Reproducible Analysis Research Articles

Abstract B049: Validation of the PGDx elio™ plasma complete for comprehensive genomic profiling of solid tumors through liquid biopsy with Epic Sciences

Abstract Introduction: To help guide treatment decisions and enable clinical trial matching for cancer patients, tissue-based comprehensive genomic profiling (CGP) is an essential precision oncology tool. Liquid biopsy may be used as a complementary approach to assess tumor-specific DNA alterations circulating in the blood when tissue is limited or unavailable. PGDx’s elio™ plasma complete is a next-generation-sequencing (NGS), cell-free DNA (cfDNA) CGP assay that identifies clinically significant variants from the plasma of patients with advanced and metastatic solid cancers. Methods: The PGDx elio plasma complete is a hybrid-capture, DNA-based kitted solution, optimized for 25 ng of cfDNA input with a targeted panel covering coding exons for 521 genes, and is designed to detect single nucleotide variants (SNVs), small insertions and deletions (indels), copy number amplifications (CNAs), and translocations. It also evaluates blood MSI and tumor mutational burden (bTMB) to guide immunotherapy selection. This study presents the validation of the PGDx elio plasma complete assay to enable the processing of patient blood samples in Epic Sciences' CAP/CLIA-certified laboratory. In the validated configuration, patient plasma will be received and isolated at Epic Sciences and subsequently shipped to PGDx/Labcorp Oncology for cfDNA extraction, library preparation, sequencing, and bioinformatics analysis. Finally, clinical report generation will be carried out by Velsera’s CGW software. Analytical validation was assessed using 105 replicates of SeraCare® (n=21) and Horizon (n=3) reference materials to establish analytical sensitivity, accuracy, and reproducibility and repeatability. Specificity was assessed using 20 non-cancerous plasma samples that were also tested orthogonally. For clinical validation a cohort of >150 cancer patient plasma samples were evaluated, including lung, breast, colorectal, head and neck, pancreatic, and prostate cancers, and compared to Illumina’s TSO500 ctDNA or Labcorp Plasma Focus results. Results: Analytical sensitivity and specificity were evaluated for SNVs, indels, CNAs, fusions, MSI and bTMB. Across all variant types, sensitivity was ≥90%, and specificity was ≥95% at LoDs. The reproducibility and repeatability study resulted in ≥95% agreement for all variant types. Analytical sensitivity was calculated using vendor-verified variants in each control. SeraCare® controls and Horizon reference materials were used to assess variant concordance with an aggregate analytical performance across all control samples with ≥95% PPA and NPA for all variant types. Conclusions: The validation of the PGDx elio plasma complete liquid biopsy assay as part of the Epic Sciences' CAP/CLIA diagnostic workflow for cfDNA-based NGS demonstrates that the liquid biopsy approach is highly sensitive, specific, accurate, reproducible, and robust for comprehensive genomic profiling to complement tissue- based testing and inform clinical decision making. Citation Format: Cynthia Maddox, Kenneth C Valkenburg, Cesar Nalvarte, Amanda Harvey, Amy Greer, Ellen L Verner, Renee L Sears, Martin Blankford, Eric A Severson, Taylor J Jensen, Marcia Eisenberg, Shakti Ramkissoon. Validation of the PGDx elio™ plasma complete for comprehensive genomic profiling of solid tumors through liquid biopsy with Epic Sciences [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B049.

