M20 Spatial
Random-primer based full-sample
spatial full transcriptome technology
By leveraging the advantages of random primers, M20 Spatial pioneers a breakthrough in spatial transcriptomics. This innovation not only extends the applicability of spatial transcriptomics across a wide range of species and sample types, but also enables to obtain the unbiased full-length sequences of diverse RNA species. This technological stride not only amplifies the practical utility of spatial transcriptomics across a spectrum of basic and clinical research fields but also dramatically expands the depth of insights derived from spatial transcriptome studies.
How M20 Spatial works
Which Sample Types are Applicable
- FFPE
- Frozen
- Fresh
What Are the Benefits
- Full-Length Sequencing
- Capturing full-length sequences of transcripts
- Revealing gene fusion and alternative splicing
- Improved Gene Detection Sensitivity
- Detecting over 5,000 genes per 50 µm spot
- Unveiling spatial cellular relationships and molecular mechanisms
- Enhanced Resolution
- Enhancing spatial resolution to 15 µm
- Supporting diverse sample types, including FFPE
- Full Spatial Transcriptome Capture
- Capturing mRNA as the dominant RNA species
- Detecting diverse non-coding RNAs, including lncRNAs and miRNAs
Use of M20 Spatial
A total of 32,971 genes across 2,389 spots with a median UMI counts of 17,901 and 5,360 genes per spot (50 µm).
A total of 31,515 genes were detected across 4,418 spots, with a median of 4,440 UMIs and 1,171 genes per spot (15 µm).
A total of 37,862 genes were detected across 2,682 spots, with a median of 28,852 UMIs and 5,421 genes per spot (50 µm).
A total of 38,434 genes were detected across 1,307 spots, with a median of 30,065 UMIs and 5,839 genes per spot (50 µm).
Read coverage along the gene body in different FFPE samples
M20 Spatial achieves unbiased, full-length coverage of gene body sequences from the 5' to 3' ends in spatial transcriptome without the need for third-generation sequencing.
Detected gene number of different types of RNA (left) and spatial mapping based on lncRNA information (right) in different FFPE samples
Across all resolutions, M20 Spatial demonstrates the capability to capture a diverse array of RNA molecules, with mRNA constituting the highest proportion. Additionally, it effectively detects a variety of non-coding RNAs,including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs). Clustering based solely on non-coding RNA data reflects a certain degree of tissue spatial structure, underscoring the potential of non-coding RNA in spatial transcriptomics.
Unsupervised cell clustering (upper left), spatial mapping (upper right) and marker expression in each cluster (lower panel) in different FFPE samples
M20 Spatial data alone can identify multiple cell subpopulations based on transcriptomic differences. High-resolution spatial mapping shows that these subpopulations correspond to distinct tissue origins, highlighting the spatial heterogeneity within the tissue.
Spatial distribution of characteristic cell type in mouse embryo (left) and olfactory bulb (right) FFPE samples based on the integration of spatial transcriptome and public single-cell transcriptome dataset
By integrating single-cell transcriptome data from public dataset, M20 Spatial allows for even more precise localization of cell types within tissue architecture.
Spatial distribution of characteristic cell type in human breast cancer (left) and peritumoral tissue (right) FFPE samples based on the integration of spatial transcriptome and single-cell transcriptome
Selected genes expression in the human breast cancer and peritumoral tissue FFPE samples
By integrating VITA single-cell transcriptome data, M20 Spatial further clarifies the precise spatial distribution of different cell types and spatial heterogeneity of gene expression with greater depth, offering deeper insights into tissue microenvironments.