StaggR: an interactive R/Shiny application for planning and visualizing staggered experimental protocols

Biological experiments often require a series of precisely timed operations, and small variations in treatment can result in inconsistent or biased results. To handle multiple samples in parallel with precise temporal resolution, experimentalists may stagger treatments by initiating the workflow of one sample during the wait or incubation time of another. However, as the number of samples processed in parallel and the number of operations increase, it becomes increasingly difficult to identify and execute valid treatment regimens that permit the handling of each sample. To address this, I developed StaggR, an interactive web application that calculates and visualizes compatible staggering intervals for parallelized execution of identical processing workflows. This tool provides a user-friendly interface for defining protocol operations, durations, and wait times. It can automatically calculate the shortest possible conflict-free interval for initiating sample treatments, or allow users to simulate specific intervals to explore potential treatment regimens or bottlenecks. Using StaggR, users of any experience level can rapidly generate complete, color-coded experimental schedules, visualize these workflows in an easy-to-read chart, and execute them using a built-in timer displaying a treatment schedule with live updates. The experimental designs can be saved, shared, and re-imported, ensuring full reproducibility and user control. The application of StaggR is expected to expedite the design and throughput of complex experimental workflows while maximizing reproducibility.

Viscosity of protein solutions is a critical product quality attribute for protein therapeutics such as monoclonal antibodies. Here we introduce a portable single-use analytical chip-based viscometer for determining the viscosity of protein solutions using low sample volumes of 10 μL. Through the combined use of a microfluidic viscometer, a smartphone camera for image capture, and an automated data processing algorithm for the calculation of the viscosity of fluids, we enable measurement of viscosity of multiple samples in parallel. We first validate the viscometer using glycerol-water mixtures and subsequently demonstrate the ability to perform rapid characterization of viscosity in four different monoclonal antibody formulations in a broad concentration (1 to 320 mg/mL) and viscosity (1 to 600 cP) range, showing excellent agreement with values obtained by a conventional cone-plate rheometer. Not only does the platform offer benefits of viscosity measurements using minimal sample volumes, but enables higher throughput compared to gold-standard methodologies owing to multiplexing of the measurement and single-use characteristics of the viscometer, thus showing great promise in developability studies. Additionally, as our platform has the capability of performing viscosity measurements at the point of sample collection, it offers the opportunity to employ viscosity measurement as an in situ quality control of therapeutic proteins and antibodies.

Laser direct-write (LDW) is a forward transfer deposition technique that has recently been adapted for 2D/3D printing of biological materials. LDW is built upon a CAD/CAM platform and scalable technologies, making it an attractive biofabrication tool for many applications, such as high-throughput in vitro testing. To truly realize these capabilities, LDW must be able to print into multi-well plates, (e.g., 24-, 48-, 96-well plates), for analysis of multiple samples in parallel (e.g., spectrophotometry, microscopy, RT-PCR), as such applications demand for high throughput. However, the geometries of standard multi-well plates interfere with the LDW print ribbon, requiring a much greater transfer distance (> 10mm between ribbon and receiving substrate). This large ribbon-substrate distance compromises print quality significantly (e.g., loss of print registry, generation of satellite beads, scattered print spots/droplets). Moreover, the wells cannot be spin-coated evenly, leading to non-uniform substrates. To overcome these hurdles, we developed a novel method to produce customizable multi-well substrates that can be spin-coated, and do not restrict ribbon-substrate spacing. This technique can be used to create LDW-compatible substrates with custom multi-well geometries, to greatly improve the flexibility, utility, and throughput of LDW.

Current biological tools to measure protein and mRNA abundance average the responses of the large number of cells present in a sample, thereby concealing cell-cell heterogeneity and limiting the analysis of rare cell types. While single cell analysis may be more desirable, the tools and methods to do these measurements are not widely available. We have developed a qRT-PCR approach to measure mRNA expression from single cell samples, and are developing a microfluidic ELISA tool to measure multiple proteins simultaneously from single cell samples. These techniques have been used to probe the spectrum and influence of cell-cell heterogeneity on immune responses by defining coordinate response patterns from individual cells and from multiple cells in parallel. Requirement for success in achieving single cell measurements include: 1) precise nanoliter-volume fluid handling; 2) sufficient sensitivity to detect single cell responses; 3) multiplexing to measure more than one analyte from each cell sample; 4) a means to biochemically process single cell samples; and 5) methods to be able to process multiple samples in parallel. Remarkably, there are technical solutions to these challenges. However, a major limitation is the current lack of understanding of the biological significance of the very heterogeneous properties of individual cells in a population. Biological understanding of cell behavior will require reference datasets to be established so that normal/diseased responses can be classified, and the significance of the absolute abundance of a given protein within a given cell can be understood. Also, current methods focus on candidate genes and proteins, and an additional challenge is identifying which analytes are most appropriately measured to be useful for TB diagnostics and monitoring responses to therapy and vaccination.

