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A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space

TL;DR: A rule-based data-informed approach to build next generation cellular consensus maps, using the human dendritic-cell and monocyte compartment in peripheral blood as an example, and providing a generalizable method for building consensus maps for the life sciences.
Abstract: Single-cell genomic techniques are opening new avenues to understand the basic units of life. Large international efforts, such as those to derive a Human Cell Atlas, are driving progress in this area; here, cellular map generation is key. To expedite the inevitable iterations of these underlying maps, we have developed a rule-based data-informed approach to build next generation cellular consensus maps. Using the human dendritic-cell and monocyte compartment in peripheral blood as an example, we performed computational integration of previous, partially overlapping maps using an approach we termed ‘backmapping’, combined with multi-color flow-cytometry and index sorting-based single-cell RNA-sequencing. Our general strategy can be applied to any atlas generation for humans and other species. Graphical Highlights Defining a consensus of the human myeloid cell compartment in peripheral blood 3 monocytes subsets, pDC, cDC1, DC2, DC3 and precursor DC make up the compartment Distinguish myeloid cell compartment from other cell spaces, e.g. the NK cell space Providing a generalizable method for building consensus maps for the life sciences

Summary (5 min read)

Introduction

  • Such single-cell technologies allow for a fully data-driven analysis to establish cell maps of an organism, such as those proposed by the Human Cell Atlas consortium (Rozenblatt-Rosen et al., 2017).
  • Reliable consensus maps are a prerequisite to reconcile conflicting data that might have been generated based on different data generating approaches (Edney, 2019; Monmonier, 2015).
  • In order to establish a consensus map of the human mononuclear myeloid cell compartment the authors allow for the integration of prior knowledge in that they define a priori criteria for the cellular compartment under study in order to increase resolution and to allow 5 building of a consensus map.

Results

  • Integrated phenotypic characterization of the myeloid cell compartment in human peripheral blood CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was notthis version posted June 3, 2019.
  • To integrate the identified DC subsets in map 1 and map 2 with each other, the authors computed a UMAP topology from the original map 1 single-cell transcriptome data comprising the DC cell space and overlaid the signatures of the map 2 DC subsets (pDC, cDC1, cDC2, pre-DC) .
  • This analysis showed that if the totality of the Lin-CD16+ compartment is mapped back onto the Lin- UMAP topology , NK cells (CD56+), monocytes (CD56-CD16+/-) and granulocyte fractions (CD16high) are included in this cellular compartment.

Discussion

  • Consensus maps are an important instrument within an iterative process of producing cellular maps of all organs and tissues in different species, including humans.
  • Because the authors propose to include prior knowledge in the respective scientific field into the algorithm for generating such consensus maps, they define the overall strategy as being ‘data-informed’, combining prior knowledge and data-driven technologies including single-cell omics.
  • CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was notthis version posted June 3, 2019.
  • BioRxiv preprint 20 providing the next iteration of this particular subspace in the myeloid cell map of human peripheral blood.

Acknowledgments:

  • The authors thank Jessica Tamanini for critical review and editing of the manuscript.
  • This work was supported by the German Research Foundation to JLS (GRK 2168, INST 217/577-1, EXC2151/1), by the HGF grant sparse2big to JLS, the FASTGenomics grant of 5 the German Federal Ministry for Economic Affairs and Energy to JLS and the EU project SYSCID under grant number 733100, also known as Funding.
  • F.G is an EMBO YIP awardee and is supported by Singapore Immunology Network (SIgN) and Shanghai Institute of Immunology core funding.
  • The authors declare that there are no competing interests.

Figure Legends

  • Generating a new consensus map of the mononuclear myeloid cell compartment in human peripheral blood.
  • (B) Visualization of ~1.4 mio. live CD45+Lin(CD3, CD19, 5 CD20, CD56)- cells after UMAP dimensionality reduction of the flow cytometry panel introduced in A (left panel), mononuclear myeloid cell compartment (second panel), overlay of index-sorted cells (third panel), UMAP topology of the index-sorted cells based on the single-cell transcriptome data .
  • (B) Heatmap of 10 most significant marker genes for each of the 11 clusters identified and visualized in Figure 2A.
  • (G) UMAP topology of scRNA-seq data derived from the map1 DC and mono subsets (left panel) and overlay of the NK cell signature onto this UMAP topology.
  • 20 25 .CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Tables S1:

  • Cell types classified in the respective studies Data Table S1: 5 Data Table S1.csv.
  • Gene signatures of the 11 clusters identified in their new scRNA-seq consensus map.

