Molecular Ecology Resources

Orchid bees (Euglossini, Apidae) are Neotropical insects whose species-level identification can depend on minute morphological characters, some difficult to see or analyze. In such cases, DNA barcoding may facilitate identification by comparing standardized DNA sequences with reference libraries. The mitochondrial cytochrome c oxidase I (COI) marker widely used in animals does not, however, provide uniform species-level resolution across bee lineages. We developed an adaptive-length nuclear ribosomal DNA (nrDNA) barcoding framework based on overlapping Nanopore-sequenced markers spanning approximately 500 to 5500 bp for 114 Euglossini species. By matching barcode length to specimen quality, material with varied preservation histories was processed within a single analysis. Leave-k-out validation with IDTAXA achieved more than 96% identification success for two longer barcodes, while performance was lower for the shortest. Combining barcode lengths within one reference library maintained high identification success, and confidence filtering reduced overclassification when species were absent from the reference library. For orchid bees, this framework permits affordable high-throughput identification and supports targeted taxonomic verification and revision. Combining adaptive barcode lengths in one analytical framework offers a general design principle for long-read reference-library construction. Its performance must now be tested in other groups.

Genetically informed conservation is critically important to ensure that interventions benefit the population of interest. In corals, preserving genetic diversity and accurate species identification are crucial for the sexual propagation. While various methods exist for species identification and measuring intraspecific variation, obtaining and analysing molecular data that enables rapid yet informed decisions on broodstock choice and progeny quality assurance remains challenging. Here we present a novel approach towards resource effective intraspecific genetic profiling by targeting extended genomic regions around ultra-conserved elements (UCEs). By sorting loci by parsimony informativeness and using a locus window size as small as 5000 bp upstream and downstream of the UCE, we identified a subset of 500 UCE-associated loci that can accurately resolve phylogenetic relationships among species and assess intraspecific variation with accuracy comparable to a whole-genome dataset, while. This method was validated using existing population genomic data from six species of staghorn coral (Acropora hyacinthus, Acropora tersa, Acropora pectinata, Acropora sp. "VI-3", Acropora kenti and Acropora cf. spathulata). The phylogeny produced by the UCE subset is congruent with the phylogeny based on complete data. With the moderate number and length of target genomic region sizes providing a balance between resolution and sequencing effort, this study provides a proof-of-concept approach towards developing fast, scalable, and cost-effective workflows using a real-time long-read sequencers such as Oxford Nanopore Technologies. The methodology has the broad potential to be applied to support genetic assessment across taxa where taxonomic uncertainty is common, improving confidence in experimental frameworks and conservation decisions.

1. Most species are neither discovered nor named, let alone included in analyses that require biological information such as trait measurements, images, ecological information and genome-scale data. Specimen-level DNA barcoding can help discover many of these species rapidly, but everything beyond discovery requires vouchers organized into putative species. Yet, existing barcoding workflows lack efficient techniques for voucher recovery, creating a post-barcoding bottleneck that limits the ability of converting barcoded specimens into biological knowledge. 2. Here we present a low-cost, open-source workflow consisting of two stages. The first safeguards barcoded specimens by separating them from DNA extracts and transferring them from microplates into ethanol-filled glass vials. The second converts the resulting voucher collection into a searchable physical resource by linking barcode-derived molecular Operational Taxonomic Unit (mOTU) assignments to vial positions and enabling specimens to be sorted into putative species either manually or automatically using a newly developed open-access robot (SORTER). 3. We evaluated the workflow using 2,024 insect specimens distributed across 21 96-well plates. For the first stage, DNA separation and specimen transfer required approximately 15 minutes per plate. For the second stage, MOTUmapper generated retrieval coordinates in a few seconds, after which the 2,024 vouchers belonging to the 452 putative species could be recovered manually in 5 days or with SORTER in 5 hours. Throughout both stages, specimen identities remained linked to barcode sequences, metadata and storage positions. 4. Vouchers are the Rosetta stones of biology because they connect different kinds of data to the same specimens. By safeguarding these vouchers and making them searchable, the workflow converts barcode projects from one-time molecular surveys into reusable resources for ecological and evolutionary research.

