BMC Methods

Fluorescent in situ hybridization (FISH) enables highly sensitive, high-resolution detection of gene transcripts. Moreover, by employing multiple probes, this technique allows for multiplexed, simultaneous detection of distinct gene expression patterns spatiotemporally, making it a valuable spatial transcriptomics approach. Owing to these advantages, FISH techniques are rapidly being adopted across diverse areas of basic biology. However, conventional protocols often rely on volatile, toxic reagents such as formalin or methanol, posing potential health risks to researchers. Here, we present a safer protocol that replaces these chemicals with low-toxicity alternatives, without compromising the high detection sensitivity of FISH. We validated this protocol using both in situ hybridization chain reaction (HCR) and signal amplification by exchange reaction (SABER)-FISH in frozen sections of various model organisms, including mouse (Mus musculus), amphibians (Xenopus laevis and Pleurodeles waltl), and medaka (Oryzias latipes). Our results demonstrate successful multiplexed detection of morphogenetic and cell-type marker genes in these model animals using this safer protocol. The protocol has the additional advantage of requiring no proteolytic enzyme treatment, thus preserving tissue integrity. Furthermore, we show that this protocol is fully compatible with EGFP immunostaining, allowing for the simultaneous detection of mRNAs and reporter proteins in transgenic animals. This protocol retains the benefits of highly sensitive, multiplexed, and multimodal detection afforded by integrating in situ HCR and SABER-FISH with immunohistochemistry, while providing a safer option for researchers, thereby offering a valuable tool for basic biology.

Extracellular vesicles (EVs) are lipid spheres released from cells. Research utilizing EVs has met several hurdles owing to the small size of the majority of EVs and other nanoparticles (<150 nm) and the lack of detection technologies capable of providing high-throughput single particle measurements at this scale. The use of high-throughput single particle measurements is critical for the assessment of EV heterogeneity and abundance which are features often used to assess the development of isolation protocols or particle characterization. The Coulter principle, known in the field as resistive pulse sensing (RPS), has been used for several decades to size and count cells. More recently, this technology has evolved to accommodate nanoparticle analysis. In the last decade a platform utilizing microfluidic resistive pulse sensing (MRPS) has been demonstrated for nanoparticles, offering ergonomic characterization of nanoparticles along with utilizing open format data. To date, assessment of MRPS accuracy and reporting standards have not been assessed. With the aim of increasing data accuracy, ergonomics, and reporting transparency, we developed a microfluidic resistive pulse sensing post-acquisition analysis software (RPSPASS) application for automated cohort calibration, population gating, statistical output, QC plot generation, alternative data file outputs, and standardized reporting templates.

Microscopy-based analysis of red blood cell (RBC) morphology is widely used to study phenotypes in sickle cell disease (SCD). Although AI models have been developed to automate classification, most are trained on pre-cropped single-cell images and thus struggle with full-scope microscopic images containing densely packed cells and diverse morphologies, which require both accurate detection and fine-grained classification. We propose an end-to-end computational framework to identify individual RBCs in full-scope microscopy images and classify them into five morphological categories: discocytes (DO), echinocytes (E), elongated and sickle-shaped cells (ES), granular cells (G), and reticulocytes (R). We first evaluate advanced detection-classification models, including You Only Look Once (YOLO) and Detection Transformers (DETR), and demonstrate that while these models effectively detect cells, their classification performance falls short of specialized classifiers trained on single-cell images, particularly for minority phenotypes. To address this limitation, we introduce a two-step framework in which a YOLO-based detector localizes and crops individual cells from full-scope images, followed by a fine-tuned DenseNet121 ensemble classifier that assigns each cell to one of the five morphological categories. The proposed framework achieves a detection-level F1-score of 0.9661 and a weighted-average classification F1-score of 0.9708, with an overall classification accuracy of 97.06%. Compared with the single-step YOLO26n baseline, the two-step pipeline yields a macro-average F1-score improvement of +0.1675, with particularly substantial gains for minority classes (E: +0.1623; G: +0.2774; R: +0.2603). Overall, this hybrid framework demonstrates a practical strategy for adapting fast, general-purpose detection models to domain-specific biomedical tasks by combining them with specialized classifiers, delivering both efficiency and high accuracy for scientific and clinical image analysis.

