Digital Spatial Profiling: Uncovering the Spatial Organization of Molecular Expression Patterns Within Tissues

The challenge: making sense of genomics and transcriptomics patterns within tissues

The fields of molecular biology and genetics have advanced rapidly in the last decades. Indeed, the information and immense amounts of data generated through high throughput molecular characterization studies have outstripped our ability to interpret and leverage these complex data.

As genetic factors contribute—at least to some extent—to the onset and progression of most human diseases, these large transcriptomics datasets hold tremendous clinical potential. Such genetic factors with potential clinical impact include mutations in the DNA, genetic polymorphisms, and alterations in the RNA levels of genes.1–3

However, uncovering the spatial organization of these genetic components, especially in the context of a 3D tissue structure, has remained a significant challenge hindering researchers and clinicians from exploiting the full potential of transcriptomics and genomics datasets to diagnose diseases and predict prognoses. The recent advances in single-cell technologies have facilitated the emergence of spatial genomics and spatial transcriptomics technologies, which put molecular data into the 2D or 3D space.

Using spatial profiling to reveal the full picture

Spatial profiling is a new and rapidly evolving technology that aims to fill a key knowledge gap—the spatial organization of molecular patterns within tissues. Like most tissues, tumor tissues are highly heterogeneous. Hence, standard molecular characterization methods do not capture the spatial heterogeneity in gene expression profiles or clinically relevant genetic polymorphisms. Although molecular profiling of whole-tissue samples provides some useful diagnostic and prognostic information, it only partially uncovers the molecular characteristics of the tissue as a whole.4

In contrast, spatial profiling can provide valuable insight into the differences in the molecular characteristics of tumors and other tissues; uncovering these differences can help us better understand the mechanisms underlying intratumoral heterogeneity.5 For example, using digital spatial gene expression profiling, Brady et al. identified significant intertumoral and intratumoral heterogeneity in metastatic prostate cancer.6

It has also become increasingly evident that spatial organization and architecture of chromatin, DNA, and RNA play a critical functional role in the phenotype of cells and tissues.7 Spatial profiling technologies enable researchers to unveil the biological architecture of complex tissues at the single-cell level by preserving the spatial relationships between cells.5

Spatial genomics holds great potential for various research and clinical areas, including oncology, immunology, and cardiology. Mounting evidence supports the diagnostic value of spatial genomics and transcriptomics patterns, which cannot be captured by conventional molecular profiling technologies. In a recent study, Cabrita et al.8 used spatial profiling technologies to identify molecular patterns in tumor-associated tertiary lymphoid structures. Importantly, these patterns could predict response to checkpoint inhibitors in patients with melanoma.8

Spatial profiling technologies have also been employed to elucidate the role of the immune system in neurodegenerative diseases. For instance, Prokop et al.9 conducted single-cell genomics and transcriptomics analyses to gain insight into the architecture of the neural tissue. Similarly, the BRAIN Initiative of the National Institutes of Health (NIH) has started efforts to map spatial transcriptomics and generate a comprehensive brain cell atlas, aiming to enhance our understanding of complex neuronal diseases.10

In situ sequencing is another spatial profiling technology that involves the sequencing of hundreds of mRNAs directly in tissue sections while preserving the spatial information of the tissue. The technology can be used to generate single-cell gene expression maps of hundreds of genes in intact biological samples.11

Examples of digital spatial profiling platforms

Owing to the advances in live DNA imaging, high throughput sequencing, and genome engineering techniques, the last two years have seen a rapid increase in the number of multiplexed, high throughput platforms. These platforms can be used to simultaneously analyze thousands of genes and transcripts in a small and intact part of the tissue.

For example, Visium Spatial Gene Expression is a high throughput and highly sensitive digital spatial profiling platform provided by 10x Genomics. The platform involves the automated labeling of tens of thousands of mRNAs in addition to routine histological staining of sections from standard tissue blocks or fresh frozen tissues. Therefore, Visium Spatial Gene Expression allows researchers to map the whole transcriptome across thousands of areas within tissues at high spatial resolution, which ranges from 1 to 10 cells per spot depending on tissue type. By doing so, the system can provide insight into gene expression patterns within tissue organization, identify robust biomarkers that provide spatial information, classify tissue regions based on their transcriptomic profiles, and uncover spatiotemporal gene expression patterns.

