Biology subjects

Nolan, G. P.

Publications and source records attributed to Nolan, G. P..

48 records · Page 3Linked to original sources

bioRxiv2019

Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front

Antitumoral immunity requires organized, spatially nuanced interactions between components of the immune tumor microenvironment (iTME). Understanding this coordinated behavior in effective versus ineffective tumor control will advance immunotherapies. We optimized CO-Detection by indEXing (CODEX) for para ffin-em bedded tissue microarrays, enabling profiling of 140 tissue regions from 35 advanced-stage colorectal cancer (CRC) patients with 56 protein markers simultaneously. We identified nine conserved, distinct cellular neighborhoods (CNs)-a collection of components characteristic of the CRC iTME. Enrichment of PD-1+CD4+ T cells only within a granulocyte CN positively correlated with survival in a high-risk patient subset. Coupling of tumor and immune CNs, fragmentation of T cell and macrophage CNs, and disruption of inter-CN communication was associated with inferior outcomes. This study provides a framework for interrogating complex biological processes, such as antitumoral immunity, demonstrating an example of how tumors can disrupt imm une functionality through interference in the concerted action of cells and spatial domains.

immunology↗

bioRxiv2019

A Systems Immunology Approach Identifies Cytokine-Induced STAT Signaling Pathways Critical to Rheumatoid Arthritis Disease Activity and Treatment Response

Rheumatoid arthritis (RA), a chronic autoimmune disease characterized by circulating autoantibodies, involves many cytokine-mediated signaling pathways in multiple immune cell subsets. Most studies of immune cells in RA have limitations, such as analysis of a small number of cell subsets or pathways, and limited longitudinal data on patient phenotypes. In this study, we used an innovative systems immunology approach to simultaneously quantify up to 882 signaling nodes (Jak/STAT signaling readouts modulated by cytokines and other stimuli) in 21 immune cell subsets. We studied 194 RA patients and 41 controls, including 146 well-characterized RA patients prior to, and 6 months after, initiation of methotrexate or biologic agents from the Treatment Efficacy and Toxicity in RA Database and Repository (TETRAD). There was strikingly attenuated signaling capacity in RA patients in IFN stimulation followed by measurement of phosphorylated STAT1 [IFN[->]p-STAT1] in six immune cell subsets. Multiple nodes showed negative association with disease activity, including IFN[->]STAT5 signaling in naive and memory B cells. In contrast, IL-6-induced STAT1 and STAT3 activation in central memory CD4-negative T cells showed a positive association with disease activity. Multiple nodes were associated with treatment response, including IFN[->]STAT1 in monocytes and IL-6[->]STAT3 in CD4+ naive T cells. Decision tree analysis identified a model combining these two nodes as a high-performing classifier of treatment response to TNF inhibitors. Our study provides novel information on RA disease mechanisms and serves as a framework for the discovery and validation of biomarkers of treatment response in RA.

immunology↗

bioRxiv2019

Profiling Myelodysplastic Syndromes by Mass Cytometry Demonstrates Abnormal Progenitor Cell Phenotype and Differentiation

PurposeWe sought to enhance the cytometric analysis of MDS by performing a pilot study of a single cell mass cytometry (MCM) assay to more comprehensively analyze patterns of surface marker expression in patients with MDS.\n\nExperimental DesignTwenty-three MDS and five healthy donor bone marrow samples were studied using a 34-parameter mass cytometry panel utilizing barcoding and internal reference standards. The resulting data were analyzed by both traditional gating and high-dimensional clustering.\n\nResultsThis high-dimensional assay provided three major benefits relative to traditional cytometry approaches: First, MCM enabled detection of aberrant surface maker at high resolution, detecting aberrancies in 27/31 surface markers, encompassing almost every previously reported MDS surface marker aberrancy. Additionally, three previously unrecognized aberrancies in MDS were detected in multiple samples at least one developmental stage: increased CD321 and CD99; and decreased CD47. Second, analysis of the stem and progenitor cell compartment (HSPCs), demonstrated aberrant expression in 21 of the 23 MDS samples, which were not detected in three samples from patients with idiopathic cytopenia of undetermined significance (ICUS). These immunophenotypically abnormal HSPCs were also the single most significant distinguishing feature between clinical risk groups. Third, unsupervised clustering of high-parameter MCM data allowed identification of abnormal differentiation patterns associated with immunophenotypically aberrant myeloid cells similar to myeloid derived suppressor cells.\n\nConclusionsThese results demonstrate that high-parameter cytometry methods that enable simultaneous analysis of all bone marrow cell types could enhance the diagnostic utility of immunophenotypic analysis in MDS.\n\nStatement of SignificanceO_LIHigh-dimensional mass cytometry enables high-resolution characterization of abnormal maker expression and myeloid development in MDS.\nC_LIO_LIThis technology could enhance MDS diagnosis and therapeutic monitoring and merits further research.\nC_LI\n\nStatement Translational RelevanceIn spite of several studies suggesting the utility of flow cytometry in the diagnosis of myelodysoplastic syndrome (MDS), this technique has not been widely adopted. We sought to enhance the utility of cytometry in MDS by performing the first high-dimensional mass cytometry characterization of a cohort of MDS patients. High-dimensional mass cytometry allowed all bone marrow cell populations to be simultaneously analyzed enabling high-resolution characterization of abnormal maker expression and myeloid development in MDS. This approach could identify almost all previously identified aberrant surface marker expression patterns in MDS while simultaneously enabling analysis by unsupervised clustering. Additionally, this mass cytometry analysis approach enabled the modeling of abnormal differentiation in MDS. This technology could enhance MDS diagnosis and therapeutic monitoring and merits further research.

