Combinatory differentiation of human induced pluripotent stem cells generates functional thymic epithelium driving dendritic cell and CD4/CD8 T cell development

Domain: neuroscience
Published: 2026-01-23
Editorial outcome: major_revision

Abstract
The thymus educates thymocytes through a selection process mediated by thymic epithelial cells (TECs). Recent advances have made the generation of T lymphocytes from induced pluripotent stem cells (iPSc) a promising therapeutic strategy. However, current approaches often fail to replicate the thymic niche, leading to impaired T cell generation. Here we address the production of functional mature iPSc-derived TECs supporting in vitro T cell generation. We optimize thymic lineage differentiation through an unbiased multifactorial experimental design. By modulating specific signaling pathways, we generate progenitors that mature into medullary and cortical TECs. Co-culture with primary hematopoietic progenitors in a 3D thymic organoid setup induces their differentiation into CD4+ and CD8+ T cells. Importantly, thymic organoids support multilineage differentiation, with dendritic cell populations also emerging. Thus, the presented thymic organoid model provides a practical platform for studying thymic cellular interactions and thymopoiesis in vitro, and opens further research perspectives towards cell-based therapies.

Introduction
The thymus plays a crucial role in the establishment of central immune self-tolerance. Its main function is the generation of a diverse yet non-autoreactive T lymphocyte repertoire1. The thymus is subdivided into two lobes and structured into two principal niches, a peripheral cortical area surrounding a central medulla. Distinct cellular populations shape the specific microenvironment of these niches. Thymic epithelial cells (TECs) are crucial actors that regulate the maturation of developing T cells, the thymocytes2. Two main populations of TECs have been identified: the cortical TECs (cTECs) and the medullary TECs (mTECs), characterized in humans by the KRT8/18 and KRT5/14 cytokeratin expression3. Although KRT8/18 tags all epithelial cells, KRT5/14 are restricted to the medullary subpopulation. In addition, the expression of the surface marker CD205 is specific to cTECs2,4. Recently, single-cell studies have revealed a higher level of heterogeneity among TECs with the identification of new mTEC subpopulations that have various functional roles such as thymocyte homing, commitment, proliferation regulation and selection3–5. TECs are central mediators of thymocyte selection by enforcing a functional and non-autoreactive T repertoire through several steps. Thymocytes originate from CD34+CD7+ early-thymic progenitors (ETPs), which derive from circulating thymus-seeding progenitors (TSPs), and differentiate through several stages characterized by CD4 and CD8 expression6–8. Positive selection is mediated by cTEC at the double positive (DP) stage and leads to the selection of thymocytes with a functional T cell receptor (TCR)4. Negative selection filters out potential autoreactive thymocytes at the single-positive (SP) stage in the medulla9,10 and is mediated by HLA-DRhi mTECs (mTEChi) expressing a great diversity of peripheral tissue antigens (PTAs) under the regulation of AIRE4,11,12. Mature CD4 and CD8 T lymphocytes are called recent thymic emigrants and migrate to the periphery13. Recent research has shown that parallel intrathymic dendritic cell (DC) differentiation occurs from the same ETPs14. Interestingly, crosstalk with this thymic DC population has been shown to support thymocyte maturation and selection14,15.

Although the mechanisms supporting the differentiation of mTECs and cTECs remain elusive, recent studies have identified one or several bipotent thymic epithelial progenitor (TEP) populations capable of giving rise to complete and functional cortical and medullary compartments16–18. Studies have identified a bipotent TEP population exhibiting a cortical-like phenotype19,20. NOTCH21,22 and RANK-CD40-LTB23–25 signaling appear to play a crucial role in regulating TEC fate decisions. The regulation of thymus organogenesis has also seen remarkable progress in recent years, notably due to the publication of the single-cell RNA sequencing (scRNA-seq) thymic cell atlas at different stages of development26–28. TEP identity is notably characterized by the expression of FOXN1, the master regulator that induces the expression of the TEC-specific transcriptional program29–32. TEPs emerge from the third pharyngeal pouch endoderm (3PPE), a transient embryonic structure formed by the involution of the endoderm into the foregut tube between weeks 3 and 4 in humans33–35. A gene cascade involving TBX1, PAX9, and EYA1 has been identified as a crucial regulator of 3PPE formation. The 3PPE derives from the ventral pharyngeal endoderm, which originates from the anterior foregut endoderm (AFE)33,36. The regulation of fate acquisition in these structures has also shown advances at the single-cell level32,37,38.

Mimicking thymus organogenesis in vitro to generate TECs from embryonic (ESc) and induced pluripotent stem cells (iPSc) has been a long-standing objective over the last decade, using both reprogramming and direct differentiation approaches29,39–44. However, the complexity of regulatory mechanisms in the differentiation process and our limited understanding of in vivo thymic development have hindered the achievement of this goal45. Indeed, thymic differentiation often yields immature and non-functional cells with TEP identity that require grafting in vivo to complete maturation40–42. Moreover, the available protocols for iPSc differentiation into TEPs have not been optimized by unbiased systematic approaches able to robustly assess the effects of the supplemented factors at each stage46–48. Reliable methods for iPSc differentiation into functional, mature TECs in vitro and their long-term maintenance in a culture system that reproduces the thymic niche are yet to be developed. Such systems could enable the generation of iPSc-derived TECs, opening promising perspectives for regenerative medicine49. Indeed, treatment of congenital pathologies affecting TECs, such as DiGeorge’s syndrome or severe combined immunodeficiency (SCID) caused by gene mutations impairing TEC development, could benefit from corrected TECs to restore thymic function. Finally, iPSc-derived TECs could be used to improve ex vivo T cell generation to counteract the dramatic loss of thymus function linked to a weakened immune response during aging39,50. In vitro T Lymphocyte generation has been greatly improved using feeder stromal cells such as OP9-DLL1 or MS5-hDLL4, which have been modified to express NOTCH ligands51–53. Formation of artificial thymic organoids has improved the maturation status of the obtained T cells, and partially replicated thymic function39,54. However, these setups only partially reproduce thymic selection, as they lack the self-antigen presentation ability of mTEChi.

Because of the complexity of thymic organoid co-cultures and iPSc thymic differentiation, due to the multiplicity of factors involved, precise optimization of the protocols is difficult to achieve. Here, we used the optimal design of experiments (DOE), a systematic statistical framework for experimental design, to collect the most information with the fewest experiments. A transcriptomics readout was used to optimize iPSc thymic differentiation based on recent single-cell datasets of thymus organogenesis. We propose a two-week protocol that efficiently yields TEPs expressing thymic identity markers. To overcome the difficulty of maturing TEPs ex vivo, we develop a 3D co-culture system with CD34+CD7+ thymic progenitor cells that have not yet passed the T lineage commitment checkpoint. These cells will be referred to as ETPs in a broader sense, encompassing TSP2 cells arriving in the thymus, ETPs and their immediate progeny at the double negative (DN) 1 stage14,55,56. We setup a fibrin hydrogel air-liquid interface culture that results in the formation of human thymic organoids (hTOs). hTOs could be maintained for up to 5 weeks, and display a phenotype similar to mature TECs, particularly HLA-DRhi medullary-like TECs, which exhibit higher AIRE expression than in a 2D setup. TEC-like cells and thymocytes show some degree of co-localization in the organoids, and hTOs demonstrate the ability to drive the maturation of diverse progenitors into SP CD4+ and CD8+ T lymphocytes. Further characterization of hTO products by scRNA-seq confirms the mature state of the generated T lymphocytes. Finally, we report the secondary differentiation of ETPs into the dendritic cell lineage in line with recent in vivo observations, thus demonstrating a complex in vitro organoid model capable of both cell maturation and multilineage potential.

Results
Optimization of directed iPSc differentiation toward a TEC fate using DOE-based factor screening and bulk transcriptomics
To setup an optimized differentiation protocol of iPSc into TEPs, we followed an approach of directed differentiation that recapitulates the events of thymic organogenesis. Recently, the understanding of thymic identity acquisition and its underlying regulation at the cellular and molecular level has greatly improved through the use of single-cell-omics approaches32,37,38,57. Definitive endoderm (DE) undergoes anteriorization into anterior foregut endoderm (AFE) from which emerges the third pharyngeal pouch endoderm (3PPE), a transitory structure that gives rise to TEPs (Fig. 1a). We aimed to recapitulate these events in vitro by modulating the main pathways involved in thymic organogenesis, such as BMP, WNT, Hedgehog (SHH) and FGF. The exit from the pluripotency state and DE induction have been thoroughly studied58, and the process of iPSc differentiation into DE has been precisely characterized. We performed this step by stimulating Nodal through Activin A exposure in combination with a 24-h pulse of WNT through CHIR99. The differentiating cells showed a peak of expression for the DE marker gene SOX17 at days (D) 3–5 (Supplementary Fig. 1a). In addition, a vast majority of cells stained positive for SOX17 and another DE marker FOXA2 at D5 (Supplementary Fig. 1b). These results reflect the high efficiency of the iPSc to DE differentiation, which does not require further optimization.

We applied DOE to test the effect of a series of factors that impact differentiation pathways and are known to be potentially active in the transitions toward AFE, 3PPE and TEP. The modulation of RA, TGFb, BMP, and WNT pathways on definitive endoderm anteriorization between D5 and D7 was first studied. Then the effect of FGF8, NOTCH, Hedgehog (SHH), BMP and WNT modulation of 3PPE was assessed between D7 and D11. Lastly, the effect of BMP, FGF10, EGF, IGF1, and RANKL modulation on TEP fate induction between D11 and D13 was investigated (Fig. 1b). These pathways were selected based on studies of thymic organogenesis regulation45,59–62 and previous thymic differentiation of hESc40,41. A Plackett-Burman combinatorial screening design was performed for each of the three transitions by testing multiple combinations of factors with two concentration levels (Supplementary Fig. 1c, d). The choice of this experimental design is justified by its ability to test a maximum number of factors within a limited number of runs, which is adapted to the experimental constraints of differentiating iPSc.

Using a low-dimensionality readout, such as measuring the expression of one or a few marker genes, can yield imprecise results due to a marked lack of specificity and gene expression variability. To overcome this weakness and robustly assess factor effects, we performed bulk RNA-seq on the DOE samples and compared their transcriptome to two scRNA-seq atlases of pharyngeal development. The atlas by Han et al.37 corresponds to early stages of development from E8.5 to 9.5, covering the AFE and 3PPE transitions. The second by Magaletta et al.32 focuses on later stages of development from E9.5 to 12.5, including the 3PPE and TEP transitions. Raw data reanalysis from these two datasets, applying uniform manifold approximation and projection (UMAP) (Supplementary Fig. 2a), led to the delineation of transcriptomic maps of the thymic differentiation trajectory from E8.5 to 9.5 (Han) and E9.5 to 12.5 (Magaletta). This was achieved by studying the pattern of expression of known genes specific to thymic precursor cell populations (Fig. 1c and Supplementary Fig. 2b). We then scored the transcriptomic similarity of each DOE sample to the different cell populations at each relevant stage of pharyngeal development, i.e., E8.5 for anterior foregut formation, E9.5 for pharynx formation and E11.5/E12.5 for TEP transition, by using the mean expression of the top marker genes of each cell population (Supplementary Data 1 and 2).

The differentiation conditions for AFE (D5 to D7) were optimized using the Han dataset. Unsupervised hierarchical clustering of DOE iPSc-derived samples and reference progenitor cell populations identified two clusters of samples with high expression of anterior foregut markers (Fig. 1d, Top Left). These samples also show strong expression of markers for developmentally close populations (lateral foregut and ventral foregut) and low expression of midgut and dorsal lateral foregut markers, confirming DE anteriorization. To identify the effect of the tested factors on anterior foregut induction, we computed their statistical main effects using factorial ANOVA on the mean expression of the anterior foregut gene markers. RA, NOGGIN and LY3 supplementation, as well as the absence of IWR1, had the most impact on DE anteriorization (Supplementary Fig. 3). Hence, BMP inhibition by NOGGIN and, to a lesser extent, TGFβ inhibition by LY3 promoted anteriorization, whereas WNT inhibition by IWR1 was globally detrimental at this step.

We applied a similar strategy to the 3PPE transition (D7 to D11) and found a cluster of DOE samples with the highest expression of pharynx markers (Fig. 1d, Bottom Left). ANOVA of the tested factors on the mean expression of the pharynx gene markers identified a statistically significant effect for the addition of CHIR99, as well as the deprivation of FLI06 and IWR1 (Supplementary Fig. 3). We then used the Magaletta dataset as reference, as it also covers the transition to 3PPE with immature pharynx 1 and 2 populations that correspond to the developmental stage of the pharynx population from the Han dataset. We confirmed the positive effect of CHIR99, as well as the negative effect of FLI06 and IWR1 on 3PPE formation (Fig. 1d, Top Right, and Supplementary Fig. 3). Hence, WNT activation by CHIR99, in contrast to its inhibition by IWR1, greatly promotes pharyngeal formation of the 3PPE, whereas NOTCH inhibition by FLI06 is detrimental. Interestingly, contrary to the Parent et al. study40, we observed no effect of Hedgehog inhibition by cyclopamine at this stage. Similarly, BMP4 and FGF8 supplementation had no significant effect on pharyngeal induction, despite being commonly used in previous studies40,42,43. We therefore specifically tested these two conditions by measuring D15 FOXN1 expression by qPCR to directly quantify differentiation efficiency, rather than using the intermediate 3PPE as a proxy. Interestingly, we observed a significantly higher expression of FOXN1 in TEP samples exposed to FGF8 and BMP4 at the earlier 3PPE stage (Supplementary Fig. 4a). Thus, the combination of these two factors at this stage seems beneficial for later TEP differentiation, but does not directly affect differentiation efficiency at 3PPE, thereby justifying keeping them in the protocol.

