1
|
Shang Y, Wang Z, Xi L, Wang Y, Liu M, Feng Y, Wang J, Wu Q, Xiang X, Chen M, Ding Y. Droplet-based single-cell sequencing: Strategies and applications. Biotechnol Adv 2024; 77:108454. [PMID: 39271031 DOI: 10.1016/j.biotechadv.2024.108454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 08/22/2024] [Accepted: 09/10/2024] [Indexed: 09/15/2024]
Abstract
Notable advancements in single-cell omics technologies have not only addressed longstanding challenges but also enabled unprecedented studies of cellular heterogeneity with unprecedented resolution and scale. These strides have led to groundbreaking insights into complex biological systems, paving the way for a more profound comprehension of human biology and diseases. The droplet microfluidic technology has become a crucial component in many single-cell sequencing workflows in terms of throughput, cost-effectiveness, and automation. Utilizing a microfluidic chip to encapsulate and profile individual cells within droplets has significantly improved single-cell research. Therefore, this review aims to comprehensively elaborate the droplet microfluidics-assisted omics methods from a single-cell perspective. The strategies for using droplet microfluidics in the realms of genomics, epigenomics, transcriptomics, and proteomics analyses are first introduced. On this basis, the focus then turns to the latest applications of this technology in different sequencing patterns, including mono- and multi-omics. Finally, the challenges and further perspectives of droplet-based single-cell sequencing in both foundational research and commercial applications are discussed.
Collapse
Affiliation(s)
- Yuting Shang
- Department of Food Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Zhengzheng Wang
- Department of Food Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Liqing Xi
- Department of Food Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Yantao Wang
- Department of Food Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Meijing Liu
- Department of Food Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Ying Feng
- Department of Food Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Juan Wang
- College of Food Science, South China Agricultural University, Guangzhou 510432, China
| | - Qingping Wu
- National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Guangdong Provincial Key Laboratory of Microbial Safety and Health, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Xinran Xiang
- Jiangsu Key Laboratory of Huaiyang Food Safety and Nutrition Function Evaluation, Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Jiangsu Key Laboratory for Eco-Agricultural Biotechnology Around Hongze Lake, School of Life Science, Huaiyin Normal University, Huai'an 223300, China; Fujian Key Laboratory of Aptamers Technology, Fuzhou General Clinical Medical School (the 900th Hospital), Fujian Medical University, Fuzhou 350001, China.
| | - Moutong Chen
- National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Guangdong Provincial Key Laboratory of Microbial Safety and Health, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
| | - Yu Ding
- Department of Food Science & Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China.
| |
Collapse
|
2
|
Eastburn DJ, White KS, Jayne ND, Camiolo S, Montis G, Ha S, Watson KG, Yeakley JM, McComb J, Seligmann B. High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.03.606484. [PMID: 39149288 PMCID: PMC11326174 DOI: 10.1101/2024.08.03.606484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
We report the development and performance of a novel genomics platform, TempO-LINC, for conducting high-throughput transcriptomic analysis on single cells and nuclei. TempO-LINC works by adding cell-identifying molecular barcodes onto highly selective and high-sensitivity gene expression probes within fixed cells, without having to first generate cDNA. Using an instrument-free combinatorial-indexing approach, all probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000+ cells per run. The TempO-LINC approach is easily scalable based on the number of barcodes and rounds of barcoding performed; however, for the experiments reported in this study, the assay utilized over 5.3 million unique barcodes. TempO-LINC has a robust protocol for fixing and banking cells and displays high-sensitivity gene detection from multiple diverse sample types. We show that TempO-LINC has an observed multiplet rate of less than 1.1% and a cell capture rate of ~50%. Although the assay can accurately profile the whole transcriptome (19,683 human or 21,400 mouse genes), it can be targeted to measure only actionable/informative genes and molecular pathways of interest - thereby reducing sequencing requirements. In this study, we applied TempO-LINC to profile the transcriptomes of 89,722 cells across multiple sample types, including nuclei from mouse lung, kidney and brain tissues. The data demonstrated the ability to identify and annotate at least 50 unique cell populations and positively correlate expression of cell type-specific molecular markers within them. TempO-LINC is a robust new single-cell technology that is ideal for large-scale applications/studies across thousands of samples with high data quality.
Collapse
|
3
|
Chow A, Lareau CA. Concepts and new developments in droplet-based single cell multi-omics. Trends Biotechnol 2024:S0167-7799(24)00184-7. [PMID: 39095258 DOI: 10.1016/j.tibtech.2024.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/31/2024] [Accepted: 07/12/2024] [Indexed: 08/04/2024]
Abstract
Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies.
Collapse
Affiliation(s)
- Arthur Chow
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Caleb A Lareau
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| |
Collapse
|
4
|
Gao X, Zhuang Q, Li Y, Li G, Huang Z, Chen S, Sun S, Yang H, Jiang L, Mao Y. Single-Cell Chromatin Accessibility Analysis Reveals Subgroup-Specific TF-NTR Regulatory Circuits in Medulloblastoma. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2309554. [PMID: 38884167 PMCID: PMC11321678 DOI: 10.1002/advs.202309554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/21/2024] [Indexed: 06/18/2024]
Abstract
Medulloblastoma (MB) stands as one of the prevalent malignant brain tumors among pediatric patients. Despite its prevalence, the intricate interplay between the regulatory program driving malignancy in MB cells and their interactions with the microenvironment remains insufficiently understood. Leveraging the capabilities of single-cell Assay for Transposase-Accessible Chromatin sequencing (scATAC-seq), the chromatin accessibility landscape is unveiled across 59,015 distinct MB cells. This expansive dataset encompasses cells belonging to discrete molecular subgroups, namely SHH, WNT, Group3, and Group4. Within these chromatin accessibility profiles, specific regulatory elements tied to individual subgroups are uncovered, shedding light on the distinct activities of transcription factors (TFs) that likely orchestrate the tumorigenesis process. Moreover, it is found that certain neurotransmitter receptors (NTRs) are subgroup-specific and can predict MB subgroup classification when combined with their associated transcription factors. Notably, targeting essential NTRs within tumors influences both the in vitro sphere-forming capability and the in vivo tumorigenic capacity of MB cells. These findings collectively provide fresh insights into comprehending the regulatory networks and cellular dynamics within MBs. Furthermore, the significance of the TF-NTR regulatory circuits is underscored as prospective biomarkers and viable therapeutic targets.
Collapse
Affiliation(s)
- Xiaoyue Gao
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100049China
| | - Qiyuan Zhuang
- Department of NeurosurgeryHuashan HospitalFudan UniversityShanghai200040China
- National Center for Neurological DisordersShanghai Key Laboratory of Brain Function Restoration and Neural RegenerationNeurosurgical Institute of Fudan University Shanghai Clinical Medical Center of NeurosurgeryHuashan HospitalFudan UniversityShanghai200040China
| | - Yun Li
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100049China
| | - Guochao Li
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100049China
| | - Zheng Huang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100049China
| | - Shenzhi Chen
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijing100049China
| | - Shaoxing Sun
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100049China
| | - Hui Yang
- Department of NeurosurgeryHuashan HospitalFudan UniversityShanghai200040China
- National Center for Neurological DisordersShanghai Key Laboratory of Brain Function Restoration and Neural RegenerationNeurosurgical Institute of Fudan University Shanghai Clinical Medical Center of NeurosurgeryHuashan HospitalFudan UniversityShanghai200040China
- State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceInstitute for Translational Brain ResearchShanghai Medical CollegeFudan UniversityShanghai200032China
| | - Lan Jiang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100049China
- Sino‐Danish CollegeUniversity of Chinese Academy of SciencesBeijing100049China
- College of Future Technology CollegeUniversity of Chinese Academy of SciencesBeijing100049China
| | - Ying Mao
- Department of NeurosurgeryHuashan HospitalFudan UniversityShanghai200040China
- National Center for Neurological DisordersShanghai Key Laboratory of Brain Function Restoration and Neural RegenerationNeurosurgical Institute of Fudan University Shanghai Clinical Medical Center of NeurosurgeryHuashan HospitalFudan UniversityShanghai200040China
| |
Collapse
|
5
|
De Rop FV, Hulselmans G, Flerin C, Soler-Vila P, Rafels A, Christiaens V, González-Blas CB, Marchese D, Caratù G, Poovathingal S, Rozenblatt-Rosen O, Slyper M, Luo W, Muus C, Duarte F, Shrestha R, Bagdatli ST, Corces MR, Mamanova L, Knights A, Meyer KB, Mulqueen R, Taherinasab A, Maschmeyer P, Pezoldt J, Lambert CLG, Iglesias M, Najle SR, Dossani ZY, Martelotto LG, Burkett Z, Lebofsky R, Martin-Subero JI, Pillai S, Sebé-Pedrós A, Deplancke B, Teichmann SA, Ludwig LS, Braun TP, Adey AC, Greenleaf WJ, Buenrostro JD, Regev A, Aerts S, Heyn H. Systematic benchmarking of single-cell ATAC-sequencing protocols. Nat Biotechnol 2024; 42:916-926. [PMID: 37537502 PMCID: PMC11180611 DOI: 10.1038/s41587-023-01881-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/22/2023] [Indexed: 08/05/2023]
Abstract
Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.
