In-situ visualization and measurement of tumor-infiltrating lymphocytes (TILs) on intact FFPE renalcellcarcinoma(RCC)
tissueusing
thespatial
molecularimager(SMI)
1211122222

Poster number:
15594

Evan W. Newell , Youngmi Kim , Heeju Ryu , Shamin Li , Yannick Simoni , Michael Leon , Sean Kim , Mark Gregory , Patrick Danaher , Sarah Warren ,Joseph Beechem2
1. Fred Hutch, Vaccine and Infectious Disease Division, Seattle WA 98109
2. Nanostring Technologies, Seattle WA 98109
SummaryCell Typing and mapping of the renal cell carcinoma (kidney tumors) overview

Selected tissue area to be analyzed

Spatially annotated 1,000 gene targets

Single cell population cluster

Many previous studies have highlighted the high degree of heterogeneity in the phenotypesof tumor infiltrating lymphocytes. We and others also reported evidence for an abundanceof non-tumor specific T cells infiltrating tumors and we’ve shown that these “bystander” Tcells do not express markers associated with terminal exhaustion, such as CD39.

To link multi-omics TIL profiling to spatial localization, we used the spatial molecular imager(SMI) , which is a novel spatial transcriptomics platform that allows spatially resolved highdimensional cellular phenotyping for comprehensive TIL profiling. SMI uses fluorescentmolecular barcodes to enable in-situ measurement of biological targets on an intact tissue
sample.

200K single cells of the renal cell carcinoma were spatially characterized
and mapped back into the neative locations.
DNAPanCK
CD45
CD3

Marker-gene expression of each cell types are visualized in a spatial map.
B cellIntegration of non-spatial single cell data to spatial single cell data
Spatially resolved tumorinfiltrating-immune-cells
info in submicron resolution
UMAP_dim2

Resectedtumors

Display spatial
locations of acell type

IGHG1

T3

T7Vimentin

SMI

Broad cellular profiles
Detection of Ag-specific cells
and their profiles

CyTOF
Clone size: 1
11% of total TCR

Clone size: 397
11% of total TCR

singlets
expandedNon-spatial single cell RNA-seq data were used to characterize subsets and gene-expression signatures of tumor-infiltrating lymphocytes
Mass cytometry analysis of RCC tumor infiltrating CD8 T cells highlights variable
abundance of bystander tumor infiltrating T cells:

Single-cell sequencing based analysis of RCC tumor infiltrating CD8 T cells allows for determination of T cell subset
transcriptional profiles:
CD69

Top 5 clones

Gene expression profiles ofimmune cell subsets

Bystander CD8 TILs are heterogeneous at bothprotein and gene levels

Conclusion
Proteins

scRNA-seq(10x or BD rhapsody)CD103

SMI assay overview

Genes

2

Sample Preparation

Integrated Readout

3

Interactive Data Analysis

PD1

TIM3

Flow cellassembly

Fully automatedcyclic chemistry &imaging

Image processingof multi-FOVs andZ-stacks

As we previously reported for lung and colon cancer, virus-specific bystander T cells can be readily detected in kidney (RCC)
tumors and display diverse phenotypic profiles.
The Spatial Molecular Imager (SMI) is a single instrument solution for subcellular spatial analysis. Provides sub-cellular
resolution of 1000+-plex transcriptomic information.

SMI is a single instrument solution for subcellular spatial analysis.

Permeabilize, fix, Hybridization ofretrieve targets RNA specificprobes

T2

Spatially resolved single-cellgene expressionPBMCs

1T0

UMAP_dim1

Dissociatedcells
SortedImmune cells

Cell type b (tumor)

Display spatial
locations of amarker gene

Info:

FFPE slides GeoMx DSP

Plasmablast

The following figure shows where those 8 T cell subsets areinriched in spatial clusters. As expected, most of T cell subsets are Each T cell subset is spatially mapped out.
found in spatial cluster 4 except T0 and T6.

CD20
Data:

0 1 2 3 4 5 6 7

Notable clusters:
0 – CD8+CD69+CD103+CD39+
2 – CD8+CD69–CD103–CD39–
3 – CD4+ non-Treg
7 – CD4+ Tregy axis

SMI analysis of matched tumor tissue was used to accurately quantify the densities and tocompare the spatial organization of various T cell and other cellular subsets. Preliminary
data show that T cells with the most terminally exhausted gene expression localize to areasof with a high degree of T cell infiltration into the tumor bed.

Assays:We used CITE-seq to profile andcluster T cell subtypes. Based onprotein expression, we identified 8clusters of T cell subsets. From thedata, we determined gene expression
signatures of T cell subsets.

UMAP_dim1

Here, using a combination of methodologies, we investigate relationships between
lymphocyte phenotypic diversity, antigen-specificity and the spatial localization of tumor
infiltrating immune cells.
Using CyTOF, scRNA-seq, and TCR sequencing analysis of dissociated, consistent with ourprevious reports, we found T cell populations could be segregated based on markers
associated with chronic T cell receptor signaling and many T cells with an exhausted
phenotype were clonally expanded in the tumor but not the blood.

Spatial mapping of identified cells

Individual cells were scored for the abundance of each cell type among
their 200 closest neighbors. Cells were then clustered according to thisneighborhood data. In this figure, cells are shown in physical space andcolored by their neighborhood cluster

X axis

UMAP_dim2

Although cancer immunotherapies can effectively restore T cell-mediated immunity leading
to sustained clinical responses, these responses are unpredictable partly due to highly
heterogeneous phenotypes of tumor-infiltrating lymphocytes (TILs) between patients. Thus,
understanding such TILs and their roles in the context of tumor microenvironments (TME)
may lead to developing better immunotherapy solutions.

Samples:

Integration of non-spatial scRNA-seq data to spatial SMI single cell data helps to visualize rare population of cell
types of interests.

The Spatial Molecular Imaging (SMI) platform applied to an RCC tumor allows for subcellular 1000-plex spatial transcriptomics

Distinct niches of the tumor microenvironment can be delineated within RCC tumor sections. By this analysis, tumor-rich,stroma, and various regions with high densities of immune cell infiltrates (e.g., regions co-enriched for T and B cells) can beclearly identified.
Cellular subsets with gene expression profiles defined using single-cell sequencing-based multi-omics can spatially definedusing SMI. T cells with an exhausted phenotype are preferentially enriched in tumor-dense regions in contrast to non-exhausted
cells with bystander-like phenotypes.