EPCO-33. OMICON: A CLOUD PLATFORM FOR FAIR RESEARCH ON GENE COEXPRESSION NETWORKS IN NORMAL HUMAN BRAIN AND BRAIN TUMORS

Abstract Genome-wide coexpression analysis of bulk human brain samples is a powerful approach for identifying highly reproducible transcriptional signatures of cell types and cell states, since it can survey huge numbers of individuals, cells, and transcripts. However, it is difficult to optimize gene coexpression network construction, compare gene coexpression modules from independent datasets, or even locate gene expression datasets with similar attributes. To address these challenges, we have developed the OMICON platform (theomicon.ucsf.edu) to promote FAIR (Findable, Accessible, Interoperable, Reusable) research practices involving human brain gene coexpression networks. OMICON contains structured gene expression data for >17K bulk human brain samples (~10K normal and ~7K malignant glioma), which were collected from diverse public repositories and consortia (e.g., GEO, TCGA, CGGA, CPTAC, REMBRANDT, IvyGAP, GTEx, NABEC, the Allen Institute). To promote interoperability, we have standardized metadata for hundreds of dataset and sample attributes, including single-nucleotide and copy-number mutations. For each dataset, we have used the FindModules R function to construct dozens of gene coexpression networks by iterating over module detection parameters. These efforts have identified ~100K gene coexpression modules, which have been extensively characterized via enrichment analysis with thousands of curated gene sets. All datasets, metadata, gene coexpression networks, enrichment results, and analysis steps can be browsed with an interactive workflow visualization tool, which promotes accessibility, reusability, and reproducibility by maintaining complete data provenance with unique identifiers. To promote findability, we have built an advanced search engine to identify datasets, samples, modules, and more, by filtering standardized metadata (e.g., find gene coexpression modules in human glioblastoma datasets that are significantly enriched with markers of T-cells). A detailed description of OMICON’s functionality is described on our Help page: theomicon.ucsf.edu/home/help. Through this functionality, OMICON seeks to build community and focus therapeutic efforts around reproducible analyses of transcriptional variation in normal human brain and brain tumors.

Physiological signal analysis and open science using the Julia language and associated software

In this mini review, we propose the use of the Julia programming language and its software as a strong candidate for reproducible, efficient, and sustainable physiological signal analysis. First, we highlight available software and Julia communities that provide top-of-the-class algorithms for all aspects of physiological signal processing despite the language’s relatively young age. Julia can significantly accelerate both research and software development due to its high-level interactive language and high-performance code generation. It is also particularly suited for open and reproducible science. Openness is supported and welcomed because the overwhelming majority of Julia software programs are open source and developed openly on public platforms, primarily through individual contributions. Such an environment increases the likelihood that an individual not (originally) associated with a software program would still be willing to contribute their code, further promoting code sharing and reuse. On the other hand, Julia’s exceptionally strong package manager and surrounding ecosystem make it easy to create self-contained, reproducible projects that can be instantly installed and run, irrespective of processor architecture or operating system.

On the use of receiver operating characteristic area under the curve in eyewitness memory research

AbstractPurposeEyewitness memory research has reformed police practices and policy and is sometimes relied upon in legal proceedings. Due to the practical implications derived from this research, it is imperative to evaluate how practical recommendations are postulated. To assess the practical relevance of research, effect sizes and their interpretation play a pivotal role.MethodsWe examined how the frequently used effect size Area Under the Curve (AUC) obtained via Receiver Operating Characteristic (ROC) curves are used and interpreted in eyewitness memory research. We identified 157 eyewitness memory related articles that conducted ROC curve analyses resulting in 1580 AUCs.ResultsApproximately 90% of the AUCs were only interpreted via statistical significance. The majority of studies did not report 95%CIs for their AUCs. Finally, power analyses were frequently not conducted or not reproducible.ConclusionsTo improve the practical inferences of eyewitness memory research, we recommend establishing a smallest effect size of interest, focusing on 95%CIs, and conducting reproducible power analyses.

