Tumor-infiltrating immature innate lymphoid cells in colorectal cancer are biased toward ILC1/tissue-resident NK cell differentiation

Domain: biology
Published: 2026-03-27
Editorial outcome: minor_revision

Abstract
Peritoneal metastases (PM) occur in 10% of patients with colorectal cancer (CRC) and are linked to poor outcomes. Although dysregulated innate lymphoid cells (ILC) have been described in CRC, their function in CRC-PM remains unclear. Here, we analyze tumor samples from CRC and CRC-PM patients using single-cell RNA sequencing (11 patients), flow cytometry (8 patients) and differentiation assays (24 patients). Healthy colon, primary CRC and CRC-PM tumors are infiltrated by heterogeneous populations of ILC3, ILC2, ILC1, tissue resident (tr) NK cells and conventional (c) NK cells. Compared to healthy colons, primary CRC and CRC-PM tumors are depleted of ILC3 but enriched for ILC1, trNK cells and cNK cells. CRC and CRC-PM tumors harbor two immature ILC populations, early NK and naïve (n) ILC, with nILCs being transcriptionally skewed toward ILC1 and trNK cells. Indeed, co-culture of isolated nILCs with OP9-DL1 cells induces intratumoral nILC differentiation into ILC1/trNK-like cells. These findings help understand the immune pathogenesis of CRC and CRC-PM and provide insights for future ILC1 and NK cell-based therapies.

Introduction
Colorectal cancer (CRC) is the second leading cause of cancer-related death globally1. Around 5% of CRC patients present with peritoneal metastasis (PM) at diagnosis2,3, and around 5% of CRC patients develop PM later3,4. Patients with right-sided colon cancer, locally advanced CRC, especially those with obstructive or perforated tumors, or those undergoing emergency or non-radical resections, are at an even higher risk3. Historically, CRC-PM patients were treated palliatively, but now hyperthermic intraperitoneal chemotherapy (HIPEC) combined with cytoreductive surgery (CRS) offers a potential cure for a subgroup of patients5. However, HIPEC efficacy is debated6 and up to 25% of patients experience serious postoperative complications, and the risk of recurrence is high7. Immune checkpoint blockade has shown encouraging results for patients with microsatellite instability (MSI-high) metastatic CRC8, but only around 8% of patients with metastatic CRC have MSI-high tumors9. Hence, more research is needed on immunotherapies for patients with microsatellite stable (MSS) CRC and especially metastatic MSS CRC because of its poor prognosis.

While T cell infiltration, particularly of cytotoxic memory CD8+ T cells, is a major determinant of CRC survival10–12 and immunotherapy response13, the role of innate lymphoid cells (ILC), including natural killer (NK) cells, in CRC and CRC-PM is less clear.

The ILC family is composed of five main subsets14. NK cells possess an arsenal of activating and inhibitor receptors that regulate their cytotoxic functions and secretion of cytokines and chemokines, ultimately aimed at eliminating virus-infected cells and tumor cells15. Related to NK cells are subsets of ILC type 1 (ILC1), which lack some prototypical NK cell features, including Eomes and perforin, but still produce interferon (IFN)-γ16. ILC3 and lymphoid tissue inducer (LTI) cells are, via their interleukin (IL)−22 production and formation of secondary lymphoid tissues, respectively, important for gut immune homeostasis14. ILC2 produces type 2 cytokines and thus contributes to parasite defense but also type 2 inflammation17.

Tumor infiltration of immune cells expressing CD57, a marker present on certain T cells and NK cells, is linked with a favorable prognosis in CRC18. This is likely since CD57 marks highly differentiated, educated, and cytotoxic NK cells and T cells with high potential for anti-tumoral function19. However, the majority of NK cells and other ILCs in CRC tumors are CD57−18. Indeed, it was shown that NK cells quickly downregulate cytotoxic functions upon entry into the CRC tumor microenvironment (TME) in mice20. Factors such as tumor growth factor (TGF)-β, produced by cancer cells and other cells in the TME, including cancer-associated fibroblasts21, are important in modulating the immune landscape in CRC22. TGF-β dampens NK cell cytotoxicity and expression of the NK cell transcription factor Eomes23, induces tissue-resident (tr) NK cell features23, and plays a key role in the differentiation of subsets of ILC1s23, including intraepithelial (ie) CD103+ ILC124. Indeed, ILC1-like cells are enriched in primary CRC, along with ILC2, as compared to the unaffected colon25. ILC2 play ambigous roles in animal models of CRC, showing anti-tumoraleffects in some settings of CRC26, while showing protumoral effects and being targets of checkpoint blockade in others26,27.

Previous studies focused at addressing the role of ILCs and NK cells in primary CRC have failed to capture the large diversity of these cells in the human intestine16 and their potential roles in CRC-PM remain unaddressed. The aim of this study was therefore to determine the ILC and NK cell landscape of primary and metastatic CRC with PM at high resolution.

Here, we use single-cell RNA sequencing (scRNAseq) to analyze healthy colon, primary CRC, and CRC-PM samples and discover that both primary CRC and PM tumors contain heterogeneous populations of ILCs and NK cells and are enriched for subsets of ILC1 and trNK cells. We also identify subsets of less differentiated intratumoral naïve (n) ILCs and early (e) NK cells with transcriptional features indicative of being precursors of more differentiated intratumoral ILCs and NK cells. Indeed, in vitro differentiation cultures reveal that intratumoral nILCs are poised for differentiation into ILC1 and NK cells with a tissue-resident phenotype, possibly contributing to their accumulation in tumors. These findings contribute to the understanding of the immune pathogenesis of CRC-PM and our large single-cell data might be used for rational design of future ILC1 and NK cell-based therapies aimed at increasing their anti-tumor activity.

Results
ILC heterogeneity in the unaffected colon, CRC, and PM tumors

We first assessed the infiltration of lymphocytes in the human unaffected colon (hereafter referred to as “colon”), primary CRC, and PM tumors. T cells dominated both primary CRC and, as previously reported28, PM tumors, while ILCs and NK cells were equally frequent in tumors as compared to the colon (Supplementary Fig. 1A). ILCs and NK cells (NKG2A−CD127+ ILCs, CD56+ NK cells and CD56−CD7+ non-conventional ILCs, total n=23407) from 11 patients in our translational cohort contributing with tissue samples of human colon, CRC primary tumors and/or PM tumors (Table 1) were sorted by flow cytometry (Supplementary Fig. 1A, B) and analyzed by scRNAseq to assess their transcriptional heterogeneity (Fig. 1A). As the high-resolution spectrum of ILCs and NK cells in the human intestine remains underexplored, and those reports that do exist challenge previous dogmatic ILC and NK cell nomenclature16,29, we paid particular attention to anchoring our annotations in existing literature.

Hierarchical clustering of 14 transcriptionally defined clusters allowed for the identification of three main branches of clusters (Fig. 1B, C) present in the colon, primary CRC and PM tumors (Supplementary Fig. 1C, D). Differential expression (DE) analysis followed by hierarchical clustering revealed modules of genes (Fig. 1D) that identified branch 1 as conventional (c) NK cells (module 6, including GZMB, FCGR3A, GZMH, and CCL3), branch 2 as CD127+ ILCs (module 8, including KIT and IL4I1 and module 9 including IL7R, LTB and GPR183) and branch 3 as ILC1/trNK cells as the clusters in this branch shared similarities with both CD127+ ILCs and cNK cells (Fig. 1C, D). Cluster 10 was not grouped with any of the three branches in hierarchical clustering (Fig. 1B) but was included in branch 3 because of high similarity with other branch 3 clusters including absence of module 6, 8 and 9 genes expressed in either branch 1 or 2 clusters (Fig. 1C). More specifically, the ILC1/trNK clusters expressed genes associated with tissue residency (module 1: CXCR6, ITGAE) as well as cytotoxicity (module 1: GZMA and GZMK) while ILC1/trNK clusters 1 and 7 expressed genes in module 10 (CD83, NFKB1 and ICAM1), also expressed by CD127+ ILCs. Noteworthy, clusters 1 and 8 both expressed genes of module 9, including SELL, typically expressed by lymphoid-homing/residing lymphocytes such as naïve T cells, nILCs, and CD56bright NK cells16,30,31.

Along with the DE genes (Fig. 1D), we further used a set of lineage and differentiation stage-defining transcription factors to annotate the clusters (Fig. 1E, F). Both early NK (eNK) cells (cluster 1) and naïve ILCs (nILCs) (cluster 8) were defined by expression of TCF7 and ID2, while ILC3 additionally expressed RORC, AHR, BCL6 and MAF (Fig. 1E). ILC2 and all clusters of ILC1 and NK cells expressed ZBTB16 (PLZF) and PRDM1 (BLIMP-1), while ILC2 showed the highest expression of GATA3 (Fig. 1E). TBX21 and IKZF3 were expressed by all clusters in the ILC1/trNK branch while EOMES was restricted to ILC1, CD160+ ILC1, IFN+ ILC1 as well as CD16+ NK cells A and HLA-DR+ NK cells. ZNF683 was uniquely expressed by ieILC1 (Fig. 1E). To further align with current nomenclature, we performed a set of scorings of our clusters versus modules of recently described NK cell gene profiles. Modulescoring for circulating NK cells and trNK cells32 showed that ILC1 and trNK cells scored the highest for a trNK signature (Fig. 1G) while cNK cells scored the highest for a circulating NK cell signature (Fig. 1H). Similarly, scoring against the recently described NK1, NK2, and NK3 signatures29 (Supplementary Fig. 1E) showed that cNK cells were most similar to NK1 cells, particularly NK1B and NK1C while HLA-DR+ NK cells scored highest for the NK3 signature, suggesting that they might be adaptive-like29. CD127+ ILCs and eNK cells scored the highest for the NK2 signature, while ILC1 and trNK cells showed an intermediate score for both the NK1 and NK2 signatures (Supplementary Fig. 1E). We also compared our clusters to recently published transcriptional profiles of ILC1s and NK cells in the human intestine, tonsil, lung, and peripheral blood16 (Supplementary Fig. 1F, G), which supported our annotations of the cNK cells, CD127+ ILCs, ILC1 and trNK cells. In particular, we confirmed the identity of ieILC1 as PRDM1-expressing ILC1 (Fig. 1E) and eNK cells (cluster 1) as closely resembling CD56bright NK cells (Supplementary Fig. 1F, G), yet with a tissue resident profile (Fig. 1G).

Two subsets of ILCs with immature signatures in colon and tumors

Whereas nILCs have been described in the healthy and inflamed colon31, and CD56bright-like NK cells, sharing similarities with the eNK cells identified in this study, observed in head and neck cancer33, their roles as progenitors to more mature ILCs and NK cells in primary and metastatic CRC remain unexplored. To define these two populations in more detail, we performed a DE analysis. Several of the top DE genes of eNK cells and nILCs were shared between the two clusters, including IL7R, SELL, GPR183, and CD55 (Fig. 2A), previously described in CD56bright NK cells16 and nILCs and ILC progenitors (ILCp)31,34. DE expression of XCL1 and XCL2 were also shared between eNK cells and nILCs, as well as with the ILC1/trNK cell branch (Fig. 2A). However, eNK cells were clearly distinct from nILCs as they resembled CD56bright NK cells in their expression of IL12RB, CRTAM, IRAK3, GNLY, COTL1, TNFSF9 and KLRC1 (Fig. 2B) and scored the highest towards a SELL+ NK cell profile16 (Supplementary Fig. 1F). The nILC cluster showed unique expression of TNFRSF4 and MFGE8 but also shared several transcripts with eNK cells (TCF7, TNFRSF18) as well as with ILC2 and ILC3, including KIT, LTB, ICOS, TIMP1, SCN1B, SSBP2 and CDKN1A (Fig. 2C). Although nILCs shared some similarities with mature CD127+ ILCs, and eNK resembled CD56bright NK cells, pseudotime analysis revealed that both clusters were located at the base of the trajectory while the most differentiated clusters ILC2, ieILC1 and HLA-DR+ NK ended up at the end of the predicted trajectory (Fig. 2D and Supplementary Fig. 2A), regardless from which tissue they were derived (Supplementary Fig. 2B–D). This supported the hypothesis that nILCs and eNK cells are the least differentiated of the subsets analyzed, along with NKp44- ILC3, the latter showing high similarity with recently described ILC3p (Supplementary Fig. 1F). Indeed, in RNA velocity analysis projected into UMAP, both NKp44- ILC3 and nILCs seemed to differentiate into NKp44+ ILC3, while eNK cells seemed more prone to differentiate into ILC1 and trNK cells (Fig. 2E and Supplementary Fig. 2E). Velocity latent time was the highest in NKp44+ ILC3, ieILC1, CD16+ NK B, and LIGHT+ NK, indicating these clusters are the most differentiated (Fig. 2F).

