Biology subjects

Simon, L. M.

Publications and source records attributed to Simon, L. M..

14 recordsLinked to original sources

bioRxiv2024

Let-7 restrains an oncogenic epigenetic circuit in alveolar type 2 cells to prevent ectopic formation of fibrogenic AT2 intermediates and pulmonary fibrosis

Analysis of lung alveolar type 2 (AT2) progenitor stem cells has highlighted fundamental mechanisms that direct their differentiation into alveolar type 1 cells (AT1s) in lung repair and disease. However, microRNA (miRNA) mediated post-transcriptional mechanisms which govern this nexus remain understudied. We show here that the let-7 miRNA family serves a homeostatic role in governance of AT2 quiescence, specifically by preventing the uncontrolled accumulation of AT2 transitional cells and by promoting AT1 differentiation. Using mice and organoid models, we demonstrate genetic ablation of let-7a1/let-7f1/let-7d cluster (let-7afd) in AT2 cells prevents AT1 differentiation and results in accumulation of AT2 transitional cells in progressive pulmonary fibrosis. Integration of AGO2-eCLIP with RNA-sequencing from AT2 cells uncovered the induction of direct targets of let-7 in an oncogene feed-forward regulatory network including BACH1/EZH2/MYC which drives an aberrant fibrotic cascade. Additional analyses using CUT&RUN-sequencing revealed an epigenetic role of let-7 in induction of chromatin histone acetylation and methylation and maladaptive AT2 cell reprogramming. This study identifies let-7 as a key gatekeeper of post-transcriptional and epigenetic chromatin signals to prevent AT2-driven pulmonary fibrosis.

cell biology↗

bioRxiv2022

Single-cell multi-omics decodes regulatory programs during development of mouse secondary palate

The abnormal perturbation in gene regulation during palatogenesis may lead to cleft palate, a major congenital birth defect in humans and mice. However, a comprehensive multi-omic map of the developing secondary palate at single-cell resolution is lacking. In this study, we performed single-cell multiome sequencing and profiled chromatin accessibility and gene expression simultaneously within the same cells (n = 36,154) isolated from mouse secondary palate across embryonic days (E) 12.5, E13.5, E14.0, and E14.5. Application of optimal transport reconstructed five trajectories, representing continuous differentiation of multipotent cells into different subpopulations in later stages. By linking open chromatin signals to gene expression changes, we discovered a list of lineage-determining transcription factors, such as Shox2 for the anterior and Dlx1/2 for the posterior palatal mesenchymal trajectories. In conclusion, this study charted epigenetic and transcriptional dynamics during palatogenesis, which provides a valuable resource for the community and facilitate future research in cleft palate. HighlightsO_LIThe first report on building a single-cell multi-omics atlas with joint chromatin accessibility and gene expression measurements from the same cells during the development of mouse secondary palate. C_LIO_LIApplication of optimal transport calculated fate probabilities to different terminal states and recovered continuous landscapes during mouse secondary palate development. C_LIO_LIBy linking cis-regulatory DNA elements to target genes, we characterized a series of transcription factors governing the differentiation of cranial neural crest-derived multipotent cells to the anterior and posterior palatal mesenchymal trajectories, respectively. C_LIO_LITranscription factors Shox2 and Dlx1/2 exhibited top regulatory roles for the anterior and posterior palatal mesenchymal trajectories, respectively, showing significant enrichment in both motif accessibility and gene expression. C_LI

developmental biology↗

bioRxiv2021

Applying computer vision to digitised natural history collections for climate change research: temperature-size responses in British butterflies

