polymer polyplex nanoparticle (polyplex) delivery has significantly advanced gene therapy in tissue regeneration and cancer treatment. However, persistent challenges related to the efficacy and safety of encapsulated polyplexes, stemming from issues such as aggregation, degradation, or difficulties in controlled release during or postintegration with hydrogel scaffolds, necessitate further exploration. Here, we introduce an injectable gene therapy gel achieved by incorporating concentrated polyplexes onto densely packed hydrogel microparticles (HMPs). Polyplexes, when uniformly adhered to the gene therapy gel through reversible electrostatic interactions, can detach from the HMP surface in a controlled manner, contrasting with free polyplexes, and thereby reducing dose-dependent toxicity during transfection. Additionally, the integration of RGD cell adhesion peptides enhances the scaffolding characteristics of the gel, facilitating cell adhesion, migration, and further minimizing toxicity during gene drug administration. Notably, despite the overall transfection efficiency showing average performance, utilizing confocal microscopy to meticulously observe and analyze the cellular states infiltrating into various depths of the gene therapy gel resulted in the groundbreaking discovery of significantly enhanced local transfection efficiency, with primary cell transfection approaching 80%. This phenomenon could be potentially attributed to the granular hydrogel-mediated delivery of polyplex nanoparticles, which revolutionizes the spatial and temporal distribution and thus the "encounter" mode between polyplexes and cells. Moreover, the gene therapy gel's intrinsic injectability and self-healing properties offer ease of administration, making it a highly promising candidate as a novel gene transfection gel dressing with significant potential across various fields, including regenerative medicine and innovative living materials.

Transcriptional dynamics orchestrating the development and integration of neurons born in the adult hippocampus.

The adult hippocampus generates new granule cells (aGCs) with functional capabilities that convey unique forms of plasticity to the preexisting circuits. While early differentiation of adult radial glia-like cells (RGLs) has been studied extensively, the molecular mechanisms guiding the maturation of postmitotic neurons remain unknown. Here, we used a precise birthdating strategy to study aGC differentiation using single-nuclei RNA sequencing. Transcriptional profiling revealed a continuous trajectory from RGLs to mature aGCs, with multiple immature stages bearing increasing levels of effector genes supporting growth, excitability, and synaptogenesis. Analysis of differential gene expression, pseudo-time trajectory, and transcription factors (TFs) revealed critical transitions defining four cellular states: quiescent RGLs, proliferative progenitors, immature aGCs, and mature aGCs. Becoming mature aGCs involved a transcriptional switch that shuts down pathways promoting cell growth, such SoxC TFs, to activate programs that likely control neuronal homeostasis. aGCs overexpressing Sox4 or Sox11 remained immature. Our results unveil precise molecular mechanisms driving adult RGLs through the pathway of neuronal differentiation.

JAK/STAT signaling promotes the emergence of unique cell states in ulcerative colitis

The intestinal epithelium ensures uptake of vital nutrients and acts as a barrier between luminal contents and the underlying immune system. In inflammatory bowel diseases, such as ulcerative colitis (UC), this barrier is compromised, and patients experience debilitating symptoms. Here, we perform single-cell RNA profiling of epithelial cells and outline patterns of cell fate decisions in healthy individuals and UC patients. We demonstrate that patterns of hierarchical behavior are altered in UC patients and identify unique cellular states associated with Janus kinase/signal transducer and activator of transcription (JAK/STAT) activation in ulcerated and non-ulcerated areas of the colonic epithelium. These transcriptional changes could be recapitulated in human colonic organoids, wherein cytokine-mediated activation of JAK/STAT led to the emergence of cell populations with augmented regenerative properties. Altogether, our findings indicate that intricate relationships between epithelial and cytokine signaling regulate cell fate during epithelial tissue regeneration in humans and have important implications for the understanding of UC biology.