Biology subjects

Chan, C.

Publications and source records attributed to Chan, C..

At least 73 records · Page 4Linked to original sources

bioRxiv2021

Modulation of gamma spectral amplitude and connectivity during reaching predicts peak velocity and movement duration

Voluntary movements are accompanied by increased oscillatory activity or synchronization in the gamma range (> 25.5 Hz) within the sensorimotor system. Despite the extensive literature about movement-related gamma synchronization, the specific role of gamma oscillations for movement control is still debated. In this study, we characterized movement-related gamma oscillatory dynamics and its relationship with movement characteristics based on 256-channels EEG recordings in 64 healthy subjects while performing fast and uncorrected reaching movements to targets located at three distances. We found that movement-related gamma synchronization occurred during both movement planning and execution, albeit with different gamma peak frequencies and topographies. Also, the amplitude of gamma synchronization in both planning and execution increased with target distance. Additional analysis of phase coherence revealed a gamma-coordinated long-range network involving occipital, frontal and central regions during movement execution. Gamma synchronization amplitude and phase coherence pattern reliably predicted peak velocity amplitude and timing, thus suggesting that cortical gamma oscillations play a significant role in the selection of appropriate kinematic parameters during planning and in their implementation during movement execution.

neuroscience↗

bioRxiv2021

Determining the architecture of nuclear ring of Xenopus laevis nuclear pore complex using integrated approaches

The nuclear pore complexes (NPCs) are large protein assemblies as a physical gate to regulate nucleocytoplasmic transport. Here, using integrated approaches including cryo-electron microscopy, hybrid homology modeling and cell experiment, we determined the architecture of the nuclear ring (NR) from Xenopus laevis oocytes NPC at subnanometer resolution. In addition to the improvement of the Y complex model, eight copies of Nup205 and ELYS were assigned in NR. Nup205 connects the inner and outer Y complexes and contributes to the assembly and stability of the NR. By interacting with both the inner Nup160 and the nuclear envelope (NE), the N-terminal {beta}-propeller and -solenoid domains of ELYS were found to be essential for accurate assembly of the NPC on the NE.

molecular biology↗

bioRxiv2021

Ad26.COV2.S Prevents SARS-CoV-2 Induced Pathways of Inflammation and Thrombosis in Hamsters

Syrian golden hamsters exhibit features of severe disease after SARS-CoV-2 challenge and are therefore useful models of COVID-19 pathogenesis and prevention with vaccines. Recent studies have shown that SARS-CoV-2 infection stimulates type I interferon, myeloid, and inflammatory signatures similar to human disease, and that weight loss can be prevented with vaccines. However, the impact of vaccination on transcriptional programs associated with COVID-19 pathogenesis and protective adaptive immune responses is unknown. Here we show that SARS-CoV-2 challenge in hamsters stimulates antiviral, myeloid, and inflammatory programs as well as signatures of complement and thrombosis associated with human COVID-19. Notably, single dose immunization with Ad26.COV2.S, an adenovirus serotype 26 vector (Ad26)-based vaccine expressing a stabilized SARS-CoV-2 spike protein, prevents the upregulation of these pathways such that the gene expression profiles of vaccinated hamsters are comparable to uninfected animals. Finally, we show that Ad26.COV2.S vaccination induces T and B cell signatures that correlate with binding and neutralizing antibody responses. These data provide further insights into the mechanisms of Ad26.COV2.S based protection against severe COVID-19 in hamsters. Author SummaryIn this study, we show that vaccination with Ad26.COV2.S protected SARS-CoV-2 challenged hamsters from developing severe COVID-19 disease by attenuating excessive proinflammatory responses, such as IL-6 and IL-1, macrophages and neutrophils signaling. Ad26 vaccination also prevented the upregulation of pathways associated with thrombosis such coagulation and clotting cascades associated with infection, and the transcriptomic profiles of vaccinated animals were largely comparable to control uninfected hamsters. In contrast, SARS-CoV-2 challenged unvaccinated hamsters showed significant increase of these proinflammatory and prothrombotic pathways and significant weight loss compared to vaccinated hamsters.

microbiology↗

bioRxiv2021

A flexible electronic strain sensor for the real-time monitoring of tumor progression

