Modeling Lymph Flow and Fluid Exchange with Blood Vessels in Lymph Nodes

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Modeling Lymph Flow and Fluid Exchange with Blood Vessels in Lymph Nodes

M. Jafarnejad1, M.C. Woodruff2, D.C. Zawieja3, M.C. Carroll4, J.E. Moore Jr.1

1 Department of Bioengineering, Imperial College, London, England SW7 2AZ

2 Emory Vaccine Center, Emory University, Atlanta, GA 30322.

3 Department of Medical Physiology, Texas A&M Health Science Center, Temple, Texas 76504

4 Program in Cellular and Molecular Medicine, Boston Childrens Hospital, Harvard Medical School, Boston, Massachusetts 02115
Running Title: Lymph Flow Patterns through Nodes
Corresponding Author:

Professor James E. Moore Jr., PhD.

The Bagrit and Royal Academy of Engineering Chair in Medical Device Design

Department of Bioengineering

Imperial College London

South Kensington Campus

Royal School of Mines Building, Room 4.14

London, SW7 2AZ, United Kingdom

Phone: +44 (0)20 7594 9795

Fax: +44 (0)20 7594 9817


Background: Lymph nodes (LNs) are positioned strategically throughout the body as critical mediators of lymph filtration and immune response. Lymph carries cytokines, antigens, and cells to the downstream LNs, and their effective delivery to the correct location within the LN directly impacts the quality and quantity of immune response. Despite the importance of this system, the flow patterns in LN have never been quantified, in part because experimental characterization is so difficult.

Methods and Results: To achieve a more quantitative knowledge of LN flow, a computational flow model has been developed based on the mouse popliteal LN, allowing for a parameter sensitivity analysis to identify the important system characteristics. This model suggests that about 90% of the lymph takes a peripheral path via the subcapsular and medullary sinuses, while fluid perfusing deeper into the paracortex is sequestered by parenchymal blood vessels. Fluid absorption by these blood vessels under baseline conditions was driven mainly by oncotic pressure differences between lymph and blood, although the magnitude of fluid transfer is highly dependent on blood vessel surface area. We also predict that the hydraulic conductivity of the medulla, a parameter that has never been experimentally measured, should be at least three orders of magnitude larger than that of the paracortex to ensure physiologic pressures across the node.

Conclusions: These results suggest that structural changes in the LN microenvironment, as well as changes in inflow/outflow conditions, dramatically alter the distribution of lymph, cytokines, antigen and cells within the LN, with great potential for modulating immune response.

Keywords: lymph node; lymph flow; computational fluid dynamics; lymph-blood fluid exchange
Condensed Abstract

Lymph carries cytokines, antigens, and cells to the downstream lymph nodes (LNs), and their effective delivery to the correct location within the LNs directly impacts the quality and quantity of immune response. To achieve a quantitative knowledge of LN flow, a computational flow model has been developed based on mouse popliteal LN, allowing for parameter sensitivity analysis to identify the important system characteristics. This study suggests that structural changes in the LN microenvironment, as well as changes in inflow/outflow conditions, dramatically alter the distribution of lymph, cytokines, antigen and cells within the LN, with great potential for modulating immune response.


Lymph nodes serve as critical outposts for the immune surveillance of peripheral tissue. The nodes also appear to be the “focal point” of the lymphatic vascular tree. In the peripheral tissues, lymph is sequestered by highly-permeable initial lymphatics and transported into the less-permeable muscularized lymphatic collectors 1. Eventually, these collectors propel the lymph into LNs, which serve as intermittent filters for the lymph on its journey back into active blood circulation via the thoracic duct 2, 3. As lymph flows from the periphery towards the nodes, it carries with it cellular debris, metabolic intermediates, immune cells and many other things found in the interstitium. Additionally, the lymph flow itself is a modulator of the lymphatic vessel contraction frequency and amplitude 4-6, acting via lymphatic endothelial cell signaling 7, and hence determines the rate by which lymph is transported to the LNs. As filters of the lymphatic system, the LN environment is directly exposed to these lymph-borne factors, and so provides as a ‘snapshot’ of the status of the upstream tissue.

In the case of a peripheral infectious challenge, pathogens and their products can be swept up in the lymph and carried to the “draining LN”, making these sites ideal as centralized points of immune surveillance 8. As lymphocyte activation is dependent on antigen exposure, lymph flow is required for the delivery of pathogenic material to the draining lymph node where it can stimulate immune responses. In the B cell follicle, lymph transported antigen can be scavenged from the lymph by subcapsular sinus macrophages and handed off to waiting B lymphocytes 9-11, or directly scavenged from the lymph via the dense conduit network 12. In the case of T lymphocyte activation, activated antigen-presenting cells (APC), most importantly dendritic cells (DCs), from the periphery capture, process and transport antigen along a carefully groomed CCL19/21 gradient to the lymphatics 13-15. Additionally we have shown that there is a population of antigen-presenting cells within the walls of the muscularized prenodal lymphatics that can also capture, process and transport antigen but do it very quickly 16. When in the collecting lymphatic vessels, APC can detach from the lymphatic endothelial cells and join others suspended in the lymph to be carried by the flow to the draining LN where they facilitate T cell response 17, 18. Additionally, cytokines and chemokines released at the site of infection can also be carried via the lymph to the draining lymph node, and have been shown to contribute to the quality and quantity of immune response 19, 20. In all three of these systems - passive antigen drainage, active antigen transport via cells, and cytokine ‘remote signaling’ - the dynamics of lymph flow within the LN have significant impact on the ability of the immune system to appropriately and rapidly respond to peripheral challenge. In the absence of lymph flow, antigen, cytokine and chemokine transport would have been limited to effective diffusion length scales of a few hundred microns 21. Moreover, DCs are bound with the crawling speed of ~6.4 μm/min in the initial lymphatics, whereas when they are transported via lymph flow in the collecting vessels, they have velocities around 1200 μm/min (~200-fold faster) 17. Despite the essential role of lymph flow in transport and distribution of molecules and cells towards and inside the node, the patterns and flow of lymph within the LNs has not yet been fully characterized.

