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| As we age, some cells enter a state called senescence, in which they stop dividing but do not die. The accumulation of these senescent cells can contribute to inflammation, tissue degeneration, cancer, and other age-related diseases. Image Credit: Scientific Frontline / stock image |
Scientific Frontline: Extended "At a Glance" Summary: RamanOmics for Identifying Cellular Senescence
The Core Concept: A noninvasive method using Raman microscopy and single-cell spatial RNA sequencing to identify specific biochemical "barcodes" associated with senescent, or "zombie," cells.
Key Distinction/Mechanism: Unlike traditional methods that require the destruction of the cell to identify senescence markers, this technique relies on a combination of near-infrared or visible light scattering and spatial genetic activity to identify senescent cells without destroying them.
Major Frameworks/Components:
- Raman microscopy evaluates chemical compositions noninvasively by tracking the scattering of near-infrared or visible light.
- Spatial RNA sequencing identifies where genes are active in a tissue section.
- In older mouse cells, both lung and skin tissues exhibited increased lipid synthesis and accumulation.
- Senescent skin cells presented alterations in pathways linked to muscle contraction, collagen remodeling, and the extracellular matrix.
- Senescent lung cells showed augmented activity in genes related to inflammation and immune activation.
Branch of Science: Bioengineering, Genetics, Mechanical Engineering.
Future Application: The development of noninvasive diagnostic tools, potentially akin to an endoscope, to identify cellular senescence internally, which could assist in diagnosing age-related disorders and directing therapies.
Why It Matters: Identifying and targeting senescent cells, which accumulate with age and contribute to conditions such as cancer, osteoarthritis, and tissue degeneration, is critical for understanding pathological conditions and normal physiological roles such as embryonic development and tissue regeneration.
In this video, Raman microscopy is used to image skin tissue. Each frame corresponds to a different wavelength of light scattered by the tissue, and brighter colors indicate a stronger Raman signal from that part of the tissue. Analyzing these signals can reveal the biochemical composition of the tissue.
As we age, some of the cells in our bodies enter a state of senescence, in which they stop dividing but do not die. These senescent cells can contribute to age-related disorders such as cancer, tissue degeneration, and inflammatory diseases.
In an advance that could lead to better ways to diagnose and treat these diseases, MIT researchers have developed a noninvasive way to detect biomarkers of senescence. Their method is based on Raman microscopy, which can reveal the biochemical composition of cells without harming them.
By combining Raman microscopy with gene expression data at single-cell resolution from the same cells, the researchers were able to identify unique “barcodes” that can be used to quickly identify senescent cells. Although this study was conducted in mouse cells, the researchers are now working on adapting the technique for use with human tissue.
“You can imagine that one day we may develop an endoscope that can look inside your body and identify cellular senescence,” says Jeon Woong Kang, an MIT research scientist and one of the study’s senior authors.
The research is part of a National Institutes of Health initiative called the Cellular Senescence Network, which is pursuing a deeper understanding of senescence in hopes of developing therapies that could combat some of the tissue-damaging effects of senescent cells.
Characterizing Senescence
Cell senescence is often triggered by DNA damage, which leads to an irreversible arrest of the cell cycle. These cells do not die, but they undergo significant changes to their shape, metabolic processes, and gene expression profiles.
The immune system is responsible for clearing out these “zombie cells,” but as people age, this process becomes less efficient. When senescent cells accumulate, they may contribute to sagging skin, muscle weakness, and chronic conditions such as osteoarthritis and type 2 diabetes.
Cellular senescence also has beneficial effects, playing critical roles in embryonic development and tissue regeneration.
“Senescence is not just a pathological condition,” So says. “The idea behind the NIH Cellular Senescence Network is to take a very comprehensive approach to understand senescence and identify senescent cells, because it plays a role in so many normal physiological conditions and many pathological conditions.”
Scientists have already identified a few biomarkers for senescence, including two proteins called p16 and p21, which are involved in halting the cell cycle. However, these proteins can only be identified using a process that ultimately destroys the cells.
The MIT team wanted to find a way to noninvasively identify senescent cells using Raman microscopy. Unlike RNA sequencing, which consumes the cells as it analyzes them, Raman microscopy is a nondestructive technique that reveals the chemical composition of tissues or cells by shining near-infrared or visible light on them.
In the new study, the researchers used Raman microscopy in conjunction with spatial RNA sequencing—a technique that reveals where genes are active within a tissue—to identify new markers of senescence. By combining these two techniques, they were able to generate a much broader picture of the distinctive features of senescent cells, including gene expression levels, spatial location, and other biochemical information.
“Our idea was to look at many different features to characterize senescence. That’s why we wanted to combine both single-cell gene expression and Raman microscopy, so that we can characterize the senescence from two complementary views,” Shu says.
Using both methods of analysis, the researchers examined skin and lung tissue from 2-month-old and 26-month-old mice.
One of the most dramatic changes seen in both lung and skin cells was an increase in lipid synthesis in older cells, along with the accumulation of lipids. How this affects the physiology of the cells is not yet known, the researchers say.
The researchers also found some effects that were specific to each tissue. In senescent skin cells, they discovered that cellular pathways associated with muscle contraction and with the remodeling of collagen and the extracellular matrix were significantly affected. In aged lung tissue, they found increased activity of genes involved in immune activation and inflammation.
In future work, the researchers hope to further study what roles these changes play in senescent cells.
Identifying Senescent Cells
Using these data, the researchers were able to identify combinations of Raman peaks that correlate with senescence. These peaks, which represent specific chemical bonds, are linked to the presence of certain lipids, proteins, or other molecules.
“Combining the most important Raman features with the most important gene signatures, we were able to create a barcode that can help us to identify senescent cells in a more unbiased way,” Sorrentino says. “Using this barcode, we can focus on a few Raman bands that emerged as the most informative in this work.”
Using these bands, researchers could identify senescent cells by looking exclusively for those segments of the Raman spectrum. This could help enable diagnostics that detect cells once they have become senescent.
To help make that possible, the researchers are now working on a higher-speed version of their Raman imaging system. Currently, it takes about 30 hours to analyze a tissue sample about 1 square millimeter in size, but they hope to develop a system that can quickly pick out the Raman barcodes they identified from larger samples.
Funding: The research was funded by the National Institutes of Health and Massachusetts General Hospital.
Published in journal: Nature Aging
Title: RamanOmics decodes the spatial vibrational–molecular architecture of senescence in aging and repair
Authors: Ke Zhang, Xingjian Chen, Francesco Monticolo, Salvatore Sorrentino, Haochun Huang, Claire Callahan, Yueqing Qiao, Judy Zhou, Sonia Brodowska, Styliani Sapantzi, Jianhuan Qi, Yinghan Wu, Thai Nam Son Dang, Yanwan Cao, Sumin Kang, Francesca Viggiani, Chia-Kang Ho, Yanxin Xu, Koseki J. Kobayashi-Kirschvink, Thang Mung, Hemali Phatnani, Zhixun Dou, Jeon Woong Kang, Peter T. C. So, and Jian Shu
Source/Credit: Massachusetts Institute of Technology | Anne Trafton
Edited by: Scientific Frontline
Reference Number: beng092126_01
