An artery-specific fluorescent dye for studying neurovascular coupling

brief communications

© 2012 Nature America, Inc. All rights reserved.

An artery-specific fluorescent dye for studying neurovascular coupling Zhiming Shen1,2, Zhongyang Lu1,2, Pratik Y Chhatbar1, Philip O’Herron1 & Prakash Kara1 We demonstrate that Alexa Fluor 633 hydrazide (Alexa Fluor 633) selectively labels neocortical arteries and arterioles by binding to elastin fibers. We measured sensory stimulus– evoked arteriole dilation dynamics in mouse, rat and cat visual cortex using Alexa Fluor 633 together with neuronal activity using calcium indicators or blood flow using fluorescein dextran. Arteriole dilation decreased fluorescence recorded from immediately underlying neurons, representing a potential artifact during neuronal functional imaging experiments.

Energy demands of electrically active neurons in the brain are met by regulating blood flow1. Active neurons and astrocytes release neurotransmitters that regulate the diameter of blood vessels, which in turn changes blood flow 1–3. However, the spatial precision of these ‘neurovascular coupling’ events in vivo is largely unknown. For example, a single functional magnetic resonance imaging (MRI) voxel of 1 mm3 in the cerebral cortex contains ~100,000 neurons4. One cubic millimeter of the visual cortex contains precise neuronal maps for all stimulus orientations5, directions6 and depths7 for a given point in visual space. Yet the extent to which blood vessels are locally ‘tuned’ within and across different categories of sensory stimuli is unknown. Thus, the efficiency of neurovascular coupling in the neocortex remains unclear and so is the interpretation of the underlying source of functional MRI signals8. Studies of neurovascular coupling would benefit from the development of dyes that specifically label the different components of the vasculature in vivo. Here we report an artery-specific dye, Alexa Fluor 633, and show that it enhances existing methods for studying vessel dynamics and neural activity in the mammalian neocortex. We originally used long-wavelength Alexa dyes9 to intra cellularly label individual neurons in the rodent and cat neocortex and reconstruct their dendrites in three dimensions. When performing these experiments, under continuous guidance of twophoton microscopy, we found that single brief (1 s) puffs of Alexa Fluor 633 from a patch pipette into the extracellular space of the

cerebral cortex resulted in instantaneous ultrabright labeling of a subset of vessels in the immediate vicinity of the injection site (Fig. 1a). The two-photon laser powers we used for imaging Alexa Fluor 633 did not lead to any autofluorescence signal from the tissue (Fig. 1b). Progressively larger puffs of Alexa Fluor 633 labeled more vessel surface area (Fig. 1c). But some adjacent and overlapping blood vessels, visible after intravenous injection of fluorescein dextran (Fig. 1d), were not labeled by Alexa Fluor 633. Examining hue differences in bright-field images of vessels on the cortical surface and/or observing the time delay between the filling of arteries and veins after intravenous injection of fluorescein dextran, we determined that Alexa Fluor 633–labeled arteries but not veins in mouse, rat and cat neocortex (Supplementary Figs. 1–3 and Supplementary Video 1). To determine feasibility of using Alexa Fluor 633 to label vessels in intact dura or thin skull10 preparations, we applied Alexa Fluor 633 via intravenous injection (in mice, rats and cats; Online Methods). As expected, the dye was initially visible in the lumen of all blood vessels. But within 30–90 min, the kidneys appeared to filter much of the luminal Alexa Fluor 633 and bright labeling of arteriole walls remained (Fig. 1e,f). Bright arteriole-wall labeling persisted for more than 24 h after injection (Supplementary Fig. 4). Whether we applied Alexa Fluor 633 in vivo (intravenously or by local micropipette injection) or on the surface of histological sections (topically), microvessels (≤10 µm diameter), venules and veins were not labeled in mice, rats and cats (Fig. 1g, Supplementary Figs. 1–10 and Supplementary Videos 2 and 3). All ≥15-µm-diameter arterioles were labeled, but no ≤ 10-µmdiameter arterioles were labeled (Supplementary Figs. 6–10). This diameter-dependent Alexa Fluor 633 labeling occurred regardless of whether the unlabeled branches were surface arterioles or offshoots from penetrating arterioles (Supplementary Figs. 6–8). Thus, rather than a progressive decrease in Alexa Fluor 633 labeling along the length of a vessel, there was a binary switch to no labeling after a branch point if the daughter branch was small enough (Supplementary Figs. 6–8). To physiologically cross-validate that only arteries and arterioles were labeled by Alexa Fluor 633, we analyzed vessel dynamics in response to sensory stimulation. We injected mice, rats and cats with Alexa Fluor 633 and fluorescein dextran, and imaged neocortical arterioles and venules with two-photon microscopy while presenting drifting square-wave grating visual stimuli. We examined vessels with diameters of 17–114 µm and quantitatively determined the time course of sensory stimulus–evoked vessel dilation (Fig. 1h). Only vessel walls labeled with Alexa Fluor 633 showed stimulus-evoked dilations (10 mouse, 15 rat and 10 cat arteriole branches in (two mice, five rats and six cats); for example, Supplementary Video 4). The dilation dynamics were consistent

1Department of Neurosciences, Medical University of South Carolina, Charleston, South Carolina, USA. 2These authors contributed equally to this work.

Correspondence should be addressed to P.K. ([email protected]).

Received 22 August 2011; accepted 22 December 2011; published online 22 january 2012; doi:10.1038/nmeth.1857

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with those observed in arterioles: relatively short latency and large amplitude11,12. Upon visual stimulation we observed an absolute increase in arteriole diameter ranging from ~1 µm for arterioles with a baseline diameter of 20 µm, to ~11 µm for arterioles with a baseline diameter of 114 µm. The average percentage change in dilation of Alexa Fluor 633–labeled arterioles was indistinguishable among the three species examined: analysis of variance (ANOVA) P = 0.24, Lilliefors P > 0.20, mean ± s.e.m. for mouse, 6.8 ± 0.7% (n = 10 arteriole branches); rat, 8.9 ± 0.8% (n = 15 branches); and cat, 7.5 ± 0.9% (n = 15 branches). When we recorded venule responses simultaneously with arteriole responses, venules never showed significant diameter changes in response to visual stimuli by any of the following criteria: ANOVA test was P > 0.05 across blank and n test visual stimuli, the percentage change of visually evoked dilation was < 1%, and visually evoked dilations did not exceed 3 s.d. above the mean prestimulation baseline diameter (n = 4 vein branches in two mice and n = 9 vein branches in two rats) (Supplementary Fig. 11). Many within and across controls in cats showed that local pipette and intravenous application of Alexa Fluor 633 did not alter individual neuronal responsiveness and selectivity to sensory visual stimuli (Supplementary Figs. 12 and 13). The cat’s heart rate and electroencephalography activity were also unaffected by application of Alexa Fluor 633 (Supplementary Fig. 14). | ADVANCE ONLINE PUBLICATION | nature methods

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Fluor 633 Fluor 633 Fluor 633 Fluor 633 and Figure 1 | Selective labeling of artery walls by a (4 Alexa c (7 Alexa e Alexa g Alexa p.s.i. puff, pipette) p.s.i. puff, pipette) (intravenous) fluorescein dextran Alexa Fluor 633. (a,b) Two-photon microscopy image of the mouse visual cortex after a 4 pounds per square inch (p.s.i.) 1-s puff of Alexa Fluor + + 633 from a micropipette (a; + indicates tip position) and an autofluorescence image (b) obtained simultaneously in vivo using 2 mW laser power. (c,d) Image from the rat visual cortex after a 7 p.s.i. 1-s puff of Alexa b Autofluorescence d Fluorescein dextran f Fluorescein dextran Fluor 633 in vivo (c) and fluorescence image of the same cortical site taken after intravenous injection of fluorescein dextran (d). (e,f) Images of arteriole walls in mouse visual cortex after intravenous injection of Alexa Fluor 633 (e) and of the same site after injection of fluorescein dextran (f). (g) Average intensity 66 µm z-dimension projection image from mouse h 25 i j 100 n = 23 visual cortex after an intravenous injection of N N O fluorescein dextran and local pipette injection of Alexa Fluor 633. (h) Dilation of an Alexa SO H S S Fluor 633–labeled arteriole from the rat visual F CO H HO S SO H n = 21 HO S SO H cortex in response to drifting grating visual O n = 27 S F stimuli (gray bar). The intersection between 1 0 O Alexa Fluor 633 NH Alexa Fluor 594 the regression line (dotted green) and zero NH H N 0 14 H N level (dashed black) represents the latency of Time (s) dilation (0.8 s). Purple shading indicates s.e.m. k Alexa Fluor 633 and α-SMA l Alexa Fluor 633 and GLUT1 m Alexa Fluor 633 n Alexa Fluor 633 and laminin (n = 16 trials). (i) Molecular structures of Alexa Fluors 633 and 594. (j) Relative brightness of arteriole walls labeled with Alexa Fluors 594, 633 and 647. Data were pooled from five experiments in the rat cortex (error bars, s.e.m.). (k) Image of an immunostained section from the macaque monkey neocortex using an antibody to α-SMA and showing Alexa Fluor 633 labeling. (l) Image of an immunostained section of mouse neocortex with an antibody to GLUT1 and showing Alexa Fluor 633 labeling. (m) Image of a mouse aorta labeled with Alexa Fluor 633 and collected by using 2 mW two-photon laser power. (n) Image of a mouse femoral artery stained with an antibody to laminin and showing Alexa Fluor 633 labeling. In vivo images (a–f) were not background-subtracted or movement-corrected. Images shown in k, l and n were collected with sequential scanning confocal microscopy. Scale bars, 100 µm (a–g), 10 µm (k,l,n) and 50 µm (m). Change in arteriole diameter (%)



