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Tian Y, Wu X, Luo S, Xiong D, Liu R, Hu L, Yuan Y, Shi G, Yao J, Huang Z, Fu F, Yang X, Tang Z, Zhang J, Hu K. A multi-omic single-cell landscape of cellular diversification in the developing human cerebral cortex. Comput Struct Biotechnol J 2024; 23:2173-2189. [PMID: 38827229 PMCID: PMC11141146 DOI: 10.1016/j.csbj.2024.05.019] [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] [Received: 02/20/2024] [Revised: 05/09/2024] [Accepted: 05/13/2024] [Indexed: 06/04/2024] Open
Abstract
The vast neuronal diversity in the human neocortex is vital for high-order brain functions, necessitating elucidation of the regulatory mechanisms underlying such unparalleled diversity. However, recent studies have yet to comprehensively reveal the diversity of neurons and the molecular logic of neocortical origin in humans at single-cell resolution through profiling transcriptomic or epigenomic landscapes, owing to the application of unimodal data alone to depict exceedingly heterogeneous populations of neurons. In this study, we generated a comprehensive compendium of the developing human neocortex by simultaneously profiling gene expression and open chromatin from the same cell. We computationally reconstructed the differentiation trajectories of excitatory projection neurons of cortical origin and inferred the regulatory logic governing lineage bifurcation decisions for neuronal diversification. We demonstrated that neuronal diversity arises from progenitor cell lineage specificity and postmitotic differentiation at distinct stages. Our data paves the way for understanding the primarily coordinated regulatory logic for neuronal diversification in the neocortex.
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Affiliation(s)
- Yuhan Tian
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Xia Wu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Songhao Luo
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Dan Xiong
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Rong Liu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Lanqi Hu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Yuchen Yuan
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Guowei Shi
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Junjie Yao
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Zhiwei Huang
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Fang Fu
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 511436, China
| | - Xin Yang
- Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 511436, China
| | - Zhonghui Tang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
| | - Jiajun Zhang
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Kunhua Hu
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510275, China
- Public Platform Laboratory, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510630, China
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2
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Du JH, Chen T, Gao M, Wang J. Joint trajectory inference for single-cell genomics using deep learning with a mixture prior. Proc Natl Acad Sci U S A 2024; 121:e2316256121. [PMID: 39226366 DOI: 10.1073/pnas.2316256121] [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: 09/18/2023] [Accepted: 08/01/2024] [Indexed: 09/05/2024] Open
Abstract
Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.
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Affiliation(s)
- Jin-Hong Du
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Tianyu Chen
- Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX 78712
| | - Ming Gao
- Booth School of Business, University of Chicago, Chicago, IL 60637
| | - Jingshu Wang
- Department of Statistics, University of Chicago, Chicago, IL 60637
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3
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Trapnell C. Revealing gene function with statistical inference at single-cell resolution. Nat Rev Genet 2024; 25:623-638. [PMID: 38951690 DOI: 10.1038/s41576-024-00750-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 07/03/2024]
Abstract
Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.
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Affiliation(s)
- Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
- Seattle Hub for Synthetic Biology, Seattle, WA, USA.
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4
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Engreitz JM, Lawson HA, Singh H, Starita LM, Hon GC, Carter H, Sahni N, Reddy TE, Lin X, Li Y, Munshi NV, Chahrour MH, Boyle AP, Hitz BC, Mortazavi A, Craven M, Mohlke KL, Pinello L, Wang T, Kundaje A, Yue F, Cody S, Farrell NP, Love MI, Muffley LA, Pazin MJ, Reese F, Van Buren E, Dey KK, Kircher M, Ma J, Radivojac P, Balliu B, Williams BA, Huangfu D, Park CY, Quertermous T, Das J, Calderwood MA, Fowler DM, Vidal M, Ferreira L, Mooney SD, Pejaver V, Zhao J, Gazal S, Koch E, Reilly SK, Sunyaev S, Carpenter AE, Buenrostro JD, Leslie CS, Savage RE, Giric S, Luo C, Plath K, Barrera A, Schubach M, Gschwind AR, Moore JE, Ahituv N, Yi SS, Hallgrimsdottir I, Gaulton KJ, Sakaue S, Booeshaghi S, Mattei E, Nair S, Pachter L, Wang AT, Shendure J, Agarwal V, Blair A, Chalkiadakis T, Chardon FM, Dash PM, Deng C, Hamazaki N, Keukeleire P, Kubo C, Lalanne JB, Maass T, Martin B, McDiarmid TA, Nobuhara M, Page NF, Regalado S, Sims J, Ushiki A, Best SM, Boyle G, Camp N, Casadei S, Da EY, Dawood M, Dawson SC, Fayer S, Hamm A, James RG, Jarvik GP, McEwen AE, Moore N, Pendyala S, Popp NA, Post M, Rubin AF, Smith NT, Stone J, Tejura M, Wang ZR, Wheelock MK, Woo I, Zapp BD, Amgalan D, Aradhana A, Arana SM, Bassik MC, Bauman JR, Bhattacharya A, Cai XS, Chen Z, Conley S, Deshpande S, Doughty BR, Du PP, Galante JA, Gifford C, Greenleaf WJ, Guo K, Gupta R, Isobe S, Jagoda E, Jain N, Jones H, Kang HY, Kim SH, Kim Y, Klemm S, Kundu R, Kundu S, Lago-Docampo M, Lee-Yow YC, Levin-Konigsberg R, Li DY, Lindenhofer D, Ma XR, Marinov GK, Martyn GE, McCreery CV, Metzl-Raz E, Monteiro JP, Montgomery MT, Mualim KS, Munger C, Munson G, Nguyen TC, Nguyen T, Palmisano BT, Pampari A, Rabinovitch M, Ramste M, Ray J, Roy KR, Rubio OM, Schaepe JM, Schnitzler G, Schreiber J, Sharma D, Sheth MU, Shi H, Singh V, Sinha R, Steinmetz LM, Tan J, Tan A, Tycko J, Valbuena RC, Amiri VVP, van Kooten MJFM, Vaughan-Jackson A, Venida A, Weldy CS, Worssam MD, Xia F, Yao D, Zeng T, Zhao Q, Zhou R, Chen ZS, Cimini BA, Coppin G, Coté AG, Haghighi M, Hao T, Hill DE, Lacoste J, Laval F, Reno C, Roth FP, Singh S, Spirohn-Fitzgerald K, Taipale M, Teelucksingh T, Tixhon M, Yadav A, Yang Z, Kraus WL, Armendariz DA, Dederich AE, Gogate A, El Hayek L, Goetsch SC, Kaur K, Kim HB, McCoy MK, Nzima MZ, Pinzón-Arteaga CA, Posner BA, Schmitz DA, Sivakumar S, Sundarrajan A, Wang L, Wang Y, Wu J, Xu L, Xu J, Yu L, Zhang Y, Zhao H, Zhou Q, Won H, Bell JL, Broadaway KA, Degner KN, Etheridge AS, Koller BH, Mah W, Mu W, Ritola KD, Rosen JD, Schoenrock SA, Sharp RA, Bauer D, Lettre G, Sherwood R, Becerra B, Blaine LJ, Che E, Francoeur MJ, Gibbs EN, Kim N, King EM, Kleinstiver BP, Lecluze E, Li Z, Patel ZM, Phan QV, Ryu J, Starr ML, Wu T, Gersbach CA, Crawford GE, Allen AS, Majoros WH, Iglesias N, Rai R, Venukuttan R, Li B, Anglen T, Bounds LR, Hamilton MC, Liu S, McCutcheon SR, McRoberts Amador CD, Reisman SJ, ter Weele MA, Bodle JC, Streff HL, Siklenka K, Strouse K, Bernstein BE, Babu J, Corona GB, Dong K, Duarte FM, Durand NC, Epstein CB, Fan K, Gaskell E, Hall AW, Ham AM, Knudson MK, Shoresh N, Wekhande S, White CM, Xi W, Satpathy AT, Corces MR, Chang SH, Chin IM, Gardner JM, Gardell ZA, Gutierrez JC, Johnson AW, Kampman L, Kasowski M, Lareau CA, Liu V, Ludwig LS, McGinnis CS, Menon S, Qualls A, Sandor K, Turner AW, Ye CJ, Yin Y, Zhang W, Wold BJ, Carilli M, Cheong D, Filibam G, Green K, Kawauchi S, Kim C, Liang H, Loving R, Luebbert L, MacGregor G, Merchan AG, Rebboah E, Rezaie N, Sakr J, Sullivan DK, Swarna N, Trout D, Upchurch S, Weber R, Castro CP, Chou E, Feng F, Guerra A, Huang Y, Jiang L, Liu J, Mills RE, Qian W, Qin T, Sartor MA, Sherpa RN, Wang J, Wang Y, Welch JD, Zhang Z, Zhao N, Mukherjee S, Page CD, Clarke S, Doty RW, Duan Y, Gordan R, Ko KY, Li S, Li B, Thomson A, Raychaudhuri S, Price A, Ali TA, Dey KK, Durvasula A, Kellis M, Iakoucheva LM, Kakati T, Chen Y, Benazouz M, Jain S, Zeiberg D, De Paolis Kaluza MC, Velyunskiy M, Gasch A, Huang K, Jin Y, Lu Q, Miao J, Ohtake M, Scopel E, Steiner RD, Sverchkov Y, Weng Z, Garber M, Fu Y, Haas N, Li X, Phalke N, Shan SC, Shedd N, Yu T, Zhang Y, Zhou H, Battle A, Jerby L, Kotler E, Kundu S, Marderstein AR, Montgomery SB, Nigam A, Padhi EM, Patel A, Pritchard J, Raine I, Ramalingam V, Rodrigues KB, Schreiber JM, Singhal A, Sinha R, Wang AT, Abundis M, Bisht D, Chakraborty T, Fan J, Hall DR, Rarani ZH, Jain AK, Kaundal B, Keshari S, McGrail D, Pease NA, Yi VF, Wu H, Kannan S, Song H, Cai J, Gao Z, Kurzion R, Leu JI, Li F, Liang D, Ming GL, Musunuru K, Qiu Q, Shi J, Su Y, Tishkoff S, Xie N, Yang Q, Yang W, Zhang H, Zhang Z, Beer MA, Hadjantonakis AK, Adeniyi S, Cho H, Cutler R, Glenn RA, Godovich D, Hu N, Jovanic S, Luo R, Oh JW, Razavi-Mohseni M, Shigaki D, Sidoli S, Vierbuchen T, Wang X, Williams B, Yan J, Yang D, Yang Y, Sander M, Gaulton KJ, Ren B, Bartosik W, Indralingam HS, Klie A, Mummey H, Okino ML, Wang G, Zemke NR, Zhang K, Zhu H, Zaitlen N, Ernst J, Langerman J, Li T, Sun Y, Rudensky AY, Periyakoil PK, Gao VR, Smith MH, Thomas NM, Donlin LT, Lakhanpal A, Southard KM, Ardy RC, Cherry JM, Gerstein MB, Andreeva K, Assis PR, Borsari B, Douglass E, Dong S, Gabdank I, Graham K, Jolanki O, Jou J, Kagda MS, Lee JW, Li M, Lin K, Miyasato SR, Rozowsky J, Small C, Spragins E, Tanaka FY, Whaling IM, Youngworth IA, Sloan CA, Belter E, Chen X, Chisholm RL, Dickson P, Fan C, Fulton L, Li D, Lindsay T, Luan Y, Luo Y, Lyu H, Ma X, Macias-Velasco J, Miga KH, Quaid K, Stitziel N, Stranger BE, Tomlinson C, Wang J, Zhang W, Zhang B, Zhao G, Zhuo X, Brennand K, Ciccia A, Hayward SB, Huang JW, Leuzzi G, Taglialatela A, Thakar T, Vaitsiankova A, Dey KK, Ali TA, Kim A, Grimes HL, Salomonis N, Gupta R, Fang S, Lee-Kim V, Heinig M, Losert C, Jones TR, Donnard E, Murphy M, Roberts E, Song S, Mostafavi S, Sasse A, Spiro A, Pennacchio LA, Kato M, Kosicki M, Mannion B, Slaven N, Visel A, Pollard KS, Drusinsky S, Whalen S, Ray J, Harten IA, Ho CH, Sanjana NE, Caragine C, Morris JA, Seruggia D, Kutschat AP, Wittibschlager S, Xu H, Fu R, He W, Zhang L, Osorio D, Bly Z, Calluori S, Gilchrist DA, Hutter CM, Morris SA, Samer EK. Deciphering the impact of genomic variation on function. Nature 2024; 633:47-57. [PMID: 39232149 DOI: 10.1038/s41586-024-07510-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 05/02/2024] [Indexed: 09/06/2024]
Abstract
Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.
