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      Single-cell transcriptomics of human embryos identifies multiple sympathoblast lineages with potential implications for neuroblastoma origin


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          Characterization of the progression of cellular states during human embryogenesis can provide insights into the origin of pediatric diseases. We examined the transcriptional states of neural crest- and mesoderm-derived lineages differentiating into adrenal glands, kidneys, endothelium, and hematopoietic tissue between post-conception weeks 6 and 14 of human development. Our results reveal transitions connecting intermediate mesoderm and progenitors of organ primordia, the hematopoietic system, and endothelial subtypes. Unexpectedly, by using a combination of single cell transcriptomics and lineage tracing, we found that intra-adrenal sympathoblasts at that stage are directly derived from the nerve-associated Schwann cell precursors similarly to local chromaffin cells, whereas the majority of extra-adrenal sympathoblasts arise from the migratory neural crest. In humans, this process persists during several weeks of development within the large intra-adrenal ganglia-like structures, which may also serve as reservoirs of originating cells in neuroblastoma.

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            Is Open Access

            Massively parallel digital transcriptional profiling of single cells

            Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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              RNA velocity of single cells

              RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena, such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

                Author and article information

                Nat Genet
                Nat Genet
                Nature genetics
                04 March 2021
                01 May 2021
                08 April 2021
                08 October 2021
                : 53
                : 5
                : 694-706
                [1 ]Department of Physiology and Pharmacology, Karolinska Institutet, 17165 Solna, Sweden
                [2 ]Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, A-1090 Vienna, Austria
                [3 ]Department of Molecular Neurosciences, Medical University of Vienna, A-1090 Vienna, Austria
                [4 ]Childhood Cancer Research Unit, Dep. of Women’s and Children’s Health, Karolinska Institutet, 171 77 Stockholm, Sweden
                [5 ]Department of Comparative Medicine, Karolinska Institutet, Solna, Sweden
                [6 ]Department of Cell and Molecular Biology, Karolinska Institutet, Solna, Sweden
                [7 ]Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
                [8 ]Department of Pathology and Laboratory Medicine, Tufts Medical Center, Boston MA USA
                [9 ]Princess Máxima Center for Pediatric Oncology, 3584 CS Utrecht, The Netherlands
                [10 ]Dept. of Pathology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
                [11 ]Institute of Translational Biomedicine, St Petersburg University, 199034 St Petersburg, Russia
                [12 ]Endocrinology Research Centre, Moscow, Russian Federation
                [13 ]Moscow Institute of Physics and Technology, 65014, Dolgoprudniy, Russian Federation
                [14 ]Institute for Regenerative Medicine, Lomonosov Moscow State University, 119192, Moscow, Russian Federation
                [15 ]Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, 420012, Russian Federation
                [16 ]RIKEN Innovation Center, RIKEN, Yokohama, 650-0047, Japan
                [17 ]Center for Life Science Technologies, RIKEN, Yokohama, 650-0047, Japan
                [18 ]Department of Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
                [19 ]Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 17164 Solna, Sweden
                [20 ]Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
                [21 ]Harvard Stem Cell Institute, Cambridge, MA 02138, USA
                Author notes
                []correspondence should be addressed to Peter Kharchenko ( Peter_Kharchenko@ 123456hms.harvard.edu ) and Igor Adameyko ( igor.adameyko@ 123456meduniwien.ac.at )

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