Biology subjects

Wolf, G.

Publications and source records attributed to Wolf, G..

29 records · Page 2Linked to original sources

bioRxiv2021

Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases

1One Sentence SummaryA novel topological machine learning approach applied to single-nucleus RNA sequencing from human retinas with age-related macular degeneration identifies interacting disease phase-specific glial activation states shared with Alzheimers disease and multiple sclerosis. 2 AbstractNeurodegeneration occurs in a wide range of diseases, including age-related macular degeneration (AMD), Alzheimers disease (AD), and multiple sclerosis (MS), each with distinct inciting events. To determine whether glial transcriptional states are shared across phases of degeneration, we sequenced 50,498 nuclei from the retinas of seven AMD patients and six healthy controls, generating the first single-cell transcriptomic atlas of AMD. We identified groupings of cells implicated in disease pathogenesis by applying a novel topologically-inspired machine learning approach called diffusion condensation. By calculating diffusion homology features and performing persistence analysis, diffusion condensation identified activated glial states enriched in the early phases of AMD, AD, and MS as well as an AMD-specific proangiogenic astrocyte state promoting pathogenic neovascularization in advanced AMD. Finally, by mapping the expression of disease-associated genes to glial states, we identified key signaling interactions creating hypotheses for therapeutic intervention. Our topological analysis identified an integrated disease-phase specific glial landscape that is shared across neurodegenerative conditions affecting the central nervous system.

neuroscience↗

bioRxiv2020

Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

1The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a continuous coarse graining process called diffusion condensation, Multiscale PHATE creates a tree of data granularities that can be cut at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hi CD66blo neutrophil and IFN{gamma}+GranzymeB+ Th17 cell responses enriched in patients who die. Further, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome.

bioinformatics↗

bioRxiv2020

Deciphering the evolution of the transcriptional and regulatory landscape in human placenta

In mammals, the placenta mediates maternal-fetal nutrient and waste exchange and provides immunomodulatory actions that facilitate maternal-fetal tolerance. The placenta is highly diversified among mammalian species, yet the molecular mechanisms that distinguish the placenta of human from other mammals are not fully understood. Using an interspecies transcriptomic comparison of human, macaque, and mouse term placentae, we identified hundreds of genes with lineage-specific expression - including dozens that are placentally-enriched and potentially related to pregnancy. We further annotated the enhancers for different human tissues using epigenomic data and demonstrate that the placenta and chorion are unique in that their enhancers display the least conservation. We identified numerous lineage-specific human placental enhancers, and found they are highly overlapped with specific families of endogenous retroviruses (ERVs), including MER21A, MER4A/B and MER39B that were previously linked to immune response and placental function. Among these ERV families, we further demonstrate that MER41 insertions create dozens of lineage-specific Serum Response Factor (SRF) binding loci in human, including one adjacent to FBN2, a placenta-specific gene with increased expression in humans that produces the peptide hormone placensin to stimulate glucose secretion and trophoblast invasion. Our results demonstrate the prevalence of lineage-specific human placental enhancers which are frequently associated with ERV insertions and likely facilitated the lineage-specific evolution of the mammalian placenta.

evolutionary biology↗

bioRxiv2020

Structural and developmental principles of neuropil assembly in C. elegans

Neuropil is a fundamental form of tissue organization within brains1. In neuropils, densely packed neurons synaptically interconnect into precise circuit architecture2,3, yet the structural and developmental principles governing nanoscale precision in bundled neuropil assembly remain largely unknown4-6. Here we use diffusion condensation, a coarse-graining clustering algorithm7, to identify nested circuit structures within the C. elegans cerebral neuropil (called the nerve ring). We determine that the nerve ring neuropil is organized into four tightly bundled strata composed of related behavioral circuits. We demonstrate that the stratified architecture of the neuropil is a geometrical representation of the functional segregation of sensory information and motor outputs, with specific sensory organs and muscle quadrants mapping onto particular neuropil strata. We identify groups of neurons with unique morphologies that integrate information across strata and that create a sophisticated honeycomb-shaped scaffold that encases the strata within the nerve ring. We resolve the developmental sequence leading to stratified neuropil organization through the integration of lineaging and cell tracking algorithms with high resolution light-sheet microscopy, and reveal principles of cell position, migration and hierarchical outgrowth that guide neuropil organization. Our results uncover conserved design principles underlying nerve ring neuropil architecture and function, and a pioneer neuron-based, temporal progression of outgrowth that guides the hierarchical development of the layered neuropil. Our findings provide a blueprint for using structural and developmental approaches to systematically understand neuropil organization within brains.

