Super-multiplex vibrational imaging

The ability to directly visualize a large number of distinct molecular species inside cells is increasingly essential for understanding complex systems and processes. Even though existing methods have been used successfully to explore structural-functional relationships in nervous systems, profile RNA in situ, reveal tumor microenvironment heterogeneity or study dynamic macromolecular assembly(1–4), it remains challenging to image many species with high selectivity and sensitivity under biological conditions. For instance, fluorescence microscopy faces a “color barrier” due to the intrinsically broad (~1500 cm(−1)) and featureless nature of fluorescence spectra(5) that limits the number of resolvable colors to 2 to 5 (or 7-9 if using complicated instrumentation and analysis)(6–8). Spontaneous Raman microscopy probes vibrational transitions with much narrower resonances (peak width ~10 cm(−1)) and thus doesn’t suffer this problem, but its feeble signals make many demanding bio-imaging applications impossible. And while surface-enhanced Raman scattering offers remarkable sensitivity and multiplicity, it cannot be readily used to quantitatively image specific molecular targets inside live cells(9). Here we show that carrying out stimulated Raman scattering under electronic pre-resonance conditions (epr-SRS) enables imaging with exquisite vibrational selectivity and sensitivity (down to 250 nM with 1-ms) in living cells. We also create a palette of triple-bond-conjugated near-infrared dyes that each display a single epr-SRS peak in the cell-silent spectral window, and that with available fluorescent probes give 24 resolvable colors with potential for further expansion. Proof-of-principle experiments on neuronal co-cultures and brain tissues reveal cell-type dependent heterogeneities in DNA and protein metabolism under physiological and pathological conditions, underscoring the potential of this super-multiplex optical imaging approach for untangling intricate interactions in complex biological systems.