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Wu S, Morotti ALM, Yang J, Wang E, Tatsis EC. Single-cell RNA sequencing facilitates the elucidation of the complete biosynthesis of the antidepressant hyperforin in St. John's wort. MOLECULAR PLANT 2024:S1674-2052(24)00258-2. [PMID: 39135343 DOI: 10.1016/j.molp.2024.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 08/05/2024] [Accepted: 08/07/2024] [Indexed: 08/27/2024]
Abstract
Hyperforin is the compound responsible for the effectiveness of St. John's wort (Hypericum perforatum) as an antidepressant, but its complete biosynthetic pathway remains unknown. Gene discovery based on co-expression analysis of bulk RNA-sequencing data or genome mining failed to discover the missing steps in hyperforin biosynthesis. In this study, we sequenced the 1.54-Gb tetraploid H. perforatum genome assembled into 32 chromosomes with the scaffold N50 value of 42.44 Mb. By single-cell RNA sequencing, we identified a type of cell, "Hyper cells", wherein hyperforin biosynthesis de novo takes place in both the leaves and flowers. Through pathway reconstitution in yeast and tobacco, we identified and characterized four transmembrane prenyltransferases (HpPT1-4) that are localized at the plastid envelope and complete the hyperforin biosynthetic pathway. The hyperforin polycyclic scaffold is created by a reaction cascade involving an irregular isoprenoid coupling and a tandem cyclization. Our findings reveal how and where hyperforin is biosynthesized, enabling synthetic-biology reconstitution of the complete pathway. Thus, this study not only deepens our comprehension of specialized metabolism at the cellular level but also provides strategic guidance for elucidation of the biosynthetic pathways of other specializied metabolites in plants.
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Affiliation(s)
- Song Wu
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China; University of Chinese Academy of Sciences, Shanghai 200032, China
| | - Ana Luisa Malaco Morotti
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Jun Yang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Ertao Wang
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Evangelos C Tatsis
- National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China; CEPAMS - CAS-JIC Centre of Excellence for Plant and Microbial Science, Shanghai 200032, China.
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2
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Barrios EL, Rincon JC, Willis M, Polcz VE, Leary JR, Darden DB, Balch JA, Larson SD, Loftus TJ, Mohr AM, Wallet S, Brusko MA, Balzano-Nogueira L, Cai G, Sharma A, Upchurch GR, Kladde MP, Mathews CE, Maile R, Moldawer LL, Bacher R, Efron PA. TRANSCRIPTOMIC DIFFERENCES IN PERIPHERAL MONOCYTE POPULATIONS IN SEPTIC PATIENTS BASED ON OUTCOME. Shock 2024; 62:208-216. [PMID: 38713581 DOI: 10.1097/shk.0000000000002379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Abstract
ABSTRACT Postsepsis early mortality is being replaced by survivors who experience either a rapid recovery and favorable hospital discharge or the development of chronic critical illness with suboptimal outcomes. The underlying immunological response that determines these clinical trajectories remains poorly defined at the transcriptomic level. As classical and nonclassical monocytes are key leukocytes in both the innate and adaptive immune systems, we sought to delineate the transcriptomic response of these cell types. Using single-cell RNA sequencing and pathway analyses, we identified gene expression patterns between these two groups that are consistent with differences in TNF-α production based on clinical outcome. This may provide therapeutic targets for those at risk for chronic critical illness in order to improve their phenotype/endotype, morbidity, and long-term mortality.
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Affiliation(s)
- Evan L Barrios
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Jaimar C Rincon
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Micah Willis
- Department of Oral Biology, College of Dentistry, Gainesville, Florida
| | - Valerie E Polcz
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Jack R Leary
- Department of Biostatistics, College of Medicine, Gainesville, Florida
| | - Dijoia B Darden
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Jeremy A Balch
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Shawn D Larson
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Tyler J Loftus
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Alicia M Mohr
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Shannon Wallet
- Department of Oral Biology, College of Dentistry, Gainesville, Florida
| | - Maigan A Brusko
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Gainesville, Florida
| | | | - Guoshuai Cai
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Ashish Sharma
- Department of Surgery, College of Medicine, Gainesville, Florida
| | | | - Michael P Kladde
- Department of Biochemistry and Molecular Biology, College of Medicine, Gainesville, Florida
| | - Clayton E Mathews
- Department of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Gainesville, Florida
| | - Robert Maile
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Lyle L Moldawer
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
| | - Rhonda Bacher
- Department of Biostatistics, College of Medicine, Gainesville, Florida
| | - Philip A Efron
- Sepsis and Critical Illness Research Center, Department of Surgery, College of Medicine, Gainesville, Florida
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3
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Chamberlin JT, Gillen AE, Quinlan AR. Improved characterization of single-cell RNA-seq libraries with paired-end avidity sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.10.602909. [PMID: 39026715 PMCID: PMC11257511 DOI: 10.1101/2024.07.10.602909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Prevailing poly(dT)-primed 3' single-cell RNA-seq protocols generate barcoded cDNA fragments containing the reverse transcriptase priming site, which is expected to be the poly(A) tail or a genomic adenine homopolymer. Direct sequencing across this priming site was historically difficult because of DNA sequencing errors induced by the homopolymeric primer at the 'barcode' end. Here, we evaluate the capability of "avidity base chemistry" DNA sequencing from Element Biosciences to sequence through this homopolymer accurately, and the impact of the additional cDNA sequence on read alignment and precise quantification of polyadenylation site usage. We find that the Element Aviti instrument sequences through the thymine homopolymer into the subsequent cDNA sequence without detectable loss of accuracy. The resulting paired-end alignments enable direct and independent assignment of reads to polyadenylation sites, which bypasses complexities and limitations of conventional approaches but does not consistently improve read mapping rates compared to single-end alignment. We also characterize low-level artifacts and arrive at an adjusted adapter trimming and alignment workflow that significantly improves the alignment of sequence data from Element and Illumina, particularly in the context of extended read lengths. Our analyses confirm that Element avidity sequencing is an effective alternative to Illumina sequencing for standard single-cell RNA-seq, particularly for polyadenylation site analyses but do not rule out the potential for similar performance from other emerging platforms.
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Affiliation(s)
- John T Chamberlin
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, 84112, USA
| | - Austin E Gillen
- RNA Bioscience Initiative, University of Colorado School of Medicine, Aurora, CO, 80045, USA
- Division of Hematology, University of Colorado School of Medicine, Aurora, CO, 80045, USA
- Rocky Mountain Regional VA Medical Center, Aurora, CO, 80045, USA
| | - Aaron R Quinlan
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, 84112, USA
- Department of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT, 84112, USA
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4
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Kuo A, Hansen KD, Hicks SC. Quantification and statistical modeling of droplet-based single-nucleus RNA-sequencing data. Biostatistics 2024; 25:801-817. [PMID: 37257175 PMCID: PMC11247185 DOI: 10.1093/biostatistics/kxad010] [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: 05/20/2022] [Revised: 03/22/2023] [Accepted: 04/19/2023] [Indexed: 06/02/2023] Open
Abstract
In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.
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Affiliation(s)
- Albert Kuo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Kasper D Hansen
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, 733 N Broadway, Baltimore, MD 21205, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
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5
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He D, Gao Y, Chan SS, Quintana-Parrilla N, Patro R. Forseti: a mechanistic and predictive model of the splicing status of scRNA-seq reads. Bioinformatics 2024; 40:i297-i306. [PMID: 38940130 PMCID: PMC11256924 DOI: 10.1093/bioinformatics/btae207] [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 Short-read single-cell RNA-sequencing (scRNA-seq) has been used to study cellular heterogeneity, cellular fate, and transcriptional dynamics. Modeling splicing dynamics in scRNA-seq data is challenging, with inherent difficulty in even the seemingly straightforward task of elucidating the splicing status of the molecules from which sequenced fragments are drawn. This difficulty arises, in part, from the limited read length and positional biases, which substantially reduce the specificity of the sequenced fragments. As a result, the splicing status of many reads in scRNA-seq is ambiguous because of a lack of definitive evidence. We are therefore in need of methods that can recover the splicing status of ambiguous reads which, in turn, can lead to more accuracy and confidence in downstream analyses. RESULTS We develop Forseti, a predictive model to probabilistically assign a splicing status to scRNA-seq reads. Our model has two key components. First, we train a binding affinity model to assign a probability that a given transcriptomic site is used in fragment generation. Second, we fit a robust fragment length distribution model that generalizes well across datasets deriving from different species and tissue types. Forseti combines these two trained models to predict the splicing status of the molecule of origin of reads by scoring putative fragments that associate each alignment of sequenced reads with proximate potential priming sites. Using both simulated and experimental data, we show that our model can precisely predict the splicing status of many reads and identify the true gene origin of multi-gene mapped reads. AVAILABILITY AND IMPLEMENTATION Forseti and the code used for producing the results are available at https://github.com/COMBINE-lab/forseti under a BSD 3-clause license.
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Affiliation(s)
- Dongze He
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, United States
- Program in Computational Biology, Bioinformatics and Genomices, University of Maryland, College Park, MD 20742, United States
| | - Yuan Gao
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, United States
- Program in Computational Biology, Bioinformatics and Genomices, University of Maryland, College Park, MD 20742, United States
| | - Spencer Skylar Chan
- Department of Computer Science, University of Maryland, College Park, MD 20742, United States
| | | | - Rob Patro
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, United States
- Department of Computer Science, University of Maryland, College Park, MD 20742, United States
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6
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Vo DN, Yuan O, Kanaya M, Telliam-Dushime G, Li H, Kotova O, Caglar E, Honnens de Lichtenberg K, Rahman SH, Soneji S, Scheding S, Bryder D, Malmberg KJ, Sitnicka E. A temporal developmental map separates human NK cells from noncytotoxic ILCs through clonal and single-cell analysis. Blood Adv 2024; 8:2933-2951. [PMID: 38484189 PMCID: PMC11176970 DOI: 10.1182/bloodadvances.2023011909] [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: 10/30/2023] [Accepted: 03/05/2024] [Indexed: 06/04/2024] Open
Abstract
ABSTRACT Natural killer (NK) cells represent the cytotoxic member within the innate lymphoid cell (ILC) family that are important against viral infections and cancer. Although the NK cell emergence from hematopoietic stem and progenitor cells through multiple intermediate stages and the underlying regulatory gene network has been extensively studied in mice, this process is not well characterized in humans. Here, using a temporal in vitro model to reconstruct the developmental trajectory of NK lineage, we identified an ILC-restricted oligopotent stage 3a CD34-CD117+CD161+CD45RA+CD56- progenitor population, that exclusively gave rise to CD56-expressing ILCs in vitro. We also further investigated a previously nonappreciated heterogeneity within the CD56+CD94-NKp44+ subset, phenotypically equivalent to stage 3b population containing both group-1 ILC and RORγt+ ILC3 cells, that could be further separated based on their differential expression of DNAM-1 and CD161 receptors. We confirmed that DNAM-1hi S3b and CD161hiCD117hi ILC3 populations distinctively differed in their expression of effector molecules, cytokine secretion, and cytotoxic activity. Furthermore, analysis of lineage output using DNA-barcode tracing across these stages supported a close developmental relationship between S3b-NK and S4-NK (CD56+CD94+) cells, whereas distant to the ILC3 subset. Cross-referencing gene signatures of culture-derived NK cells and other noncytotoxic ILCs with publicly available data sets validated that these in vitro stages highly resemble transcriptional profiles of respective in vivo ILC counterparts. Finally, by integrating RNA velocity and gene network analysis through single-cell regulatory network inference and clustering we unravel a network of coordinated and highly dynamic regulons driving the cytotoxic NK cell program, as a guide map for future studies on NK cell regulation.
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Affiliation(s)
- Dang Nghiem Vo
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Ouyang Yuan
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Minoru Kanaya
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Gladys Telliam-Dushime
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Hongzhe Li
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Olga Kotova
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Emel Caglar
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Cell Therapy Research, Novo Nordisk A/S, Måløv, Copenhagen, Denmark
| | | | | | - Shamit Soneji
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Stefan Scheding
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Department of Hematology, Skåne University Hospital, Lund, Sweden
| | - David Bryder
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Karl-Johan Malmberg
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
- Department of Medicine Huddinge, Center for Infectious Medicine, Karolinska Institute, Stockholm, Sweden
| | - Ewa Sitnicka
- Lund Stem Cell Center, Lund University, Lund, Sweden
- Division of Molecular Hematology, Department of Laboratory Medicine, Lund University, Lund, Sweden
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7
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Sawada T, Barbosa AR, Araujo B, McCord AE, D’Ignazio L, Benjamin KJM, Sheehan B, Zabolocki M, Feltrin A, Arora R, Brandtjen AC, Kleinman JE, Hyde TM, Bardy C, Weinberger DR, Paquola ACM, Erwin JA. Recapitulation of Perturbed Striatal Gene Expression Dynamics of Donors' Brains With Ventral Forebrain Organoids Derived From the Same Individuals With Schizophrenia. Am J Psychiatry 2024; 181:493-511. [PMID: 37915216 PMCID: PMC11209846 DOI: 10.1176/appi.ajp.20220723] [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] [Indexed: 11/03/2023]
Abstract
OBJECTIVE Schizophrenia is a brain disorder that originates during neurodevelopment and has complex genetic and environmental etiologies. Despite decades of clinical evidence of altered striatal function in affected patients, studies examining its cellular and molecular mechanisms in humans are limited. To explore neurodevelopmental alterations in the striatum associated with schizophrenia, the authors established a method for the differentiation of induced pluripotent stem cells (iPSCs) into ventral forebrain organoids (VFOs). METHODS VFOs were generated from postmortem dural fibroblast-derived iPSCs of four individuals with schizophrenia and four neurotypical control individuals for whom postmortem caudate genotypes and transcriptomic data were profiled in the BrainSeq neurogenomics consortium. Individuals were selected such that the two groups had nonoverlapping schizophrenia polygenic risk scores (PRSs). RESULTS Single-cell RNA sequencing analyses of VFOs revealed differences in developmental trajectory between schizophrenia and control individuals in which inhibitory neuronal cells from the patients exhibited accelerated maturation. Furthermore, upregulated genes in inhibitory neurons in schizophrenia VFOs showed a significant overlap with upregulated genes in postmortem caudate tissue of individuals with schizophrenia compared with control individuals, including the donors of the iPSC cohort. CONCLUSIONS The findings suggest that striatal neurons derived from high-PRS individuals with schizophrenia carry abnormalities that originated during early brain development and that the VFO model can recapitulate disease-relevant cell type-specific neurodevelopmental phenotypes in a dish.
