The discovery of gut microbial metabolites as modulators of host susceptibility to acetaminophen-induced hepatotoxicity.

The mammalian gut microbiota plays diverse and essential roles in modulating host physiology. Key mediators determining the outcome of the microbiota-host interactions are the small molecule metabolites produced by the gut microbiota. The liver is the organ massively exposed to gut microbial metabolites, and it serves as the nexus, maintaining healthy interactions between the gut microbiota and host. At the same time, the liver is the primary target of harmful gut microbial metabolites. This review provides an up-to-date list of gut microbial metabolites identified to increase or decrease host susceptibility to APAP-induced liver injury. Signaling pathways and molecular factors involved in the progression of APAP-induced hepatotoxicity are well-established, and we propose that the mouse model of APAP-induced hepatotoxicity serves as an excellent system for uncovering gut microbial metabolites of previously unknown function. Moreover, we envision that gut microbial metabolites identified to alter APAP-induced hepatotoxicity likely have broader implications in other liver diseases. Significance Statement This review provides an overview of recent discoveries from investigating whether and how the gut microbiota modulates the host susceptibility to APAP-induced liver injury. It focuses on the roles of gut bacterial small molecule metabolites as mediators of the interaction between the gut microbiota and the liver. It also illustrates the utility of APAP-induced liver injury as a model to identify gut microbial metabolites with biological function.

CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data.

Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.

Post-translational modifications of histone and non-histone proteins in epigenetic regulation and translational applications in alcohol-associated liver disease: Challenges and research opportunities.

Epigenetic regulation is a process that takes place through adaptive cellular pathways influenced by environmental factors and metabolic changes to modulate gene activity with heritable phenotypic variations without altering the DNA sequences of many target genes. Epigenetic regulation can be facilitated by diverse mechanisms: many different types of post-translational modifications (PTMs) of histone and non-histone nuclear proteins, DNA methylation, altered levels of noncoding RNAs, incorporation of histone variants, nucleosomal positioning, chromatin remodeling, etc. These factors modulate chromatin structure and stability with or without the involvement of metabolic products, depending on the cellular context of target cells or environmental stimuli, such as intake of alcohol (ethanol) or Western-style high-fat diets. Alterations of epigenetics have been actively studied, since they are frequently associated with multiple disease states. Consequently, explorations of epigenetic regulation have recently shed light on the pathogenesis and progression of alcohol-associated disorders. In this review, we highlight the roles of various types of PTMs, including less-characterized modifications of nuclear histone and non-histone proteins, in the epigenetic regulation of alcohol-associated liver disease (ALD) and other disorders. We also describe challenges in characterizing specific PTMs and suggest future opportunities for basic and translational research to prevent or treat ALD and many other disease states.

ChatGPT and large language models in academia: opportunities and challenges.

The introduction of large language models (LLMs) that allow iterative "chat" in late 2022 is a paradigm shift that enables generation of text often indistinguishable from that written by humans. LLM-based chatbots have immense potential to improve academic work efficiency, but the ethical implications of their fair use and inherent bias must be considered. In this editorial, we discuss this technology from the academic's perspective with regard to its limitations and utility for academic writing, education, and programming. We end with our stance with regard to using LLMs and chatbots in academia, which is summarized as (1) we must find ways to effectively use them, (2) their use does not constitute plagiarism (although they may produce plagiarized text), (3) we must quantify their bias, (4) users must be cautious of their poor accuracy, and (5) the future is bright for their application to research and as an academic tool.

Phenylpropionic acid produced by gut microbiota alleviates acetaminophen-induced hepatotoxicity.

