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Xue G, Zhang X, Li W, Zhang L, Zhang Z, Zhou X, Zhang D, Zhang L, Li Z. A logic-incorporated gene regulatory network deciphers principles in cell fate decisions. eLife 2024; 12:RP88742. [PMID: 38652107 PMCID: PMC11037919 DOI: 10.7554/elife.88742] [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] [Indexed: 04/25/2024] Open
Abstract
Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.
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Affiliation(s)
- Gang Xue
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaoyi Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Wanqi Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Zongxu Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Xiaolin Zhou
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Di Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
| | - Lei Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Beijing International Center for Mathematical Research, Center for Machine Learning Research, Peking UniversityBeijingChina
| | - Zhiyuan Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking UniversityBeijingChina
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2
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Wytock TP, Motter AE. Cell reprogramming design by transfer learning of functional transcriptional networks. Proc Natl Acad Sci U S A 2024; 121:e2312942121. [PMID: 38437548 PMCID: PMC10945810 DOI: 10.1073/pnas.2312942121] [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: 07/28/2023] [Accepted: 01/26/2024] [Indexed: 03/06/2024] Open
Abstract
Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cells. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates, thereby generating a model of the network dynamics that can be transferred to specific reprogramming goals. The approach combines transcriptional responses to gene perturbations to minimize the difference between a given pair of initial and target transcriptional states. We demonstrate our approach's versatility by applying it to a microarray dataset comprising >9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases with decreasing developmental relatedness and that fewer genes are needed to progress along developmental paths than to regress. These findings establish a proof-of-concept for our approach to computationally design control strategies and provide insights into how gene regulatory networks govern phenotype.
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Affiliation(s)
- Thomas P. Wytock
- Department of Physics and Astronomy, Northwestern University, Evanston, IL60208
- Center for Network Dynamics, Northwestern University, Evanston, IL60208
| | - Adilson E. Motter
- Department of Physics and Astronomy, Northwestern University, Evanston, IL60208
- Center for Network Dynamics, Northwestern University, Evanston, IL60208
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL60208
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL60208
- National Institute for Theory and Mathematics in Biology, Evanston, IL60208
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3
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Wytock TP, Motter AE. Cell reprogramming design by transfer learning of functional transcriptional networks. ARXIV 2024:arXiv:2403.04837v1. [PMID: 38495570 PMCID: PMC10942484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cell behavior. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates to generate a model of the functional network dynamics that can be transferred to specific reprogramming goals. The approach additively combines transcriptional responses to gene perturbations (single-gene knockdowns and overexpressions) to minimize the transcriptional difference between a given pair of initial and target states. We demonstrate the flexibility of our approach by applying it to a microarray dataset comprising over 9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of over 10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an average AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases as the developmental relatedness decreases. We also show that fewer genes are needed to progress along developmental paths than to regress. Together, these findings establish a proof-of-concept for our approach to computationally design control strategies and demonstrate their ability to provide insights into the dynamics of gene regulatory networks.
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Affiliation(s)
- Thomas P Wytock
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois 60208, USA
- Center for Network Dynamics, Northwestern University, Evanston, Illinois 60208, USA
| | - Adilson E Motter
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois 60208, USA
- Center for Network Dynamics, Northwestern University, Evanston, Illinois 60208, USA
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois 60208, USA
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois 60208, USA
- National Institute for Theory and Mathematics in Biology, Evanston, Illinois 60208, USA
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4
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Henze H, Hüttner SS, Koch P, Schüler SC, Groth M, von Eyss B, von Maltzahn J. Denervation alters the secretome of myofibers and thereby affects muscle stem cell lineage progression and functionality. NPJ Regen Med 2024; 9:10. [PMID: 38424446 PMCID: PMC10904387 DOI: 10.1038/s41536-024-00353-3] [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: 06/27/2023] [Accepted: 02/14/2024] [Indexed: 03/02/2024] Open
Abstract
Skeletal muscle function crucially depends on innervation while repair of skeletal muscle relies on resident muscle stem cells (MuSCs). However, it is poorly understood how innervation affects MuSC properties and thereby regeneration of skeletal muscle. Here, we report that loss of innervation causes precocious activation of MuSCs concomitant with the expression of markers of myogenic differentiation. This aberrant activation of MuSCs after loss of innervation is accompanied by profound alterations on the mRNA and protein level. Combination of muscle injury with loss of innervation results in impaired regeneration of skeletal muscle including shifts in myogenic populations concomitant with delayed maturation of regenerating myofibers. We further demonstrate that loss of innervation leads to alterations in myofibers and their secretome, which then affect MuSC behavior. In particular, we identify an increased secretion of Osteopontin and transforming growth factor beta 1 (Tgfb1) by myofibers isolated from mice which had undergone sciatic nerve transection. The altered secretome results in the upregulation of early activating transcription factors, such as Junb, and their target genes in MuSCs. However, the combination of different secreted factors from myofibers after loss of innervation is required to cause the alterations observed in MuSCs after loss of innervation. These data demonstrate that loss of innervation first affects myofibers causing alterations in their secretome which then affect MuSCs underscoring the importance of proper innervation for MuSC functionality and regeneration of skeletal muscle.
