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      Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq.

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          Abstract

          Despite the importance of the mammalian neocortex for complex cognitive processes, we still lack a comprehensive description of its cellular components. To improve the classification of neuronal cell types and the functional characterization of single neurons, we present Patch-seq, a method that combines whole-cell electrophysiological patch-clamp recordings, single-cell RNA-sequencing and morphological characterization. Following electrophysiological characterization, cell contents are aspirated through the patch-clamp pipette and prepared for RNA-sequencing. Using this approach, we generate electrophysiological and molecular profiles of 58 neocortical cells and show that gene expression patterns can be used to infer the morphological and physiological properties such as axonal arborization and action potential amplitude of individual neurons. Our results shed light on the molecular underpinnings of neuronal diversity and suggest that Patch-seq can facilitate the classification of cell types in the nervous system.

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          Most cited references23

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          THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL

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            Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex.

            Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.
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              Accounting for technical noise in single-cell RNA-seq experiments.

              Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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                Author and article information

                Journal
                Nat. Biotechnol.
                Nature biotechnology
                1546-1696
                1087-0156
                Feb 2016
                : 34
                : 2
                Affiliations
                [1 ] Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA.
                [2 ] Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
                [3 ] Ludwig Institute for Cancer Research, Stockholm, Sweden.
                [4 ] Bernstein Center for Computational Neuroscience, Tübingen, Germany.
                [5 ] Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
                [6 ] Werner Reichardt Center for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Tübingen, Germany.
                [7 ] Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
                [8 ] Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, USA.
                Article
                nbt.3445 NIHMS775286
                10.1038/nbt.3445
                26689543
                c4ce2124-f223-47fe-a728-261c5d89fe6e
                History

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