Spatial single cell transcriptomic analysis informs tumor developmental hierarchy of DICER1 syndrome related sarcoma

Domain: biology
Published: 2026-03-30
Editorial outcome: minor_revision

Abstract
DICER1-related tumor predisposition (DRTP), also known as DICER1 syndrome, encompasses a spectrum of malignancies mainly in children and young adults. Most are sarcomas, exhibiting histological and molecular similarities regardless of their anatomical origins, and only express the RNase IIIb domain-defective DICER1. To uncover their cellular origin and developmental hierarchy, we establish a lineage-traceable genetically engineered mouse model with controlled activation of hemizygous Dicer1 RNase IIIb mutation in Hic1+ mesenchymal stromal cells. This causes renal tumors closely mirroring the developmental continuum of human DRTP sarcoma histologically and molecularly. Spatial single-cell transcriptomic analysis reveals a Hic1+Pdgfra+Dpt+Pi16+ fibroblastic progenitor population, corresponding to universal fibroblasts subjacent to transitional epithelium of renal collecting ducts, that can undergo rhabdomyoblastic differentiation or become proliferative sarcomatous cells. Investigation of patient samples identifies analogous cell states and developmental trajectories. This study uncovers a fibroblastic origin for DRTP sarcoma and provides a faithful mouse model for future mechanistic and translational investigation.

Introduction
DICER1-related tumor predisposition (DRTP) (MONDO: 0100206), also known as DICER1 syndrome, is caused by germline pathogenic variants (PVs) in DICER1 that predisposes children and young adults to cancer1,2. This includes several tumor types which arise at distinct anatomical locations, including lung, kidney, gynecologic tract, and central nervous system (CNS), among other rarer sites1-3. Germline DICER1 PVs typically inactivate the affected allele4. In DRTP, tumor development is usually associated with the occurrence of a hotspot missense mutation on the second DICER1 allele, all of which result in the production of RNase IIIb catalytic domain-dead DICER1 protein5. This compound heterozygous state of DICER1 leads to impaired processing of pre-miRNAs and subsequent loss of mature miRNAs derived from their 5p-strands (5p-miRNA)5-7.

Although arising in different anatomical sites, it has long been noted that many DRTP cancers share key histomorphological features8. This includes subepithelial condensation of malignant mesenchyme (cambium layer), as well as frequent rhabdomyoblastic differentiation, resembling rhabdomyosarcoma (RMS). This overlap has raised important questions about whether these morphologically similar tumors arising at different sites constitute a common tumor entity. Recently we addressed this hypothesis by studying a large series of DICER1 syndrome-associated cancers9. Our study identi fied shared clinicopathological, genetic and epigenetic (DNA methylation) features in a distinct group of DICER1 syndrome-associated mesenchymal neoplasms. This group encompasses well established tumor entities and comprises three clinically meaningful classes termed low-grade mesenchymal tumor with DICER1 alteration (LGMT DICER1), sarcoma with DICER1 alteration (SARC DICER1), and primary intracranial sarcoma with DICER1 alteration (PIS DICER1). While our findings suggest a common cellular origin for DRTP sarcoma, the underlying developmental biology and processes involved in DICER1-mediated tumor cell transformation remains largely unknown.

Genetically engineered mouse models (GEMM) are powerful tools for studying tumor development in a cell type specific manner. We have recently developed a conditional Dicer1 RNase IIIb mutant allele (Dicer1+/f-D1693N)10, which, upon Cre-mediated recombination, expresses the mouse p.D1693N mutant Dicer1 (Dicer1D1693N) that is equivalent to human p.D1709N mutant DICER1, one of the most common RNase IIIb mutations in human patients1. With this tool strain and a Dicer1fl/fl strain, we also developed a GEMM of DRTP that recapitulates the compound heterozygous Dicer1 mutations seen in most DRTP-associated cancers10. Through an anti-Mullerian hormone receptor type 2 (Amhr2)-Cre driver, these Dicer1 mutations were activated in stromal cells throughout the Mullerian tract, leading to the development of adenosarcomas, a cancer type well described in DRTP11. Subsequently, Oikawa et al. developed a conditional human DICER1 RNase IIIb hotspot mutation (p.G1809R), whose activation through an Albumin-driven Cre enabled the development of epithelial lesions in the liver12. However, these and other models do not allow for controlling mutation activation and lineage tracing, limiting their application to study tumor development continuumm13,14.

In this work, we cross our Dicer1 tool strains with our recently generated CreERT2 mouse line-Hic1CreERT2, which expresses Cre in mesenchymal stromal cells (MSCs) of most adult tissues upon tamoxifen treatment15. This inducible DRTP GEMM develops renal tumors that closely recapitulate the continuum of human DRTP sarcoma development. We leverage single-cell whole transcriptomics (scRNA-seq), a targeted spatial transcriptomics platform, and primary cell cultures to uncover the putative cellular origin and developmental trajectory of sarcomagenesis in this GEMM of DRTP sarcoma, which is found conserved in human disease.

Results
Development of a lineage-traceable and genetically true mouse model of DRTP sarcoma
We utilized a tamoxifen-inducible Hic1CreERT2 driver, which marks quiescent mesenchymal progenitors in most vascularized tissues15, and the Rosa26LSL-tdtomato strain, permanently labeling cells with tdTomato fluorescence upon Cre induction16, to develop a conditional and lineage-traceable Dicer1 GEMM, Hic1CreERT2/CreERT2;Dicer1fl/fl-D1693N;Rosa26LSL-tdTomato mice (referred to as HDT hereafter, Fig. 1a) that express hemizygous Dicer1D1693N upon tamoxifen treatment in Hic1+ cells, recapitulating the compound heterozygous state of DICER1 mutations in human tumors. Postnatal activation of Dicer1 mutations in HDT mice, regardless of sex (Supplementary Fig. 1a), resulted in the development of unilateral or bilateral renal lesions (Supplementary Data 1). Macroscopic analysis revealed a tumor continuum that ranged from small tumors which maintain the integrity of the kidney structure to large masses that had completely overgrown the kidney (Fig. 1b), with 80% (52/65) penetrance and a median latency of 378 days (Fig. 1c, Supplementary Data 1). Different control genotypes did not lead to development of renal tumors and did not differ in overall survival (Supplementary Figs. 1b-f). While induction timing of Dicer1 mutations did not correlate with HDT tumor penetrance or laterality (Supplementary Fig. 1g, h), late Dicer1 mutation activation correlated with a prolonged tumor latency (Fig. 1d), suggesting that the number of cells amenable to mutant DICER1-driven oncogenesis decreases along kidney development, consistent with the predominant childhood onset of these cancers in human patients.

Microscopically, renal HDT tumors closely mirrored human DRTP-associated mesenchymal neoplasms with tumor exhibiting a continuum from LGMT-like features to frank sarcomatous tumors showing features akin to SARC DICER1/PIS DICER1 (Fig. 1e, Supplementary Data 2). No correlation was observed between tumor histology and Dicer1 mutation activation timing (Supplementary Fig. 1i and j). Notably, this continuum included histological features typically associated with cystic nephroma, which is classified under the LGMT DICER1 group, as well as anaplastic sarcoma of the kidney, categorized within the SARC DICER1 class9. In detail, this included small tumors, typically limited to the kidney’s parenchyma (Figs. 1f and g), exhibiting hypocellular and primitive mesenchyme (Fig. 1h) with focal condensation adjacent to cystically dilated renal ducts (Figs. 1i and j). Conversely, large tumors that typically showed sarcomatous overgrowth of neighboring tissue consisted of sarcomatous mesenchyme with a pattern-less or fascicular growth (Figs. 1k and l). In these tumors, subepithelial aggregation of tumor cells underneath transitional cell epithelium (bona fide cambium layer) (Fig. 1m), prominent rhabdomyoblastic differentiation (Fig. 1n), and prominent anaplasia (Fig. 1o), often accompanied with necrosis, was noted. In line with human disease2,17, tumors stained for PDGFRA and DESMIN, with PDGFRA marking areas of primitive tumor cells and DESMIN highlighting regions of rhabdomyoblastic differentiation (Fig. 1p, Supplementary Data 2). While the mesenchyme of all tumors was diffusely positive for lineage-tracing marker tdTomato, associated lining epithelium was negative for tdTomato throughout (Fig. 1q), indicating that it was not developed from the neoplastic lineage.

Targeted sequencing of HDT tumors confirmed the expected Dicer1 RNase IIIb (D1693N) mutation in all sequenced samples and revealed recurrent hotspot mutations in Kras and Trp53, which are commonly seen in human DRTP sarcoma9,18-21, in 3/35 and 3/35 tumors, respectively (Fig. 1r, Supplementary Data 3). Furthermore, qPCR for Kras identified an amplification in one tumor with a concurrent Kras missense mutation, which was confirmed by fluorescence in-situ hybridization (FISH) (Fig. 1s, Supplementary Data 3). Notably, these mutations were only identified in neoplasms with SARC-like differentiation. Shallow whole-genome sequencing (sWGS) revealed gain of chromosome 1 in a significantly greater proportion (Fig. 1r and Supplementary Figs. 1k and l, Fisher’s exact test, p=0.03051, odds ratio = 8.41408, 95% Confidence Intervals = 1.2288, 78.5872) of SARC-like lesions (23/25) than LGMT-like lesions (5/9) (Supplementary Data 3), suggesting that aneuploidy may represent a secondary event associated with tumor progression. Overall, these results demonstrate that renal neoplasms from our HDT GEMM fully recapitulate the tumor development continuum of human DICER1 syndrome-associated mesenchymal neoplasms.