The gut microbiota affects hepatic drug metabolism. However, gut microbial factors modulating hepatic drug metabolism are largely unknown. In this study, using a mouse model of acetaminophen (APAP)-induced hepatotoxicity, we identified a gut bacterial metabolite that controls the hepatic expression of CYP2E1 that catalyzes the conversion of APAP to a reactive, toxic metabolite. By comparing C57BL/6 substrain mice from two different vendors, Jackson (6J) and Taconic (6N), which are genetically similar but harbor different gut microbiotas, we established that the differences in the gut microbiotas result in differential susceptibility to APAP-induced hepatotoxicity. 6J mice exhibited lower susceptibility to APAP-induced hepatotoxicity than 6N mice, and such phenotypic difference was recapitulated in germ-free mice by microbiota transplantation. Comparative untargeted metabolomic analysis of portal vein sera and liver tissues between conventional and conventionalized 6J and 6N mice led to the identification of phenylpropionic acid (PPA), the levels of which were higher in 6J mice. PPA supplementation alleviated APAP-induced hepatotoxicity in 6N mice by lowering hepatic CYP2E1 levels. Moreover, PPA supplementation also reduced carbon tetrachloride-induced liver injury mediated by CYP2E1. Our data showed that previously known PPA biosynthetic pathway is responsible for PPA production. Surprisingly, while PPA in 6N mouse cecum contents is almost undetectable, 6N cecal microbiota produces PPA as well as 6J cecal microbiota in vitro, suggesting that PPA production in the 6N gut microbiota is suppressed in vivo. However, previously known gut bacteria harboring the PPA biosynthetic pathway were not detected in either 6J or 6N microbiota, suggesting the presence of as-yet-unidentified PPA-producing gut microbes. Collectively, our study reveals a novel biological function of the gut bacterial metabolite PPA in the gut-liver axis and presents a critical basis for investigating PPA as a modulator of CYP2E1-mediated liver injury and metabolic diseases.

Inter and transgenerational impact of H3K4 methylation in neuronal homeostasis.

Epigenetic marks and associated traits can be transmitted for one or more generations, phenomena known respectively as inter- or transgenerational epigenetic inheritance. It remains unknown if genetically and conditionally induced aberrant epigenetic states can influence the development of the nervous system across generations. Here, we show, using Caenorhabditis elegans as a model system, that alteration of H3K4me3 levels in the parental generation, caused by genetic manipulation or changes in parental conditions, has, respectively, trans- and intergenerational effects on H3K4 methylome, transcriptome, and nervous system development. Thus, our study reveals the relevance of H3K4me3 transmission and maintenance in preventing long-lasting deleterious effects in nervous system homeostasis.

Single-cell RNA-sequencing identifies disease-associated oligodendrocytes in male APP NL-G-F and 5XFAD mice.

Alzheimer's disease (AD) is associated with progressive neuronal degeneration as amyloid-beta (Aß) and tau proteins accumulate in the brain. Glial cells were recently reported to play an important role in the development of AD. However, little is known about the role of oligodendrocytes in AD pathogenesis. Here, we describe a disease-associated subpopulation of oligodendrocytes that is present during progression of AD-like pathology in the male AppNL-G-F and male 5xFAD AD mouse brains and in postmortem AD human brains using single-cell RNA sequencing analysis. Aberrant Erk1/2 signaling was found to be associated with the activation of disease-associated oligodendrocytes (DAOs) in male AppNL-G-F mouse brains. Notably, inhibition of Erk1/2 signaling in DAOs rescued impaired axonal myelination and ameliorated Aß-associated pathologies and cognitive decline in the male AppNL-G-F AD mouse model.

Neighbor-specific gene expression revealed from physically interacting cells during mouse embryonic development.

