Deconvolution Software Research Articles

Mantis: High-throughput 4D imaging and analysis of the molecular and physical architecture of cells.

High-throughput dynamic imaging of cells and organelles is essential for understanding complex cellular responses. We report Mantis, a high-throughput 4D microscope that integrates two complementary, gentle, live-cell imaging technologies: remote-refocus label-free microscopy and oblique light-sheet fluorescence microscopy. Additionally, we report shrimPy (Smart High-throughput Robust Imaging and Measurement in Python), an open-source software for high-throughput imaging, deconvolution, and single-cell phenotyping of 4D data. Using Mantis and shrimPy, we achieved high-content correlative imaging of molecular dynamics and the physical architecture of 20 cell lines every 15 min over 7.5 h. This platform also facilitated detailed measurements of the impacts of viral infection on the architecture of host cells and host proteins. The Mantis platform can enable high-throughput profiling of intracellular dynamics, long-term imaging and analysis of cellular responses to perturbations, and live-cell optical screens to dissect gene regulatory networks.

Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images

Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers. Deconwolf enables accurate quantification of crowded diffraction limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images and allows robust detection of individual transcripts in tissue sections imaged with ×20 air objectives. Deconvolution of in situ spatial transcriptomics images with Deconwolf increased the number of transcripts identified more than threefold, while the application of Deconwolf to images obtained by fluorescence in situ sequencing of barcoded Oligopaint probes drastically improved chromosome tracing. Deconwolf greatly facilitates the use of deconvolution in many bioimaging applications.

Genomic predictors of sensitivity to chemotherapy and immunotherapy in cholangiocarcinoma.

540 Background: Cholangiocarcinoma (CCA) has seen a surge immunotherapy (IO) use. Data on the sensitivity and resistance to chemotherapy and IO in CCA is limited, making it difficult to identify predictive biomarkers for guiding treatment. We hypothesize that transcriptional signatures differentiating PD-L1 high from PD-L1 low patients (pts), can be leveraged to better understand IO sensitivity. These signatures could potentially facilitate personalized treatment strategies and mitigate therapeutic resistance. Methods: We explored transcriptomic signatures related to chemotherapy and IO sensitivity in the CCA cohort of The Cancer Genome Atlas (TCGA-CHOL). We divided the pts (n=36) into 2 groups based on PD-L1 (CD274) expression levels. Pts were designated as PD-L1 high (n=11) if PD-L1 ≥ average PD-L1 expression, and PD-L1 low (n=25) if PD-L1 ≤ average expression PD-L1in the cohort. Transcriptional dysregulation was assessed by comparing transcriptional profiles of the 2 groups. Differential expression analysis, using Limma, was utilized for Gene Set Enrichment Analysis (GSEA), to assess the predicted function of transcriptional dysregulation that occurs between the 2 groups. Immune deconvolution by using the Tumor IMmune Estimation Resource (TIMER) deconvolution software to assess differences in immune infiltration and resulting scores from this analysis were used to assess how immune cell population estimates correlated with immune factors, like PD-L1 expression. Results: GSEA revealed PD-L1 low pts showed significant alterations in metabolic pathway genes that regulate cholesterol, amino acid transport and cellular respiration. As expected, GSEA revealed alterations to immune cell pathways associated with PD-L1 expression, assessed using TIMER revealed several significant correlations between immune cell deconvolution scores: CD4+ and CD8+ T-Cell (R=-0.35, p=0.02), CD8+ and neutrophils (R=0.42, p=0.009), and CD8+ and myeloid dendritic cells (R=0.3348, p=0.04). We noted significant correlations between immune cells deconvolution scores with PD-L1 expression, including PD-L1 and B Cells (R=0.54, p=0.0005), macrophages (R=0.41, p=0.01), CD8+ T Cells (R=0.43, p=0.008), neutrophils (R=0.56, p=0.0003), and myeloid dendritic cells (R=0.48, p=0.002). Significant differences in immune deconvolution scores of CD8+ T Cells, macrophages, neutrophils were noted between PD-L1 high and low pts. Conclusions: PD-L1 high pts with CCA exhibit expected differential enrichment of immune signatures. Notably, we report for the first time that CCA PD-L1 low patients uniquely enrich for differential metabolic signatures. This could lay the groundwork for understanding mechanisms that underlie IO response differences, provide valuable insights if confirmed in IO treated cohorts and serve as evidence for the utility of metabolism-targeted therapies to overcome therapeutic resistance in CCA.

