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1
Ugidos, Manuel, Sonia Tarazona, José M. Prats-Montalbán, Alberto Ferrer, and Ana Conesa. "MultiBaC: A strategy to remove batch effects between different omic data types." Statistical Methods in Medical Research 29, no. 10 (March 4, 2020): 2851–64. http://dx.doi.org/10.1177/0962280220907365.
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Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform—i.e. gene expression— is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.
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Blutt, Sarah E., Cristian Coarfa, Josef Neu, and Mohan Pammi. "Multiomic Investigations into Lung Health and Disease." Microorganisms 11, no. 8 (August 19, 2023): 2116. http://dx.doi.org/10.3390/microorganisms11082116.
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Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.
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Ramos, Marcel, Ludwig Geistlinger, Sehyun Oh, Lucas Schiffer, Rimsha Azhar, Hanish Kodali, Ino de Bruijn, et al. "Multiomic Integration of Public Oncology Databases in Bioconductor." JCO Clinical Cancer Informatics, no. 4 (October 2020): 958–71. http://dx.doi.org/10.1200/cci.19.00119.
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PURPOSE Investigations of the molecular basis for the development, progression, and treatment of cancer increasingly use complementary genomic assays to gather multiomic data, but management and analysis of such data remain complex. The cBioPortal for cancer genomics currently provides multiomic data from > 260 public studies, including The Cancer Genome Atlas (TCGA) data sets, but integration of different data types remains challenging and error prone for computational methods and tools using these resources. Recent advances in data infrastructure within the Bioconductor project enable a novel and powerful approach to creating fully integrated representations of these multiomic, pan-cancer databases. METHODS We provide a set of R/Bioconductor packages for working with TCGA legacy data and cBioPortal data, with special considerations for loading time; efficient representations in and out of memory; analysis platform; and an integrative framework, such as MultiAssayExperiment. Large methylation data sets are provided through out-of-memory data representation to provide responsive loading times and analysis capabilities on machines with limited memory. RESULTS We developed the curatedTCGAData and cBioPortalData R/Bioconductor packages to provide integrated multiomic data sets from the TCGA legacy database and the cBioPortal web application programming interface using the MultiAssayExperiment data structure. This suite of tools provides coordination of diverse experimental assays with clinicopathological data with minimal data management burden, as demonstrated through several greatly simplified multiomic and pan-cancer analyses. CONCLUSION These integrated representations enable analysts and tool developers to apply general statistical and plotting methods to extensive multiomic data through user-friendly commands and documented examples.
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Hatami, Elham, Hye-Won Song, Hongduan Huang, Zhiqi Zhang, Thomas McCarthy, Youngsook Kim, Ruifang Li, et al. "Integration of single-cell transcriptomic and chromatin accessibility on heterogenicity of human peripheral blood mononuclear cells utilizing microwell-based single-cell partitioning technology." Journal of Immunology 212, no. 1_Supplement (May 1, 2024): 1508_5137. http://dx.doi.org/10.4049/jimmunol.212.supp.1508.5137.
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Abstract Single-cell RNA sequencing (scRNA-Seq) deepens our understanding of cellular development and heterogeneity. However, limitations exist in unraveling cell states and gene regulatory programs. Chromatin state profiles assess gene expression potential and offer insights into transcriptional regulation. Integrated with gene expression data, chromatin accessibility region (CAR) profiles establish fundamental gene regulatory logic for cell fate. ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing) is a highly potent approach for profiling genome-wide CARs. To investigate the power of the multiomics assay in identifying differentiated gene regulations, we conducted multiomic snATAC-seq+ snRNA-seq on PBMCs from two different donors, by utilizing the gentle and robust microwell-based single-cell partitioning technology. The assay showed high sensitivity and specificity metrics (>10,000 median unique fragments/cell, >0.7 fragments in peak score). Integrative analysis across donors revealed enriched transcription factor motifs and fragment coverage tracks in distinct cell types, correlating significantly with gene expression data. These findings highlight mRNA's intricate connections with CARs in immune cell development. Our study underscores the power of multiomic analysis in analyzing heterogeneity of PBMC cell populations and offers a toolkit to identify gene regulation specific to diverse cell types, enhancing our comprehension of epigenetic diversity.
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Antequera-González, Borja, Neus Martínez-Micaelo, Carlos Sureda-Barbosa, Laura Galian-Gay, M. Sol Siliato-Robles, Carmen Ligero, Artur Evangelista, and Josep M. Alegret. "Specific Multiomic Profiling in Aortic Stenosis in Bicuspid Aortic Valve Disease." Biomedicines 12, no. 2 (February 6, 2024): 380. http://dx.doi.org/10.3390/biomedicines12020380.
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Introduction and purpose: Bicuspid aortic valve (BAV) disease is associated with faster aortic valve degeneration and a high incidence of aortic stenosis (AS). In this study, we aimed to identify differences in the pathophysiology of AS between BAV and tricuspid aortic valve (TAV) patients in a multiomics study integrating metabolomics and transcriptomics as well as clinical data. Methods: Eighteen patients underwent aortic valve replacement due to severe aortic stenosis: 8 of them had a TAV, while 10 of them had a BAV. RNA sequencing (RNA-seq) and proton nuclear magnetic resonance spectroscopy (1H-NMR) were performed on these tissue samples to obtain the RNA profile and lipid and low-molecular-weight metabolites. These results combined with clinical data were posteriorly compared, and a multiomic profile specific to AS in BAV disease was obtained. Results: H-NMR results showed that BAV patients with AS had different metabolic profiles than TAV patients. RNA-seq also showed differential RNA expression between the groups. Functional analysis helped connect this RNA pattern to mitochondrial dysfunction. Integration of RNA-seq, 1H-NMR and clinical data helped create a multiomic profile that suggested that mitochondrial dysfunction and oxidative stress are key players in the pathophysiology of AS in BAV disease. Conclusions: The pathophysiology of AS in BAV disease differs from patients with a TAV and has a specific RNA and metabolic profile. This profile was associated with mitochondrial dysfunction and increased oxidative stress.
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Silberberg, Gilad, Clare Killick-Cole, Yaron Mosesson, Haia Khoury, Xuan Ren, Mara Gilardi, Daniel Ciznadija, Paolo Schiavini, Marianna Zipeto, and Michael Ritchie. "Abstract 854: A pharmaco-pheno-multiomic integration analysis of pancreatic cancer: A highly predictive biomarker model of biomarkers of Gemcitabine/Abraxane sensitivity and resistance." Cancer Research 83, no. 7_Supplement (April 4, 2023): 854. http://dx.doi.org/10.1158/1538-7445.am2023-854.
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Abstract The overall survival of patients diagnosed with Pancreatic Cancer remains low. Initial responses to current therapeutic interventions are below 50%, leading to a high mortality rate shortly after diagnosis. To date, only a companion diagnostic, non-specific for pancreatic cancer, has been approved for this indication. A better understanding of the tumor cell biology and resistance mechanisms may shed light onto novel therapeutic targets that improve long-term outcome and improved patient stratification. In this study, we performed an exhaustive analysis to identify predictive biomarkers for gemcitabine/abraxane sensitivity using multiomics datasets. These datasets were integrated in a pharmaco-phenotypic-multiomic (PPMO) model predictive of therapeutic sensitivity or resistance, using sparse partial least squares (sPLS). Our results reveal major cellular discriminants in genomic variants, transcriptomics, and most pronouncedly in proteomics data. Tumors exhibiting Gemcitabine/Abraxane resistance associate with increased TPRV6 RNA expression, MUC13 protein expression, and USP42 mutation among others. Prospective application of the PPMO integration model was able to accurately predict Gemcitabine/Abraxane response profiles for 4/5 additional Pancreatic samples, therefore suggesting a potential application as a predictive diagnostic tool. Citation Format: Gilad Silberberg, Clare Killick-Cole, Yaron Mosesson, Haia Khoury, Xuan Ren, Mara Gilardi, Daniel Ciznadija, Paolo Schiavini, Marianna Zipeto, Michael Ritchie. A pharmaco-pheno-multiomic integration analysis of pancreatic cancer: A highly predictive biomarker model of biomarkers of Gemcitabine/Abraxane sensitivity and resistance [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 854.
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Culley, Christopher, Supreeta Vijayakumar, Guido Zampieri, and Claudio Angione. "A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth." Proceedings of the National Academy of Sciences 117, no. 31 (July 16, 2020): 18869–79. http://dx.doi.org/10.1073/pnas.2002959117.
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Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143Saccharomyces cerevisiaemutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.
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Pratapa, Aditya, Lydia Hernandez, Bassem Ben Cheikh, Niyati Jhaveri, and Arutha Kulasinghe. "Abstract 5503: Ultrahigh-plex spatial phenotyping of head and neck cancer tissue uncovers multiomic signatures of immunotherapy response." Cancer Research 84, no. 6_Supplement (March 22, 2024): 5503. http://dx.doi.org/10.1158/1538-7445.am2024-5503.
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Abstract Background Targeted immune checkpoint inhibitors (ICI) with anti-PD-1/PD-L1 therapy offer durable treatment of mucosal head and neck squamous cell cancer (HNSCC), in both human papillomavirus-positive (HPV+) and negative (HPV-) patients. However, currently available biomarker signatures for targeted ICI therapies have limited predictive value. Our recent ultrahigh-plex profiling of HNSCC tissue with 100+ cancer hallmarks of tumor and immunobiology uncovered distinct spatial domains that serve as defining factors for clinical response and resistance. Methods Our unbiased analysis of whole-slide metastatic HNSCC tumors is from a clinical cohort of patients treated with Pembrolizumab/Nivolumab. The cohort consisted of patients with a range of outcomes from complete vs partial vs progressive disease responses to ICI therapy. We first characterized the tumor microenvironment using our ultrahigh-plex protein panel with 100+ antibodies on the PhenoCycler®-Fusion platform. To expand upon our biomarker discovery, we included multiomic cancer hallmarks with a multimodal protein/RNA detection panel. Targeted spatial RNA detection was performed to complement and augment the microenvironment characterization achieved by our protein panel. To further consolidate the multiomic data, we leveraged MaxFuse, a state-of-the art computational framework that integrates multimodal spatial and single-cell expression data. Results Our multiomic spatial phenotyping uncovered diverse tumor regions, each with distinct biomarker expression that is reflected across modalities including protein, RNA, and metabolic activity, indicating regions likely associated with resistance to immunotherapy. Our multiomic data integration also revealed spatial signatures associated with different tissue compartments, such as the tumor and non-tumor associated tertiary lymphoid structures. Conclusions We demonstrate a multi-pronged approach that incorporated both novel experimental and computational techniques for elucidating tumor microenvironment in HNSCC tissue prior to ICI-based immunotherapy. Our multiomic approach provides deeper characterization of the HNSCC at the transcriptomic and proteomic level incorporating depth across the entire transcriptome and single-cell spatial resolution of key protein determinants for predicting and furthering our understanding of immunotherapy response to ICI therapy. Citation Format: Aditya Pratapa, Lydia Hernandez, Bassem Ben Cheikh, Niyati Jhaveri, Arutha Kulasinghe. Ultrahigh-plex spatial phenotyping of head and neck cancer tissue uncovers multiomic signatures of immunotherapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5503.
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Signorelli, Mirko, Roula Tsonaka, Annemieke Aartsma-Rus, and Pietro Spitali. "Multiomic characterization of disease progression in mice lacking dystrophin." PLOS ONE 18, no. 3 (March 31, 2023): e0283869. http://dx.doi.org/10.1371/journal.pone.0283869.
