Christoph Ziegenhain

The paper quantifies proteins and transcripts in the same cell, finding that the proteome defines stable cell states while the transcriptome reflects transitions.

This paper introduces a compound model for single-cell RNA-seq data without UMIs, using Pearson residuals to improve gene selection and embeddings in Smart-seq2 datasets.

This study shows that the uterine immune system can fully reconstitute after uterus transplantation or hematopoietic stem cell transplantation, despite immunosuppressive drugs.

The paper investigates immediate transcriptional changes in T cells upon direct contact with cancer cells using single-cell transcriptomics and identifies distinct T cell subsets associated with reduced TCR diversity.

CellCov profiles gene-body coverage at single-cell resolution, enabling analysis of coverage bias across cells and features in scRNA-seq data.

RNA BaseCode is a synthetic long-read RNA sequencing method that achieves short-read accuracy and improves full-length transcript reconstruction compared to existing long-read methods.

The paper identifies heritable transcriptional diversity within cell types by studying clonal human lymphocytes and mouse brain cells, showing clone-specific chromatin accessibility leads to distinct gene expression.

The paper investigates how chromatin state influences transcriptional burst kinetics and RNA Polymerase II pausing during Drosophila embryonic patterning.

The paper introduces molecular spikes as a gold standard to evaluate RNA counting accuracy in single-cell RNA-sequencing methods.

Prime-seq is a bulk RNA-seq method that is four-fold more cost-efficient than TruSeq while maintaining equivalent library complexity, measurement accuracy, and statistical power.

This study investigates the transcriptional dynamics and functions of long non-coding RNAs using single-cell RNA-seq, revealing distinct burst kinetics and cell-state specific roles.

anonymizeBAM removes donor-related genetic information from sequence data, enabling secure open data sharing in genomics.

The paper introduces a scalable, cost-effective single-cell RNA sequencing method that generates a detailed human blood cell atlas with high sensitivity and throughput.

This study shows transcriptional bursts explain autosomal monoallelic expression and influence allelic imbalance in scRNA-seq data.

The paper identifies Wnt/PCP-activated Lgr5+ intestinal stem cells that directly differentiate into enteroendocrine or Paneth cells, challenging previous models of multipotency.

This paper introduces Smart-seq3, a single-cell RNA sequencing method that enables allele- and isoform-resolution RNA counting, showing improved sensitivity and isoform regulation detection in mouse and human samples.

This study benchmarks 13 scRNA-seq protocols to evaluate their effectiveness in characterizing cell types and states for cell atlas projects.

The paper identifies DPPA3 as a driver of genome-wide passive DNA demethylation in mammals, showing it can bypass TET protein requirements and function across species.

This study evaluates scRNA-seq pipelines by simulating five library protocols and nine DE setups, finding that normalization and library prep have the largest impact on analysis outcomes.

The paper introduces mcSCRB-seq, a new scRNA-seq protocol that improves sensitivity and efficiency through optimized experimental conditions.

zUMIs processes RNA-seq data with UMIs and BCs, improving gene detection and cluster resolution by analyzing intronic reads.

This study quantifies biases in cross-species RNA-seq analyses and provides guidance for reliable expression quantification among closely related species.

powsimR simulates and evaluates differential expression in bulk and single-cell RNA-seq data to perform power analysis for experimental design and comparison.

This study compares five scRNA-seq protocols using mouse embryonic stem cells, finding Smart-seq2 most sensitive and SCRB-seq/Drop-seq most cost-efficient for differential gene detection.

This study evaluates how RNA-seq amplification affects differential expression analysis, finding that duplicate read removal does not improve accuracy but UMI-based protocols enhance detection power.