Spatial profiling of cancer-associated fibroblasts of sporadic early onset colon cancer microenvironment

The incidence of sporadic early-onset colon cancer (EOCC) has increased worldwide. The molecular mechanisms in the tumor and the tumor microenvironment (TME) in EOCC are not fully understood. The aim of this study is to unravel unique spatial transcriptomic and proteomic profiles in tumor epithelial cells and cancer-associated fibroblasts (CAFs). Here, we divide the sporadic colon cancer tissue samples with transcriptomic data into patients diagnosed with EOCC (<50 yrs) and late-onset colon cancer (LOCC, ≥50 yrs) and then, analyze the data using CIBERSORTx deconvolution software. EOCC tumors are more enriched in CAFs with fibroblast associated protein positive expression (FAP(+)) than LOCC tumors. EOCC patients with higher FAP mRNA levels in CAFs have shorter OS (Log-rank test, p < 0.029). Spatial transcriptomic analysis of 112 areas of interest, using NanoString GeoMx digital spatial profiling, demonstrate that FAP(+) CAFs at the EOCC tumor invasive margin show a significant upregulation of WNT signaling and higher mRNA/protein levels of fibroblast growth factor 20 (FGF20). Tumor epithelial cells at tumor invasive margin of EOCC tumors neighboring FAP(+) CAFs show significantly higher mRNA/protein levels of fibroblast growth factor receptor (FGFR2) and PI3K/Akt signaling activation. NichNET analysis show a potential interaction between FGF20 and FGFFR2. The role of FGF20 in activating FGFR2/pFGFR2 and AKT/pAKT was validated in-vitro. In conclusion, we identify a unique FAP(+) CAF population that showed WNT signaling upregulation and increased FGF20 levels; while neighbor tumor cells show the upregulation/activation of FGFR2-PI3K/Akt signaling at the tumor invasive margin of EOCC tumors.

Screening of organophosphorus flame retardant residues in rice by QuEChERS-gas chromatography-quadrupole time-of-flight mass spectrometry

Organophosphorus flame retardants (OPFRs) have emerged as good alternatives to brominated flame retardants, the use of which is globally restricted. In this study, a screening method based on QuEChERS-gas chromatography-quadrupole time-of-flight mass spectrometry (GC-Q-TOF/MS) was established for the determination of 21 OPFRs in rice. First, full scan (scanning range, m/z 50-450) was performed with a mixed standard solution of the 21 OPFRs (0.1 μg/g) by GC-Q-TOF/MS. The fragmentation pathways of these OPFRs were then investigated to explore their cleavage fragments, the interrelationships among fragments, and the possible cleavage modes of alkylated, chlorinated, and aromatic OPFRs. The retention times, isotopic abundance ratios, and molecular formulas of the characteristic fragments as well as the exact mass of the compounds were obtained to establish a mass spectral library of the OPFRs. Rice samples were extracted and purified by the QuEChERS method, and 0.5% formate acetonitrile solution was used as the extraction solvent; 4 g of magnesium sulfate, 1 g of sodium chloride, 0.5 g of disodium hydrogen citrate, and 1 g of sodium citrate as the extraction-salt combination; and 50 mg of primary secondary amine (PSA), 50 mg of octadecylsilane (C18), and 150 mg of magnesium sulfate as the purification materials. The chromatographic separation of the 21 OPFRs was completed within 16 min under optimized temperature program conditions on the DB-5MS UI column. The screening parameters were optimized, and a full scan of the samples was performed under the following conditions: number of characteristic fragment ions ≥2; accurate mass window=±2×10-5 (±20 ppm); retention time deviation=±0.2 min, and ion abundance deviation<20%. The developed method was applied to the screening 21 OPFRs in the samples. The results indicated that the matrix interference was greatly reduced by decreasing the extraction accurate mass window, thereby improving the signal-to-noise ratio of the analytes. The targets were extracted from the matrix interference and background noise using deconvolution software, which improved the match between the target compounds and the mass spectral library. The detection rates of alkyl and aromatic OPFRs increased by 22% and 25%, respectively, when the spiking level was increased from 2 to 10 ng/g. Among the chlorinated OPFRs, only tris(2-chloroisopropyl) phosphate (TCIPP) was not detected at a spiking level of 2 ng/g, indicating that chlorinated OPFRs could be identified even at low concentrations. The characteristic ions of the detected compounds matched those of the home-made mass spectral library well, indicating that the practical application of the home-made mass spectral library. The established screening method was applied in the determination of OPFRs in rice samples from different regions in China. A total of 11 OPFRs were detected, among which trimethyl phosphate (TMP), tri-iso-butyl phosphate (TiBP), and tris(3,5-dimethylphenyl) phosphate (T35DMPP) had the highest detection rates. These results indicate that these three OPFRs are widely used and can easily come into contact with rice samples through various routes. Differences in the types of OPFRs detected in the actual samples may be related to the types of OPFRs produced in local factories. OPFRs can be detected in rice samples by the developed GC-Q-TOF/MS screening method, which is helpful for the identification of OPFRs in complex matrix samples.

