Biology subjects

Huang, Y.

Publications and source records attributed to Huang, Y..

At least 415 records · Page 23Linked to original sources

bioRxiv2024

Leveraging large language models for metabolic engineering design

Establishing efficient cell factories involves a continuous process of trial and error due to the intricate nature of metabolism. This complexity makes predicting effective engineering targets a challenging task. Therefore, it is vital to learn from the accumulated successes of previous designs for advancing future cell factory development. In this study, we developed a method based on large language models (LLMs) to extract metabolic engineering strategies from research articles on a large scale. We created a database containing over 29006 metabolic engineering entries, 1210 products and 751 organisms. Using this extracted data, we trained a hybrid model combining deep learning and mechanistic approaches to predict engineering targets. Our model outperformed traditional metabolic engineering target prediction algorithms, excelled in predicting the effects of gene modifications, and generalized well to out-of-distribution products and multiple gene combinations. Our study provides a valuable dataset, a chatbot, and an engineering target prediction model for the metabolic engineering field and exemplifies an efficient method for leveraging existing knowledge for future predictions.

systems biology↗

bioRxiv2024

Targeted syndromic next-generation sequencing panel for simultaneous detection of pathogens associated with bovine reproductive failure

Bovine reproductive failure, which includes infertility, abortion, and stillbirth in cattle, leads to significant economic losses for beef and milk producers. Diagnosing the infectious causes of bovine reproductive failure is challenging as there are multiple pathogens associated with it. The traditional stepwise approach to diagnostic testing is time-consuming and can cause significant delays. In this study, we have developed a syndromic next-generation sequencing panel (BovReproSeq), for the simultaneous detection of 17 pathogens (bacteria, virus and protozoa) associated with bovine reproductive failure. This targeted approach involves amplifying multiple pathogen-specific targets using ultra-multiplex PCR, followed by sequencing with the Oxford Nanopore platform and subsequent analysis of the data using a custom bioinformatic pipeline to determine the presence or absence of pathogens. We tested 116 clinical samples and found that BovReproSeq results matched with current diagnostic methods for 93% of the samples, and most of the disagreements occurring in samples with very low pathogen loads (Ct > 35). At the optimal read-count threshold of 10 reads (minimum number of reads to classify the sample as positive), the clinical sensitivity of the assay was approximately 82%, while clinical specificity was 100%. The overall accuracy of the assay was 98.8%. Matthews Correlation Coefficient (correlation coefficient of binary classification) was approximately 0.90 and F1 score (harmonic mean of Precision and Recall) was 0.90, indicating excellent overall performance. Our study presents a significant advancement in detecting the infectious agents associated with bovine reproductive failure and the BovReproSeq panels ability to detect 17 pathogens makes it a promising tool for veterinary diagnostics. ImportanceBovine reproductive failure causes substantial economic losses to beef and milk producers, and infectious disease contributes significantly to this syndrome. Etiologic diagnosis is complicated since multiple pathogens can be involved and infections with some pathogens are asymptomatic or cause similar clinical signs. A stepwise approach to diagnostic testing is time-consuming and increases the risk of missing the correct diagnosis. BovReproSeq is a next-generation sequencing based diagnostic panel that allows detection of 17 reproductive failure pathogens simultaneously.

microbiology↗

bioRxiv2024

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

immunology↗

bioRxiv2024

Deciphering the Cell Specific Transcript Heterogeneity and Alternative Splicing during the Early Embryonic Development of Zebrafish

Cell fate determination during early embryonic development is a complex process modulated by gene expression. The intricate interplay of transcriptional and post-transcriptional regulation is integral to the developmental trajectory of embryogenesis, yet how RNA processing may contribute to early development programming is largely elusive. Leveraging recent technological advances in single-molecule nanopore sequencing, we developed a single-cell long-read transcriptome sequencing technology, allowing a clear view of transcript diversity during zebrafish embryogenesis during pre- and post-zygotic genome activation (ZGA). A closer examination of the dynamic transcript usage and potential alternative splicing revealed that abundant stage-specific transcripts with differential coding potentials are involved in distinct biological functions. Specifically, we identified two cell populations at the onset of ZGA based on isoform diversity instead of gene profiling, which followed divergent developmental trajectories toward the ectoderm and the presumptive ectoderm. These two populations of cells were characterized by divergent splicing regulations linked to differential RNA-binding proteins, including SNRPA and SFPQ. Altogether, using the single-cell long-read transcriptome sequencing strategy, we work has revealed the cell-specific transcriptome dynamics contributing to the cell fate determination during embryogenesis.

