Biology subjects

Choi, B.

Publications and source records attributed to Choi, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Systematic modeling of phenotypic drug response profiles inpatient-derived organoids

Patient-derived tumor organoids provide a physiologically relevant 3D disease model for preclinical drug discovery, surpassing the limitations of conventional 2D cell lines. To better capture the dynamic nature of organoid drug responses, we developed a new systematic evaluation method called SCOPE (Systematic Classification of Organoids for Phenotypic Evaluation), harnessing phenotypic assessments from multi-timepoint 3D imaging data. By integrating artificial intelligence (AI)-based image analysis of organoid viability with tracking and mathematical modeling of organoid growth over time, we captured temporal-and dose-dependent dynamics of phenotypic changes, culminating in two novel metrics: a combined growth and viability (GV) score as well as a cytostatic-cytotoxic transition range (CCTR) that separates drug effects on organoid growth and viability. Our approach supports classification of specific drug responses into four distinct phenotypic groups: (1) cytotoxic, (2) cytostatic plus cytotoxic, (3) late cytotoxic, and (4) cytostatic. This novel drug evaluation system can identify previously unknown drug effects or new therapeutic use cases for existing drugs, facilitating the design of alternative therapeutic options to overcome efficacy or drug resistance challenges and improving the clinical applicability of organoid-based drug discovery results.

bioengineering↗

bioRxiv2026

Recursive mutational robustness in cancer through intra- and inter-genic compensation

Mutational robustness is a fundamental property of biological systems allowing phenotypic stability against genotypic perturbation. At the gene level, this is primarily attributed to inter-genic functional redundancy driven by paralogs. Here, we show that intra-genic functional redundancy, driven by alternatively spliced isoforms, is another prevalent source of mutational robustness. Integrating isoform-resolved pan-cancer genomics and transcriptomics data across tumors and cancer cell lines, we show that alternative isoforms often bypass deleterious somatic mutations. Mutation-skipping isoforms exhibit compensatory upregulation indicative of robustness against deleterious effects, especially in highly expressed genes. We found that intra-genic robustness is strongly associated with perturbation tolerance, is highly context-specific and, notably, can be complemented and augmented recursively by inter-genic robustness. Non-tumour-suppressor genes generally show higher robustness than tumour suppressor genes, and intra-genic compensation of their mutations is associated with worse disease outcome in most cancer types. Furthermore, intra-genic compensation by mutation-skipping isoforms associates with differential isoform usage among proximal interactors, suggesting restorative protein interaction rewiring at isoform-level. Mechanistically, self-transcriptional adaptation (self-TA) following nonsense-mediated decay of perturbed isoforms provides an explanation for the observed up-regulation of mutation-skipping isoforms. Overall, the proposed recursive mutational robustness framework opens new avenues for deeper mechanistic investigation into cancer evolution and identification of selective vulnerabilities in cancer cells. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/727768v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1b0f79aorg.highwire.dtl.DTLVardef@14fa261org.highwire.dtl.DTLVardef@12da16aorg.highwire.dtl.DTLVardef@c03dee_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2026

MagNet: Computational Methods for Constructing High-Confidence Protein-Protein Interaction Networks in Magnaporthe oryzae

Magnaporthe oryzae, the rice blast fungus, plays a role as a model organism for molecular plant-microbe interaction research. Studies on the pathogenic mechanism of this fungus revealed many genes involved in signaling pathways. As multi-omics data are being available, genomic-level researches have been conducted to uncover the underlying biological processes during the pathogenesis of M. oryzae. Identifying the genome-wide protein-protein interaction (PPI) network is one of the omics-level approaches, which helps to understand signaling and regulatory pathways. However, existing biological network resources of M. oryzae are not sufficient to decipher pathogenesis mechanisms due to the abundance of false positives/negatives. In this study, a reliable PPI network database of M. oryzae, MagNet, was constructed with three methods, including homology-based Interolog search, co-expression network construction, and domain-domain interaction (DDI)-based prediction. With three approaches altogether, the pan-network with 5,600,976 interactions was generated, including 217,531 highly confident interactions supported by all three methods. Experimental data on M. oryzae PPIs supported that our PPI network can predict PPIs with higher accuracy compared to the previously constructed databases. MagNet would provide integrated biological network data, which can help to understand the molecular mechanisms of the rice blast fungus. The PPI data can be accessed via https:/magnet.scnu.ac.kr.

