INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

THIS STUDY EXAMINES HOW TWO different vasodilators influence flow patterns within the arteriolar microcirculation of the hamster cheek pouch tissue. The basis for this study comes from common findings in studies examining either local changes in flow with localized (micropipette) stimulation or whole organ resistance changes with infused agonists. These separate types of studies each suggest that low concentrations of agonists induce responses unrelated to the classically described direct pharmacological action of the agonists on vascular smooth muscle cells. Instead, connected groups of arterioles respond to low levels of an agonist, in a concerted fashion, consistent with a far-reaching coordinated hemodynamic response. Evidence suggests that the agonist-triggered response is a hemodynamic mechanism to modulate flow distribution deep within the peripheral circulation.

In the present study, we examined the behavior of a defined arteriolar network in the hamster cheek pouch, which appears to be similar to the design of arteriolar networks found in the hamster cremaster muscle preparation (11, 12, 21). We questioned whether low concentrations of vasodilators applied across the tissue would initiate vascular responses that we could identify as being agonist dependent and suggestive of differential control of flow delivery within this network. By low concentrations, we mean amounts that are just above the apparent normal interstitial levels. With adenosine, interstitial levels are 0.1-5 µM (tissue water), and with nitric oxide (NO) they are estimated to be 1-30 µM (plasma, total NO₂ + NO₃) (1, 15, 18, 19). On the basis of prior work in this laboratory and from others, we hypothesized that flow and diameter changes with adenosine would strive to maintain the wall shear stress, whereas those with NO would not. Furthermore, we predicted that the flow responses would not uniformly distribute red blood cells to all branches. Instead, we believed that there would be an anatomic location (a particular branch arteriole) that would receive more flow when inflow was elevated, especially at the higher concentrations of sodium nitroprusside (SNP), as our laboratory has seen for systemic NO stimuli (21). Thus this study examines hemodynamic mechanisms associated with low levels of interstitial adenosine or NO. The findings show evidence for organized flow distribution responses within spatially defined arteriolar networks.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

With University of Rochester approval, adult male Golden hamsters [HSD:Syr, 78 ± 8 (SD) days, 124 ± 9 g, n = 20] were anesthetized with pentobarbital sodium (70 mg/kg ip), tracheostomized, and maintained on a constant infusion of pentobarbital sodium (10 mg/ml at 0.56 ml/h ip). Systemic hematocrit (55 ± 4%) did not change during the experiment. Body temperature was maintained between 37 and 38°C by conductive and convective heat sources. Mean arterial pressure was monitored via a left femoral arterial catheter (100 ± 11 mmHg). Red blood cells from age- and weight-matched donor animals (n = 14, 78 ± 6 days, 125 ± 10 g) were labeled with a fluorescent dye [substituted tetramethyl rhodamine isothiocyanate (XRITC), Molecular Probes, Eugene, OR], by using an established protocol. The fluorescently labeled red blood cells were injected in tracer quantities via a right jugular catheter and were used to quantify blood flow changes. The left cheek pouch was prepared for in vivo microcirculatory observations (32). The preparation was continuously superfused with bicarbonate-buffered saline containing (in mmol/l) 132 NaCl, 4.7 KCl, 2.0 CaCl₂, 1.2 MgSO₄, and 20 NaHCO₃ (equilibrated with gas containing 5% CO₂-95% N₂ gas, pH 7.4 at 34°C). All chemicals were obtained from Sigma Chemical (St. Louis, MO), unless otherwise noted.

The microcirculation was observed with transillumination using a modified Nikon upright microscope (Nikon, Tokyo, Japan), with a ×25 (Nikon) objective. Epi-illumination was used to visualize the XRITC-labeled red blood cells by using a Chroma G2A filter (Chroma, Brattleboro, VT). Video images were produced by using a carge-coupled device 72s video camera and, in experiments where red blood cell velocity was obtained, a Gen/Sys II video intensifier (Dage-MTI, Michigan City, IN). During a 60-min stabilization period, the XRITC labeled cells were administered and arteriolar tone was verified by dilation to topically applied 10³ M adenosine (used to obtain maximal diameters) and constriction to 10% O₂ gas added to the superfusate.

