Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments.

Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.

Detection and characterization of chemotaxis without cell tracking.

Swarming has been observed in various biological systems from collective animal movements to immune cells. In the cellular context, swarming is driven by the secretion of chemotactic factors. Despite the critical role of chemotactic swarming, few methods to robustly identify and quantify this phenomenon exist. Here, we present a novel method for the analysis of time series of positional data generated from realizations of agent-based processes. We convert the positional data for each individual time point to a function measuring agent aggregation around a given area of interest, hence generating a functional time series. The functional time series, and a more easily visualized swarming metric of agent aggregation derived from these functions, provide useful information regarding the evolution of the underlying process over time. We extend our method to build upon the modelling of collective motility using drift-diffusion partial differential equations (PDEs). Using a functional linear model, we are able to use the functional time series to estimate the drift and diffusivity terms associated with the underlying PDE. By producing an accurate estimate for the drift coefficient, we can infer the strength and range of attraction or repulsion exerted on agents, as in chemotaxis. Our approach relies solely on using agent positional data. The spatial distribution of diffusing chemokines is not required, nor do individual agents need to be tracked over time. We demonstrate our approach using random walk simulations of chemotaxis and experiments investigating cytotoxic T cells interacting with tumouroids.

Induced endothelial cells from peripheral arterial disease patients and neonatal fibroblasts have comparable angiogenic properties.

Induced endothelial cells (iECs) generated from neonatal fibroblasts via transdifferentiation have been shown to have pro-angiogenic properties and are a potential therapy for peripheral arterial disease (PAD). It is unknown if iECs can be generated from fibroblasts collected from PAD patients and whether these cells are pro-angiogenic. In this study fibroblasts were collected from four PAD patients undergoing carotid endarterectomies. These cells, and neonatal fibroblasts, were transdifferentiated into iECs using modified mRNA. Endothelial phenotype and pro-angiogenic cytokine secretion were investigated. NOD-SCID mice underwent surgery to induce hindlimb ischaemia in a murine model of PAD. Mice received intramuscular injections with either control vehicle, or 1 × 106 neonatal-derived or 1 × 106 patient-derived iECs. Recovery in perfusion to the affected limb was measured using laser Doppler scanning. Perfusion recovery was enhanced in mice treated with neonatal-derived iECs and in two of the three patient-derived iEC lines investigated in vivo. Patient-derived iECs can be successfully generated from PAD patients and for specific patients display comparable pro-angiogenic properties to neonatal-derived iECs.

Cytotoxic T cells swarm by homotypic chemokine signalling.

Cytotoxic T lymphocytes (CTLs) are thought to arrive at target sites either via random search or following signals by other leukocytes. Here, we reveal independent emergent behaviour in CTL populations attacking tumour masses. Primary murine CTLs coordinate their migration in a process reminiscent of the swarming observed in neutrophils. CTLs engaging cognate targets accelerate the recruitment of distant T cells through long-range homotypic signalling, in part mediated via the diffusion of chemokines CCL3 and CCL4. Newly arriving CTLs augment the chemotactic signal, further accelerating mass recruitment in a positive feedback loop. Activated effector human T cells and chimeric antigen receptor (CAR) T cells similarly employ intra-population signalling to drive rapid convergence. Thus, CTLs recognising a cognate target can induce a localised mass response by amplifying the direct recruitment of additional T cells independently of other leukocytes.

Immune cells known as cytotoxic T lymphocytes, or CTLs for short, move around the body searching for infected or damaged cells that may cause harm. Once these specialised killer cells identify a target, they launch an attack, removing the harmful cell from the body. CTLs can also recognise and eliminate cancer cells, and can be infused into cancer patients as a form of treatment called adoptive cell transfer immunotherapy. Unfortunately, this kind of treatment does not yet work well on solid tumours because the immune cells often do not infiltrate them sufficiently. It is thought that CTLs arrive at their targets either by randomly searching or by following chemicals secreted by other immune cells. However, the methods used to map the movement of these killer cells have made it difficult to determine how populations of CTLs coordinate their behaviour independently of other cells in the immune system. To overcome this barrier, Galeano Niño, Pageon, Tay et al. employed a three-dimensional model known as a tumouroid embedded in a matrix of proteins, which mimics the tissue environment of a real tumour in the laboratory. These models were used to track the movement of CTLs extracted from mice and humans, as well as human T cells engineered to recognise cancer cells. The experiments showed that when a CTL identifies a tumour cell, it releases chemical signals known as chemokines, which attract other CTLs and recruit them to the target site. Further experiments and computer simulations revealed that as the number of CTLs arriving at the target site increases, this amplifies the chemokine signal being secreted, resulting in more and more CTLs being attracted to the tumour. Other human T cells that had been engineered to recognize cancer cells were also found to employ this method of mass recruitment, and collectively 'swarm' towards targeted tumours. These findings shed new light on how CTLs work together to attack a target. It is possible that exploiting the mechanism used by CTLs could help improve the efficiency of tumour-targeting immunotherapies. However, further studies are needed to determine whether these findings can be applied to solid tumours in cancer patients.

