Raman2RNA: Live-cell label-free prediction of single-cell RNA expression profiles by Raman microscopy
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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 .
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We noticed that due to photobleaching of autofluorescence, the baseline signal does decrease a little, but not the Raman spectrum itself (and also minimal photo-toxicity). The parameters were chosen so that each cell would have a total exposure time of ~1sec (assuming ~50 points per cell). 1 sec for this laser power, using our spectrograph/camera, gave us decent SNR Raman spectra.
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Thank you for the question! I think the CH stretch region would certainly help, though if you are limited to the spectrum alone its added specificity may be modest because the fingerprint region contains many more (often >10×) distinctive peaks. That said, the CH stretch region has a much stronger signal. With coherent Raman microscopes, you can get hyperspectral images in a reasonable time, which could add a whole new layer of orthogonal information for the R2R model
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Specifically, we focused on the fingerprint region of Raman spectra (600-1800 cm-1, 930 of the 1,340 features in a Raman spectrum)
Though you already report high accuracy with your method, do you think the result would improve or change if you also included the CH stretch region (~2700-2900 cm-1 or so)?
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The exposure time for each point in the Raman measurement was 20 msec, and laser power at the sample plane was 212 mW.
Thanks for sharing this work! Did you notice any alteration in Raman signal of cells due to laser exposure? How did you select the acquisition parameters?
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