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Aneta Czaplinska

Bio: Aneta Czaplinska is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Brain atlas. The author has an hindex of 1, co-authored 1 publications receiving 4270 citations.
Topics: Brain atlas

Papers
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Journal ArticleDOI
Ed S. Lein1, Michael Hawrylycz1, Nancy Ao2, Mikael Ayres1, Amy Bensinger1, Amy Bernard1, Andrew F. Boe1, Mark S. Boguski1, Mark S. Boguski3, Kevin S. Brockway1, Emi J. Byrnes1, Lin Chen1, Li Chen2, Tsuey-Ming Chen2, Mei Chi Chin1, Jimmy Chong1, Brian E. Crook1, Aneta Czaplinska2, Chinh Dang1, Suvro Datta1, Nick Dee1, Aimee L. Desaki1, Tsega Desta1, Ellen Diep1, Tim A. Dolbeare1, Matthew J. Donelan1, Hong-Wei Dong1, Jennifer G. Dougherty1, Ben J. Duncan1, Amanda Ebbert1, Gregor Eichele4, Lili K. Estin1, Casey Faber1, Benjamin A.C. Facer1, Rick Fields2, Shanna R. Fischer1, Tim P. Fliss1, Cliff Frensley1, Sabrina N. Gates1, Katie J. Glattfelder1, Kevin R. Halverson1, Matthew R. Hart1, John G. Hohmann1, Maureen P. Howell1, Darren P. Jeung1, Rebecca A. Johnson1, Patrick T. Karr1, Reena Kawal1, Jolene Kidney1, Rachel H. Knapik1, Chihchau L. Kuan1, James H. Lake1, Annabel R. Laramee1, Kirk D. Larsen1, Christopher Lau1, Tracy Lemon1, Agnes J. Liang2, Ying Liu2, Lon T. Luong1, Jesse Michaels1, Judith J. Morgan1, Rebecca J. Morgan1, Marty Mortrud1, Nerick Mosqueda1, Lydia Ng1, Randy Ng1, Geralyn J. Orta1, Caroline C. Overly1, Tu H. Pak1, Sheana Parry1, Sayan Dev Pathak1, Owen C. Pearson1, Ralph B. Puchalski1, Zackery L. Riley1, Hannah R. Rockett1, Stephen A. Rowland1, Joshua J. Royall1, Marcos J. Ruiz2, Nadia R. Sarno1, Katherine Schaffnit1, Nadiya V. Shapovalova1, Taz Sivisay1, Clifford R. Slaughterbeck1, Simon Smith1, Kimberly A. Smith1, Bryan I. Smith1, Andy J. Sodt1, Nick N. Stewart1, Kenda-Ruth Stumpf1, Susan M. Sunkin1, Madhavi Sutram1, Angelene Tam2, Carey D. Teemer1, Christina Thaller2, Carol L. Thompson1, Lee R. Varnam1, Axel Visel4, Axel Visel5, Ray M. Whitlock1, Paul Wohnoutka1, Crissa K. Wolkey1, Victoria Y. Wong1, Matthew J.A. Wood2, Murat B. Yaylaoglu2, Rob Young1, Brian L. Youngstrom1, Xu Feng Yuan1, Bin Zhang2, Theresa A. Zwingman1, Allan R. Jones1 
11 Jan 2007-Nature
TL;DR: An anatomically comprehensive digital atlas containing the expression patterns of ∼20,000 genes in the adult mouse brain is described, providing an open, primary data resource for a wide variety of further studies concerning brain organization and function.
Abstract: Molecular approaches to understanding the functional circuitry of the nervous system promise new insights into the relationship between genes, brain and behaviour. The cellular diversity of the brain necessitates a cellular resolution approach towards understanding the functional genomics of the nervous system. We describe here an anatomically comprehensive digital atlas containing the expression patterns of approximately 20,000 genes in the adult mouse brain. Data were generated using automated high-throughput procedures for in situ hybridization and data acquisition, and are publicly accessible online. Newly developed image-based informatics tools allow global genome-scale structural analysis and cross-correlation, as well as identification of regionally enriched genes. Unbiased fine-resolution analysis has identified highly specific cellular markers as well as extensive evidence of cellular heterogeneity not evident in classical neuroanatomical atlases. This highly standardized atlas provides an open, primary data resource for a wide variety of further studies concerning brain organization and function.

