Abstract
Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1,2,3,4,5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine–reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.
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Source data are provided with this paper. The other relevant data are available from the corresponding author upon request.
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Acknowledgements
We thank H. Gu, H. Xu and F. Shen for their discussions. This work was supported by grants to J.Y. (National Natural Science Foundation of China, grant no. 82272054; Science and Technology Commission of Shanghai Municipality, grant no. 21511102100; Shanghai Key Laboratory of Gynecologic Oncology; and Shanghai Jiao Tong University, grant no. YG2024LC09), D.M.C. (National Natural Science Foundation of China, grant no. 32370572) and Z.S. (National Natural Science Foundation of China, grant no. 81627801; and National Key R&D Program of China, grant no. 2020YFA0908100). Z.S. was also supported by the K. C. Wong Education Foundation (Hong Kong).
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J.Y. and X.B. conceived and designed the project. X.B. performed all the experiments. Z.S. made conceptual contributions to data analysis and interpretation and supervised the revision efforts. D.M.C. contributed to mechanistic interpretations. J.Y. and Z.S. are the senior authors of this study, and all authors participated in data analysis and paper preparation.
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Extended data figures and tables
Extended Data Fig. 1 Comparison between the analog method and dCERS.
Crystal violet (CV) was quantified in the concentration range of 10−10 to 10−15 M. (a) The summation of all acquired spectra at different concentrations of CV (10−15 to 10−10 M) and background (BG) reference (null control). The standard (STD) SERS spectra of the CV-ethanol solution is provided after scaling for clarity. In the CV-specific window of 780 to 820 cm−1, a weak peak was observable at concentrations from 10−10 to 10−12 M, but no peak was evident at concentrations lower than 10−13 M, similar to what is observed with the null control under the same conditions. (b) Typical single-molecule spectra acquired from the voxels at 10−15 M CV. Red: positive spectra with discernable CV-specific peaks (1); black: negative spectra without the targeted signals (0); blue: spectrum acquired in without CV (null control). The specific SERS band corresponding to CV (780 to 820 cm−1) is indicated by the orange stripes in panels a and b. (c) The peak intensity from the summed spectra over all voxels versus CV concentration. The standard deviation increases rapidly as the concentration decreases and no concentration below 10−12 M could be reliably measured. (d) The ratio of positive voxels (RPV) versus CV concentration. The RPV decreases as the concentration decreases with the standard deviation (error bar) following Poisson statistics, and at concentrations of 10−11 and 10−12 M, the accuracy is also better than the summed case. Furthermore, as there is no evidence of a plateau region at the lowest concentrations, it is likely that much lower concentrations could be measured by the digital approach, which would clearly not be possible with the analog method. Here for panels c and d, each data point reflects the mean and the standard deviations calculated from 3 independent measurements (5,400 voxels/mapping).
Extended Data Fig. 2 Quantitative SERS.
(a) Schematic diagrams of quantification at different concentrations. (i) High-concentration scenarios: there are multiple molecules in each excitation volume, generating recognized SERS signals with intensities higher than the threshold (TH) (positive, defined as “1”) for the majority of voxels. (ii) Low-concentration scenarios: Some of the voxels show no recognizable signals, i.e., the targeted peak presents intensity that is lower than threshold (negative, defined as “0”). (b) Heatmaps demonstrate the ability to quantify the magnitude of the target intensities in scenario (i) yet failure in scenario (ii) because of severe SIFs. By contrast, the ratio of positive voxels per mapping, reflected by digital maps (indicated by rightwards arrows) obtained by dCERS, still possess clear correlation with the concentrations in scenario (ii). (c) Schematic diagram of the depictions of the digital/heat maps. The stripe-like mapping areas are rearranged into squares for this presentation.
Extended Data Fig. 3 Characterization of the SERS colloids.
(a)-(c) Hya-Ag colloids, (d)-(f) citrate-Ag colloids and (g)-(i) citrate-Au colloids. (a), (d), (g) Shown are the colloidal suspensions contained in the vials (left) and transmission electron microscopic images of the colloids (right). (b), (e), (h) Extinction spectra. (c), (f), (i) Histograms of the hydrodynamic diameter obtained by dynamic light scattering. (j) Histogram of the intensity distribution among voxels in 10−7 M crystal violet detected by Hya-Ag colloids. The relative standard deviation is 2.8% among the 200 voxels, indicating that the hotspots present in each probed voxel were statistically comparable across voxels. To improve statistics, a relatively longer acquisition time was used for each voxel (i.e., 5 s) to accumulate more hotspots. (k) 10−10 M crystal violet (CV) and (l) 10−7 M L-cysteine were detected by the Hya-Ag colloids at 0.17, 0.25, 0.5, 1 and 1.5 nM. The detection efficiency is improved when the colloidal concentration increases, because of the increased hotspot density. With greater colloid concentration, the detection efficiency then decreases due to the increased background scattering. The histograms show the RPVs with error bars indicating the standard deviations (n = 3). The optimal concentration for Hya-Ag colloids was found around 0.5 nM.