To test for arteriole specificity of Alexa Fluor 633 among the general class of Alexa Fluor hydrazide dyes, we compared the in vivo efficacy of three red Alexa dye variants (Alexa Fluors 594, 633 and 647) in the rat neocortex. Alexa Fluor 594 was the most similar enantiomer-like variant to Alexa Fluor 633 (Fig. 1i). Alexa Fluor 633 produced the highest relative brightness of arteriole wall labeling (Online Methods) across different experiments (Fig. 1j; ANOVA P < 0.0001; unequal N honestly significant difference (HSD) = 0.0001 for Alexa Fluor 633 versus 594 and Alexa Fluor 633 versus 647). Arteriole wall binding for Alexa Fluors 594 and 647 was indistinguishable (unequal N HSD = 0.1). Having determined the in vivo selectivity of Alexa Fluor 633 for labeling arteries, we next identified the component of the arteriole wall Alexa Fluor 633 bound by using histological techniques in mouse, rat, macaque monkey and human tissue (Fig. 1k–n and Supplementary Figs. 15–18). Arterioles in the cerebral cortex have five characteristic layers, but only one layer appeared to be labeled by Alexa Fluor 633 in mice, rats and monkeys (Fig. 1k–l and Supplementary Figs. 9, 10 and 15a–c). We performed immunohistochemistry for smooth muscle (antibody to alpha smooth muscle actin (α-SMA)) or endothelial cells (antibody to glucose transporter 1 (GLUT1)) together with Alexa Fluor 633 dye application. Alexa Fluor 633 labeling overlapped with neither α-SMA labeling (Fig. 1k; n = 2 macaque monkeys,


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Fig. 15d–f). Femoral arteries had a thin endothelial basement membrane adjacent and nonoverlapping with Alexa Fluor 633 staining (Fig. 1n; Pearson’s Rr < 0), suggesting that Alexa Fluor 633 was not binding to the endothelial basement membrane. Second, we considered previously published work 13, which showed that using very high power two-photon laser excitation, elastin fibers in artery walls are autofluorescent in the blue-green spectral emission range. High laser powers are incompatible with physiology (owing to phototoxicity) and produce near instantaneous bleaching of Alexa Fluor 633 (Supplementary Fig. 15e). However, in mouse brain and aorta tissue sections labeled with Alexa Fluor 633, we performed sequential two-photon imaging on the same sample by first using low laser power (2–3 mW) to image Alexa Fluor 633 (red emission) and then using high laser power (20–30 mW) to image blue-green autofluorescence (Supplementary Fig. 16). In both brain and aorta tissue samples, Alexa Fluor 633 labeling and endogenous blue-green auto fluorescence overlapped greatly (Pearson’s Rr > 0.8; n = 2 mice).

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Figure 3 | Stimulus-dependent modulation of Cell body 1 a b e fluorescence in neurons located under cortical surface arterioles but not venules. (a–d) Mask representations of the dorsal view of a surface Arteriole 20 Cell body 2 arteriole (a), venule (c) and underlying neuronal 2 0 soma (labeled 1–4) in the rat visual cortex, and 1 Alexa Fluor 594 fluorescence in the indicated –20 0 36 neurons after a visual stimulus, in the same rat Time (s) (b,d; averages of 23 trials). (e) Rendering of a c d f Cell body 3 surface arteriole (magenta) and a neuronal soma (green) in an imaged cortical volume, illustrating Venule the potential dependence of the neuronal imaging 20 artifact on the relative positioning of an arteriole Cell body 4 3 4 and a nearby neuron. The two paths of light were 0 achieved by rotating the head of a mouse that was –20 positioned under the two-photon microscope’s 0 36 Time (s) h objective lens. In head angle 1, light passes through the surface arteriole. In head angle 2, light beam bypasses the arteriole. (f) Mask representation of an imaged area in mouse visual cortex (dorsal view) showing a surface arteriole (magenta) and a neuronal soma located directly below. (g) Alexa Fluor 594 fluorescence from the soma shown in f in response to visual stimuli. (h) Dorsal view of the same area as in f but after head rotation; soma is no longer under the arteriole. (i) Alexa Fluor 594 fluorescence in the soma shown in h in response to visual stimuli. Time courses in g and i are averages of 31 trials. All gray bars represent the period of visual stimulation; error bands, s.e.m.; scale bars, 50 µm.

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4 mice and 3 rats; Pearson’s correlation coefficient Rr < 0) nor endothelial cells (Fig. 1l; n = 4 mice and 3 rats; Pearson’s correlation coefficient Rr < 0). Pericytes were not labeled by Alexa Fluor 633 (Supplementary Fig. 9). Not present in cerebral venules and capillaries, but highly characteristic of cerebral arterioles, is a layer of elastic lamina that lies between the smooth muscle and the endothelial cell layers (Supplementary Fig. 15a). Antibodies to elastin are unreliable for distinguishing among adjacent arteriole wall layers (unpublished observations; D. Li, P.K. and Z.L.) and may compete with Alexa Fluor 633 on elastin fiber binding sites. Therefore to test whether Alexa Fluor 633 specifically labeled elastin fibers on artery walls, we used two strategies. First, we examined other organs in which it is well established that two or six layers of elastin are present on the artery: mouse femoral artery has two characteristic elastin layers and mouse aorta has six. Indeed Alexa Fluor 633 selectively labeled two and six layers on femoral artery and aorta, respectively (Fig. 1m and Supplementary

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i h 25 Figure 2 | Monitoring sensory stimulus–evoked responses in arterioles and adjacent neurons. 1 2 3 4 5 6 7 8 9 1 2 3 10 (a–d) Simultaneous tracking of neuronal activation using the calcium dye Oregon Green 488 8 4 5 6 6 Bapta-1 acetoxymethyl ester (OGB-1 AM) and changes in arteriole diameter using Alexa Fluor 633 in 4 layer 2/3 of the cat visual cortex. Image of an Alexa Fluor 633–labeled (magenta) penetrating arteriole 7 8 9 2 0 0 and adjacent neuronal cell bodies labeled with OGB-1 AM (green) (a). Scale bar, 50 µm. Time courses 0 220 Time (s) (averages of 24 trials) of responses to drifting grating visual stimuli from neurons (b,c) and arteriole (d) marked in a. ∆F/F, relative change in fluorescence. (e–g) Measurement of red blood cell velocity and vessel diameter in an arteriole of the rat visual cortex. Imaged field of view (e) showing an arteriole labeled with Alexa Fluor 633 (magenta) and its lumen labeled with fluorescein dextran (green). Scale bar, 20 µm. Time courses (averages of 32 trials) of increases in velocity (f) and diameter (g) in response to drifting grating visual stimuli measured at locations marked by dotted lines in e. (h,i) Dilation of an Alexa Fluor 633–labeled arteriole in the cat visual cortex (h) in response to the presentation of visual stimuli in each of the nine locations (3 × 3 grid) of a stimulus display monitor (i). Average responses to five repeats of the entire visual stimulus sequence are plotted in h. The response to each stimulus position is plotted in the corresponding location in the 3 × 3 grid in which the luminance value represents the dilation magnitude. All gray bars, period of visual stimulation; error bands, s.e.m.

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brief communications Our histology and in vivo data confirmed that Alexa Fluor 633 selectively bound elastin fibers on artery and arteriole walls. Alexa Fluor 633 artery wall labeling and sensory-evoked dilation measurements were compatible with the imaging of neuronal activity and blood-velocity measurements. We tracked neural activity in the cat primary visual cortex using two-photon imaging of the calcium indicator Oregon Green Bapta-1 and simultaneously monitored sensory-evoked responses in arterioles using Alexa Fluor 633 (Fig. 2a–d). In rats, we also measured blood velocity in vessels using the lumen-labeling dye fluorescein dextran (Fig. 2e–g). Additionally, in the early phase after intravenous injection of Alexa Fluor 633, that is, before it is cleared from the plasma, Alexa Fluor 633 itself can be used to measure blood velo city (Supplementary Fig. 19). To test the use of Alexa Fluor 633 for mapping the sensory-stimulus selectivity of individual arterioles, we presented cats with drifting grating visual stimuli in nine different positions and quantified the dilation in individual arterioles (Fig. 2h,i). We discovered that arteriole dilation decreased the fluorescence recorded from immediately underlying neurons, producing an imaging artifact that has, to our knowledge, not been reported and/or factored into quantifying calcium signals from neurons: we labeled single neurons in layer 2/3 of visual cortex by singlecell electroporation of Alexa Fluor 594 (31 neurons in 4 rats, 1 cat and 1 mouse). We specifically targeted neurons that were located directly below surface blood vessels with diameters of 20–80 µm (Fig. 3). When these vessels were arterioles (for example, Fig. 3a,b), visual stimuli evoked 5–20% dips in fluorescence from neurons (n = 19 neurons; P < 0.005 two-tailed t-test; Lilliefors P > 0.2). Neurons labeled below veins (for example, Fig. 3c,d) showed no dips in fluorescence upon the presentation of visual stimuli (n = 12 neurons; P > 0.05 two-tailed t-test; Lilliefors P > 0.2). To determine whether the two-photon laser beam interacting with the dilating arteriole was producing the dip in fluorescence, we imaged a given cortical neuron in two ways. First we imaged in mice and rats the cortical neuron through a surface arteriole (as above), producing a dip in its fluorescence. Then we rotated the animal’s head up to 50° so that the two-photon beam impacted directly on the neuron’s cell body (Fig. 3e–i). In this situation, the dip in neuronal fluorescence was reduced or eliminated altogether (for example, Fig. 3i; n = 8 neurons; P = 0.01 paired t-test for dependent samples). Arterioles always dilated in response to the visual stimuli irrespective of the head angle. We intentionally performed these ‘dip artifact’ experiments with a nonfunctional dye (Alexa Fluor 594) rather than using calcium indicators so as to capture the pure artifact without contamination from functional neural signals (but see Supplementary Note 1). Here we demonstrated that Alexa Fluor 633 selectively bound elastin fibers in arterial walls in mouse, rat, cat and macaque monkey brains. Alexa Fluor 633 did not label veins, capillaries, neurons, astrocytes or pericytes. Other structurally similar dyes (Alexa Fluors 594 and 647) bound arterial walls less effectively. We showed Alexa Fluor 633 is nontoxic, can be used in combination with various green dyes and to monitor changes in arterial dynamics in response to sensory stimulation. Considering the large number of research studies now using two-photon calcium indicator imaging from neurons, our in vivo arteriole wall labeling method can be used to identify, exclude and correct changes in neuronal fluorescence that are due to arteriole dilation. These assessments extend | ADVANCE ONLINE PUBLICATION | nature methods

