Biology subjects

Tang, J.

Publications and source records attributed to Tang, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

A recurrent neural network model of chronic pain development and recovery

Chronic pain presents a leading challenge in the world today for both clinicians and researchers. Because chronic pain is difficult to explain and treat, it is often managed with opioids despite providing limited relief and contributing to dependence and misuse. Persistent pain can be maintained by altered central nervous system processing even in the absence of distinct tissue damage or disease, which may limit the efficacy of conventional pharmacological therapies that target nociceptive signal transmission rather than maladaptive central nervous system dynamics often present in those with chronic pain. Although neuroimaging studies have identified this shift from nociceptive to emotional circuits during pain chronification, a quantitative framework linking these neural changes to longitudinal pain trajectories or recovery is lacking. We present a parsimonious firing-rate model that can account for the development of and recovery from chronic pain, which is based on the theoretical framework established by Wilson and Cowan. The model provides a quantitative explanation of how sensitization, anxiety, and fear maintain pain even after an injury has healed, and how calming stimulus downregulates these processes to facilitate recovery. A study applying the same principles as the model produced an average pain decrease of 3.5 on the Visual Analog Scale (VAS), with all subjects experiencing a reduction in pain. These results, coupled with our model and findings in prior studies, suggest that increasing calming stimulus can reduce pain without necessitating pharmacological or invasive, resource-intensive interventions.

neuroscience↗

bioRxiv2026

Dynamic dissociation of the IFT complex drives ciliary dysfunction during C. elegans ageing

Intraflagellar transport (IFT) is essential for cilia, and its dysfunction drives ciliopathies and systemic ageing. However, the in vivo dynamics of individual components in the IFT complexes remain obscured, leaving the mechanisms of age-dependent IFT failure largely unknown. Here, we report a dual-color super-resolution imaging strategy to dissect the structural integrity and kinetics of IFT trains in the sensory cilia of young and aged Caenorhabditis elegans. We show that IFT complexes are not static entities. Instead, distinct components undergo dynamic dissociation within IFT trains. This intra-complex dissociation causes a remarkable reduction in IFT velocity and is significantly exacerbated in the cilia of aged worms. Mechanistically, we identify the conserved TRiC/CCT chaperonin complex and daf-19/RFX, the master transcription factor driving IFT genes, as critical regulators of IFT stability. We demonstrate that their age-dependent downregulation drives the progressive IFT component dissociation. Our findings re-frame the IFT complex as a highly dynamic assembly, uncover a new dimension of IFT regulation, and identify the progressive uncoupling of IFT components as a key driver of ciliary dysfunction during ageing.

cell biology↗

bioRxiv2026

Predicting Pre-treatment Resistance or Post-treatment Effect? A Systematic Benchmarking of Single-Cell Drug Response Models

Intratumoral heterogeneity drives variable drug responses in cancer. Single-cell RNA sequencing (scRNA-seq) enables characterization of such heterogeneity and prediction of drug response at single-cell resolution. Accordingly, various computational models have been developed to infer drug response from scRNA-seq data. However, their performance, robustness, and generalizability across different biological contexts remain insufficiently evaluated. To address this gap, we benchmarked representative single-cell drug response prediction models using 26 curated datasets comprising over 760,000 cells across 12 cancer types and 21 therapeutic agents. We constructed balanced and imbalanced scenarios to reflect realistic drug-response label distributions. To address the lack of ground-truth labels in conventional scRNA-seq datasets, we incorporated lineage-tracing data with experimentally validated drug-response annotations, enabling evaluation in a clinically relevant pre-treatment prediction setting. Our results show that prediction performance was markedly higher in cell lines than in tissue samples. Under imbalanced conditions, most methods exhibited sharp performance declines, whereas scDEAL demonstrated the highest robustness. Independent validation using an in-house pancreatic ductal adenocarcinoma dataset further confirmed scDEALs robustness and ability to capture biologically meaningful state transitions. Label-substitution experiment revealed that this robustness was partially driven by the models specific training-label construction. However, benchmarking with lineage-tracing data revealed a fundamental limitation: most models capture drug-induced transcriptional changes but struggled to predict intrinsic resistance before treatment. In summary, our study defines the performance boundaries of current approaches and highlights their limitations in addressing intratumoral heterogeneity, class imbalance, and intrinsic resistance prediction, emphasizing the need for the next-generation single-cell drug response models with stronger clinical relevance.

