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Super-resolution SRS microscopy with A-PoD

Nature Methods volume 20, pages 448–458 (2023)Cite this article

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Abstract

Stimulated Raman scattering (SRS) offers the ability to image metabolic dynamics with high signal-to-noise ratio. However, its spatial resolution is limited by the numerical aperture of the imaging objective and the scattering cross-section of molecules. To achieve super-resolved SRS imaging, we developed a deconvolution algorithm, adaptive moment estimation (Adam) optimization-based pointillism deconvolution (A-PoD) and demonstrated a spatial resolution of lower than 59 nm on the membrane of a single lipid droplet (LD). We applied A-PoD to spatially correlated multiphoton fluorescence imaging and deuterium oxide (D2O)-probed SRS (DO-SRS) imaging from diverse samples to compare nanoscopic distributions of proteins and lipids in cells and subcellular organelles. We successfully differentiated newly synthesized lipids in LDs using A-PoD-coupled DO-SRS. The A-PoD-enhanced DO-SRS imaging method was also applied to reveal metabolic changes in brain samples from Drosophila on different diets. This new approach allows us to quantitatively measure the nanoscopic colocalization of biomolecules and metabolic dynamics in organelles.

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Fig. 1: Deconvolution of SRS images using A-PoD.
Fig. 2: Deconvolution results of SRS images.
Fig. 3: SA:V ratio analysis.
Fig. 4: Three-dimensional super-resolution metabolic imaging of the HeLa cell.
Fig. 5: Super-resolution metabolic imaging of Drosophila brain samples.
Fig. 6: Multiplexed super-resolution MPF-SRS imaging of mitochondria.

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Data availability

All the data supporting the findings of this study are available within the paper and its Supplementary Information.

Code availability

Exemplary data and source code for A-PoD with explanations about parameters and the installation protocol are available at https://github.com/lingyanshi2020/A-PoD/.

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Acknowledgements

We thank K. Zhang, W. Min and J. Enderlein for helpful discussions and suggestions. Thanks to M. Shtrahman and S. Saidi for providing 1 µm bead samples. We acknowledge University of California, San Diego startup funds, NIH U54CA132378, NIH 5R01NS111039, NIH R21NS125395, NIH U54DK134301, NIH U54 HL165443 and a Hellman Fellow Award.

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Authors and Affiliations

  1. Shu Chien - Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Hongje Jang, Yajuan Li, Anthony A. Fung, Pegah Bagheri, Khang Hoang, Bogdan Bintu & Lingyan Shi

  2. School of Medicine, University of California, Irvine, CA, USA

    Dorota Skowronska-Krawczyk

  3. The Ken and Ruth Davee Department of Neurology, Northwestern University, Chicago, IL, USA

    Xiaoping Chen & Jane Y. Wu

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Contributions

L.S. conceived the idea and designed the project; H.J. developed and improved the A-PoD algorithm and coded it. B.B. performed STORM imaging experiments. Y.L., A.A.F., K.H. and P.B carried out SRS imaging experiments and collected data with help from L.S.; H.J. analyzed images and generated figures with input from L.S. and B.B. D.S.-K. prepared human retina samples; X.C. and J.Y.W. performed experiments using HEK293 cells. Y.L. carried out the Drosophila work. P.B., K.H. and A.A.F. performed HeLa cell and breast cancer cell experiments. H.J. and L.S. wrote and revised the text with input from all other authors.

Corresponding author

Correspondence to Lingyan Shi.

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Competing interests

A provisional patent application has been filed by the UC San Diego patent office for L.S. and H.J. under the title ‘SUPER-RESOLUTION STIMULATED RAMA SCATTERING MICROSCOPY WITH A-POD’, U.S. provisional application serial no. _63/379,226_, filed 12 October 2022. All other authors declare no competing interests.

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Nature Methods thanks Malgorzata Baranska, Meng Wang, and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Comparison of A-PoD with Richardson-Lucy method using simulation data.

a. To compare different deconvolution methods, we generated an artificial image composed of single pixel sized 9 dots. The dots in the image have different intensity values. By convolution with an artificial PSF, a blurry image (Y) was generated. The image (Y) was deconvolved using a penalized regression method. b. When we minimize the objective function in panel b, the images, X results. Depending on the penalty parameter, R(X), X has various forms. The optimization result without any penalty parameter has strong ringing artifact as shown in panel b(i), and the result with L2-norm penalty parameter has reduced ringing artifact as shown in panel b(ii). By limiting summation of total intensity, we can reduce the ringing artifact as shown in panel b(iii). The penalty parameter limiting the total intensity as a fixed value makes the values in empty space to zeros. Accordingly, one of the main characteristics of A-PoD, the fixed total intensity of X, can increase sparsity of resulting images. c. Comparison of A-PoD with Richardson-lucy method. When we apply another characteristic of A-PoD, quantization of intensity value, together, the resulting image of A-PoD has higher resolution than that obtained using Richardson-Lucy method. The signal intensity profile shows the difference in resolutions. The dots in the A-PoD image have narrower width than Richardon-Lucy images. The calculation time of A-PoD was 1.9 s, and Deconvolutionlab2 using Richardson-Lucy algorithm calculated the image for 1.1 s (50 iteration) and 2.2 s (100 iteration).