developmental biology↗

bioRxiv2024

Generating virtual patient data for in-silico clinical trials of medical devices during extracorporeal membrane oxygenationtreatment

This study introduces a virtual patient generation model as online tool through the generation of high-quality synthetic data, addressing challenges like privacy cocerns and limited dataset sizes. Using a Conditional Tabular Generative Adversarial Network (CTGAN), we generated synthetic data from the Electronic Health Records (EHR) of 767 veno-venous extracorporeal membrane oxygenation (ECMO) patients, focusing on 55 critical therapy parameters. Rigorous preprocessing, imputation, and model tuning ensured that the synthetic data closely mirrored real patient records, achieving a 86.6% coverage score and minimal deviations in data correlations. The tool is integrated into a web platform - https://cve-sim.de/ecmo-vpg, allowing researchers to generate and visualize virtual patient cohorts, potentially enhancing ECMO research and reducing the need for extensive clinical trials.

bioinformatics↗

bioRxiv2024

Inhibition on neutrophil extracellular traps by oligomeric procyanidins alleviate chemotherapy-induced chronic kidney injury via gut-kidney axis

Cisplatin is one of the most widely used chemotherapeutic agents for various solid tumors in the clinic, but its use is limited by adverse effects in normal tissues. In particular, cisplatin administration often damages the kidneys. However, little is known about how to alleviate cisplatin-induced chronic kidney disease (CKD) specifically. Here, we found that repeated low-dose cisplatin (RLDC) recruited susceptible neutrophils to the proximal tubule, thereby promoting the progression of CKD in the mouse model. Mechanically, cisplatin destroyed the intestinal epithelium, which induced dysregulation of gut flora and intestinal leakage. Under the dual stimulation of intestinal bacteria and cisplatin, neutrophils become extremely susceptible to form Neutrophil extracellular traps (NETs), accumulating in the proximal tubule and promoted chronic inflammation, fibrosis, and chronic hypoxia, leading to poor regeneration that promoted CKD progression. NETs provided a scaffold for tissue factors (TF) adhesion and metalloid-matrix protease 9 (MMP-9) activation, which triggered local ischemia and hypoxia. In addition, NETs promoted inflammasome construction through NOD-like receptor thermal protein domain associated protein 3 (NLRP3) shear and secretion of mature interleukin-18 (IL18), which subsequently released interferon- {gamma} (IFN-{gamma}), contributing to renal interstitial fibrosis. We proposed that oligomeric procyanidins (OPCs) ameliorated RLDC-induced CKD through multi-targeting damage induced by NETs. OPCs ameliorated microcirculatory disorders and inhibited inflammation by protecting the intestinal mucosa barrier and subsequent bacterial endotoxin translocation. Furthermore, we found that OPCs altered the susceptibility of neutrophils to form NETs. In summary, OPC alleviates CKD by inhibiting NETs production via TF/MMP-9 and IL-18-NLRP3 pathways. OPCs protect the kidney through anti-inflammatory and antioxidant activities, and restore the balance of the intestinal flora.