The auxin-inducible degron (AID) technology is a convenient and powerful tool for protein functional characterization in a broad array of eukaryotic species. We recently demonstrated that the original AID and improved AID2 systems are very effective at rapid protein depletion in Candida albicans and described a limited set of reagents for their use in certain auxotrophic lab strains. With an eye towards broader applicability with improved flexibility, we report here a new series of template vectors suitable for employing AID2 technology in prototrophic C. albicans strains, including clinical isolates. We adapted a common recyclable antibiotic marker system for the required genome editing steps and developed a strategy for simultaneous CRISPR/Cas9-mediated tagging of both target alleles. We also developed a composite all-in-one tagging cassette that combines the degron tag and the OsTIR1F74A gene for single step strain engineering. We added a fluorescent protein tag option and designed and validated an approach for N-terminal tagging that retains natural promoter control. We also compared effectiveness of the two commonly used synthetic auxins, 5-Ph-IAA and 5-Ad-IAA and the two common OsTIR1 variants, F74A and F74G, and provide guidelines for using the new AID2 system. Finally, using the novel all-in-one cassette, we demonstrate that the AID2 system also works in Candida auris. The new reagents should enhance the convenience and accessibility of the AID2 system for the Candida research community. IMPORTANCEInvasive fungal infections, including those caused by Candida species, are a persistent global health problem, and their treatment is hindered by limited antifungal options and the emergence of drug resistance. There is an urgent need for tools and methods to accelerate discovery of novel therapeutic targets. The expanded and optimized auxin-inducible degron system described herein provides a versatile platform for characterizing protein function and dissecting pathways governing important traits like virulence, stress tolerance, and antifungal resistance. The new reagents make AID technology applicable to any strain. Ultimately, this enhanced toolkit has the potential to help identify and validate new high-value drug targets and deepen our understanding of molecular mechanisms that drive pathogenicity of Candida and other fungal pathogen species.

Long-read RNA sequencing (lrRNA-seq) provides advantages for transcript discovery and quantification through the sequencing of full-length transcripts. Although recent benchmarks have evaluated long-read technologies and quantification tools, to the best of our knowledge, no study to date has jointly compared sequencing technology, quantification choice, and depth across both bulk and single-cell platforms. Here, we generate a matched dataset using NGN2-induced neurons derived from Fragile X syndrome and isogenic rescue lines, profiled with bulk and single-cell Illumina, Oxford Nanopore Technologies (ONT), and Pacific Biosciences (PB) Kinnex technologies. All platforms and technologies capture the expected FMR1 reactivation signal. We find that PB bulk under-detects and under-quantifies short transcripts (less than 1.25 kb), ONT bulk under-detects and under-quantifies long transcripts (greater than 5 kb), and single-cell long-read technologies a large number of single-cell specific transcripts associated with truncations. Across six bulk and four single-cell long-read quantification tools, Isosceles, Miniquant, and Oarfish provide the best compromise between computational efficiency and performance in terms of quantification accuracy as measured by spike-ins, comparisons to Illumina, and on consensus based down-stream tasks such as differential transcript expression (DTE). Depth-equivalency analyses reveal that PB single-cell sequencing requires approximately three- to four-fold greater depth than bulk to reach comparable power for transcript discovery and differential transcript expression. Our work aims to offer practical guidance for study design, including the choice of technology, sequencing depth, and quantification method. In addition, we hope our data may serve a reference dataset to evaluate emerging long-read transcriptomic protocols and methods as well as more closely investigate FMR1 biology.

