Biology subjects

Dits, N.

Publications and source records attributed to Dits, N..

3 recordsLinked to original sources

bioRxiv2026

Small-scale bioreactor cultivation of HEK293-based suspension cells increases extracellular vesicle yield

PurposeExtracellular vesicles (EVs) are increasingly explored as natural vehicles for drug delivery and gene therapy approaches. However, reproducible yield and scalability of EV production still pose major challenges in the clinical translation of EV-based therapies. In this study, we sought to quantify and characterize EVs released by suspension-cultured HEK293 cells (Expi293F cells) grown in shaker flasks or small-scale bioreactors, to investigate how the culturing environment affects EV production yield. MethodsExpi293F cells were cultivated (N=3) in either shaker flasks or a bioreactor system, and total cell density, viability, and size were monitored. Supernatants were drawn daily post-cell seeding and were analyzed for EV quantity, size, morphology, and CD63 expression. ResultsNo significant differences were observed in terms of total cell density, viability, and cell size between both cultivation settings. However, cultivation of Expi293F cells in the bioreactor environment significantly increased EV yield by 3-fold compared to shaker flask cultivation (p < 0.01). Other parameters such as average nanoparticle size, EV morphology, and CD63 expression remained comparable between both cultivation methods. ConclusionThese results demonstrate that Expi293F-derived EV yield can be increased by culturing cells in a scalable bioreactor system. These findings pave the way towards the production of therapeutic-based EVs in a scalable and reproducible manner suitable for future (pre-)clinical applications.

bioengineering↗

bioRxiv2021

Detection of fusion transcripts and their genomic breakpoints from RNA sequencing data

Spliced fusion-transcripts are typically identified by RNA-seq without elucidating the causal genomic breakpoints. However, non poly(A)-enriched RNA-seq contains large proportions of intronic reads spanning also genomic breakpoints. Using 1.274 RNA-seq samples, we investigated what additional information is embedded in non poly(A)-enriched RNA-seq data. Here, we present our novel, graph-based, Dr. Disco algorithm that makes use of both intronic and exonic RNA-seq reads to identify not only fusion transcripts but also genomic breakpoints in gene but also in intergenic regions. Dr. Disco identified TMPRSS2-ERG fusions with genomic breakpoints and other transcribed rearrangements from multiple RNA-sequencing cohorts. In breast cancer and glioma samples Dr. Disco identified rearrangement hotspots near CCND1 and MDM2 and could directly associate this with increased expression. A comparison with matched DNA-sequencing revealed that most genomic breakpoints are not, or minimally, transcribed while also revealing highly expressed translocations missed by DNA-seq. By using the full potential of non poly(A)-enriched RNA-seq data, Dr. Disco can reliably identify expressed genomic breakpoints and their transcriptional effects.

bioinformatics↗

bioRxiv2020

EVQuant; high-throughput quantification and characterization of extracellular vesicle (sub)populations

Extracellular vesicles (EVs) reflect the cell of origin in terms of nucleic acids and protein content. They are found in biofluids and represent an ideal liquid biopsy biomarker source for many diseases. Unfortunately, clinical implementation is limited by available technologies for EV analysis. We have developed a simple, robust and sensitive microscopy-based high-throughput assay (EVQuant) to overcome these limitations and allow widespread use in the EV community. The EVQuant assay can detect individual immobilized EVs as small as 35 nm and determine their concentration in biofluids without extensive EV isolation or purification procedures. It can also identify specific EV subpopulations based on combinations of biomarkers and is used here to identify prostate-derived urinary EVs as CD9-/CD63+. Moreover, characterization of individual EVs allows analysis of their size distribution. The ability to identify, quantify and characterize EV (sub-)populations in high-throughput substantially extents the applicability of the EVQuant assay over most current EV quantification assays.

bioengineering↗