Finally, we optimized the differentiation conditions for the transition to TEP (D11 to D13), leveraging the Magaletta et al. dataset, which covers late stages of pharyngeal development, including early cTEC and mTEC, even though no bona fide transitory TEP population was identified. Since cTECs are the natural and most direct product of TEP differentiation, we focused on them as the target population for this step of differentiation. Hierarchical clustering identified a cluster of DOE samples that had the highest expression of cTEC markers, along with markers of its closely related mTEC and thyroid populations (Fig. 1d, Bottom Right). Although our statistical approach could not discriminate cTEC from mTEC or thyroid cells for their resemblance to TEPs, we took this population cluster as a distant proxy for TEPs. The ANOVA on the mean expression of cTEC markers showed that the factors with the most impact were the addition of BMP4 and IGF1, as well as the deprivation of RANKL. We also chose to retain FGF10 and EGF since we observed a direct positive effect of these two factors on the growth rate of the differentiation end product (Supplementary Fig. 4b).

Finally, we performed a post-DOE optimization considering recent publications on thymic differentiation, highlighting the benefits of FGF8 supplementation for AFE induction45. We confirmed similar results in our protocol by measuring a significant increase in FOXN1 expression at the end of differentiation in samples exposed to FGF8 from D4 to D6 (Supplementary Fig. 4c).

Results of the DOE optimization are synthesized in Fig. 1e, describing selected inhibited (−) or activated (+) pathways. We summarized our iPSc differentiation protocol to TEPs in a table displaying doses and duration of differentiation factor supplementation (Fig. 1f).

Characterization of the differentiation product demonstrates efficient differentiation into TEPs
To follow the thymic differentiation process at the transcriptomic level, we sequenced samples at multiple time points across our differentiation protocol by bulk RNA-seq. Human primary TECs, sorted as CD45-EPCAM+ and originating from thymic samples of pediatric patients undergoing cardiac surgery, were included to assess the state of maturation of the differentiation product. iPSc samples were included as the starting point of the differentiation trajectory. We performed differential gene expression analysis of each time-point sample against D0 iPSc and identified nine gene modules enriched at distinct differentiation stages and comprising known markers of thymic differentiation stages, such as SOX17 (DE), HOXA3 (AFE), EYA1 (3PPE), or EPCAM (TEP) that hit in the same module as CXCL12, whose expression is known to be a common feature of cTECs and which attracts DP thymocytes for positive selection63 (Fig. 2a, Left). However, markers associated with a more mature TEC phenotype, such as cortical proteasome components encoded by PSMB11 and PRSS16, or genes involved in antigen presentation (HLA-DRA, CD80, and CD86), were not differentially expressed in late differentiation cultures compared to D0 iPSc at the significance threshold of adjusted p-value=0.01, consistent with their immature thymic epithelial state. As a control, we showed that, apart from PRSS16, these TEC marker genes were overexpressed in primary TECs compared to D0 iPSc (Supplementary Fig. 5a, b). Furthermore, a direct comparison between primary TECs and TEPs highlighted the upregulation in TECs of maturation markers such as HLA-DRA, HLA-DRB1, and HLA-DQB2, and chemokines (CCL3, CCL19, and CCL25) together with CCR9, while TEPs were enriched for PRSS16, PRSS23, Integrins (ITGA5, ITGA6, and ITGAV), and KRT19, consistent with the acquisition in TECs of a thymic functional program, distinct from the immature epithelial state of TEPs (Supplementary Fig. 5c). To determine the biological processes enriched in the nine gene modules, Gene Ontology (GO) terms were associated using ClusterProfiler (v3.18.1). Ordering GO terms by time of differentiation showed a shift from the pluripotency state and cell proliferation to embryogenesis and organ morphogenesis (Fig. 2a, Right). GO terms in late differentiation samples and primary TEC controls were associated with lymphocyte interaction and immune response regulation, indicating the enrichment of our differentiation product in TEC-associated genes. A detailed list of GO terms is available in Supplementary Fig. 6. Together, these results show the progressive loss of pluripotency and acquisition of TEC fate through modulation of the pathways we identified using the DOE-based screen.

To finally assess the efficiency of the optimized TEP differentiation protocol, we performed a deconvolution analysis of bulk RNA-seq data from the end product against the Magaletta dataset. This approach identified cTEC as the closest reference population to our differentiation product, confirming that we generated TEPs with high efficiency (Fig. 2b). In addition, we showed that these cells express PAX9, associated with early pharyngeal development and indicative of pre-thymic identity, as well as FOXN1, which is progressively upregulated during thymic epithelial lineage commitment and maturation consistent with acquisition of a thymic epithelial fate (Supplementary Fig. 7a). These cells are generated in large quantities, yielding ~2.1 million cells from 100,000 iPSc per P12 well at the end of differentiation (Supplementary Fig. 7b). We then benchmarked our protocol against two state-of-the-art protocols from Sun et al.41 and Parent et al. studies, by measuring the expression of PAX9 and FOXN1. We found that our protocol yielded cells expressing significantly higher levels of PAX9 and FOXN1 (Supplementary Fig. 7c). To test whether these results were due to protocol overfitting to our iPSc cell line LON71 or represented a true optimization of the biological process, we differentiated two other iPSc lines (MIPS203 and LON80) and found no significant differences in FOXN1 and PAX9 expression, except for the MIPS203, which showed lower FOXN1 expression (Supplementary Fig. 7d, e).

CD205+TEPs are the main product of differentiation, exhibit mTEC fate orientation and express NOTCH functional ligands
TEPs exhibit significant cTEC/mTEC lineage plasticity, and studies have shown that these progenitors express CD20532,64. Thus, we asked whether the iPSc-derived TEPs we generated expressed the CD205 surface marker and in what proportion. We confirmed by flow cytometry that almost all cells expressed the epithelial marker EPCAM and found that a large majority of them were positive for CD205, as shown in Fig. 3a, Left. Controls also attested to the specificity of the staining (Fig. 3a, Middle). To generalize this result, we performed a series of independent differentiations and assessed the proportions of EPCAM+ and CD205+ cells at the TEP stage and the earlier 3PPE stage for comparison. We found that the median percentage of cells positive for EPCAM at the TEP stage was close to 100%, and ~70% expressed the additional CD205 (Fig. 3a, Right). As expected, CD205 expression was not detected at the 3PPE stage, in contrast to EPCAM, which marks the epithelial cell fate.

We then asked whether the bona fide CD205+ TEPs are equipped to sense maturation signals from developing thymocytes and to provide signals to promote their differentiation. To this end, we performed RNA-seq of sorted CD205+ TEP populations and analyzed the expression of a set of genes encoding proteins involved in the responsiveness of maturing TECs to signals provided by the developing thymocytes as well as in promoting thymocyte development. We identified a set of genes showing preferential expression in CD205+ TEPs and coding for proteins involved in the classical and alternative NF-κB signaling pathway, with RELA and RELB constituting the NF-κB molecular complexes, TRAF5 mediating signal transduction and RANK (TNFRSF11A) receiving RANKL signals from thymocytes and activating the NF-κB pathway (Fig. 3b, Left). Activation of the alternative NF-κB pathway is crucial for acquiring an mTEC fate and features that enable mature mTECs to sustain self-antigen expression and presentation to developing thymocytes by MHCII molecules. Thus, the CD205+ TEP population appears preferentially oriented toward an mTEC fate. We then performed a deconvolution analysis against the Magaletta dataset32 and observed that 17% of these cells with inferred TEP signature were medullary oriented (Fig. 3c). Moreover, comparison of classical cTEC marker expression between CD205+ sorted and unsorted TEPs showed lower expression in the CD205+ TEPs (Fig. 3d). We identified several NOTCH ligands, including DLL4, JAG1, and JAG2, whose coding genes showed robustly higher expression in CD205+ TEPs compared to iPSc, as well as DLL1, which was expressed at comparable levels in the two populations due to its role in maintaining pluripotent stem cells, explaining its high expression in iPSc65,66 (Fig. 3b, Right). Activation of the NOTCH pathway is crucial for promoting thymocyte development. To assess the expression of the key NOTCH ligands DLL1 and DLL4 at the protein level, we performed immunofluorescence experiments on D14 TEPs (Fig. 3e, Left), which revealed consistent surface staining supporting their potential to deliver NOTCH signaling to developing thymocytes. This was further consistent with transcript detection of DLL1 and DLL4 in our RNA-seq data (Fig. 3e, Right).

Interestingly, we observed that all CD205+ TEPs have an enhanced expression of KRT19, which has recently been identified as a marker of a separate mTEC-fated progenitor32,64,67. Together, our findings indicate that the CD205+ TEPs we generated are likely capable of sensing signals that activate medullary differentiation programs in TEPs.

Expression of mature and medullary TEC markers in TEP-derived cells after 2D co-culture with ETPs
While the maturation into functional TECs in vitro has proven challenging, with most studies grafting TEPs into mice40–43, we aimed to achieve full TEP maturation in vitro. Thymocytes have been identified as a crucial source of signaling that regulates thymic medulla formation and maturation, especially for the mTEChi compartment68. Therefore, we investigated the capacity of early thymocytes to promote TEP maturation. ETPs from human thymus samples were harvested and sorted by flow cytometry using the gating strategy CD3-CD4-CD8-CD14-CD19-CD56-(Lin-) CD34+CD7+ (Supplementary Fig. 8). This rare population constitutes multipotent hematopoietic progenitors upstream of T-cell differentiation7,56. We characterized the sorted cell population by flow cytometry and validated their ETP phenotype (Supplementary Fig. 9a). These cells were negative for CD3, double negative for CD4 and CD8, and positive for CD44, a marker of early progenitor cells uncommitted to the T-cell lineage shared by TSP2, ETP, and DN1 cell populations55. Of note, the purity of the ETP sorting strategy may vary, likely due to the heterogeneity of early thymocyte populations in donors and experimental variability. This is exemplified in Supplementary Fig. 9b, where a minor CD44− population, potentially comprising committed cells, remains.

To test whether the TEPs we generated have the potential to phenocopy TECs from the medullary lineage in addition to the cortical lineage, we performed 2D co-cultures of TEPs and primary ETPs using 20,000 TEPs and 2500 ETPs per well, supplementing the medium with cytokines stimulating TEC and T-cell growth and maturation, namely, RANKL, IL7, SCF, and FLT3L56,69 (Fig. 4a). We selected these factors due to their well-established role in supporting early thymocyte survival and expansion, to complement the endogenous secretion by differentiating TECs. Recent advances indicate that RANKL drives the expansion of the already committed mTEP population64, which is compatible with a beneficial role of RANKL in increasing medullary cell growth. Interestingly, from a seemingly random distribution shortly after co-culture setup, ETPs were nearly exclusively located at the surface of the large, differentiated epithelial cells after one week (Fig. 4b). Quantification of the co-localized cells confirmed a significant increase after one week of co-culture (Fig. 4c). Given the ability of TECs to interact with thymocytes and even to incorporate them, forming thymic nurse cell complexes, it is possible that this physical interaction is concurrent with cell-cell signaling during thymopoiesis. However, we did not investigate further whether thymocytes were incorporated by TECs or merely located at their surface.

Finally, immunofluorescence revealed surface expression of KRT5 and KRT14 in a subset of cells, highlighting the emergence of medullary features in a minor subpopulation (Fig. 4d). Indeed, in humans, KRT5 and KRT14 are mTEC markers, with KRT5 broadly expressed across all mTECs and KRT14 more restricted to mTEC (I), which represent an early stage of mTEC differentiation, as shown in the human TEC atlas by Park et al. (https://cellatlas.io/studies/thymus-development/dataset/132/cherita) (Supplementary Fig. 10a). Since CLDN3 appears to be more restricted to the mature medullary compartments (mTECII–mTECIII) in the human TEC atlas (Supplementary Fig. 10a), we performed CLDN3 staining to assess medullary maturation and observed expression with a typical focal pattern in a sparse subset of cells (Fig. 4d). We counted CLDN3 positive cells in a larger field IF and found that they comprised 14% of the total cell population, indicating heterogeneity and relatively low maturation of TECs in 2D co-culture (Supplementary Fig. 10b). In addition, we found the presence of a few cells positive for HLA-DR expression, some of them showing co-expression with CLDN3 (Fig. 4d). Since HLA-DR positive cells were sparse in immunofluorescence assays, we performed FACS analysis and identified a minority of 7% of the epithelial cells (EPCAM+) being HLA-DR+ (Supplementary Fig. 10c). Together these results show that the generated TEPs have the potential to differentiate into TEC-like cells, with a small fraction expressing markers associated with maturation (HLA-DR) and medullary fate (CLDN3), thereby strengthening the potential of the TEPs we generated to respond to signals driving medullary differentiation, albeit with a low efficiency in 2D co-culture.