Organization of the Hic1+ mesenchymal stromal cell lineage in mouse steady state kidney
As pediatric tumors often mirror the normal development of affected organs, understanding the spatial composition of Hic1+ MSC lineage in normal mouse kidney is critical for establishing the tumor development trajectory of our renal HDT tumors. Notably, despite that Hic1 effectively identifies various MSC populations, including fibroblasts and pericytes, alongside rare perineural cells in mouse skeletal muscle, heart, and skin15,22,23, the characteristics of the Hic1+ MSC lineage in kidney (Supplementary Fig. 2) remains elusive. To address this, we analyzed renal tissues from control mice using scRNA-seq and targeted spatial transcriptomics (10X Genomics, Xenium, 379 genes; Supplementary Data 4) (Fig. 2a). ScRNA-seq analysis of normal mouse kidneys from 7 mice (post-natal week (PNW) 4-36, Supplementary Table 1) identified multiple clusters correlating to physiological kidney cell composition (Supplementary Figs. 3a-d)24-26. Focused analysis of tdTomato+ MSCs identified 6 clusters that were grouped into 5 cell types based on marker expression (Fig. 2b-d, Supplementary Figs. 3e-g). This included: 1) a cluster of mural cells (Mural, Myh11+, Acta2+, Tagln+), 2) two clusters of Pdgfra+ and Dpt+ fibroblasts, one characterized by Pi16high and Ly6ahigh (Uni Fibro Pi16high) and the other by Col15a1high and Mfap4high (Uni Fibro Col15a1high), and 3) two clusters of Pdgfra+ cells that were largely negative for Dpt, Pi16 and Col15a1, one (Fibro) showing a signature resembling kidney interstitial fibroblasts and mesangial cells (Itga8+, Robo2+, Ptn+) and the other (Cycle) enriched for cycling markers (Cenpf+, Top2a+, Mki67+).

Spatial transcriptomic analysis of 22 cored kidney regions of 10 kidneys and 2 whole kidney tissues from 9 normal control mice (Supplementary Data 5) corroborated the scRNA-seq data, revealing the cellular composition of normal kidneys (Supplementary Fig. 4a-e). Uni Fibro population was lower in aged (PNW: 81) vs young (PNW: 10) adult kidney of normal control mice (1.8% vs 2.3% of all cells identified, Supplementary Fig. 4f). Targeted analysis of kidney MSCs (Fig. 2e) identified mural cells (Mural) mapping to pericytes and vascular smooth-muscle cells (Rgs5+, Myh11+, Tagln+), fibroblasts/mesangial cells (Fibro: Bgn+, Cfh+, Itga8+), and universal fibroblasts (Uni Fibro: Bgn+, Lum+, Mfap4+) that appeared to overlap with the Uni Fibro Pi16high and Uni Fibro Col15a1high populations by scRNAseq analysis. In line with their expression profile, Fibro mapped to interstitial fibroblasts of cortex and medulla, as well as to rare mesangial cells, while Uni Fibro was predominantly found in the perivascular niche in the hilar region, the basal membrane niche subjacent to transitional cell epithelium, and the renal capsular surface (Fig. 2f-h). Although the resolution of the targeted gene panel did not allow for subclassification of Uni Fibro based on UMAP, Pi16 expression was notably enriched in Uni Fibro within the perivascular niche, while Mfap4 expression was high in Uni Fibro subjacent to transitional cell epithelium (Supplementary Fig. 5). Notably, a recent study of murine fibroblasts also identified two groups of Dpt+ fibroblasts that are present universally across various tissues (in this paper kidney was not studied)27. The first group included Dpt+Pi16+ Uni Fibro that shared features with adventitial stromal cells28, which are found in perivascular niches29,30. The second group was composed of Dpt+Col15a1+ specialized fibroblasts and showed an association with the basement membrane. Therefore, our study identifies two types of fibroblastic progenitor populations originated from Hic1+ MSCs in the steady-state mouse kidney with Pi16high Uni Fibro enriched within the perivascular niche and Col15a1high Uni Fibro more associated with the subepithelial basement membrane of lining transitional epithelium.

Spatial cell composition of Dicer1 renal sarcoma from HDT mice
Next, we characterized the transcriptomic landscape of 5 primary HDT renal tumors by scRNA-seq (Fig. 3a), one of which harbored a Trp53 mutation in their corresponding FFPE tissue (Fig. 1r), revealing the presence of multiple cell lineages, such as transitional epithelial, mesenchymal, immune and endothelial cells (Supplementary Fig. 6). Unsupervised clustering of tdTomato+ MSCs (Supplementary Fig. 7a-c) revealed 10 distinct cell populations, all of which expressed some specific markers of steady kidney MSC lineages (Supplementary Fig. 7b). To further compare their developmental relatedness, we co-clustered scRNA-seq data of both HDT tumor and steady-state kidney samples (Supplementary Figs. 7d and e). This led to the identification of two clusters in HDT tumors corresponding to steady-state kidney mural cells, pericytes (Peri; Rgs5+) and vascular smooth muscle cells (SM Vasc; Acta2+), a cluster overlapping with steady-state kidney Uni Fibro that we termed Progenitor (Progen), and a ground population (Ground) closely resembling Fibro of steady-state kidney (Supplementary Fig. 7f, Figs. 3b-d). A Proliferative cluster (Prolif) enriched in genes related to cell proliferation, similar to cycling cells of steady-state kidney, was also identified, some of which also expressed signature genes of Progen or Ground clusters, suggestive of their heterogenous nature. Moreover, HDT tumors also contained three populations not identified in steady-state kidney, including a cluster of perturbed-state fibroblasts (Fibro Perturb) and two myogenic populations. The Fibro Perturb cluster expressed a high level of Pi16 with low Dpt and also expressed signature genes of perturbed-state fibroblasts (Comp+, Cpxm2+, and Itgbl1+) that were reported by Buechler et al.27. The two additional clusters expressed genes linked to skeletal muscle development (e. g. Pax7) and committed muscle cells (e.g. MyoG), termed transiting-differentiated myogenic cells (TR Diff Myo) and differentiated myogenic cells (Diff Myo)31.

To spatially visualize these clusters, we performed targeted spatial transcriptomics on renal tumors (n=24) and non-neoplastic kidneys (n=15) harvested from HDT mice (Supplementary Data 5, Supplementary Fig. 8a-c). This revealed clusters corresponding to cell states in both steady-state kidney and tumors characterized by our scRNA-seq data (Supplementary Fig. 8d-g). Notably, the Progen cluster represented the dominant MSC population in LGMT-like tumors (Supplementary Figs. 8c and e), ubiquitously expressing Mfap4 with a portion expressing Dpt and Pi16 (Fig. 3e), indicating that this subepithelial cell cluster was composed of rare tumor initiating progenitors and abundant progenitor-like tumor cells. In SARC-like tumors, Progen cells were enriched in the subepithelial condensed cambium-like layer (Fig. 3f), where abundant Diff Myo and TR Diff Myo cells were often detected, suggestive of their lineage relatedness. Cells assigned to Ground were usually scattered within other cell states, e. g. Progen and Prolif clusters (Fig. 3g). The Ground, and especially Prolif cluster cells showed frank sarcomatous differentiation (Fig. 3h).

Taken together, our scRNA-Seq and targeted spatial transcriptomic analysis not only revealed the spatial complexity of HDT tumors, but suggest that HDT tumors arise from perturbed differentiation of renal Hic1+ MSC progenitors subjacent to transitional epithelial cells of renal collecting ducts.

A Dpt+Pi16+ universal fibroblast lineage as the cell of origin for mouse Dicer1 sarcoma
To identify the tumor development trajectory, we cultured tdTomato+ cells isolated from kidneys of control and HDT mice 4 weeks post tamoxifen treatment at 3-week-old. Bulk RNA-Seq analysis demonstrated that activation of Dicer1 mutations led to increased expression of multiple Uni Fibro markers (Dpt, Dcn, Pi16, Ly6a, Ly6c1, Col15a1) and reduced expression of lineage markers for differentiated fibroblasts (Itga8) or pericytes (Myh11, Acta2) (Fig. 4a, Supplementary Fig. 9a), which were validated by real-time qRT-PCR (Supplementary Fig. 9b). This finding suggests that Dicer1 mutation reprograms the multipotent Hic1+ MSC, impairing the differentiation ability of their derivative primitive Uni Fibro, which primes them for subsequent malignant transformation towards a variety of cell states.

