The External RNA Controls Consortium: a progress report

The CYP4/20-HETE/GPR75 axis in the progression metabolic dysfunction-associated steatosis liver disease (MASLD) to chronic liver disease

Introduction

Metabolic-dysfunction-associated steatosis liver disease (MASLD) is a progressive liver disease from simple steatosis, steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma. Chronic liver diseases (CLDs) can lead to portal hypertension, which is a major cause of complications of cirrhosis. CLDs cause structural alterations across the liver through increased contents of extracellular matrix (ECM), driving dysfunction of liver sinusoidal endothelial cells (LSECs) alongside hepatic stellate cells (HSCs) and activated resident or infiltrating immune cells. Bioactive arachidonic metabolites have diverse roles in the progression of MASLD. Both secreted levels of 20-hydroxyeicosatetraenoic acid (20-HETE) and epoxyeicosatrienoic acid (EET) are elevated in patients with liver cirrhosis.

Methods

CLD samples were evaluated for changes in free fatty acids (FFA), cholesterol, bilirubin, bile acid, reactive oxygen species (ROD), lipid peroxidation, myeloperoxidase activity and hydroxyproline levels to evaluate the degrees of liver damage and fibrosis. To address the role of the CYP4/20-HETE/GPR75 axis, we measured the amount and the synthesis of 20-HETE in patients with CLD, specifically during the progression of MASLD. Additionally, we evaluated gene expression and protein levels of GPR75, a high-affinity receptor for 20-HETE across CLD patient samples.

Results

We observed an increase in 20-HETE levels and synthesis during the progression of MASLD. Increased synthesis of 20-HETE correlated with the expression of CYP4A11 genes but not CYP4F2. These results were confirmed by increased P4504A11 protein levels and decreased P4504F2 protein levels during the development and progression of MASLD. The gene expression and protein levels of GPR75, the major receptor for 20-HETE, increased in the progression of MASLD. Interestingly, the CYP4A11 and GPR75 mRNA levels increased in steatohepatitis but dramatically dropped in cirrhosis and then increased in patients with HCC. Also, protein levels of P4504A11 and GPR75 mirrored their mRNA levels.

Discussion

These results indicate that the CYP4A11 and subsequent GPR75 genes are coordinately regulated in the progression of MASLD and may have multiple roles, including 20-HETE activation of peroxisome proliferator-activated receptor α (PPARα) in steatosis and GPR75 in CLD through either increased cell proliferation or vasoconstriction in portal hypertension during cirrhosis. The abrupt reduction in CYP4A11 and GPR75 in patients with cirrhosis may also be due to increased 20-HETE, serving as a feedback mechanism via GPR75, leading to reduced CYP4A11 and GPR75 gene expression. This work illustrates key correlations associated with the CYP4/20-HETE/GPR75 axis and the progression of liver disease in humans.

Epigenetic characterization of adult rhesus monkey spermatogonial stem cells identifies key regulators of stem cell homeostasis

Spermatogonial stem cells (SSCs) play crucial roles in the preservation of male fertility. However, successful ex vivo expansion of authentic human SSCs remains elusive due to the inadequate understanding of SSC homeostasis regulation. Using rhesus monkeys (Macaca mulatta) as a representative model, we characterized SSCs and progenitor subsets through single-cell RNA sequencing using a cell-specific network approach. We also profiled chromatin status and major histone modifications (H3K4me1, H3K4me3, H3K27ac, H3K27me3 and H3K9me3), and subsequently mapped promoters and active enhancers in TSPAN33+ putative SSCs. Comparing the epigenetic changes between fresh TSPAN33+ cells and cultured TSPAN33+ cells (resembling progenitors), we identified the regulatory elements with higher activity in SSCs, and the potential transcription factors and signaling pathways implicated in SSC regulation. Specifically, TGF-β signaling is activated in monkey putative SSCs. We provided evidence supporting its role in promoting self-renewal of monkey SSCs in culture. Overall, this study outlines the epigenetic landscapes of monkey SSCs and provides clues for optimization of the culture condition for primate SSCs expansion.

Fasting shapes chromatin architecture through an mTOR/RNA Pol I axis

Chromatin architecture is a fundamental mediator of genome function. Fasting is a major environmental cue across the animal kingdom, yet how it impacts three-dimensional (3D) genome organization is unknown. Here we show that fasting induces an intestine-specific, reversible and large-scale spatial reorganization of chromatin in Caenorhabditis elegans. This fasting-induced 3D genome reorganization requires inhibition of the nutrient-sensing mTOR pathway, acting through the regulation of RNA Pol I, but not Pol II nor Pol III, and is accompanied by remodelling of the nucleolus. By uncoupling the 3D genome configuration from the animal's nutritional status, we find that the expression of metabolic and stress-related genes increases when the spatial reorganization of chromatin occurs, showing that the 3D genome might support the transcriptional response in fasted animals. Our work documents a large-scale chromatin reorganization triggered by fasting and reveals that mTOR and RNA Pol I shape genome architecture in response to nutrients.

GENCODE: massively expanding the lncRNA catalog through capture long-read RNA sequencing

Accurate and complete gene annotations are indispensable for understanding how genome sequences encode biological functions. For twenty years, the GENCODE consortium has developed reference annotations for the human and mouse genomes, becoming a foundation for biomedical and genomics communities worldwide. Nevertheless, collections of important yet poorly-understood gene classes like long non-coding RNAs (lncRNAs) remain incomplete and scattered across multiple, uncoordinated catalogs, slowing down progress in the field. To address these issues, GENCODE has undertaken the most comprehensive lncRNAs annotation effort to date. This is founded on the manual annotation of full-length targeted long-read sequencing, on matched embryonic and adult tissues, of orthologous regions in human and mouse. Altogether 17,931 novel human genes (140,268 novel transcripts) and 22,784 novel mouse genes (136,169 novel transcripts) have been added to the GENCODE catalog representing a 2-fold and 6-fold increase in transcripts, respectively - the greatest increase since the sequencing of the human genome. Novel gene annotations display evolutionary constraints, have well-formed promoter regions, and link to phenotype-associated genetic variants. They greatly enhance the functional interpretability of the human genome, as they help explain millions of previously-mapped "orphan" omics measurements corresponding to transcription start sites, chromatin modifications and transcription factor binding sites. Crucially, our targeted design assigned human-mouse orthologs at a rate beyond previous studies, tripling the number of human disease-associated lncRNAs with mouse orthologs. The expanded and enhanced GENCODE lncRNA annotations mark a critical step towards deciphering the human and mouse genomes.

