Biology subjects

Ziegenhain, C.

Publications and source records attributed to Ziegenhain, C..

At least 19 recordsLinked to original sources

bioRxiv2026

In the same cell, the proteome defines cellular state and the transcriptome marks transitions

Bulk transcriptome and proteome correlate only modestly, but this has not been investigated in the same cell or across cell-state changes. Here we introduce a scalable technology that quantifies thousands of proteins and transcripts in the same cell, separating RNA from protein by tip-based C18 capture and pairing full-length RNA sequencing with latest-generation mass spectrometry. In HeLa cells, transcript and protein abundances agree on the broad ranking within a cell (r = 0.45), but do not co-vary across the population (r = 0.038). In pluripotency transitions, only a third of matched transcripts and proteins change synchronously, yet the transcription factors defining each state stay tightly co-regulated. Transcript variance is several-fold larger than protein variance, reflecting transcriptional bursting and mRNA sampling noise. The proteome is thus the stable, low-noise definition of cell state, while the transcriptome marks cellular transitions; consequently, the proteome defines cell-state from far fewer cells.

systems biology↗

bioRxiv2026

Immediate transcriptional changes initiated by direct cell-cell contact between cytotoxic T cells and cancer cells

Many biological processes are regulated by the direct interaction between two or more cell types. However, our understanding of the immediate dynamic changes in gene transcription upon physical interaction between two cells has remained limited due to technical limitations. Here we address these limitations in a model system of cancer-specific T cell receptor (TCR)-modified CD8 T cells where single and cancer-interacting T cells were isolated by image-enabled cell sorting and transcripts from heterotypic cancer cell and T cell pairs were in silico assigned. This approach uncovers immediate, dynamic changes in gene expression following the specific interaction between TCR-modified CD8 T cells and cancer cells. In addition to dissecting transcriptional cascades dependent on the peptide sensitivity of the TCR, we for the first time directly compare these gene expression changes between single T cells and T cells in direct physical contact with cancer cells. Modeling of the observed transcriptomic activation signature identifies phenotypically distinct tumor infiltrating CD8 T cell subsets associated with reduced TCR diversity in in vivo datasets. Taken together, the paradigm developed here allows for future clonal identification of T cell receptors mediating ongoing effective cytotoxic responses in vivo.

immunology↗

bioRxiv2026

CellCov: gene-body coverage profiling for single-cell RNA-seq

MotivationGene-body coverage bias differs across scRNA-seq protocols and can influence downstream analyses, yet coverage is often assessed using bulk-level summaries that obscure cell-to-cell variability. ResultsCellCov provides gene-body coverage profiling at single-cell resolution, enabling exploration of coverage heterogeneity across both cells and features. The accompanying workflow supports flexible grouping and robust aggregation of profiles by user-provided annotations, allowing principled comparison of coverage bias across sequencing protocols. We demonstrate its use on public datasets from several full-length scRNA-seq chemistries. AvailabilityCellCov source code and documentation are available at https://github.com/ziegenhain-lab/CellCov Contactchristoph.ziegenhain@ki.se Supplementary informationSupplementary data are available at Bioinformatics online

bioinformatics↗

bioRxiv2025

RNA BaseCode: Long-Read RNA Sequences with Short-Read Accuracy

RNA sequencing has revolutionized the life sciences by enabling comprehensive profiling of gene expression across tissues and individual cells. Most mammalian genes produce multiple isoforms with distinct functional consequences, yet their accurate detection and quantification remain challenging. Here we present RNA BaseCode, a synthetic long-read RNA sequencing method that combines the high accuracy and quantitative precision of short-read sequencing with internal random barcodes to reconstruct full-length RNA sequences. We applied RNA BaseCode to purified RNA to benchmark its performance against short-read and long-read RNA sequencing methods. RNA BaseCode demonstrated superior basecalling accuracy, higher quantitative precision, and an improved ability to reconstruct full-length transcripts compared to long-read sequencing. This method enables comprehensive characterization of RNA isoform expression at scale, providing a powerful and broadly applicable tool for investigating isoform regulation across biological contexts as well as the roles of these isoforms in health and disease.