Pseudotime trajectory analysis from the Dpt+Pi16+ Uni Fibro-like population revealed 6 trajectories (Figs. 4b and c), all passing through the Progen population that was accompanied by a loss of Uni Fibro-specific markers (e. g. Dpt, Pi16, Mfap4) (Fig. 4d). Trajectories 2 and 3 led into Diff Myo (Myog+), passing through the “intermediate state” TR Diff Myo cells (Pax7+), including a highly proliferative state of TR Diff Myo. Trajectories 1, 4 and 5 transitioned into the Ground state (Itga8+). Trajectory 6 transitioned into the Pericyte state (Pdgfrb+Rgs5+). To validate the lineage relationship, we conducted copy number (CN) analysis using scRNA-Seq datasets from HDT renal tumors, which revealed high CN changes in tdTomato+ mesenchymal cells, but not other cell types (Supplementary Fig. 9c), consistently with our finding using the sWGS analysis (Supplementary Figs. 1k and l). Analysis of normal control kidneys revealed minimal CN changes (Supplementary Fig. 9d), further confirming CN changes in HDT mesenchymal cells were tumor-specific. Focused analysis of tdTomato+ mesenchymal cells of HDT tumors identified CN changes in all Hic1+ clusters except Fibro Perturb and SM Vasc populations. In contrast, a sizable subset of Progen cells accumulated gains in chromosomes 1 and 6 (Fig. 4e). Notably, the Ground and Prolif populations exhibited near-universal gains in these same chromosomes. Additionally, only a minority of the TR Diff Myo and Diff Myo populations demonstrated gains in chromosomes 1 and 15, although these CN changes were more pronounced than those observed in Progen cells, suggesting that chromosome gains can occur early before their committed differentiation but are not essential for rhabdomyogenic differentiation. These findings support a model of complex tumor progression, in which Dicer1 mutation perturbs the differentiation potential of Hic1+ MSC, priming Dicer1-mutant Dpt+Pi16+ Uni Fibro-like cells for oncogenic transformation via distinct mechanisms: a portion of these progenitors undergoes aberrant myogenic differentiation, whereas accumulation of CN alterations promotes tumorigenesis towards the Ground state, from which a subset progresses to proliferative sarcoma upon acquiring specific genomic changes (Fig. 4f). Consistent with this model and the recurrent occurrence of p53 mutations in both human and mouse tumors, deletion of p53 in Dicer1-mutant primary cells significantly accelerated cell growth (Fig. 4g). Furthermore, gene set enrichment analysis of differentially expressed genes among HDT tumor cell states revealed that the Prolif population was characterized by downregulation of p53-associated pathways and concomitant upregulation of MAPK signaling (Fig. 4h). Therefore, the identification of HDT tumor development trajectory may inform future therapeutic development.

Cell states of mouse Dicer1 sarcoma are largely recapitulated in human DRTP sarcoma
To determine whether cell states informed from the mouse Dicer1 sarcoma are present in human DRTP sarcomas, we characterized the transcriptional landscape across 16 human DRTP mesenchymal tumors (4 LGMT DICER1, 11 SARC DICER1, and 1 PIS DICER1), including Cystic nephroma, pleuropulmonary blastoma (PPB) I/II/III, nasal chondroid mesenchymal hamartoma, DICER1-mutated embryonal RMS, and primary intracranial sarcoma (PIS DICER1), using a targeted spatial transcriptomics platform (Fig. 5a). Unsupervised clustering identified 13 Louvain clusters (Supplementary Figs. 10a and b). These clusters correspond to distinct cell populations, including non-neoplastic cell types (Supplementary Figs. 10c and d). Guided by lineage markers (e. g. PDGFRA) and spatial morpho-molecular correlation, we identified fibroblast lineage clusters (Supplementary Fig. 10e)32, which were grouped into cell states that largely recapitulated those identified in our mouse HDT tumors (Figs. 5b-d). These included a neoplastic progenitor cluster (Progen) expressing PDGFRA and universal fibroblastic marker DPT, a proliferative cluster (Prolif) dominated by proliferation and cell cycle genes, two myogenic subpopulations expressing skeletal muscle-related genes (e.g., DES, PROX1) as well as committed muscle cell markers (e.g., MYBPC1), termed transiting-differentiated myogenic cells (TR Diff Myo) and differentiated myogenic cells (Diff Myo). Additionally, a PDGFRA+ fibroblastic population lacking other signatures (Ground), and a distinct fibroblast cluster (Fibro) mapping to non-neoplastic tissue and marked by PDGFRA+ and C7+ were identified. Spatial analysis revealed Progen cells in all tumors at varying frequencies (Fig. 5e), often mapping to the cambium layer of LGMT and SARC DICER1 (Fig. 5f) as seen in our HDT renal tumors (Fig. 3f). Like HDT tumors, TR Diff Myo states were associated with increased proliferation (Fig. 5g). Bona fide rhabdomyoblastic differentiation (Diff Myo) co-localized with TR Diff Myo and the committed muscle marker MYBPC1 (Fig. 5h). Ground and Prolif cells were abundant in SARC/PIS DICER1 and exhibited sarcomatous growth (Fig. 5i). In agreement with the elevated MAPK pathway in the highly proliferative state cells of HDT tumors (Fig. 4h), immunohistochemistry staining of human tumors revealed elevated p-ERK levels in SARC DICER1 compared to LGMT DICER1 (Supplementary Figs. 10f and g). These results alongside trajectory analysis (Supplementary Fig. 10h) closely mirrored spatial relationships observed in murine HDT tumors.

Human DRTP mesenchymal neoplasms closely mimic RMS clinically and morphologically. A recent scRNA-based meta-analysis identified a robust tripartite cell state landscape across various human RMS tumors, including Progenitor, Proliferative, and Differentiated metaprograms33. This paper also inspired some of our cluster names. Our transcriptomics data showed that these RMS metaprograms are enriched in signatures present in DICER1 mesenchymal neoplasms (Supplementary Fig. 10i), highlighting their transcriptional relatedness.

Methods
Model development and cohort assembly
All animal experiments were conducted following ethical approval by the University of British Columbia animal care committee (A21-0023 and A21-0047). The conditional knockin mice expressing the floxed Dicer1 p.D1693N (Dicer1+/fl-D1693N) was previously developed by our group10. The Dicer1fl/fl mice44 [RRID:MMRRC_015388-UNC] were obtained from the Mutant Mouse Resource & Research Centres (MMRRC) depository. The Rosa26LSL-tdtomato strain16 was obtained from The Jackson Laboratory (stock #: 007909). Hic1CreERT2 mice were previously generated as described elsewhere15. Mouse lines were maintained on a pure C57Bl/6J genetic background. We outcrossed these 4 strains together to develop the Hic1CreERT2/CreERT2; Dicer1fl/fl-D1693N; Rosa26LSL-tdTomato and corresponding control strains. Genotyping was performed using primers in Supplementary Table 2 using ear punches obtained at 3 weeks of age. To induce CreERT2 nuclear translocation, mice were injected at postnatal 10 (P10) days to 7 weeks old intraperitoneally with tamoxifen (dose: 2 mg every 24 hours for 5 consecutive days for mice at 3-7 weeks old; 1 mg every 24 hours for 3 consecutive days for mice at 2weeks old; 1mg for mice at P10).

For tumor development studies, we used 65 HDT mice (33 male, 32 female) and 55 control mice (26 male, 29 female). Mice were either found dead or sacrificed because they were morbid or reached the experimental end point (1.5 years). At the experimental endpoint, the tumor burden in each mouse did not reach the approved humane endpoint limit (10% of body weight). For penetrance analysis, the presence of macroscopic or microscopic tumors at death was used as the primary endpoint. For overall survival (OS) analysis, events were defined as either the death of the mice or their euthanasia due to morbid conditions. 5 tumors from 5 HDT mice (4 males and 1 female) with age ranging PNW 36 to PNW 66 were used for scRNA-Seq analysis. 24 tumors from 8 HDT males and 10 HDT females, age ranging from PNW 36 to PNW 81, and 15 non-neoplastic kidneys from 13 HDT mice (9 males, 4 females), age ranging from PNW 10 to PNW 61, were used for spatial transcriptomic analysis.

For scRNA-Seq analysis of normal mouse kidneys, 7 control mice (3 males, 4 females) were sacrificed at the age of PNW 4-36. For spatial transcriptomic analysis of normal mouse kidneys, 9 control mice (5 males and 4 females) were sacrificed at the age of PNW 10 to PNW 81.

Patient samples
A cohort of 16 patient samples of DICER1 mesenchymal neoplasms from 10 males and 6 females, with age ranging from 1 month to 69 years, was included in this study. They had all undergone histopathological evaluation by specialized pathologists, as well as molecular testing whenever applicable. Samples were collected at McGill University-affiliated hospitals in Montreal, Quebec, Canada and the Institute of Pathology, University Hospital Heidelberg, mainly from collaborating institutions in accordance with ethics review board regulations. All eligible participants, regardless of sex, were included with signing an informed consent form. Clinical data, histological features and/or sequencing results of tumors have previously been reported elsewhere9. Study of patient samples was approved by the institutional ethics committee (Heidelberg University, S660/2020) and performed in accordance with the Declaration of Helsinki. Due to the rareness of the disease, we do not have sufficient power to perform sex-specific analysis.

Tissue microarray construction
For murine tissue microarray (TMA, n=2) construction, 1.0 mm cores of formalin‐fixed, paraffin‐embedded (FFPE) tissue from control and HDT mice were used, as described previously45. For human samples, a previously constructed TMA was used, that included duplicates of 0.6 mm cores of FFPE tissues, including the 16 samples of DICER1 mesenchymal neoplasms used in this study.

Primary cell culture
The whole kidneys from Hic1CreERT2/CreERT2:Rosa26LSL-tdTomato and Hic1CreERT2/CreERT2:Dicerfl/fl-D1693N;Rosa26LSL-tdTomato GEMM mice 4 weeks post tamoxifen treatment, which occurred when mice were 3-week-old, were minced with forceps to small chunks pipette-able by a 5mL serological pipette. Tumour chunks were then incubated in Gibco HBSS (no calcium, magnesium, phenol red, Catalog #14175103) and Stemcell 10X Collagenase/hyaluronidase in DMEM (Catalog # 07912), in a glass flask at 37oC on a hot plate, until everything was dissolved, usually takes between 30-45min. Enzymatic digestion was stopped with Advanced DMEM/F-12 (Catalog #12634010). Cells and tumour chunks were spun at 1000rpm for 5min. Pellets were resuspended in Gibco 0.25% Trypsin-EDTA (Catalog #25200072) for 5min at 37oC, followed by filtering through a Falcon 70μm cell strainer (Catalog #08-771-2). Cells were pelleted and resuspended in Stemcell Mouse MesenCult™ Expansion media (Catalog #05513). tdTomato positive cells were enriched by FACS and subsequently cultured in Mesencult media or DMEM/F12 media supplemented with 10%FBS.

