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Cable DM, Murray E, Shanmugam V, Zhang S, Zou LS, Diao M, Chen H, Macosko EZ, Irizarry RA, Chen F. Cell type-specific inference of differential expression in spatial transcriptomics. Nat Methods 2022; 19:1076-1087. [PMID: 36050488 PMCID: PMC10463137 DOI: 10.1038/s41592-022-01575-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 07/15/2022] [Indexed: 12/13/2022]
Abstract
A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE's framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer's disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr .
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Affiliation(s)
- Dylan M Cable
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Evan Murray
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vignesh Shanmugam
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Simon Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luli S Zou
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard University, Boston, MA, USA
| | - Michael Diao
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Haiqi Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Evan Z Macosko
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Rafael A Irizarry
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Biostatistics, Harvard University, Boston, MA, USA.
| | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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2
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Tak YE, Horng JE, Perry NT, Schultz HT, Iyer S, Yao Q, Zou LS, Aryee MJ, Pinello L, Joung JK. Author Correction: Augmenting and directing long-range CRISPR-mediated activation in human cells. Nat Methods 2022; 19:899. [PMID: 35761069 DOI: 10.1038/s41592-022-01561-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Y Esther Tak
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Joy E Horng
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Nicholas T Perry
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Hayley T Schultz
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sowmya Iyer
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA
| | - Qiuming Yao
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Pathology, Harvard Medical School, Boston, MA, USA.,Cell Circuits and Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luli S Zou
- Cell Circuits and Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Martin J Aryee
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Pathology, Harvard Medical School, Boston, MA, USA.,Cell Circuits and Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Luca Pinello
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA.,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Pathology, Harvard Medical School, Boston, MA, USA.,Cell Circuits and Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - J Keith Joung
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA. .,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA. .,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA. .,Department of Pathology, Harvard Medical School, Boston, MA, USA.
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3
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Cable DM, Murray E, Zou LS, Goeva A, Macosko EZ, Chen F, Irizarry RA. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol 2022; 40:517-526. [PMID: 33603203 PMCID: PMC8606190 DOI: 10.1038/s41587-021-00830-w] [Citation(s) in RCA: 267] [Impact Index Per Article: 133.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 12/31/2020] [Indexed: 02/07/2023]
Abstract
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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Affiliation(s)
- Dylan M. Cable
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, 02139,Broad Institute of Harvard and MIT, Cambridge, MA, 02142,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215
| | - Evan Murray
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142
| | - Luli S. Zou
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215,Department of Biostatistics, Harvard University, Boston, MA, 02115
| | | | - Evan Z. Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142,Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge MA 02138
| | - Rafael A. Irizarry
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, 02215,Department of Biostatistics, Harvard University, Boston, MA, 02115
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4
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Tak YE, Horng JE, Perry NT, Schultz HT, Iyer S, Yao Q, Zou LS, Aryee MJ, Pinello L, Joung JK. Augmenting and directing long-range CRISPR-mediated activation in human cells. Nat Methods 2021; 18:1075-1081. [PMID: 34354266 PMCID: PMC8446310 DOI: 10.1038/s41592-021-01224-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 06/28/2021] [Indexed: 02/07/2023]
Abstract
Epigenetic editing is an emerging technology that uses artificial transcription factors (aTFs) to regulate expression of a target gene. Although human genes can be robustly upregulated by targeting aTFs to promoters, the activation induced by directing aTFs to distal transcriptional enhancers is substantially less robust and consistent. Here we show that long-range activation using CRISPR-based aTFs in human cells can be made more efficient and reliable by concurrently targeting an aTF to the target gene promoter. We used this strategy to direct target gene choice for enhancers capable of regulating more than one promoter and to achieve allele-selective activation of human genes by targeting aTFs to single-nucleotide polymorphisms embedded in distally located sequences. Our results broaden the potential applications of the epigenetic editing toolbox for research and therapeutics.
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Affiliation(s)
- Y. Esther Tak
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA,Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Joy E. Horng
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA,These authors contributed equally
| | - Nicholas T. Perry
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA,These authors contributed equally
| | - Hayley T. Schultz
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sowmya Iyer
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA
| | - Qiuming Yao
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA,Department of Pathology, Harvard Medical School, Boston, MA, USA,Cell Circuits and Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luli S. Zou
- Cell Circuits and Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Martin J. Aryee
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA,Department of Pathology, Harvard Medical School, Boston, MA, USA,Cell Circuits and Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Luca Pinello
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA,Department of Pathology, Harvard Medical School, Boston, MA, USA,Cell Circuits and Epigenomics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - J. Keith Joung
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, USA,Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA,Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA,Department of Pathology, Harvard Medical School, Boston, MA, USA,Correspondence and requests for materials should be addressed to J. Keith Joung.
