BMC Methods

Phage Display Immunoprecipitation sequencing (PhIP-Seq) is a high throughput method allowing to measure antibody binding against hundreds of thousands of potential antigens. Typically, blood samples of hundreds to thousands of individuals are measured in 96-well plates, necessitating distribution of samples on multiple plates and processing in batches. To correctly interpret PhIP-Seq results, it is crucial to understand its nature and technical limitations. Here, we analyzed 195 technical replicates of controls (anchor samples) from 49 plates measured in 13 immunoprecipitation runs to gauge technical variability and replicability. While there were few false-positive enriched peptides, we observed a substantial fraction of false negatives. The total number of enriched peptides can be affected by batch effects. Additionally, the type of biological material used (serum or plasma) influences enrichment profiles with an antigen library containing bacterial proteins due to the interaction of phages with plasma proteins. Finally, replicating measurements in different labs produces comparable enrichment profiles, but slight differences in a subset of peptides are noticeable. These results suggest best practices for PhIP-Seq experiments. Most importantly, due to batch effects, samples of cases and controls should be evenly distributed between 96-well plates to avoid confounding with biological effects. Also, for highly complex libraries, the total number of enriched peptides can be a technical artifact and potentially not a feature useful in comparisons.

Context: Cytological specimens allow minimally invasive, repeated sampling for diagnosis and molecular testing. However, cytology biobanking protocols have primarily been optimized for morphology, and the effects of preanalytical handling and storage on intracellular proteins remain unclear. Objective: To assess how fixation, staining, storage temperature, and duration affect intracellular protein preservation in cytological specimens and to identify conditions suitable for in situ proximity ligation assay (isPLA). Design: Human cancer cell lines were processed under different preanalytical conditions. Intracellular proteins and protein protein interactions were assessed by isPLA, fluorescence microscopy, and quantitative image analysis. Slides were stored at room temperature or minus 20 Celsius, with or without MayGrunwaldGiemsa (MGG) staining. Results: Storage temperature had the greatest effect on protein signal. Unstained slides stored at minus 20 Celsius retained strong signals for up to 24 months, whereas room temperature storage reduced detectable protein interactions by approximately 95%. MGG staining further reduced signal intensity during prolonged storage. Nevertheless, stained slides remained suitable for isPLA when staining, microscopic evaluation, destaining, and antibody labeling were completed on the same day. Prolonged room temperature storage of stained slides caused loss of antigenicity, increased autofluorescence, and false negative findings. Conclusions: Low temperature storage and standardized preanalytical handling preserve intracellular protein signals in cytological specimens. These conditions support reliable single cell protein profiling of archived samples and their use in biomarker research and translational pathology.

Preprocessing of spatial transcriptomics (ST) count matrices is critical for removing technical variation while preserving biological signals, but optimal strategies remain unclear across diverse platforms. We systematically benchmarked 371 preprocessing workflows across 45 data spanning 11 mainstream ST platforms, evaluating their performance using specifically designed complementary metrics. To further assess preprocessing factors upstream of count-matrix generation, we additionally evaluated image preprocessing, including cell segmentation and post-segmentation transcript processing, across representative imaging-based ST platforms. We found that no universal count-matrix preprocessing workflow performed optimally across all platforms, while data from the same platforms exhibited striking consistency in optimal count-matrix preprocessing workflows. We identified the key data structure characteristics and count-matrix preprocessing steps that critically influence preprocessing performance. We developed platform-specific and data structure-guided recommendations to assist users in selecting the optimal count-matrix preprocessing workflow, validated across a broader range of downstream tasks. We demonstrated that our data-driven recommendations uncover subtle biologically meaningful spatial patterns in new data that were obscured by suboptimal count-matrix preprocessing. Our findings suggest best practices for ST data count-matrix preprocessing.

