Lukas M. Weber

High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy

Immune-checkpoint blockade has revolutionized cancer therapy. In particular, inhibition of programmed cell death protein 1 (PD-1) has been found to be effective for the treatment of metastatic melanoma and other cancers. Despite a dramatic increase in progression-free survival, a large proportion of patients do not show durable responses. Therefore, predictive biomarkers of a clinical response are urgently needed. Here we used high-dimensional single-cell mass cytometry and a bioinformatics pipeline for the in-depth characterization of the immune cell subsets in the peripheral blood of patients with stage IV melanoma before and after 12 weeks of anti-PD-1 immunotherapy. During therapy, we observed a clear response to immunotherapy in the T cell compartment. However, before commencing therapy, a strong predictor of progression-free and overall survival in response to anti-PD-1 immunotherapy was the frequency of CD14+CD16−HLA-DRhi monocytes. We confirmed this by conventional flow cytometry in an independent, blinded validation cohort, and we propose that the frequency of monocytes in PBMCs may serve in clinical decision support.

Maximizing mutagenesis with solubilized CRISPR-Cas9 ribonucleoprotein complexes.

CRISPR-Cas9 enables efficient sequence-specific mutagenesis for creating somatic or germline mutants of model organisms. Key constraints in vivo remain the expression and delivery of active Cas9-sgRNA ribonucleoprotein complexes (RNPs) with minimal toxicity, variable mutagenesis efficiencies depending on targeting sequence, and high mutation mosaicism. Here, we apply in vitro assembled, fluorescent Cas9-sgRNA RNPs in solubilizing salt solution to achieve maximal mutagenesis efficiency in zebrafish embryos. MiSeq-based sequence analysis of targeted loci in individual embryos using CrispRVariants, a customized software tool for mutagenesis quantification and visualization, reveals efficient bi-allelic mutagenesis that reaches saturation at several tested gene loci. Such virtually complete mutagenesis exposes loss-of-function phenotypes for candidate genes in somatic mutant embryos for subsequent generation of stable germline mutants. We further show that targeting of non-coding elements in gene regulatory regions using saturating mutagenesis uncovers functional control elements in transgenic reporters and endogenous genes in injected embryos. Our results establish that optimally solubilized, in vitro assembled fluorescent Cas9-sgRNA RNPs provide a reproducible reagent for direct and scalable loss-of-function studies and applications beyond zebrafish experiments that require maximal DNA cutting efficiency in vivo. Summary: Maximal mutagenesis efficiency is achieved in vivo in zebrafish embryos using salt-solubilized, fluorescently labelled Cas9-sgRNA complexes.

Statistical methods for detecting differentially methylated loci and regions.

DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.

Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data

Recent technological developments in high‐dimensional flow cytometry and mass cytometry (CyTOF) have made it possible to detect expression levels of dozens of protein markers in thousands of cells per second, allowing cell populations to be characterized in unprecedented detail. Traditional data analysis by “manual gating” can be inefficient and unreliable in these high‐dimensional settings, which has led to the development of a large number of automated analysis methods. Methods designed for unsupervised analysis use specialized clustering algorithms to detect and define cell populations for further downstream analysis. Here, we have performed an up‐to‐date, extensible performance comparison of clustering methods for high‐dimensional flow and mass cytometry data. We evaluated methods using several publicly available data sets from experiments in immunology, containing both major and rare cell populations, with cell population identities from expert manual gating as the reference standard. Several methods performed well, including FlowSOM, X‐shift, PhenoGraph, Rclusterpp, and flowMeans. Among these, FlowSOM had extremely fast runtimes, making this method well‐suited for interactive, exploratory analysis of large, high‐dimensional data sets on a standard laptop or desktop computer. These results extend previously published comparisons by focusing on high‐dimensional data and including new methods developed for CyTOF data. R scripts to reproduce all analyses are available from GitHub (https://github.com/lmweber/cytometry-clustering-comparison), and pre‐processed data files are available from FlowRepository (FR‐FCM‐ZZPH), allowing our comparisons to be extended to include new clustering methods and reference data sets.

High-dimensional single-cell analysis reveals the immune signature of narcolepsy

Hartmann et al. show that, in narcolepsy, T cells exhibit a proinflammatory signature characterized by increased production of TNF, IL-2, and B cell–supporting cytokines.

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals).

MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions

DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.

diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering

High-dimensional flow and mass cytometry allow cell types and states to be characterized in great detail by measuring expression levels of more than 40 targeted protein markers per cell at the single-cell level. However, data analysis can be difficult, due to the large size and dimensionality of datasets as well as limitations of existing computational methods. Here, we present diffcyt, a new computational framework for differential discovery analyses in high-dimensional cytometry data, based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. Our approach provides improved statistical performance, including for rare cell populations, along with flexible experimental designs and fast runtimes in an open-source framework.

Author Correction: High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy

In the version of this article initially published, Figs. 5a,c and 6a were incorrect because of an error in a metadata spreadsheet that led to the healthy donor patient 2 (HD2) samples being used twice in the analysis of baseline samples and in the analysis at 12 weeks of anti-PD-1 therapy, while HD3 samples had not been used.

A Retrospective Study of the Effectiveness of Four Different Treatments of Periodontal Disease in Equine Cheek Teeth

Periodontal disease of equine cheek teeth is common and may lead to tooth loss if left untreated. Limited information is available comparing the effectiveness of treatment methods. The objective of this study was to retrospectively compare the effectiveness of 4 commonly used treatments in reducing periodontal pocket depth (in addition to routine dental treatment and occlusal equilibration). The 4 treatments compared were (1) removal of feed material, lavaging the pocket with dilute chlorhexidine, and then rinsing the mouth with chlorhexidine-containing mouthwash (CL); (2) CL plus placement of metronidazole into the pocket (M); (3) M plus the addition of polyvinyl siloxane temporary filling over the diastema (PVS); and (4) diastema widening to increase the interdental space, then PVS (DW). Pocket measurements were compared before and 2 to 6 months after treatment. Treatment groups CL, M, and PVS showed statistically significant reductions in pocket depth following treatment. The mean pocket depth reduction was the greatest in the DW group (and this was the only group with no cases having an increase in pocket depth), but this was not significant due to the small sample size. Additional analysis to compare effectiveness revealed a confounding effect of initial pocket depth. After accounting for this, DW was associated with smaller improvements than the other treatments, however, this was also based on a small sample size. After accounting for confounders, differences between treatments CL, M and PVS were not found to be significant, although all were associated with statistically significant reductions in pocket depth.