Histo- and Immunohistochemistry
4 µm-sections were cut and mounted on Permaflex Plus Microscopic Slides (Leica Biosystems). Sections were stained using standard protocols for Hematoxylin and Eosin (H&E). For immunohistochemistry of murine samples slides were subjected to heat-induced antigen retrieval in a high-pH buffer. Staining was performed using the Ventana Benchmark Ultra automated immunostainer (Ventana Medical Systems, Tucson, AZ, USA).

Immunohistochemical staining of murine samples was performed with antibodies to tdTomato (Rockland, anti-RFP, 600-401-379, Dilution 1:400), PDGFRA (Cell signaling, D1E1E, Dilution 1:100), p-ERK (Cell Signaling, 4376S, clone 20G11) and DESMIN (Abcam, Y66, ready-to-use).

Targeted panel sequencing
Genomic DNA was extracted with QIAamp DNA FFPE Advanced UNG Kit (Cat. # 56704). Mutations in Kras (exons 2 and 3), Nras (exons 2 and 3), Hras (exons 2 and 3), Trp53 (exons 2-11), and Dicer1 (exon 24) were sequenced on a NovaSeq 6000 machine using in house designed primers, and libraries were prepped with in house. Targeted panel data was processed using a modified version of the Nextflow46 and Sarek47 pipeline, version 3.4.2. Raw reads were trimmed with FastP and aligned with bwa-mem2. Overlapping primer intervals were merged with bedtools merge, version 2.30.0, and 100 bp of padding was added to either side of each interval. No duplicate marking was carried out and the ‘wes’ parameter was set to true. Somatic variants were called on the primer intervals with GATK Mutect2 with ‘ignore_soft_clipped_bases’ set to true, and annotated with SnpEff48 and Ensembl VEP49. For the variant calling step, the pipeline was modified to disable downsampling of the reads. Variants of interest were selected by filtering for those with variant allele fraction (VAF) > 0.1, and excluding those annotated as ‘synonymous_variant’ or ‘intron_variant’ by SnpEff.

Kras qPCR and FISH
To evaluate Kras copy number status we used a qPCR assay with SYBR Green detection and a custom primer for Kras (Chr.6:145220129) and Tfrc (Chr.16:32626732). Samples were normalized to Tfrc and evaluated for a relative gain in Kras. HDT361 showed a high count for Kras upon qPCR, and gene amplification was identified using a fluorescence in-situ hybridization (FISH) assay. For this assay we designed custom probes for Kras on 6qA1 (RP23-300B22, green; Empire genomics) and 6qG3 (RP23-118J6, red; Empire genomics).

sWGS and data analysis
DNA was extracted from cored FFPE HDT tumor or non-neoplastic kidney samples and sheared for library preparation and sequencing as we described previously50. Single-end reads were processed with a previously validated Nextflow pipeline. Read quality was assessed via FastQC 0.11.9 and reads were trimmed with Trimmomatic 0.39.29. Ilumina adapters and leading / trailing bases with quality scores below 3 were removed, as were 4 base sections with average quality scores below 15. Reads shorter than 40 base pairs were dropped. Reads were aligned to mm10 with bwa-mem2 2.1.30. Following alignment, reads were processed with QDNAseq 1.34.32 to determine segmented relative copy-number and call gains and losses using various bin sizes: 30 kbp, 50 kbp, 100 kbp. The 50 kbp bin size was selected as the best balance of resolution and variance. One sample was excluded due to a highly variable and fragmented copy-number profile, consistent with poor sample quality. For statistical comparisons of copy number alterations, a two-sided Fisher’s exact test was used as implemented in the exact2x2 R package, which implements improved selection of the matching confidence interval for such two-sided tests.

Bulk RNA-Seq
RNA from frozen cell pellets was extracted using the Zymo Quick-RNA Miniprep Kit (Zymo cat# R1054). Total RNA integrity is assessed with the TapeStation 4200 (Agilent Technologies; RNA Assay Cat No.5067-5576) and quantified with the Qubit RNA HS assay (ThermoFisher; Cat No. Q32852). Total RNA is used as input to enrich for mRNA with the NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB; Cat No. E7490). Strand-specific libraries are prepared using the DNBSEQ Fast RNA Library Prep kit (Complete Genomics; Cat No. 940-001522-00) and sequenced at Vancouver Prostate Centre. Library quality and size are checked with the TapeStation 4200 (Agilent Technologies; D1000 Assay Cat No. 5067-5582) and then quantified using the Qubit dsDNA HS Assay (ThermoFisher; Cat No. Q32854). The libraries are sequenced using a Complete Genomics DNBS EQ-G400RS High-throughput Sequencer to generate 30 million 100-bp paired-end reads per library.

Bulk RNA-Seq data analysis
Paired-end reads were processed with the nf-core rnaseq pipeline, version 3.14.0 and aligned to mm10. The trimmer was parameter was set to ‘trimgalore’ and the aligner to ‘star_salmon’, with extra salmon quant arguments supplied as ‘--seqBias –gcBias’. Following alignment and quantification, the counts were imported with tximport version 1.26.1 and differential expression between the Dicer1 mutant and control samples was assessed within each media type using DESeq2 version 1.38.3. Genes with fewer than 10 counts in at least 3 samples were excluded from analysis.

Cloning, lentivirus packaging and cell proliferation
The gRNA sequences for knocking out Trp53 in murine cells was aggagctcctgacactcgga51 and gRNA2 sequence was accctgtcaccgagacccc52. They were cloned into the lentiCRISPRv2GFP (Plasmid #82416) vector following the Zhang lab protocol53. For viral packaging, early passage HEK293T17 cells, cultured in DMEM + 10% FBS, authenticated by short tandem repeat analysis and negative for mycoplasma contamination, were seeded in 10cm dishes and then transfected with 5µg of lentiCRISPRv2GFP-p53gRNA plasmids, 4µg of 2 and 1ug of VSV.G plasmids using 20µL of Mirus Bio™ TransIT™-LT1 Transfection Reagent (Catalog #MIR2300). Virus was harvested in DMEM +10% FBS +1.1g/100ml BSA (Catalog # A2153-10G) +0.01M HEPES (Catalog #15630080). All the collected media was filtered with a 0.45 μm filter (Catalog #09-761-35). Beckman Optima XE Ultracentrifuge was prechilled to 4oC. 36-40mL of filtered harvest media was aliquoted into a 50ml tube (#NC9530407) and spun down at 25000 rpm at 4oC for 90 min. Reconstituted viral pellet was stored at -80oC. 50uL of concentrated virus was used per 50,000 cells. Plates were spun at 2000rpm at 37oC for 2 hours. Selection with eGFP sorting was carried out one week after transduction.

Growth curve generation
Primary cells were seeded in 96-well plates in triplicates (500 cells/well) and were allowed to attach for 24 hours to achieve an initial confluency of 10%. Live-image for 7 days was performed with CELLCYTE X™ from Cytena. The mean confluency of cells was plot over time for illustrating their growth behavior.

scRNA-Seq
Sample preparation. Tumour and normal kidney tissues were minced to 1mm3 with forceps and incubated at 37oC with Stemcell 10X Collagenase/hyaluronidase in DMEM (10X Genomics, C#07912) in HBSS for 30 minutes. The minced tissues were subsequently incubated in Trypsin-EDTA (0.25%) for 5 min at 37oC. Cells were then filtered through a 70 μm cell strainer. Live cells were enriched with Miltenyi Biotec Dead Cell Removal Kit. Mesenchymal cell populations were then isolated by FACS sorting with tdTomato positive enrichment and antibody depletion of Epcam, CD45, CD31, CD11b and F4/80. Cells were then prepared according to the 10X Genomics Chromium Single Cell Universal 3‘ Gene Expression kit protocol.

Alignment and quality control. Raw reads were aligned with Cell Ranger count 7.1.0 to a combined reference genome, composed of the mm10 reference with the addition of the tdTomato reporter sequence15. The combined reference genome was generated with Cell Ranger mkref 7.1.0. Following alignment, per-sample quality control was conducted. Low quality cells with > 25% of reads originating from mitochondrial genes, < 1000 UMIs detected, or < 500 genes detected, were discarded.

Integration, clustering, cell-type annotation, and pathway activity. Samples originating from HDT and/or control mice were integrated using Seurat 4.3.0.154. Counts were normalized per cell by library size followed by log1p transformation. The top 2,000 highly variable genes per sample were selected using the VST method and the top 2,000 genes across samples were used for integration via reciprocal principal components analysis (PCA). After integration and dimensionality reduction via PCA, shared nearest neighbors were identified in PCA space and clustered with the Louvain algorithm, using the first 30 components. The resulting clusters were manually annotated via the examination of differentially expressed up-regulated genes, in combination with markers of kidney and Hic1+ MSC types obtained from the literature. To achieve a pure Hic1+ MSC lineage following examination of all cell types, the data was subset to the relevant clusters in the control (Uni Fibro, Fibro, Mural, Cycle) or HDT (Fibro Perturb, Fibro, Mural, Cycle, TR Diff Myo, Diff Myo) samples. If necessary, residual tdTomato- cells, or cells expressing epithelial (Epcam+, n=102), immune (Ptprc+, n=113), or transitional epithelial (Krt19+, n=376) markers were excluded from the subset data. The resulting data, filtered for the Hic1+ MSC lineage, was reprocessed as described above for the unfiltered data; a reduced number of principal components were used for neighbor finding and clustering after selection via an elbow plot.