Healthcare professionals and scientists utilize tumor shrinkage as a key metric to establish the efficacy of cancer treatments. However, current measurement tools such as CT scanners and calipers only provide brief snapshots of the dynamic geometric changes occurring in vivo, and they are unable to characterize the continuous micrometer-scale volumetric transformations transpiring at minute timescales. Here we present a stretchable electronic strain sensor, with a 10-micron scale resolution, capable of continuously monitoring tumor volume progression in real-time. In mouse models with subcutaneously implanted lung cancer or B-cell lymphoma tumors our sensors discerned a significant change in the tumor volumes of treated mice within 5 hours after small molecule therapy or immunotherapy initiation. Histology, caliper measurements, and luminescence imaging over a one-week treatment period validated the data from the continuous sensor. We anticipate that real-time tumor progression datasets could help expedite and automate the process of screening cancer therapies in vivo.

bioengineering↗

bioRxiv2021

Genomic rearrangements have consequences for introgression breeding as revealed by genome assemblies of wild and cultivated lentil species

Understanding the genomic relationship between wild and cultivated genomes would facilitate access to the untapped variability found in crop wild relatives. We developed genome assemblies of a cultivated lentil (Lens culinaris) as well as a wild relative (L. ervoides). Comparative analyses revealed large-scale structural rearrangements and additional repetitive DNA in the cultivated genome, resulting in regions of reduced recombination, segregation distortion and permanent heterozygosity in the offspring of a cross between the two species. These novel findings provide plant breeders with better insight into how best to approach accessing the novel variability available in wild relatives.

plant biology↗

bioRxiv2021

A deep generative model of 3D single-cell organization

1 AbstractWe introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional {beta}-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to impute structures in cells where they were not imaged and to quantify the variation in the location of all subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs. 2 Author summaryIts impossible to acquire all the information we want about every cell were interested in in a single experiment. Even just limiting ourselves to imaging, we can only image a small set of subcellular structures in each cell. If we are interested in integrating those images into a holistic picture of cellular organization directly from data, there are a number of approaches one might take. Here, we leverage the fact that of the three channels we image in each cell, two stay the same across the data set; these two channels assess the cells shape and nuclear morphology. Given these two reference channels, we learn a model of cell and nuclear morphology, and then use this as a reference frame in which to learn a representation of the localization of each subcellular structure as measured by the third channel. We use {beta}-variational autoencoders to learn representations of both the reference channels and representations of each subcellular structure (conditioned on the reference channels of the cell in which it was imaged). Since these models are both probabilistic and generative, we can use them to understand the variation in the data from which they were trained, to generate instantiations of new cell morphologies, and to generate imputations of structures in real cell images to create an integrated model of subcellular organization.

cell biology↗

bioRxiv2021

Comparative analysis of RNA enrichment methods for preparation of Cryptococcus neoformans RNA sequencing libraries

Ribosomal RNA (rRNA) is the major RNA constituent of cells, therefore most RNA sequencing (RNA-Seq) experiments involve removal of rRNA. This process, called RNA enrichment, is done primarily to reduce cost: without rRNA removal, deeper sequencing would need to be performed to balance the sequencing reads wasted on rRNA. The ideal RNA enrichment method would remove all rRNA without affecting other RNA in the sample. We have tested the performance of three RNA enrichment methods on RNA isolated from Cryptococcus neoformans, a fungal pathogen of humans. We show that the RNase H depletion method unambiguously outperforms the commonly used Poly(A) isolation method: the RNase H method more efficiently depletes rRNA while more accurately recapitulating the expression levels of other RNA observed in an unenriched "gold standard". The RNase H depletion method is also superior to the Ribo-Zero depletion method as measured by rRNA depletion efficiency and recapitulation of protein-coding gene expression levels, while the Ribo-Zero depletion method performs moderately better in preserving non-coding RNA (ncRNA). Finally, we have leveraged this dataset to identify novel long non-coding RNA (lncRNA) genes and to accurately map the C. neoformans mitochondrial rRNA genes. ARTICLE SUMMARYWe compare the efficacy of three different RNA enrichment methods for RNA-Seq in Cryptococcus neoformans: RNase H depletion, Ribo-Zero depletion, and Poly(A) isolation. We show that the RNase H depletion method, which is evaluated in C. neoformans samples for the first time here, is highly efficient and specific in removing rRNA. Additionally, using data generated through these analyses, we identify novel long non-coding RNA genes in C. neoformans. We conclude that RNase H depletion is an effective and reliable method for preparation of C. neoformans RNA-Seq libraries.

