Biology subjects

Cats, D.

Publications and source records attributed to Cats, D..

9 recordsLinked to original sources

bioRxiv2026

Spatial transcriptomics defines the mechanisms of hiPSC derived stem cell mediated repair in human articular cartilage

We here determined therapeutic efficacy and mode-of-action of human induced pluripotent-derived therapeutic stem cells (hiMSCs) across in vivo mouse and ex vivo human osteoarthritis models. hiMSC treatment in DMM-mice significantly reduced OARSI damage scores, which was affirmed by a decrease in the catabolic marker Mmp13 and an increase in the anabolic marker Col2. These treatment effects appeared, irrespective of modifying factors such as xeno-free media or thermosensitive hydrogel carrier. Subsequently treatment of hiMSC+gel in human osteoarthritic cartilage explants showed a transcriptome-wide significant activation of the cholesterol and sterol synthesis pathways marked by genes such as MVD, DHCR7, MSMO1, FABP3. Additionally, we showed that these changes alleviated OA-associated imbalances of the cellular Zinc-ion homeostasis pathways, represented by genes such as MT1F, MT1G, MT1H and SLC30A1. Spatial transcriptomics then sensitively captured that hiMSC+gel treatment evoked, specifically at the superficial cartilage layer, a consistent upregulation of healthy chondrocyte markers such as CHAD, ACAN, FRZB, and SOX9, alongside a suppression of catabolic and inflammatory mediators such as SERPINE1, SPP1, MMP13, ADAMTS5. Our findings link therapeutic outcomes of hiMSC treatment to precise spatially resolved molecular changes in human tissue, that would otherwise be obscured by heterogeneous cell populations. Collectively our study highlighted that hiPSC-derived stem cell therapy (hiMSCs) could provide a scalable off-the-shelf solution to treat osteoarthritis, with strong prospects for clinical applications in the near future.

developmental biology↗

bioRxiv2025

Determining off-target effects of splice-switching antisense oligonucleotides using short read RNAseq in neuronally differentiated human induced pluripotent stem cells

Antisense oligonucleotides (AONs) are small pieces of chemically modified DNA or RNA that bind to RNA in a sequence-specific manner based on Watson-Crick base-pairing. Splice-switching AONs are designed to modulate pre-mRNA splicing, thereby for instance restoring protein expression or modifying the eventual protein to restore its function or reduce its toxicity. Given the current lack of in silico methods that adequately predict off-target splicing events, assessment of off-target effects of AONs in human cells using RNAseq could be a promising approach. The identification and prioritization of potential off-target effects for validation and further investigation into the biological relevance would contribute to the development of safe and effective AONs. In this study, we used three different splice-switching AONs targeting three different human transcripts to study their transcriptome-wide, hybridization-dependent off-target effects with short read RNAseq. Using the computational tools rMATS and Whippet, we identified differential splicing events of which only a minority could be explained by hybridization, illustrating the difficulty of predicting off-target effects based on sequence homology. The main splicing events could all be validated with RT-PCR. Furthermore, from the three AONs studied, one AON induced considerably more changes in gene expression and splicing compared to the two other AONs assessed, which was confirmed in a validation experiment. Our study demonstrates that the interpretation of short read RNAseq data to determine off-target effects is challenging. Nonetheless, we showed that valuable results can be obtained as it allows the comparison of toxicity between different AONs within an experiment and identification of AON-specific off-target profiles.

genetics↗

bioRxiv2025

LncRNA-BERT: An RNA Language Model for classifying Coding and Long Non-Coding RNA

Understanding (novel) RNA transcripts generated in next generation sequencing experiments requires accurate classification, given the increasing evidence that long non-coding RNAs (lncRNAs) play crucial regulatory roles. Recent developments in Large Language Models present opportunities for classifying RNA coding potential with sequence-based algorithms that can overcome the limitations of classical approaches that assess coding potential based on a set of predefined features. We present lncRNA-BERT, an RNA language model pre-trained and fine-tuned on human RNAs collected from the GENCODE, RefSeq, and NONCODE databases to classify lncRNAs. LncRNA-BERT matches and outperforms state-of-the-art classifiers on three test datasets, including the cross-species RNAChallenge benchmark. The pre-trained lncRNA-BERT model distinguishes coding from long non-coding RNA without supervised learning which confirms that coding potential is a sequenceintrinsic characteristic. LncRNA-BERT has been shown to benefit from pre-training on human data from GENCODE, RefSeq, and NONCODE, improving upon configurations pre-trained on the commonly used RNAcentral dataset. In addition, we propose a novel Convolutional Sequence Encoding method that is shown to be more effective and efficient than K-mer Tokenization and Byte Pair Encoding for training with long RNA sequences that are otherwise above the common context window size. lncRNA-BERT is available at https://github.com/luukromeijn/lncRNA-Py.

bioinformatics↗

bioRxiv2022

A transcriptome atlas of leg muscles from healthy human volunteers reveals molecular and cellular signatures associated with muscle location

Skeletal muscles support the stability and mobility of the skeleton but differ in biomechanical properties and physiological functions. The intrinsic factors that regulate muscle-specific characteristics are poorly understood. To study these, we constructed a large atlas of RNA-seq profiles from six leg muscles and two locations from one muscle, using biopsies from 20 healthy young males. We identified differential expression patterns and cellular composition across the seven tissues using three bioinformatics approaches confirmed by large-scale newly developed quantitative immune-histology procedures. With all three procedures, the muscle samples clustered into three groups congruent with their anatomical location. Concomitant with genes marking oxidative metabolism, genes marking fast- or slow-twitch myofibers differed between the three groups. The groups of muscles with higher expression of slow-twitch genes were enriched in endothelial cells and showed higher capillary content. In addition, expression profiles of Homeobox (HOX) transcription factors differed between the three groups and were confirmed by spatial RNA hybridization. We created an open-source graphical interface to explore and visualize the leg muscle atlas (https://tabbassidaloii.shinyapps.io/muscleAtlasShinyApp/). Our study reveals molecular specialization of human leg muscles and provides a novel resource to study muscle-specific molecular features, which could be linked with (patho)physiological processes.