cancer biology↗

bioRxiv2019

Phospho-tuning immunity through Denisovan, modern human, and mouse TNFAIP3 gene variants

Resisting or tolerating microbes are alternative strategies to survive infection, but little is known about the evolutionary mechanisms controlling this balance. Here, genomic analyses of anatomically modern humans, extinct Denisovan hominins, and mice revealed a series of missense variants in the immune response inhibitor A20 (encoded by TNFAIP3), substituting non-catalytic residues of the ubiquitin protease domain to diminish I{kappa}B-dependent phosphorylation and activation of A20. Two A20 variants with partial phosphorylation deficits appeared beneficial: one originating in Denisovans and introgressed in modern humans throughout Oceania, and another in a mouse strain resistant to Coxsackievirus. By contrast, a variant with 95% loss of phosphorylation caused spontaneous inflammatory disease in humans and mice. Analysis of the partial phosphorylation variant in mice revealed diminished tolerance of bacterial lipopolysaccharide or to poxvirus inoculation as trade-offs for enhanced immunity.\n\nOne Sentence SummaryModern and ancient variants reveal a genetically tunable element for balancing immunity and microbial tolerance.

immunology↗

bioRxiv2019

Cell type-specific monoclonal antibody cross-reactivity screening in non-human primates and development of comparative immunophenotyping panels for CyTOF

Monoclonal antibodies are a critical tool for immunologists, with applications including immunomicroscopy and cytometry, and non-human primates are essential models for drug development. Very few antibodies are raised against non-human primate antigens; instead, researchers typically use anti-human antibodies that are found to be cross-reactive with non-human primates. The NIH maintains a valuable database of cross-reactivity, but its coverage is not complete, especially for less common species. Furthermore, the database only indicates the presence or absence of staining and does not indicate if a different cell population is stained in the NHP species than in humans. We screened 332 antibodies in five immune cell populations in blood from cynomologus macaques (Macaca fascicularis), rhesus macaques (Macaca mulatta), African green monkeys (Chlorocebus aethiops) and olive/yellow baboons (Papio hamadryas anubis x Papio hamadryas cynocephalus), thereby generating a comprehensive cross-reactivity catalog that includes cell type-specificity. We subsequently used this catalog to help create large CyTOF mass cytometry panels for three of those species and humans. The curated dataset containing the primary data for each antibody has been deposited as a browsable resource at https://immuneatlas.org and https://flowrepository.org/id/FR-FCM-Z2Z7.

immunology↗

bioRxiv2019

A comprehensive atlas of immunological differences between humans, mice and non-human primates

Animal models are an integral part of the drug development and evaluation process. However, they are unsurprisingly imperfect reflections of humans, and the extent and nature of many immunological differences are unknown. With the rise of targeted and biological therapeutics, it is increasingly important that we understand the molecular differences in immunological behavior of humans and model organisms. Thus, we profiled a large number of healthy humans, along with three of the model organisms most similar to humans: rhesus and cynomolgus macaques and African green monkeys; and the most widely used mammalian model: mice. Using cross-species, universal phenotyping and signaling panels, we measured immune cell signaling responses to an array of 15 stimuli using CyTOF mass cytometry. We found numerous instances of different cellular phenotypes and immune signaling events occurring within and between species with likely effects on evaluation of therapeutics, and detail three examples (double-positive T cell frequency and signaling; granulocyte response to Bacillus anthracis antigen; and B cell subsets). We also explore the correlation of herpes simian B virus serostatus on the immune profile. The full dataset is available online at https://flowrepository.org (accession FR-FCM-Z2ZY) and https://immuneatlas.org.

immunology↗

bioRxiv2019

Variation of immune cell responses in humans reveals sex-specific coordinated signaling across cell types

Assessing the health and competence of the immune system is central to evaluating vaccination responses, autoimmune conditions, cancer prognosis and treatment. With an increasing number of studies examining immune dysregulation, there is a growing need for a curated reference of variation in immune parameters in healthy individuals. We used mass cytometry (CyTOF) to profile blood from 86 humans in response to 15 ex vivo immune stimuli. We present reference ranges for cell-specific immune markers and highlight differences that appear across sex and age. We identified modules of immune features that suggests there exists and underlying structure to the immune system based on signaling pathway responses across cell types. We observed increased MAPK signaling in inflammatory pathways in innate immune cells and greater overall coordination of immune cell responses in women. In contrast, men exhibited stronger STAT1 and TBK1 responses. These reference data are publicly available as a resource for immune profiling studies.