Germination percentage is an endpoint measure and therefore does not describe when an individual seed begins visible growth or how rapidly its radicle and plumule expand. We developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture. A single industrial camera moved along a 1 m rail and imaged three culture boxes at 1 h intervals for up to 80 h. The archive comprised 1,062 full-frame images and 6,372 seed-level repeated observations under the six-seed field-of-view configuration. A physical grid maintained seed identity through time and enabled individual regions of interest to be extracted. Whole-seed foregrounds were obtained with a pretrained U2-Net, and a masked RGB intensity rule separated newly emerging tissue from the darker hull. For each tracked seed, projected emerging-tissue area and interval growth rate were calculated. Three representative normally germinating seeds first showed measurable tissue at 48 h, yet subsequently followed distinct trajectories: final projected areas ranged from 2,605 to 4,700 pixels and peak interval growth rates ranged from 106.88 to 287.92 pixels h-1. B-1 accumulated 63.71% of its final visible area during 72-80 h, whereas B-3 accumulated 73.51% during 60-72 h. Thus, seeds with the same observed emergence interval can differ substantially in the timing and magnitude of post-emergence expansion. The workflow converts repeated images into biologically interpretable temporal phenotypes and provides a basis for nondestructive studies of rice seed vigor and germination heterogeneity.

Background: Point-of-care manufacturing of CAR-T cells could reduce cost and improve patient access, but few closed, automated systems support both flexible process development and GMP production. Methods and results: We describe LimCORE, a single-use consumable for cell incubation and processing, implemented in LimGROW (manual system) and LimONE (closed and automated system). In LimGROW, T cells expanded 246 +/-18 fold over 10 days at optimized seeding density, versus 89 +/-9 fold in a G-Rex control, with similar viability (>90%) and phenotype. Process parameters directly transferred to LimONE, where automated, closed buoyancy-based CD3+ selection achieved 90.3 +/-3.5% purity and 59.2 +/-17.0% recovery in under two hours. Using this workflow, we manufactured CD19 CAR-T cells in a fully closed, automated 7-day LimONE process requiring 155 minutes of operator intervention. LimONE manufactured CAR-T cells reached 42.6 +/-2.4 fold expansion (532 +/-29x10e6; cells at harvest), with 43.6 +/-6.0% transduction efficiency and viability similar to G-Rex controls. CD4+/CD8+ ratios, CD8+ differentiation subsets, and cytotoxicity were comparable between platforms, while LimONE-manufactured cells showed increased spare respiratory capacity and oxygen consumption. Across four manufacturing runs on the LimONE, no hardware or software failures occurred, and automated liquid transfers and volume concentration stayed within +/-5% accuracy. Conclusions: These data show that LimCORE supports T cell expansion, selection, and complete CD19 CAR-T manufacturing with performance similar to existing platforms while reducing operator time, across R&D and GMP scale closed-system formats.

In spatial transcriptomics, cell segmentation is used to draw boundaries around cells. Molecules located within a cell's boundary are assigned to it, making its gene expression profile dependent on segmentation accuracy. To identify potentially problematic cells, studies increasingly use doublet detection methods, a class of algorithms developed to recognize molecular admixture from two cells in scRNA-seq. To evaluate their suitability in spatial data, we model cell segmentation errors as a continuum of partial molecular admixture between neighboring cells, generated by varying the loss of a cell's own transcripts and the gain of transcripts from its neighbor. Evaluating 8 doublet methods across 16 spatial datasets, we characterize the conditions under which they perform well and identify their failure modes. Detection improves with increasing molecular admixture and transcriptional dissimilarity between neighboring cells, with cxds2, scDblFinder.score, and a simple baseline (unique_genes) performing best. These patterns are consistent across datasets spanning different gene panels, technological platforms, staining techniques, segmentation algorithms, and error frequencies. In unperturbed spatial data, elevated doublet scores localize to regions consistent with segmentation problems, suggesting that they can help prioritize cells or regions for inspection, segmentation refinement, or transcript reassignment. Our results establish when doublet methods can provide useful quality control signals for cell segmentation, supporting their use in spatial transcriptomics.