genomics↗

bioRxiv2026

Amplification-Free Detection of Antibiotic Resistance in Enterococcus faecium using PNA-FISH

Vancomycin-resistant Enterococcus faecium (VREfm) is a major nosocomial pathogen, with antibiotic resistance mediated by the vanA and vanB operons. Rapid and accurate detection of antibiotic resistance is critical for the timely treatment of bacteremia and sepsis. Although imaging-based approaches using fluorescence in situ hybridization (FISH) provide a potential diagnostic solution, detecting mRNAs of antibiotic resistance genes (ARGs) in individual cells remains particularly challenging due to their low copy number and transient expression. Here, we present a peptide nucleic acid (PNA)-FISH method for direct detection of vanA- and vanB-associated resistance in individual VREfm cells. A universal probe targeting the conserved region across vancomycin resistance genes and a set of probes exclusively targeting the vanB gene were designed. The universal probe showed increased fluorescence in the vanA-genotype strain upon vancomycin or teicoplanin treatment, and in the vanB-genotype strain upon vancomycin treatment. In contrast, vanB-specific probes showed increased fluorescence exclusively from the vanB-genotype strain upon vancomycin treatment, confirming their specificity to the vanB gene. Efficient cellular penetration and strong hybridization of PNA probes enabled efficient and accurate detection of antibiotic-resistant bacterial cells, even under a wide-field fluorescence microscope. No detectable signals above background were observed in other major bacterial species associated with bacteremia and sepsis. These findings demonstrate robust detection of antibiotic-resistant cells in mixed microbial populations. When integrated with microbe-capturing techniques, this method may support culture-free detection of antibiotic resistance without nucleic acid amplification or sequencing, with the potential to reduce diagnostic turnaround time.

microbiology↗

bioRxiv2026

Landscape-scale navigation unlocks antibody CDR structural logic for AI-guided rescue and therapeutic optimization

While AI offers transformative potential for therapeutic antibody design, the lack of ground-truth data fundamentally constrains our ability to model the epistatic topology of fitness landscapes. Here, we establish a high-throughput workflow to characterize tens of thousands of antibody variants per week with gold-standard biophysical precision. By combinatorially assembling functional variants from deep mutational scanning, we charted antibody fitness landscapes comprising over 17,000 data points, which revealed an extremely rugged, non-navigable epistatic topology. Yet, navigating at this unprecedented scale enabled the discovery of rare peak clusters exhibiting simultaneous enhancements in affinity and productivity. Strikingly, ProteinMPNN predicted the CDR-dependent productivity landscape with remarkable accuracy, suggesting that sequence-structure compatibility within CDRs gates cellular productivity. This insight enabled a structure-guided rescue strategy combining AlphaFold3 and ProteinMPNN, which successfully restored the cellular productivity of high-affinity, low-productivity clones via single amino acid substitutions. Two elite variants drawn directly from peak clusters further demonstrated 20- to 100-fold in vivo efficacy gains in a murine psoriasis model. Our findings establish CDR structural fitness as a fundamental determinant of antibody cellular productivity and validate landscape-scale navigation as a powerful framework for therapeutic antibody optimization.

bioengineering↗

bioRxiv2025

An evaluation of genotoxicity and 90-day repeated-dose toxicity in rats of a Dracocephalum moldavica powdered extract

