Normal brain functioning is critically dependent on the adequate and uninterrupted supply of oxygen to brain tissue (Attwell and Laughlin, 2001; Raichle and Gusnard, 2002; Raichle et al., 2001). Significant efforts have been made over the years to investigate the regulation of cerebral blood flow (CBF), including auto-regulation of CBF in response to the changes in cerebral perfusion pressure (Aaslid et al., 1989; Paulson et al., 1990) and the processes underlying neurovascular coupling (Anenberg et al., 2015; Cai et al., 2018; Girouard and Iadecola, 2006; Iordanova et al., 2015; Vazquez et al., 2010). Furthermore, neuronal and microvascular densities vary greatly between cortical layers (Blinder et al., 2013; Sakadžić et al., 2014; Weber et al., 2008; Wu et al., 2016), suggesting a laminar variation of tissue metabolism (de Kock et al., 2007; Hyder et al., 2013). However, it is still not well understood how blood flow and oxygenation are distributed through the microvascular network to support an adequate tissue oxygenation across the closely spaced, but morphologically and metabolically heterogeneous cortical layers. Answering this question is important to improve our understanding of the normal brain physiology as well as brain diseases that affect cerebral microcirculation (Berthiaume et al., 2018; Iadecola, 2016; Iadecola, 2017; Pantoni, 2010; Zlokovic, 2011).

The recent development of tools for in vivo microvascular oxygen imaging enabled investigation of brain oxygen delivery and consumption within the arteriolar, venular and capillary domains over large tissue volumes (Cao et al., 2017; Chong et al., 2015a; Hu et al., 2009; Lecoq et al., 2011; Parpaleix et al., 2013; Sakadzić et al., 2010; Sakadžić et al., 2015; Wang et al., 2011; Yaseen et al., 2009). In addition, distributions of microvascular blood flow and oxygen in mice at rest have been assessed in several studies using optical coherence tomography, two-photon microscopy and photoacoustic imaging (Cao et al., 2017; Chong et al., 2015b; Gutiérrez-Jiménez et al., 2018; Lyons et al., 2016; Moeini et al., 2018; Sakadžić et al., 2011; Sakadžić et al., 2014; Santisakultarm et al., 2012; Santisakultarm et al., 2014; Srinivasan et al., 2015). However, only in few previous studies blood flow properties at rest have been considered as a function of cortical depth, but without information about capillary oxygenation and branching order (Gutiérrez-Jiménez et al., 2016; Kleinfeld et al., 1998; Li et al., 2016; Merkle and Srinivasan, 2016). These studies in anesthetized mice showed that capillary red-blood-cell (RBC) flux and/or speed slightly decreased with cortical depth at rest, and that smaller transit time heterogeneity occurred in the deeper cortical layers. By using two-photon phosphorescence lifetime microscopy (2PLM) of oxygen (Finikova et al., 2008), distributions of capillary oxygen concentration were reported in both anesthetized (Lecoq et al., 2011; Parpaleix et al., 2013; Sakadžić et al., 2014) and awake (Lyons et al., 2016; Moeini et al., 2018) mice. The use of anesthesia in some of these studies could have significantly affected both brain CBF and metabolism (Alkire et al., 1999; Goldberg et al., 1966). Moreover, all earlier studies using 2PLM were limited by smaller sample size and insufficient imaging penetration depth due to limitations of the applied oxygen probe – PtP-C343 (Finikova et al., 2008). Lyons et al. for the first time measured the distributions of capillary blood flow and PO₂ as a function of cortical depth in the somatosensory cortex in awake mice (Lyons et al., 2016). However, the measurements were performed in less than 100 capillaries across n = 3 mice with the imaging depth of ≤410 µm. Due to these limitations, a detailed layer-specific analysis with the acquired data would be challenging. Moeini et al. conducted imaging study with 2PLM in awake mice through a thinned-skull cranial window (Moeini et al., 2018). This procedure was less invasive, but it further limited imaging depth.

In addition to the experimental observations, several recent studies using numerical modeling based on the realistic and artificial vascular anatomical networks (VANs) predicted layer-dependent blood flow (Hartung et al., 2018; Schmid et al., 2017) and oxygen distributions (Gagnon et al., 2016; Gould and Linninger, 2015; Gould et al., 2017; Linninger et al., 2013; Lücker et al., 2018a; Lücker et al., 2018b). Developing and using accurate biophysical models of oxygen advection and diffusion based on large-scale realistic VANs is a key component in our quest to better understand the regulation of microvascular blood flow. However, current numerical models need to be improved, and their predictions need to be validated by more comprehensive experimental measurements of the physiological observables involved in microvascular oxygen transport to tissue.