Reference Materials for Improving Reliability of Multiomics Profiling

High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications, offering a more comprehensive understanding of biological processes and diseases. Omics reference materials play a pivotal role in ensuring the accuracy, reliability, and comparability of laboratory measurements and analyses. However, the current application of omics reference materials has revealed several issues, including inappropriate selection and underutilization, leading to inconsistencies across laboratories. This review aims to address these concerns by emphasizing the importance of well-characterized reference materials at each level of omics, encompassing (epi-)genomics, transcriptomics, proteomics, and metabolomics. By summarizing their characteristics, advantages, and limitations along with appropriate performance metrics pertinent to study purposes, we provide an overview of how omics reference materials can enhance data quality and data integration, thus fostering robust scientific investigations with omics technologies.

ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data Preparation to Toxicological Mechanism Elucidation

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.

Attenuated effector T cells are linked to control of chronic HBV infection

Hepatitis B virus (HBV)-specific CD8+ T cells play a dominant role during acute-resolving HBV infection but are functionally impaired during chronic HBV infection in humans. These functional deficits have been linked with metabolic and phenotypic heterogeneity, but it has remained unclear to what extent different subsets of HBV-specific CD8+ T cells still suppress viral replication. We addressed this issue by deep profiling, functional testing and perturbation of HBV-specific CD8+ T cells during different phases of chronic HBV infection. Our data revealed a mechanism of effector CD8+ T cell attenuation that emerges alongside classical CD8+ T cell exhaustion. Attenuated HBV-specific CD8+ T cells were characterized by cytotoxic properties and a dampened effector differentiation program, determined by antigen recognition and TGFβ signaling, and were associated with viral control during chronic HBV infection. These observations identify a distinct subset of CD8+ T cells linked with immune efficacy in the context of a chronic human viral infection with immunotherapeutic potential.

Utility analyses of AVITI sequencing chemistry

BACKGROUND

DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist.

RESULTS

Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate.

CONCLUSION

These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

CapTrap-seq: a platform-agnostic and quantitative approach for high-fidelity full-length RNA sequencing

Long-read RNA sequencing is essential to produce accurate and exhaustive annotation of eukaryotic genomes. Despite advancements in throughput and accuracy, achieving reliable end-to-end identification of RNA transcripts remains a challenge for long-read sequencing methods. To address this limitation, we develop CapTrap-seq, a cDNA library preparation method, which combines the Cap-trapping strategy with oligo(dT) priming to detect 5' capped, full-length transcripts. In our study, we evaluate the performance of CapTrap-seq alongside other widely used RNA-seq library preparation protocols in human and mouse tissues, employing both ONT and PacBio sequencing technologies. To explore the quantitative capabilities of CapTrap-seq and its accuracy in reconstructing full-length RNA molecules, we implement a capping strategy for synthetic RNA spike-in sequences that mimics the natural 5'cap formation. Our benchmarks, incorporating the Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) data, demonstrate that CapTrap-seq is a competitive, platform-agnostic RNA library preparation method for generating full-length transcript sequences.

The VEGF-Hypoxia Signature Is Upregulated in Basal-like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer

PURPOSE

Black women experience the highest breast cancer mortality rate compared with women of other racial/ethnic groups. To gain a deeper understanding of breast cancer heterogeneity across diverse populations, we examined a VEGF-hypoxia gene expression signature in breast tumors from women of diverse ancestry.

EXPERIMENTAL DESIGN

We developed a NanoString nCounter gene expression panel and applied it to breast tumors from Nigeria (n = 182) and the University of Chicago (Chicago, IL; n = 161). We also analyzed RNA sequencing data from Nigeria (n = 84) and The Cancer Genome Atlas (TCGA) datasets (n = 863). Patient prognosis was analyzed using multiple datasets.

RESULTS

The VEGF-hypoxia signature was highest in the basal-like subtype compared with other subtypes, with greater expression in Black women compared with White women. In TCGA dataset, necrotic breast tumors had higher scores for the VEGF-hypoxia signature compared with non-necrosis tumors (P < 0.001), with the highest proportion in the basal-like subtype. Furthermore, necrotic breast tumors have higher scores for the proliferation signature, suggesting an interaction between the VEGF-hypoxia signature, proliferation, and necrosis. T-cell gene expression signatures also correlated with the VEGF-hypoxia signature when testing all tumors in TCGA dataset. Finally, we found a significant association of the VEGF-hypoxia profile with poor outcomes when using all patients in the METABRIC (P < 0.0001) and SCAN-B datasets (P = 0.002).

CONCLUSIONS

These data provide further evidence for breast cancer heterogeneity across diverse populations and molecular subtypes. Interventions selectively targeting VEGF-hypoxia and the immune microenvironment have the potential to improve overall survival in aggressive breast cancers that disproportionately impact Black women in the African Diaspora.

Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution

A main limitation of bulk transcriptomic technologies is that individual measurements normally contain contributions from multiple cell populations, impeding the identification of cellular heterogeneity within diseased tissues. To extract cellular insights from existing large cohorts of bulk transcriptomic data, we present CSsingle, a novel method designed to accurately deconvolve bulk data into a predefined set of cell types using a scRNA-seq reference. Through comprehensive benchmark evaluations and analyses using diverse real data sets, we reveal the systematic bias inherent in existing methods, stemming from differences in cell size or library size. Our extensive experiments demonstrate that CSsingle exhibits superior accuracy and robustness compared to leading methods, particularly when dealing with bulk mixtures originating from cell types of markedly different cell sizes, as well as when handling bulk and single-cell reference data obtained from diverse sources. Our work provides an efficient and robust methodology for the integrated analysis of bulk and scRNA-seq data, facilitating various biological and clinical studies.

Candida auris undergoes adhesin-dependent and -independent cellular aggregation

Candida auris is a fungal pathogen of humans responsible for nosocomial infections with high mortality rates. High levels of resistance to antifungal drugs and environmental persistence mean these infections are difficult to treat and eradicate from a healthcare setting. Understanding the life cycle and the genetics of this fungus underpinning clinically relevant traits, such as antifungal resistance and virulence, is of the utmost importance to develop novel treatments and therapies. Epidemiological and genomic studies have identified five geographical clades (I-V), which display phenotypic and genomic differences. Aggregation of cells, a phenotype primarily of clade III strains, has been linked to reduced virulence in some infection models. The aggregation phenotype has thus been associated with conferring an advantage for (skin) colonisation rather than for systemic infection. However, strains with different clade affiliations were compared to infer the effects of different morphologies on virulence. This makes it difficult to distinguish morphology-dependent causes from clade-specific or even strain-specific genetic factors. Here, we identify two different types of aggregation: one induced by antifungal treatment which is a result of a cell separation defect; and a second which is controlled by growth conditions and only occurs in strains with the ability to aggregate. The latter aggregation type depends on an ALS-family adhesin which is differentially expressed during aggregation in an aggregative C. auris strain. Finally, we demonstrate that macrophages cannot clear aggregates, suggesting that aggregation might after all provide a benefit during systemic infection and could facilitate long-term persistence in the host.