microbiology↗

bioRxiv2021

Mechanical mapping of mammalian follicle development using Brillouin microscopy

In early mammalian development, the maturation of follicles containing the immature oocytes is an important biological process as the functional oocytes provide the bulk genetic and cytoplasmic materials for successful reproduction. Despite recent work demonstrating the regulatory role of mechanical stress in oocyte growth, quantitative studies of ovarian mechanical properties remain lacking both in vivo and ex vivo. In this work, we quantify the material properties of ooplasm, follicles and connective tissues in intact mouse ovaries at distinct stages of follicle development using Brillouin microscopy, a non-invasive tool to probe mechanics in three-dimensional (3D) tissues. We find that the ovarian cortex and its interior stroma have distinct material properties associated with extracellular matrix deposition, and that intra-follicular mechanical compartments emerge during follicle maturation. Our work provides a novel approach to study the role of mechanics in follicle morphogenesis and pave the way for future understanding of mechanotransduction in reproductive biology, with potential implications for infertility diagnosis and treatment.

biophysics↗

bioRxiv2021

Restricted nucleation and piRNA-mediated establishment of H3K9me3-dependent heterochromatin during embryogenesis in Drosophila miranda

Heterochromatin is a key architectural feature of eukaryotic genomes, crucial for silencing of repetitive elements and maintaining genome stability. Heterochromatin shows stereotypical enrichment patterns around centromeres and repetitive sequences, but the molecular details of how heterochromatin is established during embryogenesis are poorly understood. Here, we map the genome-wide distribution of H3K9me3-dependent heterochromatin in individual embryos of D. miranda at precisely staged developmental time points. We find that canonical H3K9me3 enrichment patterns are established early on before cellularization, and mature into stable and broad heterochromatin domains through development. Intriguingly, initial nucleation sites of H3K9me3 enrichment appear as early as embryonic stage3 (nuclear cycle 9) over transposable elements (TE) and progressively broaden, consistent with spreading to neighboring nucleosomes. The earliest nucleation sites are limited to specific regions of a small number of TE families and often appear over promoter regions, while late nucleation develops broadly across most TEs. Early nucleating TEs are highly targeted by maternal piRNAs and show early zygotic transcription, consistent with a model of co-transcriptional silencing of TEs by small RNAs. Interestingly, truncated TE insertions lacking nucleation sites show significantly reduced enrichment across development, suggesting that the underlying sequences play an important role in recruiting histone methyltransferases for heterochromatin establishment.

genomics↗

bioRxiv2021

APOBEC3A drives acquired resistance to targeted therapies in non-small cell lung cancer

Acquired drug resistance to even the most effective anti-cancer targeted therapies remains an unsolved clinical problem. Although many drivers of acquired drug resistance have been identified1-6, the underlying molecular mechanisms shaping tumor evolution during treatment are incompletely understood. The extent to which therapy actively drives tumor evolution by promoting mutagenic processes7 or simply provides the selective pressure necessary for the outgrowth of drug-resistant clones8 remains an open question. Here, we report that lung cancer targeted therapies commonly used in the clinic induce the expression of cytidine deaminase APOBEC3A (A3A), leading to sustained mutagenesis in drug-tolerant cancer cells persisting during therapy. Induction of A3A facilitated the formation of double-strand DNA breaks (DSBs) in cycling drug-treated cells, and fully resistant clones that evolved from drug-tolerant intermediates exhibited an elevated burden of chromosomal aberrations such as copy number alterations and structural variations. Preventing therapy-induced A3A mutagenesis either by gene deletion or RNAi-mediated suppression delayed the emergence of drug resistance. Finally, we observed accumulation of A3A mutations in lung cancer patients who developed drug resistance after treatment with sequential targeted therapies. These data suggest that induction of A3A mutagenesis in response to targeted therapy treatment may facilitate the development of acquired resistance in non-small-cell lung cancer. Thus, suppressing expression or enzymatic activity of A3A may represent a potential therapeutic strategy to prevent or delay acquired resistance to lung cancer targeted therapy.

