Omics Profiling Research Articles

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

Hypertension and Dyslipidemia: the Two Partners in Endothelium-Related Crime.

The goal of this article is to characterize the endothelium's role in the development of hypertension and dyslipidemia and to point out promising therapeutic directions. Dyslipidemia may facilitate the development of hypertension, whereas the collaboration of these two silent killers potentiates the risk of atherosclerosis. The common pathophysiological denominator for hypertension and dyslipidemia is endothelial cell dysfunction, which manifests as dysregulation of homeostasis, redox balance, vascular tone, inflammation, and thrombosis. Treatment focused on mediators acting in these processes might be groundbreaking. Metabolomic research on hypertension and dyslipidemia has revealed new therapeutic targets. State-of-the-art solutions integrating interview, clinical examination, innovative imaging, and omics profiles along with artificial intelligence have been already shown to improve patients' risk stratification and treatment. Pathomechanisms underlying hypertension and dyslipidemia take place in the endothelium. Novel approaches involving endothelial biomarkers and bioinformatics advances could open new perspectives in patient management.

Investigating the effects of a FAAH inhibitor in the laterodorsal tegmental nucleus using a new ex vivo mouse preparation

As part of the brainstem reticular activating system, the laterodorsal tegmentum (LDT) is a central player in regulating sleep, arousal, and motivated behaviors including addiction. The neuronal lipids within the LDT that could regulate neuronal activity and LDT signaling are not fully characterized due, in part, to the complication of the presence of fatty acid amide hydrolases (FAAH). To determine the lipids in the LDT, mouse brain slices at different stages of brain development (16, 26 and 66 days) were exposed ex vivo to the FAAH inhibitor oleyl trifluoromethyl ketone (OTMK). Metabolomics and proteomic analyses were conducted on matched samples. Metabolomics analysis revealed differences between OTMK treated and untreated LDT. Furthermore, a distinct phenotype (proteomic profile) as a function of OTMK treatment was observed in LDT from adolescent (66 days) mice indicating an effect of treatment with OTMK at later stages of brain development. Our data indicate that this ex vivo preparation could facilitate screening of different FAAH inhibitors in mammalian tissues, and more importantly, this preparation should allow a deeper characterization of global mass spectrometry-based omics profiles within the LDT.

Anomaly detection in mixed high-dimensional molecular data.

Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly. We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high-dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by mixed graphical models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic datasets. In simulation experiments, ADMIRE outperformed the state-of-the-art methods of Local Outlier Factor, stray, and Isolation Forest. All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a Python package called adadmire which can be found at https://pypi.org/project/adadmire.

Interruption of p53-MDM2 Interaction by Nutlin-3a in Human Lymphoma Cell Models Initiates a Cell-Dependent Global Effect on Transcriptome and Proteome Level.

In most lymphomas, p53 signaling pathway is inactivated by various mechanisms independent to p53 gene mutations or deletions. In many cases, p53 function is largely regulated by alterations in the protein abundance levels by the action of E3 ubiquitin-protein ligase MDM2, targeting p53 to proteasome-mediated degradation. In the present study, an integrating transcriptomics and proteomics analysis was employed to investigate the effect of p53 activation by a small-molecule MDM2-antagonist, nutlin-3a, on three lymphoma cell models following p53 activation. Our analysis revealed a system-wide nutlin-3a-associated effect in all examined lymphoma types, identifying in total of 4037 differentially affected proteins involved in a plethora of pathways, with significant heterogeneity among lymphomas. Our findings include known p53-targets and novel p53 activation effects, involving transcription, translation, or degradation of protein components of pathways, such as a decrease in key members of PI3K/mTOR pathway, heat-shock response, and glycolysis, and an increase in key members of oxidative phoshosphorylation, autophagy and mitochondrial translation. Combined inhibition of HSP90 or PI3K/mTOR pathway with nutlin-3a-mediated p53-activation enhanced the apoptotic effects suggesting a promising strategy against human lymphomas. Integrated omic profiling after p53 activation offered novel insights on the regulatory role specific proteins and pathways may have in lymphomagenesis.

Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward.

Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.

Probabilistic tensor decomposition extracts better latent embeddings from single-cell multiomic data.