neuroscience↗

bioRxiv2020

Non-essential function of KRAB zinc finger gene clusters in retrotransposon suppression

The Kruppel-associated box zinc finger protein (KRAB-ZFP) family amplified and diversified in mammals by segmental duplications, but the function of the majority of this gene family remains largely unexplored due to the inaccessibility of the gene clusters to conventional gene targeting. We determined the genomic binding sites of 61 murine KRAB-ZFPs and genetically deleted in mouse embryonic stem (ES) cells five large KRAB-ZFP gene clusters encoding nearly one tenth of the more than 700 mouse KRAB-ZFPs. We demonstrate that clustered KRAB-ZFPs directly bind and silence retrotransposons and block retrotransposon-borne enhancers from gene activation in ES cells. Homozygous knockout mice generated from ES cells deleted in one of two KRAB-ZFP clusters were born at sub-mendelian frequencies in some matings, but heterozygous intercrosses could also yield knockout progeny with no overt phenotype. We further developed a retrotransposon capture-sequencing approach to assess mobility of the MMETn family of endogenous retrovirus like elements, which are transcriptionally activated in KRAB-ZFP cluster KOs, in a pedigree of KRAB-ZFP cluster KO and WT mice. We identified numerous somatic and several germ-line MMETn insertions, and found a modest increase in activity in mutant animals, but these events were detected in both wild-type and KO mice in stochastic and highly variable patterns. Our data suggests that the majority of young KRAB-ZFPs play a non-essential role in transposon silencing, likely due to the large redundancy with other KRAB-ZFPs and other transposon restriction pathways in mice. One Sentence SummaryMegabase-scale deletions of KRAB-ZFP gene clusters in mice leads to retrotransposon activation.

genetics↗

bioRxiv2019

Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing

Current methods for comparing scRNA-seq datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here, we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm to identify clusters of cells that are enriched or depleted in each condition is on average 57% higher than the next best-performing algorithm tested. Gene signatures derived from these clusters are more accurate compared to six alternative algorithms in ground-truth comparisons.

bioinformatics↗

bioRxiv2018

Embedding the single-cell sample manifold to reveal insights into cancer pathogenesis and disease heterogeneity

Previously, the effect of a drug on a cell population was measured based on simple metrics such as cell viability. However, as single-cell technologies are becoming more advanced, drug screen experiments can now be conducted with more complex readouts such as gene expression profiles of individual cells. The increasing complexity of measurements from these multi-sample experiments calls for more sophisticated analytical approaches than are currently available. We developed a novel method called PhEMD (Phenotypic Earth Movers Distance) and show that it can be used to embed the space of drug perturbations on the basis of the drugs effects on cell populations. When testing PhEMD on a newly-generated, 300-sample CyTOF kinase inhibition screen experiment, we find that the state space of the perturbation conditions is surprisingly low-dimensional and that the network of drugs demonstrates manifold structure. We show that because of the fairly simple manifold geometry of the 300 samples, we can accurately capture the full range of drug effects using a dictionary of only 30 experimental conditions. We also show that new drugs can be added to our PhEMD embedding using similarities inferred from other characterizations of drugs using a technique called Nystrom extension. Our findings suggest that large-scale drug screens can be conducted by measuring only a small fraction of the drugs using the most expensive high-throughput single-cell technologies--the effects of other drugs may be inferred by mapping and extending the perturbation space. We additionally show that PhEMD can be useful for analyzing other types of single-cell samples, such as patient tumor biopsies, by mapping the patient state space in a similar way as the drug state space. We demonstrate that PhEMD is scalable, compatible with leading batch effect correction techniques, and generalizable to multiple experimental designs. Altogether, our analyses suggest that PhEMD may facilitate drug discovery efforts and help uncover the network geometry of a collection of single-cell samples.

bioinformatics↗

bioRxiv2018

In vitro evaluation of the decontamination effect of cold argon plasma on selected bacteria frequently encountered in small animal bite injuries