Environmental DNA (eDNA) metabarcoding enables sensitive, non-invasive assessment of fish communities, but highly multiplexed analyses using Oxford Nanopore Technologies (ONT) platforms require stringent control of sample-index misassignment and sequencing errors. We developed a MiFish experimental workflow combining unique dual indexes, a library protocol that omitted end preparation and used 5'-phosphorylated primers, BLAST-based demultiplexing, quality-dependent clustering, consensus generation, and haplotype partitioning by SNP/INDEL patterns. Omitting pooled end-prep reduced the mean index-chimera rate from 0.0674% to 0.000420%, a 160-fold reduction. We applied the workflow to an archive of seawater samples collected weekly off Monbetsu, Hokkaido, Japan, from 2012 to 2022. Relative read abundance (RRA) data were obtained for 274 samples, and eight taxa showed significant seasonality. For six of these taxa, the three-year mean RRA peaks coincided with reported spawning periods in Hokkaido. The workflow substantially reduced index misassignment and enabled cost-efficient, highly multiplexed analysis of a decadal fish eDNA time series.

Abstract Background Many ecologically important small invertebrates are preserved in ethanol, which compromises DNA integrity and severely limits their potential to be utilized in further genomic research. The lack of soil arthropod representation ultimately results in limited availability of reference genomes and population genomic inference from pooled sequencing (Pool-seq). Results We introduce PoLoCo (Pooled Low-Coverage), a reproducible workflow integrating pooled specimen draft genome assembly from ethanol-preserved specimens with population-level allele-frequency analysis. We assembled a 411 Mb draft genome of Entomobrya nivalis from pooled short reads, achieving 97.7% BUSCO completeness despite notable fragmentation. Comparison with the published reference genome showed partial genomic correspondence while also revealing reference-dependent differences in the retained SNP datasets. The application of PoLoCo to 82 retained pooled populations produced a final distance-thinned allele-frequency dataset suitable for population-scale genomic inference. Case-study execution documented the computational requirements of the major workflow components on standard HPC infrastructure, with the workflow, documentation, and software environments openly available in Zenodo. Conclusions PoLoCo demonstrates that draft genome assemblies and reproducible Pool-seq allele-frequency analyses can be performed with long-preserved sample material from the field. PoLoCo integrates reference construction, sync-based allele-frequency analysis, and benchmarking into a free and open workflow. It establishes a reproducibility benchmark for biodiversity genomics and yields a transferable workflow for ecological and evolutionary initiatives where high-quality DNA or long-read sequencing is not feasible.

Environmental DNA (eDNA) has emerged as a transformative tool for biodiversity monitoring, offering a non-invasive means to detect and identify species across various ecosystems. However, despite its potential, current eDNA methodologies have significant limitations, including limited sensitivity, restriction to a specific taxonomic group, lengthy processing times, the need for specialized laboratory conditions and equipment, as well as computational bottlenecks in equipment, connectivity, and algorithms. Overcoming these limitations is crucial for the real-time monitoring of a high number of taxa in field-based scenarios. Here we present an integrated eDNA workflow from extraction to data analysis that mitigates several of these challenges through several technical innovations. Our approach adapts the Tagsteady protocol to Nanopore sequencing, integrates multiplex PCR for simultaneous plant and animal detection, and introduces RTBioScan, a real-time analysis pipeline employing hierarchical basecalling and OTU-level consensus generation to achieve high-accuracy taxonomic assignments during sequencing. Using a single set of universal primers, we integrate metabarcoding and barcoding into a cohesive protocol, utilizing universal markers for both animals and plants (cytochrome oxidase 1 region, COI, and Internal Transcribed Spacer 2, ITS2). This comprehensive approach can detect and identify a broad spectrum of species, from individual organisms to entire communities in a single workflow.

Structural variants (SVs) can strongly influence evolutionary processes by suppressing recombination to create jointly inherited regions and by altering gene expression and the 3D organization of DNA. Recent improvements in sequencing technologies have led to an influx of high-resolution genomic data, bolstering research on SVs. When detecting and characterizing SVs, researchers face trade-offs in data and methods concerning scale, resolution, and computational intensity. Here, we evaluate such trade-offs by quantifying how data type, analytical approach, and SV attributes affect the ability to detect and characterize SVs in Timema cristinae stick insects, with a focus on inversions. We specifically evaluate inversions characterized by (i) a population genomic approach based on patterns of local population structure inferred from genotyping-by-sequencing data, (ii) pairwise alignments of eight de novo genome assemblies and SV calling with SyRI, and (iii) a pangenome constructed from the alignment of the eight genome assemblies. Our results suggest that while broad genomic regions harboring SVs can be detected by any of the approaches, the approaches differ in how they characterize inversions, especially small or complex ones. The local population structure approach is exploratory and generally only detects the largest inversions. Pairwise comparative alignments identify the greatest number of inversions, but downstream analyses are influenced by identification of shared inversions across genome pairs. Pangenome approaches are scalable and produce network graphs that describe complex SVs but require a conceptual shift in visualization and interpretation. The collective results highlight limitations and benefits of different approaches in a burgeoning research field.

