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Cellular Raman spectra are optical signatures reflecting comprehensive molecular composition of cells. It has been shown recently that condition-dependent cellular transcriptome profiles are inferable from the changes in cellular Raman spectra. However, it remains elusive whether the other layers of omics are also inferable from Raman spectra and what constraints in gene expression profiles enable such inference. Here we show that proteome profiles ofEscherichia coliacross diverse conditions are predictable from cellular Raman spectra using simple ordinary linear regression. Employing methods in graph theory, we characterize core and peripheral stoichiometrically-conserved groups in the proteomes and reveal that major changes in cellular Raman spectra reflect low-dimensional proteome structure constructed from the global stoichiometry conservation relations. Mathematical analysis suggests a proportionality between stoichiometry conservation strength and condition-specificity of gene expression, which turns out universal across omics classes and organisms. Furthermore, stoichiometry conservation strength correlates with the essentiality and evolutionary conservation of each gene. These results provide an omics-level interpretation of cellular Raman spectra and uncover an architectural principle of global omics profiles.

Single cell RNA-Seq (scRNA-seq) and other profiling assays have opened new windows into understanding the properties, regulation, dynamics, and function of cells at unprecedented resolution and scale. However, these assays are inherently destructive, precluding us from tracking the temporal dynamics of live cells, in cell culture or whole organisms. Raman microscopy offers a unique opportunity to comprehensively report on the vibrational energy levels of molecules in a label-free and non-destructive manner at a subcellular spatial resolution, but it lacks in genetic and molecular interpretability. Here, we developed Raman2RNA (R2R), an experimental and computational framework to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and multi-modal data integration and domain translation. We used spatially resolved single-molecule RNA-FISH (smFISH) data as anchors to link scRNA-seq profiles to the paired spatial hyperspectral Raman images, and trained machine learning models to infer expression profiles from Raman spectra at the single-cell level. In reprogramming of mouse fibroblasts into induced pluripotent stem cells (iPSCs), R2R accurately (r>0.96) inferred from Raman images the expression profiles of various cell states and fates, including iPSCs, mesenchymal-epithelial transition (MET) cells, stromal cells, epithelial cells, and fibroblasts. R2R outperformed inference from brightfield images, showing the importance of spectroscopic content afforded by Raman microscopy. Raman2RNA lays a foundation for future investigations into exploring single-cell genome-wide molecular dynamics through imaging data, in vitro and in vivo.

A variety of bacteria, including Escherichia coli, are known to enter the viable but non-culturable (VBNC) state under various stress conditions. During this state, cells lose colony-forming activities on conventional agar plates while retaining signs of viability. Diverse environmental stresses including starvation induce the VBNC state. However, little is known about the genetic mechanism inducing this state. Here, we aimed to reveal the genetic determinants of the VBNC state of E. coli. We hypothesized that the VBNC state is a process wherein specific gene products important for colony formation are depleted during the extended period of stress conditions. If so, higher expression of these genes would maintain colony-forming activities, thereby restraining cells from entering the VBNC state. From an E. coli plasmid-encoded ORF library, we identified genes that were responsible for maintaining high colony-forming activities after exposure to starvation condition. Among these, cpdA encoding cAMP phosphodiesterase exhibited higher performance in the maintenance of colony-forming activities. As cpdA overexpression decreases intracellular cAMP, cAMP or its complex with cAMP-receptor protein (CRP) may negatively regulate colony-forming activities under stress conditions. We confirmed this using deletion mutants lacking adenylate cyclase or CRP. These mutants fully maintained colony-forming activities even after a long period of starvation, while wild-type cells lost most of this activity. Thus, we concluded that the lack of cAMP-CRP effectively retains high colony-forming activities, indicating that cAMP-CRP acts as a positive regulator necessary for the induction of the VBNC state in E. coli.

Raman spectroscopy is an imaging technique that can reflect whole-cell molecular compositionsin vivo,and has been applied recently in cell biology to characterize different cell types and states. However, due to the complex molecular compositions and spectral overlaps, the interpretation of cellular Raman spectra have remained unclear. In this report, we compared cellular Raman spectra to transcriptomes ofSchizosaccharomyces pombeandEscherichia coli,and provide firm evidence that they can be computationally connected and interpreted. Specifically, we find that the dimensions of high-dimensional Raman spectra and transcriptomes measured by RNA-seq can be effectively reduced and connected linearly through a shared low-dimensional subspace. Accordingly, we were able to reconstruct global gene expression profiles by applying the calculated transformation matrix to Raman spectra, and vice versa. Strikingly, highly expressed ncRNAs contributed to the Raman-transcriptome linear correspondence more significantly than mRNAs inS. pombe,which implies their major role in coordinating molecular compositions. This compatibility between whole-cell Raman spectra and transcriptomes marks an important and promising step towards establishing spectroscopic live-cell omics studies.

Summary Raman microscopy is an imaging technique that has been applied to assess molecular compositions of living cells to characterize cell types and states. However, owing to the diverse molecular species in cells and challenges of assigning peaks to specific molecules, it has not been clear how to interpret cellular Raman spectra. Here, we provide firm evidence that cellular Raman spectra and transcriptomic profiles of Schizosaccharomyces pombe and Escherichia coli can be computationally connected and thus interpreted. We find that the dimensions of high-dimensional Raman spectra and transcriptomes measured by RNA sequencing can be reduced and connected linearly through a shared low-dimensional subspace. Accordingly, we were able to predict global gene expression profiles by applying the calculated transformation matrix to Raman spectra, and vice versa. Highly expressed non-coding RNAs contributed to the Raman-transcriptome linear correspondence more significantly than mRNAs in S. pombe. This demonstration of correspondence between cellular Raman spectra and transcriptomes is a promising step toward establishing spectroscopic live-cell omics studies.