Compensatory growth and recovery of cartilage cytoarchitecture after transient cell death in fetal mouse limbs.

A major question in developmental and regenerative biology is how organ size and architecture are controlled by progenitor cells. While limb bones exhibit catch-up growth (recovery of a normal growth trajectory after transient developmental perturbation), it is unclear how this emerges from the behaviour of chondroprogenitors, the cells sustaining the cartilage anlagen that are progressively replaced by bone. Here we show that transient sparse cell death in the mouse fetal cartilage is repaired postnatally, via a two-step process. During injury, progression of chondroprogenitors towards more differentiated states is delayed, leading to altered cartilage cytoarchitecture and impaired bone growth. Then, once cell death is over, chondroprogenitor differentiation is accelerated and cartilage structure recovered, including partial rescue of bone growth. At the molecular level, ectopic activation of mTORC1 correlates with, and is necessary for, part of the recovery, revealing a specific candidate to be explored during normal growth and in future therapies.

Stromal Niche Signals That Orchestrate Intestinal Regeneration.

Stromal cell populations have a central role in providing signals that support the maintenance, differentiation, and function of the intestinal epithelium. The behavior and fate of epithelial cells is directed by the spatial organization of stromal cells that either sustain stem and progenitor cell identity or drive differentiation. A combination of single-cell analyses, mouse models, and organoid coculture assays have provided insight into the diversity of signals delivered by stromal cells. Signaling gradients are established and fine-tuned by the expression of signaling agonists and antagonists along the crypt-villus axis. On epithelial injury, there are disruptions to the abundance and organization of stromal populations. There are also distinct changes in the signals originating from these cells that impact remodeling of the epithelium. How these signals coordinate to mediate epithelial repair or sustain tissue injury in inflammatory bowel diseases is beginning to emerge. Understanding of these processes may lead to opportunities to target stromal cell populations as a strategy to modify disease states.

scPipe: an extended preprocessing pipeline for comprehensive single-cell ATAC-Seq data integration in R/Bioconductor.

scPipe is a flexible R/Bioconductor package originally developed to analyse platform-independent single-cell RNA-Seq data. To expand its preprocessing capability to accommodate new single-cell technologies, we further developed scPipe to handle single-cell ATAC-Seq and multi-modal (RNA-Seq and ATAC-Seq) data. After executing multiple data cleaning steps to remove duplicated reads, low abundance features and cells of poor quality, a SingleCellExperiment object is created that contains a sparse count matrix with features of interest in the rows and cells in the columns. Quality control information (e.g. counts per cell, features per cell, total number of fragments, fraction of fragments per peak) and any relevant feature annotations are stored as metadata. We demonstrate that scPipe can efficiently identify 'true' cells and provides flexibility for the user to fine-tune the quality control thresholds using various feature and cell-based metrics collected during data preprocessing. Researchers can then take advantage of various downstream single-cell tools available in Bioconductor for further analysis of scATAC-Seq data such as dimensionality reduction, clustering, motif enrichment, differential accessibility and cis-regulatory network analysis. The scPipe package enables a complete beginning-to-end pipeline for single-cell ATAC-Seq and RNA-Seq data analysis in R.

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures.

The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms. Alongside the ground-truth available via the sequins, we created in silico mixture samples to allow performance assessment in the absence of true positives or true negatives. Our results show that StringTie2 and bambu outperformed other tools from the six isoform detection tools tested, DESeq2, edgeR and limma-voom were best among the five differential transcript expression tools tested and there was no clear front-runner for performing differential transcript usage analysis between the five tools compared, which suggests further methods development is needed for this application.

Arginine-rich C9ORF72 ALS proteins stall ribosomes in a manner distinct from a canonical ribosome-associated quality control substrate.

Hexanucleotide expansion mutations in C9ORF72 are a frequent cause of amyotrophic lateral sclerosis. We previously reported that long arginine-rich dipeptide repeats (DPRs), mimicking abnormal proteins expressed from the hexanucleotide expansion, caused translation stalling when expressed in cell culture models. Whether this stalling provides a mechanism of pathogenicity remains to be determined. Here, we explored the molecular features of DPR-induced stalling and examined whether known mechanisms such as ribosome quality control (RQC) regulate translation elongation on sequences that encode arginine-rich DPRs. We demonstrate that arginine-rich DPRs lead to stalling in a length-dependent manner, with lengths longer than 40 repeats invoking severe translation arrest. Mutational screening of 40×Gly-Xxx DPRs shows that stalling is most pronounced when Xxx is a charged amino acid (Arg, Lys, Glu, or Asp). Through a genome-wide knockout screen, we find that genes regulating stalling on polyadenosine mRNA coding for poly-Lys, a canonical RQC substrate, act differently in the case of arginine-rich DPRs. Indeed, these findings point to a limited scope for natural regulatory responses to resolve the arginine-rich DPR stalls, even though the stalls may be sensed, as evidenced by an upregulation of RQC gene expression. These findings therefore implicate arginine-rich DPR-mediated stalled ribosomes as a source of stress and toxicity and may be a crucial component in pathomechanisms.

A specialized tyrosine-based endocytosis signal in MR1 controls antigen presentation to MAIT cells.

MR1 is a highly conserved microbial immune-detection system in mammals. It captures vitamin B-related metabolite antigens from diverse microbes and presents them at the cell surface to stimulate MR1-restricted lymphocytes including mucosal-associated invariant T (MAIT) cells. MR1 presentation and MAIT cell recognition mediate homeostasis through host defense and tissue repair. The cellular mechanisms regulating MR1 cell surface expression are critical to its function and MAIT cell recognition, yet they are poorly defined. Here, we report that human MR1 is equipped with a tyrosine-based motif in its cytoplasmic domain that mediates low affinity binding with the endocytic adaptor protein 2 (AP2) complex. This interaction controls the kinetics of MR1 internalization from the cell surface and minimizes recycling. We propose MR1 uses AP2 endocytosis to define the duration of antigen presentation to MAIT cells and the detection of a microbial metabolic signature by the immune system.

Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing.

A modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.

long-read-tools.org: an interactive catalogue of analysis methods for long-read sequencing data.

BACKGROUND: The data produced by long-read third-generation sequencers have unique characteristics compared to short-read sequencing data, often requiring tailored analysis tools for tasks ranging from quality control to downstream processing. The rapid growth in software that addresses these challenges for different genomics applications is difficult to keep track of, which makes it hard for users to choose the most appropriate tool for their analysis goal and for developers to identify areas of need and existing solutions to benchmark against. FINDINGS: We describe the implementation of long-read-tools.org, an open-source database that organizes the rapidly expanding collection of long-read data analysis tools and allows its exploration through interactive browsing and filtering. The current database release contains 478 tools across 32 categories. Most tools are developed in Python, and the most frequent analysis tasks include base calling, de novo assembly, error correction, quality checking/filtering, and isoform detection, while long-read single-cell data analysis and transcriptomics are areas with the fewest tools available. CONCLUSION: Continued growth in the application of long-read sequencing in genomics research positions the long-read-tools.org database as an essential resource that allows researchers to keep abreast of both established and emerging software to help guide the selection of the most relevant tool for their analysis needs.

Opportunities and challenges in long-read sequencing data analysis.

Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.