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Single-cell and single-nucleus RNA-sequencing technologies capture the expression of plant genes at an unprecedented resolution. Therefore, these technologies are gaining traction in plant molecular and developmental biology for elucidating the transcriptional changes across cell types in a specific tissue or organ, upon treatments, in response to biotic and abiotic stresses, or between genotypes. Despite the rapidly accelerating use of these technologies, collective and standardized experimental and analytical procedures to support the acquisition of high-quality data sets are still missing. In this commentary, we discuss common challenges associated with the use of single-cell transcriptomics in plants and propose general guidelines to improve reproducibility, quality, comparability, and interpretation and to make the data readily available to the community in this fast-developing field of research.

 

Oxirene was prepared and stabilized in matrices through resonant energy transfer prior to identification in the gas phase. 

Although methanediamine (CH 2 (NH 2 ) 2 ) has historically been the subject of theoretical scrutiny, it has never been isolated to date. Here, we report the preparation of methanediamine (CH 2 (NH 2 ) 2 )—the simplest diamine. Low-temperature interstellar analog ices composed of ammonia and methylamine were exposed to energetic electrons which act as proxies for secondary electrons produced in the track of galactic cosmic rays. These experimental conditions, which simulate the conditions within cold molecular clouds, result in radical formation and initiate aminomethyl (ĊH 2 NH 2 ) and amino ( N . H 2 ) radical chemistry. Exploiting tunable photoionization reflectron time-of-flight mass spectrometry (PI-ReToF-MS) to make isomer-specific assignments, methanediamine was identified in the gas phase upon sublimation, while its isomer methylhydrazine (CH 3 NHNH 2 ) was not observed. The molecular formula was confirmed to be CH 6 N 2 through the use of isotopically labeled reactants. Methanediamine is the simplest molecule to contain the NCN moiety and could be a vital intermediate in the abiotic formation of heterocyclic and aromatic systems such as nucleobases, which all contain the NCN moiety. 

Calculations with high accuracy for atomic and inter-atomic properties, such as nuclear magnetic resonance (NMR) spectroscopy and bond dissociation energies (BDEs) are valuable for pharmaceutical molecule structural analysis, drug exploration, and screening. It is important that these calculations should include relativistic effects, which are computationally expensive to treat. Non-relativistic calculations are less expensive but their results are less accurate. In this study, we present a computational framework for predicting atomic and inter-atomic properties by using machine-learning in a non-relativistic but accurate and computationally inexpensive framework. The accurate atomic and inter-atomic properties are obtained with a low dimensional deep neural network (DNN) embedded in a fragment-based graph convolutional neural network (F-GCN). The F-GCN acts as an atomic fingerprint generator that converts the atomistic local environments into data for the DNN, which improves the learning ability, resulting in accurate results as compared to experiments. Using this framework, the 13C/1H NMR chemical shifts of Nevirapine and phenol O–H BDEs are predicted to be in good agreement with experimental measurement.

 

Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, laboratory-based single cell fluxomics is currently impractical, and the current computational tools for flux estimation are not designed for single cell-level prediction. Given the well-established link between transcriptomic and metabolomic profiles, leveraging single cell transcriptomics data to predict single cell fluxome is not only feasible but also an urgent task. In this study, we present FLUXestimator, an online platform for predicting metabolic fluxome and variations using single cell or general transcriptomics data of large sample-size. The FLUXestimator webserver implements a recently developed unsupervised approach called single cell flux estimation analysis (scFEA), which uses a new neural network architecture to estimate reaction rates from transcriptomics data. To the best of our knowledge, FLUXestimator is the first web-based tool dedicated to predicting cell-/sample-wise metabolic flux and metabolite variations using transcriptomics data of human, mouse and 15 other common experimental organisms. The FLUXestimator webserver is available at http://scFLUX.org/, and stand-alone tools for local use are available at https://github.com/changwn/scFEA. Our tool provides a new avenue for studying metabolic heterogeneity in diseases and has the potential to facilitate the development of new therapeutic strategies.