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Blood flow modeling reveals improved collateral artery performance during the regenerative period in mammalian hearts


Collateral arteries bridge opposing artery branches, forming a natural bypass that can deliver blood flow downstream of an occlusion. Inducing coronary collateral arteries could treat cardiac ischemia, but more knowledge on their developmental mechanisms and functional capabilities is required. Here we used whole-organ imaging and three-dimensional computational fluid dynamics modeling to define spatial architecture and predict blood flow through collaterals in neonate and adult mouse hearts. Neonate collaterals were more numerous, larger in diameter and more effective at restoring blood flow. Decreased blood flow restoration in adults arose because during postnatal growth coronary arteries expanded by adding branches rather than increasing diameters, altering pressure distributions. In humans, adult hearts with total coronary occlusions averaged 2 large collaterals, with predicted moderate function, while normal fetal hearts showed over 40 collaterals, likely too small to be functionally relevant. Thus, we quantify the functional impact of collateral arteries during heart regeneration and repair—a critical step toward realizing their therapeutic potential.

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Fig. 1: Whole-organ imaging of coronary arteries at cellular resolution.
Fig. 2: Increased collateral arteries in neonate versus adult hearts after injury.
Fig. 3: Building a physiologically representative 3D model of mouse coronary arteries.
Fig. 4: Collateral arteries are predicted to perform better in neonate hearts.
Fig. 5: Evaluating collateral placement and the tradeoff between collateral number and size.
Fig. 6: Investigating hemodynamic differences between neonate and adult.
Fig. 7: Main branch coronary artery diameters remain constant while branching increases throughout postnatal development.
Fig. 8: Collateral arteries in adult and fetal human hearts.

Data availability

All images used for collateral analysis are available in the Stanford Digital Repository ( Images and meshes of the adult ( and neonate ( used for computational modeling are accessible in the vascular modeling repository (

Code availability

The code used for flow simulations can be found on the SimVascular GitHub ( Custom code for Strahler ordering and volume perfusion using publicly available modules, such as the vascular toolkit, can be found on GitHub (


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We thank A. Olson and M. Howard for technical support of light-sheet imaging and H. Wang for advice on surgical procedures. S.A. is supported by the BioX Bowes Fellowship. P.E.R.C. is supported by the NIGMS of the NIH (T32GM007276) and NSF-GRFP (DGE-1656518). M.L.D. is supported by the NSF-GRFP (DGE-1656518). D.B. is supported by the Department of Defense CMDRP in Congenital Heart Disease (W81XWH-16-1-0727). K.N. is supported by the NIH/NHLBL (R01HL141712; R01HL146754) and reports unrestricted institutional research support from Siemens Healthineers, Bayer, HeartFlow Inc., Novartis unrelated to this work, consulting for Siemens Medical Solutions USA and equity in Lumen Therapeutics. A.L.M. is supported by NIH (R01EB018302) and NSF Award (1663671). K.R.H. is supported by the NIH/NHLBL (R01-HL128503) and is an HHMI Investigator.

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Authors and Affiliations



S.A., P.E.R.C., A.L.M. and K.R.H. conceived and designed the project. S.A., P.E.R.C., A.N.L.S.-Q., C.K.C., G.D., X.F., I.M.W. and S.K.J. performed experiments. S.A., P.E.R.C., A.N.L.S.-Q., L.W.S., A.M.H. and Z.A.A. analyzed data. S.A. performed fluid simulations. P.E.R.C., B.C.R., M.Z. and D.B. performed mouse cardiac injury studies. K.N. and A.M.P. contributed human adult and fetal samples, respectively. S.A., M.L.D. and M.R.P. provided analysis tools. S.A. and P.E.R.C. prepared the figures. S.A., P.E.R.C., A.L.M. and K.R.H. wrote the article.

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Correspondence to Alison L. Marsden or Kristy Red Horse.

