Biology subjects

Rosen, B.

Publications and source records attributed to Rosen, B..

18 recordsLinked to original sources

bioRxiv2026

A pilot computer-vision model trained to identify slide-mounted scale insect pests from the family Rhizoecidae

The emerging field of image classification using computer vision has yet to address a rather challenging group of insect pests--soft-bodied insects that are preserved on microscope slides. This includes many notoriously invasive herbivores that have a significant impact on agriculture, such as members of the hemipteran suborder Sternorrhyncha. Using root mealybugs (Hemiptera: Rhizoecidae) as a test case, we gathered high-resolution extended depth of field images from museum specimens and trained a convolutional neural network (CNN) to identify and distinguish among 16 root mealybug species that are encountered as agricultural pests in plant quarantine or greenhouses. A pretrained EfficientNetV2-M model was trained using BioEncoder. Our dataset contained 1,607 images, with 41 to 239 images per category. Performance was improved through the adoption of augmentation techniques. Upon completion of training, the model achieved macro precision, recall, and F1 scores of 80.1%, 79.3%, and 79.1%, respectively. The model demonstrated classification metrics >= 80% for seven species. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize the morphological features associated with the test-image classification. Grad-CAM images highlighted meaningful diagnostic features to distinguish among species rather than focusing on uninformative visual artifacts or specimen damage, demonstrating the models utility for identification purposes. The results suggest that further development of the model should incorporate at least 200 training images per species with a minimum resolution of 600x600 pixels. The model was designed to be adaptable; images can be added to improve its performance, and more species can be incorporated as specimens become available, expanding on the taxonomic breadth of its application. Computer-vision identification tools have the potential to enhance rapid response capabilities in plant quarantine at ports-of-entry in the United States and globally.

zoology↗

bioRxiv2026

Iterative co-creation of harmonized human and non-human primate cellular and structural ontologies and 3D common coordinate frameworks for the basal ganglia

A major goal of the BRAIN Initiative Cell Atlas Network (BICAN) is to create a suite of foundational reference cell atlases and associated standards for human and non-human primate brains. Central to this goal is the creation of cross-species harmonized cellular taxonomies and structural parcellations with formal ontologies that can be mapped into 3D reference frameworks bridging neuroimaging and cellular and histological resolutions. We describe here an iterative approach, focused initially on the basal ganglia, to co-create structural and cellular ontologies in human, macaque and marmoset brains, including a Harmonized Ontology of Mammalian Brain Anatomy (HOMBA), and to map and refine structural parcellations into neuroimaging-based common coordinate frameworks. These references provide the framework for documenting and mapping all experimental sampling in BICAN, allowing analyses of cellular and molecular variation as a function of topographic position, and enabling comparisons of cellular, molecular and neuroimaging-based functional variation within and between primate species. HighlightsO_LIA hierarchical Harmonized Ontology of Mammalian Brain Anatomy (HOMBA) covering 2348 structures C_LIO_LIHOMBA-annotated 3D common coordinate frameworks (CCFs) of the basal ganglia across species C_LIO_LIHistologically informed 3D parcellation/atlas of 280 human subcortical structures indexed by HOMBA C_LIO_LIMapping and integration of structural, cellular and functional data with HOMBA and CCFs C_LI

neuroscience↗

bioRxiv2026

Mapping subarachnoid cerebrospinal fluid circulation in the human brain

The subarachnoid space is critical to cerebrospinal fluid (CSF) circulation and waste clearance, yet its flow organization in humans remains poorly characterized due to the lack of methods for direct, quantitative measurement of ultra-slow flow. Here we introduce slow-flow-sensitized phase-contrast imaging (SOPHI), which enables noninvasive quantification of ultra-slow flow velocities (e.g., [~]100 m/s) and directionality at high spatiotemporal resolution, allowing whole-brain mapping of complex CSF circulation in the human subarachnoid space. We found brain-wide, spatiotemporally coherent flow patterns characterized by strong cardiac-driven oscillations. Flow dynamics were closely coupled with vascular anatomy, exhibiting higher velocities and earlier responses in subarachnoid spaces near major arteries and a spatiotemporal propagative pattern to distal spaces. We further identified localized flow pathways associated with potential CSF efflux in ventricles and key subarachnoid regions. Together, SOPHI reveals a previously undercharacterized, highly organized CSF flow system, providing a macroscopic view of human CSF circulation and a framework for investigating brain fluid transport.

bioengineering↗

bioRxiv2026

Cortical vasodynamics exhibit multiscale propagation structure in the awake mouse