cancer biology↗

bioRxiv2021

Comparing instance segmentation methods for analyzing clonal growth of single cells in microfluidic chips

Appropriately tailored segmentation techniques can extract detailed quantitative information from biological image datasets to characterize and better understand sample distributions. Practically, high-resolution characterization of biological samples such as cell populations can provide insights into the sources of variance in biomarker expression, drug resistance, and other phenotypic aspects, but it is still unclear what is the best method for extracting this information from large image-based datasets. We present a software pipeline and comparison of multiple image segmentation methods to extract single-cell morphological and fluorescence quantitation from time lapse images of clonal growth rates using a recently reported microfluidic system. The inputs in all pipelines consist of thousands of unprocessed images and the outputs are the detection of cell counts, chamber identifiers, and individual morphological properties of each clone over time detected through multi-channel fluorescence and bright field imaging. Our conclusion is that unsupervised learning methods for cell segmentation substantially outperform supervised statistical methods with respect to accuracy and have key advantages including individual cell instance detection and flexibility through model training. We expect this system and software to have broad utility for researchers interested in high-throughput single-cell biology.

bioengineering↗

bioRxiv2020

Single-cell characterization of transcriptomic heterogeneity in lymphoblastoid cell lines

Lymphoblastoid Cell Lines (LCLs) are generated by transforming primary B cells with Epstein-Barr Virus (EBV) and are used extensively as model systems in viral oncology, immunology, and human genetics research. In this study, we characterized single-cell transcriptomic profiles of five LCLs and present a simple discrete-time simulation to explore the influence of stochasticity on LCL clonal evolution. Single-cell RNA sequencing revealed substantial phenotypic heterogeneity within and across LCLs with respect to immunoglobulin isotype; virus-modulated host pathways involved in survival, proliferation, and differentiation; viral replication state; and oxidative stress. This heterogeneity is likely attributable to intrinsic variance in primary B cells and host-pathogen dynamics. Stochastic simulations demonstrate that initial primary cell heterogeneity, random sampling, time in culture, and even mild differences in phenotype-specific fitness can contribute substantially to dynamic diversity in populations of nominally clonal cells.

immunology↗

bioRxiv2020

miR-200 deficiency promotes lung cancer metastasis by activating cancer-associated fibroblasts

Lung adenocarcinoma, the most prevalent lung cancer subtype, is characterized by its high propensity to metastasize. Despite the importance of metastasis in lung cancer mortality, its underlying cellular and molecular mechanisms remain largely elusive. Here, we identified miR-200 miRNAs as potent suppressors for lung adenocarcinoma metastasis. miR-200 expression is specifically repressed in mouse metastatic lung adenocarcinomas, and miR-200 decrease strongly correlates with poor patient survival. Consistently, deletion of mir-200c/141 in the KrasLSL-G12D/+; Trp53flox/flox lung adenocarcinoma mouse model significantly promoted metastasis, generating a desmoplastic tumor stroma highly reminiscent of metastatic human lung cancer. miR-200 deficiency in lung cancer cells promotes the proliferation and activation of adjacent cancer-associated fibroblasts (CAFs), which in turn elevates the metastatic potential of cancer cells. miR-200 regulates the functional interaction between cancer cells and CAFs, at least in part, by targeting Notch ligand Jagged1 and Jagged2 in cancer cells and inducing Notch activation in adjacent CAFs. Hence, the interaction between cancer cells and CAFs constitutes an essential mechanism to promote metastatic potential.

cancer biology↗

bioRxiv2020

Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for [~]100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN. HighlightsO_LIscMappR integrates scRNA-seq and bulk RNA-seq to re-calibrate bulk differentially expressed genes (DEGs). C_LIO_LIscMappR correctly identified immune-cell expressed DEGs from a bulk RNA-seq analysis of mouse kidney regeneration. C_LIO_LIscMappR is deployed as a user-friendly R package available at CRAN. C_LI

bioinformatics↗

bioRxiv2020

Impaired Fetal Lung Development can be Rescued by Administration of Extracellular Vesicles Derived from Amniotic Fluid Stem Cells

