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ABSTRACT Chronic pain remains a significant medical challenge with complex underlying mechanisms, and an urgent need for new treatments. Our research built and utilized the iPain single-cell

atlas to study chronic pain progression in dorsal root and trigeminal ganglia. We discovered that senescence of a small subset of pain-sensing neurons may be a driver of chronic pain. This

mechanism was observed in animal models after nerve injury and in human patients diagnosed with chronic pain or diabetic painful neuropathy. Notably, treatment with senolytics, drugs that

remove senescent cells, reversed pain symptoms in mice post-injury. These findings highlight the role of cellular senescence in chronic pain development, demonstrate the therapeutic

potential of senolytic treatments, and underscore the value of the iPain atlas for future pain research. SIMILAR CONTENT BEING VIEWED BY OTHERS AGING AND INJURY DRIVE NEURONAL SENESCENCE IN

THE DORSAL ROOT GANGLIA Article Open access 14 May 2025 SINGLE-CELL TRANSCRIPTOMIC ANALYSIS OF SOMATOSENSORY NEURONS UNCOVERS TEMPORAL DEVELOPMENT OF NEUROPATHIC PAIN Article 10 March 2021

NEUROGENESIS IN THE ADULT BRAIN FUNCTIONALLY CONTRIBUTES TO THE MAINTENANCE OF CHRONIC NEUROPATHIC PAIN Article Open access 17 September 2021 INTRODUCTION Currently, around 20% of adults

suffer from chronic pain, which would correspond to over 1.2 billion people worldwide, making it a global healthcare crisis1. While there are treatments available, these often provide only

partial relief if at all, and come with adverse side effects and a risk of drug abuse2,3. These treatments also center on pain signaling modulation and do not address the underlying

molecular and cellular causes of chronic pain. A better understanding of the molecular changes underlying chronic pain progression could enable the development of innovative, non-addictive

therapeutic strategies to effectively alleviate chronic pain. Thus, understanding the mechanisms involved in pain chronification have remained crucial for uncovering new therapeutic avenues.

Pain sensation is conveyed peripherally by the activation of pain-sensing neurons, or nociceptors, whose cell bodies reside in the peripheral sensory ganglia (mostly in the dorsal root

ganglia, DRG, alongside the spinal cord and in the trigeminal ganglia, TG). Those neurons are of different subtypes that are classified as peptidergic (PEPs), non-peptidergic (NPs), C-Low

Threshold mechanoreceptor (C-LTMRs), and a subtype of somatostatin positive neurons (SST)4,5,6. Nociceptors send peripheral nerve endings throughout the whole body, innervating the skin,

dura mater, and deep tissue. Centrally, they project within the spinal cord or the pons where the pain signal is transmitted and integrated locally and sent to upper brain centers where it

is perceived as a pain sensation. The introduction of single-cell RNA sequencing (scRNA-seq) has enabled the transformative advancement in our understanding of the intricate functional

diversity of disorder development much like the comprehensive atlases created in other fields such as cancer7 and neurological disorders8. In a substantial effort to create atlases with

further gene coverage than already available and with chromatin dynamic information, we integrated diverse single cell (sc) or single-nuclei (sn) RNA-seq from peer-reviewed datasets with our

in-house datasets5,9,10,11,12,13,14,15,16,17 (Table 1), with the aim to further unveil the molecular signatures governing the development and persistence of the different types of chronic

pain while also focusing on uncovering a shared mechanism among diverse pain models. With our somatosensory atlases called iPain available on CELLxGENE browser, we have established a

foundational framework that builds upon and extends previous research efforts5,11 and is crucial for investigating the development of chronic pain, when originating in the first anatomical

relay of pain pathway, either in the DRG (iPainDRG) or in the TG (iPainTG). Using iPainDRG, our results identify the senescence of a subset of nociceptor cells as a common mechanism across

the various rodent pain models, as well as in humans with chronic pain. Our data further reveal that treatment with senolytic compounds rescues hyperalgesia and allodynia associated with

chronic pain in mice, demonstrating significant therapeutic potential and a role for the senescence process in chronic pain disorders independent of aging. RESULTS CREATION OF IPAIN, A

SOMATOSENSORY ATLAS THAT CAPTURES CHRONIC PAIN DEVELOPMENT In recent years, numerous efforts have been made to generate sc/snRNA-seq datasets of the mouse DRG and TG in diverse models of

chronic pain (Fig. 1; Supplementary Data 1). These datasets offer different sampling timepoints and pain models at various stages of progression, providing the opportunity to create

comprehensive atlases. However, the lack of alignment among the unique genes and molecules identified in each dataset due to the use of different isolation methods and sequencing

technologies can be a challenge for integration of different datasets. To overcome this, we have used variational autoencoder (VAE)-based integration models like scANVI18, MULTIVI19, and

scGLUE20 (Fig. 1; Supplementary Fig. 1; Supplementary Fig. 2; Supplementary Fig. 3), which can effectively harmonize the datasets, control for batch correction while conserving the

biological information to capture the entire spectrum of molecular dynamics and diversity associated with pain chronicity in the DRG and TG in different chronic pain models and at different

timepoints (Fig. 1b, f; and Supplementary Fig. 3). The iPainDRG atlas, which integrates 191,798 cells from 7 sources (in-house generate: 4468 cells from multiome and 625 cells from

Smart-seq3), represents a comprehensive resource for understanding the molecular dynamics in somatosensory neurons in response to injury and the progression of pain chronification, with RNA

and ATAC information provided for each single cell (multiomic dataset allow for the use of MultiVI with ATAC imputation to all cells even to cells that did not originally provide ATAC

modality, Supplementary Fig. 2a for workflow, Supplementary Fig. 3a for atlas content). Of note, the combination of all these modalities allows the use of integration methods compatible with

the imputation of missing genes, therefore increasing the gene coverage. Similarly, the iPainTG atlas, which integrates 87,838 cells from five diverse sources (in-house generated: 124 cells

from Smart-seq3), provides insights into the TG’s response to various pain conditions (unlike in iPainDRG, not every cell in iPainTG has ATAC modality, Supplementary Fig. 2b and