Background: Facioscapulohumeral muscular dystrophy (FSHD) is a common hereditary neuromuscular disorder. The Russian FSHD Patient Registry was established in 2019 following the development of a PCR-based method for genetic confirmation of the diagnosis. Results: The registry included 470 participants (51% male). Genetic confirmation was obtained for 76% (n=356), the remainder were included based on clinical and anamnestic data. Clinical assessment forms and patient-reported questionnaires were analyzed for 310 and 142 patients, respectively. D4Z4 repeat unit (RU) distribution showed patterns consistent with European cohorts, with a predominance of patients with 3 RUs. A moderate inverse correlation was found between RUs number and clinical severity scales. Periscapular weakness was the most common onset manifestation (46.8%), followed by facial weakness (31.6%) which was often unnoticed by patients. The mean age in the Russian cohort was 37.8 years (range 0-97), indicating a younger cohort compared to international data. A delta-adjusted cluster analysis (n=215) identified three distinct trajectories: a classic phenotype with onset before age 14 and early involvement of various muscle groups (n=177), and two clusters characterized by either facial or periscapular onset with slow progression. Conclusion: The Russian FSHD registry provides a comprehensive characterization of a large national cohort, revealing a predominance of patients with 3 D4Z4 repeats and a younger demographic profile compared to international data. Cluster analysis identified three heterogeneous disease trajectories, offering a framework for improved patient stratification.

Rock-inhabiting fungi thrive in subaerial oligotrophic environments such as desert rocks, solar panels and marble monuments where organic carbon and nitrogen are scarce. We tested whether the rock-inhabiting fungus Knufia petricola showed a preference regarding nitrogen ([Formula] or [Formula]) and carbon (glucose or sucrose) sources and whether it was sensitive towards carbon and nitrogen limitation. As this fungus produces the carbon-rich, nitrogen-free 1,8-dihydroxynaphthalene (DHN) melanin, we tested whether a melanin-deficient mutant would be less sensitive to carbon limitation. The carbon and nitrogen concentrations were the primary predictors of growth, with a broad optimum partially explained by an optimal fungal C:N ratio. Limiting carbon or nitrogen supply decreased biomass formation, CO2 production and biofilm thickness but promoted substratum penetration through filamentous growth. The nitrogen content of the biomass was flexible within limits, increasing upon increasing nitrogen supply or decreasing carbon supply. The carbon use efficiency was fairly constant, whereas melanization correlated with a higher nitrogen content of the biomass despite melanin being nitrogen-free. In conclusion, in vitro, K. petricola switches to explorative growth under nutrient limitations, like fast-growing fungi, revealing universal fungal resource-acquisition patterns. Graphical abstract text and imageCarbon and nitrogen availability affect biofilm growth and morphology of the extremotolerant fungus Knufia petricola Abolfazl Dehkohneh, Julia Schumacher, Bastiaan J. R. Cockx, Karin Keil, Tessa Camenzind, Jan-Ulrich Kreft, Anna A. Gorbushina, Ruben Gerrits Growth of the rock-inhabiting fungus Knufia petricola was studied by varying carbon and nitrogen sources and concentrations. Overall, growth was best predicted by the carbon and nitrogen concentrations. Carbon and nitrogen limitation promoted substratum penetration through filamentous growth. O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/712823v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@6d98bdorg.highwire.dtl.DTLVardef@146aac5org.highwire.dtl.DTLVardef@757fa8org.highwire.dtl.DTLVardef@ff709_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundDifferential expression analysis is a central tool for studying the biological processes altered in human diseases via transcriptomic signatures. However, transcriptomic datasets are systematically confounded by latent variables from two distinct sources: unmeasured technical and biological heterogeneity within the expression data, and expression differences driven by population stratification. Correction using expression-based surrogate variables (SVs) and genotype-based principal components (PCs) addresses these sources independently, yet no study has directly evaluated their combined use against either method alone within a differential expression framework. In this study we hypothesised that simultaneously including both correction layers would produce more biologically valid and reproducible results than either approach alone, and tested this in two independent RNA-seq datasets of amyotrophic lateral sclerosis (ALS) cases and controls with matching genotype data. ResultsFour nested differential expression models (corrected for PC-only, SV-only, both SV and PC, and neither PCs nor SVs) were evaluated across the KCLBB (96 cases and 52 controls) and ALS Consortium (272 cases and 35 controls) datasets. Models were evaluated on: cross-dataset effect size concordance, cross-dataset replicability quantified by the Jaccard Similarity Index, and biological recall against a curated reference set of 66 known ALS genes. The combined SV+PC framework consistently outperformed simpler models across all metrics. Replicability improved nearly ten-fold compared to the non-corrected model, (Jaccard index: 2.28% to 19.5%), and the combined framework exhibited a statistically significant 2.1% gain over the SV-only model. The biological recall ALS genes recovered doubled comparing to the SV correction alone. Crucially, effect size stability was preserved, with the combined model expanding the shared transcriptomic signal without sacrificing consistency. These findings remained generally robust to PC number in sensitivity analyses. ConclusionsThis study found that SVs and genotype PCs address non-redundant sources of confounding, and we recommend their combined use as standard practice in differential expression analysis where matched genotype data are available. Notably PCs capturing population structure can also be derived directly from RNA-seq data, extending the applicability of this framework to studies lacking matched genotype data. Although this analysis was restricted to ALS datasets, we expect these findings to generalise to other traits.

