Clonally heritable gene expression imparts a layer of diversity within cell types

Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.

Erythroid Differentiation Enhances RNA Mis-Splicing in SF3B1-Mutant Myelodysplastic Syndromes with Ring Sideroblasts

Myelodysplastic syndromes with ring sideroblasts (MDS-RS) commonly develop from hematopoietic stem cells (HSC) bearing mutations in the splicing factor SF3B1 (SF3B1mt). Direct studies into MDS-RS pathobiology have been limited by a lack of model systems that fully recapitulate erythroid biology and RS development and the inability to isolate viable human RS. Here, we combined successful direct RS isolation from patient samples, high-throughput multiomics analysis of cells encompassing the SF3B1mt stem-erythroid continuum, and functional assays to investigate the impact of SF3B1mt on erythropoiesis and RS accumulation. The isolated RS differentiated, egressed into the blood, escaped traditional nonsense-mediated decay (NMD) mechanisms, and leveraged stress-survival pathways that hinder wild-type hematopoiesis through pathogenic GDF15 overexpression. Importantly, RS constituted a contaminant of magnetically enriched CD34+ cells, skewing bulk transcriptomic data. Mis-splicing in SF3B1mt cells was intensified by erythroid differentiation through accelerated RNA splicing and decreased NMD activity, and SF3B1mt led to truncations in several MDS-implicated genes. Finally, RNA mis-splicing induced an uncoupling of RNA and protein expression, leading to critical abnormalities in proapoptotic p53 pathway genes. Overall, this characterization of erythropoiesis in SF3B1mt RS provides a resource for studying MDS-RS and uncovers insights into the unexpectedly active biology of the "dead-end" RS.

SIGNIFICANCE

Ring sideroblast isolation combined with state-of-the-art multiomics identifies survival mechanisms underlying SF3B1-mutant erythropoiesis and establishes an active role for erythroid differentiation and ring sideroblasts themselves in SF3B1-mutant myelodysplastic syndrome pathogenesis.

Tissue-specific RNA Polymerase II promoter-proximal pause release and burst kinetics in a Drosophila embryonic patterning network

BACKGROUND

Formation of tissue-specific transcriptional programs underlies multicellular development, including dorsoventral (DV) patterning of the Drosophila embryo. This involves interactions between transcriptional enhancers and promoters in a chromatin context, but how the chromatin landscape influences transcription is not fully understood.

RESULTS

Here we comprehensively resolve differential transcriptional and chromatin states during Drosophila 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.

CONCLUSIONS

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.

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.

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

Before downstream analysis can reveal biological signals in single-cell RNA sequencing data, normalization and variance stabilization are required to remove technical noise. Recently, Pearson residuals based on negative binomial models have been suggested as an efficient normalization approach. These methods were developed for UMI-based sequencing protocols, where unique molecular identifiers (UMIs) help to remove PCR amplification noise by keeping track of the original molecules. In contrast, full-length protocols such as Smart-seq2 lack UMIs and retain amplification noise, making negative binomial models inapplicable. Here, we extend Pearson residuals to such read count data by modeling them as a compound process: we assume that the captured RNA molecules follow the negative binomial distribution, but are replicated according to an amplification distribution. Based on this model, we introduce compound Pearson residuals and show that they can be analytically obtained without explicit knowledge of the amplification distribution. Further, we demonstrate that compound Pearson residuals lead to a biologically meaningful gene selection and low-dimensional embeddings of complex Smart-seq2 datasets. Finally, we empirically study amplification distributions across several sequencing protocols, and suggest that they can be described by a broken power law. We show that the resulting compound distribution captures overdispersion and zero-inflation patterns characteristic of read count data. In summary, compound Pearson residuals provide an efficient and effective way to normalize read count data based on simple mechanistic assumptions.