O_LINatural history collections (NHCs) are invaluable resources for understanding biotic response to global change. Museums around the world are currently imaging specimens, capturing specimen data, and making them freely available online. In parallel to the digitisation effort, there have been great advancements in computer vision (CV): the computer trained automated recognition/detection, and measurement of features in digital images. Applying CV to digitised NHCs has the potential to greatly accelerate the use of NHCs for biotic response to global change research. In this paper, we apply CV to a very large, digitised collection to test hypotheses in an established area of biotic response to climate change research: temperature-size responses. C_LIO_LIWe develop a CV pipeline (Mothra) and apply it to the NHM iCollections of British butterflies (>180,000 specimens). Mothra automatically detects the specimen in the image, sets the scale, measures wing features (e.g., forewing length), determines the orientation of the specimen (pinned ventrally or dorsally), and identifies the sex. We pair these measurements and meta-data with temperature records to test how adult size varies with temperature during the immature stages of species and to assess patterns of sexual-size dimorphism across species and families. C_LIO_LIMothra accurately measures the forewing lengths of butterfly specimens and compared to manual baseline measurements, Mothra accurately determines sex and forewing lengths of butterfly specimens. Females are the larger sex in most species and an increase in adult body size with warm monthly temperatures during the late larval stages is the most common temperature size response. These results confirm suspected patterns and support hypotheses based on recent studies using a smaller dataset of manually measured specimens. C_LIO_LIWe show that CV can be a powerful tool to efficiently and accurately extract phenotypic data from a very large collection of digital NHCs. In the future, CV will become widely applied to digital NHC collections to advance ecological and evolutionary research and to accelerate the use of NHCs for biotic response to global change research. C_LI

ecology↗

bioRxiv2021

deCS: A tool for systematic cell type annotations of single-cell RNA sequencing data among human tissues

Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell-type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and less accurate. The increasing number of scRNA-seq data sets, as well as numerous published genetic studies, motivated us to build a comprehensive human cell type reference atlas. Here, we present deCS (decoding Cell type-Specificity), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth and feature selection strategies. Our results demonstrated that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deeper insights into the cellular mechanisms of disease pathogenesis. All documents, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.

bioinformatics↗

bioRxiv2021

SMARCA4 regulates spatially restricted metabolic plasticity in 3D multicellular tissue

SWI/SNF and related chromatin remodeling complexes act as tissue-specific tumor suppressors and are frequently inactivated in different cancers. Although many regulatory activities of SWI/SNF have been identified using 2D cell culture, the effects of SWI/SNF alterations in more complex 3D tissues have remained poorly understood. Here we employed 3D cell culture conditions that yield transcriptomic states mirroring primary lung adenocarcinoma (LUAD) specimens better than 2D culture. By analyzing spatial patterns of gene expression and DNA accessibility in 3D spheroids using single-cell RNA-seq and ATAC-seq, we find that the SWI/SNF ATPase SMARCA4 (BRG1) induces state-specific changes to DNA accessibility that influence spatially heterogeneous expression patterns and metabolism. In 3D conditions, SMARCA4 promotes accessibility for AP-1 transcription factors, including ATF3, a regulator of metabolism and repressor of NRF2 antioxidant signaling. These changes reduce expression of SLC7A11 in a distinct portion of cells, which sensitizes A549 spheroids to cell death via ferroptosis under oxidizing conditions. Consistent with these results, we find that SMARCA4 alterations are associated with derepression of NRF2 targets in human tumors independently of NRF2/KEAP1 status. Our work reveals new 3D-specific features and unanticipated spatial complexity associated with chromatin remodeling in multicellular tissues.

systems biology↗

bioRxiv2021

EmptyNN: A neural network based on positive-unlabeled learning to remove cell-free droplets and recover lost cells in single-cell RNA sequencing data

Droplet-based single-cell RNA sequencing (scRNA-seq) has significantly increased the number of cells profiled per experiment and revolutionized the study of individual transcriptomes. However, to maximize the biological signal robust computational methods are needed to distinguish cell-free from cell-containing droplets. Here, we introduce a novel cell-calling algorithm called EmptyNN, which trains a neural network based on positive-unlabeled learning for improved filtering of barcodes. We leveraged cell hashing and genetic variation to provide ground-truth. EmptyNN accurately removed cell-free droplets while recovering lost cell clusters, and achieved an Area Under the Receiver Operating Characteristics (AUROC) of 94.73% and 96.30%, respectively. The comparisons to current state-of-the-art cell-calling algorithms demonstrated the superior performance of EmptyNN, as measured by the number of recovered cell-containing droplets and cell types. EmptyNN was further applied to two additional datasets and showed good performance. Therefore, EmptyNN represents a powerful tool to enhance scRNA-seq quality control analyses.