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Affiliation(s)
- Tomoyo Sawada
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Bruno Araujo
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Laura D’Ignazio
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kynon J. M. Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Bonna Sheehan
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Michael Zabolocki
- South Australian Health and Medical Research Institute (SAHMRI), Laboratory for Human Neurophysiology and Genetics, Adelaide, SA, Australia
- Flinders University, Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Adelaide, SA, Australia
| | - Arthur Feltrin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Ria Arora
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Joel E. Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Cedric Bardy
- South Australian Health and Medical Research Institute (SAHMRI), Laboratory for Human Neurophysiology and Genetics, Adelaide, SA, Australia
- Flinders University, Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Adelaide, SA, Australia
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Apuā C. M. Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jennifer A. Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
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8
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Li R, Du K, Zhang C, Shen X, Yun L, Wang S, Li Z, Sun Z, Wei J, Li Y, Guo B, Sun C. Single-cell transcriptome profiling reveals the spatiotemporal distribution of triterpenoid saponin biosynthesis and transposable element activity in Gynostemma pentaphyllum shoot apexes and leaves. FRONTIERS IN PLANT SCIENCE 2024; 15:1394587. [PMID: 38779067 PMCID: PMC11109411 DOI: 10.3389/fpls.2024.1394587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024]
Abstract
Gynostemma pentaphyllum (Thunb.) Makino is an important producer of dammarene-type triterpenoid saponins. These saponins (gypenosides) exhibit diverse pharmacological benefits such as anticancer, antidiabetic, and immunomodulatory effects, and have major potential in the pharmaceutical and health care industries. Here, we employed single-cell RNA sequencing (scRNA-seq) to profile the transcriptomes of more than 50,000 cells derived from G. pentaphyllum shoot apexes and leaves. Following cell clustering and annotation, we identified five major cell types in shoot apexes and four in leaves. Each cell type displayed substantial transcriptomic heterogeneity both within and between tissues. Examining gene expression patterns across various cell types revealed that gypenoside biosynthesis predominantly occurred in mesophyll cells, with heightened activity observed in shoot apexes compared to leaves. Furthermore, we explored the impact of transposable elements (TEs) on G. pentaphyllum transcriptomic landscapes. Our findings the highlighted the unbalanced expression of certain TE families across different cell types in shoot apexes and leaves, marking the first investigation of TE expression at the single-cell level in plants. Additionally, we observed dynamic expression of genes involved in gypenoside biosynthesis and specific TE families during epidermal and vascular cell development. The involvement of TE expression in regulating cell differentiation and gypenoside biosynthesis warrant further exploration. Overall, this study not only provides new insights into the spatiotemporal organization of gypenoside biosynthesis and TE activity in G. pentaphyllum shoot apexes and leaves but also offers valuable cellular and genetic resources for a deeper understanding of developmental and physiological processes at single-cell resolution in this species.
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Affiliation(s)
- Rucan Li
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ke Du
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chuyi Zhang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaofeng Shen
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lingling Yun
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shu Wang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ziqin Li
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Zhiying Sun
- College of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jianhe Wei
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Li
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Baolin Guo
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Sun
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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9
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Barrios EL, Leary JR, Darden DB, Rincon JC, Willis M, Polcz VE, Gillies GS, Munley JA, Dirain ML, Ungaro R, Nacionales DC, Gauthier MPL, Larson SD, Morel L, Loftus TJ, Mohr AM, Maile R, Kladde MP, Mathews CE, Brusko MA, Brusko TM, Moldawer LL, Bacher R, Efron PA. The post-septic peripheral myeloid compartment reveals unexpected diversity in myeloid-derived suppressor cells. Front Immunol 2024; 15:1355405. [PMID: 38720891 PMCID: PMC11076668 DOI: 10.3389/fimmu.2024.1355405] [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: 12/13/2023] [Accepted: 04/09/2024] [Indexed: 05/12/2024] Open
Abstract
Introduction Sepsis engenders distinct host immunologic changes that include the expansion of myeloid-derived suppressor cells (MDSCs). These cells play a physiologic role in tempering acute inflammatory responses but can persist in patients who develop chronic critical illness. Methods Cellular Indexing of Transcriptomes and Epitopes by Sequencing and transcriptomic analysis are used to describe MDSC subpopulations based on differential gene expression, RNA velocities, and biologic process clustering. Results We identify a unique lineage and differentiation pathway for MDSCs after sepsis and describe a novel MDSC subpopulation. Additionally, we report that the heterogeneous response of the myeloid compartment of blood to sepsis is dependent on clinical outcome. Discussion The origins and lineage of these MDSC subpopulations were previously assumed to be discrete and unidirectional; however, these cells exhibit a dynamic phenotype with considerable plasticity.
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Affiliation(s)
- Evan L. Barrios
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Jack R. Leary
- Department of Biostatistics, University of Florida College of Medicine and Public Health and Health Sciences, Gainesville, FL, United States
| | - Dijoia B. Darden
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Jaimar C. Rincon
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Micah Willis
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Valerie E. Polcz
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Gwendolyn S. Gillies
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Jennifer A. Munley
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Marvin L. Dirain
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Ricardo Ungaro
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Dina C. Nacionales
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Marie-Pierre L. Gauthier
- Department of Biochemistry and Molecular Biology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Shawn D. Larson
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Laurence Morel
- Department of Microbiology and Immunology, University of Texas San Antonio School of Medicine, San Antonio, TX, United States
| | - Tyler J. Loftus
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Alicia M. Mohr
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Robert Maile
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Michael P. Kladde
- Department of Biochemistry and Molecular Biology, University of Florida College of Medicine, Gainesville, FL, United States
| | - Clayton E. Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL, United States
| | - Maigan A. Brusko
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL, United States
| | - Todd M. Brusko
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL, United States
| | - Lyle L. Moldawer
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
| | - Rhonda Bacher
- Department of Biostatistics, University of Florida College of Medicine and Public Health and Health Sciences, Gainesville, FL, United States
| | - Philip A. Efron
- Sepsis and Critical Illness Research Center, Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States
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10
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Su Z, Tong Y, Wei GW. Hodge Decomposition of Single-Cell RNA Velocity. J Chem Inf Model 2024; 64:3558-3568. [PMID: 38572676 PMCID: PMC11035094 DOI: 10.1021/acs.jcim.4c00132] [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: 01/24/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
RNA velocity has the ability to capture the cell dynamic information in the biological processes; yet, a comprehensive analysis of the cell state transitions and their associated chemical and biological processes remains a gap. In this work, we provide the Hodge decomposition, coupled with discrete exterior calculus (DEC), to unveil cell dynamics by examining the decomposed curl-free, divergence-free, and harmonic components of the RNA velocity field in a low dimensional representation, such as a UMAP or a t-SNE representation. Decomposition results show that the decomposed components distinctly reveal key cell dynamic features such as cell cycle, bifurcation, and cell lineage differentiation, regardless of the choice of the low-dimensional representations. The consistency across different representations demonstrates that the Hodge decomposition is a reliable and robust way to extract these cell dynamic features, offering unique analysis and insightful visualization of single-cell RNA velocity fields.
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Affiliation(s)
- Zhe Su
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yiying Tong
- Department
of Computer Science and Engineering, Michigan
State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East
Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
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11
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Kuijpers L, Hornung B, van den Hout-van Vroonhoven MCGN, van IJcken WFJ, Grosveld F, Mulugeta E. Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison. BMC Genomics 2024; 25:361. [PMID: 38609853 PMCID: PMC11010347 DOI: 10.1186/s12864-024-10285-3] [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: 11/19/2023] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used. RESULTS We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required. CONCLUSION Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.
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Affiliation(s)
- Lucas Kuijpers
- Department of Cell Biology, Erasmus University Medical Center Rotterdam (Erasmus MC), Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.
| | - Bastian Hornung
- Center for Biomics, Erasmus University Medical Center Rotterdam (Erasmus MC), Rotterdam, The Netherlands
| | | | - Wilfred F J van IJcken
- Center for Biomics, Erasmus University Medical Center Rotterdam (Erasmus MC), Rotterdam, The Netherlands
| | - Frank Grosveld
- Department of Cell Biology, Erasmus University Medical Center Rotterdam (Erasmus MC), Wytemaweg 80, Rotterdam, 3015CN, The Netherlands
| | - Eskeatnaf Mulugeta
- Department of Cell Biology, Erasmus University Medical Center Rotterdam (Erasmus MC), Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.
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12
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Chamberlin JT, Lee Y, Marth GT, Quinlan AR. Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments. Genome Res 2024; 34:179-188. [PMID: 38355308 PMCID: PMC10984380 DOI: 10.1101/gr.278253.123] [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: 07/07/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024]
Abstract
A mechanistic understanding of the biological and technical factors that impact transcript measurements is essential to designing and analyzing single-cell and single-nucleus RNA sequencing experiments. Nuclei contain the same pre-mRNA population as cells, but they contain a small subset of the mRNAs. Nonetheless, early studies argued that single-nucleus analysis yielded results comparable to cellular samples if pre-mRNA measurements were included. However, typical workflows do not distinguish between pre-mRNA and mRNA when estimating gene expression, and variation in their relative abundances across cell types has received limited attention. These gaps are especially important given that incorporating pre-mRNA has become commonplace for both assays, despite known gene length bias in pre-mRNA capture. Here, we reanalyze public data sets from mouse and human to describe the mechanisms and contrasting effects of mRNA and pre-mRNA sampling on gene expression and marker gene selection in single-cell and single-nucleus RNA-seq. We show that pre-mRNA levels vary considerably among cell types, which mediates the degree of gene length bias and limits the generalizability of a recently published normalization method intended to correct for this bias. As an alternative, we repurpose an existing post hoc gene length-based correction method from conventional RNA-seq gene set enrichment analysis. Finally, we show that inclusion of pre-mRNA in bioinformatic processing can impart a larger effect than assay choice itself, which is pivotal to the effective reuse of existing data. These analyses advance our understanding of the sources of variation in single-cell and single-nucleus RNA-seq experiments and provide useful guidance for future studies.
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Affiliation(s)
- John T Chamberlin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84108, USA
| | - Younghee Lee
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84108, USA
- Seoul National University, College of Veterinary Medicine, Seoul, 08826, South Korea
| | - Gabor T Marth
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, Utah 84112, USA
| | - Aaron R Quinlan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84108, USA;
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, Utah 84112, USA
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13
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Radmand A, Kim H, Beyersdorf J, Dobrowolski CN, Zenhausern R, Paunovska K, Huayamares SG, Hua X, Han K, Loughrey D, Hatit MZC, Del Cid A, Ni H, Shajii A, Li A, Muralidharan A, Peck HE, Tiegreen KE, Jia S, Santangelo PJ, Dahlman JE. Cationic cholesterol-dependent LNP delivery to lung stem cells, the liver, and heart. Proc Natl Acad Sci U S A 2024; 121:e2307801120. [PMID: 38437539 PMCID: PMC10945827 DOI: 10.1073/pnas.2307801120] [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: 06/26/2023] [Accepted: 09/22/2023] [Indexed: 03/06/2024] Open
Abstract
Adding a cationic helper lipid to a lipid nanoparticle (LNP) can increase lung delivery and decrease liver delivery. However, it remains unclear whether charge-dependent tropism is universal or, alternatively, whether it depends on the component that is charged. Here, we report evidence that cationic cholesterol-dependent tropism can differ from cationic helper lipid-dependent tropism. By testing how 196 LNPs delivered mRNA to 22 cell types, we found that charged cholesterols led to a different lung:liver delivery ratio than charged helper lipids. We also found that combining cationic cholesterol with a cationic helper lipid led to mRNA delivery in the heart as well as several lung cell types, including stem cell-like populations. These data highlight the utility of exploring charge-dependent LNP tropism.
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Affiliation(s)
- Afsane Radmand
- Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA30332
- Department of Chemical Engineering, Georgia Institute of Technology, Atlanta, GA30332
| | - Hyejin Kim
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Jared Beyersdorf
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Curtis N. Dobrowolski
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Ryan Zenhausern
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Kalina Paunovska
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Sebastian G. Huayamares
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Xuanwen Hua
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Keyi Han
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - David Loughrey
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Marine Z. C. Hatit
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Ada Del Cid
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Huanzhen Ni
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Aram Shajii
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Andrea Li
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Abinaya Muralidharan
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA30332
- Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA30332
| | - Hannah E. Peck
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Karen E. Tiegreen
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Shu Jia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - Philip J. Santangelo
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
| | - James E. Dahlman
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA30332
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14
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Mihai IS, Chafle S, Henriksson J. Representing and extracting knowledge from single-cell data. Biophys Rev 2024; 16:29-56. [PMID: 38495441 PMCID: PMC10937862 DOI: 10.1007/s12551-023-01091-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 06/28/2023] [Indexed: 03/19/2024] Open
Abstract
Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.
