1
|
Kawaguchi RK, Tang Z, Fischer S, Rajesh C, Tripathy R, Koo PK, Gillis J. Learning single-cell chromatin accessibility profiles using meta-analytic marker genes. Brief Bioinform 2023; 24:bbac541. [PMID: 36549922 PMCID: PMC9851328 DOI: 10.1093/bib/bbac541] [Citation(s) in RCA: 0] [Impact Index Per Article: 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] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/29/2022] [Accepted: 11/08/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate. RESULTS In this study, we perform a systematic comparison of seven scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available at https://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner.
Collapse
Affiliation(s)
| | - Ziqi Tang
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Stephan Fischer
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Chandana Rajesh
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Rohit Tripathy
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Peter K Koo
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
- Department of Physiology and Donnelly Centre for Cellular & Biomolecular Research Department, University of Toronto, Ontario M5S 3E1, Canada
| |
Collapse
|
2
|
Deng J, Meng F, Zhang K, Gao J, Liu Z, Li M, Liu X, Li J, Wang Y, Zhang L, Tang P. Emerging Roles of Microglia Depletion in the Treatment of Spinal Cord Injury. Cells 2022; 11:cells11121871. [PMID: 35741000 PMCID: PMC9221038 DOI: 10.3390/cells11121871] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Microglia, as the resident immune cells and first responder to neurological insults, play an extremely important role in the pathophysiological process of spinal cord injury. On the one hand, microglia respond rapidly and gather around the lesion in the early stage of injury to exert a protective role, but with the continuous stimulation of the injury, the excessive activated microglia secrete a large number of harmful substances, aggravate the injury of spinal cord tissue, and affect functional recovery. The effects of microglia depletion on the repair of spinal cord injury remain unclear, and there is no uniformly accepted paradigm for the removal methods and timing of microglia depletion, but different microglia depletion strategies greatly affect the outcomes after spinal cord injury. Therefore, this review summarizes the physiological and pathological roles of microglia, especially the effects of microglia depletion on spinal cord injury-sustained microglial depletion would aggravate injury and impair functional recovery, while the short-term depletion of microglial population in diseased conditions seems to improve tissue repair and promote functional improvement after spinal cord injury. Furthermore, we discuss the advantages and disadvantages of major strategies and timing of microglia depletion to provide potential strategy for the treatment of spinal cord injury.
Collapse
Affiliation(s)
- Junhao Deng
- Senior Department of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital, Beijing 100037, China;
- National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100853, China; (J.G.); (Z.L.); (M.L.); (X.L.); (J.L.)
| | - Fanqi Meng
- Department of Spine Surgery, Peking University People’s Hospital, Beijing 100044, China;
| | - Kexue Zhang
- Department of Pediatric Surgery, The Chinese PLA General Hospital, Beijing 100853, China;
| | - Jianpeng Gao
- National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100853, China; (J.G.); (Z.L.); (M.L.); (X.L.); (J.L.)
| | - Zhongyang Liu
- National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100853, China; (J.G.); (Z.L.); (M.L.); (X.L.); (J.L.)
| | - Ming Li
- National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100853, China; (J.G.); (Z.L.); (M.L.); (X.L.); (J.L.)
| | - Xiao Liu
- National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100853, China; (J.G.); (Z.L.); (M.L.); (X.L.); (J.L.)
| | - Jiantao Li
- National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100853, China; (J.G.); (Z.L.); (M.L.); (X.L.); (J.L.)
| | - Yu Wang
- Beijing Key Lab of Regenerative Medicine in Orthopaedics, Key Laboratory of Musculoskeletal Trauma and War Injuries PLA, Institute of Orthopaedics, The Chinese PLA General Hospital, Beijing 100853, China;
| | - Licheng Zhang
- Senior Department of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital, Beijing 100037, China;
- National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100853, China; (J.G.); (Z.L.); (M.L.); (X.L.); (J.L.)
- Correspondence: (L.Z.); (P.T.)
| | - Peifu Tang
- Senior Department of Orthopedics, The Fourth Medical Center of Chinese PLA General Hospital, Beijing 100037, China;
- National Clinical Research Center for Orthopedics, Sports Medicine and Rehabilitation, Beijing 100853, China; (J.G.); (Z.L.); (M.L.); (X.L.); (J.L.)
