1
|
Orouji E, Raman AT. Computational methods to explore chromatin state dynamics. Brief Bioinform 2022; 23:6751148. [PMID: 36208178 PMCID: PMC9677473 DOI: 10.1093/bib/bbac439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/25/2022] [Accepted: 09/09/2022] [Indexed: 12/14/2022] Open
Abstract
The human genome is marked by several singular and combinatorial histone modifications that shape the different states of chromatin and its three-dimensional organization. Genome-wide mapping of these marks as well as histone variants and open chromatin regions is commonly carried out via profiling DNA-protein binding or via chromatin accessibility methods. After the generation of epigenomic datasets in a cell type, statistical models can be used to annotate the noncoding regions of DNA and infer the combinatorial histone marks or chromatin states (CS). These methods involve partitioning the genome and labeling individual segments based on their CS patterns. Chromatin labels enable the systematic discovery of genomic function and activity and can label the gene body, promoters or enhancers without using other genomic maps. CSs are dynamic and change under different cell conditions, such as in normal, preneoplastic or tumor cells. This review aims to explore the available computational tools that have been developed to capture CS alterations under two or more cellular conditions.
Collapse
Affiliation(s)
- Elias Orouji
- Corresponding author: Elias Orouji, Epigenomics Lab, Princess Margaret Cancer Centre, University Health Network (UHN), 101 College St., Toronto, ON M5G 1 L7, Canada. Tel: +1 (917) 647-2202; E-mail:
| | - Ayush T Raman
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts, USA
| |
Collapse
|
2
|
Uddin M, Islam MK, Hassan MR, Jahan F, Baek JH. A fast and efficient algorithm for DNA sequence similarity identification. COMPLEX INTELL SYST 2022; 9:1265-1280. [PMID: 36035628 PMCID: PMC9395857 DOI: 10.1007/s40747-022-00846-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 08/05/2022] [Indexed: 11/22/2022]
Abstract
DNA sequence similarity analysis is necessary for enormous purposes including genome analysis, extracting biological information, finding the evolutionary relationship of species. There are two types of sequence analysis which are alignment-based (AB) and alignment-free (AF). AB is effective for small homologous sequences but becomes NP-hard problem for long sequences. However, AF algorithms can solve the major limitations of AB. But most of the existing AF methods show high time complexity and memory consumption, less precision, and less performance on benchmark datasets. To minimize these limitations, we develop an AF algorithm using a 2D \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$k-mer$$\end{document}k-mer count matrix inspired by the CGR approach. Then we shrink the matrix by analyzing the neighbors and then measure similarities using the best combinations of pairwise distance (PD) and phylogenetic tree methods. We also dynamically choose the value of k for \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$k-mer$$\end{document}k-mer. We develop an efficient system for finding the positions of \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}$$k-mer$$\end{document}k-mer in the count matrix. We apply our system in six different datasets. We achieve the top rank for two benchmark datasets from AFproject, 100% accuracy for two datasets (16 S Ribosomal, 18 Eutherian), and achieve a milestone for time complexity and memory consumption in comparison to the existing study datasets (HEV, HIV-1). Therefore, the comparative results of the benchmark datasets and existing studies demonstrate that our method is highly effective, efficient, and accurate. Thus, our method can be used with the top level of authenticity for DNA sequence similarity measurement.