Qualitative and quantitative analysis of hyaluronan oligosaccharides with high performance thin layer chromatography using reagent-free derivatization on amino-modified silica and electrospray ionization-quadrupole time-of-flight mass spectrometry coupling on normal phase

Cytokines play crucial roles in the communication between brain cells including neurons and glia, as well as in the brain-periphery interactions. In the brain, cytokines modulate long-term potentiation (LTP), a cellular correlate of memory. Whether cytokines regulate LTP by direct effects on neurons or by indirect mechanisms mediated by non-neuronal cells is poorly understood. Elucidating neuron-specific effects of cytokines has been challenging because most brain cells express cytokine receptors. Moreover, cytokines commonly increase the expression of multiple cytokines in their target cells, thus increasing the complexity of brain cytokine networks even after single-cytokine challenges. Here, we review evidence on both direct and indirect-mediated modulation of LTP by cytokines. We also describe novel approaches based on neuron- and synaptosome-enriched systems to identify cytokines able to directly modulate LTP, by targeting neurons and synapses. These approaches can test multiple samples in parallel, thus allowing the study of multiple cytokines simultaneously. Hence, a cytokine networks perspective coupled with neuron-specific analysis may contribute to delineation of maps of the modulation of LTP by cytokines.

The reconstruction of individual haplotypes can facilitate the interpretation of disease risks; however, high costs and technical challenges still hinder their assessment in clinical settings. Second-generation sequencing is the gold standard for variant discovery but, due to the production of short reads covering small genomic regions, allows only indirect haplotyping based on statistical methods. In contrast, third-generation methods such as the nanopore sequencing platform developed by Oxford Nanopore Technologies (ONT) generate long reads that can be used for direct haplotyping, with fewer drawbacks. However, robust standards for variant phasing in ONT-based target resequencing efforts are not yet available. In this study, we presented a streamlined proof-of-concept workflow for variant calling and phasing based on ONT data in a clinically relevant 12-kb region of the APOE locus, a hotspot for variants and haplotypes associated with aging-related diseases and longevity. Starting with sequencing data from simple amplicons of the target locus, we demonstrated that ONT data allow for reliable single-nucleotide variant (SNV) calling and phasing from as little as 60 reads, although the recognition of indels is less efficient. Even so, we identified the best combination of ONT read sets (600) and software (BWA/Minimap2 and HapCUT2) that enables full haplotype reconstruction when both SNVs and indels have been identified previously using a highly-accurate sequencing platform. In conclusion, we established a rapid and inexpensive workflow for variant phasing based on ONT long reads. This allowed for the analysis of multiple samples in parallel and can easily be implemented in routine clinical practice, including diagnostic testing.

Wait times in Canada are increasingly being monitored as an indicator of quality health care delivery. We created a higher resolution picture of the wait experienced by urological surgery patients beginning with the initial referral. In doing so, we hoped to (a) identify potential bottlenecks and common delays at our centre, and (b) identify predictors of wait time. The charts of 322 patients undergoing surgery from November 2007 to March 2008 were reviewed and specific dates, patient factors and delays were recorded. The data were used to detail the patient's wait and to determine the patient factors which were predictive of wait time. The mean time from decision to operate to the day of operation was 75.87 days for all patients. This number accounts for 53% of the wait time, while the time from referral to decision to operate is 47%. Predictors of a decreased wait time include cancer cases, younger age, urgency score, repeat patients and female gender in multivariate analysis. Delays were experienced by 16.8% of patients; most common delays were operating room cancellations/time constraints, patients requiring further optimization and delays in referral (4.7%, 3.4% and 3.1%, respectively). The waiting process is complex; the actual waiting time that a patient must endure is much longer than the wait times traditionally recorded and reported by hospitals. As strategies are implemented to decrease wait times, it will become increasingly important to monitor the entire wait time from referral to operation and to ensure that changes are being made that truly decrease wait times and not simply shift where and when the patient waits.