Collapse
Grants
- R01 DA047237 NIDA NIH HHS
- R00 AG059918 NIA NIH HHS
- U19 AI057266 NIAID NIH HHS
- G0B5619N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- RF1 MH128842 NIMH NIH HHS
- UM1 HG009436 NHGRI NIH HHS
- 1S80920N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- UM1 HG012076 NHGRI NIH HHS
- RM1 HG007735 NHGRI NIH HHS
- G094121N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- R35 GM124704 NIGMS NIH HHS
- UM1 HG009442 NHGRI NIH HHS
- Wellcome Trust
- H.H. received support for the project PID2020-115439GB-I00- funded by MCIN/AEI/ 10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 810287 and 874710).
- M.R.C. is supported by the National Institutes on Aging K99/R00AG059918.
- K.B.M. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).
- S.A.T. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).
- This work was supported by funding from the Rita Allen Foundation (W.J.G.), the Human Frontiers Science (RGY006S) (W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative, and the National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01- HG00990901, and U19- AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective.
- This work was supported by an ERC Consolidator Grant to S.A. (no. 724226_cis- CONTROL), KU Leuven (grant no. C14/22/125 to S.A.), Foundation Against Cancer (grant no, F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A.), Aligning Science Across Parkinson’s (ASAP, grant no. ASAP-000430 to S.A.)
Collapse
Affiliation(s)
- Florian V De Rop
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gert Hulselmans
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Chris Flerin
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Paula Soler-Vila
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Albert Rafels
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Valerie Christiaens
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Carmen Bravo González-Blas
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Domenica Marchese
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Ginevra Caratù
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | | | | | | | - Wendy Luo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Fabiana Duarte
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rojesh Shrestha
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
| | | | | | | | - Ryan Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Akram Taherinasab
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
- Division of Oncologic Sciences, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
| | - Patrick Maschmeyer
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Jörn Pezoldt
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Camille Lucie Germaine Lambert
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Marta Iglesias
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Sebastián R Najle
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Zain Y Dossani
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Luciano G Martelotto
- Adelaide Centre for Epigenetics and the South Australian Immunogenomics Cancer Institute, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- University of Melbourne Centre for Cancer Research, Victoria Comprehensive Cancer Centre, Melbourne, Victoria, Australia
| | - Zach Burkett
- Digital Biology Group, Bio-Rad, Pleasanton, CA, USA
| | | | - José Ignacio Martin-Subero
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Departament de Fonaments Clínics, Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Satish Pillai
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Arnau Sebé-Pedrós
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Department of Physics/Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Leif S Ludwig
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Theodore P Braun
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
- Division of Oncologic Sciences, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Jason D Buenrostro
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute of Integrative Cancer Research, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Stein Aerts
- VIB Center for Brain and Disease Research, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
| |
Collapse
|
6
|
Gupta T, Antanaviciute A, Hyun-Jung Lee C, Ottakandathil Babu R, Aulicino A, Christoforidou Z, Siejka-Zielinska P, O'Brien-Ball C, Chen H, Fawkner-Corbett D, Geros AS, Bridges E, McGregor C, Cianci N, Fryer E, Alham NK, Jagielowicz M, Santos AM, Fellermeyer M, Davis SJ, Parikh K, Cheung V, Al-Hillawi L, Sasson S, Slevin S, Brain O, Fernandes RA, Koohy H, Simmons A. Tracking in situ checkpoint inhibitor-bound target T cells in patients with checkpoint-induced colitis. Cancer Cell 2024; 42:797-814.e15. [PMID: 38744246 DOI: 10.1016/j.ccell.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 02/09/2024] [Accepted: 04/17/2024] [Indexed: 05/16/2024]
Abstract
The success of checkpoint inhibitors (CPIs) for cancer has been tempered by immune-related adverse effects including colitis. CPI-induced colitis is hallmarked by expansion of resident mucosal IFNγ cytotoxic CD8+ T cells, but how these arise is unclear. Here, we track CPI-bound T cells in intestinal tissue using multimodal single-cell and subcellular spatial transcriptomics (ST). Target occupancy was increased in inflamed tissue, with drug-bound T cells located in distinct microdomains distinguished by specific intercellular signaling and transcriptional gradients. CPI-bound cells were largely CD4+ T cells, including enrichment in CPI-bound peripheral helper, follicular helper, and regulatory T cells. IFNγ CD8+ T cells emerged from both tissue-resident memory (TRM) and peripheral populations, displayed more restricted target occupancy profiles, and co-localized with damaged epithelial microdomains lacking effective regulatory cues. Our multimodal analysis identifies causal pathways and constitutes a resource to inform novel preventive strategies.
Collapse
Affiliation(s)
- Tarun Gupta
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Agne Antanaviciute
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
| | - Chloe Hyun-Jung Lee
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Rosana Ottakandathil Babu
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Anna Aulicino
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Zoe Christoforidou
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Paulina Siejka-Zielinska
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Caitlin O'Brien-Ball
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford OX3 7BN, UK
| | - Hannah Chen
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - David Fawkner-Corbett
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Academic Paediatric Surgery Unit (APSU), Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Ana Sousa Geros
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Esther Bridges
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Colleen McGregor
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Nicole Cianci
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Eve Fryer
- Pathology, Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Nasullah Khalid Alham
- Nuffield Department of Surgical Sciences and Oxford NIHR Biomedical Research Centre (BRC), University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Marta Jagielowicz
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Ana Mafalda Santos
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Martin Fellermeyer
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Simon J Davis
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Kaushal Parikh
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Vincent Cheung
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Lulia Al-Hillawi
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Sarah Sasson
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Stephanie Slevin
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Oliver Brain
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford OX3 7BN, UK
| | - Hashem Koohy
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
| | - Alison Simmons
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK.
| |
Collapse
|
7
|
Zhang H, Mulqueen RM, Iannuzo N, Farrera DO, Polverino F, Galligan JJ, Ledford JG, Adey AC, Cusanovich DA. txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility. Genome Biol 2024; 25:78. [PMID: 38519979 PMCID: PMC10958877 DOI: 10.1186/s13059-023-03150-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.
Collapse
Affiliation(s)
- Hao Zhang
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Ryan M Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Natalie Iannuzo
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
| | - Dominique O Farrera
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Francesca Polverino
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Arizona, Tucson, AZ, USA
- Banner - University Medicine North, Pulmonary - Clinic F, Tucson, AZ, USA
| | - James J Galligan
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Julie G Ledford
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA.
- Oregon Health & Science University, Knight Cancer Institute, Portland, OR, USA.
- Oregon Health & Science University, Knight Cardiovascular Institute, Portland, OR, USA.
| | - Darren A Cusanovich
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA.