For six experiments with adenosine and six experiments with SNP, the test agent was applied in increasing concentrations (10⁹ to 10⁵ M) added to the suffusate, and all measurements were obtained between 5 and 10 min after initiation of exposure to that concentration. Preliminary data show that this time is sufficient to cause a steady-state change in diameter and in cell flux. In four additional experiments with adenosine, and four with SNP, observations were made during baseline, after 10⁸ M and 10⁵ M adenosine or SNP, then after 20 min of 10⁵ M N-nitro-L-arginine (L-NNA), and again with 10⁸ M and 10⁵ M adenosine or SNP during continued L-NNA.

All observations were made at defined locations within the arteriolar network, shown schematically in Fig. 1. The arteriolar classifications noted in the schematic (e.g., A1, A2, A3, A4) follow the Wiedeman system (34), whereas the terms arcade, feed, and branch describe the functional role the vessel plays in flow distribution. One arcade vessel supplies flow to a series of networks; the network consists of the feed and branches (11), and each branch supplies several capillary modules (2). Thus, in this tissue, the network consists of a feed arteriole with three to six branch arterioles. Observations were made at the branch points in the network: diameter and flow estimates (red blood cell flux and velocity) were determined for the feed segment before the branch point and for the branch immediately after the bifurcation, as previously described (11, 12, 21). In this way, flow continuing along the feed and flow supplied to each branch were evaluated.

View larger version (14K): [in this window] [in a new window]   Fig. 1.   Schematic of arteriolar vasculature in the hamster cheek pouch preparation. Diameter data were taken from the third-order arteriole (A3), which serves as the feed vessel for the arteriolar network, and the fourth-order branch arterioles (A4) along the A3 feed. Flow data were taken from whole arteriolar networks consisting of the A3 network feed and the sequential A4 branches. For convenient identification, the A4 branches are designated as 1, 2, 3, and last, sequentially as they arise within the network. These branch arterioles terminate into capillary modules. A1, first-order arteriole; A2, second-order arteriole.

Arteriolar diameter, red blood cell flux, and velocities were video recorded, and they were measured off-line from the recorded image by using image-analysis software developed for this application (Dept. of Anesthesiology, University of Rochester, Rochester, NY), calibrated with a stage micrometer. Red blood cell flux (F; in cells/s) is calculated by F = (m_t/p)/t, where m_t is the number of fluorescent cells crossing a specified vessel plane in time t, and p is the fraction of fluorescent cells in the total red blood cell population [0.32 ± 0.1% (SD)]. Individual velocities (in µm/s) were measured as the distance traveled in one video field ([1/60]th s) for all fluorescent cells crossing the specified sampling plane during the 30-s test period. The harmonic mean velocity was determined to estimate mean axial fluid velocity (v_c). The apparent viscosity (_app) was calculated from the relationship between vessel hematocrit, vessel diameter, and the relative viscosity (22). The shear rate (; in s¹) was calculated as  = 8 · v_c/D (where D is vessel diameter) and used to calculate wall shear stress (T; in dyn/cm²) as T = _app · .

The diameter change for Fig. 2 was calculated as fractional change from baseline: (test  baseline)/(maximum  baseline). The response curves were constructed by averaging the diameter change at each concentration. EC₅₀ values were determined from sigmoid-curve fitting of the diameter change vs. concentration, forcing the upper asymptote to a value of one for maximal dilation. Calculation of test parameters is indicated in the legend for each figure. Values were pooled for each concentration for the sizes of vessels to determine the means and SE. Comparisons between values were made over time, between groups by ANOVA (multiple comparisons) (3). For all statistics, differences were considered significant when P < 0.05. 

