Biology subjects

Gottardo, R.

Publications and source records attributed to Gottardo, R..

35 records · Page 2Linked to original sources

bioRxiv2021

An innate immune activation state prior to vaccination predicts responsiveness to multiple vaccines

Many factors determine whether an individual responding to vaccination will generate an immune response that can lead to protection. Several studies have shown that the pre-vaccination immune state is associated with the antibody response to vaccines. However, the generalizability and mechanisms that underlie this association remain poorly defined. Here, we sought to identify a common pre-vaccination signature and mechanisms that could predict the immune response across a wide variety of vaccines. We leveraged the "Immune Signatures Data Resource" created by the NIH Human Immunology Project Consortium (HIPC) to integrate data from 28 studies involving 13 different vaccines and associate the blood transcriptional status of 820 healthy young adults with their responses. An unsupervised analysis of blood transcriptional profiles across studies revealed three distinct pre-vaccination states, characterized by the differential expression of genes associated with a pro-inflammatory response, cell proliferation, and metabolism alterations downstream of NF{kappa}B and IRF7. Innate and adaptive immune cell subset-specific genes were also associated with the three pre-vaccination states. Importantly, individuals whose pre-vaccination state was enriched in pro-inflammatory response genes known to be downstream of NF{kappa}B tended to have higher serum antibody responses one month after vaccination. A supervised analysis of the same data resulted in a single classifier, also enriched for NF{kappa}B regulated genes, that predicted the antibody response across most of the vaccines. Projection into single-cell RNA-sequencing data suggested that this pre-vaccination state was attributable to the signature of activation of non-classical monocytes and myeloid dendritic cells. Transcriptional signatures of acute responses to bacterial and not viral infections were enriched in the high pro-inflammatory pre-vaccination state and also included NF{kappa}B regulated genes. The pro-inflammatory pre-vaccination state was highly reminiscent of the innate activation state triggered by TLR ligands or adjuvants. These results demonstrate that wide variations in the transcriptional state of the immune system in humans can be a key determinant of responsiveness to vaccination. They also define a transcriptional signature NF{kappa}B activation at baseline, that is associated with a greater magnitude of antibody response to multiple vaccines, and suggest that modulation of the innate immune system by next-generation adjuvants targeting NF{kappa}B before vaccine administration may improve vaccine responsiveness.

systems biology↗

bioRxiv2021

A faithful in vivo model of human macrophages in metastatic melanoma

AO_SCPLOWBSTRACTC_SCPLOWDespite recent therapeutic progress, advanced melanoma remains lethal for many patients. The composition of the immune tumor microenvironment (TME) has decisive impacts on therapy response and disease outcome. High dimensional analyses of patient samples can reveal the composition and heterogeneity of the immune TME. In particular, macrophages are known for their cancer-supportive role, but the underlying mechanisms are incompletely understood, and experimental in vivo systems are needed to test the functional properties of these cells. We characterized a humanized mouse model, reconstituted with a human immune system and a human melanoma, in which: (1) human macrophages support metastatic spread of the tumor; and (2) tumor-infiltrating macrophages have a specific transcriptional signature that faithfully represents the transcriptome of macrophages from patient melanoma samples and is associated with shorter survival. This model complements patient sample analyses, enabling the elucidation of fundamental principles in melanoma biology, and the development and evaluation of candidate therapies.

immunology↗

bioRxiv2021

T helper 2 transcriptional profile predicts single-cell HIV envelope-specific polyfunctional CD4+ T cells correlated with reduced risk of infection in RV144 trial

Despite the critical role antigen-specific T cells play in responding to viral infections, their aggregate frequencies in peripheral blood have not correlated with clinical protection during HIV infection. However, a subset of HIV-specific CD4+ T cells, termed polyfunctional T cells, can produce multiple effector cytokines simultaneously. In the RV144 HIV vaccine trial, polyfunctional T cells correlated with reduced risk of HIV infection. Little is known about what differentiates polyfunctional T cells from other vaccine-elicited T cells. Therefore, we developed a novel live-cell multiplexed cytokine capture assay, to identify and transcriptionally profile vaccine-specific polyfunctional CD4+ T cells. We applied these methods to samples from the HVTN 097 clinical trial of the same vaccine regimen as RV144. We discovered two surface receptors that were enriched among polyfunctional CD4+ T cells and a Th2-biased signature (IL-4, IL-5, and IL-13) that specifically predicted the envelope-specific polyfunctional CD4+ T cells that were correlated with reduced risk of HIV infection in RV144. By linking single-cell transcriptional and functional profiles, we may be able to further define the role of vaccine-elicited polyfunctional T cells in contributing to effective immunity. Key PointsO_LINovel ex vivo multiplexed cytokine capture assay to enumerate and single-cell sort polyfunctional T cells for downstream analyses C_LIO_LIPolyfunctional T cells were specifically detected among the HIV envelope-stimulated CD4+ T cells C_LIO_LISingle-cell RNA sequencing identified novel surface markers enriched among vaccine-specific polyfunctional CD4+ T cells C_LIO_LITh2 transcriptional signature predicted polyfunctional CD4+ T cell profile that had correlated with reduced risk of HIV infection in the RV144 HIV efficacy trial C_LI