Background: Perinatal mortality and morbidity remains high in Kenya over the years, and this has been attributed to the Three Delays, the third focusing on access to care once the client is in the health facility. Structured frameworks for obstetric triage have been shown to improve perinatal outcomes across the globe. In Kenya, anecdotal evidence suggests that there is no formal framework for conducting obstetric triage in public facilities. This study sought to assess the effect of a Structured Integrated Obstetric Triage Model (SIOTEL) on patient waiting time in maternity units of Kiambu Referral Hospitals. Methods: A quasi experimental design was applied in the study. The intervention facility was Kiambu County Referral Hospital and control facility was Thika Level 5 Hospital. Study population included files of patients admitted in the labour wards. The intervention (SIOTEL) was a 3-scale modified Obstetric Triage Acuity Scale, a client assessment tool that was used to conduct obstetric triage. A baseline survey was conducted followed by development, validation and implementation of the tool, then an end line survey conducted to assess effect of the model on waiting time. Quantitative data was analyzed using STATA and presented in tables and graphs. T-statistic was used to test for significance of mean waiting time in the two facilities. Results: A total of 455 files were reviewed, 231 at baseline and 224 at endline survey. The mean client waiting time at baseline was 113.19 and 124.91 minutes in the intervention and control facilities respectively. At endline, the mean waiting time was 57.46 minutes in the intervention facility and 112.92 minutes in the control facility. Findings thus revealed a significant effect of the SIOTEL on client waiting time in the intervention facility (t-3.77;p value 0.00) compared to the control facility (t-0.78; p value 0.44) Conclusions and recommendations: The study concludes that implementation of structured models for obstetric triage contributes to reduction in client waiting time, and recommendations are made to adopt such models for triage in public facilities in Kenya

Response

Implementation of a Specialty Pharmacy in an Ambulatory Care Setting in Saudi Arabia: Patient Experience and Operational Outcomes

The goal in any oil field development is to accelerate the hydrocarbon production and optimize the oil recovery at lowest cost possible. This objective becomes very challenging when managing thin oil rim reservoirs where a thin column of oil is overlain by a large gas cap and underlain by a strong aquifer. Early gas breakthrough and water coning can cause serious problems to production and hence jeopardize oil recovery. In this type of reservoir, it is very critical to control the movement of GOC and OWC. Keeping a force balance between gas cap expansion and aquifer movement is the key for maximum oil recovery. A successful project may entail plans to maintain the reservoir energy through water injection or produced gas re-injection to maintain reservoir pressure and enhance oil recovery. This study presents evaluation of a thin oil rim from Gulf of Thailand (GOT) offshore reservoir to estimate the optimum recovery factor based on dynamic flow properties. An experimental design and optimization workflow is adopted to study the effect of different flow dynamic parameters on overall recovery of a thin oil rim reservoir. Secondary recovery options (water/gas injection) are also evaluated to see their impact on incremental recovery. In this study, the impact of changing the force balance on oil recovery by horizontal well drilling is evaluated. The effect of horizontal well lateral length and distance from OWC and GOC on oil recovery is also studied. The results show a potential of improving oil recovery factor by drilling horizontal wells in thin oil rims that are farther from GOC and closer to OWC.

Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org.

Isobaric tags for relative and absolute quantitation, an approach to concurrent, relative quantification of proteins present in four cell preparations, have recently been described. To validate this approach using complex mammalian cell samples that show subtle differences in protein levels, a model stem cell-like cell line (FDCP-mix) in the presence or absence of the leukemogenic oncogene TEL/PDGFRbeta has been studied. Cell lysates were proteolytically digested, and peptides within each sample were labeled with one of four isobaric, isotope-coded tags via their N-terminal and/or lysine side chains. The four labeled samples are mixed and peptides separated by two-dimensional liquid chromatography online to a mass spectrometer (LC-MS). Upon peptide fragmentation, each tag releases a distinct mass reporter ion; the ratio of the four reporters therefore gives relative abundances of the given peptide. Relative quantification of proteins is derived using summed data from a number of peptides. TEL/PDGFRbeta leukemic oncogene-mediated changes in protein levels were compared with those seen in microarray analysis of control and transfected FDCP-mix cells. Changes at the protein level in most cases reflected those seen at the transcriptome level. Nonetheless, novel differences in protein expression were found that indicate potential mechanisms for effects of this oncogene.

The boundaries between targeted interventions, background factors, and confounders are concerns in all biological experimentation. Plant space biology takes these concerns to new territories not just geographically but also epistemically because of the operational constraints of collecting data in the multi-stressor spaceflight environment. In consequence, a central challenge in this field is the disentanglement of the various factors involved in the experiments and their tracing to biological effects on the plants. This paper characterizes the unique challenges of fundamental plant biology experiments and examines how secondary data analysis relying on metadata curation is providing novel ways to interpret, compare, and potentially integrate results obtained in these experiments. We propose an understanding of metadata workflows in comparative analyses of complex datasets as a form of post hoc experimental control that makes data tractable for interpretation in three ways: by providing a picture of the relevant intervening factors in experiments, refining the realm of comparison by keeping some conditions constant as background and others targets of analysis, and distinguishing between dependent and independent variables. More broadly, we maintain that this work can be regarded as an extension of experimental practice as it restructures the empirical resources on which researchers build analyses of past experiments and can contribute to the design of future ones.