Biology subjects

Alexandrov, T.

Publications and source records attributed to Alexandrov, T..

28 records · Page 2Linked to original sources

bioRxiv2020

Single-cell transcriptomics reveals immune response of intestinal cell types to viral infection

Human intestinal epithelial cells form a primary barrier protecting us from pathogens, yet only limited knowledge is available about individual contribution of each cell type to mounting an immune response against infection. Here, we developed a pipeline combining single-cell RNA-Seq and highly-multiplex RNA imaging and applied it to human intestinal organoids infected with human astrovirus, a model human enteric virus. We found that interferon controls the infection and that astrovirus infects all major cell types and lineages with a preferential infection of proliferating cells. Intriguingly, each intestinal epithelial cell lineage has a unique basal expression of interferon-stimulated genes and, upon astrovirus infection, undergoes an antiviral transcriptional reprogramming by upregulating distinct sets of interferon-stimulated genes. These findings suggest that in the human intestinal epithelium, each cell lineage plays a unique role in resolving virus infection. Our pipeline can be applicable to other organoids and viruses, opening new avenues to unravel roles of individual cell types in viral pathogenesis.

systems biology↗

bioRxiv2020

Towards Building a Smart Kidney Atlas: Network-based integration of multimodal transcriptomic, proteomic, metabolomic and imaging data in the Kidney Precision Medicine Project

AO_SCPLOWBSTRACTC_SCPLOWKidney Precision Medicine Project (KPMP) is building a spatially-specified human tissue atlas at the single-cell resolution with molecular details of the kidney in health and disease. Here, we describe the construction of an integrated reference tissue map of cells, pathways and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 55 subjects. We use single-cell and -nucleus transcriptomics, subsegmental laser microdissection bulk transcriptomics and proteomics, near-single-cell proteomics, 3-D nondestructive and CODEX imaging, and spatial metabolomics data to hierarchically identify genes, pathways and cells. Integrated data from these different technologies coherently describe cell types/subtypes within different nephron segments and interstitium. These spatial profiles identify cell-level functional organization of the kidney tissue as indicative of their physiological functions and map different cell subtypes to genes, proteins, metabolites and pathways. Comparison of transcellular sodium reabsorption along the nephron to levels of mRNAs encoding the different sodium transporter genes indicate that mRNA levels are largely congruent with physiological activity.This reference atlas provides an initial framework for molecular classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.

bioinformatics↗

bioRxiv2020

Critical role of type III interferon in controlling SARS-CoV-2 infection, replication and spread in primary human intestinal epithelial cells

SARS-CoV-2 is an unprecedented worldwide health problem that requires concerted and global approaches to better understand the virus in order to develop novel therapeutic approaches to stop the COVID-19 pandemic and to better prepare against potential future emergence of novel pandemic viruses. Although SARS-CoV-2 primarily targets cells of the lung epithelium causing respiratory infection and pathologies, there is growing evidence that the intestinal epithelium is also infected. However, the importance of the enteric phase of SARS-CoV-2 for virus-induced pathologies, spreading and prognosis remains unknown. Here, using both colon-derived cell lines and primary non-transformed colon organoids, we engage in the first comprehensive analysis of SARS-CoV-2 lifecycle in human intestinal epithelial cells. Our results demonstrate that human intestinal epithelial cells fully support SARS-CoV-2 infection, replication and production of infectious de-novo virus particles. Importantly, we identified intestinal epithelial cells as the best culture model to propagate SARS-CoV-2. We found that viral infection elicited an extremely robust intrinsic immune response where, interestingly, type III interferon mediated response was significantly more efficient at controlling SARS-CoV-2 replication and spread compared to type I interferon. Taken together, our data demonstrate that human intestinal epithelial cells are a productive site of SARS-CoV-2 replication and suggest that the enteric phase of SARS-CoV-2 may participate in the pathologies observed in COVID-19 patients by contributing in increasing patient viremia and by fueling an exacerbated cytokine response.

microbiology↗

bioRxiv2019

A Multimodal and Integrated Approach to Interrogate Human Kidney Biopsies with Rigor and Reproducibility: The Kidney Precision Medicine Project

