Biology subjects

Wang, F.

Publications and source records attributed to Wang, F..

At least 199 records · Page 11Linked to original sources

bioRxiv2024

Language model-guided anticipation and discovery of unknown metabolites

Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks within human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains a low-throughput endeavor. Biochemical large language models have transformed the interpretation of DNA, RNA, and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns the latent biosynthetic logic embedded within the structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective chemical synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We harness DeepMet to discover several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to accelerate the mapping of the metabolome.

bioinformatics↗

bioRxiv2024

A multi-tissue developmental gene expression atlas towards understanding the biological basis of phenotypes in sheep

Sheep (Ovis aries) represents one of the most important livestock species for animal protein and wool production worldwide. However, little is known about the genetic and biological basis of ovine phenotypes, particularly for those of high economic value and environmental impact. Here, by generating and integrating 1,413 RNA-seq samples from 51 distinct tissues across 14 developmental time points, representing early prenatal, late prenatal, neonate, lamb, juvenile, adult, and elderly stages, we built a high-resolution developmental Gene Expression Atlas (dGEA) in sheep. We observed dynamic patterns of gene expression and regulatory networks across tissues and developmental stages. When harnessing this resource for interpreting genomic associations of 48 monogenetic and 12 complex traits in sheep, we found that genes upregulated at prenatal developmental stages played more important roles in shaping these phenotypes than those upregulated at postnatal stages. For instance, genetic associations of crimp number, mean staple length (MSL), and individual birth weight were significantly enriched in the prenatal rather than postnatal skin and immune tissues. By comprehensively integrating fine-mapping results and the sheep dGEA, we identified several key genes associated with complex traits in sheep, such as SOX9 (associated with MSL), GNRHR (associated with litter size at birth), and PRKDC (associated with live weight). These results provide novel insights into the gene regulatory and developmental architecture underlying ovine phenotypes. The dGEA (https://sheepdgea.njau.edu.cn/) will serve as an invaluable resource for sheep developmental biology, genetics, genomics, and selective breeding.

genetics↗

bioRxiv2024

Annotating CryoET Volumes: A Machine Learning Challenge

Cryo-electron tomography (cryoET) has emerged as a powerful structural biology tool for understanding protein complexes in their native cellular environments. Presently, 3D volumes of cellular environments can be acquired in the thousands in a few days where each volume provides a rich and complex cellular landscape. Despite numerous innovations, localizing and identifying the vast majority of protein species in these volumes remains prohibitively difficult. Machine learning based methods provide an opportunity to automate the process of labeling and annotating cryoET volumes. Due to current bottlenecks in the annotation process, and a lack of large standardized datasets, training datasets for machine learning algorithms have been scarce. Here, we present a defined "phantom" sample, along with "ground truth" annotations, that will be the basis of a machine learning challenge to bring cryoET and ML experts together and spur creativity to address this annotation problem. We have also set up a cryoET data portal that provides additional diverse sets of annotated 3D volumes from cryoET experts across the world for the machine learning challenge.

cell biology↗

bioRxiv2024

Orexinergic projections to substantia innominata mediate arousal and analgesia

Understanding neural circuits involved in anesthesia is crucial for improving its safety and efficacy. Hypothalamic orexin neurons (LHAOX), projecting broadly, are essential in regulating arousal and pain. However, the precise targets remain unclear. Here we investigated the orexin projections to the substantia innominata (SI). Combining optogenetics, fiber photometry, and EEG/EMG allowed us to manipulate orexin activities, while simultaneously recording local ligand release and global cortical activities during anesthesia. Brain slice electrophysiology revealed the synaptic connections in the SI, while RNAscope was employed to examine the distribution of orexin receptors and downstream neuronal types. Presynaptic vesicles were identified in the orexin terminals in the SI, where 49.16% of cells expressed OX2R and 6.8% expressed OX1R. Orexin release in the SI was reversibly suppressed by isoflurane. Optogenetic activation of the LHAOX[->]SI circuit significantly increased orexin release and promoted arousal from various anesthesia stages, including reanimation during 0.75% isoflurane (p < 0.0001), prolongation of 3% isoflurane induction (p = 0.0033), and acceleration of emergence from 2% isoflurane (p < 0.0001). Furthermore, activating this circuit induced analgesia to both thermal (p = 0.0074) and inflammatory (p = 0.0127) pain. Patch-clamp recordings revealed that optogenetic activation of orexin terminals in the SI elicited excitatory postsynaptic currents, which were blocked by the OX2R antagonist. SI contains more GABAergic (28.17%) and glutamatergic (11.96%) neurons than cholinergic neurons (4.13%), all of which expressed OX2R. Thus, LHAOX neurons innervate SI neurons to regulate both arousal and pain predominantly through OX2R.