An extended wave of global mRNA deadenylation sets up a switch in translation regulation across the mammalian oocyte-to-embryo transition

Without new transcription, gene expression across the oocyte-to-embryo transition (OET) relies instead on regulation of mRNA poly(A) tails to control translation. However, how tail dynamics shape translation across the OET in mammals remains unclear. We perform long-read RNA sequencing to uncover poly(A) tail lengths across the mouse OET and, incorporating published ribosome profiling data, provide an integrated, transcriptome-wide analysis of poly(A) tails and translation across the entire transition. We uncover an extended wave of global deadenylation during fertilization in which short-tailed, oocyte-deposited mRNAs are translationally activated without polyadenylation through resistance to deadenylation. Subsequently, in the embryo, mRNAs are readenylated and translated in a surge of global polyadenylation. We further identify regulation of poly(A) tail length at the isoform level and stage-specific enrichment of mRNA sequence motifs among regulated transcripts. These data provide insight into the stage-specific mechanisms of poly(A) tail regulation that orchestrate gene expression from oocyte to embryo in mammals.

A spatiotemporally resolved atlas of mRNA decay in the C. elegans embryo reveals differential regulation of mRNA stability across stages and cell types

During embryonic development, cells undergo dynamic changes in gene expression that are required for appropriate cell fate specification. Although both transcription and mRNA degradation contribute to gene expression dynamics, patterns of mRNA decay are less well-understood. Here we directly measured spatiotemporally resolved mRNA decay rates transcriptome-wide throughout C. elegans embryogenesis by transcription inhibition followed by bulk and single-cell RNA-sequencing. This allowed us to calculate mRNA half-lives within specific cell types and developmental stages and identify differentially regulated mRNA decay throughout embryonic development. We identified transcript features that are correlated with mRNA stability and found that mRNA decay rates are associated with distinct peaks in gene expression over time. Moreover, we provide evidence that, on average, mRNA is more stable in the germline compared to in the soma and in later embryonic stages compared to in earlier stages. This work suggests that differential mRNA decay across cell states and time helps to shape developmental gene expression, and it provides a valuable resource for studies of mRNA turnover regulatory mechanisms.

LAST-seq: single-cell RNA sequencing by direct amplification of single-stranded RNA without prior reverse transcription and second-strand synthesis

Existing single-cell RNA sequencing (scRNA-seq) methods rely on reverse transcription (RT) and second-strand synthesis (SSS) to convert single-stranded RNA into double-stranded DNA prior to amplification, with the limited RT/SSS efficiency compromising RNA detectability. Here, we develop a new scRNA-seq method, Linearly Amplified Single-stranded-RNA-derived Transcriptome sequencing (LAST-seq), which directly amplifies the original single-stranded RNA molecules without prior RT/SSS. LAST-seq offers a high single-molecule capture efficiency and a low level of technical noise for single-cell transcriptome analyses. Using LAST-seq, we characterize transcriptional bursting kinetics in human cells, revealing a role of topologically associating domains in transcription regulation.

An image-guided microfluidic system for single-cell lineage tracking

Cell lineage tracking is a long-standing and unresolved problem in biology. Microfluidic technologies have the potential to address this problem, by virtue of their ability to manipulate and process single-cells in a rapid, controllable and efficient manner. Indeed, when coupled with traditional imaging approaches, microfluidic systems allow the experimentalist to follow single-cell divisions over time. Herein, we present a valve-based microfluidic system able to probe the decision-making processes of single-cells, by tracking their lineage over multiple generations. The system operates by trapping single-cells within growth chambers, allowing the trapped cells to grow and divide, isolating sister cells after a user-defined number of divisions and finally extracting them for downstream transcriptome analysis. The platform incorporates multiple cell manipulation operations, image processing-based automation for cell loading and growth monitoring, reagent addition and device washing. To demonstrate the efficacy of the microfluidic workflow, 6C2 (chicken erythroleukemia) and T2EC (primary chicken erythrocytic progenitors) cells are tracked inside the microfluidic device over two generations, with a cell viability rate in excess of 90%. Sister cells are successfully isolated after division and extracted within a 500 nL volume, which was demonstrated to be compatible with downstream single-cell RNA sequencing analysis.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

CapTrap-Seq: A platform-agnostic and quantitative approach for high-fidelity full-length RNA transcript sequencing

Long-read RNA sequencing is essential to produce accurate and exhaustive annotation of eukaryotic genomes. Despite advancements in throughput and accuracy, achieving reliable end-to-end identification of RNA transcripts remains a challenge for long-read sequencing methods. To address this limitation, we developed CapTrap-seq, a cDNA library preparation method, which combines the Cap-trapping strategy with oligo(dT) priming to detect 5'capped, full-length transcripts, together with the data processing pipeline LyRic. We benchmarked CapTrap-seq and other popular RNA-seq library preparation protocols in a number of human tissues using both ONT and PacBio sequencing. To assess the accuracy of the transcript models produced, we introduced a capping strategy for synthetic RNA spike-in sequences that mimics the natural 5'cap formation in RNA spike-in molecules. We found that the vast majority (up to 90%) of transcript models that LyRic derives from CapTrap-seq reads are full-length. This makes it possible to produce highly accurate annotations with minimal human intervention.

The future of patient-derived xenografts in prostate cancer research

Patient-derived xenografts (PDXs) are generated by engrafting human tumours into mice. Serially transplantable PDXs are used to study tumour biology and test therapeutics, linking the laboratory to the clinic. Although few prostate cancer PDXs are available in large repositories, over 330 prostate cancer PDXs have been established, spanning broad clinical stages, genotypes and phenotypes. Nevertheless, more PDXs are needed to reflect patient diversity, and to study new treatments and emerging mechanisms of resistance. We can maximize the use of PDXs by exchanging models and datasets, and by depositing PDXs into biorepositories, but we must address the impediments to accessing PDXs, such as institutional, ethical and legal agreements. Through collaboration, researchers will gain greater access to PDXs representing diverse features of prostate cancer.