CRC and PM tumors are enriched for ILC1 and trNK cells

We next observed compositional changes of CD127+ ILCs and NK cells in tumors versus unaffected colon tissue. Whereas the NKp44+ and NKp44− ILC3 clusters were predominantly derived from colon tissue (Fig. 3A, B), the primary tumors and metastases were the dominant sources of cells in the ieILC1, ILC1, CD16+ NK B, HLA-DR+ NK, IFN+ ILC1 and LIGHT+ ILC1 clusters (Fig. 3A). Assessing the total frequencies and patient contribution of these clusters revealed a statistically significant depletion of NKp44+ ILC3, and a significant enrichment of the HLA-DR+ NK cluster in primary tumors and metastases versus unaffected colon tissue (Fig. 3C). Differential abundance (DA) testing confirmed these differences, with DA cells in the NKp44+ and NKp44− ILC3 clusters being more abundant in the colon, and DA cells in the cNK and ILC1/trNK branch, including HLA-DR+ NK but also CD16+ NK A, CD16+ NK B, ILC1, ieILC1 and LIGHT+ ILC1 clusters being more frequent in primary tumors and metastases (Supplementary Fig. 3A–C). While the frequencies of nILCs and eNK cells did not differ between colon, primary CRC, or CRC-PM tumors, we detected an inverse correlation between nILC frequency and a tendency for an inverse correlation between eNK cell frequency and primary CRC tumor stage (Supplementary Fig. 4A, B). We also validated the existence of our identified cell clusters, including nILC and eNK cell-like cells, in a publically available scRNAseq dataset (GSE178341) derived from 62 primary CRC tumors (Supplementary Fig. 4C–G). Due to low cell numbers in this dataset (approximately 5600 ILC and NK cells) as compared to ours (approximately 23,400 ILC and NK cells), nILC and eNK cells were combined into one population for correlation to clinical variables. Notably, we could not validate the inverse correlation between nILC frequency and primary CRC tumor stage (Supplementary Fig. 4H), which could be due to the low cell numbers from predominantly early tumor stages in this data. Furthermore, the frequency of nILC and eNK cells was similar irrespective of MMR status, biological sex, and tumor location (Supplementary Fig. 4I–K).

To understand if the compositional changes in the tumors could be caused by changes in local differentiation of intratumoral progenitor cells, we performed velocity analysis on each tissue separately. This revealed a change in velocity within the eNK cell cluster in the tumors, which was the most pronounced in PM (Fig. 3D). In contrast to the unaffected colon, where eNK cell gene expression changes were directed towards the NKp44+ ILC3 cluster (Fig. 3D), eNK gene velocity was directed towards the LIGHT+ ILC1 and CD16+ NK B clusters in CRC primary and PM tumors (Fig. 3D).

nILCs and eNK cells are transcriptionally distinct in CRC tumors

We next sought to identify transcriptional differences between nILCs and eNK cells in colon versus tumors that could help explain the change in predicted differentiation trajectory in tumors identified by RNA velocity (Fig. 3D). DE analysis of nILCs and eNK cells in colon versus primary CRC tumors (Supplementary Data 1 and 2) revealed a potential ILC1 and NK-cell skewing of intratumoral nILCs, with higher expression of GNLY, KLRC1, RGS2, CTSW, and CEBPB as compared to nILCs in the colon (Fig. 4A, B). In contrast, nILCs in the colon seemed ILC3-skewed with higher expression of LTB, PTGER4, TIPARP, IL4I1 and IL1R1 (Fig. 4A, C), aligning with previous reports on ILC3 propensity of colon nILCs31. There were also transcriptional changes in intratumoral eNK cells with increased expression of GNLY, CRTAM, typically expressed by CD56bright NK cells, SELL, and TCF7, expressed by trNK cells35, and IL10R and HIF1A, indicating influence of the tumor environment on these cells36 (Fig. 4D, E). eNK cells in the colon expressed transcripts of early stage and trNK cells (CD3E, ZBTB16, EOMES, IRF8) as well as of ILC3 (LTB, AHR) (Fig. 4D, F), potentially explaining their predicted differentiation trajectory towards ILC3 in the colon (Fig. 3D).

Identification and isolation of intratumoral and colonic nILC and eNK cells

To validate the existence and explore the differentiation capacity of intratumoral nILC and eNK cells, we developed a flow cytometric panel based on transcripts and selected surface protein expressions in the scRNAseq data. The nILC cluster could be distinguished from other CD127+ ILCs by expression of IL7R and KIT but lack of NCR2, HLA-DRA, HLA-DRB and PTGDR2 and from ILC1 and trNK cells by absence of KLRD1 and NCAM1 expression (Fig. 5A and patient characteristics in Table 2). This phenotype could be confirmed on the protein level by using the cell surface protein libraries in the single-cell dataset, identifying nILCs as expressing CD127 and CD117 but lacking CD94 and CD336 (NKp44) (Fig. 5B). The eNK cells expressed KLRD1 and NCAM1 but also expressed IL7R, distinguishing them from the ILC1 and trNK cell clusters. Furthermore, in contrast to cNK cells, they lacked expression of FCGR3A (Fig. 5A). On the surface protein level, the eNK cluster expressed intermediate levels of CD94 and CD127 (Fig. 5B). Additionally, both nILC and eNK cells expressed intermediate levels of the CD45RA protein (Fig. 5B).

Lin-CD127+CD94− conventional ILCs, Lin− CD94+/−CD56−CD7+ non-conventional ILCs and Lin− CD94+/−CD56+ NK cells in paired colon and primary CRC tumors were analyzed by flow cytometry in a different patient group of eight patients contributing with seven colon samples and seven primary tumors (Table 2), which allowed for the identification of 17 clusters, including cells with phenotypes compatible with the eNK and nILC clusters in scRNAseq data (Fig. 5C). Cells were annotated based on unsupervised clustering followed by confirmation with manual gating for annotation (Supplementary Fig. 5). The eNK cells represented 8% of the total NK and ILC fraction (Supplementary Fig. 6) and had a profile identified as Lin− CD56+ CD94+ CD16− CD127low CD45RA+ (Fig. 5C–E). Notably, eNK cells were characterized by an intermediate CD127 expression, which distinguishes them from stage 4b NK cells37 (Fig. 5D, E). The nILCs represented 7.5% of the total NK and ILC fraction (Supplementary Fig. 6) and had a profile identified as Lin- CD127+ CD94- CD117+ CRTH2- NKp44− HLA-DR− CD45RA+ (Fig. 5C–E). Notably, similar to our scRNAseq data (Supplementary Fig. 4A, B), we detected a tendency for an inverse correlation between nILC and eNK cell frequency and primary CRC tumor stage (Supplementary Fig. 7A, B).

Intratumoral nILCs are prone to ILC1/trNK cell differentiation in the OP9-DL1 system

Prompted by the tumor-specific transcriptional features of nILC and eNK cells (Figs. 3D and 4), we next tested the hypothesis that intratumoral nILCs and eNK cells could have an increased capacity for ILC1 and trNK cell differentiation, thereby contributing to the enrichment of such cells in the tumors (Fig. 3A–C). To this end, nILCs, CD127+ eNK cells, CD127− eNK cells (defined as in Fig. 5E), NKp44+ ILC3 and CD16+ NK cells from 14 paired colon and primary CRC tumors (Table 3) were sorted by flow cytometry (Supplementary Fig. 8). Of note, due to the limited space in our flow cytometry sorting panel, CD49a+ cells, potentially representing CD49a+ stage 4b NK cells, were not excluded in the sorting of eNK cells and might represent approximately 30% of the sorted population (Fig. 4C, D).

Based on the expression of cytokine receptors in the nILCs and eNK cell clusters (Supplementary Fig. 9), we co-cultured the sorted cells with OP9-DL1 cells for 14 days with either IL-2, IL-7, IL-1β, IL-12, IL-18, and TGF-β or IL-2, IL-7, IL-1β, and IL-23 (seven paired patient samples for each cytokine mix) to assess their differentiation capacity (Fig. 6A). OP9 is a mouse stromal cell line overexpressing the Notch ligand delta-like 1, known to support development of T cells38. However, culture of blood or tonsil ILCp with OP9, OP9-DL4 or OP9-DL1 cells and IL-2, IL-7, IL-1β and IL-23 has also been shown to support differentiation of both ILC3 and ILC1/NK cells31,34.

Co-cultures of either nILCs, CD127+ or CD127- eNK cells with OP9-DL1 cells and IL-2, IL-7, IL-1β, IL-12, IL-18 and TGF-β revealed extensive generation of cells expressing CD49a, CD103, NKp44, perforin and granzyme B, compatible with an ieILC1 phenotype (Supplementary Fig. 10A, B). However, we did not detect any changes in the expression of cytotoxicity-associated proteins perforin, granzyme B or K, granulysin, CD16, cytokines (IFN-γ, IL-22, and IL-13), or the tissue residency marker CD49a for neither nILC nor for any of the eNK cell cultures derived from tumor versus colon (Supplementary Fig. 10A, B). In contrast, co-cultures of nILCs with OP9-DL1 cells and IL-2, IL-7, IL-1β, and IL-23 showed that nILCs from the tumor generated more cells expressing CD117 (Supplementary Fig. 11), typically expressed by ILC3. However, analysis of the ILC3 marker CD300F and IL-22 production did not reveal any difference in the capacity of colonic nILCs to generate CD300F+, IL-22+ or IL-22-secreting ILC3-like cells (Fig. 6B). In comparison to nILCs from the colon, intratumoral nILC generated significantly more IL-13+ ILCs (Fig. 6C). Furthermore, intratumoral nILC generated significantly more perforin+ ILCs (Fig. 6D) and also seemed to generate more granzyme B expressing cells, albeit not statistically significant (Fig. 6E). Additionally, expression of the tissue residency marker CD49a showed a tendency to be higher (Fig. 6F), whereas expression of CD16 seemed to be lower in cultures of intratumoral nILCs, although not statistically significant (Fig. 6G). None of the other markers analyzed showed any clear difference following culture of intratumoral and colon nILCs (Supplementary Fig. 11).

To assess the co-expression of these molecules on nILC-derived cells following co-culture with OP9-DL1 cells and IL-2, IL-7, IL-1β, and IL-23, we turned to multidimensional analysis visualized by UMAP, which revealed 13 clusters (Fig. 6H). Cluster 0 and 2, expressing the ILC3 markers NKp44 and CD300F, of which one was expressing intermediate levels of IL-22 (cluster 2) (Fig. 6I), were enriched for cells derived from colonic nILC (Fig. 6J). However, a small cluster with high expression of IL-22, lacking NKp44 and CD300F (cluster 10), was enriched for cells derived from intratumoral nILC. Hence, the assessment of ILC3 capacity of intratumoral nILCs was inconclusive and likely unchanged.

