Biology subjects

Soukup, J.

Publications and source records attributed to Soukup, J..

8 recordsLinked to original sources

bioRxiv2026

Plaque-associated oligodendrocyte proteostatic failure underlies myelin loss in Alzheimers disease

Alzheimers disease (AD) features amyloid-{beta} plaques and tau pathology, yet the mechanism underlying early and clinically significant myelin loss remains unresolved. Here, we report human iPSC-derived forebrain organoids with doxycycline-inducible expression of SOX10, OLIG2, and NKX6-2 (SON), which generate robust, mature oligodendrocytes and compact myelin in vitro and in vivo. Introducing amyloid precursor protein (APP) pathogenic mutations produces extracellular amyloid-{beta} plaques and phosphorylated tau, accompanied by reduced myelin basic protein (MBP) expression and disrupted myelin ultrastructure. Single-cell and spatial transcriptomics combined with amyloid plaque imaging reveal that oligodendrocytes in plaque-dense regions show pathological changes in calcium signaling, immune activation, lipid remodeling, and protein catabolic pathways. Notably, MBP protein is reduced despite elevated myelin-related transcription, suggesting plaque-induced degradation of myelin proteins. Consistent with this, proteasomal inhibition restores MBP protein levels and improves compact myelin ultrastructure in AD organoids. In human AD brain tissue, protein catabolic pathways are similarly upregulated with increasing plaque density. Together, these findings identify plaque-associated oligodendrocyte proteostatic failure as a candidate mechanism of myelin loss and a potential therapeutic pathway in AD.

neuroscience↗

bioRxiv2025

InCytokine, an open-source software, reveals a TREM2 variant specific cytokine signature

Cytokine and chemokine profiling is central to understanding inflammatory processes and the mechanisms driving diverse diseases. We introduce InCytokine, an open-source tool for semiquantitative analysis of cytokine and chemokine data generated by protein array technologies. InCytokine features robust and modular image-processing workflows, including automated spot detection, template alignment, normalization, quality-control measures and quantitative intensity summarization to deliver consistent and reliable readouts from profiling assays. We evaluated InCytokine by profiling wild-type microglia, TREM2 knockout, and Alzheimers-associated TREM2 R47H variant cells in response of lipopolysaccharide and sulfatide exposure. Differential expression analysis revealed unique sulfatide-specific and genotype-specific cytokine signatures in TREM2 variants. We also report an intriguing modulation of DPP4 and a divergent expression pattern of ENA-78 in TREM2 variants in response to lipopolysaccharide and sulfatide treatment. Such distinct expression signatures raise the possibility that TREM2 variants may play a role in modulating inflammatory signaling relevant to cardio-metabolic and Alzheimers disease. These signatures were corroborated using transcriptional profiling of the same microglia cells, revealing also a good concordance between protein array and RNA sequencing technologies. Taken together, InCytokine is an interactive, user-friendly web application for rapid, reproducible, and scalable analysis of protein array data, proven to generate meaningful insights for drug and biomarker discovery campaigns in pharmaceutical settings.

bioinformatics↗

bioRxiv2024

AnNoBrainer, an Automated Annotation of Mouse Brain Images using Deep Learning

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that less rigorous, less time-consuming methods of histopathological assessment are employed by non-pathologists, especially for early discovery and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction ([~]50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts histopathological assessment standards (>85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

neuroscience↗

bioRxiv2024

Imaging Giardia intestinalis cellular organisation using expansion microscopy revealed atypical centrin localisation.

Advanced imaging of microorganisms, including protists, is challenging due to their small size. Specimen expansion prior to imaging is thus beneficial to increase resolution and cellular details. Here, we present a sample preparation workflow for improved observations of the single-celled eukaryotic pathogen Giardia intestinalis (Excavata, Metamonada). The binucleated trophozoites colonize the small intestine of humans and animals and cause a diarrhoeal disease. Their remarkable morphology includes two nuclei and a pronounced microtubular cytoskeleton enabling cell motility, attachment and proliferation. By use of expansion and confocal microscopy, we resolved in a great detail subcellular structures and organelles of the parasite cell. The acquired spatial resolution of 43 nm enabled novel observations of centrin localisation at Giardia basal bodies. Interestingly, non-luminal centrin localization between the Giardia basal bodies was observed, which is an atypical eukaryotic arrangement. Our protocol includes antibody staining and can be used for the localisation of epitope-tagged proteins, as well as for differential organelle labelling by amino reactive esters. This fast and simple protocol is suitable for routine use without a superresolution microscopy equipment.

microbiology↗

bioRxiv2023

A Light sheet fluorescence microscopy and machine learning-based approach to investigate drug and biomarker distribution in whole organs and tumors.