bioinformatics↗

bioRxiv2026

The External Globus Pallidus is a Basal Ganglia Output Hub with Action-Specific Circuits

The external globus pallidus (GPe) is traditionally viewed as a homogeneous relay in the indirect basal ganglia pathway that broadly suppresses movement. Using whole-brain anterograde axon mapping, rabies tracing, single-neuron reconstruction, and single-nucleus RNA sequencing, we reveal that the GPe is a major basal ganglia output nucleus, composed of anatomically and molecularly distinct populations. Besides the neurons with canonical projections, and projections to striatum and cortex, the GPe contains specific neuronal populations that target the thalamus and brainstem directly, including the parafascicular thalamus (GPePf) and the pedunculopontine nucleus (GPePPN). These projection-defined subpopulations exhibit distinct behavioral functions. GPe-PPN neurons are selectively suppressed at locomotor onset, and bidirectional manipulation of this pathway is sufficient to promote or suppress locomotion. Notably, D2-MSN stimulation upstream of these neurons evokes locomotion, and co-activation of GPePPN pathway blocks this D2-MSN-evoked locomotion, demonstrating a disinhibitory circuit for action stemming from D2-MSNs. In contrast, GPePf neurons are not engaged during locomotion but are selectively recruited during skilled forelimb actions, and their activation disrupts forelimb movements without affecting locomotion. Together, these findings establish the GPe as a basal ganglia output hub composed of modules that mediate distinct behaviors. This organization revises canonical models of the indirect pathway by demonstrating that D2-MSNs can also facilitate, rather than only suppress, movement depending on the downstream GPe output channels they engage. In briefThe external globus pallidus (GPe), classically considered a relatively homogeneous relay within the indirect basal ganglia pathway, is revealed here as a major basal ganglia output hub. Distinct GPe subpopulations, defined by transcriptomic identity and projection target, form parallel channels to cortical, thalamic and brainstem structures with action-specific functions. These findings uncover a disinhibitory mechanism through which D2-MSNs can facilitate behavior, revising canonical models of basal ganglia function. HighlightsBrain-wide mapping reveals the GPe as a major basal ganglia output node GPe contains novel cell types defined by transcriptomic identity and projection specificity Projection-specific GPe circuits differentially control locomotion and skilled forelimb actions D2-MSNs can disinhibit downstream targets via specific GPe projections and promote action.

neuroscience↗

bioRxiv2026

Geometry-enhanced protein language modeling enables discovery of novel antibiotic resistance genes

The global antibiotic resistome remains largely unexplored, not because antibiotic resistance genes (ARGs) are rare in the environment, but because many are evolutionarily distant from known ARGs. Current computational approaches primarily rely on sequence homology, and thus miss distant homologues. We develop GeoARG, a geometry-enhanced framework that integrates structural features with protein language models through knowledge distillation, enabling efficient large-scale screening using sequence input alone. Across multiple benchmarks, GeoARG substantially improves the detection of remotely homologous ARGs, particularly under low sequence identity and fragmented conditions. Large-scale metagenomic analysis uncovers 1,485 high-confidence ARG candidates that are highly divergent from known ARGs, expanding the phylogenetic and functional landscape of the resistome. Structural analyses further show that these candidates preserve active-site geometry and maintain stable ligand-binding configurations consistent with known resistance mechanisms. These results demonstrate that geometric constraints enable systematic expansion of the resistome and facilitate the discovery of evolutionarily distant yet functionally conserved genes. A public web server is available at https://ycclab.cuhk.edu.cn/GeoARG/.

bioinformatics↗

bioRxiv2026

Decoding concept representations in aphasia after stroke

Many stroke survivors with aphasia struggle to map their thoughts into words or motor plans. Neuroprostheses that decode concept representations could help these individuals communicate by predicting the words, phrases, or sentences that they are struggling to produce. Here we decoded concept representations measured using functional magnetic resonance imaging (fMRI) from participants with different aphasia profiles. The decoders generated continuous word sequences that could describe the concepts that the participants were hearing about, seeing, or imagining. To forecast how this approach would generalize across the heterogeneity of aphasia profiles, we characterized how stroke affects the anatomical organization and information capacity of conceptual processing. Mapping how concepts are organized across the brain, we found that conceptual tuning during non-linguistic processing was largely consistent between the participants with aphasia and neurologically healthy participants. Comparing information processing between the participants with aphasia and neurologically healthy participants, we found that both groups processed similar amounts of non-linguistic information. Our findings indicate that concept representations can be largely spared in individuals with aphasia and demonstrate how these representations can be decoded to support communication.

