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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📋 Methods
Image preprocessing The image of the 1-μm bead was interpolated two times along the optical axis direction, and the retina image was interpolated six times in all directions. The 3D live-cell images were interpolated ten times along the optical axis direction. The measured DO-SRS images were resampled before deconvolution. For all resampling processes, the Fourier interpolation code about f-SOFI was used 58 . To increase the SNR, the PURE denoise filter was used ten times to reduce noise in imaging the standard bead; and the automatic correction of the sCMOS-related noise algorithm was used for the retina image 59 , 60 . A-PoD algorithm The A-PoD algorithm described in the paper was newly implemented for SRS analysis. We adopted the Adam solver as the optimization method and used a gradient algorithm instead of a genetic algorithm. The optimization method was changed to a gradient descent algorithm from a genetic algorithm. The optimization method used is the Adam solver 61 . Because the variables of A-PoD are positions of each virtual emitter, the numbers are set to the address value of the pixel. Therefore, all these numbers have integer values, and, for this, the gradient equation of the Adam solver was modified as follows. ∇ Φ = ( Φ ( x n + 1 , y n , z n ) − Φ ( x n − 1 , y n , z n ) ( x n + 1 ) − ( x n − 1 ) Φ ( x n , y n + 1 , z n ) − Φ ( x n , y n − 1 , z n ) ( y n + 1 ) − ( y n − 1 ) Φ ( x n , y n + 1 , z n ) − Φ ( x n , y n − 1 , z n ) ( y n + 1 ) − ( y n − 1 ) ) Here, Φ is an objective function for deconvolution of 3D images. PSFs for deconvolution processes were simulated using the PSF generator in the ImageJ plugin according to the physical conditions of each measurement 14 , 15 . To efficiently process a 3D image, the image was deconvolved by dividing the image into several pieces as used in the SPIDER algorithm 18 . A-PoD was implemented using Tensorflow 1.15 and Python 3.6. The number of virtual emitters used was manually controlled under the condition that the image contrast improved. All calculations were performed on a Xeon W-2145 CPU with 64 GB of RAM and a NVIDIA Quadro P4000 GPU.
Show full methods section
Image preprocessing The image of the 1-μm bead was interpolated two times along the optical axis direction, and the retina image was interpolated six times in all directions. The 3D live-cell images were interpolated ten times along the optical axis direction. The measured DO-SRS images were resampled before deconvolution. For all resampling processes, the Fourier interpolation code about f-SOFI was used 58 . To increase the SNR, the PURE denoise filter was used ten times to reduce noise in imaging the standard bead; and the automatic correction of the sCMOS-related noise algorithm was used for the retina image 59 , 60 . A-PoD algorithm The A-PoD algorithm described in the paper was newly implemented for SRS analysis. We adopted the Adam solver as the optimization method and used a gradient algorithm instead of a genetic algorithm. The optimization method was changed to a gradient descent algorithm from a genetic algorithm. The optimization method used is the Adam solver 61 . Because the variables of A-PoD are positions of each virtual emitter, the numbers are set to the address value of the pixel. Therefore, all these numbers have integer values, and, for this, the gradient equation of the Adam solver was modified as follows. ∇ Φ = ( Φ ( x n + 1 , y n , z n ) − Φ ( x n − 1 , y n , z n ) ( x n + 1 ) − ( x n − 1 ) Φ ( x n , y n + 1 , z n ) − Φ ( x n , y n − 1 , z n ) ( y n + 1 ) − ( y n − 1 ) Φ ( x n , y n + 1 , z n ) − Φ ( x n , y n − 1 , z n ) ( y n + 1 ) − ( y n − 1 ) ) Here, Φ is an objective function for deconvolution of 3D images. PSFs for deconvolution processes were simulated using the PSF generator in the ImageJ plugin according to the physical conditions of each measurement 14 , 15 . To efficiently process a 3D image, the image was deconvolved by dividing the image into several pieces as used in the SPIDER algorithm 18 . A-PoD was implemented using Tensorflow 1.15 and Python 3.6. The number of virtual emitters used was manually controlled under the condition that the image contrast improved. All calculations were performed on a Xeon W-2145 CPU with 64 GB of RAM and a NVIDIA Quadro P4000 GPU.
