High-throughput Processing Research Articles

High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks

High-throughput mesoscopic optical imaging technology has tremendously boosted the efficiency of procuring massive mesoscopic datasets from mouse brains. Constrained by the imaging field of view, the image strips obtained by such technologies typically require further processing, such as cross-sectional stitching, artifact removal, and signal area cropping, to meet the requirements of subsequent analyse. However, obtaining a batch of raw array mouse brain data at a resolution of 0.65×0.65×3μm3 can reach 220TB, and the cropping of the outer contour areas in the disjointed processing still relies on manual visual observation, which consumes substantial computational resources and labor costs. In this paper, we design an efficient deep differential guided filtering module (DDGF) by fusing multi-scale iterative differential guided filtering with deep learning, which effectively refines image details while mitigating background noise. Subsequently, by amalgamating DDGF with deep learning network, we propose a lightweight deep differential guided filtering segmentation network (DDGF-SegNet), which demonstrates robust performance on our dataset, achieving Dice of 0.92, Precision of 0.98, Recall of 0.91, and Jaccard index of 0.86. Building on the segmentation, we utilize connectivity analysis for ascertaining three-dimensional spatial orientation of each brain within the array. Furthermore, we streamline the entire processing workflow by developing an automated pipeline optimized for cluster-based message passing interface(MPI) parallel computation, which reduces the processing time for a mouse brain dataset to a mere 1.1 h, enhancing manual efficiency by 25 times and overall data processing efficiency by 2.4 times, paving the way for enhancing the efficiency of big data processing and parsing for high-throughput mesoscopic optical imaging techniques.

Artificial Intelligence Integration in Biochips: Enhancing Diagnostics and Precision Medicine

Abstract: The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi- functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.

High-throughput 3D shape completion of potato tubers on a harvester

Potato yield is an important metric for farmers to further optimize their cultivation practices. Potato yield can be estimated on a harvester using an RGB-D camera that can estimate the three-dimensional (3D) volume of individual potato tubers. A challenge, however, is that the 3D shape derived from RGB-D images is only partially completed, underestimating the actual volume. To address this issue, we developed a 3D shape completion network, called CoRe++, which can complete the 3D shape from RGB-D images. CoRe++ is a deep learning network that consists of a convolutional encoder and a decoder. The encoder compresses RGB-D images into latent vectors that are used by the decoder to complete the 3D shape using the deep signed distance field network (DeepSDF). To evaluate our CoRe++ network, we collected partial and complete 3D point clouds of 339 potato tubers on an operational harvester in Japan. On the 1425 RGB-D images in the test set (representing 51 unique potato tubers), our network achieved a completion accuracy of 2.8 mm on average. For volumetric estimation, the root mean squared error (RMSE) was 22.6 ml, and this was better than the RMSE of the linear regression (31.1 ml) and the base model (36.9 ml). We found that the RMSE can be further reduced to 18.2 ml when performing the 3D shape completion in the center of the RGB-D image. With an average 3D shape completion time of 10 ms per tuber, we can conclude that CoRe++ is both fast and accurate enough to be implemented on an operational harvester for high-throughput potato yield estimation. CoRe++’s high-throughput and accurate processing allows it to be applied to other tuber, fruit and vegetable crops, thereby enabling versatile, accurate and real-time yield monitoring in precision agriculture. Our code, network weights and dataset are publicly available at https://github.com/UTokyo-FieldPhenomics-Lab/corepp.git.

Development of a microhaplotype panel for steelhead/rainbow trout (Oncorhynchus mykiss) and application for phylogenetic analysis in California

AbstractAdvances in high-throughput sequencing and bioinformatic data processing have prompted a transition in wildlife and fisheries genetics from the use of allozymes, mtDNA, or microsatellites towards markers that are more amenable to genotyping by sequencing, increasing the amount of data obtained for a lower cost with less time-consuming techniques. Microhaplotypes are novel multi-allelic genetic markers that utilize a high-throughput genomic amplicon sequencing approach to genotype large numbers of individuals for parentage and kinship analysis and population genetic studies, including applications in monitoring and fisheries management. We describe the development of a panel of microhaplotypes for Oncorhynchus mykiss, a species of high cultural and economic importance throughout its native range and globally through introductions for aquaculture and due to its reputation as a prized sport fish among recreational fishers. The panel includes 124 loci presumed to be neutral, a marker for the sex determination locus (SdY), and 10 loci targeting previously identified adaptive genomic variants associated with important life-history traits in this species. We demonstrate that this panel provides high resolution for phylogeographic and other genetic analysis and on initial standardized reference population genetic baseline of California O. mykiss.