neuroscience↗

bioRxiv2026

End-to-end bimodal anti-counterfeiting by informational DNA nanoparticles

The superior stealth properties and high information density make DNA a sought-after candidate in the field of molecular steganography. Here, we developed the InfinMark end-to-end DNA steganography framework for anti-counterfeiting applications by combining the characteristics of both the Internet of Things (IoT) and DNA-of-Things (DoT). InfinMark includes five modules: Information Transcoding, Fingerprint Writing, Nano-encapsulation, Invisible Marking, and Multi-level Rapid Authentication. It ensures precise anti-counterfeiting information reading and writing through a dynamic DNA-compatible transcoding algorithm, achieves seamless embedding by developing scalable nanoparticle manufacture methods, and supports cross-scenario on-site verification, ultimately granting it comprehensive anti-counterfeiting capabilities spanning from source labeling to terminal tracing. By addressing the bottlenecks in IoT and DoT integration, lifecycle tracking, as well on-site product authentication, this research constructs a full-chain bimodal anti-counterfeiting system, thereby showcasing the practical application of informational DNA nanoparticles in various aspects of production and daily life.

synthetic biology↗

bioRxiv2026

Rapid cortical mapping with cross-participant encoding models

Voxelwise encoding models trained on functional MRI data can produce detailed maps of cortical organization. However, voxelwise encoding models must be trained on many hours of brain responses from each participant, limiting clinical applications. In this study, we introduce a cross-participant modeling framework for rapid cortical mapping. In this framework, voxelwise encoding models are trained on many hours of brain responses from previously scanned reference participants, and then transferred to a new participant by aligning brain responses using a small set of stimuli. We evaluated cross-participant encoding models on linguistic semantic mapping, non-linguistic semantic mapping, and auditory mapping. In each case we found that cross-participant encoding models had more accurate selectivity estimates and prediction performance than within-participant encoding models trained on the same amount of data from the new participant. We also found that cross-participant encoding models improved with the amount of data from each reference participant and the number of reference participants. These results demonstrate that cross-participant modeling can substantially reduce the amount of data required for accurate cortical mapping, which may facilitate new clinical applications of functional neuroimaging.

neuroscience↗

bioRxiv2026

Dynamic optimization of extrachromosomal DNA copy number drives tumour evolution

Extrachromosomal DNA (ecDNA) is common in human cancers and is associated with poor clinical outcomes, yet how ecDNA-driven genetic heterogeneity is translated into functional heterogeneity remains unclear. Using single-cell multiomics sequencing and multiplexed IF-FISH, we show that asymmetric inheritance of ecDNA generates copy number heterogeneity that propagates to gene expression programs, including oncogenic signaling and cellular stress responses. Transgenerational live-cell lineage tracking directly shows that ecDNA heterogeneity arises within only a few cell divisions and modulates daughter cell division timing in a copy number-dependent manner, a property not observed for evenly inherited chromosomal amplicons. We identify an optimal middle ecDNA copy number range that maximizes proliferative fitness at baseline, while drug selection pressure induced by low-dose CHK1 inhibition selects for cells with a new optimal range at low ecDNA copy numbers. These low ecDNA copy number cells pre-exist in the population and can be generated de novo, driving copy number shifts promoting drug resistance. In vivo experiments further demonstrate that shifts toward ecDNA copy numbers that are optimal under the tumour microenvironment enhance tumourigenicity. Together, these findings establish ecDNA copy number plasticity as a central driver of tumour evolution.

cancer biology↗

bioRxiv2026

Microencapsulation of a novel Bacteroides thetaiotaomicron strain: a promising strategy to fortify intestinal barrier fortification in weaned pig model