Comprehensive and spatially mapped molecular atlases of organs at a cellular level are a critical resource to gain insights into pathogenic mechanisms and personalized therapies for diseases. The Kidney Precision Medicine Project (KPMP) is an endeavor to generate 3-dimensional (3D) molecular atlases of healthy and diseased kidney biopsies using multiple state-of-the-art OMICS and imaging technologies across several institutions. Obtaining rigorous and reproducible results from disparate methods and at different sites to interrogate biomolecules at a single cell level or in 3D space is a significant challenge that can be a futile exercise if not well controlled. We describe a "follow the tissue" pipeline for generating a reliable and authentic single cell/region 3D molecular atlas of human adult kidney. Our approach emphasizes quality assurance, quality control, validation and harmonization across different OMICS and imaging technologies from sample procurement, processing, storage, shipping to data generation, analysis and sharing. We established benchmarks for quality control, rigor, reproducibility and feasibility across multiple technologies through a pilot experiment using common source tissue that was processed and analyzed at different institutions and different technologies. A peer review system was established to critically review quality control measures and the reproducibility of data generated by each technology before being approved to interrogate clinical biopsy specimens. The process established economizes the use of valuable biopsy tissue for multi-OMICS and imaging analysis with stringent quality control to ensure rigor and reproducibility of results and serves as a model for precision medicine projects across laboratories, institutions and consortia.

scientific communication and education↗

bioRxiv2019

ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images

MotivationImaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development.\n\nResultsWe present ColocAI, an artificial intelligence approach addressing this gap. With the help of 42 imaging MS experts from 9 labs, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using tf-idf and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings respectively). We illustrate these measures by inferring co-localization properties of 10273 molecules from 3685 public METASPACE datasets.\n\nAvailability and Implementationhttps://github.com/metaspace2020/coloc\n\nContacttheodore.alexandrov@embl.de

bioinformatics↗

bioRxiv2019

The Molecular and Microbial Microenvironments in Chronically Diseased Lungs

To visualize the personalized distributions of pathogens, chemical environments including microbial metabolites, pharmaceuticals, and their metabolic products within and between human lungs afflicted with cystic fibrosis, we generated 3D microbiome and metabolome maps of six explanted lungs from three cystic fibrosis patients. These 3D spatial maps revealed that the chemical environments are variable between patients and within the lungs of each patient. Although the patients microbial ecosystems were defined by the dominant pathogen, their chemical diversity was not. Additionally, the chemical diversity between locales in lungs of the same individual sometimes exceeded inter-individual variation. Thus, the chemistry and microbiome of the explanted lungs appear to be not only personalized but also regiospecific. Previously undescribed analogs of microbial quinolones and antibiotic metabolites were also detected. Furthermore, mapping the chemical and microbial distributions allowed visualization of microbial community interactions, such as increased production of quorum sensing quinolones in locations where Pseudomonas was in contact with Staphylococcus and Granulicatella, consistent with in vitro observations of bacteria isolated from these patients. Visualization of microbe-metabolite associations within a host organ in early-stage CF disease in animal models will help elucidate a complex interplay between the presence of a given microbial structure, antibiotics, metabolism of antibiotics, microbial virulence factors, and host responses.\n\nImportanceMicrobial infections are now recognized to be polymicrobial and personalized in nature. A comprehensive analysis and understanding of the factors underlying the polymicrobial and personalized nature of infections remains limited, especially in the context of the host. By visualizing microbiomes and metabolomes of diseased human lungs, we describe how different the chemical environments are between hosts that are dominated by the same pathogen and how community interactions shape the chemical environment, or vice versa. We highlight that three-dimensional organ mapping are hypothesis building tools that allow us to design mechanistic studies aimed at addressing microbial responses to other microbes, the host, and pharmaceutical drugs.

microbiology↗

bioRxiv2019

METASPACE: A community-populated knowledge base of spatial metabolomes in health and disease

Metabolites, lipids, and other small molecules are key constituents of tissues supporting cellular programs in health and disease. Here, we present METASPACE, a community-populated knowledge base of spatial metabolomes from imaging mass spectrometry data. METASPACE is enabled by a high-performance engine for metabolite annotation in a confidence-controlled way that makes results comparable between experiments and laboratories. By sharing their results publicly, engine users continuously populate a knowledge base of annotated spatial metabolomes in tissues currently including over 3000 datasets from human cancer cohorts, whole-body sections of animal models, and various organs. The spatial metabolomes can be visualized, explored and shared using a web app as well as accessed programmatically for large-scale analysis. By using novel computational methods inspired by natural language processing, we illustrate that METASPACE provides molecular coverage beyond the capacity of any individual laboratory and opens avenues towards comprehensive metabolite atlases on the levels of tissues and organs.