genomics↗

bioRxiv2024

Reconstitution of the uterine immune milieu after transplantation

Maintenance of tissue-specific immunity is important for immunological fitness, but its establishment have been difficult to assess in humans. Here, we investigated reconstitution of the human uterine immune system by studying women undergoing uterus solid organ transplantation (UTX) or hematopoietic stem cell transplantation (HSCT). Through single-cell identification based on SNPs and disparate HLA expression using single-cell RNA sequencing or high-parameter flow cytometry, donor vs recipient cell origin was determined, and features of these cells were studied. A full uterine immune cell reconstitution occurred after both UTX and HSCT, both at transcriptomic and phenotypic level. This occurred despite tacrolimus-induced calcineurin-mediated NFAT pathway inhibition, which affected de novo induction of tissue-residency features in vitro. Intriguingly, after HSCT, immune cells of male origin could reconstitute the uterine immune milieu. Collectively, our results proved insights into tissue immune system persistence and reconstitution capabilities in an organ undergoing continuous regeneration.

immunology↗

bioRxiv2023

Compound models and Pearson residuals for normalization of single-cell RNA-seq data without UMIs

Recent work employed Pearson residuals from Poisson or negative binomial models to normalize UMI data. To extend this approach to non-UMI data, we model the additional amplification step with a compound distribution: we assume that sequenced RNA molecules follow a negative binomial distribution, and are then replicated following an amplification distribution. We show how this model leads to compound Pearson residuals, which yield meaningful gene selection and embeddings of Smart-seq2 datasets. Further, we suggest that amplification distributions across several sequencing protocols can be described by a broken power law. The resulting compound model captures previously unexplained overdispersion and zero-inflation patterns in non-UMI data.

bioinformatics↗

bioRxiv2023

Erythroid differentiation intensifies RNA mis-splicing in SF3B1-mutant myelodysplastic syndromes with ring sideroblasts

Myelodysplastic syndromes with ring sideroblasts (MDS-RS) commonly originate from mutations in the splicing factor SF3B1 (SF3B1mt). SF3B1mt cause RNA mis-splicing, mechanistically established as the major driver of RS development. However, little is known about RS fate and biology after their initial formation in the human bone marrow. We here achieve isolation of viable RS from patient samples, enabling the first complete investigation of SF3B1mt development from stem cell to RS. We show that RS skew MACS-isolated CD34+ data towards erythroid features not recapitulated in single-cell RNAseq. We demonstrate that RS divide, differentiate, enucleate and actively respond to mis-splicing/oxidative stress, decreasing wildtype stem cell fitness via GDF15 overproduction. We identify circulating RS as a uniform clinical feature associated with disease burden. Finally, we establish that SF3B1mt mis-splicing intensifies during erythroid differentiation and demonstrate through combined transcriptomics/proteomics an uncoupling of RNA/protein biology in RS encompassing severe and dysfunctional mis-splicing of proapoptotic genes. Statement of significanceWe here combine a novel method for RS isolation with state-of-the-art multiomics to perform the first complete investigation of SF3B1mt MDS-RS hematopoiesis from stem cell to RS. We identify the survival mechanisms underlying SF3B1mt erythropoiesis and establish an active role for erythroid differentiation and RS themselves in SF3B1mt MDS-RS pathogenesis.

cancer biology↗

bioRxiv2022

Comprehensive interrogation of a Drosophila embryonic patterning network reveals the impact of chromatin state on tissue-specific burst kinetics and RNA Polymerase II promoter-proximal pause release

Formation of tissue-specific transcriptional programs underlies multicellular development, but how the chromatin landscape influences transcription is not fully understood. Here we comprehensively resolve differential transcriptional and chromatin states during Drosophila dorsoventral (DV) patterning. We find that RNA Polymerase II pausing is established at DV promoters prior to zygotic genome activation (ZGA), that pausing persists irrespective of cell fate, but that release into productive elongation is tightly regulated and accompanied by tissue-specific P-TEFb recruitment. DV enhancers acquire distinct tissue-specific chromatin states through CBP-mediated histone acetylation that predict the transcriptional output of target genes, whereas promoter states are more tissue invariant. Transcriptome-wide inference of burst kinetics in different cell types revealed that while DV genes are generally characterized by a high burst size, either burst size or frequency can differ between tissues. The data suggest that pausing is established by pioneer transcription factors prior to ZGA and that release from pausing is imparted by enhancer chromatin state to regulate bursting in a tissue-specific manner in the early embryo. Our results uncover how developmental patterning is orchestrated by tissue-specific bursts of transcription from Pol II primed promoters in response to enhancer regulatory cues.