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA.
| |
Collapse
|
8
|
Hornung BVH, Azmani Z, den Dekker AT, Oole E, Ozgur Z, Brouwer RWW, van den Hout MCGN, van IJcken WFJ. Comparison of Single Cell Transcriptome Sequencing Methods: Of Mice and Men. Genes (Basel) 2023; 14:2226. [PMID: 38137048 PMCID: PMC10743076 DOI: 10.3390/genes14122226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/04/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Single cell RNAseq has been a big leap in many areas of biology. Rather than investigating gene expression on a whole organism level, this technology enables scientists to get a detailed look at rare single cells or within their cell population of interest. The field is growing, and many new methods appear each year. We compared methods utilized in our core facility: Smart-seq3, PlexWell, FLASH-seq, VASA-seq, SORT-seq, 10X, Evercode, and HIVE. We characterized the equipment requirements for each method. We evaluated the performances of these methods based on detected features, transcriptome diversity, mitochondrial RNA abundance and multiplets, among others and benchmarked them against bulk RNA sequencing. Here, we show that bulk transcriptome detects more unique transcripts than any single cell method. While most methods are comparable in many regards, FLASH-seq and VASA-seq yielded the best metrics, e.g., in number of features. If no equipment for automation is available or many cells are desired, then HIVE or 10X yield good results. In general, more recently developed methods perform better. This also leads to the conclusion that older methods should be phased out, and that the development of single cell RNAseq methods is still progressing considerably.
Collapse
Affiliation(s)
- Bastian V. H. Hornung
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Zakia Azmani
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Alexander T. den Dekker
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Edwin Oole
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Zeliha Ozgur
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Rutger W. W. Brouwer
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Mirjam C. G. N. van den Hout
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| | - Wilfred F. J. van IJcken
- Department of Cell Biology, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands; (B.V.H.H.); (M.C.G.N.v.d.H.)
- Genomics Core Facility, Erasmus University Medical Center Rotterdam, Wytemaweg 80, 3015CN Rotterdam, The Netherlands
| |
Collapse
|
9
|
Höjer P, Frick T, Siga H, Pourbozorgi P, Aghelpasand H, Martin M, Ahmadian A. BLR: a flexible pipeline for haplotype analysis of multiple linked-read technologies. Nucleic Acids Res 2023; 51:e114. [PMID: 37941142 PMCID: PMC10711428 DOI: 10.1093/nar/gkad1010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 10/04/2023] [Accepted: 10/18/2023] [Indexed: 11/10/2023] Open
Abstract
Linked-read sequencing promises a one-method approach for genome-wide insights including single nucleotide variants (SNVs), structural variants, and haplotyping. We introduce Barcode Linked Reads (BLR), an open-source haplotyping pipeline capable of handling millions of barcodes and data from multiple linked-read technologies including DBS, 10× Genomics, TELL-seq and stLFR. Running BLR on DBS linked-reads yielded megabase-scale phasing with low (<0.2%) switch error rates. Of 13616 protein-coding genes phased in the GIAB benchmark set (v4.2.1), 98.6% matched the BLR phasing. In addition, large structural variants showed concordance with HPRC-HG002 reference assembly calls. Compared to diploid assembly with PacBio HiFi reads, BLR phasing was more continuous when considering switch errors. We further show that integrating long reads at low coverage (∼10×) can improve phasing contiguity and reduce switch errors in tandem repeats. When compared to Long Ranger on 10× Genomics data, BLR showed an increase in phase block N50 with low switch-error rates. For TELL-Seq and stLFR linked reads, BLR generated longer or similar phase block lengths and low switch error rates compared to results presented in the original publications. In conclusion, BLR provides a flexible workflow for comprehensive haplotype analysis of linked reads from multiple platforms.
Collapse
Affiliation(s)
- Pontus Höjer
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Tobias Frick
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Humam Siga
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Parham Pourbozorgi
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Hooman Aghelpasand
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Marcel Martin
- Stockholm University, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Afshin Ahmadian
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| |
Collapse
|
10
|
Zhang W, Jiang R, Chen S, Wang Y. scIBD: a self-supervised iterative-optimizing model for boosting the detection of heterotypic doublets in single-cell chromatin accessibility data. Genome Biol 2023; 24:225. [PMID: 37814314 PMCID: PMC10561408 DOI: 10.1186/s13059-023-03072-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 09/22/2023] [Indexed: 10/11/2023] Open
Abstract
Application of the widely used droplet-based microfluidic technologies in single-cell sequencing often yields doublets, introducing bias to downstream analyses. Especially, doublet-detection methods for single-cell chromatin accessibility sequencing (scCAS) data have multiple assay-specific challenges. Therefore, we propose scIBD, a self-supervised iterative-optimizing model for boosting heterotypic doublet detection in scCAS data. scIBD introduces an adaptive strategy to simulate high-confident heterotypic doublets and self-supervise for doublet-detection in an iteratively optimizing manner. Comprehensive benchmarking on various simulated and real datasets demonstrates the outperformance and robustness of scIBD. Moreover, the downstream biological analyses suggest the efficacy of doublet-removal by scIBD.
Collapse
Affiliation(s)
- Wenhao Zhang
- Department of Automation, Xiamen University, Xiamen, 361000, Fujian, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361000, Fujian, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China.
| | - Ying Wang
- Department of Automation, Xiamen University, Xiamen, 361000, Fujian, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361000, Fujian, China.
- Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision, Xiamen, 361005, Fujian, China.
| |
Collapse
|
11
|
Takeuchi F, Liang YQ, Shimizu-Furusawa H, Isono M, Ang MY, Mori K, Mori T, Kakazu E, Yoshio S, Kato N. Gene-regulation modules in nonalcoholic fatty liver disease revealed by single-nucleus ATAC-seq. Life Sci Alliance 2023; 6:e202301988. [PMID: 37491046 PMCID: PMC10368228 DOI: 10.26508/lsa.202301988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 07/14/2023] [Accepted: 07/14/2023] [Indexed: 07/27/2023] Open
Abstract
We investigated the progression of nonalcoholic fatty liver disease from fatty liver to steatohepatitis using single-nucleus and bulk ATAC-seq on the livers of rats fed a high-fat diet (HFD). Rats fed HFD for 4 wk developed fatty liver, and those fed HFD for 8 wk further progressed to steatohepatitis. We observed an increase in the proportion of inflammatory macrophages, consistent with the pathological progression. Utilizing machine learning, we divided global gene regulation into modules, wherein transcription factors within a module could regulate genes within the same module, reaffirming known regulatory relationships between transcription factors and biological processes. We identified core genes-central to co-expression and protein-protein interaction-for the biological processes discovered. Notably, a large part of the core genes overlapped with genes previously implicated in nonalcoholic fatty liver disease. Single-nucleus ATAC-seq, combined with data-driven statistical analysis, offers insight into in vivo global gene regulation as a combination of modules and assists in identifying core genes of relevant biological processes.
Collapse
Affiliation(s)
- Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Systems Genomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Yi-Qiang Liang
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hana Shimizu-Furusawa
- Department of Hygiene and Public Health, School of Medicine, Teikyo University, Tokyo, Japan
| | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Mia Yang Ang
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kotaro Mori
- Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Taizo Mori
- Department of Liver Diseases, The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, Chiba, Japan
| | - Eiji Kakazu
- Department of Liver Diseases, The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, Chiba, Japan
| | - Sachiyo Yoshio
- Department of Liver Diseases, The Research Center for Hepatitis and Immunology, National Center for Global Health and Medicine, Chiba, Japan
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Medical Genomics Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
12
|
Li J, Jaiswal MK, Chien JF, Kozlenkov A, Jung J, Zhou P, Gardashli M, Pregent LJ, Engelberg-Cook E, Dickson DW, Belzil VV, Mukamel EA, Dracheva S. Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation. Nat Commun 2023; 14:5714. [PMID: 37714849 PMCID: PMC10504300 DOI: 10.1038/s41467-023-41033-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 08/21/2023] [Indexed: 09/17/2023] Open
Abstract
A repeat expansion in the C9orf72 (C9) gene is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Here we investigate single nucleus transcriptomics (snRNA-seq) and epigenomics (snATAC-seq) in postmortem motor and frontal cortices from C9-ALS, C9-FTD, and control donors. C9-ALS donors present pervasive alterations of gene expression with concordant changes in chromatin accessibility and histone modifications. The greatest alterations occur in upper and deep layer excitatory neurons, as well as in astrocytes. In neurons, the changes imply an increase in proteostasis, metabolism, and protein expression pathways, alongside a decrease in neuronal function. In astrocytes, the alterations suggest activation and structural remodeling. Conversely, C9-FTD donors have fewer high-quality neuronal nuclei in the frontal cortex and numerous gene expression changes in glial cells. These findings highlight a context-dependent molecular disruption in C9-ALS and C9-FTD, indicating unique effects across cell types, brain regions, and diseases.
