Dynamically Quantifying Intracellular Elemental Sulfur and Predicting Pertinent Gene Transcription by Raman...

Monitoring metabolites and gene expression in living cells remains a challenge due to the invasive nature of most current methods. Traditional assays often require cell lysis, preventing real-time observation of cellular dynamics. While noninvasive techniques like Raman spectroscopy have been explored, their ability to link metabolite levels with gene transcription in live cells is limited. Prior research has shown Raman can detect elemental composition without damaging cells. However, no prior work had resolved how to connect Raman signals with specific gene expression patterns in real time. This gap motivated the development of a new approach that integrates Raman data with transcriptomic information. The study aimed to test whether Raman spectroscopy could be used to infer gene transcription levels in living cells. By focusing on intracellular elemental sulfur, the researchers sought to establish a reliable link between spectral data and mRNA levels. This work builds on existing knowledge of sulfur metabolism in bacteria but extends it to a nondestructive framework.

The primary aim of this study was to develop a noninvasive method for monitoring intracellular elemental sulfur and its associated gene transcription in living cells. The researchers sought to validate whether Raman spectroscopy could be used to quantify metabolites and predict relevant gene expression without cell disruption. A specific problem addressed was the lack of real-time, nondestructive tools for linking metabolite levels with gene transcription. The motivation stemmed from the limitations of current methods that require cell lysis or cannot track dynamic changes. The study focused on sulfur metabolism in *Thiophaeococcus mangrovi* as a model system. The goal was to create a computational model that could translate Raman spectral data into mRNA levels. This model, called mRR, was designed to infer gene transcription based on Raman intensity. The researchers also aimed to test the generalizability of their approach across different bacterial genera.

The study employed Raman spectroscopy to measure intracellular elemental sulfur in live *Thiophaeococcus mangrovi* cells. Raman signals were collected noninvasively to avoid disrupting cellular processes. A computational model called mRR was developed to correlate Raman spectral intensity with mRNA levels. The model used exponentially transformed Raman data to predict gene transcription. The researchers validated the model using real-time PCR to measure actual mRNA levels. The study included two additional bacterial genera, *Thiocapsa* and *Thiorhodococcus*, to test the model's applicability. Raman spectra were analyzed for sulfur globule proteins, which are encoded by specific genes. The model's predictions were compared with experimentally measured gene expression data. The methods combined spectroscopic analysis with transcriptomic validation to ensure accuracy.

The results showed a strong linear correlation between the Raman spectral intensity of elemental sulfur and mRNA levels of sulfur globule proteins in *Thiophaeococcus mangrovi*. The mRR model predicted gene transcription with high accuracy. When tested in *Thiocapsa* and *Thiorhodococcus*, the model's predictions matched real-time PCR results closely. The correlation coefficient between predicted and actual mRNA levels exceeded 0.8 in all tested genera. The model's performance was consistent across different bacterial species. Raman spectroscopy successfully detected intracellular sulfur without damaging cells. The predicted gene expression levels aligned with experimental data, confirming the model's reliability. These findings suggest that Raman spectroscopy can be used to monitor metabolite levels and infer gene transcription in living cells.

The study demonstrated that Raman spectroscopy can be used to quantify intracellular elemental sulfur and predict relevant gene transcription in living cells. The mRR model provided a reliable link between spectral data and mRNA levels. The model's accuracy was confirmed through real-time PCR validation in multiple bacterial genera. The approach offers a nondestructive alternative to traditional assays for monitoring metabolites and gene expression. The results suggest that Raman spectroscopy can be used to study metabolic dynamics in real time. The model's success in *Thiophaeococcus mangrovi*, *Thiocapsa*, and *Thiorhodococcus* indicates its potential for broader applications. The study supports the use of Raman spectroscopy for spectroscopic mapping of omics data in living cells. The findings align with the authors' claim that this method provides baseline data for real-time metabolic monitoring.