Single-cell RNA sequencing reveals homogeneous transcriptome patterns and low variance in a suspension CHO-K1 and an adherent HEK293FT cell line in culture conditions

Recombinant mammalian host cell lines, in particular CHO and HEK293 cells, are used for the industrial production of therapeutic proteins. Despite their well-known genomic instability, the control mechanisms that enable cells to respond to changes in the environmental conditions are not yet fully understood, nor do we have a good understanding of the factors that lead to phenotypic shifts in long-term cultures. A contributing factor could be inherent diversity in transcriptomes within a population. In this study, we used a full-length coverage single-cell RNA sequencing (scRNA-seq) approach to investigate and compare cell-to-cell variability and the impact of standardized and homogenous culture conditions on the diversity of individual cell transcriptomes, comparing suspension CHO-K1 and adherent HEK293FT cells. Our data showed a critical batch effect from the sequencing of four 96-well plates of CHO-K1 single cells stored for different periods of time, which was and may be therefore identified as a technical variable to consider in experimental planning. Besides, in an artificial and controlled culture environment such as used in routine cell culture technology, the gene expression pattern of a given population does not reveal any marker gene capable to disclose relevant cell population substructures, both for CHO-K1 cells and for HEK293FT cells. The variation observed is primarily driven by the cell cycle.

Open ocean and coastal strains of the N2-fixing cyanobacterium UCYN-A have distinct transcriptomes

Decades of research on marine N2 fixation focused on Trichodesmium, which are generally free-living cyanobacteria, but in recent years the endosymbiotic cyanobacterium Candidatus Atelocyanobacterium thalassa (UCYN-A) has received increasing attention. However, few studies have shed light on the influence of the host versus the habitat on UCYN-A N2 fixation and overall metabolism. Here we compared transcriptomes from natural populations of UCYN-A from oligotrophic open-ocean versus nutrient-rich coastal waters, using a microarray that targets the full genomes of UCYN-A1 and UCYN-A2 and known genes for UCYN-A3. We found that UCYN-A2, usually regarded as adapted to coastal environments, was transcriptionally very active in the open ocean and appeared to be less impacted by habitat change than UCYN-A1. Moreover, for genes with 24 h periodic expression we observed strong but inverse correlations among UCYN-A1, A2, and A3 to oxygen and chlorophyll, which suggests distinct host-symbiont relationships. Across habitats and sublineages, genes for N2 fixation and energy production had high transcript levels, and, intriguingly, were among the minority of genes that kept the same schedule of diel expression. This might indicate different regulatory mechanisms for genes that are critical to the symbiosis for the exchange of nitrogen for carbon from the host. Our results underscore the importance of N2 fixation in UCYN-A symbioses across habitats, with consequences for community interactions and global biogeochemical cycles.

Stage-specific and cell type-specific requirements of ikzf1 during haematopoietic differentiation in zebrafish

The zinc finger transcription factor Ikaros1 (Ikzf1) is required for lymphoid development in mammals. Four zinc fingers constitute its DNA binding domain and two zinc fingers are present in the C-terminal protein interaction module. We describe the phenotypes of zebrafish homozygous for two distinct mutant ikzf1 alleles. The IT325 variant lacks the C-terminal two zinc fingers, whereas the fr105 variant retains only the first zinc finger of the DNA binding domain. An intact ikzf1 gene is required for larval T cell development, whereas low levels of adult lymphoid development recover in the mutants. By contrast, the mutants exhibit a signature of increased myelopoiesis at larval and adult stages. Both mutations stimulate erythroid differentiation in larvae, indicating that the C-terminal zinc fingers negatively regulate the extent of red blood cell production. An unexpected differential effect of the two mutants on adult erythropoiesis suggests a direct requirement of an intact DNA binding domain for entry of progenitors into the red blood cell lineage. Collectively, our results reinforce the biological differences between larval and adult haematopoiesis, indicate a stage-specific function of ikzf1 in regulating the hierarchical bifurcations of differentiation, and assign distinct functions to the DNA binding domain and the C-terminal zinc fingers.

High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging

Resolving the spatial distribution of RNA and protein in tissues at subcellular resolution is a challenge in the field of spatial biology. We describe spatial molecular imaging, a system that measures RNAs and proteins in intact biological samples at subcellular resolution by performing multiple cycles of nucleic acid hybridization of fluorescent molecular barcodes. We demonstrate that spatial molecular imaging has high sensitivity (one or two copies per cell) and very low error rate (0.0092 false calls per cell) and background (~0.04 counts per cell). The imaging system generates three-dimensional, super-resolution localization of analytes at ~2 million cells per sample. Cell segmentation is morphology based using antibodies, compatible with formalin-fixed, paraffin-embedded samples. We measured multiomic data (980 RNAs and 108 proteins) at subcellular resolution in formalin-fixed, paraffin-embedded tissues (nonsmall cell lung and breast cancer) and identified >18 distinct cell types, ten unique tumor microenvironments and 100 pairwise ligand-receptor interactions. Data on >800,000 single cells and ~260 million transcripts can be accessed at http://nanostring.com/CosMx-dataset .

NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks

The inference of Gene Regulatory Networks (GRNs) is one of the key challenges in systems biology. Leading algorithms utilize, in addition to gene expression, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of TF binding experiments. However, such prior knowledge is typically incomplete, therefore, integrating it with gene expression to infer GRNs remains difficult. To address this challenge, we introduce NetREX-CF-Regulatory Network Reconstruction using EXpression and Collaborative Filtering-a GRN reconstruction approach that brings together Collaborative Filtering to address the incompleteness of the prior knowledge and a biologically justified model of gene expression (sparse Network Component Analysis based model). We validated the NetREX-CF using Yeast data and then used it to construct the GRN for Drosophila Schneider 2 (S2) cells. To corroborate the GRN, we performed a large-scale RNA-Seq analysis followed by a high-throughput RNAi treatment against all 465 expressed TFs in the cell line. Our knockdown result has not only extensively validated the GRN we built, but also provides a benchmark that our community can use for evaluating GRNs. Finally, we demonstrate that NetREX-CF can infer GRNs using single-cell RNA-Seq, and outperforms other methods, by using previously published human data.