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5
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Johnstone SE, Reyes A, Qi Y, Adriaens C, Hegazi E, Pelka K, Chen JH, Zou LS, Drier Y, Hecht V, Shoresh N, Selig MK, Lareau CA, Iyer S, Nguyen SC, Joyce EF, Hacohen N, Irizarry RA, Zhang B, Aryee MJ, Bernstein BE. Large-Scale Topological Changes Restrain Malignant Progression in Colorectal Cancer. Cell 2020; 182:1474-1489.e23. [PMID: 32841603 PMCID: PMC7575124 DOI: 10.1016/j.cell.2020.07.030] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 05/04/2020] [Accepted: 07/20/2020] [Indexed: 02/06/2023]
Abstract
Widespread changes to DNA methylation and chromatin are well documented in cancer, but the fate of higher-order chromosomal structure remains obscure. Here we integrated topological maps for colon tumors and normal colons with epigenetic, transcriptional, and imaging data to characterize alterations to chromatin loops, topologically associated domains, and large-scale compartments. We found that spatial partitioning of the open and closed genome compartments is profoundly compromised in tumors. This reorganization is accompanied by compartment-specific hypomethylation and chromatin changes. Additionally, we identify a compartment at the interface between the canonical A and B compartments that is reorganized in tumors. Remarkably, similar shifts were evident in non-malignant cells that have accumulated excess divisions. Our analyses suggest that these topological changes repress stemness and invasion programs while inducing anti-tumor immunity genes and may therefore restrain malignant progression. Our findings call into question the conventional view that tumor-associated epigenomic alterations are primarily oncogenic.
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Affiliation(s)
- Sarah E Johnstone
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Alejandro Reyes
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
| | - Yifeng Qi
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Carmen Adriaens
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Esmat Hegazi
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Karin Pelka
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Jonathan H Chen
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Luli S Zou
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
| | - Yotam Drier
- The Lautenberg Center for Immunology and Cancer Research, The Hebrew University, Jerusalem, Israel
| | - Vivian Hecht
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Noam Shoresh
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA
| | - Martin K Selig
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Caleb A Lareau
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02215, USA
| | - Sowmya Iyer
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Son C Nguyen
- Department of Genetics, Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eric F Joyce
- Department of Genetics, Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02129, USA
| | - Rafael A Irizarry
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Data Sciences, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA
| | - Bin Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Martin J Aryee
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02129, USA; Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, USA.
| | - Bradley E Bernstein
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02129, USA.
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6
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Rai V, Quang DX, Erdos MR, Cusanovich DA, Daza RM, Narisu N, Zou LS, Didion JP, Guan Y, Shendure J, Parker SCJ, Collins FS. Single-cell ATAC-Seq in human pancreatic islets and deep learning upscaling of rare cells reveals cell-specific type 2 diabetes regulatory signatures. Mol Metab 2020; 32:109-121. [PMID: 32029221 PMCID: PMC6961712 DOI: 10.1016/j.molmet.2019.12.006] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/12/2019] [Accepted: 12/12/2019] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity. METHODS We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations. RESULTS We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals. CONCLUSIONS Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways.
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Affiliation(s)
- Vivek Rai
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Daniel X Quang
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Michael R Erdos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Darren A Cusanovich
- Department of Genome Sciences, University of Washington, Seattle, WA, 98109, USA
| | - Riza M Daza
- Department of Genome Sciences, University of Washington, Seattle, WA, 98109, USA
| | - Narisu Narisu
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Luli S Zou
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - John P Didion
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Yuanfang Guan
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, 98109, USA
| | - Stephen C J Parker
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Francis S Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
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7
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Zou LS, Erdos MR, Taylor DL, Chines PS, Varshney A, Parker SCJ, Collins FS, Didion JP. BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues. BMC Genomics 2018; 19:390. [PMID: 29792182 PMCID: PMC5966887 DOI: 10.1186/s12864-018-4766-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 05/08/2018] [Indexed: 01/14/2023] Open
Abstract
Background Bisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to coverage depth variability. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features associated with predictive power. Results Here we describe BoostMe, a method for imputing low-quality DNA methylation estimates within whole-genome bisulfite sequencing (WGBS) data. BoostMe uses a gradient boosting algorithm, XGBoost, and leverages information from multiple samples for prediction. We find that BoostMe outperforms existing algorithms in speed and accuracy when applied to WGBS of human tissues. Furthermore, we show that imputation improves concordance between WGBS and the MethylationEPIC array at low WGBS depth, suggesting improved WGBS accuracy after imputation. Conclusions Our findings support the use of BoostMe as a preprocessing step for WGBS analysis. Electronic supplementary material The online version of this article (10.1186/s12864-018-4766-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Luli S Zou
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Michael R Erdos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - D Leland Taylor
