Immunology

Understanding immune response within tissue context using high-resolution spatial transcriptomics

The orchestration of immune response to environmental antigens and infections necessitates complex coordination between innate and adaptive immune mechanisms. This intricate interplay involves multifaceted interactions among extracellular matrix components, signaling molecules, and diverse cell types originating from various anatomical sites. High-resolution spatial transcriptomics enabled by the Curio Seeker Spatial Mapping Kit can provide a comprehensive understanding of the spatial organization of immune cells within tissues, and reveal integral cell-cell interactions that are crucial for immune responses. It empowers researchers to investigate the precise localization of immune-related genes and pathways within specific tissue microenvironments, enhancing our ability to dissect the complexity of immune-mediated processes.

The following papers highlight how researchers utilized Curio Seeker and Slide-seq, the foundational technology behind Curio Seeker, to unveil novel biological insights in the various research areas in immunology.

Inflammation

Inflammation

Exploring inflammation border zones in mouse myocarditis with high-resolution spatial transcriptomics

Mantri et al., Nature Cardiovascular Research, Aug 2022.

In this study, Mantri et al. used integrated spatial and single-cell RNA-seq to dissect the temporal, spatial, and cellular heterogeneity of reovirus-induced acute myocarditis in a neonatal mouse model. After comprehensive profiling of heart and ileum tissues after viral infection using single-cell RNA-seq and low-resolution spatial transcriptomics (resolution = 100 µm), the authors performed Curio Seeker (resolution = 10 µm) to visualize the spatial distribution and phenotypes of cardiac cell types at higher spatial resolution. Unsupervised clustering and differential gene expression analysis accurately annotated cell types within the tissue and revealed phenotypes of border zone cardiomyocytes. Furthermore, the high-resolution spatial transcriptomic data enabled the identification of additional inflammation and stress-related markers for border zone cardiomyocytes previously not found with low-resolution spatial transcriptomic technique.

Adapted from Figure 4g of Mantri et alHigh-resolution Slide-seq (Curio Seeker) spatial transcriptomics map of cardiac ventricular tissue from reovirus infected mice at 7 dpi colored by Slide-seq (Curio Seeker) bead clusters. Zoom-in shows the spatial arrangement of Slide-seq clusters within a myocarditic region.

Closer analysis of the Curio Seeker data shows the physical locations of neutrophils (Extended Data Fig 9d of Mantri et al, pea green, right), Cxcl9-expressing endothelial cells (pink, middle), and border zone cardiomyocytes (orange, left) in close proximity to infiltrating T cells (red, middle) and dendritic cells (forest green, middle) in the myocarditic regions; revealing the locations of active infection and immune response in the border zones.

Adapted from Extended Data Fig. 9d of Mantri et al.  Curio Seeker spatial transcriptomics maps showing three slide-seq clusters at a time.

Comparison of gene expression signatures from infected and unaffected cardiomyocytes identified specific gene expression patterns distinguishing border zone cardiomyocytes (Extended Data Fig. 9d of Mantri et al, orange, left) such as Ankrd1 and Nppb (Extended Data Fig. 9f of Mantri et al) as well as inflammation and stress-related markers such as Clu and Nppa (Fig 4h of Mantri et al).

Adapted from Extended Data Fig. 9f and Figure 4h of Mantri et al.: Curio Seeker identifies key genes expressed in cardiomyocytes. 

The data generated with the Curio Seeker technology suggested that tissue injury due to infection is localized to the myocarditic border zone with active remodeling and stress programs. In particular, this study demonstrates the importance of high-resolution spatially resolved analysis for studying inflammation in the tissue microenvironment.

Infectious diseases

Infectious diseases

Visualizing tissue microarchitecture and viral RNA distribution in malaria-infected mouse spleen

Williams et al., bioRxiv, Feb 2023.