PARP14 is a novel target in STAT6 mutant follicular lymphoma

The variable clinical course of follicular lymphoma (FL) is determined by the molecular heterogeneity of tumor cells and complex interactions within the tumor microenvironment (TME). IL-4 producing follicular helper T cells (TFH) are critical components of the FL TME. Binding of IL-4 to IL-4R on FL cells activates JAK/STAT signaling. We identified STAT6 mutations (STAT6MUT) in 13% of FL (N = 33/258), all clustered within the DNA binding domain. Gene expression data and immunohistochemistry showed upregulation of IL-4/STAT6 target genes in STAT6MUT FL, including CCL17, CCL22, and FCER2 (CD23). Functionally, STAT6MUT was gain-of-function by serial replating phenotype in pre-B CFU assays. Expression of STAT6MUT enhanced IL-4 induced FCER2/CD23, CCL17 and CCL22 expression and was associated with nuclear accumulation of pSTAT6. RNA sequencing identified PARP14 -a transcriptional switch and co-activator of STAT6- among the top differentially upregulated genes in IL-4 stimulated STAT6MUT lymphoma cells and in STAT6MUT primary FL cells. Quantitative chromatin immunoprecipitation (qChIP) demonstrated binding of STAT6MUT but not STAT6WT to the PARP14 promotor. Reporter assays showed increased IL-4 induced transactivation activity of STAT6MUT at the PARP14 promotor, suggesting a self-reinforcing regulatory circuit. Knock-down of PARP14 or PARP-inhibition abrogated the STAT6MUT gain-of-function phenotype. Thus, our results identify PARP14 as a novel therapeutic target in STAT6MUT FL.

Prime-seq, efficient and powerful bulk RNA sequencing

Cost-efficient library generation by early barcoding has been central in propelling single-cell RNA sequencing. Here, we optimize and validate prime-seq, an early barcoding bulk RNA-seq method. We show that it performs equivalently to TruSeq, a standard bulk RNA-seq method, but is fourfold more cost-efficient due to almost 50-fold cheaper library costs. We also validate a direct RNA isolation step, show that intronic reads are derived from RNA, and compare cost-efficiencies of available protocols. We conclude that prime-seq is currently one of the best options to set up an early barcoding bulk RNA-seq protocol from which many labs would profit.

Transcriptional kinetics and molecular functions of long noncoding RNAs

An increasing number of long noncoding RNAs (lncRNAs) have experimentally confirmed functions, yet little is known about their transcriptional dynamics and it is challenging to determine their regulatory effects. Here, we used allele-sensitive single-cell RNA sequencing to demonstrate that, compared to messenger RNAs, lncRNAs have twice as long duration between two transcriptional bursts. Additionally, we observed increased cell-to-cell variability in lncRNA expression due to lower frequency bursting producing larger numbers of RNA molecules. 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, we identified cis-functioning lncRNAs and showed that knockdown of these lncRNAs modulated the nearby protein-coding gene’s transcriptional burst frequency or size. In summary, we identified distinct transcriptional regulation of lncRNAs and demonstrated a role for lncRNAs in the regulation of mRNA transcriptional bursting.

Allele-sensitive single-cell RNA sequencing analysis of long noncoding RNA (lncRNA) transcriptional kinetics shows that their lower expression compared to mRNA is due to lower burst frequencies and highlights cell-state-specific functions for several lncRNAs.

BAMboozle removes genetic variation from 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, even in studies where donor-related genetic variant information is not of primary interest. Here, we developed BAMboozle, a versatile tool to eliminate critical types of sensitive genetic information in human sequence data by reverting aligned reads to the genome reference sequence. Applying BAMboozle to functional genomics data, such as single-cell RNA-seq (scRNA-seq) and scATAC-seq datasets, confirmed the removal of donor-related single nucleotide polymorphisms (SNPs) and indels in a manner that did not disclose the altered positions. Importantly, BAMboozle only removes the genetic sequence variants of the sample (i.e., donor) while preserving other important aspects of the raw sequence data. For example, BAMboozled scRNA-seq data contained accurate cell-type associated gene expression signatures, splice kinetic information, and can be used for methods benchmarking. Altogether, BAMboozle efficiently removes genetic variation in aligned sequence data, which represents a step forward towards open data sharing in many areas of genomics where the genetic variant information is not of primary interest.

Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance

Transcriptional bursts render substantial biological noise in cellular transcriptomes. Here, we investigated the theoretical extent of allelic expression resulting from transcriptional bursting and how it compared to the amount biallelic, monoallelic and allele-biased expression observed in single-cell RNA-sequencing (scRNA-seq) data. We found that transcriptional bursting can explain the allelic expression patterns observed in single cells, including the frequent observations of autosomal monoallelic gene expression. Importantly, we identified that the burst frequency largely determined the fraction of cells with monoallelic expression, whereas the burst size had little effect on monoallelic observations. The high consistency between the bursting model predictions and scRNA-seq observations made it possible to assess the heterogeneity of a group of cells as their deviation in allelic observations from the expected. Finally, both burst frequency and size contributed to allelic imbalance observations and reinforced that studies of allelic imbalance can be confounded from the inherent noise in transcriptional bursting. Altogether, we demonstrate that allele-level transcriptional bursting renders widespread, although predictable, amounts of monoallelic and biallelic expression in single cells and cell populations.

Genes are transcribed into RNA and further translated into proteins. The maternal and paternal copy of each gene are typically transcribed independently, and transcription itself occur in discrete stochastic bursts (transcriptional bursts). Pioneering single-cell analysis of RNA across cells revealed abundant fluctuations in the amounts of maternal and paternal RNA in cells, with frequent observations of RNA from only the maternal or paternal gene copy (monoallelic expression). In this study, we investigated to which extent the observed monoallelic expression across single cells can be explained by transcriptional bursting. We demonstrate that the process of transcriptional bursting is sufficient to explain the amount of monoallelic expression, and we further demonstrate that the frequency of bursts mainly determines the frequency of monoallelic observations. Furthermore, we show that transcriptional bursts may lead to false positive observations of monoallelic expression across cell populations. Therefore, stochastic transcription renders large fluctuations in allelic origin of RNA in cells over time, including frequent monoallelic observations when profiling single cells.

c-Rel gain in B cells drives germinal center reactions and autoantibody production

Single-nucleotide polymorphisms and locus amplification link the NF-κB transcription factor c-Rel to human autoimmune diseases and B cell lymphomas, respectively. However, the functional consequences of enhanced c-Rel levels remain enigmatic. Here, we overexpressed c-Rel specifically in mouse B cells from BAC-transgenic gene loci and demonstrate that c-Rel protein levels linearly dictated expansion of germinal center B (GCB) cells and isotype-switched plasma cells. c-Rel expression in B cells of otherwise c-Rel-deficient mice fully rescued terminal B cell differentiation, underscoring its critical B cell-intrinsic roles. Unexpectedly, in GCB cells transcription-independent regulation produced the highest c-Rel protein levels among B cell subsets. In c-Rel-overexpressing GCB cells this caused enhanced nuclear translocation, a profoundly altered transcriptional program, and increased proliferation. Finally, we provide a link between c-Rel gain and autoimmunity by showing that c-Rel overexpression in B cells caused autoantibody production and renal immune complex deposition.

Recent evolution of a TET-controlled and DPPA3/STELLA-driven pathway of passive DNA demethylation in mammals

Genome-wide DNA demethylation is a unique feature of mammalian development and naïve pluripotent stem cells. Here, we describe a recently evolved pathway in which global hypomethylation is achieved by the coupling of active and passive demethylation. TET activity is required, albeit indirectly, for global demethylation, which mostly occurs at sites devoid of TET binding. Instead, TET-mediated active demethylation is locus-specific and necessary for activating a subset of genes, including the naïve pluripotency and germline marker Dppa3 (Stella, Pgc7). DPPA3 in turn drives large-scale passive demethylation by directly binding and displacing UHRF1 from chromatin, thereby inhibiting maintenance DNA methylation. Although unique to mammals, we show that DPPA3 alone is capable of inducing global DNA demethylation in non-mammalian species (Xenopus and medaka) despite their evolutionary divergence from mammals more than 300 million years ago. Our findings suggest that the evolution of Dppa3 facilitated the emergence of global DNA demethylation in mammals.

