2024-03-22 15:21:39
A new method can track changes in the gene expression of living cells over extended periods of time. Based on Raman spectroscopy, the method does not damage cells and can be performed repeatedly. Credit: MIT News; iStock
A new MIT-developed method combines Raman spectroscopy with machine learning to non-invasively track gene expression in cells over time. This technique allows detailed study of cellular differentiation and has potential applications in cancer research, developmental biology and diagnostics.
Sequencing any RNA in a cell can reveal a lot of information about the function of that cell and what it is doing at a given point in time. However, the sequencing process destroys the cell, making it difficult to study ongoing changes in gene expression.
An alternative approach developed at MIT could allow researchers to track such changes over extended periods of time. The new method, based on a non-invasive imaging technique known as Raman spectroscopy, does not damage the cells and can be performed repeatedly.
Using this technique, the researchers showed they could monitor embryonic stem cells as they differentiated into several other cell types over several days. This technique can enable studies of long-term cellular processes such as cancer progression or embryonic development, and one day may be used to diagnose cancer and other diseases.
“With Raman imaging, you can measure many more time points, which may be important for studying cancer biology, developmental biology, and a number of neurodegenerative diseases,” says Peter Sue, professor of biological and mechanical engineering at MIT and director of the MIT Center for Laser Biomedical Research. and one of the authors of the article.
Koseki Kobayashi-Kirschvink, a postdoctoral fellow at MIT and the Broad Institute of Harvard and MIT, is lead author of the study, which was recently published in the journal Teva Biotechnology. The paper’s senior authors are Tommaso Biancalani, a former scientist at the Broad Institute; Jian Xu, assistant professor at Harvard Medical School and associate member of the Broad Institute; and Aviv Regev, senior vice president at Genentech Research and Early Development, who is on leave from faculty positions at the Broad Institute and MIT’s Department of Biology.
Gene expression imaging
Raman spectroscopy is a non-invasive technique that reveals the chemical composition of tissues or cells by shining near-infrared or visible light on them. MIT’s Laser Biomedical Research Center has been working on biomedical Raman spectroscopy since 1985, and more recently, So and others at the center have developed Raman spectroscopy-based techniques that can be used to diagnose breast cancer or measure blood glucose.
However, Raman spectroscopy alone is not sensitive enough to detect small signals such as changes in the levels of single RNA molecules. To measure RNA levels, scientists typically use a technique called single-cell RNA sequencing, which can reveal which genes are active within different types of cells in a tissue sample.
In this project, the MIT team sought to combine the advantages of single-cell RNA sequencing and Raman spectroscopy by training a computational model to translate Raman signals into RNA expression states.
“RNA sequencing gives you extremely detailed information, but it’s destructive. Raman is non-invasive, but it doesn’t tell you anything about RNA. Therefore, the idea of this project was to use machine learning to combine the strength of the two methods, thus allowing you to understand the The dynamics of gene expression profiles at the single cell level over time,” says Kobayashi-Kirschwink.
To generate data to train their model, the researchers treated mouse fibroblast cells, a type of skin cell, with factors that reprogram the cells to become pluripotent stem cells. During this process, cells can also change into several other cell types, including neurons and epithelial cells.
Using Raman spectroscopy, the researchers imaged the cells at 36 time points over 18 days as they differentiated. After each image was taken, the researchers analyzed each cell using single-molecule fluorescence in situ hybridization (smFISH), which can be used to visualize specific RNA molecules within a cell. In this case, they looked for RNA molecules encoding nine different genes whose expression patterns vary between cell types.
These smFISH data can be used as a link between Raman imaging data and single-cell RNA sequencing data. To make this link, the researchers first trained a deep learning model to predict the expression of those nine genes based on Raman images obtained from those cells.
Next, they used a computational program called Tangram, previously developed at the Broad Institute, to correlate the smFISH gene expression patterns with whole-genome profiles they obtained by performing single-cell RNA sequencing on the sample cells.
The researchers then combined these two computational models into one they call Raman2RNA, which can predict the entire genomic profiles of individual cells based on Raman images of the cells.
Monitoring cell differentiation
The researchers tested their Raman2RNA algorithm by following mouse embryonic stem cells as they differentiated into different cell types. They took Raman images of the cells four times a day for three days, and used their computational model to predict the corresponding RNA expression profiles of each cell, which they confirmed by comparing it to RNA sequencing measurements.
Using this approach, the researchers were able to observe the transitions that occurred in individual cells as they differentiated from embryonic stem cells to more mature cell types. They also showed that they could track the genomic changes that occur when mouse fibroblasts are reprogrammed into induced pluripotent stem cells over a two-week period.
“This is a demonstration that optical imaging gives additional information that allows you to directly follow the lineage of the cells and their transcriptional development,” says So.
The researchers now plan to use this technique to study other types of cell populations that change over time, such as senescent cells and cancer cells. They are currently working with cells grown in a lab dish, but in the future, they hope this approach can be developed as a potential diagnostic for use in patients.
“One of the biggest advantages of Raman is that it is a label-free method. It is a long way off, but there is potential for human translation, which could not be done using the existing invasive techniques for measuring genomic profiles,” says John Wong Kang, a research scientist at MIT who is also an author the study.
The research was funded by the Japan Society for the Promotion of Postdoctoral Fellowship for Overseas Researchers, the Naito Foundation Postdoctoral Fellowship Abroad, the MathWorks Fellowship, the Helen Hay Whitney Foundation, the US National Institutes of Health, the US National Institute of Biomedical Imaging and Bioengineering , HubMap, Howard Hughes Medical Institute and the Kellerman Cell Observatory.
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