PRAGA: A Priority-Aware Hardware/Software Co-design for High-Throughput Graph Processing Acceleration

Graph processing is pivotal in deriving insights from complex data structures but faces performance limitations due to the irregular nature of graphs. Traditional general-purpose processors often struggle with low instruction-level parallelism and energy inefficiency when handling graph data. In response, modern graph accelerators have embraced an intra-edge-parallel model to enhance parallelization, significantly outperforming conventional processors. However, the indiscriminate processing of edges in existing systems results in substantial computational redundancy, negatively impacting overall efficiency. This paper introduces PRAGA, an innovative graph accelerator designed to optimize efficiency by selectively processing edges that significantly contribute to final results while preserving high computational parallelism. PRAGA utilizes an intra-edge-sequential model, prioritizing edge processing to capitalize on coarse-grained vertex-level parallelism and minimize unnecessary computations. It incorporates a hot-value manager to alleviate network-on-chip congestion and a memory-aware coalescer to minimize redundant data accesses. Our experimental results, obtained using a Xilinx Alveo U280 FPGA accelerator card, demonstrate that PRAGA achieves speedups of 17.88 × and 5.86 × over state-of-the-art accelerators ScalaGraph and GraphDyns, respectively, and outperforms the advanced GPU-based system Gunrock by 22.52 × on average. This substantial improvement underscores PRAGA’s potential to redefine performance benchmarks in graph processing.

A Comparison of Three Automated Nucleic Acid Extraction Systems for Human Stool Samples

Automated nucleic acid extractors are useful instruments for the high-throughput processing of bio-samples and are expected to improve research throughput in addition to decreased inter-sample variability inherent to manual processing. We evaluated three commercial nucleic acid extractors Bioer GenePure Pro (Bioer Technology, Hangzhou, China), Maxwell RSC 16 (Promega Corporation, Madison, WI, USA), and KingFisher Apex (ThermoFisher Scientific, Waltham, MA, USA) based on their DNA yield, DNA purity, and 16S rRNA gene amplicon results using both human fecal samples and a mock community (ZymoBIOMICS Microbial Community Standard (Zymo Research Corp., Irvine, CA, USA)). Bead-beating provided incremental yield to effectively lyse and extract DNA from stool samples compared to lysis buffer alone. Differential abundance analysis and comparison of prevalent bacterial species revealed a greater representation of Gram-positive bacteria in samples subjected to mechanical lysis, regardless of sample type. All three commercial extractors had differences in terms of yield, inter-sample variability, and subsequent sequencing readouts, which we subsequently share in the paper and believe are significant considerations for all researchers undertaking human fecal microbiota research.

Finding environmental-friendly chemical synthesis with AI and high-throughput robotics

Recent environmental challenges have resulted in tremendous interest in Green Chemistry, which includes designing chemical products and processes that reduce the use of environmentally harmful substances. Until now, finding new environmental chemical synthesis has mainly been a trial-and-error process, requiring trained expertise and a lot of work. Here, we report a high-throughput process, combining AI techniques and robotic synthesis, allowing us to find a more environmentally friendly way to synthesize an existing material. The model materials in this study are to replace nitrate salts (NO3−), which might be responsible for algae bloom if leaked into open water, with a chloride salt (Cl−), a naturally abundant ion, in the synthesis of a metal-organic framework (MOF), Zn-HKUST-1. Our high-throughput process starts with using large language models (LLM)-based literature summary to create a database on the synthesis of Zn-HKUST-1 with NO3−, so that optimized concentrations of Cl− can be suggested. Subsequently, these suggestions are tested with automatic robotic processes, increasing the speed and precision of the experiments, and finding the optimal synthesis condition. Then, by using human verification as a foundation, we developed an AI-based automated classification algorithm to automatically sort the acquired images into crystals and non-crystals, focusing on low-resource settings. We successfully obtained MOF crystals from ZnCl2 precursors with this process, which proves that our process holds the promise to accelerate the discovery of new Green Chemistry processes.

Scalable Slot-Die Coating of Passivation Layers for Improved Performance of Perovskite Solar Cell Modules.

