Cell annotation using scRNA-seq data: A protein-protein interaction network approach.

Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.•We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes.•The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.

ORIGINS: A protein network-based approach to quantify cell pluripotency from scRNA-seq data.

Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT (Chen et al., 2019) and CytoTRACE (Gulati et al., 2020), for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set.•ORIGINS is a methodology to quantify pluripotency from scRNA-seq data implemented as a freely available R package.•ORIGINS uses the protein-protein interaction network associated with differentiation and the data set expression matrix to calculate a score (differentiation activity) that quantifies pluripotency for each cell.

Following the footprints of variability during filopodial growth.

Filopodia are actin-built finger-like dynamic structures that protrude from the cell cortex. These structures can sense the environment and play key roles in migration and cell-cell interactions. The growth-retraction cycle of filopodia is a complex process exquisitely regulated by intra- and extra-cellular cues, whose nature remains elusive. Filopodia present wide variation in length, lifetime and growth rate. Here, we investigate the features of filopodia patterns in fixed prostate tumor cells by confocal microscopy. Analysis of almost a thousand filopodia suggests the presence of two different populations: one characterized by a narrow distribution of lengths and the other with a much more variable pattern with very long filopodia. We explore a stochastic model of filopodial growth which takes into account diffusion and reactions involving actin and the regulatory proteins formin and capping, and retrograde flow. Interestingly, we found an inverse dependence between the filopodial length and the retrograde velocity. This result led us to propose that variations in the retrograde velocity could explain the experimental lengths observed for these tumor cells. In this sense, one population involves a wider range of retrograde velocities than the other population, and also includes low values of this velocity. It has been hypothesized that cells would be able to regulate retrograde flow as a mechanism to control filopodial length. Thus, we propound that the experimental filopodia pattern is the result of differential retrograde velocities originated from heterogeneous signaling due to cell-substrate interactions or prior cell-cell contacts.