Abstract

Pathological aggregation of Aβ peptides results in the deposition of amyloid in the brain parenchyma (senile plaques in Alzheimer’s disease [AD]) and around cerebral microvessels (cerebral amyloid angiopathy [CAA]). Our current understanding of the amyloid-induced microvascular changes has been limited to the structure and hemodynamics—leaving the oxygen-metabolic aspect unattended. In this Letter, we report a dual-contrast photoacoustic microscopy (PAM) technique, which integrates the molecular contrast of dichroism PAM and the physiological contrast of multi-parametric PAM for simultaneous, intravital imaging of amyloid deposition and cerebrovascular function in a mouse model that develops AD and CAA. This technique opens up new opportunities to study the spatiotemporal interplay between amyloid deposition and vascular-metabolic dysfunction in AD and CAA.

© 2021 Optical Society of America

Pathological aggregation of misfolded Aβ peptides in the brain has long been associated with both neurodegeneration [1] and cerebrovascular dysfunction [2]. Specifically, the formation of amyloid plaques in the extracellular spaces between neurons are thought to play a key role in Alzheimer’s disease (AD), the most common cause of dementia [3,4]. The deposition of amyloid in the walls of small cortical arterioles and leptomeningeal vessels can lead to cerebral amyloid angiopathy (CAA), a common form of cerebral small-vessel disease and a common feature of AD [5,6]. Notable evidence has shown that in CAA, amyloid deposition is associated with physiopathological changes in the cerebral microvasculature, including blood flow, reactivity, and oxygen delivery [711]. Thus, understanding the spatiotemporal interplay between amyloid formation and cerebrovascular function in animal models of AD and CAA might provide new insights into the disease mechanisms.

To date, simultaneous amyloid and cerebrovascular imaging in AD and CAA has been primarily carried out using confocal or two-photon microscopy [5,12]. Most of the studies have been limited to amyloid deposition-induced changes in the microvascular structure and hemodynamics, leaving the oxygen-metabolic aspect unattended. In addition to fluorescence microscopy, optical coherence tomography (OCT) has been applied for amyloid imaging [13]. However, the speckle in OCT limits its specificity and contrast. Besides, stimulated Raman scattering (SRS) microscopy has been recently applied to image amyloid plaques [14]. However, the in vivo application has been impeded by the limited imaging speed and tissue penetration.

Capitalizing on the Brownian motion of red blood cells, the distinct optical absorption spectra of oxy-hemoglobin and deoxy-hemoglobin, and the blood flow-induced signal decorrelation, we have developed multi-parametric photoacoustic microscopy (PAM) for comprehensive imaging of microvascular function, including the total concentration of hemoglobin (${{\rm C}_{{\rm Hb}}}$), oxygen saturation of hemoglobin (${{\rm sO}_2}$), and blood flow speed in the live mouse brain [1517]. In parallel, exploiting the unique dichroism contrast of Congo red (CR)-labeled amyloid plaques, we have demonstrated the feasibility of using PAM for high-sensitivity, high-specificity imaging of amyloid plaques in histological sections of the AD mouse brain [18]. When labeled with CR, amyloid plaques present linear dichroism because of the fibril orientation [19]. Specifically, if the fibril orientation is parallel to the polarization state of the light, the optical absorption (and thus the photoacoustic amplitude) reaches the maximum. In contrast, if the fibril orientation is perpendicular to the light polarization, the photoacoustic signal is diminished. The polarization-dependent optical absorption (i.e., dichroism) enables differential detection with orthogonally polarized photoacoustic excitations to achieve background-free amyloid imaging. Together, these studies show the significant potential of PAM for simultaneous imaging of amyloid deposition and cerebrovascular function in AD and CAA in vivo.

