André F. Rendeiro

The paper analyzes single-cell data from human tissues to show that aging involves both cell loss and architectural remodeling, with structural decline independent of cell composition.

StainX provides a GPU-accelerated framework for batch stain normalization in histopathology images, achieving significant speed improvements over CPU and other GPU implementations.

This study investigates how different KRAS mutations drive distinct phenotypic outcomes in pancreatic tumorigenesis, revealing mutation-specific oncogenic pathways and therapeutic implications.

This study maps human natural killer cell development in tonsil using single-cell analysis, showing spatial trajectories and tissue niches that support NK cell differentiation.

The paper profiles myeloid cells in breast cancer primary tumors and metastases to show how tumor heterogeneity drives shifts in myeloid cell phenotypes from anti- to pro-metastatic.

LazySlide provides an open-source Python package for whole-slide image analysis and multimodal integration, enabling efficient histopathology workflows with minimal setup.

The paper develops an algorithm to analyze human tissue microanatomy and finds age-related changes in domain abundance and resilience.

This study creates a comprehensive single-cell transcriptomic atlas of human endothelial cells across adult life, identifying age-related transcriptional changes and a p21-linked senescence program.

This study shows that sequential auto-processing of PIDD1 is essential for controlling ploidy in liver and heart development, with distinct roles for caspase-2 in different cell types.

SAHA creates a multimodal, subcellular-resolution reference atlas of healthy human tissues, revealing spatial organization and disease-associated disruptions across multiple organ systems.

The paper constructs and integrates protein-protein interaction networks across time and tissues to identify protein groups and networks associated with development and aging, and ranks anti-aging drug candidates.

This study profiles early-stage lung adenocarcinoma using multiplexed imaging mass cytometry to reveal two progression pathways: inflammation-driven and fibrosis-driven, with EMT masking malignant cell expansion.

This paper develops tissue clocks from histopathological images to predict biological age and disease risk from blood samples, showing non-uniform aging rates across tissues and associations with lifestyle and medical factors.

The paper identifies transcriptomic signatures of progressive and regressive liver fibrosis and portal hypertension using mouse models and patient data.

Marsilea provides a flexible visualization framework for complex datasets using a cross-layout paradigm, enabling intuitive and customizable visualizations with a Python library and web interface.

This study maps human natural killer cell development in pediatric tonsil using imaging mass cytometry and high-resolution microscopy, revealing stage-specific localization and interactions.

This paper introduces UTAG, a method to automatically identify tissue structures in multiplexed imaging data, showing it can detect higher-order tissue organization and relate it to disease states.

The paper introduces PEP, a metadata standard for biological projects, to improve data portability and tool compatibility across bioinformatics workflows.

This paper introduces scifi-RNA-seq, a high-throughput single-cell RNA sequencing method that increases throughput by 15-fold through combinatorial fluidic indexing and enables large-scale experiments.

The paper identifies functional enhancers in human embryonic stem cells using a combination of chromatin immunoprecipitation and a massively parallel reporter assay, revealing novel enhancer classes and activity patterns.

This paper combines CRISPR genome editing with single-cell RNA sequencing to enable high-throughput screening with complex molecular read-outs.