Development of multicellular organisms is orchestrated by persistent cell-cell communication between neighboring partners. Direct interaction between different cell types can induce molecular signals that dictate lineage specification and cell fate decisions. Current single-cell RNA-seq technology cannot adequately analyze cell-cell contact-dependent gene expression, mainly due to the loss of spatial information. To overcome this obstacle and resolve cell-cell contact-specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PIC-seq) and assessed them alongside similar single-cell transcriptomes derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of the PIC-seq data identified gene expression signatures that were dependent on the presence of specific neighboring cell types. Our computational predictions, validated experimentally, demonstrated that neural progenitor (NP) cells upregulate Lhx5 and Nkx2-1 genes, when exclusively interacting with definitive endoderm (DE) cells. Moreover, there was a reciprocal impact on the transcriptome of DE cells, as they tend to upregulate Rax and Gsc when in contact with NP cells. Using individual cell transcriptome data, we formulated a means of computationally predicting the impact of one cell type on the transcriptome of its neighboring cell types. We have further developed a distinctive spatial-t-distributed stochastic neighboring embedding to display the pseudospatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis.

Cell Competition in Carcinogenesis.

The majority of human cancers evolve over time through the stepwise accumulation of somatic mutations followed by clonal selection akin to Darwinian evolution. However, the in-depth mechanisms that govern clonal dynamics and selection remain elusive, particularly during the earliest stages of tissue transformation. Cell competition (CC), often referred to as 'survival of the fittest' at the cellular level, results in the elimination of less fit cells by their more fit neighbors supporting optimal organism health and function. Alternatively, CC may allow an uncontrolled expansion of super-fit cancer cells to outcompete their less fit neighbors thereby fueling tumorigenesis. Recent research discussed herein highlights the various non-cell-autonomous principles, including interclonal competition and cancer microenvironment competition supporting the ability of a tumor to progress from the initial stages to tissue colonization. In addition, we extend current insights from CC-mediated clonal interactions and selection in normal tissues to better comprehend those factors that contribute to cancer development.

Vesalius: high-resolution in silico anatomization of spatial transcriptomic data using image analysis.

Characterization of tissue architecture promises to deliver insights into development, cell communication, and disease. In silico spatial domain retrieval methods have been developed for spatial transcriptomics (ST) data assuming transcriptional similarity of neighboring barcodes. However, domain retrieval approaches with this assumption cannot work in complex tissues composed of multiple cell types. This task becomes especially challenging in cellular resolution ST methods. We developed Vesalius to decipher tissue anatomy from ST data by applying image processing technology. Vesalius uniquely detected territories composed of multiple cell types and successfully recovered tissue structures in high-resolution ST data including in mouse brain, embryo, liver, and colon. Utilizing this tissue architecture, Vesalius identified tissue morphology-specific gene expression and regional specific gene expression changes for astrocytes, interneuron, oligodendrocytes, and entorhinal cells in the mouse brain.

Systemic approaches using single cell transcriptome reveal that C/EBPγ regulates autophagy under amino acid starved condition.

Autophagy, a catabolic process to remove unnecessary or dysfunctional organelles, is triggered by various signals including nutrient starvation. Depending on the types of the nutrient deficiency, diverse sensing mechanisms and signaling pathways orchestrate for transcriptional and epigenetic regulation of autophagy. However, our knowledge about nutrient type-specific transcriptional regulation during autophagy is limited. To understand nutrient type-dependent transcriptional mechanisms during autophagy, we performed single cell RNA sequencing (scRNAseq) in the mouse embryonic fibroblasts (MEFs) with or without glucose starvation (GS) as well as amino acid starvation (AAS). Trajectory analysis using scRNAseq identified sequential induction of potential transcriptional regulators for each condition. Gene regulatory rules inferred using TENET newly identified CCAAT/enhancer binding protein γ (C/EBPγ) as a regulator of autophagy in AAS, but not GS, condition, and knockdown experiment confirmed the TENET result. Cell biological and biochemical studies validated that activating transcription factor 4 (ATF4) is responsible for conferring specificity to C/EBPγ for the activation of autophagy genes under AAS, but not under GS condition. Together, our data identified C/EBPγ as a previously unidentified key regulator under AAS-induced autophagy.

Cardiac fibroblast sub-types in vitro reflect pathological cardiac remodeling in vivo.