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Duchenne muscular dystrophy (DMD) is caused by genetic mutations leading to lack of dystrophin in skeletal muscle. A better understanding of how objective biomarkers for DMD vary across subjects and over time is needed to model disease progression and response to therapy more effectively, both in pre-clinical and clinical research. We present an in-depth characterization of disease progression in 3 murine models of DMD by multiomic analysis of longitudinal trajectories between 6 and 30 weeks of age. Integration of RNA-seq, mass spectrometry-based metabolomic and lipidomic data obtained in muscle and blood samples by Multi-Omics Factor Analysis (MOFA) led to the identification of 8 latent factors that explained 78.8% of the variance in the multiomic dataset. Latent factors could discriminate dystrophic and healthy mice, as well as different time-points. MOFA enabled to connect the gene expression signature in dystrophic muscles, characterized by pro-fibrotic and energy metabolism alterations, to inflammation and lipid signatures in blood. Our results show that omic observations in blood can be directly related to skeletal muscle pathology in dystrophic muscle.
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Silberberg, Gilad, Bandana Vishwakarama, Brandon Walling, Chelsea Riveley, Alessandra Audia, Marianna Zipeto, Ido Sloma, Amy Wesa, and Michael Ritchie. "Abstract 3907: A pheno-multiomic integration analysis of primary samples of acute myeloid leukemia reveals biomarkers of cytarabine resistance." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3907. http://dx.doi.org/10.1158/1538-7445.am2022-3907.
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Abstract The overall survival of patients diagnosed with Acute Myeloid Leukemia (AML) remains low. While initial responses to therapy are favorable, the duration of response is short and overcoming therapeutic resistance has proven difficult. A better understanding of the tumor cell biology and resistance mechanisms may shed light onto novel therapeutic targets that improve long-term outcome. In this study, we performed an exhaustive analysis to include deep tumor phenotyping, drug sensitivity profiling and comprehensive omic characterization. These datasets were included in integrative pharmaco-phenotypic-multiomic analyses to identify targets and biomarkers associated with cellular phenotype and drug response. Our results reveal that the major cellular discriminant within the cellular phenotype is CD34 expression, which associates with a high PDK-mediated metabolic profile and cytarabine sensitivity. Tumors exhibiting cytarabine resistance associate with a CD34-negative cellular phenotype and molecular characteristics such as MYC copy number gain, and increased expression of SAMDH1, FBP1 and TYMP proteins. Citation Format: Gilad Silberberg, Bandana Vishwakarama, Brandon Walling, Chelsea Riveley, Alessandra Audia, Marianna Zipeto, Ido Sloma, Amy Wesa, Michael Ritchie. A pheno-multiomic integration analysis of primary samples of acute myeloid leukemia reveals biomarkers of cytarabine resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3907.
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Baldan-Martin, M., M. Azkargorta, A. M. Aransay, R. Gil-Redondo, I. Moreno-Indias, I. Iloro, I. Soleto, et al. "DOP08 A novel multiomic approach to unravel the mechanisms of action of biologics and tofacitinib in Inflammatory Bowel Disease." Journal of Crohn's and Colitis 18, Supplement_1 (January 1, 2024): i85—i87. http://dx.doi.org/10.1093/ecco-jcc/jjad212.0048.
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Abstract Background Inflammatory bowel diseases (IBD), which includes Crohn´s disease (CD) and ulcerative colitis (UC), are complex and heterogeneous diseases characterized by a multifactorial etiology. IBD prevalence is increasing worldwide. The availability of diverse treatments with different mechanisms of action have revolutionized the ability to achieve clinical remission and endoscopic healing. The aim of this study was to achieve a deeper understanding of the mechanism of action and response to different IBD treatments using a multiomic approach. Methods We analysed the metabolome of serum and urine, metagenome of stool samples and transcriptome and proteome of mucosal biopsies from 53 patients with moderate-to-severe CD and 50 UC patients initiating biologic therapy (anti-TNF, ustekinumab or vedolizumab) or tofacitinib. Patients were assessed at 14 weeks for endoscopic remission and were categorized into responders or non-responders. The methodologies employed in this study were RNA-seq analysis, liquid chromatography-mass spectrometry, nuclear magnetic resonance, and 16S rRNA gene sequencing. Results The multiomic analyses revealed substantial alterations in the transcriptome, proteome, metabolome and metagenome of both responders and non-responders IBD patients before and after treatment (14 weeks) to different biologics and tofacitinib (Table 1). Gene enrichment analysis showed notewhorthy differences in all study comparisons, except for patients with CD and UC who did respond to anti-TNF and tofacitinib. Functional enrichment analysis of differentially expressed proteins indicated several processes that could be potentially involved in the mechanisms of action of different treatments. More differences were observed in lipoproteins than in serum and urine metabolites in the different study comparisons. Finally, metagenomic findings indicated changes in the composition of gut bacteria communities among CD patients treated with ustekinumab and UC patients treated with tofacitinib (responders vs non-responders at 14 weeks) (Figure 1). Conclusion The present study provides novel insights into the mechanisms of action of different IBD treatments from a multiomic approach. Nonetheless, multiomic data integration and validation studies are needed to confirm these findings within a more extensive and independent cohort of patients.
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Guerrero-Sánchez, Víctor M., Cristina López-Hidalgo, María-Dolores Rey, María Ángeles Castillejo, Jesús V. Jorrín-Novo, and Mónica Escandón. "Multiomic Data Integration in the Analysis of Drought-Responsive Mechanisms in Quercus ilex Seedlings." Plants 11, no. 22 (November 12, 2022): 3067. http://dx.doi.org/10.3390/plants11223067.
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The integrated analysis of different omic layers can provide new knowledge not provided by their individual analysis. This approach is also necessary to validate data and reveal post-transcriptional and post-translational mechanisms of gene expression regulation. In this work, we validated the possibility of applying this approach to non-model species such as Quercus ilex. Transcriptomics, proteomics, and metabolomics from Q. ilex seedlings subjected to drought-like conditions under the typical summer conditions in southern Spain were integrated using a non-targeted approach. Two integrative approaches, PCA and DIABLO, were used and compared. Both approaches seek to reduce dimensionality, preserving the maximum information. DIABLO also allows one to infer interconnections between the different omic layers. For easy visualization and analysis, these interconnections were analyzed using functional and statistical networks. We were able to validate results obtained by analyzing the omic layers separately. We identified the importance of protein homeostasis with numerous protease and chaperones in the networks. We also discovered new key processes, such as transcriptional control, and identified the key function of transcription factors, such as DREB2A, WRKY65, and CONSTANS, in the early response to drought.
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Liu, Hailong, Tao Jiang, and Xiaoguang Qiu. "Spatiotemporal multiomic landscape of human medulloblastoma at single cell resolution." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 2069. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.2069.
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2069 Background: Medulloblastoma is the most common malignant childhood tumor type with distinct molecular subgroups. While advances in the comprehensive treatment have been made, the mortality in the high-risk group is still very high, driven by an incomplete understanding of cellular diversity. Methods: We use single-nucleus RNA expression, chromatin accessibility and spatial transcriptomic profiling to generate an integrative multi-omic map in 40 human medulloblastomas spanning all molecular subgroups and human postnatal cerebella, which is supplemented by the bulk whole genome and RNA sequences across 300 cases. Results: This approach provides spatially resolved insights into the medulloblastoma and cerebellum transcriptome and epigenome with identification of distinct cell-type in the tumor microenvironment. Medulloblastoma exhibited three tumor subpopulations including the quiescent, the differentiated, and a stem-like (proliferating) population unique to cancer, which localized to an immunosuppressive-vascular niche. We identified and validated mechanisms of stem-like to differentiated process among the malignant cells that drive tumor progression. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing stem-like malignant cells as a hub for intercellular communication. Multiple features of potential immunosuppression and angiogenesis were observed, including Treg cells and endothelial cells co-localization in compartmentalized tumor stroma. Conclusions: Our study provides an integrative molecular landscape of human medulloblastoma and represents a reference to advance mechanistic and therapeutic studies of pediatric neuro-oncological disease.
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Louca, Stilianos, Alyse K. Hawley, Sergei Katsev, Monica Torres-Beltran, Maya P. Bhatia, Sam Kheirandish, Céline C. Michiels, et al. "Integrating biogeochemistry with multiomic sequence information in a model oxygen minimum zone." Proceedings of the National Academy of Sciences 113, no. 40 (September 21, 2016): E5925—E5933. http://dx.doi.org/10.1073/pnas.1602897113.
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Microorganisms are the most abundant lifeform on Earth, mediating global fluxes of matter and energy. Over the past decade, high-throughput molecular techniques generating multiomic sequence information (DNA, mRNA, and protein) have transformed our perception of this microcosmos, conceptually linking microorganisms at the individual, population, and community levels to a wide range of ecosystem functions and services. Here, we develop a biogeochemical model that describes metabolic coupling along the redox gradient in Saanich Inlet—a seasonally anoxic fjord with biogeochemistry analogous to oxygen minimum zones (OMZs). The model reproduces measured biogeochemical process rates as well as DNA, mRNA, and protein concentration profiles across the redox gradient. Simulations make predictions about the role of ubiquitous OMZ microorganisms in mediating carbon, nitrogen, and sulfur cycling. For example, nitrite “leakage” during incomplete sulfide-driven denitrification by SUP05 Gammaproteobacteria is predicted to support inorganic carbon fixation and intense nitrogen loss via anaerobic ammonium oxidation. This coupling creates a metabolic niche for nitrous oxide reduction that completes denitrification by currently unidentified community members. These results quantitatively improve previous conceptual models describing microbial metabolic networks in OMZs. Beyond OMZ-specific predictions, model results indicate that geochemical fluxes are robust indicators of microbial community structure and reciprocally, that gene abundances and geochemical conditions largely determine gene expression patterns. The integration of real observational data, including geochemical profiles and process rate measurements as well as metagenomic, metatranscriptomic and metaproteomic sequence data, into a biogeochemical model, as shown here, enables holistic insight into the microbial metabolic network driving nutrient and energy flow at ecosystem scales.
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Hu, Xiaohui, Masaya Ono, Nyam-Osor Chimge, Keisuke Chosa, Cu Nguyen, Elizabeth Melendez, Chih-Hong Lou, et al. "Differential Kat3 Usage Orchestrates the Integration of Cellular Metabolism with Differentiation." Cancers 13, no. 23 (November 23, 2021): 5884. http://dx.doi.org/10.3390/cancers13235884.
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The integration of cellular status with metabolism is critically important and the coupling of energy production and cellular function is highly evolutionarily conserved. This has been demonstrated in stem cell biology, organismal, cellular and tissue differentiation and in immune cell biology. However, a molecular mechanism delineating how cells coordinate and couple metabolism with transcription as they navigate quiescence, growth, proliferation, differentiation and migration remains in its infancy. The extreme N-termini of the Kat3 coactivator family members, CBP and p300, by far the least homologous regions with only 66% identity, interact with members of the nuclear receptor family, interferon activated Stat1 and transcriptionally competent β-catenin, a critical component of the Wnt signaling pathway. We now wish to report based on multiomic and functional investigations, utilizing p300 knockdown, N-terminal p300 edited and p300 S89A edited cell lines and p300 S89A knockin mice, that the N-termini of the Kat3 coactivators provide a highly evolutionarily conserved hub to integrate multiple signaling cascades to coordinate cellular metabolism with the regulation of cellular status and function.