Mapping 18F-FDG PET uptake in the aortic wall and thrombus

Abstract Background Positron Emission Tomography (PET) uptake allows for the assessment of molecular activity, being 18F-FDG uptake indicative of glucose metabolism. PET uptake is commonly quantified manually by drawing regions of interest, which is subjective, poorly-reproducible and may miss relevant information. Purpose To develop and test an image analysis method to quantify and map 18F-FDG uptake on the whole aortic wall and thrombus. Methods PET-MRI was acquired in patients with thoracic or abdominal aortic aneurysms. The aorta was semi-automatically segmented in the MR angiography using 3DSlicer, generating a surface mesh that was expanded by 3 mm inward and outward. PET uptake was interpolated over this volume with a uniform spatial resolution of 1 mm. By means of 9 reference points, the aortic wall was discretized into 52 (thoracic) or 80 (thoraco-abdominal) standardized regions (i.e. wall patches). For each aortic patch and for the whole thrombus volume, the target to background Ratio (TBR) was calculated by dividing the uptake by a reference uptake in the left atrium. Two observers segmented the aorta and thrombus, located the anatomical reference points and quantified TBR. Inter-observer agreement for segmentation and aortic wall discretization was evaluated in terms of the 95% Hausdorff Distance (HD) and Dice Score Coefficient (DSC), while both median and 95% TBR in each region were tested via intra-class correlation coefficient (absolute value, single measure). Results Data from 20 patients with thoracic or abdominal aneurysms were analysed, 10 of them presented thrombus. The reproducibility of the segmentation of the aorta (median HD 3.16 IQR [2.42 - 3.94] mm, DSC 0.90 [0.89 - 0.92]) and of the thrombus (HD 4.87 [3.51 - 8.45] mm, DSC 0.85 [0.80 - 0.87]) were excellent. The inter-observer agreement in the localization of reference points was good (HD 5.10 [3.29 - 8.73] mm), resulting in excellent overlapping of the standardized aortic wall patches (HD 3.80 [2.44 - 5.08] mm). Inter-observer agreement of median and 95% TBR in each aortic patch were excellent (ICC of 0.90 and 0.93, respectively, Figure 1), with minimal bias (Figure 2). Similarly, excellent interobserver reproducibility was observed for median and 95% TBR in the thrombus (ICC of 0.97 and 0.96, respectively, Figure 2). Conclusions A robust and reproducible image analysis method to map PET uptake along the aortic wall was developed, extending the possibilities to study aortic diseases molecular activity and its association with local aortic characteristics.

A novel portable in situ analyzer for highly sensitive monitoring of organophosphorus and carbamate pesticides in food and environment

The presence of excessive residues of organophosphorus (OPs) and carbamate (CPs) pesticides in food and the environment poses a significant threat to human health and ecological sustainability. Herein, we reported a dual-readout, portable, and highly sensitive photoelectric analyzer (DPHP-analyzer) to achieve rapid and accurate monitoring of OPs and CPs. The dual signal strategy is mainly achieved by controlling the activity of acetylcholinesterase (AChE) in the presence of OPs to catalyze acetylthiocholine (ATCh) and generate thiocholine (TCh), which specifically induced the conversion of colorless Ellman's reagent (DTNB) into yellow 5-thio-2-nitrobenzoic acid (TNB). Simultaneously, the utilization of positively charged TNB as an absorber can induce a dynamic fluorescence quenching effect based on energy transfer mechanism. Fully cooperating with the above mechanism, a CD like, disposable array-based chip manufactured by 3D printing (CD-AN-chip) was designed and integrated to deliver reagents. By use of this dual-output strategy, the analyzer and chip can provide good sensitivity and selectivity for the monitoring of diazinon and carbendazim (case analysis) with a LOD 0.38 ng/mL for carbendazim. And the reproducibility, stability and practicability analysis of real sample have been thoroughly validated simultaneously. Importantly, with the features of fluorescence fingerprint map, the detector could be employed for the distinction of various analytes and further be used for monitoring the nutritional environment within residential and public settings.

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. We present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflow simplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflow for three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. AnVILWorkflow lowers the barrier to utilizing AnVIL’s resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

AMIR: a multi-omics data platform for Asteraceae plants genetics and breeding research.