Abstract C101: A feasibility study of exposure to environmental chemicals among Black and Hispanic breast cancer survivors using silicone wristbands for passive sampling

Abstract Introduction: Exposure to environmental chemicals may be an important contributor to obesity and cancer due to effects on inflammation, immune function, hormone synthesis, and metabolism, as well as cellular mechanisms including cellular signaling and apoptosis. However, little attention has been paid to environmental exposures among cancer survivors, who are at increased risk of cancer, cardiovascular disease, and metabolic disease compared to the general population. Methods: We recruited 25 Black and Hispanic breast cancer survivors in Washington, DC and Hackensack, NJ. Survivors completed questionnaires about their demographics and breast cancer diagnosis and wore silicone wristbands for 7 days. The wristbands passively sampled participants’ exposome, including ambient air and dermal sources of exposure. After deployment, the wristbands were extracted and extract samples were analyzed to provide quantitative data on &amp;gt;1,500 chemicals by using automated deconvolution software to compare spectra from a gas chromatograph-mass spectrometer with compound libraries. Sample concentrations were adjusted for wear time and wristband size. Results: Seventeen Black and 8 Hispanic breast cancer survivors participated in the study. Study participants had a mean age of 58 years (range: 38-75 years) and most had been diagnosed with Stage I or II breast cancer (84%). Most participants were satisfied or very satisfied with their experience using the silicone wristbands (88%), found the wristbands easy to use (96%), and did not report that the wristband interfered with their daily activities (92%). Sixty distinct chemicals were detected in the wristbands with a mean of 21.8 chemicals (range: 12-29) detected on any single wristband. Nineteen chemicals were detected in &amp;gt;50% of participants. Exposure to chemicals found in personal care products and in commercial products was ubiquitous. All participants were exposed to at least 1 flame retardant (e.g., triphenyl phosphate) and at least 1 pesticide (e.g., diethyl phthalate). Four chemicals detected have been classified as possibly carcinogenic to humans according to the International Agency for Research on Cancer. The most commonly detected chemicals (&amp;gt;96%) were benzyl salicylate (fragrance, UV light absorber), galaxolide (fragrance), diethyl phthalate (plasticizer), diisobutyl phthalate (plasticizer), lilial (perfume), and triphenyl phosphate (flame retardant, plasticizer). Several of the most commonly detected chemicals have the potential for genotoxicity (i.e., lilial) or endocrine disruption (e.g., benzyl salicylate, diisobutyl phthalate), so chronic exposure may have implications for survivors’ long-term health. Conclusions: Passive sampling using silicone wristbands is a feasible and well-tolerated approach to gather data on cancer survivors’ personal exposome. Breast cancer survivors’ exposures to flame retardants and pesticides, particularly biologically active chemicals, warrant additional study. Citation Format: Traci N. Bethea, Jennifer Hicks, Erin Speiser, Gail E. Starr, Lucile L. Adams-Campbell. A feasibility study of exposure to environmental chemicals among Black and Hispanic breast cancer survivors using silicone wristbands for passive sampling [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C101.