neuroscience↗

bioRxiv2020

CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

BackgroundDrawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B-cell receptor loci, presents a unique opportunity to align exome-derived mutations with B-cell receptor clonotypes as an independent measure for clonal evolution. ResultsHere, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome sequencing, single cell RNA sequencing, and B-cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B-cell receptor clonotypes to the tumor clones. ConclusionsThe integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies.

cancer biology↗

bioRxiv2020

Synthetic Maturation of Multilineage Human Liver Organoids via Genetically Guided Engineering

Pluripotent stem cell (PSC)-derived organoids are emerging as novel human-based microphysiological models but display immature phenotypes with limited subsets of endothelial or stromal cells. Here we demonstrate that in vitro manipulation of gene regulatory networks (GRNs) in PSC-derived liver organoids selected either through computational analysis or targeted tissue design can advance tissue maturation in vitro. Through an unbiased comparison with the genetic signature of mature livers, we identify downregulated GRNs in fetal liver organoids compared to adult livers. We demonstrate that overexpression of PROX1 and ATF5, together with targeted CRISPR-based transcriptional activation of endogenous CYP3A4, drives maturation in vitro. Single cell analyses reveal hepatobiliary-, endothelial-, and stellate-like cell populations. The engineered organoids demonstrate enhanced vasculogenesis, capture native liver characteristics (e.g. FXR signaling, CYP3A4 activity), and exhibit therapeutic potential in mice. Collectively, our approach provides a genetically guided framework for engineering developmentally advanced multilineage tissues from hiPSCs. HIGHLIGHTSO_LIIn vitro tissue maturation via genetically encoded molecular programs C_LIO_LIComputational analysis to identify maturation transcription factors in liver organoids C_LIO_LIPromoting vascularization of organoids via genetically encoded molecular programs C_LIO_LISingle cell analysis of parenchymal and non-parenchymal cells C_LIO_LIModeling of native liver functions and in vivo therapeutic potential C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=168 HEIGHT=200 SRC="FIGDIR/small/087445v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@6ec32aorg.highwire.dtl.DTLVardef@1cf2eeforg.highwire.dtl.DTLVardef@1128748org.highwire.dtl.DTLVardef@16fb5a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Elucidating another level of epigenetic regulation in osteoarthritis by identifying functional cis-acting long non-coding RNAs and their targets in articular cartilage

ObjectiveTo identify robustly differentially expressed long non-coding RNAs (lncRNAs) with osteoarthritis (OA) pathophysiology in cartilage. Moreover to explore potential target mRNAs by establishing co-expression networks, followed by functional validation. MethodsRNA sequencing was performed on macroscopically lesioned and preserved OA cartilage of patients who underwent a joint replacement surgery due to OA (N=98). Differential expression (DE) analysis was performed on lncRNAs that were annotated in GENCODE and Ensembl. To identify potential interactions, correlations were calculated between the identified DE lncRNAs and previously reported DE protein-coding genes in the same samples. Modulation of chondrocyte lncRNA expression was achieved using LNA GapmeRs. ResultsBy applying our in-house pipeline we identified 5,053 lncRNAs to be robustly expressed, of which 191 were FDR significant differentially expressed between lesioned and preserved OA cartilage. Upon integrating mRNA sequencing data, we showed that intergenic and antisense DE lncRNAs show high, positive correlations with their flanking, respectively, sense genes. To functionally validate this observation we selected P3H2-AS1, which was downregulated in primary chondrocytes, resulting in downregulation of P3H2 gene expression levels. As such, we can confirm that P3H2-AS1 regulates its sense gene P3H2. ConclusionBy 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 showed insight into their regulatory networks. Our data signifies that intergenic, as well as antisense lncRNAs play an important role in regulating the pathophysiology of OA.

genomics↗

bioRxiv2019

A comparison of automatic cell identification methods for single-cell RNA-sequencing data

BackgroundSingle cell transcriptomics are 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.\n\nResultsHere, we benchmarked 20 classification methods that automatically assign cell identities including single cell-specific and general-purpose classifiers. The methods were evaluated using eight publicly available single cell RNA-sequencing datasets of different sizes, technologies, species, and complexity. The performance of the methods was evaluated based on their accuracy, percentage of unclassified cells, and computation time. We further evaluated their sensitivity to the input features, their performance across different annotation levels and datasets. We found that most classifiers performed well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose SVM classifier has overall the best performance across the different experiments.\n\nConclusionsWe 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 extension of new methods and new datasets (https://github.com/tabdelaal/scRNAseq_Benchmark/tree/snakemake_and_docker).

bioinformatics↗

bioRxiv2019

Predicting biological age based on the BBMRI-NL 1H-NMR metabolomics repository

The blood metabolome incorporates cues from the environment as well as the hosts genetic background, potentially offering a holistic view of an individuals health status. We have compiled a vast resource of 1H-NMR metabolomics and phenotypic data encompassing over 25,000 samples derived from 26 community and hospital-based cohorts. Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individuals biological age. Exploration in independent cohorts demonstrates that being judged older by ones metabolome, as compared to ones 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. 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.

epidemiology↗