In contrast, perforin-expressing clusters were clearly enriched in tumor-derived nILC cultures (clusters 4, 5, 9, 11; Fig. 6J). Notably, cluster 4 showed the highest expression of CD49a, with co-expression of intermediate levels of granzyme B, granzyme K and perforin (Fig. 6I). Cluster 9 showed the highest expression of CD103, with co-expression of NKp44 and intermediate levels of IFN-γ, NKp80 and perforin (Fig. 6I). Clusters 5 and 11 co-expressed IFN-γ and perforin, with or without CD49a (Fig. 6I). These data indicated an increased capacity of intratumoral nILC to generate cells expressing both cytotoxic proteins and tissue residency markers. Data are from a total of 14 tissue samples from 7 patients analyzed in 4 independent experiments (1–2 patients per experiment). In addition, intratumoral nILCs showed an increased capacity to generate cells expressing high levels of IL-13 (cluster 8) (Fig. 6I, J).

Together, these results indicate that while both nILCs and eNK cells can generate ILC1/trNK cells, compared to nILCs from the unaffected colon, intratumoral nILCs have an increased propensity for differentiation to cells with either an ILC1/trNK cell profile or an ILC2-like phenotype.

Intratumoral nILCs and eNK cells generate preferentially ILC1/trNK cells in co-culture with Caco-2 cells

To explore the differentiation capacity of nILC and eNK cells in a more tumor-relevant and NK cell-promoting setting, we co-cultured eNK cells and nILCs with the human colorectal adenocarcinoma cell line Caco-2 and IL-2, IL-7, IL-1β, IL-23, and IL-15, the latter to promote NK cell differentiation (Fig. 7A). Cells were sorted from the unaffected colon and primary CRC tumor of 10 CRC patients (Table 3). eNK cells were sorted as CD49a– cells to avoid inclusion of CD49a+ stage 4b cells (Fig. 4C, D and Supplementary Fig. 12). Similar to co-cultures with OP9-DL1 cells, intratumoral nILCs and eNK cells generated cells expressing granzyme B (Fig. 7B), often in combination with expression of perforin, CD49a and CD103 (Fig. 7C–E and Supplementary Fig. 13A, B), indicative of ILC1/trNK cell differentiation. However, IFN-γ and Granzyme K expression was low in these cultures (Supplementary Fig. 13C, D), potentially as a result of suppressive factors such as TGF-β or other factors produced by the Caco-2 cells and/or the absence of IL-12 and IL-18. Notably, we observed high expression of NKp44 and NKG2D (Fig. 7F–I) and to a lesser extent NKp46 and NKp80 (Fig. 7J–M) on ILC1/trNK cells generated in co-cultures with Caco-2 cells. However, we did not detect any changes in the expression of cytotoxicity-associated proteins perforin, granzyme B or K, granulysin, CD16, cytokines (IFN-γ, IL-22, and IL-13), or the tissue residency marker CD49a for neither nILC nor for any of the eNK cell cultures derived from tumor versus colon (Supplementary Fig. 10A, B). In contrast, co-cultures of nILCs with OP9-DL1 cells and IL-2, IL-7, IL-1β, and IL-23 showed that nILCs from the tumor generated more cells expressing CD117 (Supplementary Fig. 11), typically expressed by ILC3. However, analysis of the ILC3 marker CD300F and IL-22 production did not reveal any difference in the capacity of colonic nILCs to generate CD300F+, IL-22+ or IL-22-secreting ILC3-like cells (Fig. 6B). In comparison to nILCs from the colon, intratumoral nILC generated significantly more IL-13+ ILCs (Fig. 6C). Furthermore, intratumoral nILC generated significantly more perforin+ ILCs (Fig. 6D) and also seemed to generate more granzyme B expressing cells, albeit not statistically significant (Fig. 6E). Additionally, expression of the tissue residency marker CD49a showed a tendency to be higher (Fig. 6F), whereas expression of CD16 seemed to be lower in cultures of intratumoral nILCs, although not statistically significant (Fig. 6G). None of the other markers analyzed showed any clear difference following culture of intratumoral and colon nILCs (Supplementary Fig. 11).

Intratumoral nILCs additionally generated IL-22+ ILC3-like cells (Supplementary Fig. 13E). However, we observed low IL-13 expression on nILCs after co-culture (Supplementary Fig. 13F) and no divergence in the ILC1 or NK cell differentiation capacity of colon versus intratumoral nILCs (Fig. 7B, D). This contrasted with our findings with OP9-DL1 stromal cells where intratumoral nILCs generated more ILC2-as well as ILC1/NK-like cells (Fig. 6), the latter in agreement with their higher expression of NK cell genes in the tumors (Fig. 4A). It is therefore possible that the Caco-2 tumor cell microenvironment in combination with IL-15 contributed with signals that compensated for the lower intrinsic ILC1/NK cell capacity of colon nILCs while overriding the ILC2 potential of tumor nILCs. This suggests that the TME has an important role in programming the fate of nILCs.

Methods
Participants and samples
The study design included all patients planned for CRC and CRC-PM surgery, without neoadjuvant chemotherapy, at the Karolinska University Hospital, which improves the generalizability of the results and represents a patient cohort from a University hospital. From April 2020, consecutive patients with primary colon cancer or PM originating from CRC were considered for inclusion. Sample collection included primary tumor, peritoneal metastases, and non-affected reference tissue, when possible. For practical reasons, only samples that could be collected before 16.00 on Mondays–Fridays and when laboratory staff were available were included in the study. Exclusion criteria were (i) neoadjuvant or adjuvant chemotherapy in the six months preceding the surgery, (ii) inability to read and consent to study information, (iii) a history of organ transplantation, and (iv) sample collection not possible based on surgeons’ assessments. Reasons included risk of affecting pathological assessment of resected specimen due to, e.g., small tumors, or overgrowth of tumors, and/or practical logistical reasons. All patients gave informed written consent. Study participation did not affect the surgery itself. In case of colon tumor resection, tissue samples were obtained by a surgeon after removal of the specimen. The sampling procedure was developed together with pathologists at Karolinska University Hospital in order not to compromise that pathological evaluation. All surgeons were trained and as a reminder received written instructions prior to each case. The surgical specimen was carefully opened along the antimesenterical border from the distal or proximal end towards the tumor. If there was any macroscopical evidence of tumor overgrowth extra care was taken not to compromise the pathologist’s possibility to assess the specimen. When the tumor was visible from the lumen a biopsy was taken from the tumor using either a small punch biopsy instrument or scissors.

The tissue sample of unaffected colon was taken as a circular piece (approximately 1 cm) from the proximal or distal colonic marginal of the resectate, depending on the type of resection. Pathological assessment of the rest of the resected specimen followed, allowing for assessment of tumor stage reported in patient tables. Collection of data to patient characteristics tables (Tables 1–3) started prospectively and additional information regarding the pathological assessment of the tumor and oncological treatment was collected after it became registered in the medical charts. The pathological assessment was based on the Union for International Cancer Control’s Tumor-Node-Metastasis classification of malignant tumors, 8th edition64. For patients with PM, the tissue samples from macroscopical PM were obtained by a highly experienced consultant surgeon intraoperatively after initial assessment of the peritoneal tumor burden. Only tissue samples with visibly macroscopic cancerous tissue were included. The samples were taken in whole with a few millimeters of margin into the tissue. Tissue samples were placed in wash buffer containing Hanks’ balanced salt solution supplemented with antibiotics as soon as removed from the patient and transported in 3–5°C until processing. The surgery continued according to medical assessment with the planned cancer resection and/or CRS with or without HIPEC. Some patients underwent open/close procedures due to too advanced cancer burden.

Selection of samples for each experiment was based on sample availability, although a representative mix of patients (tumor stage, age, biological sex) was strived for. One patient donated samples that were used in several experimental modalities. The scRNAseq experiments required samples obtained the same day, while cryopreserved samples could be used for the other experiments.

Cell isolation from colon, colorectal primary tumors, and peritoneal metastases
Colon samples were dissected to remove fat and muscle layers. PM were dissected to isolate the tumor from the surrounding unaffected tissue, i.e., peritoneum/omentum. All samples were thinly cut into 1–2 mm pieces before subsequent enzymatic digestion into 5 ml of Iscove modified Dulbecco media (IMDM) (supplemented with antibiotics), collagenase II (0.25 mg/ml; Sigma), and deoxyribonuclease (0.25 mg/ml; Roche). Magnetic stirring was performed at 37°C and 450 rpm for 30 min. Enzymatic digestion was stopped by adding IMDM media supplemented with 10% FBS and the samples were passed through a 40 μM cell strainer before being counted.

For biobanking, maximum 40 million cells were frozen per vial. Cells were pelleted by centrifugation and resuspended in 800 μL of ice-cold FBS and 800 μL of FBS 20% DMSO was added dropwise before cells were transferred into a cell freezing container and stored overnight at –80°C. Cells were then transferred to liquid nitrogen for long term storage. Cells were thawed following 10X Genomics Demonstrated Protocol CG00039, Rev E, when needed for subsequent flow cytometric or in vitro differentiation analysis.

Cell sorting
Freshly isolated cells were sorted on the same day as the surgery for single-cell sequencing and from frozen/thawed samples for the flow cytometry analysis and in vitro differentiation assays. For sorting, cells were incubated with NIR live/dead cell marker (Invitrogen) and surface protein antibodies at 4°C for 30 min, washed with PBS and resuspended in FACS buffer (PBS with 2% FBS). Cells were sorted into 1.5 mL eppendorf tubes containing FBS on a FACSAria III sorter (BD Biosciences), with FACSDiva version 9 software.

Single cell sequencing
Maximum 15000 sorted CD127+ ILCs (CD45+, CD3−, CD19−, Lin− and CD127+), NK cells (CD45+, CD3−, CD19−, Lin−, CD94+/− and CD56+) and non-conventional (nc) ILCs (CD45+, CD3−, CD19−, Lin−, CD94+/−, CD56− and CD7+) were pooled together in one 10× reaction and 15,000 T cells (CD45+, CD3+) in another 10× reaction whenever possible. The gating strategy is available in Supplementary Fig. 1A. For two samples (PM metastasis), all cells were pooled in one 10× reaction due to a small number of sorted cells. Cells were stained with TotalSeqC antibodies CD127, CD304 (NRP1), CD94, CD161, CD196 (CCR6), CD336 (NKp44), TCRVa7-2, CD294 (CRTH2), CD1d, CD45RA, and CD117 alongside surface antibody staining for cell sorting. A 5’ library and a cell surface protein library were generated using the Chromium Next GEM Single Cell 5’ Kit v2, Chromium Next GEM Chip K Single Cell Kit, and Library Construction Kit (10× Genomics). All steps were followed as listed in the manufacturer’s instructions. Specifically, the user guide CG000331 Rev E and F.

Libraries were sequenced by SciLIfe Lab (NGI; Stockholm, Sweden) using a NovaSeq 6000 sequencer and a SP-100 v1.5 flow cell (Illumina) with a read setup of 26 cycles for read 1, 10 cycles for the i7 index, 10 cycles for the i5 index, and 90 cycles for read 2.