Collapse
Affiliation(s)
- Junhao Li
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, 92037, US
| | - Manoj K Jaiswal
- Friedman Brain Institute and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, US
| | - Jo-Fan Chien
- Department of Physics, University of California San Diego, La Jolla, CA, 92037, US
| | - Alexey Kozlenkov
- Friedman Brain Institute and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, US
| | - Jinyoung Jung
- Friedman Brain Institute and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, US
| | - Ping Zhou
- Friedman Brain Institute and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, US
| | | | - Luc J Pregent
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, US
| | | | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, US
| | | | - Eran A Mukamel
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, 92037, US.
| | - Stella Dracheva
- Friedman Brain Institute and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, US.
- Research & Development and VISN2 MIREC, James J, Peters VA Medical Center, Bronx, NY, 10468, US.
| |
Collapse
|
13
|
Zhong J, Liang M, Ai Y. DUPLETS: Deformability-Assisted Dual-Particle Encapsulation Via Electrically Activated Sorting. SMALL METHODS 2023; 7:e2300089. [PMID: 37246250 DOI: 10.1002/smtd.202300089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 04/12/2023] [Indexed: 05/30/2023]
Abstract
Co-encapsulation of bead carriers and biological cells in microfluidics has become a powerful technique for various biological assays in single-cell genomics and drug screening because of its distinct capability of single-cell confinement. However, current co-encapsulation approaches exist a trade-off between cell/bead pairing rate and probability of multiple cells in individual droplets, significantly limiting the effective throughput of single-paired cell-bead droplets production. Deformability-assisted dUal-Particle encapsuLation via Electrically acTivated Sorting (DUPLETS) system is reported to overcome this problem. The DUPLETS can differentiate the encapsulated content in individual droplets and sort out targeted droplets via a combined screening of mechanical and electrical characteristics of single droplets in label-free manners and with the highest effective throughput in comparison to current commercial platforms. The DUPLETS has been demonstrated to enrich single-paired cell-bead droplets to over 80% (above eightfold higher than current co-encapsulation techniques). It eliminates multicell droplets to 0.1% whereas up to ≈24% in 10× Chromium. It is believed that merging DUPLETS into the current co-encapsulation platforms can meaningfully elevate sample quality in terms of high purity of single-paired cell-bead droplets, low fraction of multicell droplets, and high cell viability, which can benefit a multitude of biological assay applications.
Collapse
Affiliation(s)
- Jianwei Zhong
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore
| | - Minhui Liang
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore
| | - Ye Ai
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore
| |
Collapse
|
14
|
Chen C, Ge Y, Lu L. Opportunities and challenges in the application of single-cell and spatial transcriptomics in plants. FRONTIERS IN PLANT SCIENCE 2023; 14:1185377. [PMID: 37636094 PMCID: PMC10453814 DOI: 10.3389/fpls.2023.1185377] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/26/2023] [Indexed: 08/29/2023]
Abstract
Single-cell and spatial transcriptomics have diverted researchers' attention from the multicellular level to the single-cell level and spatial information. Single-cell transcriptomes provide insights into the transcriptome at the single-cell level, whereas spatial transcriptomes help preserve spatial information. Although these two omics technologies are helpful and mature, further research is needed to ensure their widespread applicability in plant studies. Reviewing recent research on plant single-cell or spatial transcriptomics, we compared the different experimental methods used in various plants. The limitations and challenges are clear for both single-cell and spatial transcriptomic analyses, such as the lack of applicability, spatial information, or high resolution. Subsequently, we put forth further applications, such as cross-species analysis of roots at the single-cell level and the idea that single-cell transcriptome analysis needs to be combined with other omics analyses to achieve superiority over individual omics analyses. Overall, the results of this review suggest that combining single-cell transcriptomics, spatial transcriptomics, and spatial element distribution can provide a promising research direction, particularly for plant research.
Collapse
Affiliation(s)
- Ce Chen
- Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
| | - Yining Ge
- Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
| | - Lingli Lu
- Ministry of Education Key Laboratory of Environment Remediation and Ecological Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
- Key Laboratory of Agricultural Resource and Environment of Zhejiang Province, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
| |
Collapse
|
15
|
Caglayan E, Ayhan F, Liu Y, Vollmer RM, Oh E, Sherwood CC, Preuss TM, Yi SV, Konopka G. Molecular features driving cellular complexity of human brain evolution. Nature 2023; 620:145-153. [PMID: 37468639 PMCID: PMC11161302 DOI: 10.1038/s41586-023-06338-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/16/2023] [Indexed: 07/21/2023]
Abstract
Human-specific genomic changes contribute to the unique functionalities of the human brain1-5. The cellular heterogeneity of the human brain6,7 and the complex regulation of gene expression highlight the need to characterize human-specific molecular features at cellular resolution. Here we analysed single-nucleus RNA-sequencing and single-nucleus assay for transposase-accessible chromatin with sequencing datasets for human, chimpanzee and rhesus macaque brain tissue from posterior cingulate cortex. We show a human-specific increase of oligodendrocyte progenitor cells and a decrease of mature oligodendrocytes across cortical tissues. Human-specific regulatory changes were accelerated in oligodendrocyte progenitor cells, and we highlight key biological pathways that may be associated with the proportional changes. We also identify human-specific regulatory changes in neuronal subtypes, which reveal human-specific upregulation of FOXP2 in only two of the neuronal subtypes. We additionally identify hundreds of new human accelerated genomic regions associated with human-specific chromatin accessibility changes. Our data also reveal that FOS::JUN and FOX motifs are enriched in the human-specifically accessible chromatin regions of excitatory neuronal subtypes. Together, our results reveal several new mechanisms underlying the evolutionary innovation of human brain at cell-type resolution.
Collapse
Affiliation(s)
- Emre Caglayan
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
- Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Fatma Ayhan
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
- Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Yuxiang Liu
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
- Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rachael M Vollmer
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
- Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Emily Oh
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA
- Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA
| | - Chet C Sherwood
- Center for the Advanced Study of Human Paleobiology, Department of Anthropology, The George Washington University, Washington, DC, USA
| | - Todd M Preuss
- Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
- Department of Pathology, Emory University School of Medicine, Atlanta, GA, USA
| | - Soojin V Yi
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.
- Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.
- Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA.
| | - Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA.
- Peter O'Donnell Jr. Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA.
| |
Collapse
|
16
|
Wang G, Chiou J, Zeng C, Miller M, Matta I, Han JY, Kadakia N, Okino ML, Beebe E, Mallick M, Camunas-Soler J, Dos Santos T, Dai XQ, Ellis C, Hang Y, Kim SK, MacDonald PE, Kandeel FR, Preissl S, Gaulton KJ, Sander M. Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes. Nat Genet 2023; 55:984-994. [PMID: 37231096 PMCID: PMC10550816 DOI: 10.1038/s41588-023-01397-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 04/12/2023] [Indexed: 05/27/2023]
Abstract
Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.
Collapse
Affiliation(s)
- Gaowei Wang
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Joshua Chiou
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
- Biomedical Graduate Studies Program, University of California San Diego, La Jolla, CA, USA
| | - Chun Zeng
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Ileana Matta
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Jee Yun Han
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA
| | - Nikita Kadakia
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Mei-Lin Okino
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Elisha Beebe
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | - Medhavi Mallick
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA
| | | | - Theodore Dos Santos
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Xiao-Qing Dai
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Cara Ellis
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Yan Hang
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Seung K Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick E MacDonald
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Fouad R Kandeel
- Department of Clinical Diabetes, Endocrinology & Metabolism, City of Hope, Duarte, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Maike Sander
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
| |
Collapse
|
17
|
Zhu H, Wang G, Nguyen-Ngoc KV, Kim D, Miller M, Goss G, Kovsky J, Harrington AR, Saunders DC, Hopkirk AL, Melton R, Powers AC, Preissl S, Spagnoli FM, Gaulton KJ, Sander M. Understanding cell fate acquisition in stem-cell-derived pancreatic islets using single-cell multiome-inferred regulomes. Dev Cell 2023; 58:727-743.e11. [PMID: 37040771 PMCID: PMC10175223 DOI: 10.1016/j.devcel.2023.03.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 01/06/2023] [Accepted: 03/14/2023] [Indexed: 04/13/2023]
Abstract
Pancreatic islet cells derived from human pluripotent stem cells hold great promise for modeling and treating diabetes. Differences between stem-cell-derived and primary islets remain, but molecular insights to inform improvements are limited. Here, we acquire single-cell transcriptomes and accessible chromatin profiles during in vitro islet differentiation and pancreas from childhood and adult donors for comparison. We delineate major cell types, define their regulomes, and describe spatiotemporal gene regulatory relationships between transcription factors. CDX2 emerged as a regulator of enterochromaffin-like cells, which we show resemble a transient, previously unrecognized, serotonin-producing pre-β cell population in fetal pancreas, arguing against a proposed non-pancreatic origin. Furthermore, we observe insufficient activation of signal-dependent transcriptional programs during in vitro β cell maturation and identify sex hormones as drivers of β cell proliferation in childhood. Altogether, our analysis provides a comprehensive understanding of cell fate acquisition in stem-cell-derived islets and a framework for manipulating cell identities and maturity.