In this Letter, we report a dual-contrast PAM system that integrates the molecular contrast of dichroism PAM and physiological contrast of multi-parametric PAM for simultaneous, intravital imaging of amyloid deposition and cerebrovascular function in a mouse model developing both AD and CAA. The extension of PAM-based amyloid imaging from plaques, which are spatially separated from the vasculature, to amyloid deposits in the vessel wall has not been demonstrated before. Thus, we performed a two-step validation of the new technique against standard fluorescence microscopy, first in a brain section that contains only CR-labeled amyloid plaques and then in a freshly dissected mouse brain containing CR-labeled amyloid plaques, blood-perfused microvessels, and amyloid deposition in the vessel wall. Upon successful validation, we tested the performance of the dual-contrast PAM for simultaneous imaging of amyloid (in the forms of both senile plaques and deposits in the vessel wall) and cerebrovascular function (including ${{\rm C}_{{\rm Hb}}}$, ${{\rm sO}_2}$, and blood flow) in the live mouse brain.

As shown in Fig. 1, the dual-contrast PAM uses a nanosecond-pulsed laser for light excitation (GLPM-10-Y13, IPG Photonics; wavelength, 532 nm; pulse repetition rate used in this Letter, 30 kHz). Individual pulses coming out of this laser are switched between two optical paths by an acousto-optic modulator (AOM; AOMO 3080-122, Crystal Technology). The pulse energy after the AOM is ${\sim}{1000}\;{\rm nJ}$. When the AOM is off, the laser light passes it without diffraction (i.e., 0th order) and is coupled into a polarization-maintaining single-mode optical fiber (PM-SMF, HB450-SC, Fibercore) through a fiber coupler (CFC-11X-A, Thorlabs). The SRS in the PM-SMF redshifts the laser wavelength from 532 to 558 nm [20]. Then a bandpass filter (CT560/10bp, Chroma) is used to isolate the 558 nm component. Under this condition, the output from the PM-SMF is ${\sim}{520}\;{\rm nJ}$, which is reduced to ${\sim}{340}\;{\rm nJ}$ after bandpass filtering. When the AOM is on, ${\sim}{60}\%$ of the 532 nm light will be diffracted (i.e., 1st order) into the second optical path, where no wavelength conversion is implemented. The remaining 40% of undiffracted light goes through the SRS path. Based on our experimental tests using both a power meter and a high-speed photodetector, the threshold for SRS-based generation of 558 nm Stokes light is ${\sim}{50}\%$ of the input power. Thus, no 558 nm light is generated with 40% of the power, when the AOM is on. The two optical paths are merged by using a dichroic mirror (DM; FF538-FDi01, Semrock), and the dual-wavelength beam is coupled into a PM-SMF to maintain the linear polarization.

 figure: Fig. 1.

Fig. 1. Schematic of dual-contrast PAM. The boxed inset illustrates the laser excitation scheme designed for simultaneous dichroism and multi-parametric PAM. AOM, acousto-optic modulator; FC, fiber coupler; PM-SMF, polarization-maintaining single-mode optical fiber; BPF, bandpass filter; DM, dichroic mirror; EOM, electro-optic modulator; OL, objective lens; CL, correction lens; UT, ultrasonic transducer; WT, water tank. DAQ, data acquisition. H/V pol, horizontal/vertical polarization.

