Processing Of Multiple Samples Research Articles

An Automated Versatile Diagnostic Workflow for Infectious Disease Detection in Low-Resource Settings.

Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays.

Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates.

Deep neural feedforward networks are effective models for a wide array of problems, but training and deploying such networks presents a significant energy cost. Spiking neural networks (SNNs), which are modeled after biologically realistic neurons, offer a potential solution when deployed correctly on neuromorphic computing hardware. Still, many applications train SNNs offline, and running network training directly on neuromorphic hardware is an ongoing research problem. The primary hurdle is that back-propagation, which makes training such artificial deep networks possible, is biologically implausible. Neuroscientists are uncertain about how the brain would propagate a precise error signal backward through a network of neurons. Recent progress addresses part of this question, e.g., the weight transport problem, but a complete solution remains intangible. In contrast, novel learning rules based on the information bottleneck (IB) train each layer of a network independently, circumventing the need to propagate errors across layers. Instead, propagation is implicit due the layers' feedforward connectivity. These rules take the form of a three-factor Hebbian update a global error signal modulates local synaptic updates within each layer. Unfortunately, the global signal for a given layer requires processing multiple samples concurrently, and the brain only sees a single sample at a time. We propose a new three-factor update rule where the global signal correctly captures information across samples via an auxiliary memory network. The auxiliary network can be trained a priori independently of the dataset being used with the primary network. We demonstrate comparable performance to baselines on image classification tasks. Interestingly, unlike back-propagation-like schemes where there is no link between learning and memory, our rule presents a direct connection between working memory and synaptic updates. To the best of our knowledge, this is the first rule to make this link explicit. We explore these implications in initial experiments examining the effect of memory capacity on learning performance. Moving forward, this work suggests an alternate view of learning where each layer balances memory-informed compression against task performance. This view naturally encompasses several key aspects of neural computation, including memory, efficiency, and locality.

Abstract 4948: Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray

Abstract Introduction. Tissue Microarrays (TMAs), widely utilized in the field of pathology, have now found a powerful ally in Image-based Spatial Transcriptomics (ST). By analyzing various gene expression data with high resolution, image-based ST data on TMA can provide the heterogeneous patterns of tumor microenvironment in multiple samples. However, an efficient method for processing multiple samples post-data acquisition is still under development. A streamlined process would expedite the discovery of spatial gene expression patterns, thereby enhancing our understanding of the tumor microenvironment and its implications for cancer diagnostics and treatment strategies. Methods. Our automated pipeline is initiated by automatic segmentation of every core in the whole TMA, derived from the processed output of the MERSCOPE or Xenium platform. Then, it subsequently removes QC failed cells unrelated to any core. Each core receives sequential naming and undergoes automated cell type mapping utilizing a reference single-cell RNAseq data. Additionally, the pipeline computes neighborhood enrichment between cell types, providing a nuanced comprehension of spatial relationships and interactions among diverse cell populations within the tissue microenvironment from multiple samples on the TMA. Results. The automated pipeline we've developed provides several key advantages. It enables the simultaneous analysis of multiple tissue cores, effectively minimizing batch effects and ensuring the reliability of results across diverse tissue samples. Furthermore, by automating core separation, labeling, and cell type mapping, our pipeline significantly streamlines the time and effort required for TMA data analysis. This cost-effective approach allows researchers to optimize resource allocation, making our automated pipeline a valuable tool in cancer research. It facilitates the exploration of gene expression patterns within specific tissue regions, ultimately contributing to the advancement of our understanding of cancer biology. Conclusion. By seamlessly integrating TMA data with image-based ST technologies such as Xenium and MERSCOPE, we can perform comprehensive spatial transcriptomics analyses and provide detailed statistical information on cell type proportions within the tumor microenvironment. This automated pipeline not only ensures robust and reliable results across diverse tissue samples but also optimizes resource allocation by minimizing batch effects and reducing analysis time. This cost-effective solution empowers researchers to delve deeper into spatial gene expression patterns, enhancing our comprehension of the tumor microenvironment. Citation Format: Dongjoo Lee, Seungho Cook, Yeonjae Jung, Myunghyun Lim, Jae Eun Lee, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Automated tumor microenvironment analysis for multiple samples by image-based spatial transcriptomics on tissue microarray [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4948.