immunology↗

bioRxiv2024

Deep contrastive learning enables genome-wide virtual screening

Numerous protein-coding genes are associated with human diseases, yet approximately 90% of them lack targeted therapeutic intervention. While conventional computational methods, such as molecular docking, have facilitated the discovery of potential hit compounds, the development of genome-wide virtual screening against the expansive chemical space remains a formidable challenge. Here we introduce DrugCLIP, a novel framework that combines contrastive learning and dense retrieval to achieve rapid and accurate virtual screening. Compared to traditional docking methods, DrugCLIP improves the speed of virtual screening by up to seven orders of magnitude. In terms of performance, DrugCLIP not only surpasses docking and other deep learning-based methods across two standard benchmark datasets, but also demonstrates high efficacy in wet-lab experiments. Specifically, DrugCLIP successfully identified agonists with < 100 nM affinities for 5HT2AR, a key target in psychiatric diseases. For another target NET, whose structure is newly solved and not included in the training set, our method achieved a hit rate of 15%, with 12 diverse molecules exhibiting affinities better than bupropion. Additionally, two chemically novel inhibitors were validated by structure determination with Cryo-EM. Finally, a novel potential drug target TRIP12, with no experimental structures and inhibitors for reference, was used to challenge DrugCLIP. DrugCLIP achieved a hit rate of 17.5% by screening a pocket identified on an AlphaFold2-predicted structure, verified with multi-cycle SPR assays. Molecules with the highest affinities also showed a dose-dependent inhibition to the enzymatic function of TRIP12. Building on this foundation, we present the results of a pioneering trillion-scale genome-wide virtual screening, encompassing approximately 10,000 AlphaFold2 predicted proteins within the human genome and 500 million molecules from the ZINC and Enamine REAL database. This work provides an innovative perspective on drug discovery in the post-AlphaFold era, where comprehensive targeting of all disease-related proteins is within reach.

bioinformatics↗

bioRxiv2024

Leveraging Multi-Modal Feature Learning for Predictions of Antibody Viscosity

The shift towards the subcutaneous administration route for biologics therapeutics has gained momentum due to its patient-friendly nature, convenience, reduced healthcare burden, and improved compliance compared to traditional intravenous infusions. However, one of the potentially significant challenges with this transition is managing the viscosity of the administered solutions. High viscosity can pose substantial development and manufacturability challenges, directly impacting the patient by increasing injection time and pain at the injection site. Moreover, high viscosity formulations can prolong residence time at the injection site, affecting absorption kinetics and potentially altering the intended pharmacological profile and therapeutic efficacy of the biologic candidate. This publication explores the application of a multimodal feature learning workflow for predicting the viscosity of antibodies in therapeutics discovery, integrating multiple data sources such as sequence, structural, physicochemical properties, and embeddings from a language model. This approach enables the model to learn from various underlying rules, including physicochemical rules from molecular simulations and molecular protein evolutionary rules by large, pre-trained deep learning foundation models. By comparing the effectiveness of this approach against other selected published viscosity prediction methods, this study offers insights on their intrinsic viscosity predictive potential and usability in therapeutics antibody early development pipelines.

bioinformatics↗

bioRxiv2024

A two-dose regimen of Qβ virus-like particle-based vaccines elicit protective antibodies against heroin and fentanyl

Opioid overdoses and the growing rate of opioid use disorder (OUD) are major public health concerns, particularly in the United States. Current treatment approaches for OUD have failed to slow the growth of the opioid crisis. Opioid vaccines have shown pre-clinical success in targeting multiple different opioid drugs. However, the need for many immunizations can limit their clinical implementation. In this study, we investigate the development of novel opioid vaccines by independently targeting fentanyl and the active metabolites of heroin using a bacteriophage virus-like particle (VLP) vaccine platform. We establish the successful conjugation of haptens to bacteriophage Q{beta} VLPs and demonstrate immunogenicity of Q{beta}-fentanyl, Q{beta}-morphine, and Q{beta}-6-acetylmorphine in animal models after one or two immunizations. We show that in independently or in combination, these vaccines elicit high-titer, high-avidity, and durable antibody responses. Moreover, we reveal their protective capacities against heroin or fentanyl challenge after two immunizations. Overall, these findings establish Q{beta}-VLP conjugated vaccines for heroin and fentanyl as very promising opioid vaccine candidates.

immunology↗

bioRxiv2024

Multiplexed Glycan Immunofluorescence Identification of Pancreatic Cancer Cell Subpopulations in Both Tumor and Blood Samples