DNA barcoding involves the recovery of a DNA sequence for a target gene region from its source specimen. This process gains complexity when multiple sequences are recovered from a specimen, as is often the case when data are generated by high-throughput sequencers. This diversity can reflect both methodological artifacts (e.g., chimeras, PCR errors, sequencing errors, tag jumps) and real template diversity in the DNA extract (e.g., contamination, endosymbionts, NUMTs, parasites). To support analysis of the sequence data from three million specimens annually, the Centre for Biodiversity Genomics (CBG) has developed BIP, the Barcode Inference Pipeline. Compatible with all sequencing platforms, BIP processes .fastq files and returns both target DNA barcodes and non-target sequences. To generate results, BIP implements quality and size filtration, demultiplexing, primer trimming, chimera scanning, sequence error correction, OTU delineation, and sequence identification. When analysis targets the cytochrome c oxidase 1 (COI) barcode region, BIP also assigns each OTU to a known BIN or identifies its nearest neighbour BIN. As final output, BIP returns summary files ready for upload to BOLD or for other downstream analyses. They include a taxonomic assignment for each OTU, generated by comparison with a DNA barcode reference library. We describe BIPs flexibility and structure, then demonstrate its functionality by analyzing COI sequence data from 100K specimens. Because of its capacity to disentangle target and non-target sequences, BIP outperforms an alternative software package, ONTbarcoder, in several important ways. To ease access, installation, and functionality, BIP is provided as a Docker container (github.com/cbg-innov/BIP).

Polyploidy can be a critical factor for explaining plant trait variation, niche diversification, or speciation. However, inferring ploidy from silica-dried or historical samples using chromosome counts or flow cytometry is not possible, and scaling up ploidy estimation to population-level fresh contemporary samples can be challenging as well. Thus, we present a new method for estimating ploidy levels directly from sequencing data using machine learning; the Polyploid Population Genomics Tool Kit (PPGTK). The machine-learning approach is advantageous as it relaxes the assumptions of previous probabilistic methods and provides per-sample probabilities, allowing investigators to evaluate uncertainty in their system of interest.. We demonstrate performance and accuracy of the method on simulated and empirical data. Simulations showed above 99% accuracy, even for low coverage data, as long reads were mappable to the reference genome. For empirical analyses, we used target enrichment data from blueberry wild relatives (Vaccinium sect. Cyanococcus) and whole-genome data from sweetpotato wild relatives (Ipomoea ser. Batatas). Ploidy was recovered with 99% accuracy across 70 Vaccinium individuals and 97% across 82 Ipomoea individuals. Analysis of many individuals is fast and requires only a multisample VCF, which is presumably generated for the research anyway, and some samples of known ploidy for training the classifier. The approach implemented in PPGTK is promising for collections-based research as well, enabling ploidy classification of historical specimens based on present-day observations. The method is implemented in a new Python package as a single command that can run on a conventional laptop.

During biogeographical ancestry (BGA) analysis, an unknown individual for whom ancestry informative markers (AIMs) were analyzed is often assigned to one of a few reference populations. For some individuals, such an assignment is not meaningful: the two parents of the individual may come from different populations, or the individual may come from a population not included in the reference database. In this study, we examined classification methods in combination with 104 AIMs from the VISAGE Enhanced Tool to provide one of the following three possible outcomes: (i) in-reference (IR), with an assignment to one of the reference populations; (ii) admixed (AD), with parents from two in-reference populations; and (iii) out-of-reference (OR), where the individual's ancestry is not sufficiently represented by the reference populations. We therefore compared seven classifiers in combination with class-specific acceptance thresholds for the OR decision. The best-performing method according to our selection criterion was a naive Bayes classifier over pairs of populations, which we call the recent naive Bayes (RNB) classifier. Populations held out of the reference panel were correctly rejected in $84.9\%$ of cases. We further tested the method on $800$ individuals from seven additional groups not included in the reference panel. Only $29.4\%$ of individuals from four non-admixed populations were rejected, resembling joint ancestry with reference populations. In addition, we investigated the prediction accuracy in a newly collected dataset of 28 individuals with self-reported ancestry by analyzing AIMs using massively parallel sequencing.