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As shown in the boxed inset of Fig. 1, by triggering the AOM at 10 kHz, the pulse train emitted by the laser operating at a 30 kHz pulse repetition rate is packaged into multiple three-pulse packets, each of which consists of two 532 nm pulses and one 558 nm pulse. An electro-optical modulator (EOM; Model 350-80, Conoptics) is utilized to modulate the polarization states of the two 532 nm pulses to be orthogonal to each other, with a polarization extinction ratio of more than 100:1. The pulsed laser beam, containing two wavelengths (532 and 558 nm) and two polarization states at 532 nm, is focused into the object to be imaged by an objective lens, through a correction lens and then the central opening of a ring-shaped ultrasonic transducer (UT; inner diameter, 1.1 mm; outer diameter, 3.0 mm; focal length, 4.4 mm; center frequency, 40 MHz; 6 dB bandwidth, 69%). For acoustic coupling, the UT and correction lens (CL) are immersed in a homemade water tank, and a thin layer of ultrasound gel is applied between the object and a piece of polyethylene membrane at the bottom of the water tank. Light-excited acoustic waves are detected by the UT, amplified by a low-noise amplifier and acquired by a high-speed data acquisition board (DAQ, ATS9350, AlazarTech). The object is raster-scanned using two motorized stages to form images. A field-programmable gate array (FPGA, PCIe-7842r, National Instruments) is programmed to synchronize the laser, AOMs, EOM, stages, and DAQ for simultaneous acquisition of the dual-contrast images. In this experimental setting, the dwell time of each pixel is ${\sim}{100}\;\unicode{x00B5} {\rm s}$, and the pixel size is ${\sim}{0.20}\;\unicode{x00B5}{\rm m} \times {1.67}\;\unicode{x00B5}{\rm m}$. One set of dual-contrast images can be acquired within 20 min. The spatial resolution of the dual-contrast PAM system is quantified to be ${\sim}{3.1}\;\unicode{x00B5}{\rm m}$, and the imaging depth is ${\sim}{300}\;\unicode{x00B5}{\rm m}$.

With the specifically designed laser excitation scheme, three images can be simultaneously acquired using a single raster scan, including two images with the orthogonally polarized (i.e., vertical and horizontal) 532 nm light and one image with the horizontally polarized 558 nm light. Subtraction of the two images acquired at 532 nm reveals the dichroism contrast of CR-labeled amyloid deposits, while spectroscopic, statistical, and correlation analyses of the images acquired with the horizontally polarized 532 and 558 nm light provide multi-parametric quantification of microvascular ${{\rm C}_{{\rm Hb}}}$, ${{\rm sO}_2}$, and blood flow speed. Given the 30 kHz pulse repetition rate and 2 mm/s motor speed, the spatial interval between the two A-lines acquired with adjacent pulses is ${\sim}{66.7}\;{\rm nm}$, which is much smaller than the average sizes of amyloid plaques (${\gt}{10}\;\unicode{x00B5} {\rm m}$) and red blood cells (${\gt}{5}\;\unicode{x00B5} {\rm m}$). Thus, the mismatch between the three simultaneously acquired images is negligible.

A two-step validation of the dual-contrast PAM was carried out. In the first step, the brain section of a nine-month-old (APP)/PS1 mouse (Strain No: 34829, Jackson Laboratory) was incubated in a CR solution (2 µg/ml CR in 0.1 M phosphate-buffered saline [PBS], Sigma) for 10 min to label amyloid plaques, rinsed with 0.1 M PBS, and then examined by the dual-contrast PAM. Our results show that conventional, amplitude-based PAM presents considerable non-amyloid background due to the limited specificity, as shown in Fig. 2(a), acquired with the horizontally polarized 532 nm light. (Note that the amplitude-based image acquired with the vertically polarized 532 nm light shows a similar background.) In contrast, subtraction of the two amplitude-based images acquired with orthogonal polarized light yields an amyloid-specific dichroism image. As shown in Fig. 2(b), the normalized dichroism revealed by the differential detection effectively removes the background and enhances the contrast of amyloid plaques, where the green and red colors, respectively, indicate that PAM signals are predominantly generated by the absorption of horizontally and vertically polarized light. A side -by-side comparison of the plaque image acquired using dichroism PAM with that by a confocal microscope (BX61WI, Olympus; excitation, 515 nm; detection, 560–660 nm) demonstrates the high specificity of dichroism-based amyloid plaque detection [Fig. 2(c)].

 figure: Fig. 2.

Fig. 2. Validation of dual-contrast PAM in the (a)–(c) brain section and (d)–(f) dissected mouse brain. (a), (d) Amplitude-based PAM images acquired with horizontally polarized 532 nm light. (b), (e) Dichroism-based PAM images. (c), (f) Confocal fluorescence images. The white and cyan arrows show two representative vessels with and without amyloid deposits in the wall, respectively. The green arrows show a plaque-like structure in the amplitude-based PAM image which, however, is shown not to be a plaque in the dichroism and fluorescence images. Scale bars: 200 µm.