Finally, we observed fewer CD45+ ETP-derived cells in the 2D co-culture than expected, potentially due to cell death or insufficient stimulation during culture. In addition, we detected minimal differentiation into CD3+ cells, most of which were CD4-CD8+ cells, suggesting that the 2D co-culture supports only very limited ETP maturation (Supplementary Fig. 11).

A 3D thymic organoid system further promotes medullary differentiation
Classical monolayer culture is known to be unsuitable for TEC maintenance, leading to a rapid loss of TEC functional markers69. Considerable progress has been made recently, with the development of 3D culture systems in hydrogels and the setup of artificial thymic organoids52,70–72. We developed a human thymic organoid (hTO) co-culture system by reaggregating human primary ETPs with the iPSc-derived TEPs and seeding the cell mass into a fibrin hydrogel (Fig. 5a). Organoids were cultured at air-liquid interfaces on cell inserts, using the same XVIVO10 base culture medium, as other tested media, such as DMEM, did not support comparable cell growth. As for the 2D co-culture, we supplemented this medium with RANKL, IL7, SCF and FLT3L. After 24h, organoids exhibited a spheroid shape with a crown-like structure of early thymocytes (Fig. 5b, Left). Organoids showed signs of growth from D4 post-seeding, with an increase in total size and the formation of cellular projections into the hydrogel (Fig. 5b, Right).

We first validated the increased FOXN1 expression by qPCR throughout differentiation, culminating in hTOs (Supplementary Fig. 12a). We then leveraged the ease of harvesting hTO cells with low mortality in the early growth phase to perform scRNA-seq experiments and monitor early TEP differentiation at D7. We isolated the EPCAM+CD45- population from D7 organoids, likely corresponding to TEP-derived cells, and performed scRNA-seq. Analysis of the generated data revealed one main cluster 1, a smaller cluster 2 close to the former and composed of cells showing cycling features, and a distant smaller cluster 3 (Fig. 5c). To track the fate of the TEP population in the organoids, we selected a module of five common TEP marker genes, i.e., PDPN, PAX9, IL7, EBF1, and FN1, from the Magaletta dataset and Farley et al.64. We computed the expression score of this gene module and found strong expression in the main cluster 1 and in the cycling cells, revealing the strong TEP identity of these cells. In contrast, the more distant cluster 3 and a fraction of cycling cells showed low TEP identity, indicating that these cells may stem from the iPSc-derived TEPs in the organoids (Fig. 5d). Monocle3 (v1.3.3) trajectory analysis, rooted in cycling cells, revealed a fully connected graph where all cells were developmentally linked, with two trajectories: one toward high and another toward low TEP identity cells (Supplementary Fig. 12b).

To identify the nature of the low TEP identity cluster, we integrated the organoid scRNA-seq data with the human TEC atlas from Bautista et al.3. Remarkably, we found that the low TEP identity cells from cluster 3 were located within the medullary region of the UMAP, in close vicinity of mTEChi, whereas the high TEP identity cells from cluster 1 lay in the cortical region of the graph next to immature cycling cTECs, without clustering perfectly with the latter (Fig. 5e). This indicates that the induced TEPs share transcriptomic similarities with immature cTECs, but are not sufficient per se to confirm a definitive cTEC identity. In addition, the absence of detected expression of the functional markers HLA-DRA, CD80, or CD86 indicates that these cells remain at an immature stage.

To confirm that the low TEP identity cells arose after organoid formation rather than being already present in iPSc-derived TEPs, we performed a deconvolution analysis of the bulk RNA-seq data from iPSc-derived TEPs and sorted CD205+ TEPs against the three clusters identified in organoid scRNA-seq. We found that the main high TEP identity cluster 1 was the closest to iPSc-derived TEPs, which were negative for the mixture of cells with low TEP identity (Fig. 5f). Furthermore, we also showed that sorted CD205+ TEPs correspond to the high TEP identity clusters. Finally, we assessed the enrichment of TRA among 200 marker genes. We found a shift toward higher Tau values in the low TEP identity cluster 3 in comparison to cluster 1, with almost 40% of the top 200 genes classified as tissue specific (Tau > 0.7) (Fig. 5g). In addition, most identified TRA genes were of brain origin (Fig. 5h), consistent with previous findings that neural programs are activated early during the immature mTEC stage in differentiation3.

Together our findings show that upon 3D interaction with primary ETPs, the iPSc-derived TEPs initiate differentiation toward a medullary fate and exhibit TRA-favored expression in the early phase of hTO growth.

TEC maturation in the late phase of organoid differentiation
hTOs could be maintained for several weeks in the 3D culture system, reaching 5 mm in size. At D11, organoid morphology evolved from a spheroid to a complex structure with multiple projections colonizing the gel (Fig. 6a). To assess the level of medullary differentiation and the interaction of these cells with thymocytes (CD45+) in the late phase of organoid development, we performed confocal microscopy at D28 (Fig. 6b and Supplementary Fig. 12c). We observed extensive KRT5 staining in the cellular projections indicative of medullary-oriented cells. Importantly, we confirmed that KRT5 was not already present at the TEP stage (Supplementary Fig. 12d). Physical interactions between thymocytes and TECs could also be observed in the organoids with some degree of colocalization, even though only a slight majority of thymocytes (µ=65%, n=3) were located around organoid projections. Interestingly, distant z-slices from the organoid, in the hydrogel volume, showed dispersed thymocytes, highlighting their ability to freely circulate in the gel, which was originally devoid of cells during its casting (Supplementary Fig. 12c, Left). This property was also observed for KRT5+ cells, which migrated far from the initial seeded micromass (Supplementary Fig. 12c, Right).

We then investigated the cellular composition of D28 organoids by flow cytometry and identified three main populations that likely correspond to the TEC compartment (EPCAM+CD45−), thymocytes (EPCAM−CD45+) and an uncharacterized EPCAMloCD45+ population (Fig. 6c). A stromal cell population (EPCAM⁻CD45⁻), present in a lower proportion than epithelial cells, was also identified (Fig. 6c and Supplementary Fig. 12e). These cells could either be an unintended byproduct of TEP differentiation or result from TEC dedifferentiation and phenotype loss.

Further analysis of the EPCAM+CD45− compartment composition, based on HLA-DR and CD205 expression, revealed a global decrease in CD205 and a large fraction of HLA-DR+ cells with high and low HLA-DR expression (Fig. 6c). To characterize this population further, we isolated HLA-DRhi and HLA-DRlo cells and performed highly sensitive bulk RNA-seq. We found strong expression of HLA-DR encoding genes, namely, HLA-DRA and HLA-DRB1, with higher levels in HLA-DRhi than in HLA-DRlo cells, confirming HLA-DR complex expression in the organoid at D28 (Fig. 6d). HLA-DRhi cells exhibited robust expression of CD86, an essential costimulatory molecule for the priming and activation of naive T cells74. In addition, we found that the genes coding for HLA-DM but not HLA-DO were expressed, indicating that HLA-DRhi cells are able to present processed self-antigen peptides via MHCII molecules75,76, and provide activation signals to developing thymocytes, thereby recapitulating antigen presenting cell features of mature TECs.

Finally, to evaluate the cellular composition of HLA-DRhi and HLA-DRlo fractions, we performed deconvolution analysis against the human TEC atlas as an alternative to scRNA-seq and direct cell population identification. Indeed, harvesting the tightly imbricated cells from late organoids suffers from poor recovery and high mortality, hindering scRNA-seq approaches. Inference of the relative abundance of the known human TEC subpopulations in HLA-DRhi and HLA-DRlo TEC samples revealed a mixture of cells that included terminally differentiated mTEC populations, i.e., corneo-like mTECs, myoid cells and ciliated cells also referred as mTEC mimetic cells, as well as mature cTEC populations (Fig. 6e). The absence of immature TEC populations indicates that the iPSc-derived TEPs we used to form the organoids kept differentiating after D7 and lost their immature phenotype. However, despite the observation of higher levels of AIRE expression by qPCR compared to the 2D co-culture system (Fig. 6f), we did not detect its transcripts in RNA-seq data nor hints of bona fide mTEChi by deconvolution, suggesting that AIRE expression might either not reach levels sufficient for its transactivation function or be temporally desynchronized among the maturing TECs.

To monitor the dynamics of EPCAM+CD205+ and HLA-DR+ cells during hTO differentiation, we analyzed samples at D7, D14, and D21. We found that the proportion of CD205+ and CD205− cells within the EPCAM+ population was balanced, with a stable 45% of CD205+ cells at D7 and D14, before decreasing to 20% at D21 (Fig. 6g, Left and Supplementary Fig. 13). This reduction aligns with the overall decrease of CD205 observed at D28 (Fig. 6c). Notably, we observed that a large majority of these CD205+ and CD205- cells are positive for HLA-DR expression at D14 and even more markedly at D21 (Fig. 6g, Middle), consistent with the enhanced HLA-DR and HLA-DP fluorescence at D21 (Fig. 6g, Right). hTOs were also formed with TEPs generated from a slightly modified differentiation protocol that modulated the interplay between BMP4 and Activin A, known to regulate TEP differentiation and maturation77,78. BMP4 has been shown to promote the maintenance of TEPs in a progenitor state, while Activin A directs differentiation towards a medullary fate77. The inhibition of BMP4 and addition of Activin A to our protocol at the end of the AFE phase introduced a differentiation bias towards a medullary fate, with markedly reduced proportions of EPCAM+CD205+ cells in hTOs at each time point (Fig. 6g, Left and Supplementary Fig. 13).

Together, these findings show that our differentiation product acquired a phenotype close to that of human TECs, with differentiation into distinct subpopulations exhibiting cortical and medullary-like features, as well as the capacity to present self-antigen peptides to developing thymocytes via high MHCII expression.

hTOs support thymopoiesis and mature into SP CD4+ and CD8+ T lymphocytes with thymic emigrant phenotypes
To investigate the capacity of hTOs to differentiate ETPs into mature SP T cells, hTOs were dissociated and analyzed by flow cytometry. Soft mechanical dissociation yielded mostly non-adherent CD45+ hematopoietic cells. CD34 expression was absent, confirming the loss of hematopoietic progenitor identity and thus ETP differentiation (Supplementary Fig. 14a). Importantly, we observed a CD3+ population arising at D17 and rapidly expanding at D21, reaching up to 60% of the hematopoietic population at D35, confirming active T cell differentiation in our system (Fig. 7a, b). We also note substantial variation in the proportion of CD3+ cells between experiments at D21 and in later hTOs.

TCR expression was analyzed by flow cytometry to distinguish αβ vs γδ T lineage orientation in hTOs (Fig. 7c). Both αβ and γδ populations were detected in CD3+ thymocytes. The anti-TCRαβ and anti-TCRγδ antibodies used in this study detect assembled heterodimers rather than individual chains, consistent with complete V(D)J recombination. However, the αβ lineage predominated, in contrast to recent results using a similar system79. A large CD3+ population lacking αβ and γδ staining remained and may represent immature pre-TCR-bearing DNT cells. Indeed, CD3 has been reported to be part of the early pre-TCR complex, composed of a TCRβ chain and a pre-α chain, appearing as early as the DN2 stage80,81.

Although most CD3+ cells are CD4-CD8- DN thymocytes, we found that hTOs also contained CD4+CD8− SP4 and CD4−CD8+ SP8 cells, albeit in lesser proportions (Fig. 7d). While CD4+CD8+ double-positive (DP) cells were not observed, other hTO experiments have shown their presence, albeit at low frequency within the CD3+ fraction (Fig. 8a, b). In this hTO experiment, a prominent SP CD8+ population, along with a smaller SP CD4+ population, was detected, revealing SP CD8+ and CD4+ thymocyte differentiation and a preferential maturation towards the CD8 fate. We then assessed the maturation state of the SP cells by quantifying the expression of CCR7 and CD62L. Importantly, this analysis revealed populations of SP T cells within hTOs that displayed a mature CCR7+CD62L+ phenotype, demonstrating the ability of hTOs to drive thymopoiesis. This is further supported by the observation that ETPs cultured without TECs but in the same culture medium as hTOs mostly died by D15, as shown by the culture of three independent ETP isolations, compared to ETP-derived thymocytes within the organoids (Supplementary Fig. 14b). The survival rate can be as low as 0.6% living cells, as illustrated in Supplementary Fig. 14c, Top.

In addition to ETPs, we isolated already committed DN2/3 thymocytes (CD3-CD4-CD8−CD34−CD7+) (Supplementary Fig. 8) and cultured them alone in the same medium as hTOs (Supplementary Fig. 14c, Middle). Although very low, cell survival was higher than with ETPs (11.3% living cells). Among these cells, differentiation into CD3+ SP8 T cells was observed, indicating preexisting T-lineage commitment. However, they did not exhibit a mature CCR7+CD62L+ phenotype. Hence, the lack of similar differentiating thymocyte populations from ETPs cultured alone confirms the absence of committed DN thymocytes in the isolated ETPs and underscores the reliability of our ETP isolation strategy.