Polyandry, multiple mating by females, both benefits females by increases genetic variation within families and provides an opportunity for sexual selection through sperm competition and cryptic female choice. Estimating polyandry in species with large clutches is challenging because genotyping all progeny individually can be impractical. We present a likelihood-based framework for estimating the number of sires and their relative contributions from the maternal genotype and pooled sequencing of entire progeny pools. The method evaluates the probability of observed pooled offspring read counts under alternative paternal genotype mixtures, accounting for Mendelian segregation and sampling variation from finite sequencing depth. We implement this framework for both biallelic SNPs and short-read mini-haplotype markers constructed from closely linked SNPs, which provide multi-allelic information comparable to traditional microsatellite markers. Simulations show that this approach accurately recovers sire number and sire contributions across a range of conditions. We then applied the method to 275 wild-caught Drosophila melanogaster females and their offspring from a Kansas population using 3,053 biallelic SNPs and 239 four-allele mini-haplotype markers. Estimates from the two marker sets were highly concordant, and combined analyses indicate that 57% of families were sired by a single male. These results demonstrate that pooled progeny sequencing provides an efficient genomic approach for estimating polyandry in species with large families.

Global arthropod decline demands effective biodiversity monitoring strategies. However, most current monitoring approaches do not provide an exhaustive picture of arthropod community structure. In particular, biotic interactions and temporal patterns of biodiversity change are still poorly understood. Here we explore the possibility of addressing these two shortfalls using spiders, one of the most important predators of terrestrial arthropods, as natural samplers for arthropod community DNA. We conducted several experiments comparing the recovered community composition between spider gut contents and traditional monitoring methods. Additionally, we used archived spiders from long-term collections to assess the preservation of prey DNA over time. Spiders proved to be highly efficient natural DNA samplers, with gut content metabarcoding revealing similar community composition and alpha- and beta-diversity like traditional methods. Unique arthropod taxa were detected by spider gut contents and traditional methods respectively, indicating that these two approaches can serve as valuable complements to each other. Well-preserved archived spiders effectively reconstructed historical diets up to 20 years back in time, showing that historical spider collections can act as time capsules of the wider diversity that occurred in the spider's habitat. Spiders as natural samplers overcome critical shortfalls in biodiversity monitoring and contribute to our future understanding of community assembly across space and time.

High-throughput amplicon sequencing has transformed microsatellite (STR) genotyping by overcoming many of the limitations of fragment-length analysis, enabling more accurate, cost-effective, and standardized genotyping. Yet, protocols specifically designed for high-throughput sequencing (HTS)-based STR genotyping from low-template and degraded DNA remain scarce, despite the prevalence of these challenging sample types in ecological and conservation contexts. We present a methodology for the de novo development of robust STR multiplex panels together with a laboratory protocol for efficient and reliable STR genotyping by sequencing with low quantity and quality DNA samples. The protocol comprises (i) an automated bioinformatic pipeline to design large sets of short tetranucleotide markers optimized for multiplex amplicon sequencing of degraded and low-template DNA; (ii) guidelines for efficient in vitro optimization of multiplex amplification using directly low quantity/quality template DNA; and (iii) a library preparation procedure that improves detection of low-level allele signal while enabling quality assessment of STR amplicon sequencing under limiting DNA conditions. We demonstrate the approach by developing and validating STR panels for non-invasive genotyping of three large carnivore species: a 44-plex for the grey wolf (Canis lupus), a 41-plex for the Eurasian lynx (Lynx lynx), and a 30-plex for the brown bear (Ursus arctos). Multiplex performance was high, with [≥]91% of samples successfully genotyped at [≥]50% of loci (allele size range 28-110 bp across panels) and correctly assigned to known individuals, negligible levels of noise in the controls, and high discriminatory power (PIDsibs [≤]2.4 x 1e-12), also owing to sequence variation among same-length alleles at 15-50% of loci. The approach is broadly applicable to animal and plant species, a wide range of sample types, and large-scale analysis such as genetic monitoring. Our study reinforces the value of STR amplicon sequencing for ecological and conservation applications while highlighting the importance of marker design and laboratory workflows tailored to HTS-based genotyping for accurate and efficient implementation.