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Affiliation(s)
- Ionut Sebastian Mihai
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
- Industrial Doctoral School, Umeå University, Umeå, Sweden
| | - Sarang Chafle
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
| | - Johan Henriksson
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
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15
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Shahzaib M, Khan UM, Azhar MT, Atif RM, Khan SH, Zaman QU, Rana IA. Phylogenomic curation of Ovate Family Proteins (OFPs) in the U's Triangle of Brassica L. indicates stress-induced growth modulation. PLoS One 2024; 19:e0297473. [PMID: 38277374 PMCID: PMC10817133 DOI: 10.1371/journal.pone.0297473] [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: 10/19/2023] [Accepted: 01/06/2024] [Indexed: 01/28/2024] Open
Abstract
The Ovate Family Proteins (OFPs) gene family houses a class of proteins that are involved in regulating plant growth and development. To date, there is no report of the simultaneous functional characterization of this gene family in all members of U's Triangle of Brassica. Here, we retrieved a combined total of 256 OFP protein sequences and analyzed their chromosomal localization, gene structure, conserved protein motif domains, and the pattern of cis-acting regulatory elements. The abundance of light-responsive elements like G-box, MRE, and GT1 motif suggests that OFPs are sensitive to the stimuli of light. The protein-protein interaction network analysis revealed that OFP05 and its orthologous genes were involved in regulating the process of transcriptional repression through their interaction with homeodomain transcription factors like KNAT and BLH. The presence of domains like DNA binding 2 and its superfamily speculated the involvement of OFPs in regulating gene expression. The biotic and abiotic stress, and the tissue-specific expression analysis of the RNA-seq datasets revealed that some of the genes such as BjuOFP30, and BnaOFP27, BolOFP11, and BolOFP10 were highly upregulated in seed coat at the mature stage and roots under various chemical stress conditions respectively which suggests their crucial role in plant growth and development processes. Experimental validation of prominent BnaOFPs such as BnaOFP27 confirmed their involvement in regulating gene expression under salinity, heavy metal, drought, heat, and cold stress. The GO and KEGG pathway enrichment analysis also sheds light on the involvement of OFPs in regulating plant growth and development. These findings have the potential to serve as a forerunner for future studies in terms of functionally diverse analysis of the OFP gene family in Brassica and other plant species.
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Affiliation(s)
- Muhammad Shahzaib
- Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
- Centre for Advanced Studies in Agriculture and Food Security, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
| | - Uzair Muhammad Khan
- Centre for Advanced Studies in Agriculture and Food Security, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
| | - Muhammad Tehseen Azhar
- Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
| | - Rana Muhammad Atif
- Centre for Advanced Studies in Agriculture and Food Security, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
- Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
| | - Sultan Habibullah Khan
- Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
- Centre for Advanced Studies in Agriculture and Food Security, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
| | - Qamar U. Zaman
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute of Hainan University, Sanya, China
- College of Tropical Crops, Hainan University, Haikou, China
| | - Iqrar Ahmad Rana
- Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
- Centre for Advanced Studies in Agriculture and Food Security, University of Agriculture, Faisalabad, Faisalabad, Punjab, Pakistan
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16
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Sullivan DK, Min KHJ, Hjörleifsson KE, Luebbert L, Holley G, Moses L, Gustafsson J, Bray NL, Pimentel H, Booeshaghi AS, Melsted P, Pachter L. kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.21.568164. [PMID: 38045414 PMCID: PMC10690192 DOI: 10.1101/2023.11.21.568164] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The term "RNA-seq" refers to a collection of assays based on sequencing experiments that involve quantifying RNA species from bulk tissue, from single cells, or from single nuclei. The kallisto, bustools, and kb-python programs are free, open-source software tools for performing this analysis that together can produce gene expression quantification from raw sequencing reads. The quantifications can be individualized for multiple cells, multiple samples, or both. Additionally, these tools allow gene expression values to be classified as originating from nascent RNA species or mature RNA species, making this workflow amenable to both cell-based and nucleus-based assays. This protocol describes in detail how to use kallisto and bustools in conjunction with a wrapper, kb-python, to preprocess RNA-seq data.
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Affiliation(s)
- Delaney K Sullivan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | | | | | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | | | - Lambda Moses
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | | | - Nicolas L Bray
- Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Harold Pimentel
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - A Sina Booeshaghi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Páll Melsted
- deCODE Genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
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17
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George N, Fexova S, Fuentes AM, Madrigal P, Bi Y, Iqbal H, Kumbham U, Nolte N, Zhao L, Thanki A, Yu I, Marugan Calles J, Erdos K, Vilmovsky L, Kurri S, Vathrakokoili-Pournara A, Osumi-Sutherland D, Prakash A, Wang S, Tello-Ruiz M, Kumari S, Ware D, Goutte-Gattat D, Hu Y, Brown N, Perrimon N, Vizcaíno JA, Burdett T, Teichmann S, Brazma A, Papatheodorou I. Expression Atlas update: insights from sequencing data at both bulk and single cell level. Nucleic Acids Res 2024; 52:D107-D114. [PMID: 37992296 PMCID: PMC10767917 DOI: 10.1093/nar/gkad1021] [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: 09/15/2023] [Revised: 10/13/2023] [Accepted: 10/30/2023] [Indexed: 11/24/2023] Open
Abstract
Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI's knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users' understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.
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Affiliation(s)
- Nancy George
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Silvie Fexova
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Alfonso Munoz Fuentes
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Pedro Madrigal
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Yalan Bi
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Haider Iqbal
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Upendra Kumbham
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Nadja Francesca Nolte
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Lingyun Zhao
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Anil S Thanki
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Iris D Yu
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Jose C Marugan Calles
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Karoly Erdos
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Liora Vilmovsky
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Sandeep R Kurri
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | | | - David Osumi-Sutherland
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Ananth Prakash
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Shengbo Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Marcela K Tello-Ruiz
- Cold Spring Harbour Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Sunita Kumari
- Cold Spring Harbour Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Doreen Ware
- Cold Spring Harbour Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724, USA
- USDA ARS NEA, Plant Soil & Nutrition Laboratory Research Unit, Ithaca, NY 14853, USA
| | - Damien Goutte-Gattat
- FlyBase-Cambridge, Department of Physiology, Development and Neuroscience, University of Cambridge Downing Street, Cambridge CB2 3DY, UK
| | - Yanhui Hu
- Perrimon Lab, Department of Genetics, Harvard Medical School, Boston MA 02115, USA
| | - Nick Brown
- FlyBase-Cambridge, Department of Physiology, Development and Neuroscience, University of Cambridge Downing Street, Cambridge CB2 3DY, UK
| | - Norbert Perrimon
- Perrimon Lab, Department of Genetics, Harvard Medical School, Boston MA 02115, USA
- FlyBase-Harvard Biological Laboratories, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Sarah Teichmann
- Wellcome Trust Sanger Institute. Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
| | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton CB10 1SD, UK
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18
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Chen J, Yin D, Wong HYH, Duan X, Yu KHO, Ho JWK. Vulture: cloud-enabled scalable mining of microbial reads in public scRNA-seq data. Gigascience 2024; 13:giad117. [PMID: 38195165 PMCID: PMC10776309 DOI: 10.1093/gigascience/giad117] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/17/2023] [Accepted: 12/16/2023] [Indexed: 01/11/2024] Open
Abstract
The rapidly growing collection of public single-cell sequencing data has become a valuable resource for molecular, cellular, and microbial discovery. Previous studies mostly overlooked detecting pathogens in human single-cell sequencing data. Moreover, existing bioinformatics tools lack the scalability to deal with big public data. We introduce Vulture, a scalable cloud-based pipeline that performs microbial calling for single-cell RNA sequencing (scRNA-seq) data, enabling meta-analysis of host-microbial studies from the public domain. In our benchmarking experiments, Vulture is 66% to 88% faster than local tools (PathogenTrack and Venus) and 41% faster than the state-of-the-art cloud-based tool Cumulus, while achieving comparable microbial read identification. In terms of the cost on cloud computing systems, Vulture also shows a cost reduction of 83% ($12 vs. ${\$}$70). We applied Vulture to 2 coronavirus disease 2019, 3 hepatocellular carcinoma (HCC), and 2 gastric cancer human patient cohorts with public sequencing reads data from scRNA-seq experiments and discovered cell type-specific enrichment of severe acute respiratory syndrome coronavirus 2, hepatitis B virus (HBV), and Helicobacter pylori-positive cells, respectively. In the HCC analysis, all cohorts showed hepatocyte-only enrichment of HBV, with cell subtype-associated HBV enrichment based on inferred copy number variations. In summary, Vulture presents a scalable and economical framework to mine unknown host-microbial interactions from large-scale public scRNA-seq data. Vulture is available via an open-source license at https://github.com/holab-hku/Vulture.
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Affiliation(s)
- Junyi Chen
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Danqing Yin
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Harris Y H Wong
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
| | - Xin Duan
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
| | - Ken H O Yu
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Joshua W K Ho
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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19
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Gayoso A, Weiler P, Lotfollahi M, Klein D, Hong J, Streets A, Theis FJ, Yosef N. Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells. Nat Methods 2024; 21:50-59. [PMID: 37735568 PMCID: PMC10776389 DOI: 10.1038/s41592-023-01994-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 08/08/2023] [Indexed: 09/23/2023]
Abstract
RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show that veloVI compares favorably to previous approaches with respect to goodness of fit, consistency across transcriptionally similar cells and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that veloVI's posterior velocity uncertainty can be used to assess whether velocity analysis is appropriate for a given dataset. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.
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Affiliation(s)
- Adam Gayoso
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Philipp Weiler
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Dominik Klein
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Justin Hong
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Aaron Streets
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.
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20
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Schmassmann P, Roux J, Dettling S, Hogan S, Shekarian T, Martins TA, Ritz MF, Herter S, Bacac M, Hutter G. Single-cell characterization of human GBM reveals regional differences in tumor-infiltrating leukocyte activation. eLife 2023; 12:RP92678. [PMID: 38127790 PMCID: PMC10735226 DOI: 10.7554/elife.92678] [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: 12/23/2023] Open
Abstract
Glioblastoma (GBM) harbors a highly immunosuppressive tumor microenvironment (TME) which influences glioma growth. Major efforts have been undertaken to describe the TME on a single-cell level. However, human data on regional differences within the TME remain scarce. Here, we performed high-depth single-cell RNA sequencing (scRNAseq) on paired biopsies from the tumor center, peripheral infiltration zone and blood of five primary GBM patients. Through analysis of >45,000 cells, we revealed a regionally distinct transcription profile of microglia (MG) and monocyte-derived macrophages (MdMs) and an impaired activation signature in the tumor-peripheral cytotoxic-cell compartment. Comparing tumor-infiltrating CD8+ T cells with circulating cells identified CX3CR1high and CX3CR1int CD8+ T cells with effector and memory phenotype, respectively, enriched in blood but absent in the TME. Tumor CD8+ T cells displayed a tissue-resident memory phenotype with dysfunctional features. Our analysis provides a regionally resolved mapping of transcriptional states in GBM-associated leukocytes, serving as an additional asset in the effort towards novel therapeutic strategies to combat this fatal disease.
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Affiliation(s)
- Philip Schmassmann
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of BaselBaselSwitzerland
| | - Julien Roux
- Bioinformatics Core Facility, Department of Biomedicine, University of BaselBaselSwitzerland
- Swiss Institute of BioinformaticsBaselSwitzerland
| | - Steffen Dettling
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center MunichPenzbergGermany
| | - Sabrina Hogan
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of BaselBaselSwitzerland
| | - Tala Shekarian
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of BaselBaselSwitzerland
| | - Tomás A Martins
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of BaselBaselSwitzerland
| | - Marie-Françoise Ritz
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of BaselBaselSwitzerland
| | - Sylvia Herter
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center ZürichSchlierenSwitzerland
| | - Marina Bacac
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center ZürichSchlierenSwitzerland
| | - Gregor Hutter
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University of BaselBaselSwitzerland
- Department of Neurosurgery, University Hospital BaselBaselSwitzerland
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21
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Sang Y, Mo S, Zeng S, Wu X, Kashif M, Song J, Yu D, Bai L, Jiang C. Model of shrimp pond-mediated spatiotemporal dynamic distribution of antibiotic resistance genes in the mangrove habitat of a subtropical gulf. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:167199. [PMID: 37734616 DOI: 10.1016/j.scitotenv.2023.167199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/13/2023] [Accepted: 09/17/2023] [Indexed: 09/23/2023]
Abstract
Aquacultures are the main reason for the environmental selection of antibiotic resistance genes (ARGs), resulting in the enrichment of ARGs. As a filter, a marine mangrove ecosystem can reduce antimicrobial resistance (AMR) or eliminate ARGs; however, its elimination mechanism remains unclear. This study investigated the spatiotemporal dynamic distribution of ARGs in two different types of mangrove habitats (shrimp ponds and virgin forests), within a subtropical gulf located in the Beibu Gulf, China, during dry and wet seasons by using metagenomics and real time quantitative polymerase chain reaction (RT-qPCR) analysis. As the key environmental factors, sulfide, salinity, and mobile genetic elements significantly were found to contribute to ARGs distribution, respectively. Wet and dry seasons influenced the dispersal of ARGs but did not affect the microbial community structure. Three potential biomarkers, TEM-116, smeD, and smeE, played key roles in seasonal differences. The key different genes in the biological relevance of absolute abundance were demonstrated by RT-qPCR. Co-occurrence network analysis indicated that high-abundance ARGs were distributed in a modular manner. For the first time, a risk index weighted by risk rank (RIR) was proposed and used to quantify the human risk of ARGs in the mangrove metagenome. The shrimp ponds during the wet season showed the highest RIR detected. In addition to offering a perspective on reducing AMR in mangrove wetlands, this study constructed the first spatiotemporal dynamic model of ARGs in the Beibu Gulf, China and contributed to revealing the global spread of ARGs. Meanwhile, this study proposes a new pipeline for assessing the risk of ARGs, while also exploring the concept of "One Health."