To track CD4+ and CD8+ thymocyte populations throughout organoid differentiation, we analyzed hTOs at several time points from D10 to D42. In a first set of experiments covering four time points from D27 to D38 (Fig. 8a and Supplementary Figs. 15 and 17), we observed an overrepresentation of CD4+ and DP cells within the CD45+ fraction compared to the CD45+CD3+ subset, most pronounced at D27 and gradually diminishing (Fig. 8a, Bottom Right). This shows that ISP cells (CD4+)82 and their immediate DP progeny are captured in the CD45+ fraction before surface CD3 upregulation, but only rarely complete the transition to CD3+ DP cells. It also reveals that engagement into the ISP stage strongly decreases in later hTOs, while CD3+ DN thymocytes accumulate (Fig. 8a), suggesting a direct progression at a limited yield into CD3+ DP cells followed by rapid selection into CD3+ SP8 and SP4 cells, leaving little intermediate accumulation of DP cells within the CD3+ fraction.

We complemented this analysis with a second set of experiments covering five time points from D10 to D42, in which CD4 and CD8 expression was directly assessed within the CD3⁺ fraction of living cells from hTOs (Fig. 8b and Supplementary Figs. 16 and 17). Note that CD3+ SP4 and SP8 cells were already detectable at D10, consistent with previous reports that the DN-to-SP transition can occur within 4–12 days in experimental systems that promote thymocyte differentiation, such as intrathymic transfers or fetal thymus organ cultures83,84. Analyses of the combined sets of hTOs revealed a substantial variability in the proportion of CD4+ and CD8+ SP T cells within the organoids. To characterize the impact of differentiation time on T cell population dynamics within hTOs, we performed loess regression analyses on the proportions of each population (Fig. 8c, Left). We found stable proportions of DN thymocytes throughout differentiation, with a slight decrease of SP4, and stable proportions of SP8 thymocytes. However, the number of generated SP8 and SP4 cells increased over time (Fig. 8c, Right), indicating that their stable or reduced proportion among all CD3+ cells results from the accumulation of CD3+ DN thymocytes, thus masking the parallel increase of SP4 and SP8 T cells. Finally, we showed that the absolute yield of T cell generation from 2500 ETPs in a single organoid at D40 was ~15,000 CD3+ cells, including ~4000 CD8+ T cells and ~500 CD4+ T cells (Fig. 8c, Right).

scRNA-seq demonstrates multilineage differentiation of dendritic and mature T cells
To investigate the CD45+EPCAMlo compartment of hTOs, we performed highly sensitive bulk RNA-seq on the sorted population (Fig. 9a). Gene ontology analysis of differentially expressed genes, relative to iPSc, showed enrichment of terms linked to antigen processing and presentation by the MHCII complex, as well as interaction with lymphocytes (Fig. 9b). In addition, deconvolution analysis of the CD45+EPCAMlo transcriptome against the human thymic cell atlas from Park et al.26, which includes the CD45+ cell fraction, showed that dendritic cells 2 (DC2) likely constitute the majority of the CD45+EPCAMlo population (Fig. 9c). Notably, one of the two tested CD45+EPCAMlo samples showed slight positivity for DC1, indicating that this population may comprise a mixture of DCs, including an important proportion of DC2 and potentially a small number of DC1.

To further characterize the state of maturation of thymocytes in hTOs and explore their heterogeneity, we performed scRNA-seq of cells harvested at D28 by soft mechanical dissociation for CD45+ cell isolation. Three clusters were identified via UMAP projection (Fig. 9d). Two of these clusters exhibited hematopoietic identity, defined by CD45 expression, while the third and minor one was negative for CD45 expression, consistent with a stromal identity. Differential gene expression analysis pinpointed KRT8, KRT18, FN1, and PDPN as markers of the stromal cluster, indicative of a TEP-derived origin. Identification of key markers for each hematopoietic cell cluster enabled us to identify a dendritic cell population (PLEK, LY86, and HLA-DRA) of which approximately 40% expressed CD14, and a T cell population (CD3E, CD7, and TCF7) (Fig. 9e, Left). We ruled out the presence of NK and NKT cells based on the minimal expression of classical NK lineage markers (Fig. 9e, Right). T cells were divided into an actively proliferating minor cluster “T(P)”, and a larger, quiescent cluster “T(Q)” (Fig. 9d). To further refine cluster annotation and identify their in vivo counterparts, we projected our dataset onto the human thymic cell atlas of Park et al.26 using Seurat label transfer (Fig. 9f). This analysis confirmed the presence of two major DC populations (DC1 and DC2) and SP mature T cells confirming that hTOs support multilineage hematopoietic differentiation. It also identified DN thymocytes, which showed absence of CD34 and FLT3 and expression of CD3 module genes (CD3D, CD3E, CD3G, and CD247) together with upregulation of a pre-TCR/TCR signaling/response module (LAT, ZAP70, LCK, and CD5); both modules were silent in DCs (Fig. 9g). These data indicate post-commitment DN cells, with signaling activity consistent with the early assembly of TCRβ–pre-Tα with CD3 (cytoplasmic in CD3− DN; surface in CD3+ DN). In SP cells, we observed a further significant upregulation of the CD3 module and the TCR signaling/response module, which, together with our previous observation that a subset of SP cells displays a CCR7+CD62L+ phenotype (Fig. 7d), is consistent with the expectation that SP cells express a mature TCR.

As expected, single cell transcriptomic data confirmed the maturation state of hTO-derived T cells, showing expression of markers such as CCR7, CD27, or IL2RA (CD25) (Fig. 9e, Left and Supplementary Fig. 18). To validate that the DCs originated from ETPs rather than from iPSc differentiation, we performed a single nucleotide polymorphism (SNP) analysis using scSplit (v1.0.8) on the dissociated hTO scRNA-seq data. Each cell SNP profile was compared and grouped according to its donor of origin, the ETP donor or the donor from whom the iPSc were derived. As expected, the SNP analysis confirmed that cells originated from two individuals, with the stromal cluster deriving from iPSc and the hematopoietic compartment from ETPs (Fig. 9d). Therefore, both DC and T cell populations generated in hTOs originated from ETPs, revealing the hTO ability to support multilineage hematopoietic differentiation.

Finally, as a control to assess the ability of hTOs to support T cell commitment and maturation, we generated organoids by aggregating iPSc-derived TEPs and hematopoietic stem and progenitor cells (HSPCs) differentiated from the same iPSc line used for TEP generation following the protocol established by Flippe et al.85, which is devoid of any NOTCH signaling induction (Supplementary Fig. 19a). We identified two distinct CD34+ populations: a CD34hi compartment (mainly in the embryoid bodies), and a non-adherent CD34int compartment resembling cord blood (CB) hematopoietic stem cells (HSCs), predominantly found in the culture medium (Supplementary Fig. 20a). A large subset of these CD34+ cells also expressed CD7 (~40% in embryoid bodies and ~60–90% in culture medium) and CD44 (~46% in culture medium and 28% in embryoid bodies), resembling the phenotype of primary ETPs used in our hTO system (Supplementary Fig. 20b, c). In contrast to CD34+CD7− HSPCs, which do not support hTO aggregation (Supplementary Fig. 19a), the CD34+CD7+ HSPCs successfully aggregated (Supplementary Fig. 20d) and differentiated, giving rise at D35 to ~28% CD45+CD3+ cells composed almost entirely of TCRαβ+CD8+ T cells (Supplementary Fig. 21). Interestingly, we observed that hTOs at D28 generated both CD45+CD3+ SP4 and SP8 cells, suggesting that the SP4 population observed at D28, which disappears by D35, correspond to ISP cells that would entirely mature into SP8 mature T cells at D35. We also observed myeloid lineage differentiation at levels comparable to those in hTOs (Supplementary Fig. 22). Consistent with impaired hTO formation observed with CD34+CD7− HSPCs, attempts to reaggregate CB-derived HSCs, predominantly CD34+CD7−, similarly failed to generate organoids (Supplementary Fig. 21). Together, these experiments further confirm our model’s ability to support the differentiation of immature, uncommitted hematopoietic progenitors (CD34+CD7+) into both T and myeloid lineages.

Methods
Isolation of human primary ETPs and TECs
Postnatal human thymic samples were obtained as surgical waste from routine pediatric cardiac surgeries performed at CHU Nantes. The study was conducted under institutional declaration DC-2017-2987, in compliance with the French CODECOH regulatory framework. Within this framework, patients’ legal guardians were informed by the clinical team, and their non-opposition to research use was obtained. Procedural oversight and authorization were formalized through an institutional agreement (“charte d’utilisation”) between CHU Nantes and the clinical and research teams. All samples were fully anonymized prior to being provided to the research team.

ETPs isolation was performed as described in Lavaert et al.56. Briefly, fresh thymus samples were dissected on the same day in 1 mm3 fragments in RPMI1640 and dissociated by mechanical pipetting to release thymocytes. A 5 min incubation in red blood cell lysis solution (Miltenyi, 130-094-183) was performed for erythrocyte depletion. Cells were passed through 70 µm meshes and incubated with CD3 labeled Dynabeads (Thermofisher, 11031) to deplete most DP and SP thymocytes. ETP-enriched cell suspension was stained for flow cytometry and ETPs sorted with a FACS ARIA (BD Biosciences) using phenotype CD3−CD4−CD8−CD14−CD19−CD56−CD34+CD7+ and used for reaggregation culture. For TEC isolation, thymic fragments were digested using a 0.5 mg mL−1 collagenase D (Roche, 11088866001), 1 mg mL−1 dispase (Roche, 04942078001) and 0.5 mg mL−1 DNAse I (Roche, 11284932001) solution in RPMI1640 for 45 min at 37°C and dissociated using GentleMACS (Miltenyi, 130-093-235) and C tubes (Miltenyi, 130 093 237). Cell suspension was deposited on a 21% Optiprep (Sigma Aldrich, D1556 250ML) gradient and centrifuged 20 min at 500 g for TEC enrichment. Cells suspension was filtered on a 100 µm mesh and sorted based on the CD45−EPCAM+ phenotype.

Stem cell culture and banking
iPSc lines were provided by the Nantes iPSC core facility (L.D.). The LON71 and LON80 are human iPSc generated from healthy fibroblasts, from a 51-year-old male and a 57-year-old female, respectively87. The MIPS203 is a human iPSc generated from fibroblasts from a 30-year-old female88. Cells were banked at P21 and used between P21 and P35. The banks were validated by SNP arrays.

iPSc culture and differentiation into thymic epithelial progenitors (TEPs)
iPSc lines were maintained on Matrigel coated 6-well plates (StemCell Technologies, 354277) in mTeSR1 medium (StemCell Technologies). iPSc cultures were passaged every 5 to 6 days, using xeno-free passaging solution (StemCell Technologies) and were seeded in clumps. In case of abnormal colony morphology in cultures, daily cleaning was performed to guarantee optimal iPSc purity. iPSc were routinely cultured at 37°C in normoxic conditions. To control cell state variability, iPSc were passaged 4 days before induction of differentiation, then harvested 24 h before the D0 in single cell suspension using TrypLE (ThermoFisher) with 5 min incubation at 37°C. Cells were resuspended in mTeSR1 medium with 10 µM ROCK inhibitor Y27632 (Sigma Aldrich Y0503) and seeded on Matrigel-coated 12-well plates at 37×103 cells/cm2. Differentiation was induced at D0 with XVIVO10 medium (Lonza, BE04-380Q) supplemented with 5 µM CHIR99 (Miltenyi, 130-106-539) and 100 ng mL−1 Activin A (Biotechne R&D, 338-AC). Culture medium changes were performed at fixed hours, with quick rinsing by warm PBS to eliminate potential differentiation medium leftovers in wells. D1 and D2 medium contained only 50 ng mL−1 Activin A. At D3, cells were passaged in single cell as described above and seeded at low density (14.2-20×103 cells/cm2 depending on iPSc line) in Matrigel coated 12-well plate with 10 µM Y27 and 50 ng mL−1 Activin A. Culture medium was changed at D4, D5, D7, D9, D10, D11, and D13 as previously described with supplementation as detailed in Fig. 1. The supplementation included Activin A, CHIR99, BMP4 (Miltenyi, 130-111-167), retinoic acid (Sigma Aldrich, 302-79-4), FGF8 (Biotechne R&D, 423-F8), Noggin (Miltenyi 130-103-455), LY-364947 (Sigma Aldrich L6293-5MG), FGF10 (Miltenyi, 130-127-858), IGF1 (Miltenyi, 130-093-886), EGF (Miltenyi, 130-097-751), and RANK-L (Biotechne R&D: 6449-TEC).