bioinformatics↗

bioRxiv2020

INSCT: Integrating millions of single cells using batch-aware triplet neural networks

Efficient integration of heterogeneous and increasingly large single cell RNA sequencing (scRNA-seq) data poses a major challenge for analysis and in particular, comprehensive atlasing efforts. Here, we developed a novel deep learning algorithm to overcome batch effects using batch-aware triplet neural networks, called INSCT ("Insight"). Using simulated and real data, we demonstrate that INSCT generates an embedding space which accurately integrates cells across experiments, platforms and species. Our benchmark comparisons with current state-of-the-art scRNA-seq integration methods revealed that INSCT outperforms competing methods in scalability while achieving comparable accuracies. Moreover, using INSCT in semi-supervised mode enables users to classify unlabeled cells by projecting them into a reference collection of annotated cells. To demonstrate scalability, we applied INSCT to integrate more than 2.6 million transcriptomes from four independent studies of mouse brains in less than 1.5 hours using less than 25 gigabytes of memory. This feature empowers researchers to perform atlasing scale data integration in a typical desktop computer environment. INSCT is freely available at https://github.com/lkmklsmn/insct. HighlightsO_LIINSCT accurately integrates multiple scRNA-seq datasets C_LIO_LIINSCT accurately predicts cell types for an independent scRNA-seq dataset C_LIO_LIEfficient deep learning framework enables integration of millions of cells on a personal computer C_LI

bioinformatics↗

bioRxiv2020

Urban warming inverse contribution on risk of dengue transmission in the southeastern North America

O_LIPreventing diseases from becoming a problem where they are not is a common ground for disease ecology. The expectation for vector-borne diseases, especially those transmitted by mosquitos, is that warm and wet conditions favor vector traits increasing transmission potential. The advent of urbanization altering inner climate conditions hazards to increase mosquitos transmission potential on "disease-free" cooler areas as a consequence of a warming urban heat island (UHI) effect. C_LIO_LIWe assessed the realism of the anticipated dengue transmission potential into the southern United States in a causal pathway with the ongoing UHI effect, vectors spatial distribution patterns, and exogenous environment; We also measured the climatic niche similarity between both dengue vectors species. C_LIO_LIOur path model revealed that the UHI effect presents negative or no relation with dengue transmission potential. Instead, the surrounding non-urban temperature was rather suitable for the expected mosquitos transmission potential. C_LIO_LIBoth dengue vectors occurrence revealed to be more aggregated then expected by chance. These mosquitos density patterns were responsive to the warming effect of UHI-especially Aedes Aegypti-but not a reliable predictor for the anticipated dengue transmission potential pattern. The climatic niches of both vectors are not equivalent. Although currently highly overlapped, there is a wide space of their climatic niche still to be filled. C_LIO_LIPolicy implications. We highlight that the warming UHI effect on urban sites is not congruent with the expected suitability for dengue transmission. Instead, non-urban areas would be a better focus for dengue hazards into the southern United States. Our study also highlights the need for including low scale temperature on further mosquito-borne disease transmission models and track vectors niche filling under anthropogenic changes. C_LI

ecology↗

bioRxiv2019

DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data

Single cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic data sets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps. Here, we present DrivAER, a machine learning approach that scores annotated gene sets based on their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. We demonstrate that DrivAER extracts the key driving pathways and transcription factors that regulate complex biological processes from scRNA-seq data.

bioinformatics↗

bioRxiv2019

Longitudinal single cell transcriptomics reveals Krt8+ alveolar epithelial progenitors in lung regeneration