molecular biology↗

bioRxiv2021

Prime-seq, efficient and powerful bulk RNA-sequencing

With the advent of Next Generation Sequencing, RNA-sequencing (RNA-seq) has become the major method for quantitative gene expression analysis. Reducing library costs by early barcoding has propelled single-cell RNA-seq, but has not yet caught on for bulk RNA-seq. Here, we optimized and validated a bulk RNA-seq method we call prime-seq. We show that with respect to library complexity, measurement accuracy, and statistical power it performs equivalent to TruSeq, a standard bulk RNA-seq method, but is four-fold more cost-efficient due to almost 50-fold cheaper library costs. We also validate a direct RNA isolation step that further improves cost and time-efficiency, show that intronic reads are derived from RNA, validate that prime-seq performs optimal with only 1,000 cells as input, and calculate that prime-seq is the most cost-efficient bulk RNA-seq method currently available. We discuss why many labs would profit from a cost-efficient early barcoding RNA-seq protocol and argue that prime-seq is well suited for setting up such a protocol as it is well validated, well documented, and requires no specialized equipment.

genomics↗

bioRxiv2021

Molecular spikes: a gold standard for single-cell RNA counting

Molecule counting is central to single-cell sequencing, yet no experimental strategy to evaluate counting performance exists. Here, we introduce molecular spikes, novel RNA spike-ins containing inbuilt unique molecular identifiers that we use to identify critical experimental and computational conditions for accurate RNA counting across single-cell RNA-sequencing methods. The molecular spikes are a new gold standard that can be widely used to validate RNA counting in single cells.

genomics↗

bioRxiv2021

Scalable full-transcript coverage single cell RNA sequencing with Smart-seq3xpress

Plate-based single-cell RNA-sequencing methods with full-transcript coverage typically excel at sensitivity but are more costly and time-consuming. Here, we miniaturized and streamlined the Smart-seq3 protocol for drastically reduced cost and increased throughput. Applying Smart-seq3xpress to 16,349 human peripheral blood mononuclear cells revealed a highly granular atlas complete with both common and rare cell types whose identification previously relied on additional protein measurements or the integration with a reference atlas.

genomics↗

bioRxiv2021

anonymizeBAM: Versatile anonymization of human sequence data for open data sharing

The risks associated with re-identification of human genetic data are severely limiting open data sharing in life sciences. Here, we developed anonymizeBAM, a versatile tool for the anonymization of genetic variant information present in sequence data. Applying anonymizeBAM to single-cell RNA-seq and ATAC-seq datasets confirmed the complete removal of donor-related genetic information. Therefore, the accurate generation of de-identified sequence data will re-enable open sharing in sequencing-based studies for improved transparency, reproducibility, and innovation.

genomics↗

bioRxiv2020

Wnt/PCP-primed intestinal stem cells directly differentiate into enteroendocrine or Paneth cells

A detailed understanding of intestinal stem cell (ISC) self-renewal and differentiation is required to better treat chronic intestinal diseases. However, different models of ISC lineage hierarchy1-6 and segregation7-12 are debated. Here we report the identification of Lgr5+ ISCs that express Flattop (Fltp), a Wnt/planar cell polarity (PCP) reporter and effector gene. Lineage labelling revealed that Wnt/PCP-activated Fltp+ ISCs are primed either towards the enteroendocrine or the Paneth cell lineage in vivo. Integration of time-resolved lineage labelling with genome-wide and targeted single-cell gene expression analysis allowed us to delineate the ISC differentiation path into enteroendocrine and Paneth cells at the molecular level. Strikingly, we found that both lineages are directly recruited from ISCs via unipotent transition states, challenging the existence of formerly predicted bi- or multipotent secretory progenitors7-12. Transitory cells that mature into Paneth cells are quiescent and express both stem cell and secretory lineage genes, indicating that these cells are the previously described Lgr5+ labelretaining cells7. Wnt/PCP-activated Lgr5+ ISCs are indistinguishable from Wnt/{beta}-catenin-activated Lgr5+ ISCs based on the expression of stem-cell signature or secretory lineagespecifying genes but possess less self-renewal activity. This suggests that lineage priming and cell-cycle exit is triggered at the post-transcriptional level by polarity cues and a switch from canonical to non-canonical Wnt/PCP signalling. Taken together, we identified the Wnt/PCP pathway as a new niche signal and polarity cue regulating stem cell fate. Active Wnt/PCP signalling represents one of the earliest events in ISC lineage priming towards the Paneth and enteroendocrine cell fate, preceding lateral inhibition and expression of secretory lineagespecifying genes. Thus, our findings provide a better understanding of the niche signals and redefine the mechanisms underlying ISC lineage hierarchy and segregation.

