Single-cell RNA sequencing enables scientists to interrogate cells at extraordinary resolution and scale. However, the sequencing process destroys the cell, making it difficult to use the technique to study ongoing changes in gene expression.
Raman microscopy measures the vibrational energy levels of proteins and metabolites in a nondestructive manner at subcellular spatial resolution, but it is unable to interpret genetic information.
“RNA sequencing gives you extremely detailed information, but it’s destructive,” researcher Koseki Kobayashi-Kirschvink said. “Raman is noninvasive, but it doesn’t tell you anything about RNA.”
A new technique developed at MIT combines the advantages of single-cell RNA sequencing and Raman spectroscopy to track a cell’s RNA expression without damaging the cell. Known as Raman2RNA (R2R), this technique could allow scientists to study long-term cellular processes, such as cancer progression and embryonic development, using the same cells repeatedly.
To create the technique, the team trained a computational model to translate Raman signals into RNA expression states. “The idea of this project was to use machine learning to combine the strength of both modalities, thereby allowing you to understand the dynamics of gene expression profiles at the single-cell level over time,” Kobayashi-Kirschvink said.
To generate the data needed to train the machine learning model, the researchers treated mouse fibroblast cells with factors that reprogrammed the cells, causing them to become pluripotent (i.e., undifferentiated) stem cells. Using Raman spectroscopy, the team imaged the cells at 36 time points, over an 18-day period. During that time, the pluripotent cells differentiated.
The researchers then analyzed each cell using single molecule fluorescence in situ hybridization (smFISH), a molecular cytogenetic technique that enables the detection and localization of individual RNA molecules within cells. Using smFISH, the researchers searched for RNA molecules that encoded nine different genes whose expression patterns differed between cell types.
The researchers used the data acquired via smFISH to link data obtained through Raman imaging with data obtained from single-cell RNA sequencing.
To create the link, the team trained a deep learning model to predict the expression of the nine different genes, based on the images of the cells acquired using Raman spectroscopy. The researchers then used a computational program to link the gene expression patterns identified by smFISH with entire genome profiles that they obtained by performing single-cell RNA sequencing on the sample cells.
The team combined the two computational models into one model — R2R — that could predict the entire genomic profiles of individual cells based on Raman images of the cells. In experiments, the R2R model outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15).
The researchers demonstrated R2R’s ability to track mouse embryonic stem cells as the cells differentiated into several other cell types over a period of several days. The team took Raman images of the cells four times a day for three days and used the R2R computational model to predict the corresponding RNA expression profile of each cell. To confirm the computational model’s ability to predict RNA expressions, the researchers compared the model’s predictions with RNA sequencing measurements.
The researchers observed the transitions that occurred in individual cells as they differentiated from embryonic stem cells into more mature cell types. With R2R, the team also was able to track the genomic changes that occurred over a two-week period as mouse fibroblasts were reprogrammed into induced pluripotent stem cells. In the reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states.
“It’s a demonstration that optical imaging gives additional information that allows you to directly track the lineage of the cells and the evolution of their transcription,” professor Peter So said. “With Raman imaging you can measure many more time points, which may be important for studying cancer biology, developmental biology, and a number of degenerative diseases.”
The team plans to use the R2R technique to study other types of cell populations that change over time, such as aging cells and cancerous cells. Although the researchers are currently working with cells grown in a lab dish, they hope in the future to develop the technique as a potential diagnostic for use in patients. R2R lays a foundation for the exploration of live genomic dynamics.
“One of the biggest advantages of Raman is that it’s a label-free method,” researcher Jeon Woong Kang said. “It’s a long way off, but there is potential for the human translation, which could not be done using the existing invasive techniques for measuring genomic profiles.”
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