Capillary regression leads to sustained local hypoperfusion by inducing constriction of upstream transitional vessels

Abstract

In the brain, a microvascular sensory web coordinates oxygen delivery to regions of neuronal activity. This involves a dense network of capillaries that send conductive signals upstream to feeding arterioles to promote vasodilation and blood flow. Although this process is critical to the metabolic supply of healthy brain tissue, it may also be a point of vulnerability in disease. Deterioration of capillary networks is a feature of many neurological disorders and injuries and how this web is engaged during vascular damage remains unknown. We performed in vivo two-photon microscopy on young adult mural cell reporter mice and induced focal capillary injuries using precise two-photon laser irradiation of single capillaries. We found that ~59% of the injuries resulted in regression of the capillary segment 7 to 14 d following injury, and the remaining repaired to reestablish blood flow within 7 d. Injuries that resulted in capillary regression induced sustained vasoconstriction in the upstream arteriole-capillary transition (ACT) zone at least 21 days postinjury in both awake and anesthetized mice. The degree of vasomotor dynamics was chronically attenuated in the ACT zone consequently reducing blood flow in the ACT zone and in secondary, uninjured downstream capillaries. These findings demonstrate how focal capillary injury and regression can impair the microvascular sensory web and contribute to cerebral hypoperfusion.

Keywords: capillary; cerebral blood flow; microbleed; pericyte; two-photon imaging.

Fig. 1.

Focal capillary injury induced by optical laser ablation. (A) Schematic of a cortical microvascular network with location of two-photon laser-induced capillary injury. Microvascular zones are highlighted including the pial artery and PA (red), arteriole-capillary transition zone (orange), capillary zone (green), AV and pial vein (blue). (A’) Representative in vivo image of a capillary injury in a PdgfrβCre-tdTomato mouse preinjury with line-scan path (cyan) and ~10 min postinjury. Pericytes are shown in red and i.v. dye (70 kDa FITC-Dextran) labeling vessels in green. (B) Representative in vivo images of a capillary regression, repair, and sham event pre- and postinjury (~10 min), and then at various days postinjury. (C) Graphs of percent change in length of the injured (gray; Left) or sham (black; Right) capillaries based on visible i.v. dye in the lumen preinjury (0 d) and various days postinjury. Overall, 12/29 injuries (41%) resulted in capillary repairs and 17/29 (59%) resulting in regressions, in experiments conducted over 15 mice. Sham injuries = 17 conducted over 13 mice. (D) Representative in vivo images of endothelial cells after capillary regression. In 3/3 regression experiments, GFP+ endothelial labeling was no longer present 14 dpi. Pericytes are shown in red and endothelial cells are shown in green.

Fig. 2.

Focal capillary injury induces a local inflammatory response. (A) Representative in vivo images of a capillary injury in a PdgfrβCre-tdTomato; Cx3cr1-GFP mouse pre, post (~10 min), 1-, and 14-dpi. Pericytes are shown in red, Cx3cr1⁺ microglia and macrophages in green, and i.v. dye (2 MDa Alexa 680-Dextran) labeling vessels depicted in blue. Microvascular zones such as PA, ACT, and AVs are shown to depict the highly focal nature of the laser-induced capillary injuries and resulting neuroinflammatory reaction (25 μm radius; white dashed circle). Insets of these regions are shown in grayscale (1 d post blue outline and 14 d post red outline) indicating a possible mixture of soma and processes of Cx3cr1-GFP⁺ cells at the injury site. There may also be some autofluorescence occurring at the injury site in the regression example due to the robust inflammatory processes. Note: For the preinjury image of the regression event, the red and green channels were imaged separately from the far-red channel (shown in blue), within 5 min of each other, however there was a slight shift in the imaging frame resulting in an imperfect overlay of channels for that time point. (B) Graph of percent area of Cx3cr1-GFP⁺ cells at injury site preinjury (0 d) and 1-, 7-, and 14-dpi. ANOVA followed by Dunnett’s multiple comparison test were performed: Regression event: 0 vs. 1 d **P = 0.0074, 0 vs. 7 d: *P = 0.0109, 0 vs. 14 d: *P = 0.0385. Sham injuries n = 3, regression events n = 3, repair events n = 3; 2 mice. Data are shown as mean ± SEM. (C) Graph of Cx3cr1-GFP⁺ cell density surrounding injury site (excludes injury site) preinjury (0 d) and 1-, 7-, and 14-dpi. ANOVA tests were performed and no significant differences in microglia density were detected. Data are shown as mean ± SEM.