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Angione, Claudio. "Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine." BioMed Research International 2019 (June 9, 2019): 1–16. http://dx.doi.org/10.1155/2019/8304260.
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In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient’s disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, andin silicoclinical trials.
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Zawistowski, Jon, Isai Salas-Gonzalez, Tia Tate, Tatiana Morozova, Katherine Kennedy, Durga Arvapalli, Jamie Remington, et al. "Abstract 6929: Inter- and intratumoral PIK3CA subclonal diversity in breast cancer contextualized by single-cell multiomics." Cancer Research 84, no. 6_Supplement (March 22, 2024): 6929. http://dx.doi.org/10.1158/1538-7445.am2024-6929.
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Abstract Rare clonotypes within pre-cancerous tissues can drive progression to cancer. However, the evolution of rare clonotypes in tumors or normal tissue cannot be defined in the absence of single-cell resolution. At this single-cell level, multiomic interrogation across the Central Dogma of Biology provides enhanced power to reconstruct such evolutionary trajectories, defining the mutational profile, cell identify, and receptor expression within each subpopulation. Leveraging a multiomic approach, we aimed to define how different mutations in the same oncogenic driver observed in the same tumor resection associate with copy number variation (CNV) across the genome. We analyzed individual ductal carcinoma in situ/invasive ductal carcinoma cells using a unified whole-genome and full-transcript RNAseq workflow (ResolveOME™, BioSkryb Genomics) coupled with panel-level extracellular protein information through oligo-conjugated antibodies (BioLegend). We sequenced the exomes and transcriptomes of ResolveOME-amplified single cells from mastectomy samples from twelve patients. At the single nucleotide variant (SNV) level, we identified an allelic series of PIK3CA oncogenic driver mutations in the same tumor resection. A single amino acid, in-frame deletion of E109 dominated the sample, followed by H1047R and K111E in decreasing subclonal abundance. A fourth mutation, E345T, not present in the first sample, was detected as the sole PIK3CA variant in the second tumor sample. Intriguingly, each respective PIK3CA mutation class was associated with a distinct copy number alteration profile revealed by low-coverage whole-genome sequencing: Cells harboring the predominant ΔE109 mutation displayed chromosome 8p,16p, and 17 loss while the less abundant H1047R mutation was in single cells harboring 1q gain, 4q loss, and 22 loss in addition to the 8p and 16q loss present in the ΔE109 cells. PIK3CA K111E had a quiescent, 2n copy number profile. The transcriptomic arm of ResolveOME, containing an oligo-conjugated antibody readout of surface protein expression, jointly confirmed the epithelial identity for the cells harboring the oncogenic PIK3CA mutations. A subpopulation of cells harboring prototypical breast cancer CNV were typed as non-epithelial with increased stemness characteristics, indicative of the ability to resolve phenotypic cellular states. These results suggest a tight interrelationship between CNV and SNV influencing the relative rate of clonal expansion. They also provide the opportunity to explore CNV:SNV signature association with loci exclusive of PIK3CA, and to exploit the power of multiomic integration for lineage reconstruction and for defining common oncogenic signatures of the evolving tumors. Citation Format: Jon Zawistowski, Isai Salas-Gonzalez, Tia Tate, Tatiana Morozova, Katherine Kennedy, Durga Arvapalli, Jamie Remington, Jeffrey Marks, E. Shelley Hwang, Gary Harton, Victor Weigman, Jay A. West. Inter- and intratumoral PIK3CA subclonal diversity in breast cancer contextualized by single-cell multiomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6929.
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Clark, Jeremy, Rachel Hurst, Mark Simon Winterbone, Hardeve Pahndha, Antoinnette Perry, Sophie McGrath, Richard Morgan, et al. "Urine Biomarkers for Prostate Cancer Diagnosis and Progression." Société Internationale d’Urologie Journal 2, no. 3 (May 14, 2021): 159–70. http://dx.doi.org/10.48083/sawc9585.
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Prostate cancer (PCa) can be highly heterogeneous and multifocal, and accurate assessment of the volume, grade, and stage of PCa in situ is not a simple task. Urine has been investigated as a source of PCa biomarkers for over 70 years, and there is now strong evidence that analysis of urine could provide more accurate diagnosis and a better risk stratification that could aid clinical decisions regarding disease surveillance and treatment. Urine diagnostics is a developing area, moving towards multiomic biomarker integration for improved diagnostic performance. Urine tests developed by strong collaborations between scientists and clinicians have the potential to provide targeted and meaningful data that can guide treatment and improve men’s lives.
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Azulay, A., Y. Aharoni Frutkoff, Y. Shimhlash, E. Borenstein, L. Reshef, L. Plotkin, G. Focht, et al. "P1224 Predicting response to nutritional therapy in newly diagnosed children with Crohn’s Disease (CD) using multi-omics approach." Journal of Crohn's and Colitis 18, Supplement_1 (January 1, 2024): i2172. http://dx.doi.org/10.1093/ecco-jcc/jjad212.1354.
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Abstract Background Background. One third of children with Crohn's disease (CD) will fail treatment with exclusive enteral nutrition (EEN) but predictors of response have been hitherto lacking. In this prospective cohort study, we aimed to use multiomic data to predict EEN response in treatment-naïve children with CD. Methods Methods. Children commenced on EEN at CD onset were followed through 8 weeks. Stool was collected for microbiome and metabolomics, and serum for metabolomics. Disease indices and clinical data were recorded. Targeted quantitative metabolomics approach was applied to analyze fecal and serum samples using a combination of direct injection mass spectrometry with a reverse-phase LC-MS/MS custom assay. DNA extracted from stool was sequenced and 16S rRNA gene was amplified. Feature selection was first utilized, using Recursive Feature Elimination with Cross-Validation (RFECV). For each omic analysis and the multiomic integration, selected components were fitted into a machine learning random forest (RF) model. Results Results. Included were 51 children (aged 14.8±2.7 yrs) treated with EEN, of whom 34 (66%) were responders; 36 (70%) had serum metabolomics, 20 (39%) fecal metabolomics and 31 (61%) microbiome data while 17 (33%) had all components. Clinical data and standard labs did not predict response (Table). The individual omic models ranked several key metabolites and microbes (Figure) and were able to predict EEN response with an accuracy ranging at 0.806-0.850 (Table). Serum 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF), fecal phenylethylamine and fecal alpha-aminobutyric acid were higher in non-responders while serum malic acid and fecal 3-methyladipic acid were higher in responders (Fig. A-B). Moreover, levels of Veillonela and Bifidobacterium species alongside Blautia genus were higher in responders while Lachnospiraceae and Fusicatenibacter genus were higher in non-responders (Fig. C). The final multiomic model which included also clinical data, retained components from serum and fecal metabolomics but none from the microbiome; it predicted EEN response with high accuracy of 0.882 (Table). Higher level of serum 2-hydroxyglutraic acid, 2-hydroxy-2-methylbutyric acid and fecal 3-methyladipic acid were detected in responders. In non-responders there were higher level of serum alpha-aminobutyric acid, valeric acid, isovaleric acid, alpha-aminobutyric acid and kynrenine and alongside with increased fecal kynrenine/tryptophan ratio, both part of the tryptophan metabolism and kynrenine pathway (Fig. D). Conclusion Conclusion. In this multi-omic analysis, we generated a,metabolomics-based multiomic model to predict response to EEN, unravelling related biological insights.
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Winders, Dafne Alves, Riley Graham, Xiangying Mao, Ilaria De Vito, Andrea O'Hara, Laure Turner, and Haythem Latif. "Abstract 4411: Enhancing scalability and consistency in clinical multiomics via an optimized fixed cell ATAC-seq method." Cancer Research 84, no. 6_Supplement (March 22, 2024): 4411. http://dx.doi.org/10.1158/1538-7445.am2024-4411.
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Abstract ATAC-seq has an emerging role in decoding mechanisms of gene regulation, offering valuable insights into pathology and treatment response in disease models. However, clinical adoption of ATAC-seq methods has been limited by logistical hurdles, including time-sensitive processing of fresh samples and compromised viability of cryopreserved cells. These constraints, compounded by changes in open chromatin regions (OCRs) following cryopreservation, introduce unintended bias and pose significant obstacles for the translational impact of ATAC-seq experiments. Here, we introduce an optimized fixed-cell ATAC-seq approach to overcome these limitations and unlock new sample types for ATAC-seq analyses. Our solution improves and simplifies the workflow from sample collection to clinical deliverable. This method enables ATAC-seq investigation of a diverse range of samples and facilitates the execution of complex experimental designs, including time course studies and high throughput screening.To demonstrate the effectiveness of this method, we compared our optimized fixed-cell method with traditional ATAC-seq preparations in both fresh and cryopreserved GM12878 cells in parallel. Human GM12878 cell line was obtained from Coriell Institute for Medical Research5. Remarkably, we observed consistent genome-wide patterns of OCR enrichment at key regulatory elements across the three sample preparation methods. We observed consistent OCR enrichment across the promoter region of known highly-expressed B-cell genes including CD48 and LCP1, underscoring this assay’s ability to detect chromatin changes at key genes in human disease models. To investigate the potential for multiomic analysis using this method, we prepared RNA-seq libraries from fixed-cell samples in parallel to ATAC-seq. We observed significantly elevated gene expression related to B-cell function and B-cell diseases, demonstrating our method’s compatibility with RNA-seq data collection and integration. This optimized fixed-cell ATAC-seq approach offers enhanced scalability and consistency over conventional methods and presents new opportunities for the multiomic analysis of chromatin and transcriptional activity genome-wide from a single sample in both clinical and research settings. Citation Format: Dafne Alves Winders, Riley Graham, Xiangying Mao, Ilaria De Vito, Andrea O'Hara, Laure Turner, Haythem Latif. Enhancing scalability and consistency in clinical multiomics via an optimized fixed cell ATAC-seq method [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4411.
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Frétin, Marie, Amaury Gérard, Anne Ferlay, Bruno Martin, Solange Buchin, Sébastien Theil, Etienne Rifa, et al. "Integration of Multiomic Data to Characterize the Influence of Milk Fat Composition on Cantal-Type Cheese Microbiota." Microorganisms 10, no. 2 (February 1, 2022): 334. http://dx.doi.org/10.3390/microorganisms10020334.
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A previous study identified differences in rind aspects between Cantal-type cheeses manufactured from the same skimmed milk, supplemented with cream derived either from pasture-raised cows (P) or from cows fed with maize silage (M). Using an integrated analysis of multiomic data, the present study aimed at investigating potential correlations between cream origin and metagenomic, lipidomic and volatolomic profiles of these Cantal cheeses. Fungal and bacterial communities of cheese cores and rinds were characterized using DNA metabarcoding at different ripening times. Lipidome and volatolome were obtained from the previous study at the end of ripening. Rind microbial communities, especially fungal communities, were influenced by cream origin. Among bacteria, Brachybacterium were more abundant in P-derived cheeses than in M-derived cheeses after 90 and 150 days of ripening. Sporendonema casei, a yeast added as a ripening starter during Cantal manufacture, which contributes to rind typical aspect, had a lower relative abundance in P-derived cheeses after 150 days of ripening. Relative abundance of this fungus was highly negatively correlated with concentrations of C18 polyunsaturated fatty acids and to concentrations of particular volatile organic compounds, including 1-pentanol and 3-methyl-2-pentanol. Overall, these results evidenced original interactions between milk fat composition and the development of fungal communities in cheeses.