neuroscience↗

bioRxiv2024

Efficient cell-wide mapping of mitochondria in electron microscopic volumes using webKnossos

Recent technical advances in volume electron microscopy (vEM) and artificial intelligence-assisted image processing have facilitated high throughput quantifications of cellular structures, such as mitochondria that are ubiquitous and morphologically diversified. A still often overlooked computational challenge is to assign cell identity to numerous mitochondrial instances, for which both mitochondrial and cell membrane contouring used to be required. Here, we present a vEM reconstruction procedure (called mito-SegEM) that utilizes virtual path-based annotation to assign automatically segmented mitochondrial instances at the cellular scale, therefore bypassing the requirement of membrane contouring. The embedded toolset in webKnossos (an open-source online annotation platform) is optimized for fast annotation, visualization, and proofreading of cellular organelle networks. We demonstrate broad applications of mito-SegEM on volumetric datasets from various tissues, including the brain, intestine, and testis, to achieve an accurate and efficient reconstruction of mitochondria in a use-dependent fashion.

cell biology↗

bioRxiv2024

The utility of single-cell RNA sequencing data in predicting plant metabolic pathway genes

It is an ever challenging task to make genome-wide predictions for plant metabolic pathway genes (MPGs) encoding enzymes that catalyze the biosynthesis of plant natural products. Here, starting from 1,129 benchmark MPGs that have experimental evidence in Arabidopsis thaliana, we investigate the utilities of single-cell RNA sequencing (scRNA-seq) data--a recently arisen omics data that has been used in several other fields--in predicting MPGs using five machine learning (ML) algorithms that support multi-label tasks. Compared with traditional bulk RNA-seq data, scRNA-seq data lead to different but comparable co-expression networks among MPGs within metabolic classes, and significantly higher prediction accuracy of MPGs into classes. Prediction accuracy for individual metabolic classes is not associated with the co-expression network tightness, but correlated with the number of MPGs within each class, indicating that including more benchmark genes in the future will improve the MPG prediction. Splitting the RNA-seq data into genetic background/condition or tissue-specific subsets can improve the gene co-expression network tightness and MPG prediction accuracy for some classes; scRNA-seq-based models still outperform bulk RNA-seq-based models for most classes when corresponding subsets are used. In addition, deep learning approaches outperform classical machine learning approaches; approaches implemented in an ensembled workflow AutoGluon tend to have severe overfitting issues potentially due to the relative scarcity of benchmark MPGs within classes. Our results demonstrate the superiority of scRNA-seq data over bulk RNA-seq data in predicting MPGs into metabolic classes, and propose that scRNA-seq data should be included in the future to advance the identification of plant MPGs.

plant biology↗

bioRxiv2024

Brain plasticity for visual words: Elementary school teachers can drive changes in weeks that rival those formed over years

This study aims to investigate the impact of vocabulary acquisition through short-term classroom learning and its relation to broader forms of vocabulary learning through long-term exposure in daily life. Through a two week of "learning sprint" in collaboration with a local elementary school and EEG-Steady State Visual Evoked Potentials (EEG-SSVEP) paradigm, we assessed new vocabulary learning in first and second graders within their pedagogical environment. We then compared the results with the word frequency effect, a well-established phenonmenon that reflects long-term vocabulary learning. After two weeks of classroom instruction, newly acquired words elicited neural responses similar to those of high-frequency words, with the effect significantly correlated with childrens phonological decoding skills. Additionally, we successfully replicated the word frequency effect using the SSVEP paradigm for the first time. These findings highlight the potential of the "learning sprint" model for conducting neuroscience research in authentic educational settings, thereby fostering a stronger connection between education and neuroscience.

