PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells

Wolf, F. Alexander; Hamey, Fiona; Plass, Mireya; Solana, Jordi; Dahlin, Joakim S.; Göttgens, Berthold; Rajewsky, Nikolaus; Simon, Lukas M.; Theis, Fabian J.

doi:10.1186/s13059-019-1663-x

Cited by 1,227 publications

(1,238 citation statements)

References 38 publications

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“…Novel statistical methods are currently being developed to identify and reconstruct developmental trajectories from heterogeneous scRNA-seq datasets using pseudotime analysis. To validate our findings, we next applied the recently developed partition-based graph abstraction (PAGA) method (18) to reconstruct the developmental trajectories in our dataset. Consistent with our previous findings, pseudotime analysis revealed a gradient of T-cell differentiation along the naïve-memory differentiation axis, which lead to the identification of three distinct differentiation pathways associated with the acquisition of a Th1, Th17 or Treg phenotype (Fig.…”

Section: Single-cell Mrna and Protein Immunophenotyping Identifies DImentioning

confidence: 96%

Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis

Trzupek

Dunstan

Cutler

et al. 2019

Preprint

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The transcriptomic and proteomic characterisation of CD4 + T cells at the single-cell level has been performed traditionally by two largely exclusive types of technologies: single cell RNAsequencing (scRNA-seq) technologies and antibody-based cytometry. Here we demonstrate that the simultaneous targeted quantification of mRNA and protein expression in single-cells provides a high-resolution map of human primary CD4 + T cells, and reveals precise trajectories of Th1, Th17 and regulatory T-cell differentiation in blood and tissue. We report modest correlation between mRNA and protein in primary CD4 + T cells at the single-cell level, highlighting the value of protein expression data to characterise cell effector function. This transcriptomic and proteomic hybrid technology provides a massively-parallel, cost-effective, solution to dissect the heterogeneity of immune cell populations and to immunophenotype rare and potentially pathogenic immune subsets, and could have important clinical applications to redefine differentiation and activation of circulating and tissue-resident human immune cells.

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Section: Single-cell Mrna and Protein Immunophenotyping Identifies DImentioning

confidence: 96%

Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis

Trzupek

Dunstan

Cutler

et al. 2019

Preprint

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“…Besides matrix decomposition techniques, network-based methods are commonly used in the single-cell analysis [19][20][21] . In addition to a rich set of graph-based algorithms, networks bring to the realm of single-cell analysis efficient and intuitive ways to explore and visualize large-scale data.…”

Section: Introductionmentioning

confidence: 99%

A multiresolution framework to characterize single-cell state landscapes

Mohammadi

Dávila-Velderrain

Kellis

2019

Preprint

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Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although a large number of methods and approaches exist, robustly identifying underlying cell states and their associations is still a major challenge; given the nonexclusive and dynamic influence of multiple unknown sources of variability, the existence of state continuum at the time-scale of observation, and the inevitable snapshot nature of experiments. As a way to address some of these challenges, here we introduce ACTIONet, a comprehensive framework that combines archetypal analysis and network theory to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet uses multilevel matrix decomposition and network reconstruction to simultaneously learn cell state patterns, quantify single-cell states, and reconstruct a reproducible structural representation of the transcriptional state space that is geometrically mapped to a color space. A color-enhanced quantitative view of cell states enables novel visualization, prediction, and annotation approaches. Using data from multiple tissues, organisms, and developmental conditions, we illustrate how ACTIONet facilitates the reconstruction and exploration of single-cell state landscapes.

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“…Violin plots of the podocyte marker PODXL in organoids treated with IFN-g and tunicamycin revealed relatively decreased expression of PODXL in the G1 glomerular epithelial cell cluster, suggesting the G1 organoid may acquire a less differentiated state under stress ( Figure 4B). We also discovered that G1 organoids treated with both IFN-g and tunicamycin demonstrated less distinct cluster topology seen in partition-based graph abstraction (PAGA) 35 . More specifically, G1 glomerular epithelial cells became closer to the other cell types in spatial relationship, whereas G0 glomerular epithelial cells still remained more distinct from the other cell clusters ( Figure 4C).…”

Section: Resultsmentioning

confidence: 90%

“…B. Violin plots of PODXL expression in the GEC clusters of G0 and G1 organoids subjected to either IFN-g alone or both IFN-g and tunicamycin. C. Partition-based graph abstraction (PAGA) 35…”

Section: Resultsmentioning

confidence: 99%

ProfilingAPOL1Nephropathy Risk Variants in Genome-Edited Kidney Organoids with Single-Cell Transcriptomics

Liu

Radmanesh

Chung

et al. 2019

Preprint

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Running Title: APOL1 Variant Organoid scRNA-seq Word Count: 205 (Abstract), 1736 (Main Text, excluding Methods)ABSTRACT Background DNA variants in APOL1 associate with kidney disease, but the pathophysiological mechanisms remain incompletely understood. Model organisms lack the APOL1 gene, limiting the degree to which disease states can be recapitulated. Here we present single-cell RNA sequencing (scRNAseq) of genome-edited human kidney organoids as a platform for profiling effects of APOL1 risk variants in diverse nephron cell types. MethodsWe performed footprint-free CRISPR-Cas9 genome editing of human induced pluripotent stem cells (iPSCs) to knock in APOL1 high-risk G1 variants at the native genomic locus. iPSCs were differentiated into kidney organoids, treated with vehicle, IFN-g, or the combination of IFN-g and tunicamycin, and analyzed with scRNA-seq to profile cell-specific changes in differential gene expression patterns, compared to isogenic G0 controls. ResultsBoth G0 and G1 iPSCs differentiated into kidney organoids containing nephron-like structures with glomerular epithelial cells, proximal tubules, distal tubules, and endothelial cells.Organoids expressed detectable APOL1 only after exposure to IFN-g. scRNA-seq revealed cell type-specific differences in G1 organoid response to APOL1 induction. Additional stress of tunicamycin exposure led to increased glomerular epithelial cell dedifferentiation in G1 organoids. ConclusionsSingle-cell transcriptomic profiling of human genome-edited kidney organoids expressing APOL1 risk variants provides a novel platform for studying the pathophysiology of APOL1mediated kidney disease.

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PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells

Cited by 1,227 publications

References 38 publications

Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis

Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis

A multiresolution framework to characterize single-cell state landscapes

ProfilingAPOL1Nephropathy Risk Variants in Genome-Edited Kidney Organoids with Single-Cell Transcriptomics

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