Biology subjects

Senra, D.

Publications and source records attributed to Senra, D..

3 recordsLinked to original sources

bioRxiv2024

Unraveling tumor heterogeneity: Quantitative insights from scRNA-seq analysis in breast cancer subtypes

Tumors are complex systems characterized by genetic, transcriptomic, phenotypic, and microenvironmental variations. The complexity of this heterogeneity plays a crucial role in metastasis, tumor progression, and recurrence. In this work, we utilized publicly available single-cell transcriptomics data from human breast cancer samples (ER+, HER2+, and triple-negative) to evaluate key concepts pertinent to cancer biology. Quantitative assessments included measures based on copy number alterations (CNAs), entropy, transcriptomic heterogeneity, and different protein-protein interaction networks (PPINs). We found that entropy and PPIN activity related to the cell cycle delineate cell clusters with notably elevated mitotic activity, particularly elevated in aggressive breast cancer subtypes. Additionally, CNA distributions differentiate between ER+ and HER2+/TN subtypes. Further, we identified positive correlations among the CNA score, entropy, and the activities of PPINs associated with the cell cycle, as well as basal and mesenchymal cell lines. These scores reveal associations with tumor characteristics, reflecting the known malignancy spectrum across breast cancer subtypes. By bridging the gap between existing literature and a comprehensive quantitative approach, we present a novel framework for quantifying cancer traits from scRNA-seq data by establishing several scores. This approach highlights the potential for deeper insights into tumor biology compared to conventional marker-based approaches.

bioinformatics↗

bioRxiv2022

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 for each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT [1] and CytoTRACE [2], for four 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. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/491232v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1fd432forg.highwire.dtl.DTLVardef@45cdf0org.highwire.dtl.DTLVardef@d35b31org.highwire.dtl.DTLVardef@80702_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Following the footprints of variability during filopodia 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 cancer 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 filopodia 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 filopodia 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.

biophysics↗