At large scales, our ability to compare genetic diversity across species is often challenged by the difficulty of extracting range-wide sequence data from online databases. Using mitochondrial DNA from amphibians and reptiles spanning across six regions: Brazilian Atlantic Forest, Iberian Peninsula, northwestern Africa, Arabian Peninsula, Madagascar and Australian Monsoonal Tropics, we develop and implement a framework to overcome common limitations in macrogenetics. We compiled and manually curated a spatially explicit genetic dataset from public repositories, quantified the spatial representativeness of genetic sampling across species' ranges and developed a novel interpolation framework to estimate and map species-specific patterns of intraspecific genetic diversity (ISD) while accounting for heterogeneous spatial sampling. We estimated mean nucleotide diversity across species to produce regional maps for each genetic marker. Our final dataset comprised 182 amphibian and 239 reptile species, encompassing 184 genera from 54 families. Range representativeness of genetic sampling was generally high, despite some variation across study regions. Spatial patterns of ISD revealed geographic heterogeneity, and were consistent across the best-sampled mitochondrial markers. By combining automated and manual georeferencing, quantifying range representativeness, and accounting for bias in spatial interpolations, our pipeline is marker-agnostic and directly applicable to nuclear and genomic data as they become available.

Over the past decade there has been increasing interest in the use of microhaplotype markers in autopolyploid taxa. This has been driven by theoretical and observed improvements in signals of allelic dosage, linkage, and heritability. Yet, to date there has been little investigation into the suitability of microhaplotype markers for estimating kinship. Here, we develop the theory of kinship estimation from microhaplotypes, introduce the MCHap microhaplotype caller for autopolyploid populations, and apply these methods to a highly diverse germplasm population of mixed-ploidy Actinidia (kiwifruit and relatives). We find that microhaplotype-based kinship estimates are generally superior to equivalent single nucleotide variant based estimates. This is because microhaplotypes minimize the coalescent signal among alleles which may bias estimates within the context of a recent reference population. Hence, kinship estimates from microhaplotypes more accurately capture the recent demographic history of a population. These findings are supported by both coalescent simulations and the analysis of real data. Our findings are relevant to organisms of any ploidy, but most actionable in highly heterozygous taxa such as Actinidia.

Genotype-environment association (GEA) analyses are widely used to identify loci underlying local adaptation by examining correlations between allele frequencies and environmental variables across a species' range. A major challenge for this approach is distinguishing true adaptive signals from spurious associations arising from population structure. Several methods have been developed to account for population structure, but these methods can suffer from reduced statistical power or increased false positives under some conditions. To address this, we introduce a new GEA method, termed SimGEA. In essence, SimGEA infers a neutral evolutionary model that reproduces the population structure observed in empirical data and uses this model to simulate neutral alleles. By applying the same GEA statistic to both the empirical and simulated data, SimGEA evaluates the significance of observed associations against neutral expectations that account for population structure. We compared the performance of SimGEA with that of existing GEA methods, including LFMM2 and BayPass, using simulations of local adaptation in two-dimensional space. We found that SimGEA consistently controlled the false discovery rate without substantially sacrificing statistical power across the scenarios examined. These results suggest that calibrating statistics using neutral simulations provides a robust and flexible approach for accounting for population structure in GEA analyses.

Detecting dugongs at low densities remains a major challenge for conservation monitoring, particularly in remote or turbid coastal habitats where conventional surveys are difficult. Environmental DNA (eDNA) offers a complementary approach, but robust species-specific tools are needed before it can be confidently applied to dugong surveillance. We developed and analytically validated a quantitative PCR TaqMan assay for detecting dugong (Dugong dugon) eDNA and evaluated its performance across controlled aquarium conditions in Australia and Abu Dhabi, and wild populations in northern Australia and the Arabian/Persian Gulf. The assay detected dugong eDNA in aquarium and field samples, with Sanger sequencing confirming amplification of dugong mitochondrial DNA from positive field detections. Our assay provides a robust and geographically transferable tool for targeted dugong eDNA detection, performing reliably across contrasting coastal environments and widely separated populations. This approach can strengthen local-scale surveillance of low-density or difficult-to-detect dugong populations.

