Nathan J. Palpant

This study identifies latent regulatory programs from disease-associated variants to reveal biological mechanisms and clinical subtypes in complex diseases.

This study investigates the role of cardiomyocyte caveolae and cavin-1 in myocardial function, revealing sex-dependent effects on ventricular compliance and resilience through mechanisms involving NOS signaling and fibrosis.

This study shows glycaemic variability, not just hyperglycaemia, increases heart muscle dysfunction and myocardial injury risk in diabetes using genetic and experimental models.

This study compares sixteen iPSC-CM differentiation protocols to identify how protocol-dependent cell states affect disease modeling capacity for cardiovascular conditions.

This study shows how metabolic culture conditions affect cardiomyocyte maturation and sensitivity to ischaemia-reperfusion injury using hiPSC-CMs.

This study evaluates polygenic risk scores (PRS) in dilated cardiomyopathy (DCM) patients, finding higher PRS in those without monogenic variants and TTN-related DCM, suggesting polygenic contributions.

The paper shows that inhibiting contractile function in cardiomyocytes reactivates the cell cycle, suggesting active contraction maintains differentiation and cell cycle arrest.

This study identifies healthcare providers' learning needs and engagement strategies for Polygenic Risk Score education through focus groups and thematic analysis.

This paper introduces an unsupervised framework to identify genetic subtypes of disease by clustering variant co-occurrence, applied to type 2 diabetes and asthma without using phenotype data.

DNACipher predicts variant effects across cell types and assays, identifying imVars at most GWAS loci with support from experimental data.

The paper shows that codon-level scores from the CaLM model provide additional information for missense variant interpretation beyond protein-level representations.

This study identifies trace-level cytokines in PASC patients linked to cardiovascular symptoms and shows their impact on cardiomyocyte function using nanotechnology detection.

The paper identifies regions of the human genome under cellular constraint by analyzing chromatin accessibility across cell types, showing their predictive power for functional loci and applications in variant identification and drug discovery.

This paper introduces a consensus clustering method to identify robust signals from high-throughput screens by analyzing multi-scale data organization in cancer cell lines and bacterium models.

The paper develops a resource to improve cardiac modelling protocols using hPSC-CMs by analyzing decades of research data and providing tools for protocol benchmarking.

The paper demonstrates that village cultures of hiPSCs maintain consistent transcriptional profiles across uni- and village-cultured samples, enabling population-scale studies.

The paper introduces TRIAGE-Cluster and TRIAGE-ParseR to define cell diversity in scRNA-seq data using epigenetic data as a biological anchor.

This study investigates how 3D chromatin organization changes during human endothelial cell differentiation and links these changes to gene expression and transcriptional regulation.

The paper creates an iPSC differentiation atlas and identifies TMEM88 as a regulator of mesendodermal development and blood pressure.

This study introduces UnTANGLeD, an unsupervised method that organizes genes into programs based on gene-trait associations, revealing 173 clusters with distinct biological functions and disease relevance.

This study identifies HOPX as a molecular switch regulating cardiomyocyte progenitor and maturation gene programs, linking it to cardiac function and genetic variation.

The paper identifies cell type-specific genetic control of gene expression in fibroblasts and iPSCs using single-cell RNA sequencing and eQTL mapping.

scSplit demultiplexes pooled single-cell RNA-Seq samples using genetic differences inferred from the data, showing high accuracy and consistency with known truths.

The paper identifies four transcriptionally distinct subpopulations in human induced pluripotent stem cells using single-cell RNA sequencing and develops a machine learning method to classify and predict transitions between these states.

The paper maps cardiac differentiation at single-cell resolution, identifies HOPX dysregulation, and shows hypertrophic signaling is required for HOPX transcription in hPSC-derived cardiomyocytes.

The paper reconstructs cell fate trajectories in hiPSC-derived cardiomyocytes using single-cell RNA-seq and identifies genes with dynamic roles in cardiovascular disease risk.

Isolator improves isoform-level RNA-Seq analysis by reducing variance and increasing agreement between samples through a Bayesian hierarchical model and bias correction.