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.

Chivosazole A Modulates Protein-Protein Interactions of Actin

Actin is a protein of central importance for many cellular key processes. It is regulated by local interactions with a large number of actin binding proteins (ABPs). Various compounds are known to either increase or decrease the polymerization dynamics of actin. However, no actin binding compound has been developed for clinical applications yet because of selectivity issues. We provide a crystal structure of the natural product chivosazole A (ChivoA) bound to actin and show that-in addition to inhibiting nucleation, polymerization, and severing of F-actin filaments-it selectively modulates binding of ABPs to G-actin: Although unphysiological actin dimers are induced by ChivoA, interaction with gelsolin, profilin, cofilin, and thymosin-β4 is inhibited. Moreover, ChivoA causes transcriptional effects differing from latrunculin B, an actin binder with a different binding site. Our data show that ChivoA and related compounds could serve as scaffolds for the development of actin binding molecules selectively targeting specific actin functions.

Altered social behavior in mice carrying a cortical Foxp2 deletion

Genetic disruptions of the forkhead box transcription factor FOXP2 in humans cause an autosomal-dominant speech and language disorder. While FOXP2 expression pattern are highly conserved, its role in specific brain areas for mammalian social behaviors remains largely unknown. Here we studied mice carrying a homozygous cortical Foxp2 deletion. The postnatal development and gross morphological architecture of mutant mice was indistinguishable from wildtype (WT) littermates. Unbiased behavioral profiling of adult mice revealed abnormalities in approach behavior towards conspecifics as well as in the reciprocal responses of WT interaction partners. Furthermore mutant mice showed alterations in acoustical parameters of ultrasonic vocalizations, which also differed in function of the social context. Cell type-specific gene expression profiling of cortical pyramidal neurons revealed aberrant regulation of genes involved in social behavior. In particular Foxp2 mutants showed the downregulation of Mint2 (Apba2), a gene involved in approach behavior in mice and autism spectrum disorder in humans. Taken together these data demonstrate that cortical Foxp2 is required for normal social behaviors in mice.

Low spatial structure and selection against secreted virulence factors attenuates pathogenicity in Pseudomonas aeruginosa

Bacterial opportunistic pathogens are feared for their difficult-to-treat nosocomial infections and for causing morbidity in immunocompromised patients. Here, we study how such a versatile opportunist, Pseudomonas aeruginosa, adapts to conditions inside and outside its model host Caenorhabditis elegans, and use phenotypic and genotypic screens to identify the mechanistic basis of virulence evolution. We found that virulence significantly dropped in unstructured environments both in the presence and absence of the host, but remained unchanged in spatially structured environments. Reduction of virulence was either driven by a substantial decline in the production of siderophores (in treatments without hosts) or toxins and proteases (in treatments with hosts). Whole-genome sequencing of evolved clones revealed positive selection and parallel evolution across replicates, and showed an accumulation of mutations in regulator genes controlling virulence factor expression. Our study identifies the spatial structure of the non-host environment as a key driver of virulence evolution in an opportunistic pathogen.

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.

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.

Abstract 391: Evolutionary trajectories and KRAS gene dosage define pancreatic cancer phenotypes