The mosquito Aedes aegypti is an important vector of viral pathogens and serves as a model for other vector species. Pathogens are transmitted when a mosquito bites a host animal, but the neural circuits that control seeking and biting behavior are not known. Here, we detail methods and protocols for the manipulation of neural activity in the mosquito using optogenetics, a key technique to determine the causal relationship between neural circuits and behavior. These methods include rearing mosquitoes for optogenetics and three assays that are designed to measure different steps in the sequence of arousal, attraction, proboscis probing, and engorgement on the blood of host animals. These behaviors occur at different spatial scales and in response to different sensory stimuli. Each behavioral assay is outfitted with red (625 nm) LEDs for optogenetic activation. To detect arousal in response to olfactory stimuli, flight and walking are measured in all three assays. To assay attraction or landing, mosquitoes are presented with a heated blood meal in a large arena. Proboscis probing and engorgement are assayed with video resolution that enables measurement of appendages and abdomen size. The protocol describes machine vision models to enable high-resolution temporal quantification of behavior as well as endpoint measurements of feeding. These methods can be used to test the function of any population of neurons in mosquito biting behavior and can be extended to additional behaviors.

Delivery of cells or vectors in advanced therapies is probably the major challenge for genetic disorders that affect a large part of the body such as Duchenne Muscular Dystrophy (DMD). Here, we describe a novel approach for systemic cell delivery based upon an implantable bio-scaffold composed of aligned polycaprolactone nanofibers coated with laminin, able to support adhesion and extensive proliferation of mesoderm cells both in vitro and when implanted subcutaneously in a DMD mouse model. The scaffold is rapidly vascularised leading to cell entering the circulation and colonising multiple distal organs, including distant skeletal muscles and heart. Cells survive in colonized muscles and differentiate into muscle fibres that produce well detectable levels of dystrophin and -sarcoglycan. These results are game changing for cell therapy, as they allow colonization of life essential but "difficult to reach" muscles such as diaphragm and heart while avoiding invasive catheterization. Once optimised, this approach will rapidly enter clinical experimentation for DMD, other muscular dystrophies, and possibly other genetic disorders of the mesoderm. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/715524v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@11dfd34org.highwire.dtl.DTLVardef@1da6599org.highwire.dtl.DTLVardef@14427f0org.highwire.dtl.DTLVardef@19a242a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Study design and therapeutic outcome. Muscle biopsies were obtained from Duchenne muscular dystrophy (DMD) patients to isolate human DMD mesangioblasts (DMD-hMabs). Cells were genetically corrected using a lentivirus carrying a snRNA able to induce exon skipping (U7snRNA), generating U7-hMabs (1). U7-hMabs were seeded onto laminin-coated polycaprolactone (Lam-PCL) nanofiber scaffolds and implanted into the back muscle of DMD-NSG mice. This platform enabled systemic distribution of hMabs cells through circulation, resulting in engraftment across multiple muscle groups, including tibialis anterior, triceps, diaphragm and heart. C_FIG

