M20 Spatial Offers In-Depth Insights into Breast Cancer Spatial Heterogeneity

In life sciences research, complex diseases such as cancer, characterized by pronounced spatial heterogeneity within tissues, remain a central focus for spatial transcriptomics applications. Conventional spatial transcriptome platforms, however, typically require fresh-frozen samples and are constrained in their ability to analyze clinical samples like formalin-fixed paraffin embedded (FFPE) tissue. Furthermore, these platforms primarily capture only mRNA, thus omitting the abundant non-coding RNAs present in the transcriptome. Such limitations restrict our capacity to fully understand disease mechanisms.

To overcome these technical challenges, in October 2023, we launched M20 Spatial, the world’s first random-priming-based comprehensive spatial transcriptome technology, compatible with all sample types, including FFPE (https://www.m20genomics.com/1085.html ). M20 Spatial captures both mRNA and non-coding RNA, ushering in a new era of multi-species, whole-sample, full-transcriptome, and full-length sequence coverage. Last week, we announced a major upgrade to M20 Spatial, with significant enhancements in sensitivity, resolution, and analytical depth (https://www.m20genomics.com/3818.html).

We further evaluate the upgraded M20 Spatial on clinical FFPE tumor samples, confirming its exceptional performance in clinical research and its valuable contributions to understanding tumor heterogeneity. Below, we present the latest data from paired FFPE samples of human breast cancer tissue and peritumoral tissue:

• High-Sensitivity Detection of Gene Expression

M20 Spatial demonstrates high sensitivity in transcriptome capture across FFPE sections of both breast cancer and peritumoral tissues (Figure 1, 2).

In the breast cancer sample, a total of 38,404 genes are detected in 2,682 spots. Each 50 µm-diameter spot yielded a median UMI count of 28,852 and a median gene count of 5,421 (Figure 1).

Figure 1. Left: H&E staining image of the human breast cancer FFPE sample. Middle: Spatial UMI count map. Right: Spatial gene count map.

In the peritumoral tissue sample, a total of 38,924 genes are detected in 1,307 spots, with a median UMI count of 30,065 and a median gene number of 5,839 per spot (Figure 2).

Figure 2. Left: H&E staining image of the FFPE sample of the human breast cancer peritumoral tissue. Middle: Spatial UMI count map. Right: Spatial gene count map.

The performance of M20 Spatial in clinical FFPE samples demonstrates sensitivity levels comparable to those typically observed in conventional spatial transcriptome using fresh-frozen samples. Furthermore, its ability to capture the entire transcriptome allows M20 Spatial to significantly surpass platforms that are restricted to polyA or mRNA-targeted probes in terms of total gene detection.

• Unbiased Coverage of Full-Length Transcriptome

Leveraging the power of random priming, M20 Spatial achieves unbiased, full-length coverage of gene body sequences in spatial transcriptome without the need for third-generation sequencing. This advantage extends to clinical FFPE samples, where both breast cancer and peritumoral tissue specimens exhibited consistent transcriptome coverage from the 5' to 3' ends (Figure 3, 4), providing a more precise and comprehensive representation of transcriptome data.

Figure 3. Read coverage along the gene body in the human breast cancer FFPE sample.

Figure 4. Read coverage along the gene body in the FFPE sample of the human breast cancer peritumoral tissue.

• Comprehensive and Reliable Capture of Spatial Non-Coding RNAs

Since its initial launch, M20 Spatial has redefined spatial transcriptome by enabling comprehensive transcriptome capture, moving beyond previous limitations to coding regions alone. In the current study on breast cancer and peritumoral tissue FFPE samples, M20 Spatial effectively captures various RNA molecules, with mRNA as the predominant type, alongside a diverse range of non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) (Figure 5, 6).

Figure5.Detected gene number of different types of RNA captured by M20 Spatial in the human breast cancer FFPE sample.

Figure 6. Detected gene number of different types of RNA captured by M20 Spatial in the FFPE sample of the human breast cancer peritumoral tissue.

With the discovery of miRNA awarded the 2024 Nobel Prize in Physiology or Medicine, the detection of miRNAs in spatial transcriptome is highly relevant. In clinical FFPE samples, M20 Spatial captures approximately 1,000 types of miRNAs, with the exact number varying according to sample type (Figure 5, 6).