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5
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Alvarez-Kuglen M, Ninomiya K, Qin H, Rodriguez D, Fiengo L, Farhy C, Hsu WM, Kirk B, Havas A, Feng GS, Roberts AJ, Anderson RM, Serrano M, Adams PD, Sharpee TO, Terskikh AV. ImAge quantitates aging and rejuvenation. NATURE AGING 2024:10.1038/s43587-024-00685-1. [PMID: 39210148 DOI: 10.1038/s43587-024-00685-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 07/11/2024] [Indexed: 09/04/2024]
Abstract
For efficient, cost-effective and personalized healthcare, biomarkers that capture aspects of functional, biological aging, thus predicting disease risk and lifespan more accurately and reliably than chronological age, are essential. We developed an imaging-based chromatin and epigenetic age (ImAge) that captures intrinsic age-related trajectories of the spatial organization of chromatin and epigenetic marks in single nuclei, in mice. We show that such trajectories readily emerge as principal changes in each individual dataset without regression on chronological age, and that ImAge can be computed using several epigenetic marks and DNA labeling. We find that interventions known to affect biological aging induce corresponding effects on ImAge, including increased ImAge upon chemotherapy treatment and decreased ImAge upon caloric restriction and partial reprogramming by transient OSKM expression in liver and skeletal muscle. Further, ImAge readouts from chronologically identical mice inversely correlated with their locomotor activity, suggesting that ImAge may capture elements of biological and functional age. In sum, we developed ImAge, an imaging-based biomarker of aging with single-cell resolution rooted in the analysis of spatial organization of epigenetic marks.
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Affiliation(s)
| | - Kenta Ninomiya
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, Western Australia, Australia
| | - Haodong Qin
- Department of Physics, University of California San Diego, La Jolla, CA, USA
| | | | | | - Chen Farhy
- Sanford Burnham Prebys, La Jolla, CA, USA
| | - Wei-Mien Hsu
- Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Brian Kirk
- Sanford Burnham Prebys, La Jolla, CA, USA
| | | | - Gen-Sheng Feng
- School of Medicine, Univerity of California San Diego, La Jolla, CA, USA
| | | | - Rozalyn M Anderson
- University of Wisconsin, Madison, WI, USA
- GRECC, William S Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Manuel Serrano
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Spain
- Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Altos Labs, Cambridge Institute of Science, Granta Park, UK
| | | | | | - Alexey V Terskikh
- Harry Perkins Institute of Medical Research, The University of Western Australia, Perth, Western Australia, Australia.
- The Scintillon Research Institute, San Diego, CA, USA.
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6
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Xu M, Hong JJ, Zhang X, Sun M, Liu X, Kang J, Stack H, Fang W, Lei H, Lacoste X, Okada R, Jung R, Nguyen R, Shern JF, Thiele CJ, Liu Z. Targeting SWI/SNF ATPases reduces neuroblastoma cell plasticity. EMBO J 2024:10.1038/s44318-024-00206-1. [PMID: 39174852 DOI: 10.1038/s44318-024-00206-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 07/01/2024] [Accepted: 07/17/2024] [Indexed: 08/24/2024] Open
Abstract
Tumor cell heterogeneity defines therapy responsiveness in neuroblastoma (NB), a cancer derived from neural crest cells. NB consists of two primary subtypes: adrenergic and mesenchymal. Adrenergic traits predominate in NB tumors, while mesenchymal features becomes enriched post-chemotherapy or after relapse. The interconversion between these subtypes contributes to NB lineage plasticity, but the underlying mechanisms driving this phenotypic switching remain unclear. Here, we demonstrate that SWI/SNF chromatin remodeling complex ATPases are essential in establishing an mesenchymal gene-permissive chromatin state in adrenergic-type NB, facilitating lineage plasticity. Targeting SWI/SNF ATPases with SMARCA2/4 dual degraders effectively inhibits NB cell proliferation, invasion, and notably, cellular plasticity, thereby preventing chemotherapy resistance. Mechanistically, depletion of SWI/SNF ATPases compacts cis-regulatory elements, diminishes enhancer activity, and displaces core transcription factors (MYCN, HAND2, PHOX2B, and GATA3) from DNA, thereby suppressing transcriptional programs associated with plasticity. These findings underscore the pivotal role of SWI/SNF ATPases in driving intrinsic plasticity and therapy resistance in neuroblastoma, highlighting an epigenetic target for combinational treatments in this cancer.
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Affiliation(s)
- Man Xu
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Jason J Hong
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Xiyuan Zhang
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Ming Sun
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Xingyu Liu
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Jeeyoun Kang
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Hannah Stack
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Wendy Fang
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Haiyan Lei
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Xavier Lacoste
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Reona Okada
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Raina Jung
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Rosa Nguyen
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Jack F Shern
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Carol J Thiele
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
| | - Zhihui Liu
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
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7
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Kernfeld E, Keener R, Cahan P, Battle A. Transcriptome data are insufficient to control false discoveries in regulatory network inference. Cell Syst 2024; 15:709-724.e13. [PMID: 39173585 DOI: 10.1016/j.cels.2024.07.006] [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: 05/24/2023] [Revised: 05/31/2024] [Accepted: 07/22/2024] [Indexed: 08/24/2024]
Abstract
Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Eric Kernfeld
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA
| | - Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA
| | - Patrick Cahan
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA; Institute for Cell Engineering, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA; Department of Genetic Medicine, Johns Hopkins Medicine, Baltimore, MD, USA; Malone Center for Engineering and Healthcare, Johns Hopkins University, Baltimore, MD, USA; Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA.
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8
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Feng Y, Flanagan ME, Bonakdarpour B, Jamshidi P, Castellani RJ, Mao Q, Chu X, Gao H, Liu Y, Xu J, Hou Y, Martin W, Nelson PT, Leverenz JB, Pieper AA, Cummings J, Cheng F. Single-nucleus multiome analysis of human cerebellum in Alzheimer's disease-related dementia. RESEARCH SQUARE 2024:rs.3.rs-4871032. [PMID: 39184089 PMCID: PMC11343296 DOI: 10.21203/rs.3.rs-4871032/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Although human cerebellum is known to be neuropathologically impaired in Alzheimer's disease (AD) and AD-related dementias (ADRD), the cell type-specific transcriptional and epigenomic changes that contribute to this pathology are not well understood. Here, we report single-nucleus multiome (snRNA-seq and snATAC-seq) analysis of 103,861 nuclei isolated from cerebellum from 9 human cases of AD/ADRD and 8 controls, and with frontal cortex of 6 AD donors for additional comparison. Using peak-to-gene linkage analysis, we identified 431,834 significant linkages between gene expression and cell subtype-specific chromatin accessibility regions enriched for candidate cis-regulatory elements (cCREs). These cCREs were associated with AD/ADRD-specific transcriptomic changes and disease-related gene regulatory networks, especially for RAR Related Orphan Receptor A (RORA) and E74 Like ETS Transcription Factor 1 (ELF1) in cerebellar Purkinje cells and granule cells, respectively. Trajectory analysis of granule cell populations further identified disease-relevant transcription factors, such as RORA, and their regulatory targets. Finally, we prioritized two likely causal genes, including Seizure Related 6 Homolog Like 2 (SEZ6L2) in Purkinje cells and KAT8 Regulatory NSL Complex Subunit 1 (KANSL1) in granule cells, through integrative analysis of cCREs derived from snATAC-seq, genome-wide AD/ADRD loci, and Hi-C looping data. This first cell subtype-specific regulatory landscape in the human cerebellum identified here offer novel genomic and epigenomic insights into the neuropathology and pathobiology of AD/ADRD and other neurological disorders if broadly applied.
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Affiliation(s)
- Yayan Feng
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Margaret E Flanagan
- Biggs Institute, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA
- Department of Pathology, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA
| | - Borna Bonakdarpour
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Pouya Jamshidi
- Department of Pathology and Northwestern Alzheimer Disease Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Rudolph J. Castellani
- Department of Pathology and Northwestern Alzheimer Disease Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Qinwen Mao
- Department of Pathology, University of Utah, Salt Lake City, Utah, USA
| | - Xiaona Chu
- Department of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Hongyu Gao
- Department of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jielin Xu
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Hou
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - William Martin
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Peter T Nelson
- Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, Kentucky, USA
- Department of Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
| | - James B. Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
| | - Andrew A. Pieper
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center; Cleveland, OH 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland 44106, OH, USA
- Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
- Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, Nevada 89154, USA
| | - Feixiong Cheng
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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9
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Lyu R, Gao Y, Wu T, Ye C, Wang P, He C. Quantitative analysis of cis-regulatory elements in transcription with KAS-ATAC-seq. Nat Commun 2024; 15:6852. [PMID: 39127768 PMCID: PMC11316786 DOI: 10.1038/s41467-024-50680-8] [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: 03/06/2024] [Accepted: 07/18/2024] [Indexed: 08/12/2024] Open
Abstract
Cis-regulatory elements (CREs) are pivotal in orchestrating gene expression throughout diverse biological systems. Accurate identification and in-depth characterization of functional CREs are crucial for decoding gene regulation networks during cellular processes. In this study, we develop Kethoxal-Assisted Single-stranded DNA Assay for Transposase-Accessible Chromatin with Sequencing (KAS-ATAC-seq) to quantitatively analyze the transcriptional activity of CREs. A main advantage of KAS-ATAC-seq lies in its precise measurement of ssDNA levels within both proximal and distal ATAC-seq peaks, enabling the identification of transcriptional regulatory sequences. This feature is particularly adept at defining Single-Stranded Transcribing Enhancers (SSTEs). SSTEs are highly enriched with nascent RNAs and specific transcription factors (TFs) binding sites that define cellular identity. Moreover, KAS-ATAC-seq provides a detailed characterization and functional implications of various SSTE subtypes. Our analysis of CREs during mouse neural differentiation demonstrates that KAS-ATAC-seq can effectively identify immediate-early activated CREs in response to retinoic acid (RA) treatment. Our findings indicate that KAS-ATAC-seq provides more precise annotation of functional CREs in transcription. Future applications of KAS-ATAC-seq would help elucidate the intricate dynamics of gene regulation in diverse biological processes.
