Background Single-cell transcriptomics is rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification. Results Here, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified cells, and computation time. We further evaluate the methods’ sensitivity to the input features, number of cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments. Conclusions We present a comprehensive evaluation of automatic cell identification methods for single-cell RNA sequencing data. All the code used for the evaluation is available on GitHub (https://github.com/tabdelaal/scRNAseq_Benchmark). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.

SUMMARY Pluripotent stem cell (PSC)-derived organoids have emerged as novel multicellular models of human tissue development but display immature phenotypes, aberrant tissue fates, and a limited subset of cells. Here, we demonstrate that integrated analysis and engineering of gene regulatory networks (GRNs) in PSC-derived multilineage human liver organoids direct maturation and vascular morphogenesis in vitro . Overexpression of PROX1 and ATF5 , combined with targeted CRISPR-based transcriptional activation of endogenous CYP3A4 , reprograms tissue GRNs and improves native liver functions, such as FXR signaling, CYP3A4 enzymatic activity, and stromal cell reactivity. The engineered tissues possess superior liver identity when compared with other PSC-derived liver organoids and show the presence of hepatocyte, biliary, endothelial, and stellate-like cell populations in single-cell RNA-seq analysis. Finally, they show hepatic functions when studied in vivo . Collectively, our approach provides an experimental framework to direct organogenesis in vitro by systematically probing molecular pathways and transcriptional networks that promote tissue development.

Background - The blood metabolome incorporates cues from the environment as well as the host's genetic background, potentially offering a holistic view of an individual's health status. Methods - We have compiled a vast resource of 1 H-NMR metabolomics and phenotypic data encompassing over 25,000 samples derived from 26 community and hospital-based cohorts. Results - Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared to one's chronological age, confers an increased risk on future cardiovascular disease, mortality and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data. Conclusions - In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardio-metabolic health.

Mycosis fungoides (MF) is the most common cutaneous T‐cell lymphoma (CTCL). Causative genetic alterations in MF are unknown. The low recurrence of pathogenic small‐scale mutations (ie, nucleotide substitutions, indels) in the disease, calls for the study of additional aspects of MF genetics. Here, we investigated structural genomic alterations in tumor‐stage MF by integrating whole‐genome sequencing and RNA‐sequencing. Multiple genes with roles in cell physiology (n = 113) and metabolism (n = 92) were found to be impacted by genomic rearrangements, including 47 genes currently implicated in cancer. Fusion transcripts involving genes of interest such as DOT1L, KDM6A, LIFR, TP53, and TP63 were also observed. Additionally, we identified recurrent deletions of genes involved in cell cycle control, chromatin regulation, the JAK‐STAT pathway, and the PI‐3‐K pathway. Remarkably, many of these deletions result from genomic rearrangements. Deletion of tumor suppressors HNRNPK and SOCS1 were the most frequent genetic alterations in MF after deletion of CDKN2A. Notably, SOCS1 deletion could be detected in early‐stage MF. In agreement with the observed genomic alterations, transcriptome analysis revealed up‐regulation of the cell cycle, JAK‐STAT, PI‐3‐K and developmental pathways. Our results position inactivation of HNRNPK and SOCS1 as potential driver events in MF development.

Objective To identify robustly differentially expressed long noncoding RNAs (lncRNAs) with osteoarthritis (OA) pathophysiology in cartilage and to explore potential target messenger RNA (mRNA) by establishing coexpression networks, followed by functional validation. Methods RNA sequencing was performed on macroscopically lesioned and preserved OA cartilage from patients who underwent joint replacement surgery due to OA (n = 98). Differential expression analysis was performed on lncRNAs that were annotated in GENCODE and Ensembl databases. To identify potential interactions, correlations were calculated between the identified differentially expressed lncRNAs and the previously reported differentially expressed protein‐coding genes in the same samples. Modulation of chondrocyte lncRNA expression was achieved using locked nucleic acid GapmeRs. Results By applying our in‐house pipeline, we identified 5,053 lncRNAs that were robustly expressed, of which 191 were significantly differentially expressed (according to false discovery rate) between lesioned and preserved OA cartilage. Upon integrating mRNA sequencing data, we showed that intergenic and antisense differentially expressed lncRNAs demonstrate high, positive correlations with their respective flanking sense genes. To functionally validate this observation, we selected P3H2‐AS1, which was down‐regulated in primary chondrocytes, resulting in the down‐regulation of P3H2 gene expression levels. As such, we can confirm that P3H2‐AS1 regulates its sense gene P3H2. Conclusion By applying an improved detection strategy, robustly differentially expressed lncRNAs in OA cartilage were detected. Integration of these lncRNAs with differential mRNA expression levels in the same samples provided insight into their regulatory networks. Our data indicates that intergenic and antisense lncRNAs play an important role in regulating the pathophysiology of OA.