Spatial and single-cell profiling of the metabolome, transcriptome and epigenome of the aging mouse liver.

Tissues within an organism and even cell types within a tissue can age with different velocities. However, it is unclear whether cells of one type experience different aging trajectories within a tissue depending on their spatial location. Here, we used spatial transcriptomics in combination with single-cell ATAC-seq and RNA-seq, lipidomics and functional assays to address how cells in the male murine liver are affected by age-related changes in the microenvironment. Integration of the datasets revealed zonation-specific and age-related changes in metabolic states, the epigenome and transcriptome. The epigenome changed in a zonation-dependent manner and functionally, periportal hepatocytes were characterized by decreased mitochondrial fitness, whereas pericentral hepatocytes accumulated large lipid droplets. Together, we provide evidence that changing microenvironments within a tissue exert strong influences on their resident cells that can shape epigenetic, metabolic and phenotypic outputs.

Hypoxia promotes osteogenesis by facilitating acetyl-CoA-mediated mitochondrial-nuclear communication.

Bone-derived mesenchymal stem cells (MSCs) reside in a hypoxic niche that maintains their differentiation potential. While hypoxia (low oxygen concentration) was reported to critically support stem cell function and osteogenesis, the molecular events triggering changes in stem cell fate decisions in response to normoxia (high oxygen concentration) remain elusive. Here, we study the impact of normoxia on mitochondrial-nuclear communication during stem cell differentiation. We show that normoxia-cultured murine MSCs undergo profound transcriptional alterations which cause irreversible osteogenesis defects. Mechanistically, high oxygen promotes chromatin compaction and histone hypo-acetylation, particularly on promoters and enhancers of osteogenic genes. Although normoxia induces metabolic rewiring resulting in elevated acetyl-CoA levels, histone hypo-acetylation occurs due to the trapping of acetyl-CoA inside mitochondria owing to decreased citrate carrier (CiC) activity. Restoring the cytosolic acetyl-CoA pool remodels the chromatin landscape and rescues the osteogenic defects. Collectively, our results demonstrate that the metabolism-chromatin-osteogenesis axis is perturbed upon exposure to high oxygen levels and identifies CiC as a novel, oxygen-sensitive regulator of the MSC function.

RNA-sequencing of single cholangiocyte-derived organoids reveals high organoid-to organoid variability.

Over the last decades, organoids have been established from most of the tissue-resident stem and iPS cells. They hold great promise for our understanding of mammalian organ development, but also for the study of disease or even personalised medicine. In recent years, several reports hinted at intraculture organoid variability, but a systematic analysis of such heterogeneity has not been performed before. Here, we used RNA-seq of individual intrahepatic cholangiocyte organoids to address this question. We find that batch-to-batch variation is very low, whereas passage number has a profound impact on gene expression profiles. On the other hand, there is organoid-to-organoid variability within a culture. Using differential gene expression, we did not identify specific pathways that drive this variability, pointing towards possible effects of the microenvironment within the culture condition. Taken together, our study provides a framework for organoid researchers to properly consider experimental design.

Histone N-terminal acetyltransferase NAA40 links one-carbon metabolism to chemoresistance.

Aberrant function of epigenetic modifiers plays an important role not only in the progression of cancer but also the development of drug resistance. N-alpha-acetyltransferase 40 (NAA40) is a highly specific epigenetic enzyme catalyzing the transfer of an acetyl moiety at the N-terminal end of histones H4 and H2A. Recent studies have illustrated the essential oncogenic role of NAA40 in various cancer types but its role in chemoresistance remains unclear. Here, using transcriptomic followed by metabolomic analysis in colorectal cancer (CRC) cells, we demonstrate that NAA40 controls key one-carbon metabolic genes and corresponding metabolites. In particular, through its acetyltransferase activity NAA40 regulates the methionine cycle thereby affecting global histone methylation and CRC cell survival. Importantly, NAA40-mediated metabolic rewiring promotes resistance of CRC cells to antimetabolite chemotherapy in vitro and in xenograft models. Specifically, NAA40 stimulates transcription of the one-carbon metabolic gene thymidylate synthase (TYMS), whose product is targeted by 5-fluorouracil (5-FU) and accordingly in primary CRC tumours NAA40 expression associates with TYMS levels and poorer 5-FU response. Mechanistically, NAA40 activates TYMS by preventing enrichment of repressive H2A/H4S1ph at the nuclear periphery. Overall, these findings define a novel regulatory link between epigenetics and cellular metabolism mediated by NAA40, which is harnessed by cancer cells to evade chemotherapy.

The RNA-binding protein Puf5 contributes to buffering of mRNA upon chromatin-mediated changes in nascent transcription.