As the largest family of dicotyledon, the Asteraceae family comprises a variety of economically important crops, ornamental plants and numerous medicinal herbs. Advancements in genomics and transcriptomic have revolutionized research in Asteraceae species, generating extensive omics data that necessitate an efficient platform for data integration and analysis. However, existing databases face challenges in mining genes with specific functions and supporting cross-species studies. To address these gaps, we introduce the Asteraceae Multi-omics Information Resource (AMIR; https://yanglab.hzau.edu.cn/AMIR/), a multi-omics hub for the Asteraceae plant community. AMIR integrates diverse omics data from 74 species, encompassing 132 genomes, 4 408 432 genes annotated across seven different perspectives, 3897 transcriptome sequencing samples spanning 131 organs, tissues and stimuli, 42 765 290 unique variants and 15 662 metabolites genes. Leveraging these data, AMIR establishes the first pan-genome, comparative genomics and transcriptome system for the Asteraceae family. Furthermore, AMIR offers user-friendly tools designed to facilitate extensive customized bioinformatics analyses. Two case studies demonstrate AMIR's capability to provide rapid, reproducible and reliable analysis results. In summary, by integrating multi-omics data of Asteraceae species and developing powerful analytical tools, AMIR significantly advances functional genomics research and contributes to breeding practices of Asteraceae.

Evaluating survey techniques in wastewater-based epidemiology for accurate COVID-19 incidence estimation

Wastewater-based epidemiology (WBE) requires high-quality survey methods to determine the incidence of infections in wastewater catchment areas. In this study, the wastewater survey methods necessary for comprehending the incidence of infection by WBE are clarified. This clarification is based on the correlation with the number of confirmed coronavirus disease 2019 (COVID-19) cases, considering factors such as handling non-detect data, calculation method for representative values, analytical sensitivity, analytical reproducibility, sampling frequency, and survey duration. Data collected from 15 samples per week for two and a half years using a highly accurate analysis method were regarded as gold standard data, and the correlation between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater and confirmed COVID-19 cases was analyzed by Monte Carlo simulation under the hypothetical situation where the quality of the wastewater survey method was reduced. Regarding data handling, it was appropriate to replace non-detect data with estimates based on distribution, and to use geometric means to calculate representative values. For the analysis of SARS-CoV-2 RNA in samples, using a highly sensitive and reproducible method (non-detect rates of <40 %; ≤0.4 standard deviation) and surveying at least three samples, preferably five samples, per week were considered desirable. Furthermore, conducting the survey over a period of time that included at least 50 weeks was necessary. A WBE that meets these survey criteria is sufficient for the determination of the COVID-19 infection incidence in the catchment. Furthermore, WBE can offer additional insights into infection rates in the catchment, such as the estimated 48 % decrease in confirmed COVID-19 cases visiting a clinic following a COVID-19 legal reclassification in Japan.

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Abstract Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

Reproducible brain PET data analysis: easier said than done.

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future.

ThermoNet: advanced deep neural network-based thermogram processing pipeline for automatic time series analysis of specific skin areas in moving legs

Infrared thermography is an emerging technique in biomedical research, potentially providing diagnostic insights into psychological stress, physical strain, muscle fatigue, inflammation, tissue damage, and diseases with thermogenic effects. However, manual analysis strategies are frequently applied causing incomparable, non-reproducible results and hampering standardization. Moreover, widely applied manual analysis cannot recognize blood vessel-related thermal radiation patterns during physical exercise. Therefore, an enhanced processing pipeline, “ThermoNet”, has been developed to automatically process thermograms captured during running. For acquisition, an automatic temperature calibration technique has been introduced to obtain reliable pixel-temperature mapping. The thermograms are semantically segmented in the processing pipeline to extract the anatomical regions of interest (ROIs) by a state-of-the-art deep neural network rather than considering both legs as a single area. A second neural network further examines the ROIs to identify different venous and arterial (perforator) patterns. Within the segments, advanced statistical features are computed to provide time series data. Separate analysis of venous and perforator vessel patterns is carried out on individual connected components, resulting in the extraction of 276 features for each thermogram. The enhanced ROI extraction achieved a high accuracy for the left and right calf on the manually annotated test set. Each step of the ThermoNet pipeline represents a significant improvement over previous analysis methods. Finally, ThermoNet is a transferable pipeline for automatic, reproducible, and objective analysis of ROIs in thermal image sequences of moving test individuals.