Trajectory inference. The Hic1+ MSC lineage obtained from the HDT samples was used as input for trajectory inference with Monocle 3, version 1.3.455. Based on the UMAP obtained during dimensionality reduction with Seurat, trajectory was inferred, and the cells were placed in a pseudotime ordering, setting the node in the graph surrounded by the greatest number of Dpt+ cells as the earliest point. Trajectories to Ground, Cycle, and Diff Myo were examined by selecting the relevant branches, holding the start point constant and varying the end point to the most distant node in the ending cluster of interest. Selected genes of interest were visualized in pseudotime over the respective trajectories.

Copy number analysis. To infer copy-number from the scRNA-seq data, inferCNV 3.2.0 was used. The raw counts for all cells present in the control samples and the Hic1+ MSC lineage from the HDT samples were combined and used as input to inferCNV. All parameters were set to their default setting, except for ‘cutoff’, which was set to 0.1 as prescribed for 10X data in the inferCNV documentation.

Xenium spatial transcriptomics
Slide Preparation and Imaging. For spatial transcriptomic analyses (10X Genomics Xenium), four slides were prepared. Two slides each contained a section from one of the two murine TMAs. A third slide included full kidney sections from three control mice and one HDT mouse harboring a LGMT-like lesion. The fourth slide featured a section of the human TMA. Tissue sections were prepared as per the Xenium In Situ for FFPE – Tissue Preparation Guide (10X Genomics, CG000578 version Rev C) protocol. 5 μm sections were cut and mounted onto the Sample Area of a Xenium slide. The slides were air dried, followed by a 3 h incubation at 42°C. Subsequently, the slides were processed, over a two-day span, as per the Xenium In Situ for FFPE – Deparaffinization and Decrosslinking (10X Genomics, CG000580 version Rev C) and Xenium In Situ Gene Expression (10X Genomics, CG000582 version Rev C) protocols. On day one, slides were incubated at 60°C for 2 h on a Xenium thermocycler adaptor plate (BioRad, C1000 Touch Thermocycler), followed by deparaffinized through a series of immersions in xylene, 100%, 96% and 70% ethanol, and rehydration with nuclease-free water. Slides were assembled into Xenium cassettes for all downstream steps, starting with decrosslinking and permeabilization. Gene panel probes were hybridized overnight (minimum 18 h) at 50°C. The pre-designed dataset for the murine slide included 379 genes (10X Genomics, Mouse Tissue Atlassing Panel, PN1000627). The pre-designed dataset for the human slide included 379 genes (10X Genomics, Human Multi-Tissue and Cancer Panel, PN1000626). On day two, slides were subjected to a post-hybridization wash (37°C for 2 h), ligation (37°C for 2 h), amplification (30°C for 2 h), autofluorescence quenching and nuclei staining steps. Slides were then stored in PBS-T, overnight, at 4°C, in the dark. The following day, slides and consumables were loaded onto the Xenium Analyzer instrument according to the Xenium Analyzer (10X Genomics, CG000584, version Rev B) protocol. The whole slide area was selected for probe decoding and image acquisition, and the run was initiated. After run completion, slides were H&E stained as per the “Xenium in Situ Gene Expression – Post Xenium Analyzer H&E Staining” (10X Genomics, CG000613, version Rev A) protocol, and scanned using a slide scanner (AperioAT2, Leica).

Segmentation and Xenium data analysis. The Xenium bundles were segmented with Xenium Ranger 1.7.1.1, with the expansion distance around nuclei set to 5 µm. The bundles were then re-segmented with Proseg 1.1.4 using the ‘xenium’ preset, converted to the Baysor format with the ‘proseg-to-baysor’ utility bundled with Proseg, and finally re-imported with Xenium Ranger 1.7.1.156. Cell visualization and morpho-molecular correlation was performed in the 10X Xenium Explorer (Version 3.0.1). Resulting Xenium objects were analyzed using Seurat 4.3.0.154. Quality control was conducted and low-quality cells with less than 3 counts and less than 3 features, were discarded. After normalization, integration and dimensionality reduction via PCA, shared nearest neighbors were identified in PCA space and clustered with the Louvain algorithm. The resulting clusters were manually annotated via the examination of differentially expressed up-regulated genes, in combination with markers of kidney and Hic1+ MSC types obtained from the literature, as well as morpho-molecular correlation. To achieve a pure tumor cell-associated lineage following examination of all cell types, the data was subset to the relevant clusters in the patient samples (Fibro, Progen, Ground, TR Diff Myo, Diff Myo, and Prolif). The resulting data was reprocessed as described above for the unfiltered data; a reduced number of principal components were used for neighbor finding and clustering after selection via an elbow plot. Trajectory inference of patient samples was performed with Monocle 3, version 1.3.455. On the basis of the UMAP obtained during dimensionality reduction with Seurat, trajectory was inferred, and the cells were placed in a pseudotime ordering, setting the node in the graph surrounded by the greatest number of DPT+ cells as the earliest point.

For comparison of human cell states to metaprograms of RMS tumors33, RMS gene signatures were intersected with genes present on the targeted panel. Based on a limited representation of the RMS signatures, the AddModuleScore function of Seurat was used to infer metaprogram scores onto human cell states.