A high throughput immuno-affinity mass spectrometry method for detection and quantitation of SARS-CoV-2 nucleoprotein in human saliva and its comparison with RT-PCR, RT-LAMP, and lateral flow rapid antigen test.

Many reverse transcription polymerase chain reaction (RT-PCR) methods exist that can detect SARS-CoV-2 RNA in different matrices. RT-PCR is highly sensitive, although viral RNA may be detected long after active infection has taken place. SARS-CoV-2 proteins have shorter detection windows hence their detection might be more meaningful. Given salivary droplets represent a main source of transmission, we explored the detection of viral RNA and protein using four different detection platforms including SISCAPA peptide immunoaffinity liquid chromatography-mass spectrometry (SISCAPA-LC-MS) using polyclonal capture antibodies. The SISCAPA-LC MS method was compared to RT-PCR, RT-loop-mediated isothermal amplification (RT-LAMP), and a lateral flow rapid antigen test (RAT) for the detection of virus material in the drool saliva of 102 patients hospitalised after infection with SARS-CoV-2. Cycle thresholds (Ct) of RT-PCR (E gene) were compared to RT-LAMP time-to-positive (TTP) (NE and Orf1a genes), RAT optical densitometry measurements (test line/control line ratio) and to SISCAPA-LC-MS for measurements of viral protein. SISCAPA-LC-MS showed low sensitivity (37.7 %) but high specificity (89.8 %). RAT showed lower sensitivity (24.5 %) and high specificity (100 %). RT-LAMP had high sensitivity (83.0 %) and specificity (100.0 %). At high initial viral RNA loads (<20 Ct), results obtained using SISCAPA-LC-MS correlated with RT-PCR (R2 0.57, p-value 0.002). Detection of SARS-CoV-2 nucleoprotein in saliva was less frequent than the detection of viral RNA. The SISCAPA-LC-MS method allowed processing of multiple samples in <150 min and was scalable, enabling high throughput.

Model-Based Feedback Control for an Automated Micro Liquid Dispensing System Based on Contacting Droplet Generation through Image Sensing.

Over the past few decades, micro liquid dispensing technology has been widely used in biology, chemistry, material and environmental sciences due to its efficacy in processing multiple samples. For practical applications, precise and effective droplet generation is very important. Despite numerous droplet generation methods, the implementation of droplet-on-demand still faces challenges concerning system complexity, precision, cost, and robustness. In this work, a novel on-demand contacting droplet generation method incorporated with model-based feedback control with an image processing unit as a sensor was proposed. By studying droplet identification using image processing techniques, the model of droplet formation was simplified. Then model-based feedback control was implemented using volumes of dispensed samples as sensing signals by tuning related parameters adaptively to resist disturbances. The proposed method was integrated and applied to a homebuilt automated micro liquid dispensing system with droplets ranging from 20 nanoliter to 200 nanoliter. The experimental results demonstrated a high degree of accuracy and precision. Additionally, the proposed system's practical utility was evaluated by analyzing mutations in genes associated with sensorineural hearing loss, verifying its effectiveness.

Incorporation of automated buffer exchange empowers high-throughput protein and plasmid purification for downstream uses.