While DCs and emigrating lymphocytes often make use of the lymphatic vasculature for migration to and from LNs, naïve lymphocytes and other hematopoietic lineages make use of a second, circulatory-based pathway into the LN through high endothelial venules (HEVs). Through the well-established mechanism known as ‘rolling adhesion’, immigrating cells from the blood make use of specialized blood endothelial cells (BECs) to roll along vessel walls, arrest in the lumen, and ultimately cross the blood endothelial barrier into the LN parenchyma 22, 23. In addition to HEVs, the LN contains a network of traditional vasculature responsible for the homeostatic maintenance of the lymphoid organ (Figure 1). While this network of vessels maintains a tight barrier against cellular egress from the blood into the LN (unless through HEVs), the transfer of fluid between the lymphatic vasculature and blood (in either direction) has been previously documented 24. Indeed, the tight association of lymphatic vasculature and blood vasculature within the LN raises interesting questions about the variables that influence fluid transfer between these systems, and how physiological changes in those variables affect change in lymph flow, and thus, immune responsiveness.

Structurally, the lymph node is comprised of several distinct lymphatic compartments, each of which provides different resistance to lymph flow. When lymph enters the node through afferent vessels, it arrives first in the subcapsular sinus (SCS) lumen 25, 26. This sinusoidal space, often characterized by the presence of SCS macrophages, overlays the LN cortical regions (B cell follicles) in a ~10 μm sheet spanned by periodic collagen spacers which connect the outer LN capsule with the SCS floor 27. As lymph flows through the SCS lumen, it can be laterally diverted into the conduit system – a network of fine reticular collagen fibrils, which penetrate the B cell follicle and reach deep into the paracortex (Figure 2A). The conduit network has been shown to be important in the delivery of soluble antigen to B cell follicles resulting in deposition on the resident follicular dendritic cells 28. Interestingly, this system has been shown to exhibit a molecular weight cutoff at roughly 70kD, with larger antigens being excluded 29, and therefore retained in the SCS. Lymph and soluble molecules not directed into the conduit system are presumably transported into the LN medulla via lymph paths that are not well described and characterized prior to collection and egress in the efferent lymphatics 26, 30, 31. The medulla is comprised of a complex network of lymphatic vasculature densely packed by medullary macrophages as well as trafficking leukocytes27. While each of these structures has been individually evaluated in contribution to potential immune response, their individual impact on fluid flow, and thus chemokine and antigen distribution throughout the LN, has not been evaluated.

To characterize the structure of the LNs, Woodruff et al. developed whole organ imaging of cleared nodes as well as 3D reconstruction of serially sectioned nodes 32. Although the whole organ imaging technique provides valuable insight into the location and size of the each of the nodal compartments and cellular interactions, it provides no information on physical properties such as hydraulic conductivity and permeability that govern lymph and antigen transport in the node. Although most of the current immunology and drug discovery experiments are carried out on mice, no experimental data are available on the fluid exchange in the LNs of mice, presumably because of the scale of the mice nodes as well as the complexity of the numerous inlets and outlets for lymph and blood in the nodes. Others have taken the approach of analyzing LNs in larger animals such as dog and sheep. Adair and Guyton cannulated afferent and efferent vessels of canine popliteal LN and left the blood vessels intact while measuring pressure in a downstream venule 24, 33. By perfusing the node with a constant flow rate, they were able to show that about 10% of the afferent lymph is absorbed by blood vessels under physiologic pressures, whereas during imposed venous hypertension the direction of the lympho-venous fluid flow can reverse 24. They also showed elevation in efferent lymph pressure will increase the amount of fluid transported to blood vessels 33. Thus the balance of fluid flows and pressures across the nodal vasculature plays a critical role in determining the movement of lymph fluid through different parts of the node and thus overall immune function.

Mathematical and computational modeling has been widely used to improve understanding of the physiology and immunology of biological tissues 34-37. These methods can be utilized to identify key parameters of a system, suggest new hypotheses and estimate parameters that cannot be measured 35, 38. The design of the model should take into account the important characteristics of the system being modeled as well as the nature of the questions to be answered 36. Bocharov et al. used reaction-diffusion models to simulate the distribution of interferon-α in the 3D geometry of the node, but lacked advective transport in the node 39. Several other models of LNs have been developed using agent-based modeling, and cellular Potts model techniques, and have been able to simulate cellular motions, cell-cell interactions, and cell influx and efflux 40-42. All the techniques described so far are useful in studying cellular interactions, but they all fail to account for the transport of lymph and the fluid exchange in the node. A computational fluid dynamics model based on the experimental data can investigate the lymph flow in the mouse LNs, and further expand our knowledge of antigen and chemokine transport in the node.

Despite the critical role of lymph as the carrier for molecules and cells to the LNs, little is known about its transport patterns and modification while passing through the node. This work aims to construct the first ever three-dimensional computational model of the lymph flow in the LNs and to use parameter sensitivity analysis techniques to determine the important parameters in the lymph transport and exchange in the node.

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