TEP reaggregation with ETPs and hTO formation
TEPs were harvested at D13 using TrypLE with a 7 min incubation at 37°C and mechanical flushing. TEPs were aggregated in low binding 96-well plates (ThermoFisher, 174929), at an 8:1 ratio with fresh ETP sorted as described above, using 20,000 TEPs and 2,500 ETPs per well. Cultures were maintained for 24 h in XVIVO10-based medium supplemented with 1% Glutamax (Gibco, 35050-38/2165269), 1% non-essential amino acids (Gibco, 11140-035), 30 mM L-ascorbic acid, 0.1 µM retinoic acid, 50 ng mL−1 BMP4, 50 ng mL−1 RANKL, 10 ng ml−1 FGF8, 10 ng ml−1 FGF10, 10 ng ml−1 IGF1, 10 ng ml−1 EGF, 10 ng ml−1 SCF (Peprotech AF-300-07), 5 ng ml−1 IL7 (Peprotech AF-200-07), and 5 ng ml−1 FLT3L (Peprotech AF-300-19). Cell micromasses should show a typical spheroid morphology with a compact central cell mass surrounded by a crown composed mainly of ETPs. Hydrogels were formed by mixing an 8 mg mL−1 Fibrinogen (Sigma Aldrich, 341578) solution heated at 37°C and immediately used with 10 U mL−1 Thrombin (Sigma Aldrich, 605190) and 26,000 U mL−1 Aprotinin (Sigma Aldrich, 616370), and immediately casting it in the superior compartment of 12-well inserts (Merk PTHT12H48). 2–4 spheroids were individually collected using cut 200 µL pipette tips pre-coated with anti-adherence solution and seeded delicately on the hydrogels. The same culture medium described above was added in a way to form an air-liquid interface without drowning the hydrogels and changed every three days during the first week. Then retinoic acid, BMP4, RANKL, FGF8, FGF10, IGF1, and EGF were removed from the culture medium. To harvest the cells, the hydrogels were carefully collected using cut 1000 µL pipette tips and incubated 15 min at 37°C in a 0.5 mg mL−1 Collagenase (Roche, 11088866001) solution in DMEM/F12. Organoids were subsequently processed with a 10 min incubation in TrypLE at 37°C with frequent mechanical disruption by pipetting, then filtered on a 100 µm mesh.

iPSc to HSPC directed differentiation
iPSc (LON71) were differentiated toward a HSPC identity following a protocol described in ref. 85. Briefly, iPScs were incubated with Dispase (StemCell Technologies, 07913) at 37°C for 20 min, then rinsed with DMEM-F12 (Gibco, 11330). The colonies were lifted in mTeSR1 with 10 µM of Y-27632 using a 5 mL pipette placed perpendicularly to the bottom of the well, and deposited into low-attachment MW6 plates (Corning, 003471) to form embryoid bodies (EBs) for 18 h. The next day the EBs were collected, deposited into 15 mL polystyrene tubes and allowed to sediment. The medium was aspirated and the EBs were resuspended in EB medium [StemPro-34 SFM (Gibco, 10640) + 2 mM Glutamax (Gibco, 35050-38/2165269) + 50 µM 2-mercaptoethanol (Gibco, 31350010) + 1% non-essential amino acids (Gibco, 11140-035) + 50 µg mL−1 L-ascorbic acid (Sigma Aldrich, A7506-100)] with D0 cytokines (Table 1). This technique was employed for medium changes until D9 to D12, with one modification: as we observed the progressive migration of the CD34+ precursor cells from the EBs into the culture medium, we harvested those cells during medium changes at D5, D7, and D9 when applicable, by collecting and spinning the culture medium at 200 G for 5 min. The pellet was resuspended in the fresh culture medium and reintroduced into the culture wells. Cells were differentiated for 9 to 12 days to reach the HSPC stage, an early progenitor state uncommitted to the T-cell lineage and free of NOTCH signaling induction.

HSPC harvesting and iPSc-derived hTO formation
On D12 of iPSc to HSPC differentiation, the organoid bodies were dissociated in TrypLE for 15 min at 37°C, with mechanical disruption by vortexing every 5 min. The cells present in the culture medium underwent the same treatment for 5 min. The dissociated cells were spun at 300 G for 5 min, resuspended in FACS buffer and stained against CD34 and CD7 and with DAPI. The cells were sorted on an Aurora CS (Cytek Biosciences), gating on living CD34+CD7+ and CD34+CD7− cells. The iPSc-derived TEP cells were harvested as described above. TEP and CD7+ or CD7− HSPC cells were aggregated in low binding 96-well plates at a ratio of 3000 HSPCs and 20,000 TEPs per well. Culture was maintained for 24 h as described above. After 24 h, we observed the successful formation of spheroids in the CD7+ condition, and an absence of aggregation in the CD7− condition. The CD7+ hTOs were seeded on hydrogels and cultured for 5 weeks as described above, and FACS analyses were performed on D28 and D35 on an Aurora (Cytek Biosciences N7-00003).

The same protocol was followed using cord blood CD34+ cells (StemCell technologies ref 70008.1, lot# 1602190124) and a ratio of 3000 cord blood cells and 20,000 TEPs per well. After 24 h, we observed the successful formation of spheroids, which were seeded on hydrogels and cultured as described above. The culture was maintained for three weeks, at which point we observed the spontaneous lysis of the hTOs. The gels and remaining cells were treated as previously described and analyzed on an Aurora (Cytek Biosciences N7-00003).

RNA-extraction, RT and qPCR
Cell samples were lysed using RTL Lysis buffer (Qiagen, 79216). RNA extraction was performed using RNAeasy Mini Kit (Qiagen, 74104) and retrotranscription with Superscript III (Thermofisher, 18080093) following the manufacturer’s guidelines. qPCR was performed with SYBR Green Fast kit (ThermoFisher, 4385612) on a ViiA 7 Real-Time PCR System (ThermoFisher) using the primers listed in Table 2. Relative quantification of target gene expression was measured using the delta CT method with GAPDH as the endogenous control.

Bulk RNA sequencing by DGE-seq and SMART-seq
For 3′ Digital gene expression (DGE) bulk RNA sequencing, the protocol was performed according to our implementation of previously developed protocols88. Ten nanograms of total RNA were used for library preparation. mRNA was tagged via poly(A) tails-specific adapters, well-specific barcodes and universal molecular identifiers (UMI) with template-switching retrotranscription. cDNAs were pooled, amplified and tagmented by transposon-fragmentation to enrich 3′ ends. Sequencing was performed on an Illumina Novaseq 6000. Kits used were Zymo purification kit, Research D4004-1-L, Kit Advantage 2 PCR Enzyme System (Clontech, 639206), QIAquick Gel Extraction, Kit Agencourt AMPure XP magnetic beads and Nextera DNA (FC-121-1031).

For highly sensitive bulk RNA-seq, we performed SMART-seq RNA sequencing. RNA was processed with the SMART-Seq v4 Ultra Low Input RNA kit and the libraries were prepared with Illumina Nextera XT DNA Library Preparation Kit. An Illumina NextSeq500 was used for paired-end sequencing.

RNA-seq data processing and analysis
DGE RNA-seq data were processed using the SRP pipeline v1.089 (https://gitlab.univ-nantes.fr/bird_pipeline_registry/srp-pipeline), a Snakemake-based workflow for 3’ sequencing RNA profiling analysis. Read pairs used for analysis matched the following criteria: all 16 bases of the first read had quality scores of at least 10 and the first 6 bases correspond exactly to a designed well-specific barcode. The second reads were aligned to RefSeq human mRNA sequences (hg19) using BWA version 0.7.17. Reads mapping to several transcripts of different genes or containing more than three mismatches with the reference sequences were filtered out from the analysis. DGE profiles were generated by counting for each sample the number of unique UMIs associated with each RefSeq gene. Sequenced samples with at least 50,000 counts and 6000 expressed genes were retained for further analysis.

For SMART-seq data, reads were aligned to the human reference genome (hg19) using TopHat2 (v2.0.12) and Bowtie2 (v2.2.3) with default parameters. FPKM quantification was performed using Cufflinks (v2.2.1), and read counts were obtained using HTSeq-count (v0.11.x).

Batch correction and differential expression were performed with DESeq2 R package (v1.30.1), with the threshold of 1 log2 FC and 0.05 Benjamini–Hochberg p-value to classify a gene as differentially expressed vs D0 iPSc controls. Gene Ontology (GO) analysis of differentially expressed gene modules was performed using the ClusterProfiler R package (v3.18.1) on the KEGG database of biological pathways. The GO network was plotted using Cytoscape (v3.8.x). Data visualization relied on custom pipelines based on the ComplexHeatmap (v2.6.2) and ggplot2 (v3.3.3) R packages. To predict cell type proportions in the bulk samples, deconvolution was performed against multiple scRNA-seq atlases as reference (from Magaletta et al., Bautista et al., and Park et al.), applying the MuSiC_prop() function with default parameters (MuSiC v0.2.0). Bulk RNA-seq data from human mTEChi, mTEClo, and cTEC populations, used as controls in the deconvolution of hTO-derived TEPs, were generated by the Peterson lab and are available in GEO under accession number GSE176445.

scRNA-seq data generation
D7 dissociated hTOs were sorted using a FACS ARIA (BD Biosciences) to purify EPCAM+CD45− cell population and remove dead cells and debris. Cells from D28 hTOs were harvested by soft mechanical dissociation to capture those that dissociate from the 3D co-culture, which are mostly CD45+. Cells were loaded on the Chromium instrument (10X Genomics). Libraries were prepared with Chromium Single-Cell 3′ Reagent v2 Kit (10× Genomics, Pleasanton, CA) as per the manufacturer’s protocol. Illumina adapters were incorporated during library preparation. For D28 hTOs, cell samples were incubated with TotalSeq B antibodies (BioLegend) prior to droplet generation. cDNA synthesis was conducted to simultaneously capture both mRNA and hashtag oligonucleotide sequences. A targeted PCR amplification of the hashtag-derived cDNA was then performed to generate a discrete library, which was pooled with the mRNA-derived library. We considered the cell sample labeled with TotalSeq-B0252 for further analysis. Sequencing was performed on an Illumina Novaseq 6000.

scRNA-seq data processing and analysis
Primary data analysis was performed on Cell Ranger (v5). Cell count matrices were exported and secondary analysis was performed on R using Seurat v4 package90. Quality control was defined by keeping cells with gene numbers between 2000 and 15,000 and fewer than 0.1 proportion of mitochondrial genes. Variable gene identification, data scaling and dimension reduction used functions integrated in Seurat. Dimensionality reduction used UMAP and tSNE embedding on the first 10 dimensions. Clustering used the Louvain algorithm implemented in the FindCluster function with a resolution of 0.3. Visualization and marker determination relied on default Seurat functions. To determine whether D28 hTO-harvested cells originated from one or two genetically distinct human samples, we used scSplit (v1.0.8) with the options -n0 -s2, which infers sample identity based on SNP patterns91. For the analysis of public sc datasets of thymus and pharyngeal organogenesis, raw data were downloaded at the accession provided in the data availability section of the relevant papers26,32,37. Park’s dataset was downsized by random sampling by a factor of 0.25 to ease data handling. Quality control was set at 500/10,000/10 and 500/4000/10 for Han’s and Magaletta’s datasets. Dimension reduction was performed by UMAP on first 10 (Park), 21 (Magaletta) and 25 (Han) dimensions. Clustering was performed with a resolution of 0.75 (Park), 0.5 (Magaletta), 0.6 (Han). Visualization and marker identification were performed as described above. Projection of D28 hTO-harvested cells onto the Park thymic atlas was performed using Seurat’s label transfer workflow (Seurat v4.x). Anchors were computed with FindTransferAnchors (PCA reduction, 30 dimensions), and cell type labels were transferred with TransferData. Query cells were then mapped onto the UMAP structure of the Park reference with MapQuery using the stored UMAP model, to enable visualization of predicted identities in the reference topological space. The datasets from Magaletta and Han were obtained from the GEO databases (GSE182135 and GSE136689, respectively). Park’s dataset was obtained from the human thymic cell atlas portal: https://cellatlas.io/studies/thymus-development#resources. We used scRNA-seq data from Bautista (GSE147520) as provided, for integration with our scRNA-seq data and for deconvolution of bulk RNA-seq profiles, both generated from hTO-derived TEPs. For the integration, anchors were computed with FindIntegrationAnchors (2000 features, PCA reduction, 30 dimensions), and datasets were harmonized with IntegrateData. The integrated object was processed with ScaleData, PCA (30 dimensions), and UMAP embedding, followed by clustering using FindNeighbors and FindClusters, enabling joint visualization and annotation of TEC subsets across the two datasets.

Flow cytometry and immunofluorescence imaging
Cells were stained with the antibodies listed in Supplementary Data 3 according to the supplier’s instructions and analyzed on the FACS ARIA (BD Biosciences), the FACSCelesta (BD Biosciences), and the spectral FACS Aurora (Cytek). Flow cytometry data was processed on FlowJo (vX.0.7) and Cytek Spectroflo (v3.3.0). For 2D IF, cells were cultured on Ibidi 8-well plates (Ibidi, 80806), fixed with 4% PFA for 15 min and permeabilized with IF buffer (PBS, 0.2% Triton, 10% inactivated FBS) for 1 h. Cells were incubated in IF buffer overnight at 4°C with primary antibodies and for 2 h at room temperature for secondary antibodies. Plates were imaged on a confocal SIM microscope. Modifications were introduced to the protocol for DLL1, 4, PAX9 and FOXN1 staining, consisting of using 0.1% Triton and no inactivated FBS in the IF buffer, as well as omitting Triton during antibody incubation. For hTO staining, the hydrogels were harvested by using a widely cut 1000 µL pipette tip pre-coated with anti-adherence solution and carefully deposited in Ibidi 8-well plates in cold PBS. Most of the gel matrix was removed by delicate flushing and several PBS washing steps, without damaging the main cellular structures. hTOs were fixed with 4% PFA for 20 min and permeabilized with IF buffer (PBS, 0.2% Triton, 10% inactivated FBS) for 2 h. Primary staining was performed in IF buffer for 24 h and secondary staining for 4 h, and imaged on SIM confocal microscope in Z-stack mode. Images were processed on FIJI (v2.16).