the use of Alexa Fluor 633 well beyond its previously reported use in isolated and dissected arteries14,15. This is, to our knowledge, the first reported compound with selectivity for brain arterioles in vivo (see Supplementary Note 2 for discussion on selectivity of Alexa Fluor 633 and other rhodamine derivatives). Alexa Fluor 633 has great potential to be used for the study of arteriole dilation dynamics in various mammalian species and is a new and simple tool for neurovascular coupling research. Alexa Fluor 633 can be used to label arteriole walls locally using micro pipette injection or globally using intravenous administration. In the future, Alexa Fluor 633–like dyes may serve as the basis for the design of therapeutic compounds that bind to arteriole walls but do not enter the parenchyma when injected intravenously as well as for the development of MRI and positron emission tomography analogs for noninvasive imaging in humans. Methods Methods and any associated references are available in the online version of the paper at http://www.nature.com/naturemethods/. Note: Supplementary information is available on the Nature Methods website. Acknowledgments We thank W. Argraves (Medical University of South Carolina) for mouse aorta samples; J. Horton (University of California at San Francisco) for macaque monkey brain tissue; C. Beeson and Y. Peterson for modeling the structural overlap of various Alexa dyes; P. Bell for dissecting mouse kidneys; D. Leopold and A. Silva for discussion on MRI applications; R. Gourdie for discussion of Alexa Fluor 633 binding sites on artery walls; E. Vought for creating illustrations; P. Mulholland for help with using Imaris software; M. Levy and A. Schramm for comments on the manuscript. This work was supported by grants from the US National Eye Institute (R01EY017925 and R21EY020985) and funds from the Medical University of South Carolina to P.K. AUTHOR CONTRIBUTIONS P.K. conceived and designed the study, except the head angle with dip artifact experiments (Z.S.). All authors performed in vivo vessel imaging experiments. Z.S. did in vivo calcium imaging and head-angle experiments. Z.L. did histology on brain and femoral vessel tissue samples. P.K. did histology of mouse aorta, mouse kidney and human aorta tissues. P.K., Z.S., P.Y.C. and P.O. analyzed the in vivo vessel dilation and velocity data. P.K. and Z.S. analyzed the in vivo calcium imaging and single-cell electroporation data. All authors discussed results. P.K. wrote the manuscript. COMPETING FINANCIAL INTERESTS The authors declare competing financial interests: details accompany the fulltext HTML version of the paper at http://www.nature.com/naturemethods/. Published online at http://www.nature.com/naturemethods/. Reprints and permissions information is available online at http://www.nature. com/reprints/index.html. Attwell, D. et al. Nature 468, 232–243 (2010). Kleinfeld, D. et al. Front. Neuroenergetics 3, 1 (2011). Iadecola, C. & Nedergaard, M. Nat. Neurosci. 10, 1369–1376 (2007). O’Kusky, J. & Colonnier, M. J. Comp. Neurol. 210, 278–290 (1982). Ohki, K. et al. Nature 442, 925–928 (2006). Ohki, K., Chung, S., Ch’ng, Y.H., Kara, P. & Reid, R.C. Nature 433, 597–603 (2005). 7. Kara, P. & Boyd, J.D. Nature 458, 627–631 (2009). 8. Logothetis, N.K. Nature 453, 869–878 (2008). 9. Berlier, J.E. et al. J. Histochem. Cytochem. 51, 1699–1712 (2003). 10. Drew, P.J. et al. Nat. Methods 7, 981–984 (2010). 11. Tian, P.F. et al. Proc. Natl. Acad. Sci. USA 107, 15246–15251 (2010). 12. Drew, P.J., Shih, A.Y. & Kleinfeld, D. Proc. Natl. Acad. Sci. USA 108, 8473–8478 (2011). 13. Zipfel, W.R. et al. Proc. Natl. Acad. Sci. USA 100, 7075–7080 (2003). 14. Kansui, Y., Garland, C.J. & Dora, K.A. Cell Calcium 44, 135–146 (2008). 15. Clifford, P.S. et al. Arterioscler. Thromb. Vasc. Biol. 31, 2889–2896 (2011). 1. 2. 3. 4. 5. 6.


ONLINE METHODS Animals and human tissue samples. Five mammalian species were used in this study. The Institutional Animal Care and Use Committee of the Medical University of South Carolina approved all animal experiments. Wild-type C57BL/6 mice (15 males, postnatal days 35–60) and Long Evans rats (24 males, postnatal days 29–48) and cats (13 of either sex, postnatal day 28 to > 2.5 kg adult) were used for in vivo physiology and postmortem histology studies. Mice and rats were purchased from Charles River and Harlan Laboratories. Cats were purchased from Liberty Research. The mice, rats and cats were housed in independent facilities specifically designed for each species. Mice were housed in ventilated racks. Rats were housed in microisolator cages. Cats were housed in open condo-style cages with food bowls, water bottles and litter boxes and had unrestricted access to roam in the confines of the room in which they were located. All animal facilities maintained >15 air changes per hour with centrally controlled and monitored humidity and 12 h day–12 h night lighting cycles. All animals received environmental species-specific enrichment. Macaque monkey postmortem samples obtained directly from the University of California at San Francisco (two samples) and human postmortem aorta or coronary artery samples from ILSbio (two samples) were used for histochemistry and immunofluorescence only. Because the human tissue samples were postmortem, deidentified and not collected for the specific purpose of this study, our Institutional Review Board for Human Research Experiments determined that our dye labeling of human tissue was not ‘Human Subject Research’; no Institutional Review Board exemption was required. In vivo two-photon imaging in mice, rats and cats. Mice were anesthetized with urethane (1.5 g kg−1 intraperitoneally), supplemented with ketamine (10–30 mg kg−1) and xylazine (1–3 mg kg−1) during surgery. In rats, anesthesia was induced with a fentanyl cocktail (0.04–0.06 mg kg−1 fentanyl citrate, 3.75–6.25 mg kg−1 midazolam and 0.19–0.31 mg kg−1 dexmedetomidine, all administered intraperitoneally). During two-photon imaging, the doses were lowered (0.02–0.03 mg kg−1 h−1 fentanyl citrate, 1.50–2.50 mg kg−1 h−1 midazolam and 0.15–0.25 mg kg−1 h−1 dexmedetomidine) and administered via continuous intra peritoneal infusion using a catheter connected to a syringe pump. In cats, anesthesia was induced with an intramuscular injection of ketamine (10–20 mg kg−1) and acepromazine (0.2 mg kg−1), continued with isoflurane (1–2% in surgery and 0.5–1.0% during imaging). Cats were paralyzed with vecuronium bromide (0.2 mg kg−1 h−1, intravenously), and expired CO2 was regulated at 3.5–4.5% via mechanical ventilation. In each of the three species, a customized titanium or stainless steel headplate was attached to the skull using dental cement. Then a craniotomy was performed over the primary visual cortex, the dura was reflected, and the underlying cortex was covered with agarose. Removal of the dura was necessary in cats and rats but was left intact in mice. Individual Alexa dyes were injected into the visual cortex by a 2-µm-diameter micropipette (50 µM dye concentration) or administered via intravenous injection (1–2 mg kg−1; tail vein in rodents and femoral vein in cats). Thick dura made topical application of Alexa dyes ineffective. Provided that excessive laser power was not used, a single injection of Alexa Fluor 633 (Alexa 633) resulted in brightly labeled arteriole walls for the entire in vivo recording period, doi:10.1038/nmeth.1857