Wildlife biobanking depends on consistent, reproducible cell culture outcomes, yet no standardised framework exists for assessing culture quality across taxa or for integrating quality metrics into institutional collections management systems. Here, we present the Cell Viability and Utility Index (CVUI), a scored pipeline framework validated in a pilot study of 154 culture rounds from 46 species held in the Ian Potter Australian Wildlife Biobank at Museums Victoria Research Institute. The CVUI was adapted from the Wildlife Sperm Index (Jacobs et al., 2026) and assigns weighted scores encompassing four sequential culture stages -- establishment, first passage, expansion, and cryobanking -- with a continuous viability modifier applied at the cryobanking stage. Survival analysis identified individual animal identity as the primary source of variance in culture establishment, whereas taxon was the primary predictor of first-passage success. Beyond first passage, failure rates were too low to reliably assess either effect, with critical attrition concentrated at the earliest pipeline stages. These findings informed the differential weighting of CVUI components and have direct implications for workflow prioritisation and banking strategy. Monitoring can now be targeted at establishment and first passage, the stages where intervention has the greatest impact, with taxon-specific attention warranted at first passage and individual-level attention at establishment, while the robustness of the late-stage protocol means that banking yield is largely determined before a culture reaches P1. Integration of CVUI scores into collections management platforms such as Axiell EMu (Electronic Museum) would transform passive record-keeping into active decision support, enabling real-time quality tracking, early flagging of at-risk cultures, and longitudinal benchmarking of biobank performance across species, collectors, and tissue types. This framework provides a foundation for optimising wildlife fibroblast biobanking protocols, developing taxon-specific culture standards, and expanding standardised quality assessment across wildlife biobanks nationally and internationally.

Comparing quantitative trait differentiation (Q_ST) with neutral genetic differentiation (F_ST) is an established approach to detect locally adaptive trait differentiation, but empirical applications can lose power when Q_ST is deflated by extrinsic trait variance (i.e. from non-genetic sources such as environmental effects and measurement error). We present REDQuanTEA (Replication-Enhanced Detection of Quantitative Traits Evolving Adaptively), a ready-to-use computational workflow that leverages biologically replicated data to disentangle genetic and extrinsic trait variance, while using Approximate Bayesian Computation (ABC) to refine estimates of Q_ST. Instead of comparing all traits with a single F_ST-based cutoff, REDQuanTEA generates trait-specific dynamic outlier Q_ST thresholds from neutral F_ST distributions based on matching experimental properties and the trait-specific level of extrinsic variance. In addition to identification of candidate adaptive traits from empirical data, the package enables simulation-guided assessment of experimental design and statistical analysis options. Using a demographic benchmark based on Drosophila melanogaster, REDQuanTEA outperformed estimators based on analysis of variance (ANOVA), particularly when extrinsic variance was moderate to high, while controlling false positive rates (FPRs). Assuming fixed experimental effort, two replicates with more independent genotypes often outperformed three replicate designs when extrinsic variance was low, and performed similarly as extrinsic variance increased. REDQuanTEA therefore provides a framework for optimizing experimental plans and detecting adaptively differentiated traits with improved power.

Background Comparisons of single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) data have been described in some mammalian tissues and, subsequently, in Drosophila, but remain unexplored in most invertebrate lineages. The xenacoelomorphs occupy key phylogenetic positions, yet they differ anatomically from mammals. They have a reduced extracellular matrix, high-salt body fluid, and no circulatory system. Despite these differences, they possess a well-developed nervous system. One of the xenacoelomorphs, the photosymbiotic acoel (Praesagittifera naikaiensis) also harbours symbiotic Tetraselmis algae, whose RNA can be co-captured with host RNA. Results We compared scRNA-seq and snRNA-seq data from whole P. naikaiensis specimens. Both methods yielded high-quality data with comparable gene detection but a larger share of scRNA-seq reads derived from symbiotic algae. Gene-level analyses revealed that neural genes were enriched in snRNA-seq relative to non-neural genes. Cross-method label transfer and integration-based validation identified six snRNA-seq clusters, including some neural populations, that lacked a clear scRNA-seq counterpart. In contrast, three cell populations, including muscle and metabolically active clusters, were reciprocally validated as captured by both methods. Conclusions Our results show that key snRNA-seq advantages, particularly the enhanced recovery of neural transcripts, are recapitulated in our dataset, which is consistent with previous reports in mammals. Furthermore, snRNA-seq reduces symbiont-derived reads and recovers several cell populations underrepresented in scRNA-seq. These findings provide practical guidance for cell atlas construction in non-model, symbiotic invertebrates.