Collapse
|
3
|
Targets preliminary screening for the fresh natural drug molecule based on Cosine-correlation and similarity-comparison of local network. J Transl Med 2022; 20:67. [PMID: 35115019 PMCID: PMC8812203 DOI: 10.1186/s12967-022-03279-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/24/2022] [Indexed: 11/30/2022] Open
Abstract
Background Chinese herbal medicine is made up of hundreds of natural drug molecules and has played a major role in traditional Chinese medicine (TCM) for several thousand years. Therefore, it is of great significance to study the target of natural drug molecules for exploring the mechanism of treating diseases with TCM. However, it is very difficult to determine the targets of a fresh natural drug molecule due to the complexity of the interaction between drug molecules and targets. Compared with traditional biological experiments, the computational method has the advantages of less time and low cost for targets screening, but it remains many great challenges, especially for the molecules without social ties. Methods This study proposed a novel method based on the Cosine-correlation and Similarity-comparison of Local Network (CSLN) to perform the preliminary screening of targets for the fresh natural drug molecules and assign weights to them through a trained parameter. Results The performance of CSLN is superior to the popular drug-target-interaction (DTI) prediction model GRGMF on the gold standard data in the condition that is drug molecules are the objects for training and testing. Moreover, CSLN showed excellent ability in checking the targets screening performance for a fresh-natural-drug-molecule (scenario simulation) on the TCMSP (13 positive samples in top20), meanwhile, Western-Blot also further verified the accuracy of CSLN. Conclusions In summary, the results suggest that CSLN can be used as an alternative strategy for screening targets of fresh natural drug molecules.
Collapse
|
4
|
Sienkiewicz K, Chen J, Chatrath A, Lawson JT, Sheffield NC, Zhang L, Ratan A. Detecting molecular subtypes from multi-omics datasets using SUMO. CELL REPORTS METHODS 2022; 2:100152. [PMID: 35211690 PMCID: PMC8865426 DOI: 10.1016/j.crmeth.2021.100152] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 08/27/2021] [Accepted: 12/21/2021] [Indexed: 12/31/2022]
Abstract
We present a data integration framework that uses non-negative matrix factorization of patient-similarity networks to integrate continuous multi-omics datasets for molecular subtyping. It is demonstrated to have the capability to handle missing data without using imputation and to be consistently among the best in detecting subtypes with differential prognosis and enrichment of clinical associations in a large number of cancers. When applying the approach to data from individuals with lower-grade gliomas, we identify a subtype with a significantly worse prognosis. Tumors assigned to this subtype are hypomethylated genome wide with a gain of AP-1 occupancy in demethylated distal enhancers. The tumors are also enriched for somatic chromosome 7 (chr7) gain, chr10 loss, and other molecular events that have been suggested as diagnostic markers for "IDH wild type, with molecular features of glioblastoma" by the cIMPACT-NOW consortium but have yet to be included in the World Health Organization (WHO) guidelines.
Collapse
Affiliation(s)
- Karolina Sienkiewicz
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Jinyu Chen
- Department of Mathematics and Computational Biology Program, National University of Singapore, Singapore 119076, Singapore
| | - Ajay Chatrath
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - John T. Lawson
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Nathan C. Sheffield
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
- University of Virginia Cancer Center, Charlottesville, VA 22908, USA
| | - Louxin Zhang
- Department of Mathematics and Computational Biology Program, National University of Singapore, Singapore 119076, Singapore
| | - Aakrosh Ratan
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
- University of Virginia Cancer Center, Charlottesville, VA 22908, USA
| |
Collapse
|
5
|
Patty BJ, Hainer SJ. Transcription factor chromatin profiling genome-wide using uliCUT&RUN in single cells and individual blastocysts. Nat Protoc 2021; 16:2633-2666. [PMID: 33911257 PMCID: PMC8177051 DOI: 10.1038/s41596-021-00516-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 02/04/2021] [Indexed: 02/02/2023]
Abstract
Determining chromatin-associated protein localization across the genome has provided insight into the functions of DNA-binding proteins and their connections to disease. However, established protocols requiring large quantities of cell or tissue samples currently limit applications for clinical and biomedical research in this field. Furthermore, most technologies have been optimized to assess abundant histone protein localization, prohibiting the investigation of nonhistone protein localization in low cell numbers. We recently described a protocol to profile chromatin-associated protein localization in as low as one cell: ultra-low-input cleavage under targets and release using nuclease (uliCUT&RUN). Optimized from chromatin immunocleavage and CUT&RUN, uliCUT&RUN is a tethered enzyme-based protocol that utilizes a combination of recombinant protein, antibody recognition and stringent purification to selectively target proteins of interest and isolate the associated DNA. Performed in native conditions, uliCUT&RUN profiles protein localization to chromatin with low input and high precision. Compared with other profiling technologies, uliCUT&RUN can determine nonhistone protein chromatin occupancies in low cell numbers, permitting the investigation into the molecular functions of a range of DNA-binding proteins within rare samples. From sample preparation to sequencing library submission, the uliCUT&RUN protocol takes <2 d to perform, with the accompanying data analysis timeline dependent on experience level.