Lipid droplet analysis
After deconvolution, individual LDs were counted with the 3D object counter in ImageJ. Based on the information of position, volume, surface area and mean distance, we prepared the plots in Figs. 3 and 6 and Extended Data Figs. 5 and 6 . After detection of individual LDs, we mapped the SA:V ratio using home-built Matlab code. Standard beads A colloid suspension of polystyrene beads 100 nm in diameter with a solid content of 1.0% (wt) (Thermo Scientific) was used in the following experiments. To tailor the suspension for the CAPA experiments, the colloidal solution was further diluted tenfold to a concentration of 0.1% (wt) (9.33 × 1,010 parts per ml) using deionized water.
Retinal section preparation
Human retinal tissue sections were obtained from a donor (age 83) (San Diego Eye Bank, CA, USA) with appropriate consent from the San Diego Eye Bank and following a protocol approved by the University of California, San Diego Human Research Protection Program. The donor had had no history of eye disease, diabetes or any neurological diseases. Following fixation, the retina was processed for cryostat sections (12 μm) and stored at –80 °C. Frozen sections were defrosted (10 min, room temperature) and washed with 1× PBS three times, for 10 min each time, and then sandwiched between a 170-nm coverslip and a glass slide with PBS solution. The coverslips were sealed with nail polish.
MCF-7 breast cancer cells
MCF-7 cells were cultured in DMEM growth medium supplemented with 10 mg l −1 insulin (Sigma-Aldrich), 1% (vol/vol) penicillin–streptomycin mix (Fisher Scientific) and 5% (vol/vol) heat-inactivated FBS on a #1 thickness coverglass (GG-12-Laminin, Neuvitro) for 48 h. Cells were fixed with 4% (vol/vol) paraformaldehyde (PFA) solution for 15 min and then mounted on 1-mm-thick glass slides.
HEK293 cells
HEK293 cells were stably transfected with a plasmid for expressing monomeric red fluorescent protein containing a mitochondrial targeting sequence (Mito-RFP) 62 . Cells were cultured on coverglasses in 24-well cell culture dishes at 37 °C (5% CO 2 ) in DMEM supplemented with 10% FBS (Atlanta Biological) and 1% penicillin–streptomycin (Fisher Scientific). Cells were fixed with 4% PFA in PBS. Following washes with PBS, the coverglasses were mounted in PBS before imaging.
HeLa cells
HeLa cells were cultured in DMEM, supplemented with 10% FBS and 1% penicillin–streptomycin (Fisher Scientific), and incubated with 5% CO 2 at 37 °C. After passing at 80% confluence, cells were seeded at a concentration of 2 × 10 5 cells per ml onto a coverglass in a 24-well plate. DMEM with 0.5% FBS and 1% penicillin–streptomycin was used to synchronize the cells for 8 h. The medium was then changed to 50% (vol/vol) heavy water (D 2 O) and treatment medium as described below. For the excess aromatic amino acid condition, phenylalanine and tryptophan were increased as two separate test conditions at a 15× concentration. L-phenylalanine powder (SLCF3873, Sigma-Aldrich) and L-tryptophan powder (SLCF2559, Sigma-Aldrich) were added to DMEM for the excess groups. Cells were then cultured for 36 h. Next, the cells were gently rinsed with 1× PBS with calcium and magnesium ions at 37 °C (Fisher Scientific, 14040216) and fixed in 4% methanol-free PFA solution (VWR, 15713-S) for 15 min. The coverglass was finally mounted on the cleaned 1-mm-thick glass microscope slides with 120-μm spacers filled with 1× PBS for imaging and spectroscopy. These samples were stored at 4 °C when not in use. Drosophila The w 111 8 parent flies were raised in vials containing standard food (Bloomington cornmeal–yeast–sugar recipe) at 25 °C in an environment with controlled light (12–12-h light–dark cycle) and humidity (>70%) for several generations. Embryos from the young females (~7 d old) were collected in a 4-h window to synchronize larval development. Two groups of 10–15 first instar larvae were placed in vials containing 20% D 2 O-labeled standard food (100 g yeast, 50 g sucrose, 5 g agar per liter) and 3× high-glucose food (100 g yeast, 150 g sucrose, 5 g agar per liter), respectively. The larvae were allowed to develop until the wandering third-instar stage, and then brains were dissected in PBS and fixed in 4% formaldehyde for 21 min at room temperature. After fixation, brains were washed four times with PBS in glass wells and were then sandwiched between a coverglass and the slide with PBS solution. To prevent tissue drying, nail polish was used to seal the surrounding coverglass.