In this study, Williams et al. used Slide-seq to investigate the cellular interactions of CD4+ T cells and B cells as they differentiated in the mouse spleen during malaria infection. The authors delineated the immune phenotypes, spatial distributions, and co-localization patterns of distinct transcriptomic states within stromal and immune cell populations within the spleen, both before and during malaria infection. 

Mice infected with malaria plasmodium were sacrificed and their spleens were harvested seven days post-infection and analyzed with Slide-seq. Analysis of the spatial transcriptomics data revealed splenic structure defined by gene expression of markers specific for T cells, B cells, RBC, and stromal cells (Figure 1c of Williams et al.).

Adapted from Figure 1C of Williams et al.: Normalized expression of genes indicating T cells (Cd3d, Il7r), B cells (Ms4a1, Ighd), red blood cells (Hbaa1, Tfrc), and stromal cells (Cxcl13) in representative arrays in naïve and PcAS-infected spleens. 

Since malaria plasmodium expresses polyadenylated mRNA, the authors were able to capture pathogen-specific mRNA in their slide-seq data, which showed an increase in plasmodium gene expression from day 0 to day 7 of infection (Extended Data Figure 2d of Williams et al.).

Adapted from Extended Data Figure 2D of Williams et al.: Detection of Plasmodium chabaudi chabaudi AS mRNAs per spot in each array, mapped to spatial coordinates.

Utilizing their Slide-seq data, the authors examined alterations in splenic architecture during infection through Local Neighborhood Averaging (LNA) analysis. This method grouped distinct cell types based on their spatial proximity and discerned the anticipated normal structures within uninfected spleens, contrasting them with the modified structures evident in infected spleens. (Figure 1E of Williams et al.). 

Adapted from Figure 1E of Williams et al.Slide-seq reveals splenic microarchitecture before and during malaria infection. Unbiased clustering for representative Day 0 (left) and Day 7 (right) pucks.

Subsequently, the authors employed RCTD to utilize a single-cell annotated reference with the Slide-seq data, distinguishing beads bearing two cell signatures from those with a singular cell signature and assigning a cell type to each bead (Figure 2E of Williams et al.). The identification of cell types reconstructed the anticipated architecture of the unchallenged spleen.

Adapted from Figure 2E of Williams et al.: Integrating scRNA-seq and Slide-seq reveals locations of splenic immune and stromal cells. RCTD analysis of Slide-seq data recreated the expected architecture of the naive spleen. RCTD-inferred locations of naïve CD4+ T cells (gold), follicular B cells (cyan), and monocytes (red) in a whole day 0 puck with inset showing a representative follicle and T cell zone, indicated by white border above. The scale bar shown is 500 µm.

Based on their discovery that polyclonal T helper-1 cells (Th1) are drawn to and sustained by monocytes within the red pulp, and the observation that the absence of monocytes results in diminished Th1 cell differentiation, the authors aimed to pinpoint these cell populations within malaria-infected spleens. Relative to naïve counterparts, the infected splenic microarchitecture exhibited an anticipated blurring of boundaries between T cell and B cell zones, accompanied by the migration of diverse lymphocyte subsets towards the red pulp. Corresponding with their hypothesis, Th1 cells (depicted in gold in Fig 5B of Williams et al.) appeared to be closely associated with monocytes (in red) within the red pulp, while polyclonal Follicular helper cells (Tfh)-like cells (Fig 5D of Williams et al.) co-localized with follicular B cells (in blue) and activated B cells. T cell memory precursors (Tcm) exhibited a similar behavior to Tfh cells.

Adapted from Figure 5B and 5D of Williams et al.: Spatial transcriptomic co-localization analysis predicts cellular and molecular interaction partners for Th1 and Tfh cells. Locations of Th1, Tfh, and Tcm cells during infection in the spleen. 

In conclusion, the authors demonstrated that near single-cell resolution spatial transcriptomics, facilitated by Slide-seq, offers a comprehensive understanding of the spleen’s microanatomy. This approach reveals the identities, distributions, and potential interactions among numerous cell types and states throughout an immune response.