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Affiliation(s)
- Yimeng Sang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning 530004, China; National Engineering Research Center for Non-Food Biorefinery, Guangxi Research Center for Biological Science and Technology, Guangxi Academy of Sciences, Nanning 530007, China
| | - Shuming Mo
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning 530004, China; National Engineering Research Center for Non-Food Biorefinery, Guangxi Research Center for Biological Science and Technology, Guangxi Academy of Sciences, Nanning 530007, China
| | - Sen Zeng
- National Engineering Research Center for Non-Food Biorefinery, Guangxi Research Center for Biological Science and Technology, Guangxi Academy of Sciences, Nanning 530007, China
| | - Xiaoling Wu
- National Engineering Research Center for Non-Food Biorefinery, Guangxi Research Center for Biological Science and Technology, Guangxi Academy of Sciences, Nanning 530007, China
| | - Muhammad Kashif
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning 530004, China; National Engineering Research Center for Non-Food Biorefinery, Guangxi Research Center for Biological Science and Technology, Guangxi Academy of Sciences, Nanning 530007, China
| | - Jingjing Song
- Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation, Beibu Gulf University, Qinzhou 535011, China
| | - Dahui Yu
- Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation, Beibu Gulf University, Qinzhou 535011, China
| | - Lirong Bai
- Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation, Beibu Gulf University, Qinzhou 535011, China
| | - Chengjian Jiang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning 530004, China; National Engineering Research Center for Non-Food Biorefinery, Guangxi Research Center for Biological Science and Technology, Guangxi Academy of Sciences, Nanning 530007, China; Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation, Beibu Gulf University, Qinzhou 535011, China.
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22
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Kavaliauskaite G, Madsen JS. Automatic quality control of single-cell and single-nucleus RNA-seq using valiDrops. NAR Genom Bioinform 2023; 5:lqad101. [PMID: 38025048 PMCID: PMC10657416 DOI: 10.1093/nargab/lqad101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 10/05/2023] [Accepted: 11/01/2023] [Indexed: 12/01/2023] Open
Abstract
Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) measures gene expression in individual cells or nuclei enabling comprehensive characterization of cell types and states. However, isolation of cells or nuclei for sxRNA-seq releases contaminating RNA, which can distort biological signals, through, for example, cell damage and transcript leakage. Thus, identifying barcodes containing high-quality cells or nuclei is a critical analytical step in the processing of sxRNA-seq data. Here, we present valiDrops, an automated method to identify high-quality barcodes and flag dead cells. In valiDrops, barcodes are initially filtered using data-adaptive thresholding on community-standard quality metrics, and subsequently, valiDrops uses a novel clustering-based approach to identify barcodes with distinct biological signals. We benchmark valiDrops and show that biological signals from cell types and states are more distinct, easier to separate and more consistent after filtering by valiDrops compared to existing tools. Finally, we show that valiDrops can predict and flag dead cells with high accuracy. This novel classifier can further improve data quality or be used to identify dead cells to interrogate the biology of cell death. Thus, valiDrops is an effective and easy-to-use method to improve data quality and biological interpretation. Our method is openly available as an R package at www.github.com/madsen-lab/valiDrops.
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Affiliation(s)
- Gabija Kavaliauskaite
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M 5230, Denmark
- Center for Functional Genomics and Tissue Plasticity (ATLAS), Odense M 5230, Denmark
| | - Jesper Grud Skat Madsen
- Center for Functional Genomics and Tissue Plasticity (ATLAS), Odense M 5230, Denmark
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense M 5230, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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23
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Ford K, Zuin E, Righelli D, Medina E, Schoch H, Singletary K, Muheim C, Frank MG, Hicks SC, Risso D, Peixoto L. A Global Transcriptional Atlas of the Effect of Sleep Deprivation in the Mouse Frontal Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.28.569011. [PMID: 38076891 PMCID: PMC10705260 DOI: 10.1101/2023.11.28.569011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Sleep deprivation (SD) has negative effects on brain function. Sleep problems are prevalent in neurodevelopmental, neurodegenerative and psychiatric disorders. Thus, understanding the molecular consequences of SD is of fundamental importance in neuroscience. In this study, we present the first simultaneous bulk and single-nuclear (sn)RNA sequencing characterization of the effects of SD in the mouse frontal cortex. We show that SD predominantly affects glutamatergic neurons, specifically in layers 4 and 5, and produces isoform switching of thousands of transcripts. At both the global and cell-type specific level, SD has a large repressive effect on transcription, down-regulating thousands of genes and transcripts; underscoring the importance of accounting for the effects of sleep loss in transcriptome studies of brain function. As a resource we provide extensive characterizations of cell types, genes, transcripts and pathways affected by SD; as well as tutorials for data analysis.
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Affiliation(s)
- Kaitlyn Ford
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Elena Zuin
- Department of Biology, University of Padova, Italy
- Department of Statistical Sciences, University of Padova, Italy
| | - Dario Righelli
- Department of Statistical Sciences, University of Padova, Italy
| | - Elizabeth Medina
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Hannah Schoch
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Kristan Singletary
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Christine Muheim
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Marcos G Frank
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, MD, USA
| | - Davide Risso
- Department of Statistical Sciences, University of Padova, Italy
| | - Lucia Peixoto
- Department of Translational Medicine and Physiology, Sleep and Performance Research Center. Elson S. Floyd College of Medicine. Washington State University, Spokane, WA
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24
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Li H, Rahman MA, Ruesch M, Eisele CD, Anderson EM, Wright PW, Cao J, Ratnayake S, Chen Q, Yan C, Meerzaman D, Abraham RS, Freud AG, Anderson SK. Abundant binary promoter switches in lineage-determining transcription factors indicate a digital component of cell fate determination. Cell Rep 2023; 42:113454. [PMID: 37976160 PMCID: PMC10842785 DOI: 10.1016/j.celrep.2023.113454] [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: 10/28/2022] [Revised: 10/02/2023] [Accepted: 11/01/2023] [Indexed: 11/19/2023] Open
Abstract
Previous studies of the murine Ly49 and human KIR gene clusters implicated competing sense and antisense promoters in the control of variegated gene expression. In the current study, an examination of transcription factor genes defines an abundance of convergent and divergent sense/antisense promoter pairs, suggesting that competing promoters may control cell fate determination. Differentiation of CD34+ hematopoietic progenitors in vitro shows that cells with GATA1 antisense transcription have enhanced GATA2 transcription and a mast cell phenotype, whereas cells with GATA2 antisense transcription have increased GATA1 transcripts and an erythroblast phenotype. Detailed analyses of the AHR and RORC genes demonstrate the ability of competing promoters to act as binary switches and the association of antisense transcription with an immature/progenitor cell phenotype. These data indicate that alternative cell fates generated by promoter competition in lineage-determining transcription factors contribute to the programming of cell differentiation.
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Affiliation(s)
- Hongchuan Li
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Md Ahasanur Rahman
- Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Michael Ruesch
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA; Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA; Medical Scientist Training Program, The Ohio State University, Columbus, OH 43210, USA
| | - Caprice D Eisele
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA; Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Erik M Anderson
- Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Paul W Wright
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Jennie Cao
- Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA
| | - Shashikala Ratnayake
- Cancer Genomics and Bioinformatics Branch, Center for Biomedical Informatics & Information Technology, National Cancer Institute, Bethesda, MD 20892, USA
| | - Qingrong Chen
- Cancer Genomics and Bioinformatics Branch, Center for Biomedical Informatics & Information Technology, National Cancer Institute, Bethesda, MD 20892, USA
| | - Chunhua Yan
- Cancer Genomics and Bioinformatics Branch, Center for Biomedical Informatics & Information Technology, National Cancer Institute, Bethesda, MD 20892, USA
| | - Daoud Meerzaman
- Cancer Genomics and Bioinformatics Branch, Center for Biomedical Informatics & Information Technology, National Cancer Institute, Bethesda, MD 20892, USA
| | - Roshini S Abraham
- Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital, Columbus, OH 43210, USA; Department of Pathology, The Ohio State University, Columbus, OH 43210, USA
| | - Aharon G Freud
- Department of Pathology, The Ohio State University, Columbus, OH 43210, USA
| | - Stephen K Anderson
- Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA.
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25
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Yan G, Song D, Li JJ. scReadSim: a single-cell RNA-seq and ATAC-seq read simulator. Nat Commun 2023; 14:7482. [PMID: 37980428 PMCID: PMC10657386 DOI: 10.1038/s41467-023-43162-w] [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: 05/06/2023] [Accepted: 11/02/2023] [Indexed: 11/20/2023] Open
Abstract
Benchmarking single-cell RNA-seq (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) computational tools demands simulators to generate realistic sequencing reads. However, none of the few read simulators aim to mimic real data. To fill this gap, we introduce scReadSim, a single-cell RNA-seq and ATAC-seq read simulator that allows user-specified ground truths and generates synthetic sequencing reads (in a FASTQ or BAM file) by mimicking real data. At both read-sequence and read-count levels, scReadSim mimics real scRNA-seq and scATAC-seq data. Moreover, scReadSim provides ground truths, including unique molecular identifier (UMI) counts for scRNA-seq and open chromatin regions for scATAC-seq. In particular, scReadSim allows users to design cell-type-specific ground-truth open chromatin regions for scATAC-seq data generation. In benchmark applications of scReadSim, we show that UMI-tools achieves the top accuracy in scRNA-seq UMI deduplication, and HMMRATAC and MACS3 achieve the top performance in scATAC-seq peak calling.
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Affiliation(s)
- Guanao Yan
- Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA
| | - Dongyuan Song
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, 90095-7246, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA.
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, 90095-7246, USA.
- Department of Human Genetics, University of California, Los Angeles, CA, 90095-7088, USA.
- Department of Computational Medicine, University of California, Los Angeles, CA, 90095-1766, USA.
- Department of Biostatistics, University of California, Los Angeles, CA, 90095-1772, USA.
- Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA, 02138, USA.
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26
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Rodríguez-Montes L, Ovchinnikova S, Yuan X, Studer T, Sarropoulos I, Anders S, Kaessmann H, Cardoso-Moreira M. Sex-biased gene expression across mammalian organ development and evolution. Science 2023; 382:eadf1046. [PMID: 37917687 PMCID: PMC7615307 DOI: 10.1126/science.adf1046] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 09/18/2023] [Indexed: 11/04/2023]
Abstract
Sexually dimorphic traits are common among mammals and are specified during development through the deployment of sex-specific genetic programs. Because little is known about these programs, we investigated them using a resource of gene expression profiles in males and females throughout the development of five organs in five mammals (human, mouse, rat, rabbit, and opossum) and a bird (chicken). We found that sex-biased gene expression varied considerably across organs and species and was often cell-type specific. Sex differences increased abruptly around sexual maturity instead of increasing gradually during organ development. Finally, sex-biased gene expression evolved rapidly at the gene level, with differences between organs in the evolutionary mechanisms used, but more slowly at the cellular level, with the same cell types being sexually dimorphic across species.
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Affiliation(s)
- Leticia Rodríguez-Montes
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, D-69120 Heidelberg, Germany
| | | | - Xuefei Yuan
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, D-69120 Heidelberg, Germany
| | - Tania Studer
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, D-69120 Heidelberg, Germany
| | - Ioannis Sarropoulos
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, D-69120 Heidelberg, Germany
| | - Simon Anders
- BioQuant, Heidelberg University, D-69120 Heidelberg, Germany
| | - Henrik Kaessmann
- Center for Molecular Biology of Heidelberg University (ZMBH), DKFZ-ZMBH Alliance, D-69120 Heidelberg, Germany
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27
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Clark IC, Fontanez KM, Meltzer RH, Xue Y, Hayford C, May-Zhang A, D'Amato C, Osman A, Zhang JQ, Hettige P, Ishibashi JSA, Delley CL, Weisgerber DW, Replogle JM, Jost M, Phong KT, Kennedy VE, Peretz CAC, Kim EA, Song S, Karlon W, Weissman JS, Smith CC, Gartner ZJ, Abate AR. Microfluidics-free single-cell genomics with templated emulsification. Nat Biotechnol 2023; 41:1557-1566. [PMID: 36879006 PMCID: PMC10635830 DOI: 10.1038/s41587-023-01685-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 01/20/2023] [Indexed: 03/08/2023]
Abstract
Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method that does not require specialized microfluidic devices, expertise or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multiomics measurements and can accurately characterize cell types in human breast tissue compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq reveals the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible and scalable next-generation workflow that extends single-cell sequencing to new applications.
