Biology subjects

Yu, Y.

Publications and source records attributed to Yu, Y..

At least 379 records · Page 21Linked to original sources

bioRxiv2022

Reprogrammed Schwann cells organize into dynamic tracks that promote pancreatic cancer invasion

Nerves are a component of the tumor microenvironment contributing to cancer progression, but the role of cells from nerves in facilitating cancer invasion remains poorly understood. Here we show that Schwann cells (SCs) activated by cancer cells collectively function as Tumor Activated Schwann cell Tracks (TASTs) that promote cancer cell migration and invasion. Non-myelinating SCs form TASTs and have cell gene expression signatures that correlate with diminished survival in patients with pancreatic ductal adenocarcinoma. In TASTs, dynamic SCs form tracks that serve as cancer pathways and apply forces on cancer cells to enhance cancer motility. These SCs are activated by c-Jun, analogous to their reprogramming during nerve repair. This study reveals a mechanism of cancer cell invasion that co-opts a wound repair process and exploits the ability of SCs to collectively organize into tracks. These findings establish a novel paradigm of how cancer cells spread and reveal therapeutic opportunities. SIGNIFICANCEHow the tumor microenvironment participates in pancreatic cancer progression is not fully understood. Here, we show that Schwann cells are activated by cancer cells and collectively organize into tracks that dynamically enable cancer invasion in a c-Jun dependent manner.

cancer biology↗

bioRxiv2022

Arbidol inhibits esophageal squamous cell carcinoma growth in vitro and in vivo through suppressing ataxia telangiectasia and Rad3-related protein kinase

BackgroundEsophageal cancer has a global impact on human health due to its high incidence and mortality. Therefore, there is an urgent need to develop new drugs to treat or prevent the prominent pathological subtype of esophageal cancer, esophageal squamous cell carcinoma. Based upon a screening of drugs approved by the Food and Drug Administration, we discovered that Arbidol could effectively inhibit the proliferation of esophageal squamous cell carcinoma in vitro and in vivo. MethodWe first conducted a series of cell-based assays and found that Arbidol treatment inhibited the proliferation and colony formation ability of ESCC cells and promoted G1 phase cell cycle arrest. Phospho-proteomics experiments and in vitro kinase assays were subsequently performed using cell lysates derived from Arbidol-treated cells in order to identify the underlying growth inhibitory mechanism. Finally, a PDX model was used to verify the inhibitory effect of Arbidol in vivo. FindingArbidol inhibits proliferation of ESCC in vitro and in vivo through the DNA replication pathway and is associated with the cell cycle. InterpretationArbidol is a potential ATR inhibitor via binding to ATR kinase to reduce the phosphorylation and activation of MCM2 at Ser108. FundingThis research was supported by the National Natural Science Foundation of China (grant number 81872335), the National Natural Science Youth Foundation (grant number 81902486), and the Natural Science Foundation of Henan (grant number 161100510300).

cancer biology↗

bioRxiv2022

Coupling comprehensive pesticide-wide association study to iPSC dopaminergic screening identifies and classifies Parkinson-relevant pesticides

Parkinsons disease (PD) is a complex, multi-factorial neurodegenerative disease, known to involve genetic, aging-related components, but also to be highly sensitive to environmental factors. In particular, ample evidence links pesticides to PD etiology. Here, establishing a field-to-bench paradigm, we have combined record-based exposure assessment in a population-based epidemiologic study of PD with testing in dopaminergic neurons produced from iPSCs to further identify and classify PD-relevant pesticides. First, agricultural pesticide-application records in California enabled us to investigate exposure to nearly 300 specific pesticides and PD risk in a comprehensive, pesticide-wide association study (PWAS). We implicated long-term exposure to 53 pesticide active ingredients in PD risk and identified their relevant co-exposure profiles. Second, to identify which of these pesticides might contribute to PD through direct effects on dopaminergic neurons, we employed a live-cell imaging screening paradigm in which neurons, definitively identified with a tyrosine hydroxylase reporter, were exposed to 43 of the high-risk pesticides. Using detailed morphometric measures, we found 10 pesticides were directly toxic to these neurons. Further, we analyzed pesticides typically used in combinations in cotton farming. Among these "cotton cluster" pesticides, co-exposures resulted in markedly greater toxicity than any single pesticide. Trifluralin was a pivotal driver of toxicity to dopaminergic neurons and led to marked mitochondrial dysfunction. Our field-to-bench paradigm may prove useful to mechanistically dissect pesticide exposure implicated in PD risk, and guide agricultural policy in the future.