Collapse
Affiliation(s)
- Han Zhu
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093-0653, USA; Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Gaowei Wang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093-0653, USA; Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Kim-Vy Nguyen-Ngoc
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093-0653, USA; Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Dongsu Kim
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093-0653, USA; Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Michael Miller
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Georgina Goss
- Centre for Gene Therapy and Regenerative Medicine, King's College London, London SE1 9RT, UK
| | - Jenna Kovsky
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093-0653, USA; Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Austin R Harrington
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093-0653, USA; Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Diane C Saunders
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232-0475, USA
| | - Alexander L Hopkirk
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232-0475, USA
| | - Rebecca Melton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093-0653, USA; Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Alvin C Powers
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232-0475, USA; Department of Molecular Physiology & Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232-0615, USA; VA Tennessee Valley Healthcare System, Nashville, TN 37212-2637, USA
| | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Francesca M Spagnoli
- Centre for Gene Therapy and Regenerative Medicine, King's College London, London SE1 9RT, UK
| | - Kyle J Gaulton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093-0653, USA; Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Maike Sander
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093-0653, USA; Pediatric Diabetes Research Center, University of California, San Diego, La Jolla, CA, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA; Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA.
| |
Collapse
|
18
|
Jiang S, Huang Z, Li Y, Yu C, Yu H, Ke Y, Jiang L, Liu J. Single-cell chromatin accessibility and transcriptome atlas of mouse embryos. Cell Rep 2023; 42:112210. [PMID: 36881507 DOI: 10.1016/j.celrep.2023.112210] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 11/08/2022] [Accepted: 02/16/2023] [Indexed: 03/08/2023] Open
Abstract
Cis-regulatory elements regulate gene expression and lineage specification. However, the potential regulation of cis-elements on mammalian embryogenesis remains largely unexplored. To address this question, we perform single-cell assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq in embryonic day 7.5 (E7.5) and E13.5 mouse embryos. We construct the chromatin accessibility landscapes with cell spatial information in E7.5 embryos, showing the spatial patterns of cis-elements and the spatial distribution of potentially functional transcription factors (TFs). We further show that many germ-layer-specific cis-elements and TFs in E7.5 embryos are maintained in the cell types derived from the corresponding germ layers at later stages, suggesting that these cis-elements and TFs are important during cell differentiation. We also find a potential progenitor for Sertoli and granulosa cells in gonads. Interestingly, both Sertoli and granulosa cells exist in male gonads and female gonads during gonad development. Collectively, we provide a valuable resource to understand organogenesis in mammals.
Collapse
Affiliation(s)
- Shan Jiang
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zheng Huang
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yun Li
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Chengwei Yu
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; College of Future Technology College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Yu
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; College of Future Technology College, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuwen Ke
- College of Biological Science, China Agricultural University, Beijing 100193, China
| | - Lan Jiang
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; College of Future Technology College, University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Jiang Liu
- China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; College of Future Technology College, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.
| |
Collapse
|
19
|
Wang G, Chiou J, Zeng C, Miller M, Matta I, Han JY, Kadakia N, Okino ML, Beebe E, Mallick M, Camunas-Soler J, dos Santos T, Dai XQ, Ellis C, Hang Y, Kim SK, MacDonald PE, Kandeel FR, Preissl S, Gaulton KJ, Sander M. Integration of single-cell multiomic measurements across disease states with genetics identifies mechanisms of beta cell dysfunction in type 2 diabetes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2022.12.31.522386. [PMID: 36711922 PMCID: PMC9881862 DOI: 10.1101/2022.12.31.522386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Altered function and gene regulation of pancreatic islet beta cells is a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of mechanisms driving T2D is still missing. Here we integrate information from measurements of chromatin activity, gene expression and function in single beta cells with genetic association data to identify disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 non-diabetic, pre-T2D and T2D donors, we robustly identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift in T2D. Subtype-defining active chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is likely induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for identifying mechanisms of complex diseases.
Collapse
Affiliation(s)
- Gaowei Wang
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
| | - Joshua Chiou
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
- Biomedical Graduate Studies Program, University of California San Diego, La Jolla CA, USA
| | - Chun Zeng
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
| | - Ileana Matta
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
| | - Jee Yun Han
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
| | - Nikita Kadakia
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
| | - Mei-Lin Okino
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
| | - Elisha Beebe
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
| | - Medhavi Mallick
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
| | | | - Theodore dos Santos
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Xiao-Qing Dai
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Cara Ellis
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Yan Hang
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Seung K. Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Departments of Medicine and of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick E. MacDonald
- Department of Pharmacology, University of Alberta, Edmonton, AB, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB, Canada
| | - Fouad R. Kandeel
- Department of Clinical Diabetes, Endocrinology & Metabolism, City of Hope, Duarte, CA, USA
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla CA, USA
| | - Maike Sander
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
- Pediatric Diabetes Research Center, University of California San Diego, La Jolla CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla CA, USA
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| |
Collapse
|
20
|
Highly sensitive single-cell chromatin accessibility assay and transcriptome coassay with METATAC. Proc Natl Acad Sci U S A 2022; 119:e2206450119. [PMID: 36161934 PMCID: PMC9546615 DOI: 10.1073/pnas.2206450119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The thriving field of single-cell genomics allows researchers to dissect the complexity and heterogeneity of tissues at single-cell resolution at large scale, involving transcriptome and epigenome. However, single-cell chromatin accessibility profiling methods exhibit low sensitivity. Here, we increased accessible chromatin detection sensitivity in single cells with METATAC, a single-cell ATAC-seq technique, with the help of META amplification strategy and other biochemical modifications. METATAC reached the highest accessible chromatin region detection efficiency compared with existing techniques, allowing more accurate cis-regulatory element coaccessibility measurement and allele-specific chromatin accessibility analysis in complex tissue samples. In combination with a high-resolution single-cell RNA sequencing assay, we further developed a high-sensitivity joint single-cell ATAC–RNA strategy, which helps us to better resolve gene regulatory programs. Recent advances in single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) and its coassays have transformed the field of single-cell epigenomics and transcriptomics. However, the low detection efficiency of current methods has limited our understanding of the true complexity of chromatin accessibility and its relationship with gene expression in single cells. Here, we report a high-sensitivity scATAC-seq method, termed multiplexed end-tagging amplification of transposase accessible chromatin (METATAC), which detects a large number of accessible sites per cell and is compatible with automation. Our high detectability and statistical framework allowed precise linking of enhancers to promoters without merging single cells. We systematically investigated allele-specific accessibility in the mouse cerebral cortex, revealing allele-specific accessibility of promotors of certain imprinted genes but biallelic accessibility of their enhancers. Finally, we combined METATAC with our high-sensitivity single-cell RNA sequencing (scRNA-seq) method, multiple annealing and looping based amplification cycles for digital transcriptomics (MALBAC-DT), to develop a joint ATAC–RNA assay, termed METATAC and MALBAC-DT coassay by sequencing (M2C-seq). M2C-seq achieved significant improvements for both ATAC and RNA compared with previous methods, with consistent performance across cell lines and early mouse embryos.