Data Table S2:

  • CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was notthis version posted June 3, 2019.
  • Cell types classified in the respective studies .
  • CC-BY-N -ND 4.0 Internatio al licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

  • Peripheral blood mononuclear cells (PBMC) Buffy coats or venipuncture blood were obtained from healthy donors (University hospital Bonn, local ethics vote 203/09) after written consent was given according to the Declaration of Helsinki.
  • 10 Peripheral blood mononuclear cells (PBMC) were isolated by Pancoll (PAN-Biotech) density centrifugation from buffy coats.

METHOD DETAILS

  • Whole blood or buffy coat was diluted in room temperature PBS (1:2 or 1:5, respectively) and layered onto polysuccrose solution (Pancoll; PAN Biotech, Germany) for the enrichment of mononuclear cells by density gradient centrifugation according to the manufacturer's instructions.
  • Washed cells were incubated with L/D Marker DRAQ7 (BioLegend, USA) for 5 min at room temperature before acquisition and sorting of the cells using a BD FACSARIA III (BD BioSciences, USA).
  • CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The authors new index-sorted single cell transcriptome dataset was based on the Smart-Seq2 protocol (Picelli et al., 2013).
  • CDNA was diluted to an average of 200pg/µl and 100pg cDNA from each cell was tagmented by adding 1µl TD and 0.5µl ATM from a Nextera XT DNA Library Preparation Kit to 0.5µl diluted cDNA in each well of a fresh 384-well plate.

Cytospin preparation and May-Grünwald/Giemsa staining

  • Cell populations of interest were sorted into 1.5 ml reaction tubes containing 200 µl FACS-buffer 5 using a BD FACSARIA III (BD BioSciences, USA).
  • Whole blood was diluted in room temperature PBS (1:2) and layered onto polysuccrose solution (Pancoll; PAN Biotech, Germany) for the enrichment of mononuclear cells by density gradient 15 centrifugation according to the manufacturer's instructions.
  • Sequenced single-cell data was demultiplexed using bcl2fastq2 v2.20.
  • Based on the pseudoalignment estimated by Kallisto, transcript levels were quantified as transcripts per million reads (TPM).

Quality control

  • Concerning their new index-sorted and Smart-Seq2-based single cell transcriptome dataset the following quality control scheme using various meta information was performed to obtain highquality transcriptome data: 1) We removed genes that are detected in less than 6 cells (0.2 percent of cells), 2) and removed cells that have less than 1,000 uniquely detected genes.the authors.the authors.
  • Next, 25 .CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • BioRxiv preprint 36 the authors filtered further outlier cells with 3) less than 50,000 unique reads, 4) less than 30% pseudoalignment of reads to the transcriptome, 5) a lower rate of endogenous-to-mitochondrial count rate of 2, 6).
  • To reduce the influence of variation of sequencing depth among samples the authors applied a lognormalization to the data and scaled each cells gene expression profile to a total count of 10,000.
  • The residuals of this regression are scaled and centered and used for further downstream analysis.

Dimensionality reduction and clustering

  • This resulted in a total of 2491 genes, which were used as input for a principal component (PC) analysis.
  • To test for cellular heterogeneity, the authors used a shared nearest neighbor (SNN)-graph based clustering algorithm implemented in the Seurat package.
  • The authors used the first 10 principal components for constructing the SNN-graph and set the resolution to 1.
  • Monocle was used to infer differentiation trajectories by using the Louvain clustering method, umap dimensionality reduction and the SimplePPT algorithm (Qiu et al., 2017) 25 .CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was notthis version posted June 3, 2019.

Additional analysis

  • Differentially expressed (DE) genes were defined using a Wilcoxon-based test for differential gene expression built in the Seurat pipeline (v.2.3.4) (Data Table S1).
  • Top10 DE genes have been visualized using heatmap of hierarchical clustered gene expression 5 profiles.
  • Gene signature enrichment analysis Single-cell RNA-Seq data is inherently sparse and a high-dropout rate is limiting the use of single marker genes to identify cell populations.
  • In order to increase the power, the authors use both up and downregulated gene signatures for the calculation of the gene expression scores.
  • The difference between these two is scaled and visualized.