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Nature Cardiovascular Research thanks Nicola Smart, Luke Timmins, Jonathan Butcher and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Collateral formation in neonatal mice is a response of injury.

a, Maximum intensity projections of a representative, non-injured neonatal P6 heart immunolabeled with α-SMA. b, Maximum projection of traces beginning at the most proximal segment of the left coronary artery (LCA) and extended until the α-SMA signal discontinues. c, Quantification of collateral numbers in healthy and injured (4-days post MI) neonatal hearts. n = 6 uninjured, n = 9 injured hearts. Scale bars, 500 μm. Error bars are mean ± st dev: ****, p≤0.0001 by two-sided Student’s t-test.

Extended Data Fig. 2 Collateral artery assessments.

a,b, Maximum intensity projection of an entire neonate and adult heart 4-days post myocardial infarction (MI) immunolabeled with α-SMA. (a) P6 neonatal heart. (b) Adult (12-week-old) heart. c, Collateral numbers in neonate (n=9 hearts) and adult (n=6 hearts) 4 days post-MI. df, Evaluation of collateral patency in P6 neonatal heart 4-days post MI. (d) Schematic of staining method. (e) 3D reconstruction of α-SMA+ collaterals (purple) overlaid with α-SMA and CD31 staining. (f) MIPs of representative Z-stacks (30 μm) within e highlighting (solid purple arrow) a patent (α-SMA+ and CD31+) collateral. Scale bars: a and b 1 mm; e 500 μm. Error bars are mean ± st dev: ****, p≤0.0001 by two-sided Student’s t-test.

Extended Data Fig. 3 Investigation into parameters related to collateral artery placement.

a,b, Location of collateral bridges in relationship to infarcted tissue. (a) Schematic of categorization shown in b. b, Pie chart showing distribution of collateral location (n = 87 collaterals, n = 6 hearts). Distribution by collateral configuration type (n = 47 collaterals, n = 3 hearts). c, Distances between the aorta and collateral bridges (n = 47 collaterals, n = 3 hearts). df, Septal artery evaluation. (d) Distances between most distal RCA and SpA branch tips to the aorta. (e) Representative image of 3D rendered coronary artery branch tips and distances. (f) Quantification of distances in e between most distal branch tips of RCA and SpA to LCA. Right (RCA), left (LCA), and septal (SpA) coronary arteries. df, n = 3 hearts. Error bars are mean ± st dev: ****, p≤0.0001 by two-sided Student’s t-test.

Extended Data Fig. 4 0D surrogate model of pulsatile coronary flow.

a, Schematic of coronary lumped parameter network (LPN). b, Pressure quantities of the coronary LPN. Pa, Pressure at point a; Pb, pressure at point b; Paorta, pressure at aortic inlet; ΔPcor, pressure difference between Paorta and Pb.

Extended Data Fig. 5 Virtual coronary collateral configurations in adult and neonates.

ae, Adult configurations: 6 collaterals, 20 μm (a); 6 collaterals, 28 μm (b); 12 collaterals, 20 μm (c); 3 collaterals, 40 μm (d); 9 collaterals, 40 μm (e). f, Pressure increases for each collateral in c with and without a 99% stenosis. gj, Neonatal configurations: 6 collaterals, 20 μm (g); 12 collaterals, 10 μm (h); 12 collaterals, 20 μm (i); 1 collateral, 40 μm (j). Pie chart indicates number of collaterals per connection type.

Extended Data Fig. 6 Verification that collateral flows are in line with expected solutions.

a, Schematic of total collateral flow quantification. b, Total collateral flow vs. diameter of the collateral at each stenosis level. c, Total collateral flow vs. resistance of the collateral configurations 6 collaterals, 20 μm; 12 collaterals, 20 μm; 6 collaterals, 28 μm; 3 collaterals, 40 μm. Resistance was calculated based on the number, diameter, and length of the collaterals via Poiseuille’s Law.

