Mind the Map: Technology Shapes the Myeloid Cell Space

The myeloid cell system shows very high plasticity, which is crucial to quickly adapt to changes during an immune response. From the beginning, this high plasticity has made cell type classification within the myeloid cell system difficult. This publication reports on earlier attempts of cell type classification in the myeloid cell system, discusses current approaches and their pros and cons, and proposes future strategies for cell type classification within the myeloid cell system that can be easily extended to other cell types.

For more information, see the original publication: Günther P. et al. Mind the Map: Technology Shapes the Myeloid Cell Space, Front Immunol, 10: 2287, 2019. doi:10.3389/fimmu.2019.02287

Pheno-seq: Linking visual features and gene expression

Pheno-seq directly links visual phenotypes and gene expression in 3D culture systems at high-throughput. (c) DKFZ

Linking heterogeneity of morphological phenotypes and the underlying transcriptome is still limited. “Pheno-seq” is able to directly link visual features of 3D cell culture systems with profiling their transcriptome. As prototypic applications breast and colorectal cancer (CRC) spheroids were analyzed by pheno-seq. We anticipate that the ability to integrate transcriptome analysis and morphological patho-phenotypes of cancer cells will provide novel insight on the molecular origins of intratumor heterogeneity.

For more information, see the original publication: Tirier, S.M.. et al. Pheno-seq – linking visual features and gene expression in 3D cell culture systems. Sci Rep 9, 12367, 2019. doi:10.1038/s41598-019-48771-4

scGen predicts single-cell perturbation responses

Predicting cellular behavior in silico: Trained on data that capture stimulation effects for a set of cell types, scGen can be used to model cellular responses in a new cell type. © Helmholtz Zentrum München

Scientists from the Helmholtz Zentrum München developed scGen that predicts single-cell pertubation processes.

Accurately modeling cellular response to perturbations is a central goal of computational biology. scGen (https://github.com/theislab/scgen) is a model, combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. scGen can accurately model perturbation and infection response of cells across cell types, studies and species. scGen learns cell-type and species-specific responses implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in a healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.

For more information, see the press release and the original publication: Lotfollahi, M., et al. scGen predicts single-cell perturbation responses. Nat Methods 16, 715–721, 2019. doi:10.1038/s41592-019-0494-8

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

Cluster analysis results of mouse intestinal epithelium dataset from Haber et al (2017).

Single‐cell RNA‐seq has enabled gene expression to be studied at an unprecedented resolution. This review details 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.

For more information, see the original publication: Luecken M.D. et al. Current best practices in single‐cell RNA‐seq analysis: a tutorial, Mol Syst Biol 15, e8746, Mol Sys, 2019. doi:10.15252/msb.20188746

Single cell RNA-seq denoising using a deep count autoencoder

Count based loss function is necessary to identify celltypes in simulated data with high
levels of dropout noise.
(c) Helmholtz Zentrum München

Scientists from the Helmholtz Zentrum München developed a deep count autoencoder (DCA) to denoise single cell RNA-seq datasets.

The deep count autoencoder network (DCA) denoises scRNA-seq data and removes the dropout effect by taking the count structure, overdispersed nature and sparsity of the data into account using a deep autoencoder with zero-inflated negative binomial (ZINB) loss function. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery. The software can be downloaded from: https://github.com/theislab/dca

For more information, see the original publication: Eraslan G. et al. Single cell RNA-seq denoising using a deep count autoencoder. Nat. Commun., 2019. doi:10.1038/s41467-018-07931-2

Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics

A lineage tree for complex animals from single-cell transcriptomics. (c) MDC, Berlin.

Flatworms of the species Schmidtea mediterranea are immortal—adult animals contain a large pool of pluripotent stem cells that continuously differentiate into all adult cell types. Therefore, single-cell transcriptome profiling of adult animals should reveal mature and progenitor cells. By combining perturbation experiments, gene expression analysis, a computational method that predicts future cell states from transcriptional changes, and a lineage reconstruction method, we placed all major cell types onto a single lineage tree that connects all cells to a single stem cell compartment. We characterized gene expression changes during differentiation and discovered cell types important for regeneration. Our results demonstrate the importance of single-cell transcriptome analysis for mapping and reconstructing fundamental processes of developmental and regenerative biology at high resolution.

For more information, see the original publication: Plass M. et al. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics, Science, 360(6391), 2018. doi:10.1126/science.aaq1723

Tracing the origin of each cell in a zebrafish

LINNAEUS makes it possible to trace the origin of each cell of a zebrafish.
(c) Microscopic image: Junker Lab, MDC

Scientists from the Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) have used CRISPR-Cas9 genome editing to pioneer a technique capable of determining both the type and origin of all the cells in an organism.

"Whenever we use such a technology to examine an organ or an organism, we find not only familiar cell types, but also unknown and rare ones," says Dr. Jan Philipp Junker, head of the Quantitative Developmental Biology research group at MDC. "The next question is obvious: Where do these different types come from?" Junker's group describes a technique, called LINNAEUS that enables researchers to determine the cell type as well as the lineage of each cell.

For more information, see the press release by MDC, ScienceDaily, and the original publication: Spanjaard B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars. Nature Biotechnology, 2018. doi:10.1038/nbt.4124

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