Many heart diseases are associated with fibrosis, but it is unclear whether different types of heart disease correlate with different subtypes of activated fibroblasts and to which extent such diversity is modeled during in vitro activation of primary cardiac fibroblasts. Analyzing the expression of 82 fibrosis related genes in 65 heart failure (HF) patients, we identified a panel of 12 genes clearly distinguishing HF patients better from healthy controls than measurement of the collagen-related hydroxyproline content. A subcluster enriched in ischemic HF was recognized, but not for diabetes, high BMI, or gender. Single-cell transcriptomic analysis of in vitro activated mouse cardiac fibroblasts distinguished 6 subpopulations, including a contractile Acta2high precursor population, which was predicted by time trajectory analysis to develop into Acta2low subpopulations with high production of extracellular matrix molecules. The 12 gene profile identified in HF patients showed highest similarity to the fibroblast subset with the strongest expression of extracellular matrix molecules. Population markers identified were furthermore able to clearly cluster different disease stages in a murine model for myocardial infarct. These data suggest that major features of cardiac fibroblast activation in heart failure patients, in murine heart disease models, and in cell culture of primary murine cardiac fibroblast are shared.

A PDX1 cistrome and single-cell transcriptome resource of the developing pancreas.

Pancreatic and duodenal homeobox 1 (PDX1) is crucial for pancreas organogenesis, yet the dynamic changes in PDX1 binding in human or mouse developing pancreas have not been examined. To address this knowledge gap, we performed PDX1 ChIP-seq and single-cell RNA-seq using fetal human pancreata. We integrated our datasets with published datasets and revealed the dynamics of PDX1 binding and potential cell lineage-specific PDX1-bound genes in the pancreas from fetal to adult stages. We identified a core set of developmentally conserved PDX1-bound genes that reveal the broad multifaceted role of PDX1 in pancreas development. Despite the well-known dramatic changes in PDX1 function and expression, we found that PDX1-bound genes are largely conserved from embryonic to adult stages. This points towards a dual role of PDX1 in regulating the expression of its targets at different ages, dependent on other functionally congruent or directly interacting partners. We also showed that PDX1 binding is largely conserved in mouse pancreas. Together, our study reveals PDX1 targets in the developing pancreas in vivo and provides an essential resource for future studies on pancreas development.

Dormant state of quiescent neural stem cells links Shank3 mutation to autism development.

Autism spectrum disorders (ASDs) are common neurodevelopmental disorders characterized by deficits in social interactions and communication, restricted interests, and repetitive behaviors. Despite extensive study, the molecular targets that control ASD development remain largely unclear. Here, we report that the dormancy of quiescent neural stem cells (qNSCs) is a therapeutic target for controlling the development of ASD phenotypes driven by Shank3 deficiency. Using single-cell RNA sequencing (scRNA-seq) and transposase accessible chromatin profiling (ATAC-seq), we find that abnormal epigenetic features including H3K4me3 accumulation due to up-regulation of Kmt2a levels lead to increased dormancy of qNSCs in the absence of Shank3. This result in decreased active neurogenesis in the Shank3 deficient mouse brain. Remarkably, pharmacological and molecular inhibition of qNSC dormancy restored adult neurogenesis and ameliorated the social deficits observed in Shank3-deficient mice. Moreover, we confirmed restored human qNSC activity rescues abnormal neurogenesis and autism-like phenotypes in SHANK3-targeted human NSCs. Taken together, our results offer a novel strategy to control qNSC activity as a potential therapeutic target for the development of autism.

Comprehensive and unbiased multiparameter high-throughput screening by compaRe finds effective and subtle drug responses in AML models.

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.