Porcine Bacteroides thetaiotaomicron LYH5 demonstrated in vitro antimicrobial activity, suggesting probiotic potential. Due to poor gastric juice tolerance, LYH5 was encapsulated via extrusion using sodium alginate (SA) and gellan gum. Box-Behnken design optimization yielded optimal parameters: SA 1.5%, gellan gum 0.4%, CaCl2 0.9%, bacteria:glue ratio 1:4, achieving an encapsulation rate of 84.22{+/-}0.17%. Its effect on weaned piglet intestinal health was evaluated using 78 piglets (7.69{+/-}0.52 kg) randomly assigned to 4 groups for 40 days: CON (control), T (basal diet + LYH5 live bacteria, 1x10{superscript 1} CFU/mL), TJ (basal diet + LYH5 microcapsules, 1x10{superscript 1} CFU/mL, J (basal diet + empty capsules). The results of this experiment showed that compared with the control group, LYH5 microcapsule can improve the intestinal barrier function without affecting the growth performance of piglets, and provide ideas and references for the development of human next-generation probiotics (NGP). IMPORTANCEThis study addresses the key bottleneck of poor gastric acid tolerance of probiotics via microencapsulation and provides a practical reference for the development of human next-generation probiotics.

microbiology↗

bioRxiv2026

Spore-Based Biocomposite Thermoplastic Polyesters with Enhanced Toughness and Programmable Disintegration

Thermoplastic polyesters are widely used in commodity and high-performance applications due to their tunable and exceptional properties, versatile performance, and increasing relevance in sustainable materials. Integrating biological functionality into these polymers offers a promising route to enhance performance and end-of-life behavior beyond what conventional additives can achieve. Here, we report the generalization of an embedded spore-based engineered living material concept to three representative thermoplastic polyesters; polycaprolactone (PCL), polylactic acid (PLA), and poly(butylene adipate-co-terephthalate) (PBAT). Heat-shock-tolerized Bacillus subtilis spores were compounded with each polyester as a living biofiller via hot melt extrusion. The resulting biocomposite polyesters retained high spore viability (>90%) after extrusion and exhibited improved mechanical performance (up to 41% toughness improvement compared to neat polymers). End-of-life behavior was evaluated in a microbially-limited composting environment, where spore-containing PCL exhibited nearly complete disintegration within five months, corresponding to a [~]7-fold increase in degradation kinetics relative to neat PCL. Finally, 3D printing of biocomposite PCL was demonstrated through fused deposition modeling and direct ink writing methodologies. Together, this work demonstrated the successful extension of spore-based engineered living materials from thermoplastic polyurethane to multiple thermoplastic polyesters. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/707801v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@4eaafaorg.highwire.dtl.DTLVardef@bb17c2org.highwire.dtl.DTLVardef@114e4ceorg.highwire.dtl.DTLVardef@b9bd0c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

Longitudinal modality prediction learns gene regulatory patterns: insights from a single-cell competition

Simultaneous measurement of chromatin, transcriptomic, and proteomic features in single cells opens new avenues for modeling interactions between molecular layers during dynamic biological processes. Predicting one modality from another - such as inferring gene expression from chromatin profiles or protein abundance from RNA - has the potential to reveal regulatory relationships and enhance downstream analyses. However, conventional approaches for predicting gene regulation have largely failed and method development in modality prediction for regulatory inference has been limited. To explore effective modeling strategies and stimulate innovation, we generated a purpose-built longitudinal multimodal benchmarking dataset that captures early hematopoietic differentiation and organized the largest single-cell data competition to date, receiving over 27,000 submissions from 1,602 competitors worldwide. In our extensive analysis of the competition results, we demonstrated that top-performing approaches outperform state-of-the-art methods, and uncover how best-performing models captured biologically meaningful regulatory relationships between modalities. With ablation studies of the winning models, we identified feature-engineering strategies, model architectures and cross-validation schemes that are crucial for outstanding performance, and provide simplified, reproducible, light-weight code for state-of-the-art models. Together, the benchmark and analyses serve as an evaluation standard and guide future method development, including recently emerging foundation models, to advance our understanding of regulatory interactions in longitudinal, multimodal single-cell data.

bioinformatics↗

bioRxiv2026

Functional In-Context Learning in Genomic Language Models with Nucleotide-Level Supervision and Genome Compression