The safety of a powdered extract of Dracocephalum moldavica (Dracobelle Nu sd) was examined through a battery of in vitro and in vivo toxicological studies including a bacterial reverse mutation test, an in vivo mammalian micronucleus assay in male NMRI mice, and a subchronic toxicity study in Han:Wist rats. No mutagenic activity was observed in any of the tested bacterial strains with or without metabolic activation in the reverse mutation test, and no evidence of clastogenic or aneugenic activity was observed in the in vivo mouse micronucleus test. In the 90-day repeated dose oral toxicity study conducted in 80 (40 male/40 female) rats at dose levels of 0 (control), 250, 500, and 1000 mg/kg bw/day, no treatment related mortality or clinical signs of toxicity were observed, and no significant adverse effects attributable to the test item were found in any of the examined parameters in accordance with the OECD guidelines. The No Observed Adverse Effect Level (NOAEL) was determined to be 1000 mg/kg bw/day in male and female rats, the highest dose tested.

pharmacology and toxicology↗

bioRxiv2025

A gene regulatory element modulates myosin expression and controls cardiomyocyte response to stress

A hallmark of heart disease is gene dysregulation and reactivation of fetal gene programs. Reactivation of these fetal programs has compensatory effects during heart failure, depending on the type and stage of the underlying cardiomyopathy. Thousands of putative cardiac gene regulatory elements have been identified that may control these programs, but their functions are largely unknown. We profile genome-wide changes to gene expression and chromatin structure in cardiomyocytes derived from human pluripotent stem cells. We identify and characterize a gene regulatory element essential for the regulation of MYH6, which encodes human fetal myosin. Using chromatin conformation assays in combination with epigenome editing, we find that gene regulation is mediated by direct interaction between MYH6 and the enhancer. We also find that enhancer activation alters cardiomyocyte response to the hypertrophy-inducing peptide endothelin-1. Enhancer activation prevents polyploidization and changes in calcium dynamics following stress with endothelin-1. Collectively, these results identify regulatory mechanisms of cardiac gene expression programs that modulate cardiomyocyte maturation, cellular stress response, and could serve as potential therapeutic targets.

genomics↗

bioRxiv2025

Decoy-seq unlocks scalable genetic screening for regulatory small noncoding RNAs

Small noncoding RNAs (smRNAs) play critical roles in regulating various cellular processes, including development, stress response, and disease pathogenesis. However, functional characterization of smRNAs remains limited by the scale and simplicity of phenotypic readouts. Recently, single-cell perturbation screening methods, which link CRISPR-mediated genetic perturbations to rich transcriptomic profiling, have emerged as foundational and scalable approaches for understanding gene functions, mapping regulatory networks, and revealing genetic interactions. However, a comparable approach for probing the regulatory consequences of smRNA perturbations is lacking. Here, we present Decoy-seq as an extension of this approach for high-content, single-cell perturbation screening of smRNAs. This method leverages U6-driven tough decoys (TuD), which form stable duplexes with their target smRNAs, for inhibition in the cell. Lentiviral-encoded TuDs are compatible with conventional single-cell RNA-sequencing (scRNA-seq) technologies, allowing joint identification of the smRNA perturbation in each cell and its associated transcriptomic profile. We applied Decoy-seq to 336 microRNAs (miRNAs) and 196 tRNA-derived fragments (tRFs) in a human breast cancer cell line, demonstrating its ability to uncover complex regulatory pathways and novel functions of these smRNAs. Notably, we show that tRFs influence mRNA polyadenylation and regulate key cancer-associated processes, such as cell cycle progression and proliferation. Therefore, Decoy-seq provides a powerful framework for exploring the functional roles of smRNAs in normal physiology and disease, and holds promise for accelerating future discoveries.