neuroscience↗

bioRxiv2022

Antiviral DExD/H-box helicase 60 selectively inhibits translation from type II internal ribosome entry sites

During viral infection, competition ensues between viruses and their host cells to control the protein synthesis machinery. In response, certain host defense proteins globally limit mRNA translation. However, this is also detrimental for host protein synthesis. Here we describe an interferon-stimulated helicase, DDX60, that specifically inhibits translation from type II viral internal ribosome entry sites (IRESs). IRESs are RNA structures that enable mRNAs to recruit ribosomes directly, bypassing translation initiation using a 5 cap. DDX60 was previously observed to inhibit replication of a reporter hepatitis C virus (HCV). We show that DDX60 likely does not inhibit HCV replication, but surprisingly, inhibits the type II IRES used in the reporter HCV genomic RNA. Using firefly luciferase mRNAs translationally driven by different viral IRESs or a 5 cap analog, we show that DDX60 selectively reduces translation driven by type II IRESs of encephalomyocarditis virus (EMCV) and foot and mouth disease virus (FMDV), but not other IRES types or a 5 cap analog. Correspondingly, DDX60 reduces EMCV and FMDV (type II IRES) replication, but not poliovirus or bovine enterovirus 1 (type I IRES) replication. Furthermore, replacing the IRES of poliovirus with a type II IRES is sufficient for DDX60 to inhibit poliovirus replication. Finally, we demonstrate that DDX60 specifically reduces polysome binding on type II IRES mRNA, but not 5 cap-dependent mRNA. Our data demonstrate that the cellular defense factor DDX60 counteracts viral takeover of host translation by blocking ribosome access to type II IRES elements specifically.

microbiology↗

bioRxiv2022

PPRD: a comprehensive online database for expression analysis of ~45,000 plant public RNA-Seq libraries

High-throughput RNA-sequencing (RNA-seq) has become the most popular technology for profiling gene expression in the last decade due to its low cost and high coverage. As a result, the number of RNA-seq libraries from the plant community has been increasing exponentially in recent years. For major crops, such as maize, rice, soybean, wheat, and cotton, the plant community has collected a total of ~45,000 libraries by 2021. To take full advantage of the bigdata of RNA-seq libraries, an effort to integrate all publicly available libraries via a uniformed processing pipeline and curate them into an easy-to-use searchable database is urgently needed. To address this challenge, here we present a comprehensive web-based platform, Plant Public RNA-seq Database (PPRD, http://ipf.sustech.edu.cn/pub/plantrna/). PPRD consists of a large number of RNA-seq libraries of maize (11,726), rice (19,664), soybean (4,085), wheat (5,816), and cotton (3,483) from Gene Expression Omnibus (GEO), Sequence Read Archive (SRA), European Nucleotide Archive (ENA), and DNA Data Bank of Japan (DDBJ) databases.

plant biology↗

bioRxiv2022

BOLD cofluctuation 'events' are predicted from static functional connectivity

Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal. Using a combination of real and simulated data, we examined the relationship between cofluctuation and network structure and asked if this relationship was unique, or if it could arise from sampling variability alone. First, we show that events are not discrete - there is a gradually increasing relationship between network structure and cofluctuation; [~]50% of samples show very strong network structure. Second, using simulations we show that this relationship is predicted from sampling variability on static FC. Finally, we show that randomly selected points can capture network structure about as well as events, largely because of their temporal spacing. Together, these results suggest that, while events exhibit particularly strong representations of static FC, there is little evidence that events are unique timepoints that drive FC structure. Instead, a parsimonious explanation for the data is that events arise from a single static, but noisy, FC structure. HIGHLIGHTSO_LIPast results suggested high cofluctuation BOLD "events" drive fMRI functional connectivity, FC C_LIO_LIHere, events were examined in both real fMRI data and a stationary null model to test this model C_LIO_LIIn real data, >50% of BOLD timepoints show high modularity and similarity to time- averaged FC C_LIO_LIStationary null models identified events with similar behavior to real data C_LIO_LIEvents may not be a transient driver of static FC, but rather an expected outcome of it. C_LI

neuroscience↗

bioRxiv2022

An atlas of plant full-length RNA reveals tissue-specific and evolutionarily-conserved regulation of poly(A) tail length