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Affiliation(s)
- Ruitu Lyu
- Department of Chemistry, The University of Chicago, Chicago, IL, 60637, USA.
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, 60637, USA.
- Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, 60637, USA.
| | - Yun Gao
- Department of Chemistry, The University of Chicago, Chicago, IL, 60637, USA
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, 60637, USA
- Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, 60637, USA
| | - Tong Wu
- Department of Chemistry, The University of Chicago, Chicago, IL, 60637, USA
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, 60637, USA
- Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, 60637, USA
| | - Chang Ye
- Department of Chemistry, The University of Chicago, Chicago, IL, 60637, USA
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, 60637, USA
- Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, 60637, USA
| | - Pingluan Wang
- Department of Chemistry, The University of Chicago, Chicago, IL, 60637, USA
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, 60637, USA
- Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, 60637, USA
| | - Chuan He
- Department of Chemistry, The University of Chicago, Chicago, IL, 60637, USA.
- Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, 60637, USA.
- Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, 60637, USA.
- Howard Hughes Medical Institute, The University of Chicago, Chicago, IL, 60637, USA.
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10
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Kim S, Koppitch K, Parvez RK, Guo J, Achieng M, Schnell J, Lindström NO, McMahon AP. Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development. Dev Cell 2024:S1534-5807(24)00450-7. [PMID: 39121855 DOI: 10.1016/j.devcel.2024.07.013] [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: 05/20/2023] [Revised: 04/02/2024] [Accepted: 07/12/2024] [Indexed: 08/12/2024]
Abstract
The mammalian kidney maintains fluid homeostasis through diverse epithelial cell types generated from nephron and ureteric progenitor cells. To extend a developmental understanding of the kidney's epithelial networks, we compared chromatin organization (single-nuclear assay for transposase-accessible chromatin sequencing [ATAC-seq]; 112,864 nuclei) and gene expression (single-cell/nuclear RNA sequencing [RNA-seq]; 109,477 cells/nuclei) in the developing human (10.6-17.6 weeks; n = 10) and mouse (post-natal day [P]0; n = 10) kidney, supplementing analysis with published mouse datasets from earlier stages. Single-cell/nuclear datasets were analyzed at a species level, and then nephron and ureteric cellular lineages were extracted and integrated into a common, cross-species, multimodal dataset. Comparative computational analyses identified conserved and divergent features of chromatin organization and linked gene activity, identifying species-specific and cell-type-specific regulatory programs. In situ validation of human-enriched gene activity points to human-specific signaling interactions in kidney development. Further, human-specific enhancer regions were linked to kidney diseases through genome-wide association studies (GWASs), highlighting the potential for clinical insight from developmental modeling.
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Affiliation(s)
- Sunghyun Kim
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Kari Koppitch
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Riana K Parvez
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Jinjin Guo
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - MaryAnne Achieng
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Jack Schnell
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Nils O Lindström
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrew P McMahon
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.
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11
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Jeong Y, Ronen J, Kopp W, Lutsik P, Akalin A. scMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data. BMC Bioinformatics 2024; 25:257. [PMID: 39107690 PMCID: PMC11304929 DOI: 10.1186/s12859-024-05880-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Abstract
The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.
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Affiliation(s)
- Yunhee Jeong
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, Germany
- Faculty of Mathematics and Informatics, Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, Germany
| | - Jonathan Ronen
- Bioinformatics and Omics Data Science Platform, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Inceptive Nucleics, Inc., Palo Alto, CA, USA
| | - Wolfgang Kopp
- Bioinformatics and Omics Data Science Platform, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Roche Diagnostics GmbH, Penzberg, Germany
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, Germany.
- Department of Oncology, Catholic University (KU) Leuven, Leuven, Belgium.
| | - Altuna Akalin
- Bioinformatics and Omics Data Science Platform, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.
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12
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Patrick R, Naval-Sanchez M, Deshpande N, Huang Y, Zhang J, Chen X, Yang Y, Tiwari K, Esmaeili M, Tran M, Mohamed AR, Wang B, Xia D, Ma J, Bayliss J, Wong K, Hun ML, Sun X, Cao B, Cottle DL, Catterall T, Barzilai-Tutsch H, Troskie RL, Chen Z, Wise AF, Saini S, Soe YM, Kumari S, Sweet MJ, Thomas HE, Smyth IM, Fletcher AL, Knoblich K, Watt MJ, Alhomrani M, Alsanie W, Quinn KM, Merson TD, Chidgey AP, Ricardo SD, Yu D, Jardé T, Cheetham SW, Marcelle C, Nilsson SK, Nguyen Q, White MD, Nefzger CM. The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1-linked chromatin opening. Cell Metab 2024; 36:1858-1881.e23. [PMID: 38959897 DOI: 10.1016/j.cmet.2024.06.006] [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: 08/21/2023] [Revised: 03/28/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
A mechanistic connection between aging and development is largely unexplored. Through profiling age-related chromatin and transcriptional changes across 22 murine cell types, analyzed alongside previous mouse and human organismal maturation datasets, we uncovered a transcription factor binding site (TFBS) signature common to both processes. Early-life candidate cis-regulatory elements (cCREs), progressively losing accessibility during maturation and aging, are enriched for cell-type identity TFBSs. Conversely, cCREs gaining accessibility throughout life have a lower abundance of cell identity TFBSs but elevated activator protein 1 (AP-1) levels. We implicate TF redistribution toward these AP-1 TFBS-rich cCREs, in synergy with mild downregulation of cell identity TFs, as driving early-life cCRE accessibility loss and altering developmental and metabolic gene expression. Such remodeling can be triggered by elevating AP-1 or depleting repressive H3K27me3. We propose that AP-1-linked chromatin opening drives organismal maturation by disrupting cell identity TFBS-rich cCREs, thereby reprogramming transcriptome and cell function, a mechanism hijacked in aging through ongoing chromatin opening.
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Affiliation(s)
- Ralph Patrick
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Marina Naval-Sanchez
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Nikita Deshpande
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Yifei Huang
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Jingyu Zhang
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Xiaoli Chen
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Ying Yang
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Kanupriya Tiwari
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Mohammadhossein Esmaeili
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Minh Tran
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Amin R Mohamed
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Binxu Wang
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Di Xia
- Genome Innovation Hub, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Jun Ma
- Genome Innovation Hub, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Jacqueline Bayliss
- Department of Anatomy and Physiology, Faculty of Medicine Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Kahlia Wong
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Michael L Hun
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Xuan Sun
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organization, Melbourne, VIC, Australia; Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia
| | - Benjamin Cao
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organization, Melbourne, VIC, Australia; Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia
| | - Denny L Cottle
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Tara Catterall
- St. Vincent's Institute of Medical Research, Fitzroy, VIC 3065, Australia
| | - Hila Barzilai-Tutsch
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia; Institut NeuroMyoGène, University Claude Bernard Lyon 1, 69008 Lyon, France
| | - Robin-Lee Troskie
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Zhian Chen
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Andrea F Wise
- Department of Pharmacology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Sheetal Saini
- Department of Pharmacology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Ye Mon Soe
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Snehlata Kumari
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Matthew J Sweet
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; Australian Infectious Diseases Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Helen E Thomas
- St. Vincent's Institute of Medical Research, Fitzroy, VIC 3065, Australia
| | - Ian M Smyth
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Anne L Fletcher
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Konstantin Knoblich
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Matthew J Watt
- Department of Anatomy and Physiology, Faculty of Medicine Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Majid Alhomrani
- Department of Clinical Laboratories Sciences, Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia; Research Centre for Health Sciences, Taif University, Taif, Saudi Arabia
| | - Walaa Alsanie
- Department of Clinical Laboratories Sciences, Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia; Research Centre for Health Sciences, Taif University, Taif, Saudi Arabia
| | - Kylie M Quinn
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia
| | - Tobias D Merson
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia; National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ann P Chidgey
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Sharon D Ricardo
- Department of Pharmacology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Di Yu
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia; Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Thierry Jardé
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; Development and Stem Cells Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; Cancer Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; Department of Surgery, Cabrini Monash University, Malvern, VIC 3144, Australia
| | - Seth W Cheetham
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Christophe Marcelle
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia; Institut NeuroMyoGène, University Claude Bernard Lyon 1, 69008 Lyon, France
| | - Susan K Nilsson
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organization, Melbourne, VIC, Australia; Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; School of Biomedical Sciences, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Melanie D White
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; School of Biomedical Sciences, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Christian M Nefzger
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; Development and Stem Cells Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia.
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13
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Liu J, Ma J, Wen J, Zhou X. A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2401815. [PMID: 38887194 PMCID: PMC11336957 DOI: 10.1002/advs.202401815] [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: 02/20/2024] [Revised: 04/22/2024] [Indexed: 06/20/2024]
Abstract
In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but it is not clear how it will work on the integrated single-cell multi-omics data. Here, a cell cycle-aware network (CCAN) is developed to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.
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Affiliation(s)
- Jiajia Liu
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Jian Ma
- Department of Electronic Information and Computer EngineeringThe Engineering & Technical College of Chengdu University of TechnologyLeshanSichuan614000China
| | - Jianguo Wen
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Computational Systems MedicineMcWilliams School of Biomedical InformaticsThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- McGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTX77030USA
- School of DentistryThe University of Texas Health Science Center at HoustonHoustonTX77030USA
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14
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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.
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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.
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15
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Hao M, Gong J, Zeng X, Liu C, Guo Y, Cheng X, Wang T, Ma J, Zhang X, Song L. Large-scale foundation model on single-cell transcriptomics. Nat Methods 2024; 21:1481-1491. [PMID: 38844628 DOI: 10.1038/s41592-024-02305-7] [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: 06/02/2023] [Accepted: 05/10/2024] [Indexed: 08/10/2024]
Abstract
Large pretrained models have become foundation models leading to breakthroughs in natural language processing and related fields. Developing foundation models for deciphering the 'languages' of cells and facilitating biomedical research is promising yet challenging. Here we developed a large pretrained model scFoundation, also named 'xTrimoscFoundationα', with 100 million parameters covering about 20,000 genes, pretrained on over 50 million human single-cell transcriptomic profiles. scFoundation is a large-scale model in terms of the size of trainable parameters, dimensionality of genes and volume of training data. Its asymmetric transformer-like architecture and pretraining task design empower effectively capturing complex context relations among genes in a variety of cell types and states. Experiments showed its merit as a foundation model that achieved state-of-the-art performances in a diverse array of single-cell analysis tasks such as gene expression enhancement, tissue drug response prediction, single-cell drug response classification, single-cell perturbation prediction, cell type annotation and gene module inference.