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Murphy, Charlie, Kate Thompson, Lubna Nousheen, Divya Rao, and Todd E. Druley. "A Multiomic, Single-Cell Measurable Residual Disease (scMRD) Assay for Phasing DNA Mutations and Surface Immunophenotypes." Blood 142, Supplement 1 (November 28, 2023): 6055. http://dx.doi.org/10.1182/blood-2023-189360.
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The small population of cancerous cells that remain following treatment, known as measurable residual disease (MRD), is the major cause of relapse in acute myeloid leukemia (AML). Usually, these refractory cells have gained additional resistance mutations or changed their surface immunophenotypes in ways that preclude detection and phasing by current gold standard flow cytometry or bulk next-generation sequencing assays. For this reason, a multiomic single-cell MRD (scMRD) assay could offer a more comprehensive indicator of relapse and the potential for faster response. Here, we present a new scMRD assay with a 0.01% limit of detection that provides single-cell clonal architecture and immunophenotyping to not only identify residual leukemia cells, but also identify putative DNA or protein targets for salvage therapy. The assay enables rare-cell detection on a standard Mission Bio Tapestri run by adding (i) an upfront bead-based protocol to enrich for blast cells, (ii) a DNA and protein panel specifically designed for AML MRD diagnosis and treatment, and (iii) a new, automated analysis pipeline to evaluate single-cell multiomics output. By utilizing Mission Bio's single-molecule DNA sensitivity for single cells, this pipeline can identify and correlate co-occurring de novo variants, thereby reducing false positive rates over bulk assays that do not correlate variants. It furthermore can create phylogenetic trees of the detected MRD cells and present their surface protein signature and arm-level copy number. In addition, the multiplexing of up to three patient samples combined in one run via germline identification further reduces per sample costs and increases throughput. To demonstrate these features on 0.01% MRD, samples were constructed by titrating diseased cells into healthy bone marrow cells before processing them with the scMRD assay. We detected 0.01% spike-in (CD34+) and 0.1% spike-in (CD117+) in 6 of 6 samples, with an average enrichment of 41x and 13x, respectively. Further testing detected CD34+ 0.1% spike-ins in 10 of 10 samples (32x average enrichment). We applied the scMRD assay to banked bone marrow aspirate samples from 3 AML patients. The scMRD assay resolved the clonal architecture identifying multiple leukemic clones with co-occurring mutations. The assay readily distinguished pre-leukemic from leukemic clones thereby increasing the specificity of MRD results. The integration of genotype and immunophenotypic further enhanced MRD detection by identifying genotype-specific protein expression patterns. By combining high sensitivity with multiomics, this assay offers a potential scalable solution for comprehensive MRD detection that guides therapeutic decision-making.
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Lysenkova Wiklander, Mariya, Gustav Arvidsson, Ignas Bunikis, Anders Lundmark, Amanda Raine, Yanara Marincevic-Zuniga, Henrik Gezelius, et al. "A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing." Life Science Alliance 7, no. 8 (May 22, 2024): e202302481. http://dx.doi.org/10.26508/lsa.202302481.
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The B-cell acute lymphoblastic leukemia (ALL) cell line REH, with the t(12;21)ETV6::RUNX1translocation, is known to have a complex karyotype defined by a series of large-scale chromosomal rearrangements. Taken from a 15-yr-old at relapse, the cell line offers a practical model for the study of pediatric B-ALL. In recent years, short- and long-read DNA and RNA sequencing have emerged as a complement to karyotyping techniques in the resolution of structural variants in an oncological context. Here, we explore the integration of long-read PacBio and Oxford Nanopore whole-genome sequencing, IsoSeq RNA sequencing, and short-read Illumina sequencing to create a detailed genomic and transcriptomic characterization of the REH cell line. Whole-genome sequencing clarified the molecular traits of disrupted ALL-associated genes includingCDKN2A,PAX5,BTG1,VPREB1, andTBL1XR1, as well as the glucocorticoid receptorNR3C1. Meanwhile, transcriptome sequencing identified seven fusion genes within the genomic breakpoints. Together, our extensive whole-genome investigation makes high-quality open-source data available to the leukemia genomics community.
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Gao, Quanxin, Hao Huang, Peimin Liu, Xiuxin Zhao, Qiongying Tang, Zhenglong Xia, Miuying Cai, Rui Wang, Guanghua Huang, and Shaokui Yi. "Integration of Gut Microbiota with Transcriptomic and Metabolomic Profiling Reveals Growth Differences in Male Giant River Prawns (Macrobrachium rosenbergii)." Animals 14, no. 17 (August 31, 2024): 2539. http://dx.doi.org/10.3390/ani14172539.
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The giant freshwater prawn (GFP; Macrobrachium rosenbergii), a tropical species cultured worldwide, has high market demand and economic value. Male GFP growth varies considerably; however, the mechanisms underlying these growth differences remain unclear. In this study, we collected gut and hemolymphatic samples of large (ML), medium (MM), and small (MS) male GFPs and used the 16S rRNA sequencing and liquid chromatography–mass spectrometry-based metabolomic methods to explore gut microbiota and metabolites associated with GFP growth. The dominant bacteria were Firmicutes and Proteobacteria; higher growth rates correlated with a higher Firmicutes/Bacteroides ratio. Serum metabolite levels significantly differed between the ML and MS groups. We also combined transcriptomics with integrative multiomic techniques to further elucidate systematic molecular mechanisms in the GFPs. The results revealed that Faecalibacterium and Roseburia may improve gut health in GFP through butyrate release, affecting physiological homeostasis and leading to metabolic variations related to GFP growth differences. Notably, our results provide novel, fundamental insights into the molecular networks connecting various genes, metabolites, microbes, and phenotypes in GFPs, facilitating the elucidation of differential growth mechanisms in GFPs.
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Graham, Zachary A., Jacob A. Siedlik, Carlos A. Toro, Lauren Harlow, and Christopher P. Cardozo. "Boldine Alters Serum Lipidomic Signatures after Acute Spinal Cord Transection in Male Mice." International Journal of Environmental Research and Public Health 20, no. 16 (August 17, 2023): 6591. http://dx.doi.org/10.3390/ijerph20166591.
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Traumatic spinal cord injury (SCI) results in wide-ranging cellular and systemic dysfunction in the acute and chronic time frames after the injury. Chronic SCI has well-described secondary medical consequences while acute SCI has unique metabolic challenges as a result of physical trauma, in-patient recovery and other post-operative outcomes. Here, we used high resolution mass spectrometry approaches to describe the circulating lipidomic and metabolomic signatures using blood serum from mice 7 d after a complete SCI. Additionally, we probed whether the aporphine alkaloid, boldine, was able to prevent SCI-induced changes observed using these ‘omics platforms’. We found that SCI resulted in large-scale changes to the circulating lipidome but minimal changes in the metabolome, with boldine able to reverse or attenuate SCI-induced changes in the abundance of 50 lipids. Multiomic integration using xMWAS demonstrated unique network structures and community memberships across the groups.
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Rubinstein, Samuel M., and Jeremy L. Warner. "CancerLinQ: Origins, Implementation, and Future Directions." JCO Clinical Cancer Informatics, no. 2 (December 2018): 1–7. http://dx.doi.org/10.1200/cci.17.00060.
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Rapid-learning health systems have been proposed as a potential solution to the problem of quality in medicine, by leveraging data generated from electronic health systems in near-real time to improve quality and reduce cost. Given the complex, dynamic nature of cancer care, a rapid-learning health system offers large potential benefits to oncology practice. In this article, we review the rationale for developing a rapid-learning health system for oncology and describe the sequence of events that led to the development of ASCO’s CancerLinQ (Cancer Learning Intelligence Network for Quality) initiative, as well as the current state of CancerLinQ, including its importance to efforts such as the Beau Biden Cancer Moonshot. We then review the considerable challenges facing optimal implementation of a rapid-learning health system such as CancerLinQ, including integration of rapidly expanding multiomic data, capturing big data from a variety of sources, an evolving competitive landscape, and implementing a rapid-learning health system in a way that satisfies many stakeholders, including patients, providers, researchers, and administrators.
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Harris, Alexandra R., Huaitian Liu, Brittany Jenkins-Lord, Francis Makokha, Shahin Sayed, Gretchen Gierach, and Stefan Ambs. "Abstract C044: Investigation of breast tumor biology and microenvironment in women of African descent using a single cell multiomic approach." Cancer Epidemiology, Biomarkers & Prevention 32, no. 12_Supplement (December 1, 2023): C044. http://dx.doi.org/10.1158/1538-7755.disp23-c044.
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Abstract Women of African descent are at an increased risk of developing and dying from aggressive subtypes of breast cancer. A connection between aggressive disease and Western Sub-Saharan African ancestry has been postulated, but it remains largely unknown to what extent breast cancer in Africa is reminiscent of breast cancer in U.S. African American (AA) women who experience disproportionately high mortality rates. We performed ATAC- and RNA-sequencing on 9 human triple-negative breast cancer cell lines of U.S. origin and discovered that African ancestry influences the chromatin landscape, leading to disparate transcription factor activity and downstream gene expression patterns indicative of an aggressive tumor biology. Here, we describe an ambitious study that employs single-nucleus ATAC- and RNA-sequencing (snMultiome) of frozen breast tumors to characterize chromatin accessibility and gene expression patterns with single-cell resolution in AA (n=32), Kenyan (n=18), and European American (EA, n=20) women in relation to ancestry and risk factor exposure. In an initial pilot in 10 Kenyan women, we successfully isolated intact, high-quality single nuclei from frozen breast tumor tissue through an optimized combination of enzymatic digestion and an automated tissue homogenizer. We performed multiome sequencing on a total of 36525 nuclei across these 10 pilot tumors using the 10x Genomics platform. Following filtering, normalization (SCT for snRNA; LSI for snATAC), peak calling (MACS2), and integration (Seurat-v4 for snRNA; Harmony for snATAC), we characterized tumor, stromal, and immune cell populations (totaling 10 distinct cell types). We observed striking intra- and inter-tumoral heterogeneity across Kenyan women, with increased abundance of immune subpopulations (myeloid, T-cells, B-cells) in triple-negative subtypes. After demonstrating technical feasibility and success in this sample subset, we are now performing integration, cell type annotation, and downstream analyses in our larger cohort to characterize differences in both tumor biology and the tumor microenvironment in European American, African American, and Kenyan women at the single cell level. To date, 70 tumors spanning breast cancer subtypes have been sequenced. We are currently performing downstream analyses in this cohort to characterize ancestry- and risk factor-related differences in the tumor epithelium and microenvironment at the single-cell level. This project holds the potential to yield crucial insights into how ancestry or other factors may influence the etiology of different breast cancer subtypes, as well as produce clinically actionable biomarkers and therapeutic targets to enhance precision medicine within patient populations at high risk for aggressive disease. Citation Format: Alexandra R. Harris, Huaitian Liu, Brittany Jenkins-Lord, Francis Makokha, Shahin Sayed, Gretchen Gierach, Stefan Ambs. Investigation of breast tumor biology and microenvironment in women of African descent using a single cell multiomic approach [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C044.
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O’Hara, Eóin, Megan Dubois, Gabriel O. Ribeiro, and Robert J. Gruninger. "PSIX-18 Multiomic analysis to identify host and microbiome contributions to digestibility in beef cattle." Journal of Animal Science 102, Supplement_3 (September 1, 2024): 734–35. http://dx.doi.org/10.1093/jas/skae234.827.