Supplementary Fig. 3b). These atlases, with their detailed cell-type-specific markers and chromatin accessibility profiles, can serve as valuable references for the research community

studying the underlying mechanisms of chronic pain (Fig. 1d, h; and Supplementary Data 2–3). Finally, the atlases allow for the integration of datasets sequenced by any sequencing

technologies. CELL STATE PROGRESSION TOWARDS CHRONIC PAIN AND DRIVING MECHANISM To better understand the progression of cell state changes in sensory neurons during chronic pain, we used the

iPainDRG dataset, as it includes more pain models than iPainTG and covers the whole-time course of pain chronification. Indeed, all pain models used in the dataset are known to develop

within 1–3 days, peak at 2–3 weeks, and persist for weeks thereafter21. Therefore, we chose to cover the analysis of the study up to 28 days post-injury, where the pain is chronic and well

established, and in the atlas, several models share 28-day timepoints presumably reinforcing the analysis. Using iPainDRG, we further isolated and re-clustered neurons from the nociceptive

lineage in DRG, then visualized with Uniform Manifold Approximation and Projection (UMAP) to examine the molecular changes following injury (Fig. 2a–d; Supplementary Fig. 4a). We observed

that the neurons distribute in a pattern that reflects dynamics based on their experimental timepoint and connections to naïve or injury states (Fig. 2b–d and Supplementary Fig 4a). In the

UMAP visualization, neurons at a naïve (0 day) state are located at the periphery, while neurons post-injury begin to shift toward the center. By 28 days, this phenotype reversed, and most

neurons returned to the periphery of the graph (Fig. 2d). To investigate these molecular changes further, we classified the generated Leiden clusters where those spatially distinct areas

were classified into “woPain” (without pain) and “wPain” (with pain) macrostates based on their alignment with the “Reference” state (naïve and sham conditions) or not, respectively (Fig. 

2c). Differential expression analysis confirmed that the “wPain” category was associated with upregulated genes related to axon regeneration and axon guidance, suggesting a regenerative

molecular program following injury, as previously reported22,23 (Supplementary Data 4). Importantly, the “wPain” and “woPain” macrostates could be further broken down into microstates to

refine cell progression over time. These microstates, combined with injury timepoints, were named _Reference_, _Moving_, _Pain_, _Recovery,_ and _Lasting Pain_ (Fig. 2e). The _Reference_

state (timepoint 0) represents uninjured cells, naturally within “woPain”. The _Pain_ state represents the neurons in “wPain” excluding timepoint 28 days. Cells in “wPain” at 28 days

post-injury were labeled as being in the _Lasting Pain_ state. Remaining cells at post-injury timepoints but exhibited in “woPain” were further distinguished: the early phase of pain

development (up to 1.5 days) as _Moving_, and the later phase (1.5–28 days) as _Recovery_. This categorization was based on the observation that all cells were in the “wPain” macrostate at

the 1.5-day timepoint, while “woPain” state cells were seen both before and after. Therefore, cells in “woPain” prior to 1.5 days were assumed to be _Moving_ toward _Pain_, while cells after

were in _Recovery_ from _Pain_. Importantly, the defined microstates were unbiasedly recovered as pain dynamics states using RNA velocity24 (computed from CytoTrace25 score) (Fig. 2e and

Supplementary Fig. 4b) and PAGA graph analysis26 (Supplementary Fig. 4c). We also performed Generalized Perron Cluster-Cluster Analysis (GPCCA) using RNA velocity transition matrix and

observed a high degree of alignment when we color-coded the GPCCA results by our own defined states. Note that this analysis was performed for each nociceptor subtype with similar results

(Supplementary Fig. 4d). Altogether these results unbiasedly indicate that the nociceptors captured in the atlas dynamically progress between different microstates in chronic pain models. We

therefore extracted the driver genes within nociceptors responsible for the microstates transition and performed cell-cell communication analysis to comprehend the origin of cellular

changes centering on the communication between nociceptors, immune cells, and satellite glial cells. For the driver genes within nociceptors, the analysis based on the terminal states

revealed that many genes belonged to the “regeneration-associated genes” RAGs22,23 (Supplementary Data 5), with dynamic activity along the CytoTrace pseudotime. _Sox11_ was identified as the

sole transcription factor (TF) that consistently ranked among the top five driver genes in all nociceptor subtypes (Fig. 2f and Supplementary Data 5). We used SCENIC to identify the

regulons associated with the molecular programs responsible for controlling the dynamic cell state transition of nociceptors and combined it with an analysis of ATAC peaks’ accessibility

after injury (Fig. 2g and Supplementary Fig. 4e, f). Identified regulons included _Atf3_, _Jun_, and _Sox11_. Particularly, _Sox11_ regulon activity was found to increase as pseudotime

progressed and remained elevated in the _Lasting Pain_ state, in contrast to _Atf3_ and _Jun_ regulons, which exhibited an initial increase in activity followed by a decline (Supplementary

Fig. 5a–f). This suggested that SOX11 is a potential driver gene for the development and maintenance of neuropathic pain, a finding that was recently confirmed in vivo27, further supporting

the robustness of our analysis. To decipher cell-cell communication patterns (Fig. 2h), we combined all cell types, including non-neuronal cells, and employed tensor decomposition28 (Fig. 

2h, i; Supplementary Fig. 4g; and Supplementary Data 6). This method extracts multicellular gene expression patterns that vary across different cell types, pain models (contexts), and

timepoints to unbiasedly identify groups of molecular ligand-receptor pairs involved in cell-to-cell interactions. In our analysis, these groups, also called “Factors”, capture how molecular

changes in nociceptors are related to interactions with immune and satellite glial cells in _Reference_ and _non-Reference_ (_Moving, Pain, Recovery_, and _Lasting Pain_) states

(Supplementary Fig. 4g). Six Factors were revealed, amongst which Factor 3 exhibited the greatest changes between _Reference_ and _non-Reference_ conditions while affecting all pain models

that are included in the iPainDRG (Supplementary Fig. 4g). We, therefore, conducted a gene enrichment analysis using PROGENy pathways on the receptor-ligand pairs characterizing Factor 3 to

understand their biological significance. The p53 pathway was the most enriched pathway for this Factor, underlying upregulation of the p53 pathway during pain progression (Fig. 2j).

Notably, the distribution of the p53, p21, and p16 tumor suppressor genes within this “Factor” correlated with expression of these genes post-injury conditions in our atlas (Supplementary

Fig. 5g). The presence of these genes together strongly suggested a process of cellular senescence during pain chronification29,30,31. DEVELOPMENT AND PERSISTENCE OF PAIN AND SENESCENCE OF

PAIN-SENSING NEURONS The concept of cellular senescence was initially discovered in dividing cells but later extended to include post-mitotic cells, correlating this process with aging in

various tissues. Cellular senescence involves cells altering their characteristic phenotype in response to stress, resulting in a distinct secretory phenotype, called the Senescence