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Affiliation(s)
- Iain C Clark
- Department of Bioengineering, University of California, Berkeley, California Institute for Quantitative Biosciences, Berkeley, CA, USA
| | | | | | - Yi Xue
- Fluent Biosciences, Watertown, MA, USA
| | | | | | | | | | | | | | | | - Cyrille L Delley
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel W Weisgerber
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Joseph M Replogle
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marco Jost
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Kiet T Phong
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Vanessa E Kennedy
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Cheryl A C Peretz
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Esther A Kim
- Division of Plastic and Reconstructive Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Siyou Song
- Division of Plastic and Reconstructive Surgery, University of California San Francisco, San Francisco, CA, USA
| | - William Karlon
- Departments of Pathology and Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Catherine C Smith
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Adam R Abate
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
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28
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Washburn M, Alaniz-Fabián J, Scroggs T, Nelms B. Single-cell RNA-seq of maize meiocytes and pollen grains. Nat Protoc 2023; 18:3512-3533. [PMID: 37783945 DOI: 10.1038/s41596-023-00889-6] [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: 12/23/2022] [Accepted: 07/11/2023] [Indexed: 10/04/2023]
Abstract
RNA-sequencing (RNA-seq) provides invaluable knowledge on developmental pathways and the effects of mutant phenotypes. Plant reproductive cells have traditionally been difficult to isolate for genomics because they are rare and often deeply embedded within somatic tissues. Here, we present a protocol to isolate single maize meiocytes and pollen grains for RNA-seq. We discuss how to identify and isolate each sample type under a microscope, prepare RNA-seq libraries and analyze the data. This technique has several advantages over alternative methods, combining the ability to target specific rare cell types while resolving cell-to-cell heterogeneity with single-cell RNA-seq. The technique is compatible with minute amounts of starting material (e.g., a single anther), making it possible to collect dense time courses. Furthermore, developmentally synchronized anthers are saved for microscopy, allowing staging to be performed in parallel with expression analysis. Up to 200 cells can be collected in 4-5 h by someone proficient in tissue dissection, and library preparation can be completed in 2 d by researchers experienced in molecular biology and genomics. This protocol will facilitate research on plant reproduction, providing insights critical to plant breeding, genetics and agriculture.
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Affiliation(s)
| | - Jaime Alaniz-Fabián
- National Laboratory of Genomics for Biodiversity, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-LANGEBIO), Irapuato, Mexico
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29
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He D, Patro R. simpleaf: a simple, flexible, and scalable framework for single-cell data processing using alevin-fry. Bioinformatics 2023; 39:btad614. [PMID: 37802884 PMCID: PMC10580267 DOI: 10.1093/bioinformatics/btad614] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/02/2023] [Accepted: 10/05/2023] [Indexed: 10/08/2023] Open
Abstract
SUMMARY The alevin-fry ecosystem provides a robust and growing suite of programs for single-cell data processing. However, as new single-cell technologies are introduced, as the community continues to adjust best practices for data processing, and as the alevin-fry ecosystem itself expands and grows, it is becoming increasingly important to manage the complexity of alevin-fry's single-cell preprocessing workflows while retaining the performance and flexibility that make these tools enticing. We introduce simpleaf, a program that simplifies the processing of single-cell data using tools from the alevin-fry ecosystem, and adds new functionality and capabilities, while retaining the flexibility and performance of the underlying tools. AVAILABILITY AND IMPLEMENTATION Simpleaf is written in Rust and released under a BSD 3-Clause license. It is freely available from its GitHub repository https://github.com/COMBINE-lab/simpleaf, and via bioconda. Documentation for simpleaf is available at https://simpleaf.readthedocs.io/en/latest/ and tutorials for simpleaf that have been developed can be accessed at https://combine-lab.github.io/alevin-fry-tutorials.
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Affiliation(s)
- Dongze He
- Department of Cell Biology and Molecular Genetics and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, 20742, United States
| | - Rob Patro
- Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, 20742, United States
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30
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Zhang B, Upadhyay R, Hao Y, Samanovic MI, Herati RS, Blair JD, Axelrad J, Mulligan MJ, Littman DR, Satija R. Multimodal single-cell datasets characterize antigen-specific CD8 + T cells across SARS-CoV-2 vaccination and infection. Nat Immunol 2023; 24:1725-1734. [PMID: 37735591 PMCID: PMC10522491 DOI: 10.1038/s41590-023-01608-9] [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: 01/24/2023] [Accepted: 07/31/2023] [Indexed: 09/23/2023]
Abstract
The immune response to SARS-CoV-2 antigen after infection or vaccination is defined by the durable production of antibodies and T cells. Population-based monitoring typically focuses on antibody titer, but there is a need for improved characterization and quantification of T cell responses. Here, we used multimodal sequencing technologies to perform a longitudinal analysis of circulating human leukocytes collected before and after immunization with the mRNA vaccine BNT162b2. Our data indicated distinct subpopulations of CD8+ T cells, which reliably appeared 28 days after prime vaccination. Using a suite of cross-modality integration tools, we defined their transcriptome, accessible chromatin landscape and immunophenotype, and we identified unique biomarkers within each modality. We further showed that this vaccine-induced population was SARS-CoV-2 antigen-specific and capable of rapid clonal expansion. Moreover, we identified these CD8+ T cell populations in scRNA-seq datasets from COVID-19 patients and found that their relative frequency and differentiation outcomes were predictive of subsequent clinical outcomes.
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Affiliation(s)
- Bingjie Zhang
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Cell Biology, New York University Grossman School of Medicine, New York, NY, USA
| | - Rabi Upadhyay
- Department of Cell Biology, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Yuhan Hao
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Marie I Samanovic
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - Ramin S Herati
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - John D Blair
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Jordan Axelrad
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Mark J Mulligan
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - Dan R Littman
- Department of Cell Biology, New York University Grossman School of Medicine, New York, NY, USA.
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA.
- Howard Hughes Medical Institute, New York, NY, USA.
| | - Rahul Satija
- New York Genome Center, New York, NY, USA.
- Center for Genomics and Systems Biology, New York University, New York, NY, USA.
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31
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Raghubar AM, Matigian NA, Crawford J, Francis L, Ellis R, Healy HG, Kassianos AJ, Ng MSY, Roberts MJ, Wood S, Mallett AJ. High risk clear cell renal cell carcinoma microenvironments contain protumour immunophenotypes lacking specific immune checkpoints. NPJ Precis Oncol 2023; 7:88. [PMID: 37696903 PMCID: PMC10495390 DOI: 10.1038/s41698-023-00441-5] [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: 02/04/2023] [Accepted: 08/30/2023] [Indexed: 09/13/2023] Open
Abstract
Perioperative immune checkpoint inhibitor (ICI) trials for intermediate high-risk clear cell renal cell carcinoma (ccRCC) have failed to consistently demonstrate improved patient outcomes. These unsuccessful ICI trials suggest that the tumour infiltrating immunophenotypes, termed here as the immune cell types, states and their spatial location within the tumour microenvironment (TME), were unfavourable for ICI treatment. Defining the tumour infiltrating immune cells may assist with the identification of predictive immunophenotypes within the TME that are favourable for ICI treatment. To define the immunophenotypes within the ccRCC TME, fresh para-tumour (pTME, n = 2), low-grade (LG, n = 4, G1-G2) and high-grade (HG, n = 4, G3-G4) tissue samples from six patients with ccRCC presenting at a tertiary referral hospital underwent spatial transcriptomics sequencing (ST-seq). Within the generated ST-seq datasets, immune cell types and states, termed here as exhausted/pro-tumour state or non-exhausted/anti-tumour state, were identified using multiple publicly available single-cell RNA and T-cell receptor sequencing datasets as references. HG TMEs revealed abundant exhausted/pro-tumour immune cells with no consistent increase in expression of PD-1, PD-L1 and CTLA4 checkpoints and angiogenic genes. Additional HG TME immunophenotype characteristics included: pro-tumour tissue-resident monocytes with consistently increased expression of HAVCR2 and LAG3 checkpoints; an exhausted CD8+ T cells sub-population with stem-like progenitor gene expression; and pro-tumour tumour-associated macrophages and monocytes within the recurrent TME with the expression of TREM2. Whilst limited by a modest sample size, this study represents the largest ST-seq dataset on human ccRCC. Our study reveals that high-risk ccRCC TMEs are infiltrated by exhausted/pro-tumour immunophenotypes lacking specific checkpoint gene expression confirming that HG ccRCC TME are immunogenic but not ICI favourable.
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Affiliation(s)
- Arti M Raghubar
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Anatomical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Faculty of Health, Charles Darwin University, Darwin, NT, Australia
| | - Nicholas A Matigian
- QCIF Facility for Advanced Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Joanna Crawford
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Leo Francis
- Anatomical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
| | - Robert Ellis
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Department of Urology, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Helen G Healy
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
| | - Andrew J Kassianos
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Monica S Y Ng
- Kidney Health Service, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
- Conjoint Internal Medicine Laboratory, Chemical Pathology, Pathology Queensland, Health Support Queensland, Herston, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Nephrology Department, Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Matthew J Roberts
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Department of Urology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
- Centre for Clinical Research, The University of Queensland, Brisbane, QLD, Australia
| | - Simon Wood
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Department of Urology, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Andrew J Mallett
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia.
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
- College of Medicine & Dentistry, James Cook University, Townsville, QLD, Australia.
- Department of Renal Medicine, Townsville University Hospital, Townsville, QLD, Australia.
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Porter LH, Zhu JJ, Lister NL, Harrison SG, Keerthikumar S, Goode DL, Urban RQ, Byrne DJ, Azad A, Vela I, Hofman MS, Neeson PJ, Darcy PK, Trapani JA, Taylor RA, Risbridger GP. Low-dose carboplatin modifies the tumor microenvironment to augment CAR T cell efficacy in human prostate cancer models. Nat Commun 2023; 14:5346. [PMID: 37660083 PMCID: PMC10475084 DOI: 10.1038/s41467-023-40852-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 08/11/2023] [Indexed: 09/04/2023] Open
Abstract
Chimeric antigen receptor (CAR) T cells have transformed the treatment landscape for hematological malignancies. However, CAR T cells are less efficient against solid tumors, largely due to poor infiltration resulting from the immunosuppressive nature of the tumor microenvironment (TME). Here, we assessed the efficacy of Lewis Y antigen (LeY)-specific CAR T cells in patient-derived xenograft (PDX) models of prostate cancer. In vitro, LeY CAR T cells directly killed organoids derived from androgen receptor (AR)-positive or AR-null PDXs. In vivo, although LeY CAR T cells alone did not reduce tumor growth, a single prior dose of carboplatin reduced tumor burden. Carboplatin had a pro-inflammatory effect on the TME that facilitated early and durable CAR T cell infiltration, including an altered cancer-associated fibroblast phenotype, enhanced extracellular matrix degradation and re-oriented M1 macrophage differentiation. In a PDX less sensitive to carboplatin, CAR T cell infiltration was dampened; however, a reduction in tumor burden was still observed with increased T cell activation. These findings indicate that carboplatin improves the efficacy of CAR T cell treatment, with the extent of the response dependent on changes induced within the TME.
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Affiliation(s)
- L H Porter
- Prostate Cancer Research Group, Monash Biomedicine Discovery Institute, Cancer Program, Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, 3800, Australia
| | - J J Zhu
- Cancer Immunology Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - N L Lister
- Prostate Cancer Research Group, Monash Biomedicine Discovery Institute, Cancer Program, Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, 3800, Australia
| | - S G Harrison
- Prostate Cancer Research Group, Monash Biomedicine Discovery Institute, Cancer Program, Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - S Keerthikumar
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Computational Cancer Biology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
| | - D L Goode
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Computational Cancer Biology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
| | - R Quezada Urban
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Computational Cancer Biology Program, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
| | - D J Byrne
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - A Azad
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
| | - I Vela
- Queensland Bladder Cancer Initiative, School of Biomedical Science, Faculty of Health, Queensland University of Technology, Brisbane, QLD, 4102, Australia
- Australian Prostate Cancer Research Center, School of Biomedical Science, Faculty of Health, Queensland University of Technology, Brisbane, QLD, 4102, Australia
- Department of Urology, Princess Alexandra Hospital, Brisbane, QLD, 4102, Australia
| | - M S Hofman
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
- Molecular Imaging and Therapeutic Nuclear Medicine, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Prostate Cancer Theranostics and Imaging Centre of Excellence (ProsTIC), Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
| | - P J Neeson
- Cancer Immunology Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - P K Darcy
- Cancer Immunology Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - J A Trapani
- Cancer Immunology Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - R A Taylor
- Cancer Immunology Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Prostate Cancer Research Group, Monash Biomedicine Discovery Institute, Cancer Program, Department of Physiology, Monash University, Clayton, VIC, 3800, Australia.
- Prostate Cancer Theranostics and Imaging Centre of Excellence (ProsTIC), Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia.
| | - G P Risbridger
- Prostate Cancer Research Group, Monash Biomedicine Discovery Institute, Cancer Program, Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, 3800, Australia.
- Cancer Immunology Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, 3010, Australia.
- Prostate Cancer Theranostics and Imaging Centre of Excellence (ProsTIC), Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia.
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Fiévet L, Espagnolle N, Gerovska D, Bernard D, Syrykh C, Laurent C, Layrolle P, De Lima J, Justo A, Reina N, Casteilla L, Araúzo-Bravo MJ, Naji A, Pagès JC, Deschaseaux F. Single-cell RNA sequencing of human non-hematopoietic bone marrow cells reveals a unique set of inter-species conserved biomarkers for native mesenchymal stromal cells. Stem Cell Res Ther 2023; 14:229. [PMID: 37649081 PMCID: PMC10469496 DOI: 10.1186/s13287-023-03437-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: 02/07/2023] [Accepted: 07/28/2023] [Indexed: 09/01/2023] Open
Abstract
BACKGROUND Native bone marrow (BM) mesenchymal stem/stromal cells (BM-MSCs) participate in generating and shaping the skeleton and BM throughout the lifespan. Moreover, BM-MSCs regulate hematopoiesis by contributing to the hematopoietic stem cell niche in providing critical cytokines, chemokines and extracellular matrix components. However, BM-MSCs contain a heterogeneous cell population that remains ill-defined. Although studies on the taxonomy of native BM-MSCs in mice have just started to emerge, the taxonomy of native human BM-MSCs remains unelucidated. METHODS By using single-cell RNA sequencing (scRNA-seq), we aimed to define a proper taxonomy for native human BM non-hematopoietic subsets including endothelial cells (ECs) and mural cells (MCs) but with a focal point on MSCs. To this end, transcriptomic scRNA-seq data were generated from 5 distinct BM donors and were analyzed together with other transcriptomic data and with computational biology analyses at different levels to identify, characterize and classify distinct native cell subsets with relevant biomarkers. RESULTS We could ascribe novel specific biomarkers to ECs, MCs and MSCs. Unlike ECs and MCs, MSCs exhibited an adipogenic transcriptomic pattern while co-expressing genes related to hematopoiesis support and multilineage commitment potential. Furthermore, by a comparative analysis of scRNA-seq of BM cells from humans and mice, we identified core genes conserved in both species. Notably, we identified MARCKS, CXCL12, PDGFRA, and LEPR together with adipogenic factors as archetypal biomarkers of native MSCs within BM. In addition, our data suggest some complex gene nodes regulating critical biological functions of native BM-MSCs together with a preferential commitment toward an adipocyte lineage. CONCLUSIONS Overall, our taxonomy for native BM non-hematopoietic compartment provides an explicit depiction of gene expression in human ECs, MCs and MSCs at single-cell resolution. This analysis helps enhance our understanding of the phenotype and the complexity of biological functions of native human BM-MSCs.