The continued acceleration of time-to-market product development and rising demand for biotherapeutics have hastened the need for higher throughput within the biopharmaceutical industry. Automated liquid handlers (ALH) are increasingly popular due to flexible programming that enables processing of multiple samples with an array of functions. This flexibility is useful in streamlining research that requires chromatographic procedures to achieve product purity for downstream analysis. However, purification of biologics often requires additional off-deck buffer exchange steps due to undesirable elution conditions such as high acid or high salt content. Expanding the capability of ALHs to perform purification in sequence with buffer exchange would, therefore, increase workflow efficiency by eliminating the need for manual intervention, thus expediting sample preparation. Here we demonstrate two different automated purifications using pipet-based dispersive solid-phase extraction (dSPE). The first is an affinity purification of His-tagged proteins from bacterial lysate. The second is an anion-exchange purification of plasmid DNA. Both methods are followed by buffer exchange performed by an ALH. Percent recoveries for the three purified recombinant proteins ranged from 51 ± 1.2 to 86 ± 10%. The yields were inversely correlated to starting sample load and protein molecular weight. Yields for plasmid purification ranged between 11.4 ± 0.8 and 13.7 ± 0.9 µg, with the largest plasmid providing the highest yield. Both programs were rapid, with protein purification taking <80 minutes and plasmid purification <60 minutes. Our results demonstrate that high-quality, ready-to-use biologics can be obtained rapidly from a crude sample after two separate chromatographic processes without manual intervention.

Biosensor-guided rapid screening for improved recombinant protein secretion in Pichia pastoris

Pichia pastoris (Komagataella phaffii) is widely used for industrial production of heterologous proteins due to high secretory capabilities but selection of highly productive engineered strains remains a limiting step. Despite availability of a comprehensive molecular toolbox for construct design and gene integration, there is high clonal variability among transformants due to frequent multi-copy and off-target random integration. Therefore, functional screening of several hundreds of transformant clones is essential to identify the best protein production strains. Screening methods are commonly based on deep-well plate cultures with analysis by immunoblotting or enzyme activity assays of post-induction samples, and each heterologous protein produced may require development of bespoke assays with multiple sample processing steps. In this work, we developed a generic system based on a P. pastoris strain that uses a protein-based biosensor to identify highly productive protein secretion clones from a heterogeneous set of transformants. The biosensor uses a split green fluorescent protein where the large GFP fragment (GFP1-10) is fused to a sequence-specific protease from Tobacco Etch Virus (TEV) and is targeted to the endoplasmic reticulum. Recombinant proteins targeted for secretion are tagged with the small fragment of the split GFP (GFP11). Recombinant protein production can be measured by monitoring GFP fluorescence, which is dependent on interaction between the large and small GFP fragments. The reconstituted GFP is cleaved from the target protein by TEV protease, allowing for secretion of the untagged protein of interest and intracellular retention of the mature GFP. We demonstrate this technology with four recombinant proteins (phytase, laccase, β-casein and β-lactoglobulin) and show that the biosensor directly reports protein production levels that correlate with traditional assays. Our results confirm that the split GFP biosensor can be used for facile, generic, and rapid screening of P. pastoris clones to identify those with the highest production levels.

Automated and parallel microfluidic DNA extraction with integrated pneumatic microvalves/pumps and reusable open-channel columns.

A novel microfluidic DNA extraction protocol based on integrated diaphragm microvalves/pumps and silica-deposited open-channel columns was developed specifically for automated and parallel DNA solid-phase extraction (SPE). The method uses microfluidic chips with a sandwiched structure containing three layers, which are the upper fluidic layer with surface-deposited silica on glass open channels as the extraction phase, the lower actuation layer with valve actuation channels on a glass wafer, and the middle poly(dimethylsiloxane) (PDMS) membrane for reversible bonding of the two glass substrates. These two glass substrates can be reused after thoroughly cleaning and the PDMS membrane can be replaced conveniently, which could effectively decrease the time and cost of chip manufacturing. The normally closed microvalves/pumps were used to automatically control all processes of the on-chip DNA SPE without cross-contamination and leakage, enabling the processing of multiple samples in parallel without changing the microvalve control module. Using the microchip device with integrated microvalves/pumps, automated, programmable, and simultaneous λ-DNA extractions from different samples could be attained, even from complex solutions such as human blood, and the silica-deposited open-channel columns could be reused stably and reliably. Results have demonstrated that most of the eluted λ-DNA was recovered in the second 2µL of elution buffer with high-purity suitable for successful polymerase chain reaction amplification, making it possible for further integration into microfluidic devices for fully functional and high-throughput genetic analysis.

Rapid and Simple Method for Measuring Petroleum Asphaltenes by the Centrifugation Technique.

