Microenvironment components and spatially resolved single-cell transcriptome atlas of breast cancer metastatic axillary lymph nodes

Abstract

As an indicator of clinical prognosis, lymph node metastasis of breast cancer has drawn great attention. Many reports have revealed the characteristics of metastatic breast cancer cells, however, the effect of breast cancer cells on the microenvironment components of lymph nodes and spatial transcriptome atlas remains unclear. In this study, by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics, we investigate the transcriptional profiling of six surgically excised lymph node samples and the spatial organization of one positive lymph node. We identify the existence of osteoclast-like giant cells (OGC) which have high expressions of CD68 and CD163, the biomarkers of tumor-associated macrophages (TAMs). Through a spatially resolved transcriptomic method, we find that OGCs are scattered among metastatic breast cancer cells. In the lymph node microenvironment with breast cancer cell infiltration, TAMs are enriched in protumoral pathways including NF-κB signaling pathways and NOD-like receptor signaling pathways. Further subclustering demonstrates the potential differentiation trajectory in which macrophages develop from a state of active chemokine production to a state of active lymphocyte activation. This study is the first to integrate scRNA-seq and spatial transcriptomics in the tumor microenvironment of axillary lymph nodes, offering a systematic approach to delve into breast cancer lymph node metastasis.

Keywords: breast cancer; metastasis; microenvironment; single-cell RNA sequencing; spatial transcriptomics.

Figure 1

Overview of lymph nodes with breast cancer metastases (A) UMAP plots of 35,965 single cells from six positive lymph nodes, colored according to sample origins and cell types. (B) Cellular composition of samples according to sample origins. (C) Violin plot showing biomarkers of different cell types. (D) Heatmap showing differentiated expressed genes of cell types. PL, positive lymph node. OGC, osteoblast-like giant cell. pDC, peripheral dendritic cells. NK, natural killer cells.

Figure 2

Transcriptome profiles of lymphocytes in metastatic lymph nodes (A) UMAP of T lymphocytes from six positive lymph nodes, colored according to cell types. (B) T lymphocytes composition in different samples. (C) Biomarkers of T lymphocyte subclusters. (D) Heatmap showing differentiated expressed genes of T lymphocytes. (E) UMAP of B lymphocytes from six positive lymph nodes, colored according to cell types. (F) B lymphocytes composition in different samples. (G) Biomarkers of B lymphocyte subclusters. (H) Differentiated expressed genes of B lymphocytes. Treg, regulatory T cells. Tfh, T follicular helper cells.

Figure 3

Transcriptome profiles of myeloid cells in metastatic lymph nodes (A) UMAP of myeloid cells from six positive lymph nodes, colored according to cell types. (B) Cellular composition of samples according to sample origins. (C) Violin plot showing biomarkers of different macrophage subclusters. (D) Heatmap showing differentiated expressed genes of myeloid cell types, and differentiation tree showing the transcriptomic similarities of macrophage subclusters. (E) Trajectory analysis of three states of macrophage subclusters. (F) Composition of macrophage subclusters at different cell states. (G) Differentiated expressed genes of three states of macrophage differentiation. (H) Enrichment analysis of three states of macrophage differentiation. Mac, macrophage. Mig DC, migratory dendritic cells. cDC, classical dendritic cells.

Figure 4

Transcriptome characteristics of macrophage subclusters (A) Gene modules delineated the transcriptome characteristics of macrophages at the gene collection level. The red and blue part of the heatmap referred to macrophages with high or low expression of genes which were lined up together according to similarities. (B) KEGG enrichment analysis of representative modules identified in A. (C) Bioinformatics analysis based on pySCENIC delineated the characterization of macrophage subclusters from the transcription perspective, identifying the transcription factors together with the corresponding targeted genes among macrophage subclusters. Here, the expression of representative transcription factors in macrophage subclusters is shown. (D) UMAP of CAFs from positive lymph nodes. (E) Cellular composition of CAFs in six positive lymph nodes. (F) Differentially expressed genes of CAF subclusters. AUC, area under curve. CAF, cancer-associated fibroblasts.

Figure 5

Transcriptome characteristics of osteoclast-like giant cells and cancer-immune interaction in lymph node TME (A) Immunofluorescence staining of CD68 and CD163 in PL6. (B) Enrichment analysis of DEGs in OGCs. (C) Heatmap showing the incidence of ligand-receptor pairs with different frequencies between different cell clusters. Numbers to the bar refer to the number of specimens that the interaction was found (for example, “5” refers to interactions found among five positive lymph nodes). (D) Representative ligand-receptor interactions between macrophage and cancer cells. (E) Representative ligand-receptor interactions between OGCs and cancer cells.

Figure 6

Integrative analysis of spatial and single-cell transcriptome analysis (A) H&E staining of PL6. (B) Annotation of plots from PL6. (C) UMAP of cell clusters from PL6. (D) Spatial localization relationship of cells indicating cellular interactions. (E,F) Representatives of differentially expressed genes in PL6 on spatial positioning and cell types.