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Affiliation(s)
- Henriette Henze
- Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstrasse 11, 07745, Jena, Germany
| | - Sören S Hüttner
- Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstrasse 11, 07745, Jena, Germany
| | - Philipp Koch
- Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstrasse 11, 07745, Jena, Germany
| | - Svenja C Schüler
- Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstrasse 11, 07745, Jena, Germany
| | - Marco Groth
- Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstrasse 11, 07745, Jena, Germany
| | - Björn von Eyss
- Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstrasse 11, 07745, Jena, Germany
| | - Julia von Maltzahn
- Leibniz Institute on Aging - Fritz Lipmann Institute, Beutenbergstrasse 11, 07745, Jena, Germany.
- Faculty of Health Sciences Brandenburg, Brandenburg University of Technology Cottbus - Senftenberg, Universitätsplatz 1, 01968, Senftenberg, Germany.
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5
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Ritesh KC, de Boer RL, Lin M, Jeannotte L, Philippidou P. Multimodal Hox5 activity generates motor neuron diversity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.08.579338. [PMID: 38370781 PMCID: PMC10871347 DOI: 10.1101/2024.02.08.579338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Motor neurons (MNs) are the final output of circuits driving fundamental behaviors, such as respiration and locomotion. Hox proteins are essential in generating the MN diversity required for accomplishing these functions, but the transcriptional mechanisms that enable Hox paralogs to assign distinct MN subtype identities despite their promiscuous DNA binding motif are not well understood. Here we show that Hoxa5 controls chromatin accessibility in all mouse spinal cervical MN subtypes and engages TALE co-factors to directly bind and regulate subtype-specific genes. We identify a paralog-specific interaction of Hoxa5 with the phrenic MN-specific transcription factor Scip and show that heterologous expression of Hoxa5 and Scip is sufficient to suppress limb-innervating MN identity. We also demonstrate that phrenic MN identity is stable after Hoxa5 downregulation and identify Klf proteins as potential regulators of phrenic MN maintenance. Our data identify multiple modes of Hoxa5 action that converge to induce and maintain MN identity.
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Affiliation(s)
- K C Ritesh
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, USA
| | - Raquel López de Boer
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, USA
| | - Minshan Lin
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, USA
| | - Lucie Jeannotte
- Department of Molecular Biology, Medical Biochemistry & Pathology, Université Laval, Centre Recherche sur le Cancer de l'Université Laval, Centre de recherche du CHU de Québec-Université Laval (Oncology), Québec, Canada
| | - Polyxeni Philippidou
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, USA
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Tangeman JA, Rebull SM, Grajales-Esquivel E, Weaver JM, Bendezu-Sayas S, Robinson ML, Lachke SA, Del Rio-Tsonis K. Integrated single-cell multiomics uncovers foundational regulatory mechanisms of lens development and pathology. Development 2024; 151:dev202249. [PMID: 38180241 PMCID: PMC10906490 DOI: 10.1242/dev.202249] [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: 08/09/2023] [Accepted: 11/28/2023] [Indexed: 01/06/2024]
Abstract
Ocular lens development entails epithelial to fiber cell differentiation, defects in which cause congenital cataracts. We report the first single-cell multiomic atlas of lens development, leveraging snRNA-seq, snATAC-seq and CUT&RUN-seq to discover previously unreported mechanisms of cell fate determination and cataract-linked regulatory networks. A comprehensive profile of cis- and trans-regulatory interactions, including for the cataract-linked transcription factor MAF, is established across a temporal trajectory of fiber cell differentiation. Furthermore, we identify an epigenetic paradigm of cellular differentiation, defined by progressive loss of the H3K27 methylation writer Polycomb repressive complex 2 (PRC2). PRC2 localizes to heterochromatin domains across master-regulator transcription factor gene bodies, suggesting it safeguards epithelial cell fate. Moreover, we demonstrate that FGF hyper-stimulation in vivo leads to MAF network activation and the emergence of novel lens cell states. Collectively, these data depict a comprehensive portrait of lens fiber cell differentiation, while defining regulatory effectors of cell identity and cataract formation.