systems biology↗

bioRxiv2025

Targeting Osteosarcoma heterogeneity to improve therapeutic response

Intra tumor heterogeneity complicates cancer therapy by providing tumors with the ability to alter their phenotypes and become more therapy resistant. Here, we tested the hypothesis that identifying and modulating expression of key state-specific transcription factors could be used as a strategy for driving cells to a more therapy-sensitive state. Recent single-cell studies have explored the inter and intra tumoral heterogeneity of osteosarcoma and identified gene pathways enriched in specific cell states. For example, metastatic tumors are characterized by an expression of genes in the TNF-, PI3K, TGF{beta} and mTOR pathways. We identified similar profiles in osteosarcoma patient-derived xenograft-derived cell lines and potential transcription factor drivers of these states. We then used perturb-seq to downregulate expression of key transcription factors and evaluated the effect of these modulations on single cell RNA profiles and drug responses. Knockdown of NFE2L3 or NR0B1 increased the proportion of cells sensitive to targeted therapy. This approach, which could potentially be applied to other cancers, could be used as a strategy to increase the response to targeted therapies by increasing the proportion of cells in a drug-sensitive state. HighlightsO_LIDistinct transcriptomic states were identified in osteosarcoma cell lines using single-cell RNA sequencing. C_LIO_LILineage tracing identified states with differential sensitivity to therapy. C_LIO_LIUsing perturb-seq, we identified transcription factors that drive cells towards a more sensitive state. C_LIO_LIThe transcription factor NFE2L3 was identified as targets capable of reprogramming cells to a sensitive state. C_LI

genomics↗

bioRxiv2024

Acetic acid induces osmotic imbalances in drug-resistant bacteria synergistically enhancing cobalt-doped carbon quantum dots bactericidal efficiency

When pathogenic bacteria colonise a wound, they can create an alkaline ecological niche which selects for their survival by creating an inflammatory environment which restricts healthy wound healing to proceed. To aid healing, wound acidification has been exploited to disrupt this process and stimulate fibroblast growth, increase wound oxygen concentrations, minimise proteolytic activity and re-stimulate the host immune system. Within this study, we have developed unique cobalt doped carbon quantum dot nanoparticles which work together with mild acetic acid creating a potent synergistic antimicrobial therapy. The acidic environment alters the osmotic balance of microorganisms forcing them to swell and speed up the internalisation of the ultra-small particles. The particles hyperpolarise the bacterial membranes and generate damaging peroxidase species resulting in cellular lysis. In mice, cobalt doped carbon quantum dots remove MRSA infection while allowing wounds to heal at equivalent rates to uninfected wounds. This work demonstrates how synergistic antimicrobial treatment strategies can be successfully used to combat antimicrobial resistant infections.

microbiology↗

bioRxiv2024

A long context RNA foundation model for predicting transcriptome architecture

Linking DNA sequence to genomic function remains one of the grand challenges in genetics and genomics. Here, we present a large-scale compendium of single-molecule transcriptome sequencing of diverse cancer cell lines, revealing their isoform diversity and specificity. We used this compendium to build Mach-1, an RNA foundation model that learns how the nucleotide sequence of unspliced pre-mRNA dictates transcriptome architecture--the relative abundances and molecular structures of mRNA isoforms. By using the Striped-Hyena architecture, Mach-1 handles extremely long sequence inputs at nucleotide resolution (64 kilobase pairs), allowing for quantitative, zero-shot prediction of all aspects of transcriptome architecture, spanning isoform abundance, structure, and variant-induced splicing changes. To test both the interpretive and generative capabilities of Mach-1, we experimentally validated its learned regulatory grammar and predictions through perturbation of RNA-binding proteins nominated by Mach-1 to impact targeted splicing, precise CRISPR editing of variants of uncertain significance that the model predicted to alter splicing, and de novo transcript synthesis and expression in human cells. Together, this release establishes a new foundation for sequence-to-transcript modeling. Mach-1s representations can be extended and fine-tuned across a spectrum of biological contexts, from variant interpretation to RNA engineering.

bioinformatics↗

bioRxiv2024

Accurate isoform quantification by joint short- and long-read RNA-sequencing

Accurate quantification of transcript isoforms is crucial for understanding gene regulation, functional diversity, and cellular behavior. Existing RNA sequencing methods have significant limitations: short-read (SR) sequencing provides high depth but struggles with isoform deconvolution, whereas long-read (LR) sequencing offers isoform resolution at the cost of lower depth, higher noise, and technical biases. Addressing this gap, we introduce Multi-Platform Aggregation and Quantification of Transcripts (MPAQT), a generative model that combines the complementary strengths of different sequencing platforms to achieve state-of-the-art isoform-resolved transcript quantification, as demonstrated by extensive simulations and experimental benchmarks. By applying MPAQT to an in vitro model of human embryonic stem cell differentiation into cortical neurons, followed by machine learning-based modeling of transcript abundances, we show that untranslated regions (UTRs) are major determinants of isoform proportion and exon usage; this effect is mediated through isoform-specific sequence features embedded in UTRs, which likely interact with RNA-binding proteins that modulate mRNA stability. These findings highlight MPAQTs potential to enhance our understanding of transcriptomic complexity and underline the role of splicing-independent post-transcriptional mechanisms in shaping the isoform and exon usage landscape of the cell.

