When T Cells Aren’t the Whole Story: Single-Cell and Trajectory Analysis of Chronic Viral Immune Remodeling
The Situation
An academic medical research group studying virus-driven immune dysfunction sought to understand how a chronic lymphotropic viral infection reshapes myeloid cell biology in human peripheral blood. While the field has largely centered on T cell–intrinsic effects of infection, mounting evidence suggested that myeloid cells play a critical, and underexplored, role in immune dysregulation and disease progression.
The group had already generated multiple single-cell RNA sequencing datasets and initially attempted analysis using in-house bioinformatics support. However, despite technically sound pipelines, the results failed to answer the biological questions driving the project. Analyses were delivered as static outputs with minimal explanation of analytical choices, unclear pathway drivers, and cluster annotations that conflicted with the team’s deep expertise in myeloid biology.
Recognizing that the challenge was not simply computational, but interpretive and conceptual, the group evaluated several external analysis vendors. Many offered automated workflows or black-box analytics, but few demonstrated the ability to collaborate at the level of biological reasoning required for rare-cell, hypothesis-driven discovery.
Bridge Informatics was selected for its emphasis on transparent, biologically grounded single-cell analysis, and for its collaborative approach that treats domain experts as partners rather than downstream consumers of results.
The Challenge
The data presented several structural challenges:
Extreme cell-type imbalance: T cells comprised ~80–90% of all captured cells, leaving myeloid populations rare and inconsistently represented.
Insufficient depth per dataset: No single cohort contained enough myeloid cells to support statistically robust downstream analyses.
Limited healthy controls: Disease-focused cohorts included few healthy donors, complicating interpretation of infection-associated changes versus baseline immune variation.
To address these constraints, analyses required careful integration across datasets and validation beyond standard batch-correction techniques, particularly to ensure that “healthy” myeloid cells embedded within disease cohorts truly reflected baseline biology.
Our Strategy
We anchored the project around a single guiding question:
How does chronic T cell–targeting viral infection alter myeloid composition, transcriptional programs, and disease-associated trajectories?
After establishing a reproducible computational environment and auditing data quality and metadata, we:
- Performed myeloid-specific subsetting and re-clustering across all datasets.
- Integrated publicly available healthy PBMC references and explicitly validated transcriptional alignment between internal and external healthy myeloid populations.
- Treated cell annotation as an iterative, collaborative process, combining reference-based tools with direct gene-level interrogation.
- Reviewed marker genes and annotations jointly with the Client, grounding final labels in established myeloid biology rather than automated outputs alone.
With high-confidence annotations in place, we conducted differential expression and pathway analyses across disease states. Importantly, pathway results were not treated as black boxes, we traced each signal back to its gene-level drivers, enabling biological interpretation and experimental relevance.
To capture dynamic immune remodeling, we applied trajectory and pseudotime analyses, modeling myeloid state transitions across disease progression. All findings were delivered through an interactive, no-code visualization platform, paired with hands-on walkthroughs to support ongoing exploration.
Our Results
Within weeks, Bridge Informatics delivered a clear, interpretable analysis package that transformed how the Client understood their data:
- A biologically grounded myeloid cell atlas built through collaborative annotation
- Disease-associated gene programs and pathways with transparent gene-level drivers
- Quantitative shifts in myeloid composition across disease states
- Trajectory-based insights into immune remodeling during chronic infection
- Publication-ready figures and interactive datasets supporting hypothesis generation
Conclusion
By pairing rigorous single-cell analytics with collaborative interpretation and biological context, Bridge Informatics delivered more than results, we delivered understanding.
The Client gained confidence not only in the conclusions, but in the analytical decisions behind them, laying a strong foundation for publication and future discovery.
If your team needs more than black-box outputs from complex single-cell data, we’re here to help translate analysis into insight. Click here to schedule a free introductory call with a member of our Data Science team.