Extended Data Fig. 7 Performance of neonate collaterals formed 4 days post injury.

ai-ii, Coronary vasculature with (ai) and without (aii) collaterals formed in response to injury, respectively. aiii, Perfusion territories of the left (LCA), right (RCA), and septal (SpA) coronary arteries. aiv, Pressure distribution based on CFD simulation. Red arrow indicates location of occlusion that has been virtually restored. b, Pressures at tips near real collateral (red segment) attachment sites (green circles) after injury and non-attached tips (orange circles) in the injury region. c, Pressure at every tip and only distal tips of the SpA or RCA in non-injured hearts (n=81 all RCA tips, n=67 all SpA tips, n=22 distal RCA tips, n=16 distal SpA tips, n=1 heart). d, Fold change at the connection sites of the collateral (red segment) when it is present and removed. The circle on the left is the pressure on either the RCA or SpA branch and the square on the right is the pressure on the LCA branch. e, The difference in pressure across the two connection sites with and without the collateral (n=8 connections, n=1 heart). f,g, The bar graph of percent volume-at-risk (VaR) above 30% (f), and total flow in vessels downstream of the virtual occlusion (g) within the VaR. Error bars are mean ± st dev: ****, p≤0.0001.

Extended Data Fig. 8 Streamlines of adult collaterals.

ad, Streamlines of collateral flow for configurations: 16 collaterals, 20 μm (a) and 3 different types of 1 collateral, 40 μm bd, Colors represent the streamlines from 1 collateral.

Extended Data Fig. 9 User segmentation variability and sensitivity analysis.

a, Radius measurements from 5 users of 16 segmentations over 3 vessels. The coefficient of variation is 16.7%. b, Sensitivity of pressure drop along the coronary tree to the radius of segmented vessels. Adult vessel radius was reduced by 20% and neonate vessel radius was increased by 20%.

Extended Data Fig. 10 Collaterals in fetal human hearts.

a, Maximum intensity projection (MIP) of GW22 fetal human heart labeled with α-SMA, ventral side. bi, ROI of ventral side (blue boxed region). Closed orange arrows point to collateral segments. bii, Traced collateral connection (green filaments) on ventral side. ci, MIP of fetal human heart, dorsal side. cii, Traced collateral connections (green filaments) on dorsal side. df, MIP of entire fetal human GW14.5 (d), GW18.5 (e) and GW19.5 (f) hearts, ventral side. Scale bars, af, 1.5 mm.

Supplementary information

Supplementary Information

Extended Data figure legends, Supplementary Tables 1 and 2 and legends and Supplementary Video 1–3 captions

Reporting Summary

Supplementary Video 1

Collateral tracing method. Representative collateral traces of P6 neonatal heart labeled with α-SMA (white), 4 d after MI. The first clip shows a subset of optical cross-sections and the collateral connections that were semi-automatically traced (magenta) from downstream of the suture (red cross). The second clip shows 3D rendering of traced collateral arteries (magenta) overlaid with the entire heart volume (gray). Next, the traced collaterals (magenta) were isolated, quantified and categorized in 3D (cyan).

Supplementary Video 2

Collateral patency assay. 3D reconstruction of entire P6 neonatal heart 4 d after MI subjected to collateral patency assay. Coronary arteries were immunolabeled with α-SMA (white) and endothelial cells were labeled in vivo (anti-CD31, orange) to assess perfusion of α-SMA+ collaterals (purple surface). Next, the video shows optical cross-sections of the volume occupied by one representative patent collateral artery (α-SMA+ and CD31+). An outline (purple dashed line) of the collateral bridge as well as an arrow (solid purple) follows the collateral bridge throughout the heart.

Supplementary Video 3

Collateral bridge. 3D rendering of one representative collateral bridge in a P6 neonatal heart 4 d after MI labeled with α-SMA (white) and podocalyxin (cyan). Next, the video shows optical cross-sections of the isolated volume and the entire path of the representative collateral bridge (red dashed line). The infarcted zone was outlined by the lack of autofluorescence signal (yellow).

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Anbazhakan, S., Rios Coronado, P.E., Sy-Quia, A.N.L. et al. Blood flow modeling reveals improved collateral artery performance during the regenerative period in mammalian hearts. Nat Cardiovasc Res 1, 775–790 (2022).

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