To assess the single-cell RNA-Seq data of human dendritic cells and monocytes publicly available

  • Under the Gene Expression Omnibus accession number GSE94820, the authors applied the processing 25 .
  • CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was notthis version posted June 3, 2019.
  • Next, the authors followed the general data analysis scheme described at the Seurat package webpage 15 (https://satijalab.org/seurat/get_started_v1_4.html).
  • Briefly, the authors used the filtered cell-gene matrix provided by 10x Genomics and imported the data and performed the analysis with the Seurat package.

Backmapping

  • In order to compare the transcriptome profiles of monocytes isolated from the dataset derived 5 from GSE94820 (Villani et al., 2017) with the comprehensive PBMC dataset, the authors used the previously introduced canonical correlation alignment to combine datasets (Butler et al., 2018).
  • The authors determined the mutual highly variable genes as the overlap of the 4.000 genes from each dataset with highest dispersion.
  • The authors treated the different batches of the HCA dataset 25 as individual datasets and normalized them and the expression table of the consensus map .
  • CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • First, the authors repeated the steps above but without integration of the new consensus map data.

Data visualization

  • In general, the ggplot2 package was used to generate figures (Wickham, 2016).
  • 25 .CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was notthis version posted June 3, 2019.

QUANTIFICATION AND STATISTICAL ANALYSIS

  • Statistical analysis was performed using the R programming language.
  • Statistical tests used are described in the figure legend or methods part, respectively.
  • Differentially expressed genes have been identified using a Wilcoxon-based test for differential gene expression.
  • If not otherwise stated a significance level of 0.1 was applied to adjusted p-values (Benjamini Hochberg).

DATA AND SOFTWARE AVAILABILITY

  • Processed and raw scRNA-seq datasets are available through the Gene Expression Omnibus (GSE126422).
  • Additional Data tables are provided in form of EXCEL Tables (Data S1, S2) Data Table S1: Data Table S1.csv 10 Gene signatures of the 11 clusters identified in their new scRNA-seq consensus map.

ADDITIONAL RESOURCES

  • In addition, the authors provide an interactive web tool to visualize the single-cell RNA-Seq data together with the flow cytometry data at https://paguen.shinyapps.io/DC_MONO/ (external database S1).
  • .CC-BY-NC-ND 4.0 International licensea certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The copyright holder for this preprint (which was notthis version posted June 3, 2019.

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16 May 2017-Immunity
TL;DR: It is shown that monocyte differentiation occurred through de novo enhancer establishment and activation of pre‐established (poised) enhancers, and it is revealed that while Ly6C+ and Ly 6C− monocytes are homogeneous in steady state,Ly6Cint cells display heterogeneity.

216 citations


"A rule-based data-informed cellular..." refers background in this paper

  • ...…markers and results derived from genetic mouse models showing that Ly6chi monocytes (murine equivalents of classical monocytes) can transition into Ly6clow monocytes (murine equivalents of non-classical monocytes) with only a few cells detectable in the transitory state (Mildner et al., 2017)....

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19 Dec 2017-Immunity
TL;DR: A multiparametric phenotypic characterization and unbiased analysis of human dendritic cells subsets in blood, tonsil, spleen, and skin reveals interindividual heterogeneity among DC subsets, especially cDC2s and profiles the heterogeneity of human DC subset among individuals and tissues, providing comprehensive insights for the development of DC‐based therapeutics.

212 citations


"A rule-based data-informed cellular..." refers methods in this paper

  • ...The two previous maps based on single-cell RNA-seq used in our approach as well as a phenotypic analysis of the human blood and tissue myeloid cells were developed to improve our understanding of myeloid cell heterogeneity (Alcantara-Hernandez et al., 2017; See et al., 2017; Villani et al., 2017)....

    [...]

  • ...The two previous maps based on single-cell RNA-seq used in our approach as well as a 20 phenotypic analysis of the human blood and tissue myeloid cells were developed to improve our understanding of myeloid cell heterogeneity (Alcantara-Hernandez et al., 2017; See et al., 2017; Villani et al., 2017)....

    [...]