Pancreatic ductal adenocarcinoma (PDAC) has frequent alterations in few genes (KRAS, CDKN2A/TP53/SMAD4) and extensive heterogeneity of cancer drivers beyond. The expectation that mutational landscapes of rare drivers could explain phenotypic diversity has -with few exceptions- not come true. Likewise, PDAC metastasis is not understood, and comparisons of primary/metastasis pairs did not find recurrently mutated “metastasis genes”. Here we show that key aspects of PDAC biology are defined by gene-dosage variation of PDAC signature genes, evolving along distinct evolutionary routes. We found increased gene dosage of the initiating KRAS mutation (KRASMUT-iGD) in human PDAC precursors. Mouse models revealed the importance of KrasMUT-iGD for both, early progression and metastasis, rationalizing the high frequency of PDAC dissemination at diagnosis. To overcome limitations posed to gene dosage studies by PDAC´s stroma-richness, we developed murine cell culture resources comprising 135 primaries/metastases. Integrative analyses of their genomes, transcriptomes and tumor phenotypes, combined with human studies and functional analyses revealed a series of additional KrasMUT-dosage effects: different KrasMUT-levels define distinct cellular morphologies, histopathologies and clinical outcomes, with highest KrasMUT-expression underlying the most aggressive undifferentiated phenotypes. We also observed KrasMUT-dosage-associated cellular plasticity, including epithelial-to-mesenchymal transition. Mechanistically, oncogenic dosage-variation is linked to distinct evolutionary routes, characterized by defined types/states of tumor-suppressor alterations: Phylogenetic tracking studies revealed convergent evolution of KrasMUT-iGD-gains, with dependence on prior homozygous Cdkn2a- or Trp53-loss. By contrast, in Cdkn2aHET cancers, amplifications of known and novel oncogenes (Myc, Yap1, Nfkb2) collaborate with KrasMUT-HET to drive progression, yet with lower metastatic potential. These results also reveal oncogene-selective/context-dependent Cdkn2a-haploinsufficiency, for which Tgfβ pathway alterations provide permissiveness. Our study uncovers universal principles underlying PDAC biology and phenotypic diversification. It describes evolutionary trajectories, identifies their genetic hallmarks and shows how oncogenic dosage-variation is differentially licensed along individual routes to control critical disease characteristics, including early progression, histopathology, metastasis, cellular plasticity and clinical aggressiveness.

Citation Format: Sebastian Mueller, Thomas Engleitner, Roman Maresch, Magdalena Zukowska, Sebastian Lange, Thorsten Kaltenbacher, Björn Konukiewitz, Rupert Öllinger, Maximilian Zwiebel, Alex Strong, Hsi-Yu Yen, Ruby Banerjee, Sandra Louzada, Beiyuan Fu, Barbara Seidler, Juliana Götzfried, Kathleen Schuck, Zonera Hassan, Nina Schönhuber, Sabine Klein, Christian Veltkamp, Mathias Friedrich, Lena Rad, Maxim Barenboim, Christoph Ziegenhain, Julia Hess, Oliver M. Dovey, Stefan Eser, Swati Parekh, Fernando Constantino-Casas, Jorge de la Rosa, Marta I. Sierra, Mario Fraga, Julia Mayerle, Günter Klöppel, Roland M. Schmid, Juan Cadiñanos, Pentao Liu, George Vassiliou, Wilko Weichert, Katja Steiger, Wolfgang Enard, Fengtang Yang, Kristian Unger, Günter Schneider, Ignacio Varela, Allan Bradley, Dieter Saur, Roland Rad. Evolutionary trajectories and KRAS gene dosage define pancreatic cancer phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 391.

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.

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.

Micropatterning as a tool to identify regulatory triggers and kinetics of actin-mediated endothelial mechanosensing

Developmental processes, such as angiogenesis, are associated with a constant remodeling of the actin cytoskeleton in response to different mechanical stimuli. The mechanosensitive transcription factors MRTF-A (MKL1) and YAP (also known as YAP1) are important mediators of this challenging adaptation process. However, it is as yet unknown whether both pathways respond in an identical or in a divergent manner to a given microenvironmental guidance cue. Here, we use a micropatterning approach to dissect single aspects of cellular behavior in a spatiotemporally controllable setting. Using the exemplary process of angiogenesis, we show that cell-cell contacts and adhesive surface area are shared regulatory parameters of MRTF and YAP on rigid 2D surfaces. By analyzing MRTF and YAP under laminar flow conditions and during cell migration on dumbbell-shaped microstructures, we demonstrate that they exhibit different translocation kinetics. In conclusion, our work promotes the application of micropatterning techniques as a cell biological tool to study mechanosensitive signaling in the context of angiogenesis.

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, Tgfβ-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.