Spontaneous vasomotor rhythms and fluctuations are recognized contributors to functional MRI (fMRI) signals. Yet the spatial evolution of vasomotor contributions, which bear on the interpretation of fMRI signals in terms of functionally connected brain regions, remains unresolved. To clarify this issue, we analyze the 0.01 - 0.20 Hz space-frequency modes of 14T cerebral blood volume (CBV)-weighted fMRI across the awake mouse cortex. High-resolution CBV-fMRI identified single-vessel-aligned, frequency-specific modes of vasodynamic propagation, and ultra-fast CBV-fMRI enabled mapping of phase-gradients along tangential and radial axes. Tangential gradients extended maps of vasomotor traveling waves across the entire cortical mantle, with a median speed of 0.9 - 1.3 mm/s. Radial gradients revealed approximately three-fold slower changes in timing that support lamina-dependent regulation of perfusion. Region-specific differences in propagation speed and direction occur across cortical areas and between mice. These findings reframe vascular fluctuations as a feature of brain-wide physiology, as opposed to residual noise.

neuroscience↗

bioRxiv2026

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance

Precision oncology lacks scalable methods to identify the mechanisms that mediate therapeutic resistance for individual patients. Resistance often arises from focal cellular niches that are obscured by bulk profiling and costly to resolve with multi-omics. Here, we show that deep learning (DL), applied to routine histology, can localize focal tissue regions enriched for therapeutically relevant biology. Using 3111 breast cancer H&E slides with matched bulk transcriptomics, we trained weakly-supervised DL models to infer activities of immune, metabolic, and tumor-intrinsic phenotypes implicated in therapeutic resistance (AUROC>0.80; PCC>0.64). Accurate inference of these phenotypes should identify tissue regions enriched for the corresponding biological signal. Therefore, we validated phenotype inference and spatial localization with complementary analyses. Tissue-matched multiplexed immunofluorescence showed concordance between inferred immune states and corresponding cell fractions (p=0.006-0.106). Across multi-institutional cohorts, model-derived phenotypes recovered expected relationships with therapeutic outcomes (p<0.045). Finally, in a blinded evaluation, pathologists confirmed that model-derived high-attention regions were enriched for phenotype-specific morphology (p<2.408*10-5). Because evaluated phenotypes represent diverse mechanisms of resistance across therapeutic modalities, these findings provide a foundation for resistance-directed localization using therapeutic outcomes as supervision. By directing deep profiling toward model-prioritized regions, this framework could enable scalable nomination of candidate mediators of resistance for subsequent functional validation across real-world patient populations. One sentence summaryWeakly supervised deep learning localizes focal tissue regions enriched for therapeutically relevant biology in routine histology, thus offering a scalable strategy to study therapeutic resistance across large patient populations.

biophysics↗

bioRxiv2026

Direct Reconstruction of DC Cortical Conductivity from Large-Scale Electron Microscopy Data

Electrical conductivity of cortical gray matter governs the magnitude and spatial distribution of electric fields generated by brain stimulation and intrinsic neuronal activity measured with M/EEG and intracortical recordings. However, reported macroscopic conductivity values vary by more than threefold, limiting the fidelity of bioelectromagnetic models and leaving unresolved whether this variability reflects measurement uncertainty or genuine structural heterogeneity of cortical tissue. Here, we present a multiscale computational framework that, for the first time, attempts to derive mesoscale conductivity maps of mouse visual cortex at 50-{micro}m resolution directly from large-volume, segmented nanometer-scale electron microscopy data. The Minnie 65 subvolume of the MICrONS dataset is accurately subdivided into 1,224 50-{micro}m cubic blocks. Each block contains, on average, 40-50 million membrane facets of a highly convoluted and dense cellular structure. Three orthogonal electrode pairs are applied to each isolated block to estimate the three principal components of the conductivity tensor. Quasistatic electric modeling is enabled by an iterative boundary-element fast multipole method (BEM-FMM) under the approximation of non-conducting membranes (DC conductivity). Spatially averaged conductivity values predicted by our framework agree well with prior low-resolution measurements in rats, validating the approach. At the same time, the resulting mesoscale maps reveal pronounced conductivity granularity at 50-100 {micro}m scales as well as significant variations in both radial and tangential directions. These results indicate that mesoscale conductivity heterogeneity could be an intrinsic structural property of the cortex. Limitations and extensions of this study are discussed in detail.

neuroscience↗

bioRxiv2026

Are Synaptic Clefts Directionally Oriented?

