Raman2RNA: Live-cell label-free prediction of single-cell RNA expression profiles by Raman microscopy

Abstract

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.

Bringing Vibrational Imaging to Chemical Biology with Molecular Probes

“…Additionally, hyperspectral SRS , adds another layer of information for subcellular spectral analysis. With these capabilities, label-free SRS paves the way for various applications, including sensing environmental cues, studying lipid metabolism and identifying druggable targets, − tracking drug delivery and distribution, ,− multicolor cell sorting, , fast diagnosis of tumors, , and investigation of amyloid plaques in neurodegenerative diseases. , The current imaging speed, throughput, and detection sensitivity are still being continuously improved with rapid instrumental innovations. − In parallel, emerging data processing approaches, particularly machine learning algorithms, further upgrade image quality and enable data mining with rich chemical information. ,,− …”

Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF)

“…The underlying principles defining SCHAF can be extended to other cases in biology, both with similar data modalities ( e.g. , cell profiles and microscopy images measured in cultured cells(Kobayashi-Kirschvink et al 2022)), in different biological modalities (3D imaging(Wang et al 2018) or temporal tracing(Schofield et al 2018)), and non-biological settings. Finally, and more fundamentally, interpreting SCHAF’s model could have implications for our understanding of tissue biology, by helping us understand which cellular and gene programs and configurations relate to which tissue features.…”

Integration of silicon chip microstructures for in-line microbial cell lysis in soft microfluidics

“…For example, optical, electrical, electrophoretic or radio frequency interaction with single microbes may enable the development of new sorting, manipulation and spectroscopic profiling techniques. 16 With some modification, the current setup may also be employed for microbial single-cell genomics studies.…”