Robrecht Cannoodt

Computational methods for trajectory inference from single-cell transcriptomics

Recent developments in single‐cell transcriptomics have opened new opportunities for studying dynamic processes in immunology in a high throughput and unbiased manner. Starting from a mixture of cells in different stages of a developmental process, unsupervised trajectory inference algorithms aim to automatically reconstruct the underlying developmental path that cells are following. In this review, we break down the strategies used by this novel class of methods, and organize their components into a common framework, highlighting several practical advantages and disadvantages of the individual methods. We also give an overview of new insights these methods have already provided regarding the wiring and gene regulation of cell differentiation. As the trajectory inference field is still in its infancy, we propose several future developments that will ultimately lead to a global and data‐driven way of studying immune cell differentiation.

arrEYE: a customized platform for high-resolution copy number analysis of coding and noncoding regions of known and candidate retinal dystrophy genes and retinal noncoding RNAs.

Purpose:Our goal was to design a customized microarray, arrEYE, for high-resolution copy number variant (CNV) analysis of known and candidate genes for inherited retinal dystrophy (iRD) and retina-expressed noncoding RNAs (ncRNAs).Methods:arrEYE contains probes for the full genomic region of 106 known iRD genes, including those implicated in retinitis pigmentosa (RP) (the most frequent iRD), cone–rod dystrophies, macular dystrophies, and an additional 60 candidate iRD genes and 196 ncRNAs. Eight CNVs in iRD genes identified by other techniques were used as positive controls. The test cohort consisted of 57 patients with autosomal dominant, X-linked, or simplex RP.Results:In an RP patient, a novel heterozygous deletion of exons 7 and 8 of the HGSNAT gene was identified: c.634-408_820+338delinsAGAATATG, p.(Glu212Glyfs*2). A known variant was found on the second allele: c.1843G>A, p.(Ala615Thr). Furthermore, we expanded the allelic spectrum of USH2A and RCBTB1 with novel CNVs.Conclusion:The arrEYE platform revealed subtle single-exon to larger CNVs in iRD genes that could be characterized at the nucleotide level, facilitated by the high resolution of the platform. We report the first CNV in HGSNAT that, combined with another mutation, leads to RP, further supporting its recently identified role in nonsyndromic iRD.Genet Med 19 4, 457–466.

A comparison of single-cell trajectory inference methods: towards more accurate and robust tools

Using single-cell-omics data, it is now possible to computationally order cells along trajectories, allowing the unbiased study of cellular dynamic processes. Since 2014, more than 50 trajectory inference methods have been developed, each with its own set of methodological characteristics. As a result, choosing a method to infer trajectories is often challenging, since a comprehensive assessment of the performance and robustness of each method is still lacking. In order to facilitate the comparison of the results of these methods to each other and to a gold standard, we developed a global framework to benchmark trajectory inference tools. Using this framework, we compared the trajectories from a total of 29 trajectory inference methods, on a large collection of real and synthetic datasets. We evaluate methods using several metrics, including accuracy of the inferred ordering, correctness of the network topology, code quality and user friendliness. We found that some methods, including Slingshot, TSCAN and Monocle DDRTree, clearly outperform other methods, although their performance depended on the type of trajectory present in the data. Based on our benchmarking results, we therefore developed a set of guidelines for method users. However, our analysis also indicated that there is still a lot of room for improvement, especially for methods detecting complex trajectory topologies. Our evaluation pipeline can therefore be used to spearhead the development of new scalable and more accurate methods, and is available at github.com/dynverse/dynverse. To our knowledge, this is the first comprehensive assessment of trajectory inference methods. For now, we exclusively evaluated the methods on their default parameters, but plan to add a detailed parameter tuning procedure in the future. We gladly welcome any discussion and feedback on key decisions made as part of this study, including the metrics used in the benchmark, the quality control checklist, and the implementation of the method wrappers. These discussions can be held at github.com/dynverse/dynverse/issues.

Genomic amplifications and distal 6q loss : novel markers for poor survival in high-risk neuroblastoma patients