Upscaling the perovskite solar cell (PSC) while avoiding losses in the power conversion efficiency presents a substantial challenge, especially when transitioning from ≤1cm2 cells to ≥10cm2 modules. In addition to the fabrication of key functional layers, scalable technologies for surface passivation, considered indispensable for achieving high-performance PSCs, are urgently required. However, studies on this topic remain limited. In this study, an industry-ready slot-die coating method for the effective passivation of perovskite films as a practical alternative is developed to the spin-coating procedures commonly used in research. The coating conditions and molecular structure of the passivation agent are systematically optimized to achieve high-quality film morphology and substantially suppress interface recombination. 2-chloro-5-(trifluoromethyl)-phenylammonium bromide exhibited the best results, improving the open-circuit voltage of cells and subcells in a module by 80±4 and 72±10mV, respectively. Correspondingly, the larger-area (active area: 10cm2) modules sustained the highest efficiency of 21.9% under simulated 1-sun irradiation. The encapsulated devices retained 94% of their initial performances after 750h of continuous operation. The proposed surface-passivation slot-die technology is compatible with high-throughput processes and is employable for large-scale PSC fabrication.

Hierarchically Structured and Tunable Hydrogel Patches: Design, Characterization, and Application.

Recent studies show the importance of hydrogel geometry for various applications, such as encoding, micromachines, or tissue engineering. However, fabricating hydrogel structures with micrometer-sized features, advanced geometry, and precise control of porosity remains challenging. This work presents hierarchically structured hydrogels, so-called hydrogel patches, with internally deviating regions on a micron-scale. These regions are defined in a one-step, high-throughput fabrication process via stop-flow lithography. Between the specified projection pattern during fabrication, an interconnecting lower crosslinked and more porous hydrogel network forms, resulting in at least two degrees of crosslinking within the patches. A detailed investigation of patch formation is performed for two material systems and pattern variations, revealing basic principles for reliable patch formation. In addition to the two defined crosslinked regions, further regions are implemented in the patches by adapting the pattern accordingly. The variations in pattern geometry impact the mechanical characteristics of the hydrogel patches, which display pattern-dependent compression behavior due to predefined compression points. Cell culture on patches, as one possible application, reveals that the patch pattern determines the cell area of L929 mouse fibroblasts. These results introduce hierarchically structured hydrogel patches as a promising and versatile platform system with highcustomizability.

Comparative analysis of whole-genome sequences of African swine fever virus (Asfarviridae: Asfivirus) isolates сollected on the territory of the left bank of the Dnieper River in 2023.

The lack of data on the whole-genome sequences of African swine fever virus (ASFV) variants circulating on the territory of the left bank of the Dnieper River complicates the understanding of the molecular evolution of the virus and the character of the epidemic process development in Russia and Ukraine. Understanding the genetic divergence and phylogenetic relatedness of isolates can largely adjust the strategy of general and specific prevention of the disease. The aim of the study - search and description of unique mutations (deletions/insertions/substitutions) in isolates collected from domestic pigs in Donetsk, Luhansk and Zaporozhye regions in 2023; determination of relatedness and level of homology with reference strains of ASFV genotype II; sub-genotyping and clustering of isolates based on whole-genome analysis. The samples used were a culture suspension of porcine bone marrow (PBM) cells containing ASFV isolates obtained from pathologic material from domestic pig carcasses. Genomic DNA was prepared by purification and concentration of virus followed by phenol-chloroform extraction of total nucleic acid. The high-throughput sequencing process was performed using MGI technology. Consensus sequences were assembled by mapping reads to the reference genome of strain Georgia 2007/1. All isolates are assigned to genotype II, have a monophyletic origin, are phylogenetically close to the clusters «Europe» (4/5) and «Bryansk 2021» (1/5), and are divergent from the original parental genetic variants that make up the enlarged clades. In addition, numerous substitutions in the loci of the multigene family MGF 110, 505, and 360, encoding virulence proteins, were detected in 4 isolates from Donetsk and Zaporozhye regions. The phylogeny of the genotype II ASFV, which originated from the reference strain Georgia 2007/1, is shown to be sufficient for isolate differentiation. The presented data are of theoretical and practical importance for domestic and international ASFV surveillance.