Introductory paragraphPoly(A) tail is a hallmark of eukaryotic mRNA and its length plays an essential role in regulating mRNA metabolism1-3. However, a comprehensive resource for plant poly(A) tail length has yet to be established. Here, we applied a poly(A)-enrichment-free, Nanopore-based method4,5 to profile full-length RNA with poly(A) tail information in plants. Our atlas contains over 120 million polyadenylated mRNA molecules from seven different tissues of Arabidopsis, as well as the shoot tissue of maize, soybean and rice. In most tissues, the size of plant poly(A) tails shows peaks at approximately 20 and 45 nt, presumably the sizes protected by one and two poly(A) binding proteins (PABP), respectively2,6, while the poly(A) tails in pollen exhibit a distinct pattern with strong peaks centered at 55 and 80 nt. Moreover, poly(A) tail length is regulated in a gene-specific manner -- mRNAs with short half-lives in general have long poly(A) tails, while mRNAs with long half-lives are featured with relatively short poly(A) tails that peak at ~45 nt, suggesting that protection of poly(A) tail in this size by PABP is essential for mRNA stability. Across species, poly(A) tails in the nucleus are almost twice as long as in the cytoplasm, implying a conserved rapid shortening process of poly(A) tail occurs before the mRNA is stabilized in cytoplasm. Our comprehensive dataset lays the groundwork for future functional and evolutionary studies on poly(A) tail length regulation in plants.

molecular biology↗

bioRxiv2022

Therapeutic Interruption of T Cell Development Generates High-Affinity T Cells That Escape Exhaustion and Improve Cancer Immunotherapy

Availability of effective anti-tumor T cells is limited by cancer immunoediting, which depletes neoantigens, and central tolerance, which eliminates developing T cells with high-affinity T cell receptors (TCRs) against tumor self-antigens. Remaining tumor-reactive T cells are often exhausted after immune checkpoint blockade (ICB). Whether endogenous T cells with high- affinity TCRs against tumor self-antigens can be generated to circumvent exhaustion and reject neoantigen-poor tumors is unclear. We show that transiently interrupting central tolerance through RANKL blockade unleashes T cells possessing TCRs with self-reactive features that enable ICB to reject poorly immunogenic tumors. Upon recognition of tumor self-antigens, these T cells exhibit enhanced TCR signaling, enrichment in NFAT/AP-1 genes, and lymph node priming. Consequently, memory-precursor T cells against tumor self-antigens are generated, avoid exhaustion, and become effector-memory cells with transcriptional features associated with clinical ICB response. Thus, interrupting central tolerance provides T cells with tumor- directed autoreactivity that avoid exhaustion and improve immunotherapy.

immunology↗

bioRxiv2022

Pore-C Simultaneously Captures Genome-wide Multi-way Chromatin Interaction and Associated DNA Methylation Status in Arabidopsis

In the past decade, genome-wide characterization of the three-dimensional chromatin structure in plants using high-throughput methods has greatly advanced our knowledge in plant genome architecture (Liu and Weigel, 2015; Ouyang et al., 2020). However, due to the limitation of Illumina short-read sequencing, the genome-wide contact map obtained by Hi-C/ChIA-PET is pairwise, and the multi-way interaction can only be inferred from the two-way data. To directly capture multi-way interaction in Arabidopsis, we applied a long-read-based method called Pore-C that directly sequences the DNA fragments joined by proximity-based ligation (Ulahannan et al., 2019).

molecular biology↗

bioRxiv2022

MT-MAG: Accurate and interpretable machine learning based taxonomic assignment of metagenome-assembled genomes, with a partial classification option

We propose MT-MAG, a novel machine learning-based software tool for the complete or partial hierarchically-structured taxonomic classification of metagenome-assembled genomes (MAGs). MT-MAG is alignment-free, with k-mer frequencies being the only feature used to distinguish a DNA sequence from another (herein k = 7). MT-MAG is capable of classifying large and diverse metagenomic datasets: a total of 245.68 Gbp in the training sets, and 9.6 Gbp in the test sets analyzed in this study. In addition to complete classifications, MT-MAG offers a "partial classification" option, whereby a classification at a higher taxonomic level is provided for MAGs that cannot be classified to the Species level. MT-MAG outputs complete or partial classification paths, and interpretable numerical classification confidences of its classifications, at all taxonomic ranks. To assess the performance of MT-MAG, we define a "weighted classification accuracy," with a weighting scheme reflecting the fact that partial classifications at different ranks are not equally informative. For the two benchmarking datasets analyzed (genomes from human gut microbiome species, and bacterial and archaeal genomes assembled from cow rumen metagenomic sequences), MT-MAG achieves an average of 87.32% in weighted classification accuracy. At the Species level, MT-MAG outperforms DeepMicrobes, the only other comparable software tool, by an average of 34.79% in weighted classification accuracy. In addition, MT-MAG is able to completely classify an average of 67.70% of the sequences at the Species level, compared with DeepMicrobes which only classifies 47.45%. Moreover, MT-MAG provides additional information for sequences that it could not classify at the Species level, resulting in the partial or complete classification of 95.13%, of the genomes in the datasets analyzed. Lastly, unlike other taxonomic assignment tools (e.g., GDTB-Tk), MT-MAG is an alignment-free and genetic marker-free tool, able to provide additional bioinformatics analysis to confirm existing or tentative taxonomic assignments.