Gene tissue specificity calculation and measure of TRA enrichment
Calculation of gene tissue specificity scores was performed using a tool developed internally, that implements the methods proposed by Yanai et al.73 and Pan et al.92. The Tau was calculated for a list of genes using the gene tissue expression data (GTEx) from the Human Protein Atlas database (https://www.proteinatlas.org/about/download). The Tau index varies between 0 and 1, with higher values indicative of higher gene tissue specificity. The tool was deployed into a user-friendly and free to use dashboard built in Shiny for diffusion to the community and allowing customized analyses. The Shiny application is available at https://nathanprovin.shinyapps.io/GeneTissueSpe/.

Statistics and data analysis
Statistical tests were performed in R. Mean comparison significance on qPCR graphs was tested using the non-parametric Wilcoxon tests with a significance p-value threshold of p < 0.05. DOE experimental plans were designed using R DOEbase package (v1.1.x). DOE results were analyzed by factorial ANOVA as detailed in figure legends. All statistical tests were two-sided unless specified. Graphs and statistical analyses were generated on GraphPad Prism (v10.4.1).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Giraud and colleagues set out to develop an improved protocol for generating thymic epithelial cells from human iPS cells. To do this, they used a Plackett-Burman combinatorial screening approach to test the requirements for different growth factors, selected based on studies of thymic organogenesis and previous attempts to differentiate hESC into thymic epithelial cells and assayed using bulk RNAseq of the cells produced in each condition with subsequent comparison to existing pharyngeal endoderm and thymus development datasets. The authors used a four step protocol for this analysis (hiPSC to definitive endoderm (DE); DE to anterior foregut endoderm (AFE); AFE to third pharyngeal pouch endoderm (3PPE); 3PPE to thymic epithelial progenitors (TEP). As step 1 (hiPSC to DE) protocol is already well established, the authors focused on steps 2-4. Using their data in conjunction with knowledge from the literature, selected an optimised set of growth factors for each of these steps. To characterise the cells produced in further detail they then performed further bulk RNAseq at a series of timepoints across the optimised differentiation protocol. Subsequently, the hiPSC-TEP produced in the optimised protocol were tested in two functional assays – a 2D co-culture system, and a 3D ‘organoid’ model. Overall, the approach taken was interesting and based on sound rationale (although this appeared not to be applied consistently in the choice of factors), but the outcome was rather disappointing. While epithelial cells that expressed some markers characteristic of thymic epithelial cells were clearly produced it is not clear from the data presented how well (or whether) these cells could support T cell development (since the required controls are not shown), nor how well they represented the epithelial subsets present in the human thymus (indeed while the data show that mTEC-like cells may have been generated, they do not provide convincing evidence for the generation of mature cTEC). This diminishes enthusiasm for the manuscript. Therefore, although credit is given for the rational approach taken and the authors are applauded for presenting a more in depth analysis than many of the papers recently published in this area, it is not clear that the current work represents a major step forward over the best currently published protocols (e.g. Ramos et al 2023).\nSpecific comments are provided below:\nGeneral\n- Throughout, the clarity of the manuscript could be improved, it is is rather confusingly written in several places. Typos should also be tidied up including in the figures.\n- Please show negative controls for all immunofluorescence staining and also for the flow cytometry analyses presented, along with the full gating strategy for the latter (these can be provided as supplementary figures).\n- Please make sure that n is stated for all analyses, including immunofluorescence.\nSpecific comments\nFig 1: The approach taken is appropriate (and welcome) and appears to have been applied well. Overall, the differentiation protocol shares similarities with previously published protocols. There are some key differences though (e.g. activation or inhibition of BMP signalling in the DE-3PP and 3PP-TEP stages, etc) and discussion of these differences would be appreciated, as would a summary of the precise optimised differentiation conditions (in the text and in Fig1e). A weakness is that, although the aim was to be guided by the outcome of the combinatorial analysis, some factors appeared to be added to the ‘optimal’ medium for each set because they were already included in established protocols, even when the current analysis suggested no benefit.\nFig 2: The data presented in this figure assess how well the hPSC-TEP/TEC phenocopy primary human TEC. However, in most of the modules highlighted, there appears to be little correspondence with primary TEC (Fig 2). The fitting to the Magaletti data suggesting cTEC identity is interesting (Fig 2c), if surprising based on the heatmap data. However, the authors should provide details of the parameters used to allow superimposition of the two datasets in order that these data can be properly evaluated by the reader.\nFigure 2a:\n- Please provide more information as to how the modules were identified.\n- PSMB11, PRSS16, HLA-DR, CD80, CD86 are mentioned in the text but not annotated on the figure, please correct this omission.\n- What does the un-annotated module (above ‘EPCAM’ module) represent?\n- Why are the genes in the last module so over-represented?\n- These data do not convince that the protocol has developed TEC, although the lack of some canonical TEC markers in the primary TEC dataset suggests there may be a problem with those data. The hiPSC differentiation data could be checked against human TEC data in the public domain (e.g. from the Teichmann lab).\nFig 2c:\n– please provide details of how the deconvolution analysis was done i.e. what parameters were used to fit the bulk RNAseq data onto the Magaletta data? If I understand correctly, the Magaletta data are scRNAseq, whereas the dataset generated herein (for Fig 2) is bulk RNAseq. No details appear to be provided as to how these datasets were merged, again making it hard for the reader to evaluate their significance.\nRelated, SF5: The staining with FOXN1 looks real, the PAX9 stain is OK though less convincing, however, I find it surprising that all cells stain with these markers. Please provide negative control staining for both antibodies.\nFigure 3\nFig 3a - please clarify whether the controls are unstained, or FMOs.\nFig 3b - the CD205+ TEP do not appear to resemble TEP or TEC very closely, but appear to strongly express a signature associated with mTEC fate. Please show the expression patterns of cTEC associated genes (especially since CD205 is cTEC affiliated later in development), and comment on these in the text. Also relevant here is line 296 – the Krt19+ population at E12.5 is a separate mTEC-fated progenitor, not a bipotent TEP, as shown by Lucas et al and also by Farley et al 2023, and evident in Magaletta etc, while the text in line 296 rather suggests that it is part of a common TEPC signature.\nFig 4: Here, the authors set out to push the PSC-‘TEC’ towards the mTEC fate using RANKL, as well as IL7, SCF and FTL3L (Of note, it is well established that in vivo RANKL does not regulate the cTEC/mTEC fate choice but allows mTEPC to expand).\n- The ‘ETP’ population used for the co-culture is Lin-CD34+CD7+ (lin= CD3 CD4 CD8 CD14 CD19 CD56), and, from the analysis shown in supplementary figure 6 a and b appears to contain some cells (~10%) that are starting to express CD4, and some cells that express CD3. Based on this, it seems more appropriate to call the sorted population ‘DN’ rather than ETP – although the majority of the CD4- fraction of sorted cells appear to be DN1, there are clearly other cells in the sorted population including cells that may have passed the beta selection checkpoint. Please clarify this in the text.\n- The authors observe association of the ‘ETP’ with TEC, and expression of K14, K5, MHCII and Cldn3. They state that this phenotype represents a fraction of the differentiated cells, but from the data presented it looks like most of the differentiated cells express these markers. Please clarify the proportion of cells in each dish/on each slide which are positive for the above markers.\n- Fig 4 does not examine what happened to the ‘ETP’ in the co-culture, did they differentiate? Please comment on this in the text and show related data.\nFigure 5\n- It seems somewhat surprising that the authors choose to use a set of markers of ‘TEP’ (CDH10, ACKR3, TBX3 and TGFB2) that does not include any of the markers normally taken to characterise the TEP populations present in early development (or indeed the various TEP populations suggested in the adult thymus). Inclusion of some commonly agreed TEP markers in this analysis would be welcome.\n- It is not clear that the Monocle analysis adds much useful information, the authors could consider moving these data to a supplementary figure.\n- It is not clear whether in Fig 5f, the cluster 1 and 2 cells are part of a population that is present in normal human TEC, or whether they (and indeed the cluster 3 cells) are clustering separately but adjacent to the ex vivo TEC populations. It appears to be the latter, as all/almost all of the cells in those areas represent the hIPS-‘TEC’. Please clarify this in the text, not least as it affects the strength of the conclusions that can be drawn.\n- The data shown in Fig 5f (from d7 organoids) do not strongly support the notion that bona fide TEC have been made in this protocol (rather, they suggest that cells that have some resemblance to immature cTEC have been made, but that these have not generated normal differentiated TEC populations. A few cells resembling but not identical to mTEChi cells are also present). Please comment.\nFig 6\n- See Line 398 – that statement that thymocytes were concentrated around the projections does not appear to be strongly supported by the data shown. Rather, thymocytes appear scattered through the space examined (see Fig 6b, lower left panel; these are d28 data). Please provide quantitative data confirming a positive association, or tone down this comment.\n- Line 402 – please bear in mind that KRT5 does not necessarily indicate mature epithelium. There are plenty of examples (including in fetal thymus) where it marks stem or progenitor cells. The text should be adjusted accordingly.\n- In Fig 6e, without knowing more details of how the deconvolution was done, it is hard to know how significant these data are. Please provide this information. It looks as though the hiPS-‘TEC’ share features with cTEChi, and with some minor mTEC populations, but not with the major mTEClo and mTEChi populations.\n- A weakness of the bulk RNAseq approach is that it is not possible to determine the phenotype of single cells or the proportional representation of ‘TEC-like’ cells within the organoid. In view of this (see also comments above), the conclusion drawn (line 439) is overstated. Please rephrase to avoid overclaiming.\nFig 7\n- Please show the negative controls for the flow cytometry stains, as a supplementary figure (preferably FMOs)\n- Panels a and c – the levels of CD3 look consistent between weeks 3 and 5 in panel a (and fairly low), but are much higher in week 3 in panel 5. Please comment.\n- Please show CD4 v 8 profiles at timepoints earlier than week 5 (as FACS plots, not just a bar chart). Please comment on the lack of double positives at week 7 (panels d and e) - to my eyes, there are none in the plot shown in panel d or the upper plot in panel e, and very few in the lower panel e plot. I realise these are gated on CD3 but nonetheless I would expect to see DP in this population. The plots shown appear to contradict the bar chart, in which DP is the predominant population at week 4 and week 5. In line 456, the authors state that most CD3+ cells are DN, but that ‘CD4+CD8+ DP, CD4+CD8- SP4 and CD4-CD8+ SP8, albeit in lesser proportions (Fig. 7d)’. However, no DP are present in Fig 7d. This statement also appears to contradict the data shown in Fig 7f. If the input CD45+ population had been pure ETP, this would not be a concern, but since 10% of cells in that population are starting to express CD4 (and some of which express CD3), I am concerned that the SP populations present may reflect in vitro maturation of already committed DNs (including some that may already have passed beta selection) driven by the cytokines provided. Please show representative CD4 v CD8 plots at each timepoint after gating on CD45 only, and also show the negative control data (i.e. culture of the DN in the absence of the hIPSC-TEP, in the same medium as the organoids, related to SF7b).\n- Line 451 – can the authors really say commitment here, as they really start with DN rather than ETP (some of the ‘ETP’ will already have undergone commitment). Please modify the text as needed. Related, please show thymocyte subset data for the controls (SF7b) rather than just cell numbers; these data are essential.\nMinor comments:\nLine 74 – The literature on TEPC has moved on a bit from these two references, please update. In particular, whether there is a common TEPC that plays a major role in generating or replenishing TEC remains rather open.\nLine 76 – The Notch signals described in these papers include Notch signalling that is active pre-haematopoietic colonisation, so can’t be provided by cross-talk. Please clarify the text accordingly.\nLine 90 – Please make sure the referencing for the directed differentiation approaches is complete.\nLine 269 – its not clear at what point, if ever, in early development there are bipotent progenitors, though both cTEC and mTEC progenitors express b5t (and presumably, CD205). See Magaletta et al 2022, Farley et al 2023, Liu et al 2020, Li et al 2020, Nusser et al 2022 among others. Please correct this statement to accurately reflect the literature.\nLine 313 – the authors say ‘in addition to a cortical one’ [fate] but to my mind, differentiation into a cTEC fate has not been adequately demonstrated, by the authors admission the cells generated lack canonical cTEC markers. Please correct accordingly.\nLine 330 – The data presented show that something like an mTEC may have been generated, though no details of which subtype(s) of mTEC are provided. There does not appear to be any convincing evidence that mature cTEC are generated. Please modify the statement on line 330 to reflect this.\nSupplementary Figure 1d: please state in legend what 1 and -1 mean, I assume it means hi and lo dose but this is not clear – were factors also omitted?", "reviewer_3": "The creation of in vitro systems that fully recapitulate thymic stromal features/function is essential for regenerating the thymus in pathological settings and in aging. However, capturing and maintaining the complexity of the thymic microenvironment in vitro from iPSCs, especially the various types of thymic epithelial cells (TECs), has been a challenge despite numerous attempts by several groups. Moreover, while recent studies reported in vitro systems capable of fully reproducing thymocyte development, some specific steps of thymocyte selection/maturation remain an obstacle, raising the need for a fine-tuning of current thymic organoid models.