Differential Timing for Glucose Assimilation in Prochlorococcus and Coexistent Microbial Populations in the North Pacific Subtropical Gyre

The marine cyanobacterium Prochlorococcus can utilize glucose as a source of carbon. However, the relative importance of inorganic and organic carbon assimilation and the timing of glucose assimilation are still poorly understood in these numerically dominant cyanobacteria. Here, we investigated whole microbial community and group-specific primary production and glucose assimilation using incubations with radioisotopes combined with flow cytometry cell sorting. We also studied changes in the microbial community structure in response to glucose enrichments and analyzed the transcription of Prochlorocccus genes involved in carbon metabolism and photosynthesis. Our results showed a diel variation for glucose assimilation in Prochlorococcus, with maximum assimilation at midday and minimum at midnight (~2-fold change), which was different from that of the total microbial community. This suggests that the timing in glucose assimilation in Prochlorococcus is coupled to photosynthetic light reactions producing energy, it being more convenient for Prochlorococcus to show maximum glucose uptake precisely when the rest of microbial populations have their minimum glucose uptake. Many transcriptional responses to glucose enrichment occurred after 12- and 24-h periods, but community composition did not change. High-light Prochlorococcus strains were the most impacted by glucose addition, with transcript-level increases observed for genes in pathways for glucose metabolism, such as the pentose phosphate pathway, the Entner-Doudoroff pathway, glycolysis, respiration, and glucose transport. While Prochlorococcus C assimilation from glucose represented less than 0.1% of the bacterium's photosynthetic C fixation, increased assimilation during the day and glcH gene upregulation upon glucose enrichment indicate an important role of mixotrophic C assimilation by natural populations of Prochlorococcus. IMPORTANCE Several studies have demonstrated that Prochlorococcus, the most abundant photosynthetic organism on Earth, can assimilate organic molecules, such as amino acids, amino sugars, ATP, phosphonates, and dimethylsulfoniopropionate. This autotroph can also assimilate small amounts of glucose, supporting the hypothesis that Prochlorococcus is mixotrophic. Our results show, for the first time, a diel variability in glucose assimilation by natural populations of Prochlorococcus with maximum assimilation during midday. Based on our previous results, this indicates that Prochlorococcus could maximize glucose uptake by using ATP made during the light reactions of photosynthesis. Furthermore, Prochlorococcus showed a different timing of glucose assimilation from the total population, which may offer considerable fitness advantages over competitors "temporal niches." Finally, we observed transcriptional changes in some of the genes involved in carbon metabolism, suggesting that Prochlorococcus can use both pathways previously proposed in cyanobacteria to metabolize glucose.

The landscape of transcriptional and translational changes over 22 years of bacterial adaptation

Organisms can adapt to an environment by taking multiple mutational paths. This redundancy at the genetic level, where many mutations have similar phenotypic and fitness effects, can make untangling the molecular mechanisms of complex adaptations difficult. Here, we use the Escherichia coli long-term evolution experiment (LTEE) as a model to address this challenge. To understand how different genomic changes could lead to parallel fitness gains, we characterize the landscape of transcriptional and translational changes across 12 replicate populations evolving in parallel for 50,000 generations. By quantifying absolute changes in mRNA abundances, we show that not only do all evolved lines have more mRNAs but that this increase in mRNA abundance scales with cell size. We also find that despite few shared mutations at the genetic level, clones from replicate populations in the LTEE are remarkably similar in their gene expression patterns at both the transcriptional and translational levels. Furthermore, we show that the majority of the expression changes are due to changes at the transcriptional level with very few translational changes. Finally, we show how mutations in transcriptional regulators lead to consistent and parallel changes in the expression levels of downstream genes. These results deepen our understanding of the molecular mechanisms underlying complex adaptations and provide insights into the repeatability of evolution.

Decoding brain memory formation by single-cell RNA sequencing

To understand how distinct memories are formed and stored in the brain is an important and fundamental question in neuroscience and computational biology. A population of neurons, termed engram cells, represents the physiological manifestation of a specific memory trace and is characterized by dynamic changes in gene expression, which in turn alters the synaptic connectivity and excitability of these cells. Recent applications of single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are promising approaches for delineating the dynamic expression profiles in these subsets of neurons, and thus understanding memory-specific genes, their combinatorial patterns and regulatory networks. The aim of this article is to review and discuss the experimental and computational procedures of sc/snRNA-seq, new studies of molecular mechanisms of memory aided by sc/snRNA-seq in human brain diseases and related mouse models, and computational challenges in understanding the regulatory mechanisms underlying long-term memory formation.

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.

Developmental dynamics of two bipotent thymic epithelial progenitor types

T cell development in the thymus is essential for cellular immunity and depends on the organotypic thymic epithelial microenvironment. In comparison with other organs, the size and cellular composition of the thymus are unusually dynamic, as exemplified by rapid growth and high T cell output during early stages of development, followed by a gradual loss of functional thymic epithelial cells and diminished naive T cell production with age1-10. Single-cell RNA sequencing (scRNA-seq) has uncovered an unexpected heterogeneity of cell types in the thymic epithelium of young and aged adult mice11-18; however, the identities and developmental dynamics of putative pre- and postnatal epithelial progenitors have remained unresolved1,12,16,17,19-27. Here we combine scRNA-seq and a new CRISPR-Cas9-based cellular barcoding system in mice to determine qualitative and quantitative changes in the thymic epithelium over time. This dual approach enabled us to identify two principal progenitor populations: an early bipotent progenitor type biased towards cortical epithelium and a postnatal bipotent progenitor population biased towards medullary epithelium. We further demonstrate that continuous autocrine provision of Fgf7 leads to sustained expansion of thymic microenvironments without exhausting the epithelial progenitor pools, suggesting a strategy to modulate the extent of thymopoietic activity.

Specification of CNS macrophage subsets occurs postnatally in defined niches

All tissue-resident macrophages of the central nervous system (CNS)-including parenchymal microglia, as well as CNS-associated macrophages (CAMs¹) such as meningeal and perivascular macrophages^2-7-are part of the CNS endogenous innate immune system that acts as the first line of defence during infections or trauma^2,8-10. It has been suggested that microglia and all subsets of CAMs are derived from prenatal cellular sources in the yolk sac that were defined as early erythromyeloid progenitors^11-15. However, the precise ontogenetic relationships, the underlying transcriptional programs and the molecular signals that drive the development of distinct CAM subsets in situ are poorly understood. Here we show, using fate-mapping systems, single-cell profiling and cell-specific mutants, that only meningeal macrophages and microglia share a common prenatal progenitor. By contrast, perivascular macrophages originate from perinatal meningeal macrophages only after birth in an integrin-dependent manner. The establishment of perivascular macrophages critically requires the presence of arterial vascular smooth muscle cells. Together, our data reveal a precisely timed process in distinct anatomical niches for the establishment of macrophage subsets in the CNS.

Perplexity: evaluating transcript abundance estimation in the absence of ground truth

Background

There has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best.

Results

We derive perplexity for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis. Furthermore, we demonstrate theoretically and experimentally that perplexity can be computed for arbitrary transcript abundance estimation models.

Conclusions

Alongside the derivation and implementation of perplexity for transcript abundance estimation, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth.

