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The discovery of the transcriptome at tissular level: A new approach in biomedicine

Spatial resolved transcriptomics (SRT) is a technology that maps gene expression within a tissue from two perspectives: image-based and NGS-based. The applications in biomedicine are evident, especially in the characterization of tumor heterogeneity and identification of structures while shedding light to sequencing techniques such as single-cell-seq from a tissular context perspective.

How spatial transcriptomics improves scRNA-seq

While scRNA-seq has supposed a milestone in the gene expression analysis at single cell level, it has a significant limitation, the tissue context information. Cells, when dissociated from their organic context during sequencing, miss crucial information about cellular location and interactions within the tissue. Spatial transcriptomics addresses this limitation by providing high-throughput gene expression data while preserving the special tissue context of the cells. This spatial dimension offers additional advantages and significant improvements over single-cell sequencing.

By retaining cells in their tissue, SRT allows the study of location, interactions and functions, something capital in a complex system such as tumors, where cancer cell interaction and microenvironment play key roles in tumor development and progression. At the same time, the identification of specific gene expression patterns in the target region would be masked in scRNA-seq data, ultimately helping to understand tissue and tumor under these circumstances.

The combination of gene expression data and spatial information can help identify new cell types and subtypes that might not be distinguished by scRNA-seq alone. In addition, SRT is particularly valuable in the detailed characterization of tumor microenvironments (TME), by mapping the distribution of different cell types, specific areas of immune cell infiltration, stromal cell interactions and regions subjected to hypoxia conditions can be identified which also helps in the development of more effective cancer therapies.

SRT can also be used for validation of scRNA-seq assays, by spatially confirming cell type ans expression patterns, so the additional study dimension of SRT ads crucial information with the potential to revolutionize the understanding of complex biological systems and accelerate the development of new diagnostic and therapeutic strategies.

SRT and the Journey Towards Mult omics Integration

Both methods offer unique advantages and disadvantages in transcriptome research. Imaging-based methods involve the direct visualization of RNA molecules in situ, usually through fluorescence microscopy techniques with high spatial resolution event the subcellular level such as fluorescence in situ hybridization (FISH) and in situ sequencing (ISS). In contrast, sequencing-based methods employ NGS platforms for the analysis of the spatial distribution of gene transcription, computed by incorporating spatial barcodes into RNA sequencing libraries, allowing for observing transcription post-sequencing.

Imaging-based methods allow for precise localization of transcripts at the individual level within cells with high resolution, but at the cost of tissue coverage limited to the field of view of the imaging targets on the other hand, the spatial resolution of sequencing-based methods is lower and limited by the size of the aforementioned barcodes, often resulting in measurements of results at the multicellular level.

Regarding performance, sequencing-based methods have a higher throughput and allow for the analysis of a larger number of genes thanks to the fact that the entire transcript is detected and processed massively in parallel by NGS. Imaging-based methods are limited by the number of fluorescent channels and imaging cycles of the multiplex analysis.

Sequencing-based methods are generally untargeted; therefore, a predefined gene list or panel is not required for an analysis, something useful for the discovery of new transcriptome data in analysis and for giving a comprehensive view of what is being transcribed. Imaging-based methods must work with a targeted list due to the limitations of probes used in the library, although FISSEQ allows for an untargeted analysis of transcriptome distribution.

For data processing, both techniques are complex, since image-based techniques require challenging computational analysis, especially in experiments with large data sets, and sequencing-based techniques require specialized bioinformatics tools for interpretation. However, the latter make data processing easier compared to the former.

Sample compatibility is an area that has recently been under develop, and both methodologies prove to be so with different types of samples, including fresh tissue, frozen tissue, an paraffin-embedded samples, which is crucial in clinical applications and analysis of stored sample history.

Both methodologies have algo been adapted to the 3D spectrum, although analysis remain challenging due to limitations in tissue clearing and expansion techniques, as well as the computational demands for processing large 3D data sets.

In general, future developments aim to address the limitations described and expand the virtues of spatial transcriptomics, where especially the integration in multiomics stands out to have a multidimensional understanding of biological systems, generating immense potential in many fields of biomedicine including cancer research and other biological systems that are difficult to study such as neuroscience, allowing it to gradually consolidate itself as a revolutionary tool in the development of diagnostic and therapeutic strategies.

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