Introduction
Dr. Ima Rush, an immunologist, planned an experiment to characterize immune cell profiles from patient biopsies using single-cell RNA sequencing (scRNA-seq). Excited about the biological questions she hoped to address, Ima carefully designed her clinical intervention and collected samples from patients. Instead of consulting a bioinformatician, Ima proceeded to sequencing and chose to rely on generic guidelines from the sequencing core.
When Ima handed the data off to a bioinformatics consultant after sequencing, multiple critical issues surfaced:
1. Insufficient Sequencing Depth and Cell Numbers
- Problem: Ima hadn’t accurately calculated how many cells per sample were needed for robust sequencing depth and statistical analysis.
- Consequence: Many samples had too few cells sequenced, limiting her ability to confidently identify rare immune populations. Because the sequencing depth was too low many of her genes of interest were undetectable.
2. Poor Metadata Management
- Problem: The metadata associated with patient samples was inconsistently recorded, lacked standardization, and was stored across multiple Excel sheets with conflicting information.
- Consequence: Hours spent manually correcting and aligning metadata, delaying downstream analysis and increasing the risk of analysis errors.
3. Batch Effects and Experimental Biases
- Problem: All control samples were processed and sequenced in one batch, and all disease samples in another.
- Consequence: Massive batch effects confounded biological signals, necessitating complicated computational corrections that reduced confidence in the findings. Because some of her biological questions of interest were completely confounded with sequencing batch, she was unable to address them.
4. Incompatible Technology Choices
- Problem: The sequencing platform chosen by Ima was less sensitive for her specific immune-cell subpopulations of interest.
- Consequence: Key biological signals were weak or missing entirely, compromising the core objectives of her study.
The Costly Outcome
Sadly, the valuable samples Ima painstakingly gathered—at considerable time, effort, and expense—could not be replaced. Because the experiment wasn’t properly designed with bioinformatics considerations in mind, these irreplaceable resources were essentially wasted, representing a significant setback.
These issues led to months of extra analysis, delayed publication timelines, additional costly validation experiments, and frustration.
Early consultation with a bioinformatician can greatly enhance experimental planning, ensuring reliable data, saving resources, and preventing unnecessary stress.
Next time you’re planning an important experiment—don’t rush like Dr. Ima Rush. Collaborate with a bioinformatician from the start!
How Bridge Informatics (BI) Can Help
Our clients consistently bring us into their planning process early because they’ve experienced the difference it makes. They rely on us not just for data analysis, but for strategic input on experimental design that aligns with their goals. Our bioinformaticians are trained bench biologists who understand both the science and the statistics—and we know how to bridge the gap between them. Partnering early ensures your data is usable, your analysis is robust, and your results can confidently drive critical decisions.
Don’t let your experiment become a cautionary tale. Click here to schedule a free introductory call with a member of our team.
