Biology subjects

Banovich, N. E.

Publications and source records attributed to Banovich, N. E..

24 records · Page 2Linked to original sources

bioRxiv2019

Persistence of a novel regeneration-associated transitional cell state in pulmonary fibrosis

Stem cell senescence is often seen as an age associated pathological state in which cells acquire an abnormal and irreversible state. Here, we show that alveolar stem cell differentiation during lung regeneration involves a unique previously uncharacterized transitional state that exhibits cardinal features normally associated with cell senescence. Specifically, using organoid cultures, multiple in vivo injury models coupled with single cell transcriptomics and lineage tracing analysis, we find that alveolar stem cell differentiation involves a novel, pre-alveolar type-1 transitional state (PATS) en route to their terminal maturation. PATS can be distinguished based on their unique transcriptional signatures, including enrichment for TP53, TGF{beta}, and DNA damage repair signaling, and cellular senescence in both in vivo and ex vivo regenerating tissues. Significantly, PATS undergo extensive cell stretching, which makes them vulnerable to DNA damage, a feature commonly associated with most degenerative lung diseases. Importantly, we find enrichment of PATS-like state in human fibrotic lung tissues, suggesting that persistence of such transitional states underlies the pathogenesis of pulmonary fibrosis. Our study thus redefines senescence as a state that can occur as part of a normal tissue maintenance program, and can be derailed in human disease, notably fibrosis.

cell biology↗

bioRxiv2019

Single-cell RNA-sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis

Pulmonary fibrosis is a form of chronic lung disease characterized by pathologic epithelial remodeling and accumulation of extracellular matrix. In order to comprehensively define the cell types, mechanisms and mediators driving fibrotic remodeling in lungs with pulmonary fibrosis, we performed single-cell RNA-sequencing of single-cell suspensions from 10 non-fibrotic control and 20 PF lungs. Analysis of 114,396 cells identified 31 distinct cell types. We report a remarkable shift in epithelial cell phenotypes occurs in the peripheral lung in PF, and identify several previously unrecognized epithelial cell phenotypes including a KRT5-/KRT17+, pathologic ECM-producing epithelial cell population that was highly enriched in PF lungs. Multiple fibroblast subtypes were observed to contribute to ECM expansion in a spatially-discrete manner. Together these data provide high-resolution insights into the complexity and plasticity of the distal lung epithelium in human disease, and indicate a diversity of epithelial and mesenchymal cells contribute to pathologic lung fibrosis.\n\nOne Sentence SummarySingle-cell RNA-sequencing provides new insights into pathologic epithelial and mesenchymal remodeling in the human lung.

genomics↗

bioRxiv2019

Genome-wide DNA methylation and gene expression patterns reflect genetic ancestry and environmental differences across the Indonesian archipelago

Indonesia is the worlds fourth most populous country, host to striking levels of human diversity, regional patterns of admixture, and varying degrees of introgression from both Neanderthals and Denisovans. However, it has been largely excluded from the human genomics sequencing boom of the last decade. To serve as a benchmark dataset of molecular phenotypes across the region, we generated genome-wide CpG methylation and gene expression measurements in over 100 individuals from three locations that capture the major genomic and geographical axes of diversity across the Indonesian archipelago. Investigating between- and within-island differences, we find up to 10% of tested genes are differentially expressed between the islands of Mentawai (Sumatra) and New Guinea. Variation in gene expression is closely associated with DNA methylation, with expression levels of 9.7% of genes strongly correlating with nearby CpG methylation, and many of these genes being differentially expressed between islands. Genes identified in our differential expression and methylation analyses are enriched in pathways involved in immunity, highlighting Indonesia tropical role as a source of infectious disease diversity and the strong selective pressures these diseases have exerted on humans. Finally, we identify robust within-island variation in DNA methylation and gene expression, likely driven by very local environmental differences across sampling sites. Together, these results strongly suggest complex relationships between DNA methylation, transcription, archaic hominin introgression and immunity, all jointly shaped by the environment. This has implications for the application of genomic medicine, both in critically understudied Indonesia and globally, and will allow a better understanding of the interacting roles of genomic and environmental factors shaping molecular and complex phenotypes.

genomics↗

bioRxiv2018

Imputation of single-cell gene expression with an autoencoder neural network

BackgroundSingle-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses.\n\nMethodsTo overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates.\n\nResultsOn both simulated and real data, LATE and TRANSLATE outperform existing scRNA-seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene-gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1 million cells in just a few hours on a GPU.\n\nConclusionsWe demonstrate that our nonparametric approach to imputation based on autoencoders is powerful and highly efficient.

bioinformatics↗

bioRxiv2018

A comparison of gene expression and DNA methylation patterns across tissues and species

Previously published comparative functional genomic data sets from primates using frozen tissue samples, including many data sets from our own group, were collected and analyzed using non-optimal study designs and analysis approaches. In addition, when samples from multiple tissues were studied in a comparative framework, individual and tissue were confounded. We designed a multi-tissue comparative study of gene expression and DNA methylation in primates that minimizes confounding effects by using a balanced design with respect to species, tissues, and individuals. We also developed a comparative analysis pipeline that minimizes biases due to sequence divergence. We thus present the most comprehensive catalog of similarities and differences in gene expression and methylation levels between livers, kidneys, hearts, and lungs, in humans, chimpanzees, and rhesus macaques. We estimate that overall, only between 7 to 11% (depending on the tissue) of inter-species differences in gene expression levels can be accounted for by corresponding differences in promoter DNA methylation. However, gene expression divergence in conserved tissue-specific genes can be explained by corresponding inter-species methylation changes more often. We end the paper by providing recommendations for effective study design and best practices for meta-data recording for comparative functional genomic studies in primates.

genomics↗

bioRxiv2016

Impact of regulatory variation across human iPSCs and differentiated cells

Induced pluripotent stem cells (iPSCs) are an essential tool for studying cellular differentiation and cell types that are otherwise difficult to access. We investigated the use of iPSCs and iPSC-derived cells to study the impact of genetic variation across different cell types and as models for studies of complex disease. We established a panel of iPSCs from 58 well-studied Yoruba lymphoblastoid cell lines (LCLs); 14 of these lines were further differentiated into cardiomyocytes. We characterized regulatory variation across individuals and cell types by measuring gene expression, chromatin accessibility and DNA methylation. Regulatory variation between individuals is lower in iPSCs than in the differentiated cell types, consistent with the intuition that developmental processes are generally canalized. While most cell type-specific regulatory quantitative trait loci (QTLs) lie in chromatin that is open only in the affected cell types, we found that 20% of cell type-specific QTLs are in shared open chromatin. Finally, we developed a deep neural network to predict open chromatin regions from DNA sequence alone and were able to use the sequences of segregating haplotypes to predict the effects of common SNPs on cell type-specific chromatin accessibility.

genomics↗