Analysis of single-cell sequencing data
Count matrices were generated from FASTQ files using CellRanger version 7.0.1 (10× Genomics), aligned against GRCh38, and downstream analysis was performed using the R package Seurat (version 4.3)65–69. Batch effects correction on donors was performed with Harmony v0.1.170. For transcriptome analysis, Seurat was used for cell quality control, data normalization, data scaling, dimension reduction (both linear and non-linear), clustering, DE analysis, and data visualization. Low quality cells were removed according to the number of detectable genes (number of genes <200 or >3000 were removed) and percentage of mitochondrial genes for each cell (≥8%). Data were normalized by a log-transform function with a scaling factor of 10000. We used variable genes in principal component analysis (PCA) and used the top 20 principal components (PCs) in non-linear dimension reduction and clustering. High-quality cells were then clustered by Louvain algorithm implemented in Seurat under the resolution of 0.6. T cells, NK cells and CD127+ ILCs clusters were then identified and subsetted. To isolate NK cells and CD127+ ILCs from T cells, cells were filtered according to their original 10x sample (NK cells and CD127+ ILCs only and mixed reaction with T cells, but T cells only reactions were excluded) and cells with a productive VDJ library out of the mixed reaction samples were excluded. Then, reclustering was performed with a resolution of 1.5 and further identified dead cells, doublets and non-ILC or NK cells were excluded according to the percentage of mitochondrial gene, number of features, and counts, and high CD3D, MS4A or MZB1 expression, respectively. Finally, cells were clustered again with a resolution of 0.85.

Differentially expressed genes were identified with a Wilcoxon test using Seurat function FindMarkers and defined as expressed in minimum 25% of the cluster and with a logFoldchange >0.25. For nILC and eNK cells DE CRC vs. Colon analysis, only genes with a logFoldchange <−0.5 or >0.5, expressed in minimum 25% of the cluster and with a p value <0.01 were included. Volcano plots were generated using the EnhancedVolcano v1.22 package71. Differentially abundant subpopulations were identified using the DASeq v1 package in R72. For DA testing each cell’s predictive score was generated using the nearest 50 through 500 neighbors of each cell, increasing by increments of 50. Cells were then determined to be differentially abundant if they had a predictive abundance score in the top or bottom 8% of all scores, they were then divided into different subpopulations using a resolution of 0.1. Tissue composition of each cluster was performed on the dataset after subsetting each tissue to the same number of cells of the least abundant tissue (Colon with 3185 cells). Clustered dot plot was done with scCustomize v2.1.2 package with k=10. Gene signatures were calculated using the AddModuleScore command from Seurat package. Velocity analysis was performed using scvelo v0.3.1 package73,74 and plotting on the Seurat-generated UMAP. Trajectory and pseudotime analysis were done with the Monocle3 v1.3.7 package73,75–77.

Differentiation cultures with OP9-DL1 cells
Stromal cell line OP9-DL1 (mycoplasma tested and passaged less than 10 times) was provided by the laboratory of Hergen Spits and was split once a week in IMDM and 10% FBS medium. ILC-OP9-DL1 co-cultures were performed in Yssel’s medium supplemented with 2% normal human serum (Sigma). The day before culture, 3000 OP9-DL1 cells were seeded into a 96-well round-bottom plate to allow stromal cells to adhere. For bulk cultures, about 50–5000 sort-purified ILC and NK subsets were plated evenly on OP9-DL1 for 14 days with combinations of IL-2 (20 U/ml; PeproTech), IL-7 (PeproTech), IL-23 (R&D), IL-12 (R&D), IL-1β (R&D), IL-18 (R&D) 20 ng/ml each and TGF-β (R&D) 10 ng/mL replenishing the cytokines at day 5, 8 and 12. Half the media was replenished on days 8 and 12. At day 14, cells were stimulated with 25 ng of phorbol 12-myristate 13-acetate (PMA) and 0.5 μM ionomycin with GolgiPlug (1:10) and GolgiStop (1:15) (BD Biosciences) for 3h before flow cytometry analysis of intracellular protein production and surface protein expression.

Differentiation cultures with Caco-2 cells
Human colorectal adenocarcinoma cell line Caco-2 (recently authenticated, mycoplasma tested and passaged less than 10 times) was provided by the laboratory of Peter Bergman and was split once a week in IMDM and 10% FBS medium. ILC-Caco-2 co-cultures were performed in Yssel’s medium supplemented with 2% normal human serum (Sigma). The day before culture, 5000 Caco-2 cells were seeded into a 96-well round-bottom plate to allow cells to adhere. For bulk cultures, about 10–5000 sort-purified ILC and NK subsets were plated evenly on Caco-2 cells for 10 days with combinations of IL-2 (20 U/ml; PeproTech), IL-7 (PeproTech), IL-23 (R&D), IL-1β (R&D) 20 ng/ml each and IL-15 (Peprotech) 10 ng/mL replenishing the cytokines at day five and day eight. Half the media was replenished on day eight. At day 10, cells were stimulated with 25 ng of phorbol 12-myristate 13-acetate (PMA) and 0.5 μM ionomycin with GolgiPlug (1:10) and GolgiStop (1:15) (BD Biosciences) for 3h before flow cytometry analysis of intracellular protein production and surface protein expression.

Flow cytometry
For flow cytometry analysis, cells were first stained with dead cell marker (LIVE/DEAD Fixable Near-IR, Invitrogen) and a cocktail of surface antibodies for 20–30 min at 4°C. Antibodies reference list can be found in Supplementary Table 1. For experiments involving intracellular staining (Granzymes, granulysin, perforin and interleukines) cells were fixed and permeabilized using the BD Cytofix/Cytoperm™ Fixation/Permeabilization Solution Kit. If no intracellular staining was needed, samples were fixed for 5 min with PBS 2% paraformaldehyde buffer prior to acquisition. Samples were acquired on FACSymphony™ A3 or A5 (BD Biosciences) and analyzed on FlowJo version 10. Clustering and UMAP visualization of flow cytometry data was performed with Phenograph v1.5.278 and Matplotlib v3.979 package on Python version 3.12 and with CiTOFWorkflow v480 package on R version 4.4. A detailed list of antibodies and reagents is shown in Supplementary Table 1.

Bead-based multiplex immunoassays
Beads-based multiplex immunoassays plates for Granzyme B (analyte 72), IFN gamma (analyte 43), IL-13 (analyte 35), IL-17AF (analyte 36), IL-22 (analyte 76), and Perforin (analyte 53) were ordered from Thermo Fisher Scientific. Plates were prepared according to the protocol furnished with the kit PPX-06 and acquired on a MAGPIX™ instrument. Data was analyzed with Bio-Plex Manager software.

Analysis of publically available scRNA-sequencing data
Data from Gene Expression Omnibus series GSE178341 was reanalyzed in R using Seurat package v5.3.1. Integrated metadata annotation was used to select T cells and NK cells using “TNKILC” classification in clTopLevel. Low quality cells were removed based on the number of detectable genes (number of genes <200 or >3000 were removed) and percent of mitochondrial genes was plotted but not used to filter out cells given the high disparity between samples (from 4 up to 15%). Data were normalized by a log-transform function with a scaling factor of 10,000. We used variable genes in principal component analysis (PCA) and used the top 30 principal components (PCs) in non-linear dimension reduction and clustering. Batch effects correction on donors (PID) was performed with Harmony v1.2.469. High-quality cells were then clustered by Louvain algorithm implemented in Seurat under the resolution of 0.4. T cells, NK cells and CD127+ ILCs clusters were then identified and T cells and dead cells were further removed from the Seurat object based on CD3D, TCR and mitochondrial gene expression, respectively. NK cells and CD127+ ILC clusters were subsequently reclustered with a resolution of 0.4. Label transfer was done using FindTransferAnchors() function of the Seurat package and the predicted.id added in the seurat object metadata for plotting.

Clinical data correlations
Clinical data correlations were performed on Python v3.13.2 using the cluster (or cell population) count per sample and respective clinical data from that patient sample. Spearman rank correlations were calculated between cluster frequencies and continuous variables (tumor stage rank, age) using “scipy.stats.spearmanr” (scipy v1.16.2). Mann-Whitney U tests compared cluster frequencies across categorical variables (MMR status, tumor location, sex) using “scipy.stats.mannwhitneyu” with two-sided tests. Analyses were stratified by tissue type and cluster. Benjamini-Hochberg correction controlled for multiple testing false discovery rate. Visualizations were generated using matplotlib (v3.10.6), with scatter plots for correlations and box plots with overlaid individual points for group comparisons. Minimum sample size requirements: n ≥ 2 per group for Mann-Whitney tests. Data processing was performed using pandas (v2.3.3) and numpy (v2.3.3).