Statistics & Reproducibility
Sample size determination was based on anticipated variability and the effect size that was observed in the investigator’s lab for similar experiments. No data were excluded from the analyses. Kaplan–Meier plots were generated using Prism 8. For pairwise comparisons, unpaired two-tailed t tests were used to calculate P-values. Both statistical methods were carried out using Prism (GraphPad Software v. 9). p-values < 0.05 were considered significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I read the manuscript by Kommoss et al. with interest. In the paper, the authors investigated DICER1 syndrome using a genetically-modified mouse model. Single cell RNA-sequencing and spatial transcriptomics were deployed to investigate mesenchymal stromal cell populations in the mouse kidney, as well as derived renal tumors. The results were then compared against human DICER1 sarcomas.\nOverall I am impressed with the work that has done into characterising this rare disease. The mouse model would be a useful resource for translational purposes. I have only minor comments.\n1) In lines 206, the authors conclude that they identified two types of fibroblastic progenitor populations in the steady-state murine kidney with Pi16high universal fibroblasts typically enriched within the perivascular niche and Col15a1high universal fibroblasts more associated with the subepithelial basement membrane of lining transitional epithelium. While it would seem from Extended Figure 4C that Pi16 seems largely absent in the subsepthelial niches, that does not indicate association of Col15a1 here. In addition, the signals from Pi16 seem to also emanate from other fibroblast subtypes (Xenium data, from extended Figure 4C), as well as even in Identity Cluster 4 which supposedly represents Col15a1high universal fibroblasts (scRNA-seq data, from extended Figure 3f). The authors should provide strongly data to support their claims, perhaps by dual immunofluorescence or immunohistochemistry.\n2) The figure legends of the UMAPs and Xenium experiments e.g. Figure 2, Extended Figure 4C, are highly overlapping in colour and can be difficult to differentiate. For example both \"endo\" and \"collect duct\", as well as \"Podo\" and \"Immune\", \"Fibro\" and Prox Tube\" are very similar. Could some primary colours be used?\n3) It is unclear how the authors intend to use this mouse model for clinical translation, particularly for human DICER1 sarcomas and RMS. Can the authors highlight an example biomarker/target to showcase this potential?", "reviewer_2": "The study by Komoss et al., analyses a conditional mouse model with a tamoxifen inducible cre system to mark quiescent mesenchymal stem cells using Hic1. The Dicer allele they choose to generate is D1693N, though the authors do not state why they choose this lesion in particular. The authors then perform single cell transcriptomics as well as spatial transcriptomics on the sarcoma’s that developed.\nThe manuscript is extremely dense in writing. Several points remain unanswered, why do the authors think they evidences mutations in p53 and Kras, especially gain of function mutations in Kras. There is no concrete evidence of developmental trajectories, so it is unclear why the authors derive the conclusions they do derive. It is unclear what the authors actually mean by “compound heterozygous mutations’ in Dicer1, since the only allele they generate is D1693N.\nWhile generation of the conditional mouse model is interesting, the conclusions made in the study lack direct experimental evidence.", "reviewer_3": "Thank you for the opportunity to review “Spatial single cell transcriptomic analysis of a lineage-traceable mouse model of DICER1 Syndrome informs tumor developmental hierarchy\" by Kommoss et al. In this work, the authors have generated a novel mouse model of DICER1 syndrome amenable for lineage tracing. This model utilizes a tamoxifen-inducible Hic1-CreERT2 mediating conditional deletion of wild type Dicer1 and expression of RNAse IIb domain mutant Dicer1 in mesenchymal stromal cells. This model leads to 80% penetrant renal tumor development with a spectrum of tumors ranging from low-grade mesenchymal tumors to higher grade sarcomas with rhabdomyoblastic differentiation and anaplasia. Using scRNA-seq and spatial transcriptomics in normal murine kidneys and the tumor model they establish the likely fibroblastic cell of origin in these renal tumors. Similar transcriptomic approaches in human DICER1 syndrome tumors reveal similar cell states providing evidence that DICER1 tumors in humans could have mesenchymal fibroblast origins. Overall, this is a solid and impactful study in a novel Dicer1 syndrome mouse model. The findings could be of interest to researchers studying DICER1, miRNA function, impacts/defects of miRNA processing, cancer pathogenesis, and cancer origins. Prior to publication the authors should address a few concerns.\n1. The authors set out in the introduction to expand their Dicer1 models and control the timing of mutation activation (line 112). However, the data that is presented included a wide range of tamoxifen delivery from 14-61 days of age. Given the original goal of the study and the noted developmental window with decreased latency to tumor development (Figure 1D), did the authors test earlier time points and timing of the delivery of tamoxifen associated with other phenotypes (SARC-like or bilateral tumors, tumor penetrance, etc.)? Furthermore, on Line 137, the correlation between tamoxifen delivery and latency doesn’t clearly indicate a developmental window for tumor development if tumors still develop. This is compounded by the fact that the animals received different tamoxifen doses depending on age. The authors should clarify this statement.\n2. The authors should consider including tdTom IHC alongside the PDGFRA and DESMIN IHC in Figure 1P.\n3. Some of the control mice were significantly younger than the tumor mice for the scRNA-seq cohort (Supplementary Table 3, average control mouse age 72 days and the tumor mouse age 345 days). The spatial transcriptomics cohort seems more appropriately age matched. Why weren’t similar age matched samples used for the scRNA-seq?\n4. Line 153: The authors should clarify if all tumors stained for PDGFRA and DESMIN or just the SARC-like and if not all the tumors, what percentage were positive. Were other diagnostic RMS and other markers evaluated by IHC?\n5. Line 187: is there sufficient evidence to indicate the progenitor fibroblasts are more abundant in the juvenile mice compared to adults? Was this quantified and statistically significant? Is this grouping the PNW4 and 10 compared to the PNW 33 mice? PNW10 is usually not considered juvenile. This statement is repeated in the discussion (line 325-327) and needs further justification. Additionally, the extended data figure 3 legend has errors referencing panels f and g.\n6. Line 228: The population of cells termed “tumor associated fibroblasts,” If these are tdTom+ is it clear that these cells are not also transformed by mutated Dicer1? How transcriptionally distinct is this cell state from the\n7. Line 260-260: Regarding the statement that the progenitor population could give rise to highly proliferative cell states upon trp53 or Kras mutations. Did the authors observe this in the scRNA-seq data to justify this statement? Couldn’t these proliferative cell states exist independent of secondary trp53 or Kras mutations since these are not universally observed in Dicer1 tumors?\n8. The overall transcriptional analysis of tumor cells is somewhat superficial. The authors should consider further gene ontology, GSEA, and more in-depth comparisons between the mouse and human DICER1 tumors. This could even be done in the different cell states. This would provide pathways that may be of interest to the field, strengthen the study, and reinforce the value of the murine model as a representation of the human tumors.\n9. Line 314: this statement should indicate this finding is in renal tumors using mouse models.\nLine 339: This statement is not quite correct. These previous studies at least ref. 13 suggested a non-autonomous transformation mechanism where epithelial loss of Dicer1 promoted transformation of the underlying mesenchymal cells. Thus, not DICER1-driven neoplastic epithelial growth as is written. The authors may also consider citing another reference here (Yin et al. PLOS Genetics, DOI: 10.1371/journal.pgen.1005242) that draws similar conclusions.\n10. Line 350: The authors should consider also citing the original finding in addition to ref. 36 (Drummond et al. Cancer Cell, DOI: 10.1016/j.ccell.2017.12.001).\n11. The authors might consider an additional supplemental table providing the 379 genes evaluated in the panel for spatial transcriptomics.\n12. Figure 1 legend: B-D references to the figure panels are not correct.\n13. Figure legends with Kaplan-Meier plots should include the number of mice included in each study.\n14. The number of samples for the scRNA-seq is confusing since it seems like some of the controls are from multiple age-matched mice. The authors should clarify this in the methods and maybe figure legends as it is not apparent until one analyzes Supplementary Table 3 and the sample/mouse IDs."}, "author_response": "Point-by-point responses to reviewers‘ comments:\nReviewer #1 (sarcoma, scRNA-seq and spatial transcriptomics)\nI read the manuscript by Kommoss et al. with interest. In the paper, the authors investigated\nDICER1 syndrome using a genetically-modified mouse model. Single cell RNA-sequencing\nand spatial transcriptomics were deployed to investigate mesenchymal stromal cell\npopulations in the mouse kidney, as well as derived renal tumors. The results were then\ncompared against human DICER1 sarcomas. Overall I am impressed with the work that has\ndone into characterising this rare disease. The mouse model would be a useful resource for\ntranslational purposes. I have only minor comments.\nWe thank the reviewer for their positive and constructive feedback, acknowledging that\nour novel DICER1 sarcoma mouse model provides a useful resource for translational\npurposes.\n1. In lines 206, the authors conclude that they identified two types of fibroblastic progenitor\npopulations in the steady-state murine kidney with Pi16high universal fibroblasts typically\nenriched within the perivascular niche and Col15a1high universal fibroblasts more\nassociated with the subepithelial basement membrane of lining transitional epithelium.\nWhile it would seem from Extended Figure 4C that Pi16 seems largely absent in the\nsubsepthelial niches, that does not indicate association of Col15a1 here. In addition, the\nsignals from Pi16 seem to also emanate from other fibroblast subtypes (Xenium data, from\nextended Figure 4C), as well as even in Identity Cluster 4 which supposedly represents\nCol15a1high universal fibroblasts (scRNA-seq data, from extended Figure 3f). The authors\nshould provide strongly data to support their claims, perhaps by dual immunofluorescence\nor immunohistochemistry.\nResponse: As the reviewer suggested, we would love to further validate their expression by\nimmunohistochemistry. However, these protein markers of fibroblasts are often secreted\nproteins, which makes data interpretation very challenging. Instead, we have now substantially\nexpanded and refined our spatial transcriptomic analysis of fibroblastic populations in the\nsteady-state kidney. Specifically, we generated a second tissue microarray comprising\nadditional steady-state kidney samples (22 cores from 10 kidneys of 9 mice) and performed\nspatial transcriptomic profiling on two whole-kidney sections using the Xenium platform. The\nresulting datasets were integrated with our initial analysis, increasing the total number of\nsteady-state cells examined from 15,414 to 622,209. This combined dataset now provides a\nmore comprehensive and spatially resolved characterization of fibroblast and universal\nfibroblast subpopulations across the entire kidney. As shown in Fig. 2e–h and Extended Data\nFig. 5, we now present a detailed spatial mapping of clusters identified by targeted spatial\ntranscriptomics, demonstrating their localization within the perivascular, subepithelial, and\ncapsular niches. Moreover, expression patterns of the universal fibroblast marker Dpt, the\nPi16high universal fibroblast marker Pi16, and the Col15a1high universal fibroblast marker Mfap4\nare shown, further supporting niche-specific localization of these fibroblast populations within\nthe steady-state kidney.