Dendritic Spine Quantification Using an Automatic Three-Dimensional Neuron Reconstruction Software.

Synaptic connections allow for the exchange and processing of information between neurons. The post-synaptic site of excitatory synapses is often formed on dendritic spines. Dendritic spines are structures of great interest in research centered around synaptic plasticity, neurodevelopment, and neurological and psychiatric disorders. Dendritic spines undergo structural modifications during their lifespan, with properties such as total spine number, dendritic spine size, and morphologically defined subtype altering in response to different processes. Delineating the molecular mechanisms regulating these structural alterations of dendritic spines relies on morphological measurement. This mandates accurate and reproducible dendritic spine analysis to provide experimental evidence. The present study outlines a detailed protocol for dendritic spine quantification and classification using Neurolucida 360 (automatic three-dimensional neuron reconstruction software). This protocol allows for the determination of key dendritic spine properties such as total spine density, spine head volume, and classification into spine subtypes thus enabling effective analysis of dendritic spine structural phenotypes.

Enhancing of hydroquinone and catechol oxidation using a graphite and graphene composite polymer modified paste electrode

ABSTRACT An innovative electrochemical redox response of hydroquinone (HQN) and catechol (CCL) was analysed using a bare graphite and graphene composite paste electrode (BGGCPE) and polymerised L-leucine modified graphite and graphene composite paste electrode (LCN-MGGCPE) was successfully developed in 0.2 M phosphate buffer solution (PBS) with pH 6.0 at a 0.1 V/s scan rate. Fabricated electrode using simple and cost-effective method, the polymerised LCN-MGGCPE surface shows more electrochemical activity, sensitivity and selectivity towards the determination for HQN and CCL. Cyclic voltammetry (CV), linear sweep voltammetry (LSV), Differential pulse voltammetry (DPV), and electrochemical impudence spectroscopy (EIS) were utilised as analytical techniques while LCN-MGGCPE and BGGCPE surface characterisation were studied using a scanning electron microscopy (SEM) technique. LCN-MGGCPE exhibited superior electrochemical activity compared to BGGCPE towards the oxidation responses of both HQN and CCL. Additionally, the effect of pH studies, scan rate change for HQN, and concentration change of analyte molecule were examined, with the 0.025 to 0.500 V/s scan rate varied and revealing an adsorption-controlled process. The concentration changes of CVs methods ranged from 0.2 to 320 μM, DPVs ranged from 0.2 to 1100 μM and LSVs ranged from 0.2 to 850 μM, the calculated a limit of detection (LOD) of CV, DPV, and LSV, is 0.020 μM, 0.026 μM, and 0.011 μM and a limit of quantification (LOQ) of 0.068 μM, 0.088 μM, and 0.035 μM. The electrochemically developed LCN-MGGCPE shows good reproducibility, repeatability, stability, sensitivity and simultaneous analysis of analyte molecules were studied. This study presents a promising pathway for the growth of effective sensors for the determination of HQN in medicinal and water sample applications.

Evaluating the Radiation Sensitivity Index and 12-chemokine gene expression signature for clinical use in a CLIA laboratory.

The radiation sensitivity index (RSI) and 12-chemokine gene expression signature (12CK GES) are two gene expression signatures (GES) that were previously developed to predict tumor radiation sensitivity or identify the presence of tertiary lymphoid structures in tumors, respectively. To advance the use of these GES into clinical trial evaluation, their assays must be assessed within the context of the Clinical Laboratory Improvement Amendments (CLIA) process. Using HG-U133Plus 2.0 arrays, we first established CLIA laboratory proficiency. Then the accuracy (limit of detection and macrodissection impact), precision (variability by time and operator), sample type (surgery vs. biopsy), and concordance with reference laboratory were evaluated. RSI and 12CK GES were reproducible (RSI: 0.01 mean difference, 12CK GES 0.17 mean difference) and precise with respect to time and operator. Taken together, the reproducibility analysis of the scores indicated a median RSI difference of 0.06 (6.47% of range) across samples and a median 12CK GES difference of 0.92 (12.29% of range). Experiments indicated that the lower limit of input RNA is 5 ng. Reproducibility with a second CLIA laboratory demonstrated reliability with the median RSI score difference of 0.065 (6% of full range) and 12CK GES difference of 0.93 (12 % of observed range). Overall, under CLIA, RSI and 12CK GES were demonstrated by the Moffitt Cancer Center Advanced Diagnostic Laboratory to be reproducible GES for clinical usage.