AO_SCPLOWBSTRACTC_SCPLOWGenomic foundation models aim to learn general-purpose representations directly from DNA sequence, enabling sequence understanding, generation, and probabilistic reasoning across a wide range of biological tasks. Scaling such models to genomic lengths, however, remains challenging due to the tension between long-range context, nucleotide-level resolution, and practical computational efficiency. Architectural innovations have enabled increasingly long nominal inputs, but often struggle to translate additional context into meaningful performance gains, particularly in the presence of sparse functional signal along eukaryotic genomes. In this work, we revisit the design of long-context genomic foundation models from the perspective of training objective and data construction. We introduce Factorized Nucleotide Supervision (FNS), which reconciles efficient k-mer tokenization with single-nucleotide likelihoods through probability marginalization, and Genome Compression Pretraining (GCP), which reshapes the training distribution by concentrating on gene-centric and regulatory regions. Together, these techniques enable standard transformer-based models to perform functional in-context learning without sacrificing nucleotide-level fidelity or computational efficiency. Building on these ideas, we present a family of autoregressive genomic foundation models supporting contexts of up to 98k base pairs across eukaryotic and prokaryotic genomes. Across training-free evaluations and downstream fine-tuning benchmarks, our models consistently improve over prior approaches and match or exceed state-of-the-art baselines while enabling substantially more efficient inference. Together, these results demonstrate that aligning supervision and data regimes with the biological structure of genomic sequence provides a principled and effective path toward scalable and biologically faithful genomic language modeling. Models, data, and scripts for downstream analyses are publicly available at https://huggingface.co/GenerTeam.

genomics↗

bioRxiv2026

Monounsaturated fatty acid biosynthesis is critical for streptococcal envelope homeostasis and stress tolerance

AbstractThe genus Streptococcus contains some of the most important commensals and pathogens of the human microbiome. To obtain the fatty acids required for cell membranes, Streptococcus either produce fatty acids de novo through the fatty acid biosynthesis (fab) pathway or uptake host fatty acids through the fatty acid kinase (fak) pathway. Although both the fab and fak pathways represent potential therapeutic targets to prevent or treat infection, progress is limited because of an incomplete understanding of taxon-to-taxon variability in streptococcal lipid metabolism. Here, we examined the role of de novo monounsaturated fatty acid (MUFA) synthesis in physiology and virulence-associated traits in Streptococcus mutans, Streptococcus pyogenes, and Streptococcus pneumoniae, three major pathogens that cause disease at distinct body sites. In all three species, deletion of fabM abolished MUFA production and caused severe growth defects, decreased stress tolerance, increased antibiotic susceptibility, and defects in cell viability, morphology, and division. In S. mutans, loss of fabM also markedly reduced competence signaling and production of the mutacin IV bacteriocin. Deletion of fabM increased susceptibility to killing by human neutrophils in S. mutans and S. pneumoniae, but not S. pyogenes. Together, these findings illustrate that MUFA synthesis is broadly important for streptococcal physiology and cell membrane homeostasis, while its contribution to pathogenesis is strongly species- and context-dependent, providing leads to guide development of novel therapeutic and/or preventative strategies.

microbiology↗

bioRxiv2026

Isthmin-1 is a Key Regulator of Induced Pluripotent Stem Cell-Derived Cardiomyocytes Maturation through Activation of p53 Signaling

AimsHuman induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) exhibit an immature structural and metabolic phenotype that limits their utility for cardiac disease modeling and regenerative therapy. Although multiple extrinsic strategies have been proposed to enhance iPSC-CM maturation, intrinsic molecular regulators governing this process remain incompletely defined. This study aimed to identify and characterize a novel molecular factor that promotes cardiomyocyte maturation. Methods and ResultsCross-species transcriptomic analyses comparing adult versus fetal hearts and hypertrophic cardiomyopathy versus healthy myocardium identified Isthmin-1 (ISM1) as a conserved maturation-associated gene. In human iPSC-CMs, ISM1 overexpression enhanced sarcomere organization, mitochondrial oxidative phosphorylation, ATP production, and calcium-handling properties, whereas ISM1 knockdown impaired these maturation features. RNA sequencing revealed global transcriptional reprogramming toward an adult-like state, characterized by suppression of cell-cycle-related gene programs and activation of oxidative metabolic pathways. KEGG pathway enrichment, GSVA, and GSEA consistently identified p53 signaling as the most significantly activated pathway in ISM1-overexpressing iPSC-CMs. Mechanistically, ISM1 directly interacted with p53, enhanced its protein stability, promoted nuclear localization, and increased transcription of p53 downstream targets involved in metabolic remodeling and cell-cycle exit. Pharmacological inhibition of p53 abolished ISM1-induced structural and metabolic maturation, demonstrating that ISM1 promotes cardiomyocyte maturation in a p53-dependent manner. ConclusionsISM1 is a previously unrecognized molecular driver of cardiomyocyte maturation that promotes structural, metabolic, and functional maturation of iPSC-CMs through activation of p53-dependent transcriptional programs. These findings provide new mechanistic insight into cardiomyocyte maturation and identify ISM1 as a promising target for improving the maturity of stem cell-derived cardiomyocytes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/698535v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@15daca3org.highwire.dtl.DTLVardef@f1396dorg.highwire.dtl.DTLVardef@f13400org.highwire.dtl.DTLVardef@18d0b41_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2026