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Affiliation(s)
- Jared A. Tangeman
- Department of Biology and Center for Visual Sciences, Miami University, Oxford, OH 45056, USA
- Cell, Molecular, and Structural Biology Program, Miami University, Oxford, OH 45056, USA
| | - Sofia M. Rebull
- Department of Biology and Center for Visual Sciences, Miami University, Oxford, OH 45056, USA
| | - Erika Grajales-Esquivel
- Department of Biology and Center for Visual Sciences, Miami University, Oxford, OH 45056, USA
| | - Jacob M. Weaver
- Department of Biology and Center for Visual Sciences, Miami University, Oxford, OH 45056, USA
- Cell, Molecular, and Structural Biology Program, Miami University, Oxford, OH 45056, USA
| | - Stacy Bendezu-Sayas
- Department of Biology and Center for Visual Sciences, Miami University, Oxford, OH 45056, USA
- Cell, Molecular, and Structural Biology Program, Miami University, Oxford, OH 45056, USA
| | - Michael L. Robinson
- Department of Biology and Center for Visual Sciences, Miami University, Oxford, OH 45056, USA
- Cell, Molecular, and Structural Biology Program, Miami University, Oxford, OH 45056, USA
| | - Salil A. Lachke
- Department of Biological Sciences, University of Delaware, Newark, DE 19716, USA
- Center for Bioinformatics & Computational Biology, University of Delaware, Newark, DE 19713, USA
| | - Katia Del Rio-Tsonis
- Department of Biology and Center for Visual Sciences, Miami University, Oxford, OH 45056, USA
- Cell, Molecular, and Structural Biology Program, Miami University, Oxford, OH 45056, USA
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7
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Sandoval L, Mohammed Ismail W, Mazzone A, Dumbrava M, Fernandez J, Munankarmy A, Lasho T, Binder M, Simon V, Kim KH, Chia N, Lee JH, Weroha SJ, Patnaik M, Gaspar-Maia A. Characterization and Optimization of Multiomic Single-Cell Epigenomic Profiling. Genes (Basel) 2023; 14:1245. [PMID: 37372428 PMCID: PMC10297939 DOI: 10.3390/genes14061245] [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: 04/30/2023] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
The snATAC + snRNA platform allows epigenomic profiling of open chromatin and gene expression with single-cell resolution. The most critical assay step is to isolate high-quality nuclei to proceed with droplet-base single nuclei isolation and barcoding. With the increasing popularity of multiomic profiling in various fields, there is a need for optimized and reliable nuclei isolation methods, mainly for human tissue samples. Herein we compared different nuclei isolation methods for cell suspensions, such as peripheral blood mononuclear cells (PBMC, n = 18) and a solid tumor type, ovarian cancer (OC, n = 18), derived from debulking surgery. Nuclei morphology and sequencing output parameters were used to evaluate the quality of preparation. Our results show that NP-40 detergent-based nuclei isolation yields better sequencing results than collagenase tissue dissociation for OC, significantly impacting cell type identification and analysis. Given the utility of applying such techniques to frozen samples, we also tested frozen preparation and digestion (n = 6). A paired comparison between frozen and fresh samples validated the quality of both specimens. Finally, we demonstrate the reproducibility of scRNA and snATAC + snRNA platform, by comparing the gene expression profiling of PBMC. Our results highlight how the choice of nuclei isolation methods is critical for obtaining quality data in multiomic assays. It also shows that the measurement of expression between scRNA and snRNA is comparable and effective for cell type identification.
