The Experts below are selected from a list of 1216086 Experts worldwide ranked by ideXlab platform
Alexander Van Oudenaarden - One of the best experts on this subject based on the ideXlab platform.
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Cell Type Purification by Single-Cell Transcriptome-Trained Sorting.
Cell, 2019Co-Authors: Chloé S. Baron, Aditya Barve, Mauro J. Muraro, Gitanjali Dharmadhikari, Reinier Van Der Linden, Anna Lyubimova, Eelco J.p. De Koning, Alexander Van OudenaardenAbstract:Much of current molecular and Cell biology research relies on the ability to purify Cell Types by fluorescence-activated Cell sorting (FACS). FACS typically relies on the ability to label Cell Types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for Cell Types without a priori knowledge of Cell-Type markers. Here, we propose GateID, a computational method that combines single-Cell transcriptomics with FACS index sorting to purify Cell Types of choice using only native Cellular properties such as Cell size, granularity, and mitochondrial content. We validate GateID by purifying various Cell Types from zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.
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Cell Type Purification by Single-Cell Transcriptome-Trained Sorting
SSRN Electronic Journal, 2019Co-Authors: Chloé S. Baron, Aditya Barve, Mauro J. Muraro, Gitanjali Dharmadhikari, Reinier Van Der Linden, Anna Lyubimova, Eelco J.p. De Koning, Alexander Van OudenaardenAbstract:Much of modern molecular and Cell biology research relies on the ability to purify Cell Types by fluorescence activated Cell sorting (FACS). FACS typically relies on the ability to label Cell Types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for Cell Types without a priori knowledge of Cell Type markers. Here we propose GateID, a computational method that combines single-Cell transcriptomics with FACS index sorting to purify Cell Types of choice, using only native Cellular properties such as Cell size, granularity and mitochondrial content. We validate GateID by purifying various Cell Types from the zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.
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Cell Type purification by single-Cell transcriptome-trained sorting
2018Co-Authors: Chloé S. Baron, Aditya Barve, Mauro J. Muraro, Gitanjali Dharmadhikari, Reinier Van Der Linden, Anna Lyubimova, Eelco J.p. De Koning, Alexander Van OudenaardenAbstract:Traditional Cell Type enrichment using fluorescence activated Cell sorting (FACS) relies on methods that specifically label the Cell Type of interest. Here we propose GateID, a computational method that combines single-Cell transcriptomics for unbiased Cell Type identification with FACS index sorting to purify Cell Types of choice. We validate GateID by purifying various Cell Types from the zebrafish kidney marrow and the human pancreas without resorting to specific antibodies or transgenes.
Rafael A. Irizarry - One of the best experts on this subject based on the ideXlab platform.
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Robust decomposition of Cell Type mixtures in spatial transcriptomics
Nature Biotechnology, 2021Co-Authors: Dylan M. Cable, Evan Murray, Luli S. Zou, Aleksandrina Goeva, Evan Z. Macosko, Fei Chen, Rafael A. IrizarryAbstract:Cell Type mapping in spatial transcriptomics is enabled by accounting for compositional mixtures and differences in sequencing technologies. A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple Cells, hindering the discovery of Cell-Type-specific spatial patterns of localization and expression. Here, we develop robust Cell Type decomposition (RCTD), a computational method that leverages Cell Type profiles learned from single-Cell RNA-seq to decompose Cell Type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify Cell Types on simulated datasets. Furthermore, RCTD accurately reproduces known Cell Type and subType localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD’s recovery of Cell Type localization enables the discovery of genes within a Cell Type whose expression depends on spatial environment. Spatial mapping of Cell Types with RCTD enables the spatial components of Cellular identity to be defined, uncovering new principles of Cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD .
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Robust decomposition of Cell Type mixtures in spatial transcriptomics
2020Co-Authors: Dylan M. Cable, Evan Murray, Luli S. Zou, Aleksandrina Goeva, Evan Z. Macosko, Fei Chen, Rafael A. IrizarryAbstract:Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual Cells. However, a limitation of current technologies is that spatial measurements may contain contributions from multiple Cells, hindering the discovery of Cell Type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages Cell Type profiles learned from single-Cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in Cell Type assignment in Slide-seq data by accurately reproducing known Cell Type and subType localization patterns in the cerebellum and hippocampus. We further show the advantages of RCTD by its ability to detect mixtures and identify Cell Types on an assessment dataset. Finally, we show how RCTD9s recovery of Cell Type localization uniquely enables the discovery of genes within a Cell Type whose expression depends on spatial environment. Spatial mapping of Cell Types with RCTD has the potential to enable the definition of spatial components of Cellular identity, uncovering new principles of Cellular organization in biological tissue.
Chloé S. Baron - One of the best experts on this subject based on the ideXlab platform.
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Cell Type Purification by Single-Cell Transcriptome-Trained Sorting.
Cell, 2019Co-Authors: Chloé S. Baron, Aditya Barve, Mauro J. Muraro, Gitanjali Dharmadhikari, Reinier Van Der Linden, Anna Lyubimova, Eelco J.p. De Koning, Alexander Van OudenaardenAbstract:Much of current molecular and Cell biology research relies on the ability to purify Cell Types by fluorescence-activated Cell sorting (FACS). FACS typically relies on the ability to label Cell Types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for Cell Types without a priori knowledge of Cell-Type markers. Here, we propose GateID, a computational method that combines single-Cell transcriptomics with FACS index sorting to purify Cell Types of choice using only native Cellular properties such as Cell size, granularity, and mitochondrial content. We validate GateID by purifying various Cell Types from zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.
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Cell Type Purification by Single-Cell Transcriptome-Trained Sorting
SSRN Electronic Journal, 2019Co-Authors: Chloé S. Baron, Aditya Barve, Mauro J. Muraro, Gitanjali Dharmadhikari, Reinier Van Der Linden, Anna Lyubimova, Eelco J.p. De Koning, Alexander Van OudenaardenAbstract:Much of modern molecular and Cell biology research relies on the ability to purify Cell Types by fluorescence activated Cell sorting (FACS). FACS typically relies on the ability to label Cell Types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for Cell Types without a priori knowledge of Cell Type markers. Here we propose GateID, a computational method that combines single-Cell transcriptomics with FACS index sorting to purify Cell Types of choice, using only native Cellular properties such as Cell size, granularity and mitochondrial content. We validate GateID by purifying various Cell Types from the zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.
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Cell Type purification by single-Cell transcriptome-trained sorting
2018Co-Authors: Chloé S. Baron, Aditya Barve, Mauro J. Muraro, Gitanjali Dharmadhikari, Reinier Van Der Linden, Anna Lyubimova, Eelco J.p. De Koning, Alexander Van OudenaardenAbstract:Traditional Cell Type enrichment using fluorescence activated Cell sorting (FACS) relies on methods that specifically label the Cell Type of interest. Here we propose GateID, a computational method that combines single-Cell transcriptomics for unbiased Cell Type identification with FACS index sorting to purify Cell Types of choice. We validate GateID by purifying various Cell Types from the zebrafish kidney marrow and the human pancreas without resorting to specific antibodies or transgenes.
Dylan M. Cable - One of the best experts on this subject based on the ideXlab platform.
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Robust decomposition of Cell Type mixtures in spatial transcriptomics
Nature Biotechnology, 2021Co-Authors: Dylan M. Cable, Evan Murray, Luli S. Zou, Aleksandrina Goeva, Evan Z. Macosko, Fei Chen, Rafael A. IrizarryAbstract:Cell Type mapping in spatial transcriptomics is enabled by accounting for compositional mixtures and differences in sequencing technologies. A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple Cells, hindering the discovery of Cell-Type-specific spatial patterns of localization and expression. Here, we develop robust Cell Type decomposition (RCTD), a computational method that leverages Cell Type profiles learned from single-Cell RNA-seq to decompose Cell Type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify Cell Types on simulated datasets. Furthermore, RCTD accurately reproduces known Cell Type and subType localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD’s recovery of Cell Type localization enables the discovery of genes within a Cell Type whose expression depends on spatial environment. Spatial mapping of Cell Types with RCTD enables the spatial components of Cellular identity to be defined, uncovering new principles of Cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD .
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Robust decomposition of Cell Type mixtures in spatial transcriptomics
2020Co-Authors: Dylan M. Cable, Evan Murray, Luli S. Zou, Aleksandrina Goeva, Evan Z. Macosko, Fei Chen, Rafael A. IrizarryAbstract:Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual Cells. However, a limitation of current technologies is that spatial measurements may contain contributions from multiple Cells, hindering the discovery of Cell Type-specific spatial patterns of localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD), a computational method that leverages Cell Type profiles learned from single-Cell RNA sequencing data to decompose mixtures, such as those observed in spatial transcriptomic technologies. Our approach accounts for platform effects introduced by systematic technical variability inherent to different sequencing modalities. We demonstrate RCTD provides substantial improvement in Cell Type assignment in Slide-seq data by accurately reproducing known Cell Type and subType localization patterns in the cerebellum and hippocampus. We further show the advantages of RCTD by its ability to detect mixtures and identify Cell Types on an assessment dataset. Finally, we show how RCTD9s recovery of Cell Type localization uniquely enables the discovery of genes within a Cell Type whose expression depends on spatial environment. Spatial mapping of Cell Types with RCTD has the potential to enable the definition of spatial components of Cellular identity, uncovering new principles of Cellular organization in biological tissue.
Carol A. Tamminga - One of the best experts on this subject based on the ideXlab platform.
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Cell Type-specific epigenetic links to schizophrenia risk in the brain
Genome biology, 2019Co-Authors: Isabel Mendizabal, Stefano Berto, Noriyoshi Usui, Kazuya Toriumi, Paramita Chatterjee, Connor Douglas, Iksoo Huh, Hyeonsoo Jeong, Thomas Layman, Carol A. TammingaAbstract:The importance of Cell Type-specific epigenetic variation of non-coding regions in neuropsychiatric disorders is increasingly appreciated, yet data from disease brains are conspicuously lacking. We generate Cell Type-specific whole-genome methylomes (N = 95) and transcriptomes (N = 89) from neurons and oligodendrocytes obtained from brain tissue of patients with schizophrenia and matched controls. The methylomes of the two Cell Types are highly distinct, with the majority of differential DNA methylation occurring in non-coding regions. DNA methylation differences between cases and controls are subtle compared to Cell Type differences, yet robust against permuted data and validated in targeted deep-sequencing analyses. Differential DNA methylation between control and schizophrenia tends to occur in Cell Type differentially methylated sites, highlighting the significance of Cell Type-specific epigenetic dysregulation in a complex neuropsychiatric disorder. Our results provide novel and comprehensive methylome and transcriptome data from distinct Cell populations within patient-derived brain tissues. This data clearly demonstrate that Cell Type epigenetic-differentiated sites are preferentially targeted by disease-associated epigenetic dysregulation. We further show reduced Cell Type epigenetic distinction in schizophrenia.
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Cell-Type specific epigenetic links to schizophrenia risk in brain
2019Co-Authors: Isabel Mendizabal, Stefano Berto, Noriyoshi Usui, Kazuya Toriumi, Paramita Chatterjee, Connor Douglas, Iksoo Huh, Hyeonsoo Jeong, Thomas Layman, Carol A. TammingaAbstract:Abstract The importance of Cell-Type specific epigenetic variation of non-coding regions in neuropsychiatric disorders is increasingly appreciated, yet data from disease brains are conspicuously lacking. We generated Cell-Type specific whole-genome methylomes (N=95) and transcriptomes (N=89) from neurons and oligodendrocytes from brains of schizophrenia and matched controls. The methylomes of these two Cell-Types are highly distinct, with the majority of differential DNA methylation occurring in non-coding regions. DNA methylation difference between control and schizophrenia brains is subtle compared to Cell-Type difference, yet robust against permuted data and validated in targeted deep-sequencing analyses. Differential DNA methylation between control and schizophrenia tends to occur in Cell-Type differentially methylated sites, highlighting the significance of Cell-Type specific epigenetic dysregulation in a complex neuropsychiatric disorder. Our resource provides novel and comprehensive methylome and transcriptome data from distinct Cell populations from schizophrenia brains, further revealing reduced Cell-Type epigenetic distinction in schizophrenia.