Asphaltenes are heavy constituents of crude oil which affect the flow and viscosity of crude oil. They also stabilize water-in-oil emulsions which makes the separation process of water from oil during the primary treatment processes for crude oils more difficult and costly. Measuring asphaltenes has great importance, especially for crude oil production companies. Gravimetric and spectroscopic measurement methods are the basic techniques used by international references such as ASTM and IP. A new methodology has been introduced as a modification of ASTM D6560 gravimetric methodology by using the centrifugation technique in the separation of asphaltenes for different oil samples with the API gravity change from 17.4 (oil S1) to 39.8 (oil S5). The new methodology has the advantages of consuming little time, and multiple sample processing and can be done in the field and also in the lab. Moreover, it has good repeatability, reproducibility, and working range values compared to the reference gravimetric ASTM and IP methods. The repeatability of the new method was found to be 8.0% at its maximum value (S1, has a low asphaltene content), while the minimum value was found to be 3.75% (S10, has the highest asphaltene content). It was found that the maximum reproducibility value was 17.0% for the S1 sample and the minimum was 0.0% for S9 and S10 samples.

Spiking Neural Network With Weight-Sharing Synaptic Array for Multi-input Processing

A multi-input processing spiking neural network inference system (MSS) is proposed to enhance the parallel processing capabilities of the spiking neural network (SNN) inference relative to the conventional SNN inference. Processing multiple input samples using MSS with shared synaptic arrays for compute-in-memory drastically reduces the number of synaptic arrays assigned for parallel processing, thereby minimizing the effort required by parallel processing networks. A shared weight array is evaluated using the 4-bit quantization capabilities of a manufactured ferroelectric field-effect-transistor as a synaptic device. A batch action was implemented as a multi-input on a 3-layer fully connected network to verify the MSS. The benefits of MSS in energy consumption and area throughput are rigorously investigated and estimated based on the number of multi-input processing. It is proven that the simultaneously processing of multiple input samples using the proposed MSS boosts the energy and area efficiency by up to 9.12 and 242 times, respectively.

High-Throughput Neurite Outgrowth Assay Using GFP-Labeled iPSC-Derived Neurons.

The potential neurotoxicity from an increasing number of drugs and untested environmental chemicals creates a need to develop reliable and efficient in vitro methods for identifying chemicals that may adversely affect the nervous system. An important process in neurodevelopment is neurite outgrowth, which can be affected by developmental neurotoxicity. Currently, neurite outgrowth assays rely mainly on staining, which requires multiple sample processing steps, particularly washing steps, that may introduce variation and limit throughput. Here, we describe a neurite outgrowth assay that uses induced pluripotent stem cell (iPSC)-derived human cortical glutamatergic neurons and/or spinal motor neurons labeled with green fluorescent protein (GFP) to test compounds in a high-content and high-throughput format. This method enables live and time-lapse imaging of GFP-labeled neurons using an assay plate that is continuously imaged at multiple times after chemical treatment. In this article, we describe how to thaw frozen GFP-labeled neurons, culture them, treat them with a compound of interest, and analyze neurite outgrowth using a high-content imaging platform. In this assay, GFP-labeled iPSC-derived human neurons represent a promising tool for identifying and prioritizing compounds with potential developmental neurotoxicity for further hazard characterization. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. Basic Protocol 1: Thawing and seeding of iPSC-derived neurons Basic Protocol 2: Compound plate preparation and treatment of neurons Basic Protocol 3: High-content imaging and analysis.

Hemin-Functionalized Microfluidic Chip with Dual-Electric Signal Outputs for Accurate Determination of Uric Acid.