Biology has seen huge advances in technology in recent years. This has led to state-of-the-art techniques which can test hundreds of conditions simultaneously, such as how cancer cells respond to different drugs. In addition to this, each of the tens of thousands of cells studied can be screened for multiple variables, such as certain proteins or genes. This generates massive datasets with large numbers of parameters, which researchers can use to find similarities and differences between the tested conditions. Analyzing these 'high-throughput' experiments, however, is no easy task, as the data is often contaminated with meaningless information, or 'background noise', as well as sources of bias, such as non-biological variations between experiments. As a result, most analysis methods can only probe one parameter at a time, or are unautomated and require manual interpretation of the data. Here, Chalabi Hajkarim et al. have developed a new toolkit that can analyze multiparameter datasets faster and more robustly than current methods. The kit, which was named 'compaRe', combines a range of computational tools that automatically 'clean' the data of background noise or bias: the different conditions are then compared and any similarities are visually displayed using a graphical interface that is easy to explore. Chalabi Hajkarim et al. used their new method to study data from patients with acute myeloid leukemia (AML) and myelodysplastic syndrome, two forms of cancer that disrupt the production of functional immune cells. The toolkit was able to identify subtle differences between the patients and categorize them into groups based on the proteins present on immune cells. Chalabi Hajkarim et al. also applied compaRe to high-throughput data on cells from patients and mouse models with AML that had been treated with large numbers of specific drugs. This revealed that different cell types in the samples responded to the treatments in distinct ways. These findings suggest that the toolkit created by Chalabi Hajkarim et al. can automatically, rapidly and precisely compare large multiparameter datasets collected using high-throughput screens. In the future, compaRe could be used to identify drugs that illicit a specific response, or to predict how newly developed treatments impact different cell types in the body.

Flower lose, a cell fitness marker, predicts COVID-19 prognosis.

Risk stratification of COVID-19 patients is essential for pandemic management. Changes in the cell fitness marker, hFwe-Lose, can precede the host immune response to infection, potentially making such a biomarker an earlier triage tool. Here, we evaluate whether hFwe-Lose gene expression can outperform conventional methods in predicting outcomes (e.g., death and hospitalization) in COVID-19 patients. We performed a post-mortem examination of infected lung tissue in deceased COVID-19 patients to determine hFwe-Lose's biological role in acute lung injury. We then performed an observational study (n = 283) to evaluate whether hFwe-Lose expression (in nasopharyngeal samples) could accurately predict hospitalization or death in COVID-19 patients. In COVID-19 patients with acute lung injury, hFwe-Lose is highly expressed in the lower respiratory tract and is co-localized to areas of cell death. In patients presenting in the early phase of COVID-19 illness, hFwe-Lose expression accurately predicts subsequent hospitalization or death with positive predictive values of 87.8-100% and a negative predictive value of 64.1-93.2%. hFwe-Lose outperforms conventional inflammatory biomarkers and patient age and comorbidities, with an area under the receiver operating characteristic curve (AUROC) 0.93-0.97 in predicting hospitalization/death. Specifically, this is significantly higher than the prognostic value of combining biomarkers (serum ferritin, D-dimer, C-reactive protein, and neutrophil-lymphocyte ratio), patient age and comorbidities (AUROC of 0.67-0.92). The cell fitness marker, hFwe-Lose, accurately predicts outcomes in COVID-19 patients. This finding demonstrates how tissue fitness pathways dictate the response to infection and disease and their utility in managing the current COVID-19 pandemic.

The Transcriptome and Epigenome Reveal Novel Changes in Transcription Regulation During Pancreatic Rat Islet Maturation.