Valency-driven division of labor balances chromatin compaction and structural plasticity

Chromatin folding is regulated by multivalent protein complexes and condensates, yet how the multivalent binding quantitatively controls chromatin compaction, domain organization, and cell-to-cell heterogeneity remains unresolved. Here we develop the Chromatin-associated Protein Complex Maximum Entropy Model (CPC-MEM), a data-driven, physics-based polymer framework in which Hi-C contacts are realized through an explicit, finite pool of diffusing chromatin-associated protein complexes (CPCs) with prescribed CPC-chromatin interaction valency. When fitted to Hi-C, CPC-MEM generates chromatin structural ensembles that simultaneously reproduce population-averaged contact maps and single-cell super-resolution distance statistics. We uncover a clear division of labor across valency: low-valency CPCs act as abundant linkers that compact chromatin, homogenize folding, and refine local structure, whereas high-valency CPCs form sparse co-bridging hubs that nucleate chromatin domains and increase conformational heterogeneity. Mixtures of valencies naturally produce a "hub-and-matrix" chromatin architecture that optimally balances compaction with structural plasticity. Our study provides a quantitative mechanism by which locus-specific chromatin interaction preferences, together with the valency and abundance of CPCs and condensates in the nucleus, jointly shape functional 3D genome organization.

biophysics↗

bioRxiv2025

MATERNAL AUTOPHAGY CONTRIBUTES TO GRAIN YIELD IN MAIZE

Maize is an important crop species that is cultivated worldwide, and increasing maize yield is one of the major goals of plant breeding. The subcellular degradation and recycling pathway known as autophagy is involved in many plant developmental processes including vegetative growth, reproductive development, seed development, and senescence. Here, we demonstrate that maize autophagy-defective atg10 mutants have delayed flowering and reduced kernel size, weight and number, leading to reduced grain yield. Reciprocal crosses between mutant and parental lines indicate that the maternal plant is the major contributor to the kernel phenotype in atg10 mutants, rather than the seed genotype. We hypothesize that this may be due to detrimental effects on nutrient remobilization from maternal tissue to seeds during kernel development, leading to reduced yield.

plant biology↗

bioRxiv2025

tBid mediated genetic ablation of connective tissue cells reveal their key regulatory function during limb regeneration in axolotls

Limb regeneration in adulthood is a fascinating phenomenon unique to salamanders, requiring precise coordination and interaction of various cell types. Connective tissue (CT) constitutes a major component of the limb and serves as the primary reservoir of positional information during regeneration. However, the direct evidence and the extent of CT in positional contribution remain limited. Here we develop an inducible Cre-LoxP-mediated tBid cell ablation system in axolotls and demonstrate efficient elimination of targeted muscle tissue and muscle stem cells through transient electroporation or genetic approach. Ablation of CT cells at early stages of regeneration results in delayed regeneration and loss of proximal limb segments (e.g., upper and lower limb), with minimal impact on hand differentiation. CT ablation during development yields similar defects as observed during regeneration, indicating an essential role for CT cells in determining the positional identity of developing and regenerating limb structures. Further scRNA-seq reveal a progressive proximal-to-distal transition among CT cells and identify distinct CT subtypes that contribute to proximal and distal segments. CT ablation results in reduction of the differentiated CT1--a subpopulation that maintains proximal identity throughout regeneration, and a delay of distal transformation in the proliferative CT1--a major CT subtypes showing progenitor property. These cellular shifts likely lead to the observed morphological deficits in regenerating limbs after CT ablation. Moreover, differentiated CT1 cells enhanced their interactions with surrounding cell types after CT ablation, to modulate adaptive proliferation in other populations, e.g., muscle stem cells. Our work establishes a tBid-based ablation strategy for functional studies of CT and other cell types in axolotls, and provides direct evidence demonstrating pivotal roles of CT cells in guiding positional memory and segmental patterning during organ regeneration.

molecular biology↗