immunology↗

bioRxiv2021

Superscan: Supervised Single-Cell Annotation

Automated cell type annotation of single-cell RNA-seq data has the potential to significantly improve and streamline single cell data analysis, facilitating comparisons and meta-analyses. However, many of the current state-of-the-art techniques suffer from limitations, such as reliance on a single reference dataset or marker gene set, or excessive run times for large datasets. Acquiring high-quality labeled data to use as a reference can be challenging. With CITE-seq, surface protein expression of cells can be directly measured in addition to the RNA expression, facilitating cell type annotation. Here, we compiled and annotated a collection of 16 publicly available CITE-seq datasets. This data was then used as training data to develop Superscan, a supervised machine learning-based prediction model. Using our 16 reference datasets, we benchmarked Superscan and showed that it performs better in terms of both accuracy and speed when compared to other state-of-the-art cell annotation methods. Superscan is pre-trained on a collection of primarily PBMC immune datasets; however, additional data and cell types can be easily added to the training data for further improvement. Finally, we used Superscan to reanalyze a previously published dataset, demonstrating its applicability even when the dataset includes cell types that are missing from the training set.

bioinformatics↗

bioRxiv2021

Individual Level Differential Expression Analysis for Single Cell RNA-seq data

Bulk RNA-seq data quantify the expression of a gene in an individual by one number (e.g., fragment count). In contrast, single cell RNA-seq (scRNA-seq) data provide much richer information: the distribution of gene expression across many cells. To assess differential expression across individuals using scRNA-seq data, a straightforward solution is to create "pseudo" bulk RNA-seq data by adding up the fragment counts of a gene across cells for each individual, and then apply methods designed for differential expression using bulk RNA-seq data. This pseudo-bulk solution reduces the distribution of gene expression across cells to a single number and thus loses a good amount of information. We propose to assess differential expression using the gene expression distribution measured by cell level data. We find denoising cell level data can substantially improve the power of this approach. We apply our method, named IDEAS (Individual level Differential Expression Analysis for scRNA-seq), to study the gene expression difference between autism subjects and controls. We find neurogranin-expressing neurons harbor a high proportion of differentially expressed genes, and ERBB signals in microglia are associated with autism.

bioinformatics↗

bioRxiv2020

Integrated analysis of multimodal single-cell data

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce weighted-nearest neighbor analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. AvailabilityInstallation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

genomics↗

bioRxiv2020

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

Recently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpaces improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data. In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

bioinformatics↗

bioRxiv2020

Single-cell analysis of chromatin silencing programs in developmental and tumor progression

Single-cell analysis has become a powerful approach for the molecular characterization of complex tissues. Methods for quantifying gene expression1 and chromatin accessibility2 of single cells are now well-established, but analysis of chromatin regions with specific histone modifications has been technically challenging. Here, we adapt the recently published CUT&Tag method3 to scalable single-cell platforms to profile chromatin landscapes in single cells (scCUT&Tag) from complex tissues. We focus on profiling Polycomb Group (PcG) silenced regions marked by H3K27 trimethylation (H3K27me3) in single cells as an orthogonal approach to chromatin accessibility for identifying cell states. We show that scCUT&Tag profiling of H3K27me3 distinguishes cell types in human blood and allows the generation of cell-type-specific PcG landscapes from heterogeneous tissues. Furthermore, we use scCUT&Tag to profile H3K27me3 in a brain tumor patient before and after treatment, identifying cell types in the tumor microenvironment and heterogeneity in PcG activity in the primary sample and after treatment.

genomics↗

bioRxiv2020

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8+ and CD4+ T cells, and cytotoxic CD4+ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

immunology↗

bioRxiv2020

Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

Modern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.

immunology↗

bioRxiv2019

A targeted multi-omic analysis approach measures protein expression and low abundance transcripts on the single cell level

High throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets.\n\nHere, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.

immunology↗

bioRxiv2019

A new data-driven cell population discovery and annotation method for single-cell data, FAUST, reveals correlates of clinical response to cancer immunotherapy