View larger version (18K): [in this window] [in a new window]   Fig. 2.   Fractional dilation of the pooled feed arterioles and all branches combined for adenosine (A) or sodium nitroprusside (SNP; B). EC50 values are reported in the text, although note that no clear maximal dilation was achieved with SNP. Values are means ± SE. Fraction dilation is calculated as (peak  initial)/[maximum (max)  initial] for each vessel segment where the maximum dilation was obtained with 103 M adenosine. Significantly different (P < 0.05) from baseline: * (entrance),  (end). Square brackets denote concentration.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

The arteriolar network is schematized in Fig. 1. Diameter, red blood cell flux, and velocity were measured for the feed and branch at the first, second, third, and last branches in the network. Data for the arteriolar feed segment were obtained before each bifurcation, and data for the branch were obtained immediately after the bifurcation. The interbranch lengths and total length of the arteriolar network, and the diameters for individual locations, are given in Table 1. First, the data were examined by combining all data collected at each location along the feed arteriole (4 locations) and by combining all data for each branch arteriole (4 branches). Baseline arteriolar diameters for all feed locations combined were 11.0 ± 0.96 (SE) µm and for all branches combined were 9.6 ± 0.92 µm.

The concentration-response relationships for suffusate applied adenosine or SNP for the combined data are shown in Fig. 2. For comparison to current literature, we obtained an estimate of the EC₅₀. When responses for all locations within the network were combined, the estimated EC₅₀ values were identical for the feed and branches. With adenosine, the EC₅₀ was 3 ± 3 × 10⁷ M for feed and 3 ± 2 × 10⁷ M for branches; and with SNP, the EC₅₀ was 1 ± 3 × 10⁶ M for feed and 1 ± 4 × 10⁶ M for branches. When noting the maximal diameters obtained with 10³ M adenosine in Table 1, it is clear that the vessels were capable of dilating further, and yet a plateau maximum response of ~30% dilation was suggested by the data with adenosine. Most striking is the significant constriction of the feed and branches at lower concentrations with adenosine and the biphasic response with SNP showing significant dilation of the feed at 10 M.

We next examined diameter changes by location across the arteriolar network. Along the feed arteriole, the beginning (entrance) and end of the vessel had different diameter responses, with both adenosine and SNP (Fig. 3). Opposite responses were seen with adenosine compared with with SNP. With adenosine, the entrance of the feed significantly constricted at lower concentrations, whereas the terminating portion did not change in diameter (Fig. 3A). With SNP, in contrast, the end of the feed constricting significantly, whereas the entrance displayed a significant biphasic dilatory response to SNP (Fig. 3B). Thus the tissue-wide stimulation resulted in heterogeneous responses based on location within the network, even at concentrations higher than the established tissue concentrations for these agonists.

View larger version (31K): [in this window] [in a new window]   Fig. 3.   Diameter changes (test  baseline) with adenosine (A and C) and with SNP (B and D) for the entrance and end portions of the feed (A and B) and for the first, third, and last branch arterioles (C and D). Values are means ± SE. Square brackets denote concentration. Significantly different (P < 0.05) from baseline: * (entrance),  (end),  (first branch), ** (third branch), § (last branch).

The location-specific changes in resistance were examined further, by noting the diameter changes for the branch arterioles (Fig. 3, C and D). With adenosine, the first branch constricted, even at higher doses, whereas the third and "last" branches dilated; the first branch was significantly more constricted than the others at all concentrations. In contrast, with SNP, all branches dilated significantly at both 10⁹ and 10⁵ M SNP and, at the remaining concentrations, behaved differently from each other.

The focus of this study was to determine how the changes in hemodynamic parameters were interrelated for these agonists. To evaluate the hemodynamic mechanism(s), we measured red blood cell flux as an estimate of flow changes and calculated the wall shear stress.