CARTANA, a Swedish spatial genomics company recently acquired by 10x Genomics, has developed in situ sequencing kits and gene panels. Their next-generation in situ sequencing technology enables the sequencing of over 100 genes in tissue sections at single-cell resolution. The technology can be used to map genes in fresh/fixed frozen or formalin-fixed paraffin-embedded (FFPE) samples. The technology has been successfully applied to better understand neurodegenerative disorders, inflammation, and cancer.12

GeoMx Digital Spatial Profiler, made commercially available by NanoString, is another spatial profiling platform. It can be used to generate digital whole transcriptomes and map hundreds of proteins in tissue slides. Hence, the digital profiling platform allows researchers to reproducibly and rapidly uncover heterogeneity in gene expression patterns within tissue samples.13

Challenges and future perspectives

Despite their many advantages over more conventional transcriptomics and genomics technologies, there are certain challenges to be addressed. As spatial profiling technologies are relatively new and rapidly evolving, researchers, laboratory staff, and clinicians may not be so familiar with them, how they work, and their potential.5

As with many emerging technologies, their cost is considerably high, making them inaccessible to many diagnostic and research laboratories.13 Identifying ways to expand their applications and make their use more efficient and economical is required before researchers are able to leverage the full potential of spatial profiling technologies.

Furthermore, there is significant room to improve the spatial resolution, scale, reproducibility, standardization, and multiplexing ability of spatial profiling technologies. Merging high throughput sequencing data with tissue images can also be technically challenging.5,14

Visium Clinical Translational Research Network (CTRN) was recently established to improve spatial profiling workflows and accelerate the adoption of spatial genomics and transcriptomics technologies in clinical research. CTRN comprises over 40 international partners in the space of oncology, immunology, oncoimmunology, and neurosciences. The establishment of such international collaborative networks and web portals may aid in standardizing spatial profiling protocols and maximizing the clinical benefit of using spatial genomics and transcriptomics technologies.


References

  1. Marco-Puche G, Lois S, Benítez J, Trivino JC. RNA-Seq Perspectives to Improve Clinical Diagnosis. Front Genet. 2019;10:1152. doi:10.3389/fgene.2019.01152
  2. Ombrello MJ, Sikora KA, Kastner DL. Genetics, genomics, and their relevance to pathology and therapy. Best Pract Res Clin Rheumatol. 2014;28(2):175-189. doi:10.1016/j.berh.2014.05.001
  3. Byron SA, Van Keuren-Jensen KR, Engelthaler DM, Carpten JD, Craig DW. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet. 2016;17(5):257-271. doi:10.1038/nrg.2016.10
  4. Graf JF, Zavodszky MI. Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures. PLoS One. 2017;12(11):e0188878. doi:10.1371/journal.pone.0188878
  5. Nerurkar SN, Goh D, Cheung CCL, Nga PQY, Lim JCT, Yeong JPS. Transcriptional Spatial Profiling of Cancer Tissues in the Era of Immunotherapy: The Potential and Promise. Cancers (Basel). 2020;12(9). doi:10.3390/cancers12092572
  6. Brady L, Kriner M, Coleman I, et al. Inter- and intra-tumor heterogeneity of metastatic prostate cancer determined by digital spatial gene expression profiling. Nat Commun. 2021;12(1):1426. doi:10.1038/s41467-021-21615-4
  7. Ramani V, Shendure J, Duan Z. Understanding Spatial Genome Organization: Methods and Insights. Genomics Proteomics Bioinformatics. 2016;14(1):7-20. doi:10.1016/j.gpb.2016.01.002
  8. Cabrita R, Lauss M, Sanna A, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561-565. doi:10.1038/s41586-019-1914-8
  9. Prokop S, Miller KR, Labra SR, et al. Impact of TREM2 risk variants on brain region-specific immune activation and plaque microenvironment in Alzheimer’s disease patient brain samples. Acta Neuropathol. 2019;138(4):613-630. doi:10.1007/s00401-019-02048-2
  10. Ecker JR, Geschwind DH, Kriegstein AR, et al. The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas. Neuron. 2017;96(3):542-557. doi:10.1016/j.neuron.2017.10.007
  11. Payne AC, Chiang ZD, Reginato PL, et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science (80- ). 2021;371(6532). doi:10.1126/science.aay3446
  12. Gyllborg D, Langseth CM, Qian X, et al. Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue. Nucleic Acids Res. 2020;48(19):E112. doi:10.1093/nar/gkaa792
  13. Van TM, Blank CU. A user’s perspective on GeoMxTM digital spatial profiling. Immuno-Oncology Technol. 2019;1:11-18. doi:https://doi.org/10.1016/j.iotech.2019.05.001
  14. Marx V. Method of the Year: spatially resolved transcriptomics. Nat Methods. 2021;18(1):9-14. doi:10.1038/s41592-020-01033-y

 

Christos received his Masters in Cancer Biology from Heidelberg University and PhD from the University of Manchester.  After working as a scientist in cancer research for ten years, Christos decided to switch gears and start a career as a medical writer and editor. He is passionate about communicating science and translating complex science into clear messages for the scientific community and the wider public.

Share This Post