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Affiliation(s)
- Minsheng Hao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
- BioMap, Beijing, China
| | | | | | | | | | | | | | - Jianzhu Ma
- Department of Electrical Engineering, Tsinghua University, Beijing, China.
- Institute for AI Industry Research, Tsinghua University, Beijing, China.
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
- School of Life Sciences and School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China.
| | - Le Song
- BioMap, Beijing, China.
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE.
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16
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Pepin AS, Schneider R. Emerging toolkits for decoding the co-occurrence of modified histones and chromatin proteins. EMBO Rep 2024; 25:3202-3220. [PMID: 39095610 PMCID: PMC11316037 DOI: 10.1038/s44319-024-00199-2] [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: 02/28/2024] [Revised: 05/10/2024] [Accepted: 06/10/2024] [Indexed: 08/04/2024] Open
Abstract
In eukaryotes, DNA is packaged into chromatin with the help of highly conserved histone proteins. Together with DNA-binding proteins, posttranslational modifications (PTMs) on these histones play crucial roles in regulating genome function, cell fate determination, inheritance of acquired traits, cellular states, and diseases. While most studies have focused on individual DNA-binding proteins, chromatin proteins, or histone PTMs in bulk cell populations, such chromatin features co-occur and potentially act cooperatively to accomplish specific functions in a given cell. This review discusses state-of-the-art techniques for the simultaneous profiling of multiple chromatin features in low-input samples and single cells, focusing on histone PTMs, DNA-binding, and chromatin proteins. We cover the origins of the currently available toolkits, compare and contrast their characteristic features, and discuss challenges and perspectives for future applications. Studying the co-occurrence of histone PTMs, DNA-binding proteins, and chromatin proteins in single cells will be central for a better understanding of the biological relevance of combinatorial chromatin features, their impact on genomic output, and cellular heterogeneity.
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Affiliation(s)
- Anne-Sophie Pepin
- Institute of Functional Epigenetics (IFE), Helmholtz Zentrum München, Neuherberg, Germany
| | - Robert Schneider
- Institute of Functional Epigenetics (IFE), Helmholtz Zentrum München, Neuherberg, Germany.
- Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany.
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17
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Chromatin state and BACH2 control T H17 cell functional heterogeneity. Nat Immunol 2024; 25:1330-1331. [PMID: 39020023 DOI: 10.1038/s41590-024-01904-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/19/2024]
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18
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Ma H, Qu J, Pang Z, Luo J, Yan M, Xu W, Zhuang H, Liu L, Qu Q. Super-enhancer omics in stem cell. Mol Cancer 2024; 23:153. [PMID: 39090713 PMCID: PMC11293198 DOI: 10.1186/s12943-024-02066-z] [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: 04/19/2024] [Accepted: 07/12/2024] [Indexed: 08/04/2024] Open
Abstract
The hallmarks of stem cells, such as proliferation, self-renewal, development, differentiation, and regeneration, are critical to maintain stem cell identity which is sustained by genetic and epigenetic factors. Super-enhancers (SEs), which consist of clusters of active enhancers, play a central role in maintaining stemness hallmarks by specifically transcriptional model. The SE-navigated transcriptional complex, including SEs, non-coding RNAs, master transcriptional factors, Mediators and other co-activators, forms phase-separated condensates, which offers a toggle for directing diverse stem cell fate. With the burgeoning technologies of multiple-omics applied to examine different aspects of SE, we firstly raise the concept of "super-enhancer omics", inextricably linking to Pan-omics. In the review, we discuss the spatiotemporal organization and concepts of SEs, and describe links between SE-navigated transcriptional complex and stem cell features, such as stem cell identity, self-renewal, pluripotency, differentiation and development. We also elucidate the mechanism of stemness and oncogenic SEs modulating cancer stem cells via genomic and epigenetic alterations hijack in cancer stem cell. Additionally, we discuss the potential of targeting components of the SE complex using small molecule compounds, genome editing, and antisense oligonucleotides to treat SE-associated organ dysfunction and diseases, including cancer. This review also provides insights into the future of stem cell research through the paradigm of SEs.
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Affiliation(s)
- Hongying Ma
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China
| | - Jian Qu
- Department of Pharmacy, the Second Xiangya Hospital, Institute of Clinical Pharmacy, Central South University, Changsha, 410011, People's Republic of China
- Hunan key laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, 410219, China
| | - Zicheng Pang
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China
- Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China
| | - Jian Luo
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China
| | - Min Yan
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China
| | - Weixin Xu
- Department of Pharmacy, the Second Xiangya Hospital, Institute of Clinical Pharmacy, Central South University, Changsha, 410011, People's Republic of China
| | - Haihui Zhuang
- Department of Pharmacy, the Second Xiangya Hospital, Institute of Clinical Pharmacy, Central South University, Changsha, 410011, People's Republic of China
| | - Linxin Liu
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, 410011, People's Republic of China
| | - Qiang Qu
- Department of Pharmacy, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, 410008, People's Republic of China.
- Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China.
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, 410011, People's Republic of China.
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19
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Zhao Z, Liu A, Citu C, Enduru N, Chen X, Manuel A, Sinha T, Gorski D, Fernandes B, Yu M, Schulz P, Simon L, Soto C. Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease. RESEARCH SQUARE 2024:rs.3.rs-4622123. [PMID: 39149497 PMCID: PMC11326379 DOI: 10.21203/rs.3.rs-4622123/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis-regulatory elements (cCREs) across brain regions. We prioritized seven conservative transcription factors in glial cells in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in regulating sEOAD-associated genes. Moreover, we identified the top 25 altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. We reported 38 cCREs linked to sEOAD-associated genes overlapped with late-onset AD risk loci, and sEOAD cCREs enriched in neuropsychiatric disorder risk loci. This atlas helps dissect transcriptional and chromatin dynamics in sEOAD, providing a key resource for AD research.
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Affiliation(s)
- Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Andi Liu
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Citu Citu
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston
| | - Nitesh Enduru
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xian Chen
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston
| | - Astrid Manuel
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston
| | - Tirthankar Sinha
- Mitchell Center for Alzheimer's Disease and Related Brain Disorders, Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Damian Gorski
- Mitchell Center for Alzheimer's Disease and Related Brain Disorders, Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Brisa Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston
| | - Meifang Yu
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston
| | - Paul Schulz
- Department of Neurology, McGovern School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Lukas Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Claudio Soto
- Mitchell Center for Alzheimer's Disease and Related Brain Disorders, Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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20
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Zhou T, Zhang R, Jia D, Doty RT, Munday AD, Gao D, Xin L, Abkowitz JL, Duan Z, Ma J. GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells. Nat Genet 2024; 56:1701-1711. [PMID: 38744973 PMCID: PMC11323187 DOI: 10.1038/s41588-024-01745-3] [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: 07/21/2023] [Accepted: 04/05/2024] [Indexed: 05/16/2024]
Abstract
The organization of mammalian genomes features a complex, multiscale three-dimensional (3D) architecture, whose functional significance remains elusive because of limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we introduce genome architecture and gene expression by sequencing (GAGE-seq), a scalable, robust single-cell co-assay measuring 3D genome structure and transcriptome simultaneously within the same cell. Applied to mouse brain cortex and human bone marrow CD34+ cells, GAGE-seq characterized the intricate relationships between 3D genome and gene expression, showing that multiscale 3D genome features inform cell-type-specific gene expression and link regulatory elements to target genes. Integration with spatial transcriptomic data revealed in situ 3D genome variations in mouse cortex. Observations in human hematopoiesis unveiled discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level. GAGE-seq provides a powerful, cost-effective approach for exploring genome structure and gene expression relationships at the single-cell level across diverse biological contexts.
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Affiliation(s)
- Tianming Zhou
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ruochi Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Deyong Jia
- Department of Urology, University of Washington, Seattle, WA, USA
| | - Raymond T Doty
- Division of Hematology and Oncology, Department of Medicine/Fred Hutch Cancer Center, University of Washington, Seattle, WA, USA
| | - Adam D Munday
- Division of Hematology and Oncology, Department of Medicine/Fred Hutch Cancer Center, University of Washington, Seattle, WA, USA
| | - Daniel Gao
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Department of Chemistry, Pomona College, Claremont, CA, USA
| | - Li Xin
- Department of Urology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Janis L Abkowitz
- Division of Hematology and Oncology, Department of Medicine/Fred Hutch Cancer Center, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Zhijun Duan
- Division of Hematology and Oncology, Department of Medicine/Fred Hutch Cancer Center, University of Washington, Seattle, WA, USA.
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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21
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Yang X, Mann KK, Wu H, Ding J. scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration. Genome Biol 2024; 25:198. [PMID: 39075536 PMCID: PMC11285326 DOI: 10.1186/s13059-024-03338-z] [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: 12/15/2023] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.
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Affiliation(s)
- Xiuhui Yang
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Koren K Mann
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Hao Wu
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China.
| | - Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada.
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada.
- Mila-Quebec AI Institute, Montreal, QC, H2S 3H1, Canada.
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22
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Metzner E, Southard KM, Norman TM. Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.26.605307. [PMID: 39091800 PMCID: PMC11291144 DOI: 10.1101/2024.07.26.605307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Pseudotime analysis of perturbations connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state.
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Affiliation(s)
- Eli Metzner
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Kaden M. Southard
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Thomas M. Norman
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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23
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Loers JU, Vermeirssen V. A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data. Brief Bioinform 2024; 25:bbae382. [PMID: 39207727 PMCID: PMC11359808 DOI: 10.1093/bib/bbae382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 06/27/2024] [Accepted: 07/23/2024] [Indexed: 09/04/2024] Open
Abstract
Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.
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Affiliation(s)
- Jens Uwe Loers
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Corneel Heymanslaan 10, 9000 Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Zwijnaarde-Technologiepark 71, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium
| | - Vanessa Vermeirssen
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Corneel Heymanslaan 10, 9000 Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Zwijnaarde-Technologiepark 71, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Corneel Heymanslaan 10, 9000 Ghent, Belgium
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24
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Li Y, Ma A, Wang Y, Guo Q, Wang C, Fu H, Liu B, Ma Q. Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data. Brief Bioinform 2024; 25:bbae369. [PMID: 39082647 PMCID: PMC11289686 DOI: 10.1093/bib/bbae369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/19/2024] [Accepted: 07/15/2024] [Indexed: 08/03/2024] Open
Abstract
Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.