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 figure: Fig. 3.

Fig. 3. (a)–(d) Multi-parametric PAM images of a 10-month-old APP/PS1 mouse brain in vivo, including the cerebrovascular structure, ${{\rm C}_{{\rm Hb}}}$, ${{\rm sO}_2}$, and blood flow speed. (e) Photo of the same region taken by a wide-field microscope. (f) Dichroism PAM image showing the distribution of CR-stained amyloid plaques and deposits in the vessel wall. (g) Close-up of the boxed area in (f). (h) Confocal image of the boxed area in (f). Scale bars: 200 µm.

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In the second step, a CR-stained, freshly dissected APP/PS1 mouse brain was imaged to further investigate whether the dual-contrast PAM can image amyloid deposits in the vessel wall, which was more challenging due to the presence of strong photoacoustic signals of the hemoglobin. Following anesthesia with ${\sim}{1.0}\%$ vaporized isoflurane, the mouse was placed in a stereotactic holder. A midline incision was made, and the periosteum was removed from the cranium. After identification of the Bregma, appropriate coordinates (1 mm lateral and 0.5 mm posterior) were registered for the stereotactic injection. Then a 0.5 mm burr hole was created using a dental drill, and a microsyringe was directed to the recorded coordinates for injection of 5 µL 0.4% CR. After removing the syringe, we closed the surgical incision with a 4–0 nylon suture. The mouse was returned to its home cage after recovering from anesthesia in a temperature-controlled incubator. Two days after the CR injection, the animal was euthanized, and the brain was dissected and examined by the dual-contrast PAM. Our results show that the amyloid deposits in the vessel wall, which is completely indistinguishable from the blood in the amplitude-based PAM images, can be clearly identified using the dichroism contrast and well agree with those observed by the confocal microscope [white and cyan arrows in Figs. 2(d)–2(f)]. Moreover, a plaque-like structure in the amplitude-based PAM image [green arrow in Fig. 2(d)] is shown not to be an amyloid plaque in the dichroism image [Fig. 2(e)] and the fluorescence image [Fig. 2(f)], once again demonstrating the high specificity of the dichroism contrast for amyloid imaging.

To examine the utility of the dual-contrast PAM for simultaneous imaging of amyloid and cerebrovascular function in vivo, the parietal cortex of a 10-month-old APP/PS1 mouse was imaged through a ${3} \times {3}\;{{\rm mm}^2}$ cranial window two days after the CR injection. The pulse energy at the tissue surface was kept ${\sim}{100}\;{\rm nJ}$. All experimental procedures were carried out in conformity with the animal protocol approved by the Institutional Animal Care and Use Committee at Washington University in St. Louis.

As shown in Figs. 3(a)–3(d), the dual-contrast PAM clearly measures the cerebrovascular structure, ${{\rm C}_{{\rm Hb}}}$, ${{\rm sO}_2}$, and blood flow speed over the ${2} \times {2}\;{{\rm mm}^2}$ region of interest (ROI), providing additional microvascular and functional insights in comparison to the picture taken by a wide-field microscope (SM-3TZ-54S, AmScope) over the same ROI [Fig. 3(e)]. The simultaneously acquired dichroism image shows amyloid deposition across the entire ROI [Fig. 3(f)]. A side-by-side comparison of the boxed region imaged by dichroism PAM [Fig. 3(g)] and confocal microscope [Fig. 3(h)] shows similar distributions of both amyloid plaques (white arrows) and amyloid deposits in the vessel wall (yellow arrows).

In summary, we have developed a dual-contrast PAM technique for simultaneous, intravital imaging of cerebral microvascular physiology and amyloid dichroism. This technique opens up new opportunities to study the spatiotemporal interplay between amyloid and vascular-metabolic dysfunction in AD and CAA.

Funding

Hope Center for Neurological Disorders.