Herein, we develop a hemin-functionalized microfluidic chip with dual-electric signal outputs for accurate determination of uric acid (UA). Hemin is designed as the catalyst, which could trigger a built-in reference signal. Carbon nanotube (CNT) and alkalinized titanium carbide (alk-Ti3C2Tx) are used as attachment substrates to strengthen the signal. Benefiting from the synergistic action of hemin, CNT, and alk-Ti3C2Tx, the hybrid functionalized sensor shows prominent electrochemical capacity, desirable catalytic activity, and unique built-in signal ability. Through density functional theory calculations, the structure-reactivity relationship and possible signal output mechanism are deeply investigated. The functionalized sensor is further integrated into a microfluidic chip to prepare a portable electrochemical sensing platform, in which multiple sample processing steps including primary filtration, target enrichment, and reliable analysis can be conducted step-by-step. Based on the abovementioned designs, the developed functionalized microfluidic platform presents desirable performance in UA determination with a detection limit of 0.41 μM. Furthermore, it is capable of accurately detecting UA in urine samples, providing a promising idea for biomolecule monitoring.

Expanding the use of cork as biosorbent through the determination of new psychoactive substances in postmortem blood samples

This study aimed to investigate the applicability of cork in a solid phase-based extraction method coupled to a 96-deepwell plate for LC-MS/MS determination of new psychoactive substances and other drugs of abuse in blood. Nowadays, there is a concern in developing new analytical methods that follows the principles of “green analytical chemistry”. These include the miniaturization of methods, utilization of minimal sample and solvent volumes, development of greener solvents and sorbents, among others. Regarding sorbents, natural compounds such as cork can be a great choice to be employed as extraction phase due to its large availability and low cost. Nevertheless, these new materials can help overcome analytical challenges related to postmortem analysis. Regarding sample preparation, blood samples were primarily submitted to protein precipitation by mixing 200 μL of blood, 250 μL of water, 250 μL of zinc sulfate and 50 μL of NaOH, followed by agitation and centrifugation. An aliquot of 500 μL of the supernatant was added to a 96-deepwell plate containing 30 mg of cork powder with particle size ≤ 250 μm. Extraction was performed by agitation for 15 minutes in an orbital shaker at 300 rpm, with consecutive removal of the aqueous phase. Desorption was performed by the addition of 1150 μL of acetonitrile with further agitation at 300 rpm for 2 minutes. The organic layers were recovered and dried under nitrogen stream, reconstituted in 30 μL of methanol and 2 μL was injected in the analytical system. Sample preparation parameters were optimized through univariate and multivariate strategies with the software Statistica 8.0 (Statsoft, Tulsa, USA). A Nexera UFLC system coupled to a LCMS-8045 triple quadrupole mass spectrometer (Shimadzu, Japan) was used for the analysis. Separation was performed on an Acquity UPLC® BEH C18 (1.7 μm, 2.1 × 50 mm) column (Waters, USA) using water (A) and acetonitrile (B) both containing 0.1% formic acid as mobile phases. Total run time was 7.5 minutes. The method was validated according to ANSI/ASB Standard 036 for the following parameters: lower limit of quantification (LLOQ), linearity, carryover, selectivity, precision, bias and dilution integrity. To verify the applicability of the method, three real postmortem blood samples were analyzed. The optimization of extraction parameters was carried out for the following items: choice of cork conformation (powder or sheet), cork particle size, cork mass, extraction time, desorption time, solvent type and solvent volume. The best parameters values are described in “Methods” session. LLOQs ranged between 0.5 and 5 ng/mL. Calibration curves were linear through the ranges of 0.5–85 ng/mL for ethylone, MDA, MDMA, N-ethylpentylone, 25B-NBOMe, 25C-NBOMe, 25I-NBOMe, fentanyl, pentylone and LSD; 1–85 ng/mL for cathinone, mephedrone and PCP; and 5–100 ng/mL for 25-NBOH, with determination coefficient (r2) > 0.99 and absence of carryover effects. Bias, within-run and between-run precision and dilution integrity results were within the acceptable values. The three samples were positive for the presence of MDA and MDMA with concentrations ranging between 23.11–26.59 ng/mL and 79.21–198.13 ng/mL, respectively. Sample preparation optimization was found to be relevant for the achievement of better analytical responses. Additionally, the use of the 96-deepwell plate allows simultaneous processing of multiple samples, with further possibility of automation. This study demonstrate that cork can be employed as a biosorbent for the determination of NPS and other drugs of abuse in postmortem blood through proper optimization, validation and application of the methodology. Therefore, it can be an alternative extraction phase to be considered in method development for toxicological purposes.