- Correspondence: (L.Z.); (P.T.)
| |
Collapse
|
3
|
Schiebout C, Frost HR. CAMML: Multi-Label Immune Cell-Typing and Stemness Analysis for Single-Cell RNA-sequencing. Pac Symp Biocomput 2022; 27:199-210. [PMID: 34890149 PMCID: PMC8669732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Inferring the cell types in single-cell RNA-sequencing (scRNA-seq) data is of particular importance for understanding the potential cellular mechanisms and phenotypes occurring in complex tissues, such as the tumor-immune microenvironment (TME). The sparsity and noise of scRNA-seq data, combined with the fact that immune cell types often occur on a continuum, make cell typing of TME scRNA-seq data a significant challenge. Several single-label cell typing methods have been put forth to address the limitations of noise and sparsity, but accounting for the often overlapped spectrum of cell types in the immune TME remains an obstacle. To address this, we developed a new scRNA-seq cell-typing method, Cell-typing using variance Adjusted Mahalanobis distances with Multi-Labeling (CAMML). CAMML leverages cell type-specific weighted gene sets to score every cell in a dataset for every potential cell type. This allows cells to be labelled either by their highest scoring cell type as a single label classification or based on a score cut-off to give multi-label classification. For single-label cell typing, CAMML performance is comparable to existing cell typing methods, SingleR and Garnett. For scenarios where cells may exhibit features of multiple cell types (e.g., undifferentiated cells), the multi-label classification supported by CAMML offers important benefits relative to the current state-of-the-art methods. By integrating data across studies, omics platforms, and species, CAMML serves as a robust and adaptable method for overcoming the challenges of scRNA-seq analysis.
Collapse
|
4
|
Cui Y, Zhang S, Liang Y, Wang X, Ferraro TN, Chen Y. Consensus clustering of single-cell RNA-seq data by enhancing network affinity. Brief Bioinform 2021; 22:6308199. [PMID: 34160582 PMCID: PMC8574980 DOI: 10.1093/bib/bbab236] [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] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 05/29/2021] [Accepted: 06/01/2021] [Indexed: 12/18/2022] Open
Abstract
Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell Clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU + GPU (Central Processing Units + Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.
Collapse
Affiliation(s)
- Yaxuan Cui
- College of Computer and Information Engineering, Tianjin Normal University, China
| | - Shaoqiang Zhang
- College of Computer and Information Engineering, Tianjin Normal University, China
| | - Ying Liang
- College of Computer and Information Engineering, Tianjin Normal University, China
| | - Xiangyun Wang
- College of Computer and Information Engineering, Tianjin Normal University, China
| | - Thomas N Ferraro
- Department of Biomedical Sciences at CMSRU, Rowan University, NJ 08028, USA
| | - Yong Chen
- Department of Molecular and Cellular Biosciences at Rowan University, Rowan University, NJ 08028, USA
| |
Collapse
|
5
|
Tan Y, Cahan P. SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species. Cell Syst 2019; 9:207-213.e2. [PMID: 31377170 DOI: 10.1016/j.cels.2019.06.004] [Citation(s) in RCA: 155] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 04/18/2019] [Accepted: 06/12/2019] [Indexed: 11/28/2022]
Abstract
Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.
Collapse
Affiliation(s)
- Yuqi Tan
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Patrick Cahan
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| |
Collapse
|
6
|
Zhu Y, Clair G, Chrisler WB, Shen Y, Zhao R, Shukla AK, Moore RJ, Misra RS, Pryhuber GS, Smith RD, Ansong C, Kelly RT. Proteomic Analysis of Single Mammalian Cells Enabled by Microfluidic Nanodroplet Sample Preparation and Ultrasensitive NanoLC-MS. Angew Chem Int Ed Engl 2018; 57:12370-12374. [PMID: 29797682 DOI: 10.1002/anie.201802843] [Citation(s) in RCA: 151] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/21/2018] [Indexed: 01/22/2023]
Abstract
We report on the quantitative proteomic analysis of single mammalian cells. Fluorescence-activated cell sorting was employed to deposit cells into a newly developed nanodroplet sample processing chip, after which samples were analyzed by ultrasensitive nanoLC-MS. An average of circa 670 protein groups were confidently identified from single HeLa cells, which is a far greater level of proteome coverage for single cells than has been previously reported. We demonstrate that the single-cell proteomics platform can be used to differentiate cell types from enzyme-dissociated human lung primary cells and identify specific protein markers for epithelial and mesenchymal cells.
Collapse
Affiliation(s)
- Ying Zhu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Geremy Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - William B Chrisler
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Yufeng Shen
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Rui Zhao
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Anil K Shukla
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Ravi S Misra
- Department of Pediatrics-Neonatology, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Gloria S Pryhuber
- Department of Pediatrics-Neonatology, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Charles Ansong
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Ryan T Kelly
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| |
Collapse
|