Statistics
GraphPad Prism 10.0 (GraphPad Software, La Jolla, CA) was used for statistical analysis. Differences between two groups were analyzed using multiple t-tests with correction for multiple comparisons. Differences among more than two groups were analyzed using two-way ANOVA followed by Tukey’s multiple comparisons test with correction. Adjusted p<0.05 was considered statistically significant. Parametric tests were applied as the data showed no major deviations from normality and ANOVA is robust for balanced designs with small sample sizes. Adjusted p value < 0.05 was considered statistically significant. R was used for statistics of differentially expressed genes using a Wilcoxon test. Python was used to generate statistics of correlations with clinical data using a Spearman test for numerical metadata and Mann–Whitney tests for 2-group comparison.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, Anne Marchalot et al. analyze the spectrum of innate lymphoid cell (ILC) infiltrates in primary colorectal tumors and peritoneal metastases. They employ single-cell RNA sequencing (on 6 primary tumors and 7 peritoneal metastases), flow cytometry (on 8 primary tumors), and in vitro differentiation assays (on 7 primary tumors and 7 peritoneal metastases). Their main finding is that tumor-infiltrating immature ILCs exhibit a bias toward differentiation into tissue-resident NK cell/ILC1 phenotypes.\n\nThe study represents a detailed and compelling investigation of relatively underexplored immune cell populations. The manuscript is well-prepared, and the analyses appear technically robust. However, I have some concerns regarding the reproducibility of the results, given the limited sample size. My specific comments are as follows:\n\n1. The small number of tumors analyzed is concerning, particularly given the known heterogeneity of immune cell infiltrates in colorectal cancer. It raises the possibility that findings may not be generalizable. The inclusion of an independent validation cohort would substantially strengthen the study. While datasets for peritoneal metastases may not be publicly available, several single-cell RNA-seq datasets exist for primary colorectal tumors. The authors might consider leveraging one of these to validate key findings. Additionally, it would be valuable, though perhaps beyond the scope of this manuscript, to assess key phenotypes in situ using multiplex immunohistochemistry or similar approaches in a larger patient cohort.\n2. More detailed information about the patient cohorts is needed. When were the patients recruited? Were they a consecutive series meeting specific inclusion criteria? Do the cohorts overlap across the different experimental modalities? I recommend referring to the STROBE guidelines to enhance reporting of study participant characteristics.\n3. MMR status (or microsatellite instability, MSI) is a significant confounder in colorectal cancer immunology. For instance, NK cell infiltration is typically enriched in MMR deficient tumors. It would be helpful if the authors could analyze or at least discuss the cases with “unknown” MMR status. Notably, MMR deficient tumors appear to be present among the primary tumor cases, but absent among those with peritoneal metastases, which may influence the findings and should be acknowledged in the text.\n4. The biopsy procedure is currently described only briefly (\"A biopsy was sampled from the primary tumor according to a protocol developed after directions from the pathology department at Karolinska University Hospital\"). This description should be expanded to include key details such as method of collection, and handling.\n5. Clarify how the authors ensured that the tumor and peritoneal metastasis samples were representative. This is particularly important for peritoneal lesions, where macroscopic appearance may not reliably reflect tumor presence or cellular content.\n6. Given the known issues in cell culture work, including misidentification, genetic drift, and microbial contamination, the authors should describe steps taken to confirm the authenticity and integrity of the cell lines used. For example, when were the cells last tested for mycoplasma contamination? After how many passages were cells discarded or re-thawed to minimize phenotypic drift?\n7. Please include catalog numbers for all reagents to improve reproducibility. RRIDs would also be useful for applicable resources such as antibodies.\n8. The manuscript would benefit from including version numbers for all R packages used in the analyses to facilitate reproducibility.\n9. The \"Statistics\" section mentions only GraphPad Prism, yet R appears to have been used for parts of the analysis. This should be clarified, and the statistical methods in R should be appropriately described.\n10. In Tables I–III, T stages should be formatted as pT1–2, pT3, and pT4, rather than p1–2, p3, and p4. Similarly, N stages should be listed as pN0, pN1, and pN2.\n11. Harmonize the presentation of biological sex across the tables. Table I reports percentages for both sexes, while Tables II and III only report percentages for females.\n12. The discussion section would be improved by adding a dedicated paragraph acknowledging the limitations of the study, including small sample size, cohort heterogeneity, and any technical or methodological constraints.", "reviewer_2": "This manuscript describes the composition and heterogeneity of innate lymphoid cells in unaffected (non-tumoral) colon samples, colorectal tumors and peritoneal metastases. The authors employed scCITEseq and flow cytometry to characterize the ILC compartment leading to the identification of previously uncharacterized immature ILC subsets. They used an in-vitro system to assess the development potential of these cells showing their differentiation into ILC1/tissue resident NK cells. The novelty here lies in the characterization of the ILC infiltrate in peritoneal metastases and the identification of immature ILC populations. This is a valuable resource that presents the transcriptomic profiles of >20,000 ILCs in healthy colon and tumors which is of significant interest to the community. However, most of the results remain descriptive.\n\nA key concern here is that the patient cohort is very heterogenous and only includes a relatively small number of samples, which somewhat limits the clinical utility of the data. Stratification analyses based on sex, primary tumor location or mismatch repair status should be performed as these factors are known to influence intratumor immune cell composition and function.\n\nThe developmental potential of nILC and eNK subsets need be further investigated, including using in vivo models and analyzing the generated intestinal ILC subsets. The use of NSG mice that express human cytokines or the use of NSG mice injected with human cytokines would represent a more physiological system to understand the developmental potential of these immature ILC subsets in the intestine. Based on the gating strategy used to cell sort eNK cells (Fig S5), it is possible that this population was contaminated by CD49a+ stage 4b NK cells. These cells express all the eNK cell markers and therefore could not be discriminated by the sorting strategy employed by the authors, unless CD49a expression was used to exclude them. Could the authors comment on their gating strategy and the purity of their eNK cell subset for their in vitro assays.\n\nFinally, as mentioned by the authors, ILCs represent yet untapped therapeutic targets and as such, the authors should extend their flow cytometric analyses and map intratumoral ILC infiltration to correlate the proportion of eNK, nILCs and or other identified ILC subsets with disease prognosis, including the likelihood of developing peritoneal metastases. Such analyses would significantly enhance the scope of this manuscript and highlight the clinical importance of these ILC populations in colorectal cancer.", "reviewer_3": "This paper provides new insights into the populations of ILCs and NK cells present in CRC-PM by utilising single cell sequencing and flow cytometry. The authors identified putative precursor ILC populations that may be of interest for future research endeavours. This work is original and provides value to the field.\n\nBelow are some points to be considered.\n\n1. The statistical tests used in figure 3 and figure 6 are not referenced in the figure legends, nor is a legend for p-value indicators – this information should be included. The methods indicate that only t-tests have been used for statistical comparisons. Particularly when multiple groups are being compared, an omnibus test should be used followed by a posthoc test with adjusted p-values so that significance is not overestimated.\n2. An effort should be made to improve the interpretability of figure 1, particularly around the numbering and naming of the clusters. For example, in 1D, the key should be titled ‘cluster’ instead of ‘identity’. A table or otherwise should be included to correlated annotated clusters with original cluster numbers rather than having to infer between UMAP plots. Additionally, cluster 10 appears to have been included as part of Branch 3 despite not clustering as such in 1B.\n3. The axis labels in 2D should be cleaned up.\n4. It may be useful to include the latent time plots alongside your existing RNA velocity cluster plots to improve interpretability of start and endpoints.\n5. Figure S3 should have ‘differential’ rather than ‘differencial’ in the titles.\n6. You need to include information about the clustering and annotation method for the flow data in figure 5 e.g. unsupervised clustering followed by confirmation with manual gating for annotation.\n7. You should include the gating strategy used for the flow cytometry in figure 5 as a supplementary figure.\n8. Line 241-244 – be more clear about what comparison you are trying to make here.\n9. In figure S7B you should include the concentration in your concentration/cell CBA plots. I am not sure it is correct to display your concentrations per cell here when you only know the final cell numbers but haven’t measured the evolution of these populations over time. This is also true for your CBA plots in figure 6.\n10. Line 244-246 – There is no significant difference here, be careful not to bias your interpretation.\n11. Line 253 – there is a significant difference indicated for CD117.\n12. You should include discussion around (or mention of) organoids as an alternative differentiation platform for ILCs e.g. https://doi.org/10.1016/j.celrep.2022.111281. Could also discuss WNT signalling as it may affect ILC differentiation in CRC tumours."}, "author_response": "Authors’ response to the reviewer’s Comments\nReviewer #1 (Remarks to the Author)\nIn this study, Anne Marchalot et al. analyze the spectrum of innate lymphoid cell (ILC)\ninfiltrates in primary colorectal tumors and peritoneal metastases. They employ single-\ncell RNA sequencing (on 6 primary tumors and 7 peritoneal metastases), flow\ncytometry (on 8 primary tumors), and in vitro differentiation assays (on 7 primary tumors\nand 7 peritoneal metastases). Their main finding is that tumor-infiltrating immature ILCs\nexhibit a bias toward differentiation into tissue-resident NK cell/ILC1 phenotypes.\nThe study represents a detailed and compelling investigation of relatively underexplored\nimmune cell populations. The manuscript is well-prepared, and the analyses appear\ntechnically robust. However, I have some concerns regarding the reproducibility of the\nresults, given the limited sample size. My specific comments are as follows:\n1. The small number of tumors analyzed is concerning, particularly given the known\nheterogeneity of immune cell infiltrates in colorectal cancer. It raises the possibility that\nfindings may not be generalizable. The inclusion of an independent validation cohort\nwould substantially strengthen the study. While datasets for peritoneal metastases may\nnot be publicly available, several single-cell RNA-seq datasets exist for primary\ncolorectal tumors. The authors might consider leveraging one of these to validate key\nfindings. Additionally, it would be valuable, though perhaps beyond the scope of this\nmanuscript, to assess key phenotypes in situ using multiplex immunohistochemistry or\nsimilar approaches in a larger patient cohort.\nReply: We thank the reviewer for this comment and agree that our results need to be\nfollowed up in a larger patient cohort in the future. We have highlighted this in the\nsection related to strengths and limitations of our study (pages 14-15, lines 437-463).\nOur study however represents the most detailed analysis of the ILC-NK cell spectrum of\nprimary CRC to date, and the first ever in CRC-PM tumors. Our scRNAseq dataset\ncontains 23,407 CD127+ ILCs, CD56+ NK cells as well as Lin-CD7+CD56-CD127- non-\nconventional ILCs, which, to our knowledge, no previously generated single-cell RNA-\nsequencing (scRNAseq) data can be compared to. In addition to the 11 patients\nincluded for scRNAseq analysis, we included 8 more CRC patients in our flow cytometry\nvalidation experiments, and another 24 CRC patients in our functional validation\nexperiments. Hence, data from a total of 43 patients, obtained by four different\nexperimental methods, univocally showed the existence of immature subsets of ILCs\nand NK cells in CRC tumors. We are therefore confident that the main message of our\npaper, which is that intratumoral tissue-resident immature ILCs and NK cells can\ngenerate differentiated subsets of ILC1 and trNK cells in CRC tumors, stands strong.