\nIn this report, Provin and colleagues take on the challenge and use an original methodology to rationalize the culture conditions required for differentiation of iPSCs to TEPs, coupling statistical-mathematical approaches to previously published gene expression datasets on early thymic organogenesis, to recreate optimal experimental designs. Focusing on 4 key steps of thymic organogenesis the authors were able to confirm/demonstrate which specific pathways previously used in the field were needed for the induction of TEP fates from iPSC. The authors also identified a new pathway (IGF-1) to be targeted in vitro for TEP induction. In addition, they showed the higher efficiency of their protocol for TEP generation in comparison to two previously published reference methods, and its reproducibility across iPSC lines.\nAfter extensive phenotypic and transcriptomic characterization of iPSC-derived TEPs, they proceeded to test their potential to further differentiate into TEC subsets in vitro, by aggregating cells with CD34+CD7+ human thymic progenitors to create human thymic organoids (hTOs). They briefly assessed the phenotype of TECs in later stage hTOs and show the possible presence of mature mTEC subsets. This maturation process was obtained in vitro, bypassing the need for in vivo transplantation of organoids usually described in the field. Finally, the authors identified CD8SP and CD4SP and dendritic cell subsets derived in hTOs.\nThis work is original in its DOE approach although this analysis relies on previously published protocols. While the methodology for deriving TEPs and hTOs has some novelty, the evidence showing this system can generate sufficient and functional T cells in a way that surpasses current models in the field is limited.\nBelow is a detailed list of major and minor comments/points to be addressed:\nMajor comments:\n- There is a lack of quantitative data throughout the manuscript: how many TEPs can be obtained from iPSCs? How many cells are needed (TEPs and ETPs) to make hTOs? How many hematopoietic cells (T, DC) can be harvested from hTOs (the FACS profiles suggest this is very limited)? How reproducible is the hTO system in showing T cell output from TEPs?\n- Clarification of what is different from other published protocols using the DOE revised methodology is essential to evaluate what is new in the paper.\n- Figure 7: the authors mention the presence of all thymocyte subsets in hTOs, but there is no data on the kinetics of T cell development from early to late time point in hTO cultures including the development of precursors, most importantly DPs. CD4 versus CD8 flow data would be an important addition to the bar graph in Figure 7f.\nThe authors describe the presence of mature T cells generated in the hTOs, based on the expression of CD3+ cells at selected time points but TCRab expression data is very weak. CD3 is also expressed by NKT cells so additional makers should be used to discriminate between T and NKT cells. What are the CD3+TCRa/b- cells? Moreover, a better characterization of the maturation of SP4/SP8 cells in hTOs is needed (CD3 vs TCRab expression) at all stages of hTOs development.\n- While the authors provide some phenotypic characterization of TEP, a more detailed phenotypic analysis of other stromal cells in 3D conditions (hTOs) throughout the length of the cultures is missing. In particular, the frequency of EpCAM+ and/or EpCAM+CD205+ cells between D0 and D35 in hTOs is not shown. Is FOXN1 detected at early/mid/late stages of hTO cultures?\n- Is there any spatial evidence of distinct cortical/medullary areas in hTOs?\n- Thymic cross-talk between epithelial cells and thymic progenitors/T cells has not been demonstrated and without stronger data this term should be deleted from the title and conclusions.\nEssential experiments/data\n- Quantification of TEP and hTO cell output (stromal and T cell) is essential to show the system is superior to others in the field.\n- Stronger evidence of thymopoiesis including CD4+CD8+ development and kinetics of T cell differentiation is required as described above.\n- ETPs are a heterogeneous population, containing cells already primed for T-cell fate in the thymus. Of note, the cited paper Lavaert et al, 2020 defines the earliest thymic progenitors (TSP1) as CD7- (by protein) and only CD7+ by RNA, so the authors have seeded TSP2 cells into the cultures, a population that has already committed to T cell lineage. Evidence that the TEPs produced from iPSCs in this system can support T cell development from less mature progenitor sources (cord blood CD34+ for instance) is required.\n- Flow cytometry data showing CD205 and HLA-DR expression in hTO TECs is unclear; a better characterization of the stromal compartment of hTO (and cellular compartmentalization) should be achieved by immunofluorescence.\n- Immunofluorescence experiments would better demonstrate the cross-talk between stromal and hematopoietic progenitor/cells instead of the current data shown in Figure 4.\n- DLL1 and DLL4 expression are key markers of TECs. RNA-Seq expression of these are relatively weak in CD205+ TEPs whereas Jag1 and 2 expression appears to be more prominent (Fig 3b). Protein expression of these key markers (at least DLL4) should be given with appropriate controls.\n- The statement (line 258) that FOXN1 expression showed a complete differentiation into TEC-fate (Fig S5B)” is misleading. Only a few cells are shown and no summarized data on the % of FOXN1 cells in culture is given. In addition, while FOXN1 is necessary for proof of fully differentiated TECs it is not sufficient.\n- Figures throughout need more complete explanation in legends.\nMinor comments:\n- Discussion: hTOs are said to contain fibroblasts, but there is no evidence showing the presence of fibroblasts in the Results section. Mesenchymal support of TECs would be expected to be required for maintenance of the hTOs. Where do the fibroblasts originate from?\n- Co-culture of ETPs was used to help mature the TEPs in vitro-discussion of how/if this would be used practically for generation of TECs for clinical use would be helpful.\n- Are there any regulatory T regs in hTO with CD4+ T cells?\n- Inconsistent time point labeling: some figures specify D35, others W5.\n- Introduction: line 54-57 needs citations for the human TEC markers cited\n- Line 269: the statement that CD205 marks TEP that are bipotent should be clarified as CD205 is not specific -the ref for this should be cited\n- Careful editing of grammar and meaning is needed throughout: the use of “remarkably” should be kept to a minimum if used at all.\nsome examples in the Intro:\nLine 77: Understanding of regulation ….has shown progress\nLine 86-understanding of regulation…has shown progress\nLine 100-this is confusing as most forms of SCID are due to mutations in hematopoietic cells and so will not benefit from TEC replacement\nLine 105-confusing example of application of TECs", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "In recent years, many techniques have been reported to induce differentiation and functional maturation of TECs. However, the myriad of protocols has left a need for systematic testing of combinatorial effects of different factors to induce differentiation of TECs at early stages and to establish an in vitro maturation protocol without grafting of TEPs in vivo. The current work systemically investigates the effects of many factors at three early stages (from DE to AFE to 3PPE to TEP) on the differentiation of human iPSCs into TEPs as well as the maturation of cortical and medullary TECs in hydrogel-based 3D co-cultures with primary hematopoietic progenitors ETPs. The authors could also show that ETPs differentiate into CD4 and CD8 T cells and dendritic cells in the 3D hTO co-culture system. The experiments are well designed and the results add to the overall incremental understanding of the factors todifferentiation induction at the different transition stages. Impressively, in the 3D hTO co-culture system, the iPS-derived TEPs continue to mature into functional TECs that support human ETPs to differentiate into T cells and dendritic cells.\nSome comments are provided to improve the manuscript.\n1. Fig. 1d and Supplementary Fig. 1d: It would be interesting to know how the authors used Plackett-Burman designs as a screening method to evaluate the most significant factors with the fewest experiments. For example, why is it necessary to have 36 runs for 5 factors on two levels (AFE: D5 – D7)?\n2. “However, we chose to keep BMP4 and FGF8 because of their consensual use to achieve 3PPE differentiation in previous studies37,39,40.” “Similarly to above, we chose to keep FGF10 and EGF since we observed higher growth rate in cultures supplemented with EGF.” Given this, some of the key findings would warrant testing in a combination without these factors at the transition stages of 3PPE (D7 to D11) and TEP (D11 to D131) to determine whether they really add any significant benefit or can be eliminated to simplify the protocol.\n3. Lines 291-293: “We also identified the NOTCH ligands DLL1, DLL4, JAG1 and JAG2 whose coding genes show robust higher expression in TEP vs iPSc (Fig 3b, Right)”. Do the authors mean in CD205+ TEP vs TEP (or iPSC)?\n4. Supplementary Fig. 6a: Please check the method section, should keep consistent, either using CD4/CD8 depletion or CD3 depletion, or both?\n5. Supplementary method section is missing, please provide information on antibodies used in the study, also information and concentrations of factors used for TEP aggregation with ETPs and 2D co-culture, including RANKL, FGF10, IGF1, EGF, SCF, IL7, and FTL3L, lines 637-639.\n6. 2D co-culture and 3D hTO co-culture: please provide the ratio and cell numbers of TEPs and ETPs used.\n7. Fig. 4: b, what are differences between left and right images? c, How is the co-localization quantified? d, What percentage of TEPs become mTECs?\n8. Fig. 4b, 5b, 6a, S1: please insert scale bars.\n9. The discussion does not compare their hTO co-culture results to previously published work with grafting of TEPs in vivo for maturation induction of TECs and efficiency to support T cell and dendritic cell development."}, "author_response": "We would first like to thank the reviewers for their insightful and constructive comments, which have allowed us to significantly improve our manuscript. We are particularly grateful for the recognition of the rationale behind our Design of Experiment (DoE) approach and the analyses performed. We provide a detailed point-by-point response in blue for each reviewer below. We have highlighted changes to the revised manuscript using tracked changes in red.\nMAIN REVISIONS INCLUDE:\n* Enhanced immunofluorescence characterization of iPSc-derived thymic epithelial progenitors (TEPs). To confirm the identity of the cells generated by our DoE-based protocol, we performed additional immunofluorescence experiments. These new data demonstrate that iPSc-derived TEPs express key NOTCH ligands (DLL1, DLL4) and thymic epithelial markers (FOXN1, PAX9), with appropriate negative controls to validate specificity (new Fig 3e, revised Supp Fig 7a).\n* Improved characterization of TEC maturation dynamics in 2D and 3D systems: We now provide a more comprehensive analysis of the maturation status of TECs derived from iPSc, in both 2D co-cultures and 3D human thymic organoids (hTOs) (new Fig 6g, new Supp Fig. 10, 12-13).\n* hTO experiments with detailed time-course analysis. We have thoroughly reworked this section and now show a total of 11 independent hTO experiments (including newly conducted replicates) with comprehensive flow cytometry gating strategies and negative controls. The detailed characterization demonstrates consistent generation of single-positive CD4 and CD8 T cells as the organoids mature (new Fig 8).\n* New hTO experiments using iPSc-derived hematopoietic stem and progenitor cells (HSPCs). To further validate the capacity of hTOs to support T cell commitment and differentiation, we performed a new series of experiments using iPSc-derived HSPCs. Specifically, we seeded hTOs with uncommitted CD34+CD44+CD7+ HSPCs, and demonstrated their successful differentiation into TCRαβ-positive single positive T cells, along with CD14+ myeloid cells comparable to ETP-seeded hTOs (Supp Fig 19-22).\n* Significant revision and clarification of figure legends.\n* Careful editing of the manuscript for language, including grammar and typos."}, {"round": 2, "reviewer_comments": {"reviewer_3": "In the previous version of the manuscript “Combinatory differentiation of human induced pluripotent stem cells generates functional thymic epithelium driving dendritic- and CD4/CD8 T-cell full development”, by Provin, d’Arco, Giraud et al., we identified major points to address to justify statements made by the authors.\nIn this new revised version of the manuscript, the authors made significant improvements pertaining to the major suggestions for review, notably:\n- Adding quantitative information (cell numbers) generated from iPSC (TEPs) and T-cell population differentiated in hTOs throughout the manuscript;\n- Detailing the number of TEPs and progenitor cells needed for making hTOs;\n- Clarifying the use of the DOE method;\n- Verifying the reproducibility of the hTO by increasing the number of experiments;\n- Adding data on the kinetics of T-cell development;\n- Testing the efficiency of the hTOs in generating T cells when starting from HSPC not primed towards T-cell fates;\n- A better characterization of TECs in hTO by showing gene and/or protein expression of markers of TEC identity and functon(notably mTECs);\n- A better characterization of non-T hematopoietic populations (DCs) and non-TEC stromal cells in hTOs;\n- Confirming hTOs do not generate cells with NK and NKT fates;\n- Improving figure legends, texts and revising statements based on new experiments.\nOur minor points were also considered and addressed in this revised manuscript.\nTwo main issues remain in the current version\n1. There is no functional analysis of the T cells produced, likely because of low cell yield. The authors’ statement that this will be solved using future changes in methodology is rather overconfident and the issue should be directly addressed in the discussion as to why the output is so low. This is also an essential hurdle to overcome before translation is feasible and will also limit the experimental usefulness of the model.\nA major point that should be corrected throughout the manuscript is of the nature of the seeding hematopoietic cells that can generate T cells in the hTOs. The only hematopoietic progenitors (whether from CB or iPSCs or thymus) able to generate T cells in the hTOs are those that express CD7( i.e. CD34+CD7+ cells). As mentioned by Reviewer 1, CD34+ CD7+ cells are not HSCs nor true thymic ETPs as described by the authors. In CB they are mostly lymphoid-committed with DC and NK potential. In the case of thymic progenitors, CD34+CD7+ cells are largely T committed lineage. True ETPs with multilineage potential are CD34+CD7- (Le et al, Immunity 2020 doi: 10.1016/j.immuni.2020.05.010). The inability of the hTOs to generate T cells from CD34+CD7- cells suggests that critical factors required for induction of T cell specification and commitment (most likely Notch signaling), present in normal thymus, are missing in the hTO microenvironment. This deficiency in the system is also seen in some (although not the OP9DLL4/4 or ATO) models and should be clearly acknowledged as such in the manuscript.