Collapse
Affiliation(s)
- Benjamin J Patty
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Sarah J Hainer
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
| |
Collapse
|
6
|
Rai MF, Wu CL, Capellini TD, Guilak F, Dicks AR, Muthuirulan P, Grandi F, Bhutani N, Westendorf JJ. Single Cell Omics for Musculoskeletal Research. Curr Osteoporos Rep 2021; 19:131-140. [PMID: 33559841 PMCID: PMC8743139 DOI: 10.1007/s11914-021-00662-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/19/2021] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW The ability to analyze the molecular events occurring within individual cells as opposed to populations of cells is revolutionizing our understanding of musculoskeletal tissue development and disease. Single cell studies have the great potential of identifying cellular subpopulations that work in a synchronized fashion to regenerate and repair damaged tissues during normal homeostasis. In addition, such studies can elucidate how these processes break down in disease as well as identify cellular subpopulations that drive the disease. This review highlights three emerging technologies: single cell RNA sequencing (scRNA-seq), Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), and Cytometry by Time-Of-Flight (CyTOF) mass cytometry. RECENT FINDINGS Technological and bioinformatic tools to analyze the transcriptome, epigenome, and proteome at the individual cell level have advanced rapidly making data collection relatively easy; however, understanding how to access and interpret the data remains a challenge for many scientists. It is, therefore, of paramount significance to educate the musculoskeletal community on how single cell technologies can be used to answer research questions and advance translation. This article summarizes talks given during a workshop on "Single Cell Omics" at the 2020 annual meeting of the Orthopedic Research Society. Studies that applied scRNA-seq, ATAC-seq, and CyTOF mass cytometry to cartilage development and osteoarthritis are reviewed. This body of work shows how these cutting-edge tools can advance our understanding of the cellular heterogeneity and trajectories of lineage specification during development and disease.
Collapse
Affiliation(s)
- Muhammad Farooq Rai
- Department of Orthopaedic Surgery, Washington University, St. Louis, MO, USA
| | - Chia-Lung Wu
- Department of Orthopaedic Surgery, Washington University and Shriners Hospitals for Children, St. Louis, MO, USA
| | - Terence D Capellini
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Farshid Guilak
- Department of Orthopaedic Surgery, Washington University and Shriners Hospitals for Children, St. Louis, MO, USA
| | - Amanda R Dicks
- Department of Orthopaedic Surgery, Washington University and Shriners Hospitals for Children, St. Louis, MO, USA
| | | | - Fiorella Grandi
- Department of Orthopedic Surgery, Stanford University, Stanford, CA, USA
| | - Nidhi Bhutani
- Department of Orthopedic Surgery, Stanford University, Stanford, CA, USA
| | | |
Collapse
|
7
|
Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity. Comput Struct Biotechnol J 2021; 19:1701-1712. [PMID: 33815693 PMCID: PMC7997026 DOI: 10.1016/j.csbj.2021.03.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/05/2021] [Accepted: 03/18/2021] [Indexed: 12/16/2022] Open
Abstract
The global pandemic caused by the SARS-CoV-2 virus continues to spread. Infection with SARS- CoV-2 causes COVID-19, a disease of variable severity. Mutation has already altered the SARS-CoV-2 genome from its original reported sequence and continued mutation is highly probable. These mutations can: (i) have no significant impact (they are silent), (ii) result in a complete loss or reduction of infectivity, or (iii) induce increase in infectivity. Physical generation, for research purposes, of viral mutations that could enhance infectivity are controversial and highly regulated. The primary purpose of this project was to evaluate the ability of the DeepNEU machine learning stem-cell simulation platform to enable rapid and efficient assessment of the potential impact of viral loss-of-function (LOF) and gain-of-function (GOF) mutations on SARS-CoV-2 infectivity. Our data suggest that SARS-CoV-2 infection can be simulated in human alveolar type lung cells. Simulation of infection in these lung cells can be used to model and assess the impact of LOF and GOF mutations in the SARS-CoV2 genome. We have also created a four- factor infectivity measure: the DeepNEU Case Fatality Rate (dnCFR). dnCFR can be used to assess infectivity based on the presence or absence of the key viral proteins (NSP3, Spike-RDB, N protein, and M protein). dnCFR was used in this study, not to only assess the impact of different mutations on SARS-CoV2 infectivity, but also to categorize the effects of mutations as loss of infectivity or gain of infectivity events.