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Affiliation(s)
- Yang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, United States
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, United States
| | - Yizhong Wang
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, United States
| | - Cankun Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, United States
| | - Hongjun Fu
- Department of Neuroscience, College of Medicine, The Ohio State University, Columbus, OH 43210, United States
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, United States
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25
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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26
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Chang Z, Xu Y, Dong X, Gao Y, Wang C. Single-cell and spatial multiomic inference of gene regulatory networks using SCRIPro. Bioinformatics 2024; 40:btae466. [PMID: 39024032 PMCID: PMC11288411 DOI: 10.1093/bioinformatics/btae466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/05/2024] [Accepted: 07/17/2024] [Indexed: 07/20/2024] Open
Abstract
MOTIVATION The burgeoning generation of single-cell or spatial multiomic data allows for the characterization of gene regulation networks (GRNs) at an unprecedented resolution. However, the accurate reconstruction of GRNs from sparse and noisy single-cell or spatial multiomic data remains challenging. RESULTS Here, we present SCRIPro, a comprehensive computational framework that robustly infers GRNs for both single-cell and spatial multi-omics data. SCRIPro first improves sample coverage through a density clustering approach based on multiomic and spatial similarities. Additionally, SCRIPro scans transcriptional regulator (TR) importance by performing chromatin reconstruction and in silico deletion analyses using a comprehensive reference covering 1,292 human and 994 mouse TRs. Finally, SCRIPro combines TR-target importance scores derived from multiomic data with TR-target expression levels to ensure precise GRN reconstruction. We benchmarked SCRIPro on various datasets, including single-cell multiomic data from human B-cell lymphoma, mouse hair follicle development, Stereo-seq of mouse embryos, and Spatial-ATAC-RNA from mouse brain. SCRIPro outperforms existing motif-based methods and accurately reconstructs cell type-specific, stage-specific, and region-specific GRNs. Overall, SCRIPro emerges as a streamlined and fast method capable of reconstructing TR activities and GRNs for both single-cell and spatial multi-omic data. AVAILABILITY SCRIPro is available at https://github.com/wanglabtongji/SCRIPro. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhanhe Chang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Frontier Science Center for Stem Cell Research, Tongji University, Shanghai, China
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Yunfan Xu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Frontier Science Center for Stem Cell Research, Tongji University, Shanghai, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Frontier Science Center for Stem Cell Research, Tongji University, Shanghai, China
| | - Yawei Gao
- Frontier Science Center for Stem Cell Research, Tongji University, Shanghai, China
- Institute for Regenerative Medicine, Shanghai East Hospital, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
- Frontier Science Center for Stem Cell Research, Tongji University, Shanghai, China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai 200120, China
- Frontier Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 200120, China
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27
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Dong C, Meng X, Zhang T, Guo Z, Liu Y, Wu P, Chen S, Zhou F, Ma Y, Xiong H, Shu S, He A. Single-cell EpiChem jointly measures drug-chromatin binding and multimodal epigenome. Nat Methods 2024:10.1038/s41592-024-02360-0. [PMID: 39025969 DOI: 10.1038/s41592-024-02360-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 06/25/2024] [Indexed: 07/20/2024]
Abstract
Studies of molecular and cellular functions of small-molecule inhibitors in cancer treatment, eliciting effects by targeting genome and epigenome associated proteins, requires measurement of drug-target engagement in single-cell resolution. Here we present EpiChem for in situ single-cell joint mapping of small molecules and multimodal epigenomic landscape. We demonstrate single-cell co-assays of three small molecules together with histone modifications, chromatin accessibility or target proteins in human colorectal cancer (CRC) organoids. Integrated multimodal analysis reveals diverse drug interactions in the context of chromatin states within heterogeneous CRC organoids. We further reveal drug genomic binding dynamics and adaptive epigenome across cell types after small-molecule drug treatment in CRC organoids. This method provides a unique tool to exploit the mechanisms of cell type-specific drug actions.
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Affiliation(s)
- Chao Dong
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Xiaoxuan Meng
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Tong Zhang
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Zhifang Guo
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China
- Peking University International Cancer Institute, Beijing, China
- Peking University-Yunnan Baiyao International Medical Research Center, Beijing, China
| | - Yaxi Liu
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Peihuang Wu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China
| | - Shiwei Chen
- Peking University International Cancer Institute, Beijing, China
- Peking University-Yunnan Baiyao International Medical Research Center, Beijing, China
| | - Fanqi Zhou
- State Key Laboratory of Medical Molecular Biology, Haihe laboratory of Cell Ecosystem, Key Laboratory of RNA and Hematopoietic Regulation, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Yanni Ma
- State Key Laboratory of Medical Molecular Biology, Haihe laboratory of Cell Ecosystem, Key Laboratory of RNA and Hematopoietic Regulation, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Haiqing Xiong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Shaokun Shu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China.
- Peking University International Cancer Institute, Beijing, China.
- Peking University-Yunnan Baiyao International Medical Research Center, Beijing, China.
| | - Aibin He
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
- Key laboratory of Carcinogenesis and Translational Research of Ministry of Education of China, Peking University Cancer Hospital & Institute, Beijing, China.
- Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, China.
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28
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Shwab EK, Gingerich DC, Man Z, Gamache J, Garrett ME, Crawford GE, Ashley-Koch AE, Serrano GE, Beach TG, Lutz MW, Chiba-Falek O. Single-nucleus multi-omics of Parkinson's disease reveals a glutamatergic neuronal subtype susceptible to gene dysregulation via alteration of transcriptional networks. Acta Neuropathol Commun 2024; 12:111. [PMID: 38956662 PMCID: PMC11218415 DOI: 10.1186/s40478-024-01803-1] [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: 05/22/2024] [Accepted: 05/27/2024] [Indexed: 07/04/2024] Open
Abstract
The genetic architecture of Parkinson's disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis.
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Affiliation(s)
- E Keats Shwab
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Daniel C Gingerich
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Zhaohui Man
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Julia Gamache
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Melanie E Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, 27701, USA
| | - Gregory E Crawford
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, 27708, USA
- Center for Advanced Genomic Technologies, Duke University Medical Center, Durham, NC, 27708, USA
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, 27701, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, 27708, USA
| | - Geidy E Serrano
- Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Thomas G Beach
- Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Michael W Lutz
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA.
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA.
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29
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Hu H, Wang X, Feng S, Xu Z, Liu J, Heidrich-O'Hare E, Chen Y, Yue M, Zeng L, Rong Z, Chen T, Billiar T, Ding Y, Huang H, Duerr RH, Chen W. A unified model-based framework for doublet or multiplet detection in single-cell multiomics data. Nat Commun 2024; 15:5562. [PMID: 38956023 PMCID: PMC11220103 DOI: 10.1038/s41467-024-49448-x] [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: 11/30/2023] [Accepted: 06/03/2024] [Indexed: 07/04/2024] Open
Abstract
Droplet-based single-cell sequencing techniques rely on the fundamental assumption that each droplet encapsulates a single cell, enabling individual cell omics profiling. However, the inevitable issue of multiplets, where two or more cells are encapsulated within a single droplet, can lead to spurious cell type annotations and obscure true biological findings. The issue of multiplets is exacerbated in single-cell multiomics settings, where integrating cross-modality information for clustering can inadvertently promote the aggregation of multiplet clusters and increase the risk of erroneous cell type annotations. Here, we propose a compound Poisson model-based framework for multiplet detection in single-cell multiomics data. Leveraging experimental cell hashing results as the ground truth for multiplet status, we conducted trimodal DOGMA-seq experiments and generated 17 benchmarking datasets from two tissues, involving a total of 280,123 droplets. We demonstrated that the proposed method is an essential tool for integrating cross-modality multiplet signals, effectively eliminating multiplet clusters in single-cell multiomics data-a task at which the benchmarked single-omics methods proved inadequate.
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Affiliation(s)
- Haoran Hu
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Xinjun Wang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Site Feng
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- School of Medicine, Tsinghua University, 100084, Beijing, China
| | - Zhongli Xu
- School of Medicine, Tsinghua University, 100084, Beijing, China
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, 15224, USA
| | - Jing Liu
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, 15224, USA
| | | | - Yanshuo Chen
- Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Center of Bioinformatics and Computational Biology, College Park, MD, 20740, USA
| | - Molin Yue
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Lang Zeng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Ziqi Rong
- School of Information, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Tianmeng Chen
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Timothy Billiar
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Ying Ding
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Heng Huang
- Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Center of Bioinformatics and Computational Biology, College Park, MD, 20740, USA
| | - Richard H Duerr
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Wei Chen
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, 15224, USA.
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
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30
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Peeters F, Cappuyns S, Piqué-Gili M, Phillips G, Verslype C, Lambrechts D, Dekervel J. Applications of single-cell multi-omics in liver cancer. JHEP Rep 2024; 6:101094. [PMID: 39022385 PMCID: PMC11252522 DOI: 10.1016/j.jhepr.2024.101094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/18/2024] [Accepted: 03/27/2024] [Indexed: 07/20/2024] Open
Abstract
Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.
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Affiliation(s)
- Frederik Peeters
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Sarah Cappuyns
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Marta Piqué-Gili
- Liver Cancer Translational Research Laboratory, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Gino Phillips
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Chris Verslype
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Centre for Cancer Biology, Leuven, Belgium
| | - Jeroen Dekervel
- Digestive Oncology, Department of Gastroenterology, University Hospitals Leuven, Leuven, Belgium
- Laboratory of Clinical Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
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31
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Otto DJ, Jordan C, Dury B, Dien C, Setty M. Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon. Nat Methods 2024; 21:1185-1195. [PMID: 38890426 DOI: 10.1038/s41592-024-02302-w] [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: 07/11/2023] [Accepted: 05/08/2024] [Indexed: 06/20/2024]
Abstract
Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.
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Affiliation(s)
- Dominik J Otto
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Cailin Jordan
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Brennan Dury
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christine Dien
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Manu Setty
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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32
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Sun K, Liu X, Xu R, Liu C, Meng A, Lan X. Mapping the chromatin accessibility landscape of zebrafish embryogenesis at single-cell resolution by SPATAC-seq. Nat Cell Biol 2024; 26:1187-1199. [PMID: 38977847 DOI: 10.1038/s41556-024-01449-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/30/2024] [Indexed: 07/10/2024]
Abstract
Currently, the dynamic accessible elements that determine regulatory programs responsible for the unique identity and function of each cell type during zebrafish embryogenesis lack detailed study. Here we present SPATAC-seq: a split-pool ligation-based assay for transposase-accessible chromatin using sequencing. Using SPATAC-seq, we profiled chromatin accessibility in more than 800,000 individual nuclei across 20 developmental stages spanning the sphere stage to the early larval protruding mouth stage. Using this chromatin accessibility map, we identified 604 cell states and inferred their developmental relationships. We also identified 959,040 candidate cis-regulatory elements (cCREs) and delineated development-specific cCREs, as well as transcription factors defining diverse cell identities. Importantly, enhancer reporter assays confirmed that the majority of tested cCREs exhibited robust enhanced green fluorescent protein expression in restricted cell types or tissues. Finally, we explored gene regulatory programs that drive pigment and notochord cell differentiation. Our work provides a valuable open resource for exploring driver regulators of cell fate decisions in zebrafish embryogenesis.