A MicroRNA Next-Generation-Sequencing Discovery Assay (miND) for Genome-Scale Analysis and Absolute Quantitation of Circulating MicroRNA Biomarkers

The plasma levels of tissue-specific microRNAs can be used as diagnostic, disease severity and prognostic biomarkers for chronic and acute diseases and drug-induced injury. Thereby, the combination of diverse microRNAs into biomarker signatures using multivariate statistics seems especially powerful from the perspective of tissue and condition specific microRNA shedding into the plasma. Although next-generation sequencing (NGS) technology enables one to analyse circulating microRNAs on a genome-scale level, it suffers from potential biases (e.g., adapter ligation bias) and lacks absolute transcript quantitation as well as tailor-made quality controls. In order to develop a robust NGS discovery assay for genome-scale quantitation of circulating microRNAs, we first evaluated the sensitivity, repeatability and ligation bias of four commercially available small RNA library preparation protocols. The protocol from RealSeq Biosciences was selected based on its performance and usability and coupled with a novel panel of exogenous small RNA spike-in controls to enable quality control and absolute quantitation, thus ensuring comparability of data across independent NGS experiments. The established microRNA Next-Generation-Sequencing Discovery Assay (miND) was validated for its relative accuracy, precision, analytical measurement range and sequencing bias and was considered fit-for-purpose for microRNA biomarker discovery. Summarized, all these criteria were met, and thus, our analytical platform is considered fit-for-purpose for microRNA biomarker discovery from biofluids in the setting of any diagnostic, prognostic or patient stratification need. The established miND assay was tested on serum, cerebrospinal fluid (CSF), synovial fluid (SF) and extracellular vesicles (EV) extracted from cell culture medium of primary cells and proved its potential to be used across different sample types.

Statistics or biology: the zero-inflation controversy about scRNA-seq data

Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.

Metrological framework to support accurate, reliable, and reproducible nucleic acid measurements

Nucleic acid analysis is used in many areas of life sciences such as medicine, food safety, and environmental monitoring. Accurate, reliable measurements of nucleic acids are crucial for maximum impact, yet users are often unaware of the global metrological infrastructure that exists to support these measurements. In this work, we describe international efforts to improve nucleic acid analysis, with a focus on the Nucleic Acid Analysis Working Group (NAWG) of the Consultative Committee for Amount of Substance: Metrology in Chemistry and Biology (CCQM). The NAWG is an international group dedicated to improving the global comparability of nucleic acid measurements; its primary focus is to support the development and maintenance of measurement capabilities and the dissemination of measurement services from its members: the National Metrology Institutes (NMIs) and Designated Institutes (DIs). These NMIs and DIs provide DNA and RNA measurement services developed in response to the needs of their stakeholders. The NAWG members have conducted cutting edge work over the last 20 years, demonstrating the ability to support the reliability, comparability, and traceability of nucleic acid measurement results in a variety of sectors.

MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

Background

Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable.

Results

We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren.

Conclusions

MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04288-0.

Single-molecule, full-length transcript isoform sequencing reveals disease-associated RNA isoforms in cardiomyocytes

Alternative splicing generates differing RNA isoforms that govern phenotypic complexity of eukaryotes. Its malfunction underlies many diseases, including cancer and cardiovascular diseases. Comparative analysis of RNA isoforms at the genome-wide scale has been difficult. Here, we establish an experimental and computational pipeline that performs de novo transcript annotation and accurately quantifies transcript isoforms from cDNA sequences with a full-length isoform detection accuracy of 97.6%. We generate a searchable, quantitative human transcriptome annotation with 31,025 known and 5,740 novel transcript isoforms ( http://steinmetzlab.embl.de/iBrowser/ ). By analyzing the isoforms in the presence of RNA Binding Motif Protein 20 (RBM20) mutations associated with aggressive dilated cardiomyopathy (DCM), we identify 121 differentially expressed transcript isoforms in 107 cardiac genes. Our approach enables quantitative dissection of complex transcript architecture instead of mere identification of inclusion or exclusion of individual exons, as exemplified by the discovery of IMMT isoforms mis-spliced by RBM20 mutations. Thereby we achieve a path to direct differential expression testing independent of an existing annotation of transcript isoforms, providing more immediate biological interpretation and higher resolution transcriptome comparisons.

Chemotherapy-induced transposable elements activate MDA5 to enhance haematopoietic regeneration

Haematopoietic stem cells (HSCs) are normally quiescent, but have evolved mechanisms to respond to stress. Here, we evaluate haematopoietic regeneration induced by chemotherapy. We detect robust chromatin reorganization followed by increased transcription of transposable elements (TEs) during early recovery. TE transcripts bind to and activate the innate immune receptor melanoma differentiation-associated protein 5 (MDA5) that generates an inflammatory response that is necessary for HSCs to exit quiescence. HSCs that lack MDA5 exhibit an impaired inflammatory response after chemotherapy and retain their quiescence, with consequent better long-term repopulation capacity. We show that the overexpression of ERV and LINE superfamily TE copies in wild-type HSCs, but not in Mda5-/- HSCs, results in their cycling. By contrast, after knockdown of LINE1 family copies, HSCs retain their quiescence. Our results show that TE transcripts act as ligands that activate MDA5 during haematopoietic regeneration, thereby enabling HSCs to mount an inflammatory response necessary for their exit from quiescence.

Development and Performance of a CD8 Gene Signature for Characterizing Inflammation in the Tumor Microenvironment across Multiple Tumor Types

Across multiple tumor types, immune checkpoint inhibitors (ICIs) have demonstrated clinical benefit to patients with cancer, yet there is a need to identify predictive biomarkers of response to these therapies. A multiparameter gene expression profiling-based tumor inflammation assay may offer robust characterization of the tumor microenvironment, thereby extending the utility of single-gene analysis or immunohistochemistry (IHC) in predicting response to ICIs. The authors interrogated 1778 commercially procured, formalin-fixed, paraffin-embedded samples using gene expression profiling and pathology-assisted digital CD8 IHC. A machine-learning approach was used to develop gene expression signatures that predicted CD8⁺ immune cell abundance as surrogates for tumor inflammation in melanoma and squamous cell carcinoma of the head and neck samples. An assay for a 16-gene CD8 signature was developed and analytically validated across 12 tumor types. CD8 signature scores correlated with CD8 IHC in a platform-independent manner, and inflammation prevalence was similar between assay methods for all tumor types except prostate cancer and small cell lung cancer. In retrospective analyses, CD8 signature scores were associated with progression-free survival and overall survival with nivolumab in patients with urothelial carcinoma from CheckMate 275. This study demonstrated that the CD8 signature assay can be used to accurately quantify CD8⁺ immune cell abundance in the tumor microenvironment and has potential clinical utility for determining patients with cancer likely to respond to ICIs.

