Malaria remains one of the major infectious threats, and vaccines provide only partial protection. The liver stage of the parasite lasts about 48 hours, and during this time T cells must find and destroy all infected hepatocytes, otherwise the disease will develop. Previously it was thought that cells are attracted to the site of infection by chemical signals, but direct evidence was lacking. The work stands somewhat apart: nearby there are studies of cell motility in channels and microfluidics, but no one had turned the living vascular network of the liver into a graph for search analysis.
Mice were injected with activated T cells, then infected with sporozoites of the malaria parasite. Using two-photon microscopy, the movement of T cells, the position of the parasite, and the structure of sinusoids stained with rhodamine dextran were simultaneously recorded. Vessel images were manually digitized in the Imaris program and converted into a graph: vertices are branching points, edges are vessel segments with a weight equal to their length. Then each position of a cell and the parasite was projected onto the nearest vertex of the graph. To assess attraction, a metric was developed based on the change in the shortest path to the infected area and compared with the classical method for open space.
In open-space experiments, a small fraction of T cells appeared attracted to the parasite, especially at the average cluster size. But when analyzing movement along the sinusoid graph, the number of attracted and repelled cells turned out to be equal, indicating the absence of a systematic bias. Simulations of random search showed that one T cell per imaging volume (about 62 500 cells per liver) finds the infected area on average in 12 hours, and within 48 hours with a probability above 94%. Even in the absence of attraction, 10 cells within 6 hours form a cluster at the parasite, which casts doubt on the previous model of directed recruitment.
The work shows for the first time that to effectively search for a rare target in dense tissue, chemical attraction is not needed: random movement along the natural vascular network is sufficient. This distinguishes it from neighboring studies that focused on cell motility in artificial channels or on the analysis of open space. The method of converting the vascular network into a graph opens the way to quantitative analysis of search strategies of immune cells in real tissues.
The technique can be transferred to other organs and cell types, for example, to the search for tumor cells or inflammatory foci. It will be necessary to automate the digitization of vessels and take into account tissue heterogeneity. In the future, this will make it possible to predict how many immune cells are needed to control infection under different conditions.
The results will influence the development of malaria vaccines and the understanding of immune surveillance in the liver and other dense tissues.
To check whether random search persists in T cells induced by a vaccine rather than by transfer of activated cells. To expand the analysis to deeper layers of the liver and to a larger number of parasites.
The work links the spatial structure of tissue with the effectiveness of the immune response, which remains an open question in immunology. It shows how environmental constraints can replace active guidance mechanisms and offers a tool to test this in other systems.
The fractal dimension of the sinusoid graph turned out to be about 1.7–2.0, less than the three dimensions of the liver. This means that T cells search for a target in a space whose properties are closer to a plane than to a volume, and therefore random search works faster.
Not published on arXiv. Here are its page, the PDF and the formalization: you can check it yourself, not only read our account.