Associated Secretory Phenotype (SASP), and characterized by the release of inflammatory cytokines, growth factors, and proteases. SASP operates through diverse mechanisms, often involving

autocrine or paracrine signaling. To facilitate standardized and streamlined senescence analysis, the Mayo Clinic has recently introduced the SenMayo gene set32, encompassing an extensive

collection of SASP-related genes (Supplementary Data 7). Using this method, we identified SASP-positive cells by selecting those within the top 10 percent of the SenMayo z-score (Fig. 3a, b)

and highlighted them in the scatter plot, depicting GenAge score (gene set associated with aging in model organisms) in relation to CellAge score (gene set associated with aging in human

cells)33 (Fig. 3a, b and Supplementary Data 8). This analysis demonstrated that cells affected by injury exhibited advancement in both GenAge and CellAge compared to naïve cells. Upon

evaluating this with the CytoTrace score which demonstrated a lower potential for cell plasticity after injury in a subset of nociceptors (Supplementary Fig. 4b), we formulated a hypothesis

suggesting that the persistence of pain in the chronic injury model might be attributed to the persistence of injury-induced senescent cells which are unable to reverse their phenotype to

the _Reference_ state. To test this hypothesis, we initially identified senescent cells among the nociceptive lineage cells by computing a gene module score based on the genes listed in

SenMayo. Upon the evaluation of the score, we observed that the SenMayo z-score increased in every type of neuropathic pain model when compared to the control (Fig. 3c). Statistical testing

of the SenMayo z-score revealed a high enrichment of senescent cellular characteristics within nociceptors following injury in all pain models except Paclitaxel (Fig. 3c), across the dynamic

states (Fig. 3d) and for all nociceptors subtypes, regardless of the biological sexes (Fig. 3e). Additionally, the SenMayo z-score was found to be significantly lower in the _Recovery_

state when compared to the _Pain_ and _Lasting Pain_ states (Fig. 3d). Interestingly, every subtype of the nociceptive lineage cells exhibited the increase of the SenMayo z-score when we

compared the _Pain_ state to _Reference_ state. We then verified the time course of senescence development in silico (Fig. 3f) and in vivo by detecting the Senescence-Associated

Beta-galactosidase (SA-β-Gal) staining in DRG after nerve injury (CCI, Fig. 3g, h). We identified a senescent phenotype in a subset of nociceptors, which were characterized by high

expression of the neuronal nuclei marker (NeuN) and their smaller size (Fig. 3i). Furthermore, the injury was accompanied by an increased nuclear expression of the senescence marker p21

starting from day 7 post-injury34 (Supplementary Fig. 5g, h). These in vivo findings support the reliability of using the SenMayo z-score as a hallmark of senescence in our in silico

analysis. For TG, the atlas covered limited timepoints for proper analysis. We, therefore, assessed the SenMayo z-score in a pain model of compression of the trigeminal root entry zone 

(TREZ) of the TG nerve, modeling excruciating pain disorders such as trigeminal neuralgia (TN, also called the suicidal disease)35, and cluster headache (CH, a trigeminal autonomic

cephalalgia) which are both associated with nerve compression by blood vessels (called microvascular compression)36. To do this, we used the bulk RNA-seq dataset37 from the rat model with TN

to compute the score. We identified a clear increase in the SenMayo z-score compared to control conditions suggesting that both the DRG and TG use senescence as a pain chronification

mechanism (Fig. 3j). We then assessed the SenMayo z-score in human samples focusing on the DRG (TG dataset or samples from pain sufferers are not currently available online nor in biobank).

SenMayo z-score was elevated in DRG from human patients diagnosed with chronic pain (Fig. 4a) irrespective of their biological age (Fig. 4b, c), in an online dataset38. Deconvolution into

cell types using human reference map39 (Fig. 4d–f; Supplementary Fig. 5i) showed that the nociceptors (Fig. 4g) have a SenMayo z-score significantly elevated in chronic pain condition,

further emphasizing the involvement of senescence in nociceptors in human with chronic pain. Similar results were observed when the SenMayo z-score was applied to DRG bulk-seq40 extracted

from patients with diabetes neuropathy (Fig. 4h, i). These results in humans support the translational value of our findings. Importantly in mice, the senescence score was significantly

higher in all pain models, regardless of the biological sex (Fig. 3c, e). TARGETING SENESCENT CELLS TREATS CHRONIC PAIN While the presence of senescent neurons in peripheral somatosensory

neurons was unexpected in the context of chronic pain independent of aging, drugs targeting senescence cells, known as senolytic agents, have been well characterized and are used already in

preclinical and clinical trials. Senolytic drugs capitalize on senescent-cell anti-apoptotic pathways (SCAPs)41. Disrupting the expression of proteins within these pathways can lead to the

selective elimination of senescent cells while preserving neighboring cells and tissue physiology. The senolytic agents used in this study included the bioavailable small molecule inhibitors

of B-cell lymphoma-2 protein (Bcl-2) family proteins, such as Navitoclax (ABT-263), Venetoclax, and proteolysis targeting chimera (PROTAC) compound, PROTAC Bcl-XL degrader, the latter

aiming at controlling the linked side effects that this class of molecules has on thrombocytes counts. To test the hypothesis that targeting senescent cells could suppress hypersensitivity

to pain in a chronic pain model, we gave these various senolytic agents or vehicles to mice which have been subjected to Chronic Constriction Injury (CCI) and developed hyperalgesia and

allodynia by day 7 (Fig. 5a). Remarkably, mice treated with senolytic agents exhibited significantly less or no signs of mechanical pain 4 weeks post nerve injury when compared with baseline

or vehicle at the same timepoint (Fig. 5b). The drugs however did not significantly impact thermal sensation during the timeframe of the experiment or at the administered doses. The

restoration of normal pain behavior was associated with the selective removal of at least 50% of SA-β-galactosidase positive cells, on average, among the small diameter, peripherin-positives

 neurons for all drugs (Fig. 5c, d). Ultimately, the removed cells represent a small fraction of the total peripherin-positive neurons. Moreover, body weight remained unchanged

(Supplementary Fig. 5j) throughout the experiment and blood counts were similar between the drug-treated and control groups (Fig. 5e), indicating that the senolytic compounds did not show

obvious adverse systemic effects and did not significantly affect the thrombocytes counts (Fig. 5e). Importantly, as a control to assess the specific role of the senolytic compounds on

pain-sensing neurons and to control for off-target effects, we tested the mice for anxiety balance and motor skills (Fig. 5f, g, and Supplementary Fig. 5k, l). No significant difference was

observed when comparing the drug-treated mice to the control conditions. These results provide a more comprehensive understanding of the effects of the senolytic compounds highlighting their

selective impact on mechanical hypersensitivity without affecting other sensory modalities or general health parameters. Together these data indicate that a senescence process occurs in a

subset of nociceptive neurons in various chronic pain models and participates in the persistence of the pain state and that targeting these senescent cells represents a promising treatment

option for patients affected with chronic pain. DISCUSSION The extensive prevalence of chronic pain in the human population aligns with the pressing need to develop novel approaches to

medical treatment. A major limitation to progress in this field has been a lack of comprehensive understanding of the overall phenotypic alterations that underlie the dysfunction of

nociceptive neurons following nerve injury, leading to the onset of the chronic pain state. Our study introduces an integrated pain atlas (iPain) for systematically exploring the trajectory

of chronic pain development through the analysis of single-cell omics data from various sources. A salient discovery from our research is the consistent induction of a senescence phenotype

in the transcriptome of a subset of somatosensory neurons, principally in nociceptors, following peripheral nerve injury, a phenomenon observed in both rodents and human datasets.