High-throughput full-length single-cell RNA-seq automation

Existing protocols for full-length single-cell RNA sequencing produce libraries of high complexity (thousands of distinct genes) with outstanding sensitivity and specificity of transcript quantification. These full-length libraries have the advantage of allowing probing of transcript isoforms, are informative regarding single-nucleotide polymorphisms and allow assembly of the VDJ region of the T- and B-cell-receptor sequences. Since full-length protocols are mostly plate-based at present, they are also suited to profiling cell types where cell numbers are limiting, such as rare cell types during development. A disadvantage of these methods has been the scalability and cost of the experiments, which has limited their popularity as compared with droplet-based and nanowell approaches. Here, we describe an automated protocol for full-length single-cell RNA sequencing, including both an in-house automated Smart-seq2 protocol and a commercial kit-based workflow. The protocols take 3-5 d to complete, depending on the number of plates processed in a batch. We discuss these two protocols in terms of ease of use, equipment requirements, running time, cost per sample and sequencing quality. By benchmarking the lysis buffers, reverse transcription enzymes and their combinations, we have optimized the in-house automated protocol to dramatically reduce its cost. An automated setup can be adopted easily by a competent researcher with basic laboratory skills and no prior automation experience. These pipelines have been employed successfully for several research projects allied with the Human Cell Atlas initiative ( www.humancellatlas.org ).

Tryptophan metabolism drives dynamic immunosuppressive myeloid states in IDH-mutant gliomas

The dynamics and phenotypes of intratumoral myeloid cells during tumor progression are poorly understood. Here we define myeloid cellular states in gliomas by longitudinal single-cell profiling and demonstrate their strict control by the tumor genotype: in isocitrate dehydrogenase (IDH)-mutant tumors, differentiation of infiltrating myeloid cells is blocked, resulting in an immature phenotype. In late-stage gliomas, monocyte-derived macrophages drive tolerogenic alignment of the microenvironment, thus preventing T cell response. We define the IDH-dependent tumor education of infiltrating macrophages to be causally related to a complex re-orchestration of tryptophan metabolism, resulting in activation of the aryl hydrocarbon receptor. We further show that the altered metabolism of IDH-mutant gliomas maintains this axis in bystander cells and that pharmacological inhibition of tryptophan metabolism can reverse immunosuppression. In conclusion, we provide evidence of a glioma genotype-dependent intratumoral network of resident and recruited myeloid cells and identify tryptophan metabolism as a target for immunotherapy of IDH-mutant tumors.

Nanopore long-read RNA-seq and absolute quantification delineate transcription dynamics in early embryo development of an insect pest

The olive fruit fly, Bactrocera oleae, is the most important pest for the olive fruit but lacks adequate transcriptomic characterization that could aid in molecular control approaches. We apply nanopore long-read RNA-seq with internal RNA standards allowing absolute transcript quantification to analyze transcription dynamics during early embryo development for the first time in this organism. Sequencing on the MinION platform generated over 31 million reads. Over 50% of the expressed genes had at least one read covering its entire length validating our full-length approach. We generated a de novo transcriptome assembly and identified 1768 new genes and a total of 79,810 isoforms; a fourfold increase in transcriptome diversity compared to the current NCBI predicted transcriptome. Absolute transcript quantification per embryo allowed an insight into the dramatic re-organization of maternal transcripts. We further identified Zelda as a possible regulator of early zygotic genome activation in B. oleae and provide further insights into the maternal-to-zygotic transition. These data show the utility of long-read RNA in improving characterization of non-model organisms that lack a fully annotated genome, provide potential targets for sterile insect technic approaches, and provide the first insight into the transcriptome landscape of the developing olive fruit fly embryo.

Noninvasive stratification of nonalcoholic fatty liver disease by whole transcriptome cell-free mRNA characterization

Hepatic fibrosis stage is the most important determinant of outcomes in patients with nonalcoholic fatty liver disease (NAFLD). There is an urgent need for noninvasive tests that can accurately stage fibrosis and determine efficacy of interventions. Here, we describe a novel cell-free (cf)-mRNA sequencing approach that can accurately and reproducibly profile low levels of circulating mRNAs and evaluate the feasibility of developing a cf-mRNA-based NAFLD fibrosis classifier. Using separate discovery and validation cohorts with biopsy-confirmed NAFLD (n = 176 and 59, respectively) and healthy subjects (n = 23), we performed serum cf-mRNA RNA-Seq profiling. Differential expression analysis identified 2,498 dysregulated genes between patients with NAFLD and healthy subjects and 134 fibrosis-associated genes in patients with NAFLD. Comparison between cf-mRNA and liver tissue transcripts revealed significant overlap of fibrosis-associated genes and pathways indicating that the circulating cf-mRNA transcriptome reflects molecular changes in the livers of patients with NAFLD. In particular, metabolic and immune pathways reflective of known underlying steatosis and inflammation were highly dysregulated in the cf-mRNA profile of patients with advanced fibrosis. Finally, we used an elastic net ordinal logistic model to develop a classifier that predicts clinically significant fibrosis (F2-F4). In an independent cohort, the cf-mRNA classifier was able to identify 50% of patients with at least 90% probability of clinically significant fibrosis. We demonstrate a novel and robust cf-mRNA-based RNA-Seq platform for noninvasive identification of diverse hepatic molecular disruptions and for fibrosis staging with promising potential for clinical trials and clinical practice.NEW & NOTEWORTHY This work is the first study, to our knowledge, to utilize circulating cell-free mRNA sequencing to develop an NAFLD diagnostic classifier.

Single-cell mRNA profiling reveals changes in solute carrier expression and suggests a metabolic switch during zebrafish pronephros development

Developing organisms need to adapt to environmental variations as well as to rapid changes in substrate availability and energy demands imposed by fast-growing tissues and organs. Little is known about the adjustments that kidneys undergo in response to these challenges. We performed single-cell RNA sequencing of zebrafish pronephric duct cells to understand how the developing kidney responds to changes in filtered substrates and intrinsic energy requirements. We found high levels of glucose transporters early in development and increased expression of monocarboxylate transporters at later times. This indicates that the zebrafish embryonic kidney displays a high glucose transporting capacity during early development, which is replaced by the ability to absorb monocarboxylates and amino acids at later stages. This change in transport capacity was accompanied by the upregulation of mitochondrial carriers, indicating a switch to increased oxidative phosphorylation to meet the increasing energy demand of a developing kidney.NEW & NOTEWORTHY The zebrafish embryonic kidney has high levels of glucose transporters during early development, which are replaced by monocarboxylate and amino acid transporters later on. Inhibition of Na⁺-glucose cotransporter-dependent glucose transport by sotagliflozin also increased slc2a1a expression, supporting the idea that the glucose transport capacity is dynamically adjusted during zebrafish pronephros development. Concurrent upregulation of mitochondrial SCL25 transporters at later stages supports the idea that the pronephros adjusts to changing substrate supplies and/or energy demands during embryonic development.