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Abstract This study evaluated beef heifers selected for high (efficient) or low (inefficient) digestible fiber intake (DFI). Initial analysis showed that high DFI animals had reduced methane production versus low-DFI under a high forage diet. Using the same cohort of animals maintained on a further 4 diets of varying forage:concentrate ratios, we employed multi-kingdom amplicon sequencing and metagenome shotgun sequencing of rumen digesta and feces alongside RNA sequencing of rumen epimural samples to evaluate the compositional and functional interplay between different microbial groups, and their relationship with host gene expression in cattle divergent for DFI. Samples were collected from 16 cattle during 2 metabolism trials, comprising 5 diets. Amplicon sequencing analysis was conducted using QIIME2; 16S rRNA and 18S rRNA reads were analyzed using the SILVA database, while LSU (fungal) sequences were analyzed using a custom D1/D2 database. Additional analysis of archaeal 16S rRNA sequences was conducted using the Rumen and Intestinal Methanogens (RIM) database. Metagenome shotgun reads underwent a two-pass classification with Kraken2 using a database of prokaryotic genomes derived from the GTDB taxonomy, with the unclassified output undergoing classification using a custom database containing all NCBI protozoa, fungi, and phage genomes, enriched with selected rumen-specific ciliate and fungal genomes. Downstream analysis of taxonomic data from all microbiome work was conducted in R, and differentially abundant taxa were identified using ANCOM-BC and Aldex2. Functional analysis of metagenome contigs using the CAZY database implemented in dbCAN3 is ongoing. RNA-seq data were analyzed using the ARS-UCD reference genome, with identification of DE genes conducted using DeSeq2. Preliminary results indicate no major effect of DFI ranking on host gene expression, bacterial 16S rRNA, or metagenome compositional profiles. Several bacterial genera were differentially abundant between digestibility groups (P < 0.05), but these were all minor (< 0.01%) members of the microbiome. Fungal and methanogen communities different significantly (P < 0.05) according to DFI group, with efficient (high DFI) containing and more diverse communities under high-grain diet (P < 0.05). The same difference showed a tendency toward significance for the 18S rRNA protozoa data (P < 0.1). These preliminary data indicate that the microbial factors underpinning divergence in efficiency measured by DFI vary according to diet and may be more prominent in the non-bacterial fraction of the microbiome. Ongoing functional analysis of metagenome data as well as integration of multiomic data will provide deeper insight into these relationships and how they contribute to feed digestibility and efficiency in cattle.
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Karasarides, Maria, Alexandria P. Cogdill, Paul B. Robbins, Michaela Bowden, Elizabeth M. Burton, Lisa H. Butterfield, Alessandra Cesano, et al. "Hallmarks of Resistance to Immune-Checkpoint Inhibitors." Cancer Immunology Research 10, no. 4 (March 14, 2022): 372–83. http://dx.doi.org/10.1158/2326-6066.cir-20-0586.
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Abstract Immune-checkpoint inhibitors (ICI), although revolutionary in improving long-term survival outcomes, are mostly effective in patients with immune-responsive tumors. Most patients with cancer either do not respond to ICIs at all or experience disease progression after an initial period of response. Treatment resistance to ICIs remains a major challenge and defines the biggest unmet medical need in oncology worldwide. In a collaborative workshop, thought leaders from academic, biopharma, and nonprofit sectors convened to outline a resistance framework to support and guide future immune-resistance research. Here, we explore the initial part of our effort by collating seminal discoveries through the lens of known biological processes. We highlight eight biological processes and refer to them as immune resistance nodes. We examine the seminal discoveries that define each immune resistance node and pose critical questions, which, if answered, would greatly expand our notion of immune resistance. Ultimately, the expansion and application of this work calls for the integration of multiomic high-dimensional analyses from patient-level data to produce a map of resistance phenotypes that can be utilized to guide effective drug development and improved patient outcomes.
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Godbole, Shweta, Hannah Voss, Simon Schlumbohm, Yannis Schumann, Bojia Peng, Martin Mynarek, Stefan Rutkowski, et al. "MDB-19. MULTIOMIC PROFILING OF MEDULLOBLASTOMA REVEALS SUBTYPE-SPECIFIC TARGETABLE ALTERATIONS AT THE PROTEOME AND N-GLYCAN LEVEL." Neuro-Oncology 25, Supplement_1 (June 1, 2023): i66. http://dx.doi.org/10.1093/neuonc/noad073.252.
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Abstract Medulloblastomas (MBs) are malignant pediatric brain tumors which are clinically and histologically very heterogeneous. Epigenomic and transcriptomic analyses have advanced the understanding of these tumors and four main molecular subgroups – which themselves comprise several subtypes- have been defined: WNT-activated MB, Sonic hedgehog (SHH)-activated MB, Group3 and Group4 MB. Despite tremendous advances in classification and stratification, the pathogenesis of subtypes is still poorly understood and there is still a lack of targeted therapies. In contrast to nucleic acids, proteins more directly account for the phenotype and hold the potential to discover clinically relevant phenotypes, new biomarkers and therapy targets. In this study, we put together a harmonized cohort of 167 MB samples and integrated proteome data with DNA methylome and N-glycan data. At proteome level, we found six stable subtypes which could be assigned to two main molecular signatures namely-transcriptional/translational processes (pWNT, pG3myc and pSHHt) and synaptic/immunological processes (pG3, pG4 and pSHHs). pG3myc MB showed poor survival and accumulated high risk features such as anaplastic histology, epigenetic subtype II and MYC amplification. They showed an overlap of proteome patterns with favorable pWNT MBs but displayed significantly different protein abundancies of the vincristine resistance associated TriC/CCT complex and N-glycan turnover associated factors. Of note. N-glycan profiles distinguished MB subtypes and pG3myc MBs were enriched by complex bisecting N-glycans. We further identified Tenascin C (TNC) and Palmdelphin (PALMD) as suitable biomarkers for the pWNT and pG3myc MBs, respectively. Integration of proteome and DNA methylome data revealed that pWNT MBs showed the highest correlation of data modalities among MB subtypes, indicating a higher conservation of biological processes compared to other proteome subtypes. Our results shed light on new targetable alterations in MB and set a foundation for potential immunotherapies targeting glycan structures.
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Irineu, Luiz Eduardo Souza da Silva, Cleiton de Paula Soares, Tatiane Sanches Soares, Felipe Astolpho de Almeida, Fabrício Almeida-Silva, Rajesh Kumar Gazara, Carlos Henrique Salvino Gadelha Meneses, et al. "Multiomic Approaches Reveal Hormonal Modulation and Nitrogen Uptake and Assimilation in the Initial Growth of Maize Inoculated with Herbaspirillum seropedicae." Plants 12, no. 1 (December 22, 2022): 48. http://dx.doi.org/10.3390/plants12010048.
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Herbaspirillum seropedicae is an endophytic bacterium that can fix nitrogen and synthesize phytohormones, which can lead to a plant growth-promoting effect when used as a microbial inoculant. Studies focused on mechanisms of action are crucial for a better understanding of the bacteria-plant interaction and optimization of plant growth-promoting response. This work aims to understand the underlined mechanisms responsible for the early stimulatory growth effects of H. seropedicae inoculation in maize. To perform these studies, we combined transcriptomic and proteomic approaches with physiological analysis. The results obtained eight days after inoculation (d.a.i) showed increased root biomass (233 and 253%) and shoot biomass (249 and 264%), respectively, for the fresh and dry mass of maize-inoculated seedlings and increased green content and development. Omics data analysis, before a positive biostimulation phenotype (5 d.a.i.) revealed that inoculation increases N-uptake and N-assimilation machinery through differentially expressed nitrate transporters and amino acid pathways, as well carbon/nitrogen metabolism integration by the tricarboxylic acid cycle and the polyamine pathway. Additionally, phytohormone levels of root and shoot tissues increased in bacterium-inoculated-maize plants, leading to feedback regulation by the ubiquitin-proteasome system. The early biostimulatory effect of H. seropedicae partially results from hormonal modulation coupled with efficient nutrient uptake-assimilation and a boost in primary anabolic metabolism of carbon–nitrogen integrative pathways.
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Bulusu, Krishna C., Jake Cohen-Setton, Ioannis Kagiampakis, Miguel Goncalves, Gavin Edwards, Sanddhya Jayabalan, Shruti Shikare, Kelvin Tsang, Ben Sidders, and Etai Jacob. "Abstract 3531: PRESSNET: Patient stratification and biomarker discovery using multi-modal knowledge graph framework." Cancer Research 84, no. 6_Supplement (March 22, 2024): 3531. http://dx.doi.org/10.1158/1538-7445.am2024-3531.
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Abstract Background: Multiomics data is critical to obtain a near comprehensive picture of disease progression and drug response. In addition, the generation of response and survival biomarkers, and the segmentation of patients into subtypes with distinct, actionable ‘omic signatures and survival trajectories, is vital for personalised medicine research and successful trial design. However, as the volume and diversity of data increases, so too does the challenge of effective multiomic data integration. Knowledge graphs (KGs) can capture heterogeneous data and relationships between entities in a flexible and scalable data structure, making them suitable for this domain. We have developed PRESSnet, a framework for building Patient KGs and analysing clinical data for novel patient stratification hypotheses and clinical biomarker discovery. Methods: PRESSnet is an end-to-end framework that turns raw patient data into graph-derived insights. Firstly, the user chooses which modality files to include, what assumptions to make about data processing, and which graph algorithms to use. PRESSnet then automatedly creates a patient KG of the input data, where nodes represent patients and their associated features. In addition, biomedical prior knowledge, for example in the form of gene-pathway or gene-gene relationship data, is also integrated with the graphs. Insights are generated from the KG via community detection, graph embedding generation, and graph neural networks; these generate hypotheses for novel patient subtypes or biomarkers for clinical outcomes. As graph algorithms capture interrelationships between nodes, PRESSnet offers biomarkers that are composite, i.e. that can contain features from multiple ‘omics and clinical features. Results: We applied PRESSnet to the MSK 20221 cohort of IO-treated LUAD patients, and it uncovered prognostic composite biomarkers that stratified biomarker-positive patients from the whole cohort with a p value < 0.001 (95% CI) for OS, including known markers of poor prognosis such as STK11, RBM10, KRAS and KEAP1 mutations, and high neutrophil/lymphocyte ratio. We also generated embeddings of patients in the cohort and predicted survived/deceased status with an AUC of 0.82, outperforming published state-of-the-art. Conclusions: We have successfully developed a generalisable framework for generating insights from patient data using state-of-the art knowledge graph data science. PRESSnet can generate novel stratification and biomarker hypotheses that can potentially inform the next generation of IO targets and clinical biomarkers. Footnotes1 Vanguri et al., “Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer”, Nat Cancer. 2022 Citation Format: Krishna C. Bulusu, Jake Cohen-Setton, Ioannis Kagiampakis, Miguel Goncalves, Gavin Edwards, Sanddhya Jayabalan, Shruti Shikare, Kelvin Tsang, Ben Sidders, Etai Jacob. PRESSNET: Patient stratification and biomarker discovery using multi-modal knowledge graph framework [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3531.
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Patti, Gary, Ethan Stancliffe, Adam Richardson, Ashima Mehta, Monil Gandhi, and Kevin Cho. "Abstract 4428: Integrated multi-omics analysis reveals systemic and localized metabolic disruptions in colorectal cancer." Cancer Research 84, no. 6_Supplement (March 22, 2024): 4428. http://dx.doi.org/10.1158/1538-7445.am2024-4428.
