Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs
Bone marrow mononuclear cells (BMMCs) are a heterogeneous pool of hematopoietic progenitors and mature immune cells that collectively sustain hematopoiesis and coordinate immune responses. The bone marrow serves not only as the primary site for blood cell production but also as a niche for various disorders, including blood cancers. Advances in single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have significantly enhanced our understanding of the cellular biology and molecular dynamics within this complex microenvironment. The choice between these two approaches, however, is often shaped or constrained by the study design, such as research objectives, sample type, and preservation conditions. Consequently, methodological differences in library preparation and transcript capture efficiency can introduce systematic biases that complicate downstream analyses and interpretation, underscoring the need to identify and account for method-specific features. In this study, we conducted a comparative analysis of matched snRNA-seq and scRNA-seq datasets from 11 pairs of healthy donor bone marrow mononuclear cell samples, generated using the popular 10x Genomics platform. We evaluated method-specific biases using multiple quality metrics and compared cell type proportions and transcriptomic signatures captured by each approach. Integrative analysis of these datasets is feasible but not advisable due to systematic gene length biases that were observed between these approaches. Our results showed that despite inherent differences in library complexity, both protocols reliably captured all major cell types. This comparative analysis highlights intrinsic differences between snRNA-seq and scRNA-seq data, providing valuable insights into their respective advantages, limitations, and trade-offs. These findings can assist researchers in selecting the optimal method tailored to specific biological questions and sample characteristics, and also enable more method-aware data analysis and interpretation.