Performance of an in-house multiplex PCR assay for HIV-1 drug resistance testing – A cheaper alternative

BackgroundCurrently, most HIV drug resistance PCR assays amplify the protease-reverse transcriptase (PR-RT) fragment separately from the integrase (IN) fragment. The aim of this study was to develop a multiplex PCR assay that simultaneously amplifies PR-RT and IN fragments for HIV-1 drug-resistance testing. MethodsThe in-house multiplex PCR assay was evaluated on extracted total nucleic acids obtained from the National Health Laboratory Service (NHLS) and Lancet laboratories. Sanger sequencing was performed on amplicons, and HIV-1 drug-resistance mutations (DRMs) were assessed using HIV Stanford drug resistance database. ResultsThis study tested 59 patient samples with known HIV-1 viral load and DRM results; 41 from Lancet and 18 from NHLS. In-house multiplex PCR assay detected one or both fragments in most samples but had higher sensitivity for detection of IN fragment (93.2 %) compared to PR-RT fragment (83.1 %). There was 100 % concordance between Lancet assay versus in-house assay sequence data for IN DRMs, but lower concordance with PR-RT (87.0 %). The in-house multiplex PCR assay’s precision and reproducibility analysis showed ≥99.9 % sequence similarity and yielded similar DRM results for both PR-RT and IN fragments. ConclusionsThe in-house multiplex PCR assay demonstrated satisfactory performance and higher sensitivity for IN fragment amplification. This could be a cost-effective method for HIV-1 drug resistance testing as both PR-RT and IN fragments are successfully amplified in one reaction in most samples.

Easy analysis of bacterial whole-genome sequencing data for clinical microbiologists using open-source Galaxy platform: Characterization of ESBL-producing Enterobacterales from bloodstream infections

ObjectivesClinical microbiologists require easy-to-use open access tools with graphical interfaces to perform bacterial whole-genome sequencing (WGS) in routine practice. This study aimed to build a bioinformatics pipeline on the open-source Galaxy platform, facilitating comprehensive and reproducible analysis of bacterial WGS data in a few steps. We then used it to characterize our local epidemiology of ESBL-producing Enterobacterales isolated from patients with bacteremia. MethodsWe built a bioinformatics pipeline consisting of the following sequential tools: Fastp (input data trimming); FastQC (read quality control); SPAdes (genome assembly); Quast (quality control of genome assembly); Prokka (gene annotation); Staramr (ResFinder database) and ABRicate (CARD database) for antimicrobial resistance (AMR) gene screening and molecular strain typing. Paired-end short read WGS data from all ESBL-producing Enterobacterales strains isolated from patients with bacteremia over one year were analysed. ResultsThe Galaxy platform does not require command line tools. The bioinformatics pipeline was constructed within one hour. It only required uploading fastq files and facilitated systematization of de novo assembly of genomes, MLST typing, and AMR gene screening in one step. Among the 66 ESBL-producing strains analysed, the two most frequent ESBL genes were blaCTX−M-15 (62.1%) and blaCTX−M-27 (13.6%). ConclusionsThe open-access Galaxy platform provides a graphical interface and easy-to-use tools suitable for routine use in clinical microbiology laboratories without bioinformatics specialists. We believe that this platform will facilitate fast and low-cost analysis of bacterial WGS data, especially in resource-limited settings.

ImageGP 2 for enhanced data visualization and reproducible analysis in biomedical research.

