review · Acta Biochimica Polonica
Single-cell RNA sequencing technologies have transformed the profiling of genetic information by examining individual cells rather than providing population-averaged data. This high-throughput approach reveals hidden cellular diversity, identifying cell subtypes and gene expression variations that bulk sequencing overlooks. However, because tissue dissociation is required to isolate individual cells, standard single-cell sequencing loses critical spatial information about where genes are expressed. Spatial transcriptomics overcomes this limitation by identifying molecules such as RNA directly within their original positions in tissue sections at single-cell resolution. Together, these technologies and their associated computational analysis methods provide valuable insights into dynamic cellular states across diverse biomedical fields, including oncology, neurology, immunology, embryology, histology, and microbiology.
Traditional genetic sequencing averages measurements across entire tissue samples, hiding vital differences between individual cells. Combining single-cell sequencing with spatial transcriptomics allows researchers to see precisely which genes are active and where they sit within intact tissues. This detailed view helps scientists understand how diseases like cancer develop and how complex organs function at a microscopic level.
The technologies discussed serve biomedical researchers, clinical scientists, and biotechnology developers working in areas such as cancer research, neuroscience, and immunology. As this work represents a review of current techniques, technological developments, and analytical challenges, the methodologies are largely in the research and development phase rather than offering a finalised commercial product.
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In recent years, significant advancements in biochemistry, materials science, engineering, and computer-aided testing have driven the development of high-throughput tools for profiling genetic information. Single-cell RNA sequencing (scRNA-seq) technologies have established themselves as key tools for dissecting genetic sequences at the level of single cells. These technologies reveal cellular diversity and allow for the exploration of cell states and transformations with exceptional resolution. Unlike bulk sequencing, which provides population-averaged data, scRNA-seq can detect cell subtypes or gene expression variations that would otherwise be overlooked. However, a key limitation of scRNA-seq is its inability to preserve spatial information about the RNA transcriptome, as the process requires tissue dissociation and cell isolation. Spatial transcriptomics is a pivotal advancement in medical biotechnology, facilitating the identification of molecules such as RNA in their original spatial context within tissue sections at the single-cell level. This capability offers a substantial advantage over traditional single-cell sequencing techniques. Spatial transcriptomics offers valuable insights into a wide range of biomedical fields, including neurology, embryology, cancer research, immunology, and histology. This review highlights single-cell sequencing approaches, recent technological developments, associated challenges, various techniques for expression data analysis, and their applications in disciplines such as cancer research, microbiology, neuroscience, reproductive biology, and immunology. It highlights the critical role of single-cell sequencing tools in characterizing the dynamic nature of individual cells.
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DOI: 10.3389/abp.2025.13922
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