Extended Data Fig. 2 Precision and speed of A-PoD in comparison with SPIDER.

a. To compare the localization microscopy image with A-PoD result, we deconvolved a mitochondrial image. The image stack is composed of 100 frames. Each image frame contains information about blinking emitters. The emitters were localized using SPIDER deconvolution algorithm. By averaging the image stack, we generated a widefield image, and the widefield image was deconvolved using A-PoD. The intensity profiles of the cross-section in the deconvolved images show the similarity between the two results. b. Two optimization methods for the deconvolution process were compared. An image composed of 100000 virtual emitters was deconvolved using the two different optimizers. The results of Adam solver (i) finished calculation within 2 s. By increasing the iteration number, the deconvolution results using genetic solver (ii, iii, and iv with different iteration numbers) were compared with the result of Adam solver. The deconvolution result with a high iteration number shows more precise image. However, to generate an image having same quality as that obtained with the Adam solver, we need to increase the iteration number further beyond 5 × 106 more.

Extended Data Fig. 3 Comparison of the deconvolution results on STORM images using DAO STORM versus A-PoD.

a. (i) A single ‘epifluorescence’-like image was calculated by averaging the STORM-stack. (ii) We selected an area with low emitter density (yellow rectangle region in (i)) than other areas. (iii) The averaged image stack of the chosen area was deconvolved using A-PoD. (iv) From the whole stack of the selected area, the individual single emitters were localized using DAOSTORM. b. The two areas marked by the blue and red rectangle areas in (a. i and b. ii) were selected. (iii and iv) The intensity profiles and auto-correlation data shows the periodicity of the structure of the membrane-associated periodic skeleton (MPS) in neurons. c. Another bright area with high emitter density (green rectangle area in a.i) where we cannot localize the individual molecules using DAOSTORM was selected. (i) From the image stack of the selected area, we chose a single frame. (ii) Using A-PoD, we deconvolved the chosen frame. (iii and iv) The intensity profile and the auto-correlation result show the periodicity. Due to the strong intensity, the periodic structure was clearly revealed, and the interval in the MPS is also close to the previous published result, 190 nm.

Extended Data Fig. 4 Comparison of two PSF models.

a. Experimental PSF was extracted from 100 nm bead image. As shown in a, by deconvolving the measured bead image with artificial 2D Gaussian image having 100 nm FWHM, experimental PSF was calculated. The FWHM of the experimental PSF was 471.2 nm. b. Single LD image was deconvolved using simulated PSF and experimental PSF. After deconvolution, the raw LD image (in b, i) was converted to the two images (in b, ii and iii). Two PSF has almost similar size with about 5% error (bar graphs in b, iv). From the intensity profiles of the two deconvolved images, membrane thickness was measured. The thinnest part has 59 nm and 76 nm for experimental PSF and simulated PSF, respectively. Spatial resolutions measured with the decorrelation method were 54 nm and 57 nm for experimental PSF and simulated PSF, respectively.

Extended Data Fig. 5 SRS images of a HeLa cell cultured in the standard medium.

a. Raw DO-SRS images of the HeLa cell. b. Deconvolution results of the images. The images show the shape and distribution of the lipid droplets in sub-micron scale. c. After measuring the surface area and volume of individual lipid droplets, the surface area to volume ratio of individual LDs was mapped.

Extended Data Fig. 6 LD size and lipid turnover rate distribution.

a. In flies fed on different diets, LDs have different size distribution. In high glucose group, the LD size was widely distributed, and the number of LDs in 0.1~0.2 µm2 range was higher than the other size. In control dietary condition, the control group with standard diet, the number of LDs in 0.2~0.3 µm2 range was high. LDs were labeled on the images with three colors according to the size (Blue, 0.05~0.2 µm2; Red, 0.2~0.3 µm2; Green, 0.3~0.45 µm2). b. To compare the LD size and lipid turnover rate, the two parameters of individual LDs were plotted. Under both conditions, LD size and lipid turnover rate show positive correlation. Correlation coefficient: 0.40 (3x glucose), 0.44 (control).

Extended Data Fig. 7 SRS images of larvae brain samples from flies fed on different diets.

a. DO-SRS images of a drosophila larvae brain in 3x glucose group. The wide range new lipid (CD) and old lipid (CH2) signal show the distribution of newly synthesized lipids and old lipids in whole sample, respectively. In the zoomed-in images, the microscopic distribution of two different lipid components is clearly shown. After deconvolution, the nanoscopic distribution and shape of lipid droplets are getting clearer. By using the particle analysis method, we can remove the background and focus on the areas of lipid droplets. b. SRS images of a drosophila larvae brain in the control group were processed with the same manner in a. These images were analyzed, and the analysis result is explained in Fig. 6.

Extended Data Fig. 8 Comparison between A-PoD and the Richardson-Lucy method.

a. USAF-1951 resolution target. The fluorescence image of the resolution target in the paper65 was deconvolved using Richardson-Lucy algorithm (Deconvolutionlab2 program)20. b. Intensity profiles of the yellow dotted line in figure A show the resolution difference. A-PoD result resolved each line perfectly, but Richardson-Lucy result could not resolve them. c. Deconvolution results of retinal tissue image. The raw image (i) was deconvolved with Richardson-Lucy algorithm (ii) and A-PoD (iii). The image contrast was significantly improved when we used A-PoD for deconvolution.

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Jang, H., Li, Y., Fung, A.A. et al. Super-resolution SRS microscopy with A-PoD. Nat Methods 20, 448–458 (2023). https://doi.org/10.1038/s41592-023-01779-1

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