A Data Driven Black Box Approach for the Inverse Quantification of Set-Theoretical Uncertainty

Abstract Inverse uncertainty quantification commonly uses the well established Bayesian framework. Recently, alternative interval methodologies have been introduced. However, in their current state of the art implementation, both techniques suffer from a large and usually unpredictable computational effort. Thus, both techniques are not applicable in a real-time context. To achieve a low-cost, real-time solution to this inverse problem, we introduce a deep-learning framework consisting of unsupervised auto-encoders and a shallow neural network. This framework is trained by means of a numerically generated dataset that captures typical relations between the model parameters and selected measured system responses. The performance and efficacy of the technique is illustrated using two distinct case studies. The first case involves the DLR AIRMOD, a benchmark case that has served as reference case for the inverse uncertainty quantification problem. The results demonstrate that the achieved accuracy is on par with the existing interval method found in literature, while requiring only a fraction of its computational resources. The second case study examines a resistance pressure welding process, which is known to require extremely fast monitoring and control due to the high process throughput. Based on the proposed method, and with only a limited selection of simulated responses of the process, it is possible to identify the interval uncertainty of the crucial parameters of the process. The computational cost in this case makes it possible for an inverse uncertainty quantification in a real-time setting.

Assessing the potential for upscaling the continuous production of lignin oils through reductive catalytic depolymerization

Lignin represents up to one third of lignocellulosic biomass and is a sustainable carbon source available at a sufficient scale to replace an impactful amount of fossil resources when converted into materials and chemicals. The molecular structure of lignin renders it ideal to obtain renewable aromatic building blocks for the polymer industry. Nevertheless, in polymeric form, lignin poses serious challenges for material applications and chemical conversion, namely poor solubility, and low reactivity. This problem can be circumvented by depolymerization of isolated lignins. To meet the demand for renewable aromatic building blocks, it is crucial to utilize technical lignin streams generated on large scale. This will eventually allow the development of continuous, high-throughput processes which are required for viable industrial-scale operations. Our work demonstrates for the first time that “technical grade” hydrolysis lignin from an operating biorefinery can be successfully subjected to continuous reductive catalytic depolymerization (RCD) in a packed bed reactor using a commercially available Pd/Al2O3 catalyst. The impact of catalyst amount, feed concentration (in methanol), and temperature with regards to process stability and product quality was investigated. The product was characterized by NMR, GPC, and GC-FID. Feed concentrations up to 7 wt% could be continuously processed for 77 h at 200 °C whereas at 225 °C, the operation time was restricted to 21 h. In all cases, a stable output was obtained over time in terms of molar mass, OH-group distributions, and monomer content. At 200 °C, carbon yields above 90 % of the soluble lignin fraction were feasible.

Inertial and Deterministic Lateral Displacement Integrated Microfluidic Chips for Epithelial-Mesenchymal Transition Analysis.

With the aim of efficiently sorting rare circulating tumor cells (CTCs) from blood and minimizing damage to CTCs during isolation, we constructed an inertia-assisted single-cell focusing generator (I-SCF) and a water droplet deterministic lateral displacement cell sorting (D-DLD) microfluidic system (IDIC) based on different sizes, the device is initially sorted by a continuous fluid swing and Dean flow-assisted helical micromixers, then flows through a droplet shaped DLD region, enabling single-cell focused sequencing and precise separation, improving cell separation efficiency (>95%) and purity, while ensuring a high single cells survival rate of more than 98.6%. Subsequently, breast cancer cell lines were run through our chip, and then the downstream epithelial-mesenchymal transition (EMT) process induced by TGF-β was detected, and the levels of three proteins, EpCAM, PD-L1, and N-cadherin, were analyzed to establish the relationship between PD-L1 and the EMT process. Compared with other analytical techniques such as the filtration method, the enrichment method and immunoaffinity capture methods, this method not only ensures the separation efficiency and purity, but also ensures the cell activity, and avoids missing the different results caused by the heterogeneity of CTCs due to the isolation of high purity (84.01%). The device has a high throughput processing capacity (5 mL of diluted whole blood/∼2.8 h). By using the chip, we can more easily and conveniently predict tumor stage and carry out cancer prevention and treatment in advance, and it is expected to be further developed into a clinical liquid biopsy technology in the future.

Optoelectronic Reconfigurable Logic Gates Based on Two-Dimensional Vertical Field-Effect Transistors.