Azacitidine combined with the selective FLT3 kinase inhibitor crenolanib disrupts stromal protection and inhibits expansion of residual leukemia-initiating cells in FLT3-ITD AML with concurrent epigenetic mutations

Effectively targeting leukemia-initiating cells (LIC) in FLT3-ITD-mutated acute myeloid leukemia (AML) is crucial for cure. Tyrosine kinase inhibitors (TKI) have limited impact as single agents, failing to eradicate LIC in the bone marrow. Using primary AML samples and a patient-derived xenograft model, we investigated whether combining the FLT3-selective TKI crenolanib with the hypomethylating agent azacitidine (AZA) eliminates FLT3-ITD LIC and whether efficacy of this combination depends on co-existing mutations. Using multiparameter flow cytometry, we show FLT3-ITD occurs within the most primitive Lin^-/CD33⁽⁺⁾/CD45^dim/CD34⁺CD38^- LIC compartment. Crenolanib alone could not target FLT3-ITD LIC in contact with niche cells while addition of AZA overcame stromal protection resulting in dramatically reduced clonogenic capacity of LIC in vitro and severely impaired engraftment in NSG mice. Strikingly, FLT3-mutated samples harboring TET2 mutations were completely resistant to crenolanib whereas neither NPM1 nor DNMT3A mutations influenced response. Conversely, primary AML LIC harboring either TET2, DNMT3A or NPM1 mutations did not show increased sensitivity to AZA.

In summary, resistance of FLT3-ITD LIC to TKI depends on co-existing epigenetic mutations. However, AZA + crenolanib effectively abrogates stromal protection and inhibits survival of FLT3-ITD LIC irrespective of mutations, providing evidence for this combination as a means to suppress residual LIC.

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.

Chromatin-remodeling factor SMARCD2 regulates transcriptional networks controlling differentiation of neutrophil granulocytes

We identify SMARCD2 (SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily D, member 2), also known as BAF60b (BRG1/Brahma-associated factor 60b), as a critical regulator of myeloid differentiation in humans, mice, and zebrafish. Studying patients from three unrelated pedigrees characterized by neutropenia, specific granule deficiency, myelodysplasia with excess of blast cells, and various developmental aberrations, we identified three homozygous loss-of-function mutations in SMARCD2. Using mice and zebrafish as model systems, we showed that SMARCD2 controls early steps in the differentiation of myeloid-erythroid progenitor cells. In vitro, SMARCD2 interacts with the transcription factor CEBPɛ and controls expression of neutrophil proteins stored in specific granules. Defective expression of SMARCD2 leads to transcriptional and chromatin changes in acute myeloid leukemia (AML) human promyelocytic cells. In summary, SMARCD2 is a key factor controlling myelopoiesis and is a potential tumor suppressor in leukemia.

Niche WNT5A regulates the actin cytoskeleton during regeneration of hematopoietic stem cells

Schreck et al. show that environmental Wnt5a regulates the transcriptome of HSCs during regeneration, particularly the expression of actin-regulatory mediators. In this manner, the niche affects engraftment through regulation of adhesion, migration, and homing of both normal and malignant cells.

Here, we show that the Wnt5a-haploinsufficient niche regenerates dysfunctional HSCs, which do not successfully engraft in secondary recipients. RNA sequencing of the regenerated donor Lin⁻ SCA-1⁺ KIT⁺ (LSK) cells shows dysregulated expression of ZEB1-associated genes involved in the small GTPase-dependent actin polymerization pathway. Misexpression of DOCK2, WAVE2, and activation of CDC42 results in apolar F-actin localization, leading to defects in adhesion, migration and homing of HSCs regenerated in a Wnt5a-haploinsufficient microenvironment. Moreover, these cells show increased differentiation in vitro, with rapid loss of HSC-enriched LSK cells. Our study further shows that the Wnt5a-haploinsufficient environment similarly affects BCR-ABL^p185 leukemia-initiating cells, which fail to generate leukemia in 42% of the studied recipients, or to transfer leukemia to secondary hosts. Thus, we show that WNT5A in the bone marrow niche is required to regenerate HSCs and leukemic cells with functional ability to rearrange the actin cytoskeleton and engraft successfully.

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.