Single-cell transcriptome identified ddx43+ cell types critical for maintenance of transient slow-cycling stem cells in planaria

Single-cell transcriptome identified ddx43+ cell types critical for maintenance of transient slow-cycling stem cells in planaria | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Single-cell transcriptome identified ddx43 + cell types critical for maintenance of transient slow-cycling stem cells in planaria View ORCID Profile Nikhil Kumar Jaligam , Mohamed Mohamed Haroon , Mainak Basu , Atriya Mazumdar , Swathi Pavithran , Vinay Kumar Dubey , Praveen Kumar Vemula , Ankit Arora , View ORCID Profile Dasaradhi Palakodeti doi: https://doi.org/10.1101/2025.08.22.671706 Nikhil Kumar Jaligam 1 Institute for Stem Cell Science and Regenerative Medicine , Bangalore, Karnataka, India 2 Regional Centre for Biotechnology , Faridabad, Haryana, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nikhil Kumar Jaligam Mohamed Mohamed Haroon 1 Institute for Stem Cell Science and Regenerative Medicine , Bangalore, Karnataka, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mainak Basu 1 Institute for Stem Cell Science and Regenerative Medicine , Bangalore, Karnataka, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Atriya Mazumdar 1 Institute for Stem Cell Science and Regenerative Medicine , Bangalore, Karnataka, India 2 Regional Centre for Biotechnology , Faridabad, Haryana, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Swathi Pavithran 3 Cincinnati children’s hospital medical centre (CCHMC) , Cincinnati, OH 45229, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vinay Kumar Dubey 4 Duke University School of Medicine , Durham, North Carolina 27710, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Praveen Kumar Vemula 1 Institute for Stem Cell Science and Regenerative Medicine , Bangalore, Karnataka, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ankit Arora 1 Institute for Stem Cell Science and Regenerative Medicine , Bangalore, Karnataka, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: dasaradhip{at}instem.res.in aankit{at}instem.res.in Dasaradhi Palakodeti 1 Institute for Stem Cell Science and Regenerative Medicine , Bangalore, Karnataka, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dasaradhi Palakodeti For correspondence: dasaradhip{at}instem.res.in aankit{at}instem.res.in Abstract Full Text Info/History Metrics Preview PDF Summary Cell cycle dynamics are fundamental to stem cell maintenance and differentiation. While the G1 phase is known to influence stem cell fate decisions, the mechanisms regulating its duration remain poorly understood. Using Schmidtea mediterranea , a highly tractable model for studying regeneration, we uncovered how the interplay of systemic and local cues from specialized cell types regulates G1 phase progression, thereby priming the cells in a slow-cycling state poised to differentiate. Single-cell transcriptomics identified G1-enriched, non-committed neoblasts that give rise to a previously uncharacterized ddx43 ⁺ cell type, localized near sub-epidermal muscle and closely associated with surrounding neoblasts. Functional analyses revealed that ddx43⁺ cells act as gatekeepers, maintaining adjacent neoblasts in an extended G1 phase and a differentiation-primed state. ddx43 knockdown led to neoblast hyperproliferation, highlighting their niche-like regulatory role. Following injury, extracellular signal-regulated kinase (Erk) signalling that propagates to distal regions via subepidermal muscle cells is critical for suppression of ddx43⁺ cells by downregulating Notch signalling. This establishes a critical cross-talk between sub-epidermal muscle and ddx43⁺ cells, mediated by the Erk–Notch axis, essential for maintaining G1 phase extension. In summary, we reveal a ddx43 -dependent, non–cell autonomous mechanism that regulates a transient, slow-cycling neoblast population, offering new insights into how extrinsic cues orchestrate stem cell cycling during tissue homeostasis and regeneration. Introduction Planarians are known for their remarkable ability to regenerate damaged or lost tissues, a capability driven by a unique population of adult pluripotent stem cells known as neoblasts ( Baguñà et al., 1989 ; Reddien, 2018 ; Wagner et al., 2012 ). The maintenance and differentiation of specific lineages are regulated both spatially and temporally by intrinsic and extrinsic cues( Miyares & Lee, 2019 ; Palani & Sarkar, 2009 ; Paratore et al., 2002 ). While a significant body of work focused on understanding intrinsic factors( Petzold & Gentleman, 2021 ; Steens & Klein, 2022 ) the roles of extrinsic factors, comprising surrounding tissues and their cell types, extracellular components, and systemic cues, remain poorly understood. Previously published work has identified muscle and extracellular matrix (ECM) as key components that provide extrinsic cues critical for the maintenance and differentiation of neoblasts to specific lineages ( Chan et al., 2021 ; Dubey et al., 2022 ; Gattazzo et al., 2014 ). A salient feature of planarian neoblasts is their heterogeneity, primarily due to the commitment of distinct subpopulations of neoblasts to specific lineages( Adler & Sánchez Alvarado, 2015 ; Molina & Cebrià, 2021 ). Additionally, this heterogeneity is influenced by the variation in the duration of the cell cycle phases among the neoblasts. A study by Molinaro et al (2021) identified a subset of slow-cycling neoblasts characterised by their ability to retain BrdU over extended periods ( Molinaro et al., 2021 ; Wagner et al., 2011 ). However, the mechanisms that maintain these slow-cycling cells in a G1/G0 phase remain unclear. In this study, we identify a mechanism that maintains a small fraction of smedwi-1 + cells in an extended G1 phase. A state maintained by their physical proximity to ddx43 + cells, a population of differentiated cells associated with subepidermal muscles. Our previous work, based on the mitochondrial content and membrane potential, identified a distinct population of neoblasts termed X2 MTG low HFSC cells residing in the G1 phase. These cells exhibit enhanced pluripotency in irradiated planarian as demonstrated through rescue experiments via transplantation ( Hayashi et al., 2006 ; Mohamed Haroon et al., 2021 ). Herein, we performed single-cell RNA sequencing (scRNA-seq) of X2 MTG populations followed by extensive analysis which identified a population of differentiated cells marked by the expression of ddx43, derived from the smedwi-1 + and ddx43 + neoblast. Our study identified a small fraction of neoblasts closely associated with ddx43 + cells located near the musculature. BrdU labelling experiments indicate these neoblasts reside in the extended G1 phase and are resistant to sublethal doses of irradiation. We termed this population as “transient slow cycling neoblast”. Knockdown of ddx4 3 showed increased proliferation of these neoblasts and rendered them sensitive to sublethal doses of radiation. Together, our results suggest that this small pool of transient slow-cycling neoblasts serves as a reserve population that can re-enter the cell cycle in response to physiological stress, such as radiation-induced depletion of neoblasts. Upon injury, the wound signal propagates to the distal tissues via longitudinal muscles through Erk signalling ( Fan et al., 2023 ). This signalling is essential for the distal proliferation of neoblasts critical for regeneration. We found Erk signalling is required to downregulate ddx43 + cells, thereby allowing the neoblast to re-enter the cell cycle. Moreover, our results also suggest that the Notch signalling downregulation is also essential for reducing the ddx43 + cell numbers, further facilitating neoblast proliferation. Overall, our study highlights an essential Erk-Notch axis governs the cross-talk between muscle and ddx43 + cells and is crucial for the maintenance of slow cycling neoblasts. Results Single cell transcriptome analysis identified specific cluster within X2 MTG low HFSC enriched for neoblast markers Cell populations in Schmidtea mediterranea (planaria) are broadly categorized into 3 classes-X1, X2 and Xins-based on their nuclear-to-cytoplasmic ratios and radiation sensitivity. The X1 population consist of actively dividing neoblasts in the S/G2/M phase of the cell cycle. The X2 population comprises neoblasts in the G1 phase and early progenitor cells ( Hayashi et al., 2006 ). In our previous work, we employed MitoTracker TM -based staining to identify four subpopulations within X2 compartment based on their mitochondrial content/potential and cell size. These are classified as X2 MTG low HFSC, X2 MTG low LFSC, X2 MTG high HFSC and X2 MTG high LFSC. Through transplantation experiments into lethally irradiated planarians, we demonstrated X2 MTG low HFSC represent neoblast population with enhanced pluripotency compared to other X2-derived cell types ( Mohamed Haroon et al., 2021 ). To uncover the molecular markers enriched in X2 MTG low HFSC, we performed scRNA-seq on all four X2 subpopulations ( Figure 1A ) . From this analysis, we obtained 19,015 high-quality cells, which were visualized as 16 distinct clusters using Uniform Manifold Approximation and Projection (UMAP) ( Figure 1B ) with an average of 4,500 cells per sample ( Figure 1C ) . Download figure Open in new tab Figure 1: scRNA-seq illustration and expression of known markers in X2 MTG population (A) Schematic overview of X2 MTG population from planaria (B) Uniform manifold approximation and projection (UMAP) plot represents cell clusters (n=19,015 single cells) identified from integrative analysis. (C) UMAP plot depicting cell clusters with respect to each sample namely X2 MTG low HFSC, X2 MTG low LFSC, X2 MTG high HFSC and X2 MTG high LFSC. (D) Dot plot expression representation of neoblast markers and post mitotic markers across each sample. Colors constitute average log2 expression levels scaled to unique molecular identifier (UMI) values for each cell. In it, red defines the higher expression and sky-blue defines the lower expression. Dot size represents proportionality to percentage of cells expressing particular gene. (E) Dot plot demonstrating expression of neoblast markers and post mitotic markers obtained across each clusters. Color code identify expression levels, while size of dot represents percent of cells. We next analyzed the expression of canonical neoblast and progenitor markers across four cell populations and within individual clusters. Neoblast markers such as smedwi-1 , bruli , vasa-1 , mcm-5 & rad51 were markedly enriched in the X2 MTG low HFSC population. In contrast, pan-differentiation markers such as xbp1 , p4hb ( Raz et al., 2021 ) were predominantly enriched in the X2 MTG high HFSC & LFSC population. Dot plot analysis further confirmed that a majority of the cells in X2 MTG low HFSC population exhibited high expression of neoblast markers ( Figure 1D ) . Cluster-wise analysis identified Cluster-7 as a key subset exhibiting strong expression of neoblast markers (s medwi-1 , bruli , vasa-1 , mcm-5 & rad51 ), while lacking expression of post-mitotic or lineage committed markers. In contrast, xbp1 , p4hb and other post-mitotic progenitor markers are enriched in the clusters 0 and 12 ( Figure 1E ) ( Figure S1A ) . As visualized using UMAP in addition to cluster 7, smedwi-1 expression was also detected in 1,2,& 10 ( Figure 1E ) ( Figure S1A ). Further, we examined the expression of known X1/X2-enriched genes across four cell populations and clusters. Markers such as H2A, Lamin-B2, unidentified-6, DNA-helicase, vasa-2, zfp-1, Fhl-1, ap-2, khd-1 & Imo-1 were specifically enriched in X2 MTG low HFSC population and in cluster 7 ( Figure S1B & C) . These cells lacked expression of committed progenitor marker such as pou2/3, six1/2-2, soxP-5, nkx2.2, neuroD-1, sp6-9, runt-1, myoD and several others ( Figure S1B & C) . In summary, our single-cell transcriptomic analysis identifies cluster 7 as a unique sub-population within X2 MTG low HFSC enriched for neoblast markers and largely devoid of lineage committed gene expression. This supports the notion that cluster 7 represents a pluripotent neoblast subpopulation with high regenerative potential. Gene Regulatory Network analysis (GRN) identified translation regulators and ribosome biogenesis as top candidates enriched in neoblast clusters Single cell transcriptomic analysis identifies smedwi-1 expression in sub-populations corresponding to clusters 1,2, 7 and 10, with highest expression levels (log2FC > 1 & adjusted p-value < 0.05) observed in clusters 1 and 7 ( Figure 2A ) . To gain insights into the molecular signature of neoblast clusters 1 and 7, we performed Gene Regulatory Network (GRN) analysis. By applying high-dimensional Weighted Gene Co-expression Network Analysis (WGCNA) ( Morabito et al., 2023 ), we identified six modules of co-expressed genes across 4,500 cells that exhibited the highest smedwi-1 expression ( Figure 2B ) . Download figure Open in new tab Figure 2: Gene Regulatory Network (GRN) analysis across neoblast clusters (A) Volcano plot illustrating the smedwi-1 expression across obtained cell clusters. In it star signify the significant expression of smedwi-1 compare to different clusters (B) Weighted gene co-expression network (WGCNA) dendrogram contemplate hierarchical clustering of genes based on their expression pattern. Each leaf in dendrogram represents each gene, while the colour specified at the bottom signifies the co-expression modules. (C) UMAP plot identifying the expression pattern of different modules. Color coding explain the pattern of each module as blue for module-1,cyan for module-2, maroon for module-3,red for module-4, green for module-5 and yellow for module-6. (D) Dot-plot demonstrating average expression pattern of each module specific hub genes across each sample. In similar fashion color identify the expression levels and dot size represents the percent of cells. (E) Dot-plot representing average expression pattern of each module specific hub genes across each cluster obtained from total cells. To visualize the distribution of these 4500 cells, we generated UMAP plots in which each module is colour coded as blue (module-1), cyan (module-2), maroon (module-3), red (module-4), green (module-5) and yellow (module-6) ( Figure 2C ) . Examining the expression pattern of genes from these six modules across the four subpopulations of X2 MTG and 16 clusters revealed that module-1 genes were most enriched in X2 MTG low HFSC and cluster 7 ( Figure 2D & 2E ) . In addition, gene enrichment analysis (GO annotation) of module-1 genes identifies translation regulators as top enriched GO terms ( Figure S2A ) . This indicates that the translation regulatory mechanisms play a pivotal role in maintaining neoblast identity and function. Whereas, GO annotation from modules 2, 3, 4, 5 and 6 genes identified cilium organization ( Figure S2B ) , synapse organization ( Figure S2C ) , mRNA splicing ( Figure S2D ) , transport of ER to golgi vesicle ( Figure S2E ) and mRNA processing ( Figure S2F ) respectively. Together, our findings suggest that the molecular signatures represented in module-1 might be essential for neoblast maintenance. Differential expression analysis identify genes specific to pluripotent neoblast in the G1 phase of the cell cycle In the previous section, we identified molecular signatures specific to neoblast clusters and found many of these are highly enriched in the X2 MTG low HFSC subpopulation. This provides a mechanistic explanation for why the X2 subpopulation with low mitochondrial content is highly pluripotent. To further characterize this population, we performed differential gene expression analysis to identify genes specifically enriched in the X2 MTG low HFSC compared to other subpopulations. Differential expression analysis identified 209 genes that were significantly enriched (Adjusted p-value less than 0.05 and log2FC greater than 1) in the X2 MTG low HFSC population (Table S1) . Gene Ontology (GO) enrichment analysis of 209 genes revealed top enriced GO terms with molecular functions such as ribosome biogenesis, piRNA processing, Tricarboxylic Acid (TCA), metabolic process and stress granule assembly ( Figure 3A ) . We further investigated the enrichment of obtained 209 genes in 16 clusters derived from single-cell transcriptomic analysis of the X2 MTG population. Most of these genes showed enrichment in cluster 7. It also suggests that enriched genes are crucial for maintaining neoblast function ( Figure S3.1A , Figure S3.1B , Figure S3.1C , Figure S3.3A and Figure S3.3B ) . Download figure Open in new tab Figure 3: Differential expression and sub-clustering of neoblast enriched clusters (A) Bar plot identifying the top 10 gene ontology (GO) terms obtained using differentially expressed genes (B) Dot plot depicting the expression of differentially expressed markers and Transcription factors across X2 MTG population (C) Dot plot demonstrating the expression of differentially expressed markers and Transcription factors across X1 population (D) UMAP plot describing the sub-clustering of cells from smedwi-1 enriched clusters in form of new trajectory A subpopulation of neoblasts actively undergoing division is represented by the X1 population, characterized by high nuclear content and sparse cytoplasm, corresponding to cells in S/G2/M phase of the cell cycle. Single-cell transcriptomic analysis of X1 population identified distinct neoblast clusters, including both pluripotent and lineage committed cells ( Zeng et al., 2018 ). Further, Raz et al. 2021 have shown that neoblasts attain differentiation marks as they progress through the S phase and undergo asymmetric division to generate progenitor cells and non-committed neoblasts in the G1 phase. To demonstrate that X2 MTG low HFSC are non-committed G1 enriched neoblasts, we first compared the X1 enriched markers to the single cell transcriptome data of X2 MTG low HFSC population and found that few markers, zfp -1, sycp -1, rhombotin -1 seem to be enriched in the neoblast cluster of X2 MTG low HFSC ( Figure S3.5B ) . Conversely, to identify genes that are specifically enriched in X2 MTG low HFSC but not in X1, we examined the expression of the X2 MTG low HFSC enriched genes (209 genes) across X1 neoblast clusters ( Figure S3.2A , Figure S3.2B , Figure S3.2C , Figure S3.4A and Figure S3.4B ) . Among the 209 genes, 11 genes were found to be highly expressed in X2 MTG low HFSC ( Figure 3B ) and either sparsely or not expressed in X1 neoblast clusters ( Figure 3C ) . This include pyr1, trap1, hsp7c, pesc, rl37a, h1b, ddx43, e2f5, cebpz, and two with unidentified function (unidentified-1, unidentified-2). Two of the genes cepbz and e2f5 are transcription factors involved in the regulation of the cell cycle and cellular differentiation. The