Optoelectronic reconfigurable logic gates are promising candidates to meet the multifunctional and energy-efficient requirements of integrated circuits. However, complex device architectures need more power and hinder multifunctional device applications. Here, we design vertical field-effect transistors (VFET) based on the two-dimensional (2D) graphene/MoS2/WSe2/graphene van der Waals heterojunction forming ohmic and Schottky contact. The modulation of the Schottky barrier via gate bias enables the device to switch between positive and negative photocurrents, which can effectively achieve optoelectronic reconfigurable logic gates (XNOR, NOR, NAND, AND, OR, and Inhibit) in a single device. Particularly, the transistor number is reduced by 75% for ("XNOR", "NOR", "NAND") and 83% for ("AND", "OR") gates compared to traditional logic circuits. This work provides a promising route for using a single device to realize optoelectronic reconfigurable logic gates, which can advance the development of high-speed, high-throughput, and intricate data processing in future optical computing applications.

Advanced Plant Phenotyping: Unmanned Aerial Vehicle Remote Sensing and CimageA Software Technology for Precision Crop Growth Monitoring

In production activities and breeding programs, large-scale investigations of crop high-throughput phenotype information are needed to help improve management and decision-making. The development of UAV (unmanned aerial vehicle) remote sensing technology provides a new means for the large-scale, efficient, and accurate acquisition of crop phenotypes, but its practical application and popularization are hindered due to the complicated data processing required. To date, there is no automated system that can utilize the canopy images acquired through UAV to conduct a phenotypic character analysis. To address this bottleneck, we developed a new scalable software called CimageA. CimageA uses crop canopy images obtained by UAV as materials. It can combine machine vision technology and machine learning technology to conduct the high-throughput processing and phenotyping of crop remote sensing data. First, zoning tools are applied to draw an area-of-interest (AOI). Then, CimageA can rapidly extract vital remote sensing information such as the color, texture, and spectrum of the crop canopy in the plots. In addition, we developed data analysis modules that estimate and quantify related phenotypes (such as leaf area index, canopy coverage, and plant height) by analyzing the association between measured crop phenotypes and CimageA-derived remote sensing eigenvalues. Through a series of experiments, we confirmed that CimageA performs well in extracting high-throughput remote sensing information regarding crops, and verified the reliability of retrieving LAI (R2 = 0.796) and estimating plant height (R2 = 0.989) and planting area using CimageA. In short, CimageA is an efficient and non-destructive tool for crop phenotype analysis, which is of great value for monitoring crop growth and guiding breeding decisions.

Digital Magnetic Sorting for Fractionating Cell Populations with Variable Antigen Expression in Cell Therapy Process Development

Cellular therapies exhibit immense potential in treating complex diseases with sustained responses. The manufacture of cell therapies involves the purification and engineering of specific cells from a donor or patient to achieve a therapeutic response upon injection. Magnetic cell sorting targeting the presence or absence of surface markers is commonly used for upfront purification. However, emerging research shows that optimal therapeutic phenotypes are characterized not only by the presence or absence of specific antigens but also by antigen density. Unfortunately, current cell purification tools like magnetic or fluorescence-activated cell sorting (FACS) lack the resolution to differentiate populations based on antigen density while maintaining scalability. Utilizing a technique known as digital magnetic sorting (DMS), we demonstrate proof of concept for a scalable, magnetic-based approach to fractionate cell populations based on antigen density level. Targeting CD4 on human leukocytes, DMS demonstrated fractionation into CD4Hi T cells and CD4Low monocytes and neutrophils as quantified by flow cytometry and single-cell RNA seq. DMS also demonstrated high throughput processing at throughputs 3–10× faster than FACS. We believe DMS can be leveraged and scaled to enable antigen density-based sorting in cell therapy manufacturing, leading to the production of more potent and sustainable cellular therapies.

Broadband and parallel multiple-order optical spatial differentiation enabled by Bessel vortex modulated metalens

Optical analog image processing technology is expected to provide an effective solution for high-throughput and real-time data processing with low power consumption. In various operations, optical spatial differential operations are essential in edge extraction, data compression, and feature classification. Unfortunately, existing methods can only perform low-order or selectively perform a particular high-order differential operation. Here, we propose and experimentally demonstrate a Bessel vortex modulated metalens composed of a single complex amplitude metasurface, which can perform multiple-order radial differential operations over a wide band by presetting the order of the corresponding Bessel vortex. This architecture further enables angle multiplexing to create multiple information channels that synchronously perform multi-order spatial differential operations, indicating the superiority of the proposed devices in parallel processing. Our approach may find various applications in artificial intelligence, machine vision, autonomous driving, and advanced biomedical imaging.