The numbers of red blood cells entering the network (entrance inflow) and their distribution throughout the network (into the branches) were significantly different with adenosine vs. SNP (Fig. 4). Baseline flux (no. of red blood cells/s) into the network is given in Fig. 4A and for the branch arterioles was 126 ± 20 (SE) cells/s (first branch), 104 ± 25 cells/s (third branch), and 112 ± 30 cells/s (last branch). Initially, the red blood cell flux into the network (Fig. 4A) decreased at lower doses with each agonist, increasing above baseline at 10⁷ M with adenosine and at 10⁶ M with SNP. At 10⁶ M, the network influx was significantly greater with SNP, compared with with adenosine, despite similar changes in diameter for the entrance to the network ( in Fig. 3, A and B). Thus changes in flux and in diameter were not directly associated.

View larger version (26K): [in this window] [in a new window]   Fig. 4.   Red blood cell (RBC) influx to the network (A) and the cell flux for the first, third, and last branches in experiments with adenosine (B) and with SNP (C). Values are means ± SE. Significantly different (P < 0.05) from baseline: * (entrance). Significantly different (P < 0.05) from others at same concentration:  (first branch), ** (third branch), § (last branch).

Figure 4, B and C, shows the red blood cell flux into the first, third, and last branches. Major changes in the numbers of red blood cells going into the branches were apparent at the higher concentrations. With adenosine, the excess flux into the network appeared to preferentially pass into the middle branches (third branch is shown in Fig. 4B). With SNP, the excess flux to the network passed preferentially to the last branch (Fig. 4C).

To further examine the flux distribution within the network, we examined the fractional flow distribution. That is, of the available number of red blood cells entering the network, the fractional flow represents what fraction went to each branch arteriole; this addresses the question of local heterogeneity of flow. During baseline conditions in this tissue, each branch received ~15% of the total inflow to the network; the first branch tended to receive more than the others, although this was not statistically significant for this tissue.

Figure 5 shows the fractional flow distribution into the first, third, and last branches, as a function of adenosine or SNP concentrations. Flow distribution patterns were not identical for equivalent changes in the inflow to the network. With adenosine, the flux distribution into each branch remained constant during significant changes in the total available flow (e.g., total flow decreased by 50% at 10⁸ M and increased by 40% at 10⁷ M). Only at the higher concentrations of adenosine did one branch receive more cells. Specifically, when total inflow increased by 50%, 40% of this went to the third branch, at the expense of the last branches further downstream. With SNP, at lower concentrations, whereas inflow was decreased, the last branch lost red blood cell flux. At increasing concentrations, the last branch received progressively more of the available flow, at the expense of upstream branches. Thus, with adenosine, flow was diverted to the middle branches at the expense of the last branch. With SNP, flow was diverted to the last branches at the expense of the first branch.

View larger version (15K): [in this window] [in a new window]   Fig. 5.   Change in normalized RBC distribution for the first, third, and last branches in experiments with adenosine (A) and SNP (B). Values are means ± SE. Normalized flux was calculated as test flux/test inflow, where inflow refers to the total flow entering the network for that condition. Thus the data show how the total flow entering the network was distributed to the branches compared with the baseline flow distribution. Significantly different (P < 0.05) from others at same concentrations: ** (third branch), § (last branch).

Wall shear stress for the entrance to the network (feed) is shown in Fig. 6A. With adenosine, wall shear stress was unchanged from baseline until the highest dose tested, where it decreased significantly. Shear was maintained at the baseline values with adenosine by a combination of diameter, red blood cell velocity, and flux changes. With SNP, wall shear stress decreased significantly between 10⁹ and 10⁷ M, and, at 10⁶, there was a step increase in shear that followed the changes in red blood cell influx to the network and not the changes in resistance or velocity. Thus, with SNP, shear passively followed the change in flux.