remaining genes play a role in pyrimidine biosynthesis ( pyr1 ), piRNA biogenesis and translation regulation (ATP-dependent RNA helicase ( ddx43 )), ribosome assembly and biogenesis ( rl37a ), histone packaging ( h1b ) and stress response/protein folding ( hsp7c ). However, the function of most of these genes involved in cell cycle regulation and stem cell function remains largely unknown. Although all 11 genes were enriched in cluster 7– pyr1, h1b, unidentified-1, unidentified-2, cepbz and e2f5 – were exclusive to cluster 7, while others ( trap1, hsp7c, pesc, rl37a, ddx43) were also expressed to lesser extent in the clusters marked by post mitotic markers ( Figure 3B ) . Furthermore, sub-clustering based on smedwi-1 expression identified eight clusters with ddx43, trap1, pyr1,rl37a and e2f5 enriched in clusters 0,2,3 & 6 ( Figure 3D ) ( Figure S3.5A ) . This confirms that the 11 genes are part of the G1 enriched neoblast. We next performed pseudotime analysis to investigate the dynamic trajectories of these cell clusters. From our analysis, we identified cluster 7 as a prominent cluster expressing neoblast markers, while lacking expression of pan-differentiation markers ( xbp1, p4hb ) ( Raz et al., 2021 ). Using ‘Monocle’ ( Qiu et al., 2017 ), we set cluster 7 as the root to define lineage trajectories. The directionality of pseudotime from high to lower pseudo-time was validated using smedwi-1, which showed decreasing expression along the axis. We then used additional markers including xbp1, p4hb , prog-1 and vasa-1 to track their expression pattern along the pseudotime. Neoblast markers followed the trend of higher to lower pseudo time, whereas the differentiation markers followed the opposite trajectory ( Figure 4A ) . We obtained the list of Fate Specific Transcription Factors (FSTF) from H. O. King et al., (2024) & Raz et al., (2021) and checked the expression along the pseudo-temporal pattern. The epidermal markers such as p53 and soxP-3 followed the expression pattern of lower to higher pseudotime, whereas the markers for muscle such as myoD followed the opposite pattern from higher to lower pseudotime ( Figure 4B ) . Download figure Open in new tab Figure 4: Neoblast and Post mitotic markers modulates the pseudotime trajectory inference (A) Heatmap showing the dynamics of markers changing gradually over smedwi-1 higher expression to lower expression. Genes (row) and cells (column) are arranged according to the pseudotime progression. Genes mentioned in first set are represented for the post mitotic markers and second set of genes are represented for neoblast markers. (B) Heatmap exhibiting pseudotime progression of Fate Specific Transcription Factors (FSTF) obtained from ( H. O. King et al., 2024 ; Raz et al., 2021 ). Therein epidermal markers showed pseudotime expression from lower to higher whereas muscle, intestinal markers showed opposite trend from higher to lower expression (C) Heatmap depicting the trajectory inference of 11 genes enriched in X2 MTG population in compare to X1 population. Likewise all the eleven genes follow the similar pseudotime progression like neoblast marker as smedwi-1 . Further, we checked pseudotime progression of 11 genes enriched in X2 MTG low HFSC, which followed pseudo-temporal progression similar to smedwi-1 , transitioning from higher to lower pseudotime ( Figure 4C ) . To infer lineage trajectories, we performed Slingshot analysis ( Street et al., 2018 ), using cluster 7 as the root. This revealed a bifurcating trajectory from cluster 7, with one branch representing a neoblast lineage and the other a post-mitotic lineage, as visualized in the UMAP plot (Figures S4A–S4C) . In summary, our in-depth single-cell transcriptomic analysis of X2 MTG population identified 11 genes specifically enriched in X2 neoblast, representing the G1 phase of the cell cycle. While these genes appear to be important for neoblast maintenance, their precise role in regulation of G1 phase and pluripotency remains to be elucidated. RNAi Screening identify unique role of ddx43 and h1b in negative regulation of neoblast proliferation in planaria To investigate the role of 11 genes enriched in the X2 MTG low HFSC population, we performed RNA interference (RNAi) mediated knockdowns. Animals were fed with dsRNA at a final concentration of 250 ng/µL mixed with beef liver extract. A total of six feeds were administered at three-day intervals, followed by post-treatment observations for 7 days ( Figure 5A ) . Five out of 11 knockdowns resulted in lethal phenotypes at varying time points. Among the 11 genes, e2f5 , pesc , unidentified 2 (un2) , and cebpz knockdowns caused lesions leading to animal lysis, while knockdown of rl37a resulted in body paralysis, followed by lysis ( Figure S5A ) . Animals that did not exhibit phenotypic defects were fixed seven days after the final feed for further analysis. Quantitative PCR was performed to validate the RNAi efficiency ( Figure S5B ). Download figure Open in new tab Figure 5: RNAi screening revealed hyperproliferation in ddx43 and h1b knockdown animals (A) Schematic representation of the RNAi knockdown strategy applied to screen 11 candidate genes. Animals were fed with gene-specific dsRNA six times with 3-day interval between feeding. Animals exhibiting phenotypes were fixed immediately upon observation, while those without observable phenotypes were fixed at 28 th day. (B) Immunostaining using anti-phospho-histone H3 (H3p) for all eleven gene knockdowns. Scale bars:100 µm. (C) RNA-FISH for smedwi-1 staining in gene knockdowns of e2f5 , pesc , rl37a , unidentified-1 (un1), ddx43 , and h1b . Scale bars: 100 µm. (D, F) Quantification of H3p + and smedwi-1 + cells in ddx43 knockdown animals. p value: ** <0.01; *** <0.001 (E, G) Quantification of H3p + and smedwi-1 + cells in h1b knockdown animals. p value: ** <0.01 Immunostaining for phosphorylated histone H3 (H3P), a mitotic marker, revealed a reduction in proliferating cells in e2f5 , pesc , rl37a , cebpz , and unidentified 1 (un1) knockdowns ( Figure 5B , S5C ) . In contrast ddx43 , h1b , trap1, and un1 knockdowns showed an increase in H3P + cells indicating altered proliferative dynamics ( Figure 5B , 5D , 5E and S5C ) .To further assess the impact on neoblast populations, we performed RNA fluorescence in situ hybridisation (RNA-FISH) for smedwi-1 , a neoblast marker ( Figure 5C ) . e2f5 , pesc , cebpz , and un1 knockdowns resulted in a reduction of neoblasts, whereas rl37a knockdown led to neoblast clustering or compacting. Although trap1 and un2 knockdowns showed an increase in mitotic cells ( Figure S5C ) , while neoblast numbers remained unchanged ( Figure S5D ) . Interestingly, ddx43 and h1b knockdowns exhibited a significant increase in the number of smedwi-1 + cells ( Figure 5F , 5G ) . Together, our results show increased numbers of H3P + and smedwi-1 + cells, suggesting hyperproliferation of neoblasts in ddx43 and h1b knockdown animals ( Figure S5E ) . ddx43 -dependent regulation of h1b establishes hierarchical relationship critical in controlling neoblast proliferation Given that both ddx43 and h1b knockdowns result in a similar hyperproliferation phenotype, a relatively uncommon outcome in neoblast-targeted RNAi screens suggesting a role in restraining stem cell proliferation. We investigated whether these genes are expressed within the same cell types, which might explain the similar phenotype between both gene knockdowns. Analysis of our single-cell transcriptomic data revealed a negative correlation between ddx43 and h1b expression, indicating limited co-expression between ddx43 and h1b ( Figure S6A ) . To better understand this relationship and its phenotypic consequences, we examined their spatial expression patterns in planarians by performing a series of double fluorescence in situ hybridisation (dFISH) experiments using probes against ddx43 or h1b along with neoblast marker smedwi-1 in wild-type (WT) animals. RNA-FISH for ddx43 and smedwi-1 revealed ddx43 is expressed in smedwi-1 + neoblasts and differentiated cells. These differentiated cells expressing ddx43 are in physical proximity and are surrounded by neoblasts ( Figure 6A ) . Similarly, dFISH for h1b and smedwi-1 revealed ∼50% of h1b + cells co-express smedwi-1 ( Figure 6B ) . We next explored lineage relationship of ddx43 ⁺ and h1b⁺ cell types using scRNA-seq from X2 MTG population. UMAP analysis revealed 92.58% of smedwi-1⁺ cells in the X2 MTG population express ddx43 . Among these, a subset (55.11%) co-expresses h1b , forming a distinct smedwi-1⁺/ddx43⁺/h1b⁺ triple-positive population ( Figure S6B ) . Additionally, we identified a separate smedwi-1⁺/h1b⁺ population (∼55%) that lacked ddx43 expression. These findings suggest a hierarchical lineage structure, wherein smedwi-1⁺/ddx43⁺ neoblasts represent a parent lineage that gives rise to both smedwi-1⁺/ddx43⁺/h1b⁺ and smedwi-1⁺/h1b ⁺ progeny ( Figure S6B ) . This lineage relationship was further validated by dFISH experiments ( Figure 6C ) . We observed co-expression of smedwi-1 with both ddx43 and h1b , as well as co-expression of ddx43 and h1b in a subset of cells. These findings suggest ddx43 + and h1b + cell types might have originated from the same parent lineage. Further, using dFISH experiments for ddx43 and h1b reveals h1b + cells are in close physical proximity to ddx43 ⁺ cells ( Figure 6C ) . This observation prompted us to test whether ddx43 + cells