Acknowledgment

This work was supported by a Pilot Project Award from the Hope Center for Neurological Disorders at Washington University. The authors thank Dr. Yu-Yo Sun for his assistance with the CR injection.

Disclosures

The authors declare no conflicts of interest.

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

REFERENCES

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3. H. Mathys, J. Davila-Velderrain, Z. Peng, F. Gao, S. Mohammadi, J. Z. Young, M. Menon, L. He, F. Abdurrob, and X. Jiang, Nature 570, 332 (2019). [CrossRef]  

4. C. H. Heo, K. H. Kim, H. J. Kim, S. H. Baik, H. Song, Y. S. Kim, J. Lee, I. Mook-Jung, and H. M. Kim, Chem. Commun. 49, 1303 (2013). [CrossRef]  

5. S. J. van Veluw, M. P. Frosch, A. A. Scherlek, D. Lee, S. M. Greenberg, and B. J. Bacskai, J. Cereb. Blood Flow Metab. 41, 82 (2020). [CrossRef]  

6. J. Götz, J. R. Streffer, D. David, A. Schild, F. Hoerndli, L. Pennanen, P. Kurosinski, and F. Chen, Mol. Psychiatry 9, 664 (2004). [CrossRef]  

7. F. C. Maier, H. F. Wehrl, A. M. Schmid, J. G. Mannheim, S. Wiehr, C. Lerdkrai, C. Calaminus, A. Stahlschmidt, L. Ye, and M. Burnet, Nat. Med. 20, 1485 (2014). [CrossRef]  

8. A. R. Switzer, I. Cheema, C. R. McCreary, A. Zwiers, A. Charlton, A. Alvarez-Veronesi, R. Sekhon, C. Zerna, R. B. Stafford, and R. Frayne, Neurology 95, e1333 (2020). [CrossRef]  

9. H. Fukuyama, M. Ogawa, H. Yamauchi, S. Yamaguchi, J. Kimura, Y. Yonekura, and J. Konishi, J. Nucl. Med. 35, 1 (1994).

10. C. Iadecola and R. F. Gottesman, Circ. Res. 123, 406 (2018). [CrossRef]  

11. B. H. Han, M. Zhou, F. Abousaleh, R. P. Brendza, H. H. Dietrich, J. Koenigsknecht-Talboo, J. R. Cirrito, E. Milner, D. M. Holtzman, and G. J. Zipfel, J. Neurosci. 28, 13542 (2008). [CrossRef]  

12. P. Kelly, E. Hudry, S. S. Hou, and B. J. Bacskai, Front Aging Neurosci. 10, 219 (2018). [CrossRef]  

13. J. Gesperger, A. Lichtenegger, T. Roetzer, M. Augustin, D. J. Harper, P. Eugui, C. W. Merkle, C. K. Hitzenberger, A. Woehrer, and B. Baumann, Appl. Sci. 9, 2100 (2019). [CrossRef]  

14. M. Ji, M. Arbel, L. Zhang, C. W. Freudiger, S. S. Hou, D. Lin, X. Yang, B. J. Bacskai, and X. S. Xie, Sci. Adv. 4, eaat7715 (2018). [CrossRef]  

15. B. Ning, N. Sun, R. Cao, R. Chen, K. Kirk Shung, J. A. Hossack, J.-M. Lee, Q. Zhou, and S. Hu, Sci. Rep. 5, 18775 (2015). [CrossRef]  

16. T. Wang, N. Sun, R. Cao, B. Ning, R. Chen, Q. Zhou, and S. Hu, Neurophotonics 3, 045006 (2016). [CrossRef]  

17. S. Jeon, J. Kim, D. Lee, J. W. Baik, and C. Kim, Photoacoustics 15, 100141 (2019). [CrossRef]  

18. S. Hu and L. V. Wang, Biophys. J. 105, 841 (2013). [CrossRef]  

19. L.-W. Jin, K. A. Claborn, M. Kurimoto, M. A. Geday, I. Maezawa, F. Sohraby, M. Estrada, W. Kaminksy, and B. Kahr, Proc. Natl. Acad. Sci. USA 100, 15294 (2003). [CrossRef]  

