DiSiR: fast and robust method to identify ligand-receptor interactions at subunit level from single-cell RNA-sequencing data.

Most cell-cell interactions and crosstalks are mediated by ligand-receptor interactions. The advent of single-cell RNA-sequencing (scRNA-seq) techniques has enabled characterizing tissue heterogeneity at single-cell level. In the past few years, several methods have been developed to study ligand-receptor interactions at cell type level using scRNA-seq data. However, there is still no easy way to query the activity of a specific user-defined signaling pathway in a targeted way or to map the interactions of the same subunit with different ligands as part of different receptor complexes. Here, we present DiSiR, a fast and easy-to-use permutation-based software framework to investigate how individual cells are interacting with each other by analyzing signaling pathways of multi-subunit ligand-activated receptors from scRNA-seq data, not only for available curated databases of ligand-receptor interactions, but also for interactions that are not listed in these databases. We show that, when utilized to infer ligand-receptor interactions from both simulated and real datasets, DiSiR outperforms other well-known permutation-based methods, e.g. CellPhoneDB and ICELLNET. Finally, to demonstrate DiSiR's utility in exploring data and generating biologically relevant hypotheses, we apply it to COVID lung and rheumatoid arthritis (RA) synovium scRNA-seq datasets and highlight potential differences between inflammatory pathways at cell type level for control versus disease samples.

Expression and subcellular localization of USH1C/harmonin in human retina provides insights into pathomechanisms and therapy.

Usher syndrome (USH) is the most common form of hereditary deaf-blindness in humans. USH is a complex genetic disorder, assigned to three clinical subtypes differing in onset, course and severity, with USH1 being the most severe. Rodent USH1 models do not reflect the ocular phenotype observed in human patients to date; hence, little is known about the pathophysiology of USH1 in the human eye. One of the USH1 genes, USH1C, exhibits extensive alternative splicing and encodes numerous harmonin protein isoforms that function as scaffolds for organizing the USH interactome. RNA-seq analysis of human retinae uncovered harmonin_a1 as the most abundant transcript of USH1C. Bulk RNA-seq analysis and immunoblotting showed abundant expression of harmonin in Müller glia cells (MGCs) and retinal neurons. Furthermore, harmonin was localized in the terminal endfeet and apical microvilli of MGCs, presynaptic region (pedicle) of cones and outer segments (OS) of rods as well as at adhesive junctions between MGCs and photoreceptor cells (PRCs) in the outer limiting membrane (OLM). Our data provide evidence for the interaction of harmonin with OLM molecules in PRCs and MGCs and rhodopsin in PRCs. Subcellular expression and colocalization of harmonin correlate with the clinical phenotype observed in USH1C patients. We also demonstrate that primary cilia defects in USH1C patient-derived fibroblasts could be reverted by the delivery of harmonin_a1 transcript isoform. Our studies thus provide novel insights into PRC cell biology, USH1C pathophysiology and development of gene therapy treatment(s).

Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation.

MOTIVATION: Single-cell Chromatin ImmunoPrecipitation DNA-Sequencing (scChIP-seq) analysis is challenging due to data sparsity. High degree of sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from the ENCODE project to impute missing protein-DNA interacting regions of target histone marks or transcription factors. RESULTS: Imputations using machine learning models trained for each single cell, each ChIP protein target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene identification on real human data. Results on bulk data simulating single cells show that the imputations are single-cell specific as the imputed profiles are closer to the simulated cell than to other cells related to the same ChIP protein target and the same cell type. Simulations also show that 100 input genomic regions are already enough to train single-cell specific models for the imputation of thousands of undetected regions. Furthermore, SIMPA enables the interpretation of machine learning models by revealing interaction sites of a given single cell that are most important for the imputation model trained for a specific genomic region. The corresponding feature importance values derived from promoter-interaction profiles of H3K4me3, an activating histone mark, highly correlate with co-expression of genes that are present within the cell-type specific pathways in 2 real human and mouse datasets. The SIMPA's interpretable imputation method allows users to gain a deep understanding of individual cells and, consequently, of sparse scChIP-seq datasets. AVAILABILITY AND IMPLEMENTATION: Our interpretable imputation algorithm was implemented in Python and is available at https://github.com/salbrec/SIMPA.

Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data.

Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA sequencing (scRNA-seq) data. This need emerged from the expansion of scRNA-seq techniques, the increasing interest in antibody-based drug development and the importance of BCR repertoire changes in cancer and autoimmune disease progression. However, a comprehensive assessment of performance-influencing factors such as the sequencing depth, read length or number of somatic hypermutations (SHMs) as well as guidance regarding the choice of methodology is still lacking. In this work, we evaluated the ability of six available methods to reconstruct full-length BCRs using one simulated and three experimental SMART-seq datasets. In addition, we validated that the BCRs assembled in silico recognize their intended targets when expressed as monoclonal antibodies. We observed that methods such as BALDR, BASIC and BRACER showed the best overall performance across the tested datasets and conditions, whereas only BASIC demonstrated acceptable results on very short read libraries. Furthermore, the de novo assembly-based methods BRACER and BALDR were the most accurate in reconstructing BCRs harboring different degrees of SHMs in the variable domain, while TRUST4, MiXCR and BASIC were the fastest. Finally, we propose guidelines to select the best method based on the given data characteristics.

RNA Sequencing of Human Peripheral Blood Cells Indicates Upregulation of Immune-Related Genes in Huntington's Disease.

Huntington's disease (HD) is an autosomal dominantly inherited neurodegenerative disorder caused by a trinucleotide repeat expansion in the Huntingtin gene. As disease-modifying therapies for HD are being developed, peripheral blood cells may be used to indicate disease progression and to monitor treatment response. In order to investigate whether gene expression changes can be found in the blood of individuals with HD that distinguish them from healthy controls, we performed transcriptome analysis by next-generation sequencing (RNA-seq). We detected a gene expression signature consistent with dysregulation of immune-related functions and inflammatory response in peripheral blood from HD cases vs. controls, including induction of the interferon response genes, IFITM3, IFI6 and IRF7. Our results suggest that it is possible to detect gene expression changes in blood samples from individuals with HD, which may reflect the immune pathology associated with the disease.

Computational identification of cell-specific variable regions in ChIP-seq data.

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is used to identify genome-wide DNA regions bound by proteins. Given one ChIP-seq experiment with replicates, binding sites not observed in all the replicates will usually be interpreted as noise and discarded. However, the recent discovery of high-occupancy target (HOT) regions suggests that there are regions where binding of multiple transcription factors can be identified. To investigate ChIP-seq variability, we developed a reproducibility score and a method that identifies cell-specific variable regions in ChIP-seq data by integrating replicated ChIP-seq experiments for multiple protein targets on a particular cell type. Using our method, we found variable regions in human cell lines K562, GM12878, HepG2, MCF-7 and in mouse embryonic stem cells (mESCs). These variable-occupancy target regions (VOTs) are CG dinucleotide rich, and show enrichment at promoters and R-loops. They overlap significantly with HOT regions, but are not blacklisted regions producing non-specific binding ChIP-seq peaks. Furthermore, in mESCs, VOTs are conserved among placental species suggesting that they could have a function important for this taxon. Our method can be useful to point to such regions along the genome in a given cell type of interest, to improve the downstream interpretative analysis before follow-up experiments.

cis-regulatory variation modulates susceptibility to enteric infection in the Drosophila genetic reference panel.

BACKGROUND: Resistance to enteric pathogens is a complex trait at the crossroads of multiple biological processes. We have previously shown in the Drosophila Genetic Reference Panel (DGRP) that resistance to infection is highly heritable, but our understanding of how the effects of genetic variants affect different molecular mechanisms to determine gut immunocompetence is still limited. RESULTS: To address this, we perform a systems genetics analysis of the gut transcriptomes from 38 DGRP lines that were orally infected with Pseudomonas entomophila. We identify a large number of condition-specific, expression quantitative trait loci (local-eQTLs) with infection-specific ones located in regions enriched for FOX transcription factor motifs. By assessing the allelic imbalance in the transcriptomes of 19 F1 hybrid lines from a large round robin design, we independently attribute a robust cis-regulatory effect to only 10% of these detected local-eQTLs. However, additional analyses indicate that many local-eQTLs may act in trans instead. Comparison of the transcriptomes of DGRP lines that were either susceptible or resistant to Pseudomonas entomophila infection reveals nutcracker as the only differentially expressed gene. Interestingly, we find that nutcracker is linked to infection-specific eQTLs that correlate with its expression level and to enteric infection susceptibility. Further regulatory analysis reveals one particular eQTL that significantly decreases the binding affinity for the repressor Broad, driving differential allele-specific nutcracker expression. CONCLUSIONS: Our collective findings point to a large number of infection-specific cis- and trans-acting eQTLs in the DGRP, including one common non-coding variant that lowers enteric infection susceptibility.