bioinformatics↗

bioRxiv2024

CDC42 Inhibitors Alter Patterns of Vessel Arborization in Skin and Tumors in vivo

Tumors that arise in the epidermis must develop a vascular supply to grow beyond a millimeter in depth. This process requires CDC42 GTPases such as CDC42, RhoJ and RhoQ. Despite this dependence on angiogenesis for growth, melanoma tumors are minimally responsive to current anti-angiogenesis agents, highlighting the need for more effective drugs in this class. Here we integrate antibody infusion, optical tissue clearing, multiphoton imaging, and three-dimensional semi-automated tracing to develop a quantitative approach to measure changes in vascular architecture in skin and skin tumors. This new approach uncovered differences in vessel arborization in the skin of RhoJ KO mice as compared to wild-type mice. Furthermore, novel small molecules that inhibit CDC42 GTPases inhibited both tumor growth and vessel branching within tumors to a similar degree as Braf inhibitors, which are commonly used to treat melanoma. In contrast to Braf inhibitors, however, which only affected tumor vasculature, CDC42 inhibitors affected vascularization in both tumor and normal skin without apparent toxicity to endothelial or stromal cells. These novel CDC42 inhibitors similarly blocked vessel branching in human cell-based micro-physiological models of normal and tumor vessels. RNA sequencing revealed reduced expression of multiple angiogenesis-related genes in drug-treated skin. Taken together, these studies identify a new class of pharmacologic agents that inhibit vessel branching in both normal skin and tumors with potential utility for treating skin cancer and skin diseases characterized by pathologic angiogenesis.

cancer biology↗

bioRxiv2023

A large-scale bioinformatic study of graspimiditides and structural characterization of albusimiditide

Graspetides are a class of ribosomally synthesized and post-translationally modified peptides (RiPPs) that exhibits an impressive diversity in patterns of side chain-to-side chain {omega}-ester or {omega}-amide linkages. Recent studies have uncovered a significant portion of graspetides to contain an additional post-translational modification involving aspartimidylation catalyzed by an O-methyltransferase, predominantly found in the genomes of Actinomycetota. Here, we present a comprehensive bioinformatic analysis focused on graspetides harboring aspartimide for which we propose the name graspimiditides. From Protein BLAST results of 5,000 methyltransferase sequences, we identified 962 unique putative graspimiditides, which we further classified into eight main clusters based on sequence similarity along with several smaller clusters and singletons. The previously studied graspimiditides, fuscimiditide and amycolimiditide, are identified in this analysis; fuscimiditide is a singleton while amycolimiditide is in the fifth largest cluster. Cluster 1, by far the largest cluster, contains 641 members, encoded almost exclusively in the Streptomyces genus. To characterize an example of a graspimiditide in Cluster 1, we conducted experimental studies on the peptide from Streptomyces albus J1074, which we named albusimiditide. By tandem mass spectrometry, hydrazinolysis, and amino acid substitution experiments, we elucidated the structure of albusimiditide to be a large tetracyclic peptide with four {omega}-ester linkages generating a stem-loop structure with one aspartimide. The ester crosslinks form 22-, 46-, 22-, and 44-atom macrocycles, last of which, the loop, contains the enzymatically installed aspartimide. Further in vitro experiments revealed that the aspartimide hydrolyzes in a 3:1 ratio of isoaspartate to aspartate residues. Overall, this study offers a comprehensive insight into the diversity and structural features of graspimiditides, paving the way for future investigations of this unique class of natural product. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/545981v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@bd68f2org.highwire.dtl.DTLVardef@1350705org.highwire.dtl.DTLVardef@5ff593org.highwire.dtl.DTLVardef@1f3c4a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2023

