Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses

SummaryHigh-dimensional data, such as those generated using single-cell RNA sequencing, present challenges in interpretation and visualization. Numerical and computational methods for dimensionality reduction allow for low-dimensional representation of genome-scale expression data for downstream clustering, trajectory reconstruction, and biological interpretation. However, a comprehensive and quantitative evaluation of the performance of these techniques has not been established. We present an unbiased framework that defines metrics of global and local structure preservation in dimensionality reduction transformations. Using discrete and continuous scRNA-seq datasets, we find that input cell distribution and method parameters are largely determinant of global, local, and organizational data structure preservation by eleven published dimensionality reduction methods. Code available atgithub.com/KenLauLab/DR-structure-preservationallows for rapid evaluation of further datasets and methods.

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Genome-scale oscillations in DNA methylation during exit from pluripotency

10.1101/338822 ◽

2018 ◽

Author(s):

Steffen Rulands ◽

Heather J Lee ◽

Stephen J Clark ◽

Christof Angermueller ◽

Sébastien A Smallwood ◽

...

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Epigenetic Mark ◽

List Type ◽

Dna Hypomethylation ◽

Cell Fate Decisions ◽

Oscillatory Dynamics ◽

Genome Scale

SummaryPluripotency is accompanied by the erasure of parental epigenetic memory with naïve pluripotent cells exhibiting global DNA hypomethylation both in vitro and in vivo. Exit from pluripotency and priming for differentiation into somatic lineages is associated with genome-wide de novo DNA methylation. We show that during this phase, coexpression of enzymes required for DNA methylation turnover, DNMT3s and TETs, promotes cell-to-cell variability in this epigenetic mark. Using a combination of single-cell sequencing and quantitative biophysical modelling, we show that this variability is associated with coherent, genome-scale, oscillations in DNA methylation with an amplitude dependent on CpG density. Analysis of parallel single-cell transcriptional and epigenetic profiling provides evidence for oscillatory dynamics both in vitro and in vivo. These observations provide fresh insights into the emergence of epigenetic heterogeneity during early embryo development, indicating that dynamic changes in DNA methylation might influence early cell fate decisions.HighlightsCo-expression of DNMT3s and TETs drive genome-scale oscillations of DNA methylationOscillation amplitude is greatest at a CpG density characteristic of enhancersCell synchronisation reveals oscillation period and link with primary transcriptsMultiomic single-cell profiling provides evidence for oscillatory dynamics in vivo

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Genome‐scale, single‐cell‐type resolution of microRNAactivities within a whole plant organ

The EMBO Journal ◽

10.15252/embj.2018100754 ◽

2019 ◽

Vol 38 (13) ◽

Cited By ~ 5

Author(s):

Christopher Andrew Brosnan ◽

Alexis Sarazin ◽

PeiQi Lim ◽

Nicolas Gerardo Bologna ◽

Matthias Hirsch‐Hoffmann ◽

...

Keyword(s):

Single Cell ◽

Cell Type ◽

Plant Organ ◽

Whole Plant ◽

Single Cell Type ◽

Genome Scale

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Genome-scale single-cell mechanical phenotyping reveals disease-related genes involved in mitotic rounding

Nature Communications ◽

10.1038/s41467-017-01147-6 ◽

2017 ◽

Vol 8 (1) ◽

Cited By ~ 17

Author(s):

Yusuke Toyoda ◽

Cedric J. Cattin ◽

Martin P. Stewart ◽

Ina Poser ◽

Mirko Theis ◽

...

Keyword(s):

Single Cell ◽

Disease Related Genes ◽

Genome Scale

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Plant genome-scale metabolic reconstruction and modelling

Current Opinion in Biotechnology ◽

10.1016/j.copbio.2012.08.007 ◽

2013 ◽

Vol 24 (2) ◽

pp. 271-277 ◽

Cited By ~ 45

Author(s):

Cristiana Gomes de Oliveira Dal’Molin ◽

Lars Keld Nielsen

Keyword(s):

Plant Genome ◽

Metabolic Reconstruction ◽

Genome Scale

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Insights from the Applications of Single-Cell Transcriptomic Analysis in Germ Cell Development and Reproductive Medicine

International Journal of Molecular Sciences ◽

10.3390/ijms22020823 ◽

2021 ◽

Vol 22 (2) ◽

pp. 823

Author(s):

Hyeonwoo La ◽

Hyunjin Yoo ◽

Eun Joo Lee ◽

Nguyen Xuan Thang ◽

Hee Jin Choi ◽

...

Keyword(s):

Germ Cell ◽

Single Cell ◽

Germ Cells ◽

Cell Formation ◽

Cell Development ◽

Transcriptomic Analysis ◽

Germ Cell Development ◽

A Genome ◽

Genome Scale ◽

Germ Cell Formation

Mechanistic understanding of germ cell formation at a genome-scale level can aid in developing novel therapeutic strategies for infertility. Germ cell formation is a complex process that is regulated by various mechanisms, including epigenetic regulation, germ cell-specific gene transcription, and meiosis. Gonads contain a limited number of germ cells at various stages of differentiation. Hence, genome-scale analysis of germ cells at the single-cell level is challenging. Conventional genome-scale approaches cannot delineate the landscape of genomic, transcriptomic, and epigenomic diversity or heterogeneity in the differentiating germ cells of gonads. Recent advances in single-cell genomic techniques along with single-cell isolation methods, such as microfluidics and fluorescence-activated cell sorting, have helped elucidate the mechanisms underlying germ cell development and reproductive disorders in humans. In this review, the history of single-cell transcriptomic analysis and their technical advantages over the conventional methods have been discussed. Additionally, recent applications of single-cell transcriptomic analysis for analyzing germ cells have been summarized.