High-dimensional single-cell cytometry is routinely used to characterize patient responses to cancer immunotherapy and other treatments. This has produced a wealth of datasets ripe for exploration but whose biological and technical heterogeneity make them difficult to analyze with current tools. We introduce a new interpretable machine learning method for single-cell mass and flow cytometry studies, FAUST, that robustly performs unbiased cell population discovery and annotation. FAUST processes data on a per-sample basis and returns biologically interpretable cell phenotypes that can be compared across studies, making it well-suited for the analysis and integration of complex datasets. We demonstrate how FAUST can be used for candidate biomarker discovery and validation by applying it to a flow cytometry dataset from a Merkel cell carcinoma anti-PD-1 trial and discover new CD4+ and CD8+ effector-memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. We then use FAUST to validate these correlates in an independent CyTOF dataset from a published metastatic melanoma trial. Importantly, existing state-of-the-art computational discovery approaches as well as prior manual analysis did not detect these or any other statistically significant T cell sub-populations associated with anti-PD-1 treatment in either data set. We further validate our methodology by using FAUST to replicate the discovery of a previously reported myeloid correlate in a different published melanoma trial, and validate the correlate by identifying it de novo in two additional independent trials. FAUSTs phenotypic annotations can be used to perform cross-study data integration in the presence of heterogeneous data and diverse immunophenotyping staining panels, enabling hypothesis-driven inference about cell sub-population abundance through a multivariate modeling framework we call Phenotypic and Functional Differential Abundance (PFDA). We demonstrate this approach on data from myeloid and T cell panels across multiple trials. Together, these results establish FAUST as a powerful and versatile new approach for unbiased discovery in single-cell cytometry.

bioinformatics↗

bioRxiv2019

Reply to \"A discriminative learning approach to differential expression analysis for single-cell RNA-seq\"

Multivariate logistic regression (mLR) has been recently proposed by Ntranos et al. to perform gene differential expression analyses of single-cell RNA-sequencing (scRNAseq) data. Herein we reproduce and extend some of their findings. We notably show that while mLR performs better in simulated datasets, these simulations do not recapitulate important features of experimental datasets. Indeed, our results suggest that MAST followed by Sidak aggregation of the p-values perform better than mLR on experimental datasets. Overall, we highlight that most of the new results obtained by Ntranos et al is likely due to the quantification of scRNAseq data at the transcript or transcript compatibility classes level, rather than the use of mLR.

bioinformatics↗

bioRxiv2019

Orchestrating Single-Cell Analysis with Bioconductor

Recent developments in experimental technologies such as single-cell RNA sequencing have enabled the profiling a high-dimensional number of genome-wide features in individual cells, inspiring the formation of large-scale data generation projects quantifying unprecedented levels of biological variation at the single-cell level. The data generated in such projects exhibits unique characteristics, including increased sparsity and scale, in terms of both the number of features and the number of samples. Due to these unique characteristics, specialized statistical methods are required along with fast and efficient software implementations in order to successfully derive biological insights. Bioconductor - an open-source, open-development software project based on the R programming language - has pioneered the analysis of such high-throughput, high-dimensional biological data, leveraging a rich history of software and methods development that has spanned the era of sequencing. Featuring state-of-the-art computational methods, standardized data infrastructure, and interactive data visualization tools that are all easily accessible as software packages, Bioconductor has made it possible for a diverse audience to analyze data derived from cutting-edge single-cell assays. Here, we present an overview of single-cell RNA sequencing analysis for prospective users and contributors, highlighting the contributions towards this effort made by Bioconductor.

genomics↗

bioRxiv2018

DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis.

A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code and primary data be made available with publications. There has been little practical advice on implementing reproducible research work-flows for large omics or systems biology data sets used by teams of analysts working in collaboration. In such instances it is important to ensure all analysts use the same version of a data set for their analyses. Yet, instantiating relational databases and standard operating procedures can be unwieldy, with high \"startup\" costs and poor adherence to procedures when they deviate substantially from an analysts usual work-flow. Ideally a reproducible research work-flow should fit naturally into an individuals existing work-flow, with minimal disruption. Here, we provide an overview of how we have leveraged popular open source tools, including Bioconductor, Rmarkdown, git version control, R, and specifically Rs package system combined with a new tool DataPackageR, to implement a lightweight reproducible research work-flow for preprocessing large data sets, suitable for sharing among small-to-medium sized teams of computational scientists. Our primary contribution is the DataPackageR tool, which decouples time-consuming data processing from data analysis while leaving a traceable record of how raw data is processed into analysis-ready data sets. The software ensures packaged data objects are properly documented and performs checksum verification of these along with basic package version management, and importantly, leaves a record of data processing code in the form of package vignettes. Our group has implemented this work-flow to manage, analyze and report on pre-clinical immunological trial data from multi-center, multi-assay studies for the past three years.