High-Throughput Deconvolution of Intact Protein Mass Spectra for the Screening of Covalent Inhibitors.

Deconvolution from intact protein mass-to-charge spectra to mass spectra is essential to generate interpretable data for mass spectrometry (MS) platforms coupled to ionization sources that produce multiply charged species. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) can be used to analyze intact proteins in multiwell microtiter plates with speed matching small molecule analyses (at least 1 Hz). However, the lack of compatible deconvolution software has limited its use in high-throughput screening applications. Most existing automated deconvolution software packages work best for data generated from LC-MS, and to the best of our knowledge, there is no software capable of performing fast plate-based mass spectral deconvolution. Herein we present the use of a new workflow in ProSight Native for the deconvolution of protein spectra from entire well plates that can be completed within 3 s. First, we successfully demonstrated the potential increased throughput benefits produced by the combined IR-MALDESI-MS - ProSight Native platform using protein standards. We then conducted a screen for Bruton's tyrosine kinase (BTK) covalent binders against a well-annotated compound collection consisting of 2232 compounds and applied ProSight Native to deconvolute the protein spectra. Seventeen hits including five known BTK covalent inhibitors in the compound set were identified. By alleviating the data processing bottleneck using ProSight Native, it may be feasible to analyze and report covalent screening results for >200,000 samples in a single day.

Determination of Bioactive Compounds Against Bacterial Wilt of Potato (Ralstonia pseudosolanacearum sp. nov.) in Psidium guajava and Pelargonium zonale Leaf Extracts

Common guava (Psidium guajava) and Pelargonium (Pelargonium zonale) have shown in-vitro antibacterial activity against Ralstonia pseudosolanacearum sp. nov. in previous studies. However, their phytochemical constituents and bioactive compounds against the pathogen have not been identified. The present study investigated the phytochemical components of P. guajava and P. zonale leaf extracts by phytochemical screening and gas chromatography-mass spectrometry (GC-MS). Phytochemical screening was done using different solvents while 100 mg of the dried ethanolic extract pastes from each plant sample was subjected to GC-MS analysis. Automated mass spectral deconvolution and identification system software (AMDIS, US) was used to analyze chromatograms and spectra representing individual compounds. Compound identification was performed by comparing each of the mass spectra with the database of NIST 11 (Gaithersburg, MD, USA), Wiley 7N (John Wiley, NY, USA) and by comparing the calculated Kovats linear retention indices using retention times of n-alkane series against the values in the NIST webbook. Flavonoids, phenols, alkaloids, saponins, terpenoids and tannins were detected in both plant samples. GC-MS analysis revealed presence of 35 and 26 compounds from P. zonale and P. guajava respectively. Both P. zonale and P. guajava had 7 similar compounds with antibacterial properties; Fumaric acid, Phytol, Pyrogallol, 4-Hydroxybenzoic acid, Shikimic acid, Protocatechuic acid and 3, 4, 5-Trihydroxybenzoic acid ethyl ester but P. zonale had one additional antibacterial compound; Lactic acid. In both cases, Shikimic acid had the highest percent peak areas of 3.2% for P. zonale and 6.8% P. guajava respectively. Therefore, P. zonale and P. guajava can serve as alternative sources of active ingredients for formulation of commercial botanicals for the management of bacterial wilt of potato.