developmental biology↗

bioRxiv2020

Deducing transcriptional kinetics and molecular functions of long non-coding RNAs using allele-sensitive single-cell RNA-sequencing

An increasing number of studies have demonstrated the regulatory importance of long non-coding RNAs (lncRNAs), yet little is known about their transcriptional dynamics and it remains challenging to determine their regulatory functions. Here, we used allele-sensitive single-cell RNA-seq (scRNA-seq) to demonstrate that lncRNAs have lower burst frequencies with twice as long duration between bursts, compared to mRNAs. Additionally, we observed an increased cell-to-cell variability in lncRNA expression that was due to more sporadic bursting (lower frequency) with larger numbers of RNA molecules being produced. Exploiting heterogeneity in asynchronously growing cells, we identified and experimentally validated lncRNAs with cell-state specific functions involved in cell cycle progression and apoptosis. Finally, utilizing allele-resolved RNA expression, we identified cis functioning lncRNAs and observed that knockdown of these lncRNAs modulated either transcriptional burst frequency or size of the nearby protein-coding gene. Collectively, our results identify distinct transcriptional regulation of lncRNAs and we demonstrate a role for lncRNAs in the regulation of transcriptional bursting of mRNAs.

genomics↗

bioRxiv2019

Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3

Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells2,3. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5 unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.

genomics↗

bioRxiv2019

Benchmarking Single-Cell RNA Sequencing Protocols for Cell Atlas Projects

Single-cell RNA sequencing (scRNA-seq) is the leading technique for charting the molecular properties of individual cells. The latest methods are scalable to thousands of cells, enabling in-depth characterization of sample composition without prior knowledge. However, there are important differences between scRNA-seq techniques, and it remains unclear which are the most suitable protocols for drawing cell atlases of tissues, organs and organisms. We have generated benchmark datasets to systematically evaluate techniques in terms of their power to comprehensively describe cell types and states. We performed a multi-center study comparing 13 commonly used single-cell and single-nucleus RNA-seq protocols using a highly heterogeneous reference sample resource. Comparative and integrative analysis at cell type and state level revealed marked differences in protocol performance, highlighting a series of key features for cell atlas projects. These should be considered when defining guidelines and standards for international consortia, such as the Human Cell Atlas project.

genomics↗

bioRxiv2019

A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines

The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not been established, yet. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in[~] 3,000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.

bioinformatics↗

bioRxiv2018

TET1 drives global DNA demethylation via DPPA3-mediated inhibition of maintenance methylation

Genome-wide DNA demethylation is a unique feature of mammalian development and naive pluripotent stem cells. So far, it was unclear how mammals specifically achieve global DNA hypomethylation, given the high conservation of the DNA (de-)methylation machinery among vertebrates. We found that DNA demethylation requires TET activity but mostly occurs at sites where TET proteins are not bound suggesting a rather indirect mechanism. Among the few specific genes bound and activated by TET proteins was the naive pluripotency and germline marker Dppa3 (Pgc7, Stella), which undergoes TDG dependent demethylation. The requirement of TET proteins for genome-wide DNA demethylation could be bypassed by ectopic expression of Dppa3. We show that DPPA3 binds and displaces UHRF1 from chromatin and thereby prevents the recruitment and activation of the maintenance DNA methyltransferase DNMT1. We demonstrate that DPPA3 alone can drive global DNA demethylation when transferred to amphibians (Xenopus) and fish (medaka), both species that naturally do not have a Dppa3 gene and exhibit no post-fertilization DNA demethylation. Our results show that TET proteins are responsible for active and - indirectly also for - passive DNA demethylation; while TET proteins initiate local and gene-specific demethylation in vertebrates, the recent emergence of DPPA3 introduced a unique means of genome-wide passive demethylation in mammals and contributed to the evolution of epigenetic regulation during early mammalian development.

developmental biology↗