Collapse
|
21
|
He J, Lin L, Chen J. Practical bioinformatics pipelines for single-cell RNA-seq data analysis. BIOPHYSICS REPORTS 2022; 8:158-169. [PMID: 37288243 PMCID: PMC10189648 DOI: 10.52601/bpr.2022.210041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/01/2022] [Indexed: 11/05/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool to explore cells. With an increasing number of scRNA-seq data analysis tools that have been developed, it is challenging for users to choose and compare their performance. Here, we present an overview of the workflow for computational analysis of scRNA-seq data. We detail the steps of a typical scRNA-seq analysis, including experimental design, pre-processing and quality control, feature selection, dimensionality reduction, cell clustering and annotation, and downstream analysis including batch correction, trajectory inference and cell-cell communication. We provide guidelines according to our best practice. This review will be helpful for the experimentalists interested in analyzing their data, and will aid the users seeking to update their analysis pipelines.
Collapse
Affiliation(s)
- Jiangping He
- Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510320, China
| | - Lihui Lin
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Jiekai Chen
- Center for Cell Lineage and Atlas (CCLA), Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510320, China
- Key Laboratory of Regenerative Biology of the Chinese Academy of Sciences and Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| |
Collapse
|
22
|
Deterministic scRNA-seq captures variation in intestinal crypt and organoid composition. Nat Methods 2022; 19:323-330. [DOI: 10.1038/s41592-021-01391-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 12/22/2021] [Indexed: 12/20/2022]
|
23
|
McGarvey AC, Kopp W, Vučićević D, Mattonet K, Kempfer R, Hirsekorn A, Bilić I, Gil M, Trinks A, Merks AM, Panáková D, Pombo A, Akalin A, Junker JP, Stainier DY, Garfield D, Ohler U, Lacadie SA. Single-cell-resolved dynamics of chromatin architecture delineate cell and regulatory states in zebrafish embryos. CELL GENOMICS 2022; 2:100083. [PMID: 36777038 PMCID: PMC9903790 DOI: 10.1016/j.xgen.2021.100083] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/24/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022]
Abstract
DNA accessibility of cis-regulatory elements (CREs) dictates transcriptional activity and drives cell differentiation during development. While many genes regulating embryonic development have been identified, the underlying CRE dynamics controlling their expression remain largely uncharacterized. To address this, we produced a multimodal resource and genomic regulatory map for the zebrafish community, which integrates single-cell combinatorial indexing assay for transposase-accessible chromatin with high-throughput sequencing (sci-ATAC-seq) with bulk histone PTMs and Hi-C data to achieve a genome-wide classification of the regulatory architecture determining transcriptional activity in the 24-h post-fertilization (hpf) embryo. We characterized the genome-wide chromatin architecture at bulk and single-cell resolution, applying sci-ATAC-seq on whole 24-hpf stage zebrafish embryos, generating accessibility profiles for ∼23,000 single nuclei. We developed a genome segmentation method, ScregSeg (single-cell regulatory landscape segmentation), for defining regulatory programs, and candidate CREs, specific to one or more cell types. We integrated the ScregSeg output with bulk measurements for histone post-translational modifications and 3D genome organization and identified new regulatory principles between chromatin modalities prevalent during zebrafish development. Sci-ATAC-seq profiling of npas4l/cloche mutant embryos identified novel cellular roles for this hematovascular transcriptional master regulator and suggests an intricate mechanism regulating its expression. Our work defines regulatory architecture and principles in the zebrafish embryo and establishes a resource of cell-type-specific genome-wide regulatory annotations and candidate CREs, providing a valuable open resource for genomics, developmental, molecular, and computational biology.
Collapse
Affiliation(s)
- Alison C. McGarvey
- Computational Regulatory Genomics, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany,Quantitative Developmental Biology, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany
| | - Wolfgang Kopp
- Computational Regulatory Genomics, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany,Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max Delbrück Centre for Molecular Medicine, Berlin 10115, Germany
| | - Dubravka Vučićević
- Computational Regulatory Genomics, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany
| | - Kenny Mattonet
- Department of Developmental Genetics, Max Planck Institute for Heart and Lung Research, Bad Nauheim 61231, Germany
| | - Rieke Kempfer
- Epigenetic Regulation and Chromatin Architecture, Berlin Institute for Medical Systems Biology, Max Delbrück Centre for Molecular Medicine, Berlin, Germany,Institute for Biology, Humboldt Universität Berlin, Berlin 10115, Germany
| | - Antje Hirsekorn
- Computational Regulatory Genomics, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany
| | - Ilija Bilić
- Computational Regulatory Genomics, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany
| | - Marine Gil
- Computational Regulatory Genomics, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany
| | - Alexandra Trinks
- IRI Life Sciences, Humboldt Universität Berlin, Berlin 10115, Germany
| | - Anne Margarete Merks
- Electrochemical Signaling in Development and Disease, Max Delbrück Centre for Molecular Medicine, Berlin, Germany,DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin 13125, Germany
| | - Daniela Panáková
- Electrochemical Signaling in Development and Disease, Max Delbrück Centre for Molecular Medicine, Berlin, Germany,DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin 13125, Germany
| | - Ana Pombo
- Epigenetic Regulation and Chromatin Architecture, Berlin Institute for Medical Systems Biology, Max Delbrück Centre for Molecular Medicine, Berlin, Germany,Institute for Biology, Humboldt Universität Berlin, Berlin 10115, Germany
| | - Altuna Akalin
- Bioinformatics and Omics Data Science Platform, Berlin Institute for Medical Systems Biology, Max Delbrück Centre for Molecular Medicine, Berlin 10115, Germany
| | - Jan Philipp Junker
- Quantitative Developmental Biology, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany
| | - Didier Y.R. Stainier
- Department of Developmental Genetics, Max Planck Institute for Heart and Lung Research, Bad Nauheim 61231, Germany
| | - David Garfield
- IRI Life Sciences, Humboldt Universität Berlin, Berlin 10115, Germany
| | - Uwe Ohler
- Computational Regulatory Genomics, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany,Institute for Biology, Humboldt Universität Berlin, Berlin 10115, Germany,Corresponding author
| | - Scott Allen Lacadie
- Computational Regulatory Genomics, Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine, Berlin 10115, Germany,Berlin Institute of Health, Berlin 10178, Germany,Corresponding author
| |
Collapse
|
24
|
Sunaga-Franze DY, Muino JM, Braeuning C, Xu X, Zong M, Smaczniak C, Yan W, Fischer C, Vidal R, Kliem M, Kaufmann K, Sauer S. Single-nucleus RNA sequencing of plant tissues using a nanowell-based system. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 108:859-869. [PMID: 34390289 DOI: 10.1111/tpj.15458] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 07/16/2021] [Accepted: 08/02/2021] [Indexed: 05/25/2023]
Abstract
Single-cell genomics provides unprecedented potential for research on plant development and environmental responses. Here, we introduce a generic procedure for plant nucleus isolation combined with nanowell-based library preparation. Our method enables the transcriptome analysis of thousands of individual plant nuclei. It serves as an alternative to the use of protoplast isolation, which is currently a standard methodology for plant single-cell genomics, although it can be challenging for some plant tissues. We show the applicability of our nucleus isolation method by using different plant materials from different species. The potential of our single-nucleus RNA sequencing method is shown through the characterization of transcriptomes of seedlings and developing flowers from Arabidopsis thaliana. We evaluated the transcriptome dynamics during the early stages of anther development, identified stage-specific activities of transcription factors regulating this process, and predicted potential target genes of these transcription factors. Our nucleus isolation procedure can be applied in different plant species and tissues, thus expanding the toolkit for plant single-cell genomics experiments.
