Biology subjects

Ballllosera Navarro, F.

Publications and source records attributed to Ballllosera Navarro, F..

3 recordsLinked to original sources

bioRxiv2025

Streamlining Multiplexed Tissue Image Analysis with PIP{Sigma}X: An Integrated Automated Pipeline for Image Processing and EXploration for Diverse Tissue Types

Spatial proteomics via multiplexed tissue imaging is transforming how we study biology, enabling researchers to investigate dozens of markers in a single tissue section and explore how cells behave in their native habitat. While imaging technologies have advanced rapidly, data analyses remain a bottleneck. To address this, we developed PIP{Sigma}X (Pipeline for Image Processing and EXploration), a user-friendly, end-to-end open-source software designed to make complex image analysis approachable, even for those with little or no programming skills. PIP{Sigma}X combines robust automation with an intuitive graphical user interface, guiding users through each step of the analysis, from image preprocessing and membrane-aware cell segmentation to signal quantification and spatial data exploration. Each feature includes built-in explanations, recommendations, and quality controls to help users make confident choices throughout the process. PIP{Sigma}X is compatible with a wide range of multiplexed imaging platforms, and its outputs integrate seamlessly with visualization tools like TissUUmaps and QuPath. Also, it supports downstream applications by enabling direct export of selected cell coordinates for laser microdissection. This functionality facilitates precise isolation of target cell populations for deep proteomic or transcriptomic profiling. With PIP{Sigma}X, researchers can extract meaningful biological insights from multiplexed images more easily and robustly, helping to bridge the gap between powerful imaging technologies and real-world scientific discovery. HighlightsO_LIPIP{Sigma}X offers a user-friendly "one-stop shop" pipeline for multiplexed tissue image analysis C_LIO_LIwithout coding C_LIO_LIWorks across diverse tissue types and imaging platforms at whole-slide scale C_LIO_LIIncludes membrane-aware segmentation and quality control features C_LIO_LISeamlessly integrates with visualization platforms like TissUUmaps and QuPath for data exploration C_LIO_LIEnables export for automated laser microdissection and spatial single-cell profiling C_LI

bioinformatics↗

bioRxiv2024

SubCell: Vision foundation models for microscopycapture single-cell biology

Cell morphology and subcellular protein organization provide important insights into cellular function and behavior. These cellular features can be studied using large-scale fluorescence microscopy, and machine learning has become a powerful tool to interpret the resulting images for biological insights. Here, we introduce SubCell, a deep learning model for fluorescence microscopy designed to accurately capture cellular morphology, protein localization, cellular forganization, and biological function beyond what humans can readily perceive. SubCell was trained on the proteome-wide image collection from the Human Protein Atlas with a novel proteome-aware learning objective. SubCell outperforms state-of-the-art methods across a variety of tasks relevant to single-cell biology and generalizes to other fluorescence microscopy datasets without any fine-tuning. Additionally, we construct the first proteome-wide hierarchical map of proteome organization that is directly learned from image data. This vision-based multiscale cell map defines cellular subsystems down to protein complex resolution, reveals proteins with similar functions, and distinguishes dynamic and stable behaviors within cellular compartments. Finally, combining SubCell with a protein sequence model enables a rich multimodal approach to capture gene function better than either vision-only or sequence-only models alone. In conclusion, SubCell creates deep, image-driven representations of cellular architecture that are applicable across diverse biological contexts and datasets.

cell biology↗

bioRxiv2024

High-parametric protein maps reveal the spatial organization in early-developing human lung

The respiratory system, including the lungs, is essential for terrestrial life. While recent research has advanced our understanding of lung development, much still relies on animal models and transcriptome analyses. In this study conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the protein-level spatiotemporal organization of the lung during the first trimester of human gestation. Using high-parametric tissue imaging with a 30-plex antibody panel, we analyzed human lung samples from 6 to 13 post-conception weeks, generating data from over 2 million cells across five developmental timepoints. We present a resource detailing spatially resolved cell type composition of the developing human lung, including proliferative states, immune cell patterns, spatial arrangement traits, and their temporal evolution. This represents an extensive single-cell resolved protein-level examination of the developing human lungs and provides a valuable resource for further research into the developmental roots of human respiratory health and disease.

developmental biology↗