Pharmacologically targeting senescent cells further proved effective in rescuing pain behaviors in a mouse model of chronic pain, suggesting a role of senescent cells in sustaining a

neuropathic pain phenotype. Our data provide functional evidence for the role of senescent cells in the progression and persistence of chronic pain in a sex-independent manner. This study

reveals a dynamic “intrinsic” shift in gene expression patterns in time following peripheral nerve injury. A common transcriptomic signature across all subtypes is _Sox11_. SOX11 is a

transcription factor involved in nervous system development and in neurogenesis in adults where it induces the expression of neuronal traits42,43. This suggests a role in the regeneration

process. However, while the expression of the known regeneration-associated genes like _Atf3_ and _Jun_ increases at early timepoints post-injury, they decrease later. In contrast, _Sox11_

remains elevated at a later timepoint, suggesting a different role than regeneration which would align with persistent pain development and maintenance. Interestingly, in relation to chronic

pain a role of _Sox11_ was previously predicted using the neuropathic pain model of spared nerve injury44, and in a sex-stratified genome-wide association study of Multisite Chronic Pain

(MCP), SOX11 was shown to be associated with MCP in females44. Finally, it is now reported using loss and gain of function that SOX11 has an important role in the initiation and maintenance

of neuropathic pain25. Those findings further support the resourcefulness of iPain. Moreover, our approach and our workflow differ from a cross-species atlas45, which also integrates data

from multiple sources, but focuses on addressing molecular conservation across different species. In contrast, iPain is specifically tailored to charting the trajectory of chronic pain

development in a targeted manner. Previous research has linked cellular senescence with chronic pain, specifically in the spinal cord46,47, presumably as a secondary process occurring

several months after injury and exhibiting a male-specific effect46 in certain cases. Nevertheless, using senolytics to treat the spinal cord locally did not provide sustained relief from

neuropathic pain, indicating that this approach did not address the primary mechanism underlying neuropathic pain in the studied model. However, our study found that the connection between

cellular senescence and chronic pain begins just after the acute phase, and is linked to the onset of neuropathic/chronic pain. We observed senescence in nociceptors across various models of

peripheral nerve injury and inflammation, as well as in the TG for the TREZ compression model. Furthermore, we discovered that this effect is not sex-specific; both male and female animals

showed similar levels of cellular senescence in nociceptors. This suggests an underlying mechanism different from those previously reported. Lastly, while our study focuses on young and

middle-aged adult animals, this mechanism might also function or even intensify in older individuals in which the development of a senescent phenotype in nociceptors following nerve injury

or in control conditions might be more easily induced48. In conclusion, our single-cell transcriptomic atlases could provide substantial advances in drug discovery efforts by defining cells

and pathways relevant to pain disorders following peripheral nerve injury. Our discovery suggests an alternative therapeutic approach to the currently limited options for chronic pain

treatment, a hypothesis that has been validated in mice with translational potential for human subjects according to in silico analysis of chronic pain patients and patients with diabetes 

painful neuropathy. The selective removal of the few dysfunctional, senescent neurons may be a worthwhile trade-off to achieve lasting well-being for the patient, especially in the case of

excruciating pain conditions with limited options, such as TN and CH which are linked to trigeminal nerve compression36. The current clinical development of several senolytic compounds,

including those used in this study, for anti-aging and cancer-related diseases, supports the strategy of targeting senescence to treat persistent pain in humans. This progress suggests these

senolytic compounds could soon be repurposed for treating chronic pain and headache disorders resulting from nerve tissue injury. Targeting senescent cells through apoptosis induction is a

straightforward method, and there are various agents demonstrating the effectiveness of senolytic agents in vivo, as well as in clinical trials39. Therefore, the use of senolytics represents

a promising approach in managing chronic pain. METHODS The basic analysis of the data was done with the functions from Scanpy49 package (v1.9.3) of Python3 (v3.10.10) and default parameters

according to Scanpy pipeline unless otherwise specified. MICE Cohorts of adult mice of both biological sexes, aged from 8 to 12 weeks were used for sequencing in all the different published

studies5,9,10,11,12,13. Our sequencing data on TG and DRG were obtained from mice aged between 8 and 12 weeks using the Smart-seq3Xpress, 10x multiomic technologies, in iPain these mice are

referred to as young. A middle-aged cohort (34–62 weeks) was processed using 10x multiomic sequencing, in iPain these mice are referred to as aged. For all details on biological sex, and

the number of cells per timepoints see tables in Fig. S2 and the CellxGene50 iPain (iPain). For the senescence kinetic and for the drug treatment experiment, cohorts of mice aged between 17

and 27 weeks of both sexes (3 males and 3 females per group) were used. All the mice were on a C57BL/6 background and were kept under a 12-h light–dark cycle, at 24 °C with unlimited food

and water. All animal care and experimental procedures were permitted by the Ethical Committee on Animal Experiments (Stockholm North committee) and conducted according to The Swedish Animal

Agency’s Provisions and Guidelines for Animals Experimentation recommendations. Ethical permit numbers 9702-2018 and 17396-2022. DRUG ADMINISTRATION The senolytic compounds were given

orally, by gavage. The stock solution was diluted as 10% DMSO in corn oil prior to administration. The treatment starts seven days post CCI injury, the senolytic compound or vehicle control

(10% DMSO in corn oil) was administered by oral gavage once daily for either 10 consecutive days (Navitoclax CAS No. CAS. 923564-51-6 or Venetoclax CAS No. 1257044-40-8) or once weekly for 2

weeks PROTAC Bcl-xL degrader (CAS No. 2920415-08-1). FULL BLOOD COUNT Mice were deeply anesthetized with isoflurane. The full blood was taken from the hearts of the mice and kept in a blood

collection tube (containing EDTA) for further testing by IDEXX BioAnalytics. CHRONIC CONSTRICTIVE INJURY MICE MODEL Chronic constriction injury of the sciatic nerve (CCI) has been widely

described with minor differences in rodents51,52,53. Briefly, mice were first anesthetized with isoflurane, and a small incision was made at the mid-thigh level on the right side. Ligatures