at least 26 h in our acute experiments (Supplementary Fig. 4). The cell-permeant calcium indicator Oregon Green 488 Bapta-1 AM (Invitrogen) was injected into layer 2/3 visual cortex as previously described7. Fluorescence from neurons and vessels was monitored with a customized Prairie Technologies microscope and light path (Supplementary Fig. 20) that was optimized for in vivo experiments in mouse, rat and cat preparations, and coupled with a Mai Tai XF (Newport Spectra-Physics) mode-locked Ti:sapphire laser (810 nm). Excitation light was focused by a high-numericalaperture (1.0 NA) water-immersion objective (Olympus XLUMPLFLN 20×) and beam expansion optics was used to overfill the back aperture (Supplementary Fig. 20). For deep in vivo imaging up to 550 µm below the cortical surface (for example, Supplementary Fig. 7), we used a 1.1 NA water-immersion objective (Nikon CFI Apo LWD 25×). Two non-descanned photo multiplier tube (PMT) detector channels (Hamamatsu) were used with barrier filters (Chroma). Barrier filters were customized as needed to match particular fluorophores of interest: ET 525/50m-2p for Oregon Green 488 Bapta-1 AM and fluores cein dextran 70 kDa and 2,000 kDa (Sigma-Aldrich); ET 665/40m-2p for Alexa Fluors 633 and 647 hydrazide (Invitrogen), HQ 645/70m-2p for Alexa Fluor 594 hydrazide (Invitrogen) and BG22 for blue-green autofluorescence. In addition to barrier filter and PMT dichroic selection, detection of long-wavelength dyes such as Alexa Fluors 633 and 647 hydrazide also required careful selection of the primary dichroic (700dcxr, Chroma) and blocking filter (ET 700sp-2p8; Chroma). In vivo imaging of the arterial and venous filling phases in cerebral cortex. Using a 4× air objective (Olympus UPLFLN, NA 0.13), time-lapse video of the arterial and venous filling phases upon intravenous administration of fluorescein dextran (~50 mg kg−1 via a 5% stock solution, injecting 0.1 ml per 100 g of body weight) was recorded from the cat visual cortex with an Adimec 1000 monochrome charge-coupled device (CCD) camera (50 Hz frame rate, 498 × 498 pixels; see Supplementary Figure 1 and Supplementary Video 1). The Adimec 1000 was attached directly to the c-mount camera port of the Prairie Technologies microscope. An Exfo X-CITE 120Q light source was used in combination with a FITC epi-fluorescence filter set (Chroma) placed before the CCD camera to collect fluorescein dextran fluorescence. Visual stimulation. Mice, rats and cats viewed drifting square wave grating stimuli (1.5–2 Hz, 100% contrast) presented on a computer display monitor while we performed two-photon imaging at a particular site in the primary visual cortex. In experiments that required the assessment of vessel dilation and/ or red blood cell velocity to sensory stimulation, visual stimuli were presented for 4–8 s interspersed by a two- to fivefold longer period of blank (equiluminant gray screen). The duration of the stimulation period, for example, 8 s, and the duration of the blank display period, for example, 24 s, was always identical across all 16 or more repeats of a particular two-photon imaging session. For control experiments that required calcium imaging from neurons without an assessment of vessel dilation or red blood cell velocity (for example, Supplementary Figs. 12 and 13), blank display periods were of equal duration to visual stimulation periods7. This is because unlike arteriole dilation, nature methods


neural calcium transients returned to baseline nearly imme diately upon extinguishing the visual stimulus7 (Fig. 2b,c). In vivo data-analysis overview. All two-photon images of vessels and neurons collected during in vivo physiology experiments were analyzed using customized Matlab (MathWorks) software. Some statistical functions not available in Matlab were implemented in Statistica (Statsoft). Three-dimensional rendering and masking of cells and vessels in imaged volumes were performed using Imaris (Bitplane) (for example, Fig. 3e). Typically a square region of cortex was imaged at 128 × 128 pixels (200 µm per side) to 512 × 512 pixels (600 µm per side) at 0.16–1.64 s per frame. For blood-velocity measurements, line-scan parameters were optimized for a given caliber vessel (2–6 µs pixel−1, 0.23–0.93 µm pixel−1 and 0.63–1.7 ms line−1). Movements of the brain from respiratory and heartbeat pulsations were negligible with our established imaging and surgical methods, which sometimes included a lumbar suspension and pneumothorax in cats5–7. However, in our vessel dilation analyses, to compensate for very small drifts in baseline vessel diameter over a long recording session containing many hundreds to thousands of imaging frames, the mean vessel diameter for adjacent baseline frames during each blank period epoch and subsequent adjacent baseline frames during the next blank period epoch were linearly interpolated. For calcium imaging from neurons, images were realigned by maximizing the correlation between frames6. As brain movement was minimal, realignment did not influence the statistical outcome of responsiveness and tuning-width measurements. Quantifying vessel dilation and blood velocity in vivo. To determine the change in vessel diameter upon sensory stimulation when artery walls were labeled with Alexa 633, we first used a Gaussian filter (σ ≈ 1 µm) to smooth each imaging frame of the time series. Vessel cross-sections were then selected manually using a graphical user interface. When vessels were imaged longitudinally (Fig. 1a,c,e), the cross-section was drawn perpendicular to the two parallel lines that represented each side of the vessel wall. When a vessel was imaged transversely (Fig. 2a), cross-section line segments passed through the center of the circular vessel. Because Alexa 633 labeled the arterioles brightly with minimal dye in the lumen of the vessel and the brain parenchyma (Fig. 1), the profile of image brightness along the cross-section line segment always had only two maxima. Each maximum corresponded to each side of the vessel. The distance between the two maxima of each cross-section line segment represented the diameter of the vessel. The above procedures were used to calculate the diameter in each imaging frame of the time series, resulting in a time course of vessel dilation for each cross-section examined (for example, Figs. 1h and 2d,g,h). The Lilliefors test for normality was satisfied for all vessels examined. Vessels selectively tuned for particular receptive field location was defined by ANOVA across n stimulus periods (P < 0.05). Typically, our two-photon imaging frame rates for vessel imaging were slow (1–1.64 s frame−1) but well within the range used in our previous calcium imaging work5–7. With faster imaging frame rates (0.16–0.2 s frame −1) we could extract the latency of arteriole dilation relative to the onset of the grating visual stimulus. To determine the latency of dilation upon sensory stimulation, each of n baseline and m stimulus frames were averaged across all repeats. We then performed a linear regression nature methods

on the rising phase of the vessel dilation response using data points between 20% and 80% to the peak dilation11 (for example, Fig. 1h). Visually evoked latencies of dilation for arterioles ranged from 0.67 s to 0.99 s (n = 3 arteriole branches in two mice and 5 arteriole branches in two rats). To quantify the change in vessel diameter upon sensory stimulation when the lumen of veins and arteries were labeled with fluorescein dextran, we applied the same Gaussian smoothing as described above for Alexa 633– labeled walls. However, maxima with fluorescein dextran labeling did not necessarily correspond with the vessel diameter because the lumen could have near equal brightness at more than two points along the cross-section line segment. Thus, for fluorescein dextran labeling, we calculated the first derivative of the profile of image brightness along the cross-section line segment. The first derivative had a maximum at the wall-lumen interface. Red blood cell velocity was determined from line scans of fluorescein dextran– (or Alexa 633–) labeled lumen of the blood vessel of interest (Supplementary Fig. 19). We used the Radon transform to calculate velocity (in millimeters per second) as described previously16. Typically, we calculated the average velocity per block of 300 sequential line scans with an overlap of 100 lines between blocks. The algorithm was implemented using the Matlab function ‘radon’. The time courses of visual stimulus evoked velocity responses (blank versus stimulation periods; for example, Fig. 2f) were smoothed by a 10-point sliding mean. Quantifying the efficacy of three red Alexa dye variants in vivo (Alexa 594, 633 and 647). We adopted the following standardized protocol in five rats: for each rat, two dyes were compared simultaneously in two adjacent craniotomies or injection sites. Each dye (50 µM solution) was applied locally in the cortex with a 2-µm-diameter tip patch pipette. The patch pipette was positioned ~50 µm below the pial surface and ~50 µm from a wall of the artery of interest. We always applied three pulses to puff out dye from the pipette tip (4 p.s.i. per pulse, 1 s pulse duration). After all unbound dye washed away, we collected two-photon images of longitudinal sections of the vessel walls using very low, 2–3 mW laser power (for example, Fig. 1a). We drew crosssection line segments perpendicular to the two parallel lines that represented each side of the vessel wall, as was done for vessel diameter calculations (see above). The binding efficacy of a given dye in vivo was determined as the brightness of the vessel wall versus brightness outside of the vessel wall along individual cross-section line segments. Tukey’s HSD post-ANOVA comparisons were used to determine whether the aggregate mean differences in labeling brightness among the Alexa 594, 633 and 647 groups were significantly different (Fig. 1j). Quantifying neuronal calcium transients. The time course of calcium transients from neurons was used to determine statistically significant visually evoked responses and selectivity using established procedures5–7. Briefly, neurons were identified through a series of morphological filters that defined the contours of cell bodies based on intensity, size and shape. Time courses of individual cells were extracted by calculating mean pixel values within cell contours. Visually responsive cells were defined by ANOVA across blank and n test visual stimuli (P < 0.05). Cells selective for particular stimuli were defined by ANOVA across n stimulus periods (P < 0.05). doi:10.1038/nmeth.1857