The development of 2PLM of oxygen also enabled measurements of erythrocyte-associated transients (EAT) in cortical capillaries (Lecoq et al., 2011). EAT were first theoretically predicted by Hellums (1977) and extensively investigated over the last four decades using analytical and numerical approaches (Federspiel and Popel, 1986; Hellums, 1977; Lücker et al., 2015; Lücker et al., 2017; Popel, 1989). Originally, they were experimentally observed in peripheral capillaries (Barker et al., 2007; Golub and Pittman, 2005), but the full confirmation within the more challenging three-dimensional cortical capillary network was made possible only recently with advent of 2PLM (Lecoq et al., 2011; Parpaleix et al., 2013). Since EAT is tightly related to the intravascular resistance to oxygen transport to tissue (Golub and Pittman, 2005; Hellums, 1977), their direct measurements are critical for better understanding of the oxygen delivery through the capillary network. However, the dependence of EAT on cortical layer has not been fully explored.

To this end, in the present work, we applied 2PLM to measure the absolute intravascular PO₂ in a large number of arterioles, venules and capillaries, as well as RBC-PO₂, InterRBC-PO₂, EAT and RBC flow properties in capillaries as a function of cortical depth within the range of 0–600 µm. We used a new phosphorescent oxygen probe – Oxyphor2P (Esipova et al., 2019). Compared to its predecessor – PtP-C343 (Finikova et al., 2008), Oxyphor2P exhibits longer excitation and emission wavelength maxima, higher quantum yield and a larger two-photon absorption cross-section, facilitating simultaneous mapping of microvascular PO₂ and capillary RBC flux in cortical layers I-V in mice and making it possible to significantly increase both the sample size and imaging penetration depth in comparison to the previous studies (Lecoq et al., 2011; Lyons et al., 2016; Moeini et al., 2018; Parpaleix et al., 2013; Sakadzić et al., 2010; Sakadžić et al., 2014). Moreover, our measurements were performed in head-restrained awake mice, and thus were free of the confounding effects of anesthesia on neuronal activity, CBF and brain metabolism. With the results, we report that 1) in the whisker barrel cortex in awake mice the oxygen extraction fraction (OEF), measured at different cortical depths (i.e., depth-dependent OEF), reaches its maximum at the depth range of 320–450 µm, corresponding to cortical layer IV, where the neuron and capillary densities are the largest, and, presumably, oxygen consumption is the highest; and 2) the increased OEF is accompanied by the more homogenously distributed capillary PO₂ and RBC flux, as well as by a decrease in the intracapillary resistance to oxygen delivery (inferred from the EAT magnitude). These experimental results enabled quantification of parameters of importance for oxygen transport to tissue and put forward a potential mechanism, by which the microvascular networks at rest may adapt to the heterogeneous metabolic demands in different cortical layers. We anticipate that our results will improve our understanding of the normal brain function as well as of diseases that affect the cerebral microcirculation (Girouard and Iadecola, 2006; Iadecola, 2016; Müller et al., 2017; Pantoni, 2010; Wardlaw et al., 2013; Zlokovic, 2011). In addition, detailed knowledge of the distributions of capillary RBC flux and oxygenation at different depths should inform the next generation of biophysical models of the layer-specific blood flow, oxygen delivery and consumption (Gagnon et al., 2016; Gould and Linninger, 2015; Gould et al., 2017; Guibert et al., 2010; Guibert et al., 2012; Hartung et al., 2018; Linninger et al., 2013; Lorthois and Lauwers, 2012; Lorthois et al., 2011; Peyrounette et al., 2018; Schmid et al., 2017), as well as modeling of the impact of various RBC flow properties on oxygen delivery (Lücker et al., 2018a; Lücker et al., 2018b).

We have performed measurements of absolute intra-vascular PO₂ in arterioles, venules and a large number of capillaries (6544 capillaries across n = 15 mice), as well as EAT, RBC flux, speed and line-density in a subset of capillaries (978 capillaries across n = 15 mice). These parameters were measured simultaneously in the whisker barrel cortex in head-restrained awake C57BL/6 mice and thus were free of the confounding effects of anesthesia on neuronal activity, CBF and brain metabolism.