View larger version (29K): [in this window] [in a new window]   Fig. 6.   Wall shear stress (WSS) for the entrance to the network (A) and change in normalized WSS for the first, third, and last branches in experiments with adenosine (B) and SNP (C). Values are means ± SE. Normalized shear change in B and C was calculated as 1  (test shear/baseline shear). Thus the data show whether shear was maintained constant at individual positions compared with baseline. Significantly different (P < 0.05) from baseline: * (entrance),  (1st branch), ** (third branch), § (last branch). Note that significance marks are left off in C for clarity.

Constant shear with adenosine and disregulated shear with SNP are reinforced by data from the branch arterioles. The wall shear stress remained constant in the branches as well as in the feed with adenosine, during complex changes in flux and in resistance (Fig. 6B). In contrast, with SNP, shear changed greatly for each branch at each concentration of SNP (Fig. 6C). Together, this suggests that, with adenosine, the network retains the ability to regulate wall shear stress (maintain shear through dilation or constriction), whereas, with SNP, it does not. This is reinforced by the data shown in Fig. 7, which are combined data from 10⁹ to 10⁷ M for adenosine data and for SNP data for all branch arterioles. From Fig. 7, A and C, it is clear that the changes in wall shear stress with adenosine are minimal compared with the changes with SNP.

View larger version (32K): [in this window] [in a new window]   Fig. 7.   Changes in WSS and in RBC flux as a function of change in diameter for the branch arterioles for experiments with adenosine (ADO; A and B) or with SNP (C and D). Values are means ± SE. Data are averaged for experiments with adenosine (n = 6) or SNP (n = 6) and for experiments with adenosine plus N-nitro-L-arginine (L-NNA; n = 4) or SNP plus L-NNA (n = 4). These normalized changes were calculated as 1  (test/baseline).

To test for the involvement of flow-dependent dilation, N-nitro-L-arginine (L-NNA; 10⁵ M) was added to the suffusate with low concentrations of adenosine (10⁸ M, n = 4) or SNP (10⁸ M, n = 4). The presence of L-NNA resulted in failure to maintain wall shear stress with low adenosine and had no effect on shear with low SNP. In comparison, the changes in red blood cell flux (Fig. 7, B and D) were equivalent for the two agents, with or without L-NNA. Thus coordinated changes in diameter, flux and red blood cell velocity together maintain wall shear stress with adenosine through flow-dependent dilatory pathways, whereas coordination of this type is lacking with SNP.

To further examine the role of flow-dependent dilation, higher concentrations of adenosine or SNP were tested with L-NNA. Figure 8 compares the data with adenosine or SNP alone with the data with each agonist plus L-NNA (10⁵ M). The first key finding of Fig. 8 is shown at low concentrations of adenosine and SNP. With adenosine (no L-NNA), changes in resistance and in wall shear stress were minimal. With adenosine plus L-NNA, wall shear stress was not held constant, and the flow pattern was altered. For the responses with SNP plus L-NNA, the flow distribution did not change, yet changes in diameter and in shear were very different from that without L-NNA.

View larger version (43K): [in this window] [in a new window]   Fig. 8.   Fractional inflow (A and B), diameter change (C and D), and fractional WSS (E and F) for experiments with adenosine (A, C, E) or with SNP (B, D, F) alone at 105 and 108 M and with these concentrations during 105 M L-NNA. Fractional inflow was calculated as test flux/test inflow, where inflow refers to the total flow entering the network for that condition. Diameter change was calculated as test  baseline. Fractional WSS was calculated as 1  (test/baseline). Values are means ± SE. Data are averaged for experiments with adenosine alone (n = 10) or SNP alone (n = 10) and for experiments with adenosine plus L-NNA (n = 4) or SNP plus L-NNA (n = 4). * Significantly different from baseline value for that parameter at that branch, P < 0.05.