Single-cell sequencing technology enables the simultaneous capture of multiomic data from multiple cells. The captured data can be represented by tensors, i.e.the higher-rank matrices. However, the existing analysis tools often take the data as a collection of two-order matrices, renouncing the correspondences among the features. Consequently, we propose a probabilistic tensor decomposition framework, SCOIT, to extract embeddings from single-cell multiomic data. SCOIT incorporates various distributions, including Gaussian, Poisson, and negative binomial distributions, to deal with sparse, noisy, and heterogeneous single-cell data. Our framework can decompose a multiomic tensor into a cell embedding matrix, a gene embedding matrix, and an omic embedding matrix, allowing for various downstream analyses. We applied SCOIT to eight single-cell multiomic datasets from different sequencing protocols. With cell embeddings, SCOIT achieves superior performance for cell clustering compared to nine state-of-the-art tools under various metrics, demonstrating its ability to dissect cellular heterogeneity. With the gene embeddings, SCOIT enables cross-omics gene expression analysis and integrative gene regulatory network study. Furthermore, the embeddings allow cross-omics imputation simultaneously, outperforming current imputation methods with the Pearson correlation coefficient increased by 3.38-39.26%; moreover, SCOIT accommodates the scenario that subsets of the cells are with merely one omic profile available.

AttOmics: attention-based architecture for diagnosis and prognosis from omics data.

The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially the ones based on deep-learning approaches, to improve diagnosis. Due to the high-dimensional small-sample nature of omics data, current deep-learning models end up with many parameters and have to be fitted with a limited training set. Furthermore, interactions between molecular entities inside an omics profile are not patient specific but are the same for all patients. In this article, we propose AttOmics, a new deep-learning architecture based on the self-attention mechanism. First, we decompose each omics profile into a set of groups, where each group contains related features. Then, by applying the self-attention mechanism to the set of groups, we can capture the different interactions specific to a patient. The results of different experiments carried out in this article show that our model can accurately predict the phenotype of a patient with fewer parameters than deep neural networks. Visualizing the attention maps can provide new insights into the essential groups for a particular phenotype. The code and data are available at https://forge.ibisc.univ-evry.fr/abeaude/AttOmics. TCGA data can be downloaded from the Genomic Data Commons Data Portal.

Clonostachys rosea ‘omics profiling: identification of putative metabolite-gene associations mediating its in vitro antagonism against Fusarium graminearum

BackgroundClonostachys rosea is an established biocontrol agent. Selected strains have either mycoparasitic activity against known pathogens (e.g. Fusarium species) and/or plant growth promoting activity on various crops. Here we report outcomes from a comparative ‘omics analysis leveraging a temporal variation in the in vitro antagonistic activities of C. rosea strains ACM941 and 88–710, toward understanding the molecular mechanisms underpinning mycoparasitism.ResultsTranscriptomic data highlighted specialized metabolism and membrane transport related genes as being significantly upregulated in ACM941 compared to 88–710 at a time point when the ACM941 strain had higher in vitro antagonistic activity than 88–710. In addition, high molecular weight specialized metabolites were differentially secreted by ACM941, with accumulation patterns of some metabolites matching the growth inhibition differences displayed by the exometabolites of the two strains. In an attempt to identify statistically relevant relationships between upregulated genes and differentially secreted metabolites, transcript and metabolomic abundance data were associated using IntLIM (Integration through Linear Modeling). Of several testable candidate associations, a putative C. rosea epidithiodiketopiperazine (ETP) gene cluster was identified as a prime candidate based on both co-regulation analysis and transcriptomic-metabolomic data association.ConclusionsAlthough remaining to be validated functionally, these results suggest that a data integration approach may be useful for identification of potential biomarkers underlying functional divergence in C. rosea strains.

364-OR: Unsupervised Approach Systematically Analyzes the Multiomics Profiles Associated with T2D Insulin Insensitivity—An IMI-RHAPSODY Study