Synapses are fundamental building blocks of cortical circuits, yet their geometry is typically regarded as a local property, independent of mesoscale architecture. The prevailing assumption is that synaptic clefts are isotropically oriented in space. Here, we test this assumption by analyzing approximately 117 million synaptic clefts from two independent 1 mm3 electron microscopy datasets: the human H01 middle temporal gyrus and the mouse MICrONS primary visual cortex, using three independent cleft-extraction methods. Across both volumes, we observe that synaptic cleft orientations are not randomly distributed, but instead show statistically significant and spatially coherent directional biases across cortical layers. This mesoscale anisotropy is conserved across species, yet is stronger and more consistent in human association cortex than in mouse sensory cortex, a difference that may reflect the expanded dendritic arbors and greater integrative demands of human pyramidal neurons. We propose that cleft orientation bias is a geometric consequence of the axonal and dendritic architecture that shapes synapse formation, representing a new candidate organizational feature of cortical microarchitecture with potential implications for circuit computation and neuromodulation. These findings motivate targeted physiological studies to determine whether synaptic orientation contributes causally to cortical function.

neuroscience↗

bioRxiv2025

A cross-species spatial transcriptomic atlas of the human and non-human primate basal ganglia

The basal ganglia are interconnected subcortical nuclei with complex topographical organization that orchestrate goal-directed behaviors and are implicated in neurodegenerative movement disorders. We generated a cellular-resolution, spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, sampling over one million cells in each species. By integrating spatial data with a cross-species, consensus snRNA-seq cell type taxonomy, this atlas reveals conserved principles of molecular organization within and across structures. The cellular architecture is complex but highly stereotyped, with gene expression gradients superimposed onto discrete compartments. Extensive spatial sampling illuminates 3D gradients of molecular organization in the striatum and reveals cell type-specific core and shell compartments in the primate internal globus pallidus, which is conserved with mouse. This unified, cross-species spatial transcriptomic atlas will be a foundational resource for characterizing the molecular and functional organization of the basal ganglia and their roles in health and disease.

neuroscience↗

bioRxiv2025

Inferring domestic goat demographic history through ancient genome imputation

Goats were among the earliest managed animals, making them a natural model to explore the genetic consequences of domestication. However, a challenge in ancient genomic analysis is the relatively low genome coverage for most samples, limiting analysis to pseudohaploid genotypes. Genotype imputation offers potential to alleviate this limitation by improving information content and accuracy in low coverage genomes. To test this we used published high coverage (>8x) goat palaeogenomes, imputing downsampled genomes using the VarGoats dataset (1,372 individuals) as a reference panel. Measuring concordance between imputed and high coverage genotypes, we find high concordance after filtering for common (>5%), high confidence variants, with 0.5x genomes reaching >0.97 concordance. There is a trade-off between coverage, genotype probability (GP) thresholds, and genotype recovery, where higher coverage and more lenient GP thresholds result in higher recovery, and a reduction in heterozygous false-positive rates with stricter thresholds. We then imputed 36 goat palaeogenomes with [≥]0.5x coverage to examine runs-of-homozygosity (ROH) and identity-by-descent (IBD) patterns. Using a novel approach combining ROH profiles across tools, we find that among Neolithic goats, ROH increases with distance from the Zagros Mountains, suggesting a large effect of the initial dispersal of managed herds. Inbreeding levels decrease across Southwest Asia in more recent periods. IBD mirrored this pattern, with less relatedness in the early herding site of Ganj Dareh compared to higher relatedness in goats from later in the dispersal process. These findings provide insights into the genetic consequences of early goat management on demography, and confirm the utility of imputation in leveraging low coverage palaeogenomes. SignificancePaleogenomics offers crucial insight into how animals were domesticated, but poor DNA preservation in ancient remains often limits the reach of genetic analyses. We utilise a cutting-edge technique, genotype imputation, to recover missing genetic information from ancient low coverage goat genomes and shine light on their domestication process. Early domestic goats showed low overall runs-of-homozygosity (ROH) and relatedness. We find that during the Neolithic, runs-of-homozygosity (ROH) and relatedness among goats increased, likely a consequence of the movement of herds beyond their natural range by humans. Inbreeding levels decline in more recent periods, potentially due to expanded herd sizes, animal trade networks, or improved husbandry practices. These findings challenge long-standing assumptions about domestication bottlenecks and highlight how ancient DNA can be used to uncover complex evolutionary histories, even from low coverage ancient samples.

genomics↗

bioRxiv2024

The Human Cerebellum: A Digital Anatomical Atlas at the Level of Individual Folia