Semi-quantitative analysis of cannabinoids in hemp (Cannabis sativa L.) using gas chromatography coupled to mass spectrometry

BackgroundHemp (Cannabis sativa L.) is a producer of cannabinoids. These organic compounds are of increasing interest due to their potential applications in the medicinal field. Advances in analytical methods of identifying and quantifying these molecules are needed.MethodThis study describes a new method of cannabinoid separation from plant material using gas chromatography-mass spectrometry (GC-MS) as the analytical tool to detect low abundance cannabinoids that will likely have implications for future therapeutical treatments. A novel approach was adopted to separate trichomes from plant material to analyse cannabinoids of low abundance not observed in raw plant extract. Required plant sample used for analysis was greatly reduced compared to other methods. Derivatisation method was simplified and deconvolution software was utilised to recognise unknown cannabinoid compounds of low abundance.ResultsThe method produces well-separated spectra and allows the detection of major and minor cannabinoids. Ten cannabinoids that had available standards could be identified and quantified and numerous unidentified cannabinoids or pathway intermediates based on GC-MS spectra similarities could be extracted and analysed simultaneously with this method.ConclusionsThis is a rapid novel extraction and analytical method from plant material that can identify major and minor cannabinoids using a simple technique. The method will be of use to future researchers seeking to study the multitude of cannabinoids whose values are currently not understood.

Mass Spectrometry-Based Nontargeted and Targeted Analytical Approaches in Fingerprinting and Metabolomics of Food and Agricultural Research.

Mass spectrometry (MS)-based techniques have been extensively applied in food and agricultural research. This review aims to address the advances and applications of MS-based analytical strategies in nontargeted and targeted analysis and summarizes the recent publications of MS-based techniques, including flow injection MS fingerprinting, chromatography-tandem MS metabolomics, direct analysis using ambient mass spectrometry, as well as development in MS data deconvolution software packages and databases for metabolomic studies. Various nontargeted and targeted approaches are employed in marker compounds identification, material adulteration detection, and the analysis of specific classes of secondary metabolites. In the newly emerged applications, the recent advances in computer tools for the fast deconvolution of MS data in targeted secondary metabolite analysis are highlighted.

Dosimetric characteristics of Gd-doped silica glass subjected to neutron and gamma irradiations

The dosimetric characteristics of newly developed gadolinium (Gd) glass dosimeter produced via sol-gel method are reported. Irradiation were made using a 750 kW neutron flux thermal power and 1.25 MeV 60Co gamma rays with entrance doses from 2 to 10 Gy. Investigation has been done on various Gd dopant concentrations, ranging from 1 to 10 mol%. The Gd-doped silica glass have been characterised for thermoluminescence (TL) dose response, sensitivity, linearity index, glow curve and kinetic parameter analysis. For particular dopant concentration obtained in 6 mol% Gd, the least squares fit shows the change in TL yield, correlation coefficient (r2) of better than 0.980 (at 95% confidence level), with neutron and gamma exposure to be 8 and 4 times greater than that of 1 mol% Gd, respectively. Broad peaks in the absence of any sharp peak observed in the glow curve confirms the amorphous nature of the prepared glass. A glow curve of Gd-doped SiO2 sample is observed with a single prominent peak (Tm) within 200–250 °C (peak shifting appears with respect to the increment of dopant concentration) and 350 °C (for all respective Gd dopants) for neutron and gamma irradiations, respectively. Deconvolution shows the glow curves of the Gd-doped SiO2 glass to be formed of seven and five overlapping peaks, with figures of merit below 2% (FOM) of between 1.38-1.79 and 1.30–1.97 for the particular neutron and gamma irradiations, respectively. Through use of Glowfit deconvolution software, the key trapping parameters of activation energy, E and frequency factor, s−1 were calculated for the Gd-doped SiO2 glass. The mechanism of TL yield with the gradual increase in Gd concentrations and doses is explained upon the incorporation of Gd and radiation damage that change the structure of the electron traps in the glass matrix. These early results indicate that selectively screened Gd-SiO2 glass can be developed into a promising TL system towards dosimetric applications.

