Muzlifah A. Haniffa

The paper builds a spatiotemporal map of human pilosebaceous unit development using single-cell multiomics and machine learning, showing how epithelial-mesenchymal interactions drive organogenesis and how sebaceous gland programs are disrupted in tumors.

TERRA is a foundation model for spatial transcriptomics that predicts tissue behavior under perturbation and identifies multi-scale biological insights across tissues.

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.

TarDis disentangles multiple covariates in single-cell genomics, improving data integration and out-of-distribution predictions by generating structured latent representations.

Tripso, a self-supervised transformer model, identifies gene program-specific embeddings and discovers novel gene programs, revealing biological insights in hematopoietic development and disease.

This study creates a spatially-resolved molecular atlas of human skin and uses deep learning to identify 26 niches, revealing novel immune memory structures in inflammatory skin diseases.

PerturbGen predicts how genetic perturbations affect cellular trajectories across time in human datasets, showing effects on immune responses, hematopoiesis, and skin development.

This study creates a spatial genomics atlas of human skin fibroblasts in health and disease, identifying six major populations and three disease-specific subtypes.

This study identifies TGF-β as a key mediator of macrophage specialization in the skin through interaction with sensory nerves.

The paper generates a single-cell and spatial transcriptomic atlas of human fetal meninges and finds that meningiomas likely originate from dura lineage cells.

MintFlow is a generative AI algorithm that predicts how tissue perturbations reprogram cell states in disease, revealing novel cell populations and therapeutic targets in three human conditions.

This paper introduces scTRAM, a framework to quantitatively evaluate single-cell trajectory representations, revealing model-specific trade-offs and structural biases in embeddings.

CellDISECT improves cell type discovery and counterfactual predictions by disentangling covariates and using synthetic data for training.

The paper creates a regulon atlas mapping TF activity across human cell types and uses it to link disease signatures to cell types and TFs, improving disease risk locus analysis.

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

This study identifies non-canonical NF-κB signalling as a therapeutic target in CYLD cutaneous syndrome and shows that IKK inhibition reduces tumor viability in patient-derived models.

The paper integrates single-cell RNA-seq datasets across the GI tract to identify epithelial metaplasia in inflammatory diseases, showing its role in inflammation through immune cell interactions.

WebAtlas integrates single cell and spatial transcriptomic data into a cloud-optimised format for interactive exploration.

This study constructs a multiomic atlas of human early skeletal development using single-nuclei transcriptomics, epigenomics, and spatial transcriptomics to uncover regulatory mechanisms and cell states involved in bone and joint formation.

The paper introduces mvTCR, a multimodal generative model for integrating TCR and transcriptome data to improve analysis of single-cell immune profiling data.

The paper identifies Th2-like malignant cells in CTCL supported by a B cell-rich microenvironment through single-cell and spatial transcriptomics analysis.

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.

This paper describes OME-Zarr, a cloud-optimized bioimaging file format developed through community collaboration to improve scalability and data access.

The paper develops GASPACHO to identify dynamic eQTLs in antiviral responses using single-cell RNA-seq, uncovering thousands of eQTLs and linking them to disease risk loci.

The paper introduces a method to determine X-inactivation states in single-cell transcriptomes and uses it to infer cellular phylogenies in human females.

This study creates a single-cell atlas of human liver development, identifying pathways and factors that guide hepatic cell fates and improving hepatocyte-like cell functionality.

The paper maps the multi-organ functions of the human yolk sac during early development, revealing its roles in hematopoiesis, nutrition, and oxygen regulation.

The paper identifies a distinct developmental state in KMT2A-rearranged infant B-cell acute lymphoblastic leukemia using single-cell transcriptome analysis.

MultiMAP integrates multimodal data by reducing dimensionality and preserving structure, outperforming existing methods in various tasks.

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