Extended Data Fig. 4 Single-molecule sensitivity by BiASERS.
The concentration-dependent heatmaps of each analyte involved in the BiASERS measurements (left and middle) and the histograms of the contribution of (a, c, d) crystal violet (CV) or (b) 4-NBT in each spectrum (right). The pure events are defined as the probability of one of the analytes approaching 0 (or 1) and the mixed events are defined as the probability in between. (a) Mixtures of CV and Nile blue (NB) measured using Hya-Ag colloids. At 10−8 M of CV/NB, both analytes show clear signal in nearly all voxels, exhibiting a Gaussian-like histogram of the probability of CV. On the contrary, at 10−9 M CV, some spectra exhibit neither the CV nor NB signal but within all signal spectra, there is a growing proportion of pure-signaled ones, leading to an increase of 1/0 measurements than those in the middle. At 10−10 M CV, most signal spectra exhibit pure signals with extremely rare ones showing both signals. Since these pure-signaled spectra should be statistically dominated by single-molecule events, the single-molecule regime for CV should be below 10−9 M. (b) Mixture of 4-NBT and 4-chlorothiophenol (4-CBT) measured using Hya-Ag colloids, exhibiting the expected single-molecule behavior for 4-NBT below 10−8 M. (c) Mixtures of CV and NB measured by citrate-Ag colloids and (d) citrate-Au colloids. The same trend can be observed in the other types of SERS colloids though at different analyte concentrations due to differences in hotspot density/intensity, surface binding affinity, among other properties.
Extended Data Fig. 5 Standard SERS spectra.
(a) The standard SERS spectrum of 10−7 M crystal violet enhanced by Hya-Ag colloids (red) and the averaged spectrum of the null control (black). The signature window of crystal violet at 780 to 820 cm−1 is indicated by the red stripe. (b) The standard SERS spectrum of 10−7 M Nile blue enhanced by Hya-Ag colloids. The signature window of Nile blue at 565 to 605 cm−1 is indicated by the red stripe. For both panels, the noise window is at 1,700 to 1,740 cm−1 as indicated by the gray stripes.
Extended Data Fig. 6 dCERS of different analytes and using different colloids.
The relationship between RPV and analyte concentrations of (a) crystal violet (CV), (b) 4-NBT, (c) cysteine, (d) A12, (e) hemoglobin, (f) D-glucose, (g) Nile blue (NB), (h) paraquat and (i) thiram measured using Hya-Ag colloids plotted in a linear-linear scale. The calibration curves of (j) cysteine, (k) NB, (l) paraquat and (m) thiram measured using Hya-Ag colloids. The cysteine shows a much lower efficiency than 4-NBT likely owing to a smaller molecular cross section assuming that both have a high affinity to Hya-Ag colloids via covalent binding. (n) The calibration curves of CV using Hya-Ag colloids (blue), citrate-Ag colloids (red) and citrate-Au colloids (black). Though the three different types of colloids have comparable surface potential and concentration, citrate-Au colloids exhibit a much lower efficiency than the other two Ag colloids due to a poorer plasmonic effect. Additionally, the efficiency of citrate-Ag colloids is slightly lower than Hya-Ag colloids probably because of a layer of citrate residues which may cause steric hindrance to impede CV from getting close to the metallic surface to some extent even though the size of citrate-Ag colloids is larger than Hya-Ag colloids. For the calibration curves, the error bars indicate the standard deviations calculated with 3 independent measurements. Fitting formula: \(\log RPV=k\log M+b\).
Extended Data Fig. 7 Verification of Poisson dominated accuracy for dCERS.
(a) The number of positive voxels increases with an increase in the total number of voxels acquired per dCERS test for crystal violet concentrations of 10−11 to 10−15 M crystal violet (from top to bottom) measured using Hya-Ag colloids. (b) The number of positive voxels increased with an increase in the concentration of 4-NBT, hemoglobin, A12 and glucose (from top to bottom) measured using Hya-Ag colloids. The total number of measured voxels per dCERS test was 5,400 for 4-NBT and 1,200 for the others. The gray bars show the mean number of positive voxels with the error bars indicating the standard deviations from 3 independent tests. The relative standard deviation (RSD, red dots) is calculated as the ratio of the standard deviation to the mean of the positive count, which agrees with the Poisson estimation (\(\frac{1}{\sqrt{N}}\), blue lines) from the mean positive counts (N).
Extended Data Fig. 8 Photographs of the lab-grown bean sprouts prepared under normal culture conditions.
In brief, 50 g mung beans were immersed in pure water for 2 h and then placed in the bean sprouter with 1 L fresh pure water without thiram. From Day 1 to Day 2, the beans doubled in size and started to sprout. On each of the following days, the length of several bean sprouts was measured by a ruler.
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Bi, X., Czajkowsky, D.M., Shao, Z. et al. Digital colloid-enhanced Raman spectroscopy by single-molecule counting. Nature 628, 771–775 (2024). https://doi.org/10.1038/s41586-024-07218-1
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DOI: https://doi.org/10.1038/s41586-024-07218-1
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