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.,European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Peter S Chines
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Arushi Varshney
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Stephen C J Parker
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Francis S Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - John P Didion
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
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8
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Sievers CK, Zou LS, Pickhardt PJ, Matkowskyj KA, Albrecht DM, Clipson L, Bacher JW, Pooler BD, Moawad FJ, Cash BD, Reichelderfer M, Vo TN, Newton MA, Larget BR, Halberg RB. Subclonal diversity arises early even in small colorectal tumours and contributes to differential growth fates. Gut 2017; 66:2132-2140. [PMID: 27609830 PMCID: PMC5342955 DOI: 10.1136/gutjnl-2016-312232] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 08/10/2016] [Accepted: 08/12/2016] [Indexed: 12/18/2022]
Abstract
OBJECTIVE AND DESIGN The goal of the study was to determine whether the mutational profile of early colorectal polyps correlated with growth behaviour. The growth of small polyps (6-9 mm) that were first identified during routine screening of patients was monitored over time by interval imaging with CT colonography. Mutations in these lesions with known growth rates were identified by targeted next-generation sequencing. The timing of mutational events was estimated using computer modelling and statistical inference considering several parameters including allele frequency and fitness. RESULTS The mutational landscape of small polyps is varied both within individual polyps and among the group as a whole but no single alteration was correlated with growth behaviour. Polyps carried 0-3 pathogenic mutations with the most frequent being in APC, KRAS/NRAS, BRAF, FBXW7 and TP53. In polyps with two or more pathogenic mutations, allele frequencies were often variable, indicating the presence of multiple populations within a single tumour. Based on computer modelling, detectable mutations occurred at a mean polyp size of 30±35 crypts, well before the tumour is of a clinically detectable size. CONCLUSIONS These data indicate that small colon polyps can have multiple pathogenic mutations in crucial driver genes that arise early in the existence of a tumour. Understanding the molecular pathway of tumourigenesis and clonal evolution in polyps that are at risk for progressing to invasive cancers will allow us to begin to better predict which polyps are more likely to progress into adenocarcinomas and which patients are at greater risk of developing advanced disease.
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Affiliation(s)
- Chelsie K Sievers
- Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Luli S Zou
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Perry J Pickhardt
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Kristina A Matkowskyj
- Department of Pathology and Laboratory Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,US Department of Veterans Affairs, William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin, USA
| | - Dawn M Albrecht
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Linda Clipson
- Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Jeffery W Bacher
- Genetic Analysis Group, Promega Corporation, Madison, Wisconsin, USA
| | - B Dustin Pooler
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Fouad J Moawad
- Gastroenterology Service, Department of Medicine, Walter Reed National Military Medical Center, Bethesda, Maryland, USA
| | - Brooks D Cash
- Gastroenterology Service, Department of Medicine, Walter Reed National Military Medical Center, Bethesda, Maryland, USA,Gastroenterology Division, Department of Medicine, University of South Alabama, Mobile, Alabama, USA
| | - Mark Reichelderfer
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Tien N Vo
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Michael A Newton
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA,Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Bret R Larget
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA,Department of Botany, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Richard B Halberg
- Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,Division of Gastroenterology and Hepatology, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,Carbone Cancer Center, University of Wisconsin–Madison, Madison, Wisconsin, USA
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Ji J, Xie QM, Chen CY, Bai SW, Zou LS, Zuo KJ, Cao YC, Xue CY, Ma JY, Bi YZ. Molecular detection of Muscovy duck parvovirus by loop-mediated isothermal amplification assay. Poult Sci 2010; 89:477-83. [PMID: 20181863 DOI: 10.3382/ps.2009-00527] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Muscovy duck parvovirus (MDPV) usually causes high morbidity and mortality in 1- to 3-wk-old Muscovy ducklings due to serious infections, which is an imminent threat to the commercial duck industry in China. The objectives of this study were to develop and evaluate a simple, rapid, and inexpensive loop-mediated isothermal amplification (LAMP) method for specific detection of MDPV and to compare it with the PCR method in rapidity, sensitivity, and accuracy. The novel LAMP assay used a set of 4 specific primers to recognize 6 distinct genomic sequences of capsid protein (VP3) from MDPV, which could be completed within 50 min at 63 degrees C in a simple water bath. The diagnostic results demonstrated that the LAMP assay detected all 7 preserved MDPV isolates, had no cross-reactivity with other duck pathogens (i.e., goose parvovirus, duck plague virus, H9N2 avian influenza virus, duck hepatitis type virus I, and Muscovy duck reovirus). The LAMP assay was at least 10-fold more sensitive than the routine PCR assay and obtained more sensitivity in 61 clinical samples. Therefore, the newly developed LAMP assay provides a specific and sensitive means for detecting MDPV and can be simply applied both in field conditions and in laboratory operations in a cost-effective manner with primary care facilities.
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Affiliation(s)
- J Ji
- College of Animal Science, South China Agricultural University, Guangzhou 510642, People's Republic of China
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10
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Ma CM, Zou LS, Zhu FG. [Anastomosis following resection of esophageal or cardial cancer]. Zhonghua Wai Ke Za Zhi 1985; 23:419-20, 445. [PMID: 3902405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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