09-10, 14:58–15:01 (Europe/Berlin), Main conference room - MW 0350
Background. Integration analysis not only offer a panoramic view by making annotation consistent across studies, but also amplify the statistical power restricted by sample size in the individual studies. Commonly used integration methods for scRNA-seq data often struggle with strong batch effects, which can distort or dilute the biological signals. Furthermore, conventional clustering built on improper integration can lead to impure clusters containing multiple cell types, compromising the purity and interpretability of the data.
Methods. To circumvent these issues, we employed a two-step solution that minimizes the need for data integration. In the first step, a combination of cell annotation techniques are used to identify high-level cellular compartments. In the second step we mainly utilize SingleR augmented with curated references from pan-cancer studies in a hierarchical framework. The clustering-free second step, which we term deep-phenotyping, is particularly advantageous for resolving cell states.
Results. We applied this computational framework to annotate 11 scRNA-seq datasets of patients treated with immune checkpoint inhibitors (IBI) with multiple timepoints. Altogether our dataset included longitudinally paired samples from 163 patients. We accurately portrayed the complex landscape of diverse cellular states in the tumor microenvironment (TME) at an individual patient level. Our analysis revealed consistent compositional changes in 19 cell subtypes following ICI treatment. We uncovered co-regulated cell communities within the TME, highlighting the coordinated interplay between adaptive and innate immune cells, as well as immune and non-immune components. Furthermore, we identified two distinct patient groups exhibiting tightly correlated cellular dynamics within the TME post-treatment. The first group, enriched for responders, displayed a marked expansion of naive lymphocytes, while the second group, predominantly composed of non-responders, showed an increased abundance of immune experienced/suppressive cell states. This dichotomy in TME dynamics offers a potential predictive biomarker for patient stratification and personalized therapeutic strategies.
Conclusions. Our study presents a comprehensive landscape of the cellular dynamics within the TME during ICI treatment, enabled by a powerful deep phenotyping approach showcasing the importance of a systems-level understanding of the TME dynamics in improving patient stratification and advancing personalized cancer immunotherapy.
The annotation of single-cell RNA-seq data is performed at 2 levels, built on the reference-based SingleR annotation tool. At Level 1, cells are divided into discrete lineage populations. Level 2 focuses on the annotation of cells to the most granular cell types/states.
Level 1 starts with the assignment of major lineage components utilizing SingleR with the BlueprintEncodeData as a reference and using the built-in pruning approach in SingleR. The pruning will cause low-quality assignments to not be labeled. To assign those low-quality cells, we utilized the scGate as a complementary method.
Level 2 annotations were designed to cater to various cellular lineages, and consists of two primary steps - preparation of reference sets and subsequent deep phenotyping.
The reference data for T-cells, NK cells, myeloid cells, endothelial cells and CAFs were retrieved from the pan-cancer atlas, then individually and aggregated at different levels. Immune cells were first classified into main cell types within each lineage by SingleR based on the pan-cancer reference at the main level.
Due to the distinct marker expression, the scGate was applied to separate cells from the T and NK cells group. After separating the NK and gd T cells, the remaining CD4+/CD8+ T cells underwent further ‘gating’ by the scGate. Cells with overlapped labels were identified by SingleR, based on an aggregated pan-cancer reference at the main level. Following the main-level annotation, the immune cells were further annotated within each main cell type by SingleR with pan-cancer reference at the deep level.
Previous knowledge expected
PhD student at Technion - Israel Institute of Technology