A new two-photon-excitable phosphorescence oxygen probe – Oxyphor2P was used in this study. Oxyphor2P belongs to the class of dendritically protected oxygen probes (Lebedev et al., 2009), and it is built around a newly developed Pt tetraarylphthalimidoporphyrin (PtTAPIP) (Esipova et al., 2017). In comparison to its predecessor (PtP-C343) (Finikova et al., 2008), Oxyphor2P exhibits red-shifted two-photon excitation and emission maxima, higher phosphorescence quantum yield and larger two-photon absorption cross-section (Esipova et al., 2019). Furthermore, in contrast to PtP-C343 the phosphorescence decay of Oxyphor2P exhibits well-defined single-exponential kinetics, which helps improve the accuracy of PO₂ calculation. These improvements of Oxyphor2P facilitated large-scale sampling of both capillary RBC flow and PO₂ deeper in the brain, for example, 600 µm vs. 450 µm (Lyons et al., 2016; Sakadžić et al., 2014), and with higher signal-to-noise ratios, which, in turn, powered the statistical analyses beyond what could be achievable with PtP-C343 (Devor et al., 2011; Lecoq et al., 2011; Lyons et al., 2016; Parpaleix et al., 2013; Sakadzić et al., 2010; Sakadžić et al., 2014). In addition, the very high phosphorescence quantum yield of Oxyphor2P (Esipova et al., 2017; Esipova et al., 2019) enabled detection of RBC-passages in capillaries based on phosphorescence alone, without the need to enhance the signal from the blood plasma using additional chromophores (Lyons et al., 2016).

We first assessed oxygenation in the diving arterioles and surfacing venules and observed a decrease in PO₂ with cortical depth for both vessel types (Figure 2a). The PO₂ decrease in the diving arterioles between layer I and V was about 15 mmHg, equivalent to the difference in SO₂ of 3.8%. This modest oxygen extraction along the diving arterioles implies that much more oxygen was extracted from the rest of the microvascular network, including arteriolar branches, capillaries and potentially venules. Previous measurements performed in mice (Kisler et al., 2017; Moeini et al., 2018) and rats (Devor et al., 2011; Sakadžić et al., 2016) reported pronounced PO₂ gradients in the periarteriolar tissue around the cortical diving arterioles, implying extraction of oxygen from cortical diving arterioles. Specifically, a significantly steeper decrease in PO₂ with cortical depth was observed directly in diving arterioles in isoflurane-anesthetized mice by Kazmi et al. (2013) and indirectly, based on the depth dependence of the extravascular (tissue) PO₂ measured immediately next to the diving arterioles, in α-chloralose anesthetized rats by Devor et al. (2011). The most likely reason for these discrepancies is the higher suppression of CBF than CMRO₂ under different anesthesia regimes. In awake mice, Lyons et al. (2016) measured approximately constant PO₂ in the diving arterioles over cortical depth of 0–400 µm. This result is consistent with our current observation that in awake mice, the amount of oxygen extracted along the diving arterioles over depth represents a small fraction of the total amount of oxygen transported by these arterioles.

In contrast to the moderate PO₂ decrease along the penetrating arterioles, the decrease in the venular PO₂ from layer I to V was much smaller (~6 mmHg), but resulted in a larger fractional decrease in SO₂ (8.3%) than in the penetrating arterioles (Figure 2b). An increase in PO₂ in ascending venues towards the cortical surface has been previously observed in isoflurane-anesthetized mice (Sakadzić et al., 2010). However, the PO₂ values, measured previously along the ascending venules in the fore-paw and hind-paw regions of the somatosensory cortex in awake mice, were reported to be nearly constant (Lyons et al., 2016). The difference between these observations may be attributed to the difference in the studied cortical regions and/or to the lower measurement accuracy associated with the old probe – PtP-C343, because of its lower brightness and inability to resolve small PO₂ changes. In addition, some discrepancies in the absolute baseline PO₂ in arterioles and venules at different depths were observed between our present and some previous studies (Kazmi et al., 2013; Lecoq et al., 2011; Lyons et al., 2016; Parpaleix et al., 2013; Sakadzić et al., 2010). Different anesthesia regimes, different cortical areas and, most importantly, use of anesthetized vs. awake animals would be the most obvious factors underlying the observed discrepancies. Furthermore, cortical temperature typically was not controlled in the previous studies, while the presence of a cranial window can cause reduction in the cortical temperature (Shirey et al., 2015), especially when imaging is carried out using a non-heated water-immersion objective. Temperature affects all physiological parameters and chemical properties, including O₂ diffusion and solubility, hemoglobin affinity to O₂, CBF, CMRO₂ and the triplet decay time of the oxygen probe (Benesch et al., 1969; Croughwell et al., 1992; Finikova et al., 2008; Hanks and Wallace, 1949; Jones and Siegel, 1969; Pray, 1952; Rosomoff and Holaday, 1954; Rossing and Cain, 1966; Soukup et al., 2002) – all of which may affect the experimental results.