ImageGP is an extensively utilized, open-access platform for online data visualization and analysis. Over the past 7 years, it has catered to more than 700,000 usages globally, garnering substantial user feedback. The updated version, ImageGP 2 (available at https://www.bic.ac.cn/BIC), introduces a redesigned interface leveraging cutting-edge web technologies to enhance functionality and user interaction. Key enhancements include the following: (i) Addition of modules for data format transformation, facilitating operations such as matrix merging, subsetting, and transformation between long and wide formats. (ii) Streamlined workflows with features like preparameter selection data validation and grouping of parameters with similar attributes. (iii) Expanded repertoire of visualization functions and analysis tools, including Weighted Gene Co-Expression Network Analysis, differential gene expression analysis, and FASTA sequence processing. (iv) Personalized user space for uploading large data sets, tracking analysis history, and sharing reproducible analysis data, scripts, and results. (v) Enhanced user support through a simplified error debugging feature accessible with a single click. (vi) Introduction of an R package, ImageGP, enabling local data visualization and analysis. These updates position ImageGP 2 as a versatile tool serving both wet-lab and dry-lab researchers with expanded capabilities.

Impact of Preanalytical Procedures on Complement Biomarkers in Cerebrospinal Fluid and Plasma from Controls and Alzheimer's Disease Patients.

Studies comparing cerebrospinal fluid (CSF) and plasma complement proteins in Alzheimer's disease (AD) patients versus healthy controls (HC) have yielded inconsistent results. Discrepancies in the preanalytical sample handling could contribute to the heterogeneity in the reported findings. Using qualified immunoassays, we aimed at assessing the impact of preanalytical procedures on complement proteins in blood and CSF from AD patients and HCs. We supplemented HC and AD CSF/plasma with complement stabilizers and measured the complement proteins C4a, C4, C3a, C3, Factor Bb and Factor B by immunoassay. We tested the impact of freeze-thaw (FT) cycles on fluid complement proteins. Most complement proteins were mildly impacted by FT cycles in plasma but not CSF, except for C3a which displayed greater sensitivity to FTs in CSF than in plasma. In CSF, the effect of FTs on C3a was reduced but not prevented by the supplementation with EDTA (±Futhan). Our findings provide recommendations for CSF/plasma sample handling to ensure robust and reproducible complement biomarker analyses in AD.

Brain tumoroids: treatment prediction and drug development for brain tumors with fast, reproducible and easy-to-use personalized models.

generation of patient avatar is critically needed in neuro-oncology for treatment prediction and preclinical therapeutic development. Our objective was to develop a fast, reproducible, low-cost and easy-to-use method of tumoroids generation and analysis, efficient for all types of brain tumors, primary and metastatic. tumoroids were generated from 89 patients: 81 primary tumors including 77 gliomas, and 8 brain metastases. Tumoroids morphology, cellular and molecular characteristics were compared with the ones of the parental tumor by using histology, methylome profiling, pTERT mutations and multiplexed spatial immunofluorescences. Their cellular stability overtime was validated by flow cytometry. Therapeutic sensitivity was evaluated and predictive factors of tumoroid generation were analyzed. All the tumoroids analyzed had similar histological (N=21) and molecular features (N=7) than the parental tumor. Median generation time was 5 days. Success rate was 65 %: it was higher for high grade gliomas and brain metastases versus IDH mutated low grade gliomas. For high-grade gliomas, neither other clinical, neuro-imaging, histological nor molecular factors were predictive of tumoroid generation success. The cellular organization inside tumoroids analyzed by MACSima revealed territories dedicated to specific cell subtypes. Finally, we showed the correlation between tumoroid and patient treatment responses to radio-chemotherapy and their ability to respond to immunotherapy thanks to a dedicated and reproducible 3D analysis workflow. patient-derived tumoroid model that we developed offers a robust, user-friendly, low-cost and reproducible preclinical model valuable for therapeutic development of all type of primary or metastatic brain tumors, allowing their integration into forthcoming early-phase clinical trials.