(7–0 surgical silk) were tied loosely around the sciatic nerve with approximately 0.5 mm between ligatures. And then, the skin was closed by a 5–0 silk suture. PAIN-RELATED BEHAVIORAL TESTS

Mechanical sensitivity was assessed by the von Frey test in an up-down testing paradigm as previously described54,55. Briefly, mice were placed in glass cylinders on a 6 × 6 mm wire mesh

grid floor and were allowed to acclimate for 20–60 min. The plantar surface of the hind paw was stimulated with a series of calibrated monofilaments (von Frey hairs; Stoelting, IL) ranging

between 0.008 and 2 g and withdrawal or jerking of the paw is recorded as the pain threshold. Mice were tested 6 times on each paw following the testing paradigm. Thermal sensitivity was

assessed by the Hargreaves thermal paw withdrawal test as previously described56,57. Briefly, mice were placed in Plexiglas chambers on top of a glass sheet and were allowed to acclimate for

20–60 min. A thermal stimulator (IITC Inc., Woodland Hills, CA, United States) was precisely aimed at the middle of the plantar surface of the mice through the glass sheet. The laser

intensity was set to 30% and a cutoff latency of 20 s was set to avoid tissue damage. Noxious mechanical sensitivity was assessed by pinprick test as previously described with minor

modification58. Briefly, mice were placed in Plexiglas chambers on top of a glass sheet and were allowed to acclimate for 20–60 min. A 25-gauge needle connected with a 1 g filament (von Frey

hairs; Stoelting, IL) was applied uniformly to the plantar surface of the hind paw without penetrating the skin. A score system was used according to the extent of the response. 0 = no

response; 1 = move, look around to see what happened; 2 = brief quick lift or withdrawal or removal away of hind paw; 3 = brief quick shakes of the hind paw, or jumps; 4 = high frequency of

shaking, licking, flinching, or guarding. Mice were tested 4 times on each paw with a waiting time of 5min58,59. ANXIETY AND BALANCE MOTOR FUNCTION-RELATED BEHAVIOR ASSESSMENTS Anxiety was

assessed by the open field test (OFT) and elevated plus maze (EPM) tests60,61. For OFT, animals were placed in a roofless 45 × 45 cm square arena enclosed with 40 cm walls and allowed to

explore for 15 min while being recorded by a camera secured on the ceiling. Tracking and analysis were done using the EthoVision XT software where the arena was further partitioned into two

zones: center (square area of 22.5 × 22.5 cm in the middle of the arena) and periphery (rest of arena close to walls). The proportion of time spent in the two different zones with respect to

the center-point of each animal was used as an indicator of anxiety. For EPM, animals were placed in a plus (+) shaped arena elevated at 60 cm from the ground and allowed to explore for 5 

min while being recorded by a camera secured on the ceiling. Tracking and analysis were done using the EthoVision XT software where the arena was partitioned according to its design: two

closed arms opposite to each other that are each 6 × 35 cm enclosed by 15 cm walls, two open arms opposite to each other with 6 × 35 cm which are all connected to one another by a small

6x6cm center area. The total distance traveled, and the proportion of time spent in the different types of zones with respect to the center-point of each animal were used as indicators of

anxiety. Motor coordination was assessed by beam walk and rotarod tests62,63,64,65. For the beam walk test, each animal and some bedding material was first placed for 2 min for habituation

inside the goal box, which is 12 × 12 × 12 cm elevated to 64 cm from the ground and enclosed on all sides except for the entry side. A round beam with a diameter of 11 mm (Small) and a 

length of 105 cm were used. The test beam was placed straight with the endpoint in front of the goal box parallel to the ground with start and finish lines defined for a total length of 80 

cm in the middle of the beam. Animals were first habituated on a 20 mm beam until the ability to fully cross the beam without hesitation was demonstrated. A total of three trials were

performed on the 11 mm beam, with at least 1 min rest between trials in the goal box. Each trial was recorded on camera and the time required for the mouse to cross the beam from start to

finish and the usage of the injured leg as a scoring system were used as indicators of motor function. 7 = traverses beam normally with no more than two footslips; 6 = traverses beam

successfully and uses affected limb in more than 50% of the steps; 5 = traverses beam successfully and uses affected limb in less than 50% of the steps; 4 = traverses beam and places

affected limb on horizontal surface at least once; 3 = traverses beam by dragging affected limbs; 2 = the animals cannot traverse but were able to place limbs on horizontal surface and

maintain balance; 1 = the animal cannot traverse and were unable to put affected limb on horizontal surface. For rotarod, the equipment from Ugo Basile S.R.L. model 47,600 was used with a

start speed of 4 rpm/min, a max speed of 40 rpm, and a ramp speed of 1.8 min (20 rpm/min). Mice were placed on the rotating dowel set to minimum speed and allowed to habituate for 5 min on

the first trial. A total of 8 trials were performed for each animal with a rest time of at least 5 min between each trial in the home cage. The time and final rpm of the mouse at falling or

the first occurrence of passive rotation (clinging to the dowel) were used as indicators of motor function. The first 2 trials were seen as habituation trials and the last 5 trials were

taken for analysis. SENESCENCE-ASSOCIATED Β-GALACTOSIDASE STAINING Mice were deeply euthanized with isoflurane and perfused with pre-cooled PBS. L4- L6 DRGs were quickly dissected,

immediately frozen, and then quickly imbedded in optimal cutting temperature embedding medium (OCT). The SA-β-galactosidase activity was detected by using a SA-β-galactosidase staining kit

(Cell Signaling Technology, #9860) at PH 6.0 according to the manufacturer’s instructions with minor modification. Briefly, DRG sections were rinsed twice by PBS before fixation, followed by

x-gal (PH 6.0) staining. 4 days after incubation (at 37 °C), the staining solution was removed, rinsed three times with PBS, sealed, and then immediately taken imaging. IMMUNOSTAINING The

snap-frozen DRG tissues were collected as described above (in “Methods” section, SA-β-galactosidase staining section). The snap-frozen DRG tissue was either freshly sectioned and then fixed

in PFA overnight at 4 °C, or reused from SA-β-galactosidase staining (double staining). DRG sections were washed with PBS, and permeabilized with 0.3% PBST for 10 min, followed by incubation

in blocking buffer (PBS containing 0.25% Triton X-100 and 1% bovine serum albumin) for 30 min at room temperature (RT). And then, DRG sections were incubated with primary antibody (diluted

in blocking buffer) at 4 °C overnight. After 3 washes with blocking buffer, a secondary antibody (1:1000, diluted in blocking buffer) was applied for 1 h at RT. Cells were then incubated in

