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Zhang Z, Wiencke JK, Kelsey KT, Koestler DC, Molinaro AM, Pike SC, Karra P, Christensen BC, Salas LA. Hierarchical deconvolution for extensive cell type resolution in the human brain using DNA methylation. Front Neurosci 2023; 17:1198243. [PMID: 37404460 PMCID: PMC10315586 DOI: 10.3389/fnins.2023.1198243] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/30/2023] [Indexed: 07/06/2023] Open
Abstract
Introduction The human brain comprises heterogeneous cell types whose composition can be altered with physiological and pathological conditions. New approaches to discern the diversity and distribution of brain cells associated with neurological conditions would significantly advance the study of brain-related pathophysiology and neuroscience. Unlike single-nuclei approaches, DNA methylation-based deconvolution does not require special sample handling or processing, is cost-effective, and easily scales to large study designs. Existing DNA methylation-based methods for brain cell deconvolution are limited in the number of cell types deconvolved. Methods Using DNA methylation profiles of the top cell-type-specific differentially methylated CpGs, we employed a hierarchical modeling approach to deconvolve GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells. Results We demonstrate the utility of our method by applying it to data on normal tissues from various brain regions and in aging and diseased tissues, including Alzheimer's disease, autism, Huntington's disease, epilepsy, and schizophrenia. Discussion We expect that the ability to determine the cellular composition in the brain using only DNA from bulk samples will accelerate understanding brain cell type composition and cell-type-specific epigenetic states in normal and diseased brain tissues.
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Affiliation(s)
- Ze Zhang
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - John K. Wiencke
- Department of Neurological Surgery, Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, United States
| | - Karl T. Kelsey
- Department of Epidemiology, Department of Pathology and Laboratory Medicine, Brown University School of Public Health, Providence, RI, United States
| | - Devin C. Koestler
- Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, United States
| | - Annette M. Molinaro
- Department of Neurological Surgery, Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, United States
| | - Steven C. Pike
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
- Department of Neurology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Prasoona Karra
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Brock C. Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
| | - Lucas A. Salas
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
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Bonnycastle LL, Gildea DE, Yan T, Narisu N, Swift AJ, Wolfsberg TG, Erdos MR, Collins FS. Single-cell transcriptomics from human pancreatic islets: sample preparation matters. Biol Methods Protoc 2020; 5:bpz019. [PMID: 31984226 DOI: 10.1093/biomethods/bpz019] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/26/2019] [Accepted: 12/11/2019] [Indexed: 12/27/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) of human primary tissues is a rapidly emerging tool for investigating human health and disease at the molecular level. However, optimal processing of solid tissues presents a number of technical and logistical challenges, especially for tissues that are only available at autopsy, which includes pancreatic islets, a tissue that is highly relevant to diabetes. To assess the possible effects of different sample preparation protocols on fresh islet samples, we performed a detailed comparison of scRNA-seq data generated with islets isolated from a human donor but processed according to four treatment strategies, including fixation and cryopreservation. We found significant and reproducible differences in the proportion of cell types identified, and more minor effects on cell-specific patterns of gene expression. Fresh islets from a second donor confirmed gene expression signatures of alpha and beta subclusters. These findings may well apply to other tissues, emphasizing the need for careful consideration when choosing processing methods, comparing results between different studies, and/or interpreting data in the context of multiple cell types from preserved tissue.
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Affiliation(s)
- Lori L Bonnycastle
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Derek E Gildea
- Bioinformatics and Scientific Programming Core, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tingfen Yan
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Narisu Narisu
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amy J Swift
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Tyra G Wolfsberg
- Bioinformatics and Scientific Programming Core, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michael R Erdos
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Francis S Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
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Yin L, Zhang Z, Liu Y, Gao Y, Gu J. Recent advances in single-cell analysis by mass spectrometry. Analyst 2019; 144:824-845. [PMID: 30334031 DOI: 10.1039/c8an01190g] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cells are the most basic structural units that play vital roles in the functioning of living organisms. Analysis of the chemical composition and content of a single cell plays a vital role in ensuring precise investigations of cellular metabolism, and is a crucial aspect of lipidomic and proteomic studies. In addition, structural knowledge provides a better understanding of cell behavior as well as the cellular and subcellular mechanisms. However, single-cell analysis can be very challenging due to the very small size of each cell as well as the large variety and extremely low concentrations of substances found in individual cells. On account of its high sensitivity and selectivity, mass spectrometry holds great promise as an effective technique for single-cell analysis. Numerous mass spectrometric techniques have been developed to elucidate the molecular profiles at the cellular level, including electrospray ionization mass spectrometry (ESI-MS), secondary ion mass spectrometry (SIMS), laser-based mass spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In this review, the recent advances in single-cell analysis by mass spectrometry are summarized. The strategies of different ionization modes to achieve single-cell analysis are classified and discussed in detail.
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Affiliation(s)
- Lei Yin
- Research Institute of Translational Medicine, The First Hospital of Jilin University, Jilin University, Dongminzhu Street, Changchun 130061, PR China.
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