Spatial Transcriptomics refers to omics studies performed on tissue sections that preserve the spatial information of the sample. It can display the gene expression patterns in different regions of a tissue section, reveal activated signaling pathways within fine pathological zones, and facilitate the mechanistic interpretation of how molecular features drive pathological characteristics. Spatial Transcriptomics represents a technological innovation that integrates digital pathology with pathological imaging. It plays a crucial role in emerging fields such as diagnostic biomarker discovery, identification of drug resistance sites, development of targeted drugs, and immunotherapy.

1.Advanced Cryostat Equipment: We are equipped with the advanced LEICA CM1950 cryostat, paired with an experienced sectioning team. We test and optimize section thickness and temperature for various tissue types, providing both hardware and technical assurance for obtaining high-quality frozen sections that reflect the true expression levels within the tissue.

2.High-Resolution Digital Slide Scanning: We possess the advanced PANNORAMIC series digital slide scanners from 3DHISTECH, capable of rapid brightfield and fluorescence scanning of frozen sections. Z-Stack multi-layer focusing and Extended Focus depth-of-field extension fusion scanning modes further enhance resolution and clarity, ensuring accurate capture of high-resolution digital image information from the sections.

3.Extensive Experience in Spatial Transcriptomics: We have rich experience in spatial transcriptome sequencing and data analysis, along with robust protocols for integrated analysis of spatial and single-cell transcriptome data, enabling personalized and customized data analysis services.

4.Comprehensive End-to-End Service: Novel Bio provides a full workflow service for spatial transcriptomics, including sample embedding, sectioning, slide selection, H&E staining, tissue permeabilization, library preparation & sequencing, and subsequent FISH validation, offering you a worry-free service experience.

Sample Types: OCT-embedded frozen tissue, FFPE tissue.
OCT-embedded Tissue: Fresh tissue snap-frozen in isopentane followed by OCT embedding, or fresh tissue directly embedded in OCT and then snap-frozen at -80°C.
FFPE Tissue: Can be previously preserved paraffin-embedded tissue blocks, or fresh tissue collected and embedded in paraffin.

Important Notes:
1.Samples must be kept in a low-temperature environment during both processing and transportation.
2.If sample quantity permits, we recommend preparing two or more aliquots: one for RNA quality control and subsequent formal sectioning experiments, and the other(s) reserved as backup.
3.For particularly precious or scarce tissue samples, the number of aliquots may be reduced based on specific circumstances.

Single-cell sequencing samples:
1.Peripheral blood samples: 3 vs 8；Nasal tissue samples: 4 vs 8
2.Visium samples: CIT vs NP = 2 vs 3
3.Visium HD samples: CIT vs NP = 4 vs 6
Single-Cell Capture Platform: 10 × Genomics
Primary Technical Methods:
Single-cell RNA sequencing (scRNA-seq), T cell receptor sequencing (scTCR-seq), Spatial Transcriptomics (Visium / Visium HD), Immunofluorescence (IF), etc.
NovelBrain Cloud Platform Analysis:
Cell clustering analysis, Pseudotime analysis, Pathway analysis, Cell-cell communication analysis, etc.

Previous single-cell transcriptomic analyses of nasal tissues from patients with Chronic Rhinosinusitis (CRS) have revealed contributions from epithelial stem cells, CD38hi CD27hi mast cells, ALOX15+ macrophages, and Th2 cells to the pathogenesis of type 2 inflammatory processes, with T cells identified as the major lymphocyte subset in Nasal Polyps (NPs). This research integrated multiple sequencing technologies - including scRNA-seq, scTCR-seq, and Spatial Transcriptomics (Visium / Visium HD) - to describe the transcriptional landscape of CD45+ lymphocytes, focusing specifically on CD8+ T cells and their spatial interactions within the nasal mucosa of CRSwNP patients. It utilized various wet-lab experiments and public cohort data to validate the corresponding findings. This work helps advance our understanding of stromal-immune cell signaling interactions in promoting inflammation in allergic and autoimmune diseases, as well as in driving tumorigenesis.

The study initially used scRNA-seq data analysis to define the research direction. UMAP clustering identified 25 distinct clusters and revealed:

① CD8+ T cells, ILC2s, and myeloid cells were enriched in nasal tissues. Among them, γδ T cells and NK cells were enriched in Control Inferior Turbinates (CITs), while ILC2s were enriched in Nasal Polyps (NPs).

② CRSwNP patients could be divided into eosinophilic (E) and non-eosinophilic (NE) groups based on eosinophil infiltration levels. ILC2s were preferentially enriched in E-NPs, while B cells were enriched in NE-NPs.

③ The immune cell composition in the peripheral blood of patients was similar to controls, suggesting the immune response in CRSwNP is localized to the tissue (subsequent analyses focused on tissue).

Notably, GZMK+ CD8+ T cells were increased in NPs (a finding confirmed at the wet-lab level by IF and Flow Cytometry). These cells highly expressed GZMK and effector memory T cell gene signatures, but had low expression of other granzymes, distinguishing them from other granzyme-expressing CD8+ T cells. In contrast, GZMB+ CD8+ T cells were prevalent in the blood. No significant differences in CD8+ T cell subtype composition were found between CBL and NP-BL, between E-BL and NE-BL groups, or between E-NP and NE-NP groups.