might regulate h1b + cells in a non-cell autonomous or hierarchical manner. To test this, we analyzed h1b expression in ddx43 knockdown animals and vice versa ( Figure 6D , 6E ) . Strikingly, the number of h1b ⁺ cells were significantly reduced in ddx43 knockdown, while ddx43 + cells remained unaffected in h1b knockdown ( Figure 6F , 6G ) . This suggests that ddx43 acts upstream of h1b and plays a critical role in supporting the h1b + cell population. These results are further corroborated by QRT-PCR analysis, wherein h1b transcript levels were reduced by 50% in ddx43 knockdown animals ( Figure S6C ) . In contrast, ddx43 transcript levels remained unchanged in h1b knockdown animals ( Figure S6D ) . Further, bulk transcriptome sequencing of ddx43 knockdown animals followed by bioinformatics analysis revealed increased expression of differentiated markers in the knockdown animals compared to the controls ( Figure S6E ) . This suggests that ddx43 + cell types might play a pivotal role in maintaining the adjacent neoblast in extended G1 phase, which were primed to differentiate upon entering the cell cycle. Download figure Open in new tab Figure 6: In-situ staining conferred ddx43 is essential for h1b expression (A) RNA-FISH signiting by using ddx43 and smedwi-1 probes in wild-type animals. First insert shows co-localization of ddx43 with smedwi-1 in the tail region. Second inset shows smedwi-1 + cells surrounding ddx43 + cells in the tail region. (B) RNA-FISH staining by using h1b and smedwi-1 probes in wild-type animals. Inset shows h1b colocalizing with smedwi-1 in the tail region. (C) RNA-FISH staining using ddx43 and h1b probes in wild-type animals. Inset shows ddx43 + cells in close proximity to h1b + cells. (D) RNA-FISH for h1b in ddx43 knockdown animals. (E) RNA-FISH for ddx43 in h1b knockdown animals. (F, G) Quantification of h1b + and ddx43 + cells in ddx43 and h1b knockdowns. Scale bars: Whole-animal: 100 µm; tail insets: 50 µm; single cell insets: 10 µm. p value: * <0.05 Together, our data reveal a hierarchical regulatory relationship between ddx43 and h1b , where ddx43 promotes h1b expression and function. This axis appears to be essential for controlling neoblast proliferation under homeostasis condition. ddx43 positive cells play pivotal role in maintaining nearby neoblasts in slow-cycling state and confer resistance to sublethal doses of radiation To investigate the cell cycle state of the neoblasts in close physical proximity to ddx43 ⁺ cells, we performed RNA-FISH using probe against ddx43 and immunostaining using antibody against H3p, a marker of cells in the G2/M phase of the cell cycle. Only a small fraction (∼10%) of proliferating neoblasts located near the ddx43 + cells are H3p + which further suggests that these neoblast are predominantly in the G1 phase and may be slow cycling. In an effort to directly access whether these neoblasts are indeed in the slow-cycling state, we conducted a long-term BrdU pulse chase experiment. Planarians were fed with BrdU and examined for label retention in neoblast on 15 days post feeding (dpf). We performed immunofluorescent staining with anti-BrdU antibody followed by dFISH using probes against smedwi-1 and ddx43 . Strikingly, neoblast in the close proximity to ddx43 + cells retained BrdU signal, indicating a slow-cycling nature ( Figure 7A ) . Download figure Open in new tab Figure 7: Sublethal doses of irradiation exhibit ddx43 importanat for transient slow cycling neoblasts (A) Triple labelling of ddx43 , smedwi-1 (RNA-FISH), and BrdU immunostaining in 15-day BrdU chased animals. Arrow indicates smedwi-1 + BrdU + cell near a ddx43 + cell. (B) RNA-FISH for ddx43 and smedwi-1 in wild-type and sub-lethally irradiated animals (15 Gy) at 1, 2, 3, and 5 days post-irradiation (DPI). Gamma radiation was used to irradiate the animals. (C) Quantification illustrating smedwi-1 + cells in proximity to ddx43 + cells at indicated timepoints as 1, 2, 3 and 5 dpi along with wildtype animals after irradiation. smedwi-1 + cells in physical proximity to ddx43 + cells are considered and are divided with total smedwi-1 + cells and multiplied with 100 to obtain the percentage. p value: **** <0.0001 (D, E) RNA-FISH for smedwi-1 at 2dpi and 5dpi in ddx43 knockdown animals subjected to sublethal radiation (15Gy). (F, G) Quantification of smedwi-1 + cells at 2DPI and 5DPI in ddx43 and h1b knockdowns. Each condition having four animals. p value: *<0.05; ***<0.001 (H) Model depicting ddx43 + cells maintaining transient, slow-cycling neoblasts. Loss of ddx43 or h1b leads to hyperproliferation. While upon sublethal doses of irradiation, slow-cycling neoblasts re-enter the cell cycle and proliferate. When planarians were exposed to sublethal dose of radiation (15 Gy) ( Figure S6F ) , partial loss of neoblasts was observed. Existing literature suggests that slow cycling cells are resistant to radiation ( Lyle & Moore, 2011 ; Skvortsova et al., 2015 ). This led to our hypothesis that neoblasts resistant to sublethal doses of radiation constitute a slow cycling population residing near the ddx43 + cells. To test this, we irradiated the planarians with 15 Gy and performed dFISH using probes against smedwi-1 and ddx43. At 1-day post-irradiation (dpi), the surviving neoblasts were predominantly located near ddx43 ⁺ cells. Notably, by 3 dpi, many of the neoblasts had migrated away from ddx43 ⁺ cells, likely to repopulate the tissue ( Figure 7B , 7C ) . These findings suggest that neoblasts surviving sublethal doses of radiation are slow-cycling in nature which are maintained in the vicinity of ddx43 + cells. To determine whether ddx43 ⁺ cells are essential for maintaining the radiation resistant and slow-cycling neoblast population, we performed RNAi-mediated knockdown of ddx43 followed by sublethal doses of radiation and stained with smedwi-1 ( Figure 7D , 7E ) . At 2 and 5 dpi, ddx43 knockdown animals exhibited a significant reduction in surviving neoblasts, indicating compromised radiation resistance ( Figure 7F , 7G ) . These results demonstrate that ddx43 ⁺ cells are critical for the survivability of neoblast to sublethal doses of radiation by maintaining them in transient slow-cycling state. In summary, our findings highlight a pivotal role of ddx43 ⁺ cells in regulating the local microenvironment to maintain neighbouring neoblast in a transient slow-cycling state ( Figure 7H ) . However, the molecular mechanisms by which ddx43 ⁺ cells sustain this state and how this regulation is modulated in response to injury remains to be elucidated. Erk signalling downregulate ddx43 + cells by suppressing Notch, critical for neoblast proliferation A recent study by Fan et al., (2023) demonstrated Erk signalling propagates rapidly from the site of injury towards the distal end along the longitudinal muscle. This signalling was shown to be essential for neoblast proliferation occurring within 6 hours post amputation (hpa), in the distal from the site of injury. We know that ddx43 + cells negatively regulate proliferation and ERrk promotes proliferation, therefore, we hypothesised that Erk signalling might deplete ddx43 + cells to promote proliferation in distal tissues. To investigate the fate of ddx43 ⁺ cells and their associated neoblast following injury, we amputated the anterior region of the planaria just below the head and fixed them at 6 hpa. We performed FISH using probes against ddx43 ( Figure 8A ) . In the proximal region near the wound, ddx43 ⁺ cell numbers remained unchanged comparable to homeostasis ( Figure 8B ) . In contrast, ddx43 + cell numbers were significantly reduced in the distal region ( Figure 8C ) . These findings led us to hypothesize that the Erk-dependent neoblast proliferation at the distal end, as reported in Fan et al. (2023) might be facilitated by the depletion of ddx43 + cells. This in turn could enable the nearby neoblast to exit slow-cycling state and enter proliferation. To test this, we treated regenerating animals with the Erk inhibitor FR180204 (25 µM conc.), which blocks the activity of phosphorylated Erk. dFISH with ddx43 and smedwi-1 probes revealed that Erk-inhibition significantly increased ddx43 ⁺ cell numbers in the distal region compared to untreated controls ( Figure 8D , 8E ) . The ddx43 + cell numbers in Erk inhibitor condition remain unchanged in proximal regions compared to untreated control ( Figure S7A ) . These results indicate that Erk signalling is required to reduce ddx43 ⁺ cell numbers, thereby promoting neoblast proliferation. Download figure Open in new tab Figure 8: Erk activity crucial for downregulation of ddx43 in distal region (A) RNA-FISH for ddx43 in homeostasis and 6 hours post-amputation (6 hpa). Heads were amputated just below the eyes and fixed after 6 hours. (B, C) Quantification of ddx43 + cells in proximal and distal regions in uncut and 6 hpa condition. In homeostasis conditions, the prepharynx is compared with proximal region of 6HPA animal and postpharynx region is compared to distal region of 6hpa animal. p value: **<0.01 (D) RNA-FISH showing for ddx43 at 6 hpa in untreated and Erk inhibitor (FR180204) treated animals. (E) Quantification of ddx43 + cells in distal regions of planaria in untreated and Erk inhibitor treated animals. p value: ** <0.01 (F) Quantification showing smedwi-1 + cells in close proximity to ddx43+ cells in distal regions of untreated and Erk-inhibited animals. smedwi-1 + cells in physical