A faster decrease in SO₂ with cortical depth in the ascending venules than in the penetrating arterioles resulted in a higher depth-dependent OEF in the deeper cortical layers, reaching the maximum in layer IV (Figure 2b). In addition, the total blood flow may be higher in layer IV than in layer I, as suggested by the measurements of capillary RBC flux (~13% lower in layer IV than in layer I; Figure 3a) and capillary segment density (~50% higher in layer IV than in layer I; Gould et al., 2017; Sakadžić et al., 2014). Altogether, this implies that oxygen extraction was higher in the deeper cortical layers, which would be in agreement with the findings that layer IV of mouse cortex has the highest neuronal and capillary densities (Blinder et al., 2013; Lefort et al., 2009; Patel, 1983; Wu et al., 2016), and that the cells in layer IV exhibit the highest cytochrome oxidase labeling activity, suggestive of the highest oxidative metabolism (Land and Simons, 1985). We estimated the depth-dependent OEF, which implies existence of a laminar flow pattern in the brain cortical microvascular network. This was recently confirmed by Schmid et al. (2017), who applied numerical modeling of blood flow and tracking of the trajectories of individual RBCs in realistic mouse cortical vasculature, which led to conclusion that RBCs predominantly flow in plane and no significant RBC flow in the direction of cortical depth takes place. Since oxygen extraction depends on both total blood flow and arterio-venous oxygen saturation difference, it will be important in the future to further experimentally investigate the total blood flow differences across different cortical layers.

The mean capillary RBC flux decreased only slightly with cortical depth, without reaching statistically significant difference across layers I-V (Figure 3a). This might be due to the redistribution of the blood flow over the denser capillary network in the deeper cortical layers, especially in layer IV (Blinder et al., 2013; Sakadžić et al., 2014; Wu et al., 2016), causing the RBC flux to be lower in individual capillaries. The observed mean capillary RBC flux (41.5 ± 1.2 RBCs/s; Figure 1—figure supplement 2) is in good agreement with the value of 41.9 ± 1.8 RBCs/s, reported by Lyons et al. (2016), and somewhat lower than ~48 RBC/s reported by Moeini et al. (2018) for somatosensory cortex in awake mice. The average capillary Mean-PO₂ increased slightly by ~7 mmHg from layer I to V (Figure 3d), which is in agreement with the previously reported increase in capillary Mean-PO₂ in both awake (Lyons et al., 2016) and isoflurane-anesthetized mice (Sakadžić et al., 2014) across the upper 400 µm of the cortex. One potential explanation for this increase could be related to the finding by Blinder et al. (2013), who reported that, in the upper 600 µm of somatosensory cortex, the probability of penetrating arterioles giving off side branches increased with cortical depth, whereas the probability for the ascending venules decreased with depth. This suggests that the ratio between the number of the more oxygenated (upstream) capillary branches and that of the less oxygenated (downstream) capillary branches increases with depth, contributing to the higher capillary PO₂ at greater depth.

Importantly, we found that the distributions of capillary RBC flux, speed and Mean-PO₂ were more homogeneous in cortical layers IV and V (Figure 3). The layer-dependent homogeneity of blood flow has been predicted by modeling the blood flow distribution in large-scale mouse brain microvasculature covering the whisker barrel cortex (Hartung et al., 2018). Both analytical and numerical models of oxygen advection and diffusion in microvascular networks predict that more homogenous capillary blood flow facilitates oxygen delivery to tissue (Jespersen and Østergaard, 2012; Østergaard et al., 2013; Østergaard et al., 2014a; Østergaard et al., 2014b; Rasmussen et al., 2015). Indeed, capillary blood flow homogeneity, measured by the distribution of capillary transit time or RBC flux during functional activation in the brain in healthy anesthetized animals, have been reported previously (Gutiérrez-Jiménez et al., 2016; Lee et al., 2016; Stefanovic et al., 2008). Interestingly, capillary flow homogeneity was absent in the mouse model of Alzheimer’s disease (Gutiérrez-Jiménez et al., 2018), while increased capillary blood flow heterogeneity was found in patients of Alzheimer’s disease (Eskildsen et al., 2017; Nielsen et al., 2017), suggesting a possible link between disturbed capillary flow patterns, reduced oxygen supply to tissue, and the progression of neurodegeneration. Our observations are in agreement with the previously reported lower capillary transit time heterogeneity in cortical layers IV and V than in the upper layers in anesthetized mice (Merkle and Srinivasan, 2016). Furthermore, capillary Mean-PO₂ also exhibited a similar homogeneous trend (Figure 3e,f). This could be expected since capillary RBC flow is correlated positively with Mean-PO₂ (Figure 7—figure supplement 2) (Lyons et al., 2016).

