Cole Trapnell

The paper shows that multipotent progenitors and HSC arise from distinct HE populations during mouse embryogenesis, identified through clonal assays and single-cell transcriptomics.

This paper reconstructs cellular trajectories of mammalian embryogenesis using scRNA-seq data and identifies key transcription factors at branch points.

The paper identifies key signaling molecules involved in HSC development by analyzing single-cell transcriptomics data from AGM-derived cells and uses them to engineer a supportive niche for HSC generation.

TransMPRA combines CRISPR perturbation and MPRAs to identify trans-acting factors influencing enhancer activity.

The paper introduces sci-fate, a method to profile both newly synthesized and total transcriptomes in single cells, and applies it to study cortisol response dynamics in over 6,000 cells.

The paper applies UMAP to visualize genetic interactions in yeast transcriptome data, identifying protein complexes and novel interactions.

This paper introduces Scribe, a toolkit for causal gene regulatory network inference from single-cell data, showing that pseudotime data performs worse than true time series data.

Connect-seq combines viral tracing and single-cell transcriptomics to map molecular identities and signaling molecules in neural circuits, revealing upstream neuron subsets and their anatomical locations in the hypothalamus.

This paper introduces a method to physically separate live cells based on visual phenotypes and uses it to study nuclear localization sequences and paclitaxel resistance in human cancers.

Garnett enables rapid cell type annotation in scRNA-seq and scATAC-seq data using a hierarchical markup language of cell type-specific genes.

The paper maps lineage and transcriptome relationships in C. elegans embryogenesis using single-cell transcriptomics, revealing lineage-transcriptome correlation breakdown in late divisions.

The paper investigates how thyroid hormone regulates different maturation paths in zebrafish pigment cell lineages through transcriptomic profiling and trajectory analysis.

This study uses single-cell RNA-seq to analyze gene expression in A. thaliana root cells, identifying cell-type-specific genes and developmental trajectories, and shows cell-type differences in heat stress responses.

The paper presents a single-cell chromatin accessibility map of the mouse hippocampus using sci-ATAC-seq, identifies cell-type-specific regulatory networks, and provides a resource for future studies.

The paper analyzes MYOD-mediated reprogramming of human fibroblasts using single-cell RNA-Seq to identify molecular determinants of reprogramming success and failure.

This paper introduces crisprQTL mapping to identify gene regulation by using CRISPR perturbations and single-cell RNA-seq in human cells, finding 128 cis-regulatory elements affecting nearby genes.

The paper introduces sci-MET, a scalable single-cell DNA methylation sequencing method, achieving 69% read alignment rates and successfully deconvolving a mixture of three human cell lines.

The paper profiles chromatin accessibility in Drosophila embryos to uncover cis-regulatory programs and cell fate determinants during development.

The paper identifies a major issue with CRISPR-based single cell screens due to guide-barcode swapping and presents an optimized method to improve accuracy.

This study uses single-cell RNA sequencing to show extreme variation in viral gene transcript production among infected cells, with some cells producing over half their mRNA from virus.

Monocle 2 learns branched single-cell trajectories without supervision via reversed graph embedding, revealing a non-fusing myoblast outcome and knockout myeloid cells diverted onto opposite fate branches.

This paper introduces sci-RNA-seq, a combinatorial indexing method for single-cell RNA sequencing, and applies it to profile 42,035 C. elegans cells, identifying 27 cell types and their specific genes.

The paper introduces Cicero, a statistical method to link regulatory elements to target genes using single-cell chromatin accessibility data, and applies it to study myogenesis.

The paper uses pseudotemporal ordering to analyze single-cell data and reveals dynamics and regulators of cell fate decisions.