At the Wisconsin National Primate Research Center, we have identified a family of rhesus carrying the microtubule-associated protein tau (MAPT) R406W mutation linked to frontotemporal dementia (FTD). Rhesus induced pluripotent stem cells (RhiPSCs) derived from these monkeys present a unique opportunity for in vitro modeling and comparison with cells derived from MAPT R406W human carriers. Here, we report the development of a reproducible method to generate RhiPSCs compliant with the standards of the International Society for Stem Cell Research (ISSCR) to support in vitro modeling of FTD-MAPT R406W. Our stepwise approach identified efficient methods for fibroblast derivation, fibroblast reprogramming to RhiPSC, and RhiPSC maintenance over continued culture. To derive fibroblasts from MAPT wild type (WT) and R406W monkeys, a combination of manual processing and overnight enzymatic digestion was required to maximize the number of low passage fibroblasts available for reprogramming. Fibroblast reprogramming to RhiPSC using Sendai viral vectors versus oriP/EBNA1 episomal plasmids revealed the latter as most efficient. Electroporation conditions for oriP/EBNA1 reprogramming were optimized to maximize plasmid uptake and cell survival. Ultimately, eight RhiPSC lines were derived from 4 donor rhesus monkeys (n=2 WT, n=2 R406W; two clonal lines per donor) and fully characterized according to ISSCR standards. RhiPSC stemness and genetic stability was best maintained on mouse embryonic fibroblast feeders in Universal Primate Pluripotency Stem Cell medium, as opposed to Essential 12 medium supplemented with IWR1, which produced cytogenetic abnormalities. Rhesus neural progenitor cells were generated using a monolayer protocol and expressed PAX6 and NESTIN after 21 days of differentiation. Our reliable method will be useful to labs seeking to derive RhiPSCs for preclinical studies. Overall, the RhiPSCs generated from MAPT R406W carriers will be a critical resource for evaluating the molecular underpinnings of tau-related neurodegeneration across primate species.

Accurate quantification of transplanted cardiac spheroids requires three-dimensional localisation within intact myocardium, yet this remains technically challenging. Optical clearing and light-sheet microscopy enable volumetric imaging of injection sites, but automated segmentation is difficult when transplanted spheroids and host tissue are labelled with the same fluorescent markers and cannot be separated by simple thresholding. We developed a random forest based pixel classification workflow for 3D detection of injected hiPSC derived cardiomyocyte and H9c2 spheroids in optically cleared rabbit myocardium. A supervised classifier trained on intensity, edge, and texture features generated a segmentation then grouped pixels via connected component analysis to reconstruct individual spheroids. The method showed good agreement with manual annotation and enabled automated extraction of spheroid size and spatial metrics. This accessible workflow enables reproducible three-dimensional quantification of transplanted spheroids in large light-sheet microscopy datasets and provides a practical route from volumetric imaging to spatial metrics in cardiac regeneration studies.