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Abstract Despite technological advances in molecular medicine over the last 30 years, no single approach has proven to be sufficient to meet the diagnostic needs in cancer care. Here we use a multiomic approach leveraging proteomic, metabolomic, and transcriptomic technologies to study the metabolic shifts that occur during colorectal cancer (CRC) progression, Our integrated analysis identified systemic changes to beta oxidation pathways and localized changes to tyrosine metabolism within the tumor, creating a mechanistic, actionable description of the core CRC metabolic program. We conducted untargeted proteomics and metabolomics profiling on serum samples from CRC patients (n=10) and healthy controls (n=10). Our serum proteomics data is generated through sample enrichment on the Seer Proteograph system followed by LC/MS analysis with the Thermo Orbitrap Astral. Our metabolomics approach employs LC/MS assays for unbiased profiling of the serum metabolome, exposome, and lipidome. We supplemented this discovery work with both targeted serum and tumor-specific approaches including targeted proteomics to quantify select targets, inflammatory profiling with Alamar proteomics, public transcriptomics data from TCGA, and in vitro metabolic flux data. These large and diverse datasets were then integrated through joint pathway analysis and network integration. The global serum proteomics and metabolomics profiles generated show distinct separation between healthy and diseased individuals. Joint pathway analysis of these discovery datasets highlighted enrichment in beta oxidation pathways and tyrosine metabolism, among others. Interestingly, the Alamar inflammatory panel revealed that many of the analytes in the tyrosine metabolism pathway were well correlated with inflammatory status, while the beta oxidation signature had a lower correlation. To determine the relationship of these systemic findings from serum to tumor metabolism itself, we integrated tumor-specific transcriptomics data and found alterations to tyrosine metabolism with differences in tyrosine aminotransferase expression. The beta oxidation related genes, on the other hand, were not concordant with the serum findings. However, our in vitro metabolic flux studies have shown beta oxidation in CRC cells is upregulated to provide additional fuel for oxidative phosphorylation. This result suggests that beta oxidation may not be transcriptionally regulated in the tumor but rather a consequence of organismal metabolic rewiring. The results of this work, which is currently expanding into a larger and more diverse patient population, underscores the power of multiomic profiling for enhancing our understanding of metabolic dysregulation in CRC. Ultimately, a comprehensive model of the molecular alterations in CRC will yield a better understanding of tumor phenotypes and inform better diagnostics and therapies. Citation Format: Gary Patti, Ethan Stancliffe, Adam Richardson, Ashima Mehta, Monil Gandhi, Kevin Cho. Integrated multi-omics analysis reveals systemic and localized metabolic disruptions in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4428.
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Harris, Alexandra R., Huaitian Liu, Brittany Jenkins-Lord, Tiffany H. Dorsey, Francis Makokha, Shahin Sayed, Gretchen Gierach, and Stefan Ambs. "Abstract 6108: Investigation of breast tumor biology and microenvironment in women of African descent using a single cell multiomic approach." Cancer Research 84, no. 6_Supplement (March 22, 2024): 6108. http://dx.doi.org/10.1158/1538-7445.am2024-6108.
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Abstract Women of African descent are at an increased risk of developing and dying from aggressive subtypes of breast cancer. A connection between aggressive disease and Western Sub-Saharan African ancestry has been postulated, but it remains largely unknown to what extent breast cancer in Africa is reminiscent of breast cancer in U.S. African American (AA) women who experience disproportionately high mortality rates. We performed ATAC- and RNA-sequencing on 9 human triple-negative breast cancer cell lines of U.S. origin and discovered that African ancestry influences the chromatin landscape, leading to disparate transcription factor (TF) activity and downstream gene expression patterns indicative of an aggressive tumor biology. Here, we describe an ambitious study that employs single-nucleus (sn) ATAC- and RNA-sequencing (snMultiome) of frozen breast tumors to characterize chromatin accessibility and gene expression patterns with single-cell resolution in AA (n=33), Kenyan (n=25), and European American (EA, n=24) women in relation to genetic ancestry, risk factor exposures, clinical characteristics, and 5-year survival. To achieve this, we successfully isolated intact, high-quality single nuclei from archival frozen breast tumor tissue through an optimized combination of enzymatic digestion and automated tissue homogenization. We performed snMultiome sequencing of 82 tumors using the 10x Genomics platform. Following filtering, normalization (SCT for snRNA; LSI for snATAC), peak calling (MACS2), and integration (Harmony), our dataset includes a total of 296,557 nuclei. Cancerous (163,419 nuclei) and non-cancerous (133,138 nuclei) cells were distinguished based on DNA copy number (CopyKat). Within the microenvironment, 11 major immune, epithelial, and stromal cell types were successfully annotated, exhibiting distinct patterns by population group (e.g. AA tumors showed markedly increased abundance of myeloid and T-cells, while Kenyan tumors showed increased abundance of pericytes and fibroblasts). A large number of enriched TFs within each cell type varied significantly by population group, suggesting distinct chromatin accessibility patterns related to genetic ancestry. Within cancerous cells, striking intra- and inter-tumoral heterogeneity was observed by genetic ancestry even within molecular subtype groups. Current efforts focus on in-depth molecular characterization of ancestry- and risk factor-related differences in the tumor epithelium and microenvironment and distinct signatures present in lethal disease. This project holds the potential to yield crucial insights into how ancestry or other factors may influence the etiology of different breast cancer subtypes, as well as produce clinically actionable biomarkers and therapeutic targets to enhance precision medicine within patient populations at high risk for aggressive disease. Citation Format: Alexandra R. Harris, Huaitian Liu, Brittany Jenkins-Lord, Tiffany H. Dorsey, Francis Makokha, Shahin Sayed, Gretchen Gierach, Stefan Ambs. Investigation of breast tumor biology and microenvironment in women of African descent using a single cell multiomic approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6108.
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Gambacorta, Valentina, Stefano Beretta, Martina Ciccimarra, Laura Zito, Kety Giannetti, Angela Andrisani, Daniela Gnani, et al. "Abstract LB563: Integrated multiomic profiling identifies the epigenetic regulator PRC2 as a therapeutic target to counteract leukemia immune escape and relapse." Cancer Research 82, no. 12_Supplement (June 15, 2022): LB563. http://dx.doi.org/10.1158/1538-7445.am2022-lb563.
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Abstract Background: Evasion from immune control represents one of the main drivers of acute myeloid leukemia (AML) relapse after allogeneic hematopoietic cell transplantation (allo-HCT). In particular, up to 40% of AML relapses display complete loss of surface expression of HLA class II molecules without any genetic lesion explaining this phenotype (Christopher et al, N Engl J Med, 2018; Toffalori et al, Nat Med, 2019). This led us to investigate the links between epigenetic changes, immune evasion and post-transplantation relapse. Methods: Starting from primary AML samples pairwise collected from five patients at diagnosis and relapse with non-genomic loss of HLA class II expression, we generated Patients-Derived Xenografts (PDXs) into NOD-SCID γ-chain null mice. Leukemic cells expanded in the mice and their original human counterparts were characterized for changes in gene expression (by RNA-Seq), DNA methylation profile (by RRBS), and chromatin accessibility (by ATAC-Seq). The results obtained by all these approaches and in the different patients were integrated by Multi-Omics Factor Analysis (MOFA) and Gene Set Enrichment Analysis (GSEA). Finally, we tested the immunological effects of epigenetic drugs on AML cells and on their recognition by T cells in ex-vivo short-term cultures and in PDXs. Results: We verified that PDXs faithfully recapitulate immune-related differences between AML diagnosis and post-transplantation relapse, including loss of expression of HLA class II molecules. Differences between diagnosis and post-transplantation relapse samples were mostly explained by changes in chromatin accessibility, and largely unrelated to the DNA methylation profile. In particular, in all five patients analyzed, we documented genomewide chromatin compaction at time of relapse, that was particularly evident for HLA class II genes and their master regulator CIITA, and not detected in relapses after sole chemotherapy. Integration of all the high-throughput technologies by MOFA, and of results from different patients by GSEA, pointed to the Polycomb Repressive Complex 2 (PRC2) as the main candidate mediator of HLA class II silencing.Pharmacological inhibition of PRC2 subunits rescued HLA class II expression in AML relapses ex vivo and in vivo, with consequent recovery of leukemia recognition by CD4+ T cells. Conclusions: Our results uncover a novel link between epigenetics and leukemia immune escape, which may rapidly translate into innovative strategies to cure or prevent AML post-transplantation relapse. Citation Format: Valentina Gambacorta, Stefano Beretta, Martina Ciccimarra, Laura Zito, Kety Giannetti, Angela Andrisani, Daniela Gnani, Lucia Zanotti, Giacomo Oliveira, Matteo G. Carrabba, Davide Cittaro, Ivan Merelli, Fabio Ciceri, Raffaella Di Micco, Luca Vago. Integrated multiomic profiling identifies the epigenetic regulator PRC2 as a therapeutic target to counteract leukemia immune escape and relapse [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB563.
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Bayless, Nicholas L., Jeffrey A. Bluestone, Samantha Bucktrout, Lisa H. Butterfield, Elizabeth M. Jaffee, Christian A. Koch, Bart O. Roep, et al. "Development of preclinical and clinical models for immune-related adverse events following checkpoint immunotherapy: a perspective from SITC and AACR." Journal for ImmunoTherapy of Cancer 9, no. 9 (September 2021): e002627. http://dx.doi.org/10.1136/jitc-2021-002627.
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Recent advances in cancer immunotherapy have completely revolutionized cancer treatment strategies. Nonetheless, the increasing incidence of immune-related adverse events (irAEs) is now limiting the overall benefits of these treatments. irAEs are well-recognized side effects of some of the most effective cancer immunotherapy agents, including antibody blockade of the cytotoxic T-lymphocyte-associated protein 4 and programmed death protein 1/programmed-death ligand 1 pathways. To develop an action plan on the key elements needed to unravel and understand the key mechanisms driving irAEs, the Society for Immunotherapy for Cancer and the American Association for Cancer Research partnered to bring together research and clinical experts in cancer immunotherapy, autoimmunity, immune regulation, genetics and informatics who are investigating irAEs using animal models, clinical data and patient specimens to discuss current strategies and identify the critical next steps needed to create breakthroughs in our understanding of these toxicities. The genetic and environmental risk factors, immune cell subsets and other key immunological mediators and the unique clinical presentations of irAEs across the different organ systems were the foundation for identifying key opportunities and future directions described in this report. These include the pressing need for significantly improved preclinical model systems, broader collection of biospecimens with standardized collection and clinical annotation made available for research and integration of electronic health record and multiomic data with harmonized and standardized methods, definitions and terminologies to further our understanding of irAE pathogenesis. Based on these needs, this report makes a set of recommendations to advance our understanding of irAE mechanisms, which will be crucial to prevent their occurrence and improve their treatment.
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Waeijen-Smit, Kiki, Antonio DiGiandomenico, Jessica Bonnell, Kristoffer Ostridge, Ulf Gehrmann, Bret R. Sellman, Tara Kenny, et al. "Early diagnostic BioMARKers in exacerbations of chronic obstructive pulmonary disease: protocol of the exploratory, prospective, longitudinal, single-centre, observational MARKED study." BMJ Open 13, no. 3 (March 2023): e068787. http://dx.doi.org/10.1136/bmjopen-2022-068787.