Systematic Identification of Post-Transcriptional Regulatory Modules

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we performed a systematic annotation of RBP combinatorial interactions via multimodal data integration. We built a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we used CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generated an integrated map of functional RBP interactions. We then used this map to match RBPs to their context-specific functions and validated the predicted functions biochemically for four RBPs. This study highlights the previously underappreciated scale of the inter-RBP interactions, be it genetic or physical, and is a first step towards a more comprehensive understanding of post-transcriptional regulatory processes and their underlying molecular grammar.

systems biology↗

bioRxiv2023

Predicting the efficacy of BH3 mimetics through the profiling of multiple protein complexes

BH3 mimetics are protein-protein interaction (PPI) inhibitors that saturate anti-apoptotic proteins in the BCL2 family to induce apoptosis in cancer cells, a prominent example of protein complex-targeting therapies. Despite the remarkable success of the BH3 mimetic ABT-199 in treating hematological malignancies, only a fraction of patients respond to ABT-199 and eventually develop resistance, necessitating the predictive biomarkers for both initial responses and resistance development. We here used the single-molecule pull-down and co-immunoprecipitation platform to quantify more than 20 different types of PPI complexes using [~]1.2x106 cells in total, revealing the rewired status of the BCL2 family PPI network. By comparing the obtained multi-dimensional data with BH3 mimetic efficacies determined ex vivo, we constructed an analysis model for ABT-199 efficacy that designates the BCL2-BAX and BCLxL-BAK complexes as the primary mediators of drug effectiveness and resistance. We then applied this model to assist in therapeutic decision-making for acute myeloid leukemia patients in a prospective manner. Our work demonstrates a capability for the extensive characterization of PPI complexes in clinical specimens, potentially opening a new avenue of precision medicine for protein complex-targeting therapies.

molecular biology↗

bioRxiv2022

Inferring delays in partially observed gene regulatory networks

MotivationCell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality. ResultsWe develop a simulation-based Bayesian MCMC method for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: An activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. With prior information about observed fluorescent protein synthesis, our method successfully infers the dynamics of the unobserved regulatory protein. We can estimate the delay and kinetic parameters characterizing target regulation including transcription, translation, and target searching of an unobserved protein from experimental measurements of the products of its target gene. Our method is scalable and can be used to analyze non-Markovian models with hidden components. AvailabilityAccompanying code in R is available at https://github.com/Mathbiomed/SimMCMC. Contactjaekkim@kaist.ac.kr or kresimir.josic@gmail.com or cbskust@korea.ac.kr Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Semi-automated protocol to quantify and characterize fluorescent, 3D vascular images

The microvasculature facilitates gas exchange, provides nutrients to cells, and regulates blood flow in response to stimuli. Vascular abnormalities are an indicator of pathology for various conditions, such as compromised vessel integrity in small vessel disease and angiogenesis in tumors. Traditional immunohistochemistry enables visualization of tissue cross-sections containing exogenously labeled vasculature. Although this approach can be utilized to quantify vascular changes within small fields-of-view, it is not a practical way to study the vasculature on the scale of whole organs. Three-dimensional (3D) imaging presents a more appropriate method to visualize the vascular architecture in tissue. Here we describe the complete protocol that we use to characterize the vasculature of different organs in mice encompassing the methods to fluorescently label vessels, optically clear tissue, collect 3D vascular images, and quantify these vascular images with a semi-automated approach. To validate the automated segmentation of vascular images, one user manually segmented fifty random regions of interest across different vascular images. The automated segmentation results had an average sensitivity of 80{+/-}8% and an average specificity of 90{+/-}5% when compared to manual segmentation. Applying this procedure of image analysis presents a method to reliably quantify and characterize vascular networks in a timely fashion. This procedure is also applicable to other methods of tissue clearing and vascular labels that generate 3D images of microvasculature.

bioengineering↗