Perfusion and histology. A mixture of pentobarbital and phenytoin sodium (Euthasol, 180 mg kg−1, Virbac) was given intraperitoneally to deeply anesthetize (EEG flatlined) and kill (heart rate and respiration fell to zero) mice and rats. The mice and rats were then transcardially perfused with saline followed by 4% paraformaldehyde in phosphate buffer using a peristaltic pump. Brain, aorta, kidneys and femoral vessels were then dissected and incubated with 30% sucrose in phosphate buffer. Then the tissues were placed on dry ice and embedded into optimal cutting temperature (OCT) compound (Tissue-Tek). Tissue sections (40 µm thick) were cut on a vibratome. As we had to perform immunohistochemistry on multiple adjacent components of the vessel wall, it was necessary to ensure that paraformaldehyde fixation itself did not influence antibody binding to vessel lamina. Therefore in a few mice and rats, we performed histo logy without paraformaldehyde perfusion. In these instances, tissues were dissected immediately after killing, placed on dry ice, embedded in OCT compound and stored at −80 °C. OCTembedded frozen tissue was cryosectioned (8–10-µm-thick sections). Postmortem blocks of macaque monkey cerebral cortex were also cut on a vibratome (5- and 40-µm-thick sections). Human aorta and coronary samples were flash-frozen directly by ILSbio before our study began and before our request for such samples from them. ILSbio obtained these tissues from healthy subjects with a postmortem interval of ~2 h or less. Human tissue samples were cut in 5-µm-thick sections. During our study, no local or national human tissue bank had cerebral cortex tissue sections from healthy subjects with short postmortem intervals. Short postmortem interval samples from healthy human tissue samples appeared to be necessary for robust labeling of artery and arteriole walls by Alexa 633 because our anecdotal observations from postmortem tissue samples that had undocumented postmortem intervals, stored in fixative for years and/or associated with extensive neurodegenerative pathology did not yield bright artery- or arteriole-specific labeling with Alexa 633. Our rat and mouse tissues essentially had zero postmortem intervals to transcardial paraformaldehyde perfusion or OCT embedding and flash-freezing. Furthermore, rodent samples were not stored in paraformaldehyde or other fixative for months. We observed no difference in the quality of Alexa 633 binding to artery and arteriole walls using our two methods of histological preparation of rat or mouse tissue. For flash-frozen human tissue, the quality of Alexa 633 labeling of artery walls was equally good irrespective of whether the dye was applied to unfixed or acetone-fixed tissue sections or whether endogenous peroxidase was quenched. Immunofluorescence analysis, autofluorescence analysis, Alexa 633 labeling and bleaching. Tissue sections were bathed in blocking solution: 3% normal goat serum in phosphate buffered saline (PBS; pH 7.4) for 60 min. The following primary antibodies were applied overnight (one antibody per section, except for triple labeling described below): rabbit antibody to GLUT1 (1:400, Abcam, AB652) as an endothelial cell marker; mouse antibody to actin-α-smooth muscle clone 1A4 (1:400, Sigma-Aldrich, A2547) as a marker for smooth muscle; rabbit antibody to laminin (1:200, Abcam, AB11575) as a maker for endothelial basement membrane, and goat antibody PDGFR-β (1:40, R&D Systems, AF1042)

doi:10.1038/nmeth.1857

as a marker for pericytes. After three washes for 5 min each in PBS, the following secondary antibodies were applied (one antibody per section, except for triple labeling described below): goat anti-rabbit IgG (FITC) or goat anti-rabbit IgG (Alexa Fluor 488) for GLUT1; goat anti-mouse IgG (FITC) for actin-α-smooth muscle; anti-rabbit IgG (FITC) for laminin and rabbit anti-goat IgG (FITC) for PDGFR-β. The primary antibodies we used were tested for specificity in previous studies17–20. But even in our study, the specificity of antibodies used to label smooth muscle, endothelial cells, basement membrane and pericytes was unambiguous via unique morphological features that were labeled (Supplementary Figs. 9, 10 and 15) and negative controls. Using ‘green’ secondary antibodies for smooth muscle, endothelial cells, basement membrane and pericytes enabled us to perform colocalization studies with the ‘red’ arteriole-specific Alexa 633. Consequently, Alexa 633 (1:1,000 dilution from 2 mM stock) was added to the histological tissue sections for 1–2 min and rinsed. The tissue sections on the glass slides were then prepared for confocal, two-photon or epi-fluorescence microscopy by applying Fluoromount aqueous mounting medium (Sigma) and covered by a coverslip. Two-color histological fluorescence images (2,048 × 2,048 pixels) were typically collected on a Leica TCS SP5 confocal laser scanning microscope using a very high NA (1.47) oilimmersion objective (Leica HCX PL APO 63×). Using individual laser lines (Ar 488, He 543 and 633 nm) together with the 1 nm tuning resolution of the prism-based spectral emission filters on the Leica confocal microscope ensured that red and green fluorescence detection bandwidths were completely nonoverlapping for the dyes of interest. To further minimize bleed through, we acquired confocal images exclusively by sequential scanning21,22. Some histological sections, particularly those labeled with one exogenous fluorescent label, for example, Alexa 633, were examined using our in vivo two-photon microscope. Two-photon excitation of brain and aorta tissue sections was critical for determining whether the signature autofluorescence of elastin fibers13 (blue-green emission) visible with high two-photon laser power (>20–30 mW, measured at the front aperture of the objective lens) overlapped with Alexa 633 labeling that was evident with low (2 mW) power (Supplementary Fig. 16). For these autofluorescence tests, we used emission filters ET 665/40m-2p for Alexa 633 on one PMT channel and BG22 for blue-green autofluorescence on a second PMT channel. No bleaching of Alexa 633 occurred with low two-photon laser power, but using >20–30 mW to thin tissue sections on glass slides produced near-instantaneous bleaching (Supplementary Fig. 15e). Because our Leica confocal and Prairie Technologies two-photon imaging systems were each equipped with only two PMTs, triple-labeled tissue sections with Alexa 633, PDGFR-β antibodies and 4′,6-diamidino-2-phenylindole (DAPI) or PDGFR-β antibodies, GLUT1 antibodies and DAPI were examined on a Nikon 90i epi-fluorescence microscope, using a CFI Plan Fluor 40× oil NA 1.3 objective and FITC, Texas Red and DAPI filter sets. PDGFR-β antibody, GLUT1 antibody and DAPI triple labeling (Supplementary Fig. 9b) required the following secondary antibodies: donkey anti-goat IgG (Alexa 594) for PDGFR-β and bovine IgG (FITC) for GLUT1. Quantitative localization21,22 analysis including Pearson’s correlation coefficient after background subtraction 21,22 was performed using CoLocalizer Pro (CoLocalization Research Software).

nature methods

19. Svedin, P., Hagberg, H., Savman, K., Zhu, C. & Mallard, C. J. Neurosci. 27, 1511–1518 (2007). 20. Bell, R.D. et al. Neuron 68, 409–427 (2010). 21. Bolte, S. & Cordelieres, F.P. J. Microsc. 224, 213–232 (2006). 22. Zinchuk, V. & Grossenbacher-Zinchuk, O. Prog. Histochem. Cytochem. 44, 125–172 (2009).


16. Drew, P.J., Blinder, P., Cauwenberghs, G., Shih, A.Y. & Kleinfeld, D. J. Comput. Neurosci. 29, 5–11 (2010). 17. Carlson, T.R. et al. Proc. Natl. Acad. Sci. USA 102, 9884–9889 (2005). 18. Li, Y., Lu, Z., Keogh, C.L., Yu, S.P. & Wei, L. J. Cereb. Blood Flow Metab. 27, 1043–1054 (2007).

nature methods

doi:10.1038/nmeth.1857

Nature Methods

An artery-specific fluorescent dye for studying neurovascular coupling Zhiming Shen, Zhongyang Lu, Pratik Y Chhatbar, Philip O’Herron & Prakash Kara

Supplementary Figure 1

Delay between the filling of arteries and veins after intravenous dye injection confirms artery specificity of Alexa 633 labeling in vivo


Vessel classification using brightfield vs. two-photon fluorescein dextran + Alexa 633 in vivo


Vessel classification using brightfield vs. two-photon green tomato lectin + Alexa 633 in vivo


Persistent in vivo Alexa 633 labeling > 24 hours post-injection


Surface and penetrating arterioles ≥ 15 µm in diameter are labeled in vivo by Alexa 633 while veins, venules, and microvessels are not


The smallest surface arteriole branches (≤ 10 µm diameter) are not labeled by Alexa 633 in vivo


A deep penetrating arteriole labeled by Alexa 633 in vivo


Small (< 10-µm-diameter) branchlets off penetrating arterioles are not labeled by Alexa 633 in vivo


PDGFR-β immunohistochemistry suggests that pericytes are not labeled by Alexa 633


Alexa 633 does not label the small microvessels in histological sections of the cerebral cortex


Arterioles but not venules in the visual cortex respond to drifting grating visual stimuli


Neuronal responses are unaffected by local micropipette application of Alexa 633 in the visual cortex


Neuronal responses are unaffected before and after intravenous application of Alexa 633 in the same animal


Nature Methods: doi:10.1038/nmeth.1857

EEG and heart rate are unaffected by Alexa 633


Binding site of Alexa 633 on artery/arteriole wall


Alexa 633 labels elastin fibers on artery walls in brain and aorta


Alexa 633 labeling of human aorta


Alexa 633 labeling of mouse kidney


Red blood cell velocity in cortical microvessels after intravenous injection of fluorescent dyes


Light path used for in vivo two-photon imaging

Supplementary Note 1

Fluorescence dip artifact caused by arteriole dilation

Supplementary Note 2

Selectivity of Alexa 633 vs. other rhodamine derivatives

Note: Supplementary Videos 1–4 are available on the Nature Methods website.