Enteric infection induces Lark-mediated intron retention at the 5' end of Drosophila genes.

BACKGROUND: RNA splicing is a key post-transcriptional mechanism that generates protein diversity and contributes to the fine-tuning of gene expression, which may facilitate adaptation to environmental challenges. Here, we employ a systems approach to study alternative splicing changes upon enteric infection in females from classical Drosophila melanogaster strains as well as 38 inbred lines. RESULTS: We find that infection leads to extensive differences in isoform ratios, which results in a more diverse transcriptome with longer 5' untranslated regions (5'UTRs). We establish a role for genetic variation in mediating inter-individual splicing differences, with local splicing quantitative trait loci (local-sQTLs) being preferentially located at the 5' end of transcripts and directly upstream of splice donor sites. Moreover, local-sQTLs are more numerous in the infected state, indicating that acute stress unmasks a substantial number of silent genetic variants. We observe a general increase in intron retention concentrated at the 5' end of transcripts across multiple strains, whose prevalence scales with the degree of pathogen virulence. The length, GC content, and RNA polymerase II occupancy of these introns with increased retention suggest that they have exon-like characteristics. We further uncover that retained intron sequences are enriched for the Lark/RBM4 RNA binding motif. Interestingly, we find that lark is induced by infection in wild-type flies, its overexpression and knockdown alter survival, and tissue-specific overexpression mimics infection-induced intron retention. CONCLUSION: Our collective findings point to pervasive and consistent RNA splicing changes, partly mediated by Lark/RBM4, as being an important aspect of the gut response to infection.

Assessment of computational methods for the analysis of single-cell ATAC-seq data.

BACKGROUND: Recent innovations in single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enable profiling of the epigenetic landscape of thousands of individual cells. scATAC-seq data analysis presents unique methodological challenges. scATAC-seq experiments sample DNA, which, due to low copy numbers (diploid in humans), lead to inherent data sparsity (1-10% of peaks detected per cell) compared to transcriptomic (scRNA-seq) data (10-45% of expressed genes detected per cell). Such challenges in data generation emphasize the need for informative features to assess cell heterogeneity at the chromatin level. RESULTS: We present a benchmarking framework that is applied to 10 computational methods for scATAC-seq on 13 synthetic and real datasets from different assays, profiling cell types from diverse tissues and organisms. Methods for processing and featurizing scATAC-seq data were compared by their ability to discriminate cell types when combined with common unsupervised clustering approaches. We rank evaluated methods and discuss computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the effects of sequencing coverage and noise, and clustering performance. Running times and memory requirements are also discussed. CONCLUSIONS: This reference summary of scATAC-seq methods offers recommendations for best practices with consideration for both the non-expert user and the methods developer. Despite variation across methods and datasets, SnapATAC, Cusanovich2018, and cisTopic outperform other methods in separating cell populations of different coverages and noise levels in both synthetic and real datasets. Notably, SnapATAC is the only method able to analyze a large dataset (> 80,000 cells).

GADD45 promotes locus-specific DNA demethylation and 2C cycling in embryonic stem cells.

Mouse embryonic stem cell (ESC) cultures contain a rare cell population of "2C-like" cells resembling two-cell embryos, the key stage of zygotic genome activation (ZGA). Little is known about positive regulators of the 2C-like state and two-cell stage embryos. Here we show that GADD45 (growth arrest and DNA damage 45) proteins, regulators of TET (TET methylcytosine dioxygenase)-mediated DNA demethylation, promote both states. Methylome analysis of Gadd45a,b,g triple-knockout (TKO) ESCs reveal locus-specific DNA hypermethylation of ∼7000 sites, which are enriched for enhancers and loci undergoing TET-TDG (thymine DNA glycosylase)-mediated demethylation. Gene expression is misregulated in TKOs, notably upon differentiation, and displays signatures of DNMT (DNA methyltransferase) and TET targets. TKOs manifest impaired transition into the 2C-like state and exhibit DNA hypermethylation and down-regulation of 2C-like state-specific genes. Gadd45a,b double-mutant mouse embryos display embryonic sublethality, deregulated ZGA gene expression, and developmental arrest. Our study reveals an unexpected role of GADD45 proteins in embryonic two-cell stage regulation.