Pancreatic ductal adenocarcinoma (PDAC) tumor heterogeneity impedes the development of biomarker assays suitable for early disease detection that would improve patient outcomes. The CA19-9 glycan is currently used as a standalone biomarker for PDAC. Furthermore, previous studies have shown that cancer cells may display aberrant membrane-associated glycans. We therefore hypothesized that PDAC cancer cell subpopulations could be distinguished by aberrant glycan signatures. We used multiplexed glycan immunofluorescence combined with pathologist annotation and automated image processing to distinguish between PDAC cancer cell subpopulations within tumor tissue. Using a training-set/test-set approach, we found that PDAC cancer cells may be identified by signatures comprising 4 aberrant glycans (VVL, CA19-9, sTRA, and GM2) and that there are three glycan-defined PDAC tumor types: sTRA type, CA19-9 type, and intermixed. To determine whether the aberrant glycan signatures could be detected in blood samples, we developed hybrid glycan sandwich assays for membrane-associated glycans. In both patient-matched tumor and blood samples, the proportion of aberrant glycans detected was consistent. Furthermore, our multiplexed glycan immunofluorescent approach proved to be more sensitive and more specific than CA19-9 alone. Our results provide proof of concept for a novel methodology to improve early PDAC detection and patient outcomes.

cancer biology↗

bioRxiv2024

Inflammatory Signatures of Microglia in the Mouse Model of Corticosterone-Induced Depression

Microglia-mediated inflammation has been recognized as a key feature of major depressive disorder. Although hypercortisolemia commonly occurs in depressed patients and can be predictive of treatment response, how chronic exposure to this stress hormone influences microglia is incompletely characterized. Here, we exploited a standard mouse model of depressive-like behaviors induced by peripheral administration of corticosterone. Microglia in the prefrontal cortex of mice were profiled by bulk RNA sequencing, which exhibited the up-regulation of inflammatory markers. In addition, single-cell RNA sequencing identified distinct molecular patterns of microglial responses. Moreover, we revealed the elevation of Pu.1 and Irf8, the two central transcription factors governing microglia-mediated inflammation, in the prefrontal cortex and hippocampus of corticosterone-treated mice, which was similarly observed in the single-nucleus RNA sequencing dataset of depressed patients microglia. These results have established inflammatory signatures of microglia in the mouse model recapitulating hypercortisolemia-related depression, providing new insights into diagnostic and therapeutic strategies.

neuroscience↗

bioRxiv2024

Orally surface engineered probiotic system for ulcerative colitis therapy via modulating gut microbiota and immune homeostasis

Oral probiotics have emerged as a promising therapeutical strategy for effectively managing ulcerative colitis (UC) in the world. The existing researches successfully preserve probiotic viability in the upper gastrointestinal tract, but they fall short in achieving precise release profile and effective colonization of probiotics at the site of colitis in colon as well as the understanding of therapeutic mechanism with suboptimal efficacy. This constraint poses a significant barrier to rational design and effective development of oral probiotic system. Here, we fabricate an orally layered-by-layered probiotic (Escherichia coli Nissle 1917, EcN) system (so-called CML@EcN) based on carboxymethyl modified lignin (CML) which can significantly protect EcN in gastric and small intestinal microenvironment and efficiently control release EcN in colon. Interestingly, the release and proliferation process of EcN in the colon are detected and can be modeled as a plug-flow mode. We derive a mathematical expression that highly matches the experimental results, providing a theoretical basis for quantitatively calculation of the release process. Furthermore, CML@EcN significantly alleviates symptoms in dextran sulfate sodium (DSS)-induced UC mice by modulating the multiple organ immune disorder and gut microbiota (GM)/metabolites profile as well as reconstructing the colon barrier. Importantly, we explore the mechanism of immune homeostasis regulation via metabolism from GM. Moreover, the CML armour is excreted and would not influence the process. This work not only reports a novel oral therapeutic for UC treatment with high efficacy as well as the exploration of mechanism between GM and immune homeostasis, but also provides a general approach to engineer probiotics for oral administration.

microbiology↗

bioRxiv2024

Endothelial MICU1 protects against vascular inflammation and atherosclerosis by inhibiting mitochondrial calcium uptake