Fig. 3.

Chronic constriction of arteriole-capillary transition vessels occurs following laser-induced capillary regression. (A) Schematic of vessel network mapping by tracing pericyte territories (pink) and branch order from PA and arteriole-capillary transition (ACT) zone through the capillary network including the injury site to the AV. (A’) Representative in vivo image of a capillary injury with pericyte territories in a PdgfrβCre-tdTomato mouse preinjury with the line-scan path (cyan) and ~10 min postinjury (Inset). Pericytes are shown in red and i.v. dye (70 kDa FITC-Dextran) labeling vessels depicted in green. In the Right panel, pericyte territories are shown in pink, as well as vascular branch order and blood flow direction (dash line and arrow). (B) Graph of percent change in vessel diameter from baseline across the microvascular zones (PA, ACT, Capillary, AV) in a vessel network of a sham (black), regression (purple), and repair (blue) event. Changes in diameter are reported for each branch order in the acute phase (3 and 7 d) and the chronic phase (14 and 21 d). Branch order of injury sites in the capillary networks of the three case examples is denoted with color coded arrows. (C) Representative in vivo image of upstream ACT vessel segments from a sham, regression, and repair event preinjury and in the acute and chronic phase following capillary injury. Pericytes are shown in red and i.v. dye (70 kDa FITC-Dextran) labeling vessels depicted in green and grayscale. Soma of ensheathing pericytes are indicated with arrows. Baseline vessel diameter is indicated (dashed blue lines) to demonstrate ACT zone constriction following capillary regression. (D) Graphs of percent change in the diameter of vessel segments throughout the microvascular zones (PA, ACT, Capillary, AV) of sham (black), repair (blue), and regression (purple) events. Change from preinjury is shown during the acute (3 or 7 d) and chronic (14 or 21 d) phase following capillary injury. ANOVA followed by Tukey’s or Dunnett’s multiple comparison tests were performed depending on distribution of data. ACT zone: Acute: sham vs. regression ****P < 0.0001; repair vs. regression ***P = 0.0002. Chronic: sham vs. regression ***P = 0.0005; repair vs. regression **P = 0.0039. Each datapoint is the diameter from a single vessel segment. Sham = 12 experiments in 9 mice; repair = 8 experiments in 8 mice; regression = 14 experiments in 10 mice.

Fig. 4.

Proximity to the capillary injury site does not influence arteriole-capillary transition zone constriction. (A) Schematic showing Euclidean distance between arteriole-capillary transition (ACT) zone and capillary injury site. (A’) Euclidean distance between ACT zone and injury site in sham (black), repair (blue), and regression (purple) events. ANOVA indicated no significant difference. Sham = 12 experiments in 9 mice; repair = 8 experiments in 8 mice; regression = 14 experiments in 10 mice. (B) Scatter plots of Euclidean distance of ACT zone to capillary injury site versus ACT zone diameter change from baseline during the acute (3 or 7 d) and chronic (14 or 21 d) phases in sham, repair, and regression events. Pearson correlation tests did not show a correlation between diameter change and proximity to the injury site. Respective r and P values are indicated on all graphs for each experimental group. (C) Schematic showing vessel distance between ACT zone and capillary injury site. (C’) Shortest vessel distance between ACT zone and injury site between sham, repair, and regression events. ANOVA indicated no significant difference. (D) Scatter plots of vessel distance versus ACT zone diameter change from baseline during the acute (3 or 7 d) and chronic (14 or 21 d) phases in sham, repair, and regression events. Pearson correlation tests did not show a correlation between diameter change and proximity to the injury site. (E) Schematic showing capillary branch order from PA with (E’) scatter plots of vessel diameter changes at each branch order during the acute (3 or 7 d) and chronic (14 or 21 d) phases in sham, repair, and regression events. Pearson correlation indicated a significant correlation between diameter change in upstream vessels and proximity to the PA. (F) Schematic showing capillary branch order from AVs with (F’) scatter plots of vessel diameter changes at each branch order during the acute (3 or 7 d) and chronic (14 or 21 d) phases in sham, repair, and regression events. Pearson correlation tests did not show a correlation between diameter change in downstream vessels and proximity to the injury site.