20. P. Hajireza, A. Forbrich, and R. J. Zemp, Opt. Lett. 38, 2711 (2013). [CrossRef]  

References

  • View by:

  1. P. J. Muchowski, Neuron 35, 9 (2002).
    [Crossref]
  2. V. Vasilevko, G. Passos, D. Quiring, E. Head, M. Fisher, and D. H. Cribbs, Ann. N.Y. Acad. Sci. 1207, 58 (2010).
    [Crossref]
  3. H. Mathys, J. Davila-Velderrain, Z. Peng, F. Gao, S. Mohammadi, J. Z. Young, M. Menon, L. He, F. Abdurrob, and X. Jiang, Nature 570, 332 (2019).
    [Crossref]
  4. C. H. Heo, K. H. Kim, H. J. Kim, S. H. Baik, H. Song, Y. S. Kim, J. Lee, I. Mook-Jung, and H. M. Kim, Chem. Commun. 49, 1303 (2013).
    [Crossref]
  5. S. J. van Veluw, M. P. Frosch, A. A. Scherlek, D. Lee, S. M. Greenberg, and B. J. Bacskai, J. Cereb. Blood Flow Metab. 41, 82 (2020).
    [Crossref]
  6. J. Götz, J. R. Streffer, D. David, A. Schild, F. Hoerndli, L. Pennanen, P. Kurosinski, and F. Chen, Mol. Psychiatry 9, 664 (2004).
    [Crossref]
  7. F. C. Maier, H. F. Wehrl, A. M. Schmid, J. G. Mannheim, S. Wiehr, C. Lerdkrai, C. Calaminus, A. Stahlschmidt, L. Ye, and M. Burnet, Nat. Med. 20, 1485 (2014).
    [Crossref]
  8. A. R. Switzer, I. Cheema, C. R. McCreary, A. Zwiers, A. Charlton, A. Alvarez-Veronesi, R. Sekhon, C. Zerna, R. B. Stafford, and R. Frayne, Neurology 95, e1333 (2020).
    [Crossref]
  9. H. Fukuyama, M. Ogawa, H. Yamauchi, S. Yamaguchi, J. Kimura, Y. Yonekura, and J. Konishi, J. Nucl. Med. 35, 1 (1994).
  10. C. Iadecola and R. F. Gottesman, Circ. Res. 123, 406 (2018).
    [Crossref]
  11. B. H. Han, M. Zhou, F. Abousaleh, R. P. Brendza, H. H. Dietrich, J. Koenigsknecht-Talboo, J. R. Cirrito, E. Milner, D. M. Holtzman, and G. J. Zipfel, J. Neurosci. 28, 13542 (2008).
    [Crossref]
  12. P. Kelly, E. Hudry, S. S. Hou, and B. J. Bacskai, Front Aging Neurosci. 10, 219 (2018).
    [Crossref]
  13. J. Gesperger, A. Lichtenegger, T. Roetzer, M. Augustin, D. J. Harper, P. Eugui, C. W. Merkle, C. K. Hitzenberger, A. Woehrer, and B. Baumann, Appl. Sci. 9, 2100 (2019).
    [Crossref]
  14. M. Ji, M. Arbel, L. Zhang, C. W. Freudiger, S. S. Hou, D. Lin, X. Yang, B. J. Bacskai, and X. S. Xie, Sci. Adv. 4, eaat7715 (2018).
    [Crossref]
  15. B. Ning, N. Sun, R. Cao, R. Chen, K. Kirk Shung, J. A. Hossack, J.-M. Lee, Q. Zhou, and S. Hu, Sci. Rep. 5, 18775 (2015).
    [Crossref]
  16. T. Wang, N. Sun, R. Cao, B. Ning, R. Chen, Q. Zhou, and S. Hu, Neurophotonics 3, 045006 (2016).
    [Crossref]
  17. S. Jeon, J. Kim, D. Lee, J. W. Baik, and C. Kim, Photoacoustics 15, 100141 (2019).
    [Crossref]
  18. S. Hu and L. V. Wang, Biophys. J. 105, 841 (2013).
    [Crossref]
  19. L.-W. Jin, K. A. Claborn, M. Kurimoto, M. A. Geday, I. Maezawa, F. Sohraby, M. Estrada, W. Kaminksy, and B. Kahr, Proc. Natl. Acad. Sci. USA 100, 15294 (2003).
    [Crossref]
  20. P. Hajireza, A. Forbrich, and R. J. Zemp, Opt. Lett. 38, 2711 (2013).
    [Crossref]