bioinformatics↗

bioRxiv2022

GR13-type plasmids in Acinetobacter potentiate the accumulation and horizontal transfer of diverse accessory genes

Carbapenem resistance and other antibiotic resistance genes (ARGs) can be found in plasmids in Acinetobacter, but many plasmid types in this genus have not been well-characterised. Here we describe the distribution, diversity and evolutionary capacity of rep group 13 (GR13) plasmids that are found in Acinetobacter species from diverse environments. Our investigation was prompted by the discovery of two GR13 plasmids in A. baumannii isolated in an intensive care unit (ICU). The plasmids harbour distinct accessory genes: pDETAB5 contains blaNDM-1 and genes that confer resistance to four further antibiotic classes, while pDETAB13 carries putative alcohol tolerance determinants. Both plasmids contain multiple dif modules, which are flanked by pdif sites recognised by XerC/XerD tyrosine recombinases. The ARG-containing dif modules in pDETAB5 are almost identical to those found in pDETAB2, a GR34 plasmid from an unrelated A. baumannii isolated in the same ICU a month prior. Examination of a further 41 complete, publicly available plasmid sequences revealed that the GR13 pangenome consists of just four core but 1086 accessory genes, 123 in the shell and 1063 in the cloud, reflecting substantial capacity for diversification. The GR13 core genome includes genes for replication and partitioning, and for a putative tyrosine recombinase. Accessory segments encode proteins with diverse putative functions, including for metabolism, antibiotic/heavy metal/alcohol tolerance, restriction-modification, an anti-phage system and multiple toxin-antitoxin systems. The movement of dif modules and actions of insertion sequences play an important role in generating diversity in GR13 plasmids. Discrete GR13 plasmid lineages are internationally disseminated and found in multiple Acinetobacter species, which suggests they are important platforms for the accumulation, horizontal transmission and persistence of accessory genes in this genus. Impact statementAcinetobacter species are particularly well-adapted for persistence in hospital environments where they pose a life-threatening infection risk to the most clinically-vulnerable patients. Plasmids with the potential to transfer multiple antibiotic resistance determinants between Acinetobacter species are therefore concerning, but most are not well-characterised. This work sheds further light on the poorly-understood mobile gene pool associated with Acinetobacter. We show here that GR13 plasmids carry a small set of core genes but have access to a highly diverse set of accessory segments that might provide fitness advantages under certain conditions. The complex evolutionary dynamics of GR13 plasmids appear to be driven by the exchange of dif modules and by the actions of a diverse population of insertion sequences. The novel dif modules characterised here emphasise the broader importance of these elements to the dissemination of accessory genes in Acinetobacter. This study has improved our understanding of the diversity and distribution of dif modules, plasmids that carry them, and how both disseminate in the continuum of Acinetobacter populations that link hospitals and the wider environment.

microbiology↗

bioRxiv2022

SLC38A2 provides proline to fulfil unique synthetic demands arising during osteoblast differentiation and bone formation.

Cellular differentiation is associated with the acquisition of a unique protein signature which is essential to attain the ultimate cellular function and activity of the differentiated cell. This is predicted to result in unique biosynthetic demands that arise during differentiation. Using a bioinformatic approach, we discovered osteoblast differentiation is associated with increased demand for the amino acid proline. When compared to other differentiated cells, osteoblast-associated proteins including RUNX2, OSX, OCN and COL1A1 are significantly enriched in proline. Using a genetic and metabolomic approach, we demonstrate that the neutral amino acid transporter SLC38A2 acts cell autonomously to provide proline to facilitate the efficient synthesis of proline-rich osteoblast proteins. Genetic ablation of SLC38A2 in osteoblasts limits both osteoblast differentiation and bone formation in mice. Mechanistically, proline is primarily incorporated into nascent protein with little metabolism observed. Collectively, these data highlight a requirement for proline in fulfilling the unique biosynthetic requirements that arise during osteoblast differentiation and bone formation.

