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Matthew Kroll

Bio: Matthew Kroll is an academic researcher from Allen Institute for Brain Science. The author has contributed to research in topics: Neocortex & Cell type. The author has an hindex of 14, co-authored 22 publications receiving 1782 citations.

Papers
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Journal ArticleDOI
31 Oct 2018-Nature
TL;DR: This study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex and identifies 133 transcriptomic types of glutamatergic neurons to their long-range projection specificity.
Abstract: The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

1,184 citations

Posted ContentDOI
06 Dec 2017-bioRxiv
TL;DR: A combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct regions of the mouse cortex is established and correspondence between excitatory transcriptomic types and their region-specific long-range target specificity is demonstrated.
Abstract: Neocortex contains a multitude of cell types segregated into layers and functionally distinct regions. To investigate the diversity of cell types across the mouse neocortex, we analyzed 12,714 cells from the primary visual cortex (VISp), and 9,035 cells from the anterior lateral motor cortex (ALM) by deep single-cell RNA-sequencing (scRNA-seq), identifying 116 transcriptomic cell types. These two regions represent distant poles of the neocortex and perform distinct functions. We define 50 inhibitory transcriptomic cell types, all of which are shared across both cortical regions. In contrast, 49 of 52 excitatory transcriptomic types were found in either VISp or ALM, with only three present in both. By combining single cell RNA-seq and retrograde labeling, we demonstrate correspondence between excitatory transcriptomic types and their region-specific long-range target specificity. This study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct regions of the mouse cortex.

403 citations

Journal ArticleDOI
26 Dec 2018-PLOS ONE
TL;DR: It is demonstrated that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis, and the high information content of nuclear RNA for characterization of cellular diversity in brain tissues is illustrated.
Abstract: Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

368 citations

Journal ArticleDOI
Nathan W. Gouwens1, Staci A. Sorensen1, Jim Berg1, Changkyu Lee1, Tim Jarsky1, Jonathan T. Ting1, Susan M. Sunkin1, David Feng1, Costas A. Anastassiou1, Eliza Barkan1, Kris Bickley1, Nicole Blesie1, Thomas Braun1, Krissy Brouner1, Agata Budzillo1, Shiella Caldejon1, Tamara Casper1, Dan Castelli1, Peter Chong1, Kirsten Crichton1, Christine Cuhaciyan1, Tanya L. Daigle1, Rachel A. Dalley1, Nick Dee1, Tsega Desta1, Songlin Ding1, Samuel Dingman1, Alyse Doperalski1, Nadezhda Dotson1, Tom Egdorf1, Michael S. Fisher1, Rebecca de Frates1, Emma Garren1, Marissa Garwood1, Amanda Gary1, Nathalie Gaudreault1, Keith B. Godfrey1, Melissa Gorham1, Hong Gu1, Caroline Habel1, Kristen Hadley1, James Harrington1, Julie A. Harris1, Alex M. Henry1, DiJon Hill1, Samuel R Josephsen1, Sara Kebede1, Lisa Kim1, Matthew Kroll1, Brian Lee1, Tracy Lemon1, Katherine E. Link1, Xiaoxiao Liu1, Brian Long1, Rusty Mann1, Medea McGraw1, Stefan Mihalas1, Alice Mukora1, Gabe J. Murphy1, Lindsay Ng1, Kiet Ngo1, Thuc Nghi Nguyen1, Philip R. Nicovich1, Aaron Oldre1, Daniel Park1, Sheana Parry1, Jed Perkins1, Lydia Potekhina1, David Reid1, Miranda Robertson1, David Sandman1, Martin Schroedter1, Cliff Slaughterbeck1, Gilberto J. Soler-Llavina1, Josef Sulc1, Aaron Szafer1, Bosiljka Tasic1, Naz Taskin1, Corinne Teeter1, Nivretta Thatra1, Herman Tung1, Wayne Wakeman1, Grace Williams1, Rob Young1, Zhi Zhou1, Colin Farrell1, Hanchuan Peng1, Michael Hawrylycz1, Ed Lein1, Lydia Ng1, Anton Arkhipov1, Amy Bernard1, John W. Phillips1, Hongkui Zeng1, Christof Koch1 
TL;DR: A single-cell characterization pipeline is established using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions to establish a morpho-electrical taxonomy of cell types for the mouse visual cortex via unsupervised clustering analysis of multiple quantitative features.
Abstract: Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.

328 citations

Journal ArticleDOI
10 Jun 2021-Cell
TL;DR: The isocortex and hippocampal formation (HPF) in the mammalian brain play critical roles in perception, cognition, emotion, and learning as discussed by the authors, and a transcriptomic cell type taxonomy revealing a comprehensive repertoire of glutamatergic and GABAergic neuron types.

310 citations


Cited by
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Journal ArticleDOI
13 Jun 2019-Cell
TL;DR: 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.

7,892 citations

Posted ContentDOI
02 Nov 2018-bioRxiv
TL;DR: This work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets, and demonstrates how anchoring can harmonize in-situ gene expression and scRNA-seq datasets.
Abstract: Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat

2,037 citations

Journal ArticleDOI
21 Aug 2019-Nature
TL;DR: RNA-sequencing analysis of cells in the human cortex enabled identification of diverse cell types, revealing well-conserved architecture and homologous cell types as well as extensive differences when compared with datasets covering the analogous region of the mouse brain.
Abstract: Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

1,044 citations

Journal ArticleDOI
TL;DR: This work investigates ACE2 and TMPRSS2 expression levels and their distribution across cell types in lung tissue and in cells derived from subsegmental bronchial branches by single nuclei and single cell RNA sequencing, suggesting increased vulnerability for SARS‐CoV‐2 infection.
Abstract: The SARS-CoV-2 pandemic affecting the human respiratory system severely challenges public health and urgently demands for increasing our understanding of COVID-19 pathogenesis, especially host factors facilitating virus infection and replication. SARS-CoV-2 was reported to enter cells via binding to ACE2, followed by its priming by TMPRSS2. Here, we investigate ACE2 and TMPRSS2 expression levels and their distribution across cell types in lung tissue (twelve donors, 39,778 cells) and in cells derived from subsegmental bronchial branches (four donors, 17,521 cells) by single nuclei and single cell RNA sequencing, respectively. While TMPRSS2 is strongly expressed in both tissues, in the subsegmental bronchial branches ACE2 is predominantly expressed in a transient secretory cell type. Interestingly, these transiently differentiating cells show an enrichment for pathways related to RHO GTPase function and viral processes suggesting increased vulnerability for SARS-CoV-2 infection. Our data provide a rich resource for future investigations of COVID-19 infection and pathogenesis.

808 citations

Journal ArticleDOI
TL;DR: A protocol is introduced to help avoid common shortcomings of t-SNE, for example, enabling preservation of the global structure of the data.
Abstract: Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structure of the data is not represented accurately. Here we describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations. It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we additionally use exaggeration and downsampling-based initialisation. We use published single-cell RNA-seq data sets to demonstrate that this protocol yields superior results compared to the naive application of t-SNE. t-SNE is widely used for dimensionality reduction and visualization of high-dimensional single-cell data. Here, the authors introduce a protocol to help avoid common shortcomings of t-SNE, for example, enabling preservation of the global structure of the data.

576 citations