ABSTRACT The islets of Langerhans are complex micro-organs comprised of multiple cell types essential for the maintenance of glucose homeostasis. The endocrine cell types of the islets engage in intimate, intercellular communication that is necessary for normal secretory activity. Disruption of this intercellular communication, which is at least partially dependent on the spatial organization of individual islets, leads to secretory dysfunction and exacerbation of the symptomology of disease states such as type 2 diabetes. However, the molecular determinants of the mechanisms underlying disrupted intercellular communication remain incompletely understood. Herein we describe the utilization of CosMx™ Spatial Molecular Imaging (SMI) to interrogate transcriptomic changes associated with the transition from the obese, prediabetic state to overt type 2 diabetes. Using SMI, we verified previously reported findings regarding islet composition in the obese and type 2 diabetic states, including loss of beta cells and expansion of alpha cell mass. In addition, we identified changes in the islet neighborhood that have implications for the function of islet endocrine cells. In particular, we identified a subset of alpha cells oriented in the periphery of the islets that appear to exhibit a transcriptomic profile suggestive of de-differentiation toward a beta cell-like transcriptome-type. To our knowledge, this is the first study utilizing spatial molecular imaging to investigate single-cell transcriptomes of individual islets. Further exploration of the intersection of islet architecture and gene expression using spatial technologies is expected to yield novel insights into the mechanisms underlying the development and progression of metabolic diseases like type 2 diabetes.

Phenotypic analysis of the CD4 compartment in patients with CRLM

Expression levels of genes involved in the MHC class I pathway in the primary tumor and CRLM of patient CRI 3280

CD4+ Th cells play a key role in orchestrating immune responses, but the identity of the CD4+ Th cells involved in the antitumor immune response remains to be defined. We analyzed the immune cell infiltrates of head and neck squamous cell carcinoma and colorectal cancers and identified a subset of CD4+ Th cells distinct from FOXP3+ Tregs that coexpressed programmed cell death 1 (PD-1) and ICOS. These tumor-infiltrating lymphocyte CD4+ Th cells (CD4+ Th TILs) had a tissue-resident memory phenotype, were present in MHC class II-rich areas, and proliferated in the tumor, suggesting local antigen recognition. The T cell receptor repertoire of the PD-1+ICOS+ CD4+ Th TILs was oligoclonal, with T cell clones expanded in the tumor, but present at low frequencies in the periphery. Finally, these PD-1+ICOS+ CD4+ Th TILs were shown to recognize both tumor-associated antigens and tumor-specific neoantigens. Our findings provide an approach for isolating tumor-reactive CD4+ Th TILs directly ex vivo that will help define their role in the antitumor immune response and potentially improve future adoptive T cell therapy approaches.

Accurate cell typing is fundamental to analysis of spatial single-cell transcriptomics, but legacy scRNA-seq algorithms can underperform in this new type of data. We have developed a cell typing algorithm, Insitutype, designed for statistical and computational efficiency in spatial transcriptomics data. Insitutype is based on a likelihood model that weighs the evidence from every expression value, extracting all the information available in each cell’s expression profile. This likelihood model underlies a Bayes classifier for supervised cell typing, and an Expectation-Maximization algorithm for unsupervised and semi-supervised clustering. Insitutype also leverages alternative data types collected in spatial studies, such as cell images and spatial context, by using them to inform prior probabilities of cell type calls. We demonstrate rapid clustering of millions of cells and accurate fine-grained cell typing of kidney and non-small cell lung cancer samples.

Even though blocking the interaction of NKG2A with its ligand HLA-E is a promising strategy to restore the function of cytotoxic T cells and induce tumor cell killing, several aspects of the biology of NKG2A+ CD8 T cells remain to be understood to fully benefit from this therapeutic approach. Here, we characterize the expression of NKG2A on tumor-infiltrating CD8 T cells (CD8 TILs) in patients with head and neck squamous cell carcinoma (HNSCC) and microsatellite-stable colorectal cancer (CRC). Our results show that NKG2A is primarily expressed by a population of tumor-reactive CD8 TILs identified by coexpression of CD39 and CD103 (DP CD8). NKG2A expression was stable and maintained during in vitro expansion. Interestingly, while TCR stimulation in presence of IL-15 and TGF-β are thought to be responsible for NKG2A upregulation, our in vitro results suggest that other factors might be necessary to induce its expression on naive CD8 T cells. Ex vivo analysis of NKG2A+ DP CD8 TILs by scRNAseq and flow cytometry revealed that those cells were activated, more differentiated, and displayed a cytolytic profile suggesting a role in tumor recognition and killing. Finally, we found that, even though there was a high degree of clonal overlap between NKG2A+ and NKG2A− DP CD8, some CDR3 TCR sequences were enriched in one versus the other subset. Consequently, HPV-reactive cells were preferentially enriched in NKG2A− DP CD8 TILs. Altogether, our results provide evidence that NKG2A is expressed by tumor-reactive CD8 TILs in HNSCC and CRC. However, NKG2A-inducing factor(s) remain to be identified and might provide new potential drug targets to bolster anti-tumor immune responses in cancer patients. Supported by the Providence Portland Medical Foundation.

