Biology subjects

Robben, M.

Publications and source records attributed to Robben, M..

3 recordsLinked to original sources

bioRxiv2023

Single-cell RNA-seq reveals TCR clonal expansion and a high frequency of transcriptionally distinct double-negative T cells in NOD mice

T cells primarily drive the autoimmune destruction of pancreatic beta cells in Type 1 diabetes (T1D). However, the profound yet uncharacterized diversity of the T cell populations in vivo has hindered obtaining a clear picture of the T cell changes that occur longitudinally during T1D onset. This study aimed to identify T cell clonal expansion and distinct transcriptomic signatures associated with T1D progression in Non-Obese Diabetic (NOD) mice. Here we profiled the transcriptome and T cell receptor (TCR) repertoire of T cells at single-cell resolution from longitudinally collected peripheral blood and pancreatic islets of NOD mice using single-cell RNA sequencing technology. Surprisingly, we detected a considerable high frequency of islet-matching T cell clones in the peripheral circulation and blood-matching T cell clones in the islets. Our analysis showed that transcriptional signatures of the T cells are associated with the matching status of the T cells, suggesting potential future applications as a marker for early prediction of diabetes onset using peripheral T cells. In addition, we discovered a high frequency of transcriptionally distinct double negative (DN) T cells that might arise from naive and effector backgrounds through the loss of CD4 or CD8 in a yet unknown biological pathway. This study provides a single-cell level transcriptome and TCR repertoire atlas of T cells in NOD mice and opens the door for more research into the causes of type 1 diabetes and inflammatory autoimmune disease using mouse models.

immunology↗

bioRxiv2023

scRNA-seq reveals novel genetic pathways and sex chromosome regulation in Tribolium spermatogenesis

Insights into single cell expression data are generally collected through well conserved biological markers that separate cells into known and unknown populations. Unfortunately for non-model organisms that lack known markers, it is often impossible to partition cells into biologically relevant clusters which hinders analysis into the species. Tribolium castaneum, the red flour beetle, lacks known markers for spermatogenesis found in insect species like Drosophila melanogaster. Using single cell sequencing data collected from adult beetle testes, we implement a strategy for elucidating biologically meaningful cell populations by using transient expression stage identification markers, weighted principal component leiden clustering. We identify populations that correspond to observable points in sperm differentiation and find species specific markers for each stage. We also develop an innovative method to differentiate diploid from haploid cells based on scRNA-Seq reads and use it to corroborate our predicted demarcation of meiotic cell stages. Our results demonstrate that molecular pathways underlying spermatogenesis in Coleoptera are highly diverged from those in Diptera, relying on several genes with female meiotic pathway annotations. We find that the X chromosome is almost completely silenced throughout pre-meiotic and meiotic cells. Further evidence suggests that machinery homologous to the Drosophila dosage compensation complex (DCC) may mediate escape from meiotic sex chromosome inactivation and postmeiotic reactivation of the X chromosome.

molecular biology↗

bioRxiv2022

Selection of an Ideal Machine Learning Framework for Predicting Perturbation Effects on Network Topology of Bacterial KEGGPathways

Biological networks for bacterial species are used to assign functional information to newly sequenced organisms but network quality can be largely affected by poor gene annotations. Current methods of gene annotation use homologous alignment to determine orthology, and have been shown to degrade network accuracy in non-model bacterial species. To address these issues in the KEGG pathway database, we investigated the ability for machine learning (ML) algorithms to re-annotate bacterial genes based on motif or homology information. The majority of the ensemble, clustering, and deep learning algorithms that we explored showed higher prediction accuracy than CD-hit in predicting EC ID, Map ID, and partial Map ID. Motif-based, machine-learning methods of annotation in new species were more accurate, faster, and had higher precisionrecall than methods of homologous alignment or orthologous gene clustering. Gradient boosted ensemble methods and neural networks also predicted higher connectivity of networks, finding twice as many new pathway interactions than blast alignment. The use of motif-based, machine-learning algorithms in annotation software will allow researchers to develop powerful network tools to interact with bacterial microbiomes in ways previously unachievable through homologous sequence alignment. CCS CONCEPTS* Applied computing [->] Computational biology; Life and medical sciences; Bioinformatics; * Computing methodologies [->] Machine learning algorithms; Machine learning approaches. ACM Reference FormatMichael Robben, Mohammad Sadegh Nasr, Avishek Das, Manfred Huber, Justyn Jaworski, Jon Weidanz, and Jacob Luber. 2022. Selection of an Ideal Machine Learning Framework for Predicting Perturbation Effects on Network Topology of Bacterial KEGG Pathways. In The 13th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, August 07-10, 2022, Chicago, IL. ACM, New York, NY, USA, 11 pages. https://doi.org/XXXXXXX.XXXXXXX

bioinformatics↗