Incomplete lung development, also known as pulmonary hypoplasia, is a recognized cause of neonatal death and poor outcome for survivors. To date, there is no effective treatment that promotes fetal lung growth and maturation. Herein, we describe a novel stem cell-based approach that enhances fetal lung development via the administration of extracellular vesicles (EVs) derived from amniotic fluid stem cells (AFSCs). In experimental models of pulmonary hypoplasia, administration of AFSC-EVs promoted lung branching morphogenesis and alveolarization, and stimulated pulmonary epithelial cell and fibroblast differentiation. This regenerative ability was confirmed in two models of injured human lung cells, where human AFSC-EVs obtained following good manufacturing practices restored pulmonary epithelial homeostasis. AFSC-EV beneficial effects were exerted via the release of RNA cargo, primarily miRNAs, that regulate the expression of genes involved in fetal lung development. Our findings suggest that AFSC-EVs hold regenerative ability for underdeveloped fetal lungs, demonstrating potential for therapeutic application. One Sentence SummaryFetal lung regeneration via administration of extracellular vesicles derived from amniotic fluid stem cells

developmental biology↗

bioRxiv2020

Wireless AI-Powered IoT Sensors for Laboratory Mice Behavior Recognition

According to the U.S. Department of Agriculture in 2018, there are more than 100 million animals used in research, education, and testing per year. Of the laboratory animals used for research, 95 percent are mice and rats as reported by the Foundation for Biomedical Research (FBR). We present here our work in developing wireless Artificial Intelligent (AI)-powered IoT Sensors (AIIS) for laboratory mice motion recognition utilizing embedded micro-inertial measurement units (uIMUs). Based on the AIIS, we have demonstrated a small-animal motion tracking and recognition system that could recognize 5 common behaviors of mice in cages with accuracy of ~76.23%. The key advantage of this AIIS-based system is to enable high throughput behavioral monitoring of multiple to a large group of laboratory animals, in contrast to traditional video tracking systems that usually track only single or a few animals at a time. The system collects motion data (i.e., three axes linear accelerations and three axes angular velocities) from the IoT sensors attached to different mice, and classifies these data into different behaviors using machine learning algorithms. One of the challenging problems for data analysis is that the distribution of behavior samples is extremely imbalanced. Behaviors such as sleeping and walking dominate the entire sample set from different mice. However, machine learning algorithms often require a balanced sample set to achieve optimal performance. Thus, several methods are proposed to solve the imbalanced sample problem. Data processing methods for data segmentation, feature extraction, feature selection, imbalanced learning, and machine learning are explored to process motion data including sleeping, walking, rearing, digging, shaking, grooming, drinking and scratching. For example, by tuning the parameters of a machine-learning algorithm (i.e., Support Vector Machine (SVM)), the average accuracy of classifying five behaviors (i.e., sleeping, walking, rearing, digging and shaking) is 48.07% before solving the imbalance sample issue. To address this problem, an iteration of sample and feature selection is applied to improve the SVM performance. A combination of oversampling and undersampling is used to handle imbalanced classes, and feature selection provides the optimal number of features. The accuracy increases from 48.07% to 76.23% when the optimized combination is used. We further obtained an average accuracy of 86.46% by removing shaking, which is proved to have a negative effect on the overall performance, out of these five behaviors. Furthermore, we were able to classify less frequent behaviors including rearing, digging, grooming, drinking and scratching at an average accuracy of 96.35%.

animal behavior and cognition↗

bioRxiv2020

An integrative structural model of the full-length gp16 ATPase in bacteriophage phi29 DNA packaging motor