Supplementary Figure 1 Delay between the filling of arteries and veins after intravenous dye injection confirms artery specificity of Alexa 633 labeling in vivo

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Two-photon images of Alexa 633 (red) and fluorescein dextran (green) labeling

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Time-lapse cortical images after an i.v. injection of fluorescein dextran

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Supplementary Figure 1 Delay between the filling of arteries and veins after intravenous dye injection confirms artery specificity of Alexa 633 labeling in vivo (a) Brightfield image of a craniotomy over the cat visual cortex, viewed through a surgical dissecting microscope. No dyes were present in the circulation at this phase of the experiment. Arteries and veins were classified based on small difference in hue—arteries orange and veins purple. These differences can be enhanced by using the ‘Auto Levels’ function in Adobe Photoshop, as done in panel a of this figure and the brightfield images shown in Supplementary Figs. 2–3. Large dashed square indicates the region imaged in b with a monochrome CCD camera and the two smaller square boxes (sites 1 and 2) are sub-regions corresponding to two-photon images shown in c. (b) Time-lapse epifluorescence imaging of cortical vessels immediately after fluorescein dextran was injected intravenously in the femoral vein. The upper panels display two still images from Supplementary Video 1. The left panel was captured shortly after the dye reached the cortex when only the arteries were labeled—because the dye must pass from the femoral vein (in the leg) through the heart to get to the cerebral cortex, the arteries must label first. The right panel, captured 7 seconds later—when the dye was predominantly in the veins. Because it takes a few circulatory cycles for the dye to become equally distributed in the blood plasma, the arteries darken as the dye moves into the veins. The bottom panel is an RGB image created by overlaying the top two panels. The pixel intensity values from the arterial phase (left panel) are displayed in the red channel and those from the venous phase (right panel) in the green and blue channels—hence the cyan shading for veins. (c) Two-photon images corresponding to the sites identified in a, after intravenous injection of Alexa 633 and fluorescein dextran. The subset of vessel walls labeled with Alexa 633 (red) correlate precisely with the classification of arteries by the two independent methods shown in a and b. The two-photon image from site 1 is an average intensity 100-µm-z-projection through the cortex while the two-photon image from site 2 is a single imaging plane on the cortical surface. Hue differences between arteries and veins (as shown in panel a) were consistently observed across all three species examined: cat—Supplementary Fig. 1, rat—Supplementary Figs. 2-3, and mouse (data not shown).


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Supplementary Figure 2 Vessel classification using brightfield vs. two-photon fluorescein dextran + Alexa 633 in vivo

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Supplementary Figure 2 Vessel classification using brightfield vs. two-photon fluorescein dextran + Alexa 633 in vivo (a) Brightfield image of a craniotomy over rat visual cortex, equivalent to the approach described for cat visual cortex in Supplementary Fig. 1. Orange arteries (A) and purple veins (V) are readily discernable. (b) Two-photon image of a sub-region shown in a, after intravenous injection of Alexa 633 and fluorescein dextran. Image is a 46 µm maximum intensity z projection and shows an arteriole (A) and daughter branches labeled with Alexa 633. A venule (V) running in parallel to the larger arteriole is not labeled by Alexa 633. (c) Higher zoom two-photon image corresponding to a sub-region shown in b. The image is from a single imaging plane corresponding to the z stack shown in Supplementary Video 2. The arteriole branches are well labeled with Alexa 633 while the nearby venule is unlabeled.


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Supplementary Figure 3 Vessel classification using brightfield vs. two-photon green tomato lectin + Alexa 633 in vivo

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Supplementary Figure 3 Vessel classification using brightfield vs. two-photon green tomato lectin + Alexa 633 in vivo (a) Brightfield image of a craniotomy over rat visual cortex, equivalent to the approach described in Supplementary Figs. 1–2. Orange arteries and purple veins are readily discernable. (b) Two-photon image of a sub-region shown in a, after intravenous injection of Alexa 633 and green tomato lectin. Endothelial cells in all vessels were labeled with green tomato lectin but only arteriole branches were labeled by the Alexa 633 injection. Image is an average intensity 64-µm-z-projection.


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Supplementary Figure 4 Persistent in vivo Alexa 633 labeling > 24 hours post-injection

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Supplementary Figure 4 Persistent in vivo Alexa 633 labeling > 24 hours post-injection (a) Two-photon image from the cat visual cortex obtained four hours after intravenous injection of Alexa 633 is an average intensity 20-µm-z-projection and shows a cortical artery brightly labeled with Alexa 633. (b) Another two-photon image (average intensity 10-µm-z-projection) showing a different cortical artery in the same cat as shown in a. In a given craniotomy, tissue growth over the cortex (especially in cats) reduces overall imaging quality over long time periods (24 h). Therefore to ensure that we only measured the loss in Alexa 633 wall labeling, we opened a new craniotomy 26 h after the injection and imaged a new artery. The vessel was still brightly labeled. Images shown in a and b were obtained with the identical laser power and PMT value.


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Supplementary Figure 5 Surface and penetrating arterioles ≥ 15 µm in diameter are labeled in vivo by Alexa 633 while veins, venules, and microvessels are not

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Supplementary Figure 5 Surface and penetrating arterioles ≥ 15 µm in diameter are labeled in vivo by Alexa 633 while veins, venules, and microvessels are not (a) Single z plane two-photon image from mouse visual cortex, 78 µm below the pial surface. The entire vasculature is visible (green) after an intravenous injection of fluorescein dextran. The wall of a penetrating arteriole is clearly labeled with Alexa 633 (red) while surrounding micro-vessels are not. (b) A surface two-photon image of a large arteriole labeled with Alexa 633. Surrounding venules and microvessels are not labeled by Alexa 633. Note the hexagonal microvessel loop (white arrows), as described by Blinder and colleagues (Proc. Natl. Acad. Sci. USA 2010). The image is a maximum intensity 15-µm-zprojection, from the same craniotomy used to obtain the image shown in a. (c) Dorsal view perspective of a 3D volume rendering of the nearly identical region shown in a, but with a higher 2× optical zoom. The surface and penetrating arterioles are labeled by Alexa 633 while the vein on the right edge and microvessels are not. (d) Left side view perspective of the same 3D volume shown in c. The penetrating arteriole is labeled throughout the imaged depth. In the current example, Alexa 633 labeling was obtained by local micropipette injection (cf. intravenous injection in experiments shown in Supplementary Figs. 1–4). x-y-z volume dimensions in c–d: 220 × 234 × 123 µm. A 360° rendering of the volume is shown in Supplementary Video 3.


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Supplementary Figure 6 The smallest surface arteriole branches (≤ 10 µm diameter) are not labeled by Alexa 633 in vivo

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Supplementary Figure 6 The smallest surface arteriole branches (≤ 10 µm diameter) are not labeled by Alexa 633 in vivo (a) Average intensity 66-µm-z-projection two-photon image from mouse visual cortex. The entire vasculature is visible (green) after an intravenous injection of fluorescein dextran. The smallest surface arteriole branch ≈ 10 µm diameter (white arrow) is unlabeled by local micropipette injection of Alexa 633. (b) Average intensity 12-µm-z-projection two-photon image from rat visual cortex. After intravenous injection of Alexa 633, the ≈ 25-µm-diameter arteriole was labeled. But the smaller ≈ 7-µm-diameter offshoot was not labeled by Alexa 633 (white arrow). Alexa 633 labeling ends abruptly at the vessel branch point (red arrow). The vasculature lumen is visible (green) via an intravenous injection of fluorescein dextran.


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Supplementary Figure 7 A deep penetrating arteriole labeled by Alexa 633 in vivo a

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Supplementary Figure 7 A deep penetrating arteriole labeled by Alexa 633 in vivo (a) Side view perspective of a 3D volume rendering of a two-photon imaged site in rat visual cortex—from the pial surface to a depth of 550 µm. The lumen of all vessels is labeled with fluorescein dextran (green). After intravenous injection of Alexa 633, a penetrating arteriole ≈ 17-µm-diameter is labeled with Alexa 633 (red) while the surrounding micro-vessels are not. x-y-z volume dimensions: 135 × 135 × 550 µm. Gray slices correspond to z cross-sections shown in c and d. (b) Dorsal view of the same cortical region shown in a. The dashed white square indicates the sub-region used to render the 3D volume shown in a. The white arrow shows a small branch coming off the larger ≈ 17-µmdiameter penetrating arteriole. This small branch (≤ 10 µm diameter) is not labeled by the intravenous injection of Alexa 633. (c) Image of a maximum intensity 30-µm-z-projection centered 355 µm below the cortical surface. The penetrating arteriole is labeled by Alexa 633 while two small < 10-µm-diameter branches (white arrows) and surrounding micro-vessels are not. (d) Image of a maximum intensity 20-µm-z-projection centered 480 µm below the cortical surface. The penetrating arteriole is still labeled by Alexa 633 while the small < 10-µm-diameter branchlet (arrowed) and surrounding vessels are not.