Immuno-oncology

Immuno-oncology

Unveiling spatial localizations of TCR clones in renal cell carcinoma and melanoma

Liu et al., Immunity, Oct 2022.

In this study, the authors developed Slide-TCR-seq, which adapts the Slide-seq technique to analyze whole transcriptomes alongside TCRs within the same tissue section at 10 µm resolution. Using Slide-TCR-seq, they mapped the locations of T cells and their receptors in the mouse spleen and identified spatially distinct TCR repertoires in human lymphoid germinal centers. Additionally, using the same method they studied the heterogeneous T cell responses in renal cell carcinoma and melanoma specimens. Here, we summarize a portion of their findings in mouse lymph nodes and lung metastasis of renal cell carcinoma.

When juxtaposed with H&E staining of an adjacent section, the transcriptome fraction of Slide-TCR-seq data recapitulated the splenic structure with high concordance with H&E staining. Additionally, cell type assignment to beads performed using robust cell-type decomposition (RCTD) with a murine spleen scRNA-seq reference aligned well with expected splenic architecture (Figure 1B and C of Liu et al.)

Adapted from Figure 1B-C of Liu et al.: (B) Serial sections of the OT-I mouse spleen with hematoxylin and eosin stain showing the characteristic architecture of red pulp and white pulp separation. (C) Spatial reconstruction of a representative Slide-TCR-seq array from three replicates for a corresponding section of OT-I mouse spleen, with RCTD cell-type assignment.

The authors showcased the precision of Slide-TCR-seq in recovering the CDR3 sequence for both TCR alpha and beta chains while accurately mapping their spatial distributions. Notably, the constant regions – Trac and Trbc2 (depicted in the left panel of F and G in Fig 2) and the alpha and beta subunits (illustrated in the right panel of F and G in Fig 2) of the receptors exhibit the expected spatial overlap. 

Adapted from Figure 2F-G of Liu et al.: Slide-TCR-seq locates TCR constant and variable region expression in mouse spleen. Comparing the spatial distribution of constant (left) and variable (right) sequences of a representative Slide-TCR-seq array of 3 replicates for TCRa (F) and TCRb (G) with superimposed density plot. UMI, unique molecular identifier. All scale bars: 500 mm.

Having established the robustness of the method in healthy tissue, the authors used Slide-TCR-seq to investigate lung metastasis of renal carcinoma. The study delineated three distinct cell compartments demarcating the tumor from lung tissue and the intervening boundary (Fig 3B of Liu et al.). Within the lung metastasis sample, the authors identified 1,223 unique TCRb T cell clonotypes, some of which displayed discernible spatial distributions (Fig 3C of Liu et al.). Furthermore, upon scrutinizing tumor clonality, the authors successfully distinguished three distinct tumor clones with specific spatial arrangements (Fig 3D of Liu et al.). Subsequently, leveraging an annotated single-cell reference via RCTD, the authors mapped the locations of various cell populations within the tumor, including B cells, dendritic cells, fibroblasts, macrophages, and endothelial cells. This analysis revealed that immune cells such as B cells, dendritic cells, and macrophages were predominantly situated along the tumor periphery, while endothelial cells and fibroblasts exhibited a more uniform distribution within the tumor (Fig 3E of Liu et al.).

Adapted from Figure 3A-E of Liu et al.: Slide-TCR-seq identifies spatial differences between T cell clonotypes in renal cell carcinoma. (A) H&E stain of an RCC metastasis to the lung following treatment with a PD-1 inhibitor. (B) Compartment assignment of the lung (green), boundary (orange), and tumor (blue) of a representative Slide-TCR-seq array from three replicates by applying K-nearest neighbors to cell types determined by unsupervised clustering. (C) Spatial localization of T cell clonotypes (n = 549 clonotypes, colored by clonotype) of a representative Slide-TCR-seq array from three replicates. (D) Within-tumor spatial localization of 3 distinct RCC cell subtypes (STAR Methods) of a representative Slide-TCR-seq array from three replicates. (E) Within-tumor spatial localization of a representative Slide-TCR-seq array from three replicates of the five most abundant non-tumor non-T cell types as determined by RCTD and using the combination of five scRNA-seq datasets as reference. DC, dendritic cell.