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Affiliation(s)
- Keyong Sun
- School of Medicine, Tsinghua University, Beijing, China
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Tsinghua University, Beijing, China
| | - Xin Liu
- School of Life Sciences, Tsinghua University, Beijing, China
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China
| | - Runda Xu
- School of Medicine, Tsinghua University, Beijing, China
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China
| | - Chang Liu
- School of Medicine, Tsinghua University, Beijing, China
| | - Anming Meng
- School of Life Sciences, Tsinghua University, Beijing, China.
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China.
| | - Xun Lan
- School of Medicine, Tsinghua University, Beijing, China.
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Tsinghua University, Beijing, China.
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China.
- Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China.
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33
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Zenk F, Fleck JS, Jansen SMJ, Kashanian B, Eisinger B, Santel M, Dupré JS, Camp JG, Treutlein B. Single-cell epigenomic reconstruction of developmental trajectories from pluripotency in human neural organoid systems. Nat Neurosci 2024; 27:1376-1386. [PMID: 38914828 PMCID: PMC11239525 DOI: 10.1038/s41593-024-01652-0] [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: 04/20/2023] [Accepted: 04/17/2024] [Indexed: 06/26/2024]
Abstract
Cell fate progression of pluripotent progenitors is strictly regulated, resulting in high human cell diversity. Epigenetic modifications also orchestrate cell fate restriction. Unveiling the epigenetic mechanisms underlying human cell diversity has been difficult. In this study, we use human brain and retina organoid models and present single-cell profiling of H3K27ac, H3K27me3 and H3K4me3 histone modifications from progenitor to differentiated neural fates to reconstruct the epigenomic trajectories regulating cell identity acquisition. We capture transitions from pluripotency through neuroepithelium to retinal and brain region and cell type specification. Switching of repressive and activating epigenetic modifications can precede and predict cell fate decisions at each stage, providing a temporal census of gene regulatory elements and transcription factors. Removing H3K27me3 at the neuroectoderm stage disrupts fate restriction, resulting in aberrant cell identity acquisition. Our single-cell epigenome-wide map of human neural organoid development serves as a blueprint to explore human cell fate determination.
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Affiliation(s)
- Fides Zenk
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
- Brain Mind Institute, School of Life Sciences EPFL, Lausanne, Switzerland.
| | - Jonas Simon Fleck
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | | | - Bijan Kashanian
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Benedikt Eisinger
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Małgorzata Santel
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Jean-Samuel Dupré
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - J Gray Camp
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
| | - Barbara Treutlein
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
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Liu A, Citu C, Enduru N, Chen X, Manuel AM, Sinha T, Gorski D, Fernandes BS, Yu M, Schulz PE, Simon LM, Soto C, Zhao Z. Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600720. [PMID: 38979371 PMCID: PMC11230393 DOI: 10.1101/2024.06.25.600720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate cis- regulatory elements (cCREs) across brain regions. We prioritized seven conservative transcription factors in glial cells in multiple brain regions, including RFX4 in astrocytes and IKZF1 in microglia, which are implicated in regulating sEOAD-associated genes. Moreover, we identified the top 25 altered intercellular signaling between glial cells and neurons, highlighting their regulatory potential on gene expression in receiver cells. We reported 38 cCREs linked to sEOAD-associated genes overlapped with late-onset AD risk loci, and sEOAD cCREs enriched in neuropsychiatric disorder risk loci. This atlas helps dissect transcriptional and chromatin dynamics in sEOAD, providing a key resource for AD research.
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Yang Y, Pe’er D. REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data. Bioinformatics 2024; 40:i567-i575. [PMID: 38940155 PMCID: PMC11211829 DOI: 10.1093/bioinformatics/btae234] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Profiling of gene expression and chromatin accessibility by single-cell multi-omics approaches can help to systematically decipher how transcription factors (TFs) regulate target gene expression via cis-region interactions. However, integrating information from different modalities to discover regulatory associations is challenging, in part because motif scanning approaches miss many likely TF binding sites. RESULTS We develop REUNION, a framework for predicting genome-wide TF binding and cis-region-TF-gene "triplet" regulatory associations using single-cell multi-omics data. The first component of REUNION, Unify, utilizes information theory-inspired complementary score functions that incorporate TF expression, chromatin accessibility, and target gene expression to identify regulatory associations. The second component, Rediscover, takes Unify estimates as input for pseudo semi-supervised learning to predict TF binding in accessible genomic regions that may or may not include detected TF motifs. Rediscover leverages latent chromatin accessibility and sequence feature spaces of the genomic regions, without requiring chromatin immunoprecipitation data for model training. Applied to peripheral blood mononuclear cell data, REUNION outperforms alternative methods in TF binding prediction on average performance. In particular, it recovers missing region-TF associations from regions lacking detected motifs, which circumvents the reliance on motif scanning and facilitates discovery of novel associations involving potential co-binding transcriptional regulators. Newly identified region-TF associations, even in regions lacking a detected motif, improve the prediction of target gene expression in regulatory triplets, and are thus likely to genuinely participate in the regulation. AVAILABILITY AND IMPLEMENTATION All source code is available at https://github.com/yangymargaret/REUNION.
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Affiliation(s)
- Yang Yang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, United States
| | - Dana Pe’er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, United States
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Jiang J, Hiron TK, Agbaedeng TA, Malhotra Y, Drydale E, Bancroft J, Ng E, Reschen ME, Davison LJ, O’Callaghan CA. A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease. Circ Res 2024; 135:6-25. [PMID: 38747151 PMCID: PMC11191562 DOI: 10.1161/circresaha.123.324172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types such as macrophages. METHODS We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo. RESULTS We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus. CONCLUSIONS Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.
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Affiliation(s)
- Jiahao Jiang
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Thomas K. Hiron
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Thomas A. Agbaedeng
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Yashaswat Malhotra
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Edward Drydale
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - James Bancroft
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Esther Ng
- Nuffield Department of Orthopaedics, Kennedy Institute of Rheumatology, Rheumatology and Musculoskeletal Sciences (E.N.), University of Oxford, United Kingdom
| | - Michael E. Reschen
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, United Kingdom (M.E.R.)
| | - Lucy J. Davison
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
- Department of Clinical Science and Services, Royal Veterinary College, Hatfield, United Kingdom (L.J.D.)
| | - Chris A. O’Callaghan
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
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Zhou M, Zhang H, Bai Z, Mann-Krzisnik D, Wang F, Li Y. Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique. STAR Protoc 2024; 5:103066. [PMID: 38748882 PMCID: PMC11109308 DOI: 10.1016/j.xpro.2024.103066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/21/2023] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.
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Affiliation(s)
- Manqi Zhou
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA; Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY 10021, USA
| | - Hao Zhang
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10021, USA
| | - Zilong Bai
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY 10021, USA; Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10021, USA
| | | | - Fei Wang
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY 10021, USA; Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10021, USA
| | - Yue Li
- Quantitative Life Science, McGill University, Montréal, QC H3A 0G4, Canada; School of Computer Science, McGill University, Montréal, QC H3A 0G4, Canada; Mila - Quebec AI Institute, Montréal, QC H2S 3H1, Canada.
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Tian Y, Wu L, Huang CC, Wang L. Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599704. [PMID: 38948854 PMCID: PMC11213234 DOI: 10.1101/2024.06.19.599704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
While genome-wide association studies and expression quantitative trait loci (eQTL) analysis have made significant progress in identifying noncoding variants associated with prostate cancer risk and bulk tissue transcriptome changes, the regulatory effect of these genetic elements on gene expression remains largely unknown. Recent developments in single-cell sequencing have made it possible to perform ATAC-seq and RNA-seq profiling simultaneously to capture functional associations between chromatin accessibility and gene expression. In this study, we tested our hypothesis that this multiome single-cell approach allows for mapping regulatory elements and their target genes at prostate cancer risk loci. We applied a 10X Multiome ATAC + Gene Expression platform to encapsulate Tn5 transposase-tagged nuclei from multiple prostate cell lines for a total of 65,501 high quality single cells from RWPE1, RWPE2, PrEC, BPH1, DU145, PC3, 22Rv1 and LNCaP cell lines. To address data sparsity commonly seen in the single-cell sequencing, we performed targeted sequencing to enrich sequencing data at prostate cancer risk loci involving 2,730 candidate germline variants and 273 associated genes. Although not increasing the number of captured cells, the targeted multiome data did improve eQTL gene expression abundance by about 20% and chromatin accessibility abundance by about 5%. Based on this multiomic profiling, we further associated RNA expression alterations with chromatin accessibility of germline variants at single cell levels. Cross validation analysis showed high overlaps between the multiome associations and the bulk eQTL findings from GTEx prostate cohort. We found that about 20% of GTEx eQTLs were covered within the significant multiome associations (p-value ≤ 0.05, gene abundance percentage ≥ 5%), and roughly 10% of the multiome associations could be identified by significant GTEx eQTLs. We also analyzed accessible regions with available heterozygous SNP reads and observed more frequent association in genomic regions with allelically accessible variants (p = 0.0055). Among these findings were previously reported regulatory variants including rs60464856-RUVBL1 (multiome p-value = 0.0099 in BPH1) and rs7247241-SPINT2 (multiome p-value = 0.0002- 0.0004 in 22Rv1). We also functionally validated a new regulatory SNP and its target gene rs2474694-VPS53 (multiome p-value = 0.00956 in BPH1 and 0.00625 in DU145) by reporter assay and SILAC proteomics sequencing. Taken together, our data demonstrated the feasibility of the multiome single-cell approach for identifying regulatory SNPs and their regulated genes.
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Affiliation(s)
- Yijun Tian
- Department of Tumor Biology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, United States
| | - Lang Wu
- Population Sciences in the Pacific Program, University of Hawai i Cancer Center, University of Hawai i at Mānoa, Honolulu, HI 96813, USA
| | - Chang-Ching Huang
- Zilber College of Public Health, University of Wisconsin, Milwaukee, WI 53226, United States
| | - Liang Wang
- Department of Tumor Biology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, United States
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Pang QY, Chiu YC, Huang RYJ. Regulating epithelial-mesenchymal plasticity from 3D genome organization. Commun Biol 2024; 7:750. [PMID: 38902393 PMCID: PMC11190238 DOI: 10.1038/s42003-024-06441-w] [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: 09/26/2022] [Accepted: 06/11/2024] [Indexed: 06/22/2024] Open
Abstract
Epithelial-mesenchymal transition (EMT) is a dynamic process enabling polarized epithelial cells to acquire mesenchymal features implicated in development and carcinoma progression. As our understanding evolves, it is clear the reversible execution of EMT arises from complex epigenomic regulation involving histone modifications and 3-dimensional (3D) genome structural changes, leading to a cascade of transcriptional events. This review summarizes current knowledge on chromatin organization in EMT, with a focus on hierarchical structures of the 3D genome and chromatin accessibility changes.