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Affiliation(s)
- Leticia Sandoval
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Wazim Mohammed Ismail
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Amelia Mazzone
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Mihai Dumbrava
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
- Mayo Clinic Medical Scientist Training Program, Mayo Clinic Alix School of Medicine and Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Jenna Fernandez
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.F.); (T.L.)
| | - Amik Munankarmy
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Terra Lasho
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.F.); (T.L.)
| | - Moritz Binder
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.F.); (T.L.)
| | - Vernadette Simon
- Medical Genome Facility, Genome Analysis Core, Mayo Clinic, Rochester, MN 55905, USA;
| | - Kwan Hyun Kim
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - Nicholas Chia
- Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Jeong-Heon Lee
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
| | - S. John Weroha
- Department of Medical Oncology, Mayo Clinic, Rochester, MN 55905, USA;
| | - Mrinal Patnaik
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
- Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA; (J.F.); (T.L.)
| | - Alexandre Gaspar-Maia
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; (L.S.); (W.M.I.); (A.M.); (M.D.); (A.M.); (J.-H.L.)
- Epigenomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA; (M.B.); (K.H.K.); (M.P.)
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Naval-Sanchez M, Deshpande N, Tran M, Zhang J, Alhomrani M, Alsanie W, Nguyen Q, Nefzger CM. Benchmarking of ATAC Sequencing Data From BGI’s Low-Cost DNBSEQ-G400 Instrument for Identification of Open and Occupied Chromatin Regions. Front Mol Biosci 2022; 9:900323. [PMID: 35874611 PMCID: PMC9302965 DOI: 10.3389/fmolb.2022.900323] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 05/18/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Chromatin falls into one of two major subtypes: closed heterochromatin and euchromatin which is accessible, transcriptionally active, and occupied by transcription factors (TFs). The most widely used approach to interrogate differences in the chromatin state landscape is the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). While library generation is relatively inexpensive, sequencing depth requirements can make this assay cost-prohibitive for some laboratories. Findings: Here, we benchmark data from Beijing Genomics Institute’s (BGI) DNBSEQ-G400 low-cost sequencer against data from a standard Illumina instrument (HiSeqX10). For comparisons, the same bulk ATAC-seq libraries generated from pluripotent stem cells (PSCs) and fibroblasts were sequenced on both platforms. Both instruments generate sequencing reads with comparable mapping rates and genomic context. However, DNBSEQ-G400 data contained a significantly higher number of small, sub-nucleosomal reads (>30% increase) and a reduced number of bi-nucleosomal reads (>75% decrease), which resulted in narrower peak bases and improved peak calling, enabling the identification of 4% more differentially accessible regions between PSCs and fibroblasts. The ability to identify master TFs that underpin the PSC state relative to fibroblasts (via HOMER, HINT-ATAC, TOBIAS), namely, foot-printing capacity, were highly similar between data generated on both platforms. Integrative analysis with transcriptional data equally enabled direct recovery of three published 3-factor combinations that have been shown to induce pluripotency. Conclusion: Other than a small increase in peak calling sensitivity for DNBSEQ-G400 data (BGI), both platforms enable comparable levels of open chromatin identification for ATAC-seq library sequencing, yielding similar analytical outcomes, albeit at low-data generation costs in the case of the BGI instrument.
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Affiliation(s)
- Marina Naval-Sanchez
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
- *Correspondence: Marina Naval-Sanchez, ; Christian M. Nefzger,
| | - Nikita Deshpande
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
| | - Minh Tran
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
| | - Jingyu Zhang
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
| | - Majid Alhomrani
- Department of Clinical Laboratories Sciences, Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
- Centre of Biomedical Sciences Research (CBSR), Deanship of Scientific Research, Taif University, Taif, Saudi Arabia
| | - Walaa Alsanie
- Department of Clinical Laboratories Sciences, Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
- Centre of Biomedical Sciences Research (CBSR), Deanship of Scientific Research, Taif University, Taif, Saudi Arabia
| | - Quan Nguyen
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
| | - Christian M. Nefzger
- Institute for Molecular Bioscience, University of Queensland, St Lucia, QLD, Australia
- *Correspondence: Marina Naval-Sanchez, ; Christian M. Nefzger,
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