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Affiliation(s)
- Loïc Fiévet
- RESTORE, Université de Toulouse, EFS Occitanie, INP-ENVT, Inserm U1301, UMR CNRS 5070, France, Université de Toulouse, Toulouse, France
- CHU de Toulouse, IFB, Hôpital Purpan, Toulouse, France
| | - Nicolas Espagnolle
- RESTORE, Université de Toulouse, EFS Occitanie, INP-ENVT, Inserm U1301, UMR CNRS 5070, France, Université de Toulouse, Toulouse, France
| | - Daniela Gerovska
- Group of Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, 20014, San Sebastián, Spain
- Basque Foundation for Science, IKERBASQUE, 48009, Bilbao, Spain
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of Basque Country (UPV/EHU), 48940, Leioa, Spain
| | - David Bernard
- RESTORE, Université de Toulouse, EFS Occitanie, INP-ENVT, Inserm U1301, UMR CNRS 5070, France, Université de Toulouse, Toulouse, France
| | - Charlotte Syrykh
- Department d'Anatomie Pathologique, Institut Universitaire du Cancer, CHU de Toulouse, Toulouse, France
| | - Camille Laurent
- Department d'Anatomie Pathologique, Institut Universitaire du Cancer, CHU de Toulouse, Toulouse, France
| | - Pierre Layrolle
- Tonic Inserm/UPS UMR 1214, CHU Purpan Hospital, Toulouse, France
- UMR 1238 Inserm, Phy-OS, Bone Sarcoma and Remodeling of Calcified Tissues, School of Medicine, University of Nantes, Nantes, France
| | - Julien De Lima
- UMR 1238 Inserm, Phy-OS, Bone Sarcoma and Remodeling of Calcified Tissues, School of Medicine, University of Nantes, Nantes, France
| | - Arthur Justo
- Department de Chirurgie Orthopédique, Pierre Paul Riquet, Hôpital Purpan, Toulouse, France
| | - Nicolas Reina
- Department de Chirurgie Orthopédique, Pierre Paul Riquet, Hôpital Purpan, Toulouse, France
| | - Louis Casteilla
- RESTORE, Université de Toulouse, EFS Occitanie, INP-ENVT, Inserm U1301, UMR CNRS 5070, France, Université de Toulouse, Toulouse, France
| | - Marcos J Araúzo-Bravo
- Group of Computational Biology and Systems Biomedicine, Biodonostia Health Research Institute, 20014, San Sebastián, Spain
- Basque Foundation for Science, IKERBASQUE, 48009, Bilbao, Spain
- Department of Cell Biology and Histology, Faculty of Medicine and Nursing, University of Basque Country (UPV/EHU), 48940, Leioa, Spain
| | - Abderrahim Naji
- Department of Environmental Medicine, Cooperative Medicine Unit, Research and Education Faculty, Medicine Science Cluster, Nankoku, Kochi Prefecture, Japan
| | - Jean-Christophe Pagès
- RESTORE, Université de Toulouse, EFS Occitanie, INP-ENVT, Inserm U1301, UMR CNRS 5070, France, Université de Toulouse, Toulouse, France
- CHU de Toulouse, IFB, Hôpital Purpan, Toulouse, France
| | - Frédéric Deschaseaux
- RESTORE, Université de Toulouse, EFS Occitanie, INP-ENVT, Inserm U1301, UMR CNRS 5070, France, Université de Toulouse, Toulouse, France.
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Oh K, Yoo YJ, Torre-Healy LA, Rao M, Fassler D, Wang P, Caponegro M, Gao M, Kim J, Sasson A, Georgakis G, Powers S, Moffitt RA. Coordinated single-cell tumor microenvironment dynamics reinforce pancreatic cancer subtype. Nat Commun 2023; 14:5226. [PMID: 37633924 PMCID: PMC10460409 DOI: 10.1038/s41467-023-40895-6] [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: 04/16/2022] [Accepted: 08/14/2023] [Indexed: 08/28/2023] Open
Abstract
Bulk analyses of pancreatic ductal adenocarcinoma (PDAC) samples are complicated by the tumor microenvironment (TME), i.e. signals from fibroblasts, endocrine, exocrine, and immune cells. Despite this, we and others have established tumor and stroma subtypes with prognostic significance. However, understanding of underlying signals driving distinct immune and stromal landscapes is still incomplete. Here we integrate 92 single cell RNA-seq samples from seven independent studies to build a reproducible PDAC atlas with a focus on tumor-TME interdependence. Patients with activated stroma are synonymous with higher myofibroblastic and immunogenic fibroblasts, and furthermore show increased M2-like macrophages and regulatory T-cells. Contrastingly, patients with 'normal' stroma show M1-like recruitment, elevated effector and exhausted T-cells. To aid interoperability of future studies, we provide a pretrained cell type classifier and an atlas of subtype-based signaling factors that we also validate in mouse data. Ultimately, this work leverages the heterogeneity among single-cell studies to create a comprehensive view of the orchestra of signaling interactions governing PDAC.
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Affiliation(s)
- Ki Oh
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Yun Jae Yoo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Luke A Torre-Healy
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Manisha Rao
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Department of Pathology, Stony Brook University, Stony Brook, NY, USA
| | - Danielle Fassler
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Pei Wang
- Department of Cell Systems & Anatomy, University of Texas Health Science Center, San Antonio, TX, USA
| | - Michael Caponegro
- Department of Pharmacology, Stony Brook University, Stony Brook, NY, USA
| | - Mei Gao
- Department of Surgery, University of Kentucky and Markey Cancer Center, Lexington, KY, USA
| | - Joseph Kim
- Department of Surgery, University of Kentucky and Markey Cancer Center, Lexington, KY, USA
| | - Aaron Sasson
- Department of Surgery, Stony Brook University, Stony Brook, NY, USA
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA
| | - Georgios Georgakis
- Department of Surgery, Stony Brook University, Stony Brook, NY, USA
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA
| | - Scott Powers
- Department of Pathology, Stony Brook University, Stony Brook, NY, USA
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA.
- Department of Hematology and Medical Oncology, Emory University, Atlanta, GA, USA.
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA.
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35
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Wang Y, Sarfraz I, Pervaiz N, Hong R, Koga Y, Akavoor V, Cao X, Alabdullatif S, Zaib SA, Wang Z, Jansen F, Yajima M, Johnson WE, Campbell JD. Interactive analysis of single-cell data using flexible workflows with SCTK2. PATTERNS (NEW YORK, N.Y.) 2023; 4:100814. [PMID: 37602214 PMCID: PMC10436054 DOI: 10.1016/j.patter.2023.100814] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 03/27/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Analysis of single-cell RNA sequencing (scRNA-seq) data can reveal novel insights into the heterogeneity of complex biological systems. Many tools and workflows have been developed to perform different types of analyses. However, these tools are spread across different packages or programming environments, rely on different underlying data structures, and can only be utilized by people with knowledge of programming languages. In the Single-Cell Toolkit 2 (SCTK2), we have integrated a variety of popular tools and workflows to perform various aspects of scRNA-seq analysis. All tools and workflows can be run in the R console or using an intuitive graphical user interface built with R/Shiny. HTML reports generated with Rmarkdown can be used to document and recapitulate individual steps or entire analysis workflows. We show that the toolkit offers more features when compared with existing tools and allows for a seamless analysis of scRNA-seq data for non-computational users.
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Affiliation(s)
- Yichen Wang
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Irzam Sarfraz
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Nida Pervaiz
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Rui Hong
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Yusuke Koga
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Vidya Akavoor
- Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA
| | - Xinyun Cao
- Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA
| | - Salam Alabdullatif
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Syed Ali Zaib
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Zhe Wang
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Frederick Jansen
- Software & Application Innovation Lab, Rafik B. Hariri Institute for Computing and Computational Science and Engineering, Boston, MA, USA
| | - Masanao Yajima
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - W. Evan Johnson
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
| | - Joshua D. Campbell
- Bioinformatics Program, Boston University, Boston, MA, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA
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36
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Xing L, Simon JM, Ptacek TS, Yi JJ, Loo L, Mao H, Wolter JM, McCoy ES, Paranjape SR, Taylor-Blake B, Zylka MJ. Autism-linked UBE3A gain-of-function mutation causes interneuron and behavioral phenotypes when inherited maternally or paternally in mice. Cell Rep 2023; 42:112706. [PMID: 37389991 PMCID: PMC10530456 DOI: 10.1016/j.celrep.2023.112706] [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: 08/17/2022] [Revised: 04/15/2023] [Accepted: 06/12/2023] [Indexed: 07/02/2023] Open
Abstract
The E3 ubiquitin ligase Ube3a is biallelically expressed in neural progenitors and glial cells, suggesting that UBE3A gain-of-function mutations might cause neurodevelopmental disorders irrespective of parent of origin. Here, we engineered a mouse line that harbors an autism-linked UBE3AT485A (T503A in mouse) gain-of-function mutation and evaluated phenotypes in animals that inherited the mutant allele paternally, maternally, or from both parents. We find that paternally and maternally expressed UBE3AT503A results in elevated UBE3A activity in neural progenitors and glial cells. Expression of UBE3AT503A from the maternal allele, but not the paternal one, leads to a persistent elevation of UBE3A activity in neurons. Mutant mice display behavioral phenotypes that differ by parent of origin. Expression of UBE3AT503A, irrespective of its parent of origin, promotes transient embryonic expansion of Zcchc12 lineage interneurons. Phenotypes of Ube3aT503A mice are distinct from Angelman syndrome model mice. Our study has clinical implications for a growing number of disease-linked UBE3A gain-of-function mutations.
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Affiliation(s)
- Lei Xing
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jeremy M Simon
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Campus Box #7255, Chapel Hill, NC 27599, USA; Department of Genetics, The University of North Carolina at Chapel Hill, Campus Box #7264, Chapel Hill, NC 27599, USA
| | - Travis S Ptacek
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Campus Box #7255, Chapel Hill, NC 27599, USA
| | - Jason J Yi
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Campus Box #7255, Chapel Hill, NC 27599, USA
| | - Lipin Loo
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hanqian Mao
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Campus Box #7255, Chapel Hill, NC 27599, USA
| | - Justin M Wolter
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Campus Box #7255, Chapel Hill, NC 27599, USA; Department of Genetics, The University of North Carolina at Chapel Hill, Campus Box #7264, Chapel Hill, NC 27599, USA
| | - Eric S McCoy
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Smita R Paranjape
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bonnie Taylor-Blake
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mark J Zylka
- UNC Neuroscience Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Campus Box #7255, Chapel Hill, NC 27599, USA.
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37
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Olivier T, Blomet J, Desmecht D. Central role of lung macrophages in SARS-CoV-2 physiopathology: a cross-model single-cell RNA-seq perspective. Front Immunol 2023; 14:1197588. [PMID: 37350967 PMCID: PMC10282834 DOI: 10.3389/fimmu.2023.1197588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/19/2023] [Indexed: 06/24/2023] Open
Abstract
Cytokine storms are considered a driving factor in coronavirus disease 2019 (COVID-19) severity. However, the triggering and resolution of this cytokine production, as well as the link between this phenomenon and infected cells, are still poorly understood. In this study, a cross-species scRNA-seq analysis showed that cytokine-producing macrophages together with pneumocytes were found to be the main contributors of viral transcripts in both Syrian hamsters and African green monkeys. Whatever the cell type, viral read-bearing cells show an apoptotic phenotype. A comparison of SARS-CoV-2 entry receptor candidates showed that Fc receptors are better correlated with infected cells than ACE2, NRP1, or AXL. Although both species show similar interferon responses, differences in adaptive immunity were highlighted. Lastly, Fc receptor and cytokine upregulation in M1 macrophages was found to correlate with a comprehensive interferon response. Based on these results, we propose a model in which lung macrophages play a central role in COVID-19 severity through antibody-dependent enhancement.
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Affiliation(s)
- Thibaut Olivier
- GAS Department, Prevor Research Laboratories, Valmondois, France
- Department of Pathology, Fundamental and Applied Research for Animals & Health (FARAH), Faculty of Veterinary Medicine, University of Liege, Liège, Belgium
| | - Joël Blomet
- GAS Department, Prevor Research Laboratories, Valmondois, France
| | - Daniel Desmecht
- Department of Pathology, Fundamental and Applied Research for Animals & Health (FARAH), Faculty of Veterinary Medicine, University of Liege, Liège, Belgium
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38
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Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, Wang Y, Pollard J, Mendez M, Hill J, Kumar N, Cao X, Chen X, Khaladkar M, Wen J, Leach A, Ferran E. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 2023; 22:496-520. [PMID: 37117846 PMCID: PMC10141847 DOI: 10.1038/s41573-023-00688-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2023] [Indexed: 04/30/2023]
Abstract
Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.