\nStill, the reviewer’s point is well taken, and we have performed an extensive inventory of\nprevious scRNAseq studies of primary CRC to identify one or several datasets to use as\nan independent validation cohort (Reviewer Table I). Unfortunately, many studies do not\nmake their data publicly accessible. We have for several years tried to get in touch with\nthe authors of Qi et al, Cell Rep Med, 2021 (PMID 34467243) who have deposited their\ndata in a Chinese repository with restricted access. This data contains 31,246 CD127+\nILCs (but no NK cells) from 4 patients (CRC tumor+adjacent colon), which could have\nbeen useful for validation of our findings related to CD127+ ILCs. Unfortunately, our\nmultiple data requests have not been answered.\nAnother challenge is that most previous studies include few patients (max 10, i.e.\nsmaller than our dataset) and analyses total single cell suspensions from tumors. Since\nILCs and NK cells make up only 0.1-10% of total CD45+ lymphocytes (Fig S1A), which in\nturn represent maximally 10% of total viable cells in the tumors (Fig S1A), a scRNAseq\ndataset of e.g. 200 000 total cells, which is considered large, would contain only 20-\nenriching for the target populations of interest. This makes our dataset unique in\ngranularity, i.e. making it possible to discover novel ILC/NK cell states.\nOur inventory did however reveal one study with large numbers of patients (n=62) and\ndecent amounts of cells (Reviewer Table I; Pelka et al: GSE178341, yellow-marked)\nReviewer Table I. Available scRNAseq data from selected CRC studies.\nAccession No of primary CRC No of total CD45+ and CD45-\nno samples cells\nGSE221575 9 39,484\nGSE178341 62 127,695\nGSE146771 18 43,817\nGSE178318 6 111,292\nGSE225857 4 196,473\nGSE232525 Not stated 7,343\nGSE242271 5 24,111\nGSE200997 16 26,173\nWe reanalyzed this dataset (Reviewer Fig. 1) and could conclude that the number of\ntotal ILC and NK cells per sample and cell quality was low compared to our dataset,\ndespite filtering of low-quality cells (Reviewer Fig. 1 A-B). This shows the strength of our\nscRNAseq dataset that although derived from 11 patients, the cell counts and data\nquality is high. Nevertheless, we performed cell label transfer analysis. We could indeed\nidentify cells in the Pelka et al data that matched with the clusters that we revealed in\nour own dataset (Reviewer Fig. 1C) but the cell numbers were low (Reviewer Fig. 1D-E),\nmaking correlations to clinical data challenging.\n4000\n3000\n2000\n1000\nReviewer Figure 1. A. Total ILC and NK cells count per sample found in Pelka et al.\n(GSE178341) and our data (Marchalot et al). B. Mean count RNA per sample. C-D. Label\ntransfer displayed on UMAP. E. Cell counts per cluster.\nelpmas\nrep\ntnuoc\nllec\nlatoT\nTumor 6000\n4000\n2000\nk a et al al ot et al P el ar c h\nM\nelpmas\nrep\nANR_tnuoCn\nnaeM\nA. B.\nColon\nTumor\nk a et al al ot et al P el ar c h\nM\nTo understand the distribution of eNK cells and nILCs across clinical and molecular\nphenotypes we therefore combined these cells into one “immature subsets” (reviewer\nFig. 1D) and could see that the majority of the tumors in this dataset had equal\nfrequencies of such cells irrespective of MMR status, biological sex, tumor location and\ntumor stage (Reviewer Fig. 2). Of note, of the 62 patients in the dataset, 28 had clinical\nmetadata and were included in the analysis.\nA. B.\nC.\nD.\nReviewer Figure 2. A-C: Total nILC and eNK cells per tumor sample combined as a\nfrequency of “immature subsets” in A. MMR proficience (p) or MMR deficient (d) tumors,\nB. Males (M) or females (F) and in tumors from right or left colon. D: Frequency of\n“immature subsets” (nILCs plus eNK cells) across tumor stage (T).\nAll scRNAseq data from Pelka et al. (GSE178341) using cell labels from our scRNAseq\ndata.\nWe have now included part of these data in Figure S4 and they are mentioned in the text\non page 7, lines 188-196.\nFinally, the reviewer suggested that we substantiate our findings with multiplex\nimmunohistochemistry or similar approaches in a larger patient cohort. While we fully\nagree that this would be valuable, we do consider, which the reviewer also indicates,\nthat this is beyond the scope of this manuscript.\n2. More detailed information about the patient cohorts is needed. When were the\npatients recruited? Were they a consecutive series meeting specific inclusion criteria?\nDo the cohorts overlap across the different experimental modalities? I recommend\nreferring to the STROBE guidelines to enhance reporting of study participant\ncharacteristics.\nReply: Thank you for this comment which helped us improve the clarity regarding the\npatient cohorts in the manuscript. We have followed the STROBE guidelines to improve\nthe clarity in study patient characteristics and study design including how patients were\nrecruited, how samples were obtained and for what analysis. Due to low cell numbers\nobtained from these small tissue pieces, only one patient had enough sample to be\nused for several experimental modalities (scRNAseq and dijerentiation assay with IL-\n2/7/12/18/TGF-b).\nThe changes related to this issue can be found on page 4, 98-99; page 8, 239-240; page\n9, 263-264; page 16, 487-528.\n3. MMR status (or microsatellite instability, MSI) is a significant confounder in colorectal\ncancer immunology. For instance, NK cell infiltration is typically enriched in MMR\ndeficient tumors. It would be helpful if the authors could analyze or at least discuss the\ncases with “unknown” MMR status. Notably, MMR deficient tumors appear to be\npresent among the primary tumor cases, but absent among those with peritoneal\nmetastases, which may influence the findings and should be acknowledged in the text.\nReply: We thank the reviewer for their comment and for highlighting the association\nbetween MMR deficiency, tumor stage and NK cell infiltration. We have reported all\navailable data on MMR and have been able to access additional information on two\npatients that had unknown MMR status (see patient tables I-III). Unfortunately, not all\nCRC cases have been tested for MMR status at our hospital during the study period. As\nthe reviewer highlights, MMR deficiency is more common in early-stage CRC than in\nmetastatic disease. While our results mirror real world data on MMR status by stage, the\ndifferences in MMR status between groups could have affected comparisons between\nprimary tumor and metastasis. This has been added to the discussion pages 14-15,\nlines 437-463.\nInspired by the reviewer’s question we compared MSS status in our scRNAseq data.\nWe detected two interesting differences where the data was non-overlapping for the two\ngroups (shown below for the reviewer’s inspection only). However, given the low\nfrequency of MSI cases, we cannot draw any conclusions from this data and the cell\nnumbers for these two clusters in Pelka et al (GSE178341) were too small to validate\nthese findings (Reviewer Fig. 1E).\nReviewer Figure 3. Frequencies of nILCs and IFN+ ILC1 in MSS and MSI primary tumors.\n4. The biopsy procedure is currently described only briefly (\"A biopsy was sampled from\nthe primary tumor according to a protocol developed after directions from the pathology\ndepartment at Karolinska University Hospital\"). This description should be expanded to\ninclude key details such as method of collection, and handling.\nReply: Thank you for this suggestion. We have expanded on this (page 17, lines 508-\n520).\n5. Clarify how the authors ensured that the tumor and peritoneal metastasis samples\nwere representative. This is particularly important for peritoneal lesions, where\nmacroscopic appearance may not reliably reflect tumor presence or cellular content.\nReply: Thank you for this suggestion. All sampling was conducted by highly experienced\nsurgeons, each with extensive expertise in CRC surgeries, including HIPEC. As the\nreviewer is probably aware, pathological reviews are often performed during surgeries\nwhen there is uncertainty about the presence of carcinomatosis. However, in these\ncases, no formal pathological review was carried out since only tissue samples with\nvisibly macroscopic cancerous tissue were included. This is clarified in the manuscript\npage 17, lines 514-517.\n6. Given the known issues in cell culture work, including misidentification, genetic drift,\nand microbial contamination, the authors should describe steps taken to confirm the\nauthenticity and integrity of the cell lines used. For example, when were the cells last\ntested for mycoplasma contamination? After how many passages were cells discarded\nor re-thawed to minimize phenotypic drift?\nReply: OP9-DL1 cells were tested for mycoplasma once every month and were always\nmycoplasma negative. They were discarded at the latest after 10 passages and cells\nfrom re-thawing were passaged only once before freezing down a new stock of cells. For\nthe Caco-2 cell line (new figure 7) we were able to confirm its authenticity and integrity\n(see report below). We have also added this information in the Reporting summary.\nHowever, for the OP9 mouse stromal cell line, it requires a separate kit from ATCC for\nauthentication which unfortunately is backordered until after the deadline of this\nrevision. We were therefore unable to perform this analysis. However, we have used this\ncell line since 2016 (max 10 passages) and published results (Kokkinou et al, Sci Imm,\n2022) in line with others’ results from this cell line (Nagasawa et al, J Exp Med, 2019),\nstrengthening the authenticity of the OP9 cell line. We have not used any other cell lines\nin parallel in our lab, making mix-ups unlikely.\n7. Please include catalog numbers for all reagents to improve reproducibility. RRIDs\nwould also be useful for applicable resources such as antibodies.\nReply: We thank the reviewer for highlighting this and we have now included catalogue\nnumbers or RRIDs for all reagents. Changes can be found in Table S3.\n8. The manuscript would benefit from including version numbers for all R packages used\nin the analyses to facilitate reproducibility.\nReply: We agree with reviewer that this is good practice. We have included version\nnumbers for the R packages used. Changes can be found on after each package\nmention in the methods section (page 19, lines 596, 597, 604, 606 and 607).\n9. The \"Statistics\" section mentions only GraphPad Prism, yet R appears to have been\nused for parts of the analysis. This should be clarified, and the statistical methods in R\nshould be appropriately described.\nReply: While R has been used for data exploration and visualization, only GraphPad\nPrism was used for statistical analyses except for differentially expressed genes\nstatistics and new correlations statistics asked for during revisions. A clarification has\nbeen added page 22, line 686-690.\n10. In Tables I–III, T stages should be formatted as pT1–2, pT3, and pT4, rather than p1–\n2, p3, and p4. Similarly, N stages should be listed as pN0, pN1, and pN2.\nReply: Thank you for this suggestion. We have updated tables I-III accordingly.\n11. Harmonize the presentation of biological sex across the tables. Table I reports\npercentages for both sexes, while Tables II and III only report percentages for females.\nReply: Thank you for noticing this inconsistency. Tables I-III have been updated.\n12. The discussion section would be improved by adding a dedicated paragraph\nacknowledging the limitations of the study, including small sample size, cohort\nheterogeneity, and any technical or methodological constraints.\nReply: We agree with the reviewer and have added a section “Strengths and limitations\nof the study” to the discussion on pages 14-15, lines 437-463.\nReviewer #2 (Remarks to the Author)\nThis manuscript describes the composition and heterogeneity of innate lymphoid cells\nin unaffected (non-tumoral) colon samples, colorectal tumors and peritoneal\nmetastases. The authors employed scCITEseq and flow cytometry to characterize the\nILC compartment leading to the identification of previously uncharacterized immature\nILC subsets. They used an in-vitro system to assess the development potential of these\ncells showing their differentiation into ILC1/tissue resident NK cells. The novelty here\nlies in the characterization of the ILC infiltrate in peritoneal metastases and the\nidentification of immature ILC populations. This is a valuable resource that presents the\ntranscriptomic profiles of >20,000 ILCs in healthy colon and tumors which is of\nsignificant interest to the community. However, most of the results remain descriptive.\n1. A key concern here is that the patient cohort is very heterogenous and only includes a\nrelatively small number of samples, which somewhat limits the clinical utility of the\ndata. Stratification analyses based on sex, primary tumor location or mismatch repair\nstatus should be performed as these factors are known to influence intratumor immune\ncell composition and function.\nReply: We thank the reviewer for this suggestion. As the reviewer rightly points out, our\nstudy aimed at performing the first in-depth analysis of the intratumoral ILC/NK cell\ncompartment in a limited sample cohort, rather than a shallower analysis across more\npatients. While our strategy is warranted, we agree that stratification across biological\nsex, primary tumor location and MMR status would significantly strengthen our study.\nAlthough our study is too small to statistically verify differences in cell frequencies\ndepending on clinical variables, we did identify some interesting cell types where the\ncell frequencies were non-overlapping between groups. Two such differences are seen\nbelow (Reviewer Fig. 4). The frequencies of nILCs and IFN+ ILC1 were higher in the two\nMSI cases as compared to the four MSS cases but we have refrained from adding it to\nthe paper due to lack of statistical power. We identified no such differences for sex or\ntumor location, which could be due to a lack of statistical power in our cohort.\nReviewer Figure 4. Frequencies of nILCs and IFN+ ILC1 in MSS and MSI primary tumors.