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Affiliation(s)
- Qing You Pang
- Neuro-Oncology Research Laboratory, National Neuroscience Institute, Singapore, 308433, Singapore
| | - Yi-Chia Chiu
- School of Medicine, College of Medicine, National Taiwan University, Taipei, 10051, Taiwan
| | - Ruby Yun-Ju Huang
- School of Medicine, College of Medicine, National Taiwan University, Taipei, 10051, Taiwan.
- Center for Advanced Computing and Imaging in Biomedicine, National Taiwan University, Taipei, 10051, Taiwan.
- Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119077, Singapore.
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40
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Li H, Humphreys BD. Protocol for multimodal profiling of human kidneys with simultaneous high-throughput ATAC and RNA expression with sequencing. STAR Protoc 2024; 5:103049. [PMID: 38900631 PMCID: PMC11239685 DOI: 10.1016/j.xpro.2024.103049] [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: 03/05/2024] [Revised: 03/31/2024] [Accepted: 04/15/2024] [Indexed: 06/22/2024] Open
Abstract
Simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq) profiles transcriptomics and chromatin accessibility in the same cells at high throughput. Here, we present a protocol for multimodal profiling of human kidneys with SHARE-seq. We describe steps for processing fixed nuclei for SHARE-seq split-pool barcoding and library preparation. We also detail how to determine the optimal working concentration of Tn5 transposase for transposition and tagmentation. This protocol allows researchers to generate large-scale single-cell multiomics data at low reagent cost. For complete details on the use and execution of this protocol, please refer to Li et al.1.
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Affiliation(s)
- Haikuo Li
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63105, USA.
| | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO 63105, USA; Department of Developmental Biology, Washington University in St. Louis, St. Louis, MO 63105, USA.
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Regner MJ, Garcia-Recio S, Thennavan A, Wisniewska K, Mendez-Giraldez R, Felsheim B, Spanheimer PM, Parker JS, Perou CM, Franco HL. Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.13.598858. [PMID: 38948758 PMCID: PMC11212881 DOI: 10.1101/2024.06.13.598858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.
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Affiliation(s)
- Matthew J. Regner
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Susana Garcia-Recio
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Aatish Thennavan
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX, USA, 77030
| | - Kamila Wisniewska
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Raul Mendez-Giraldez
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Brooke Felsheim
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Philip M. Spanheimer
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Joel S. Parker
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Charles M. Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Hector L. Franco
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Genetics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Division of Clinical and Translational Cancer Research, University of Puerto Rico Comprehensive Cancer Center, San Juan, PR 00935
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Chen Y, Wang Z, Qu X, Song B, Tang Y, Li B, Cao G, Yi G. An intronic SNP affects skeletal muscle development by regulating the expression of TP63. Front Vet Sci 2024; 11:1396766. [PMID: 38933706 PMCID: PMC11199888 DOI: 10.3389/fvets.2024.1396766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
Background Porcine skeletal muscle development is pivotal for improving meat production. TP63, a transcription factor, regulates vital cellular processes, yet its role in skeletal muscle proliferation is unclear. Methods The effects of TP63 on skeletal muscle cell viability and proliferation were investigated using both mouse and porcine skeletal muscle myoblasts. Selective sweep analysis in Western pigs identified TP63 as a potential candidate gene for skeletal muscle development. The correlation between TP63 overexpression and cell proliferation was assessed using quantitative real-time PCR (RT-qPCR) and 5-ethynyl-2'-deoxyuridine (EDU). Results The study revealed a positive correlation between TP63 overexpression and skeletal muscle cell proliferation. Bioinformatics analysis predicted an interaction between MEF2A, another transcription factor, and the mutation site of TP63. Experimental validation through dual-luciferase assays confirmed that a candidate enhancer SNP could influence MEF2A binding, subsequently regulating TP63 expression and promoting skeletal muscle cell proliferation. Conclusion These findings offer experimental evidence for further exploration of skeletal muscle development mechanisms and the advancement of genetic breeding strategies aimed at improving meat production traits.
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Affiliation(s)
- Yufen Chen
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- College of Animal Science, Shanxi Agricultural University, Jinzhong, China
| | - Zhen Wang
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Xiaolu Qu
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Bangmin Song
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yueting Tang
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Bugao Li
- College of Animal Science, Shanxi Agricultural University, Jinzhong, China
| | - Guoqing Cao
- College of Animal Science, Shanxi Agricultural University, Jinzhong, China
| | - Guoqiang Yi
- Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Kunpeng Institute of Modern Agriculture at Foshan, Chinese Academy of Agricultural Sciences, Foshan, China
- Bama Yao Autonomous County Rural Revitalization Research Institute, Bama, China
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Ma Y, Guo S, Chen Y, Peng Y, Su X, Jiang H, Lin X, Zhang J. Single-nucleus chromatin landscape dataset of mouse brain development and aging. Sci Data 2024; 11:616. [PMID: 38866804 PMCID: PMC11169343 DOI: 10.1038/s41597-024-03382-1] [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: 02/26/2024] [Accepted: 05/15/2024] [Indexed: 06/14/2024] Open
Abstract
The development and aging of the brain constitute a lifelong dynamic process, marked by structural and functional changes that entail highly coordinated cellular differentiation and epigenetic regulatory mechanisms. Chromatin accessibility serves as the foundational basis for genetic activity. However, the holistic and dynamic chromatin landscape that spans various brain regions throughout development and ageing remains predominantly unexplored. In this study, we employed single-nucleus ATAC-seq to generate comprehensive chromatin accessibility maps, incorporating data from 69,178 cells obtained from four distinct brain regions - namely, the olfactory bulb (OB), cerebellum (CB), prefrontal cortex (PFC), and hippocampus (HP) - across key developmental time points at 7 P, 3 M, 12 M, and 18 M. We delineated the distribution of cell types across different age stages and brain regions, providing insight into chromatin accessible regions and key transcription factors specific to different cell types. Our data contribute to understanding the epigenetic basis of the formation of different brain regions, providing a dynamic landscape and comprehensive resource for revealing gene regulatory programs during brain development and aging.
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Affiliation(s)
- Yuting Ma
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, 050035, China
- BGI Genomics, Shenzhen, 518083, China
| | - Sicheng Guo
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, 050035, China
- BGI Genomics, Shenzhen, 518083, China
| | - Yixi Chen
- BGI Research, Shenzhen, 518083, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | | | - Xi Su
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, 050035, China
- BGI Genomics, Shenzhen, 518083, China
| | - Hui Jiang
- BGI Genomics, Shenzhen, 518083, China
| | - Xiumei Lin
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
- BGI Research, Shenzhen, 518083, China.
| | - Jianguo Zhang
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, 050035, China.
- BGI Genomics, Shenzhen, 518083, China.
- BGI Research, Shenzhen, 518083, China.
- School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China.
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Hua H, Wang Y, Wang X, Wang S, Zhou Y, Liu Y, Liang Z, Ren H, Lu S, Wu S, Jiang Y, Pu Y, Zheng X, Tang C, Shen Z, Li C, Du Y, Deng H. Remodeling ceramide homeostasis promotes functional maturation of human pluripotent stem cell-derived β cells. Cell Stem Cell 2024; 31:850-865.e10. [PMID: 38697109 DOI: 10.1016/j.stem.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] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 03/21/2024] [Accepted: 04/12/2024] [Indexed: 05/04/2024]
Abstract
Human pluripotent stem cell-derived β cells (hPSC-β cells) show the potential to restore euglycemia. However, the immature functionality of hPSC-β cells has limited their efficacy in application. Here, by deciphering the continuous maturation process of hPSC-β cells post transplantation via single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), we show that functional maturation of hPSC-β cells is an orderly multistep process during which cells sequentially undergo metabolic adaption, removal of negative regulators of cell function, and establishment of a more specialized transcriptome and epigenome. Importantly, remodeling lipid metabolism, especially downregulating the metabolic activity of ceramides, the central hub of sphingolipid metabolism, is critical for β cell maturation. Limiting intracellular accumulation of ceramides in hPSC-β cells remarkably enhanced their function, as indicated by improvements in insulin processing and glucose-stimulated insulin secretion. In summary, our findings provide insights into the maturation of human pancreatic β cells and highlight the importance of ceramide homeostasis in function acquisition.
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Affiliation(s)
- Huijuan Hua
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Yaqi Wang
- School of Life Sciences, Center for Bioinformatics, Center for Statistical Science, Peking University, Beijing, China
| | | | - Shusen Wang
- Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
| | - Yunlu Zhou
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Yinan Liu
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Zhen Liang
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Huixia Ren
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Sufang Lu
- Hangzhou Reprogenix Bioscience, Hangzhou, China
| | | | - Yong Jiang
- Hangzhou Reprogenix Bioscience, Hangzhou, China
| | - Yue Pu
- Hangzhou Reprogenix Bioscience, Hangzhou, China
| | - Xiang Zheng
- Hangzhou Repugene Technology, Hangzhou, China
| | - Chao Tang
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Zhongyang Shen
- Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
| | - Cheng Li
- School of Life Sciences, Center for Bioinformatics, Center for Statistical Science, Peking University, Beijing, China.
| | - Yuanyuan Du
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
| | - Hongkui Deng
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China; Changping Laboratory, Beijing, China.
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45
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Wagle MM, Long S, Chen C, Liu C, Yang P. Interpretable deep learning in single-cell omics. Bioinformatics 2024; 40:btae374. [PMID: 38889275 PMCID: PMC11211213 DOI: 10.1093/bioinformatics/btae374] [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: 02/25/2024] [Revised: 05/11/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them 'black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations. RESULTS In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions.
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Affiliation(s)
- Manoj M Wagle
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Siqu Long
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Carissa Chen
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Chunlei Liu
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Pengyi Yang
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia
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46
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Jindal K, Adil MT, Yamaguchi N, Yang X, Wang HC, Kamimoto K, Rivera-Gonzalez GC, Morris SA. Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes. Nat Biotechnol 2024; 42:946-959. [PMID: 37749269 PMCID: PMC11180607 DOI: 10.1038/s41587-023-01931-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 07/31/2023] [Indexed: 09/27/2023]
Abstract
Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage-tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics. However, reliance on transcriptional profiling limits adaptation to other single-cell assays. With CellTag-multi, we present an approach that enables direct capture of heritable random barcodes expressed as polyadenylated transcripts, in both single-cell RNA sequencing and single-cell Assay for Transposase Accessible Chromatin using sequencing assays, allowing for independent clonal tracking of transcriptional and epigenomic cell states. We validate CellTag-multi to characterize progenitor cell lineage priming during mouse hematopoiesis. Additionally, in direct reprogramming of fibroblasts to endoderm progenitors, we identify core regulatory programs underlying on-target and off-target fates. Furthermore, we reveal the transcription factor Zfp281 as a regulator of reprogramming outcome, biasing cells toward an off-target mesenchymal fate. Our results establish CellTag-multi as a lineage-tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.