Pairing label-free microscopy with virtual staining could reduce the cost and experimental burden of fluorescence microscopy, but its impact is conditional on generalizable inference. Most virtual staining studies assess performance using image quality assessment (IQA) metrics developed for natural images, yet how well these metrics translate to microscopy remains unknown. Here, we examined the behavior of seven commonly-used full-reference training objectives and metrics, MAE, PSNR, SSIM, foreground PSNR and SSIM, LPIPS, and DISTS, under controlled image degradation and realistic out-of-distribution virtual staining. We applied graded intensity, textural, and morphological transformations to Cell Painting images spanning 18 cell lines, seeding densities, and fluorescence channels. Channel, cell line identity and seeding density explained substantial metric variation after controlling for degradation magnitude. DISTS and foreground metrics showed more favorable balance between degradation sensitivity and biological invariance, although no metric reported performance independent of biological context. Incrementally degrading images and evaluating concomitant metric degradation further revealed that most metrics used only a small fraction of their nominal numerical ranges and frequently plateaued while image degradation visibly continued. We next trained three popular virtual staining model architectures (UNet, WGAN-GP, UNeXt) on five U2-OS seeding densities separately, and computed metrics on model predictions across 17 unseen cell lines. We observed that architecture and training U2-OS seeding density together explain less than 2% of metric variation. Visual inspection suggested comparable scores across cell lines correspond to qualitatively distinct errors, such as differences in cell morphology and marker intensity. These findings show that conventional IQA metrics do not effectively translate to virtual staining applications. Selection or optimization of virtual staining models against real application such as in label-free high content drug screening should instead be approached in an application-oriented fashion.

Speckle artifacts and isolated foreground pixels are common in fluorescence microscopy and can interfere with segmentation and subsequent quantitative image analysis. Conventional denoising methods often modify image intensities through filtering or smoothing, potentially altering biologically relevant fluorescence signals. We introduce Sparse Pixel Cluster Cleaning (SPC-Clean), a topology-aware method that removes poorly supported foreground pixels through iterative neighborhood analysis of a thresholded mask. SPC-Clean is deterministic, training-free, preserves original fluorescence intensities for practical microscopy workflows.

Integrating spatial metabolomics and spatial proteomics on the same tissue section remains challenging because matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and laser capture microdissection (LCM)-based proteomics impose different requirements on sample slides. Here, we developed and systematically evaluated a conductive indium tin oxide-coated polyethylene terephthalate (ITO-PET) slide that enables sequential MALDI-MSI and LCM-liquid chromatography-mass spectrometry (LCM-LC-MS) analysis of the same tissue section. Using mouse brain tissue as a model, ITO-PET provided MALDI-MSI performance closely comparable to conventional ITO-glass, including spectral concordance (Pearson correlation, R = 0.90), ion detection coverage, metabolite annotation, signal intensity distribution, and preservation of spatial molecular patterns. Following MALDI-MSI, the ITO-PET slide enabled cutting-mode LCM and yielded proteomic signal intensities and numbers of identified protein groups comparable to those obtained with conventional PEN-glass slides. Across different tissue sampling areas, proteomic signal intensity distributions, precursor ion counts, and protein group identifications remained broadly comparable before and after MALDI-MSI, with substantial overlap in identified protein groups. Similar patterns were observed in mouse kidney, lung, spleen, and liver tissues, further supporting the applicability of the workflow across different tissue types. By combining the electrical conductivity required for MALDI-MSI with the mechanical properties required for LCM cutting, the ITO-PET slide addresses a major material incompatibility between the two analytical modalities and enables sequential spatial metabolomic and proteomic analysis from the same tissue section. This workflow provides a practical analytical platform for obtaining complementary molecular information from spatially limited biological specimens.

Pathogenic missense variants in the X chromosome gene UBQLN2 cause amyotrophic lateral sclerosis (ALS), often accompanied by frontotemporal dementia (FTD). As an X-linked gene, UBQLN2 is subject to X chromosome inactivation (XCI), a process wherein one X chromosome in each cell is randomly inactivated to a Barr body throughout the body in females, creating a mosaic of allelic expression in the tissues of heterozygotes. Despite heterozygous females constituting a majority of reported cases of UBQLN2-linked ALS/FTD, and the known influence of XCI on neurological disorders at large, no current disease models account for XCI. Here we report the characterisation of 12 iPSC clones carrying the ALS/FTD-causing p.T487I (c.1460C>T) UBQLN2 variant. These clones, originally derived from 3 heterozygous carrier fibroblast lines, underwent validation of homeostatic Barr body retention. Erosion of XCI in a subset of the lines was correlated with biallelic expression (of both wildtype and mutant UBQLN2), as measured through a novel allele-selective qPCR (AS-qPCR) assay and verified by amplicon-based Illumina sequencing and Sanger chromatogram quantification, enabling selection of iPSC clones best retaining XCI. Together, this UBQLN2 AS-qPCR assay and selected iPSC clones will enable studies of the role of XCI and its skew in female resilience to UBQLN2 p.T487I-linked ALS/FTD and enable development of allele-selective therapies.