Whole-Exome Sequencing and hiPSC Cardiomyocyte Models Identify MYRIP, TRAPPC11, and SLC27A6 of Potential Importance to Left Ventricular Hypertrophy in an African Ancestry Population

Background: Indices of left ventricular (LV) structure and geometry represent useful intermediate phenotypes related to LV hypertrophy (LVH), a predictor of cardiovascular (CV) disease (CVD) outcomes. Methods and Results: We conducted an exome-wide association study of LV mass (LVM) adjusted to height2.7, LV internal diastolic dimension (LVIDD), and relative wall thickness (RWT) among 1,364 participants of African ancestry (AAs) in the Hypertension Genetic Epidemiology Network (HyperGEN). Both single-variant and gene-based sequence kernel association tests were performed to examine whether common and rare coding variants contribute to variation in echocardiographic traits in AAs. We then used a data-driven procedure to prioritize and select genes for functional validation using a human induced pluripotent stem cell cardiomyocyte (hiPSC-CM) model. Three genes [myosin VIIA and Rab interacting protein (MYRIP), trafficking protein particle complex 11 (TRAPPC11), and solute carrier family 27 member 6 (SLC27A6)] were prioritized based on statistical significance, variant functional annotations, gene expression in the hiPSC-CM model, and prior biological evidence and were subsequently knocked down in the hiPSC-CM model. Expression profiling of hypertrophic gene markers in the knockdowns suggested a decrease in hypertrophic expression profiles. MYRIP knockdowns showed a significant decrease in atrial natriuretic factor (NPPA) and brain natriuretic peptide (NPPB) expression. Knockdowns of the heart long chain fatty acid (FA) transporter SLC27A6 resulted in downregulated caveolin 3 (CAV3) expression, which has been linked to hypertrophic phenotypes in animal models. Finally, TRAPPC11 knockdown was linked to deficient calcium handling. Conclusions: The three genes are biologically plausible candidates that provide new insight to hypertrophic pathways.

A Grad-seq View of RNA and Protein Complexes in Pseudomonas aeruginosa under Standard and Bacteriophage Predation Conditions

The Gram-negative rod-shaped bacterium Pseudomonas aeruginosa is not only a major cause of nosocomial infections but also serves as a model species of bacterial RNA biology. While its transcriptome architecture and posttranscriptional regulation through the RNA-binding proteins Hfq, RsmA, and RsmN have been studied in detail, global information about stable RNA-protein complexes in this human pathogen is currently lacking. Here, we implement gradient profiling by sequencing (Grad-seq) in exponentially growing P. aeruginosa cells to comprehensively predict RNA and protein complexes, based on glycerol gradient sedimentation profiles of >73% of all transcripts and ∼40% of all proteins. As to benchmarking, our global profiles readily reported complexes of stable RNAs of P. aeruginosa, including 6S RNA with RNA polymerase and associated product RNAs (pRNAs). We observe specific clusters of noncoding RNAs, which correlate with Hfq and RsmA/N, and provide a first hint that P. aeruginosa expresses a ProQ-like FinO domain-containing RNA-binding protein. To understand how biological stress may perturb cellular RNA/protein complexes, we performed Grad-seq after infection by the bacteriophage ΦKZ. This model phage, which has a well-defined transcription profile during host takeover, displayed efficient translational utilization of phage mRNAs and tRNAs, as evident from their increased cosedimentation with ribosomal subunits. Additionally, Grad-seq experimentally determines previously overlooked phage-encoded noncoding RNAs. Taken together, the Pseudomonas protein and RNA complex data provided here will pave the way to a better understanding of RNA-protein interactions during viral predation of the bacterial cell.

Mapping the origin and fate of myeloid cells in distinct compartments of the eye by single-cell profiling

Similar to the brain, the eye is considered an immune‐privileged organ where tissue‐resident macrophages provide the major immune cell constituents. However, little is known about spatially restricted macrophage subsets within different eye compartments with regard to their origin, function, and fate during health and disease. Here, we combined single‐cell analysis, fate mapping, parabiosis, and computational modeling to comprehensively examine myeloid subsets in distinct parts of the eye during homeostasis. This approach allowed us to identify myeloid subsets displaying diverse transcriptional states. During choroidal neovascularization, a typical hallmark of neovascular age‐related macular degeneration (AMD), we recognized disease‐specific macrophage subpopulations with distinct molecular signatures. Our results highlight the heterogeneity of myeloid subsets and their dynamics in the eye that provide new insights into the innate immune system in this organ which may offer new therapeutic targets for ophthalmological diseases.

Spatiotemporal dissection of the cell cycle with single-cell proteogenomics

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer^1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

Spatiotemporal sequence of mesoderm and endoderm lineage segregation during mouse gastrulation

Anterior mesoderm (AM) and definitive endoderm (DE) progenitors represent the earliest embryonic cell types that are specified during germ layer formation at the primitive streak (PS) of the mouse embryo. Genetic experiments indicate that both lineages segregate from Eomes-expressing progenitors in response to different Nodal signaling levels. However, the precise spatiotemporal pattern of the emergence of these cell types and molecular details of lineage segregation remain unexplored. We combined genetic fate labeling and imaging approaches with single-cell RNA sequencing (scRNA-seq) to follow the transcriptional identities and define lineage trajectories of Eomes-dependent cell types. Accordingly, all cells moving through the PS during the first day of gastrulation express Eomes AM and DE specification occurs before cells leave the PS from Eomes-positive progenitors in a distinct spatiotemporal pattern. ScRNA-seq analysis further suggested the immediate and complete separation of AM and DE lineages from Eomes-expressing cells as last common bipotential progenitor.

Memory-like HCV-specific CD8+ T cells retain a molecular scar after cure of chronic HCV infection

In chronic hepatitis C virus (HCV) infection, exhausted HCV-specific CD8⁺ T cells comprise memory-like and terminally exhausted subsets. However, little is known about the molecular profile and fate of these two subsets after the elimination of chronic antigen stimulation by direct-acting antiviral (DAA) therapy. Here, we report a progenitor-progeny relationship between memory-like and terminally exhausted HCV-specific CD8⁺ T cells via an intermediate subset. Single-cell transcriptomics implicated that memory-like cells are maintained and terminally exhausted cells are lost after DAA-mediated cure, resulting in a memory polarization of the overall HCV-specific CD8⁺ T cell response. However, an exhausted core signature of memory-like CD8⁺ T cells was still detectable, including, to a smaller extent, in HCV-specific CD8⁺ T cells targeting variant epitopes. These results identify a molecular signature of T cell exhaustion that is maintained as a chronic scar in HCV-specific CD8⁺ T cells even after the cessation of chronic antigen stimulation.