neuroscience↗

bioRxiv2024

Probing the role of Nogo receptor homolog NgR2 in setting up cochlear connectivity

Sound encoding depends on the precise and reliable neurotransmission at the afferent synapses between the sensory inner hair cells (IHCs) and spiral ganglion neurons (SGNs). The molecular mechanisms contributing to the formation, as well as interplay between the pre- and postsynaptic components remain largely unclear. Here, we tested the role of the synaptic adhesion molecule and Nogo/RTN4 receptor homolog RTN4RL2 (also referred to as NgR2) in the development and function of afferent IHC-SGN synapses. Upon deletion of RTN4RL2 in mice (RTN4RL2-/-), presynaptic IHC active zones showed enlarged synaptic ribbons and a depolarized shift in the activation of CaV1.3 Ca2+ channels. The postsynaptic densities (PSDs) of SGNs were smaller and deficient of GluA2-4 AMPA receptor subunits despite maintained Gria2 mRNA expression in SGNs. Next to synaptically engaged PSDs we observed "orphan" PSDs located away from IHCs. They likely belong to a subset of SGN peripheral neurites that do not contact the IHCs in RTN4RL2-/- cochleae as found by volume electron microscopy reconstruction of SGN neurites. Auditory brainstem responses of RTN4RL2-/- mice showed increased sound thresholds indicating impaired hearing. Together, these findings suggest that RTN4RL2 contributes to the proper formation and function of auditory afferent synapses and is critical for normal hearing.

neuroscience↗

bioRxiv2024

Unveiling multi-scale architectural features in single-cell Hi-C data using scCAFE

Single-cell Hi-C (scHi-C) has provided unprecedented insights into the heterogeneity of 3D genome organization. However, its sparse and noisy nature poses challenges for computational analyses, such as chromatin architectural feature identification. Here, we introduce scCAFE, a deep learning model for the multi-scale detection of architectural features at the single-cell level. scCAFE provides a unified framework for annotating chromatin loops, TAD-like domains (TLDs), and compartments across individual cells. Our model outperforms previous scHi-C loop calling methods and delivers accurate predictions of TLDs and compartments that are biologically consistent with previous studies. The resulting single-cell annotations also offer a measure to characterize the heterogeneity of different levels of architectural features across cell types. We leverage this heterogeneity and identify a series of marker loop anchors, which demonstrate the potential of the 3D genome data to annotate cell identities without the aid of simultaneously sequenced omics data. Overall, scCAFE not only serves as a useful tool for analyzing single-cell genomic architecture, but also paves the way for precise cell-type annotations solely based on 3D genome features.

bioinformatics↗

bioRxiv2024

BST2 induces vascular smooth muscle cell plasticity and phenotype switching during cancer progression

BackgroundSmooth muscle cell (SMC) plasticity and phenotypic switching play prominent roles in the pathogenesis of multiple diseases, but their role in tumorigenesis is unknown. We investigated whether and how SMC diversity and plasticity plays a role in tumor angiogenesis and the tumor microenvironment. Methods and ResultsWe use SMC-specific lineage-tracing mouse models and single cell RNA sequencing to observe the phenotypic diversity of SMCs participating in tumor vascularization. We find that a significant proportion of SMCs adopt a phenotype traditionally associated with macrophage-like cells. These cells are transcriptionally similar to resolution phase M2b macrophages, which have been described to have a role in inflammation resolution. Computationally predicted by the ligand-receptor algorithm CellChat, signaling from BST2 on the surface of tumor cells to PIRA2 on SMCs promote this phenotypic transition; in vitro SMC assays demonstrate upregulation of macrophage transcriptional programs, and increased proliferation, migration, and phagocytic ability when exposed to BST2. Knockdown of BST2 in the tumor significantly decreases the transition towards a macrophage-like phenotype, and cells that do transition have a comparatively higher inflammatory signal typically associated with anti-tumor effect. ConclusionAs BST2 is known to be a poor prognostic marker in multiple cancers where it is associated with an M2 macrophage-skewed TME, these studies suggest that phenotypically switched SMCs may have a previously unidentified role in this immunosuppressive milieu. Further translational work is needed to understand how this phenotypic switch could influence the response to anti-cancer agents and if targeted inhibition of SMC plasticity would be therapeutically beneficial.