Biological motors, ubiquitous in living systems, convert chemical energy into different kinds of mechanical motions critical to cellular functions. Most of these biomotors belong to a group of enzymes known as ATPases, which adopt a multi-subunit ring-shaped structure and hydrolyze adenosine triphosphate (ATP) to generate forces. The gene product 16 (gp16), an ATPase in bacteriophage {square}29, is among the most powerful biomotors known. It can overcome substantial resisting forces from entropic, electrostatic, and DNA bending sources to package double-stranded DNA (dsDNA) into a preformed protein shell (procapsid). Despite numerous studies of the {square}29 packaging mechanism, a structure of the full-length gp16 is still lacking, let alone that of the packaging motor complex that includes two additional molecular components: a connector gp10 protein and a prohead RNA (pRNA). Here we report the crystal structure of the C-terminal domain of gp16 (gp16-CTD). Structure-based alignment of gp16-CTD with related RNase H-like nuclease domains revealed a nucleic acid binding surface in gp16-CTD, whereas no nuclease activity has been detected for gp16. Subsequent molecular dynamics (MD) simulations showed that this nucleic acid binding surface is likely essential for pRNA binding. Furthermore, our simulations of a full-length gp16 structural model highlighted a dynamic interplay between the N-terminal domain (NTD) and CTD of gp16, which may play a role in driving DNA movement into the procapsid, providing structural support to the previously proposed inchworm model. Lastly, we assembled an atomic structural model of the complete {square}29 dsDNA packaging motor complex by integrating structural and experimental data from multiple sources. Collectively, our findings provided a refined inchworm-revolution model for dsDNA translocation in bacteriophage {square}29 and suggested how the individual domains of gp16 work together to power such translocation. ABSTRACT (SHORT)Biological motors, ubiquitous in living systems, convert chemical energy into different kinds of mechanical motions critical to cellular functions. The gene product 16 (gp16) in bacteriophage {square}29 is among the most powerful biomotors known, which adopts a multi-subunit ring-shaped structure and hydrolyzes ATP to package double-stranded DNA (dsDNA) into a preformed procapsid. Here we report the crystal structure of the C-terminal domain of gp16 (gp16-CTD). Structure-based alignment and molecular dynamics (MD) simulations revealed an essential binding surface of gp16-CTD for prohead RNA (pRNA), a unique component of the motor complex. Furthermore, our simulations highlighted a dynamic interplay between the N-terminal domain (NTD) and CTD of gp16, which may play a role in driving DNA movement into the procapsid. Lastly, we assembled an atomic structural model of the complete {square}29 dsDNA packaging motor complex by integrating structural and experimental data from multiple sources. Collectively, our findings provided a refined inchworm-revolution model for dsDNA translocation in bacteriophage {square}29 and suggested how the individual domains of gp16 work together to power such translocation.

biophysics↗

bioRxiv2020

Ontology-guided segmentation and object identification for developmental mouse lung immunofluorescent images

BackgroundImmunofluorescent confocal microscopy uses labeled antibodies as probes against specific macromolecules to discriminate between multiple cell types. For images of the developmental mouse lung, these cells are themselves organized into densely packed higher-level anatomical structures. These types of images can be challenging to segment automatically for several reasons, including the relevance of biomedical context, dependence on the specific set of probes used, prohibitive cost of generating labeled training data, as well as the complexity and dense packing of anatomical structures in the image. The use of an application ontology surmounts these challenges by combining image data with its metadata to provide a meaningful biological context, and hence constraining and simplifying the process of segmentation and object identification. ResultsWe propose an innovative approach for the automated analysis of complex and densely packed anatomical structures from immunofluorescent images that utilizes an application ontology to provide a simplified context for image segmentation and object identification. We describe how the logical organization of biological facts in the form of an ontology can provide useful constraints that enhance automatic processing of complex images. We demonstrate the results of ontology-guided segmentation and object identification in mouse developmental lung images from the Bioinformatics REsource ATlas for the Healthy lung (BREATH) database of the Molecular Atlas of Lung Development (LungMAP1) program. ConclusionThe microscopy analysis pipeline library (micap) is available at https://github.com/duke-lungmap-team/microscopy-analysis-pipeline. Code to reproduce our analysis of LungMAP images is also available at https://github.com/duke-lungmap-team/lungmap-pipeline. Finally, the application ontology is available at https://github.com/duke-lungmap-team/lung_ontology and includes example SPARQL queries. ContactAnna Maria Masci email: annamaria.masci@duke.edu

bioinformatics↗