The temporal resolution of our measurements (0.6 s) ensured that the blood flow and PO₂ fluctuations due to cardiac and respiratory cycles, as they occurred to awake mice, were averaged out, but it is sufficient to capture the dynamics related to the rate of oxygen consumption, which could be estimated as the time for tissue PO₂ to drop to zero after blood flow stoppage. As reported in cats, that time was at least several seconds (Acker and Lübbers, 1977; Whalen and Nair, 1975). In addition, blood flow responses to neuronal activation could typically be resolved with such temporal resolution (Uhlirova et al., 2016). However, it is important to note that our experiments probed just one frequency window within a wide range of both faster and slower fluctuations present in the microvascular network. The cause of the dampening of the temporal fluctuations with depth is unclear. It may be related to the anatomical and functional differences between cortical layers; it also can be due to the expected dampening along the vascular paths if the temporal fluctuations originate from the upstream arteriolar blood flow.

We observed strong positive correlations between the temporal fluctuations of capillary RBC flux and Mean-PO₂ (Figure 5). This observation is in agreement with the previously reported positive correlation between the fluctuations of RBC flux and extravascular (tissue) PO₂ measured adjacent to the capillaries in rat tumor, although the latter occurred at a lower frequency range (Braun et al., 1999; Kimura et al., 1996). Importantly, the fluctuation of capillary Mean-PO₂ was found to be correlated positively with the fluctuation of RBC flux significantly stronger than with the fluctuations of RBC speed or line-density (Figure 5b,c), although these three variables are closely related as RBC flux is proportional to the product of RBC speed and line-density (Kleinfeld et al., 1998). This result could be expected, since most oxygen in the blood is bound to the hemoglobin inside RBCs (Pittman, 2011). We also observed strong positive correlation between the fluctuations of RBC flux and speed, but the fluctuations of line-density was poorly correlated with both of them (Figure 5—figure supplement 1). These correlations between the temporal fluctuations are in agreement with the trends measured using the populations of capillaries (Figure 7—figure supplement 2) as well as with the previous studies (Kleinfeld et al., 1998; Santisakultarm et al., 2012; VanTeeffelen et al., 2008). Despite poor correlation between the RBC flux and line-density, we observed reasonably positive correlation between the temporal fluctuations of capillary Mean-PO₂ and line-density (Figure 5). Similarly, shown in Figure 7—figure supplement 2 and in Lyons et al. (2016), capillary Mean-PO₂ and line-density, measured in the populations of capillaries, were also positively correlated, emphasizing the specific role of RBC line-density in oxygen transport (Lücker et al., 2017).

Capillary PO₂ gradients were measured to assess the intracapillary resistance to oxygen delivery to tissue and to calculate capillary SO₂ (Figure 6). For measuring EAT, we followed the previously developed methods (Lecoq et al., 2011; Lyons et al., 2016; Parpaleix et al., 2013). Compared to the previous studies performed with the old probe – PtP-C343 (Lecoq et al., 2011; Lyons et al., 2016; Parpaleix et al., 2013), the superior properties of Oxyphor2P enabled us to measure PO₂ gradients in a much larger number of capillaries over a greater cortical depth (down to 600 µm below the cortical surface) and using a shorter acquisition time (9 s) per measurement location. In agreement with Lyons et al. (2016), we observed EAT in capillaries (Figure 6), although the magnitude was lower than previously reported. EAT did not appear to depend significantly on RBC flux, speed or Mean-PO₂, but were correlated negatively with the line-density (Figure 7—figure supplement 3). Our measurements are in agreement with the previous observations (Lyons et al., 2016) as well as with the theoretical predictions (Hellums, 1977; Lücker et al., 2017). Importantly, we detected a significant decrease in EAT towards the deeper cortical layers (Figure 6c). Since EAT is directly related to the intravascular resistance to the diffusive oxygen transport from RBCs to tissue (Hellums, 1977), reduced EAT in the deeper layers represents another example of the adaptation of the microvascular network in order to facilitate local oxygen delivery in cortical regions with higher oxygen demand (Pries et al., 1994; Pries et al., 1998; Reglin et al., 2009; Reglin et al., 2017). However, it is important to note that mechanisms that govern EAT are multifactorial. EAT may be affected by multiple parameters, such as RBC spacing, shape and wall-to-RBC spatial clearance (Golub and Pittman, 2005; Lücker et al., 2017; Popel, 1989). In addition, our EAT measurements provide average values based on multiple RBC-passages. Therefore, they do not account for fluctuations of the parameters that affect EAT, which may differ between cortical layers and/or proximal and distal capillaries, and differentially affect the EAT measurements. Smaller difference between RBC-PO₂ and InterRBC-PO₂ and, consequently, smaller EATs at greater depths are likely a consequence of more narrowly distributed capillary blood flow and oxygenation. However, it is less clear why the mean RBC-PO₂ and InterRBC-PO₂ are increasing with depth (Figure 3—figure supplement 1). The observed increases in the mean RBC-PO₂ and InterRBC-PO₂ may be in part due to the potential change in proportion of the more oxygenated upstream capillaries to the less oxygenated downstream capillaries. Our measurement protocol (i.e., measuring PO₂ in most of the capillaries identified within the field of view at each imaging plane) together with the large number of capillaries interrogated at each depth, ensured 1) that the upstream and downstream capillaries were sampled proportional to their number densities, and 2) that the mean PO₂ values were properly estimated, but could not guarantee an equal sampling of the upstream and downstream capillaries unless the equal distributions of the upstream and downstream capillaries are naturally occurring. Other mechanisms, such as the effect of the increased homogeneity of capillary RBC flux and PO₂ on the mean intravascular PO₂, may also contribute.