2020 (2)

S. J. van Veluw, M. P. Frosch, A. A. Scherlek, D. Lee, S. M. Greenberg, and B. J. Bacskai, J. Cereb. Blood Flow Metab. 41, 82 (2020).
[Crossref]

A. R. Switzer, I. Cheema, C. R. McCreary, A. Zwiers, A. Charlton, A. Alvarez-Veronesi, R. Sekhon, C. Zerna, R. B. Stafford, and R. Frayne, Neurology 95, e1333 (2020).
[Crossref]

2019 (3)

H. Mathys, J. Davila-Velderrain, Z. Peng, F. Gao, S. Mohammadi, J. Z. Young, M. Menon, L. He, F. Abdurrob, and X. Jiang, Nature 570, 332 (2019).
[Crossref]

J. Gesperger, A. Lichtenegger, T. Roetzer, M. Augustin, D. J. Harper, P. Eugui, C. W. Merkle, C. K. Hitzenberger, A. Woehrer, and B. Baumann, Appl. Sci. 9, 2100 (2019).
[Crossref]

S. Jeon, J. Kim, D. Lee, J. W. Baik, and C. Kim, Photoacoustics 15, 100141 (2019).
[Crossref]

2018 (3)

M. Ji, M. Arbel, L. Zhang, C. W. Freudiger, S. S. Hou, D. Lin, X. Yang, B. J. Bacskai, and X. S. Xie, Sci. Adv. 4, eaat7715 (2018).
[Crossref]

C. Iadecola and R. F. Gottesman, Circ. Res. 123, 406 (2018).
[Crossref]

P. Kelly, E. Hudry, S. S. Hou, and B. J. Bacskai, Front Aging Neurosci. 10, 219 (2018).
[Crossref]

2016 (1)

T. Wang, N. Sun, R. Cao, B. Ning, R. Chen, Q. Zhou, and S. Hu, Neurophotonics 3, 045006 (2016).
[Crossref]

2015 (1)

B. Ning, N. Sun, R. Cao, R. Chen, K. Kirk Shung, J. A. Hossack, J.-M. Lee, Q. Zhou, and S. Hu, Sci. Rep. 5, 18775 (2015).
[Crossref]

2014 (1)

F. C. Maier, H. F. Wehrl, A. M. Schmid, J. G. Mannheim, S. Wiehr, C. Lerdkrai, C. Calaminus, A. Stahlschmidt, L. Ye, and M. Burnet, Nat. Med. 20, 1485 (2014).
[Crossref]

2013 (3)

S. Hu and L. V. Wang, Biophys. J. 105, 841 (2013).
[Crossref]

C. H. Heo, K. H. Kim, H. J. Kim, S. H. Baik, H. Song, Y. S. Kim, J. Lee, I. Mook-Jung, and H. M. Kim, Chem. Commun. 49, 1303 (2013).
[Crossref]

P. Hajireza, A. Forbrich, and R. J. Zemp, Opt. Lett. 38, 2711 (2013).
[Crossref]

2010 (1)

V. Vasilevko, G. Passos, D. Quiring, E. Head, M. Fisher, and D. H. Cribbs, Ann. N.Y. Acad. Sci. 1207, 58 (2010).
[Crossref]

2008 (1)