developmental biology↗

bioRxiv2021

Structural insights into the ion selectivity of the MgtE channel for Mg2+ over Ca2+

MgtE is a Mg2+-selective ion channel whose orthologs are widely distributed from prokaryotes to eukaryotes, including humans, and play an important role in the maintenance of cellular Mg2+ homeostasis. Previous functional analyses showed that MgtE transports divalent cations with high selectivity for Mg2+ over Ca2+. Whereas the high-resolution structure determination of the MgtE transmembrane (TM) domain in complex with Mg2+ ions revealed a Mg2+ recognition mechanism of MgtE, the previous Ca2+-bound structure of the MgtE TM domain was determined only at moderate resolution (3.2 [A] resolution), which was insufficient to visualize the water molecules coordinated to Ca2+ ions. Thus, the structural basis of the ion selectivity of MgtE for Mg2+ over Ca2+ has remained unclear. Here, we showed that the metal-binding site of the MgtE TM domain binds to Mg2+ [~]500-fold more strongly than Ca2+. We then determined the crystal structure of the MgtE TM domain in complex with Ca2+ ions at a higher resolution (2.5 [A] resolution), allowing us to reveal hexahydrated Ca2+, which is similarly observed in the previously determined Mg2+-bound structure but with extended metal-oxygen bond lengths. Our structural, biochemical, and computational analyses provide mechanistic insights into the ion selectivity of MgtE for Mg2+ over Ca2+.

biophysics↗

bioRxiv2021

Influence of sequencing depth on the fidelity and sensitivity of 1%-5% low-frequency mutation detection and recommendation for standardization of sequencing depth

Sequencing depth has always played an important role in the accurate detection of low-frequency mutations. The increase of sequencing depth and the reasonable setting of threshold can maximize the probability of true positive mutation, or sensitivity. Here, we found that when the threshold was set as a fixed number of positive mutated reads, the probability of both true and false-positive mutations increased with depth. However, When the number of positive mutated reads increased in an equal proportion with depth (the threshold was transformed from a fixed number to a fixed percentage of mutated reads), the true positive probability still increased while false positive probability decreased. Through binomial distribution simulation and experimental test, it is found that the "fidelity" of detected-VAFs is the cause of this phenomenon. Firstly, we used the binomial distribution to construct a model that can easily calculate the relationship between sequencing depth and probability of true positive (or false positive), which can standardize the minimum sequencing depth for different low-frequency mutation detection. Then, the effect of sequencing depth on the fidelity of NA12878 with 3% mutation frequency and circulating tumor DNA (ctDNA of 1%, 3% and 5%) showed that the increase of sequencing depth reduced the fluctuation range of detected-VAFs around the expected VAFs, that is, the fidelity was improved. Finally, based on our experiment result, the consistency of single-nucleotide variants (SNVs) between paired FF and FFPE samples of mice increased with increasing depth, suggesting that increasing depth can improve the precision and sensitivity of low-frequency mutations. HighlightsThe normalized relationship between sequencing depth and the probability of true positive mutation (sensitivity) is established based on binomial distribution. The probability of true positive increases and the probability of false positive decreases when the number of positive mutated reads increases (threshold) in an equal proportion with depth. Detected-VAFs fluctuates regularly around expected-VAFs. The amplitude of detected-VAFs fluctuation decreases with sequencing depth and the "fidelity" increases. The increase of "fidelity" leads to a higher degree of differentiation between true and false positive mutations, which ultimately increases the true positive probability and decreases the false positive probability.

genomics↗

bioRxiv2021

An ultrapotent RBD-targeted biparatopic nanobody neutralizes broad SARS-CoV-2 variants