DAPI solution (D212, Wako) for another 10 min, followed by 3 washes with PBS. Images were obtained with a fluorescence microscope or confocal microscope. The following primary antibodies

were used: anti-p21 antibody (1:100, gift from Doctor Sylvain Peuget), anti-peripherin (1:200; ab39374, Abcam), anti-NeuN (1:200; MAB377, Merck Millipore). IMAGING Brightfield images were

acquired using the Zeiss Axio Imager.Z2. Images were analyzed with ImageJ (v1.52 h) by (1) manual segmentation of ganglion while excluding large fibers; (2) general isolation of the blue

stain with the Color Deconvolution plugin66,67 with the built-in Brilliant Blue vector; (3) thresholding by pixel intensity from 5 to 185 with Li’s method and (4) positive stain

identification by the Analyze Particles function (size inclusion from 400-Infinity). Identified positive regions were refined manually before measurement of the area which were summed and

normalized against the total area of the segmented DRG to determine the percentage positive area for each DRG. Statistical significance was calculated with the Mann–Whitney U test. DRG

DISSOCIATION AND SINGLE-NUCLEI ISOLATION51,52,53 Mice were deeply euthanized by isoflurane and perfused with 4% pre-cooled PBS. L4-L6 DRGs were collected at 14 days after CCI injury in aged

mice or from young mice (without injury), and then immediately put on dry ice. Frozen tissues are stored at −80 °C before nuclei isolation. Isolation of nuclei from frozen tissue was

performed as described by the Allen Institute for Brain Science68,69. All steps were performed in 4 °C. Briefly, tissues were homogenized in 2 ml chilled homogenization buffer (10 mM Tris pH

8, 250 mM Sucrose, 25 mM KCl, 5 mM MgCl2, 0.1 mM DTT, 1× Protease inhibitor cocktail, 0.2 U/μl RNasin Plus, 0.1% Triton X-100), and filtered through 70 µm and 30 µm cell strainers. Nuclei

pellets were collected by 10 min, 900 × _g_ suspension, and then resuspended in 250 μl chilled homogenization buffer. Finally, debris was removed by Iodixanol 25–29%, and the nuclei pellet

was resuspended. For single-nuclei Multiomic sequencing, the nuclei pellet was resuspended in 30 µL 1× chilled nuclei buffer (10x Genomics, 2000207) and then proceeded immediately for

single-nuclei Multiomic sequencing protocol. For neuron-specific- single-nuclei RNA sequencing, the nuclei pellet was resuspended in 500 ul blocking buffer (1× PBS, 1% BSA, 0.2 U/μl RNase

inhibitor) for fluorescent-activated nuclei sorting and Smart-seq3xpress. FLUORESCENT-ACTIVATED NUCLEI SORTING AND SINGLE-NUCLEI RNA SEQUENCING (SMART-SEQ3XPRESS AND 10X MULTIOMIC

SEQUENCING) To enable sorting of nuclei derived from neurons, nuclei suspension was incubated with anti-NeuN PE-conjugated antibody (1:500, FCMAB317PE, Merck) for 30 min on ice. Next, nuclei

pellet was collected by 5 min, 400 × _g_ suspension, and resuspended in PBS containing 0.1 μg/mL DAPI (D3571, Invitrogen) and 0.2 U/ul RNase inhibitor. Single NeuN+ and DAPI+ nuclei were

sorted into each well of a 384-well plate containing 0.3 µL lysis reaction mix by a flow cytometer (DB FACSAria Fusion or BD FACSAria III) at 4 °C. Gating was performed based on DAPI and

phycoerythrin signal of NeuN. After sorting, each plate was immediately spun down and stored at −80 °C before single-nuclei RNA sequencing with Smart-seq3xpress. The smart-seq3xpress

protocol was performed by Rickard Sandberg’s Group, Karolinska Institute. Nuclei isolated for single nuclei Multiomic sequencing were stained with DAPI and sent for sequencing by Eukaryotic

Single Cell Genomics Facility at SciLifeLab, Stockholm. OBTAINING AND PREPROCESSING THE PUBLICLY AVAILABLE SCRNA-SEQ DATA The count matrices and associated metadata of DRG were obtained from

the Gene Expression Omnibus (GEO) with the following accession numbers presented on Table 1. Additionally, the data were preprocessed according to the original papers before being loaded

into Python3 as AnnData objects. PREPROCESSING THE LAB-GENERATED DATA DRG 10X MULTIOMICS The samples were sequenced on NovaSeq6000 (NovaSeq Control Software 1.7.5/RTA v3.4.4) with a

50nt(Read1)−8nt(Index1)−24nt(Index2)−49nt(Read2) (ATAC part) and 28nt(Read1)−10nt(Index1)−10nt(Index2)−90nt(Read2) (GEX part) setup using ‘NovaSeqStandard’ workflow in ‘S2’ mode flowcell.

The Bcl to FastQ conversion was performed using bcl2fastq_v2.20.0.422 from the CASAVA software suite. The quality scale used is Sanger/phred33/Illumina 1.8+. The sequenced reads of each

sample were aligned using Cell Ranger Arc pipeline version 2.0.2 to the mouse genome (mm10). After the alignment, the chromatin peaks from each sample were aggregated together to obtain a

common peak set with “aggr” function from Cell Ranger Arc. The count matrices were loaded into Python3 as AnnData objects. The quality control was done by removing features that were

detected in less than 3 cells, and cells with a the natural log pseudo count (log1p) of the number of genes or peaks less than 4 and more than 9 were removed. Cells with percent

mitochondrial genes of more than 1.5 and a total UMI count of more than 20,000 were also filtered out. SMART-SEQ3 DRG & TG The sequenced reads were aligned using the zUMIs pipeline which

makes use of the STAR-SOLO70 alignment tool, and the reference genome was mouse (mm39). After alignment, the count matrices were loaded into Python3 for analysis. Furthermore, the count

matrices were filtered to only yield high-quality cells. For DRG, genes found in less than 20 cells, and cells with less than 1000 genes were filtered out. For TG, genes detected in less

than 3 cells, and cells with more than 3 percent of mitochondrial genes and less than 2000 detected genes were removed. MAIN UMAP OF DRG The AnnData objects were concatenated to create a

unified AnnData object, and we made use of two models which were based on the scVI71 framework (v0.20.3) for the integration of DRG data. First, we trained the scVI model to integrate the

DRG cells unbiasedly on 3000 highly variable genes. The model architecture was designed to obtain 50 dimensions in the latent space with 5 hidden layers and 128 nodes per hidden layer, where

the distribution of gene likelihood was negative binomial. Then we constructed a scANVI model based on the trained scVI model to obtain the latent space that could clearly separate the

respective cell types for visualization. We used the cell-type nomenclature from Renthal et al. 5 to train the model and perform label transfer. The scANVI model was trained until the model

was converged. After the models were trained, the latent spaces from scVI and scANVI were extracted. The neighbor graph was built from 50 dimensions with 100 neighbors and the method to

calculate the distance was cosine based on the latent space from scANVI. The projection of UMAP of constructed from the neighbor graph with parameter min_dist = 0.5 before plotting. FEATURE