cancer biology↗

bioRxiv2024

Distinct roles of general anesthesia activated CeA neurons in acute versus late phase of neuropathic pain

A previous study discovered a distinct population of GABAergic neurons in the central amygdala (CeA) that can be activated by general anesthesia (CeAGA) and exert analgesic functions (Hua et al., 2020). To independently reproduce these prior findings and to investigate the electrophysiological properties of CeAGA neurons, we first used 1.2% isoflurane to induce c-Fos activation in the mouse brain and validated the Fos expression by RNAscope in situ hybridization. Indeed, isoflurane induced robust Fos expression in CeA and these Fos+ CeAGA neurons are GABAergic neurons (Vgat+). We next used Fos-TRAP2 method (different from the CANE method used in the prior study) to label CeAGA neurons (tdTomato+). Our ex vivo electrophysiological recordings in brain slices revealed that compared to Fos-negative CeA neurons, CeAGA neurons had significantly higher excitability and exhibited distinct patterns of action potentials. Chemogenetic activation of Fos-TRAPed CeAGA neurons was effective at increasing pain thresholds in naive mice and mice with early-phase neuropathic pain 2 weeks after spared nerve injury (SNI). However, the same chemogenetic activation of CeAGA neurons only had modest analgesia in the late phase of SNI at 8 weeks, although it was highly effective in reducing chronic pain-associated anxiety behaviors at this stage. We found that Fos-negative CeA neurons, but not CeAGA neurons, exhibited increased excitability in the late-phase of SNI, suggesting that chronic pain causes a shift in the relative activity of the CeA microcircuit. Interestingly, Fos-negative neurons exhibited much higher expression of K+-Cl- cotransporter-2 (KCC2), and KCC2 expression was downregulated in the CeA in the late-phase of neuropathic pain. These results support the idea that targeting CeAGA neurons may provide therapeutic benefits for pain relief and chronic pain-associated anxiety. Our findings also suggest distinct roles of CeAGA neurons in regulating physiological pain, acute pain, and chronic pain with a possible involvement of KCC2.

neuroscience↗

bioRxiv2024

Ethylene Modulates the Phenylpropanoid Pathway by Enhancing VvMYB14 Expression via the ERF5-Melatonin-ERF104 Pathway in Grape Seeds

Ethylene plays a crucial role in regulating polyphenol metabolism, however the underlying mechanism remains largely unknown. This work demonstrated that ethylene release occurred earlier than melatonin during seed ripening. Ethylene treatment increased the VvASMT expression and melatonin content. VvERF5 was elucidated to bind to the ERE element in the VvASMT promoter. VvERF5 overexpression increased ASMT expression and melatonin content while its suppression generated the opposite results in grape seeds, calli and/or Arabidopsis seeds. Using the promoter of VvMYB14, which was strongly induced by melatonin, a melatonin responsive element (MTRE) was identified. VvERF104 was revealed not only to be strongly induced by melatonin but to bind to the MTRE of the VvMYB14. VvERF104 overexpression and suppression largely increased and decreased the MYB14 expression, respectively, in grape seeds, calli and/or Arabidopsis seeds. VvMYB14 overexpression widely modified the expression of genes in phenylpropanoid pathway and phenolic compound content in grape seeds. DAP-seq revealed that the MEME-1 motif was the most likely binding sites of VvMYB14. VvPAL, VvC4H and VvCHS were verified to be the target genes of VvMYB14. Additionally, the roles of VvERF5, VvASMT and VvERF104 in mediating ethylene-induced changes in phenylpropanoid pathway were elucidated using their suppressing seeds. Collectively, ethylene increased the VvMYB14 expression via the pathway of ERF5-melatonin-ERF104 and thereby modified phenylpropanoid pathway.

plant biology↗

bioRxiv2024

Deep Generative Optimization of mRNA Codon Sequences for Enhanced Protein Production and Therapeutic Efficacy