Collapse
Affiliation(s)
- Daniele Y Sunaga-Franze
- Genomics Platforms, Max Delbrück Center for Molecular Medicine in the Helmholtz Association/Berlin Institute of Health, Berlin, Germany
| | - Jose M Muino
- Systems Biology of Gene Regulation, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Caroline Braeuning
- Genomics Platforms, Max Delbrück Center for Molecular Medicine in the Helmholtz Association/Berlin Institute of Health, Berlin, Germany
| | - Xiaocai Xu
- Plant Cell and Molecular Biology, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Minglei Zong
- Plant Cell and Molecular Biology, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Cezary Smaczniak
- Plant Cell and Molecular Biology, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Wenhao Yan
- Plant Cell and Molecular Biology, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Cornelius Fischer
- Genomics Platforms, Max Delbrück Center for Molecular Medicine in the Helmholtz Association/Berlin Institute of Health, Berlin, Germany
| | - Ramon Vidal
- Genomics Platforms, Max Delbrück Center for Molecular Medicine in the Helmholtz Association/Berlin Institute of Health, Berlin, Germany
| | - Magdalena Kliem
- Genomics Platforms, Max Delbrück Center for Molecular Medicine in the Helmholtz Association/Berlin Institute of Health, Berlin, Germany
| | - Kerstin Kaufmann
- Plant Cell and Molecular Biology, Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sascha Sauer
- Genomics Platforms, Max Delbrück Center for Molecular Medicine in the Helmholtz Association/Berlin Institute of Health, Berlin, Germany
| |
Collapse
|
25
|
Wang M, Gu M, Liu L, Liu Y, Tian L. Single-Cell RNA Sequencing (scRNA-seq) in Cardiac Tissue: Applications and Limitations. Vasc Health Risk Manag 2021; 17:641-657. [PMID: 34629873 PMCID: PMC8495612 DOI: 10.2147/vhrm.s288090] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 09/14/2021] [Indexed: 12/16/2022] Open
Abstract
Cardiovascular diseases (CVDs) are a group of disorders of the blood vessels and heart, which are considered as the leading causes of death worldwide. The pathology of CVDs could be related to the functional abnormalities of multiple cell types in the heart. Single-cell RNA sequencing (scRNA-seq) technology is a powerful method for characterizing individual cells and elucidating the molecular mechanisms by providing a high resolution of transcriptomic changes at the single-cell level. Specifically, scRNA-seq has provided novel insights into CVDs by identifying rare cardiac cell types, inferring the trajectory tree, estimating RNA velocity, elucidating the cell-cell communication, and comparing healthy and pathological heart samples. In this review, we summarize the different scRNA-seq platforms and published single-cell datasets in the cardiovascular field, and describe the utilities and limitations of this technology. Lastly, we discuss the future perspective of the application of scRNA-seq technology into cardiovascular research.
Collapse
Affiliation(s)
- Mingqiang Wang
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Mingxia Gu
- Perinatal Institute, Division of Pulmonary Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA
- Center for Stem Cell and Organoid Medicine, CuSTOM, Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Ling Liu
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Yu Liu
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Lei Tian
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA
| |
Collapse
|
26
|
Mimitou EP, Lareau CA, Chen KY, Zorzetto-Fernandes AL, Hao Y, Takeshima Y, Luo W, Huang TS, Yeung BZ, Papalexi E, Thakore PI, Kibayashi T, Wing JB, Hata M, Satija R, Nazor KL, Sakaguchi S, Ludwig LS, Sankaran VG, Regev A, Smibert P. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat Biotechnol 2021; 39:1246-1258. [PMID: 34083792 PMCID: PMC8763625 DOI: 10.1038/s41587-021-00927-2] [Citation(s) in RCA: 206] [Impact Index Per Article: 68.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 04/16/2021] [Indexed: 02/04/2023]
Abstract
Recent technological advances have enabled massively parallel chromatin profiling with scATAC-seq (single-cell assay for transposase accessible chromatin by sequencing). Here we present ATAC with select antigen profiling by sequencing (ASAP-seq), a tool to simultaneously profile accessible chromatin and protein levels. Our approach pairs sparse scATAC-seq data with robust detection of hundreds of cell surface and intracellular protein markers and optional capture of mitochondrial DNA for clonal tracking, capturing three distinct modalities in single cells. ASAP-seq uses a bridging approach that repurposes antibody:oligonucleotide conjugates designed for existing technologies that pair protein measurements with single-cell RNA sequencing. Together with DOGMA-seq, an adaptation of CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) for measuring gene activity across the central dogma of gene regulation, we demonstrate the utility of systematic multi-omic profiling by revealing coordinated and distinct changes in chromatin, RNA and surface proteins during native hematopoietic differentiation and peripheral blood mononuclear cell stimulation and as a combinatorial decoder and reporter of multiplexed perturbations in primary T cells.
Collapse
Affiliation(s)
- Eleni P Mimitou
- Technology Innovation Lab, New York Genome Center, New York, NY, USA
| | - Caleb A Lareau
- Department of Pathology, Stanford University, Stanford, CA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology / Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Kelvin Y Chen
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Department of Experimental Pathology, Institute for Frontier Medical Sciences, Kyoto University, Kyoto, Japan
| | | | - Yuhan Hao
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Yusuke Takeshima
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Wendy Luo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology / Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | | | | | - Efthymia Papalexi
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | | | - Tatsuya Kibayashi
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - James Badger Wing
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Laboratory of Human Immunology (Single Cell Immunology), Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Mayu Hata
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | | | - Shimon Sakaguchi
- Department of Experimental Immunology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Department of Experimental Pathology, Institute for Frontier Medical Sciences, Kyoto University, Kyoto, Japan
| | - Leif S Ludwig
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology / Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Vijay G Sankaran
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology / Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- New York Genome Center, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Biology and Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Peter Smibert
- Technology Innovation Lab, New York Genome Center, New York, NY, USA.
| |
Collapse
|
27
|
Zhao S, Tsibris A. Leveraging Novel Integrated Single-Cell Analyses to Define HIV-1 Latency Reversal. Viruses 2021; 13:1197. [PMID: 34206546 PMCID: PMC8310207 DOI: 10.3390/v13071197] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/11/2021] [Accepted: 06/16/2021] [Indexed: 01/24/2023] Open
Abstract
While suppressive antiretroviral therapy can effectively limit HIV-1 replication and evolution, it leaves behind a residual pool of integrated viral genomes that persist in a state of reversible nonproductive infection, referred to as the HIV-1 reservoir. HIV-1 infection models were established to investigate HIV-1 latency and its reversal; recent work began to probe the dynamics of HIV-1 latency reversal at single-cell resolution. Signals that establish HIV-1 latency and govern its reactivation are complex and may not be completely resolved at the cellular and regulatory levels by the aggregated measurements of bulk cellular-sequencing methods. High-throughput single-cell technologies that characterize and quantify changes to the epigenome, transcriptome, and proteome continue to rapidly evolve. Combinations of single-cell techniques, in conjunction with novel computational approaches to analyze these data, were developed and provide an opportunity to improve the resolution of the heterogeneity that may exist in HIV-1 reactivation. In this review, we summarize the published single-cell HIV-1 transcriptomic work and explore how cutting-edge advances in single-cell techniques and integrative data-analysis tools may be leveraged to define the mechanisms that control the reversal of HIV-1 latency.
Collapse
Affiliation(s)
| | - Athe Tsibris
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02139, USA;
| |
Collapse
|
28
|
Lareau CA, Ludwig LS, Muus C, Gohil SH, Zhao T, Chiang Z, Pelka K, Verboon JM, Luo W, Christian E, Rosebrock D, Getz G, Boland GM, Chen F, Buenrostro JD, Hacohen N, Wu CJ, Aryee MJ, Regev A, Sankaran VG. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat Biotechnol 2021; 39:451-461. [PMID: 32788668 PMCID: PMC7878580 DOI: 10.1038/s41587-020-0645-6] [Citation(s) in RCA: 138] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 07/17/2020] [Indexed: 12/24/2022]
Abstract
Natural mitochondrial DNA (mtDNA) mutations enable the inference of clonal relationships among cells. mtDNA can be profiled along with measures of cell state, but has not yet been combined with the massively parallel approaches needed to tackle the complexity of human tissue. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), a method that combines high-confidence mtDNA mutation calling in thousands of single cells with their concomitant high-quality accessible chromatin profile. This enables the inference of mtDNA heteroplasmy, clonal relationships, cell state and accessible chromatin variation in individual cells. We reveal single-cell variation in heteroplasmy of a pathologic mtDNA variant, which we associate with intra-individual chromatin variability and clonal evolution. We clonally trace thousands of cells from cancers, linking epigenomic variability to subclonal evolution, and infer cellular dynamics of differentiating hematopoietic cells in vitro and in vivo. Taken together, our approach enables the study of cellular population dynamics and clonal properties in vivo.