\n2. The figure legends of the UMAPs and Xenium experiments e.g. Figure 2, Extended Figure\n4C, are highly overlapping in colour and can be difficult to differentiate. For example both\n\"endo\" and \"collect duct\", as well as \"Podo\" and \"Immune\", \"Fibro\" and Prox Tube\" are very\nsimilar. Could some primary colours be used?\nResponse: We thank the reviewer for this helpful suggestion. We have revised the color\nschemes in Figure 2 and Extended Data Fig. 5 to improve visual distinction between clusters.\n3. It is unclear how the authors intend to use this mouse model for clinical translation,\nparticularly for human DICER1 sarcomas and RMS. Can the authors highlight an example\nbiomarker/target to showcase this potential?\nResponse: Informed by the pathway enrichment analysis, we have shown that the MAPK\npathway is highly activated in the proliferative population of mouse Dicer1 sarcoma (Fig. 4h).\nConsistently, using immunohistochemistry we demonstrated that in human patient samples\nhigh-grade DICER1 sarcoma (SARC) expresses more abundant p-ERK, a marker of MAPK\npathway activation, in comparison to low-grade tumors (Extended Data Figs. 10f and g). This\nfinding further strengthens the translational potential of our mouse model for identifying novel\ndisease biomarkers and/or druggable targets that are also noted in our discussion (page 10,\nlast paragraph).\nReviewer #2 (Dicer1 in cancer and mouse models, knows scRNA-seq)\nThe study by Kommoss et al., analyses a conditional mouse model with a tamoxifen inducible\ncre system to mark quiescent mesenchymal stem cells using Hic1.\n1. The Dicer allele they choose to generate is D1693N, though the authors do not state\nwhy they choose this lesion in particular.\nResponse: We have edited the 1st and 3rd paragraphs of the INTRODUCTION to explain\nthis. In the 1st paragraph, we have detailed that all RNase IIIb hotspot missense mutations\nfrom patients led to inactivation of their catalytic activity. In the 3rd paragraph, we have\nadded that the D1693N mutation in mouse is equivalent to D1709N mutation in human, one\nof the most common hotspot mutations in patient tumors as supported by a newly added\nreference (PMID: 31342592).\nThe authors then perform single cell transcriptomics as well as spatial transcriptomics on the\nsarcoma’s that developed. The manuscript is extremely dense in writing.\nResponse: We appreciate the comments and have edited through the manuscript to improve\nthe clarity.\nSeveral points remain unanswered,\n1. why do the authors think they evidences mutations in p53 and Kras, especially gain of\nfunction mutations in Kras.\nResponse: We performed targeted sequencing on the tumors and identified p53 and Kras\noncogenic mutations. Data is presented in Fig. 1r and Supplementary Table 2 with\ndescription in the first paragraph of page 4.\n2. There is no concrete evidence of developmental trajectories, so it is unclear why the\nauthors derive the conclusions they do derive.\nResponse: We have conducted additional studies to support the developmental trajectory\nanalysis. First, we isolated and cultured tdTomato+ primary mesenchymal cells from control\nand mutant Dicer1 activated mouse kidneys within a month of tamoxifen treatment. Bulk\nRNAseq analysis along with qPCR validation revealed the elevated expression of universal\nfibroblast markers, e.g. Dpt, Pi16, and Ly6a, and decreased expression of differentiation\nmarkers in Dicer1-mutant cells in comparison to control cells (Fig. 4a, Extended Data Figs.9a\nand b). This suggests that activation of Dicer1 mutation sustains the self-renewal rather than\ndifferentiation of universal fibroblast. Second, we performed copy number (CN) analysis using\nthe scRNA-Seq dataset of our mouse Dicer1 tumors, which led to the discovery of\nheterogeneous CN changes across each cell state. In particular, we found that Dpt+Pi16+ Uni\nFibro-like cells did not have detectable CN change, whereas nearly all other cell states had\nvariable amounts of CN changes, e.g. chromosome 1 and 6 gains (Fig. 4e), that correlated\nwith their pseudotime in trajectory analysis. We further validated chromosome 1/6 gains using\nshallow whole genome sequencing on a large collection of mouse Dicer1 tumors (Fig. 1r).\nThese findings strongly support that HDT tumors arise from mutant Dicer1-primed Dpt+Pi16+\nUni Fibro-like cells, differentiating into either rhabdomyoblastic cell lineage (CN change is not\nessential) or perturbed fibroblastic cell lineages accompanied with accumulated CN changes.\nAccumulation of certain CN changes, e.g. chromosome 1/6 gains, promotes the proliferation\nof perturbed fibroblastic cells towards sarcomagenesis, which may encompass additional\ngenetic mutations, e.g. p53 or Kras mutations, to establish aggressive anaplastic sarcoma.\nSupporting this, we knocked out p53 into Dicer1-mutant primary mesenchymal cells and\nobserved substantial increase in their proliferation (Fig. 4g).\n3. It is unclear what the authors actually mean by “compound heterozygous mutations’ in\nDicer1, since the only allele they generate is D1693N.\nResponse: Human DICER1 syndrome-associated cancers encompass two hits on the\nDICER1 gene, one germline inactivating event and one hotspot missense mutation on the 2nd\nallele. This pattern of compound heterozygous DICER1 mutations in patients, described in the\nfirst paragrah of the Introduction section, are fully recapitulated in HDT mice with one of two\nDicer1 alleles having a conditional D1693N mutation (null allele before induction) and the other\nhas a conditional mutation leading to Dicer1 knockout (wildtype allele before induction), which\nis mentioned in third paragraph of the Introduction section with more details described in our\ninitial publication (cited, PMID: 37494476). To avoid confusion, we have edited the first\nparagraph of the Results section with ‘‘… to develop a conditional and lineage-traceable\nDicer1 GEMM, Hic1CreERT2/CreERT2;Dicer1fl/fl-D1693N;Rosa26LSL-tdTomato mice (referred to as HDT\nhereafter, Fig. 1a) that express hemizygous Dicer1D1693N upon tamoxifen treatment in Hic1+\ncells, recapitulating the compound heterozygous state of DICER1 mutations in human\ntumors.“\nReviewer #3 (GEMM including Dicer1 cKO mouse model, sarcoma, knows lineage\ntracing)\nThank you for the opportunity to review “Spatial single cell transcriptomic analysis of a lineage-\ntraceable mouse model of DICER1 Syndrome informs tumor developmental hierarchy\" by\nKommoss et al. In this work, the authors have generated a novel mouse model of DICER1\nsyndrome amenable for lineage tracing. This model utilizes a tamoxifen-inducible Hic1-\nCreERT2 mediating conditional deletion of wild type Dicer1 and expression of RNAse IIb\ndomain mutant Dicer1 in mesenchymal stromal cells. This model leads to 80% penetrant renal\ntumor development with a spectrum of tumors ranging from low-grade mesenchymal tumors\nto higher grade sarcomas with rhabdomyoblastic differentiation and anaplasia. Using scRNA-\nseq and spatial transcriptomics in normal murine kidneys and the tumor model they establish\nthe likely fibroblastic cell of origin in these renal tumors. Similar transcriptomic approaches in\nhuman DICER1 syndrome tumors reveal similar cell states providing evidence that DICER1\ntumors in humans could have mesenchymal fibroblast origins. Overall, this is a solid and\nimpactful study in a novel Dicer1 syndrome mouse model. The findings could be of interest to\nresearchers studying DICER1, miRNA function, impacts/defects of miRNA processing, cancer\npathogenesis, and cancer origins. Prior to publication the authors should address a few\nconcerns.\nWe thank the reviewer for their positive comments and very helpful suggestions.\nAdditional analyses were included in the revised manuscript, as suggested by the\nreviewer, and outlined below.\n1. The authors set out in the introduction to expand their Dicer1 models and control the timing\nof mutation activation (line 112). However, the data that is presented included a wide range\nof tamoxifen delivery from 14-61 days of age. Given the original goal of the study and the\nnoted developmental window with decreased latency to tumor development (Figure 1D),\ndid the authors test earlier time points and timing of the delivery of tamoxifen associated\nwith other phenotypes (SARC-like or bilateral tumors, tumor penetrance, etc.)?\nFurthermore, on Line 137, the correlation between tamoxifen delivery and latency doesn’t\nclearly indicate a developmental window for tumor development if tumors still develop. This\nis compounded by the fact that the animals received different tamoxifen doses depending\non age. The authors should clarify this statement.\nResponse: We have indeed tried to induce the mutations at embryonic stage and new natal\nstages. However, we experienced a big challenge to have viable pups from embryonic\nactivation. For pups that survived, unfortunately none had correct genotype. Similarly, very\nfew new natal mice injected were healthy. For a few that survived, they did develop tumors\nwith latency varying from 4 months to 1y. We are working on improving the induction method\nto have sufficient power to fully address the question as a follow-up study.\nAs being recommended, we have tested whether earlier induction of Dicer1 mutations\ncorrelates with tumor penetrance, laterality and histology. As shown in Extended Data Figs.\n1g-j, we did not detect significant correlation between mutation activation time and tumor\npenetrance, laterality or histology.\nRegarding the comment on the developmental window for tumor development, we agree\nthat the concept is confusing as tumors still develop when tamoxifen was injected at ~40 days.\nThe different doses of tamoxifen given to mice at different ages were chosen based on\nliteratures showing their success of induction of transgenes for ERT2 mice, which also takes\nboth body weight and age into consideration. In general, 2-week old pups may weigh around\n8g, while a 4 week-old mouse weighs ~15g. Due to animal welfare concerns by our institution,\nwe are not allowed to keep mice beyond 1.5 years old after tamoxifen injection. Consequently,\nwe were unable to address whether induction of Dicer1 mutations at adulthood, e.g. 8week\nold, will still lead to tumor development after 1.5 years. However, given that almost no DICER1\nsyndrome patients developed DICER1-associated cancer in their adulthood, it is reasonable\nto suggest that induction in adult mice may not lead to cancer development. To avoid the\nconfusion, we have revised our statement to ‘‘late Dicer1 mutation activation correlated with a\nprolonged latency (Fig. 1d), suggesting that cells amenable to mutant DICER1-driven\noncogenesis are decreasing along the development, consistent with the predominant\nchildhood onset of these cancers in human patients“ in the first paragraph of the Results\nsection.\n2. The authors should consider including tdTom IHC alongside the PDGFRA and DESMIN\nIHC in Figure 1P.\nResponse: We have now included tdTom IHC alongside the PDGFRA and DESMIN IHC in\nFigure 1P.\n3. Some of the control mice were significantly younger than the tumor mice for the scRNA-\nseq cohort (Supplementary Table 3, average control mouse age 72 days and the tumor\nmouse age 345 days). The spatial transcriptomics cohort seems more appropriately age\nmatched. Why weren’t similar age matched samples used for the scRNA-seq?