Spatial Genomics Enables Multi-Modal Study of Clonal Heterogeneity in Tissues
8th August 2023
Given the highly dynamic state of the environment which gives rise to tumorigenic growth, it is highly desirable to characterise not only the promoters of said growth but to also decipher the genetic conditions required for the growth to occur.
Despite the advances in sequencing techniques such as deep sequencing and single cell whole genome sequencing, there are numerous challenges facing scientists in the fight against all types of cancers, understanding the spatial location of and the role each tissue and gene plays in tumour development is a high priority.
Here, the authors describe a technique utilising a unique barcoded bead array known as Slide-DNA-Seq which allows for the spatially resolved DNA sequencing from intact tissues. The technique circumvents the requirement for tissue imaging. The 10µm polystyrene beads are used to house the barcode which uniquely identifies a spatial region in the tissue. The bead array in conjunction with a HCL treated tissue section, are used to construct a sequencing library which has a primary output in paired-end sequencing to detect Copy Number Alterations (CNAs), mutations etc.
The utilisation of the afore-mentioned technique is most effective when used as part of a multi-disciplinary and -omic approach, as both histopathological and transcriptomic information can combine to give depth of tissue-tumour data which can guide further research.
Firstly, healthy mouse cerebellum was accurately characterised utilising slide-DNA-Seq to show a marked distinction between the soma (nuclei rich) and neurites (mitochondrial rich) regions. Immunofluorescence (IF) and DAPI staining were used to confirm patterns noted.
Following on, mouse liver metastases was analysed for CNA by sectioning the liver metastases and subjecting them to a H & E staining, along with IF for the late-stage tumour marker Hmga2 and Slide-DNA-Seq. Beads were smoothed based on spatial proximity with subsequent Principal Component Analysis (PCA) performed which identified co-associated genomic regions across the tissue. Spatial patterning was shown to be concordant across all platforms.
CNAs were characterised by visualising the genomic coverage of the bead clusters above at a 1Mb resolution. Amplification of chromosome 6, an indicator of kras-induced tumours, was noted. This was further validated by serial sections of a biological replica which showed a high correlation. The copy number analysis was further quantified using diploid mouse cerebellum data.
Clones within a tissue were accurately distinguished by applying both H & E staining and IHC (immunohistochemistry) to a section of liver metastases which showed two distinct clonal populations with varying levels of the protein marker HMGA2. A further section was used for Slide-DNA-Seq along with the PCA approach and this found that three different clusters could be assigned to the two clonal types. Statistical analysis was applied to the spatially localise CNA gains/losses in the metastases, thus confirming that they were indeed seeded from different clones.
Using a stage IIIB human colorectal tumour sample, clonal heterogeneity was assessed by using the same combinational approach of H&E staining, IHC and Slide-DNA-Seq on serial sections. A high abundance of localised aggregates was noted in the course of H & E staining. Spatial restrictions identified by PC1 scores indicated that the aggregates were from a single lineage. This tumour environment was further validated by co-registration of the Slide-DNA-Seq array, H&E stain and IHC using tumour marker MK167 and immune marker CD45.
All tumour regions were found to share the same genetic mutations, which included chr8q (Chromosome 8q arm) amplification including MYC and MYBLI (proto-oncogenes known to promote tumour growth) and deletion of chr15 (includes loss of genes known to promote genome stability such as RAD51 and FAN1) and chr18 leading to the conclusion that these mutations likely occurred at the early stages of tumorigenesis. Adding to this, subclonal mutations were also identified, including amplification of chr1q, chr7 and chr20.
Single Cell Whole Genome Sequencing (scWGS) was performed on the tumour by sampling cells from the whole tumour. This found five tumour clusters and one normal cluster. The findings were akin to the Slide-DNA-Seq data except, given its wider coverage, scWGS assessment also noted a further region of genetic aberrations during the course of analysis.
Serial sections of a nearby region of the colorectal tumour were further assessed using the various techniques previously described. Spatially distinct regions of the tumour cells were identified. Identities to these subclones was assigned and local tumour density was quantified.
Variance decomposition analysis was applied, where gene expression variance for each gene was calculated based on subclonal identity, tumour density and unexplained variance. 412 genes were found to be significantly associated with subclonal identity (including PLAG1 on chr8q and MYC on chr7q) , 638 genes with tumour density (including LGALS3 which contributes to immunosuppression) and 1098 genes a combination of both.
Pathway analysis showed that cell growth and proliferation (primarily MYC and E2F genes) were altered by subclonal identity. Whereas high tumour density related genes conferred enrichment of cell adhesion molecule and cadherin-binding properties including COL3A1 (a component of extracellular matrix, ECM), actin modulators FLNB and CALDI1 and mechanotransduction regulator ITGB1.
Given the complexity of assessing the various factors governing tumorigenic initiation, growth and metastases, the technique of Slide-DNA-Seq appears to be a valuable tool to elucidate the spatial compartmentalisation and mechanisms of tumour growth control at both the cellular and genetic level in a quantifiable manner, especially when used in combination with transcriptomic and histochemical approaches. Other powerful transcriptomic tools, such as high throughput deep sequencing, can also be deployed in tandem to confirm the data output.
In a similar manner to the techniques described here, at Epistem (www.epistem.co.uk) there is a routine deployment of multi-modal assessment studies of a wide variety of tissues and cells utilising a range of expertise to delineate cellular compartmentalisation, form and function.
To aid this assessment, Laser Capture Microscopy (LCM) is a powerful tool used at Epistem to select individual cells from a population in a tissue for input into downstream histochemical profiling or transcriptomic analysis via single cell RNA-Seq (scRNA-Seq) allowing for precise categorisation of the cell state with a comparably high resolution to Slide-seq.
This high resolution is especially important given the complexity created by the heterogeneity of population cells, where subpopulations in a pool are a primary target but could be missed. The subpopulations can be identified and spatially assigned to aid understanding of the driving virulence force behind disease or tumour progression.