The elevated SO₂ in the surfacing venules in layers I-III suggests that in the upper cortical layers (e.g. layers I-III) the downstream capillaries and post-capillary venules that feed the surfacing venules are more oxygenated compared to the surfacing venules in the deeper layers (e.g. layers IV and V). We therefore assigned branching orders to the capillary segments in cortical layers I-III and investigated the distributions of capillary RBC flow and oxygenation along the capillary paths. We found that the average SO₂ in the V1 capillaries selected across layers I-III was indeed higher than the SO₂ in the ascending venules in both layers I and II/III, confirming that high oxygen content in the superficial capillaries contributed to the increase in the venular SO₂ toward brain surface. This result is in line with the recent experimental and theoretical findings that capillary transit time is shorter in the surface cortical layers (Gutiérrez-Jiménez et al., 2016; Schmid et al., 2017), causing less efficient release of oxygen by the RBCs, thus elevating the SO₂ in the surfacing venules (Schmid et al., 2017). This result also suggests that the baseline oxygen extraction may be different between the cortical layers, which has been predicted by several theoretical studies using realistic vascular anatomical models (Gould and Linninger, 2015; Gould et al., 2017; Linninger et al., 2013). If true, this may have implications on the interpretation of results from different imaging modalities such as BOLD fMRI (Blockley et al., 2015; Griffeth and Buxton, 2011; Siero et al., 2011; Silva and Koretsky, 2002; Vazquez et al., 2006; Yu et al., 2014). Secondly, approximately 1/3 of the total oxygen extraction took place along the paths from the pial arterioles to the A1 capillary segments in layers I-III. This observation differs from our previous study in isoflurane-anesthetized mice, where we found, based on the measurements in the upper 450 µm of somatosensory cortex, that ~50% of the oxygen delivered to brain tissue was extracted from arterioles (Sakadžić et al., 2014). The discrepancy might be due to the effect of anesthesia, which suppressed cerebral oxygen metabolism and possibly increased both CBF and arteriolar surface area due to vasodilation (Alkire et al., 1999; Goldberg et al., 1966; Ogawa et al., 1990). As a result of these perturbations, oxygen extraction could be shifted towards the upstream microvascular segments (Sakadžić et al., 2014). Based on the observed difference in oxygen extraction and the assumed difference in oxygen metabolism between cortical layers, we anticipate that in cortical layers IV and V, the fraction of the extracted oxygen from arterioles may be smaller than in layers I-III, but still significant.

Our data provided additional evidence that the distributions of capillary flow and oxygenation were highly heterogeneous and strongly positively correlated with one-another, in particular in the downstream capillaries (Figure 7). This implies that the mixed venous blood oxygenation was, therefore, a result of the contributions from the wide distribution of capillary paths, carrying blood that was both more and less oxygenated than the blood in the postcapillary venules (Figure 7). At the extreme ends of this distribution are capillary paths with very low and very high oxygenation and blood flow. The paths with very low oxygenation may be the first sites of coupling of microvascular dysfunction and progression of various brain pathologies (Erdener et al., 2017; Lücker et al., 2018a). Indeed, it has been recently reported that only ~0.4% of the cortical capillaries in healthy mice had ‘stalled flow’, but in transgenic mice of Alzheimer’s disease the fraction was as high as 2% (Cruz Hernandez et al., 2016; Momjian-Mayor and Baron, 2005). As an example of such poorly oxygenated/perfused capillaries, we identified a capillary segment with PO₂ of ~15 mmHg and no detectable RBC flow during the 9-s-long acquisition (Figure 7—figure supplement 4). In contrast, the paths with very high oxygenation and blood flow (Figure 7—figure supplement 5) may be especially good sites for implementing blood flow control in response to locally increased oxygen demand, since increasing their resistance to flow may quickly redistribute blood over the nearby capillary network. However, the prevalence of such highly oxygenated/perfused capillaries in the cortical microvascular network and the existence of mechanisms of their site-specific control are unknown (Hudetz et al., 1996). Further studies should address these questions in relation to both normal and pathological brain conditions.