Atherosclerosis is triggered by endothelial activation and vascular inflammation, which is closely related to mitochondrial dysfunction. Mitochondrial calcium uptake 1 (MICU1), as the gatekeeper of mitochondrial Ca2+ homeostasis, is a critical player in mitochondrial function and implicated in a plethora of pathophysiological conditions. However, the role of MICU1 in the pathogenesis of vascular inflammation and atherosclerosis is unknown. We ask whether endothelial MICU1 can prevent vascular inflammation and atherosclerosis by maintaining mitochondrial homeostasis. We observed that vascular inflammation in response to LPS was aggravated in EC-specific Micu1 knockout mice (Micu1ECKO) and reduced in EC-specific Micu1 transgenic mice (Micu1ECTg). Furthermore, hypercholesterolemic Micu1ECKO mice also showed accelerated formation of atherosclerotic plaques, while Micu1ECTg mice protected against atherosclerosis. Mechanistically, MICU1 regulated mitochondrial Ca2+ influx, thereby reducing the expression of the mitochondrial deacetylase SIRT3 and the ensuing deacetylation of SOD2, leading to the burst of mitochondrial reactive oxygen species (mROS). Of clinical relevance, we identified decreased MICU1 level in endothelial layer of human atherosclerotic plaques, as well as in primary human aortic endothelial cells (HAECs) exposed to serum from patients with coronary artery diseases. Two-sample Wald ratio Mendelian randomization further revealed that increased expression of MICU1 was associated with decreased risk of coronary artery bypass grafting (CABG). Our findings unravel a critical and unrecognized role of MICU1 in preventing vascular inflammation and atherosclerosis by maintaining mitochondrial Ca2+ homeostasis.

molecular biology↗

bioRxiv2024

Sapphire-Based Optrode for Low Noise Neural Recording and Optogenetic Manipulation

Electrophysiological recordings of neurons in deep brain regions using optogenetic stimulation are essential to understanding and regulating the role of complex neural activity in biological behavior and cognitive function. Optogenetic techniques have significantly advanced neuroscience research by enabling the optical manipulation of neural activities. Because of the significance of the technique, constant advancements in implantable optrodes that integrate optical stimulation with low-noise, large-scale electrophysiological recording are in demand to improve the spatiotemporal resolution for various experimental designs and future clinical applications. However, robust and easy-to-use neural optrodes that integrate neural recording arrays with high-intensity light emitting diodes (LEDs) are still lacking. Here, we propose a neural optrode based on Gallium Nitride (GaN) on sapphire technology, which integrates a high-intensity blue LED with a 5x2 recording array monolithically for simultaneous neural recording and optogenetic manipulation. To reduce the noise interference between the recording electrodes and the LED, which is in close physical proximity, three metal grounding interlayers were incorporated within the optrode, and their ability to reduce LED-induced artifacts during neural recording was confirmed through both electromagnetic simulations and experimental demonstrations. The capability of the sapphire optrode to record action potentials has been demonstrated by recording the firing of mitral/tuft cells in the olfactory bulbs of mice in vivo. Additionally, the elevation of action potential firing due to optogenetic stimulation observed using the sapphire probe in medial superior olive (MSO) neurons of the gerbil auditory brainstem confirms the capability of this sapphire optrode to precisely access neural activities in deep brain regions under complex experimental designs.

neuroscience↗

bioRxiv2024

Selection shapes plant performance in a grassland biodiversity experiment

O_LIThe increasing strength of positive biodiversity effects on plant community productivity in long-term biodiversity experiments has been shown to be related to mixed responses at species level. However, it is still not well understood if the varying environments in plant communities with different diversity also exert different selection pressures to which plant species respond adaptively or whether their responses are mainly due to phenotypic plasticity. C_LIO_LIWe conducted a transplant experiment for nine plant species in a 17-year-old grassland biodiversity experiment (Jena Experiment). We used offspring of plants selected in communities with different diversity or from plants which did not experience selection in the experiment (naive). In a Community History Experiment, offspring of selected plants were planted in three test environments: their original plant communities with original soil, newly assembled plant communities with original soil, and newly assembled plant communities with new soil. Additionally, in a Selection Experiment we compared the performance of selected plants grown in their original environment to the performance of naive plants grown in the same environment. C_LIO_LIIn all test environments, increasing species richness was on average associated with a decrease in plant individual biomass, reproductive output, relative growth rate, plant height, leaf greenness, and leaf nitrogen (N) concentration and an increase in SLA. C_LIO_LIIn the Community History Experiment survival was lower, while plant height, SLA, leaf N and C concentrations were highest in the test environment without plant and soil history. In high-diversity communities, individuals produced more biomass, grew taller and had higher leaf greenness in the original environment than in the other test environments. C_LIO_LIIn the Selection Experiment selected plants had weaker decline in their biomass, taller stature, and higher leaf C and N concentrations than C_LIO_LInaive plants with increasing species richness. C_LIO_LIIn both experiments, we found evidences of adaptive phenotypic responses. Specifically, in high-diversity communities, selected plants showed higher performance in their original environment, largely explained by plant community history and positive plant-soil feedbacks established over time. C_LIO_LISynthesis: Our study highlights the important role of eco-evolutionary feedbacks of species diversity, which affect plant performance over time. C_LI