Studying algae requires specialized equipment to characterize culture growth and physiological performance and non-invasive monitoring of these cultures typically relies on optical density (OD) measurements. Currently, OD is often measured using standard spectrometers that suffer from detector saturation at high cell densities, which requires time consuming sample dilutions and lacks compatibility with flat-walled cell culture flasks like Roux bottles. Here we describe a 3D-printable device to measure optically dense algal cultures directly within these cultivation vessels. This instrument combines light attenuation and scattering, resulting in a dual-sensor system that utilizes near-infrared light (950 nm) to precisely quantify biomass without interference from changing photosynthetic pigments. Cultures that reach high cell densities can be assumed to be accurately quantified in situ using the scattered signal, preserving the integrity of the culture and eliminating the need for subsampling. This method is adaptable to the most common geometries of Roux bottles (T-25, T-75, and T-175) and could be used to monitor small-scale batch cultures without contamination risks. A cyanobacteria algal species (Limnospira platensis) and three flask sizes were analysed on light attenuation linearity and scattering detection limits at different cell concentration, demonstrating that the light attenuation module can reliably measure biomass up to an OD of 3.1 (equivalent to >1.5 g/L) without requiring dilution.

The sea anemone Aiptasia is a model system for understanding cnidarian loss of symbiotic algae under heat stress (bleaching). While Aiptasia polyps have been widely used to study this process, accurate symbiosis phenotyping grapples with discordant length scales: fine spatial resolution (~100 um) is needed across a whole organism (~5 mm). To address this, we consider small (~100 um), optically transparent Aiptasia larvae as a bleaching model suitable for whole-organism phenotyping by fluorescence microscopy with larvae classified as symbiotic when algae are localized within gastrodermal cells. To expedite phenotyping, we introduce a machine-learning (ML) image-analysis pipeline (SYMPHONY) designed for single-larva resolution analysis of intact larvae. SYMPHONY efficiently identifies the cellular location of internalized algae (accuracy: 79%, precision: 82%, recall: 79%, F1 score: 79%; training dataset composed of 1611 total objects). Additionally, SYMPHONY reports statistically significant larval bleaching under heat stress and corroborates manual phenotyping results, while significantly reducing operator labor from hours to minutes. The combination of the Aiptasia larvae model and the SYMPHONY pipeline aims to accelerate our understanding of symbiosis breakdown.

Extracellular Vesicles (EVs) are small membrane-bound particles secreted by cells that play key roles in intercellular communication, gene regulation and modulation of cell function. They are involved in both physiological and pathological processes and, due to their ability to transport biomolecules across biological barriers, have emerged as promising tools for use as drug delivery vehicles and biomarkers with diagnostic and prognostic applications. Various methodologies are currently employed for the isolation, characterization, and analysis of EVs, including Ultracentrifugation (UC), Transmission Electron Microscopy (TEM), Nanoparticle Tracking Analysis (NTA), and Flow Cytometry. Flow Cytometry has emerged as a powerful technique capable of providing a multiparametric analysis of individual EVs. Recent advancements have led to the development of cytometers with higher sensitivity and increased limit of detection, enabling the detection and sorting of nanoscale particles--a technique known as Nano-Flow Cytometry. In this study, we show the optimization of small particle sorting, termed nanoFACS, via the CytoFLEX SRT. This method enables sorting based on size or fluorescence, enhancing reproducibility and broadening the potential for application in biological and clinical assays. Furthermore, we demonstrate the utility of nanoFACS in isolating nanoparticles from complex biofluids and in detecting miRNA using molecular beacons (MBs) highlighting its potential in both basic research and translational applications.