Considerable heterogeneity exists in Type 2 diabetes disease onset and subsequent outcomes. Multi-omics analysis may provide a means to better assess inter-patient variations and design personalised approaches. Previous T2D biomarker discovery mainly focused on associating biomarkers with clinical pre-assigned patients. Here, we attempt to group T2D patients with similar multi-omics profiles using a multi-omics integrating clustering approach, Similarity Network Fusion (SNF). Each T2D subgroup’s unique omics profiles were then associated with T2D progression. In both cohorts, 180 and 1195 circulating lipids and proteins from a total of 1,134 subjects in DCS and GoDARTS cohorts group patients into two subgroups. Two subgroups are likely to represent the different stages of T2D insulin insensitivity with differences at HOMA2 (p=0.0008;4.2e-11;1.1e-09, -B, -IR and -S, DCS), C-peptide (p=3.7e-11;2.5e-06) and overall disease progression (HR=0.6;0.7). This is the first study solely relying on integrated lipidomics and proteomics to capture the stages of T2D insulin insensitivity, allowing potential novel biomarker discovery which may be masked by traditional approaches. In both cohorts, a number of discriminative omics features can be observed. Immune proteins, such as IL-18R, CLP, IL-1R and COD antigen showed strong but differing associations with insulin insensitivity, which may be related to the inflammation associated with T2D. Moreover, growth factors such as GHR and IGFs also exhibit strong associations with the present study. For lipids, patients with less severe insulin insensitivity exhibit elevated levels of sphingomyelins. In conclusion, we systematically analysed the multi-omics profiles associated with T2D insulin insensitivity. The molecular signatures may be used to investigate molecular mechanisms underpinning progression and provide insights for precision medicine. Disclosure S.Li: None. I.J.Dragan: None. K.Simons: Other Relationship; Lipotype, I have no there compoanz. L.A.Donnelly: None. V.T.Tran: None. F.Mehl: None. L.M.'t hart: Research Support; European Union. M.Ibberson: Consultant; Novo Nordisk Foundation. G.A.Rutter: Consultant; Sun Pharmaceutical Industries Ltd. E.Pearson: Speaker's Bureau; Novo Nordisk, Lilly, Illumina. R.Slieker: None. J.Beulens: None. M.K.Hansen: None. D.Kuznetsov: None. M.J.Gerl: Employee; Lipotype GmbH.

Pathway-level multi-omics analysis of the molecular mechanisms underlying the toxicity of long-term tacrolimus exposure

Tacrolimus (TAC)-based treatment is associated with nephrotoxicity and hepatotoxicity; however, the underlying molecular mechanisms responsible for this toxicity have not been fully explored. This study elucidated the molecular processes underlying the toxic effects of TAC using an integrative omics approach. Rats were sacrificed after 4 weeks of daily oral TAC administration at a dose of 5 mg/kg. The liver and kidney underwent genome-wide gene expression profiling and untargeted metabolomics assays. Molecular alterations were identified using individual data profiling modalities and further characterized by pathway-level transcriptomics-metabolomics integration analysis. Metabolic disturbances were mainly related to an imbalance in oxidant-antioxidant status, as well as in lipid and amino acid metabolism in the liver and kidney. Gene expression profiles also indicated profound molecular alterations, including in genes associated with a dysregulated immune response, proinflammatory signals, and programmed cell death in the liver and kidney. Joint-pathway analysis indicated that the toxicity of TAC was associated with DNA synthesis disruption, oxidative stress, and cell membrane permeabilization, as well as lipid and glucose metabolism. In conclusion, our pathway-level integration of transcriptome and metabolome and conventional analyses of individual omics profiles, provided a more comprehensive picture of the molecular changes resulting from TAC toxicity. This study also serves as a valuable resource for subsequent investigations aiming to understand the mechanism underlying the molecular toxicology of TAC.

Molecular and immune landscape of hepatocellular carcinoma to guide therapeutic decision-making.

Liver cancer, primarily HCC, exhibits highly heterogeneous histological and molecular aberrations across tumors and within individual tumor nodules. Such intertumor and intratumor heterogeneities may lead to diversity in the natural history of disease progression and various clinical disparities across the patients. Recently developed multimodality, single-cell, and spatial omics profiling technologies have enabled interrogation of the intertumor/intratumor heterogeneity in the cancer cells and the tumor immune microenvironment. These features may influence the natural history and efficacy of emerging therapies targeting novel molecular and immune pathways, some of which had been deemed undruggable. Thus, comprehensive characterization of the heterogeneities at various levels may facilitate the discovery of biomarkers that enable personalized and rational treatment decisions, and optimize treatment efficacy while minimizing the risk of adverse effects. Such companion biomarkers will also refine HCC treatment algorithms across disease stages for cost-effective patient management by optimizing the allocation of limited medical resources. Despite this promise, the complexity of the intertumor/intratumor heterogeneity and ever-expanding inventory of therapeutic agents and regimens have made clinical evaluation and translation of biomarkers increasingly challenging. To address this issue, novel clinical trial designs have been proposed and incorporated into recent studies. In this review, we discuss the latest findings in the molecular and immune landscape of HCC for their potential and utility as biomarkers, the framework of evaluation and clinical application of predictive/prognostic biomarkers, and ongoing biomarker-guided therapeutic clinical trials. These new developments may revolutionize patient care and substantially impact the still dismal HCC mortality.