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Gregarine single-cell transcriptomics reveals differential mitochondrial remodeling and adaptation in apicomplexans

BMC Biology ◽

10.1186/s12915-021-01007-2 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Eric D. Salomaki ◽

Kristina X. Terpis ◽

Sonja Rueckert ◽

Michael Kotyk ◽

Zuzana Kotyková Varadínová ◽

...

Keyword(s):

Single Cell ◽

Evolutionary Relationship ◽

Mitochondrial Metabolism ◽

Human Pathogens ◽

Cryptosporidium Hominis ◽

Microbial Eukaryotes ◽

Transport Chain ◽

Phylogenomic Analyses ◽

Mitochondrial Remodeling ◽

Genome Scale

Abstract Background Apicomplexa is a diverse phylum comprising unicellular endobiotic animal parasites and contains some of the most well-studied microbial eukaryotes including the devastating human pathogens Plasmodium falciparum and Cryptosporidium hominis. In contrast, data on the invertebrate-infecting gregarines remains sparse and their evolutionary relationship to other apicomplexans remains obscure. Most apicomplexans retain a highly modified plastid, while their mitochondria remain metabolically conserved. Cryptosporidium spp. inhabit an anaerobic host-gut environment and represent the known exception, having completely lost their plastid while retaining an extremely reduced mitochondrion that has lost its genome. Recent advances in single-cell sequencing have enabled the first broad genome-scale explorations of gregarines, providing evidence of differential plastid retention throughout the group. However, little is known about the retention and metabolic capacity of gregarine mitochondria. Results Here, we sequenced transcriptomes from five species of gregarines isolated from cockroaches. We combined these data with those from other apicomplexans, performed detailed phylogenomic analyses, and characterized their mitochondrial metabolism. Our results support the placement of Cryptosporidium as the earliest diverging lineage of apicomplexans, which impacts our interpretation of evolutionary events within the phylum. By mapping in silico predictions of core mitochondrial pathways onto our phylogeny, we identified convergently reduced mitochondria. These data show that the electron transport chain has been independently lost three times across the phylum, twice within gregarines. Conclusions Apicomplexan lineages show variable functional restructuring of mitochondrial metabolism that appears to have been driven by adaptations to parasitism and anaerobiosis. Our findings indicate that apicomplexans are rife with convergent adaptations, with shared features including morphology, energy metabolism, and intracellularity.

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METABOLIC: High-throughput Profiling of Microbial Genomes for Functional Traits, Biogeochemistry, and Community-scale Metabolic Networks

10.21203/rs.3.rs-113327/v1 ◽

2020 ◽

Author(s):

Zhichao Zhou ◽

Patricia Q Tran ◽

Adam M Breister ◽

Yang Liu ◽

Kristopher Kieft ◽

...

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Microbial Ecology ◽

Single Cell ◽

Metabolic Networks ◽

Biogeochemical Cycling ◽

Microbial Interactions ◽

Microbial Genomes ◽

Biogeochemical Models ◽

Genome Scale

Abstract Background: Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent, however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and contributions to biogeochemical cycling. Results: We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, identification of metabolism markers, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the community, potential microbial metabolic handoffs and metabolite exchange, and calculation of microbial community contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or from single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, and community-scale metabolic networks using a newly defined metric ‘MN-score’ (metabolic network score). METABOLIC takes ~3 hours with 40 CPU threads to process ~100 genomes and metagenomic reads within which the most compute-demanding part of hmmsearch takes ~45 mins, while it takes ~5 hours to complete hmmsearch for ~3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut.Conclusion: METABOLIC enables consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available at https://github.com/AnantharamanLab/METABOLIC under GPLv3.

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Single-cell tracing of the first hematopoietic stem cell generation in human embryos

10.1101/750158 ◽

2019 ◽

Author(s):

Yang Zeng ◽

Jian He ◽

Zhijie Bai ◽

Zongcheng Li ◽

Yandong Gong ◽

...

Keyword(s):

Stem Cells ◽

Single Cell ◽

Human Embryos ◽

Cellular Interactions ◽

Hematopoietic Stem ◽

Human Embryogenesis ◽

Genome Scale ◽

Cellular Components ◽

Shed Light ◽

Carnegie Stage

AbstractTracing the emergence of the first hematopoietic stem cells (HSCs) in human embryos, particularly the scarce and transient precursors thereof, is so far challenging, largely due to technical limitations and material rarity. Here, using single-cell RNA sequencing, we constructed the first genome-scale gene expression landscape covering the entire course of endothelial-to-HSC transition during human embryogenesis. The transcriptomically defined HSC-primed hemogenic endothelial cells (ECs) were captured at Carnegie stage 12-14 in an unbiased way, showing an unambiguous arterial EC feature with the up-regulation ofRUNX1,MYBandANGPT1. Importantly, subcategorizing CD34+CD45−ECs into CD44+population strikingly enriched hemogenic ECs by over 10-fold. We further mapped the developmental path from arterial ECs via HSC-primed hemogenic ECs to hematopoietic stem progenitor cells, and revealed a distinct expression pattern of genes that were transiently over-represented upon the hemogenic fate choice of arterial ECs, includingEMCN,PROCRandRUNX1T1. We also uncovered another temporally and molecularly distinct intra-embryonic hemogenic EC population, which was detected mainly at earlier CS 10 and lacked the arterial feature. Finally, we revealed the cellular components of the putative aortic niche and potential cellular interactions acting on the HSC-primed hemogenic ECs. The cellular and molecular programs and interactions that underlie the generation of the first HSCs from hemogenic ECs in human embryos, together with distinguishing HSC-primed hemogenic ECs from others, will shed light on the strategies for the production of clinically useful HSCs from pluripotent stem cells.

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