\nWe additionally performed correlation analyses between cell cluster frequencies and\ntumor stage and identified that the frequencies of nILCs and eNK cells were inversely\ncorrelated to tumor stage. This was seen in both the scRNAseq data and for the flow\ncytometry data. These data have been added to Fig. S4A-B and Fig. S7A-B and are being\nmentioned on page 7, lines 186-188 and page 9, lines 249-250.\nStill, our data were derived from relatively few patients. So, to potentially better answer\nthe reviewer’s question, we also made use of a publicly available scRNAseq dataset\nfrom 62 primary CRC tumors from an equal number of patients (Pelka et al,\nGSE178341). We reanalyzed this dataset (Reviewer Fig. 5) and could conclude that the\nnumber of total ILC and NK cells per sample and cell quality was low compared to our\ndataset, despite filtering of low-quality cells (Reviewer Fig. 5A-B). The total ILC+NK cell\nnumber in this dataset was 5631 cells (of which 4369 cells had associated clinical\nmetadata), to be compared to our dataset of 23 400 ILC+NK cells.\nThis shows the strength of our scRNAseq dataset that although derived from 11\npatients, the cell counts and data quality is high. Nevertheless, we performed cell label\ntransfer analysis. We could indeed identify cells in the Pelka et al data that matched\nwith the clusters that we revealed in our own dataset (Reviewer Fig. 5C), but the cell\nnumbers were low (Reviewer Fig. 5D), making correlations to clinical data challenging.\n4000\n3000\n2000\n1000\nReviewer Figure 5. A. Total ILC and NK cells count per sample found in Pelka et al.\n(GSE178341) and our data (Marchalot et al). B. Mean count RNA per sample. C-D. Label\ntransfer displayed on UMAP. E. Cell counts per cluster.\nelpmas\nrep\ntnuoc\nllec\nlatoT\nTumor 6000\n4000\n2000\nk a et al al ot et al P el ar c h\nM\nelpmas\nrep\nANR_tnuoCn\nnaeM\nA. B.\nColon\nTumor\nk a et al al ot et al P el ar c h\nM\nA. B.\nC.\nD.\nReviewer Figure 6. A-C: Total nILC and eNK cells per tumor sample combined as a\nfrequency of “immature subsets” in A. MMR proficience (p) or MMR deficient (d) tumors,\nB. Males (M) or females (F) and in tumors from right or left colon. D: Frequency of\n“immature subsets” (nILCs plus eNK cells) across tumor stage (T).\nAll scRNAseq data from Pelka et al. (GSE178341) using cell labels from our scRNAseq\ndata.\nWe have added parts of these data to Fig S4C-G and included text regarding this on page\n7, lines 188-196.\nFinally, while our study was limited in terms of samples, it provides a basis for future\nresearch. This is now discussed more explicitly in the manuscript (discussion -\nStrengths and limitations) on pages 14-15, lines 437-463.\n2. The developmental potential of nILC and eNK subsets need be further investigated,\nincluding using in vivo models and analyzing the generated intestinal ILC subsets. The\nuse of NSG mice that express human cytokines or the use of NSG mice injected with\nhuman cytokines would represent a more physiological system to understand the\ndevelopmental potential of these immature ILC subsets in the intestine. Based on the\ngating strategy used to cell sort eNK cells (Fig S5), it is possible that this population was\ncontaminated by CD49a+ stage 4b NK cells. These cells express all the eNK cell\nmarkers and therefore could not be discriminated by the sorting strategy employed by\nthe authors, unless CD49a expression was used to exclude them. Could the authors\ncomment on their gating strategy and the purity of their eNK cell subset for their in vitro\nassays.\nReply: We thank the reviewer for observing that the cell subset that we defined and\nsorted as eNK cells are very similar to CD49a+ stage 4b NK cells in our flow cytometry\ncluster analyses. Indeed, eNK cells do not express ITGA1, the gene for CD49a, and\nshould therefore be defined as CD49a- in our gating strategy. Unfortunately, we did not\ninclude CD49a in our gating strategy due to lack of space in the sorting panel, so it is\nindeed possible that some CD49a+ stage 4b NK cells were included in the eNK cell gate.\nWe confirmed that by using data from our more extensive flow cytometry panel (Fig 5C-\nD) where we observed variable CD49a expression on eNK cells (Reviewer figure 7) gated\nas in the functional differentiation experiments (Fig 6).\n80\n60\n40\n20\nol o\nn\nT u\nm\nor\nC\nR\nC\nReviewer figure 7. Frequency of CD49a+ cells among CD127+ eNK cells in normal colon\nand primary CRC tumors.\nImportantly, CD49a expression did not differ between the colon and CRC tumor\n(Reviewer Fig. 7) and we also did not observe any difference in the differentiation\ncapacity of eNK cells (main Fig. 6) that could be attributed to different phenotypes of\neNK cells in colon versus CRC tumors. Since stage 4b NK cells are also NK cell\nprecursors we find that the data generated are still informative in terms of determining\nthe capacity of intratumoral eNK cells to generate ILC1 and NK cells. For transparency\nwe have added a sentence regarding this in the results section page 9, lines 258-260.\nWe also excluded CD49a+ stage 4b NK cells from the additional experiments that we\nperformed (page 11, lines 313-314), prompted by the reviewer’s question about the\nrelevance of our in vitro differentiation culture system (see below).\nWe further agree with the reviewer that in vivo experiments would substantiate the\nobservational and in vitro findings in our study. One could envisage an experiment\nwhere nILCs or eNK cells are injected in mice. However, we would like to point out that\nthe nILC and eNK cells that we identify here have a clear tissue resident profile (Fig. 1G)\nand while intratumoral nILCs show similarities to circulating ILC precursors and\nintratumoral eNK cells are similar to circulating CD56bright NK cells (Fig. S1G), we are not\nsure that circulating cells would give rise to intratumoral nILCs or eNK cells or if they are\nfo\ntuo\nsllec\n+a94DC\n%\netag\ntros\n+721DC\nKNe\nFlow cytometry data figure 5\nderived from local CD34+ hematopoetic stem cells, which are located in the intestine\n(PMID: 25500367 and PMID: 34453879). Also, it is unclear if injection of intratumoral\nnILCs or eNK cells would lead to their homing and penetrance into tumors. Another\nchallenge is the cell numbers since we can typically only sort 10-100 nILCs per tumor\nsample and finding so few cells back in the mouse would be challenging. For the mouse\nmodel to be at all feasible and relevant, human CRC tumor cells would have to be\northotopically implanted in the colon, and then nILCs or eNK cells would have to be\ndelivered directly into, or along with the human tumor. While we are aware of a few labs\nin the world that have developed mouse orthotopic CRC models, we have yet not\nidentified a lab that has ever injected human (or mouse) tumor cells in the colon wall of\nNSG mice. This would mean developing an entirely new CRC model. Discussions with a\ngroup in Oxford, UK that recently developed an orthotopic CRC model in\nimmunocompetent mice used approximately 6 months to just learn the complex\ninjections needed. In addition, we would still not know if all the differentiation cues in\nthe NGS mouse system are cross-reactive, even if human cytokines are provided. We\ntherefore find this beyond the scope of the current study.\nHowever, since we agree that our in vitro system with OP9-DL1 stromal cells does not\nrepresent the tumor microenvironment, we developed a novel in vitro system with\ncolorectal cancer cells. We cultured nILCs and eNK cells (now sorted as CD49a–) with\nCaco-2 cells (genetically verified by ATCC) and a modified cytokine mix IL-2+IL-7+IL-23\n+ IL-1β (as in figure 6) with the addition of IL-15 to enhance NK cell dijerentiation. These\ndata have been added as Fig. 7, and Fig. S12-13.\nThe results recapitulate our findings from the OP9-DL1 cultures in that while nILCs\ngenerate both IL-22+ ILC3-like cells as well as Perforin+Granzyme B+ ILC1/NK-like cells,\neNK cells differentiate preferentially to ILC1/NK-like cells (Fig. 7, and Fig. S12-13). This\nis an important validation, also because the eNK cells were sorted as CD49a–, validating\nthe existence and function of CD49a– eNK cells.\nFurthermore, validating our OP9-DL1 findings, we observed no difference in the\ndifferentiation capacity between colon and intratumoral eNK cells. However, we also\ndid not detect any difference in intratumoral nILCs to generate ILC1/NK and ILC2-like\ncells. This contrasted with our findings with OP9-DL1 stomal cells where intratumoral\nnILCs generated more ILC1/NK and ILC2-like cells (Fig. 6), the former in agreement with\ntheir higher expression of NK cell genes such as KLRC1 (NKG2A) as compared to colon\nnILCs (Fig. 4A). It is possible that the Caco-2 tumor cell microenvironment in\ncombination with IL-15 contributes with signals that compensates for the lower intrinsic\nILC1/NK cell capacity of colon nILCs and overrides the ILC2 potential of tumor nILCs.\nThis would indicate that the tumor microenvironment has a key role in shaping the\ndijerentiation capacity of nILCs. It is also worth noting that when comparing the OP9-\nDL1 and Caco-2 co-culture systems, the latter generated fewer cells expressing CD16,\nIFN-g and Granzyme K as well as IL-13 from intratumoral nILCs. Again, this could be due\nto the tumor microenvironment e.g. impact of TGF-b. In support of that, we detected\nslightly higher CD103 expression, known to be induced by TGF-b, in the Caco-2 co-\nculture system as compared to the OP9-DL1. While we are eager to experimentally\ndissect such mechanisms, that is challenging due to the low numbers of nILCs and eNK\ncells, and will hence be the subject of future work.\nIn the Caco-2 co-culture system we were also able to expand our panel of potential\nreceptors that could mediate the cytotoxic activation of ILC1/NK-like cells generated\nfrom nILCs and eNK cells. We detected NKp44, NKp46 and NKG2D on ILC1/NK-like\ncells (Fig. 7 and Fig. S13), giving clues as to how such cells could be activated in the\ntumor microenvironment.\nWe have added these data as Fig. 7, and Fig. S12-13 and included text and discussing\nregarding these findings on page 11, lines 307-332, page 13, line 398 and page 14, lines\n416-430.\n3. Finally, as mentioned by the authors, ILCs represent yet untapped therapeutic targets\nand as such, the authors should extend their flow cytometric analyses and map\nintratumoral ILC infiltration to correlate the proportion of eNK, nILCs and or other\nidentified ILC subsets with disease prognosis, including the likelihood of developing\nperitoneal metastases. Such analyses would significantly enhance the scope of this\nmanuscript and highlight the clinical importance of these ILC populations in colorectal\ncancer.\nReply: We thank the reviewer for being positive about the potential therapeutic\nimplications of our findings and we agree that it is important to relate our findings to key\nclinical outcomes and variables. We agree that our results need to be followed up in a\nlarger patient cohort in the future. We have highlighted this in the section related to\nstrengths and limitations of our study (pages 14-15, lines 437-463).\nOur study however represents the most detailed analysis of the ILC-NK cell spectrum of\nprimary CRC to date, and the first ever in CRC-PM tumors. Our scRNAseq dataset\ncontains 23,407 CD127+ ILCs, CD56+ NK cells as well as Lin-CD7+CD56-CD127- non-\nconventional ILCs, which, to our knowledge, no previously generated single-cell RNA-\nsequencing (scRNAseq) data can be compared to. In addition to the 11 patients\nincluded for scRNAseq analysis, we included 8 more CRC patients in our flow cytometry\nvalidation experiments, and another 24 CRC patients in our functional validation\nexperiments. Hence, data from a total of 43 patients, obtained by three different\nexperimental modalities, univocally showed the existence of immature subsets of ILCs\nand NK cells. We are therefore confident that the main message of our paper, which is\nthat intratumoral tissue-resident immature ILCs and NK cells can generate\ndifferentiated subsets of ILC1/trNK cells in CRC tumors, stands strong.\nStill, the reviewer’s point is well taken. We therefore re-analyzed our scRNAseq and flow\ncytometry data and identified a few interesting correlations that we have now added to\nthe manuscript (Fig. S4 and S7). The most consistent finding in both the scRNAseq and\nflow cytometry data was that nILC and eNK cell frequencies tended to be higher in\ntumors with less advanced tumor stage (Fig. S4 and S7). These new analyses are\nmentioned in the text on page 7, lines 186-188 and page 9, lines 249-250.\nWe additionally observed that patients who relapsed within the first two years following\nprimary colon cancer surgery tended to have higher frequencies of intratumoral ieILC1\nCD45RA- and CD49a+CD103+ St4a NK cells as identified by flow cytometry (Reviewer\nfigure 8, clusters from main Fig 4C-D). However, since only two of the patients relapsed,\nthe statistical power is too low. We have therefore refrained from adding these data to\nthe manuscript.\nReviewer figure 8. Frequency of ILC/NK cell subsets from figure 4 stratified by relapse\n(n=2) or not (n=5).\nAs explained in our response to the reviewer’s question 2, we additionally performed an\nextensive inventory of previous scRNAseq studies of primary CRC to identify one or\nseveral datasets to use as an independent validation cohort. Our inventory revealed a\nstudy with a large number of patients and decent amounts of cells (see table below for\nthe reviewer’s inspection only; GSE178341) that we reanalyzed and performed cell label\ntransfer analysis (reviewer figure 5 above).\nReviewer Table II. Available scRNAseq data from selected CRC studies.