Abstract
Single-cell RNA sequencing and other profiling assays have helped interrogate cells at unprecedented resolution and scale, but are inherently destructive. Raman microscopy reports on the vibrational energy levels of proteins and metabolites in a label-free and nondestructive manner at subcellular spatial resolution, but it lacks genetic and molecular interpretability. Here we present Raman2RNA (R2R), a method to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and domain translation. We predict single-cell RNA sequencing profiles nondestructively from Raman images using either anchor-based integration with single molecule fluorescence in situ hybridization, or anchor-free generation with adversarial autoencoders. R2R outperformed inference from brightfield images (cosine similarities: R2R >0.85 and brightfield <0.15). In reprogramming of mouse fibroblasts into induced pluripotent stem cells, R2R inferred the expression profiles of various cell states. With live-cell tracking of mouse embryonic stem cell differentiation, R2R traced the early emergence of lineage divergence and differentiation trajectories, overcoming discontinuities in expression space. R2R lays a foundation for future exploration of live genomic dynamics.
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Data availability
Raw data for the mouse reprogramming scRNA-seq are available for download from NCBI Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo) under project number: GSE122662. mES cell differentiation scRNA-seq data are available publicly20. All other data are available at https://console.cloud.google.com/storage/browser/raman2rna.
Code availability
Code for R2R are available at https://github.com/kosekijkk/raman2rna. Control software for the multimodal Raman microscope are available at https://github.com/kosekijkk/multimodal-raman-acq.
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Acknowledgements
This work is part of the Human Cell Atlas. We thank L. Gaffney and A. Hupalowska for graphical editing of figures and E. Lander, R. Jaenisch, D. Hekstra and J. Kirschvink for their helpful discussion and insights. K.J.K.-K. was supported by the Japan Society for the Promotion of Science Postdoctoral Fellowship for Overseas Researchers, and the Naito Foundation Overseas Postdoctoral Fellowship. B.G. was supported by the MathWorks Fellowship. J.S. was supported by the Helen Hay Whitney Foundation and NIH, and funds from the Broad Institute of MIT and Harvard and Massachusetts General Hospital. This research was funded by NIH National Institute of Biomedical Imaging and Bioengineering, grant P41EB015871 (J.W.K. and P.T.C.S.), NIH grant UG3 UG3CA275687 (J.W.K., P.T.C.S. and J.S.), NIH grant U19 MH114821 (A.R. and T.B.), HubMap UH3CA246632 (A.R. and T.B.), 4R00HD096049-03 (J.S.), 5R00HD096049-04 (J.S.) and HHMI and the Klarman Cell Observatory (A.R.). A.R. was a Howard Hughes Medical Institute Investigator when this work was initiated.
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K.J.K.-K. and A.R. conceived the research. K.J.K.-K., J.S., T.B. and A.R. developed the methodology. J.S., T.B., P.T.C.S. and A.R. funded and supervised research. K.J.K.-K., J.S. and J.R.O. performed reprogramming and differentiation experiments. K.J.K.-K. developed the Raman microscope and control software with supervision from J.W.K. and P.T.C.S. K.J.K.-K., E.I.G. and K.Z. performed smFISH. K.J.K.-K., S.G., T.J. and T.B. developed the Raman spectral preprocessing and classification pipeline. K.J.K.-K. developed the image registration, cell tracking, anchor-based inference and feature importance analysis pipeline. K.J.K.-K., C.S.C. and B.G. performed brightfield-based classification and regression. C.S.C. and K.J.K.-K. designed fully connected neural nets and AAEs with guidance from A.R. K.J.K.-K., J.S. and A.R. wrote the manuscript with input from all the authors.
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A.R. is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas, and was a scientific advisory board member of ThermoFisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov until 31 July 2020. A.R. is an employee of Genentech from 1 August 2020 with equity in Roche. T.B., S.G. and T.J. are employees of Genentech from 1 Feburary 2021, 29 March 2021 and 5 June 2023, respectively. J.S. is a scientific advisor for Arcadia Science. A patent application has been filed by the Broad Institute related to this work. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 A multi-modal Raman microscope capable of fluorescence imaging and Raman microscopy.
Schematic of a Raman microscope integrated with a wide-field fluorescence microscope for simultaneous detection of nuclei staining, bright field, fluorescence channels, and Raman images.
Extended Data Fig. 2 Raman-predicted and scRNA-seq measured pseudo-bulk profiles are well correlated across cell types.
scRNA-seq measured (y axis) and R2R-predicted (x axis) expression for each gene (dot) in pseudo-bulk RNA profiles averaged across cells labeled as iPSC (top left), epithelial (top right), stromal (bottom left) and MET (bottom right). Cosine similarity is denoted at the top left corner.
Extended Data Fig. 3 Measured and Raman-predicted single cell profiles co-embed well as reflected by gene scores for each cell type.
UMAP co-embedding of Raman predicted RNA profiles and measured scRNA-seq profiles (dots) colored by scores of marker gene for different cell types (rows) determined by smFISH measurements (left, for cells with Raman-predicted profiles) or real scRNA-seq measurements (right, for cells with scRNA-seq profiles).
Extended Data Fig. 4 Measured and Raman-predicted single cell profiles co-embed well as reflected by smFISH measurement of Raman cells.
UMAP co-embedding of Raman predicted RNA profiles and measured scRNA-seq profiles (dots) where the Raman cells are colored by smFISH measurement of each of nine anchor genes.
Extended Data Fig. 5 Measured and Raman-predicted single cell profiles co-embed well as reflected by scRNA-seq based expression of nine anchor genes.
UMAP co-embedding of Raman predicted RNA profiles and measured scRNA-Seq profiles (dots) where the scRNA-seq profiled cells are colored by scRNA-seq measured expression of each of nine anchor genes.
Extended Data Fig. 6 Distributions of expression of marker genes based on R2R-predicted profiles.
Distributions (density plots) of the predicted expression in Raman2RNA inferred profiles for each marker gene (panel) in its expected corresponding cell type (blue, based on the predicted expression profiles) and all other cells (orange).
Extended Data Fig. 7 Distributions of expression of marker genes based on real smFISH profiles.
Distributions (density plots) of the real smFISH profiles for each marker gene (panel) in its expected corresponding cell type (blue, based on the R2R predicted expression profiles) and all other cells (orange).
Extended Data Fig. 9 Adversarial autoencoder (AAE) based model for anchor-free generation of scRNA-seq from Raman profiles.
Top: Two autoencoders (AEs) – one for Raman and the other for scRNA-seq – are trained adversarially to learn two indistinguishable latent spaces. Once a common latent space is found, new Raman spectra are encoded using the encoder part of the Raman AE and decoded to scRNA-seq using the decoder part of the scRNA-seq AE (bottom).
Extended Data Fig. 10 Anchor-free R2R profiles capture variance in single cell profiles as indicated by co-embedding.
UMAP co-embedding of anchor-free R2R-generated profiles and real scRNA-seq (dots) colored by cell types determined by Tangram label-transfer on smFISH measurements (left, for cells with R2R-generated profiles) or by ground truth scRNA-seq (right, for cells with scRNA-seq profiles).
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Kobayashi-Kirschvink, K.J., Comiter, C.S., Gaddam, S. et al. Prediction of single-cell RNA expression profiles in live cells by Raman microscopy with Raman2RNA. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-023-02082-2
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DOI: https://doi.org/10.1038/s41587-023-02082-2