Liquid chromatography coupled with photodiode array and a multivariate curve resolution – Alternating least square algorithm for identification and quantification of co-eluting impurities in pharmaceutical analysis

This paper systematically investigated and reported for the first time the identification and quantification of co-eluting impurities as low as 0.05 area% by PDA with i-PDeA II deconvolution software in the LabSolutions Chromatographic Data System (CDS) using an integrated multivariate curve resolution-alternating least squares (MCR-ALS) algorithm with a bidirectional exponentially modified Gaussian (BEMG) model function. The algorithm was able to consistently identify 0.05% impurities when co-eluting with the main component (Rs ≥ 0.8) as well as when co-eluting with another impurity (Rs ≥ 0.5). In the case of two co-eluting impurities from 0.05% to 1% (Rs ≥ 0.5), the quantification error ranged from +10.6% to -16.7%. In the case of an impurity co-eluting with the main component (Rs ≥ 0.8), the quantification error was 4.4–8.9% for 1% impurity and 109–184% for 0.05% impurity. The precision was excellent for the range of 0.05–1.0% impurities with the RSD being 1.4–3.0% for 1% impurity and 4.0–8.7% for 0.05% impurity. The identification rate and quantitation accuracy were not affected by the spectral similarity of the molecules, as comparable results were obtained by analyzing two molecules with low similarity (4,4-difluorobenzophenone and valerophenone) and two molecules with high similarity (diazepam and oxazepam) based on simulated data. This peak resolution by MCR-ALS approach provides fast and robust identification and quantification of co-eluting impurities even when method development efforts do not provide complete separation of the target peaks, and could therefore find a wide range of applications in pharmaceutical and other types of analyses.

Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1–3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Development and Validation of a Single Step GC/MS Method for the Determination of 41 Drugs and Drugs of Abuse in Postmortem Blood

A toxicology laboratory often receives a high number of samples from cases (autopsies or clinical) that may require the quick delivery of trustworthy, accurate results. Thus, there is a great need for a fast and reliable method that is capable of identifying and determining a large number of drugs and drugs of abuse in biological matrices, and especially in blood. In the present study, we describe the development of a fast and simple gas chromatography–mass spectrometry (GC-MS) method for the determination of 41 drugs and drugs of abuse (DOA) in blood. Sample pre-treatment by alkaline liquid–liquid extraction (LLE) was studied through the utilization of different solvents and solvent-to-sample ratios (v/v), which aimed to achieve a greater extraction efficiency and detection sensitivity with a decreased need for large sample volumes. Butyl acetate with a sample-to-solvent ratio of 4:1 (1 mL blood: 0.25 mL butyl acetate) was the most efficient. The method was validated for all analytes, and the evaluation parameters were within the acceptance criteria. The coefficient of determination (R2) was between 0.9934 and 1, the limits of detection (LODs) ranged between 1 ng/mL and 113 ng/mL, and the limits of quantification (LOQs) were between 4 ng/mL and 375 ng/mL for all analytes. The determinations were accurate (accuracy% from 84% to 114%) and precise (RSD% from 0.66% to 14.8% for low concentrations). Deconvolution Reporting Software (DRS) for GC-MS was optimized and applied for data analysis to enhance the identification potential, thereby avoiding false identifications (false positives) and increased productivity. The NIST Automated Mass Spectral Deconvolution and Identification Software (AMDIS) and the analytical utility Retention Time Lock (RTL) Database Library assisted in data evaluation. The method was applied to 89 postmortem cases (history of mental disorders and use of psychiatric pharmaceuticals) in which diazepam (0.13 to 4.34 μg/mL), citalopram (0.04 to 0.24 μg/mL), alprazolam (0.01 to 0.12 μg/mL), olanzapine (0.009 to 0.083 μg/mL), mirtazapine (0.01 to 0.33 μg/mL), venlafaxine (0.006 to 0.92 μg/mL), haloperidol (0.007 to 0.13 μg/mL), and zolpidem (0.01 to 0.16 μg/mL) were successfully quantitated.

