Next-Generation Spatial Transcriptomic Tools Transform RNA Detection in Degraded Samples
RNA is one of the types of analytes that are sensitive to degradation. Unlike proteins, which can remain for years, the RNA molecule itself has inherent instability and degrades easily, especially because there are high numbers of enzymes in the environment. As a result measurement of RNA is very challenging in archival or clinical samples where RNA is often more degraded.
Although RNA-based measurements have the potential for application across diverse areas of human health, inadequate sample collection techniques can release cellular RNA, and improper handling and storage can further degrade RNA. In clinical settings, samples are stored and left at room temperature, exposed to the ambient environment for years. This typically leads to a high degree of degradation, which affects downstream analysis.
In addition, for tissue processing, the sample has to go through an elaborate and complex sample fixation and preparation step that tends to heavily fix the sample. As a result, cross-linking happens between molecules, and due to the creation of cross-link bonds, the RNA can bind into surrounding chemical groups. It’s harder to get access to a molecule by other binding reagents when it is heavily cross-linked. This makes detection and measurement more challenging in lower quality samples, especially formalin-fixed paraffin-embedded (FFPE) samples.
Challenges of traditional spatial transcriptomics
Traditionally, spatial transcriptomics depended on using probes to bind with the target, but efficient binding is difficult because of the heavy cross-linking. They are also too fragmented and can easily float away during sample preparation, which makes detection challenging. In traditional spatial transcriptomics, there is not an easy way to retain the RNA fragments in the sample.
Another challenge is the background can be very high in a variety of samples. For example, human liver samples, heart samples and brain samples have various degrees of autofluorescence background. When you deal with this, you’ll have fragmented pieces that can easily get lost in samples preparation. At the same time, the sample itself has variable degrees of background that will confound the detection.
To overcome these issues, a rethinking about the process and method of how to do RNA detection was needed. New tools, including new chemistry, had to be developed. They have spawned a new generation of spatial transcriptomics.
The new generation of degraded RNA analysis
A new approach recently developed by Vizgen to better handle degraded RNA is to modify the RNA molecules, so that they can be anchored in hydrogel. A universal sample preparation protocol that includes tissue clearing is then applied to lower the autofluorescence background, and the protocol is optimized to achieve best signal-to-noise ratio during imaging. When you perform tissue clearing, the fragmented RNA pieces won’t be lost, as the optimized anchoring help retaining the molecules inside the gel. Once the probe binds, the signal will be boosted through a new generation of chemistry for spatial omics that uses a signal enhancement procedure to amplify the signal. This way it’s possible to get a much better signal-to-noise ratio for imaging.
RNA anchoring, tissue clearing, probe structure optimization and signal enhancement are what make next-generation chemistry for spatial transcriptomics better than the original chemistry to detect degraded RNA.
Diverse applications
The opportunities that this new approach to RNA creates span across diverse applications, especially neuroscience, oncology, and developmental biology. The advancement to better analyzing degraded RNA is significant, especially in light of the extension in the BRAIN Initiative from mapping the mouse brain to embracing human brain samples recently.
The RNA quality of human brain samples is highly variable and much poorer—much more so than mouse brain or animal brain samples, which are usually harvested in a controlled environment. However, the analysis of degraded RNA in human samples offers a higher promise of bigger breakthroughs in brain research.
The emergence of tools enabling researchers to perform high-plex spatial transcriptomics with single-cell resolution has revolutionized our understanding of tumor development. The new chemistry helps to identify rare cell types and cell-cell interaction in tumor microenvironment as well.
More insights with increased sensitivity
With the new technique and new chemistry for spatial transcriptomics along with the improved sensitivity of new generation technology, researchers are able to gain much more insights from RNA measurement than was possible in the past.
To understand how cells function, researchers often need to be able to discern between the differentially expressed genes and the different signaling pathways that are responding to stimuli. Sensitivity is a key to success for such applications, as researchers move toward more nuanced findings. A boost in sensitivity is highly beneficial in spatial transcriptomics when profiling very low expressed genes.