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Affiliation(s)
| | | | | | - Bart Naughton
- Computational Neurobiology, Eisai, Cambridge, MA, USA
| | - Wendi Bacon
- EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
- The Open University, Milton Keynes, UK
| | | | - Yong Wang
- Precision Bioinformatics, Prometheus Biosciences, San Diego, CA, USA
| | | | - Melissa Mendez
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | - Jon Hill
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Namit Kumar
- Informatics & Predictive Sciences, Bristol Myers Squibb, San Diego, CA, USA
| | - Xiaohong Cao
- Genomic Research Center, AbbVie Inc., Cambridge, MA, USA
| | - Xiao Chen
- Magnet Biomedicine, Cambridge, MA, USA
| | - Mugdha Khaladkar
- Human Genetics and Computational Biology, GlaxoSmithKline, Collegeville, PA, USA
| | - Ji Wen
- Oncology Research and Development Unit, Pfizer, La Jolla, CA, USA
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39
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Stuart T, Hao S, Zhang B, Mekerishvili L, Landau DA, Maniatis S, Satija R, Raimondi I. Nanobody-tethered transposition enables multifactorial chromatin profiling at single-cell resolution. Nat Biotechnol 2023; 41:806-812. [PMID: 36536150 PMCID: PMC10272075 DOI: 10.1038/s41587-022-01588-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
Chromatin states are functionally defined by a complex combination of histone modifications, transcription factor binding, DNA accessibility and other factors. Current methods for defining chromatin states cannot measure more than one aspect in a single experiment at single-cell resolution. Here we introduce nanobody-tethered transposition followed by sequencing (NTT-seq), an assay capable of measuring the genome-wide presence of up to three histone modifications and protein-DNA binding sites at single-cell resolution. NTT-seq uses recombinant Tn5 transposase fused to a set of secondary nanobodies (nb). Each nb-Tn5 fusion protein specifically binds to different immunoglobulin-G antibodies, enabling a mixture of primary antibodies binding different epitopes to be used in a single experiment. We apply bulk-cell and single-cell NTT-seq to generate high-resolution multimodal maps of chromatin states in cell culture and in human immune cells. We also extend NTT-seq to enable simultaneous profiling of cell surface protein expression and multimodal chromatin states to study cells of the immune system.
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Affiliation(s)
- Tim Stuart
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Stephanie Hao
- Technology Innovation Lab, New York Genome Center, New York, NY, USA
| | - Bingjie Zhang
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Levan Mekerishvili
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Dan A Landau
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Silas Maniatis
- Technology Innovation Lab, New York Genome Center, New York, NY, USA
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Ivan Raimondi
- Technology Innovation Lab, New York Genome Center, New York, NY, USA.
- Weill Cornell Medicine, New York, NY, USA.
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40
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Suppinger S, Zinner M, Aizarani N, Lukonin I, Ortiz R, Azzi C, Stadler MB, Vianello S, Palla G, Kohler H, Mayran A, Lutolf MP, Liberali P. Multimodal characterization of murine gastruloid development. Cell Stem Cell 2023:S1934-5909(23)00170-4. [PMID: 37209681 DOI: 10.1016/j.stem.2023.04.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/16/2023] [Accepted: 04/25/2023] [Indexed: 05/22/2023]
Abstract
Gastruloids are 3D structures generated from pluripotent stem cells recapitulating fundamental principles of embryonic pattern formation. Using single-cell genomic analysis, we provide a resource mapping cell states and types during gastruloid development and compare them with the in vivo embryo. We developed a high-throughput handling and imaging pipeline to spatially monitor symmetry breaking during gastruloid development and report an early spatial variability in pluripotency determining a binary response to Wnt activation. Although cells in the gastruloid-core revert to pluripotency, peripheral cells become primitive streak-like. These two populations subsequently break radial symmetry and initiate axial elongation. By performing a compound screen, perturbing thousands of gastruloids, we derive a phenotypic landscape and infer networks of genetic interactions. Finally, using a dual Wnt modulation, we improve the formation of anterior structures in the existing gastruloid model. This work provides a resource to understand how gastruloids develop and generate complex patterns in vitro.
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Affiliation(s)
- Simon Suppinger
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland; University of Basel, 4001 Basel, Switzerland
| | - Marietta Zinner
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland
| | - Nadim Aizarani
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland
| | - Ilya Lukonin
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland; Roche Institute of Human Biology, 4058 Basel, Switzerland
| | - Raphael Ortiz
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland
| | - Chiara Azzi
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland; Babraham Institute, Cambridge CB22 3AT, UK
| | - Michael B Stadler
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland; University of Basel, 4001 Basel, Switzerland; Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - Stefano Vianello
- School of Life Sciences, Federal Institute of Technology EPFL, 1015 Lausanne, Switzerland
| | - Giovanni Palla
- Institute of Computational Biology, Helmholtz Center Munich, 85764 Munich, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, 80333 Munich, Germany
| | - Hubertus Kohler
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland
| | - Alexandre Mayran
- School of Life Sciences, Federal Institute of Technology EPFL, 1015 Lausanne, Switzerland
| | - Matthias P Lutolf
- Roche Institute of Human Biology, 4058 Basel, Switzerland; School of Life Sciences, Federal Institute of Technology EPFL, 1015 Lausanne, Switzerland
| | - Prisca Liberali
- Friedrich Miescher Institute for Biomedical Research (FMI), 4058 Basel, Switzerland; University of Basel, 4001 Basel, Switzerland.
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41
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He D, Patro R. simpleaf: A simple, flexible, and scalable framework for single-cell transcriptomics data processing using alevin-fry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.28.534653. [PMID: 37034702 PMCID: PMC10081176 DOI: 10.1101/2023.03.28.534653] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Summary The alevin-fry ecosystem provides a robust and growing suite of programs for single-cell data processing. However, as new single-cell technologies are introduced, as the community continues to adjust best practices for data processing, and as the alevin-fry ecosystem itself expands and grows, it is becoming increasingly important to manage the complexity of alevin-fry ’s single-cell preprocessing workflows while retaining the performance and flexibility that make these tools enticing. We introduce simpleaf , a program that simplifies the processing of single-cell data using tools from the alevin-fry ecosystem, and adds new functionality and capabilities, while retaining the flexibility and performance of the underlying tools. Availability and implementation Simpleaf is written in Rust and released under a BSD 3-Clause license. It is freely available from its GitHub repository https://github.com/COMBINE-lab/simpleaf , and via bioconda. Documentation for simpleaf is available at https://simpleaf.readthedocs.io/en/latest/ and tutorials for simpleaf are being developed that can be accessed at https://combine-lab.github.io/alevin-fry-tutorials .
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Affiliation(s)
- Dongze He
- Department of Cell Biology and Molecular Genetics and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Rob Patro
- Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
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42
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Gervais O, Peñaloza C, Gratacap R, Papadopoulou A, Beltrán M, Henderson NC, Houston RD, Hassan MA, Robledo D. Understanding host response to infectious salmon anaemia virus in an Atlantic salmon cell line using single-cell RNA sequencing. BMC Genomics 2023; 24:161. [PMID: 36991327 DOI: 10.1186/s12864-023-09254-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 03/17/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Infectious Salmon Anaemia Virus (ISAV) is an Orthomixovirus that represents a large problem for salmonid aquaculture worldwide. Current prevention and treatment methods are only partially effective. Genetic selection and genome engineering have the potential to develop ISAV resistant salmon stocks. Both strategies can benefit from an improved understanding of the genomic regulation of ISAV pathogenesis. Here, we used single-cell RNA sequencing of an Atlantic salmon cell line to provide the first high dimensional insight into the transcriptional landscape that underpins host-virus interaction during early ISAV infection. RESULTS Salmon head kidney (SHK-1) cells were single-cell RNA sequenced at 24, 48 and 96 h post-ISAV challenge. At 24 h post infection, cells showed expression signatures consistent with viral entry, with genes such as PI3K, FAK or JNK being upregulated relative to uninfected cells. At 48 and 96 h, infected cells showed a clear anti-viral response, characterised by the expression of IFNA2 or IRF2. Uninfected bystander cells at 48 and 96 h also showed clear transcriptional differences, potentially suggesting paracrine signalling from infected cells. These bystander cells expressed pathways such as mRNA sensing, RNA degradation, ubiquitination or proteasome; and up-regulation of mitochondrial ribosome genes also seemed to play a role in the host response to the infection. Correlation between viral and host genes revealed novel genes potentially key for this fish-virus interaction. CONCLUSIONS This study has increased our understanding of the cellular response of Atlantic salmon during ISAV infection and revealed host-virus interactions at the cellular level. Our results highlight various potential key genes in this host-virus interaction, which can be manipulated in future functional studies to increase the resistance of Atlantic salmon to ISAV.
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Affiliation(s)
- Ophélie Gervais
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, UK
| | - Carolina Peñaloza
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, UK
| | - Remi Gratacap
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, UK
| | - Athina Papadopoulou
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, UK
| | - Mariana Beltrán
- Centre for Inflammation Research, the Queen´s Medical Research Institute, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Neil C Henderson
- Centre for Inflammation Research, the Queen´s Medical Research Institute, University of Edinburgh, Edinburgh, EH16 4TJ, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Crewe Road South, Edinburgh, UK
| | - Ross D Houston
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, UK
- Benchmark Genetics, 1 Pioneer Building, Edinburgh Technopole, Penicuik, EH26 0GB, UK
| | - Musa A Hassan
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, UK
| | - Diego Robledo
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, UK.
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43
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Naydenov DD, Vashukova ES, Barbitoff YA, Nasykhova YA, Glotov AS. Current Status and Prospects of the Single-Cell Sequencing Technologies for Revealing the Pathogenesis of Pregnancy-Associated Disorders. Genes (Basel) 2023; 14:756. [PMID: 36981026 PMCID: PMC10048492 DOI: 10.3390/genes14030756] [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: 01/13/2023] [Revised: 03/12/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is a method that focuses on the analysis of gene expression profile in individual cells. This method has been successfully applied to answer the challenging questions of the pathogenesis of multifactorial diseases and open up new possibilities in the prognosis and prevention of reproductive diseases. In this article, we have reviewed the application of scRNA-seq to the analysis of the various cell types and their gene expression changes in normal pregnancy and pregnancy complications. The main principle, advantages, and limitations of single-cell technologies and data analysis methods are described. We discuss the possibilities of using the scRNA-seq method for solving the fundamental and applied tasks related to various pregnancy-associated disorders. Finally, we provide an overview of the scRNA-seq findings for the common pregnancy-associated conditions, such as hyperglycemia in pregnancy, recurrent pregnancy loss, preterm labor, polycystic ovary syndrome, and pre-eclampsia.
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Affiliation(s)
- Dmitry D. Naydenov
- Faculty of Biology, St. Petersburg State University, 199034 Saint-Petersburg, Russia
| | - Elena S. Vashukova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia
| | - Yury A. Barbitoff
- Faculty of Biology, St. Petersburg State University, 199034 Saint-Petersburg, Russia
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia
| | - Yulia A. Nasykhova
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia
| | - Andrey S. Glotov
- Faculty of Biology, St. Petersburg State University, 199034 Saint-Petersburg, Russia
- D. O. Ott Research Institute of Obstetrics, Gynaecology and Reproductology, 199034 Saint-Petersburg, Russia
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44
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Păun O, Tan YX, Patel H, Strohbuecker S, Ghanate A, Cobolli-Gigli C, Llorian Sopena M, Gerontogianni L, Goldstone R, Ang SL, Guillemot F, Dias C. Pioneer factor ASCL1 cooperates with the mSWI/SNF complex at distal regulatory elements to regulate human neural differentiation. Genes Dev 2023; 37:218-242. [PMID: 36931659 PMCID: PMC10111863 DOI: 10.1101/gad.350269.122] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/28/2023] [Indexed: 03/19/2023]
Abstract
Pioneer transcription factors are thought to play pivotal roles in developmental processes by binding nucleosomal DNA to activate gene expression, though mechanisms through which pioneer transcription factors remodel chromatin remain unclear. Here, using single-cell transcriptomics, we show that endogenous expression of neurogenic transcription factor ASCL1, considered a classical pioneer factor, defines a transient population of progenitors in human neural differentiation. Testing ASCL1's pioneer function using a knockout model to define the unbound state, we found that endogenous expression of ASCL1 drives progenitor differentiation by cis-regulation both as a classical pioneer factor and as a nonpioneer remodeler, where ASCL1 binds permissive chromatin to induce chromatin conformation changes. ASCL1 interacts with BAF SWI/SNF chromatin remodeling complexes, primarily at targets where it acts as a nonpioneer factor, and we provide evidence for codependent DNA binding and remodeling at a subset of ASCL1 and SWI/SNF cotargets. Our findings provide new insights into ASCL1 function regulating activation of long-range regulatory elements in human neurogenesis and uncover a novel mechanism of its chromatin remodeling function codependent on partner ATPase activity.