Optical redox ratio (ORR) is a commonly used metric to quantify the metabolic state of biological samples based on autofluorescence of NAD(P)H and FAD. Despite its relevance and popularity, little work has been done to quantify the estimation error of ORR, especially in low-photon count data. Here, we provide an analytical solution to determine ORR variability in the presence of shot noise and dark counts. We then validate this analytical model with simulated and experimental data. This work will enable researchers to quantify the uncertainty in their ORR measurements, aiding in more accurate and efficient experimental design and data analysis.

Magnetic field effects (MFEs) in biological systems are typically small and experimentally challenging to measure reproducibly across large sample populations. Existing approaches to measure such effects often rely on low-throughput microscopy or custom-built magnetic stimulation systems that provide limited control over magnetic field geometry, synchronization, or experimental automation. Here, we present an open-source magnetofluorescence imaging platform designed for bacterial plate-scale screening of MFEs in live colonies. The instrument integrates a programmable three-axis vector electromagnet, synchronized fluorescence excitation and imaging, and integrated acquisition software with per-frame metadata logging on a hardware-synchronized data acquisition card. An extensive calibration procedure enables accurate generation of arbitrary magnetic field vectors, while synchronized triggering ensures deterministic alignment between field application, illumination, and image acquisition. The system images an entire 100 mm Petri dish in a single acquisition. Typical experiments monitor hundreds of bacterial colonies simultaneously over multi-hour acquisition sequences. Control software, calibration routines, mechanical design files, and acquisition workflows are provided openly to facilitate replication. Instrument performance is demonstrated through detection of magnetic field-dependent fluorescence changes in E. coli expressing the engineered magnetosensitive fluorescent protein MagLOV2. This instrument provides a flexible and scalable platform for high-throughput magnetobiology, synthetic biology, and quantum biology experiments.

Recent advances in spatial transcriptomics have enabled the profiling of increasingly larger numbers of genes while retaining single-cell and subcellular resolution in situ. However, standardized bioinformatics workflows for analyzing these datasets have lagged behind, with existing pipelines focusing primarily on image processing and cell segmentation. To address this gap, we present nf_xpatial, a best-practices Nextflow pipeline for the downstream analysis of 10x Genomics Xenium data. The pipeline performs quality control, filtering, log and cell area normalization, multi-sample integration, and both expression-driven and spatially informed clustering across systematic parameter sweeps, allowing users to evaluate and compare clustering resolutions and spatial modeling parameters within a single reproducible run. Overall, nf_xpatial streamlines the processing of Xenium data from platform outputs to integrated single-cell and spatial clustering datasets, providing a standardized starting point from which biologists can fine-tune parameters and proceed to hypothesis-driven spatial analyses.

Preeclampsia arises from early placental dysfunction that precedes clinical presentation, creating a need for molecular readouts that enable timely risk assessment. Placenta derived exosomes carry nucleic acid biomarkers with diagnostic potential, yet current analytical workflows rely on multistep isolation and extraction procedures that are slow and resource intensive. Here, we report Exo-LAMP, an extraction-free isothermal assay for detecting placental exosomal KISS1 mRNA, representing the first isothermal approach for exosomal mRNA detection. Placental alkaline phosphatase (PLAP) positive syncytiotrophoblast derived exosomes are selectively captured from biofluids using PLAP antibody conjugated magnetic beads within 20 minutes. Captured exosomes are treated with RNase inhibitor and lysed at 90C for 5 minutes, enabling direct RNA release while preserving transcript integrity and eliminating conventional extraction steps. The released RNA is analyzed in the same vessel by colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP), providing semi-quantitative readout within 25 to 30 minutes. The assay quantifies KISS1 mRNA across 103 to 108 exosomes per reaction, with an estimated limit of detection of about 2 exosomes and a limit of quantification of 103 exosomes. Stoichiometric analysis indicates approximately one KISS1 transcript per 27 exosomes, compared with one per 38 exosomes measured by reverse transcription quantitative polymerase chain reaction (RT-qPCR). In both the exosome-depleted serum spike-in and the plasma spike-in samples containing mixed vesicle populations, the assay detects target derived transcripts at 1% abundance and retains measurable signal at the quantification limit in the presence of up to 108 non-target exosomes. These results establish Exo-LAMP as a rapid and simplified platform for exosomal mRNA analysis that integrates selective capture, lysis, and amplification in a single workflow completed in less than 1 hour. The method provides a general framework for semi-quantitative detection of exosomal transcripts and can be adapted to other coding and non-coding RNA targets for liquid biopsy applications.