Collapse
Affiliation(s)
- Caleb A Lareau
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Division of Medical Sciences, Harvard Medical School, Boston, MA, USA.
| | - Leif S Ludwig
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Christoph Muus
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Satyen H Gohil
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Academic Haematology, UCL Cancer Institute, London, UK
| | - Tongtong Zhao
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Zachary Chiang
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Karin Pelka
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey M Verboon
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wendy Luo
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elena Christian
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Medical Sciences, Harvard Medical School, Boston, MA, USA
| | - Daniel Rosebrock
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gad Getz
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Genevieve M Boland
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fei Chen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jason D Buenrostro
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Nir Hacohen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Catherine J Wu
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Martin J Aryee
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
- Department of Biology and Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Stem Cell Institute, Cambridge, MA, USA.
| |
Collapse
|
29
|
Hassman LM, Paley MA, Esaulova E, Paley GL, Ruzycki PA, Linskey N, Laurent J, Feigl-Lenzen L, Springer L, Montana CL, Hong K, Enright J, James H, Artyomov MN, Yokoyama WM. Clinicomolecular Identification of Conserved and Individualized Features of Granulomatous Uveitis. OPHTHALMOLOGY SCIENCE 2021; 1:100010. [PMID: 35937550 PMCID: PMC9352144 DOI: 10.1016/j.xops.2021.100010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/17/2021] [Accepted: 03/08/2021] [Indexed: 12/17/2022]
Abstract
Objective To identify molecular features that distinguish individuals with shared clinical features of granulomatous uveitis. Design Cross-sectional, observational study. Participants Four eyes from patients with active granulomatous uveitis. Methods We performed single-cell RNA-sequencing with antigen-receptor sequence analysis to obtain an unbiased gene expression survey of ocular immune cells and identify clonally expanded lymphocytes. Main Outcomes Measures For each inflamed eye, we measured the proportion of distinct immune cell types, the amount of B or T cell clonal expansion, and the transcriptional profile of T and B cells. Results Each individual had robust clonal expansion arising from a single T or B cell lineage, suggesting distinct, antigen-driven pathogenic processes in each patient. This variability in clonal expansion was mirrored by individual variability in CD4 T cell populations, whereas ocular CD8 T cells and B cells were more transcriptionally similar between patients. Finally, ocular B cells displayed evidence of class-switching and plasmablast differentiation within the ocular microenvironment, providing additional support for antigen-driven immune responses in granulomatous uveitis. Conclusions Collectively, our study identified both conserved and individualized features of granulomatous uveitis, illuminating parallel pathophysiologic mechanisms, and suggesting that future personalized therapeutic approaches may be warranted.
Collapse
Affiliation(s)
- Lynn M. Hassman
- Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, Missouri
| | - Michael A. Paley
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Ekaterina Esaulova
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Grace L. Paley
- Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, Missouri
| | - Philip A. Ruzycki
- Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, Missouri
| | - Nicole Linskey
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Jennifer Laurent
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Lacey Feigl-Lenzen
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Luke Springer
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Cynthia L. Montana
- Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, Missouri
| | - Karen Hong
- Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, Missouri
| | - Jennifer Enright
- Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, Missouri
| | - Hayley James
- Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, Missouri
| | - Maxim N. Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Wayne M. Yokoyama
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| |
Collapse
|
30
|
Andrews TS, Kiselev VY, McCarthy D, Hemberg M. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat Protoc 2020; 16:1-9. [PMID: 33288955 DOI: 10.1038/s41596-020-00409-w] [Citation(s) in RCA: 147] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 09/08/2020] [Indexed: 01/01/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. However, the analysis of the large volumes of data generated from these experiments requires specialized statistical and computational methods. Here we present an overview of the computational workflow involved in processing scRNA-seq data. We discuss some of the most common tasks and the tools available for addressing central biological questions. In this article and our companion website ( https://scrnaseq-course.cog.sanger.ac.uk/website/index.html ), we provide guidelines regarding best practices for performing computational analyses. This tutorial provides a hands-on guide for experimentalists interested in analyzing their data as well as an overview for bioinformaticians seeking to develop new computational methods.
Collapse
Affiliation(s)
| | | | - Davis McCarthy
- Bioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia.,Melbourne Integrative Genomics, Faculty of Science, University of Melbourne, Melbourne, Victoria, Australia
| | | |
Collapse
|
31
|
Abstract
Recent advancements in paired B-cell receptor sequencing technologies have accelerated the development of simpler, high-throughput pipelines for generating native antibody heavy and light chain pairs used to elucidate novel antibodies and provide insights into antibody response against pathogenic targets. These technologies involve single-cell isolation, using either single wells or emulsified droplets to maintain physical separation of individual cells, followed by sequencing. The development of novel single wells and emulsion-based workflows addresses key challenges by improving throughput of single-cell analyses, reducing method complexity, and integrating functional assays into existing workflows. Enabled by paired B-cell receptor sequencing, functional characterization of pathogen-specific antibodies reveals immunological insights beyond bulk sequencing.
Collapse
Affiliation(s)
- Nicholas C Curtis
- Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH 03755, United States
| | - Jiwon Lee
- Thayer School of Engineering, Dartmouth College, 14 Engineering Drive, Hanover, NH 03755, United States
| |
Collapse
|
32
|
Corridoni D, Antanaviciute A, Gupta T, Fawkner-Corbett D, Aulicino A, Jagielowicz M, Parikh K, Repapi E, Taylor S, Ishikawa D, Hatano R, Yamada T, Xin W, Slawinski H, Bowden R, Napolitani G, Brain O, Morimoto C, Koohy H, Simmons A. Single-cell atlas of colonic CD8 + T cells in ulcerative colitis. Nat Med 2020; 26:1480-1490. [PMID: 32747828 DOI: 10.1038/s41591-020-1003-4] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 06/04/2020] [Indexed: 12/17/2022]
Abstract
Colonic antigen-experienced lymphocytes such as tissue-resident memory CD8+ T cells can respond rapidly to repeated antigen exposure. However, their cellular phenotypes and the mechanisms by which they drive immune regulation and inflammation remain unclear. Here we compiled an unbiased atlas of human colonic CD8+ T cells in health and ulcerative colitis (UC) using single-cell transcriptomics with T-cell receptor repertoire analysis and mass cytometry. We reveal extensive heterogeneity in CD8+ T-cell composition, including expanded effector and post-effector terminally differentiated CD8+ T cells. While UC-associated CD8+ effector T cells can trigger tissue destruction and produce tumor necrosis factor (TNF)-α, post-effector cells acquire innate signatures to adopt regulatory functions that may mitigate excessive inflammation. Thus, we identify colonic CD8+ T-cell phenotypes in health and UC, define their clonal relationships and characterize terminally differentiated dysfunctional UC CD8+ T cells expressing IL-26, which attenuate acute colitis in a humanized IL-26 transgenic mouse model.
Collapse
Affiliation(s)
- Daniele Corridoni
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Agne Antanaviciute
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
- MRC WIMM Centre For Computational Biology, MRC WIMM, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Tarun Gupta
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - David Fawkner-Corbett
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Anna Aulicino
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Marta Jagielowicz
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Kaushal Parikh
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Emmanouela Repapi
- Computational Biology Research Group, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Steve Taylor
- Computational Biology Research Group, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Dai Ishikawa
- Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan
| | - Ryo Hatano
- Department of Therapy Development and Innovation for Immune Disorders and Cancers, Juntendo University, Tokyo, Japan
| | - Taketo Yamada
- Department of Pathology, Saitama Medical University, Saitama, Japan
| | - Wei Xin
- Department of Pathology, Case Western Reserve University, Cleveland, OH, USA
| | - Hubert Slawinski
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Rory Bowden
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Giorgio Napolitani
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Oliver Brain
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Chikao Morimoto
- Department of Therapy Development and Innovation for Immune Disorders and Cancers, Juntendo University, Tokyo, Japan
| | - Hashem Koohy
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK.
- MRC WIMM Centre For Computational Biology, MRC WIMM, John Radcliffe Hospital, University of Oxford, Oxford, UK.
| | - Alison Simmons
- Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, UK.
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK.
| |
Collapse
|