One potential limitation of this study is that the assignment of the cortical layers as a function of the cortical depth was performed based on the literature data (Blinder et al., 2013; Lefort et al., 2009), as opposed to identification of the layer-specific anatomical landmarks. Nevertheless, while slight shifts in the layer boundaries, which may have resulted from such an assignment, may have affected the exact values, it is unlikely that the observed general trends and significant differences in the observables between the layers would have changed. In addition, layer V in mouse barrel cortex spans the depth range of 450–700 μm (Blinder et al., 2013; Lefort et al., 2009), so that the range of 450–600 μm, interrogated in this study, likely overlaps the best with the upper part of layer V (i.e., layer Va) (Blinder et al., 2013; Lefort et al., 2009).

Another limitation is that at greater cortical depths, expanding shadows below large pial vessels, partial obstruction of the optical paths by the edge of the cranial window, and gradual loss of resolution, contributed to the smaller sampling size. It is possible that due to the smaller number of measured capillaries in the deeper cortical layers (e.g., 104 capillaries in layer V vs. 400 capillaries in layer I; Figure 3), some investigated variables did not show statistically significant difference between cortical layers.

The capillaries were identified based on their morphology, without taking into account the smooth muscle cell coverage and pericyte types (Attwell et al., 2016; Hall et al., 2014; Hartmann et al., 2015; Hill et al., 2015; Mishra et al., 2014; Peppiatt et al., 2006; Secomb, 2017), and the assignment of capillary branching order indices was performed manually, which may be operator-dependent. In principle, misclassifying vessel types and/or branching orders could potentially influence our analysis. However, based on the overwhelmingly larger number of capillaries compared to the non-penetrating arterioles and venules, our conclusions in general are unlikely to be different. Furthermore, identification of capillaries based on the biochemical staining of smooth muscle cells and pericytes (Hall et al., 2014; Hartmann et al., 2015) will add significant complexity to the study, especially considering a large sample size used in our analysis, but in the end may not provide more accurate results, as the debate on what is a proper classification of vessel-types based on staining is still ongoing (Hill et al., 2015).

The RBC speed was calculated by assuming a constant RBC size (6 µm) (Unekawa et al., 2010) along the capillary axis, without considering its potential variations with RBC speed, line-density and capillary diameter. In a separate set of experiments, we performed line-scan measurements (Kleinfeld et al., 1998) in 58 capillaries (2-s-long acquisition, 2 kHz line-scan rate) in n = 2 awake mice, and obtained close mean RBC speed values by processing the data by using two methods: by the line-scan method described in Kleinfeld et al. (1998) (mean RBC speed = 0.61 mm/s) and by the RBC-passage (or point-scan) method used in this work (mean RBC speed = 0.69 mm/s). The mean RBC longitudinal size was estimated by following the procedures described in Chaigneau et al. (2003), and it did not vary significantly as a function of RBC speed, line-density and capillary diameter, except for the fast RBCs (>1 mm/s), which were also associated with the noisier measurements by both techniques. However, the mean fluctuation of the RBC longitudinal size over the 2-s-long acquisition was moderate (STD = 2.3 ± 0.6 µm). Therefore, group comparison of the mean RBC speed values measured by the RBC-passage method may be conducted with reasonable accuracy, but the instantaneous speed of individual RBCs obtained by this method may have larger measurement errors (please see Figure 1—figure supplement 2 and text for additional details). These limitations of the technique should be considered for particular experimental designs.

The RBCs that are touching each other might be counted mistakenly as a single RBC, causing underestimation of RBC flux and speed. We estimated that approximately 6% of the ‘valleys’ might be caused by the passing of multiple RBCs through the optical focus. However, as no difference in RBC line-density across different cortical layers was observed, we do not anticipate that our layer-specific analysis is affected as it is unlikely that the RBCs-touching phenomenon differentially affects the measurements in different cortical layers.