bioinformatics↗

bioRxiv2018

cytometree: a binary tree algorithm for automatic gating in cytometry analysis

MotivationFlow cytometry is a powerful technology that allows the high-throughput quantification of dozens of surface and intracellular proteins at the single-cell level. It has become the most widely used technology for immunophenotyping of cells over the past three decades. Due to the increasing complexity of cytometry experiments (more cells and more markers), traditional manual flow cytometry data analysis has become untenable due to its subjectivity and time-consuming nature.\n\nResultsWe present a new unsupervised algorithm called \"cytometree\" to perform automated population discovery (aka gating) in flow cytometry. cytometree is based on the construction of a binary tree, the nodes of which are subpopulations of cells. At each node, the marker distributions are modeled by mixtures of normal distribution. Node splitting is done according to a normalized difference of Akaike information criteria (AIC) between the two models. Post-processing of the tree structure and derived populations allows us to complete the annotation of the derived populations. The algorithm is shown to perform better than the state-of-the-art unsupervised algorithms previously proposed on panels introduced by the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP I) project. The algorithm is also applied to a T-cell panel proposed by the Human Immunology Project Consortium (HIPC) program; it also outperforms the best unsupervised open-source available algorithm while requiring the shortest computation time.\n\nAvailabilityAn R package named \"cytometree\" is available on the CRAN repository.\n\nContactdaniel.commenges@u-bordeaux.fr; rodolphe.thiebaut@u-bordeaux.fr\n\nSupplementary informationSupplementary data are available.

bioinformatics↗

bioRxiv2018

CD32+ and PD-1+ Lymph Node CD4 T Cells Support Persistent HIV-1 Transcription in Treated Aviremic Individuals

A recent study conducted in blood has proposed CD32 as the marker identifying the elusive HIV reservoir. We have investigated the distribution of CD32+ CD4 T cells in blood and lymph nodes(LNs) of healthy HIV-1 uninfected, viremic untreated and long-term treated HIV-1 infected individuals and their relationship with PD-1+ CD4 T cells. The frequency of CD32+ CD4 T cells was increased in viremic as compared to treated individuals in LNs and a large proportion(up to 50%) of CD32+ cells co-expressed PD-1 and were enriched within T follicular helper cells(Tfh) cells. We next investigated the role of LN CD32+ CD4 T cells in the HIV reservoir. Total HIV DNA was enriched in CD32+ and PD-1+ CD4 T cells as compared to CD32- and PD-1- cells in both viremic and treated individuals but there was no difference between CD32+ and PD-1+ cells. There was not enrichment of latently infected cells with inducible HIV-1 in CD32+ versus PD-1+ cells in ART treated individuals. HIV-1 transcription was then analyzed in LN memory CD4 T cell populations sorted on the basis of CD32 and PD-1 expression. CD32+PD-1+ CD4 T cells were significantly enriched in cell associated HIV RNA as compared to CD32-PD-1-(average 5.2 fold in treated and 86.6 fold in viremics), to CD32+PD-1-(2.2 fold in treated and 4.3 fold in viremics) and to CD32-PD-1+ cell populations(2.2 fold in ART treated and 4.6 fold in viremics). Similar levels of HIV-1 transcription were found in CD32+PD-1- and CD32-PD-1+ CD4 T cells. Interestingly, the proportion of CD32+ and PD-1+ CD4 T cells negatively correlated with CD4 T cell counts and length of therapy while positively correlated with viremia. Therefore, the expression of CD32 identifies, independently of PD-1, a CD4 T cell population with persistent HIV-1 transcription and CD32 and PD-1 co-expression the CD4 T cell population with the highest levels of HIV-1 transcription in both viremic and treated individuals.\n\nImportanceThe existence of long-lived latently infected resting memory CD4 T cells represents a major obstacle to the eradication of HIV infection. Identifying cell markers defining latently infected cells containing replication competent virus is important in order to determine the mechanisms of HIV persistence and to develop novel therapeutic strategies to cure HIV infection. We provide evidence that PD-1 and CD32 may have a complementary role in better defining CD4 T cell populations infected with HIV-1. Furthermore, CD4 T cells co-expressing CD32 and PD-1 identify a CD4 T cell population with high levels of persistent HIV-1 transcription.

immunology↗