\nResponse: In our HDT GEMM of DICER1 sarcoma, the tumor latency was significantly\nreduced when mutations were induced at later developmental stages, suggesting that the\namount of cells amenable to tumorigenesis is declining during development. We performed\nscRNA-Seq analysis on control kidneys of both young (PNW4, PNW10) and adult mice\n(PNW33-36) to identify potential mesenchymal cell populations more abundantly present in\nkidneys of young mice, some of which may be the potential cell of origin of these tumors.\nNotably, while we observed the Uni Fibro and mural cells were more abundant in young mice,\nthe rarity of the tdTomato+ cells isolated (n=604 in total from 7 mice analyzed) alerts that the\nobserved age-dependent composition requires validation with more mouse kidneys.\nTo overcome the challenge with cell isolation, we opted to use spatial transcriptomics on\narchived fixed samples to address the question. We initially performed analysis using cored\nkidney materials from aged adult mice to obtain the data for initial submission. To strengthen\nthe data with unbiased sampling, we performed targeted Xenium transcriptomic analysis on\nthe whole kidneys of control mice at PNW4, PNW10, and PNW81 during the revision.\nUnfortunately, during sample preparation, PNW4 sample failed due to a technical error.\nRegardless, with the targeted transcriptomics analysis, we did not notice major difference in\ncell composition between PNW10 and PNW81 kidneys (Extended Data Fig. 4f) except that\nthere is a mild reduction of Uni Fibro population in PNW81 kidney (1.8% vs 2.3% in PNW10).\nAs stated in the Discussion section, future analysis of these kidneys with more comprehensive\npanel or whole transcriptomic analysis will provide more insights into precise difference in\nfibroblast progentiors between juvenile and adult as well as aged kidneys.\n4. Line 153: The authors should clarify if all tumors stained for PDGFRA and DESMIN or just\nthe SARC-like and if not all the tumors, what percentage were positive. Were other\ndiagnostic RMS and other markers evaluated by IHC?\nResponse: We thank the reviewer for this suggestion. We have now performed DESMIN and\nPDGFRA staining on the available tumor samples and reported the results for each individual\nsample in Supplementary Table 2. No additional myogenic markers were evaluated by IHC.\n5. Line 187: is there sufficient evidence to indicate the progenitor fibroblasts are more\nabundant in the juvenile mice compared to adults? Was this quantified and statistically\nsignificant? Is this grouping the PNW4 and 10 compared to the PNW 33 mice? PNW10 is\nusually not considered juvenile. This statement is repeated in the discussion (line 325-327)\nand needs further justification. Additionally, the extended data figure 3 legend has errors\nreferencing panels f and g.\nResponse: Thank you for pointing out our mis-classification of PNW10 mice as juvenile mice.\nDue to the low number of cells identified to each mouse age, we have limited power to make\nthis conclusion using scRNA-Seq. More discussion is included in our response to your\nquestion #3. Instead, we turned to use targeted spatial transcriptomics on archived fixed\nsamples to address the question. We did not notice major difference in cell composition\nbetween PNW10 and PNW81 kidneys (Extended Data Fig. 4f) except that there was a mild\nreduction of Uni Fibro population in PNW81 kidney (1.8% vs 2.3% in PNW10). We have\nupdated our statement in the Results section and the Discussion section accordingly. For the\ndiscussion, we stated that “Given that tumors from the DICER1 sarcoma spectrum\npredominantly affect children and young adults, the tumor susceptibility in humans may also\nbe influenced by the developmental stage. Mirroring this, we observed prolonged tumor\nlatency in HDT mice with a late activation of Dicer1 mutations (Fig. 1d). Therefore, earlier\ndevelopmental windows may lead to the retention of certain primitive fibroblast populations\nmore prone to malignant transformation. Given the potential sampling bias during kidney\ndissociation for scRNA-Seq, future global spatial transcriptomic analysis of mouse kidneys\nand other organs during HDT tumor development continuum is needed to identify such unique\nand rare cell population.“\n6. Line 228: The population of cells termed “tumor associated fibroblasts,” If these are tdTom+\nis it clear that these cells are not also transformed by mutated Dicer1? How transcriptionally\ndistinct is this cell state from the\nResponse: We thank the reviewer for this important point. To avoid confusion, we have\nremoved the term “tumor associated fibroblasts“. Instead, we have improved our description\nas highlighted here. “The Fibro Perturb cluster expressed a high level of Pi16 with low Dpt\nexpression and also expressed signature genes of perturbed-state fibroblasts (Comp+,\nCpxm2+, and Itgbl1+) that were reported by Buechler et al. (PMID: 33981032). While these\ndata support a fibroblastic identity, this cluster had no lineage link with identified cellular origin\n(Dpt+Pi16+ cells) revealed by the trajectory analysis (Fig. 4b) and had no copy number\nalterations (Fig. 4e), suggesting that they are likely untransformed tdTomato+ cells. Notably,\nHic1+ MSC cells are a mixture of cells at various states including primitive universal fibroblasts\nand also differentiated cells, e.g. fibroblast and pericytes, which are abundant in the kidney at\nall stages but appear not transformable by Dicer1 mutations as evidenced by the prolonged\ntumor latency when Dicer1 mutations were activated late (Fig. 1d). Therefore, it is possible\nthese tdTomato+ cells may arise from activating Dicer1 mutations in Hic1+ fibroblasts.\nSimilarly, we assume the tdTomato+ vascular smooth muscle cells (SM Vasc) may derive from\nactivating Dicer1 mutations in Hic1+ mural cells. However, we cannot fully exclude that these\nrare subsets, particularly the Fibro Perturb cluster, may be transformed and contribute to\nsarcomagenesis upon accumulating additional genomic mutations. We plan to investigate this\nfurther in future work.“ We have added this into the Discussion section (3rd paragraph).\n7. Line 260-260: Regarding the statement that the progenitor population could give rise to\nhighly proliferative cell states upon trp53 or Kras mutations. Did the authors observe this in\nthe scRNA-seq data to justify this statement? Couldn’t these proliferative cell states exist\nindependent of secondary trp53 or Kras mutations since these are not universally observed\nin Dicer1 tumors?\nResponse: The scRNA-Seq analysis sequences the 3’end of transcripts and thus does not\nhave the resolution to identify genetic mutations. While we don’t have direct evidence to\nsupport that our samples used for scRNAseq analysis have p53 or Kras mutations, a missense\nhotspot p53 mutation was detected in the corresponding FFPE sample of the one tumor in\nscRNAseq (HDT340) (Fig. 1r, Supplementary Table S2). We have also established primary\ncell culture from tdtomato+ cells of HDT kidneys, which represents the progenitor population.\nIntroducing trp53 knockout mutations into this cell line significantly increased cell proliferation\n(Fig. 4g), supporting that these mutations are growth promoting events along tumor\ndevelopment continuum. However, as not all high-grade tumors have Trp53 or Kras mutations,\nother events will likely also contribute to tumor progression, which are currently being studied\nin our lab.\n8. The overall transcriptional analysis of tumor cells is somewhat superficial. The authors\nshould consider further gene ontology, GSEA, and more in-depth comparisons between\nthe mouse and human DICER1 tumors. This could even be done in the different cell states.\nThis would provide pathways that may be of interest to the field, strengthen the study, and\nreinforce the value of the murine model as a representation of the human tumors.\nResponse: We have conducted gene set enrichment analysis for different cell states of mouse\ntumors. Notably, the MAPK pathway is highly activated in the proliferative population of mouse\nDicer1 sarcoma (Fig. 4h). Consistently, using immunohistochemistry we demonstrated that in\nhuman patient samples high-grade DICER1 sarcoma (SARC) expresses more abundant p-\nERK, a marker of the MAPK pathway activation, in comparison to low-grade tumors (Extended\nData Figs. 10f and g). This finding further strengthens the translational potential of our mouse\nmodel for identifying novel disease biomarkers and/or druggable targets.\n9. Line 314: this statement should indicate this finding is in renal tumors using mouse models.\nResponse: While our finding is largely in mouse renal tumors, the identified cell composition\nand their developmental hierarchy were also observed in human paitent tumors we\nassembled. Therefore, we have updated the statement to more precisely describe these\nfindings: “Thus, for the first time we identified universal fibroblasts as the putative cellular origin\nof mouse DICER1 sarcoma and deconvoluted their developmental trajectory, which appears\nconserved in human patients.“\n10. Line 339: This statement is not quite correct. These previous studies at least ref. 13\nsuggested a non-autonomous transformation mechanism where epithelial loss of Dicer1\npromoted transformation of the underlying mesenchymal cells. Thus, not DICER1-driven\nneoplastic epithelial growth as is written. The authors may also consider citing another\nreference here (Yin et al. PLOS Genetics, DOI: 10.1371/journal.pgen.1005242) that draws\nsimilar conclusions.\nResponse: Thank you. We have amended the manuscript accordingly and cited both studies.\nNotably, DICER1 syndrome is driven by specific missense mutations of DICER1, but not those\nleading to total loss of DICER1, in the mesenchymal cells. Therefore, while previous studies\nreported that epithelial loss of Dicer1 promotes transformation of the underlying mesenchymal\ncells, their relevance to DICER1 syndrome tumor biology is unclear. We propose that “It is\npossible that DICER1 may be epigenetically silenced in the epithelium of a subset of these\ntumors, potentially influencing the growth of the subepithelial mesenchymal cells that are\nalready transformed by RNase IIIb-mutant DICER1 through this non-autonomous\nmechanism.“\n11. Line 350: The authors should consider also citing the original finding in addition to ref.\n36 (Drummond et al. Cancer Cell, DOI: 10.1016/j.ccell.2017.12.001).\nResponse: We have updated the manuscript to also cite the original finding by Drummond et\nal..\n12. The authors might consider an additional supplemental table providing the 379 genes\nevaluated in the panel for spatial transcriptomics.\nResponse: We have included the list of 379 genes used for spatial transcriptomics (new\nSupplementary Table 3)\n13. Figure 1 legend: B-D references to the figure panels are not correct.\nResponse: We have fixed the mistake.\n14. Figure legends with Kaplan-Meier plots should include the number of mice included in\neach study.\nResponse: We have added the numbers of mice used for each condition in pictures and figure\nlegends of Fig. 1 and Supplementary Fig. 1“.\n15. The number of samples for the scRNA-seq is confusing since it seems like some of\nthe controls are from multiple age-matched mice. The authors should clarify this in the\nmethods and maybe figure legends as it is not apparent until one analyzes Supplementary\nTable 3 and the sample/mouse IDs.\nResponse: We have updated the figure legend of Fig. 2a to “profiled by scRNA-seq (n=7 in 3\nbatches with postnatal week [PNW] at 4, 10 and 33-36)“."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have significantly improved on their results by providing additional spatial transcriptomic data. I have no further comments.", "reviewer_2": "The authors have done a nice job of addressing the reviewer recommendations. I have no further comments.", "reviewer_3": "The authors have sufficiently addressed my concerns and I do not have any additional comments. Congratulations to the authors on this interesting work."}, "author_response": ""}]

Decision letter