We would also like to mention that some discrepancy exists between the EAT magnitudes measured using 2PLM by different groups. For example, rather large EAT was reported in Lecoq et al. (2011); Lyons et al. (2016); Parpaleix et al. (2013) with the PtP-C343 probe. Much smaller EAT (only a few mmHg) were measured previously using also PtP-C343 (unpublished data from Sakadžić et al., 2014), and moderate EAT are reported in the present work with Oxyphor2P. It should be mentioned that the data underlying the EAT measurements are typically noisier than those used to derive mean intravascular and tissue PO₂'s, since to quantify EAT the signals have to be split into multiple distance or time bins. In addition, the previously used probe PtP-C343 has intrinsically non-single-exponential phosphorescence decay and much lower emission quantum yield. These limitations, in combination with potentially slightly different implementations of acquisition protocols and algorithms for fitting the phosphorescence decay data, are likely to be the dominant factor contributing to the differences in the reported EAT magnitudes. By using Oxyphor2P, which has much stronger signal and much better defined single-exponential decay, making data analysis much more robust, here we greatly reduced uncertainty in the EAT measurements, which was inherent to the previous probe PtP-C343. Nevertheless, we still would like to emphasize the importance of aligning the acquisition and data analysis protocols across the labs as well as using the same data acquisition protocols during measurement as used for probe calibration.

We averaged all the phosphorescence decays within the valleys (i.e. RBC-passages) in the segmented phosphorescence intensity time courses to calculate RBC-PO₂. This approach does not consider that within the valley PO₂ may vary as a function of distance to the center of the valley, and the extent of this variation may be different among capillaries of different diameter and/or RBC speeds.

In conclusion, we have experimentally mapped the distributions of microvascular flow and oxygenation in the whisker barrel cortex in awake mice using two-photon phosphorescence lifetime microscopy. We have found evidence that oxygen was extracted differently in different cortical layers, and that the distributions of microvascular blood flow and oxygenation were adjusted across layers in a way that facilitated oxygen delivery in the deeper layers. Specifically, the depth-dependent OEF was measured higher in cortical layers IV and V, where the oxidative metabolism is presumably the highest in the cortex. This increase was accompanied by the more homogenous capillary RBC flow and oxygenation, as well as by the reduction of intracapillary resistance to oxygen diffusion to tissue (inferred from the EAT changes). In addition, we have found that arterioles in the superficial cortical layers (e.g. layers I-III) contributed significantly to the oxygen extraction from blood (34%) even in awake mice. We anticipate that our results and analysis will help better understand the normal brain physiology, and the progression of brain pathologies that affect cerebral microcirculation. They will also inform more accurate biophysical models of the cortical layer-specific oxygen delivery and consumption, as well as improve the interpretation of the results from other brain imaging modalities.

All data generated or analyzed during this study are included in this paper and the supporting files.

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Author details

1. Baoqiang Li

   Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Tatiana V Esipova

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2992-3303

2. Tatiana V Esipova

   1. Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, United States
   2. Department of Chemistry, University of Pennsylvania, Philadelphia, United States

   Contribution

   Investigation, Methodology

   Contributed equally with

   Baoqiang Li

   Competing interests

   No competing interests declared

3. Ikbal Sencan

   Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

4. Kıvılcım Kılıç

   Department of Neurosciences, University of California, San Diego, La Jolla, United States

   Contribution

   Investigation, Methodology, Writing—original draft

   Competing interests

   No competing interests declared

5. Buyin Fu

   Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

6. Michele Desjardins

   Department of Radiology, University of California, San Diego, La Jolla, United States

   Contribution

   Validation, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

7. Mohammad Moeini

   1. Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Canada
   2. Research Centre, Montreal Heart Institute, Montréal, Canada

   Contribution

   Software, Validation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

8. Sreekanth Kura

   Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States

   Contribution

   Software, Visualization, Writing—original draft

   Competing interests

   No competing interests declared

9. Mohammad A Yaseen

   Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States

   Contribution

   Software, Validation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4154-152X

10. Frederic Lesage

    1. Institute of Biomedical Engineering, École Polytechnique de Montréal, Montréal, Canada
    2. Research Centre, Montreal Heart Institute, Montréal, Canada

    Contribution

    Validation, Investigation, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

11. Leif Østergaard

    Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Contribution

    Validation, Writing—review and editing

    Competing interests

    No competing interests declared

12. Anna Devor

    1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States
    2. Department of Neurosciences, University of California, San Diego, La Jolla, United States
    3. Department of Radiology, University of California, San Diego, La Jolla, United States

    Contribution

    Resources, Funding acquisition, Validation, Investigation, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5143-3960

13. David A Boas

    1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States
    2. Department of Biomedical Engineering, Boston University, Boston, United States

    Contribution

    Resources, Funding acquisition, Validation, Investigation, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

14. Sergei A Vinogradov

    1. Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, United States
    2. Department of Chemistry, University of Pennsylvania, Philadelphia, United States

    Contribution

    Resources, Funding acquisition, Validation, Investigation, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4649-5534

15. Sava Sakadžić

    Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, United States

    Contribution

    Conceptualization, Resources, Software, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing—review and editing

    For correspondence

    sava.sakadzic@mgh.harvard.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6318-1193

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