Bastian V. H. Hornung
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Bastian V. H. Hornung has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Alexander T. Den Dekker
Genomics Core Facility, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
1 shared publication - Edwin Oole
Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
1 shared publication - Mirjam C. G. N. van Den Hout
Erasmus MC
1 shared publication - Rutger W. W. Brouwer
Center for Biomics, Erasmus University Medical Center, Rotterdam, the Netherlands
1 shared publication - Wilfred F. J. van Ijcken
Erasmus MC
1 shared publication - Zakia Azmani
Department of Cell Biology, Erasmus University Medical Center, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands
1 shared publication
Latest Publications
- Open Access
Comparison of Single Cell Transcriptome Sequencing Methods: Of Mice and Men
Single cell RNAseq has been a big leap in many areas of biology. Rather than investigating gene expression on a whole organism level, this technology enables scientists to get a detailed look at rare single cells or within their cell population of interest. The field is growing, and many new methods appear each year. We compared methods utilized in our core facility: Smart-seq3, PlexWell, FLASH-seq, VASA-seq, SORT-seq, 10X, Evercode, and HIVE. We characterized the equipment requirements for each method. We evaluated the performances of these methods based on detected features, transcriptome diversity, mitochondrial RNA abundance and multiplets, among others and benchmarked them against bulk RNA sequencing. Here, we show that bulk transcriptome detects more unique transcripts than any single cell method. While most methods are comparable in many regards, FLASH-seq and VASA-seq yielded the best metrics, e.g., in number of features. If no equipment for automation is available or many cells are desired, then HIVE or 10X yield good results. In general, more recently developed methods perform better. This also leads to the conclusion that older methods should be phased out, and that the development of single cell RNAseq methods is still progressing considerably.
Open AccessComparison of Single Cell Transcriptome Sequencing Methods: Of Mice and Men
Single cell RNAseq has been a big leap in many areas of biology. Rather than investigating gene expression on a whole organism level, this technology enables scientists to get a detailed look at rare single cells or within their cell population of interest. The field is growing, and many new methods appear each year. We compared methods utilized in our core facility: Smart-seq3, PlexWell, FLASH-seq, VASA-seq, SORT-seq, 10X, Evercode, and HIVE. We characterized the equipment requirements for each method. We evaluated the performances of these methods based on detected features, transcriptome diversity, mitochondrial RNA abundance and multiplets, among others and benchmarked them against bulk RNA sequencing. Here, we show that bulk transcriptome detects more unique transcripts than any single cell method. While most methods are comparable in many regards, FLASH-seq and VASA-seq yielded the best metrics, e.g., in number of features. If no equipment for automation is available or many cells are desired, then HIVE or 10X yield good results. In general, more recently developed methods perform better. This also leads to the conclusion that older methods should be phased out, and that the development of single cell RNAseq methods is still progressing considerably.