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Affiliation(s)
- Kunal Jindal
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Mohd Tayyab Adil
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Naoto Yamaguchi
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Xue Yang
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Helen C Wang
- Department of Pediatrics, Division of Hematology and Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kenji Kamimoto
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Guillermo C Rivera-Gonzalez
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Samantha A Morris
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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47
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Liu Q, Ma W, Chen R, Li S, Wang Q, Wei C, Hong Y, Sun H, Cheng Q, Zhao J, Kang J. Multiome in the Same Cell Reveals the Impact of Osmotic Stress on Arabidopsis Root Tip Development at Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308384. [PMID: 38634607 PMCID: PMC11199978 DOI: 10.1002/advs.202308384] [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: 11/04/2023] [Revised: 02/27/2024] [Indexed: 04/19/2024]
Abstract
Cell-specific transcriptional regulatory networks (TRNs) play vital roles in plant development and response to environmental stresses. However, traditional single-cell mono-omics techniques are unable to directly capture the relationships and dynamics between different layers of molecular information within the same cells. While advanced algorithm facilitates merging scRNA-seq and scATAC-seq datasets, accurate data integration remains a challenge, particularly when investigating cell-type-specific TRNs. By examining gene expression and chromatin accessibility simultaneously in 16,670 Arabidopsis root tip nuclei, the TRNs are reconstructed that govern root tip development under osmotic stress. In contrast to commonly used computational integration at cell-type level, 12,968 peak-to-gene linkage is captured at the bona fide single-cell level and construct TRNs at an unprecedented resolution. Furthermore, the unprecedented datasets allow to more accurately reconstruct the coordinated changes of gene expression and chromatin states during cellular state transition. During root tip development, chromatin accessibility of initial cells precedes gene expression, suggesting that changes in chromatin accessibility may prime cells for subsequent differentiation steps. Pseudo-time trajectory analysis reveal that osmotic stress can shift the functional differentiation of trichoblast. Candidate stress-related gene-linked cis-regulatory elements (gl-cCREs) as well as potential target genes are also identified, and uncovered large cellular heterogeneity under osmotic stress.
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Affiliation(s)
- Qing Liu
- State Key Laboratory of North China Crop Improvement and RegulationKey Laboratory of Vegetable Germplasm Innovation and Utilization of HebeiMinistry of Education of China‐Hebei Province Joint Innovation Center for Efficient Green Vegetable IndustryInternational Joint R & D Center of Hebei Province in Modern Agricultural BiotechnologyCollege of Life SciencesCollege of HorticultureHebei Agricultural UniversityBaoding071000China
| | - Wei Ma
- State Key Laboratory of North China Crop Improvement and RegulationKey Laboratory of Vegetable Germplasm Innovation and Utilization of HebeiMinistry of Education of China‐Hebei Province Joint Innovation Center for Efficient Green Vegetable IndustryInternational Joint R & D Center of Hebei Province in Modern Agricultural BiotechnologyCollege of Life SciencesCollege of HorticultureHebei Agricultural UniversityBaoding071000China
| | - Ruiying Chen
- BGI ResearchBeijing102601China
- BGI ResearchShenzhen518083China
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | | | - Qifan Wang
- State Key Laboratory of North China Crop Improvement and RegulationKey Laboratory of Vegetable Germplasm Innovation and Utilization of HebeiMinistry of Education of China‐Hebei Province Joint Innovation Center for Efficient Green Vegetable IndustryInternational Joint R & D Center of Hebei Province in Modern Agricultural BiotechnologyCollege of Life SciencesCollege of HorticultureHebei Agricultural UniversityBaoding071000China
| | - Cai Wei
- BGI ResearchBeijing102601China
| | - Yiguo Hong
- State Key Laboratory of North China Crop Improvement and RegulationKey Laboratory of Vegetable Germplasm Innovation and Utilization of HebeiMinistry of Education of China‐Hebei Province Joint Innovation Center for Efficient Green Vegetable IndustryInternational Joint R & D Center of Hebei Province in Modern Agricultural BiotechnologyCollege of Life SciencesCollege of HorticultureHebei Agricultural UniversityBaoding071000China
- School of Life SciencesUniversity of WarwickCoventryCV4 7ALUK
| | - Hai‐Xi Sun
- BGI ResearchBeijing102601China
- BGI ResearchShenzhen518083China
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijing100049China
| | - Qi Cheng
- State Key Laboratory of North China Crop Improvement and RegulationKey Laboratory of Vegetable Germplasm Innovation and Utilization of HebeiMinistry of Education of China‐Hebei Province Joint Innovation Center for Efficient Green Vegetable IndustryInternational Joint R & D Center of Hebei Province in Modern Agricultural BiotechnologyCollege of Life SciencesCollege of HorticultureHebei Agricultural UniversityBaoding071000China
| | - Jianjun Zhao
- State Key Laboratory of North China Crop Improvement and RegulationKey Laboratory of Vegetable Germplasm Innovation and Utilization of HebeiMinistry of Education of China‐Hebei Province Joint Innovation Center for Efficient Green Vegetable IndustryInternational Joint R & D Center of Hebei Province in Modern Agricultural BiotechnologyCollege of Life SciencesCollege of HorticultureHebei Agricultural UniversityBaoding071000China
| | - Jingmin Kang
- BGI ResearchBeijing102601China
- BGI ResearchShenzhen518083China
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48
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Zhou P, Bocci F, Li T, Nie Q. Spatial transition tensor of single cells. Nat Methods 2024; 21:1053-1062. [PMID: 38755322 PMCID: PMC11166574 DOI: 10.1038/s41592-024-02266-x] [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: 07/03/2023] [Accepted: 04/02/2024] [Indexed: 05/18/2024]
Abstract
Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.
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Affiliation(s)
- Peijie Zhou
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- Center for Machine Learning Research, Peking University, Beijing, China
- AI for Science Institute, Beijing, China
- National Engineering Laboratory for Big Data Analysis and Applications, Beijing, China
| | - Federico Bocci
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
| | - Tiejun Li
- LMAM and School of Mathematical Sciences, Peking University, Beijing, China
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- Department of Cell and Developmental Biology, University of California, Irvine, Irvine, CA, USA.
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49
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Chen H, Ryu J, Vinyard ME, Lerer A, Pinello L. SIMBA: single-cell embedding along with features. Nat Methods 2024; 21:1003-1013. [PMID: 37248389 PMCID: PMC11166568 DOI: 10.1038/s41592-023-01899-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).
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Affiliation(s)
- Huidong Chen
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jayoung Ryu
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Michael E Vinyard
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Adam Lerer
- Facebook AI Research, New York, NY, USA.
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
- Department of Pathology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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50
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Shiraishi R, Cancila G, Kumegawa K, Torrejon J, Basili I, Bernardi F, Silva PBGD, Wang W, Chapman O, Yang L, Jami M, Nishitani K, Arai Y, Xiao Z, Yu H, Lo Re V, Marsaud V, Talbot J, Lombard B, Loew D, Jingu M, Okonechnikov K, Sone M, Motohashi N, Aoki Y, Pfister SM, Chavez L, Hoshino M, Maruyama R, Ayrault O, Kawauchi D. Cancer-specific epigenome identifies oncogenic hijacking by nuclear factor I family proteins for medulloblastoma progression. Dev Cell 2024:S1534-5807(24)00330-7. [PMID: 38834071 DOI: 10.1016/j.devcel.2024.05.013] [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: 11/02/2023] [Revised: 03/01/2024] [Accepted: 05/10/2024] [Indexed: 06/06/2024]
Abstract
Normal cells coordinate proliferation and differentiation by precise tuning of gene expression based on the dynamic shifts of the epigenome throughout the developmental timeline. Although non-mutational epigenetic reprogramming is an emerging hallmark of cancer, the epigenomic shifts that occur during the transition from normal to malignant cells remain elusive. Here, we capture the epigenomic changes that occur during tumorigenesis in a prototypic embryonal brain tumor, medulloblastoma. By comparing the epigenomes of the different stages of transforming cells in mice, we identify nuclear factor I family of transcription factors, known to be cell fate determinants in development, as oncogenic regulators in the epigenomes of precancerous and cancerous cells. Furthermore, genetic and pharmacological inhibition of NFIB validated a crucial role of this transcription factor by disrupting the cancer epigenome in medulloblastoma. Thus, this study exemplifies how epigenomic changes contribute to tumorigenesis via non-mutational mechanisms involving developmental transcription factors.
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Affiliation(s)
- Ryo Shiraishi
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan
| | - Gabriele Cancila
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay 91400, France
| | - Kohei Kumegawa
- Cancer Cell Diversity Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo 135-8550, Japan
| | - Jacob Torrejon
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay 91400, France
| | - Irene Basili
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay 91400, France
| | - Flavia Bernardi
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay 91400, France
| | - Patricia Benites Goncalves da Silva
- Hopp Children's Cancer Center Heidelberg (KiTZ), German Cancer Research Center (DKFZ) and Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Wanchen Wang
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan
| | - Owen Chapman
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Liying Yang
- Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo 135-8550, Japan
| | - Maki Jami
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan
| | - Kayo Nishitani
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan
| | - Yukimi Arai
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan
| | - Zhize Xiao
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan
| | - Hua Yu
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay 91400, France
| | - Valentina Lo Re
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay 91400, France
| | - Véronique Marsaud
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay 91400, France
| | - Julie Talbot
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay 91400, France
| | - Bérangère Lombard
- Institut Curie, PSL Research University, CurieCoreTech Mass Spectrometry Proteomics, Paris 75005, France
| | - Damarys Loew
- Institut Curie, PSL Research University, CurieCoreTech Mass Spectrometry Proteomics, Paris 75005, France
| | - Maho Jingu
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan; Department of Biomolecular Science, Graduate School of Science, Toho University, Chiba 274-8510, Japan
| | - Konstantin Okonechnikov
- Hopp Children's Cancer Center Heidelberg (KiTZ), German Cancer Research Center (DKFZ) and Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Masaki Sone
- Department of Biomolecular Science, Graduate School of Science, Toho University, Chiba 274-8510, Japan
| | - Norio Motohashi
- Department of Molecular Therapy, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan
| | - Yoshitsugu Aoki
- Department of Molecular Therapy, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan
| | - Stefan M Pfister
- Hopp Children's Cancer Center Heidelberg (KiTZ), German Cancer Research Center (DKFZ) and Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Lukas Chavez
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Mikio Hoshino
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan
| | - Reo Maruyama
- Cancer Cell Diversity Project, NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo 135-8550, Japan; Project for Cancer Epigenomics, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo 135-8550, Japan.
| | - Olivier Ayrault
- Institut Curie, PSL Research University, CNRS UMR, INSERM, Université Paris Sud, Université Paris-Saclay, CNRS UMR 3347, INSERM U1021, Orsay 91400, France.
| | - Daisuke Kawauchi
- Department of Biochemistry and Cellular Biology, National Center of Neurology and Psychiatry (NCNP), Tokyo 187-8502, Japan.
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