BackgroundThe quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. ResultsWe demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10 spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense, a model for Pseudocercospora fijiensis, and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. ConclusionsMIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.

Melanoma incidence continues to rise globally, with formalin-fixed paraffin-embedded (FFPE) tissue archives representing an invaluable resource for large-scale retrospective proteomic studies. However, inconsistent deparaffinization remains a critical pre-analytical bottleneck limiting protein yield, reproducibility, and downstream data quality. In this study, we developed and validated a fully automated FFPE deparaffinization workflow using the Fluent(R) 780 liquid handling workstation (Tecan (C)) and evaluated its performance against a conventional manual protocol in a cohort of 54 patients with primary cutaneous melanoma, predominantly at early AJCC 8th edition stage I-II. The automated workflow achieved superior protein identification (6,146 {+/-} 860 vs. 4,941 {+/-} 1,091 proteins; p < 0.0001) with lower technical variability, while maintaining highly comparable global proteomic profiles as confirmed by principal component analysis and hierarchical clustering. A total of 8,305 proteins (96.1%) were identified by both methods, supporting the reproducibility and equivalence of the automated approach. Patients were stratified by the presence (N=21) or absence (N=33) of histological regression in the primary tumor. Proteomic comparison revealed 97 upregulated and 226 downregulated proteins in regressing melanomas, with pathway enrichment analysis demonstrating elevated mitochondrial and translational activity alongside reduced innate immune and complement pathway activation in the regression group. No statistically significant differences in overall, disease-free, or progression-free survival were observed between groups, consistent with the early-stage composition of the cohort. Digital pathology validated tissue morphology preservation across processing conditions. These findings support the integration of automated FFPE processing with proteomic and digital pathology workflows as a scalable platform for precision melanoma research. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/744404v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1d51629org.highwire.dtl.DTLVardef@a1f126org.highwire.dtl.DTLVardef@1df1b0aorg.highwire.dtl.DTLVardef@686f1c_HPS_FORMAT_FIGEXP M_FIG C_FIG

Uniform, reproducible blood smears are critical for reliable hematological evaluation. Manual smear preparation, however, is user-dependent and introduces variability that limits quantitative microscopy. Here we developed BUsmear, a low-cost, 3D-printed, motorized blood smear device that prepares two smears simultaneously from a printed stage and a micro-motor drive with tunable linear velocity, controlled through a joystick-operated driver module. By spreading two slides in parallel with fully repeatable slide-to-slide motion, the device doubles throughput while eliminating operator-dependent motion artifacts, and can be fabricated on any benchtop 3D printer in under one day. To validate smear quality, we analyzed blood films from three donors together with a manual smear prepared by an expert from the blood of one of the same donors, giving a matched device-versus-manual pair. Automated Cellpose segmentation of 14,046 red blood cells across 12 bright-field fields showed that cell diameter was preserved and closely matched the expert smear (6.7-7.8 um across groups, within the 6.2-8.2 um human reference range; 6.7 vs 6.9 um in the matched pair), and that all films formed non-aggregated monolayers (Clark-Evans index of aggregation 1.04-1.24). Critically, red blood cells in the device films were markedly more circular than in the expert manual smear (mean eccentricity 0.405 vs 0.502; 0.443 vs 0.502 in the matched pair), with complete separation between the two methods at the level of whole fields of view. Because eccentricity reports smear-induced cell distortion, this indicates that a constant, mechanically controlled spreading velocity preserves red blood cell morphology better than skilled manual technique. BUsmear offers an accessible route to standardized smear geometry for quantitative analysis, including AI-based morphometry, and its low cost may be particularly advantageous in low-income countries with a high prevalence of malaria.