The second key finding of Fig. 8 is shown for flow at high concentrations of adenosine and SNP (compare A and B). At high concentrations of agonists, excess flux was diverted to the middle branches (not first or last) with adenosine and to the last branches with SNP. With L-NNA, the flow pattern is inverted with adenosine (none to the third, and all to the first and last branches). The flow pattern with SNP was not altered by L-NNA. Thus the mechanism that adenosine stimulates to alter flow distribution appears related to flow-dependent dilatory pathways, whereas that with SNP does not.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION REFERENCES

This study examines how arteriolar network flow is coordinated in the hamster cheek pouch microcirculation during stimulation with two vasodilators. Hemodynamic mechanisms for coordinated flow distribution responses are defined by this study for arteriolar networks in this tissue during elevated interstitial adenosine and SNP. The concentrations used bracket the normal physiological levels found in many mammalian tissues.

The first key finding is that tissue-wide stimulation does not provide the pharmacological response attributed to these agonists; the responses within the network are attenuated compared with that expected from local micropipette stimulation of individual arteriolar segments, although the estimated EC₅₀ remains unchanged (26). The changes in resistance within the network that are not predicted by direct pharmacological action could rely on vascular communication between multiple stimulated sites (11, 20, 26, 31). In the present study, we examined how the responses were related to both the locations within the network and the prevailing conditions of the network, in an effort to define hemodynamic mechanism(s) for network blood flow control.

The second key finding is that changes in wall shear stress are minimal with adenosine, and widely variable with SNP, for normal to mild elevations (above normal) of adenosine or NO (SNP). Important links have been uncovered showing how shear stress is modulated by tissue exposure to these agonists. With adenosine, the hemodynamic changes across the network are coordinated, providing a constant wall shear stress, which is linked to flow-dependent dilatory pathways. With SNP, instead, wall shear stress changes passively with changes in red blood cell flux.

The third key finding is that the way red blood cells are recruited and distributed at the network level is agonist dependent. With adenosine, flux remains uniformly distributed until higher concentrations, where elevated red blood cell flux is not longer uniformly distributed but instead is channeled through the middle branches of the network; again, this involves a flow-dependent mechanism. In contrast, at low concentrations of SNP, more flux is channeled into upstream branches, at the expense of the last branch, whereas, at higher concentrations, the elevated red blood cell flux coming into the network is channeled to the terminating branch point of the network, at the expense of upstream branches. Thus flow heterogeneity can be defined within a scale as small as the arteriolar network in the hamster cheek pouch; patterns of flow heterogeneity are agonist dependent.

Heterogeneity of resistance and flow occurs at the microvascular level (8, 17, 23, 28) and is likely due to a number of factors, including sympathetic tone, myogenic activity, flow and local vascular communication, as well as the tissue type and species being studied. Heterogeneity has been predicted to be an important element of microvascular control (25), but the hemodynamic mechanisms remain poorly understood.

The vasculature modulates responses to agonists in a way that is related to the local pressure and flow. There are now many examples showing that the resistance change for an arteriolar segment to locally applied agonists does not match the resistance change for the same size and classification of vessels with global drug application (e.g., coronary vessels: Ref. 4 vs. Ref. 16; cheek pouch tissue: Ref. 26 vs. present study). In a recent review, the functional location of the resistance vessels was acknowledged to be critical to our understanding of how heterogeneous responses are interrelated so that coordinated flow delivery would occur (5). Two lines of experimental data converge at this point. First, prescribed patterns of resistance change occur within resistance arterioles that are located near each other (e.g., Ref. 8). In fact, flow is organized within functionally defined networks, and intact flow-dependent dilatory pathways are required for flow to remain organized (12). Second, anatomically, these microvascular networks (units) are aligned along a common skeletal muscle fiber bundle (9), where flow can be recruited and derecruited during exercise (20). Although the studies are from different tissues and species, together this reinforces the idea that microvascular flow is a highly ordered process and perhaps that heterogeneity of flow is merely repeating homogeneous patterns of behavior on a local level. The present study now adds the identification of one hemodynamic mechanism responsible for microvascular flow coordination. We show for the hamster cheek pouch that red blood cell flux remains organized during moderately elevated interstitial adenosine but not during elevated NO levels. The key hemodynamic mechanism responsible for network-wide flow organization with adenosine is network maintenance of wall shear stress through flow-dependent dilatory pathways because red blood cell flux and shear are no longer maintained with L-NNA. The key hemodynamic mechanism for disregulation of red blood cell flux with SNP remains unknown.