Fluorescent reporters such as fluorescent proteins or chemigenetic indicators are indispensable tools for studying biological processes using light microscopy. Choosing an appropriate fluorescent tag is a crucial step in experimental design not only for imaging but also for quantitative measurements such as fluorescence fluctuation spectroscopy. Two key parameters should be considered: Fluorescent brightness and photo-bleaching. Change to fluorescence intensity due to photobleaching is relatively easy to assess in different biological environments, while brightness is more elusive. Here, we develop and employ a fluorescence correlation spectroscopy (FCS) based excitation scan assay that determines fluorescent protein performance and validate it in tissue culture and zebrafish embryos. We employ our FCS pipeline to compare a set of 10 established fluorescent proteins as well as HALO and SNAP tags for both cellular imaging and measurements of diffusion dynamics with FCS. We show that mNeonGreen outperforms mEGFP in tissue culture and zebrafish embryos. We also compare StayGold variants against other green fluorescent proteins and chemigenetic reporters in tissue culture. Overall, we present a broadly applicable approach for determining fluorescent reporter brightness in the living system of interest.

Local ancestry inference (LAI) predicts a discrete ancestry label for each segment of an individuals genome and has become integral to studying population history, genetic variation, and polygenic trait association. We present a new local ancestry paradigm that eschews discrete categorical labels and instead performs inference in a continuous coordinate space. We call this method "point cloud local ancestry inference" (PCLAI), since it represents an individuals genetic ancestry as a point cloud with each point corresponding to a small haplotypic segment in their genome. This formulation works in any co-ordinate space (for instance, geographic or principal components) permitting the representation of continuous genetic variation at the haplotypic-segment level without resorting to artificially constructed discrete labels. We illustrate PCLAI by training on ancient samples from multiple time periods separately, yielding chromosome paintings based on geography that are time-stratified and provide insight into how individuals genomic segments moved across space and time.

Whole-genome sequencing (WGS) enables comprehensive analysis of tumour genomes, but its use in formalin-fixed paraffin-embedded (FFPE) samples is limited by DNA fragmentation and low yields. Whole-genome amplification (WGA) methods such as multiple displacement amplification (MDA) can boost DNA availability but distort copy-number alteration (CNA) profiles. DNA ligation-mediated MDA (DLMDA) mitigates this bias by reconstituting fragmented templates, yet its performance in FFPE-derived DNA remains uncertain. We compared paired DLMDA pre-amplified (2h, 8h) and non-pre-amplified FFPE prostate tumour samples from 22 archival blocks (5, 15 and 20 years old). DLMDA increased DNA yield by 42- to 86-fold, with global CNA patterns largely preserved. However, DLMDA significantly reduced the number of detected CNA deletions and amplifications. These effects were independent of both block age and reaction time. CNA dropouts were randomly distributed across the genome, indicating that DLMDA does not introduce regional bias. Our results show that DLMDA enables robust DNA yield recovery and avoids false-positive CNA artefacts, but at the cost of reduced CNA sensitivity. While suitable for CNA screening pipelines through WGS, further improvements are required to minimise the false-negative risk and improve the techniques sensitivity for FFPE-based genomics.

Accurate detection of RNA splice variants is often hindered when transcripts lack large distinguishable exonic regions, making conventional PCR strategies challenging. We developed a simple melting temperature (Tm)-guided exon-exon junction (EEJ) RT-PCR method to enable variant-specific detection under these conditions. Uni-directional primers spanning exon-exon junctions were designed so that approximately each half anneals to adjacent exons. The Tm of each half-site was set >7{degrees}C below the annealing temperature, preventing stable binding to individual exons and enforcing junction-dependent amplification. The method was evaluated using HTRA1-AS1 long noncoding RNA variants that share overlapping exon sequences but differ in splice connectivity. HTRA1-AS1 comprises five variants, only one with a large distinguishable exon. Tm-guided EEJ primers robustly discriminated the remaining four variants. After optimization, amplification yielded sharp, single bands with minimal cross-reactivity. Compared with conventional designs, this approach reduced heteroduplex and heteroquadruplex formation, improving band clarity. Sanger sequencing confirmed junction specificity, and the method performed well in multiplex settings. Overall, Tm-guided EEJ RT-PCR is a cost-effective, high-resolution approach for detecting RNA variants lacking easily distinguishable exonic regions, readily compatible with standard RT-PCR and qPCR workflows.