Collapse
|
8
|
Sinha S, Satpathy AT, Zhou W, Ji H, Stratton JA, Jaffer A, Bahlis N, Morrissy S, Biernaskie JA. Profiling Chromatin Accessibility at Single-cell Resolution. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:172-190. [PMID: 33581341 PMCID: PMC8602754 DOI: 10.1016/j.gpb.2020.06.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 03/04/2020] [Accepted: 08/15/2020] [Indexed: 01/22/2023]
Abstract
How distinct transcriptional programs are enacted to generate cellular heterogeneity and plasticity, and enable complex fate decisions are important open questions. One key regulator is the cell’s epigenome state that drives distinct transcriptional programs by regulating chromatin accessibility. Genome-wide chromatin accessibility measurements can impart insights into regulatory sequences (in)accessible to DNA-binding proteins at a single-cell resolution. This review outlines molecular methods and bioinformatic tools for capturing cell-to-cell chromatin variation using single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) in a scalable fashion. It also covers joint profiling of chromatin with transcriptome/proteome measurements, computational strategies to integrate multi-omic measurements, and predictive bioinformatic tools to infer chromatin accessibility from single-cell transcriptomic datasets. Methodological refinements that increase power for cell discovery through robust chromatin coverage and integrate measurements from multiple modalities will further expand our understanding of gene regulation during homeostasis and disease.
Collapse
Affiliation(s)
- Sarthak Sinha
- Department of Comparative Biology & Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
| | - Ansuman T Satpathy
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Weiqiang Zhou
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Hongkai Ji
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Jo A Stratton
- Department of Comparative Biology & Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Arzina Jaffer
- Department of Comparative Biology & Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Nizar Bahlis
- Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB T2N 4Z6, Canada
| | - Sorana Morrissy
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB T2N 4Z6, Canada; Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Jeff A Biernaskie
- Department of Comparative Biology & Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
| |
Collapse
|
9
|
Ji Z, Zhou W, Hou W, Ji H. Single-cell ATAC-seq signal extraction and enhancement with SCATE. Genome Biol 2020; 21:161. [PMID: 32620137 PMCID: PMC7333383 DOI: 10.1186/s13059-020-02075-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 06/15/2020] [Indexed: 01/25/2023] Open
Abstract
Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample.
Collapse
Affiliation(s)
- Zhicheng Ji
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205 USA
| | - Weiqiang Zhou
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205 USA
| | - Wenpin Hou
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205 USA
| | - Hongkai Ji
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205 USA
| |
Collapse
|
10
|
Seweryn MT, Pietrzak M, Ma Q. Application of information theoretical approaches to assess diversity and similarity in single-cell transcriptomics. Comput Struct Biotechnol J 2020; 18:1830-1837. [PMID: 32728406 PMCID: PMC7371753 DOI: 10.1016/j.csbj.2020.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 04/24/2020] [Accepted: 05/06/2020] [Indexed: 02/09/2023] Open
Abstract
Single-cell transcriptomics offers a powerful way to reveal the heterogeneity of individual cells. To date, many information theoretical approaches have been proposed to assess diversity and similarity, and characterize the latent heterogeneity in transcriptome data. Diversity implies gene expression variations and can facilitate the identification of signature genes; while, similarity unravels co-expression patterns for cell type clustering. In this review, we summarized 16 measures of information theory used for evaluating diversity and similarity in single-cell transcriptomic data, provide references and shed light on selected theoretical properties when there is a need to select proper measurements in general cases. We further provide an R package assembling discussed approaches to improve the researchers own single-cell transcriptome study. At last, we prospected further applications of diversity and similarity measures in support of depicting heterogeneity in single-cell multi-omics data.