Abstract Background Neuroblastoma is characterized by substantial clinical heterogeneity. Despite intensive treatment, the survival rates of high-risk neuroblastoma patients are still disappointingly low. Somatic chromosomal copy number aberrations have been shown to be associated with patient outcome, particularly in low- and intermediate-risk neuroblastoma patients. To improve outcome prediction in high-risk neuroblastoma, we aimed to design a prognostic classification method based on copy number aberrations. Methods In an international collaboration, normalized high-resolution DNA copy number data (arrayCGH and SNP arrays) from 556 high-risk neuroblastomas obtained at diagnosis were collected from nine collaborative groups and segmented using the same method. We applied logistic and Cox proportional hazard regression to identify genomic aberrations associated with poor outcome. Results In this study, we identified two types of copy number aberrations that are associated with extremely poor outcome. Distal 6q losses were detected in 5.9% of patients and were associated with a 10-year survival probability of only 3.4% (95% confidence interval [CI] = 0.5% to 23.3%, two-sided P = .002). Amplifications of regions not encompassing the MYCN locus were detected in 18.1% of patients and were associated with a 10-year survival probability of only 5.8% (95% CI = 1.5% to 22.2%, two-sided P < .001). Conclusions Using a unique large copy number data set of high-risk neuroblastoma cases, we identified a small subset of high-risk neuroblastoma patients with extremely low survival probability that might be eligible for inclusion in clinical trials of new therapeutics. The amplicons may also nominate alternative treatments that target the amplified genes.

SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development

Recent advances in RNA sequencing enable the generation of genome-wide expression data at the single-cell level, opening up new avenues for transcriptomics and systems biology. A new application of single-cell whole-transcriptomics is the unbiased ordering of cells according to their progression along a dynamic process of interest. We introduce SCORPIUS, a method which can effectively reconstruct an ordering of individual cells without any prior information about the dynamic process. Comprehensive evaluation using ten scRNA-seq datasets shows that SCORPIUS consistently outperforms state-of-the-art techniques. We used SCORPIUS to generate novel hypotheses regarding dendritic cell development, which were subsequently validated in vivo. This work enables data-driven investigation and characterization of dynamic processes and lays the foundation for objective benchmarking of future trajectory inference methods.

IncGraph : incremental graphlet counting for topology optimisation

Motivation Graphlets are small network patterns that can be counted in order to characterise the structure of a network (topology). As part of a topology optimisation process, one could use graphlet counts to iteratively modify a network and keep track of the graphlet counts, in order to achieve certain topological properties. Up until now, however, graphlets were not suited as a metric for performing topology optimisation; when millions of minor changes are made to the network structure it becomes computationally intractable to recalculate all the graphlet counts for each of the edge modifications. Results IncGraph is a method for calculating the differences in graphlet counts with respect to the network in its previous state, which is much more efficient than calculating the graphlet occurrences from scratch at every edge modification made. In comparison to static counting approaches, our findings show IncGraph reduces the execution time by several orders of magnitude. The usefulness of this approach was demonstrated by developing a graphlet-based metric to optimise gene regulatory networks. IncGraph is able to quickly quantify the topological impact of small changes to a network, which opens novel research opportunities to study changes in topologies in evolving or online networks, or develop graphlet-based criteria for topology optimisation. Availability IncGraph is freely available as an open-source R package on CRAN (incgraph). The development version is also available on GitHub (rcannood/incgraph).

A comprehensive evaluation of module detection methods for gene expression data

A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods.Modules composed of groups of genes with similar expression profiles tend to be functionally related and co-regulated. Here, Saelens et al evaluate the performance of 42 computational methods and provide practical guidelines for module detection in gene expression data.

The Transcription Factor ZEB2 Is Required to Maintain the Tissue-Specific Identities of Macrophages

SUMMARY Heterogeneity between different macrophage populations has become a defining feature of this lineage. However, the conserved factors defining macrophages remain largely unknown. The transcription factor ZEB2 is best described for its role in epithelial to mesenchymal transition; however, its role within the immune system is only now being elucidated. We show here that Zeb2 expression is a conserved feature of macrophages. Using Clec4f‐cre, Itgax‐cre, and Fcgr1‐cre mice to target five different macrophage populations, we found that loss of ZEB2 resulted in macrophage disappearance from the tissues, coupled with their subsequent replenishment from bone‐marrow precursors in open niches. Mechanistically, we found that ZEB2 functioned to maintain the tissue‐specific identities of macrophages. In Kupffer cells, ZEB2 achieved this by regulating expression of the transcription factor LXR&agr;, removal of which recapitulated the loss of Kupffer cell identity and disappearance. Thus, ZEB2 expression is required in macrophages to preserve their tissue‐specific identities. Graphical Abstract Figure. No caption available. HighlightsZEB2 is highly expressed across the macrophage lineageZEB2 preserves the tissue‐specific identities of macrophages across tissuesZEB2 deficient macrophages are outcompeted by WT counterpartsLXR&agr; is crucial for Kupffer cell identity and is maintained by ZEB2 &NA; Scott et al. demonstrate that ZEB2 is critical for maintaining the tissue identities of macrophages. Loss of ZEB2 results in tissue‐specific changes in different macrophage populations and their subsequent disappearance. In Kupffer cells, ZEB2 maintains LXR&agr; expression, loss of which reproduces the change in Kupffer cell identity and their disappearance.