John C. Marioni

The paper introduces CellMAPP and CHART to analyze multiplexed antibody imaging data, revealing modular morphological programs in response to genetic perturbations.

The paper integrates multiple single-cell and single-nucleus RNA-sequencing datasets to create a comprehensive Human Kidney Cell Atlas, identifying cell types, states, and disease-associated populations.

This paper introduces STERN, a framework that quantitatively compares developmental trajectories across embryos by learning spatiotemporal representations from 4D imaging data.

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.

RIMA enables cross-species comparison of transcriptomic atlases, identifying conserved developmental programs and revealing species-specific differences in gene expression timing.

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.

This study creates a spatiotemporal atlas of mouse gastrulation and early organogenesis using spatial transcriptomics and scRNA-seq, revealing gene expression dynamics and mesodermal fate decisions.

This study creates a single-cell spatial atlas of the developing human reproductive tract, identifying genes and mechanisms involved in sexual dimorphism and organ development.

The paper uses single-cell multi-omic lineage tracing to identify transcriptional, epigenetic, and genetic factors that predict cancer evolution and drug tolerance.

This study investigates how GLP-1R agonists affect human hypothalamic neurons derived from pluripotent stem cells, finding they induce depolarization, increased firing, and gene expression changes.

This study links single-cell transcriptomes to genotypes to assess the functional impact of single nucleotide variants, classifying missense variants into three categories.

The paper identifies mitochondrial reverse electron transport in myeloid cells as a driver of neuroinflammation and shows that blocking it protects the CNS in disease models.

This paper creates a human embryonic limb cell atlas using single-cell and spatial transcriptomics, identifying novel cell populations and developmental patterns.

The paper uses scRNA-Seq and computational methods to predict and validate differentiation trajectories during early mammalian organogenesis, showing agreement between computational predictions and experimental outcomes.

Milo2.0 enables population genetic analysis of cell state abundance using a count-based mixed model, identifying over 500 csQTLs and linking them to immune-mediated disease.

The paper develops statistical tools to analyze gene knockouts in mouse embryos and applies them to study developmental disruptions and cancer therapy responses.

CellBender removes systematic background noise from droplet-based single-cell data using a deep generative model, improving alignment with established gene expression patterns.

The paper identifies a drug combination targeting human POMC neurons to reduce food intake and body weight using hPSC-derived neurons and mouse models.

This study constructs a rabbit developmental atlas and compares it with mouse data to identify species-specific differences in gene regulation and extra-embryonic tissues.

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 investigates human primordial germ cell specification using in vitro models and in vivo datasets, revealing the role of TFAP2A and TFAP2C in PGC fate and amnion development.

The paper constructs a single-cell spatio-temporal atlas of molecular trajectories during gastrulation in mouse embryos, revealing asymmetric BMP signaling and its role in left-right body plan asymmetry.

The paper introduces miloDE, a cluster-free method for differential expression testing in single-cell RNA sequencing data, improving sensitivity and specificity by avoiding discrete clustering.

This study maps the transcriptional and regulatory changes during mouse somitogenesis, showing accelerated differentiation and lineage-specific programs.

This study identifies asymmetrically expressed genes in the mouse cardiac crescent during left-right symmetry breaking using single-cell RNA sequencing and validates their expression patterns.

The paper constructs a human fetal lung cell atlas from 5-22 post conception weeks, identifying new cell states and regulators of epithelial fates.

The paper identifies and characterizes a reference iPSC line, KOLF2.1J, for standardized use in collaborative studies across multiple differentiation protocols and assays.

The paper compares using cell atlases versus matched controls for identifying disease-associated cell states in scRNA-seq data, finding that combining atlas data with matched controls improves accuracy.

The paper uses single-cell RNA-sequencing to study heterogeneity in mouse gastruloids over time, revealing distinct mesodermal and neural differentiation trajectories and their implications for perturbation studies.

CellRegMap identifies context-specific regulatory variants using scRNA-seq by analyzing allelic effects across cell types and states.