proximity to ddx43 + cells are considered and are divided with total smedwi-1 + cells and multiplied with 100 to obtain the percentage. Each condition exhibiting four animals. (G) 3D image rendering of muscle (6G10) and ddx43 stain showing the close association of muscle with ddx43 + cells. The 3D redering was done in FV3000 software from Olympus. (H) RNA-FISH for ddx43 and notch2 in wild-type animals. notch2 expression was observed in ddx43 + cells. Scale bars: 10 µm. (I) Quantification of ddx43 + cells in the distal region at 6 HPA in untreated, Erk-inhibited, and Erk+Notch (E+N) co-inhibited animals. (J) Model illustration for ddx43+ cells during injury. Upon injury, Erk-mediated Notch signalling leads to downregulation of ddx43 + cells in distal regions post-injury, and promotes proliferation. Next, we quantified the proportion of neoblasts near the ddx43 ⁺ cells in both control and Erk-inhibited conditions. A significantly higher fraction of neoblasts were found in the proximity to ddx43 ⁺ cells in Erk-inhibited animals ( Figure 8F ) . Given that Erk signalling in planaria is known to act via muscle cells ( Fan et al., 2023 ), we investigated spatial relationship between ddx43 ⁺ cells and muscle. Co-staining for the muscle markers, 6G10 and ddx43 in uninjured wild-type animals reveals that ddx43 ⁺ cells are in close proximity with the muscle cells ( Figure 8G , S7B , S7C ) . Together, these results suggest that Erk signalling plays a pivotal role in downregulating the ddx43 + cell number in the distal region during regeneration crucial for neoblast proliferation. To further explore the signalling cross talk between muscle and ddx43 + cells, we analyzed single cell transcriptomic data to identify signalling pathway, using genes enriched in ddx43 + /smedwi-1 - cells. Notably, notch2 is highly expressed in ddx43 + /smedwi-1 - cells suggesting Notch signalling may play a role in maintaining ddx43 + cells (Table S2) .Previous studies have shown that Notch signalling regulate stem cell dynamics in planarians. For instance, inhibition of Notch using DAPT in Dugesia japonica have shown to increase neoblast proliferation by 6hpa ( Dong et al., 2021 ) . Furthermore, dFISH of notch2 with ddx43 confirmed the expression of notch2 in ddx43 + cells ( Figure 8H , S7D , S7E ) and notch2 KD also showed increased proliferation ( Figure S7F , S7G ) . This observation led to our hypothesis that Erk signalling may suppress ddx43 + cells by inhibiting Notch activity. To test this hypothesis, we treated animals with both Erk and Notch inhibitors and examined ddx43 + cell numbers in the distal region from the site of injury. Strikingly, ddx43 + cells were significantly reduced under dual Erk and Notch inhibition, reaching comparable levels comparable to untreated controls ( Figure 8I ) . In summary, our data suggests that Erk signalling, transmitted via muscle following injury suppress Notch activity in ddx43 + cells, leading to a reduction in the cell numbers. This down regulation is a necessary step to facilitate the proliferation of adjacent neoblast during regeneration. In summary, the pERK signalling downregulates notch in distal tissues, thereby, depleting ddx43 + cell number, an essential step for neoblast proliferation ( Figure 8J ) Discussion Cell cycle regulation in stem cells has traditionally been viewed as a mechanism to expand the stem cell pool, essential for their maintenance. However, emerging evidence across diverse systems has shown that the duration of the cell cycle, particularly the G1 phase, acts as an active regulatory node in stem cell renewal and fate specification. The G1 phase acts as a critical window during which stem cells integrate extrinsic and intrinsic cues to determine whether to self-renew, enter quiescence, or undergo lineage commitment ( Dalton, 2015 ; Pardee, 1974 ; Zaveri & Dhawan, 2018 ). In human and mouse embryonic stem cells (ESCs), dynamic changes in the G1 duration have been shown to influence fate outcomes. For instance, shortened G1 was associated with maintenance of naïve pluripotency, driven by extrinsic signals such as Leukemia Inhibitory factor LIF ( Coronado et al., 2013 ), while prolonged G1 phase increases sensitivity to differentiation cues like retinoic acid ( Pauklin & Vallier, 2013 ). G1-specific chromatin accessibility also appears to prime cells for fate transitions ( Sela et al., 2012 ; Trouth et al., 2025 ). Furthermore, molecular regulators like Cyclin E1 and p27 Kip1 have been shown to directly link G1 progression with stem cell maintenance and differentiation ( Chetty et al., 2015 ; Menchón et al., 2011 ). Despite these insights, the link between G1 duration and differentiation is not universally consistent. Li et al., (2012) demonstrated that mere elongation of G1 in mouse ESCs via overexpression of cell cycle inhibitors like p21 and p27 did not accelerate differentiation. Conversely, shortening G1 by overexpressing cyclins did not delay it. These findings suggest that G1 length alone may be insufficient to dictate fate decisions in certain contexts and that additional regulatory mechanisms likely buffer stem cells from differentiation cues. In this study, we used Schmidtea mediterranea , a model organism with robust regenerative capabilities, to interrogate how cell cycle dynamics regulate the maintenance and function of adult pluripotent stem cells. Planarians maintain a population of adult pluripotent stem cells, a sub-population of neoblasts, capable of regenerating all somatic cell types. Our study identified a transient slow-cycling population of neoblasts with an extended G1 phase. These cells appear to exist in a state of differentiation readiness, without being lineage committed and may play a crucial role in facilitating differentiation. Further, we found that this slow-cycling G1 neoblasts are in close proximity to previously uncharacterized ddx43- expressing cell type , which we term “Janitor cells”-gatekeepers of cell cycle. The discovery of ddx43 ⁺ cell was serendipitous arising from our search for molecular markers specific to G1-phase of the neoblast. To characterize G1 neoblast population, we focused on the X2 MTG low HFSC subset described by Haroon et al., (2021), a G1-enriched neoblast population. The earlier study identified four distinct populations of X2 based on mitochondrial content and cell size: X2 MTG low HFSC, X2 MTG low LFSC, X2 MTG high HFSC, and X2 MTG high LFSC. Among these, X2 MTG low HFSC cells, characterized by low mitochondrial content and larger cell size exhibited the highest pluripotency, as evidenced by their ability to rescue lethally irradiated animals upon transplantation. Building on these insights, we isolated all four X2 MTG subpopulations and performed single-cell transcriptomic sequencing to identify G1-phase-specific signatures. Differential gene expression analysis comparing X2 MTG low HFSC cells to the other X2 populations and X1 neoblasts (S/G2/M phase cells) revealed 11 genes specifically enriched in G1-phase neoblasts. Additionally, we also checked the pseudo-temporal trajectory differences, which concur with the biological trajectory of non-committed neoblasts and the post-mitotic markers. Our obtained eleven X2 enriched genes showed expression of higher to lower pseudo-time similar to neoblast pattern suggesting the co-expression of these genes in the G1 enriched neoblast population. Among these, ddx43 exhibited distinct expression pattern of its expression in the smedwi-1 + and in differentiated cells. Lineage tracing based on the single transcriptome data revealed that ddx43 + cells types arise from a ddx43 + /smedwi-1 + G1 enriched neoblast population. To investigate the functional relevance of ddx43 + cell types, we performed ddx43 knockdown, which resulted wide-spread neoblast hyperproliferation. This suggests that ddx43 ⁺ cells exert non-cell autonomous control over neoblast cycling. Remarkably, these transient slow cycling cells adjacent to ddx43 ⁺ cells are resistant to sub-lethal radiation and repopulate the animals with neoblasts, indicating to a protective niche-like function of ddx43 ⁺ cells. Together, our data highlight a critical role for spatial cues from ddx43 ⁺ cell in regulating stem cell dynamics, particularly in the G1 phase, essential for tissue homeostasis. Importantly, the transient slow-cycling neoblasts described are distinct from the slow-cycling cells reported by Molinaro et al., (2021) , which were characterized by a metabolically distinct profile. In contrast, the population we described here are microenvironment-dependent, shaped by local interactions with ddx43 ⁺ cells. To explore the role of ddx43 ⁺ cells during regeneration, we performed anterior amputations and analyzed the neoblast response at 6 hours post amputation (hpa), corresponding to the first mitotic peak ( Wenemoser & Reddien, 2010 ). We observed a marked reduction in ddx43 ⁺ cell numbers at the distal end, but not the proximal end of the wound. This inversely correlated with the findings of Fan et al., (2023) , who showed Erk-dependent proliferation of neoblast at the distal end of the wound. we hypothesized that Erk signalling may promotes neoblast proliferation by suppressing ddx43⁺ cells. This was tested by treating the anterior amputated animals with a well-known Erk inhibitor, which led to a significant decrease of the ddx43 ⁺ cell types in the distal region of the wound. This supports our