An in vivo model of functional and vascularized human brain organoids

Content introduction:

  • An in vivo model of functional and vascularized human brain organoids
  • Highly scalable generation of DNA methylation profiles in single cells
  • Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars
  • Metabolomics activity screening for identifying metabolites that modulate phenotype
  • Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors

1. An in vivo model of functional and vascularized human brain organoids

Differentiation of human pluripotent stem cells to small brain-like structures known as brain organoids offers an unprecedented opportunity to model human brain development and disease. To provide a vascularized and functional in vivo model of brain organoids, Abed AlFatah Mansour at The Salk Institute for Biological Studies in La Jolla, California, USA and his colleagues established a method for transplanting human brain organoids into the adult mouse brain. Organoid grafts showed progressive neuronal differentiation and maturation, gliogenesis, integration of microglia, and growth of axons to multiple regions of the host brain. In vivo two-photon imaging demonstrated functional neuronal networks and blood vessels in the grafts. Finally, in vivo extracellular recording combined with optogenetics revealed intragraft neuronal activity and suggested graft-to-host functional synaptic connectivity. This combination of human neural organoids and an in vivo physiological environment in the animal brain may facilitate disease modeling under physiological conditions.

Read more, please click https://www.nature.com/articles/nbt.4127

2. Highly scalable generation of DNA methylation profiles in single cells

Ryan M Mulqueen at Oregon Health & Science University in Portland, Oregon, USA and his colleagues present a highly scalable assay for whole-genome methylation profiling of single cells. They use their approach, single-cell combinatorial indexing for methylation analysis (sci-MET), to produce 3,282 single-cell bisulfite sequencing libraries and achieve read alignment rates of 68 ± 8%. They apply sci-MET to discriminate the cellular identity of a mixture of three human cell lines and to identify excitatory and inhibitory neuronal populations from mouse cortical tissue.

Read more, please click https://www.nature.com/articles/nbt.4112

3. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars

A key goal of developmental biology is to understand how a single cell is transformed into a full-grown organism comprising many different cell types. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the developmental origin of cells remains challenging. Here Bastiaan Spanjaard at Max Delbrück Center for Molecular Medicine in Berlin, Germany and his colleagues present LINNAEUS (lineage tracing by nuclease-activated editing of ubiquitous sequences)—a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, they reconstruct developmental lineage trees in zebrafish larvae, and in heart, liver, pancreas, and telencephalon of adult fish. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Read more, please click https://www.nature.com/articles/nbt.4124

4. Metabolomics activity screening for identifying metabolites that modulate phenotype

Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype. Although appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell’s or an organism’s phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.

Read more, please click https://www.nature.com/articles/nbt.4101

5. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors

Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. Laleh Haghverdi at European Bioinformatics Institute (EMBL-EBI) in Cambridge, UK and his colleagues present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Their approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. They demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, they demonstrate that their MNN batch-effect-correction method can be scaled to large numbers of cells.

Read more, please click https://www.nature.com/articles/nbt.4091

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