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Supplementary Figure 8 Small (< 10 µm diameter) branchlets off penetrating arterioles are not labeled by Alexa 633 in vivo

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Supplementary Figure 8 Small (< 10-µm-diameter) branchlets off penetrating arterioles are not labeled by Alexa 633 in vivo Two-photon image of an average intensity 16-µm-z-projection centered 105 µm below the pial surface. The lumen of all vessels is labeled with fluorescein dextran (green). The schematic of a micropipette indicates the tip location where Alexa 633 was ejected. Penetrating arterioles with branchlets (# 1–4) are labeled with Alexa 633 but diving venules (V) and microvessels are not. The white arrow indicates an unlabeled ≈ 8-µm-diameter branchlet from the same artery as # 1 (≈ 12 µm diameter and labeled by Alexa 633). Inset overlaid in the center of the two-photon image provides further details where Alexa 633 labeling ends on arterioles by showing a mask rendering of the penetrating arteriole that gives rise to the two branchlets: # 1 and the other indicated by the white arrow. The mask was created using the automatic masking function in Imaris software. The best visualization of the mask of the two-branchlets with their parent arteriole was via a ventral projection (as shown). Note that the lack of label in the ≈ 8-µm-diameter branchlet could not be due to diffusion distance from the injection site since artery # 2 is much further away from the pipette tip and is clearly labeled.


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Supplementary Figure 9 PDGFR-β immunohistochemistry suggests that pericytes are not labeled by Alexa 633

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Supplementary Figure 9 PDGFR-β immunohistochemistry suggests that pericytes are not labeled by Alexa 633 (a) Triple labeling of Alexa 633/PDGFR-β/DAPI in mouse visual cortex shows no overlap of labeling between Alexa 633 (red) and PDGFR-β (green). Furthermore, pericytes on microvessels, e.g., white arrow, show no labeling by Alexa 633. (b) Triple labeling of PDGFR-β/Glut1/DAPI in mouse visual cortex shows pericytes labeled (red) along microvessels visualized with Glut1 (green). We present numerous lines of in vivo and other histological data to show that there is no labeling of Alexa 633 on microvessels < 10 µm in diameter (also see Supplementary Figs. 5–8, 10). Furthermore, there are no pericytes in the aorta (Wagenseil and Mecham, Physiol. Rev. 2009), yet Alexa 633 labels distinct elastin bands in the aorta (Supplementary Fig. 15). The labeling of Alexa 633 is inconsistent with pericytes as a target.


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Supplementary Figure 10 Alexa 633 does not label the small microvessels in histological sections of the cerebral cortex Alexa 633

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Supplementary Figure 10 Alexa 633 does not label the small microvessels in histological sections of the cerebral cortex Glut-1 or Laminin were used as a green counterstain for microvessels (white arrows) in individual tissue sections. Not a single microvessel was co-labeled by Alexa 633 (red). All data shown are from mouse visual cortex. Scale bars = 20 µm.


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Supplementary Figure 11 Arterioles but not venules in the visual cortex respond to drifting grating visual stimuli

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Supplementary Figure 11 Arterioles but not venules in the visual cortex respond to drifting grating visual stimuli (a–b) Two-photon images collected from two adjacent cortical sites in the same mouse. The vascular lumen (green) was labeled by intravenous injection of fluorescein dextran. A single micropipette injection of Alexa 633 (red) selectively labeled arterioles in both sites. White hatched lines correspond to the cross-sections used to analyze the time course of change in vessel diameter with sensory stimulation (c–d). White arrow in a denotes the ≤ 10-µm-diameter arteriole branchlet that was not labeled by Alexa 633. (c–d) Time courses of percent change in the vessel diameter to visual stimulation for arterioles (red traces) and venules (blue traces) recorded simultaneously in each of the two sites using two-photon microscopy. The grey shaded rectangle denotes the period of visual stimulation. Each time course is shown as an average of twentyfour trials to a drifting grating visual stimulus. Error bands represent s.e.m. At both imaged sites, arterioles showed large sensory stimulus evoked responses. Venules were unresponsive to visual stimulation—they never showed visually-evoked responses: ANOVA test was always P > 0.05 across blank and n test visual stimuli, the percentage change of visually-evoked dilation was always < 1%, and visually-evoked dilations never exceeded three standard deviations above the mean pre-stimulation baseline.


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Supplementary Figure 12 Neuronal responses are unaffected by local micropipette application of Alexa 633 in the visual cortex

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Supplementary Figure 12 Neuronal responses are unaffected by local micropipette application of Alexa 633 in the visual cortex (a–c) Masks of identified somata, local orientation map, and time course of calcium transients from a control cat (no Alexa 633 applied). Calcium transients were measured after bulk loading Oregon Green 488 Bapta-1 AM (OGB 1-AM). (d–f) Data from another cat, but here Alexa 633 was co-injected with OGB 1-AM. Cell numbers listed in time courses (c,f) match corresponding numbers shown for somata masks (a,d). For the cell masks shown in a and d, cells painted in white were significantly tuned for orientation and cells painted in gray were untuned. Each time course in c and f show individual cell responses—four trials superimposed. Greater than 90% of neurons in cat visual cortex were visually responsive both in ‘control’ (where no Alexa 633 was co-injected with the calcium indicator) and Alexa 633 injected animals. The orientation selectivity index (OSI) of neurons was indistinguishable between control and Alexa 633 animals (control OSI = 0.736 ± 0.014 vs. Alexa 633 OSI = 0.737 ± 0.019, mean ± s.e.m; control n = 120 cells, Alexa 633 n = 110 cells; P = 0.46 MannWhitney). Cats have distinct iso-orientation domains and thus OGB 1-AM can be injected into specific orientation domains guided by intrinsic signal optical imaging. We deliberately chose cortical locations in control and Alexa 633 treated sites in cat visual cortex that had the same overall orientation and direction preference—to avoid domain-based differences (cardinal stimulus orientations over-represented in visual cortex).


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Supplementary Figure 13 Neuronal responses are unaffected before and after intravenous application of Alexa 633 in the same animal Identical cells in the same cat

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Supplementary Figure 13 Neuronal responses are unaffected before and after intravenous application of Alexa 633 in the same animal (a–c) Masks of identified somata, local orientation map, and time course of calcium transients before Alexa 633 was applied. Calcium transients were measured after bulk loading Oregon Green 488 Bapta-1 AM (OGB 1AM). (d–f) Data from same cat—exactly the same cells as shown in a–c, but after Alexa 633 was applied intravenously (femoral vein). Cell numbers listed in time courses (c,f) match corresponding numbers shown for somata masks (a,d). For the cell masks shown in a and d, cells painted in white were significantly tuned for orientation and cells painted in gray were untuned. Each time course in c and f show individual cell responses— four trials superimposed. For the identical neurons, before and after Alexa 633 application injected showed no difference in the amplitude of visually evoked responses (control amplitude ∆F/F = 16.52 ± 0.69 % vs. Alexa 633 amplitude ∆F/F = 15.93 ± 0.71 %, mean ± s.e.m; n = 105 cells; P = 0.22 Wilcoxon signed rank test) and orientation selectivity index (control OSI = 0.880 ± 0.012 vs. Alexa 633 OSI = 0.867 ± 0.014, mean ± s.e.m; n = 105 cells; P = 0.43 Wilcoxon signed rank test).


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Supplementary Figure 14 EEG and heart rate are unaffected by Alexa 633

a. Before Alexa 633 i.v. injection heart beat

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Supplementary Figure 14 EEG and heart rate are unaffected by Alexa 633 (a) Heart beat, raw EEG traces, and spectrogram of EEG records before intravenous injection of Alexa 633 in an anesthetized and paralyzed cat. (b) Heart beat, raw EEG traces, and spectrogram of EEG records after intravenous injection of Alexa 633 (1 mg kg–1) in the same anesthetized and paralyzed cat used to collect the data shown in a. No difference in heart rate and EEG activity was observed before and after Alexa 633 injection. Alexa 633 labeling of arterioles was visible when data shown in b were collected.


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Supplementary Figure 15 Binding site of Alexa 633 on artery/arteriole wall

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Supplementary Figure 15 Binding site of Alexa 633 on artery/arteriole wall (a) Schematic of the five distinct layers in the brain arteriole wall: 1. Outermost covering containing collagen, 2. smooth muscle, 3. elastin fibers, 4. endothelial basement membrane, 5. endothelial cells. (b) Anti-SMA-α (green) was used to label smooth muscle in brain arterioles. Anti-SMA-α shows no overlap with Alexa 633 (red) labeling. Data shown are from macaque monkey but identical results were obtained in rodents. (c) Anti-Glut1 (green) was used to label endothelial cells in brain arterioles—no overlap with Alexa 633 labeling. Data shown are from mouse but identical results were obtained in other species. Images shown in b, c, g were collected with sequential scanning confocal microscopy. (d) Alexa 633 labeled six characteristic elastin fiber layers in mouse aorta, images collected by using low (2 mW) two-photon laser power. (e) Higher zoom of mouse aorta also collected using low two-photon laser power. Black square within the center of the labeled arteriole wall is from a preceding intentional high power two-photon laser bleach of Alexa 633, to demonstrate that Alexa 633 labeling was not contaminated by autofluorescence. (f) The signature pattern of thin external elastic lamina and thick internal elastic lamina of mouse femoral artery was visible with Alexa 633 staining. (g) Anti-laminin (green) was used to label endothelial basement membrane in mouse femoral artery. Antilaminin staining did not overlap with Alexa 633 labeling. The schematic shown in panel a was made specifically for the current publication and is an original drawing by Emma Vought, a medical illustrator in the Department of Neurosciences at the Medical University of South Carolina. The schematic is based on drawings shown in Principles of Anatomy & Physiology (13th Edition) by Tortora & Derrickson (Wiley 2012).