hypothesis that Erk signalling is a critical for regulation of cells marked by ddx43 . Fan et al., (2023) also showed that Erk activation propagates through the longitudinal muscle from the injury site towards the distal end. We speculated that Erk-mediated downregulation ddx43⁺ cell types requires proximity to muscle tissue. Indeed, our data showed that the ddx43 ⁺ cells are closely surrounded by the subepidermal muscle, suggesting juxtacrine signalling between muscle and ddx43 ⁺ cells. To identify the signalling component within the ddx43 + cells that might respond to Erk, we analysed single-cell transcriptome data from the ddx43- enriched cells, which identified Notch2 as a potential candidate. This also led to the hypothesis that Erk activation modulates ddx43 + cell types by altering Notch function. Previous studies have shown interactions between Erk and Notch pathways in various systems. For example in mouse cochlea, Mek/Erk signalling supresses hair cell differentiation by maintaining Notch activity, while Erk inhibition downregulates Notch components ( Ma et al., 2024 ). In breast cancer, MAPK-ERK controls Notch activation via regulation of Jagged1 (JAG1), and Erk inhibition disrupts Notch-driven tumor progression ( Izrailit et al., 2013 ). Similarly, in oral squamous cell carcinoma, Erk activation induces Jagged1 expression, activating the Notch pathway and enhancing cancer stemness ( L. J. Li et al., 2022 ). These examples suggest a conserved Erk–Notch axis, which may also operate in planarians to coordinate injury responsive niche function. To test this, we performed dual inhibition of Erk and Notch signalling. This restored ddx43 ⁺ cell numbers in distal end from the site of amputation to control levels, unlike Erk inhibition alone, which led to increased levels ddx43 ⁺ cells. These results establish the Erk-Notch axis as a critical regulatory mechanism modulating ddx43 ⁺ cell function during early wound response and regeneration. Beyond injury response, our study also reveals a unique mechanism for maintaining the neoblast in an extended G1 phase. While Raz et al., (2021) showed that planarian neoblasts can initiate fate specification during the S/G2/M phases of the cell cycle without compromising potency, our findings highlight an additional regulator layer. Here, extrinsic cues from ddx43⁺ cells maintain neighbouring neoblasts in a poised, pluripotent and slow-cycling G1 state. These neoblasts are not fate primed but serve as a reserve population, capable of rapid activation upon injury, enhancing regenerative resilience. In conclusion, our findings reveal that G1 phase regulation is an active, microenvironment-mediated mechanism of stem cell maintenance in planaria. The spatial association between ddx43 ⁺ cells and neoblasts adds a new dimension to our understanding of how regenerative capacity-not solely regulated by intrinsic transcriptional program, but also by extrinsic, niche-derived restraint. Rather than a passive interval, G1 extension emerges as a key regulatory feature supporting stem cell identity and regenerative potential. Methods View this table: View inline View popup Download powerpoint Table 1 View this table: View inline View popup Table 2 View this table: View inline View popup Download powerpoint Table 3 View this table: View inline View popup Table 4 View this table: View inline View popup Download powerpoint Table 5. Primers used for dsRNA and Riboprobe synthesis. View this table: View inline View popup Download powerpoint Table 6. Primers used for qPCR. Animal husbandry The planarian species ‘ Schmidtea mediterranea’ was used for our study. All animals were maintained in 1x Montjuïc water at 20°C in dark conditions within Sanyo incubators. ph range of 7-7.4 had been maintained for the 1x Montjuïc water. Planaria were fed with beef liver extract at every 4 th or 5 th day. The animals were starved for a week before taking them for the experiment. Single-cell RNA-sequencing library preparation Staining and FACS sorting of X2 MTG populations were performed as described previously ( Haroon et al., 2022 ; Mohamed Haroon et al., 2021 ). Briefly, ∼0.7 cm planaria were used and the animals were diced into small pieces using a scalpel in CMFB+1% Bovine serum albumin (BSA). After chopping, the fragments were transferred to a 50 mL centrifuge tube using a wide bore pipette tip. Cells were mechanically dissociated by repeated pipetting and the resulting suspension is filtered using 40um cell strainer. The strained solution is centrifuged at 290g for 10mins at 4 degree. The supernatant is discarded and the cells were resuspended in isotonic planaria media (IPM) with 10% Fetal Bovine Serum (FBS). The cells were then stained with 40ug/ml hoechst 33342 for 50 mins at Room Temperature (RT). Then Mito Tracker Green (MTG) was added at 100 nM concentration for 20 mins at RT. After staining, the sample is centrifuged at 290g for 10mins at 4 degree and then cells were resuspended in IPM+10% FBS. The samples were immediately analysed through flow cytometry. The X1 and X2 gates were set based on Hoechst staining. X1 MTG populations and X2 MTG were gated as described in Haroon et al., (2022) and Haroon et al., (2021). Sorting was performed in BD FACS ARIA III cell sorter, using a 100 µm nozzle with the sheath fluid pressure set at 20 psi. The sorted cells were counted using a haemocytometer, and the viability was tested using trypan blue exclusion assay. The sorted cells had more than 90% viability. The cells were then subjected to single cell RNA (scRNA) sequencing library preparation using 10X Chromium Next GEM Single Cell 3ʹ Reagent Kit v3.1 according to the manufacturer’s instruction. Sequencing was done using Ilumina Nova seq 6000. scRNA-seq data processing Raw scRNA-seq reads were aligned to the planaria genome Schmidtea mediterranea S2F2 (SMESG.1) genome using 10X Genomics CellRanger count (v6.0.0) ( Zheng et al., 2017 ) to attain gene/cell count matrices. The corresponding genome and General Feature Format (GFF) were obtained from planmine database ( Rozanski et al., 2019 ) and prepared with CellRanger ‘mkref’ function. Each sample in form of UMI count were merged into a table and imported into Seurat (v4.0.0)( Butler et al., 2018 ) ( Stuart et al., 2019 ) within the R environment (v4.0.3). Herein obtained Seurat object were implemented for filtering, normalization, integration, clustering and differential expression analysis for different samples. Cells were filtered for genes expressing at least 100 cells. Normalization on cells was performed using ‘sctransform’ normalization method and integration of scRNA-seq data was performed on multiple samples using the top 3000 variable features. After integration, genes were used as input for executing principal component analysis (PCA) using function ‘RunPCA’ and genes were used for visualization 2D data in Uniform Manifold Approximation and Projection (UMAP) form using function ‘RunUMAP’. In Seurat, clustering was performed using the ‘FindClusters’ function adopting the resolution of 0.1 and the shared-nearest neighbor (SNN) graph was constructed using the first 30 PCA dimensions. In case of differential expression analysis, we used ‘FindAllMarkers’ with default parameters using negative binomial distribution (test.use = DESeq2) and minimum difference & minimum fraction between two groups with threshold of 0.1. Genes with log2FC above 1 and adjusted p-value less than 0.05 were considered as differentially expressed. To understand the enrichment of GO annotation from planaria, Swiss-prot blastx-id against the planarian annotation were aligned using the uniprot database. Furthermore, we also performed pseudo-time trajectory analysis and cell ordering through a pseudo-temporal continum using Monocle (v2) ( Qiu et al., 2017 ) and Slingshot (v2) ( Street et al., 2018 ). Gene ontology enrichment analysis for enriched genes was performed using EnrichR ( E. Y. Chen et al., 2013 ). Construction of Gene Regulatory Networks Gene regulatory networks (GRNs) were designed on the scRNA-seq data using R package hdWGCNA (v0.2.19) ( Morabito et al., 2023 ) which incorporates the WGCNA (v1.72-1) to perform the co-expression network. Firstly, we constructed metacells to transcriptionally enable neighboring cells. hdWGCNA uses K-nearest neighbors (KNN) for building metacells. These obtained metacells were further processed for normalization by using function ‘NormalizeMetacells’ and after executing the function ‘TestSoftPowers’ appropriate co-expression networks were executed. We applied the AddModuleScore function in Seurat to compute the score for module related genes and obtain the expression pattern for our scRNA-seq datasets. In it module eigengenes were computed for gene expression profile in each sample alongside module connectivity were also calculated and average gene expression for each module were plotted in all clusters. Along with GRN analysis we also obtained Transcription factors (TF) from module-1 genes of co-expression network and TF from differentially expressed genes using ChEA3 ( Keenan et al., 2019 ) with the GTEx library. RNA extraction and cDNA synthesis To extract RNA, animals were kept in TRIzol (500ul) and stored at −80°C for overnight in 1.5ml Eppendorf tube. Using a pestle the tissue was homogenized and added 1/5 th volume of ice cold chloroform and mixed thouroughly using a pipette. The solution was kept on ice for 10 imn and