ecology↗

bioRxiv2024

Ubiquitination mediates protein localization in RNA virus-infected cells

Viruses trigger monocytes proinflammatory and antiviral responses. Ubiquitination, a post-translational modification primarily marking proteins for degradation, regulates cellular responses to virus infection. However, a comprehensive analysis of virus-induced ubiquitination in monocytes is lacking. Here we identified a widespread increase of ubiquitination under viral RNA challenge or influenza infection in monocytes. Systematic proteome studies revealed that influenza infection elicits dynamic ubiquitinome alterations, with a notable transition from early to late stage. Most of this increased ubiquitination is not proteolytic and targets proteins involved in subcellular localization, such as the mitochondrial protein COA7 which, when ubiquitinated during infection, translocates to the nucleus and inhibits stress granules formation and TNF- expression. Blocking ubiquitination halts viral ribonucleoproteins nuclear export, highlighting ubiquitinations importance for protein localization during virus infection.

molecular biology↗

bioRxiv2024

Predicting mutation-disease associations through protein interactions via deep learning

Disease is one of the primary factors affecting life activities, with complex etiologies often influenced by gene expression and mutation. Currently, wet-lab experiments have analyzed the mechanisms of mutations, but these are usually limited by the costs of wet experiments and constraints in sample types and scales. Therefore, this paper constructs a real-world mutation-induced disease dataset and proposes Capsule networks and Graph topology networks with multi-head attention (CGM) to predict the mutation-disease associations. CGM can accurately predict protein mutation-disease associations, and in order to further elucidate the pathogenicity of protein mutations, we also verified that protein mutations lead to protein structural alterations by Swiss-model, which suggests that mutation-induced conformational changes may be an important pathogenic factor. Limited by the size of the mutated protein dataset, we also performed experiments on benchmark and imbalanced datasets, where CGM mined 22 unknown protein interaction pairs from the benchmark dataset, better illustrating the potential of CGM in predicting mutation-disease associations. In summary, this paper curates a real dataset and proposes CGM to predict the protein mutations-disease associations, providing a novel tool for further understanding of biomolecular pathways and disease mechanisms.

bioinformatics↗

bioRxiv2024

Optics-free reconstruction of 2D images via DNA barcode proximity graphs

Spatial genomics technologies include imaging- and sequencing-based methods. Sequencing-based spatial methods typically require surfaces coated with coordinate-associated DNA barcodes, but the physical registration of these barcodes to spatial coordinates is challenging, necessitating either high density printing of oligonucleotides or in situ sequencing/probing of randomly deposited, DNA-barcode-bearing beads. As a consequence, the surface areas available to sequencing-based spatial genomic methods are constrained by the time, labor, cost and instrumentation required to either print or decode a coordinate-tagged surface. To address this challenge, we developed SCOPE (Spatial reConstruction via Oligonucleotide Proximity Encoding), an optics-free, DNA microscopy-inspired method. With SCOPE, the relative positions of DNA-barcoded beads within a 2D shape, 2D image or 3D volume are inferred from the ex situ sequencing of chimeric molecules formed from diffusing "sender" and tethered "receiver" oligonucleotides. To demonstrate the potential of this approach, we applied SCOPE to reconstruct 2D shapes, 2D images or 3D volumes defined by 104-106 x 20-100 {micro}m DNA barcoded beads, including an asymmetric "swoosh" resembling the Nike logo (44 mm2), a "color" Snellen eye chart (704 mm2) and the surface topology of 3D molds of a teddy bear, star, butterfly or block letter (75-100 mm3). Each of the resulting "DNA barcode proximity graphs" was computationally reconstructed in an automated fashion, across fields of view and at resolutions that were determined by sequencing depth, bead size and diffusion kinetics, rather than by microarray or microscope instrument time. Because the ground truth shapes are known, these datasets may be particularly useful for the further development of computational algorithms by this nascent field.

genomics↗