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Affiliation(s)
- Oana Păun
- Neural Stem Cell Biology Laboratory, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Yu Xuan Tan
- Neural Stem Cell Biology Laboratory, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Harshil Patel
- Bioinformatics and Biostatistics Science and Technology Platform, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Stephanie Strohbuecker
- Bioinformatics and Biostatistics Science and Technology Platform, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Avinash Ghanate
- Bioinformatics and Biostatistics Science and Technology Platform, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Clementina Cobolli-Gigli
- Neural Stem Cell Biology Laboratory, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Miriam Llorian Sopena
- Bioinformatics and Biostatistics Science and Technology Platform, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Lina Gerontogianni
- Bioinformatics and Biostatistics Science and Technology Platform, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Robert Goldstone
- Bioinformatics and Biostatistics Science and Technology Platform, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - Siew-Lan Ang
- Neural Stem Cell Biology Laboratory, the Francis Crick Institute, London NW1 1AT, United Kingdom
| | - François Guillemot
- Neural Stem Cell Biology Laboratory, the Francis Crick Institute, London NW1 1AT, United Kingdom;
| | - Cristina Dias
- Neural Stem Cell Biology Laboratory, the Francis Crick Institute, London NW1 1AT, United Kingdom;
- Medical and Molecular Genetics, School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King's College London, London SE1 9RT, United Kingdom
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45
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Lonsdale A, Halman A, Brown L, Kosasih H, Ekert P, Oshlack A. Toblerone: detecting exon deletion events in cancer using RNA-seq. F1000Res 2023; 12:130. [PMID: 37767021 PMCID: PMC10521068 DOI: 10.12688/f1000research.129490.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/27/2023] [Indexed: 09/29/2023] Open
Abstract
Cancer is driven by mutations of the genome that can result in the activation of oncogenes or repression of tumour suppressor genes. In acute lymphoblastic leukemia (ALL) focal deletions in IKAROS family zinc finger 1 (IKZF1) result in the loss of zinc-finger DNA-binding domains and a dominant negative isoform that is associated with higher rates of relapse and poorer patient outcomes. Clinically, the presence of IKZF1 deletions informs prognosis and treatment options. In this work we developed a method for detecting exon deletions in genes using RNA-seq with application to IKZF1. We developed a pipeline that first uses a custom transcriptome reference consisting of transcripts with exon deletions. Next, RNA-seq reads are mapped using a pseudoalignment algorithm to identify reads that uniquely support deletions. These are then evaluated for evidence of the deletion with respect to gene expression and other samples. We applied the algorithm, named Toblerone, to a cohort of 99 B-ALL paediatric samples including validated IKZF1 deletions. Furthermore, we developed a graphical desktop app for non-bioinformatics users that can quickly and easily identify and report deletions in IKZF1 from RNA-seq data with informative graphical outputs.
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Affiliation(s)
- Andrew Lonsdale
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, 3010, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, 3052, Australia
- Peter MacCallum Cancer Centre, Parkville, VIC, 3052, Australia
| | - Andreas Halman
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, 3010, Australia
- Peter MacCallum Cancer Centre, Parkville, VIC, 3052, Australia
| | - Lauren Brown
- Murdoch Children’s Research Institute, Parkville, VIC, 3052, Australia
- School of Women’s and Children’s Health, UNSW Sydney, Sydney, NSW, 2052, Australia
- Children's Cancer Institute Australia, Sydney, NSW, 2052, Australia
| | - Hansen Kosasih
- Murdoch Children’s Research Institute, Parkville, VIC, 3052, Australia
| | - Paul Ekert
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, 3010, Australia
- Murdoch Children’s Research Institute, Parkville, VIC, 3052, Australia
- Peter MacCallum Cancer Centre, Parkville, VIC, 3052, Australia
- School of Women’s and Children’s Health, UNSW Sydney, Sydney, NSW, 2052, Australia
- Children's Cancer Institute Australia, Sydney, NSW, 2052, Australia
| | - Alicia Oshlack
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, 3010, Australia
- Peter MacCallum Cancer Centre, Parkville, VIC, 3052, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, 3010, Australia
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46
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Lazure F, Farouni R, Sahinyan K, Blackburn DM, Hernández-Corchado A, Perron G, Lu T, Osakwe A, Ragoussis J, Crist C, Perkins TJ, Jahani-Asl A, Najafabadi HS, Soleimani VD. Transcriptional reprogramming of skeletal muscle stem cells by the niche environment. Nat Commun 2023; 14:535. [PMID: 36726011 PMCID: PMC9892560 DOI: 10.1038/s41467-023-36265-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 01/23/2023] [Indexed: 02/03/2023] Open
Abstract
Adult stem cells are indispensable for tissue regeneration, but their function declines with age. The niche environment in which the stem cells reside plays a critical role in their function. However, quantification of the niche effect on stem cell function is lacking. Using muscle stem cells (MuSC) as a model, we show that aging leads to a significant transcriptomic shift in their subpopulations accompanied by locus-specific gain and loss of chromatin accessibility and DNA methylation. By combining in vivo MuSC transplantation and computational methods, we show that the expression of approximately half of all age-altered genes in MuSCs from aged male mice can be restored by exposure to a young niche environment. While there is a correlation between gene reversibility and epigenetic alterations, restoration of gene expression occurs primarily at the level of transcription. The stem cell niche environment therefore represents an important therapeutic target to enhance tissue regeneration in aging.
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Affiliation(s)
- Felicia Lazure
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada
| | - Rick Farouni
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada.,McGill Genome Centre, Victor Phillip Dahdaleh Institute of Genomic Medicine, 740 Dr Penfield Avenue, Montreal, QC, H3A 0G1, Canada
| | - Korin Sahinyan
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada
| | - Darren M Blackburn
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada
| | - Aldo Hernández-Corchado
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada.,McGill Genome Centre, Victor Phillip Dahdaleh Institute of Genomic Medicine, 740 Dr Penfield Avenue, Montreal, QC, H3A 0G1, Canada
| | - Gabrielle Perron
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada.,McGill Genome Centre, Victor Phillip Dahdaleh Institute of Genomic Medicine, 740 Dr Penfield Avenue, Montreal, QC, H3A 0G1, Canada
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada.,Quantitative Life Sciences, McGill University, Montreal, Canada
| | - Adrien Osakwe
- Quantitative Life Sciences, McGill University, Montreal, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada.,McGill Genome Centre, Victor Phillip Dahdaleh Institute of Genomic Medicine, 740 Dr Penfield Avenue, Montreal, QC, H3A 0G1, Canada
| | - Colin Crist
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada
| | - Theodore J Perkins
- Sprott Center for Stem Cell Research, Ottawa Hospital Research Institute, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada.,Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, K1H 8M5, Canada
| | - Arezu Jahani-Asl
- Department of Cellular and Molecular Medicine and University of Ottawa Brain and Mind Research Institute, University of Ottawa, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada
| | - Hamed S Najafabadi
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada. .,McGill Genome Centre, Victor Phillip Dahdaleh Institute of Genomic Medicine, 740 Dr Penfield Avenue, Montreal, QC, H3A 0G1, Canada. .,Quantitative Life Sciences, McGill University, Montreal, Canada.
| | - Vahab D Soleimani
- Department of Human Genetics, McGill University, 3640 rue University, Montréal, QC, H3A 0C7, Canada. .,Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada.
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47
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Zhang B, Upadhyay R, Hao Y, Samanovic MI, Herati RS, Blair J, Axelrad J, Mulligan MJ, Littman DR, Satija R. Multimodal characterization of antigen-specific CD8 + T cells across SARS-CoV-2 vaccination and infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.24.525203. [PMID: 36747786 PMCID: PMC9900816 DOI: 10.1101/2023.01.24.525203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The human immune response to SARS-CoV-2 antigen after infection or vaccination is defined by the durable production of antibodies and T cells. Population-based monitoring typically focuses on antibody titer, but there is a need for improved characterization and quantification of T cell responses. Here, we utilize multimodal sequencing technologies to perform a longitudinal analysis of circulating human leukocytes collected before and after BNT162b2 immunization. Our data reveal distinct subpopulations of CD8 + T cells which reliably appear 28 days after prime vaccination (7 days post boost). Using a suite of cross-modality integration tools, we define their transcriptome, accessible chromatin landscape, and immunophenotype, and identify unique biomarkers within each modality. By leveraging DNA-oligo-tagged peptide-MHC multimers and T cell receptor sequencing, we demonstrate that this vaccine-induced population is SARS-CoV-2 antigen-specific and capable of rapid clonal expansion. Moreover, we also identify these CD8 + populations in scRNA-seq datasets from COVID-19 patients and find that their relative frequency and differentiation outcomes are predictive of subsequent clinical outcomes. Our work contributes to our understanding of T cell immunity, and highlights the potential for integrative and multimodal analysis to characterize rare cell populations.
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Affiliation(s)
- Bingjie Zhang
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- Department of Cell Biology and Regenerative Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Rabi Upadhyay
- Department of Cell Biology and Regenerative Medicine, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Yuhan Hao
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Marie I. Samanovic
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - Ramin S. Herati
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - John Blair
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Jordan Axelrad
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Mark J. Mulligan
- Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
- New York University Langone Vaccine Center, New York, NY, USA
| | - Dan R. Littman
- Department of Cell Biology and Regenerative Medicine, New York University Grossman School of Medicine, New York, NY, USA
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA
- Howard Hughes Medical Institute, New York, NY, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
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48
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Peng X, Dorman KS. Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers. Bioinformatics 2023; 39:6971842. [PMID: 36610988 PMCID: PMC9891248 DOI: 10.1093/bioinformatics/btad002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 11/16/2022] [Accepted: 01/04/2023] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Amplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during polymerase chain reaction (PCR) and sequencing. One solution attaches unique molecular identifiers (UMIs) to sample sequences before amplification. Counting UMIs instead of sequences provides unbiased estimates of abundance. While modern methods improve over naïve counting by UMI identity, most do not account for UMI reuse or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences. RESULTS We introduce Deduplication and Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological amplicon sequences and accurately estimate their deduplicated abundance. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods. AVAILABILITY AND IMPLEMENTATION Source code is available at https://github.com/DormanLab/AmpliCI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiyu Peng
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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49
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He D, Soneson C, Patro R. Understanding and evaluating ambiguity in single-cell and single-nucleus RNA-sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.04.522742. [PMID: 36711921 PMCID: PMC9881993 DOI: 10.1101/2023.01.04.522742] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Recently, a new modification has been proposed by Hjörleifsson and Sullivan et al. to the model used to classify the splicing status of reads (as spliced (mature), unspliced (nascent), or ambiguous) in single-cell and single-nucleus RNA-seq data. Here, we evaluate both the theoretical basis and practical implementation of the proposed method. The proposed method is highly-conservative, and therefore, unlikely to mischaracterize reads as spliced (mature) or unspliced (nascent) when they are not. However, we find that it leaves a large fraction of reads classified as ambiguous, and, in practice, allocates these ambiguous reads in an all-or-nothing manner, and differently between single-cell and single-nucleus RNA-seq data. Further, as implemented in practice, the ambiguous classification is implicit and based on the index against which the reads are mapped, which leads to several drawbacks compared to methods that consider both spliced (mature) and unspliced (nascent) mapping targets simultaneously - for example, the ability to use confidently assigned reads to rescue ambiguous reads based on shared UMIs and gene targets. Nonetheless, we show that these conservative assignment rules can be obtained directly in existing approaches simply by altering the set of targets that are indexed. To this end, we introduce the spliceu reference and show that its use with alevin-fry recapitulates the more conservative proposed classification. We also observe that, on experimental data, and under the proposed allocation rules for ambiguous UMIs, the difference between the proposed classification scheme and existing conventions appears much smaller than previously reported. We demonstrate the use of the new piscem index for mapping simultaneously against spliced (mature) and unspliced (nascent) targets, allowing classification against the full nascent and mature transcriptome in human or mouse in <3GB of memory. Finally, we discuss the potential of incorporating probabilistic evidence into the inference of splicing status, and suggest that it may provide benefits beyond what can be obtained from discrete classification of UMIs as splicing-ambiguous.
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Affiliation(s)
- Dongze He
- Department of Cell Biology and Molecular Genetics and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
| | - Charlotte Soneson
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Rob Patro
- Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
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50
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Sun S, Shen X, Li Y, Li Y, Wang S, Li R, Zhang H, Shen G, Guo B, Wei J, Xu J, St-Pierre B, Chen S, Sun C. Single-cell RNA sequencing provides a high-resolution roadmap for understanding the multicellular compartmentation of specialized metabolism. NATURE PLANTS 2023; 9:179-190. [PMID: 36522449 DOI: 10.1038/s41477-022-01291-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 10/26/2022] [Indexed: 06/17/2023]
Abstract
Monoterpenoid indole alkaloids (MIAs) are among the most diverse specialized metabolites in plants and are of great pharmaceutical importance. We leveraged single-cell transcriptomics to explore the spatial organization of MIA metabolism in Catharanthus roseus leaves, and the transcripts of 20 MIA genes were first localized, updating the model of MIA biosynthesis. The MIA pathway was partitioned into three cell types, consistent with the results from RNA in situ hybridization experiments. Several candidate transporters were predicted to be essential players shuttling MIA intermediates between inter- and intracellular compartments, supplying potential targets to increase the overall yields of desirable MIAs in native plants or heterologous hosts through metabolic engineering and synthetic biology. This work provides not only a universal roadmap for elucidating the spatiotemporal distribution of biological processes at single-cell resolution, but also abundant cellular and genetic resources for further investigation of the higher-order organization of MIA biosynthesis, transport and storage.
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Affiliation(s)
- Sijie Sun
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaofeng Shen
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yi Li
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ying Li
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shu Wang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Rucan Li
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huibo Zhang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guoan Shen
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Baolin Guo
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianhe Wei
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang Xu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Benoit St-Pierre
- EA2106 Biomolécules et Biotechnologies Végétales, Université de Tours, Tours, France.
| | - Shilin Chen
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China.
- Chengdu University of Traditional Chinese Medicine, Chengdu, China.
| | - Chao Sun
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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