Fourier ptychographic microscopy (FPM) uses sequential multi-angle illumination and iterative phase retrieval to recover a high-resolution complex image from a series of low-resolution brightfield and darkfield images. We present OpenFPM, an open-source FPM platform in which conventional and optomechanical hardware is replaced with compact, low-cost 3D printed components. Illumination, sample and objective positioning, and camera triggering are controlled using a Python-based interface on a Raspberry Pi microcomputer. With a 10 x /0.25 NA objective lens and 636 nm illumination, OpenFPM experimentally achieves amplitude and phase reconstructions with an effective synthetic NA of 0.90 over a 1 mm field-of-view. This platform gives researchers accessible and affordable hardware for developing and testing LED-array microscopy techniques for a range of biomedical imaging applications.

SummaryThe combination of target capture metagenomics and long-read sequencing represents a powerful approach for the characterisation of rare microbial taxa and their functional genes. However, standard Nanopore library preparations are incompatible with established capture protocols. A possible workaround is the preparation of Illumina libraries prior to ONT sequencing. Currently, this hybrid approach is hindered by a lack of specialised demultiplexing software capable of handling residual adapter fragments; Nanopores higher error rates and positional variability. Here, we present deluxpore: a Nextflow pipeline that demultiplexes Nanopore reads from Illumina dual-indexed libraries (NEBNext and Nextera) using BLAST alignment and Levenshtein distance matching. Extensive benchmarking across 18 replicates validates the viability and precision of this hybrid indexing approach. Benchmarking demonstrates that accurate demultiplexing requires minimum Q20 data quality and strategic index selection. Unique index-to-sample designs achieved 91.7% sample recovery at Q20 versus 46.1% for combinatorial approaches. We also identified high-crosstalk index pairs within NEBNext Primer Set A and provide an optimized 8-sample configuration achieving ~95% accuracy at Q20. deluxpore enables reliable, automated demultiplexing for hybrid capture-long-read sequencing workflows. Availability and implementationdeluxpore is implemented in Nextflow, Python, and Bash under the GNU GPL v3.0. Source code, documentation, and benchmarking workflows are available at https://github.com/compgenomicslab/deluxpore and https://github.com/compgenomicslab/deluxpore-benchmarking.

Local ancestry inference classifies segments of DNA in admixed individuals by their originating population. However, as the date of admixture becomes older, these segments become shorter and determining their ancestry becomes increasingly difficult. This limits many existing segment-based methods to relatively recent historical admixture events and more highly diverged populations. The rapidly expanding availability of ancient DNA offers a promising opportunity to use these ancient samples as references for local ancestry inference. A recent approach integrates ancient samples into the ancestral recombination graph (ARG) for local ancestry inference. Here, we introduce recent advances in deep learning for graphs into this ARG framework to create ARGMix, a graph transformer that infers local ancestry using the coalescent trees of the inferred ARG. Our approach employs ancient samples as references in the marginal trees to predict local ancestry. We train ARGMix on data reflecting the well-understood ancient European demography and demonstrate improved accuracy and robustness even under demographic misspecification. We then apply ARGMix to an ARG of ancient and present-day European samples for ancestry-specific analyses, finding evidence of continuity between Otzi the Iceman and present-day individuals from nearby regions.

Short-read Illumina sequencing of 10x Genomics single-nucleus multiome libraries captures only the 3 end of RNA transcripts, losing transcription start site (TSS) information. Here we demonstrate nanopore sequencing of 10x multiome libraries, which enables the profiling of full length transcripts. We show concordance with common short-read sequencing based workflows including successful genetic demultiplexing of nanopore data despite its higher error rate. We compare TSS identified using nanopore sequencing of multiome cDNA to those identified using a short-read 5 assay, and provide an optimized approach for the preprocessing of nanopore reads prior to TSS identification. We find that nanopore sequencing of multiome cDNA captures a median of 63% of the TSS detected by the 5 assay.