We report constriction of some locations in the hamster cheek pouch with adenosine. Vasoconstriction with adenosine has been localized to A₃-receptor-stimulated mast cell degranulation with consequent constriction of nearby arterioles to histamine or thromboxane in the hamster cheek pouch (29). Although this may explain part of the response we report in the present study (i.e., entrance to the feed), it cannot explain the pattern with adenosine. Nor can this finding explain constriction of the end of the feed arteriole with SNP. Thus, although the pharmacological mechanism responsible for mast cell degranulation is likely occurring, that alone cannot explain the total network-wide response that we report in the present study.

Elements of the hemodynamic responses to tissue-wide stimulation with adenosine and NO have been previously described by vascular communication studies. Specifically, with local (micropipette) stimulation, adenosine and NO each induce remote vasodilatory responses in skeletal muscle arterioles (10- to 50-µm diameter) (11, 26, 31). This remote response recruits flow, with much more flow in response to NO compared with adenosine for similar diameter changes (11, 26, 31). The remote dilation (but not the local dilation) with NO donors is influenced by the initial wall shear stress before stimulation at key network locations. In fact, in the present study, for the higher concentrations of SNP (10⁵ and 10⁶ M), the changes in diameter and baseline wall shear stress are correlated for the feed arteriole (R² = 0.66 and R² = 0.69, respectively); with adenosine, the changes in diameter and baseline wall shear stress are unrelated (R² = 0.05 and R² = 0.18, respectively). Hence, we conclude that a portion of the "tissue-wide" exposure stimulates a remote response.

In previous studies, we have shown that a significant portion of the remote dilation with adenosine serves to return wall shear stress to baseline conditions through a NO-dependent mechanism (e.g., flow-dependent dilation; Ref. 11), and this finding is reinforced by the present study. Thus, in terms of vascular control, adenosine responses are tightly linked to maintaining wall shear stress, whereas NO responses are related to the location in the network or the baseline flow conditions of the network.

There is a growing body of evidence that flow control is agonist dependent in multiple tissue types, with some common elements. The common elements are that NO recruits more flow for the same resistance change than does adenosine and that change with adenosine is largely linked to flow-dependent pathways. In the coronary microcirculation, with similar dilations in response to adenosine or NO donors, flow increases much more with NO, compared with with adenosine (14). A portion, but not all, of a hyperemic response with adenosine is blocked by inhibiting NO formation (6), thus suggesting a portion of the response may involve flow-dependent dilation. NO, instead, regulates microcirculatory diameter independent of the flow change per se (3). In the skeletal muscle microcirculation, interstitial adenosine, more so than interstitial NO, is implicated in exercise-induced hyperemia (13, 15, 24, 27). For exercise-induced hyperemia, an ascending, flow-dependent dilation appears to amplify the resistance changes (13). In the present study in the hamster cheek pouch, data with adenosine are similarly consistent with flow-dependent dilation playing an important role in maintaining flow distribution. In comparison, elevated systemic NO alters microvascular flow patterns in a highly uniform manner (10, 21), with evidence that increased flow into the network is channeled to the terminating branch points of the network and not uniformly distributed among all branches, as found in the present study. We conclude that, specific to each agonist, flow distribution is location dependent within the network. We speculate that this is due to the cellular mechanism(s) by which that agonist initiates the hemodynamic response. The precise manner by which this occurs will require further study.