4,944 citations


Cited by
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Journal ArticleDOI
TL;DR: A set of Cre reporter mice with strong, ubiquitous expression of fluorescent proteins of different spectra is generated and enables direct visualization of fine dendritic structures and axonal projections of the labeled neurons, which is useful in mapping neuronal circuitry, imaging and tracking specific cell populations in vivo.
Abstract: The Cre/lox system is widely used in mice to achieve cell-type-specific gene expression. However, a strong and universally responding system to express genes under Cre control is still lacking. We have generated a set of Cre reporter mice with strong, ubiquitous expression of fluorescent proteins of different spectra. The robust native fluorescence of these reporters enables direct visualization of fine dendritic structures and axonal projections of the labeled neurons, which is useful in mapping neuronal circuitry, imaging and tracking specific cell populations in vivo. Using these reporters and a high-throughput in situ hybridization platform, we are systematically profiling Cre-directed gene expression throughout the mouse brain in several Cre-driver lines, including new Cre lines targeting different cell types in the cortex. Our expression data are displayed in a public online database to help researchers assess the utility of various Cre-driver lines for cell-type-specific genetic manipulation.

5,365 citations

Journal ArticleDOI
TL;DR: These findings call into question the concept of a “glial” cell class as the gene profiles of astrocyte and oligodendrocytes are as dissimilar to each other as they are to neurons, for better understanding of neural development, function, and disease.
Abstract: Understanding the cell–cell interactions that control CNS development and function has long been limited by the lack of methods to cleanly separate neural cell types. Here we describe methods for the prospective isolation and purification of astrocytes, neurons, and oligodendrocytes from developing and mature mouse forebrain. We used FACS (fluorescent-activated cell sorting) to isolate astrocytes from transgenic mice that express enhanced green fluorescent protein (EGFP) under the control of an S100β promoter. Using Affymetrix GeneChip Arrays, we then created a transcriptome database of the expression levels of >20,000 genes by gene profiling these three main CNS neural cell types at various postnatal ages between postnatal day 1 (P1) and P30. This database provides a detailed global characterization and comparison of the genes expressed by acutely isolated astrocytes, neurons, and oligodendrocytes. We found that Aldh1L1 is a highly specific antigenic marker for astrocytes with a substantially broader pattern of astrocyte expression than the traditional astrocyte marker GFAP. Astrocytes were enriched in specific metabolic and lipid synthetic pathways, as well as the draper/Megf10 and Mertk/integrin αvβ5 phagocytic pathways suggesting that astrocytes are professional phagocytes. Our findings call into question the concept of a “glial” cell class as the gene profiles of astrocytes and oligodendrocytes are as dissimilar to each other as they are to neurons. This transcriptome database of acutely isolated purified astrocytes, neurons, and oligodendrocytes provides a resource to the neuroscience community by providing improved cell-type-specific markers and for better understanding of neural development, function, and disease.

2,838 citations

Journal ArticleDOI
06 Mar 2015-Science
TL;DR: Large-scale single-cell RNA sequencing is used to classify cells in the mouse somatosensory cortex and hippocampal CA1 region and found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex.
Abstract: The mammalian cerebral cortex supports cognitive functions such as sensorimotor integration, memory, and social behaviors. Normal brain function relies on a diverse set of differentiated cell types, including neurons, glia, and vasculature. Here, we have used large-scale single-cell RNA sequencing (RNA-seq) to classify cells in the mouse somatosensory cortex and hippocampal CA1 region. We found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex. We identified numerous marker genes, which allowed alignment with known cell types, morphology, and location. We found a layer I interneuron expressing Pax6 and a distinct postmitotic oligodendrocyte subclass marked by Itpr2. Across the diversity of cortical cell types, transcription factors formed a complex, layered regulatory code, suggesting a mechanism for the maintenance of adult cell type identity.

2,675 citations

Journal ArticleDOI
TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Abstract: The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony ( https://github.com/immunogenomics/harmony ), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data. Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.

2,459 citations

Journal ArticleDOI
08 Aug 2018-Nature
TL;DR: It is shown that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols, and expected to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.
Abstract: 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 between 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.

2,285 citations