Among non-coding RNAs, lncRNAs represent the largest proportion and have garnered significant attention for their regulatory roles in tumors in recent years. In both breast cancer and peritumoral FFPE samples, M20 Spatial reliably captures lncRNA across various spatial locations within the tissue (Figure 7, 8).

Figure 7. The spatial distribution of lncRNAs in the human breast cancer FFPE sample.

Figure 8. The spatial distribution of lncRNAs in the FFPE sample of the human breast cancer peritumoral tissue.

The ability to detect both non-coding RNAs and mRNAs makes M20 Spatial an invaluable tool for in-depth exploration of disease mechanisms, identifying potential biomarkers and therapeutic targets.

• In-Depth Exploration in Spatial Heterogeneity of Gene Expression

Leveraging data from M20 Spatial allows us to explore the spatial heterogeneity of gene expression with greater depth, uncovering associations with tumor pathology and key prognostic markers. Figure 9 presents the expression profiles of selected genes in both breast cancer and peritumoral tissue samples.

Figure 9. Selected genes expression in the human breast cancer and peritumoral tissue FFPE samples.

Compared to the peritumoral tissue, the breast cancer sample shows significant upregulation of ERBB2 (HER2) and downregulation of estrogen receptor (ESR1) and progesterone receptor (PGR), suggesting a possible HER2-positive breast cancer subtype. IGHG1 expression is also notably elevated in the breast cancer sample, indicating possible infiltration by B cells and/or plasma cells.

In addition, tumor suppressor genes such as PTEN ¹and TP53² shows lower expression in the breast cancer sample relative to the peritumoral tissue, while genes associated with tumor progression, like MMP9³, are upregulated. Notably, the spatial expression patterns of prognostic markers JCHAIN (associated with favorable prognosis)⁴ and MUCL1 (associated with poor prognosis)⁵ reveal distinct spatial distributions, highlighting intratumoral heterogeneity.

Furthermore, we observe elevated CD24 expression within regions of high HER2 expression, potentially suggesting an immune evasion mechanism. Meanwhile, other immune checkpoints, such as CD47, are expressed at lower levels in the tumor than in the peritumoral tissue.

Figure 10. Tumor purity in the human breast cancer FFPE sample.

Figure 11. Tumor purity in the FFPE sample of the human breast cancer peritumoral tissue.

• Integration of VITA Single-Cell Transcriptome Data for Enhanced Spatial Cellular Heterogeneity Profiling

In the breast cancer and peritumoral FFPE samples, M20 Spatial data alone enables identification of multiple subpopulations based on transcriptome differences, confirming the distinct spatial distribution of these subpopulations (Figure 12, 13). Within a specific subpopulation in the breast cancer sample, we observe high expression of DKK1⁷, SLC3A1⁸, MUC5B⁹, ACKR1¹⁰, and APOC1¹¹—genes associated with breast cancer development and/or prognosis (Figure 12).

Figure 12. Unsupervised cell clustering (upper left), spatial projection (upper right) and marker expression in each cluster (lower panel) in the human breast cancer FFPE sample.

Figure 13. Unsupervised cell clustering (upper left), spatial projection (upper right) and marker expression in each cluster (lower panel) in the FFPE sample of the human breast cancer peritumoral tissue.

By integrating VITA single-cell transcriptome data, M20 Spatial allows for even more precise localization of cell types within tissue architecture, providing highly detailed spatial heterogeneity information (Figure 14, 15).

Figure 14. Spatial distribution of characteristic cell type in the human breast cancer FFPE sample based on the integration of M20 Spatial and VITA single-cell transcriptome data.

Figure 15. Spatial distribution of characteristic cell type in the FFPE sample of the human breast cancer peritumoral tissue based on the integration of M20 Spatial and VITA single-cell transcriptome data.

In line with findings outlined above, macrophage locations identified in breast cancer and peritumoral samples correspond with the spatial distribution of macrophage marker CD163 (Figure 9). Plasma cell infiltration observed in both samples also aligns with the spatial distribution of IGHG1 (Figure 9), while epithelial cell spatial localization corresponds closely with tumor purity analysis results (Figure 10, 11), underscoring the reliability of M20 Spatial and VITA integration.

Looking Ahead: Future Prospects

These data from clinical breast cancer and peritumoral FFPE samples underscore M20 Spatial’s superior performance and its capacity to deliver novel insights into tumor heterogeneity and underlying disease mechanisms. We are excited to equip clinical researchers with this powerful and reliable tool to accelerate groundbreaking discoveries.

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