Andrew J. Filby

Human Developmental Cell Atlas integrates ~4.6 million cells/nuclei from 4-22 post-conception weeks into 454 refined cell types and maps 114 spatial niches across 6PCW whole-embryo sections.

This study uses imaging mass cytometry to analyze changes in glioblastoma tumor microenvironment before and after treatment, finding increased normal brain cells and altered cellular interactions post-treatment.

The paper develops a flow cytometry-based method combined with ultra-sensitive proteomics to study host-pathogen interactions in Salmonella-infected macrophages, revealing new insights into virulence factor induction and phagosomal dynamics.

This study identifies distinct myeloid DC subsets in synovial tissue across health and RA states, showing their roles in inflammation and tolerance.

This paper develops an ex-vivo model of hepatocellular carcinoma using precision-cut tumour slices to enable efficient and ethical drug screening.

The paper introduces ChannelNet, a deep learning architecture for segmenting cells and nuclei in multiplexed images, and demonstrates its effectiveness when combined with InstanSeg.

This study creates a prenatal human skin cell atlas, revealing immune cells' role in skin development and showing their impact on vascular and hair follicle formation.

OPTIMAL benchmarks segmentation and analysis methods for Imaging Mass Cytometry data, identifying optimal parameters for cell type identification and spatial analysis.

The paper introduces SCIP, a scalable image processing pipeline for morphological profiling of image cytometry data, demonstrating its application on three datasets to classify and analyze cell types and drug responses.

This study uses single-cell RNA-sequencing to analyze fetal bone marrow development and identifies disruptions in hematopoiesis in Down syndrome.

This study compares classical machine learning and deep learning for classifying white blood cells using stain-free imaging flow cytometry, achieving balanced accuracies of 0.776 and 0.697 for white blood cells and 0.866 and 0.867 for eosinophils.

The paper uses single-cell transcriptomics to map hematopoiesis in human fetal liver, identifying cell differentiation trajectories and shifts in hematopoietic composition during development.

The paper profiles single-cell transcriptomes from the human first trimester fetal-maternal interface, revealing cellular composition and interactions critical for placentation.

This paper uses deep learning to reconstruct cell cycle stages and disease progression from image data, achieving lower error rates and faster analysis.