6. Transcriptomics

6.5. Applications and limitations

RNA-seq, particularly with short reads, is an incredibly versatile tool for gaining high quality transcriptomic data. However, long-read RNA sequencing data align more accurately to the genome and can help to identify the complete length of isoforms that are differentially expressed or differentially spliced. Long-read sequencing is thus filling an important gap in our knowledge of isoform expression patterns in honey bees.

Single-cell transcriptomics is a relatively new field, and while the potential resolution of this type of sequencing data is unparalleled, current practices do not yet reach the full potential. For example, to obtain a diverse representation of different cell types from targeted tissue, pooled samples are often still used. To harvest enough cells (50,000-100,000) for a good coverage of honey bee brain samples, brains from several honey bees are pooled together (Traniello et al., 2023, 2020). In the future, a key objective should be to decrease the quantity of the input cell numbers. Ideally, the ultimate goal would be to obtain deep sequencing data from individual bees instead of relying on pooled bee samples. This approach would significantly enhance statistical power and unlock the full potential of single-cell sequencing, providing exceptional resolution. A further limitation is that few cell types can currently be recognized based on the gene markers available. To overcome this limitation, it is crucial to conduct more extensive studies on individual gene markers that can accurately represent a broader range of honey bee cell types beyond neurons and glia.

Transcriptomics and RNA-seq analyses can provide information on the expression, isoforms abundance, alternative splicing, and chemical modifications of genes. Additional information about the activity of proteins or other cellular processes is essential for understanding gene function, and can be added by designing additional experiments using functional genomics (Section 7) proteomics (Section 8) and metabolomics (Section 9).