In summary, the paper highlights Slide-TCR-seq’s versatility in investigating T cell biology, TCR repertoires, and spatial relationships within tissues across diseases such as infectious diseases and cancer immunology. Slide-TCR-seq’s ability to integrate spatial and clonotypic transcriptional profiles promises insights into complex immune responses and spatially organized mechanisms underlying T cell behaviors.

Allergy & autoimmune diseases

Allergy & Autoimmune Diseases

Spatial dissection of the Th2 response to inhaled allergens

Poholek et al., Research Square Preprint, Oct 2023

This study investigates the mechanisms behind Th2 cell differentiation in barrier tissues, focusing on house dust mite (HDM)-specific T cells. Through temporal, spatial, and single-cell transcriptomic tracking, the authors identify the molecular pathways driving the differentiation and migration of allergen-specific Th2 cells, revealing the crucial role of early expression of the transcriptional repressor Blimp-1 and IL-2-mediated spatial microniches in lymph nodes for Th2 initiation and migration to the lung, thus shedding light on the pathogenesis of allergic asthma.

To study the role of Blimp-1 in the development of Th2 cells in the lungs and the development of asthma, a TCR transgenic mouse specific to HDM was crossed to BLIMP-1 YFP reporter mice. CD4+ T cells from this transgenic mouse were adoptively transferred into recipients followed by daily intranasal administration of HDM for up to 10 days. To better understand the spatial T cell response to HDM, the authors performed spatial transcriptomics via Visium Spatial Gene expression (10X Genomics) (Figure 6a-b of Poholek et al.) and Slide-seq (Curio Bioscience) (Figure S5g,h,j,k of Poholek et al.) at day 3 and day 5 in the mediastinal lymph node (medLN).

Adapted from Figure 6 of Poholek et al.: IF imaging (left) and RCTD deconvolution analysis (right three panels) of Visium Spatial Gene expression of the mediastinal lymph node (medLN) collected at indicated time points. IF staining: B220 (blue), CD4 (green), CD45.1 (red) and CD11c (magenta). Proportions of TFH, Th2, and B cells transcriptomes were identified by RCTD using matched scRNAseq data. Representative images from two medLNs on each time point.

Adapted from Figure S5 of Poholek et al: IF imaging (g, j) and RCTD deconvolution analysis (h, k) of Curio Seeker data of the medLN collected at indicated time points. Serial sections were collected and stained for immunofluorescence of B220 (blue), CD4 (green), CD45.1 (red), and CD11c (magenta) (g, j). RCTD deconvolution using match scRNAseq data was performed to assign regions with 7 cell types (h, k). Isolated regions mapped with 1366 Th2 transcriptomes or B cell transcriptomes were visualized by spatial location (i, l)

Spatial analysis using both Visium and Slide-seq platforms underscores differences in resolution and spatial distribution. Due to the larger size of Visium spots compared to Slide-seq, the detection of Th2 cells among B cells or resting T cells is limited to regions with a substantial Th2 cell presence, resulting in Visium data identifying Th2 cells solely at the T-B border at day 5. In contrast, the near single-cell resolution offered by Slide-seq facilitates the capture of a higher density of events, revealing the presence of Th2 cells both at the T-B border and dispersed throughout the T cell zone at day 5 (as depicted in Figure S5i and l of Poholek et al.). This discrepancy in findings between the two spatial platforms underscores the critical role of resolution in accurately elucidating biological phenomena.