Sensitive Multiplexed MicroRNA Spatial Profiling and Data Classification Framework Applied to Murine Breast Tumors
MicroRNAs (miRNAs) are small RNAs that are often dysregulated in many diseases, including cancers. They are highly tissue specific and stable, thus making them particularly useful as biomarkers. As the spatial transcriptomics field advances, protocols that enable highly sensitive and spatially resol...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156323 https://orcid.org/0000-0002-7255-4606 |
| Summary: | MicroRNAs (miRNAs) are small RNAs that are often dysregulated in many diseases, including cancers. They are highly tissue specific and stable, thus making them particularly useful as biomarkers. As the spatial transcriptomics field advances, protocols that enable highly sensitive and spatially resolved detection become necessary to maximize the information gained from samples. This is especially true of miRNAs where the location of where they are expressed within tissue can provide prognostic value with regards to patient outcome. Equally as important as detection are ways to assess and visualize the miRNA’s spatial information in order to leverage the power of spatial transcriptomics over that of traditional non-spatial bulk assays. We present a highly sensitive methodology that simultaneously quantitates and spatially detects seven miRNAs in situ on formalin-fixed paraffin embedded tissue sections. This method utilizes rolling circle amplification (RCA) in conjunction with a dual scanning approach in nanoliter well arrays with embedded hydrogel posts. The hydrogel posts are functionalized with DNA-probes that enable the detection of miRNAs across a large dynamic range (four orders of magnitude) and a limit of detection of 0.17 zeptomoles (1.7×10⁻⁴ attomoles). We applied our methodology coupled with a data analysis pipeline to K14-Cre Brca1 superscript f/f Tp53 superscript f/f murine breast tumors to showcase the information gained from this approach. |
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