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TL;DR: In this article, the authors studied the relevance of slanDCs as inflammatory dermal dendritic cells (DCs) in psoriasis and identified their strong capacity to induce T h 17/T h 1 responses.
Abstract: Background Psoriasis is a chronic inflammatory skin disease that is considered to result from activated T cells stimulated by a population of inflammatory dermal dendritic cells (DCs). The origin and identity of these inflammatory dermal DCs are largely unknown. Objective We previously identified slanDCs (6-sulfo LacNAc) DCs as a rich source of TNF-α and as the early major source of IL-12. Here we studied the relevance of slanDCs as inflammatory dermal DCs in psoriasis. Methods Psoriasis skin samples were stained for the presence of activated slanDCs. Functional studies were carried out to determine the cytokine production of slanDCs, their T h 17/T h 1 T-cell programming, and their migration behavior. Results Large numbers of IL-23, TNF-α, and inducible nitric oxide synthase expressing slanDCs were found in psoriatic skin samples, which can be recruited by C5a, CX3CL1, and CXCL12. SlanDCs isolated from blood produced high levels of IL-1s, IL-23, IL-12, and IL-6. Compared with classic CD1c + DCs, slanDCs were far more powerful in programming T h 17/T h 1 T cells that secrete IL-17, IL-22, TNF-α, and IFN-γ, yet CD1c + DCs induced a higher IL-10 production of T cells. Self–nucleic acids complexed to cathelicidin LL37 trigger endosomal Toll-like receptor (TLR) signaling (TLR7, TLR8, TLR9) and are key factors for the activation of DCs in psoriasis. We show that slanDCs respond particularly well to complexes formed of self-RNA and LL37. Similarly, slanDCs stimulated with a synthetic TLR7/8 ligand produced high levels of proinflammatory cytokines. Conclusion Our study defines slanDCs as inflammatory dermal DCs in psoriasis and identifies their strong capacity to induce T h 17/T h 1 responses.

180 citations

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TL;DR: The history of cartography has been freely available online as mentioned in this paper for the last few years, including volumes one and two of The History of Cartography (Vol. 1, ed. Harley and Woodward, 1987); the traditional Asian societies (Vols. 2.1 and 2.2.3, ed., Woodward and Lewis, 1998).
Abstract: The University of Chicago Press has made all four books of Volumes One and Two of The History of Cartography freely available online. These books cover cartography in the ancient and classical world and medieval Europe (Vol. 1, ed. Harley and Woodward, 1987); the traditional Asian societies (Vols. 2.1 and Vol. 2.2, ed. Harley and Woodward, 1992 and 1994); and indigenous societies across the rest of the world (Vol. 2.3, ed. Woodward and Lewis, 1998). The books brought sustained attention to societies and cultures other than those on which map historians had generally focused. They demonstrated the validity of a socio-cultural approach to map history and encouraged much new scholarship. While many thousands of copies of these groundbreaking books have been sold, they are still not readily available to all map historians because of their cost. Online publication now makes this scholarship available to a wider audience.

160 citations


"A rule-based data-informed cellular..." refers background in this paper

  • ...Reliable consensus maps are a prerequisite to reconcile conflicting data that might have been generated based on different data generating approaches (Edney, 2019; Monmonier, 2015)....

    [...]

  • ...These iterations improve the precision, accuracy and available content per data point (Edney, 2019; Monmonier, 2015; Ridpath, 2007)....

    [...]

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TL;DR: Recent advances in understanding of the organization of the DC network in mice and humans, the functional specialization of theDC subsets that compose these networks, and how this has enabled us to begin to elucidate cross-species parallels are outlined.

146 citations


"A rule-based data-informed cellular..." refers result in this paper

  • ...In conclusion, these analyses demonstrate that map 1 DC5 and map 2 pre-DCs represent, to a large extent, the same pre-DC identities and therefore, might be best named according to already published guidelines (Guilliams et al., 2014; Schlitzer and Ginhoux, 2014) as pre-DCs....

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  • ...In conclusion, these analyses demonstrate that map 1 DC5 and map 2 preDCs represent, to a large extent, the same pre-DC identities and therefore, might be best named according to already published guidelines (Guilliams et al., 2014; Schlitzer and Ginhoux, 2014) as pre-DCs....

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