Almut Lütge

Afferent lymphatic vessels (LVs) are present in most vascularized tissues and exert important immune and drainage functions, yet human afferent LVs remain poorly studied. Performing single-cell RNA sequencing of lymphatic endothelial cells (LECs) from human skin and subcutaneous adipose tissue, we identified various LEC subsets, including two valve LEC populations located on the upstream and downstream sides of the valve leaflets. The cell adhesion molecule CD24 emerged as a specific marker of upper valve leaflet LECs in human skin and contributed to lymphatic valve development in murine mesentery. Three-dimensional imaging further revealed several unique features of the human dermal lymphatic network, including a high proportion of LYVE-1+ pre-collecting vessels containing intraluminal valves, virtually no collectors, and absence of lymphatic muscle cell coverage. Moreover, LECs in blind-ended capillaries and around valves in pre-collectors displayed mixed junctional and morphological phenotypes. These findings reveal key differences between human and murine dermal afferent lymphatics and provide a deeper understanding of human lymphatic-related (patho)physiological processes.

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.

Chronic lymphocytic leukemia (CLL) has a complex pattern of driver mutations and much of its clinical diversity remains unexplained. We devised a method for simultaneous subgroup discovery across multiple data types and applied it to genomic, transcriptomic, DNA methylation and ex vivo drug response data from 217 patients with CLL. We uncovered a biological axis of heterogeneity strongly associated with clinical behavior and orthogonal to known biomarkers. We validated its presence and clinical relevance in four independent cohorts (n = 547 patients). We found that this axis captures the proliferative drive (PD) of CLL cells, as it associates with lymphocyte doubling rate, global hypomethylation, accumulation of driver aberrations and response to pro-proliferative stimuli. CLL–PD was linked to the activation of mTOR–MYC–oxidative phosphorylation through transcriptomic, proteomic and single-cell resolution analysis. CLL–PD is a key determinant of disease outcome in CLL. Our multi-table integration approach may be applicable to other tumors whose inter-individual differences are currently unexplained.

Despite the extensive catalogue of recurrent mutations in chronic lymphocytic leukaemia (CLL), the diverse molecular driving events and the resulting range of disease phenotypes remain incompletely understood. To study the molecular heterogeneity of CLL, we performed RNA-sequencing on 184 CLL patient samples. Unsupervised analysis revealed two major independent axes of gene expression variation: the first one aligned with the mutational status of the immunoglobulin heavy variable (IGHV) genes, and concomitantly, with the three-group stratification of CLL by global DNA methylation pattern, and affected biological functions including B- and T-cell receptor signaling. The second one aligned with trisomy 12 status and affected chemokine signaling. Furthermore, we searched for differentially expressed genes associated with gene mutations and copy-number aberrations and detected strong signatures for TP53, BRAF and SF3B1 , as well as for del(11)(q22.3), del(17)(p13) and del(13)(q14) beyond the dosage effect. We discovered strong non-additive effects (i.e., genetic interactions, or epistasis) of IGHV mutation status and trisomy 12 on multiple phenotypes, including the expression of 893 genes. Multiple types of epistasis were observed, including synergy, buffering, suppression and inversion. Our study reveals previously underappreciated gene expression signatures for (epi)genomic variants in CLL and the presence of epistasis between them. The findings will serve as a reference for a functional resolution of CLL molecular heterogeneity.

A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects that can obscure the biological signal of interest. Although there are various tools and methods to correct for batch effects, their performance can vary. Therefore, it is important to understand how batch effects manifest to adjust for them. Here, we systematically explore batch effects across various scRNA-seq datasets according to magnitude, cell type specificity, and complexity. We developed a cell-specific mixing score (cms) that quantifies mixing of cells from multiple batches. By considering distance distributions, the score is able to detect local batch bias as well as differentiate between unbalanced batches and systematic differences between cells of the same cell type. We compare metrics in scRNA-seq data using real and synthetic datasets and whereas these metrics target the same question and are used interchangeably, we find differences in scalability, sensitivity, and ability to handle differentially abundant cell types. We find that cell-specific metrics outperform cell type–specific and global metrics and recommend them for both method benchmarks and batch exploration.

A key challenge in single cell RNA-sequencing (scRNA-seq) data analysis are dataset- and batch-specific differences that can obscure the biological signal of interest. While there are various tools and methods to perform data integration and correct for batch effects, their performance can vary between datasets and according to the nature of the bias. Therefore, it is important to understand how batch effects manifest in order to adjust for them in a reliable way. Here, we systematically explore batch effects in a variety of scRNA-seq datasets according to magnitude, cell type specificity and complexity. We developed a cell-specific mixing score ( cms ) that quantifies how well cells from multiple batches are mixed. By considering distance distributions (in a lower dimensional space), the score is able to detect local batch bias and differentiate between unbalanced batches (i.e., when one cell type is more abundant in a batch) and systematic differences between cells of the same cell type. We implemented cms and related metrics to detect batch effects or measure structure preservation in the CellMixS R/Bioconductor package. We systematically compare different metrics that have been proposed to quantify batch effects or bias in scRNA-seq data using real datasets with known batch effects and synthetic data that mimic various real data scenarios. While these metrics target the same question and are used interchangeably, we find differences in inter- and intra-dataset scalability, sensitivity and in a metric’s ability to handle batch effects with differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.