Lung Single-Cell Signaling Interaction Map Reveals Basophil Role in Macrophage Imprinting

Cohen, Michal; Giladi, Amir; Gorki, Anna-Dorothea; Solodkin, Dikla Gelbard; Zada, Mor; Hladik, Anastasiya; Miklósi, András G.; Salame, Tomer-Meir; Halpern, Keren Bahar; David, Eyal; Itzkovitz, Shalev; Harkany, Tibor; Knapp, Sylvia; Amit, Ido

doi:10.1016/j.cell.2018.09.009

Cited by 348 publications

(416 citation statements)

References 48 publications

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“…Molecular Systems Biology 15: e8746 | 2019 obtained from the recent CellPhoneDB (Vento-Tormo et al, 2018) and used to interpret the highly expressed genes across clusters using statistical models (Zepp et al, 2017;Zhou et al, 2017;Cohen et al, 2018;Vento-Tormo et al, 2018).…”

Section: Gene Set Analysismentioning

confidence: 99%

“…Here, interaction between cell clusters is inferred from the expression of receptors and their cognate ligands. Ligand–receptor pair labels can be obtained from the recent CellPhoneDB (Vento‐Tormo et al , ) and used to interpret the highly expressed genes across clusters using statistical models (Zepp et al , ; Zhou et al , ; Cohen et al , ; Vento‐Tormo et al , ).…”

Section: Introductionmentioning

confidence: 99%

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Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,525

1,386

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Single‐cell RNA ‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one's data. Here, we detail the steps of a typical single‐cell RNA ‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. We formulate current best‐practice recommendations for these steps based on independent comparison studies. We have integrated these best‐practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

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Section: Gene Set Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,525

1,386

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“…Compared with microbial cells, the more mobile living cells (especially human and animal cells) have different surface morphology and protein distribution . Thus, the direct imprinting of human and animal cells requires milder conditions, and sometimes pretreatment of cells (such as fixing cells with paraformaldehyde) is required . Therefore, the imprinting of living cells is mainly induced by the receptor–ligand interaction between the cell membrane and the imprinted polymer to induce specific cellular responses and trigger relevant cellular behaviors.…”

Section: Biological Molecular Imprinting With Different Scalementioning

confidence: 99%

Molecularly Imprinted Materials for Selective Biological Recognition

Zhang

et al. 2019

Macromol. Rapid Commun.

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Molecular imprinting is an approach of generating imprinting cavities in polymer structures that are compatible with the target molecules. The cavities have memory for shape and chemical recognition, similar to the recognition mechanism of antigen–antibody in organisms. Their structures are also called biomimetic receptors or synthetic receptors. Owing to the excellent selectivity and unique structural predictability of molecularly imprinted materials (MIMs), practical MIMs have become a rapidly evolving research area providing key factors for understanding separation, recognition, and regenerative properties toward biological small molecules to biomacromolecules, even cell and microorganism. In this review, the characteristics, morphologies, and applicability of currently popular carrier materials for molecular imprinting, especially the fundamental role of hydrogels, porous materials, hierarchical nanoparticles, and 2D materials in the separation and recognition of biological templates are discussed. Moreover, through a series of case studies, emphasis is given on introducing imprinting strategies for biological templates with different molecular scales. In particular, the differences and connections between small molecular imprinting (bulk imprinting, “dummy” template imprinting, etc.), large molecular imprinting (surface imprinting, interfacial imprinting, etc.), and cell imprinting strategies are demonstrated in detail. Finally, future research directions are provided.

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“…The neuronal data collection was downloaded from GEO accession number GSE71585 (Tasic (2016) [37]), GSE115746 [38]), and GSE102827 (Hrvatin [12]). The mouse lung development dataset was downloaded from GEO accession number GSE119228 [7]. The healthy mouse kidney dataset was downloaded from GEO accession number GSE107585 [29].…”

Section: Data Collections and Data Processingmentioning

confidence: 99%

“…We refined the annotation of the Tabula Muris lung FACS data as a query set using a mouse lung development dataset from of E12.5 to Day7 having 20,931 cells [7] from 22 cell types as reference. scClassify correctly annotated the common cell types such as endothelial, natural killer, T and B cells.…”

Section: Case Studies: Identification Of Small Cell Subpopulationmentioning

confidence: 99%

scClassify: hierarchical classification of cells

Lin

Cao

Kim

et al. 2019

Preprint

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17Cell type identification is a key computational challenge in single-cell RNA-sequencing 18 (scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq datasets, 19 we present scClassify, a hierarchical classification framework based on ensemble learning. 20 scClassify can identify cells from published scRNA-seq datasets more accurately and more 21 finely than in the original publications. We also estimate the cell number needed for accurate 22 classification anywhere in a cell type hierarchy. 23 24Single cell RNA-sequencing (scRNA-seq) datasets have become larger and more diverse, driving the 25 need for classification methods that can provide more refined discrimination between cell types. 26Many major cell types can be divided into subtypes in a hierarchical fashion, forming what we call a 27 'cell type hierarchy' 1,2 . Approaches to scRNA-seq studies that account for cell type hierarchies in 28 experimental design and cell type identification will permit more nuanced interpretations of the 29 resulting data. 3 30 31The most common approach to identifying cell types within scRNA-seq data is unsupervised 32 clustering 4-6 followed by manual annotation using known marker genes. However, the number of 33

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Lung Single-Cell Signaling Interaction Map Reveals Basophil Role in Macrophage Imprinting

Cited by 348 publications

References 48 publications

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Molecularly Imprinted Materials for Selective Biological Recognition

scClassify: hierarchical classification of cells

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