centrifuged at 13,000 rpm for 30 mins at 4°C. the aquesous phase was transferred into a freshtube and pre-chilled isopropanol of equal volume is added and mixed. The solution was kept at −20°C for 1h/overnight. The solution was then centrifuged at13,000 rpm for 30 mins. The pelleted RNA was black in colourand washed with 70% ethanol (in nuclease-free water) twice. After washing, air dry the pellet and resuspend in nuclease free water. The RNA as stored at −80°C. 500 ng of RNA was used to prepare cDNA using TAKARA cDNA synthesis kit. dsRNA and Riboprobe preparation Genes were amplified using gene-specific primers and cloned using the TA cloning kit. Primer sequences are mentioned in Tabel 5. XL10 Escherichia coli cells were used for transformation. By using blue-white colony screening to identify successful transformants in the presence of kanamycin resistance, positive clones were selected. Gene-specific primers with T7 promoter overhang were used to generate the template for dsRNA synthesis. Dig RNA labeling mix or Fluorescein RNA labeling mix was used to make riboprobes. Both dsRNA and riboprobe were generated using T7 polymerase-dependent in-vitro transcription. dsRNA was purified using the sodium acetate precipitation method and Riboprobe was purified using Megaclear transcription purification kit. RNAi dsRNA was mixed with beef liver extract with a final concentration of 250 ng/μl. This feed was fed to one week-starved animals every 4 th day for 6 feeds. After the 6 th feed, animals were observed for one week for phenotype. Gene knockdowns (KD) that did not show any phenotype after 7 days were validated for KD using qRT-PCR. dsRNA of Green fluorescent protein (GFP) was used as the negative control. qRT-PCR To perform qRT-PCR, the RNA was isolated using TRIzol after knockdown. The RNA was treated with DNase and then used 500ng of RNA to synthesise cDNA. PowerUP SYBRGreen master mix was used to perform Quantitative real-time PCR (qPCR). Run for every gene is done in triplicate using the QuantStudio 5 system. Refer to Table 6 for the primer sequences. Whole-mount in situ hybridization (ISH) and immunostaining (IHC) Riboprobes were prepared using rNTPs labelled with either Digoxigenin or Fluorescein. Reverse primer with T7 overhang and the forward primer were used to amplify the gene. In it amplicon has been used to synthesize antisense Riboprobe. The used primers are listed in table.x. For both ISH and IHC, animals were killed using 5% N-Acetyl-L-Cysteine in pbstx for 5 min and fixed using 4% formaldehyde in pbstx (0.3% Triton-× 100, unless mentioned otherwise) for 20 mins. The fixed planaria were then dehydrated using methanol and stored at −20°C. Before staining, animals were rehydrated and bleached (5% deionized formamide, 0.5x SSX, 1.2% H2O2 in NFW) under bright light. To perform in situ hybridisation, animals were permeabilised using proteinase-k and fixed using 4% formaldehyde. Riboprobe incubation was done in hybridization buffer for 16-18 hours at 56°C. The riboprobe concentrations used are 0.5 ng/μl for smedwi-1, 0.3 ng/μl for ddx43, and 0.5 ng/μl for h1b. Then the samples were washed with SSC, TNTx (10% 1M Tris-Cl pH: 7.5, XM NACL, 0.3% TritonX-100) and incubated with Anti-Digoxigenin POD (1:1000) or Anti-Fluorescein POD (1:1000) at 4°C overnight in blocking solution (5% RWBR, 5% Horse Serum in TNTx). The samples were washed with TNTX and developed using FITC/CY3/CY5 – tyramide signal Amplification ( R. S. King & Newmark, 2013 ). For developing a second Riboprobe, the peroxidase enzyme on the first riboprobe antibody was inhibited using 0.215M sodium azide for 2 hours and further steps were continued form blocking. To perform immunostaining, the animals were washed with pbstx after bleaching and incubated in blocking solution (10% Horse serum in pbstx) for 2 hours. The primary antibody is then added with fresh blocking solution (1:100 for H3p) for 36-40 hours at 4°C. The samples are then washed with pbstx and incubated with secondary antibody (1:1000) in 10% Horse serum blocking solution. For BrdU staining, after the tyramide signal development the samples were treated with 2N HCL in 0.5% pbstx for 15 mins at 37°C. This is followed by primary (1:100) and secondary antibody(1:1000) incubations, as previously mentioned earlier in immunostaining. BrdU labeling strategy Planaria were treated with a higher salt concentration (5x montjuic juice) for at least 2 days before BrdU feeding ( Molinaro et al., 2021 ; Newmark & Sánchez Alvarado, 2000 ). To prepare BrdU stocks, 5mg of BrdU is dissolved in 100 μl of 50% DMSO in nuclease-free water and stored in −80. 20ul of this stock is then mixed with 80ul of beef liver and 100ul of 1% Ultra LowMelting Agarose. This Brdu feed (5mg/ml final concentration) was fed to planaria. Planaria were maintained in high salt concentration during the chase period. After the desired chase period, animals were are killed using 5% NAC and fixed with 4% formaldehyde as mentioned in the IHC section. Only the animals that didn’t developed any lesions during the chase period were fixed. BrdU staining was done in-addition to in situ hybridisation. After developing the probe, the animals were washed twice with pbstx. Samples were then incubated with 2N HCL in 0.5% pbstx at 37°C for 20 mins. The samples were then washed with pbstx and blocked using 10% Horse serum. Post blocking 1° antibody incubation was done for 36 hours at 4°C. Then 1° antibody was removed and samples are washed with pbstx multiple times. Secondary incubation was done in 10% HS at RT for 2 hours. Later samples were then washed and proceeded for DAPI staining. Irradiation Cobalt based gamma irradiator (LDI-2000, Manufacturer: BRIT, INDIA) was used to irradiate planaria exposure time was calculated based on the dosage rate. Media is changed everyday and the animals are fixed using 4% Formaldehyde and stored in 100% methanol at −20°C. Inhibitor treatment Erk inhibitor FR180204 and Notch inhibitor DAPT were both prepared as 50 mM stock solutions in 50% DMSO. Working concentrations of 25 µM (FR180204) and 35 µM (DAPT) were then made using Montjuïc water. Animals are pretreated for 4-hours with a freshly prepared inhibitor solution. Amputation was then performed just below the head. Following amputation, fresh inhibitor media was added to the animals, and they were fixed 6 hours later using 4% formaldehyde. Finally, samples were stored in 100% methanol at −20°C. Microscopy, Quantification and Software Brightfield images were taken using Olympus SZX16 microscope. Confocal imaging was done on an FV3000 Olympus microscope with 40X 1.3 NA objective and 60X 1.42 NA objective. 3D reconstruction was done using FV3000 software from Olympus. Quantification of immunostaining and FISH stainings were done using Fiji software (V 1.54p). Statistical significance and Graphs were generated using Graphpad PRISM software. Models were made using Biorender. Bulk RNA Sequencing and Analysis Animals were fed with ddx43 dsRNA for 6 feeds and RNA was isolated after 7 days of the last feed. The RNA was isolated using TriZOL and coprecipitated using glycoblue before submitting for sequencing. Samples were sequenced in triplicate. mRNA was isolated using NEBNext® Poly(A) mRNA Magnetic Isolation. Library preparation was done using NEBNext® Ultra™ II Directional RNA Library Prep Kit and sequenced on Illumina Nova seq 6000. Raw RNA-seq reads were subjected to quality control and trimming using fastp [v0.20.1] ( S. Chen et al., 2018 ) followed by aggregation of the QC report data in MultiQC [v1.9] ( Ewels et al., 2016 ). STAR [v2.7.9a] ( Dobin et al., 2013 ) was used for mapping using S. mediterranea ( S2F2) as a reference genome. Mapped reads were processed to obtain raw counts for gene expression estimation using featureCounts ( Liao et al., 2014 ) from the subread package [v1.5.2]. Differential gene expression was analyzed for differential expression using DESeq2 [v1.30.1] ( Love et al., 2014 ) in the R environment [v4.0.3]. In order to determine upregulated and downregulated genes for ddx43 knockdown and GFP control, differentially expressed genes with an absolute value of log2foldchange greater than 1 and false discover rate less than 0.05 were filtered using general linearized model. Heatmap were created using default parameters by obtaining z-scores from normalized read counts generated by DESeq2. Competing Interests The Authors declare no competing or financial interests. Author contributions N.K.J., M.M.H., A.A., and D.P. contributed to the conceptualization of the study and manuscript preparation. N.K.J. performed the majority of the experiments and supervised all experimental work. Confocal imaging was carried out by N.K.J. Single-cell isolation and sequencing were performed by M.M.H., with assistance from P.V. All bioinformatic analyses were conducted by A.A. BrdU experiments were performed by M.B. Radiation experiments were conducted by A.M. and V.K.D. M.B., A.M., and S.P. assisted with in situ hybridization and immunostaining. D.P. acquired funding and supervised the project. Resources Single cell X1 data used in this paper are taken from NCBI GEO: GSE107873 Funding N.K.J and A.M would like to thank Department of Biotechnology (DBT), India and Institute for Stem Cell Science and Regenerative Medicine (inStem) for PhD fellowship. This work was supported by inStem core grant, Swarnajayanti fellowship and Frontiers grant. Supplementary