ObjectiveThe beneficial effects of cold argon plasma (CAP) on wound healing and its capacity for bacterial decontamination has recently been documented. However, despite favourable reports from experimental trials and human applications, the first in vivo studies in small animals did not prove any decontamination effect in canine bite wounds.\n\nThe present study therefore aimed to evaluate the decontamination effect of CAP in different bacteria frequently encountered in canine bite wounds in vitro.\n\nMethodsStandard strains of Escherichia (E.) coli, Staphylococcus (S.) pseudintermedius, S. aureus, Streptococcus (Sc.) canis, Pseudomonas (P.) aeruginosa and Pasteurella multocida were investigated. To evaluate the influence of the bacterial growth phase, each bacterium was incubated in nutrient broth for 3 and 8 hours, respectively, before argon plasma treatment. Three different bacterial concentrations were created per bacterium and growth phase, and each was exposed to cold plasma at a gas flow rate of 5 standard litres/minute of argon for 30 seconds, 1 minute and 2 minutes.\n\nResultsArgon treatment resulted in acceptable decontamination rates (range 98.9-99.9%) in all bacteria species in vitro; however, differences in susceptibility were detected in the different tested bacteria. Treatment time significantly (P<0.05) correlated with the decontamination rate in E. coli, Sc. canis and S. aureus, with an exposure time of 2 minutes being most effective. The initial bacterial concentration significantly (P<0.05) influenced decontamination in Pasteurella multocida and P. aeruginosa, in which treatment time was not as important. The growth phase only influenced decontamination in S. pseudintermedius.\n\nConclusionCAP exerts effective antibacterial activity against the tested bacteria strains in vitro, with species specific effects of treatment time, growth phase and concentration.

microbiology↗

bioRxiv2017

Exploring Single-Cell Data with Multitasking Deep Neural Networks

Biomedical researchers are generating high-throughput, high-dimensional single-cell data at a staggering rate. As costs of data generation decrease, experimental design is moving towards measurement of many different single-cell samples in the same dataset. These samples can correspond to different patients, conditions, or treatments. While scalability of methods to datasets of these sizes is a challenge on its own, dealing with large-scale experimental design presents a whole new set of problems, including batch effects and sample comparison issues. Currently, there are no computational tools that can both handle large amounts of data in a scalable manner (many cells) and at the same time deal with many samples (many patients or conditions). Moreover, data analysis currently involves the use of different tools that each operate on their own data representation, not guaranteeing a synchronized analysis pipeline. For instance, data visualization methods can be disjoint and mismatched with the clustering method. For this purpose, we present SAUCIE, a deep neural network that leverages the high degree of parallelization and scalability offered by neural networks, as well as the deep representation of data that can be learned by them to perform many single-cell data analysis tasks, all on a unified representation.\n\nA well-known limitation of neural networks is their interpretability. Our key contribution here are newly formulated regularizations (penalties) that render features learned in hidden layers of the neural network interpretable. When large multi-patient datasets are fed into SAUCIE, the various hidden layers contain denoised and batch-corrected data, a low dimensional visualization, unsupervised clustering, as well as other information that can be used to explore the data. We show this capability by analyzing a newly generated 180-sample dataset consisting of T cells from dengue patients in India, measured with mass cytometry. We show that SAUCIE, for the first time, can batch correct and process this 11-million cell data to identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue on the basis of single-cell measurements.

bioinformatics↗

bioRxiv2017

PHATE: A Dimensionality Reduction Method for Visualizing Trajectory Structures in High-Dimensional Biological Data

With the advent of high-throughput technologies measuring high-dimensional biological data, there is a pressing need for visualization tools that reveal the structure and emergent patterns of data in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure in data by an information-geometric distance between datapoints. We perform extensive comparison between PHATE and other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data including continual progressions, branches, and clusters. We define a manifold preservation metric DEMaP to show that PHATE produces quantitatively better denoised embeddings than existing visualization methods. We show that PHATE is able to gain unique insight from a newly generated scRNA-seq dataset of human germ layer differentiation. Here, PHATE reveals a dynamic picture of the main developmental branches in unparalleled detail, including the identification of three novel subpopulations. Finally, we show that PHATE is applicable to a wide variety of datatypes including mass cytometry, single-cell RNA-sequencing, Hi-C, and gut microbiome data, where it can generate interpretable insights into the underlying systems.

bioinformatics↗

bioRxiv2017

MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data

Single-cell RNA-sequencing is fast becoming a major technology that is revolutionizing biological discovery in fields such as development, immunology and cancer. The ability to simultaneously measure thousands of genes at single cell resolution allows, among other prospects, for the possibility of learning gene regulatory networks at large scales. However, scRNA-seq technologies suffer from many sources of significant technical noise, the most prominent of which is dropout due to inefficient mRNA capture. This results in data that has a high degree of sparsity, with typically only ~10% non-zero values. To address this, we developed MAGIC (Markov Affinity-based Graph Imputation of Cells), a method for imputing missing values, and restoring the structure of the data. After MAGIC, we find that two- and three-dimensional gene interactions are restored and that MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and our newly generated epithelial-to-mesenchymal transition dataset.

bioinformatics↗