Messenger RNA (mRNA) therapeutics show immense promise, but their efficacy is limited by suboptimal protein expression. Here, we present RiboDecode, a deep learning framework that generates mRNA codon sequences for enhanced mRNA translation. RiboDecode introduces several advances, including direct learning from large-scale ribosome profiling data and generative exploration of a large sequence space. In silico analysis demonstrates RiboDecode s robust predictive accuracy for unseen genes and cellular environments. In vitro experiments showed substantial improvements in protein expression, significantly outperforming past methods. In addition, RiboDecode enables mRNA design with consideration of cellular context and demonstrates robust performance across different mRNA formats, including m1{Psi}-modified and circular mRNAs, an important feature for mRNA therapeutics. In vivo mouse studies showed that optimized influenza hemagglutinin mRNAs induce ten times stronger neutralizing antibody responses against influenza virus compared to the unoptimized sequence. In an optic nerve crush model, optimized nerve growth factor mRNAs achieve equivalent neuroprotection of retinal ganglion cells at one-fifth the dose of the unoptimized sequence. Collectively, RiboDecode represents a paradigm shift from rule-based to a data-driven, context-aware approach for mRNA therapeutic applications, enabling the development of more potent and dose-efficient treatments.

bioinformatics↗

bioRxiv2024

An antibiotic that mediates immune destruction of senescent cancer cells

Drugs that eliminate senescent cells, senolytics, can be powerful when combined with pro-senescence cancer therapies. Using a CRISPR/Cas9-based genetic screen, we identify here SLC25A23 as a vulnerability of senescent cancer cells. Suppressing SLC25A23 disrupts cellular calcium homeostasis, impairs oxidative phosphorylation and interferes with redox signaling, leading to death of senescent cells. These effects can be replicated by salinomycin, a cation ionophore antibiotic. Salinomycin prompts a PANoptosis-like cell death in senescent cells, including apoptosis and two forms of immunogenic cell death: necroptosis and pyroptosis. Notably, we observed that salinomycin treatment or SLC25A23 suppression elevates reactive oxygen species, upregulating death receptor 5 via JNK pathway activation. We show that a combination of a DR5 agonistic antibody and salinomycin is a robust senolytic cocktail. We provide evidence that this drug combination provokes a potent NK and CD8+ T cell mediated immune destruction of senescent cancer cells, mediated by the pyroptotic cytokine IL18. SignificanceThe efficacy of multiple cancer drugs is limited by the induction of senescence, which enables cancer cells to evade cell death. We uncover here a new selective vulnerability of senescent cancer cells and we show that this vulnerability can be targeted with a commonly-used antibiotic. We show that a combination of senescence-inducing therapy, combined with this antibiotic causes a highly immunogenic cell death phenotype that further stimulates potent immune destruction of senescent cancer cells.

cancer biology↗

bioRxiv2024

The T-type voltage-gated Ca2+ channel CaV3.1 as a candidate receptor for Pasteurella multocida toxin and contributes to the disruption of respiratory epithelial barrier induced by the toxin

