Insight into the cellular distribution of RNA, which is closely linked to cell functions, could help scientists better understand the relation between cellular processes and disease. Potentially, this could lead to more targeted treatments for neurodegenerative disorders and aging.
While many methods have been developed to study RNA distribution within cells, only a few have been applied on a transcriptome-wide scale.
To capture the transcriptome of target cell types at the tissue level and RNA content within subcellular compartments, a research team at the UT Southwestern Medical Center, led by professor Haiqi Chen, developed Photoselection of Transcriptome over Nanoscale (PHOTON).
PHOTON combines high-resolution imaging with high-throughput sequencing to achieve spatial transcriptome profiling of RNA at subcellular resolution. It identifies RNA molecules at their native locations within cells, showing where different RNA species are distributed spatially in response to cellular cues.
To build PHOTON, the researchers designed DNA-based molecular cages that bound to all the RNA in cells. The molecular cages open when they are exposed to light, allowing for further chemical labeling.
After observing microscopically that the cells bound to the molecular cages, the researchers shined a narrow, 200-300-nm, near-ultraviolet (NUV) laser beam on regions of interest, such as specific organelles. The light caused the molecular cages to open, allowing only the RNA molecules located in the illuminated regions to be labeled. The researchers then collected the labeled RNA molecules and sequenced them to learn their identities and functions.
The team used PHOTON to examine RNAs present in the nucleolus and mitochondria, showing that RNAs identified through PHOTON closely matched those in published databases that were produced by isolating the organelles from the cells.
The researchers applied PHOTON to stress granules — transient, membraneless structures formed by cells when the cells are under stress. Although most stress granule RNAs that were identified matched those in published databases, the researchers found some discrepancies using PHOTON.
At the tissue scale, PHOTON accurately captured the transcriptome of cells within their native tissue microenvironment. At the subcellular scale, it enabled selective sequencing of the RNA content in the nucleoli, the mitochondria, and the stress granules.
The researchers used PHOTON to investigate whether m6A, a chemical modification found on some RNA molecules, played a role in moving RNAs into stress granules. By analyzing RNA molecules identified through PHOTON, the researchers found that the RNAs in the stress granules carried significantly more m6A than those outside the granules, suggesting that m6A contributes to the movement of specific RNAs into stress granules.
The researchers showed that PHOTON could be flexibly applied across regions of interest that spanned different scales, from specific regions of mouse ovarian tissue to various subcellular compartments. In-line image segmentation enabled the researchers to generate regions of interest based on an extensive range of spatial features, and automated the targeted photocleavage process over large numbers of cells or features.
These results show that PHOTON has the potential to uncover connections between spatial and transcriptomic information at diverse length scales.
Existing techniques to spatially identify RNA species can be prohibitively expensive and typically require specialized technical expertise and sophisticated image processing and data analysis to complete.
Chen said that he and his colleagues plan to use PHOTON to study the distributions of RNA in various conditions, particularly in neurodegenerative disease and aging. By comparing distributions in diseased cells to those in healthy cells, Chen said, researchers may be able to identify new targets for therapies to treat these conditions.
“Aging and many neurodegenerative diseases impose significant stress on cells, causing a subset of cellular RNA to redistribute into various subcellular compartments such as the stress granules,” Chen said. “PHOTON allows us to detect the spatial redistribution of cellular RNA in diseases versus health, helping us understand how these diseases cause damage to cellular functions.”
While many methods have been developed to study RNA distribution within cells, only a few have been applied on a transcriptome-wide scale.
To capture the transcriptome of target cell types at the tissue level and RNA content within subcellular compartments, a research team at the UT Southwestern Medical Center, led by professor Haiqi Chen, developed Photoselection of Transcriptome over Nanoscale (PHOTON).
PHOTON combines high-resolution imaging with high-throughput sequencing to achieve spatial transcriptome profiling of RNA at subcellular resolution. It identifies RNA molecules at their native locations within cells, showing where different RNA species are distributed spatially in response to cellular cues.
To build PHOTON, the researchers designed DNA-based molecular cages that bound to all the RNA in cells. The molecular cages open when they are exposed to light, allowing for further chemical labeling.
After observing microscopically that the cells bound to the molecular cages, the researchers shined a narrow, 200-300-nm, near-ultraviolet (NUV) laser beam on regions of interest, such as specific organelles. The light caused the molecular cages to open, allowing only the RNA molecules located in the illuminated regions to be labeled. The researchers then collected the labeled RNA molecules and sequenced them to learn their identities and functions.
The team used PHOTON to examine RNAs present in the nucleolus and mitochondria, showing that RNAs identified through PHOTON closely matched those in published databases that were produced by isolating the organelles from the cells.
The researchers applied PHOTON to stress granules — transient, membraneless structures formed by cells when the cells are under stress. Although most stress granule RNAs that were identified matched those in published databases, the researchers found some discrepancies using PHOTON.
At the tissue scale, PHOTON accurately captured the transcriptome of cells within their native tissue microenvironment. At the subcellular scale, it enabled selective sequencing of the RNA content in the nucleoli, the mitochondria, and the stress granules.
The researchers used PHOTON to investigate whether m6A, a chemical modification found on some RNA molecules, played a role in moving RNAs into stress granules. By analyzing RNA molecules identified through PHOTON, the researchers found that the RNAs in the stress granules carried significantly more m6A than those outside the granules, suggesting that m6A contributes to the movement of specific RNAs into stress granules.
The researchers showed that PHOTON could be flexibly applied across regions of interest that spanned different scales, from specific regions of mouse ovarian tissue to various subcellular compartments. In-line image segmentation enabled the researchers to generate regions of interest based on an extensive range of spatial features, and automated the targeted photocleavage process over large numbers of cells or features.
These results show that PHOTON has the potential to uncover connections between spatial and transcriptomic information at diverse length scales.
Existing techniques to spatially identify RNA species can be prohibitively expensive and typically require specialized technical expertise and sophisticated image processing and data analysis to complete.
Chen said that he and his colleagues plan to use PHOTON to study the distributions of RNA in various conditions, particularly in neurodegenerative disease and aging. By comparing distributions in diseased cells to those in healthy cells, Chen said, researchers may be able to identify new targets for therapies to treat these conditions.
“Aging and many neurodegenerative diseases impose significant stress on cells, causing a subset of cellular RNA to redistribute into various subcellular compartments such as the stress granules,” Chen said. “PHOTON allows us to detect the spatial redistribution of cellular RNA in diseases versus health, helping us understand how these diseases cause damage to cellular functions.”
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