A comparison of the pro-angiogenic potential of human induced pluripotent stem cell derived endothelial cells and induced endothelial cells in a murine model of peripheral arterial disease.

BACKGROUND: Endothelial cells derived from human induced pluripotent stem cells (iPSC-ECs) promote angiogenesis, and more recently induced endothelial cells (iECs) have been generated via fibroblast trans-differentiation. These cell types have potential as treatments for peripheral arterial disease (PAD). However, it is unknown whether different reprogramming methods produce cells that are equivalent in terms of their pro-angiogenic capabilities. OBJECTIVES: We aimed to directly compare iPSC-ECs and iECs in an animal model of PAD, in order to identify which cell type, if any, displays superior therapeutic potential. METHODS: IPSC-ECs and iECs were generated from human fibroblasts, and transduced with a reporter construct encoding GFP and firefly luciferase for bioluminescence imaging (BLI). Endothelial phenotype was confirmed using in vitro assays. NOD-SCID mice underwent hindlimb ischaemia surgery and received an intramuscular injection of either 1×106 iPSC-ECs, 1×106 iECs or control vehicle only. Perfusion recovery was measured by laser Doppler. Hindlimb muscle samples were taken for histological analyses. RESULTS: Perfusion recovery was enhanced in iPSC-EC treated mice on day 14 (Control vs. iPSC-EC; 0.35±0.04 vs. 0.54±0.08, p<0.05) and in iEC treated mice on days 7 (Control vs. iEC; 0.23±0.02 vs. 0.44±0.06, p<0.05), 10 (0.31±0.04 vs. 0.64±0.07, p<0.001) and 14 (0.35±0.04 vs. 0.68±0.07, p<0.001) post-treatment. IEC-treated mice also had greater capillary density in the ischaemic gastrocnemius muscle (Control vs. iEC; 125±10 vs. 179±11 capillaries/image; p<0.05). BLI detected iPSC-EC and iEC presence in vivo for two weeks post-treatment. CONCLUSIONS: IPSC-ECs and iECs exhibit similar, but not identical, endothelial functionality and both cell types enhance perfusion recovery after hindlimb ischaemia.

Modeling biological tissue growth: discrete to continuum representations.

There is much interest in building deterministic continuum models from discrete agent-based models governed by local stochastic rules where an agent represents a biological cell. In developmental biology, cells are able to move and undergo cell division on and within growing tissues. A growing tissue is itself made up of cells which undergo cell division, thereby providing a significant transport mechanism for other cells within it. We develop a discrete agent-based model where domain agents represent tissue cells. Each agent has the ability to undergo a proliferation event whereby an additional domain agent is incorporated into the lattice. If a probability distribution describes the waiting times between proliferation events for an individual agent, then the total length of the domain is a random variable. The average behavior of these stochastically proliferating agents defining the growing lattice is determined in terms of a Fokker-Planck equation, with an advection and diffusion term. The diffusion term differs from the one obtained Landman and Binder [J. Theor. Biol. 259, 541 (2009)] when the rate of growth of the domain is specified, but the choice of agents is random. This discrepancy is reconciled by determining a discrete-time master equation for this process and an associated asymmetric nonexclusion random walk, together with consideration of synchronous and asynchronous updating schemes. All theoretical results are confirmed with numerical simulations. This study furthers our understanding of the relationship between agent-based rules, their implementation, and their associated partial differential equations. Since tissue growth is a significant cellular transport mechanism during embryonic growth, it is important to use the correct partial differential equation description when combining with other cellular functions.