Peter Smibert

The paper identifies target genes and pathways of blood trait loci using CRISPR screens and single-cell RNA sequencing, revealing cis and trans regulatory effects of noncoding variants.

The paper introduces CaRPool-seq, a method using Cas13 for combinatorial RNA perturbations in single cells, and applies it to study myeloid differentiation regulators in AML.

The paper uses single-cell multi-omics to study how pathogenic mtDNA mutations are subject to purifying selection in immune cells from Pearson syndrome patients.

The paper introduces scCUT&Tag-pro and scChromHMM to analyze chromatin states in single cells, showing their ability to characterize dynamic changes and regulatory priming across immune cell types.

This paper demonstrates direct detection of RNA modifications and structure using single molecule nanopore sequencing, showing it can identify endogenous and exogenous modifications in rRNA from E. coli and S. cerevisiae.

PySeq2500 converts the HiSeq 2500 into an open platform for programmable fluidics and imaging, enabling automated multi-day workflows for multiplexed imaging of biological samples.

Weighted-nearest neighbor (WNN) analysis in Seurat v4 learns per-cell modality weights to define cell states, enabling a 228-antibody human PBMC CITE-seq reference for RNA-only query mapping.

This paper introduces CRISPR-sciATAC, a method linking genetic perturbations to chromatin accessibility in single cells, showing its effectiveness in human and mouse experiments.

This study investigates how antibody concentration, staining volume, and cell number affect signal quality in oligo-conjugated antibody single-cell analysis, offering guidelines to reduce background and improve sensitivity.

The paper uses multi-modal single-cell screens to identify regulators of PD-L1 expression and develops a computational framework to improve analysis of perturbation data.

This paper introduces ASAP-seq, a method to simultaneously profile chromatin accessibility, protein levels, and mtDNA in single cells, demonstrating its application in hematopoietic differentiation and T cell perturbations.

Seurat v3 finds scored mutual-nearest-neighbor anchors in canonical correlation space to integrate scRNA-seq batches and transfer labels, protein levels and transcriptome-wide expression across modalities.

The paper introduces cell hashing using barcoded antibodies to enable multiplexing and doublet detection in single-cell genomics, demonstrating its effectiveness with human PBMC samples.

The paper introduces ECCITE-seq, a method that simultaneously captures transcriptomes, immune clonotypes, surface markers, sample identity, and CRISPR perturbations from single cells.

Seurat combines canonical correlation analysis and dynamic time warping to co-embed scRNA-seq datasets from different conditions, platforms, or species, outscoring ComBat and limma in alignment score.

The paper introduces a method to simultaneously measure epitopes and transcriptomes in single cells using oligonucleotide-labeled antibodies.