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Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We propose a novel machine learning algorithm, CELESTA, which uses both cell’s protein expression and spatial information to identify cell type of individual cells. We demonstrate the performance of CELESTA on multiplexed immunofluorescence in situ images of colorectal cancer and head and neck cancer. Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, which we validate in an independent cohort. By coupling our in situ spatial analysis with single-cell RNA-sequencing data on proximal sections of the same tissue specimens, we identify and validate cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of spatial biology to reveal clinically-relevant cellular interactions.

BackgroundNatural killer (NK) cells comprise a subset of the innate lymphoid cell (ILC) family. Although NK cells have been observed to be present in most solid tumors, their role in the protection against tumor formation in humans has been unclear. Studies have been hampered by the heterogeneity of NK cells within the tumor microenvironment (TME) and lack of information about the broader ILC subsets found in tumors. Further, there is an increasing recognition of plasticity between NK cells and other ILC family members in various disease contexts, calling for a broader examination of ILCs within solid tumors. We previously analyzed the ILC population in primary samples from human head and neck squamous cell carcinoma (HNSCC) and matched blood by single-cell RNA sequencing (scRNA-seq).1 Those studies revealed that peripheral NK cells differentiate along two divergent trajectories in the TME, resulting in different end-states: one with a hyporesponsive phenotype and another possessing potent anti-tumor activity and resembling intraepithelial ILC1s (ieILC1s).MethodsIn vitro co-culture approaches and in vivo mouse models were used to investigate the ability of peripheral NK cells to differentiate into alternate ILC states with heterogeneous functions. Cytotoxicity assays were used to assess functional activity of in vitro derived ieILC1-like cells. Adoptive cell transfer of ieILC1-like cells into tumor-bearing mice was also used to assess anti-tumor function.ResultsPeripheral human NK cells could be efficiently differentiated into ieILC1-like cells using an in vitro co-culture system. These ieILC1-like cells had enhanced natural cytotoxicity against target cells compared to conventional IL-15-activated and K562-expanded NK cells. In addition, they infiltrated the TME efficiently and were a more effective means of adoptive cell therapy against HNSCC solid tumor xenografts in vivo compared to conventional NK cells.ConclusionsOur data indicate that peripheral NK cells change cell states within the TME of HNSCC. The heterogeneity in the relative proportion of the cell states may influence host response to tumors. We identified the ieILC1-like cell state to be the phenotype with the most potent anti-tumor activity within the TME. Importantly, this cell state can be induced from peripheral donor NK cells ex vivo for differentiation into and expansion of highly active ieILC1-like cells, providing a platform for a novel class of effector cells for adoptive cell immunotherapy.AcknowledgementsThese studies were supported by the Lokey Stem Cell Research Building (SIM1) Flow Cytometry core facility for cell sorting and flow cytometric analysis and the Stanford Cancer Institute Tissue Bank for procurement of tumor samples and blood. This work was supported by funding from the National Institutes of Health (R01CA158516; R35DE030054; U54CA209971) to J.B.S.ReferenceMoreno-Nieves UY, Tay JK, Saumyaa S, Shin JH, Horowitz NB, Mohammad IA, Luca B, Mundy DC, Gulati GS, Bedi N, Chang S, Chen C, Kaplan MJ, Rosenthal EL, Holsinger FC, Divi V, Baik FM, Sirjani DB, Gentles AJ, Newman AM, Freud AG, Sunwoo JB. Landscape of ILCs in human head and neck cancer reveals divergent NK cell states in the tumor microenvironment. Proc Natl Acad Sci U S A 2021;118(28):e2101169118.Ethics ApprovalThe studies reported here were approved by the Stanford Institutional Review Board (IRB 11402) and the Stanford Administrative Panel on Laboratory Animal Care (APLAC 20547).

Natural killer (NK) cells comprise one subset of the innate lymphoid cell (ILC) family. Despite reported antitumor functions of NK cells, their tangible contribution to tumor control in humans remains controversial. This is due to incomplete understanding of the NK cell states within the tumor microenvironment (TME). Here, we demonstrate that peripheral circulating NK cells differentiate down two divergent pathways within the TME, resulting in different end states. One resembles intraepithelial ILC1s (ieILC1) and possesses potent in vivo antitumor activity. The other expresses genes associated with immune hyporesponsiveness and has poor antitumor functional capacity. Interleukin-15 (IL-15) and direct contact between the tumor cells and NK cells are required for the differentiation into CD49a(+)CD103(+) cells, resembling ieILC1s. These data explain the similarity between ieILC1s and tissue-resident NK cells, provide insight into the origin of ieILC1s, and identify the ieILC1-like cell state within the TME to be the NK cell phenotype with the greatest antitumor activity. Because the proportions of the different ILC states vary between tumors, these findings provide a resource for the clinical study of innate immune responses against tumors and the design of novel therapy.

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell’s marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell–cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions.

The presence and function of innate lymphoid cells (ILC), including natural killer (NK) cells, within human tumors has been poorly characterized. Here, we have assessed the heterogeneity of NK and ILC populations by single-cell RNA sequencing (scRNAseq) of hundreds of individual NK cells and ILCs within human head and neck squamous cell carcinoma (HNSCC), matched lymph node metastases, matched peripheral blood, and blood from healthy donors. Fresh tumor specimens and blood were obtained from 8 patients undergoing surgical resection of HNSCC. Unsupervised clustering revealed 8 different clusters of NK cells and ILC subsets. We observed significant heterogeneity, with distinct subsets showing profiles consistent with that of conventional NK cells, ILC1-like cells and ILC3-like cell. Importantly, peripheral NK cells were distinct from intratumoral NK cells. The presence of these different cell subsets within primary HNSCC tumors was confirmed by flow cytometry. Further, plasticity between the NK cell subsets was supported by in vitro and in vivo experimental data. Given the ability of NK and ILCs to polarize the immune responses through the secretion of cytokines and the ability of certain subsets to kill target cells, we hypothesize that the differences observed in NK and ILC populations and their plasticity may result in different immune responses, and may influence clinical outcomes following therapy.