B. H. Han, M. Zhou, F. Abousaleh, R. P. Brendza, H. H. Dietrich, J. Koenigsknecht-Talboo, J. R. Cirrito, E. Milner, D. M. Holtzman, and G. J. Zipfel, J. Neurosci. 28, 13542 (2008).
[Crossref]

2004 (1)

J. Götz, J. R. Streffer, D. David, A. Schild, F. Hoerndli, L. Pennanen, P. Kurosinski, and F. Chen, Mol. Psychiatry 9, 664 (2004).
[Crossref]

2003 (1)

L.-W. Jin, K. A. Claborn, M. Kurimoto, M. A. Geday, I. Maezawa, F. Sohraby, M. Estrada, W. Kaminksy, and B. Kahr, Proc. Natl. Acad. Sci. USA 100, 15294 (2003).
[Crossref]

2002 (1)

P. J. Muchowski, Neuron 35, 9 (2002).
[Crossref]

1994 (1)

H. Fukuyama, M. Ogawa, H. Yamauchi, S. Yamaguchi, J. Kimura, Y. Yonekura, and J. Konishi, J. Nucl. Med. 35, 1 (1994).

Abdurrob, F.

H. Mathys, J. Davila-Velderrain, Z. Peng, F. Gao, S. Mohammadi, J. Z. Young, M. Menon, L. He, F. Abdurrob, and X. Jiang, Nature 570, 332 (2019).
[Crossref]

Abousaleh, F.

B. H. Han, M. Zhou, F. Abousaleh, R. P. Brendza, H. H. Dietrich, J. Koenigsknecht-Talboo, J. R. Cirrito, E. Milner, D. M. Holtzman, and G. J. Zipfel, J. Neurosci. 28, 13542 (2008).
[Crossref]

Alvarez-Veronesi, A.

A. R. Switzer, I. Cheema, C. R. McCreary, A. Zwiers, A. Charlton, A. Alvarez-Veronesi, R. Sekhon, C. Zerna, R. B. Stafford, and R. Frayne, Neurology 95, e1333 (2020).
[Crossref]

Arbel, M.

M. Ji, M. Arbel, L. Zhang, C. W. Freudiger, S. S. Hou, D. Lin, X. Yang, B. J. Bacskai, and X. S. Xie, Sci. Adv. 4, eaat7715 (2018).
[Crossref]

Augustin, M.

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Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Figures (3)

Fig. 1.
Fig. 1. Schematic of dual-contrast PAM. The boxed inset illustrates the laser excitation scheme designed for simultaneous dichroism and multi-parametric PAM. AOM, acousto-optic modulator; FC, fiber coupler; PM-SMF, polarization-maintaining single-mode optical fiber; BPF, bandpass filter; DM, dichroic mirror; EOM, electro-optic modulator; OL, objective lens; CL, correction lens; UT, ultrasonic transducer; WT, water tank. DAQ, data acquisition. H/V pol, horizontal/vertical polarization.
Fig. 2.
Fig. 2. Validation of dual-contrast PAM in the (a)–(c) brain section and (d)–(f) dissected mouse brain. (a), (d) Amplitude-based PAM images acquired with horizontally polarized 532 nm light. (b), (e) Dichroism-based PAM images. (c), (f) Confocal fluorescence images. The white and cyan arrows show two representative vessels with and without amyloid deposits in the wall, respectively. The green arrows show a plaque-like structure in the amplitude-based PAM image which, however, is shown not to be a plaque in the dichroism and fluorescence images. Scale bars: 200 µm.
Fig. 3.
Fig. 3. (a)–(d) Multi-parametric PAM images of a 10-month-old APP/PS1 mouse brain in vivo, including the cerebrovascular structure, ${{\rm C}_{{\rm Hb}}}$ , ${{\rm sO}_2}$ , and blood flow speed. (e) Photo of the same region taken by a wide-field microscope. (f) Dichroism PAM image showing the distribution of CR-stained amyloid plaques and deposits in the vessel wall. (g) Close-up of the boxed area in (f). (h) Confocal image of the boxed area in (f). Scale bars: 200 µm.

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