The wide transmission and host adaptation of SARS-CoV-2 have led to the rapid accumulation of mutations, posing significant challenges to the effectiveness of vaccines and therapeutic antibodies. Although several neutralizing antibodies were authorized for emergency clinical use, convalescent patients derived natural antibodies are vulnerable to SARS-CoV-2 Spike mutation. Here, we describe the screen of a panel of SARS-CoV-2 receptor-binding domain (RBD) targeted nanobodies (Nbs) from a synthetic library and the design of a biparatopic Nb, named Nb1-Nb2, with tight affinity and super wide neutralization breadth against multiple SARS-CoV-2 variants of concern. Deep-mutational scanning experiments identify the potential binding epitopes of the Nbs on the RBD and demonstrate that biparatopic Nb1-Nb2 has a strong escape resistant feature against more than 60 tested RBD amino acid substitutions. Using pseudovirion-based and trans-complementation SARS-CoV-2 tools, we determine that the Nb1-Nb2 broadly neutralizes multiple SARS-CoV-2 variants, including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), Lambda (C.37), Kappa (B.1.617.1) and Mu (B.1.621). Furthermore, a heavy chain antibody is constructed by fusing the human IgG1 Fc to Nb1-Nb2 (designated as Nb1-Nb2-Fc) to improve its neutralization potency, yield, stability and potential half-life extension. For the new Omicron variant (B.1.1.529) that harbors unprecedented multiple RBD mutations, Nb1-Nb2-Fc keeps a firm affinity (KD < 1.0x10-12 M) and strong neutralizing activity (IC50 = 0.0017 nM). Together, we developed a tetravalent biparatopic human heavy chain antibody with ultrapotent and broad-spectrum SARS-CoV-2 neutralization activity which highlights the potential clinical applications.

microbiology↗

bioRxiv2021

Explainable autoencoder-based representation learning for gene expression data

Modern machine learning methods have been extensively utilized in gene expression data analysis. In particular, autoencoders (AE) have been employed in processing noisy and heterogenous RNA-Seq data. However, AEs usually lead to "black-box" hidden variables difficult to interpret, hindering downstream experimental validation and clinical translation. To bridge the gap between complicated models and biological interpretations, we developed a tool, XAE4Exp (eXplainable AutoEncoder for Expression data), which integrates AE and SHapley Additive exPlanations (SHAP), a flagship technique in the field of eXplainable AI (XAI). It quantitatively evaluates the contributions of each gene to the hidden structure learned by an AE, substantially improving the expandability of AE outcomes. By applying XAE4Exp to The Cancer Genome Atlas (TCGA) breast cancer gene expression data, we identified genes that are not differentially expressed, and pathways in various cancer-related classes. This tool will enable researchers and practitioners to analyze high-dimensional expression data intuitively, paving the way towards broader uses of deep learning. AvailabilityOpen source at https://github.com/QingrunZhangLab/Explainable-Deep-Autoencoder. Contactsqingrun.zhang@ucalgary.ca and wliao@ucalgary.ca.

bioinformatics↗

bioRxiv2021

Adjustment for SNPs instead of demographic and clinical covariates in GWAS

With capabilities of sequencing ancient DNA to high coverage often limited by sample quality or cost, imputation of missing genotypes presents a possibility to increase power of inference as well as cost-effectiveness for the analysis of ancient data. However, the high degree of uncertainty often associated with ancient DNA poses several methodological challenges, and performance of imputation methods in this context has not been fully explored. To gain further insights, we performed a systematic evaluation of imputation of ancient data using Beagle 4.0 and reference data from phase 3 of the 1000 Genomes project, investigating the effects of coverage, phased reference and study sample size. Making use of five ancient samples with high-coverage data available, we evaluated imputed data with respect to accuracy, reference bias and genetic affinities as captured by PCA. We obtained genotype concordance levels of over 99% for data with 1x coverage, and similar levels of accuracy and reference bias at levels as low as 0.75x. Our findings suggest that using imputed data can be a realistic option for various population genetic analyses even for data in coverage ranges below 1x. We also show that a large and varied phased reference set as well as the inclusion of low-to moderate-coverage ancient samples can increase imputation performance, particularly for rare alleles. In-depth analysis of imputed data with respect to genetic variants and allele frequencies gave further insight into the nature of errors arising during imputation, and can provide practical guidelines for post-processing and validation prior to downstream analysis.

genetics↗

bioRxiv2021

Cell transcriptomic atlas of the non-human primate Macaca fascicularis

Studying tissue composition and function in non-human primates (NHP) is crucial to understand the nature of our own species. Here, we present a large-scale single-cell and single-nucleus transcriptomic atlas encompassing over one million cells from 43 tissues from the adult NHP Macaca fascicularis. This dataset provides a vast, carefully annotated, resource to study a species phylogenetically close to humans. As proof of principle, we have reconstructed the cell-cell interaction networks driving Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases and intersected our data with human genetic disease orthologous coordinates to identify both expected and unexpected associations. Our Macaca fascicularis cell atlas constitutes an essential reference for future single-cell studies in human and NHP.

cell biology↗