Tissue clearing and Light sheet fluorescence microscopy (LSFM) provide spatial information at a subcellular resolution in intact organs and tumors which is a significant advance over tools that limit imaging to a few representative tissue sections. The spatial distribution of drugs, targets, and biomarkers can help inform relationships between exposure at the site of action, efficacy, and safety during drug discovery. We demonstrate the use of LSFM to investigate distribution of an oncolytic virus (OV) and vasculature in xenograft tumors, as well as brain A{beta} pathology in an Alzheimers disease (AD) mouse model. Machine learning-based image analysis tools developed to segment vasculature in tumors showed that random forest and deep learning methods provided superior segmentation accuracy vs intensity-based thresholding. Sub-cellular resolution enabled detection of punctate and diffuse intracellular OV distribution profiles. LSFM investigation in the brain in a TgCRND8 AD mouse model at 6.5 months of age enabled evaluation of A{beta} plaque density in different brain regions. The utility of LSFM data to support quantitative systems pharmacology (QSP) and physiology-based pharmacokinetics (PBPK) modeling to inform drug development are also discussed. In summary, we showcase how LSFM can expand our understanding of macromolecular drug and biomarker distribution to advance drug discovery and development.

cancer biology↗

bioRxiv2023

Transcriptomic response in pyroxsulam-resistant and susceptible Bromus sterilis identified three distinct mechanisms of resistance

Bromus sterilis has evolved into a more predominant weed in the Czech Republics winter wheat fields, owing largely to the widespread application of pyroxsulam for its management. In this study, we report a biotype that has developed resistance to pyroxsulam and has also shown cross- resistance to other herbicides. Although no differences in ploidy levels or no mutations of acetolactate synthase (ALS) were detected, a significant elevation of ALS enzyme activity was observed in the R biotype. Through combined analysis of enzyme inhibition and total transcript expression (RNA-Seq), we have identified differentially expressed transcripts that potentially contribute to pyroxsulam metabolism. Furthermore, we observed a significant increase in the expression of genes involved in redox mechanisms and transporters that could contribute to enhanced resistance to pyroxulam in the R biotype. Our results present a novel understanding of herbicide resistance in B. sterilis through three distinct resistance mechanisms (ALS gene overexpression, enhanced metabolism and reduced translocation) without mutation in the herbicide target protein. This understanding is the foundation for improving management strategies for herbicide resistant B. sterilis.

plant biology↗

bioRxiv2023

Large Extracellular Vesicles Induce Stronger Prion Infection in Cell Culture than Small Extracellular Vesicles

Prions are responsible for a number of lethal neurodegenerative and transmissible diseases in humans and animals. Extracellular vesicles, especially small exosomes, have been extensively studied in connection with various diseases. In contrast, larger microvesicles are often overlooked. In this work, we compared the ability of large extracellular vesicles (lEVs) and small extracellular vesicles (sEVs) to spread prions in cell culture. We utilized CAD5 cell culture model of prion infection and isolated lEVs by 20,000 x g force and sEVs by 110,000 x g force. The lEV fraction was enriched in {beta}-1 integrin with a vesicle size starting at 100 nm. The fraction of sEVs was depleted of {beta}-1 integrin with a mean size of 79 nm. Both fractions were enriched in prion protein, but the lEVs contained a higher prion-converting activity. In addition, lEV infection led to stronger prion signals in both cell cultures, as detected by cell and western blotting. These results were verified on N2a-PK1 cell culture. Our data suggest the importance of lEVs in the trafficking and spread of prions over extensively studied small EVs.

cell biology↗

bioRxiv2018

A Deep Learning Genome-Mining Strategy Improves Biosynthetic Gene Cluster Prediction

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers more accurate BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing tools. We supplemented this with downstream random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a significant step forward for in-silico BGC identification.

bioinformatics↗