Figure legends Download figure Open in new tab Figure S1: scRNA-seq entraps X1 and X2 markers expression over MTG population (A) UMAP expression representation for neoblast marker as smedwi-1 and P4HB as post-mitotic marker (B) Dot plot illustration for previously published X1 and X2 population markers obtained from Raz et al., (2021) across different X2 MTG samples. In it mcm-10, ap-2, cyclin B2, lamin B2, vasa-2 and sycp-1 have shown to be expressed in pluripotent (X2 MTG low HFSC) sample (C) Dot plot identification of published X1 and X2 population markers obtained along clusters from X2 MTG population. In it cluster 7 seems to represent higher expression for markers like mcm-10, ap-2, cyclin B2, lamin B2, vasa-2 and sycp-1 . Download figure Open in new tab Figure S2: Enrichment analysis for identified GRN modules (A) Bar plot representing top 10 gene ontology terms for the genes obtained from GRN analysis for each specific module. In module-1, Cytoplasmic Translation and Ribosome biogenesis appear to be highlighted terms. While star(*) mentioned in respective terms constitute adjusted p-value less than 0.05 (B) Bar plot identifying obtained top 10 gene ontology terms for the genes obtained in module-2. In it, Cilium assembly, membrane organization and maintenance of basal cell polarity appeared to be enriched terms from it (C) Bar plot for characterization of module-3 revealed terms like Synapse organization, Neuron projection, Cell junction organization and Intracellular signal transduction for its set of genes (D) Bar plot outlining enriched terms from module-4 genes such as RNA splicing, regulation of RNA metabolic process and mRNA processing (E) Bar plot illustrating enriched terms from module-5 genes such as Endoplasmic reticulum to golgi vesicle mediated transport, Protein localization and Intracellular protein transport (F) Module-6 genes exhibiting GO terms like Chromatin Remodeling, Chromatin Organization and RNA splicing Download figure Open in new tab Figure S3(1): Expression pattern of genes across X2 MTG samples derived clusters (A) Dot plot identifying the expression from first set differentially expressed gene across X2 MTG population. Differentially expressed genes (n=209) were distributed into different sets for plotting across different population. (B,C) Differentially expressed genes from second and third set across X2 MTG population Download figure Open in new tab Figure S3(2): Expression pattern of genes across X1 population derived clusters (A) Dot plot depicting the expression from first set of differentially expressed genes across X1 population (B,C) Differentially expressed genes in the form second and third set across X1 population Download figure Open in new tab Figure S3(3): Expression pattern of genes across X2 MTG samples derived clusters (A) Dot plot for fourth set of differentially expressed genes across cell clusters of X2 MTG population (B) Transcription factors (TF) obtained from GRN and Differential expression analysis were considered for expression quantification over X2 MTG clusters Download figure Open in new tab Figure S3(4): Expression pattern of genes across X1 population derived clusters (A) Fourth set of differentially expressed genes were represented using dot plot (B) Similar set of TF were used for expression computation across cell clusters of X1 population Download figure Open in new tab Figure S3(5): Expression outline of X2 gained markers in sub-clustering of neoblast cluster (A) Dot plot describing the eleven enriched markers over sub clustering of neoblast cluster. In it, Cluster 0,2,3 and 6 portrays enriched cluster for obtained markers (B) Dot plot depicting the expression levels of previously published X1 population markers over scRNA-seq data of X2 MTG population. From it markers namely zfp-1 , sycp-1, rhombotin-1 seems to be enriched in neoblast cluster Download figure Open in new tab Figure S4: scRNA-seq identifies trajectory inference from intergal cells (A) UMAP plot illustrating the trajectory inference taking cluster 7 (neoblast cluster) as root for distribution of total cells across 16 clusters using slingshot (B) UMAP plot identifying the two different lineages across our total cells from root cluster. smedwi-1 lineage shown on the left and P4HB lineage shown on the right. (C) UMAP plot describing the smooth trajectory using the principal curves from the obtained lineages by taking neoblast cluster as root Download figure Open in new tab Figure S5: Ectopic phenotype observation for enriched markers (A) Darkfield images of rl37a, e2f5, un1, pesc , and cebpz knockdowns at 9, 11, 14, 16, and 18 days post first feeding (DP1F). (B) qPCR analysis showing relative gene expression for 11 knockdowns. (C) Quantification for H3p + cells in hsp7c, pyr1, trap1 , and un2 knockdown animals. p value: *<0.05; ** <0.01 (D) Quantification for smedwi -1 + cells in trap1 and un2 knockdown animals. (E) Table summarizing obtained phenotypes, H3p + and smedwi-1 + cell quantification description for 11 genes. Download figure Open in new tab Figure S6 Bulk RNA-seq and scRNA-seq captures interaction for X2 enriched markers (A) Correlation coefficient heatmap between enriched markers featured in pluripotent sample of X2 MTG low HFSC obtained from differential expression with other progenitors sample in X2 MTG population. In it blue color represents the positive correlation while the brown color represents the negative correlation. (B) UMAP plot illustrating the positive cells in form of higher density for respective markers like smedwi-1, ddx43 and h1b . All together it represents triple positive cells including markers smedwi-1, ddx43 and h1b for quantification in form of joint density. (C) Relative expression of ddx43 and h1b in ddx43 knockdown animals. (D) Relative expression of h1b and ddx43 in h1b knockdown animals. (E) Heatmap showing expression pattern of previously published differentiation markers from H. O. King et al., (2024) and Raz et al., (2021) across different replicates for GFP control and ddx43 RNAi sample using normalized read counts (z-score). In it, red color depicts the higher expression and blue color depicts the lower expression. (F) RNA-FISH showing smedwi-1 + cells in wild-type and irradiated animals exposed to 5, 10, 15, and 20 Gy respectively. Download figure Open in new tab Figure S7: Notch activity required for ddx43 cell maintenance (A) Quantification of ddx43 + cells in the proximal region at 6 hpa in untreated and Erk-inhibited animals. Each condition comprising four animals. (B,C) RNA-FISH for ddx43 and immunostaining with 6G10 (muscle marker) shows ddx43 + cells closely associated with sub epidermal muscles on the dorsal side. Scale bars: 100 µm. (D) RNA-FISH showing notch2 expression in wild-type animals. Scale bars: 100 µm. (E) RNA-FISH for ddx43 and notch2 in wild-type animals. notch2 expression was observed in ddx43 + cells. Scale bars: 10 µm. (F) Quantification indicating H3p + cells in notch2 knockdown animals. pvalue: *<0.05. Acknowledgements We would like to thank Dr Tina Mukherjee and Dr Kai Lei (Wuhan University) for their discussion and valuable feedback on our manuscript. We acknowledge the central imaging and flow cytometry facility (CIFF), NGS facility and Irradiation facility at the BLiSc Bio-Cluster for their constant technical support. We also thank Johan from Colin lab for Erk Inhibitor and Raj lab (NCBS) for DAPT. Footnotes ↵ * Co-first authors References ↵ Adler , C. E. , & Sánchez Alvarado , A . ( 2015 ). Types or States? Cellular Dynamics and Regenerative Potential . In Trends in Cell Biology (Vol. 25 , Issue 11 , pp. 687 – 696 ). Elsevier Ltd . doi: 10.1016/j.tcb.2015.07.008 OpenUrl CrossRef PubMed ↵ Baguñà , J. , Saló , E. , & Auladell , C . ( 1989 ). Regeneration and pattern formation in planarians: III. Evidence that neoblasts are totipotent stem cells and the source of blastema cells . 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Share Single-cell transcriptome identified ddx43 + cell types critical for maintenance of transient slow-cycling stem cells in planaria Nikhil Kumar Jaligam , Mohamed Mohamed Haroon , Mainak Basu , Atriya Mazumdar , Swathi Pavithran , Vinay Kumar Dubey , Praveen Kumar Vemula , Ankit Arora , Dasaradhi Palakodeti bioRxiv 2025.08.22.671706; doi: https://doi.org/10.1101/2025.08.22.671706 Share This Article: Copy Citation Tools Single-cell transcriptome identified ddx43 + cell types critical for maintenance of transient slow-cycling stem cells in planaria Nikhil Kumar Jaligam , Mohamed Mohamed Haroon , Mainak Basu , Atriya Mazumdar , Swathi Pavithran , Vinay Kumar Dubey , Praveen Kumar Vemula , Ankit Arora , Dasaradhi Palakodeti bioRxiv 2025.08.22.671706; doi: https://doi.org/10.1101/2025.08.22.671706 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Developmental Biology Subject Areas All Articles Animal Behavior and Cognition (7618) Biochemistry (17635) Bioengineering (13859) Bioinformatics (41846) Biophysics (21401) Cancer Biology (18534) Cell Biology (25422) Clinical Trials (138) Developmental Biology (13352) Ecology (19860) Epidemiology (2067) Evolutionary Biology (24285) Genetics (15582) Genomics (22463) Immunology (17700) Microbiology (40298) Molecular Biology (17141) Neuroscience (88424) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4813) Physiology (7633) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4284) Systems Biology (9808) Zoology (2267)