Thermoluminescence (TL), kinetic parameters and dosimetric features of Pakistani limestone

Thermoluminescence (TL), kinetic parameters and dosimetric features of Pakistani limestone (CaCO3) is reported in this study. Both compositional and structural analyses reveal that the material has a crystalline nature with rhombohedral structure and non-uniform crystallite size having major content of CaCO3. A powdered limestone sample of 30 mg is found to be the optimized weight for TL and other dosimetric studies. After irradiating the samples with a test dose of 100 Gy using a β source three composite glow peaks termed as P1, P2 and P3 are visible at 100, 230 and 330 °C respectively using a linear heating rate of 1 °C/s during the TL readout. The Coefficient of Variation (COV) is found to be about 4%. Kinetic parameters (i.e., frequency factor (f), activation energy (E), and the kinetic order (b)) are estimated using both first and second Order of kinetics using an in-house Computerized Glow Curve Deconvolution (GCD) software. The figure-of-merit (FOM) is found to be 2.12%. The distribution of continuum traps with activation energy in the range of 0.77–2.59 eV is observed in the kinetic parameter analysis of the glow peaks of the sample. The TL response in the dose range of 1–5 Gy (not reported previously) and linearity in the dose response in the dose range of 1–10 Gy is observed in samples of Pakistani limestone. The Minimum Detectable Dose (MDD) is 1.01 Gy clearly resembling the experimentally linear fitted results. After a fading study for a period of thirty days, only the first peak i.e., P1 majorly fades while no major change is observed in the amplitude of peaks P2 and P3. In addition, P1 is the main contributor fading by 92% within the first 24 h of irradiation while P2 fades by 30 %. However, P3 shows stability with a very minor fading of 0.05% within 24 h of irradiation. This study concludes that Pakistani limestone can be further assessed as a potential radiation dosimeter for various applications.

Advances in rapid scan EPR spectroscopy.

Progress has been made in hardware for low frequencies, demonstrations of rapid frequency scans, hybrid instrumentation, and improved deconvolution software. The recent availability of the commercial Bruker BioSpin rapid scan accessory for their X-band EMX and Elexsys systems makes this technique available to a wide range of users without the need to construct their own system. Developments at lower frequencies are underway in several labs with the goal of facilitating in vivo and preclinical rapid scan imaging. Development of new deconvolution algorithms will make data processing more robust. Frequency scans have substantial promise at higher frequencies. New examples of applications show the wide applicability and advantages of rapid scan.

Development and application of an in-house library and workflow for gas chromatography–electron ionization–accurate-mass/high-resolution mass spectrometry screening of environmental samples

This work presents an optimized gas chromatography–electron ionization–high-resolution mass spectrometry (GC-EI-HRMS) screening method. Different method parameters affecting data processing with the Agilent Unknowns Analysis SureMass deconvolution software were optimized in order to achieve the best compromise between false positives and false negatives. To this end, an accurate-mass library of 26 model compounds was created. Then, five replicates of mussel extracts were spiked with a mixture of these 26 compounds at two concentration levels (10 and 100 ng/g dry weight in mussel, 50 and 500 ng/mL in extract) and injected in the GC-EI-HRMS system. The results of these experiments showed that accurate mass tolerance and pure weight factor (combination of reverse-forward library search) are the most critical factors. The validation of the developed method afforded screening detection limits in the 2.5–5 ng range for passive sampler extracts and 1–2 ng/g for mussel sample extracts, and limits of quantification in the 0.6–3.2 ng and 0.1–1.8 ng/g range, for the same type of samples, respectively, for 17 model analytes. Once the method was optimized, an accurate-mass HRMS library, containing retention indexes, with ca. 355 spectra of derivatized and non-derivatized compounds was generated. This library (freely available at https://doi.org/10.5281/zenodo.5647960), together with a modified Agilent Pesticides Library of over 800 compounds, was applied to the screening of passive samplers, both of polydimethylsiloxane and polar chemical integrative samplers (POCIS), and mussel samples collected in Galicia (NW Spain), where a total of 75 chemicals could be identified.