Utilising the precision nutrition toolkit in the path towards precision medicine.

The overall aim of precision nutrition is to replace the 'one size fits all' approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more 'precision' dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene-diet or microbiota-diet interactions. Third, precision nutrition can be used for 'personalised nutrition' advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet-disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Proteome analysis of patients with early onset colorectal cancer in Ireland.

e15639 Background: Early onset colorectal cancer (EOCRC) is defined as colorectal cancer (CRC) in those under the age of 50. The incidence of EOCRC is rising at an alarming rate. Unlike in the USA, screening for CRC in Ireland begins at 60 years of age. It has been hypothesized that the signatures of CRC in younger populations could differ from that in elderly patients. Deep characterization of the tissue using omics profiling, including proteomics, could provide the answer to this question. Moreover, omics profiling could identify new biomarkers for patient stratification and earlier diagnosis. Methods: Forty micrograms of healthy and matched frozen tumors were taken from 8 patients with locally advanced EOCRC for whole proteome analysis. The samples were digested with trypsin and mass spectrometry was performed using data dependent analysis parallel accumulation serial fragmentation (DDA-PASEF). The raw data was searched against the Homo sapiens subset of the Uniprot Swissprot database (reviewed) with the proteomics pipeline software Fragpipe (Version 19.1) Statistical analysis was performed using Perseus software. Results: When we compared proteomes of 8 tumor tissues versus matched normal tissues, out of 5,717 protein IDs, there were more than 1,300 differentially expressed proteins. Our pathway enrichment analysis, using differentially expressed proteins, has identified a number of molecular functions altered in EOCRC samples. The most enriched signaling pathways were associated with FAT10 signaling, neutrophil extracellular trap (NET) signaling and oxidative stress response. The upstream regulator analysis (URA) has identified LASP1 and LONP1 proteins. LASP1 is known to promote CRC progression via the Hippo signaling pathway, while LONP1 is known to play a role in mitochondrial functions, metabolism, and epithelial–mesenchymal transition (EMT) in CRC cells. Importantly, the NET signaling activation in all samples suggests unfavorable immunomodulation that could stimulate cell proliferation and invasiveness. The observed activation of oxidative stress signaling components is in line with the inflammatory nature of CRC, which could signify potential response to immune checkpoint inhibitors. Conclusions: Our findings in a small cohort of patients indicate that proteomics can provide a deeper insight into aberrant signalling in EOCRC. We have identified novel vulnerabilities that might be used as therapeutic targets as well as patient stratification biomarkers. Additional proteome studies with corresponding genomic and transcriptomic analysis are needed to fully understand the potential of this approach.

MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously.

Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a "multi-omics" network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes. Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance. The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep.

Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases.

Whilst the incidence of neurological diseases is increasing worldwide, treatment remains mostly limited to symptom management. The gut-brain axis, which encompasses the communication routes between microbiota, gut and brain, has emerged as a crucial area of investigation for identifying new preventive and therapeutic targets in neurological disease. Due to the inter-organ, systemic nature of the gut-brain axis, together with the multitude of biomolecules and microbial species involved, molecular systems biology approaches are required to accurately investigate the mechanisms of gut-brain communication. High-throughput omics profiling, together with computational methodologies such as dimensionality reduction or clustering, machine learning, network inference and genome-scale metabolic models, allows novel biomarkers to be discovered and elucidates mechanistic insights. In this review, the general concepts of experimental and computational methodologies for gut-brain axis research are introduced and their applications are discussed, mainly in human cohorts. Important aspects are further highlighted concerning rational study design, sampling procedures and data modalities relevant for gut-brain communication, strengths and limitations of methodological approaches and some future perspectives. Multi-omics analyses, together with advanced data mining, are essential to functionally characterize the gut-brain axis and put forward novel preventive or therapeutic strategies in neurological disease.