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "The authors have addressed all of my previous comments; however, there are still some issues that require further clarification. In particular, the authors have added new experiments to support their conclusions.\n1. Please avoid using the same abbreviation, DE, for both \"definitive endoderm\" and \"differentially expression\" in the manuscript, as this may cause confusion. Additionally, please carefully review the manuscript including the figures for grammatical errors and typos, for example, \"differentially expression\", “caracterization”, “PAX9 et FOXN1”, “Protocol reproductibility”, “An 3D thymic”, “FTL3L”, “reagregation”, “ HLA-DRhi et lo”, “pharyngealn” …..\n2. Please provide the gene lists for the different reference cell populations used in the comparison of iPSC-derived samples to the two atlases. Additionally, include the GEO accession codes for the DGE-seq data from iPSc to TEP.\n3. Supplementary Fig. 4b: Are the proliferating cells in the differentiation end product TEPs or another cell type? How is the TEP population defined here? What percentage of the differentiation end product are TEPs? Considering Fig. 2a and Supplementary Fig. 6 (modules 4 and 6), a set of genes related to muscle development appears to be highly expressed in the differentiation end product (D14-D22). Could you provide an explanation for this observation?\n4. Supplementary Fig. 5b: It would be informative to include a comparison between primary TECs and D14-D22 iPSC-derived samples.\n5. What percentage of cells are double positive for FOXN1 and PAX9 (Supplementary Fig. 7a) in the differentiation end product?\n6. Fig. 3c and line 334: The manuscript states that “one third of these cells with inferred TEC signature are medullary oriented”, how was this proportion calculated? In Fig. 3c, the fraction appears to be less than 20%.\n7. Fig. 3d and line 335: The manuscript states, “Moreover, classic cTEC markers are less expressed in the CD205+ TEPs”. Less expressed compared to what population? This statement is unclear and needs further clarification.\n8. Lines 336-337: the manuscript states, “identified NOTCH ligands DLL1, DLL4, JAG1 and JAG2, whose coding genes exhibit robustly higher expression in TEPs compared to iPSc (Fig. 3b, Right).” However, in Fig. 3b (Right), these genes appear to be expressed at lower levels in TEPs, with the exception of JAG2. As noted in my initial review, the authors may be referring to CD205+ TEPs, but in this case, DLL1 expression appears comparable to, or even lower than that in iPSCs, not higher. This should be clarified.", "reviewer_6": "While the authors have been largely responsive to reviewer 1’s concerns, there are a still a few questions that remain, especially about the maturation and development of thymocytes within the organoid system. This group has done a commendable job on generating what look to be organoids that resemble thymic epithelium in many ways. The major claim of this manuscript is that unlike other approaches that require an in vivo transplantation step, this is a fully ex vivo organoid able to generate mature T cells. However, I am not yet fully convinced that is what they are doing.\nFirstly, CD4/CD8 expression seems to follow a curious pattern. On Line 566-568 the authors claim that there are CD4+CD8+ double positive cells (Fig. 7D) but that is not apparent from the flow plots being presented (a point that was raised in the prior reviewer comments). In Fig. 8 there are more DP cells, but as far as I can gather the reason why is that in Fig. 8 there is a broader CD45+ gate, not excluding CD3- cells. Based on this it seems that most DP cells are CD3-. This is surprising given the proportion of CD3+ cells that seem to be generated. In Fig. 8a it seems that there are a lot of both CD4+ and CD8+ single positive cells that are also CD3- (and very few that are CD3+). Finally, it also seems that most CD3+ cells are not TCRab or gd+. Based on the argument that the abundant CD3+TCR- cells are DN cells expressing preTCR (which would explain the expression of CD3 even early in the cultures), this would suggest downregulation of CD3 between the preTa stage and DP (and later) stages? Ideally, characterization of these earlier DN stages of T cell development using markers such as CD34, CD7 and CD5 would reveal what is happening within the DN compartment with respect to this unexpected expression of CD3. There also seems to be significant variation in outcomes with Fig. 7a showing 17% CD3+ at day 21 but Fig. 7c showing 47% (this is reflected by the graph in Fig. 7b which shows a very large spread of CD3+ cells at day 21). Is there a difference in TCR expression in the very high CD3 expressing organoids vs the low CD3 expressing? In Fig. 7D the authors would like to claim differentiation to a mature CCR7+CD62L+ phenotype, but are these cells TCR+ or just CD3+? Ultimately, what evidence do the authors have that there are a significant fraction of CD3+ cells have actually undergone TCR recombination with selection? RAG induction? CD5 signaling? TRECs? Are they generated but is it just an extremely inefficient process? The biggest innovation of this study is the ex vivo generation of mature T cells in thymic organoids – even if in small numbers to start - so ensuring the robustness of this claim is important. Furthermore, the authors do not seem to have fully addressed these same questions about TCR expression from Reviewer 3 in their response.\nIn Fig. 8c the authors present kinetic data but there is no change in proportion of SP4 or SP8 cells from a very early timepoint of day 10. Do the authors think there are mature T cells that early and then do not change in proportion (despite the fact that no new hematopoietic progenitors are being seeded) across the timecourse of the organoid? This does not seem to fit with what we know about T cell development kinetics in the thymus and what could be expected to happen to SP proportions once earlier precursors are depleted.\nFinally, I would suggest that the authors could again spend some time working on the wording in the manuscript (including greater experimental detail in the figure legends) to make it easier to follow. In addition, while I applaud the inclusion of a lot of validation data, 22 supplemental figures seems excessive and adds to the complexity. Including only the truly pertinent supplemental data may assist in the flow of the manuscript."}, "author_response": "We would first like to thank the reviewers for their insightful and constructive comments, which have allowed us to significantly improve our manuscript. We are particularly grateful for the recognition of the rationale behind our Design of Experiment (DoE) approach and the analyses performed. We provide a detailed point-by-point response in blue for each reviewer below. We have highlighted changes to the revised manuscript using tracked changes in red.\nMAIN REVISIONS INCLUDE:\n* Enhanced immunofluorescence characterization of iPSc-derived thymic epithelial progenitors (TEPs). To confirm the identity of the cells generated by our DoE-based protocol, we performed additional immunofluorescence experiments. These new data demonstrate that iPSc-derived TEPs express key NOTCH ligands (DLL1, DLL4) and thymic epithelial markers (FOXN1, PAX9), with appropriate negative controls to validate specificity (new Fig 3e, revised Supp Fig 7a).\n* Improved characterization of TEC maturation dynamics in 2D and 3D systems: We now provide a more comprehensive analysis of the maturation status of TECs derived from iPSc, in both 2D co-cultures and 3D human thymic organoids (hTOs) (new Fig 6g, new Supp Fig. 10, 12-13).\n* hTO experiments with detailed time-course analysis. We have thoroughly reworked this section and now show a total of 11 independent hTO experiments (including newly conducted replicates) with comprehensive flow cytometry gating strategies and negative controls. The detailed characterization demonstrates consistent generation of single-positive CD4 and CD8 T cells as the organoids mature (new Fig 8).\n* New hTO experiments using iPSc-derived hematopoietic stem and progenitor cells (HSPCs). To further validate the capacity of hTOs to support T cell commitment and differentiation, we performed a new series of experiments using iPSc-derived HSPCs. Specifically, we seeded hTOs with uncommitted CD34+CD44+CD7+ HSPCs, and demonstrated their successful differentiation into TCRαβ-positive single positive T cells, along with CD14+ myeloid cells comparable to ETP-seeded hTOs (Supp Fig 19-22).\n* Significant revision and clarification of figure legends.\n* Careful editing of the manuscript for language, including grammar and typos."}, {"round": 3, "reviewer_comments": {"reviewer_3": "Most important issues are now addressed. However I continue to believe that the conclusions of how well the hTO can recapitulate normal thymopoiesis should be more circumspect. Given that the hTO model was only shown to generate T cells from primary thymic or iPSC-derived CD34+CD7+ cells, and not CB CD34+CD7- cells it is clear that T cells cannot be produced from uncommitted HSC or HSPC in the hTO system (unlike other in vitro systems). This very likely because of insufficient notch ligand expression in the iPSC derived epithelial cells, possibly because of immaturity. The poor output of CD4+CD8+ (DPs) also suggests that T cell differentiation is not proceeding normally (as suggested by reviewer #6). The statement (line 776) that “T-cell output remains modest mainly because the number of hematopoietic progenitors available from primary thymic samples restricts input and thus limits the size of organoid batches” begs the question of why the hTO system only worked with thymocyte progenitors.\nWhile an alternative approach to produce TECs from iPSCs is a valuable addition to the literature I believe that it is important for the readership to understand the limits to the conclusions that can be made from the data (modifications should be made for example: line 663 “…functional thymic organoids supporting full thymopoiesis in vitro”)\nI would also suggest removing the new sentence at the end of the abstract (line 41-42) “…generating mature thymic immune cells of clinical relevance and, through optimization of their production, paving the way for future cellular therapies.”\n-It would seem that the clinical relevance of the cells and their optimization of production are yet to be explored and should be made clear are speculative at this point.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "In the new revised manuscript, the authors have thoroughly addressed all of my comments, I have no further questions or concerns.", "reviewer_6": "Let me begin by saying that I like the approach the authors have taken and they have done a commendable job to advances the differentiation of functional TECs from pluripotent cells. I also appreciate the authors have tried to do as much as they can with their existing data to answer my questions and concerns – and I do genuinely try to avoid asking for experiments where they are not always needed (especially having been brought in after another reviewer dropped out). However, I am still concerned that the data presented are not entirely convincing that there are truly TCR rearranged and functionally expressing T cells being generated (let alone being MHC restricted and functional). Ideally, I would have loved to see additional data such as TRECs, dual staining for both CD3 and TCR, and even a functional measure of TCR engagement; however, I appreciate that may be beyond the scope of the manuscript and do not feel that these concerns should hold up publication.\nFurther digging suggests that, unlike mouse equivalents that typically detect only single TCR chains, the TCR antibodies used do seem to bind both alpha and beta chains (or gamma and delta chains) so should be detecting fully rearranged TCR (and not pre-TCR rearranged beta chain for instance). This should be highlighted as evidence of full TCR rearrangement. \nProgress has certainly been made in these systems, and it would be beneficial for the community to have these studies published, but ultimately they suggest that there is extremely inefficient generation of mature T cells.\nI appreciate that the discussion does acknowledge a likely bottle neck between DN and DP, but to address my concerns, I suggest that the authors should consider working on some of the language to more carefully describe what is being generated and the limitations as they stand. For instance, the title referencing “full T cell development” seems a little premature since there is no functional evidence that these are indeed fully mature T cells. In the abstract, perhaps the language could be softened (further) to suggest that further optimization will be needed as the generation of mature T cells is extremely inefficient and the cells generated have not yet been tested functionally."}, "author_response": "We would first like to thank the reviewers for their insightful and constructive comments, which have allowed us to significantly improve our manuscript. We are particularly grateful for the recognition of the rationale behind our Design of Experiment (DoE) approach and the analyses performed. We provide a detailed point-by-point response in blue for each reviewer below. We have highlighted changes to the revised manuscript using tracked changes in red.\nMAIN REVISIONS INCLUDE:\n* Enhanced immunofluorescence characterization of iPSc-derived thymic epithelial progenitors (TEPs). To confirm the identity of the cells generated by our DoE-based protocol, we performed additional immunofluorescence experiments. These new data demonstrate that iPSc-derived TEPs express key NOTCH ligands (DLL1, DLL4) and thymic epithelial markers (FOXN1, PAX9), with appropriate negative controls to validate specificity (new Fig 3e, revised Supp Fig 7a).\n* Improved characterization of TEC maturation dynamics in 2D and 3D systems: We now provide a more comprehensive analysis of the maturation status of TECs derived from iPSc, in both 2D co-cultures and 3D human thymic organoids (hTOs) (new Fig 6g, new Supp Fig. 10, 12-13).\n* hTO experiments with detailed time-course analysis. We have thoroughly reworked this section and now show a total of 11 independent hTO experiments (including newly conducted replicates) with comprehensive flow cytometry gating strategies and negative controls. The detailed characterization demonstrates consistent generation of single-positive CD4 and CD8 T cells as the organoids mature (new Fig 8).\n* New hTO experiments using iPSc-derived hematopoietic stem and progenitor cells (HSPCs). To further validate the capacity of hTOs to support T cell commitment and differentiation, we performed a new series of experiments using iPSc-derived HSPCs. Specifically, we seeded hTOs with uncommitted CD34+CD44+CD7+ HSPCs, and demonstrated their successful differentiation into TCRαβ-positive single positive T cells, along with CD14+ myeloid cells comparable to ETP-seeded hTOs (Supp Fig 19-22).\n* Significant revision and clarification of figure legends.\n* Careful editing of the manuscript for language, including grammar and typos."}]

Decision letter
[Editorial Note: See attachment]