Gene expression involves regulation of chromatin structure and transcription, as well as processing of the transcribed mRNA. While there are feedback mechanisms, it is not clear whether these include crosstalk between chromatin architecture and mRNA decay. To address this, we performed a genome-wide genetic screen using a Saccharomyces cerevisiae strain harbouring the H3K56A mutation, which is known to perturb chromatin structure and nascent transcription. We identified Puf5 (also known as Mpt5) as essential in an H3K56A background. Depletion of Puf5 in this background leads to downregulation of Puf5 targets. We suggest that Puf5 plays a role in post-transcriptional buffering of mRNAs, and support this by transcriptional shutoff experiments in which Puf5 mRNA targets are degraded slower in H3K56A cells compared to wild-type cells. Finally, we show that post-transcriptional buffering of Puf5 targets is widespread and does not occur only in an H3K56A mutant, but also in an H3K4R background, which leads to a global increase in nascent transcription. Our data suggest that Puf5 determines the fate of its mRNA targets in a context-dependent manner acting as an mRNA surveillance hub balancing deregulated nascent transcription to maintain physiological mRNA levels.

Chromatin remodeling due to degradation of citrate carrier impairs osteogenesis of aged mesenchymal stem cells.

Aging is accompanied by a general decline in the function of many cellular pathways. However, whether these are causally or functionally interconnected remains elusive. Here, we study the effect of mitochondrial-nuclear communication on stem cell aging. We show that aged mesenchymal stem cells exhibit reduced chromatin accessibility and lower histone acetylation, particularly on promoters and enhancers of osteogenic genes. The reduced histone acetylation is due to impaired export of mitochondrial acetyl-CoA, owing to the lower levels of citrate carrier (CiC). We demonstrate that aged cells showed enhanced lysosomal degradation of CiC, which is mediated via mitochondrial-derived vesicles. Strikingly, restoring cytosolic acetyl-CoA levels either by exogenous CiC expression or via acetate supplementation, remodels the chromatin landscape and rescues the osteogenesis defects of aged mesenchymal stem cells. Collectively, our results establish a tight, age-dependent connection between mitochondrial quality control, chromatin and stem cell fate, which are linked together by CiC.

Benchmarking single-cell RNA-sequencing protocols for cell atlas projects.

Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear. In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas.

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 yet been established. 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 ~3000 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.

Ageing and sources of transcriptional heterogeneity.

Cellular heterogeneity is an important contributor to biological function and is employed by cells, tissues and organisms to adapt, compensate, respond, defend and/or regulate specific processes. Research over the last decades has revealed that transcriptional noise is a major driver for cell-to-cell variability. In this review we will discuss sources of transcriptional variability, in particular bursting of gene expression and how it could contribute to cellular states and fate decisions. We will highlight recent developments in single cell sequencing technologies that make it possible to address cellular heterogeneity in unprecedented detail. Finally, we will review recent literature, in which these new technologies are harnessed to address pressing questions in the field of ageing research, such as transcriptional noise and cellular heterogeneity in the course of ageing.

Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq.

Single-cell RNA sequencing (scRNA-seq) has emerged as a central genome-wide method to characterize cellular identities and processes. Consequently, improving its sensitivity, flexibility, and cost-efficiency can advance many research questions. Among the flexible plate-based methods, single-cell RNA barcoding and sequencing (SCRB-seq) is highly sensitive and efficient. Here, we systematically evaluate experimental conditions of this protocol and find that adding polyethylene glycol considerably increases sensitivity by enhancing cDNA synthesis. Furthermore, using Terra polymerase increases efficiency due to a more even cDNA amplification that requires less sequencing of libraries. We combined these and other improvements to develop a scRNA-seq library protocol we call molecular crowding SCRB-seq (mcSCRB-seq), which we show to be one of the most sensitive, efficient, and flexible scRNA-seq methods to date.

zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs.

Background: Single-cell RNA-sequencing (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplification of the cDNA. The high throughput is made possible by the early introduction of sample-specific bar codes (BCs), and the amplification bias is alleviated by unique molecular identifiers (UMIs). Thus, the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI. Findings: zUMIs is a pipeline that can handle both known and random BCs and also efficiently collapse UMIs, either just for exon mapping reads or for both exon and intron mapping reads. If BC annotation is missing, zUMIs can accurately detect intact cells from the distribution of sequencing reads. Another unique feature of zUMIs is the adaptive downsampling function that facilitates dealing with hugely varying library sizes but also allows the user to evaluate whether the library has been sequenced to saturation. To illustrate the utility of zUMIs, we analyzed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to introns. Also, we show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution. Conclusions: zUMIs flexibility makes if possible to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs and is the most feature-rich, fast, and user-friendly pipeline to process such scRNA-seq data.