Scientific interest in the cerebellum has surged in the last few decades with an emerging consensus on a multifaceted functionality and intricate, but not yet fully understood, functional topography over the cerebellar cortex. To further refine this structure-function relationship and quantify its inter-subject variability, a high-resolution digital anatomical atlas is fundamental. Using a combination of manual labeling and image processing, we turned a recently published reconstruction of the human cerebellum, the first such reconstruction fine enough to resolve the individual folia, into a digital atlas with both surface and volumetric representations. Its unprecedented granularity (0.16 mm) and detailed expert labeling make the atlas usable as an anatomical ground truth, enabling new ways of analyzing and visualizing cerebellar data through its digital format.

neuroscience↗

bioRxiv2024

Connecting the Dots: Approaching a Standardized Nomenclature for Molecular Connectivity Combining Data and Literature

PET-based connectivity computation is a molecular approach that complements fMRI-derived functional connectivity. However, the diversity of methodologies and terms employed in PET connectivity analysis has resulted in ambiguities and confounded interpretations, highlighting the need for a standardized nomenclature. Drawing parallels from other imaging modalities, we propose "molecular connectivity" as an umbrella term to characterize statistical dependencies between PET signals across brain regions at the individual level (within-subject). Like fMRI resting-state functional connectivity, "molecular connectivity" leverages temporal associations in the PET signal to derive brain network associations. Another within-subject approach evaluates regional similarities of tracer kinetics, which are unique in PET imaging, thus referred to as "kinetic connectivity". On the other hand, "molecular covariance" denotes group-level computations of covariance matrices across-subject. Further specification of the terminology can be achieved by including the employed radioligand, such as "metabolic connectivity/covariance" for [18F]FDG as well as "tau/amyloid covariance" for [18F]flutemetamol / [18F]flortaucipir. To augment these distinctions, high-temporal resolution functional [18F]FDG PET scans from 17 healthy participants were analysed with common techniques of molecular connectivity and covariance, allowing for a data-driven support of the above terminology. Our findings demonstrate that temporal band-pass filtering yields structured network organization, whereas other techniques like 3rd order polynomial fitting, spatio-temporal filtering and baseline normalization require further methodological refinement for high-temporal resolution data. Conversely, molecular covariance from across-subject data provided a simple means to estimate brain region interactions through regularized or sparse inverse covariance estimation. A standardized nomenclature in PET-based connectivity research can reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers and imaging modalities. Via a data-driven approach, this work provides a transparent framework for categorizing and comparing PET-derived connectivity and covariance metrics, laying the foundation for future investigations in the field.

neuroscience↗

bioRxiv2023

Decoding Heterogenous Single-cell Perturbation Responses

Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets. One sentence summaryWe present a method to quantify diverse perturbation responses and discover novel biological insights in single-cell perturbation datasets.

bioinformatics↗

bioRxiv2021

Ultrafast high-resolution multi-parametric brain MRI using 3D Echo Planar Time-resolved Imaging

Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this long-standing challenge, we introduce a novel approach, termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 folds in the k-t space. A subspace reconstruction was employed to resolve thousands of artifact-free high-quality multi-contrast images spaced at a time interval of ~1 ms. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T1, T2, T2*, PD and B1+ mapping at high isotropic resolution within minutes (e.g., 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures. HighlightsO_LIUltra-fast acquisition for 3D multi-parametric quantitative MRI. C_LIO_LISimultaneous T1 T2 T2* PD and B1+ mapping. C_LIO_LI3-minute scan at 1-mm isotropic resolution with whole-brain coverage. C_LIO_LIMulti-parametric mapping at 700-m isotropic resolution in 10 minutes. C_LIO_LIRepeatable quantification and cortical-depth analysis. C_LI

bioengineering↗

bioRxiv2021

DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics

Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data could provide a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wide clinical use. We designed and validated a deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data, including strain rate (SR) and regional strain polar maps, consisting of segmentation and motion estimation convolutional neural networks developed and trained using healthy and cardiovascular disease (CVD) subjects (n=150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an end-point-error of 2.9 {+/-} 1.5 mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 4 healthy subjects, intraclass correlation coefficient for intra-scanner repeatability was excellent (>0.95) for strain, moderate to excellent for SR (0.690-0.963), and good to excellent (0.826-0.994) in most polar map segments. Absolute relative change was within ~5% for strain, within ~10% for SR, and <1% in half of polar map segments. In conclusion, we developed and evaluated a DL-based, end-to-end fully-automatic workflow for global and regional myocardial strain analysis to quantitatively characterize cardiac mechanics of healthy and CVD subjects based on ubiquitously acquired cine-MRI data.

