Biology subjects

Torro, R.

Publications and source records attributed to Torro, R..

4 recordsLinked to original sources

bioRxiv2024

Celldetective: an AI-enhanced image analysis toolfor unraveling dynamic cell interactions

Analysis of multimodal and multidimensional data capturing dynamic interactions between diverse cell populations is a current challenge in bioimaging, especially in the context of immunology and immunotherapy research. Here, we introduce Celldetective, an open-source Python-based software tool designed for high-performance, end-to-end analysis of image-based in vitro immune and immunotherapy assays. Celldetective is purpose-built for multicondition, 2D multi-channel time-lapse microscopy of mixed cell populations. Although it is optimised for the needs of immunology assays, it is nevertheless broadly applicable to any biological system involving interacting cell populations. The software seamlessly integrates AI-based segmentation, tracking, and automated single-cell event detection, all within an intuitive graphical interface that supports interactive visualisation, annotation, and training options. We showcase its capabilities with original datasets of single immune effector cell interactions with an activating surface mediated by bispecific antibodies, and pairwise interactions in antibody-dependent cell cytotoxicity events.

bioinformatics↗

bioRxiv2024

Antigen density and applied force control enrichment of nanobody-expressing yeast cells in microfluidics

In vitro display technologies such as yeast display have been instrumental in developing the selection of new antibodies, antibody fragments or nanobodies that bind to a specific target, with affinity towards the target being the main factor that influences selection outcome. However, the roles of mechanical forces are being increasingly recognized as a crucial factor in the regulation and activation of effector cell function. It would thus be of interest to isolate binders behaving optimally under the influence of mechanical forces. We developed a microfluidic assay allowing the selection of yeast displaying nanobodies through antigen-specific immobilization on a surface under controlled hydrodynamic flow. This approach enabled enrichment of model yeast mixtures using tunable antigen density and applied force. This new force-based selection method opens the possibility of selecting binders by relying on both their affinity and force resistance, with implications for the design of more efficient immunotherapeutics.

bioengineering↗

bioRxiv2023

Establishment of Wnt ligand-receptor organization and cell polarity in the C. elegans embryo

Different signaling mechanisms concur to ensure robust tissue patterning and cell fate instruction during animal development. Most of these mechanisms rely on signaling proteins that are produced, transported and detected. The spatiotemporal dynamics of signaling molecules is largely unknown, yet it determines signal activitys range and time frame. Here, we use the Caenorhabditis elegans embryo to study how Wnt ligands, an evolutionarily conserved family of signaling proteins, dynamically organize to establish cell polarity in a developing tissue. We identify how locally produced Wnt ligands spread to transmit information to distant target cells. With quantitative live imaging, we show that the Wnt ligands diffuse extracellularly through the embryo over a timescale shorter than the cell cycle. We extract diffusion coefficients of Wnt ligands and their receptor Frizzled (Fz) and characterize their co-localization. Integrating our different measurements and observations in a simple computational framework, we show how fast diffusion in the embryo can polarize target cells. Our results support diffusion-based long-range Wnt signaling, which is consistent with the dynamics of developing processes.

developmental biology↗

bioRxiv2022

Cellular forces during early spreading of T lymphocytes on ultra-soft substrates

Mechanical forces are increasingly recognized as critical regulators of T cell activation, yet their earliest dynamics remain poorly resolved. Here, we use traction force microscopy on ultra-soft, antigen-presenting-cell-like polyacrylamide substrates to quantify the first 15 minutes of force generation by Jurkat and primary human CD4 T cells under controlled activating conditions. By combining time-resolved stress mapping with spatial tensor analysis, we uncover previously unrecognized heterogeneity in early T cell mechanosensing. Rather than producing a single stereotyped mechanical response, T cells exhibit three distinct temporal force regimes: low-amplitude active fluctuations, intermittent force bursts, and sustained sigmoidal buildups of stress. These temporal programs tightly couple to spatial organization: fluctuating and intermittent behaviors associate with disordered stress distributions, whereas sustained sigmoidal responses predominantly accompany polarized, dipolar, and unexpectedly extensile stress patterns. Substrate stiffness strongly reshapes this distribution, with stiffer gels suppressing sustained high-energy responses and biasing cells toward fragmented mechanical engagement. Primary T cell subsets likewise display distinct force phenotypes: naive cells exhibit weak, fluctuating behaviors, whereas memory cells more readily enter sustained high-force states. Together, these findings support a two-stage model of early T cell mechanosensing in which filopodia-mediated probing generates low, intermittent forces that, upon sustained engagement, transition to a lamellipodia-driven spreading phase producing larger, persistent, and predominantly outward-directed stresses. This framework provides a mechanistic explanation for how substrate mechanics, receptor context, and immune cell state shape force generation during the earliest stages of activation. More broadly, our results identify force as a dynamic and structured component of antigen recognition rather than a passive consequence of T cell signaling. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/480084v4_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@29e24dorg.highwire.dtl.DTLVardef@1c8399forg.highwire.dtl.DTLVardef@3f29edorg.highwire.dtl.DTLVardef@d9a581_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