IMPUTATION Next, we train the MultiVI model to imputed for the missing features across data modalities by using our lab-generated multiomics data as an anchor. We aimed to impute as many

features as possible. The MultiVI was constructed by using the default parameters and was trained until it converged. To train the model, we employed NVIDIA A100 GPU to be able to hold the

count matrix on the VRAM, and this resource was provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS). After the training, the imputation of the gene

expression modality was done by calling get_normalized_expression method from the model, while the chromatin accessibility modality was imputed using get_accessibility_estimates method with

custom parameters of threshold = 0.1, normalize_cells = True, and normalize_regions = True. The imputed matrices were then multiplied by 10,000. ANALYSIS OF THE NOCICEPTIVE LINEAGE CELLS The

nociceptive lineage cells were selected and subset to yield high-resolution clusters. The neighbor graph and UMAP projection were calculated using the same parameters as mentioned above.

Then we clustered the nociceptive lineage cells with the Leiden clustering algorithm at 0.1 resolution. To visualize the distribution of cells along the time kinetic after injury, the

embedding_density method from the Scanpy package was utilized to compute the cell density on the UMAP projection for cells at different timepoints. Next, we ran the pySCENIC72 pipeline to

calculate the regulon activity in the nociceptive lineage. Yet, not all TFs had the information for regulons and thus we fit the univariate linear model (ULM) from the decoupleR73 package by

using the RNA expression of each cell as an independent variable and the interaction weight of each TF from the adjacency table from pySCENIC as a target. The t-value of the slope of the

fitted model represented the TF activity. CELL STATE POTENCY The relative cell state potency of the nociceptive lineage cells was calculated in R using the CytoTrace package. Before running

CytoTrace, we reconstructed the raw count matrix of the gene expression data from the imputed normalized count with the PyTorch distribution module for the negative binomial distribution.

After the reconstruction, the AnnData object was converted into a SingleCellExperiment (SCE) object using rpy2 and anndata2ri Python packages. Once converted, we ran CytoTrace on the

reconstructed raw count matrix with data sources passed to the batch parameter. The CytoTrace was normalized to have the range from 0 to 1. Then we calculated the CytoTrace pseudotime by

taking the difference between 1 and the normalized CytoTrace score. We performed an independent _t_-test with 7000 random permutations on the CytoTrace scores between the naïve and injury

states with the SciPy package to confirm that the score in the naïve state was significantly higher than that of the injured state. TEMPORAL ANALYSIS OF NOCICEPTIVE SUBTYPES We employed

CellRank74 (v2.0.0) to perform the temporal analysis of each nociceptive subtype between control and pain states based on RNA modality. For each subtype, we made use of the CytroTrace kernel

from CellRank to compute the transition matrix. Then we passed the transition matrix CytroTrace kernel to Generalized Perron Cluster-Cluster Analysis (GPCCA) for the identification of

terminal states and fate probability toward the terminal states with n_states=3. With GPCCA, we identified the lineage driver genes that drove the transition of cells toward the terminal

state of pain. A generalized additive model was utilized to fit the expression profile for each gene as a function of pseudotime to visualize the expression trend of the lineage driver

genes. Furthermore, we also applied the same analytical approach on the regulons level calculated from SCENIC. CELL-CELL COMMUNICATION The inference of cell-cell communication (CCC) was done

with the LIANA+75 package (v0.1.8). Here, we passed the count matrix of the atlas as input to the rank_aggregate.by_samplen function of LIANA and we selected “mouseconsensus” as the

resource for CCC inference. Due to multi-conditions and multi-timepoints information held within the atlas, the communications were very complex. Thus, tensor decomposition was employed to

extract meaningful communications as factors. This was accomplished by calling run_tensor_cell2cell_pipeline function provided by the cell2cell28 package. IDENTIFICATION OF SENESCENCE The

SenMayo gene score was calculated based on the scaled data of the imputed gene expression count matrix by using the score_genes function from the Scanpy package. The SenMayo gene set can be

found in Supplementary Data 7. Additionally, we selected cells within the top 10 percentile and assigned them as SASP. To study the relative age of SASP cells, we performed gene set

enrichment analysis with the run_gsea function from the decoupleR73 package to extract the enrichment scores of GenAge and CellAge gene sets. This was done on the normalized unscaled imputed

gene expression count matrix. Mann–Whitney U test was performed to test for the statistical significance of the SenMayo score between the naïve and injury groups. ANALYSIS OF HUMAN DRG

RNA-SEQ WITHIN For the analysis of human DRG RNA-seq, we first obtained the bulk RNA-seq data of different pain conditions of neuron-rich samples from Ray et al.38. As the count matrix was

normalized using the TPM method, we multiplied 1e6 to the matrix to unnormalize it back to the raw transcript count values. Then we normalized the raw count to obtain the total transcript

count per sample after normalization equal to the median of the total transcript count for that sample before normalization. The normalized count matrix was log-transformed and scaled with

log1p and scale functions from Scanpy. SenMayo score was calculated with the score_gene function on the scaled count matrix. Regarding the deconvolution of hDRG, we retrieved the snRNA-seq

of hDRG under normal conditions from Jung et al.39 with the accession number GSE201654 from GEO. The data was preprocessed and analyzed as described in the original paper. The deconvolution

was done using a VAE-based model called Bulk2Single provided by the omicverse76 package in Python3. The bulk RNA-seq data of hDRG was separated into two count matrices, one from donors

without known pain conditions and the other from donors with pain. As the data was split in two, we trained two models separately for 3500 epochs each. After the training, the predicted

snRNA-seq count matrices were reconstructed and merged for further analysis. There were more than 250,000 cells generated by the models in total. We processed and clustered the matrix

according to Scanpy workflow with the Leiden clustering algorithm, and we noticed that there was a lot of noise within the generated data as there were many clusters comprised of 50–1000

cells. Thus, Leiden clusters with less than 900 cells were removed to retain cells with clean signals. Furthermore, we selected only nociceptive cells to calculate the SenMayo score after

scaling the count matrix. Regarding the analysis of hDRG, we noted that the cell proportions between the reference snRNA-seq and the deconvolution of bulk RNA-seq were not equal. This result

was expected as we performed the deconvolution on the samples from the group being classified by the authors as neuron-rich samples36 (51 out of 70 samples), to focus our analysis on

nociceptive lineage cells. The deconvolution yielded ~60% neurons whereas the reference snRNA-seq contained a more representative population of the cell types in DRG with around ~2% neurons.