Lung injury activates quiescent stem and progenitor cells to regenerate alveolar structures. The sequence and coordination of transcriptional programs during this process has largely remained elusive. Using single cell RNA-seq, we first generated a whole-organ birds-eye view on cellular dynamics and cell-cell communication networks during mouse lung regeneration from [~]30,000 cells at six timepoints. We discovered an injury-specific progenitor cell state characterized by Krt8 in flat epithelial cells covering alveolar surfaces. The number of these cells peaked during fibrogenesis in independent mouse models, as well as in human acute lung injury and fibrosis. Krt8+ progenitors featured a highly distinct connectome of receptor-ligand pairs with endothelial cells, fibroblasts, and macrophages. To sky dive into epithelial differentiation dynamics, we sequenced >30,000 sorted epithelial cells at 18 timepoints and computationally derived cell state trajectories that were validated by lineage tracing genetic reporter mice. Airway stem cells within the club cell lineage and alveolar type-2 cells underwent transcriptional convergence onto the same Krt8+ progenitor cell state, which later resolved by terminal differentiation into alveolar type-1 cells. We derived distinct transcriptional regulators as key switch points in this process and show that induction of TNF-alpha/NFkappaB, p53, and hypoxia driven gene expression programs precede a Sox4, Ctnnb1, and Wwtr1 driven switch towards alveolar type-1 cell fate. We show that epithelial cell plasticity can induce non-gradual transdifferentiation, involving intermediate progenitor cell states that may persist and promote disease if checkpoint signals for terminal differentiation are perturbed.

systems biology↗

bioRxiv2019

A cellular census of healthy lung and asthmatic airway wall identifies novel cell states in health and disease

Human lungs enable efficient gas exchange, and form an interface with the environment which depends on mucosal immunity for protection against infectious agents. Tightly controlled interactions between structural and immune cells are required to maintain lung homeostasis. Here, we use single cell transcriptomics to chart the cellular landscape of upper and lower airways and lung parenchyma in health. We report location-dependent airway epithelial cell states, and a novel subset of tissue-resident memory T cells. In lower airways of asthma patients, mucous cell hyperplasia is shown to stem from a novel mucous ciliated cell state, as well as goblet cell hyperplasia. We report presence of pathogenic effector Th2 cells in asthma, and find evidence for type-2 cytokines in maintaining the altered epithelial cell states. Unbiased analysis of cell-cell interactions identify a shift from airway structural cell communication in health to a Th2-dominated interactome in asthma.

immunology↗

bioRxiv2018

An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics

Aging promotes lung function decline and susceptibility to chronic lung diseases, which are the third leading cause of death worldwide. We used single cell transcriptomics and mass spectrometry to quantify changes in cellular activity states of 30 cell types and the tissue proteome from lungs of young and old mice. Aging led to increased transcriptional noise, indicating deregulated epigenetic control. We observed highly distinct effects of aging on cell type level, uncovering increased cholesterol biosynthesis in type-2 pneumocytes and lipofibroblasts as a novel hallmark of lung aging. Proteomic profiling revealed extracellular matrix remodeling in old mice, including increased collagen IV and XVI and decreased Fraser syndrome complex proteins and Collagen XIV. Computational integration of the aging proteome and single cell transcriptomes predicted the cellular source of regulated proteins and created a first unbiased reference of the aging lung. The lung aging atlas can be accessed via an interactive user-friendly webtool at: https://theislab.github.io/LungAgingAtlas

systems biology↗

bioRxiv2018

Single cell RNA-seq denoising using a deep count autoencoder

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNAseq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a zero-inflated negative binomial noise model, and nonlinear gene-gene or gene-dispersion interactions are captured. Our method scales linearly with the number of cells and can therefore be applied to datasets of millions of cells. We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery.

bioinformatics↗

bioRxiv2018

MetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq data

BackgroundWith the advent of the age of big data in bioinformatics, large volumes of data and high performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts, but its generic nature also enables the detection of microbial and viral transcripts.\n\nFindingsWe developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from 6 independent controlled infection experiments of cell line models and comparison with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from >17,000 samples from >400 studies relevant to human disease using state-of-the-art high performance computing systems. The resulting data of this large-scale re-analysis are made available in the presented MetaMap resource.\n\nConclusionsOur results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation towards the role of the microbiome in human disease.

bioinformatics↗