How the Biotech Industry is Using Spatial Analysis

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There is no doubt that one industry that we have come to depend on is the biotech industry. While this industry has gained a lot of attention with its rapid and historic efforts to develop COVID-19 vaccines, there are also signs that this industry will increasingly depend on spatial analysis as part of its efforts to develop rapid medical advancements that can help fight some of our most challenging diseases.

Molecular cartography is one area that the biotech industry has grown dependent on. Mapping and assessing complex molecular structures, including spatial relationships of these structures, helps to determine if compounds created have given efficacy.

This includes creating 3D maps that allow researchers and biotech industry experts to assess potential benefits of drugs before they are developed. This can save development time and costs by limiting the range of candidate drugs to test, improving chances that a drug could ultimately be accepted for use.

One example is a recent startup, called Resolve Biosciences, which is using spatial multiomics.[1] In this field, researchers are combining big data analysis and deep learning methods with spatial mapping to assess which complex combinations of molecular structures are likely to be effective or produce given desired effects.[2],[3]

The pipeline for identification of tumor-infiltrating lymphocyte (TIL) maps on whole-slide histopathologic images. Image: Lu et al., 2020.
The pipeline for identification of tumor-infiltrating lymphocyte (TIL) maps on whole-slide histopathologic images. Image: Lu et al., 2020, doi: 10.1200/CCI.19.00126, CC BY 4.0.

Such companies could increasingly work with biotech manufacturers to help produce desired drugs while also limiting development time similar to how we have seen the COVID-19 vaccine develop. In fact, in a recent breakthrough, Google’s DeepMind was able to demonstrate that given a DNA sequence the protein’s three-dimensional shape could be predicted using the AlphaFold 2 software. The commercial application is clear for biotech companies who can potentially develop drugs using DNA, mRNA, or even creating synthetic alternatives that can be incorporated within complex compounds.

Mapping Molecular Structure to Better Understand Disease

In addition to new advances being predicted or tested using mapping and machine learning techniques, scientists are also mapping molecular structures to better understand disease.

For instance, Chagas disease is a tropical disease that has varied physical effects over the course of the disease. Using molecular mapping, it has become possible to determine how the disease has differential effects on cells at a molecular level. Researchers were able to determine metabolic and microbiome alterations due to the disease using spatial localization analysis. This can help lead to new advancements that better target the regions where given effects of the disease are evident.[4]

Spatial impact of T. cruzi infection is reflected by spatial modulation of the tissue small-molecule profile. Cropped figure showing Parasite burden at each sampling site (A) and mapped sampling position (B). Figure: Hossain et al., 2020. DOI: 10.1126/sciadv.aaz2015, CC BY 4.0
Spatial impact of T. cruzi infection is reflected by spatial modulation of the tissue small-molecule profile. Cropped figure showing Parasite burden at each sampling site (A) and mapped sampling position (B). Figure: Hossain et al., 2020. DOI: 10.1126/sciadv.aaz2015, CC BY 4.0

One benefit is seen in gene editing, where scientists can map molecular and gene structures and determine locations that can be edited or altered.[5]

Scientists are also mapping larger scale structures such as cells and their locations across different types of body tissue. Spatial transcriptomics is a type of technology used to allow scientists to measure varied gene activity within a tissue sample and map this across that tissue where given activity is evident.[6] The application is it can help determine the efficacy of drugs or even map how diseases and infections have different effects across body tissue.

We are witnessing many changes in the biotech industry that is in large part driven by major scientific advancements. Among these developments are better spatial methods that include mapping and machine learning techniques to aid in the analysis of cellular or molecular activity and compounds. These changes have greatly assisted capabilities in developing drugs and other biomedical products that could be developed more rapidly and potentially respond to emerging diseases as well as solve long-lived diseases.

References

[1] For more on Resolve Biosciences, see: https://www.genomeweb.com/business-news/spatial-analysis-startup-resolve-biosciences-launches-24m-series-financing-round#.X98pH9bLduQ.

[2] For more on an example spatial multiomics and its use in spatial analysis, see: Liu, Y., Yang, M., Deng, Y., Su, G., Enninful, A., Guo, C.C., Tebaldi, T., Zhang, D., Kim, D., Bai, Z., Norris, E., Pan, A., Li, J., Xiao, Y., Halene, S., Fan, R., 2020. High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue. Cell 183, 1665-1681.e18. https://doi.org/10.1016/j.cell.2020.10.026.

[3] For more on an example of deep learning methods with multiomics, see: Lu, Z., Xu, S., Shao, W., Wu, Y., Zhang, J., Han, Z., Feng, Q., Huang, K., 2020. Deep-Learning–Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data. JCO Clinical Cancer Informatics 480–490. https://doi.org/10.1200/CCI.19.00126.

[4] For more on how molecular mapping can aid in understanding Chagas disease, see: Hossain, E., Khanam, S., Dean, D.A., Wu, C., Lostracco-Johnson, S., Thomas, D., Kane, S.S., Parab, A.R., Flores, K., Katemauswa, M., Gosmanov, C., Hayes, S.E., Zhang, Y., Li, D., Woelfel-Monsivais, C., Sankaranarayanan, K., McCall, L.-I., 2020. Mapping of host-parasite-microbiome interactions reveals metabolic determinants of tropism and tolerance in Chagas disease. Sci. Adv. 6, eaaz2015. https://doi.org/10.1126/sciadv.aaz2015.

[5] For more on gene editing using mapping to determine locations for editing, see an example: Lin, Q., Low, L.W.L., Lau, A., Chua, E.W.L., Matsuoka, Y., Lian, Y., Monteiro, A., Tate, S., Gunaratne, J., Carney, T.J., 2019. Tracking genome-editing and associated molecular perturbations by SWATH mass spectrometry. Sci Rep 9, 15240. https://doi.org/10.1038/s41598-019-51612-z.

[6] For more on spatial transcriptomics, see: https://www.10xgenomics.com/spatial-transcriptomics/.

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