Collapse
Affiliation(s)
- Michal T. Seweryn
- Center for Medical Genomics, Jagiellonian University, Cracow, Poland
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus OH, United States
| | - Qin Ma
- Department of Biomedical Informatics, The Ohio State University, Columbus OH, United States
| |
Collapse
|
11
|
Wang W, Fasolino M, Cattau B, Goldman N, Kong W, Frederick MA, McCright SJ, Kiani K, Fraietta JA, Vahedi G. Joint profiling of chromatin accessibility and CAR-T integration site analysis at population and single-cell levels. Proc Natl Acad Sci U S A 2020; 117:5442-5452. [PMID: 32094195 PMCID: PMC7071901 DOI: 10.1073/pnas.1919259117] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Chimeric antigen receptor (CAR)-T immunotherapy has yielded impressive results in several B cell malignancies, establishing itself as a powerful means to redirect the natural properties of T lymphocytes. In this strategy, the T cell genome is modified by the integration of lentiviral vectors encoding CAR that direct tumor cell killing. However, this therapeutic approach is often limited by the extent of CAR-T cell expansion in vivo. A major outstanding question is whether or not CAR-T integration itself enhances the proliferative competence of individual T cells by rewiring their regulatory landscape. To address this question, it is critical to define the identity of an individual CAR-T cell and simultaneously chart where the CAR-T vector integrates into the genome. Here, we report the development of a method called EpiVIA (https://github.com/VahediLab/epiVIA) for the joint profiling of the chromatin accessibility and lentiviral integration site analysis at the population and single-cell levels. We validate our technique in clonal cells with previously defined integration sites and further demonstrate the ability to measure lentiviral integration sites and chromatin accessibility of host and viral genomes at the single-cell resolution in CAR-T cells. We anticipate that EpiVIA will enable the single-cell deconstruction of gene regulation during CAR-T therapy, leading to the discovery of cellular factors associated with durable treatment.
Collapse
Affiliation(s)
- Wenliang Wang
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
| | - Maria Fasolino
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
| | - Benjamin Cattau
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
| | - Naomi Goldman
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
| | - Weimin Kong
- Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Abramson Family Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
| | - Megan A Frederick
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
| | - Sam J McCright
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
| | - Karun Kiani
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
| | - Joseph A Fraietta
- Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Abramson Family Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Center for Cellular Immunotherapies, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Parker Institute for Cancer Immunotherapy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Golnaz Vahedi
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104;
- Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Epigenetics Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
- Abramson Family Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104
| |
Collapse
|
12
|
A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases. Med Biol Eng Comput 2020; 58:1015-1029. [PMID: 32124223 DOI: 10.1007/s11517-020-02146-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 02/13/2020] [Indexed: 12/20/2022]
Abstract
The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is important for CAD, we propose a multi-level method to measure the similarity between the CISLs. The CISLs are characterized in the low-level visual scale, mid-level attribute scale, and high-level semantic scale, for a rich representation. The similarity at multiple levels is calculated and combined in a weighted sum form as the final similarity. The proposed multi-level similarity method is capable of computing the level-specific similarity and optimal cross-level complementary similarity. The effectiveness of the proposed similarity measure method is evaluated on a dataset of 511 lung CT images from clinical patients for CISLs retrieval. It can achieve about 80% precision and take only 3.6 ms for the retrieval process. The extensive comparative evaluations on the same datasets are conducted to validate the advantages on retrieval performance of our multi-level similarity measure over the single-level measure and the two-level similarity methods. The proposed method can have wide applications in radiology and decision support. Graphical abstract.
Collapse
|