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Supplementary Figure 16 Alexa 633 labels elastin fibers on artery walls in brain and aorta

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Supplementary Figure 16 Alexa 633 labels elastin fibers on artery walls in brain and aorta (a,b) Two-photon images of a mouse brain arteriole and a mouse aorta labeled with Alexa 633 (laser power = 2 mW). Data collected on a red emission channel. (c,d) No autofluorescence was measured from the blue-green emission channel with 2 mW laser power. (e,f) Robust elastin autofluorescence was detected from the blue-green emission channel using high (22–32 mW) two-photon laser power. (g,h) Alexa 633 and elastin autofluorescence on the artery wall overlapped near perfectly (Pearson’s Rr = 0.84 and 0.96 in g and h, respectively). Images shown were collected from postmortem tissue sections.


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Supplementary Figure 17 Alexa 633 labeling of human aorta

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Supplementary Figure 17 Alexa 633 labeling of human aorta (a) Two-photon image from a human aorta—postmortem tissue cross-section. Unlike the signature six layers of elastin fibers in mouse aorta (see Supplementary Fig. 15 d–e), the human aorta has many more tightly packed bands of elastin fibers. From left to right, the image shows Alexa 633 labeling of the tunica media of the aorta and an abrupt transition to the adventitial layer. (b) Higher zoom of the tunica media shown in a. Individual elastin bundles of fibers were well labeled by Alexa 633. Not shown but analogous to our observations in animal tissues (see Supplementary Figs. 15–16), human tissue had no blue-green autofluorescence when using low two-photon laser power and high laser power bleached Alexa 633 labeling. Images are not background subtracted.


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Supplementary Figure 18 Alexa 633 labeling of mouse kidney

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Supplementary Figure 18 Alexa 633 labeling of mouse kidney (a) Two-photon image of a freshly dissected kidney (no fixative). 2 µM Alexa 633 was applied directly to the dissected kidney. An arteriole shown as a longitudinal section was brightly labeled by Alexa 633. The Alexa 633 was applied immediately after the kidney was dissected. The image was collected approximately one hour after the topical Alexa 633 application. (b,c) Two-photon images from another freshly dissected kidney. But here Alexa 633 labeling was done in vivo by tail vein injection (1 mg kg–1). An individual two-photon optical slice is shown in b and a maximum intensity 100-µm-z-projection is shown in c. The kidneys were removed approximately 1 hour after the tail vein injection and immediately imaged. (d–f). No blue-green autofluorescence detected with the same laser power used to collect red channel Alexa 633 fluorescence shown in a–c. Image pairs a,d; b,e; and c,f were each collected simultaneously on two separate detector channels. Images are not background subtracted.


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Supplementary Figure 19 Red blood cell velocity in cortical microvessels after intravenous injection of fluorescent dyes

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Supplementary Figure 19 Red blood cell velocity in cortical microvessels after intravenous injection of fluorescent dyes (a) Rat 1. Red blood cell velocity from line scans after an intravenous injection of Alexa 633. No fluorescein dextran was injected in this rat. Line scans were collected before Alexa 633 cleared the blood plasma. (b) Rat 2. Red blood cell velocity from line scans after intravenous injection of fluorescein dextran. No Alexa 633 was injected in this rat. (c) Rat 3. Red blood cell velocity from line scans after intravenous injection of Alexa 633 (recorded on PMT Channel 1) and fluorescein dextran (recorded on PMT Channel 2). Cross talk between the two PMT channels was eliminated by careful selection of PMT gain and the wide separation of emission bands between the red and green PMT filters used for Alexa 633 and fluorescein dextran imaging. As expected, line scan imaging on Alexa 633 and fluorescein dextran channels resulted in virtually identical mean velocity measurements. Raw line scan data shown for each of the three rat experiments are from a block of 200 line scans. In the mean velocity plots (right panels) error bars are standard deviations.


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Supplementary Figure 20 Light path used for in vivo two-photon imaging

Partially Reflective Mirror 2-photon IR laser (Mai Tai)

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Supplementary Figure 20 Light path used for in vivo two-photon imaging Note that the specifications for objective lenses and a detailed protocol for line & frame scans are provided in the Methods.


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Supplementary Note 1 Fluorescence dip artifact caused by arteriole dilation Guided by Alexa 633, we showed that arteriole dilation acts like a stimulus-evoked filter and leads to stimulusevoked dips in fluorescence from neurons located directly under arterioles but not veins. We suggest that claims in the literature of the presence of reductions in neural spiking activity (relative to spontaneous baseline firing) from calcium indicator imaging should also include the location of these neurons relative to surface arterioles. Also, the reports should explicitly demonstrate that arteriole dilation is not the source of decreases in neuronal fluorescence. Measurements of neuronal tuning bandwidth inferred from calcium indicator imaging also should include careful consideration of surface arteriole locations. The exact mechanism of how two-photon light passage through the dilating arteriole leads to a reduction in neuronal fluorescence remains to be determined. Perhaps, compared to the unstimulated baseline period, the relatively rapid dilation of arterioles upon sensory visual stimulation produces a larger local hemoglobin volume. This increased volume of hemoglobin absorbs more two-photon excitation light and emitted fluorescence from neurons. However, light scattering from the complex architecture of the arterial wall including the molecular dynamics of elastin fibers23 should not be ruled out. Supplementary Note 2 Selectivity of Alexa 633 vs. other rhodamine derivatives Alexa 633 is a sulfonated rhodamine derivative24. Different rhodamine derivatives have been reported to have selectivity for various cell types. Rhodamine-6G labels leukocytes25. Rhodamine-123 labels mitochondria26. Sulforhodamine-101 and sulforhodamine-B label astrocytes27,28. Astrocyte-selective markers like sulforhodamine-101 and sulforhodamine-B will, by definition, outline many vessels including capillaries in the brain via astrocyte end-feet apposition to blood vessels27. More recently, sulforhodamine-101 has been suggested to also label endothelial cells lining arteries in the cerebral cortex29, but such labeling of multiple cell classes with sulforhodamine-101 means that distinguishing arterioles from venules remains difficult. The labeling of different cell types alongside vessels of different classes also makes it difficult to use sulforhodamine-101 to track changes in vessel diameter. Finally, sulforhodamine-101 via astrocyte labeling has been reported to induce epileptic seizures by lowering the neuronal action potential threshold30, further undermining the use of this compound in neurovascular coupling studies in vivo. Our methods provide strong evidence for artery and arteriole specificity of Alexa 633 in the brain—bright labeling of a continuous band of elastin fibers wedged between smooth muscle and endothelial basement membrane (with no labeling of neurons, astrocytes, veins, venules, or pericytes). It is possible that excessively high concentrations of Alexa 633 application—via many dozens of high pressure pulses through the micropipette rather than just a few low


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pressure pulses of a few psi, or higher than our suggested intravenous dose, combined use of detergents like DMSO, or any stress (e.g., physical, chemical, oxidative, metabolic), that induces astrocytes to become reactive may all lead to non-specific labeling. Poor PMT filter design and resulting cross talk from OGB or GFP labeled neurons and/or astrocytes may also lead to the appearance of non-specific labeling. Although we have examined labeling in five tissue types across five species, we cannot exclude the possibility that some veins in other species or tissues may also take up Alexa 633. Possibly, some peripheral vasculature in the extremities of the limbs, large veins like the superior vena cava in some species, or parenchymal cells in the liver could take up Alexa 633. For example, we have observed some dis-continuous Alexa 633 labeling in femoral veins (data not shown). Note that the astrocyte-specific dye sulforhodamine-101 (discussed above) also labels oligodendrocytes in the retina31 and motor neurons that innervate peripheral muscles32. Our review of the literature on the anatomy of veins and venules throughout the body suggest that the presence of a continuous band of elastin in these structures is rather unlikely. But regardless of any future uncovering of accumulation of Alexa 633 in cells or connective tissue in some part of the body, our study establishes the utility for Alexa 633 in neurovascular coupling research.

Supplementary References 23. Tamburro, A.M. Nanomedicine (Lond.) 4, 469-487 (2009). 24. Agnew, B., Gee, K.R. & Nyberg, T.G., United States Patent Application No. 0249014 (Oct. 25, 2007). 25. Schwarzmaier, S.M., Kim, S.W., Trabold, R. & Plesnila, N. J. Neurotrauma 27, 121-130 (2010). 26. Johnson, L.V., Walsh, M.L. & Chen, L.B. Proc. Natl. Acad. Sci. USA 77, 990-994 (1980). 27. Nimmerjahn, A., Kirchhoff, F., Kerr, J.N. & Helmchen, F. Nat. Methods 1, 31-37 (2004). 28. Verant, P., Ricard, C., Serduc, R., Vial, J.C. & van der Sanden, B. J. Biomed. Opt. 13, 064028 (2008). 29. McCaslin, A.F., Chen, B.R., Radosevich, A.J., Cauli, B. & Hillman, E.M. J. Cereb. Blood Flow Metab. 31, 795-806 (2011). 30. Kang, J. et al. Neuroscience 169, 1601-1609 (2010). 31. Ehinger, B., Zucker, C.L., Bruun, A. & Adolph, A. Glia 10, 40-48 (1994). 32. Lichtman, J.W., Wilkinson, R.S. & Rich, M.M. Nature 314, 357-359 (1985).


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