STORM imaging
Mouse hippocampal neuronal culture and immunostaining were performed as described previously 63 . STORM imaging 64 was performed on a custom inverted microscope (Applied Scientific Imaging) with a 60× Nikon objective (MRD01605). A custom Lumencor CELESTA system was used to illuminate the sample. A laser line (~1 W, 640 nm) was used to image a hippocampal neuron immunostained using anti-β II spectrin antibody conjugated to the Alexa 647 dye conjugated to the anti-spectrin antibody, and a laser line (~200 mW, 405 nm) was used to stimulate the cycling of the dyes. The Teledyne Kinetix camera was used for imaging at 50 Hz. The other imaging conditions and the parameters for the DAOSTORM fitting and processing were set as described previously 63 . The neuron culture was performed as described previously 65 .
Stimulated Raman scattering microscopy
A custom-built upright laser-scanning microscope (Olympus) with a 25× water objective (XLPLN, WMP2, 1.05 NA, Olympus) was applied for near-IR throughput. A synchronized pulsed pump beam (tunable wavelength, 720–990 nm; pulse width, 5–6 ps; repetition rate, 80 MHz) and a Stokes beam (wavelength at 1,032 nm; pulse width, 6 ps; repetition rate, 80 MHz) were supplied by a picoEmerald system (Applied Physics & Electronics) and coupled into the microscope. The pump and Stokes beams were collected in transmission by a high-NA oil condenser (1.4 NA). A high-O.D. shortpass filter (950 nm, Thorlabs) was used that would completely block the Stokes beam and transmit the pump beam only onto an Si photodiode for detecting the stimulated Raman loss signal. The output current from the photodiode was terminated, filtered and demodulated by a lock-in amplifier at 20 MHz. The demodulated signal was fed into the FV3000 software module FV-OSR (Olympus) to form an image during laser scanning. All images obtained were 512 × 512 pixels, with a dwell time of 80 μs and imaging speed of ~23 s per image.
Fluorescence microscopy
MPF microscopy was integrated with the DIY SRS microscopy together for imaging the same ROI with different modalities (DO-SRS signals and fluorescence signals). The Mitored signal was imaged with 800-nm ultrafast laser-scanning two-photon fluorescence excitation and detected by PMT with a 610-nm bandpass filter in front of it.
Supplementary Material supplement figure
📊 Figures
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 ar...
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 blinkin...
Extended Data Fig. 3 |
Comparison of the deconvolution results on STORM images using DAO STORM versus A-PoD.
a . (i) A single u2018epifluorescenceu2019-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. ...
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...
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 a...
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 u03bcm 2 range was higher than th...
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 (CH 2 ) signal show the distribution of newly synthesized lipids and old lipids in whole...
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 paper 65 was deconvolved using Richardson-Lucy algorithm (Deconvolutionlab2 program) 20 . b . Intensity profiles...
Fig. 1 |
Deconvolution of SRS images using A-PoD.
a , Schematic of superresolution SRS image processing. b , Three-dimensional deconvolution result of LDs (2,850 cm u22121 ) in a live cell. Following deconvolution, the membrane of an individual LD wa...
Fig. 2 |
Deconvolution results of SRS images.
a , Images of standard beads (100 nm and 1 u03bcm). Left, after the deconvolution of a two-dimensional (2D) image of a 100-nm bead, FWHM of the intensity profile was decreased from 608.5 nm to 101.0 n...
Fig. 3 |
SA:V ratio analysis.
a , The SA:V ratio of LDs in the breast cancer cell image in Fig. 2c was mapped. b , k -mean clustering shows that the three groups of LDs have different SA:V ratios. c , The LD images in different gr...
Fig. 4 |
Three-dimensional super-resolution metabolic imaging of the HeLa cell.
a , DO-SRS images of LDs in CH 2 and CD channels. The CH 2 channel represents the distribution of old LDs (left), and the CD vibration image shows the distribution of newly synthesized LDs (middle). T...
Fig. 5 |
Super-resolution metabolic imaging of Drosophila brain samples.
a , Schematic of the analysis method. The whole-sample image represents the overall lipid distribution. The image was magnified to compare the signal distribution of old and new lipids. The nanoscopic...
Figure images are served from the NIH/NLM PubMed Central Open Access Subset or Europe PMC; copyright remains with the publishers and authors.
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