bioinformatics↗

bioRxiv2020

Ultra-high field (7T) functional magnetic resonance imaging in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease of the central nervous system that results in a progressive loss of motor function and ultimately death. It is critical, yet also challenging, to develop non-invasive biomarkers to identify, localize, measure and/or track biological mechanisms implicated in ALS. Such biomarkers may also provide clues to identify potential molecular targets for future therapeutic trials. Herein we report on a pilot study involving twelve participants with ALS and nine age-matched healthy controls who underwent high-resolution resting state functional magnetic resonance imaging at an ultra-high field of 7 Tesla. A group-level whole-brain analysis revealed a disruption in long-range functional connectivity between the superior sensorimotor cortex (in the precentral gyrus) and bilateral cerebellar lobule VI. Post hoc analyses using atlas-derived left and right cerebellar lobule VI revealed decreased functional connectivity in ALS participants that predominantly mapped to bilateral postcentral and precentral gyri. Cerebellar lobule VI is a transition zone between anterior motor networks and posterior non-motor networks in the cerebellum, and is associated with a wide range of key functions including complex motor and cognitive processing tasks. Our observation of the involvement of cerebellar lobule VI adds to the growing number of studies implicating the cerebellum in ALS. Future avenues of scientific investigation should consider how high-resolution imaging at 7T may be leveraged to visualize differences in functional connectivity disturbances in various genotypes and phenotypes of ALS along the ALS-frontotemporal dementia spectrum.

neuroscience↗

bioRxiv2020

Automatic Reconstruction of Cerebellar Cortex from Standard MRI Using Diffeomorphic Registration of a High-Resolution Template (ARCUS)

As cumulating evidence points to a wider range of functional tasks and neurological conditions that involve the cerebellum than previously known, the interest for examining the cerebellum with non-invasive neuroimaging techniques is growing. However, the standard methods of computational neuroanatomy for segmenting and reconstructing the cerebral cortex work poorly for the cerebellar cortex at the resolutions attainable with contemporary MRI technology because of its extremely intricate folding, making detailed and topologically correct reconstructions of the geometry of the cerebellar cortical surface unfeasible. Recently, a detailed surface reconstruction of the human cerebellar cortex was achieved from an ex-vivo specimen. These novel anatomical data enable a new reconstruction technique where this detailed surface reconstruction is morphed to subject space based on standard in-vivo MRI data. The result is an approximate reconstruction of the cerebellar cortex that requires only standard-resolution MRI data and can be used e.g., in functional neuroimaging, for integrating topographic population data or for visualizing topographic data on flattened surface patches.

neuroscience↗

bioRxiv2019

Resting-state \"Physiological Networks\"

Slow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which may manifest as structured spatial patterns of temporal correlations between distant brain regions. These correlations can appear similar to large-scale networks typically attributed to coupled neuronal activity. However, little effort has been devoted to a systematic investigation of such \"physiological networks\"--sets of segregated brain regions that exhibit similar physiological responses--and their potential influence on estimates of resting-state brain networks. Here, by analyzing a large group of subjects from the 3T Human Connectome Project database, we demonstrate brain-wide and noticeably heterogenous dynamics attributable to either respiratory variation or heart rate changes. We show that these physiologic dynamics can give rise to apparent \"connectivity\" patterns that resemble previously reported resting-state networks derived from fMRI data. Further, we show that this apparent \"physiological connectivity\" cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiological responses. Possible mechanisms causing these apparent \"physiological networks\", and their broad implications for interpreting functional connectivity studies are discussed.

physiology↗

bioRxiv2017

Analysis of the Aedes albopictus C6/36 genome provides insight into cell line adaptations to in vitro viral propagation

BackgroundThe 50-year old Aedes albopictus C6/36 cell line is a resource for the detection, amplification, and analysis of mosquito-borne viruses including Zika, dengue, and chikungunya. The cell line is derived from an unknown number of larvae from an unspecified strain of Aedes albopictus mosquitoes. Toward improved utility of the cell line for research in virus transmission, we present an annotated assembly of the C6/36 genome.\n\nResultsThe C6/36 genome assembly has the largest contig N50 (3.3 Mbp) of any mosquito assembly, presents the sequences of both haplotypes for most of the diploid genome, reveals independent null mutations in both alleles of the Dicer locus, and indicates a male-specific genome. Gene annotation was computed with publicly available mosquito transcript sequences. Gene expression data from cell line RNA sequence identified enrichment of growth-related pathways and conspicuous deficiency in aquaporins and inward rectifier K+ channels. As a test of utility, RNA sequence data from Zika-infected cells was mapped to the C6/36 genome and transcriptome assemblies. Host subtraction reduced the data set by 89%, enabling faster characterization of non-host reads.\n\nConclusionsThe C6/36 genome sequence and annotation should enable additional uses of the cell line to study arbovirus vector interactions and interventions aimed at restricting the spread of human disease.

genomics↗