Islet function is critical for normal glucose homeostasis. Unlike adult ß cells, fetal and neonatal islets are more proliferative and have decreased insulin secretion in response to stimuli. However, the underlying mechanisms governing functional maturity of islets have not been completely elucidated. Pancreatic islets comprise different cell types. The microenvironment of islets and interactions between these cell types are critical for ß-cell development and maturation. Thus, the study of intact islets is optimal to identify novel molecular mechanisms controlling islet functional development. Transcriptomes and genome-wide histone landscapes of H3K4me3, H3K27me3, and H3K27Ac from intact islets isolated from 2- and 10-week-old Sprague-Dawley rats were integrated to elucidate genes and pathways modulating islet development, as well as the contribution of epigenetic regulation. A total of 4489 differentially expressed genes were identified; 2289 and 2200 of them were up- and down-regulated in 10-week islets, respectively. Ingenuity Pathway Analysis revealed critical pathways regulating functional maturation of islets, including nutrient sensing, neuronal function, immune function, cell replication, and extracellular matrix. Furthermore, we identified significant changes in enrichment of H3K4me3, H3K27me3, and H3K27Ac marks, which correlated with expression changes of genes critical for islet function. These histone marks were enriched at critical transcription factor-binding motifs, such as Hoxa9, C/EBP-ß, Gata1, Foxo1, E2f1, E2f3, and Mafb. In addition, our chromatin immunoprecipitation sequencing data revealed multiple potential bivalent genes whose poised states changed with maturation. Collectively, our current study identified critical novel pathways for mature islet function and suggested a role for histone modifications in regulating islet development and maturation.

Cell competition in intratumoral and tumor microenvironment interactions.

Tumors are complex cellular and acellular environments within which cancer clones are under continuous selection pressures. Cancer cells are in a permanent mode of interaction and competition with each other as well as with the immediate microenvironment. In the course of these competitive interactions, cells share information regarding their general state of fitness, with less-fit cells being typically eliminated via apoptosis at the hands of those cells with greater cellular fitness. Competitive interactions involving exchange of cell fitness information have implications for tumor growth, metastasis, and therapy outcomes. Recent research has highlighted sophisticated pathways such as Flower, Hippo, Myc, and p53 signaling, which are employed by cancer cells and the surrounding microenvironment cells to achieve their evolutionary goals by means of cell competition mechanisms. In this review, we discuss these recent findings and explain their importance and role in evolution, growth, and treatment of cancer. We further consider potential physiological conditions, such as hypoxia and chemotherapy, that can function as selective pressures under which cell competition mechanisms may evolve differently or synergistically to confer oncogenic advantages to cancer.

Decreased KATP Channel Activity Contributes to the Low Glucose Threshold for Insulin Secretion of Rat Neonatal Islets.

Transitional hypoglycemia in normal newborns occurs in the first 3 days of life and has clinical features consistent with hyperinsulinism. We found a lower threshold for glucose-stimulated insulin secretion from freshly isolated embryonic day (E) 22 rat islets, which persisted into the first postnatal days. The threshold reached the adult level by postnatal day (P) 14. Culturing P14 islets also decreased the glucose threshold. Freshly isolated P1 rat islets had a lower threshold for insulin secretion in response to 2-aminobicyclo-(2, 2, 1)-heptane-2-carboxylic acid, a nonmetabolizable leucine analog, and diminished insulin release in response to tolbutamide, an inhibitor of ß-cell KATP channels. These findings suggested that decreased KATP channel function could be responsible for the lower glucose threshold for insulin secretion. Single-cell transcriptomic analysis did not reveal a lower expression of KATP subunit genes in E22 compared with P14 ß cells. The investigation of electrophysiological characteristics of dispersed ß cells showed that early neonatal and cultured cells had fewer functional KATP channels per unit membrane area. Our findings suggest that decreased surface density of KATP channels may contribute to the observed differences in glucose threshold for insulin release.

VeTra: a tool for trajectory inference based on RNA velocity.

MOTIVATION: Trajectory inference (TI) for single cell RNA sequencing (scRNAseq) data is a powerful approach to interpret dynamic cellular processes such as cell cycle and development. Still, however, accurate inference of trajectory is challenging. Recent development of RNA velocity provides an approach to visualize cell state transition without relying on prior knowledge. RESULTS: To perform TI and group cells based on RNA velocity we developed VeTra. By applying cosine similarity and merging weakly connected components, VeTra identifies cell groups from the direction of cell transition. Besides, VeTra suggests key regulators from the inferred trajectory. VeTra is a useful tool for TI and subsequent analysis. AVAILABILITY AND IMPLEMENTATION: The Vetra is available at https://github.com/wgzgithub/VeTra. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.