Biogenic gas nanostructures as ultrasonic molecular reporters

Journal name:
Nature Nanotechnology
Volume:
9,
Pages:
311–316
Year published:
DOI:
doi:10.1038/nnano.2014.32
Received
Accepted
Published online

Abstract

Ultrasound is among the most widely used non-invasive imaging modalities in biomedicine1, but plays a surprisingly small role in molecular imaging due to a lack of suitable molecular reporters on the nanoscale. Here, we introduce a new class of reporters for ultrasound based on genetically encoded gas nanostructures from microorganisms, including bacteria and archaea. Gas vesicles are gas-filled protein-shelled compartments with typical widths of 45–250 nm and lengths of 100–600 nm that exclude water and are permeable to gas2, 3. We show that gas vesicles produce stable ultrasound contrast that is readily detected in vitro and in vivo, that their genetically encoded physical properties enable multiple modes of imaging, and that contrast enhancement through aggregation permits their use as molecular biosensors.

At a glance

Figures

  1. Gas vesicles produce ultrasound contrast.
    Figure 1: Gas vesicles produce ultrasound contrast.

    a, Diagram of a gas vesicle: a hollow gas nanocompartment (solid shading) surrounded by a gas-permeable protein shell (ribbed shading). b, TEM images of intact (left) and hydrostatically collapsed (right) Ana gas vesicles. c, TEM images of intact (left) and collapsed (right) Halo gas vesicles. All scale bars, 200 nm. d, Ultrasound images of a gel phantom containing PBS buffer, Ana gas vesicles at optical densities ranging from OD 0.25 to 2 (concentrations of 150 pM to 1.2 nM) or collapsed Ana gas vesicles (OD 2.0). Images were acquired at multiple frequencies, as indicated. e, Ultrasound images of a gel phantom containing PBS buffer, Halo gas vesicles at optical densities ranging from OD 0.25 to 2 (concentrations of 5 to 40 pM) or collapsed Halo gas vesicles (OD 2.0). Conversion between OD, molar concentration and gas volume fraction is described in the Methods. The size of each field of view is indicated in the lower right corner of the images. f, Total backscattered signal relative to PBS at each frequency and Ana gas vesicle (GV) concentration (N = 4 per sample). g, Total backscattered signal relative to PBS at each frequency and Halo gas vesicle concentration (N = 4 per sample). Detailed image acquisition and analysis parameters are provided in Supplementary Table 1, and colour maps for ultrasound images in Supplementary Fig. 9. All error bars represent ± standard error of the mean (s.e.m.).

  2. Nonlinear imaging and genetic diversity enable enhanced contrast specificity and selective disruption imaging.
    Figure 2: Nonlinear imaging and genetic diversity enable enhanced contrast specificity and selective disruption imaging.

    a, Power spectrum of signal backscattered from Halo gas vesicles (black) and 4.78 µm polystyrene (PS) microspheres (red) in response to 6 MHz transmitted pulses (peak amplitude 98 kPa, labelled ‘Transmit’ in the figure). Each point on the spectrum represents an average of 48 points from three samples (16 points per sample). The orange, green and blue shaded areas correspond to frequency bands used to generate the images in b. b, Ultrasound images of Halo gas vesicles and PS microspheres acquired with 6 MHz transmission and bandpass-filtered around 6, 12 and 18 MHz. c, Ratio of total backscattered signal from Halo gas vesicles and PS microspheres after filtering at the indicated frequencies (N = 4). d, Ultrasound images of Halo gas vesicles, Ana gas vesicles and PS microspheres at 8.6 MHz acquired before (Pre) and after (Post) destructive collapse with 650 kPa insonation, and the difference (Difference) between these images. e, Ratio of total backscattered signal from gas vesicles and PS microspheres in pre-collapse and difference images (N = 4). The concentrations used in ae were OD 0.5 Halo gas vesicles, OD 2.0 Ana gas vesicles and 0.83% (wt/vol) polystyrene. f, Ultrasound images of a phantom containing wells with PBS, a mixture of Ana and Halo gas vesicles, or each type of gas vesicle on its own (all gas vesicles at OD 1.0 in PBS), acquired at 8.6 MHz. Top: before collapse. Middle: after collapse at 300 kPa. Bottom: after collapse at 650 kPA. g, Top: Difference between the top and middle images in f. Bottom: Difference between the middle and bottom images in f. h, Overlay of the two images in g. Detailed image acquisition and analysis parameters are provided in Supplementary Table 1, and colour maps for ultrasound images in Supplementary Fig. 9. The size of each field of view is indicated in the lower right corner of the images. All error bars represent ± s.e.m.