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IntroductionAcute exacerbations of chronic obstructive pulmonary disease (AECOPD) play a pivotal role in the burden and progressive course of chronic obstructive pulmonary disease (COPD). As such, disease management is predominantly based on the prevention of these episodes of acute worsening of respiratory symptoms. However, to date, personalised prediction and early and accurate diagnosis of AECOPD remain unsuccessful. Therefore, the current study was designed to explore which frequently measured biomarkers can predict an AECOPD and/or respiratory infection in patients with COPD. Moreover, the study aims to increase our understanding of the heterogeneity of AECOPD as well as the role of microbial composition and hostmicrobiome interactions to elucidate new disease biology in COPD.Methods and analysisThe ‘Early diagnostic BioMARKers in Exacerbations of COPD’ study is an exploratory, prospective, longitudinal, single-centre, observational study with 8-week follow-up enrolling up to 150 patients with COPD admitted to inpatient pulmonary rehabilitation at Ciro (Horn, the Netherlands). Respiratory symptoms, vitals, spirometry and nasopharyngeal, venous blood, spontaneous sputum and stool samples will be frequently collected for exploratory biomarker analysis, longitudinal characterisation of AECOPD (ie, clinical, functional and microbial) and to identify host–microbiome interactions. Genomic sequencing will be performed to identify mutations associated with increased risk of AECOPD and microbial infections. Predictors of time-to-first AECOPD will be modelled using Cox proportional hazards’ regression. Multiomic analyses will provide a novel integration tool to generate predictive models and testable hypotheses about disease causation and predictors of disease progression.Ethics and disseminationThis protocol was approved by the Medical Research Ethics Committees United (MEC-U), Nieuwegein, the Netherlands (NL71364.100.19).Trial registration numberNCT05315674.
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Ayoub, Edward, Vakul Mohanty, Yuki Nishida, Tallie Patsilevas, Mahesh Basyal, Russell Pourebrahim, Muharrem Muftuoglu, Ken Chen, Ghayas C. Issa, and Michael Andreeff. "Abstract A41: Single Cell Multiomic Analysis Reveals Association of TP53-mut Loss of Heterozygosity with Primitive Phenotype in Acute Myeloid Leukemia." Blood Cancer Discovery 4, no. 3_Supplement (May 1, 2023): A41. http://dx.doi.org/10.1158/2643-3249.aml23-a41.
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Abstract TP53 mutations in acute myeloid leukemia (AML) are associated with copy number abnormalities (CNA), structural variants and high risk of relapse (Döhner et al., 2017; Giacomelli et al., 2018; Bernard et al. 2020). In spite of relatively high remission rates obtained by targeted therapies, TP53 mutant (TP53-mut) clones persist, invariably resulting in relapse (Short et al., 2021; Takahashi et al., 2016). Delineating the clonal architecture and the immunophenotypes of TP53-mut clones during AML therapy may provide a better understanding of the role of TP53-mutations in AML biology. Recent progress in sequencing technologies allows the integration of genotyping and phenotyping at the single cell level. Here, we took advantage of MissionBio Tapestri’s newest platform: single cell DNA + protein for simultaneous genotyping and phenotyping with 45 surface oligo-conjugated antibodies in 10 paired samples from 5 patients with TP53-mut AML before and after therapy. Samples with at least 70% viability were stained with the TotalSeq™-D Human Heme Oncology Cocktail, V1.0. Following surface marker staining, single-cell suspension, encapsulation, and barcoding were performed according to manufacturer’s instruction. For scDNA library preparation, we utilized a validated custom panel (Morita et al. 2020) consisting of 279 amplicons covering recurrent mutations in 37 genes in AML. We sequenced a total of 44,550 cells from 10 samples. We confirmed mutations reported by MD Anderson molecular diagnostic laboratory in the genes covered by the scDNA custom panel. The clonal architecture analysis distinguished between TP53-mut clones with or without loss of heterozygosity (LOH) of the normal TP53 allele. Our data show a primitive immunophenotype in TP53-mut with LOH (LOH+) clones in comparison to TP53-mut LOH- clones. We see 2.4 LOG2FC increase in CD34 and 1.8 LOG2FC increase in CD117 (p<0.001) in TP53-mut LOH+ clones in comparison to TP53-mut LOH- clones. Clonal evolution analysis shows that TP53-mut LOH+ clones are significantly more resistant to therapy than TP53-mut LOH-, consistent with previous publications. On the other hand, TP53-mut LOH- clones showed significantly higher levels of CD2, CD16, CD5, CD3, and CD8 among other lineage markers (LOG2FC= 1.1, 1.2, 1.4, 1.1, and 1.1 respectively; p.value <0.0001) compared to TP53-mut LOH+. This data indicates that TP53-mut LOH- cells can express lymphoid phenotypic markers. Single cell cytokine analysis (IsoPlexis) reveals profound lack of secreted cytokines in T-cells from TP53-mut AML. Further data from bulk-RNA sequencing, ddPCR, and CyTOF will be presented that validates a lymphoid phenotype of TP53-mut LOH-. In summary, we utilize a scDNA+protein multiomic approach to dissect clonal architecture and provide a link between genotype-phenotype in TP53-mut AML. We show that while TP53-mut LOH+ clones are exclusively primitive, TP53-mut LOH- clones retain the capacity to exist outside primitive immunophenotype and might lack differentiation block. Citation Format: Edward Ayoub, Vakul Mohanty, Yuki Nishida, Tallie Patsilevas, Mahesh Basyal, Russell Pourebrahim, Muharrem Muftuoglu, Ken Chen, Ghayas C. Issa, Michael Andreeff. Single Cell Multiomic Analysis Reveals Association of TP53-mut Loss of Heterozygosity with Primitive Phenotype in Acute Myeloid Leukemia [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr A41.
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Johnston, Michael, John J. Y. Lee, Bo Hu, Ana Nikolic, Audrey Baguette, Seungil Paik, Haifen Chen, et al. "EPCO-38. TYPE B ULTRA LONG-RANGE INTERACTIONS IN PFAS (TULIPS) ARE RECURRENT EPIGENOMIC FEATURES OF PFA EPENDYMOMA." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii124. http://dx.doi.org/10.1093/neuonc/noac209.472.
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Abstract Posterior Fossa Group A (PFA) ependymomas are pediatric brain tumors with extremely poor survival outcomes. As protein-coding mutations in PFA are exceedingly rare, the underlying etiology of these tumors remains elusive. Elevated CpG island methylation and depletion of H3K27me3 have been described in PFA, leading to the hypothesis that PFA may be driven by a dysregulated epigenetic state. In this study, we sought to determine how three-dimensional (3D) genome features (such as DNA loops, domains, and compartments) differ between pediatric brain tumors. We performed Hi-C sequencing on a collection of 64 patient specimens and patient-derived primary cultures that collectively span multiple subgroups of ependymoma, medulloblastoma, high-grade glioma, and non-neoplastic brain. For certain samples, we further performed RNA-seq, histone modification ChIP-seq, or whole-genome bisulfite sequencing to allow multiomic data integration. Overall, the 3D genome organization of PFA samples appeared distinct from other tumor types. We identified and defined TULIPs: a subset of type B compartments, separated by genomic distances greater than 10 Mbp, that exhibit a striking fivefold increase in reciprocal interaction strength. These TULIPs recurred at the same genomic positions across the vast majority of PFA samples with minimal representation among other tumor or non-tumor samples. TULIPs displayed enrichment for heterochromatic features such as H3K9me3 and late replication timing and were depleted of euchromatic features such as H3K27ac and protein-coding genes. By using immuno-fluorescence for H3K9me3 and oligo-FISH to label TULIP regions, we demonstrated that TULIP regions are more compact in PFA than other tumors. Finally, by applying inhibitors of H3K9 lysine methylation to PFA cultures we showed that TULIPs become more diffuse and cell viability is reduced. Altogether, this work defines TULIPs as highly recurrent epigenetic features of PFA tumors.
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Valle, Filippo, Matteo Osella, and Michele Caselle. "Multiomics Topic Modeling for Breast Cancer Classification." Cancers 14, no. 5 (February 23, 2022): 1150. http://dx.doi.org/10.3390/cancers14051150.
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The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of ’omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs, and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or “topics” that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development and are associated to the survival probability.
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Rotroff, Daniel M., and Alison A. Motsinger-Reif. "Embracing Integrative Multiomics Approaches." International Journal of Genomics 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/1715985.
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As “-omics” data technology advances and becomes more readily accessible to address complex biological questions, increasing amount of cross “-omics” dataset is inspiring the use and development of integrative bioinformatics analysis. In the current review, we discuss multiple options for integrating data across “-omes” for a range of study designs. We discuss established methods for such analysis and point the reader to in-depth discussions for the various topics. Additionally, we discuss challenges and new directions in the area.
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Demetci, Pinar, Rebecca Santorella, Björn Sandstede, William Stafford Noble, and Ritambhara Singh. "Single-Cell Multiomics Integration by SCOT." Journal of Computational Biology 29, no. 1 (January 1, 2022): 19–22. http://dx.doi.org/10.1089/cmb.2021.0477.
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Santiago, Raoul. "Multiomics integration: advancing pediatric cancer immunotherapy." Immuno Oncology Insights 04, no. 07 (August 5, 2023): 267–72. http://dx.doi.org/10.18609/ioi.2023.038.
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Cameron, Andrew J., Assya Legrini, Colin S. Wood, Craig Nourse, Yoana Doncheva, Claire Kennedy Dietrich, Colin Nixon, et al. "Abstract A028: Multiomic modelling of pancreatic IPMN stroma reveals distinct tertiary lymphoid structure distribution: Mapping the transcriptomic landscape via regional bulk, single-cell and subcellular approaches." Cancer Research 84, no. 2_Supplement (January 16, 2024): A028. http://dx.doi.org/10.1158/1538-7445.panca2023-a028.
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Abstract Background Intraductal Papillary Mucinous Neoplasms (IPMN) remain the largest subtype of cystic pancreatic cancer precursors. Cancer risk has been observed to vary between histological grades and histological subtypes. Spatial characterisation has revealed variation in immune cell distribution disease subtypes. Methods A cohort of 14 surgically resected IPMN tumors across a range of histological grades (Low-grade LG, high-grade HG and invasive IPMN cancers IPMN-PDAC) and subtypes (gastric, intestinal, pancreaticobiliary) underwent multimodal spatial interrogation. After expert histopathological annotation; tissue sections underwent regional whole transcriptome analysis with TempO-seq and 10x Visium. Extensive region analysis with NanoString GeoMx employing segmentation using staining for epithelium (PanCK) fibroblast stroma (aSMA) was performed. A tissue microarray was then constructed, and representative cores underwent single-cell and subcellular spatial transcriptomic analysis with NanoString CosMx. Raw count data and digital images were exported and analysed using Seurat, Giotto, SPATA2 and custom R pipelines. Region deconvolution, Harmony integration, Leiden Clustering, Gene Set Enrichment Analysis, Trajectory Mapping, and Moran’s I Analysis were performed. Results TempO-seq revealed up-regulation of canonical oncogene expression in all IPMN-PDAC cases as compared to LG and HG IPMN. There were no statistically significant differences in expression comparing LG and HG IPMN lesions. Visium differential gene expression and clustering models identified cancer epithelium, stroma and lymphatic components that correlated with histopathological annotations. Immune cell rich spots were identified via gene ontology of top 50 marker genes. The spatial trajectory of B-cell expression signatures in IPMN-PDAC showed greater concentration and less variability than HG tumors, Moran’s I -0.34 vs 0.12 (P = 0.021). B-cells in HG IPMN were showed greater concentration and less variability than LG IPMN, Moran’s I -0.15 vs 0.02 (P=0.05). Much of this concentration was in identified lymphoid aggregates or tertiary lymphoid structures. The spatial trajectory of T-cell expression. The spatial trajectory of macrophage expression showed greater concentration and less variability than in LG and HG tumors. GeoMx analysis of stromal regions identified up regulation of B cell signatures in IPMN PDAC and HG IPMN when compared to LG IPMN. Markers associated with TLS formation were found to be significantly upregulated including CXCL13, CXCR5 and LAMP3. Pancreaticobiliary subtypes were found to have the least concentrated B-cell distribution across subtypes. Conclusions Immune cell composition was found to vary across histological grades and subtypes of IPMN. A paradoxical relationship was observed between T-cell infiltrates and B-cell and macrophage populations. The greater proportion of tertiary lymphoid structures, and high levels of TLS specific gene expression, may indicate a propensity for b-cell activation in IPMN tumors which progress to malignancy. Citation Format: Andrew J. Cameron, Assya Legrini, Colin S. Wood, Craig Nourse, Yoana Doncheva, Claire Kennedy Dietrich, Colin Nixon, Jennifer Hay, Fraser Duthie, Pawel Herzyk, Jennifer Morton, Nigel B. Jamieson. Multiomic modelling of pancreatic IPMN stroma reveals distinct tertiary lymphoid structure distribution: Mapping the transcriptomic landscape via regional bulk, single-cell and subcellular approaches [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A028.