Fig. 5.

Vasomotor dynamics along the arteriole-capillary transition zone are attenuated following capillary regression. (A) Representative image of arteriole-capillary transition (ACT) zones from a regression and repair event in an awake PdgfrβCre-GCaMP6f mouse. Pericytes are shown in green and i.v. dye (70 kDa Texas Red-Dextran) labeling vessels depicted in red. Grayscale images show location of ACT diameter sampling (yellow crosslines) and region of interest (ROI, yellow outline) to measure GCaMP6 fluorescence intensity in ensheathing pericyte. (B) Scatter plots of change in mural cell GCaMP6f signal (dF/F0) versus ACT zone diameter over at least 2 min (data point collected every 0.512 s or 1.951 Hz) preinjury, acute (3 or 7 dpi) and chronic (14 or 21 dpi). Plots are shown following cross-correlation analysis (SI Appendix, Fig. S8B) with strongest correlation time shift shown. Regression correlation time: Pre t = 2.048 s, Acute t = 1.536 s, Chronic t = 1.024 s; Repair correlation time: Pre t = 1.024 s, Acute t = 1.024 s, Chronic t = 1.536 s. Pearson correlation tests were performed, and respective r and P values are reported on graphs along with the coupling slope (S). (C–E) Graphs of (C) coupling slope, (D) diameter variance, and (E) mural cell Ca²⁺ signal variance over the course of 21 d following injury in ACT zones upstream of sham (black), repair (blue), and regression (purple) events. ANOVA followed by Dunnett’s multiple comparison tests were performed: Coupling slope: Regression: 0 vs. 3 d: *P = 0.015, 0 vs. 7 d: *P = 0.027, 0 vs. 14 d: *P = 0.022, 0 vs. 21 d: *P = 0.038. Diameter variance: ACT zone–Regression: 0 vs. 7 d: **P = 0.002, 0 vs. 21 d: **P = 0.003. Sham n = 4, repair n = 3, regression n = 7; 5 mice. (F) Schematic demonstrating that capillary regression results in attenuated vasomotion (orange) with normal mural cell Ca²⁺ signaling (black) in the upstream ACT zone.

Fig. 6.

Capillary regression and chronic constriction of the ACT zone results in hypoperfusion of secondary off-shoot vessels. (A) Image and schematic showing location of secondary off-shoots relative to injury site. (B) Graph of upstream ACT zone blood volume flux over the course of 21 d following sham, repair, and regression events in awake mice. A mixed-effects model was performed: Time: P = 0.6531, Condition: P = 0.1137, Interaction: P = 0.6922. Sham n = 3 vessels (3 mice), repair n = 2 vessels (2 mice), regression n = 6 vessels (2 mice). Data are shown as mean ± SEM. (C) Graph of red blood cell flux over the course of 21 d in secondary off-shoots from ACT zone following sham (black), repair (blue), and regression (purple) events in awake mice. A mixed-effects model followed by Tukey’s multiple comparisons test was performed: Time: P = 0.2798, Condition: *P = 0.0329, *Interaction: P = 0.0356. Sham vs. regression day 3: *P = 0.0243. Sham n = 9 vessels (3 mice), repair n = 3 vessels (3 mice), regression n = 7 vessels (3 mice). Data are shown as mean ± SEM. (D) Graph of vessel diameter over the course of 21 d following injury in secondary off-shoots from ACT zone following sham, repair, and regression events in awake mice. A mixed-effects model was performed: Time: P = 0.0809, Condition: P = 0.6315, Interaction: P = 0.8773. Sham n = 9 vessels (3 mice), repair n = 3 vessels (3 mice), regression n = 7 vessels (3 mice). Data are shown as mean ± SEM. (E) Scatter plot of ACT zone blood volume flux versus RBC flux in secondary off-shoots over 21 dpi in sham, repair, and regression events in awake mice. Pearson correlation tests were performed, respective r and P values are reported on graph. (F) Schematic demonstrating that capillary regression results in ACT zone constriction reducing blood flow into the microvascular network including uninjured, secondary off-shoots.