We thus applied a broad classification on the hDRG in contrast to the single-cell analysis in mice model, as we suspected that the deconvolution model could not perform well in classifying

the high-resolution cell types (neuronal subtypes) that were closely related to each other due to it being trained on the reference sample with lower neuron representation. Nevertheless,

even with the broad classification the model seems to have some difficulties in clearly distinguishing between the two neuronal cell types of NFs and nociceptors that are more highly

correlated than other cell types (Fig. 4h). Despite this, the Spearman correlation still indicated the highest correlation between the transcriptome profiles of the nociceptors identified in

the reference snRNA-seq and the deconvolved RNA-seq nociceptors, indicating the applicability of the model. We then proceeded to perform a SenMayo score comparison between the NoPain and

Pain groups to find that it is increased in the Pain group indicating for cellular senescence, aligning with our other results. ANALYSIS OF HUMAN DRG RNA-SEQ WITH DIABETIC NEUROPATHY The

count matrix was obtained from Hall et al.40. The count matrix was normalized into transcript per million and pseudo-log transformed. The effect of different sexes was regressed out with

Scanpy’s regress_out function. Samples with their ages being outliers between the healthy and diabetic groups were removed; these were samples with ages below 30 and ages above 60. The

SenMayo score was calculated after the matrix was being scaled. The Z-score of SenMayo was computed before performing a one-sided _t_-test to statistically compare the scores between healthy

and diabetic groups. UMAP OF TG For the TG, we generate an integrated atlas separately for TG using scGLUE20 (v0.3.2), by first computing the latent space representation of each source and

modality and passing them as input. For the latent space representation of RNA, we updated the weights of the scANVI model trained on the DRG data to obtain the updated latent space of the

RNA gene expression modality for the TG datasets. LSA was performed on the ATAC modality to obtain the latent semantic indexing from the TG datasets. Furthermore, scGLUE required an

additional input besides the latent space representations which was the guidance graph. The guidance graph was constructed using the highly variable features from RNA and ATAC as described

in the original paper20. After the inputs were prepared, the model was trained with the default parameters. Once the model was trained, we extracted cell embedding from the model to build a

neighbor graph using “cosine” as a metric. The UMAP was computed from the neighbor graph. To facilitate us with cell-type annotation, we perform label transfer using the scANVI model that

was previously updated with the weights. STATISTICAL ANALYSIS The statistical analysis and tests were done in Python3 with the “stats” module from the SciPy v1.10.1 package. The data were

either imported as DataFrame or Array through Pandas v2.0.3 or Numpy v1.22.4 package respectively. In general, a _t_-test was applied when the data followed a normal distribution. Otherwise,

the Mann–Whitney U test was performed if the data did not appear to follow the normal distribution. For the behavior analysis with paired data paired _t_-test was used for comparison, where

an independent _t_-test was used on unpaired data. REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

DATA AVAILABILITY The sequencing data and count matrices generated from this study have been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE253345. All the

datasets with their GEO accessions used to generate iPain can be found in Table 1. The interactive visualization of the atlases can be accessed through CELLxGENE platform from the Chan

Zuckerberg Initiative (iPain) and UCSC cell browser77. Source data are provided with this paper. CODE AVAILABILITY The script for the core analysis of this work can be found at

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  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS The authors are grateful to Professor Francois Lallemend and team members for discussion and proofreading the

manuscript. The authors are grateful to Professor Patrik Ernfors and team members, especially Doctors Jie Su and Mingdong Zhang for training in performing pain models and pain behavior

assessments. The authors are grateful to Doctor Marek Bartosovic for the discussion on epigenomic analysis. The authors thank Doctor Olga Kharchenko for drawings, Doctor Sylvain Peuget for

the gift of the p21 antibody. The authors would like to acknowledge the KI innovation, Bic facility, the BFC facility, the Animal Behavior Core Facility at Karolinska Institutet, the

Eukaryotic Single Cell Genomics (ESCG) Facility, and the National Genomics Infrastructure (NGI) at the Science for Life Laboratory, Sweden, for the sc/snRNA-seq. The authors acknowledge

support from the National Genomics Infrastructure in Stockholm funded by Science for Life Laboratory, the Knut and Alice Wallenberg Foundation and the Swedish Research Council, and

SNIC/Uppsala Multidisciplinary Center for Advanced Computational Science for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure. The

computations and data handling were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) partially funded by the Swedish Research

Council through grant agreement no. 2022-06725. S.H., P.T., and K.Z. are supported by the KID funding at Karolinska Institutet, S.H. and X.F. are supported by the Wenner Gren Foundation.

S.H. was further supported by StratNeuro, Tysta Skolan, the Swedish Research Council, and Hjärnfonden. FUNDING Open access funding provided by Karolinska Institute. AUTHOR INFORMATION Author

notes * These authors contributed equally: Prach Techameena, Xiaona Feng. AUTHORS AND AFFILIATIONS * Laboratory of Neurobiology of Pain & Therapeutics, Department of Neuroscience,

Karolinska Institutet, Stockholm, Sweden Prach Techameena, Xiaona Feng, Kaiwen Zhang & Saida Hadjab Authors * Prach Techameena View author publications You can also search for this

author inPubMed Google Scholar * Xiaona Feng View author publications You can also search for this author inPubMed Google Scholar * Kaiwen Zhang View author publications You can also search

for this author inPubMed Google Scholar * Saida Hadjab View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Study design and supervision: S.H.

Tissue collection and acquisition of data: X.F., K.Z., P.T., and S.H. Animal behavior: X.F. and K.Z. Analysis of data: P.T., X.F., K.Z, and S.H. Computational analysis and integrated

atlases: P.T. under supervision of S.H. Figures: P.T. and S.H. with input from all co-authors. Drafting of manuscript: P.T., K.Z., and S.H. with input from X.F. CORRESPONDING AUTHOR

Correspondence to Saida Hadjab. ETHICS DECLARATIONS COMPETING INTERESTS S.H. is an inventor on pending patents describing senolytic compounds for the treatment of a headache disorder and/or

chronic pain (application numbers # 2351493-8 and # 2450321-1). The other authors do not have competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Communications_ thanks Yi Zhang

and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains

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permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Techameena, P., Feng, X., Zhang, K. _et al._ The single-cell transcriptomic atlas iPain identifies senescence of nociceptors as a

therapeutical target for chronic pain treatment. _Nat Commun_ 15, 8585 (2024). https://doi.org/10.1038/s41467-024-52052-8 Download citation * Received: 07 May 2024 * Accepted: 21 August 2024

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