Filter paper disks as a matrix for manipulation of recombinant proteins

Filter paper provides an excellent matrix for retention of proteins containing a cellulose binding domain. To use this capability for manipulating recombinant fusion proteins, binding and elution parameters were explored and procedures developed for small scale purification, modification and assay. Proteins were tagged with the cellulose binding domain from the Clostridium thermocellum CipB gene via a cleavable linker. Filter paper disks of 6 mm diameter were able to bind up to 80 μg protein although there was a substantial dependence on molecular size. Different means of introducing fusion proteins to the disks allow either binding within 20 min from microliter volumes or slower binding from milliliter volumes. Elution with protease in small volumes yielded greater than 10 μg amounts with concentrations in the 1–2 mg/ml range. To demonstrate their utility, disks were used for small scale protein purification, covalent modification of protein, immunoprecipitation, and in a binding assay. These versatile methods allow parallel processing of multiple samples and may find many uses when only small amounts of protein are needed.

Remote Learning Versus Traditional Learning: Attitudes of University Students

The years 2020 and 2021 brought new challenges to teaching and learning in the institutions of tertiary education due to the global COVID-19 Pandemic. They have been the devastating years for many teachers. Innumerable difficulties in professional and personal life increased the stress - striving to survive with the least losses. Temporary measures for remote teaching/learning in spring of 2020 extended until summer of 2021 worldwide and seem unlikely to stop in the nearest future. New challenge of novel online activities made teachers reconsider their teaching philosophy due to an overall lack of students engagement in spite of the usual and familiar learning procedures. The current crisis outlines the following framework to teaching philosophy: capability, reliability, inability, suitability, ingenuity, and sustainability. This article aims at researching University students attitudes to remote and traditional learning of English for Specific Purposes. A specially designed survey was administered to 180 full-time students (9 groups, 20 students in each group). Their responses were statistically processed by a means of the SPSS software to compute the Means and Standard Variations and compare the estimates of the between-group and within-group variances. Statistical processing of multiple samples reveals whether observed differences in responses occur at random, i. e. are due to chance, or they are significant, real and meaningful. The scientific analysis of computation data might allow drawing conclusions about students preferences: which mode of learning - remote or traditional - is beneficial and how much is each of them supportive.

Influence of process and formulation parameters on the preparation of solid lipid nanoparticles by dual centrifugation

A promising strategy to formulate poorly water-soluble active pharmaceutical ingredients (APIs) is the application of these substances in solid lipid nanoparticles. These drug carrier systems are commonly prepared by high-pressure homogenization above the melting temperature of the utilized lipid. While being very useful for large-scale production this method is quite resource-consuming and does not allow simultaneous processing of multiple samples, e.g. for screening purposes. For this reason, an alternative manufacturing process, dual centrifugation, is introduced to prepare solid lipid nanoparticles. The ingredients of the dispersions were directly weighed into 2 mL vessels at room temperature without the need to prepare a pre-mix emulsion. Due to an additional rotation of the samples in the heated centrifuge as well as the addition of grinding media an intensive stressing of the samples was achieved. The emulsification process was finished within 10 min with sample temperatures of up to 90 °C being obtained. Dependent on the process set-up like grinding media size, filling ratio or process temperature and the composition of the lipid formulation, the achieved particles sizes were below 200 nm and had a narrow, monomodal size distribution.

Electrochemical sensor array with nanoporous gold nanolayer and ceria@gold corona-nanocomposites enhancer integrated into microfluidic for simultaneous ultrasensitive lead ion detection