Physiological response in E. coli to YdgR overexpression depends on whether the protein has an intact function

Membrane transport proteins are essential for the transport of a wide variety of molecules across the cell membrane to maintain cellular homeostasis. Generally, these transport proteins can be overexpressed in a suitable host (bacteria, yeast, or mammalian cells), and it is well documented that overexpression of membrane proteins alters the global metabolomic and proteomic profiles of the host cells. In the present study, we investigated the physiological consequences of overexpression of a membrane transport protein YdgR that belongs to the POT/PTR family from E. coli by using the lab strain BL21 (DE3)pLysS in its functional and attenuated mutant YdgR-E33Q. We found significant differences between the omics (metabolomics and proteomics) profiles of the cells expressing functional YdgR as compared to cells expressing attenuated YdgR, e.g., upregulation of several uncharacterized y-proteins and enzymes involved in the metabolism of peptides and amino acids. Furthermore, molecular network analysis suggested a relatively higher presence of proline-containing tripeptides in cells expressing functional YdgR. We envisage that an in-depth investigation of physiological alterations due to protein over-expression may be used for the deorphanization of the y-gene transportome.

Abstract 5639: High throughput spatially resolved laser-activated cell sorting links the genomic molecules with its spatial information

Abstract Spatial omics profiling technologies have been recognized recently for its ability to decipher the genetic molecules that are structurally relevant in pathology. Especially, in tumor biology, tumor is not the group of malignant tumor cells, but rather group of various cells such as tumor cells, immune cells, fibroblasts, etc. gathers together, constructing the tumor microenvironments. Technologies to analyze such microstructures have evolved from bulk sequencing, single cell sequencing to spatial omics profiling technologies. Spatial omics profiling technologies have highly influenced in decoding cancerous mechanisms by questioning the tumor heterogeneity, tumor microenvironment and spatial biomarkers. Most of the spatial omics technologies focus on mapping the spatial omics landscape in a large scale. They rather introduces the spatially-barcoded capture probes or fluorescence labeled target probes to spatially locate the genetic molecules. The information depth and the scalability of the techniques varies according to the purpose of the spatial assay techniques. Such technologies are capable of discovering the spatial heterogeneity and the spatial landscape of the consisting cell types due to relatively low depth of the omics information. To effectively address the target molecules for therapeutics or diagnostics, higher depth of the omics information are required. To meet the needs, region of interest (ROI)-based spatial technologies isolated the target regions and applies chemistries for higher coverage omics data. Conventional cell sorters utilizes microfluidic channels to sort cells of interest which requires cell dissociation in a solution phase. For instance, Fluorescence activated cell sorter (FACS) or Magnetic-activated cell sorting (MACS) uses fluorescence or magnetic particles, respectively, to designate the cells of interest in dissociated cell solutions. Spatially isolating techniques such as laser capture microdissection (LCM) is able to sort out the ROIs while preserving the spatial context, but it approximately takes an hour for isolating the targets. Also, it uses rather UV laser to dissect out cells or IR-activated melting of polymers to stick out cells which might cause damage to cells. Here, I developed the automated spatially resolved laser activated cell sorter that isolates the cells in target per second while preserving the spatial context of the cells. Specific region of indium tin oxide (ITO) coated slide glass evaporates when illuminated by IR laser pulse, plunging the cells into the desired reservoir. The applicability of the suggested cell sorter are demonstrated in omics profiling chemistries such as DNA sequencing, RNA sequencing, mass spectrometry, etc. Citation Format: Sumin Lee, Amos C. Lee, Sunghoon Kwon. High throughput spatially resolved laser-activated cell sorting links the genomic molecules with its spatial information. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5639.

An agroecological structure model of compost—soil—plant interactions for sustainable organic farming

Compost is used worldwide as a soil conditioner for crops, but its functions have still been explored. Here, the omics profiles of carrots were investigated, as a root vegetable plant model, in a field amended with compost fermented with thermophilic Bacillaceae for growth and quality indices. Exposure to compost significantly increased the productivity, antioxidant activity, color, and taste of the carrot root and altered the soil bacterial composition with the levels of characteristic metabolites of the leaf, root, and soil. Based on the data, structural equation modeling (SEM) estimated that amino acids, antioxidant activity, flavonoids and/or carotenoids in plants were optimally linked by exposure to compost. The SEM of the soil estimated that the genus Paenibacillus and nitrogen compounds were optimally involved during exposure. These estimates did not show a contradiction between the whole genomic analysis of compost-derived Paenibacillus isolates and the bioactivity data, inferring the presence of a complex cascade of plant growth-promoting effects and modulation of the nitrogen cycle by the compost itself. These observations have provided information on the qualitative indicators of compost in complex soil-plant interactions and offer a new perspective for chemically independent sustainable agriculture through the efficient use of natural nitrogen.

Charting plant gene functions in the multi-omics and single-cell era.

Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.