Quantitative single-cell transcriptomics.

Single-cell RNA sequencing (scRNA-seq) is currently transforming our understanding of biology, as it is a powerful tool to resolve cellular heterogeneity and molecular networks. Over 50 protocols have been developed in recent years and also data processing and analyzes tools are evolving fast. Here, we review the basic principles underlying the different experimental protocols and how to benchmark them. We also review and compare the essential methods to process scRNA-seq data from mapping, filtering, normalization and batch corrections to basic differential expression analysis. We hope that this helps to choose appropriate experimental and computational methods for the research question at hand.

Evolutionary routes and KRAS dosage define pancreatic cancer phenotypes.

The poor correlation of mutational landscapes with phenotypes limits our understanding of the pathogenesis and metastasis of pancreatic ductal adenocarcinoma (PDAC). Here we show that oncogenic dosage-variation has a critical role in PDAC biology and phenotypic diversification. We find an increase in gene dosage of mutant KRAS in human PDAC precursors, which drives both early tumorigenesis and metastasis and thus rationalizes early PDAC dissemination. To overcome the limitations posed to gene dosage studies by the stromal richness of PDAC, we have developed large cell culture resources of metastatic mouse PDAC. Integration of cell culture genomes, transcriptomes and tumour phenotypes with functional studies and human data reveals additional widespread effects of oncogenic dosage variation on cell morphology and plasticity, histopathology and clinical outcome, with the highest KrasMUT levels underlying aggressive undifferentiated phenotypes. We also identify alternative oncogenic gains (Myc, Yap1 or Nfkb2), which collaborate with heterozygous KrasMUT in driving tumorigenesis, but have lower metastatic potential. Mechanistically, different oncogenic gains and dosages evolve along distinct evolutionary routes, licensed by defined allelic states and/or combinations of hallmark tumour suppressor alterations (Cdkn2a, Trp53, Tgfß-pathway). Thus, evolutionary constraints and contingencies direct oncogenic dosage gain and variation along defined routes to drive the early progression of PDAC and shape its downstream biology. Our study uncovers universal principles of Ras-driven oncogenesis that have potential relevance beyond pancreatic cancer.

powsimR: power analysis for bulk and single cell RNA-seq experiments.

SUMMARY: Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses. AVAILABILITY AND IMPLEMENTATION: The R package and associated tutorial are freely available at https://github.com/bvieth/powsimR. CONTACT: vieth@bio.lmu.de or hellmann@bio.lmu.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparative Analysis of Single-Cell RNA Sequencing Methods.

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.

Characterization of Rare, Dormant, and Therapy-Resistant Cells in Acute Lymphoblastic Leukemia.

Tumor relapse is associated with dismal prognosis, but responsible biological principles remain incompletely understood. To isolate and characterize relapse-inducing cells, we used genetic engineering and proliferation-sensitive dyes in patient-derived xenografts of acute lymphoblastic leukemia (ALL). We identified a rare subpopulation that resembled relapse-inducing cells with combined properties of long-term dormancy, treatment resistance, and stemness. Single-cell and bulk expression profiling revealed their similarity to primary ALL cells isolated from pediatric and adult patients at minimal residual disease (MRD). Therapeutically adverse characteristics were reversible, as resistant, dormant cells became sensitive to treatment and started proliferating when dissociated from the in vivo environment. Our data suggest that ALL patients might profit from therapeutic strategies that release MRD cells from the niche.

The impact of amplification on differential expression analyses by RNA-seq.

Currently, quantitative RNA-seq methods are pushed to work with increasingly small starting amounts of RNA that require amplification. However, it is unclear how much noise or bias amplification introduces and how this affects precision and accuracy of RNA quantification. To assess the effects of amplification, reads that originated from the same RNA molecule (PCR-duplicates) need to be identified. Computationally, read duplicates are defined by their mapping position, which does not distinguish PCR- from natural duplicates and hence it is unclear how to treat duplicated reads. Here, we generate and analyse RNA-seq data sets prepared using three different protocols (Smart-Seq, TruSeq and UMI-seq). We find that a large fraction of computationally identified read duplicates are not PCR duplicates and can be explained by sampling and fragmentation bias. Consequently, the computational removal of duplicates does improve neither accuracy nor precision and can actually worsen the power and the False Discovery Rate (FDR) for differential gene expression. Even when duplicates are experimentally identified by unique molecular identifiers (UMIs), power and FDR are only mildly improved. However, the pooling of samples as made possible by the early barcoding of the UMI-protocol leads to an appreciable increase in the power to detect differentially expressed genes.