To minimize the plumbic poisoning, the construction of powerful portable chip for ultrasensitive Pb2+ assay plays a significant role in clinical diagnosis and environmental monitoring. Herein, inspired by communicating vessels, a microfluidic electrochemical sensing chip array was proposed for ultrasensitive Pb2+ detection of multiple samples. An editable nanoporous gold nanolayer with a feature of all-round conductivity and abundant active sites favoring biological ligand attachment was employed as electrochemical sensing substrate. Ceria@gold corona-nanocomposites (CeO2@Au NPs) was designed and utilized as a differential pulse voltammetry response enhancer of the o-phenylenediamine-hydrogen peroxide electrochemical sensing system. In addition, the proposed chip contains ten detection units and no complex components (such as pump or valve), which makes it capable of processing multiple samples in batch for the in-situ detection with low cost. Under the optimized experimental conditions, the Pb2+ detection limit of 3.1 pM was achieved with good stability and reliability, which was benefited from the excellent sensing substrate and appropriate CeO2@Au NPs-trigger amplification strategy. The results indicated that the integration of novel microfluidic chip design and nanomaterials catalytic strategy provides a new paradigm for the field of in-situ analysis, and shows promise for potential applications in many analytic fields.

Quantitative Approach Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-ToF) Mass Spectrometry.

Quantitation using mass spectrometry (MS) is a routine approach for multiple analytes, including small molecules and peptides. Electrospray-based MS platforms are typically employed, as they provide highly reproducible outputs for batch processing of multiple samples. Quantitation using matrix-assisted laser desorption/ionization (MALDI) time-of-flight (ToF) mass spectrometry, while less commonly adopted, offers the ability to monitor analytes at significantly higher throughput and lower cost compared with ESI MS. Achieving accurate quantitation using this approach requires the development of appropriate sample preparation, spiking of appropriate internal standards, and acquisition to minimize spot-to-spot variability. Here we describe the preparation of samples for accurate quantitation using MALDI-ToF MS. The methodology presented shows the ability to quantitate perfluorooctanesulfonic acid (PFOS) from contaminated water.

Rapid enrichment of mitochondria from mammalian cell cultures using digitonin

We describe here a simple method to enrich mitochondrial fractions from mammalian cells for downstream analyses in the lab. Mitochondria purification involves cell lysis followed by separation of the organelles from the rest of the cellular components. Here, we use detergent to rupture the cell membrane of mammalian cells followed by differential centrifugation to enrich the organelles. Optimum conditions with respect to detergent concentration, time, sample size, and yield are discussed. The method's utility in downstream analyses and ease of processing multiple samples simultaneously is also described. All the reagents in this method can be assembled in-house, are economical, and are comparable, if not superior, to commercially available kits in terms of mitochondrial yield and integrity.• Rapid enrichment of mitochondria from mammalian cells using commonly available reagents.• Multiple samples can be processed simultaneously.• Works over a wide range of sample size (1 million to 100 million cells).

Development of a new semi-automated closed modular system for CAR-T cell therapy manufacturing

Background & Aim CAR T-cell therapy is a transformative cancer treatment that utilizes genetically modified cells from a patient's own immune system to attack cancer cells. Manufacturing of these cells represents a major challenge for the industry. Today, the manufacturing process lacks specific cell therapy equipment, is labor intensive, and has many “open” (vs closed) procedures. Herein we discuss the development of a new semi-automated closed modular system for CAR-T cell therapy manufacturing. Methods, Results & Conclusion The system integrates existing and newly developed platforms (modules). Specifically, we incorporated and controlled these modules via TrueBio software and the DeltaV Control system. In addition to automating the process, the DeltaV system provides a 21 CFR Part 11 compliant control platform . The DeltaV PK Controller is ideal for the smaller scale process and can be operated in stand-alone mode or as part of a larger system. The associated Syncade MES and Electronic Batch Records will allow material tracking as well as electronic workflows and data collection. Our goal is to engineer a small footprint, closed, semi-automated system to improve process reliability, robustness and flexibility while at the same time affording a significant reduction in cell manufacturing cost. The modularity of the system provides process flexibility and allows the processing of multiple samples in parallel since the operation of individual modules does not block the use of other modules within the train. As processing needs change, the cell manufacturing train can be adapted by selecting the appropriate modules for the task in hand. We believe that this semi-automated process will help alleviate the challenges that the cell therapy manufacturing represents today and ultimately reduce the cost of these live-saving therapies so they can be accessible to a larger group of patients.