Pasteurella multocida toxin (PMT) is an exotoxin produced by several members of the zoonotic respiratory pathogen P. multocida. The role of PMT in disrupting the mammalian respiratory barrier remains to be elucidated. In this study, we discovered that inoculation of recombinantly expressed PMT increased the permeability of the respiratory epithelial barrier in mouse and respiratory cell models. This was evidenced by a decreased expression of tight junctions (ZO-1, occludin) and adherens junctions ({beta}-catenin, E-cadherin), as well as enhanced cytoskeletal rearrangement. In mechanism, we demonstrated that PMT inoculation induced cytoplasmic Ca2+ inflow, leading to an imbalance of cellular Ca2+ homeostasis and endoplasmic reticulum stress. This process further stimulated the RhoA/ROCK signaling, promoting cytoskeletal rearrangement and reducing the expression of tight junctions and adherens junctions. Notably, the T-type voltage-gated Ca2+ channel CaV3.1 was found to participate in PMT-induced cytoplasmic Ca2+ inflow. Knocking out CaV3.1 significantly reduced the cytotoxicity induced by PMT on swine respiratory epithelial cells and mitigated cytoplasmic Ca2+ inflow stimulated by PMT. Further analysis identified Ser (aa92), Glu (aa155), Tyr (aa167), and Leu (aa448) as crucial sites utilized by PMT to interact with CaV3.1. These findings suggest CaV3.1 serves as an important host receptor of PMT and contributes to PMT-induced respiratory epithelial barrier disruption. ImportancePMT is a significant toxin produced by the zoonotic respiratory pathogen P. multocida, yet little is known about its pathogenesis beyond causing progressive atrophic rhinitis in pigs. In our study, we have discovered that PMT has the capacity to disrupt the integrity of the mammalian respiratory epithelial barrier. This disruption involves an imbalance in cellular Ca2+ homeostasis, endoplasmic reticulum stress, and activation of the RhoA/ROCK signaling pathway induced by PMT. Importantly, we have identified CaV3.1 as a pivotal receptor that plays a crucial role in the pathogenic effects of PMT. Our findings highlight the potential of CaV3.1 as a target for intervention strategies aimed at combating the detrimental effects of PMT.

microbiology↗

bioRxiv2024

Vibrio alginolyticus is the pathogen of 'Baotou disease causing serious damage to Gracilariopsis lemaneiformis cultivation in China

Gracilariopsis lemaneiformis and Saccharina japonica, the two very important cultivated economic seaweeds, are often co-cultured in China due to their growth periods overlapped for about 3 months. These two seaweeds are often plagued with bleaching diseases during their high-density commercial cultivation. In recent years, a disease called Baotou has been causing large-scale yield reductions of G. lemaneiformis in China. Interestingly, Vibrio alginolyticus once reported to be beneficial to S. japonica by reducing the bleaching disease risk, was strongly proved to be the pathogen causing Baotou disease in this study. Analysis of 16S rRNA gene profiling revealed that V. alginolyticus was the most abundant and dominant bacterium on the algal thalli suffering from Baotou disease, whereas its presence was scarcely detected on healthy thalli. Scanning electron microscope (SEM) analysis revealed that a large number of V. alginolyticus cells were found to be attached to the algal thalli with Baotou disease and the rotten thalli acquired by the lab infection treatment. V. alginolyticus could cause the rotten symptoms which were consistent with those of Baotou disease. According to Kochs postulates, V. alginolyticus was identified as the pathogen causing Baotou disease. IMPORTANCEA highly contagious disease known as Baotou disease has persistently triggered significant yield reductions in G. lemaneiformis throughout China. The pathogen of Baotou disease was isolated and identified as V. alginolyticus which was once reported to be beneficial to S. japonica by reducing the bleaching disease risk. The study indicates that V. alginolyticus is closely involved in the competition or co-existence between G. lemaneiformis and S. japonica co-cultured in China, and provides a novel perspective and evidence that bacteria are closely involved in the competition or co-existence among different species of seaweeds that grow together.

microbiology↗

bioRxiv2024

Random Sanitization in DNA information storage using CRISPR-Cas12a

DNA information storage provides an excellent solution for metadata storage due to its high density, programmability, and long-term stability. However, current research in DNA storage primarily focuses on the processes of storing and reading data, lacking comprehensive solutions for the secure metadata wiping. Herein, we present a method of random sanitization in DNA information storage using CRISPR-Cas12a (RSDISC) based on precise control of the thermodynamic energy of primer-template hybridization. We utilize the collateral cleavage (trans-activity) of single-stranded DNA (ssDNA) by CRISPR-Cas12a to achieve selective sanitization of files in metadata. This method enables ssDNA degradation with different GC content, lengths, and secondary structures to achieve a sanitization efficiency up to 99.9% for 28,258 oligonucleotides in DNA storage within one round. We demonstrate that the number of erasable files could reach 1011.7 based on a model of primer-template hybridization efficiency. Overall, RSDISC provides a random sanitization approach to set the foundation of information encryption, file classification, memory deallocation and accurate reading in DNA data storage.

synthetic biology↗

bioRxiv2024

Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

biochemistry↗