Summary/Introduction
The development of spatial omics technologies has revolutionized genomics, allowing us to understand cellular functions at the molecular level and within their native spatial context. This dual capability is invaluable since it provides insights into how cells interact with their niche, providing a more comprehensive picture of biological processes than single-cell omics.
There are, however, several challenges facing spatial omics. Although single-cell sequencing computational tools are well developed, spatial genomics introduces new complexities, such as increased noise and cellular mixing, that require significant modifications to existing tools. Furthermore, strategies that isolate single cells while maintaining spatial barcoding suffer from limitations in spatial resolution and tissue sampling depth. In an ideal scenario, spatial genomics would efficiently capture cellular profiles from tissue sections, achieve detailed cell positioning at a micrometer scale, and be compatible with any single-cell analysis technique.
Russell, A.J.C. et al. recently published a new spatial methodology, Slide tag, in the journal Nature, which successfully overcomes these challenges. The slide-tag technique provides high-throughput data on single cells at the spatial level and can also be used to measure epigenomics, transcriptomics, and proteomics.
Unveiling Cellular Complexity: Nuclei Barcoding for High-Resolution SIngle Cell Spatial Analysis
Slide-tags offer a novel approach for marking cellular nuclei in intact fresh frozen tissue sections using spatial barcode oligonucleotides. These barcodes are derived from DNA-barcoded beads positioned at known locations. Once the nuclei are isolated, they can be analyzed using standard single-cell methods, which are now enhanced with spatial data.
Slide-tags can seamlessly integrate into existing single-cell computational workflows such as single-nucleus RNA-sequencing (snRNA-seq), single-nucleus ATAC–seq (snATAC–seq), TCR sequencing, and copy-number variation (CNV) inference. This integration unlocks the potential to explore cell-type-specific variations in gene expression, contextualize receptor-ligand interactions, and investigate genetic and epigenetic elements within tumor microenvironments, all with a precise spatial resolution.
Single-Cell Spatial Dynamics: A Comprehensive Look with Slide-tags
Profiling Slide-tags nuclei isolated from adult mouse and human brains using snRNA-seq demonstrated RNA data of indistinguishable quality and high spatial positioning accuracy. Furthermore, it enabled the identification of cell-specific genes whose expression differs between cortical layers. An application of snRNA-seq to densely packed human tonsil tissue enabled the spatial contextualization of receptor-ligand interactions.
Finally, the researchers demonstrated the multimodal capabilities of Slide-tags by simultaneously profiling the transcriptome, epigenome, and TCR repertoire in metastatic melanoma tissue and inferring CNV from transcriptome data. Transcriptome data revealed spatial differences in immune cells among genomically distinct clones based on copy-number alterations. There were two transitional tumor cell states within a cytogenetically uniform subclone. In addition, single-nucleus spatial chromatin accessibility data were utilized to identify transcription factor motifs that are likely to contribute to this mesenchymal-like transition.
Together, these findings demonstrate Slide-tags’ versatility in providing comprehensive multi-omic insights across diverse tissues and diseases, highlighting its potential as an invaluable tool in biomedical research.
Slide-Tag: High Sensitivity in Single Cell Meets High Throughput Sequencing
There are numerous advantages to using slide-tags as a spatial genomics technology. The method effectively integrates into existing sequencing workflows, adding spatial resolution without requiring specialized equipment or compromising data quality. The technology inherently achieves data at single-cell resolution, removing the need for complex deconvolution. It also boasts high sensitivity and throughput, allowing the processing of numerous tissue sections simultaneously. Slide-tags are adaptable to various genomic and epigenetic profiling methods, promising further innovations in DNA profiling, epigenetic modifications, and protein analysis. Even though Slide-tags have many advantages, there are still areas for improvement. Due to losses in dissociation and barcoding, only a subset of nuclei are captured, with an estimated 75% lost during these processes. This significantly impacts the discovery of cellular interactions. Additionally, it is unclear whether existing sc-RNA sequencing workflows can handle this level of sparsity. Moreover, Slide-tags are primarily limited to single-nucleus sequencing. While effective for certain applications, this restricts the use of methodologies that benefit from whole single-cell data, such as lineage tracing and transcriptional kinetics analysis.
Overall, the slide-tag platform significantly advances spatial genomics through high-resolution, high-throughput profiling capabilities and seamless integration with existing computational workflows. Its adaptability and potential for methodological expansion, including integration with multi-omics, highlight its potential for broader applications in genomics research.
Outsourcing Bioinformatics Analysis: How Bridge Informatics (BI) Can Help
We are passionate about empowering life science companies with cutting-edge technologies. BI’s data scientists prioritize studying, understanding, and reporting on the latest developments so we can advise our clients confidently. Our bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis.
From pipeline development and software engineering to deploying your existing bioinformatic tools, BI can help you on every step of your research journey. As experts across data types from leading sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Click here to schedule a free introductory call with a member of our team.
Shahrzad Ghazisaeidi, PhD, Data Scientist, Bridge Informatics
Shahrzad specializes in high-throughput sequencing, data pre-processing, and downstream analysis of transcriptomic and epigenetic landscapes. She is particularly passionate about developing innovative tools for drug repurposing.
Prior to joining Bridge Informatics, Shahrzad served as a Postdoctoral Associate at the Hospital for Sick Children in Toronto, Canada where she researched the epigenetics of peripheral nerve injury models.
Shahrzad earned her Ph.D. in Physiology and Neuroscience from the University of Toronto. Her studies focused on the sex-dependent and independent gene regulation of peripheral nerve injury. Currently based in Toronto, Shahrzad balances her professional pursuits with personal interests by making time for yoga, martial arts, and poetry.