To deeply explore cellular interactions within NPs, the study performed 10X Visium Spatial Transcriptomics (ST) analysis on NP sections from 3 patients and CIT sections from 2 control participants. However, the gene expression level obtained by this method was low and not significant for specific T cell populations. Therefore, feature gene sets from the scRNA-seq data were used to generate enrichment scores for each Visium spot to visualize the distribution of GZMK+ CD8+ T cells.

Neighborhood enrichment analysis showed a positive correlation indicating co-enrichment between GZMK+ CD8+ T cells and fibroblasts, implying close physical proximity between these two cell types. In contrast, co-enrichment between GZMK+ CD8+ T cells and epithelial cells was not significant. The study defined spots with an enrichment pattern indicating GZMK+ CD8+ T cell/fibroblast co-enrichment as "inner spots," and the spots surrounding them as "intermediate spots." Both inner and intermediate spots suggest spatial proximity between the two cell types, while other spots indicate a significant distance between them.

Since the Visium platform does not achieve single-cell resolution (spots of 55 μm diameter containing 1-10 cells), the study further applied the newly launched Visium HD platform (10X Genomics) in 2024 to analyze an additional 4 control samples and 6 NP samples. The Visium HD spatial gene expression platform provides spatial resolution at the single-cell scale, with 2 μm x 2 μm bins and no gaps. To balance cellular resolution and the average transcript count per bin for analysis, a bin size of 16 μm x 16 μm was used for visualization and analysis in this dataset. For the Visium HD dataset, after integration and unsupervised clustering, the integrated scRNA-seq dataset (HRA000772 and GSE175930) was used as a reference for reference-based deconvolution and cell annotation. Using reference-based deconvolution to assign unified labels across different samples facilitated the analysis of spatial heterogeneity in nasal tissue.

Visium HD data provided high-resolution maps of various immune cell types and structural cells (e.g., in representative sample HD_NP4). These data not only validated the previous single-cell sequencing data but, more importantly, identified and visualized these cells, even visualizing both CD8T_GZMK and CD8T_GZMB cells within the lamina propria.

Compared to peripheral blood samples, clonal expansion was more pronounced in nasal tissue samples, but TCR clonal diversity was significantly reduced in both CITs and NPs, as indicated by lower Abundance-based Coverage Estimator (ACE), Chao1 richness estimator, Inverse Simpson index, and Shannon entropy scores. Furthermore, the degree of clonal expansion varied among different CD8+ T cell subtypes: CD8+ TRM, GZMB+ CD8+ T cells, and GZMK+ CD8+ T cells all exhibited high levels of clonal expansion in both CITs and NPs, with the clonal expansion of GZMK+ CD8+ T cells being more pronounced in NPs.

Developmental trajectory analysis of CD8+ T cells identified 3 paths, each starting with CD8+ Naive T cells, followed by CD8+ TRM cells. Path 1 ended with CD8+ MAIT cells, Path 2 ended with GZMK+ CD8+ T cells, and Path 3 went through GZMB+ CD8+ T cells and ended with GNLY+ CD8+ T cells. These trajectory analyses support distinct developmental paths for GZMK+ CD8+ T cells and GZMB+ CD8+ T cells.

The study input the top 10 most abundant β-chain CDR3 amino acid sequences of CD8+ TRM, GZMK+ CD8+ T cells, and GZMB+ CD8+ T cells from both NPs and CITs into TCR matching tools to identify epitopes and associated antigens. Results showed that TCR sequences from CD8+ TRM and GZMB+ CD8+ T cells in both NPs and CITs were mapped to epitopes from SARS-CoV-2. In contrast, several EBV epitopes matched the CDR3 sequences of GZMK+ CD8+ T cells in NPs, whereas no epitopes matched GZMK+ CD8+ T cell CDR3 sequences in CITs.

Concurrently, the study utilized public datasets, collecting 108,969 CD8+ T cells from scRNA-seq data of Type 2 inflammatory diseases (atopic dermatitis, allergic asthma, eosinophilic esophagitis), non-Type 2 chronic inflammatory diseases (COPD, cystitis glandularis), autoimmune diseases (cutaneous lupus erythematosus, thyroiditis), and infectious diseases (sepsis, Hepatitis B virus infection).

* scRNA-seq revealed the enrichment of GZMK+ CD8+ T cells within NP tissues, exhibiting a unique pro-inflammatory but low cytotoxicity profile distinct from GZMB+ CD8+ T cells. The GZMK+ and GZMB+ subsets also differed in their differentiation trajectories and the clonal specificity for recognizing different viral epitopes.
* Integrated scRNA-seq and scTCR-seq characterized that GZMK+ CD8+ T cells (non-GZMB+ T cells) are phenotypically very close to fibroblasts within NPs and activate fibroblasts, conferring the potential to promote neutrophilic inflammation.
* Spatial Transcriptomics (Visium / Visium HD) demonstrated that the spatial localization and signaling interactions between GZMK+ CD8+ T cells and fibroblasts are also evident across a broad range of inflammatory diseases.

Link to Original Article:https://doi.org/10.1038/s41467-024-54685-1