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Ravera, Francesco, Martina Dameri, Isabella Lombardo, Mario Stabile, Alberto Tagliafico, Massimo Calabrese, Alberto Ballestrero, Lorenzo Ferrando, and Gabriele Zoppoli. "Abstract OT1-23-01: Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE trial." Cancer Research 83, no. 5_Supplement (March 1, 2023): OT1–23–01—OT1–23–01. http://dx.doi.org/10.1158/1538-7445.sabcs22-ot1-23-01.
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Abstract Background: The detection of breast lesions through self-examination or during screening tests is a frequent finding. Breast biopsy is required in case of radiologically suspect lesions, bestowing a high burden on both patients and national healthcare system, since only one every four biopsies is breast cancer (BC). To date, the assessment of circulating biomarkers failed to demonstrate clinical utility in the early diagnosis of BC, for its suboptimal accuracy and difficult transferability to clinical practice. The combination of novel cutting-edge methods for the assessment of circulating analytes in an integrated multiomic classifier may overcome such limitations, possibly allowing liquid biopsy to become a novel noninvasive procedure for the differential diagnosis of BC. Design: In the RENOVATE trial (NCT04781062), women with suspect (BI-RADS-4/5) breast lesions ≤ 2 cm (cT1) are asked, before biopsy, to donate ~ 35 mL of blood collected in four dedicated tubes and ~ 50 mL of urine at the Diagnostic Senology Unit of Ospedale Policlinico San Martino (Genoa, IT). Plasma cell-free DNA methylation and copy number alterations are assessed in a cohort of patients diagnosed with early BC and a matched set of patients with histologically proven benign lesions through cell-free methylated DNA immunoprecipitation and high throughput sequencing (cfMeDIPseq), as well as ultra-low pass whole genome sequencing (ULP-WGS). Thanks to the volume and quality of our sample set, other experimental techniques will be tested as well. Results from cfMeDIP-seq and ULP-WGS, possibly in combination with other findings, will be integrated in a unique classifier for the noninvasive differential diagnosis of suspect breast lesions. Eligibility criteria: Patients with radiologically suspect breast lesions ≤ 2 cm (i.e. BIRADS 4/5) are eligible. Patients with previous history of cancer, or diagnosed with autoimmune or active allergic diseases, acute or chronic hepatic, renal, or cardiac diseases, or acute or chronic infectious diseases are excluded from the present trial. Specific aims: The primary aim of the present trial is to develop a noninvasive classifier for the differential diagnosis of suspect breast lesions detected through mammography and/or ultrasound. For such purpose we will assess the performance of plasma cfMeDIPseq, ULP-WGS, and other promising techniques for the differential diagnosis of BC. Such techniques will be integrated in a unique classifier in order to reach the maximum possible accuracy. Statistical methods: Sample size was calculated with a semi-parametric simulation-based approach from beta-distributions of PBMC datasets: assuming to test 20,000 CpG regions, with 300 differentially methylated target CpGs, a target maximal difference in DNA methylation of 0.2 between groups and an FDR of 0.05, 1 – beta ~ 0.90 would be achieved with an overall sample size of 150 samples split in a 1:2 ratio. Target accrual and present accrual: Minimum target accrual is set at 49 patients with BC and 98 patients with benign lesions. To date, we have collected plasma samples from 74 eligible patients with BC and 115 eligible patients with benign lesions. A validation cohort accounting for ~30% of our sample set will be recruited at Istituto Nazionale dei Tumori (Milan, IT). Contact information: For further information, please contact Gabriele Zoppoli at gabriele.zoppoli@unige.it. Citation Format: Francesco Ravera, Martina Dameri, Isabella Lombardo, Mario Stabile, Alberto Tagliafico, Massimo Calabrese, Alberto Ballestrero, Lorenzo Ferrando, Gabriele Zoppoli. Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT1-23-01.
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Khoury, Haia, Stefano Cairo, Mara Gilardi, Michael Ritchie, and Gilad Silberberg. "Abstract 7115: Mutational signatures in PDXs for improved understanding of drug response and companion biomarkers identification." Cancer Research 84, no. 6_Supplement (March 22, 2024): 7115. http://dx.doi.org/10.1158/1538-7445.am2024-7115.
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Abstract Genomic DNA editing is a continuous process that occurs during the entire cell life span. The type and frequency of these modifications can be related to the physiological or pathological activity of intrinsic mechanisms such as DNA surveillance and repair or to extrinsic events that may induce an alteration of DNA sequence by exposure to agents that directly or indirectly induce accumulations of DNA alterations. In the past few years, large-scale analyses have revealed mutational signatures across human cancer types. These signatures can be used as markers of defective internal processes, such as DNA repair deficiency, or external exposures, such as carcinogens, like tobacco, or genotoxic therapies such as radiation and chemotherapy. Our TumorGraft platform, a collection of 1500 patient-derived xenograft generated from more than 50 different types of cancer, is one of the most comprehensive preclinical oncology platforms worldwide, and aims to recapitulate the variety of the patient population and tumor biology complexity. This platform is currently used to evaluate the efficacy of new drugs, and all our models are very well characterized at the molecular level, including whole transcriptome, proteomics and phospho-proteomics, quantification, and genomic variation calls. This allows accurate selection of models with the molecular characteristics of the target patient population, as well as to identify biomarkers predictive of treatment efficacy. This could eventually lead to the development of companion biomarkers in the clinical setting to improve the identification of patients that will benefit from the treatment and those that should be spared. To maximize the chances of identifying relevant molecular traits associated with tumor response, we have been utilizing the Pharmaco-Pheno-Multiomic (PPMO) integration workflow, a machine learning approach which combines phenotypic and therapeutic response profiles with multiple omics datatypes to generate complex biomarker profiles. To provide additional insights on the molecular characteristics of our tumors, we decided to perform the mutational signatures profiling of our models by using whole exome sequencing data. The results obtained were crossed with patient’s clinical history and showed good correlation between the mutational signatures identified and the treatments received by the patient, in particular for exposure to platinum salts. The addition of PDXs mutational signatures strongly improves our knowledge on these models and confers an important parameter for model selection. Moreover, the integration of mutational signature profiles in the PPMO workflow would make a significant contribution for companion biomarkers identification. Citation Format: Haia Khoury, Stefano Cairo, Mara Gilardi, Michael Ritchie, Gilad Silberberg. Mutational signatures in PDXs for improved understanding of drug response and companion biomarkers identification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7115.
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Boroń, Dariusz, Nikola Zmarzły, Magdalena Wierzbik-Strońska, Joanna Rosińczuk, Paweł Mieszczański, and Beniamin Oskar Grabarek. "Recent Multiomics Approaches in Endometrial Cancer." International Journal of Molecular Sciences 23, no. 3 (January 22, 2022): 1237. http://dx.doi.org/10.3390/ijms23031237.
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Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.
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Maier, Keith E., Matthew R. Marunde, Vishnu U. Sunitha Kumary, Carolina P. Lin, Danielle N. Maryanski, Liz Albertorio-Saez, Dughan J. Ahimovic, et al. "Automated Cut&run Brings Scalable Epigenomic Profiling to Hematology." Blood 142, Supplement 1 (November 28, 2023): 7150. http://dx.doi.org/10.1182/blood-2023-186100.
Full textAbstract:
Genome-wide association studies (GWAS) and corresponding transcriptomics research have been leveraged to identify disease risk variants in hematological disorders with the hopes of developing more personalized treatment regimens. Interestingly, >90% of GWAS-identified variants are found in non-coding regions of the genome, indicating they likely affect the regulatory machinery that governs chromatin structure. Non-coding elements, such as enhancers, play important roles in supporting chromatin mechanisms, including cell-type specific gene expression, cell fates and disease states. However, these variants are difficult to study compared to protein-coding mutations and have been challenging to directly associate with phenotypic readouts. To date, efforts to understand the effects of non-coding variants on chromatin dynamics have been focused on chromatin accessibility (ATAC-seq) and DNA methylation profiling. Notably, these assays can only provide a binary, “open-or-closed,” view of chromatin and often fail to provide mechanistic insight into disease etiology. Epigenomic features - such as histone post-translational modifications (PTMs) and chromatin-associated proteins - mark distinct genomic compartments (e.g., promoters, enhancers) and regulate chromatin structure, gene expression, and cell function. Mapping these features provides a rich context to study cell fate and has great potential for discovering new biomarkers and drug targets. Despite this, efforts to integrate epigenomics into large scale lymphatic and myeloid research have been hampered by the poor sensitivity, high background, and low throughput of traditional chromatin mapping technology (i.e., ChIP-seq). Here, we present autoCUT&RUN, a high throughput assay for rapid, ultra-sensitive profiling of epigenomic features from FACS-isolated primary cells or tissues. This workflow generates reliable profiles from <10,000 cells per reaction, and is supported by a rigorous optimization strategy, high-quality antibodies, and quantitative spike-in controls. As part of a multi-site collaboration with the Immunological Genome Consortium, we used our autoCUT&RUN platform to build a comprehensive epigenomic database of mouse immune cells - composed of >1,500 epigenomic profiles from >100 different FACS-isolated primary immune cell types. These studies set the stage to leverage high-throughput epigenomics for multi-site clinical studies to identify and/or characterize novel biomarkers for precision medicine applications. Further, we will discuss our integration of CUT&RUN and Enzymatic Methylation-sequencing (CUT&RUN-EM) to deliver a true multiomic view of the co-occurrence of epigenomic features and DNA methylation, which can reveal multivalent cellular mechanisms and deconvolute sample heterogeneity.
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Ashuach, Tal, Mariano I. Gabitto, Rohan V. Koodli, Giuseppe-Antonio Saldi, Michael I. Jordan, and Nir Yosef. "MultiVI: deep generative model for the integration of multimodal data." Nature Methods, June 29, 2023. http://dx.doi.org/10.1038/s41592-023-01909-9.
Full textAbstract:
AbstractJointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI, a probabilistic model to analyze such multiomic data and leverage it to enhance single-modality datasets. MultiVI creates a joint representation that allows an analysis of all modalities included in the multiomic input data, even for cells for which one or more modalities are missing. It is available at scvi-tools.org.
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Williams, Amanda. "Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont." FEMS Microbiology Ecology, April 23, 2024. http://dx.doi.org/10.1093/femsec/fiae058.
Full textAbstract:
Abstract Since their radiation in the Middle Triassic period ∼ 240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.