Notch signaling and natural killer cell infiltration in tumor tissues underlie medulloblastoma prognosis

Medulloblastoma is the most common embryonic brain tumor in children. We investigated a cohort of 52 Asian medulloblastoma patients aged between 0 and 19 years old, who received surgical resections and post-resection treatments in the Taipei Medical University Hospital and the Taipei Veterans General Hospital. Genome-wide RNA sequencing was performed on fresh-frozen surgical tissues. These data were analyzed using the CIBERSORTx immune deconvolution software. Two external clinical and molecular datasets from United States (n = 62) and Canada (n = 763) were used to evaluate the transferability of the gene-signature scores across ethnic populations. The abundance of 13 genes, including DLL1, are significantly associated with overall survival (All Cox regression P < 0.001). A gene-signature score was derived from the deep transcriptome, capable of indicating patients’ subsequent tumor recurrence (Hazard Ratio [HR] 1.645, confidence interval [CI] 1.337–2.025, P < 0.001) and mortality (HR 2.720, CI 1.798–4.112, P < 0.001). After the adjustment of baseline clinical factors, the score remains indicative of recurrence-free survival (HR 1.604, CI 1.292–1.992, P < 0.001) and overall survival (HR 2.781, CI 1.762–4.390, P < 0.001). Patients stratified by this score manifest not only distinct prognosis but also different molecular characteristics: Notch signaling ligands and receptors are comparatively overexpressed in patients with poorer prognosis, while tumor infiltrating natural killer cells are more abundant in patients with better prognosis. Additionally, immunohistochemical staining showed the DLL1 protein, a major ligand in the Notch signaling pathway, and the NCAM1 protein, a representative biomarker of natural killer cells, are present in the surgical tissues of patients of four molecular subgroups, WNT, SHH, Group 3 and Group 4. NCAM1 RNA level is also positively associated with the mutation burden in tumor (P = 0.023). The gene-signature score is validated successfully in the Canadian cohort (P = 0.009) as well as its three molecular subgroups (SHH, Group 3 and Group 4; P = 0.047, 0.018 and 0.040 respectively). In conclusion, pediatric medullablastoma patients can be stratified by gene-signature scores with distinct prognosis and molecular characteristics. Ligands and receptors of the Notch signaling pathway are overexpressed in the patient stratum with poorer prognosis. Tumor infiltrating natural killer cells are more abundant in the patient stratum with better prognosis.

AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning.

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input function (AIF) and the venous output function, with the AIF as the most critical model input. When manually performed, the vascular function selection is time demanding, suffers from poor reproducibility and is subject to the professionals' experience. This leads to potentially unreliable quantification of the penumbra and core lesions and, hence, might harm the treatment decision process. In this work we automatize the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. Unlike previous methods using clustering or segmentation techniques to select vascular voxels, AIFNet is directly optimized at the vascular function estimation, which allows to better recognise the time-curve profiles. Validation on the public ISLES18 stroke database shows that AIFNet almost reaches inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification obtained through deconvolution. We conclude that AIFNet has potential for clinical transfer and could be incorporated in perfusion deconvolution software.

Visualizing minute details in light-sheet and confocal microscopy data by combining 3D rolling ball filtering and deconvolution.

We developed an open-source deconvolution software that stunningly increases the visibility of minute details, as for example, neurons or nerve fibers in light-sheet microscopy or confocal microscopy data by combining rolling ball background subtraction in three directions with deconvolution using a synthetic or measured point spread function. Via automatic block-wise processing image stacks of virtually unlimited size can be deconvolved even on small computers with 8 or 16 GB RAM. By parallelization and optional GPU-acceleration, the software works with high speed: On a PC equipped with a state-of-the-art NVidia graphic board a three dimensional (3D)-stack of about 1 billion voxels can be deconvolved within 5 to 10minutes. The implemented variation of the Richardson-Lucy deconvolution algorithm preserves the photogrammetry of the image data by using flux-preserving regularization, an approach that to our knowledge has not been applied for deconvolving microscopy data before.