A statistical inference approach to reconstruct intercellular interactions in cell migration experiments

Inverse modeling of time-delayed interactions via the dynamic-entropy formalism

Although instantaneous interactions are unphysical, a large variety of maximum entropy statistical inference methods match the model-inferred and the empirically measured equal-time correlation functions. Focusing on collective motion of active units, this constraint is reasonable when the interaction timescale is much faster than that of the interacting units, as in starling flocks, yet it fails in a number of counterexamples, as in leukocyte coordination (where signaling proteins diffuse among two cells). Here, we relax this assumption and develop a path integral approach to maximum-entropy framework, which includes delay in signaling. Our method is able to infer the strength of couplings and fields, but also the time required by the couplings to completely transfer information among the units. We demonstrate the validity of our approach providing excellent results on synthetic datasets of non-Markovian trajectories generated by the Heisenberg-Kuramoto and Vicsek models equipped with delayed interactions. As a proof of concept, we also apply the method to experiments on dendritic migration, where matching equal-time correlations results in a significant information loss.

Retinal Progenitor Cells Exhibit Cadherin-Dependent Chemotaxis across Transplantable Extracellular Matrix of In Vitro Developmental and Adult Models

Retinal degeneration is an escalating public health challenge, as diseases such as age-related macular degeneration, diabetic retinopathy, and retinitis pigmentosa cause irreversible vision loss in millions of adults each year. Regenerative medicine has pioneered the development of stem cell replacement therapies, which treat degeneration by replacing damaged retinal neurons with transplanted stem-like cells (SCs). While the collective migration of SCs plays critical roles during retinal development, our understanding of collective SC behaviors within biomaterials transplanted into adult tissue remains understudied. This project examines the potential therapeutic impacts of collective SC migration during transplantation by correlating the expression of cadherin, cell-cell cohesion molecules that maintain intercellular communication during development, with receptor proteins of chemoattractant molecules prevalent in degenerated adult tissue. Experiments examine these well-conserved biomechanisms by using two different model organisms: Drosophila melanogaster, a seminal model for retinal development, and Mus, an important preclinical model for transplantation. Results indicate that SCs from both animal models significantly upregulate cadherin expression to achieve more directed collective migration towards species-specific chemoattractants and exhibit longer distance motility upon different extracellular matrix substrates.

Quantifying heterogeneity to drug response in cancer-stroma kinetics

A crucial challenge in medicine is choosing which drug (or combination) will be the most advantageous for a particular patient. Usually, drug response rates differ substantially, and the reasons for this response unpredictability remain ambiguous. Consequently, it is central to classify features that contribute to the observed drug response variability. Pancreatic cancer is one of the deadliest cancers with limited therapeutic achievements due to the massive presence of stroma that generates an environment that enables tumor growth, metastasis, and drug resistance. To understand the cancer-stroma cross talk within the tumor microenvironment and to develop personalized adjuvant therapies, there is a necessity for effective approaches that offer measurable data to monitor the effect of drugs at the single-cell level. Here, we develop a computational approach, based on cell imaging, that quantifies the cellular cross talk between pancreatic tumor cells (L3.6pl or AsPC1) and pancreatic stellate cells (PSCs), coordinating their kinetics in presence of the chemotherapeutic agent gemcitabine. We report significant heterogeneity in the organization of cellular interactions in response to the drug. For L3.6pl cells, gemcitabine sensibly decreases stroma-stroma interactions but increases stroma-cancer interactions, overall enhancing motility and crowding. In the AsPC1 case, gemcitabine promotes the interactions among tumor cells, but it does not affect stroma-cancer interplay, possibly suggesting a milder effect of the drug on cell dynamics.

Gramian angular fields for leveraging pretrained computer vision models with anomalous diffusion trajectories

Anomalous diffusion is present at all scales, from atomic to large ones. Some exemplary systems are ultracold atoms, telomeres in the nucleus of cells, moisture transport in cement-based materials, arthropods' free movement, and birds' migration patterns. The characterization of the diffusion gives critical information about the dynamics of these systems and provides an interdisciplinary framework with which to study diffusive transport. Thus, the problem of identifying underlying diffusive regimes and inferring the anomalous diffusion exponent α with high confidence is critical to physics, chemistry, biology, and ecology. Classification and analysis of raw trajectories combining machine learning techniques with statistics extracted from them have widely been studied in the Anomalous Diffusion Challenge [Muñoz-Gil et al., Nat. Commun. 12, 6253 (2021)2041-172310.1038/s41467-021-26320-w]. Here we present a new data-driven method for working with diffusive trajectories. This method utilizes Gramian angular fields (GAF) to encode one-dimensional trajectories as images (Gramian matrices), while preserving their spatiotemporal structure for input to computer-vision models. This allows us to leverage two well-established pretrained computer-vision models, ResNet and MobileNet, to characterize the underlying diffusive regime and infer the anomalous diffusion exponent α. Short raw trajectories of lengths between 10 and 50 are commonly encountered in single-particle tracking experiments and are the most difficult ones to characterize. We show that GAF images can outperform the current state-of-the-art while increasing accessibility to machine learning methods in an applied setting.

Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors

pH balance and regulation within organelles are fundamental to cell homeostasis and proliferation. The ability to track pH in cells becomes significantly important to understand these processes in detail. Fluorescent sensors based on micro- and nanoparticles have been applied to measure intracellular pH; however, an accurate methodology to precisely monitor acidification kinetics of organelles in living cells has not been established, limiting the scope of this class of sensors. Here, silica-based fluorescent microparticles were utilized to probe the pH of intracellular organelles in MDA-MB-231 and MCF-7 breast cancer cells. In addition to the robust, ratiometric, trackable, and bioinert pH sensors, we developed a novel dimensionality reduction algorithm to automatically track and screen massive internalization events of pH sensors. We found that the mean acidification time is comparable among the two cell lines (ΔT_MCF-7 = 16.3 min; ΔT_MDA-MB-231 = 19.5 min); however, MCF-7 cells showed a much broader heterogeneity in comparison to MDA-MB-231 cells. The use of pH sensors and ratiometric imaging of living cells in combination with a novel computational approach allow analysis of thousands of events in a computationally inexpensive and faster way than the standard routes. The reported methodology can potentially be used to monitor pH as well as several other parameters associated with endocytosis.

Pannexin Channel Regulation of Cell Migration: Focus on Immune Cells

The role of Pannexin (PANX) channels during collective and single cell migration is increasingly recognized. Amongst many functions that are relevant to cell migration, here we focus on the role of PANX-mediated adenine nucleotide release and associated autocrine and paracrine signaling. We also summarize the contribution of PANXs with the cytoskeleton, which is also key regulator of cell migration. PANXs, as mechanosensitive ATP releasing channels, provide a unique link between cell migration and purinergic communication. The functional association with several purinergic receptors, together with a plethora of signals that modulate their opening, allows PANX channels to integrate physical and chemical cues during inflammation. Ubiquitously expressed in almost all immune cells, PANX1 opening has been reported in different immunological contexts. Immune activation is the epitome coordination between cell communication and migration, as leukocytes (i.e., T cells, dendritic cells) exchange information while migrating towards the injury site. In the current review, we summarized the contribution of PANX channels during immune cell migration and recruitment; although we also compile the available evidence for non-immune cells (including fibroblasts, keratinocytes, astrocytes, and cancer cells). Finally, we discuss the current evidence of PANX1 and PANX3 channels as a both positive and/or negative regulator in different inflammatory conditions, proposing a general mechanism of these channels contribution during cell migration.