immunology↗

bioRxiv2019

Ion beam subcellular tomography

Multiplexed ion beam imaging (MIBI) has been previously used to profile multiple parameters in two dimensions in single cells within tissue slices. Here, a mathematical and technical framework for three-dimensional subcellular MIBI is presented. We term the approach ion beam tomography (IBT) wherein ion beam images are acquired iteratively across successive, multiple scans and later compiled into a 3D format. For IBT, cells were imaged at 0.2-4 pA ion current across 1,000 axial scans. Consecutive subsets of ion beam images were binned over 3 to 20 slices (above and below) to create a resolved image, wherein binning was incremented one slice at a time to yield an enhanced multi-depth data without loss of depth resolution. Algorithmic deconvolution, tailored for ion beams, was then applied to the transformed ion image series using a hybrid deblurring algorithm and an ion beam current-dependent point-spread function. Three-dimensional processing was implemented by segmentation, mesh, molecular neighborhoods, and association maps. In cultured cancer cells and tissues, IBT enabled accessible visualization of three-dimensional volumetric distributions of genomic regions, RNA transcripts, and protein factors with 65-nm lateral and 5-nm axial resolution. IBT also enabled label-free elemental mapping of cells, allowing "point of source" cellular component measurements not possible for most optical microscopy targets. Detailed multiparameter imaging of subcellular features at near macromolecular resolution should now be made possible by the IBT tools and reagents provided here to open novel venues for interrogating subcellular biology.

systems biology↗

bioRxiv2018

Continuous visualization of differences between biological conditions in single-cell data

In high-dimensional single cell data, comparing changes in functional markers between conditions is typically done across manual or algorithm-derived partitions based on population-defining markers. Visualizations of these partitions is commonly done on low-dimensional embeddings (eg. t-SNE), colored by per-partition changes. Here, we provide an analysis and visualization tool that performs these comparisons across overlapping k-nearest neighbor (KNN) groupings. This allows one to color low-dimensional embeddings by marker changes without hard boundaries imposed by partitioning. We devised an objective optimization of k based on minimizing functional marker KNN imputation error. Proof-of-concept work visualized the exact location of an IL-7 responsive subset in a B cell developmental trajectory on a t-SNE map independent of clustering. Per-condition cell frequency analysis revealed that KNN is sensitive to detecting artifacts due to marker shift, and therefore can also be valuable in a quality control pipeline. Overall, we found that KNN groupings lead to useful multiple condition visualizations and efficiently extract a large amount of information from mass cytometry data. Our software is publicly available through the Bioconductor package Sconify.

systems biology↗

bioRxiv2017

Evolutionary Origin of the Mammalian Hematopoietic System Found in a Colonial Chordate

Hematopoiesis is an essential process that evolved in multicellular animals. At the heart of this process are hematopoietic stem cells (HSCs), which are multipotent, self-renewing and generate the entire repertoire of blood and immune cells throughout life. Here we studied the hematopoietic system of Botryllus schlosseri, a colonial tunicate that has vasculature, circulating blood cells, and interesting characteristics of stem cell biology and immunity. Self-recognition between genetically compatible B. schlosseri colonies leads to the formation of natural parabionts with shared circulation, whereas incompatible colonies reject each other. Using flow-cytometry, whole-transcriptome sequencing of defined cell populations, and diverse functional assays, we identified HSCs, progenitors, immune-effector cells, the HSC niche, and demonstrated that self-recognition inhibits cytotoxic reaction. Our study implies that the HSC and myeloid lineages emerged in a common ancestor of tunicates and vertebrates and suggests that hematopoietic bone marrow and the B. schlosseri endostyle niche evolved from the same origin.

immunology↗

bioRxiv2017

Meta-analysis of Cytometry Data Reveals Racial Differences in Immune Cells

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of 10 heterogeneous cytometry studies totaling 2926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between White and Asian adults. Software is released to the public through GitHub (github.com/hzc363/MetaCyto).

bioinformatics↗

bioRxiv2017

Scalable Multi-Sample Single-Cell Data Analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject.\n\nAuthor SummaryRecently, the cytometry field has experienced rapid advancement in the development of mass cytometry (CyTOF). CyTOF enables a significant increase in the ability to monitor 50 or more cellular markers for millions of cells at the single-cell level. Initial studies with CyTOF focused on few samples, in which expert manual discovery of cell types were acceptable. As the technology matures, it is now feasible to collect more samples, which enables systematic studies of cell types across multiple samples. However, the statistical and computational issues surrounding multi-sample analysis have not been previously examined in detail. Furthermore, it was not clear how the data analysis could be scaled for hundreds of samples, such as those in clinical studies. In this work, we present a scalable analysis pipeline that is grounded in strong statistical foundation. Partition-Assisted Clustering (PAC) offers fast and accurate clustering and Multiple Alignments of Networks (MAN) utilizes network structures learned from each homogeneous cluster to organize the data into data-set level clusters. PAC-MAN thus enables the analysis of a large CyTOF dataset that was previously too large to be analyzed systematically; this pipeline can be extended to the analysis of similarly large or larger datasets.

bioinformatics↗