\nAccession No of primary CRC No of total CD45+ and CD45-\nno samples cells\nGSE221575 9 39,484\nGSE178341 62 127,695\nGSE146771 18 43,817\nGSE178318 6 111,292\nGSE225857 4 196,473\nGSE232525 Not stated 7,343\nGSE242271 5 24,111\nGSE200997 16 26,173\nWe could indeed identify cells matching with several of our clusters (reviewer Fig. 5\nabove). These cells were found equally prevalent across MMR status, biological sex,\ntumor location and tumor stages (reviewer Fig. 6 above, note that only 28 of the 62\npatients had tumor stage and MMR status data). We have now included these data as\nsupplementary in Fig. S4C-G and they are mentioned in the text on page 7, lines 188-\n196.\nUnfortunately, this dataset (Pelka et al, GSE178341), which is the largest we could find,\ndoes not contain information on subsequent development of peritoneal metastases.\nWe have also not found any other scRNAseq dataset with this information. However,\nusing The Cancer Atlas Genome (TCGA) program database, containing whole primary\nCRC tumor bulk RNA sequencing data, we were able to link the cell clusters we\nidentified here to overall survival. For this we performed deconvolution analysis using\nInstaPrism which uses the scRNAseq clusters as reference and infers cluster fractions\nfor each sample in the query bulk RNAseq dataset. We then identified tumors with high\nversus low gene signatures of a particular cluster using the median frequency as the\ncutoff. Unfortunately, we could not identify any significant differences for any of the cell\nclusters, including CD16+ NK cells, in terms of predicting better or worse overall\nsurvival. The examples of nILCs and eNK cells are provided below for the reviewer’s\ninspection only (reviewer Fig. 9).\nA. B.\nReviewer figure 9. Kaplan-Meier curves for overall survival probability versus time (days)\nfor tumors with a high (red) and low (blue) gene signature of A. nILCs or B. eNK cells as\ndefined in main figure 1. The numbers of patients/tumors for each group are stated\nbelow the Kaplan-Meier curves.\nReviewer #3 (Remarks to the Author):\nThis paper provides new insights into the populations of ILCs and NK cells present in\nCRC-PM by utilising single cell sequencing and flow cytometry. The authors identified\nputative precursor ILC populations that may be of interest for future research\nendeavours. This work is original and provides value to the field.\nBelow are some points to be considered.\n1. The statistical tests used in figure 3 and figure 6 are not referenced in the figure\nlegends, nor is a legend for p-value indicators – this information should be included. The\nmethods indicate that only t-tests have been used for statistical comparisons.\nParticularly when multiple groups are being compared, an omnibus test should be used\nfollowed by a posthoc test with adjusted p-values so that significance is not\noverestimated.\nReply: We thank the reviewer for observing our mistake. We have now added\ninformation about the statistical test to figure 3 and 6 legends. We also followed the\nreviewer’s advice to apply more rigorous statistical tests to identify differences between\ngroups. We used Two-way ANOVA followed by a Tukey's multiple comparison test with\nTukey correction for figure 3. After correction the only significant differences remaining\nwas for NKp44+ ILC3 and HLA-DR+ NK cells, being depleted or enriched in the tumors\nversus colon, respectively (Fig. 3). Still, our differential abundance test, which analyses\nchanges in abundance of cell states rather than clusters, showed enrichment of cells in\nseveral clusters of the ILC1/trNK branch (Fig. S3). We have made changes related to this\ntopic on page 7, lines 179-180.\nFor Figure 6, we performed multiple t-test comparisons and added Holm-Šídák\ncorrection, considering adjusted p-values. This reduced the numbers of statistical\ndifferences. However, our main findings, which was an increased frequency of IL-13+\nand perforin+ ILCs following culture of intratumoral versus colon nILCs, was still\nstatistically significant. There was also a statistical tendency (p<0.1) for increased\nexpression of CD49a on these cells. We also carefully interpreted what seems to be an\nincrease in granzyme B and reduction of CD16 on intratumoral nILCs after culture but\nhighlighted in the text that these were not statistically different. We have made changes\nrelated to this on page 10, lines 277-282. We would like to emphasize that our co-\nexpression analysis (Fig. 6H-I) still supports the increased capacity of intratumoral nILC\nto generate ILC1/trNK-like cells expressing both cytotoxic proteins (perforin and\ngranzyme B and tissue residency markers CD49a and CD103).\n2. An ejort should be made to improve the interpretability of figure 1, particularly\naround the numbering and naming of the clusters. For example, in 1D, the key should be\ntitled ‘cluster’ instead of ‘identity’. A table or otherwise should be included to correlated\nannotated clusters with original cluster numbers rather than having to infer between\nUMAP plots. Additionally, cluster 10 appears to have been included as part of Branch 3\ndespite not clustering as such in 1B.\nReply: We agree with the reviewer and have added cluster numbers to Fig 1E-H to\nimprove interpretability.\nRegarding cluster 10, the reviewer is right that it was not grouped with any of the 3\nbranches during hierarchical clustering. However, it was included in branch 3 because\nof his similarity with other branch 3 clusters including absence of module 6, 8 and 9\ngenes expressed in either branch 1 or 2 clusters. We have now explained this on page 5,\nlines 112-115.\n3. The axis labels in 2D should be cleaned up.\nReply: This has been done.\n4. It may be useful to include the latent time plots alongside your existing RNA velocity\ncluster plots to improve interpretability of start and endpoints.\nReply: We have added this to Fig 2F. Changes to the text related to this can be seen on\npage 6, lines 168-170.\n5. Figure S3 should have ‘dijerential’ rather than ‘dijerencial’ in the titles.\nReply: We thank the reviewer for observing this misspelling, which has now been\ncorrected.\n6. You need to include information about the clustering and annotation method for the\nflow data in figure 5 e.g. unsupervised clustering followed by confirmation with manual\ngating for annotation.\nReply: We fully agree and have added this information to the manuscript text on page 9,\nlines 242-243 and in the figure legend for Fig 5. As suggested by the reviewer we\nperformed manual gating to confirm the annotation which has now been added as\nFigure S5.\n7. You should include the gating strategy used for the flow cytometry in figure 5 as a\nsupplementary figure.\nReply: We have added the manual gating for Figure 5 as Figure S5. Changes to the text\nrelated to this can be seen on page 9, lines 242-243.\n8. Line 241-244 – be more clear about what comparison you are trying to make here.\nReply: This has been done. Changes to the text related to this can be seen on page 9,\nlines 269-272.\n9. In figure S7B you should include the concentration in your concentration/cell CBA\nplots. I am not sure it is correct to display your concentrations per cell here when you\nonly know the final cell numbers but haven’t measured the evolution of these\npopulations over time. This is also true for your CBA plots in figure 6.\nReply: We agree that data expressed as concentration per cell must be interpreted with\ncaution, and this is also why we additionally provided the raw concentrations per well in\nFig S7 (now Fig. S10). To further clarify we have added the concentration units (pg/mL) to\nFigure 6. As the reviewer can also appreciate, we have measured the majority of the\nproteins by both Luminex and FACS to enable robust interpretations.\n10. Line 244-246 – There is no significant dijerence here, be careful not to bias your\ninterpretation.\nReply: We agree and this has been done on page 10, line 275-276\n11. Line 253 – there is a significant dijerence indicated for CD117.\nReply: We have highlighted this on page 10, line 274\n12. You should include discussion around (or mention of) organoids as an alternative\ndijerentiation platform for ILCs\ne.g. https://doi.org/10.1016/j.celrep.2022.111281. Could also discuss WNT signalling as\nit may aject ILC dijerentiation in CRC tumours.\nReply: We agree that tumor organoids could serve as an attractive approach to study\nILC/NK cell differentiation and we have added a section in the discussion regarding this\npossibility (page 14, lines 421-422).\nWith regards to Wnt signalling, we have now discussed this in relation to the OP9-DL1\ncultures and Notch signaling, as well as in the context of colorectal cancer on page 13-\n14, lines 403-413."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I think the authors have appropriately addressed the comments.", "reviewer_2": "I would like to thank the authors for their thorough response to my comments. They have addressed all my comments satisfactorily, and I am confident that this manuscript will serve as a strong reference in the field of ILCs in CRC.\n\nI have a few additional minor comments regarding the newly added data.\n\n1. Lines 259-260. Purity of the sorted eNK CD127+ cells. Could the authors indicate in the figure legends that CD49+ stage 4B NK cells represent approximatively ~30% [range:0-70%] of eNK cells. This would help readers better interpret the data shown in Figure 6 and Figure S10.\n2. Line, 319, the authors suggest that TGF-b production by Caco-2 cells impairs the expression of IFN-g and granzyme K. However, in the OP9-DL1 system, the culture of nILC or eNK cells (+/-stage 4B NK cells) in the presence of TGF-b results in the accumulation of IFN-g and granzyme K in the supernatant (Fig. S10). Could the authors comment on these potential discrepancies? Given the potential uncertainties regarding the involvement of TGF-b in this phenotype, I recommend softening this claim and possibly removing TGF-b from the sentence.\n3. Line 320, the figure number is missing; it should read (Fig. 7F-I).\n4. In Fig 7C, E, G, I, K, M, could the authors include representative FACS plots for NKp44+ ILC3s.\n5. Line 386. There is a typo – it should read IL-1b.", "reviewer_3": "The revised manuscript and response to reviewers by Mjosberg et al have largely covered the concerns and suggestions I had with one minor point remaining.\n\n1. Regarding the updated statistical analysis, you are now utilising two-way ANOVA but this is not actually indicated in the text. You should include this and also justification for using a parametric test on a relatively small dataset."}, "author_response": "Authors’ response to the reviewer’s Comments\nReviewer #1 (Remarks to the Author):\nI think the authors have appropriately addressed the comments.\nReply: We thank the reviewer for this comment.\nReviewer #2 (Remarks to the Author):\nI would like to thank the authors for their thorough response to my comments. They\nhave addressed all my comments satisfactorily, and I am confident that this manuscript\nwill serve as a strong reference in the field of ILCs in CRC.\nI have a few additional minor comments regarding the newly added data.\n1. Lines 259-260. Purity of the sorted eNK CD127+ cells. Could the authors indicate in\nthe figure legends that CD49+ stage 4B NK cells represent approximatively ~30%\n[range:0-70%] of eNK cells. This would help readers better interpret the data shown in\nFigure 6 and Figure S10.\nReply: We thank the reviewer for this comment, additional information has been added\nto the text line 259 to help interpretation of the figures\n2. Line, 319, the authors suggest that TGF-b production by Caco-2 cells impairs the\nexpression of IFN-g and granzyme K. However, in the OP9-DL1 system, the culture of\nnILC or eNK cells (+/-stage 4B NK cells) in the presence of TGF-b results in the\naccumulation of IFN-g and granzyme K in the supernatant (Fig. S10). Could the authors\ncomment on these potential discrepancies? Given the potential uncertainties regarding\nthe involvement of TGF-b in this phenotype, I recommend softening this claim and\npossibly removing TGF-b from the sentence.\nReply: Thank you for this comment which highlights an important point. Indeed, the\nculture systems used were different not only in terms of the cell lines but also in which\ncytokine cocktail was used. It is therefore difficult to trace exactly which factors\ninfluenced IFN- and granzyme K expression. We agree with the reviewer that there\nmight be other factors than TGF- explaining these discrepancies. We have therefore\nsoftened the claim related to TGF- and also highlighted the fact that IL-12 and IL-18\nwere missing in the Caco-2 cell co-culture (line 322-323).\n3. Line 320, the figure number is missing; it should read (Fig. 7F-I).\nReply: Thank you for this, we have corrected the figure number line 324\n4. In Fig 7C, E, G, I, K, M, could the authors include representative FACS plots for\nNKp44+ ILC3s.\nReply: Thank you for this suggestion, corresponding FACS plots for NKp44+ ILC3s have\nbeen added in Figure 7.\n5. Line 386. There is a typo – it should read IL-1b.\nReply: Thank you for this, we have corrected the typo now line 390\nReviewer #3 (Remarks to the Author):\nThe revised manuscript and response to reviewers by Mjosberg et al have largely\ncovered the concerns and suggestions I had with one minor point remaining.\n1. Regarding the updated statistical analysis, you are now utilising two-way ANOVA but\nthis is not actually indicated in the text. You should include this and also justification for\nusing a parametric test on a relatively small dataset.\nReply: Thank you for this comment. We have modified the statistical analysis paragraph\nto add the mention of two-way ANOVA before Tukey's multiple comparison and added a\njustification of why we chose to use a parametric test despite the small dataset lines\n697-702."}]

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