Workflow improvement and financial gain after integration of high-throughput sample processing system with flow cytometer in a high-volume pathology laboratory: Results from a prospective comparative study using Lean principles

BackgroundHighly efficient clinical laboratories are essential for monitoring many human illnesses. Ampath Laboratory Services, the largest pathology lab in South Africa, analyzes large numbers of peripheral blood samples for CD4 levels yearly. ObjectiveTo assess productivity and quality of a newer integrated automated solution, the BD FACSDuet™ Sample Preparation System/BD FACSLyric™ Flow Cytometer using conventional assessment methods and Lean concepts. Materials and methodsThis prospective study compared the performance of the BD FACS™ Sample Preparation Assistant [SPA] III and BD FACSCanto™ II Flow Cytometer with the newly introduced integrated system (BD FACSDuet™/BD FACSLyric™). They were validated for accuracy, precision, and external quality assessment. Process mapping and Lean assessment helped identify steps leading to waste. An economic model was developed to characterize workflow and economic impact associated with total daily hands-on time, processing time, and reworks. ResultsStrong linear correlation was present between both systems. Precision and accuracy studies revealed that all coefficient of variation (CV)% values were below 20% of allowable limits. External proficiency assessments were within limits. The fully automated workflow of BD FACSDuet™/BD FACSLyric™ permitted better consistency with significantly shorter processing time and batch processing and reduced operator interventions. Lean assessment identified defects with motion, over-processing, waiting, and non-utilized talent. Significant reductions in hands-on and total daily processing time that could increase daily specimen testing efficiency and fewer reworks were associated with the BD FACSDuet™/BD FACSLyric™. Lean improvements translated to significant economic savings associated with operator costs and unnecessary reagent consumption. ConclusionBD FACSDuet™/BD FACSLyric™ is an accurate, reliable, and cost-effective fully automated system for high-volume flow cytometry labs that perform T-cell enumeration using a single-platform and single-tube approach.

Fabrication of Spherical Colloidal Supraparticles via Membrane Emulsification.

Colloidal supraparticles are micrometer-scale assemblies of primary particles. These supraparticles have potential application in photonic materials, catalysis, gas adsorption, and drug delivery. Thus, the synthesis of colloidal supraparticles with a narrow size distribution and high yield has become essential. Here, we demonstrate membrane emulsification as a high-throughput approach for fabricating spherical supraparticles with a narrow size distribution and control over particle size and crystallinity. Spherical supraparticles with well-ordered surface structures are synthesized by generating emulsion droplets of an aqueous colloidal dispersion in fluorocarbon oil using a Shirasu porous glass membrane followed by the consolidation of particles through water removal within the emulsion. We systematically investigate process parameters, including the flow rate of the particle dispersion, the particle concentration, and the average pore diameter of the membrane, on the mean size and size distribution of the supraparticles, revealing key factors governing supraparticle properties and production throughput. A comparative evaluation with commonly employed methods highlights the advantage of membrane emulsification, which combines well-defined internal structure and controlled supraparticle sizes with comparably high yields on the order of tens of grams per day. Importantly, in contrast to widely used droplet-based microfluidics, membrane emulsification allows fabrication of supraparticles in nonfluorinated oil. Overall, membrane emulsification offers a simple yet versatile method for fabricating colloidal supraparticles with high quality and yield and may serve as a bridge between existing high-precision techniques, such as droplet-based microfluidics, and high-throughput processes with less control, such as spray-drying.

Recommendations for Accurate Lipid Annotation and Semi-absolute Quantification from LC-MS/MS Datasets.

Recent developments in LC-MS instrumentation and analytical technologies together with bioinformatics tools supporting high-throughput processing of large omics datasets significantly enhanced our capabilities and efficiency of identification and quantification of lipids in diverse biological materials. However, each biological matrix is characterized by its unique lipid composition, thus requiring optimization of analytical and bioinformatics workflows for each studied lipidome. Here, we describe an integrated workflow for deep lipidome profiling, accurate annotation, and semi-absolute quantification of complex lipidomes based on reversed phase chromatography and high resolution mass spectrometry. This chapter provides details on selection of the optimal extraction protocol, acquisition of LC-MS/MS data for accurate annotation of lipid molecular species, and design of lipidome-specific mixtures of internal standards to assist quantitative analysis of complex, native lipidomes.