Resolving the spatial distribution of RNA and protein in tissues at subcellular resolution is a challenge in the field of spatial biology. We describe spatial molecular imaging, a system that measures RNAs and proteins in intact biological samples at subcellular resolution by performing multiple cycles of nucleic acid hybridization of fluorescent molecular barcodes. We demonstrate that spatial molecular imaging has high sensitivity (one or two copies per cell) and very low error rate (0.0092 false calls per cell) and background (~0.04 counts per cell). The imaging system generates three-dimensional, super-resolution localization of analytes at ~2 million cells per sample. Cell segmentation is morphology based using antibodies, compatible with formalin-fixed, paraffin-embedded samples. We measured multiomic data (980 RNAs and 108 proteins) at subcellular resolution in formalin-fixed, paraffin-embedded tissues (nonsmall cell lung and breast cancer) and identified >18 distinct cell types, ten unique tumor microenvironments and 100 pairwise ligand–receptor interactions. Data on >800,000 single cells and ~260 million transcripts can be accessed at http://nanostring.com/CosMx-dataset .

The Spatial Molecular Imaging platform (CosMx TM SMI, NanoString Technologies, Seattle, WA) utilizes high-plex in-situ imaging chemistry for both RNA and protein detection. This automated instrument provides 1000’s of plex, at high sensitivity (1 to 2 copies/cell), very low error rate (0.0092 false calls/cell) and background (∼0.04 counts/cell). The imaging system generates three-dimensional super-resolution localization of analytes at ∼2 million cells per sample, four samples per run. Cell segmentation is morphology-based using antibodies, compatible with FFPE samples. Multiomic data (980 RNAs, 108 proteins) were measured at subcellular resolution using FFPE tissues (non-small cell lung (NSCLC) and breast cancer) and allowed identification of over 18 distinct cell types, 10 unique tumor microenvironments, and 100 pairwise ligand-receptor interactions. Over 800,000 single cells and ∼260 million transcripts data are released into the public domain allowing extended data analysis by the entire spatial biology research community.

Spatial Molecular Imager (SMI) is an automated microscope imaging system with microfluidic reagent cycling, for high-plex, spatial in-situ detection of multiomic targets (RNA and protein) on FFPE and other intact samples with subcellular resolution. The key attributes of the CosMxTM SMI platform (NanoString®, Seattle, WA) include: 1) high-plex and high-sensitivity imaging chemistry that works for both RNA and protein detection, 2) three-dimensional subcellular-resolution image analysis with a target localization accuracy of ∼50 nm in the XY plane, 3) large Hamming-distance encoding scheme with low error rate (0.0092 false calls per cell per gene) and low background (∼ 0.04 counts per cell per gene), 4) high-throughput (up to 1 million cells per sample, four samples per run), 5) antibody-based cell segmentation methods, and 6) compatibility with formalin-fixed, paraffin-embedded (FFPE) samples. In this study, 980 RNAs and 80 proteins were measured at subcellular resolution in FFPE cultured cell pellets, as well as FFPE tissues from biobanked samples of non-small cell lung cancer (NSCLC) and breast cancer. Cross-platform analysis using 16 cancer cell lines validated high-correlation (R2 ∼0.77) and high sensitivity (∼1.44 FPKM/TPM; roughly 1 to 2 copies of RNA per cell) when compared to RNA-seq. Real-world archived NSCLC FFPE tumor sections revealed greater than 94% cell detection efficiency for RNA, despite the low RNA quality QV200 20% to the medium quality 65%. The accuracy of protein expression measurements was independent of the level of multiplexing, as demonstrated by the linear behavior of nested multiplexing panels (R2 > 0.9). At 980-plex RNA detection, data analysis allowed identification of over 18 distinct cell types, at least 10 unique tumor microenvironment neighborhoods, and over 100 pairwise ligand-receptor interactions. Data from 8 NSCLC samples comprising over 800,000 single cells and ∼260 million transcripts are released into the public domain (www.nanostring.com) to allow for extended data analysis by the entire spatial biology research community.

Abstract Microsatellite-stable (MSS) colorectal cancers are characterized by low mutation burden and limited immune-cell infiltration and thereby respond poorly to immunotherapy. Here, we report a case of metastatic MSS colorectal cancer with a robust anticancer immune response. The primary tumor was resected in 2012, and the patient received several cycles of chemotherapy until 2017. In 2018, the patient underwent a left hepatectomy to remove a new metastasis. Analysis of the metastatic tumor revealed a strong CD8+ T-cell response. A high frequency of CD8+ T cells coexpressed CD39 and CD103, a phenotype characteristic of tumor-reactive cells. Using whole-exome sequencing, we identified somatic mutations that generated peptides recognized by CD39+CD103+CD8+ T cells. The observed reactivity against the tumor was dominated by the response to a single mutation that emerged in the metastasis. Somatic mutations that were not immunogenic in the primary tumor led to robust CD8+ T-cell expansion later during disease progression. Our data suggest that the cytotoxic treatment regimen received by the patient might be responsible for this effect. Hence, the capacity of cytotoxic regimens to prime the immune system in colorectal cancer patients should be investigated further and might provide a rationale for combination with immunotherapy.