  3. Gas vesicles act as biomolecular sensors and report cellular integrity.
    Figure 3: Gas vesicles act as biomolecular sensors and report cellular integrity.

    a, Illustration of predicted aggregation interactions between surface-biotinylated gas vesicles (hexagons with grey arrows) and streptavidin (SA) at different SA:GV ratios. b, An image, acquired at 17 MHz, of OD 1.0 biotinylated Ana gas vesicles mixed with the indicated ratio of streptavidin. c, Integrated signal intensity relative to phantom background corresponding to the SA:GV conditions in b (N = 4 per condition). d, TEM images of Ana gas vesicles incubated with streptavidin at the indicated molar ratios on the top right-hand corner of each panel. At the higher magnification (right), arrows indicate apparent streptavidin molecules on the vesicle surface. Scale bars, 2 µm (left) and 40 nm (right). e, Illustration of gas vesicles (black hexagons) confined inside intact cells (orange) or released following lysis. f, Ultrasound image (17 MHz pulses) of Ana cells treated with water (intact) or with 25% sucrose (lysed). g, Integrated signal intensity relative to phantom background for intact and lysed cells (N = 4 per condition). Detailed image acquisition and analysis parameters are provided in Supplementary Table 1, and colour bars for ultrasound images in Supplementary Fig. 9. The size of each field of view is indicated in the lower right corner of the images. All error bars represent ± s.e.m.

  4. Gas vesicles produce ultrasound contrast in vivo.
    Figure 4: Gas vesicles produce ultrasound contrast in vivo.

    a, Overlay of second-harmonic image (6 MHz pulses) in green on a greyscale broadband anatomical image of mouse lower abdomen injected subcutaneously with 150 µl OD 6.0 Halo gas vesicles on the right side and 150 µl PBS on the left side. b,c, Second-harmonic ultrasound images before (b) and after (c) vesicle collapse with destructive insonation (650 kPa). Dashed outlines show regions of interest (ROI) used to quantify signals. d, Total backscattered second harmonic signal from ROIs covering vesicle-injected (orange) and PBS-injected (blue) tissues, before and after collapse (N = 5). eg, Nonlinear contrast images acquired using a high-frequency ultrasound scanner system (operating at 18 MHz and 2% power) of SCID nude mice infused intravenously with 50 µl OD 6.0 Halo gas vesicles. The images show contrast at 4.5 s (e) and 64 s (f) after the start of infusion, or after the application of a burst pulse (g). Locations of the IVC and liver are indicated. h, Time course of the smoothed average nonlinear signal in the IVC (blue) and liver (orange) during infusion. i, Mean average signal intensity in the IVC before (pre), during (peak) and after (steady) infusion, and after the burst pulse (post) (N = 5). j, Mean average signal intensity in the liver before (pre) and after (steady) infusion, and after the burst pulse (post) (N = 5). k,l, Dose–response relationship of 50 µl Halo gas vesicles infused at OD 0–6.0 determined from the AUC of average contrast in the IVC (k) and liver (l) (N = 5). Detailed image acquisition and analysis parameters are provided in Supplementary Table 1, and colour maps for ultrasound images in Supplementary Fig. 9. The size of each field of view is indicated in the lower right corner of the images. All error bars represent ± s.e.m.

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Author information

Affiliations

  1. Miller Research Institute, University of California at Berkeley, 2536 Channing Way, Berkeley, California 94720, USA

    • Mikhail G. Shapiro
  2. Department of Bioengineering, 306 Stanley Hall MC #1762, University of California at Berkeley, Berkeley, California 94720, USA

    • Mikhail G. Shapiro,
    • Patrick W. Goodwill,
    • David V. Schaffer &
    • Steven M. Conolly
  3. Department of Molecular and Cell Biology, 142 LSA #3200, University of California at Berkeley, Berkeley, California 94720, USA

    • Mikhail G. Shapiro
  4. Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, California 94720, USA

    • Arkosnato Neogy &
    • Steven M. Conolly
  5. Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada

    • Melissa Yin &
    • F. Stuart Foster
  6. Department of Medical Biophysics, University of Toronto, 610 University Avenue, Toronto, Ontario M4N 3M5, Canada

    • F. Stuart Foster
  7. Department of Chemical and Biomolecular Engineering, University of California at Berkeley, Berkeley, California 94720, USA

    • David V. Schaffer
  8. Present address: Division of Chemistry and Chemical Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, California 91125, USA

    • Mikhail G. Shapiro

Contributions

M.G.S. conceived and directed the study, planned the experiments, prepared the specimens, collected, analysed and interpreted the data, and wrote the manuscript, with input from all other authors. P.W.G. designed and constructed the imaging instrument and accompanying signal processing software, and assisted with initial experiments. A.N. designed, constructed and optimized the imaging instrument and accompanying signal processing software. F.S.F. and M.Y. designed, performed and analysed the data from in vivo experiments. All authors provided input on the study and experimental design, data analysis, data interpretation and the manuscript.

Competing financial interests

F. Stuart Foster is a consultant to VisualSonics.

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