systems biology↗

bioRxiv2019

Recognizing off-sample mass spectrometry images with machine and deep learning

MotivationImaging mass spectrometry (imaging MS) is a powerful technology for revealing localizations of hundreds of molecules in tissue sections. However, imaging MS data is polluted with off-sample ions caused by caused by sample preparation, particularly by the MALDI matrix application. The presence of the off-sample ion images confounds and hinders metabolite identification and downstream analysis.\n\nResultsWe created a high-quality gold standard of 23238 manually tagged ion images from 87 public datasets from the METASPACE knowledge base. We developed several machine and deep learning methods for recognizing off-sample ion images. Deep residual learning performed the best with the F1 score of 0.97. Spatio-molecular biclustering method achieved the F1 scores of 0.96 and 0.93 in semi- and fully-automated scenarios, respectively. Molecular co-localization method achieved the F1 score of 0.90. We investigated the clusters of the DHB matrix, the most common MALDI matrix, and characterized parameters of a clusters combinatorial model. This work addresses an important issue in imaging MS and illustrates how public data, modern web technologies, and machine and deep learning open novel avenues in imaging MS.\n\nAvailability and ImplementationData and source code are available at: https://github.com/metaspace2020/offsample.\n\nContacttheodore.alexandrov@embl.de

bioinformatics↗

bioRxiv2019

Spatial single-cell profiling of intracellular metabolomes in situ

The recently unveiled extent of cellular heterogeneity demands for single-cell investigations of intracellular metabolomes to reveal their roles in intracellular processes, molecular microenvironment and cell-cell interactions. To address this, we developed SpaceM, a method for in situ spatial single-cell metabolomics of cell monolayers which detects >100 metabolites in >10000 individual cells together with fluorescence and morpho-spatial cellular features. We discovered that the intracellular metabolomes of co-cultured human HeLa cells and mouse NIH3T3 fibroblasts predict the cell type with 90.4% accuracy and revealed a short-distance metabolic intermixing between HeLa and NIH3T3. We characterized lipid classes composing lipid droplets in steatotic differentiated human hepatocytes, and discovered a preferential accumulation of long-chain phospholipids, a co-regulation of oleic and linoleic acids, and an association of phosphatidylinositol monophosphate with high cell-cell contact. SpaceM provides single-cell metabolic, phenotypic, and spatial information and enables spatio-molecular investigations of intracellular metabolomes in a variety of cellular models.

molecular biology↗

bioRxiv2017

Curatr: a web application for creating, curating, and sharing a mass spectral library

MotivationIdentification from metabolomics mass spectrometry experiments requires comparison of fragmentation spectra from experimental samples to spectra from analytical standards. As the quality of identification depends directly on the quality of the reference spectra, manual curation is routine during the selection of reference spectra to include in a spectral library. Whilst building our own in-house spectral library we realised that there is currently no vendor neutral open access tool for for facilitating manual curation of spectra from raw LC-MS data into a custom spectral library.\n\nResultsWe developed a web application curatr for the rapid generation of high quality mass spectral fragmentation libraries for liquid-chromatography mass spectrometry analysis. Curatr handles datasets from single or multiplexed standards, automatically extracting chromatographic profiles and potential fragmentation spectra for multiple adducts. These are presented through an intuitive interface for manual curation before being documented in a custom spectral library. Searchable molecular information and the providence of each standard is stored along with metadata on the experimental protocol. Curatr support the export of spectral libraries in several standard formats for easy use with third party software or submission to community databases, maximising the return on investment for these costly measurements. We demonstrate the use of curatr to generate the EMBL Metabolomics Core Facility spectral library which is publicly available at http://curatr.mcf.embl.de.\n\nAvailabilityThe source code is freely available at http://github.com/alexandrovteam/curatr/ along with example data.\n\nSupplementary informationA step-by step user manual is available in the supplementary information

bioinformatics↗