High-throughput Mode Research Articles

Acoustic black hole effect enhanced micro-manipulator

Microparticle manipulation is a critical concern across various fields including microfabrication, flexible electronics and tissue engineering. Acoustic-activated sharp structures have been designed as simple and flexible tools to manipulate microparticles with their good compatibility, fast response, and broad tunability. However, there still lacks rational acoustic-structure design for effective energy concentration at the acoustic-activated sharp structures for microparticle manipulation. Here, we present the acoustic black hole (ABH) effect as enhancement for the acoustic micro-manipulator. It provides great reliability, simplicity and ease of use, supporting custom design of high-throughput patterning modes. Moreover, compared to commonly used configurations, such as cylindrical or conical microneedles, those microneedles with ABH profile exhibit superior acoustic energy focusing at the tip and induce stronger acoustofluidic effects. The average acoustic flow velocity induced by the ABH microneedle is 154 times greater than that of the conical one and 45 times greater than that of the cylindrical microneedle. Besides, the average acoustic radiation force (ARF) produced by the ABH microneedle against acrylic microparticles is about 319 times greater than that of the cylindrical one and 16 times greater than that of the conical one. These results indicate that ABH design significantly enhances microparticle manipulation. We demonstrate this concept with ABH effect enhanced microparticle manipulation and study the parameters influencing its performance including operating frequency, operating voltage and particle diameter. Furthermore, considering the flexibility of this system, we employ it for various patterning and high-throughput microparticle manipulation. This work paves the way for controllable microparticle manipulation, holding great potential for applications in microfabrication and biomedicine.

Development of a carboxymethyl chitosan functionalized slide for small molecule detection using oblique-incidence reflectivity difference technology.

Label-free optical biosensors have become powerful tools in the study of biomolecular interactions without the need for labels. High throughput and low detection limit are desirable for rapid and accurate biomolecule detection. The oblique-incidence reflectivity difference (OI-RD) technique is capable of detecting thousands of biomolecular interactions in a high-throughput mode, specifically for biomolecules larger than 1000 Da. In order to enhance the detection capability of OI-RD for small molecules (typically < 500 Da), we have developed a three-dimensional biochip that utilized carboxymethyl chitosan (CMCS) functionalized slides. By investigating various factors such as sonication time, protein immobilization time, CMCS molecular weight, and glutaraldehyde (GA) functionalization time, we have achieved a detection limit of 6.8 pM for avidin (68 kDa). Furthermore, accurate detection of D-biotin with a molecular weight of 244 Da has also been achieved. This paper presents an effective solution for achieving both high throughput and low detection limits using the OI-RD technique in the field of biomolecular interaction detection.

High-throughput detection of Src kinase through cathodic photoelectrochemistry

Photoelectrochemical (PEC) bioanalysis has recently become a perspective pathway for biomolecular sensing, presenting new insights for disease diagnosis and treatment. Herein, we present a new cathodic PEC platform for detecting Src kinase in a label-free, non-immobilized, and high-throughput mode. Specifically, treatment of the tyrosine-containing peptide by tyrosine kinase produced an ortho-diphenol structure of tyrosine that can in situ introduce oxygen vacancies (Ov) on the surface of NiTiO3 material, which led to the augmentation of the photoelectric signal. While the presence of the target Src kinase phosphorylated tyrosine in substrate peptide and blocked the surface Ov formation onto NiTiO3. Such a phenomenon was correlated with the Src kinase detection in terms of good sensitivity and selectivity, achieving the linear detection range of 0.001–2.0 μg/mL and the detection limit of 3.0×10−4 μg/mL (S/N=3). For Src kinase detection, this is a superior strategy to some classical methods in terms of easy operation, rapid response, and cost effectiveness. This study features the first exploration of cathodic photoelectrochemistry for Src kinase detection and is expected to expand its application in probing more disease related targets with unknown possibilities.

Addressing Reproducibility Challenges in High-Throughput Photochemistry.

Light-mediated reactions have emerged as an indispensable tool in organic synthesis and drug discovery, enabling novel transformations and providing access to previously unexplored chemical space. Despite their widespread application in both academic and industrial research, the utilization of light as an energy source still encounters challenges regarding reproducibility and data robustness. Herein we present a comprehensive head-to-head comparison of commercially available batch photoreactors, alongside the introduction of the use of batch and flow photoreactors in parallel synthesis. Hence, we aim to establish a reliable and consistent platform for light-mediated reactions in high-throughput mode. Herein, we showcase the identification of several platforms aligning with the rigorous demands for efficient and robust high-throughput experimentation screenings and library synthesis.

Discovery of Delirium Biomarkers through Minimally Invasive Serum Molecular Fingerprinting.

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.

A new methodology for a rapid and high-throughput comparison of molecular profiles and biological activity of phytoextracts.

To robustly discover and explore phytocompounds, it is necessary to evaluate the interrelationships between the plant species, plant tissue, and the extraction process on the extract composition and to predict its cytotoxicity. The present work evaluated how Fourier Transform InfraRed spectroscopy can acquire the molecular profile of aqueous and ethanol-based extracts obtained from leaves, seeds, and flowers of Cynara Cardunculus, and ethanol-based extracts from Matricaria chamomilla flowers, as well the impact of these extracts on the viability of mammalian cells. The extract molecular profile enabled to predict the extraction yield, and how the plant species, plant tissue, and extraction process affected the extract's relative composition. The molecular profile obtained from the culture media of cells exposed to extracts enabled to capture its impact on cells metabolism, at a higher sensitivity than the conventional assay used to determine the cell viability. Furthermore, it was possible to detect specific impacts on the cell's metabolism according to plant species, plant tissue, and extraction process. Since spectra were acquired on small volumes of samples (25 µL), after a simple dehydration step, and based on a plate with 96 wells, the method can be applied in a rapid, simple, high-throughput, and economic mode, consequently promoting the discovery of phytocompounds.

Predicting Cellular Rejection of Renal Allograft Based on the Serum Proteomic Fingerprint.

Kidney transplantation is an essential medical procedure that significantly enhances the survival rates and quality of life for patients with end-stage kidney disease. However, despite advancements in immunosuppressive therapies, allograft rejection remains a leading cause of organ loss. Notably, predictions of cellular rejection processes primarily rely on biopsy analysis, which is not routinely performed due to its invasive nature. The present work evaluates if the serum proteomic fingerprint, as acquired by Fourier Transform Infrared (FTIR) spectroscopy, can predict cellular rejection processes. We analyzed 28 serum samples, corresponding to 17 without cellular rejection processes and 11 associated with cellular rejection processes, as based on biopsy analyses. The leave-one-out-cross validation procedure of a Naïve Bayes model enabled the prediction of cellular rejection processes with high sensitivity and specificity (AUC > 0.984). The serum proteomic profile was obtained in a high-throughput mode and based on a simple, rapid, and economical procedure, making it suitable for routine analyses and large-scale studies. Consequently, the current method presents a high potential to predict cellular rejection processes translatable to clinical scenarios, and that should continue to be explored.

Abstract 1450: Novel whole slide imaging modes for imaging mass cytometry reveal cellular and structural composition of mouse glioblastoma

Abstract Mouse tumors have become widely used models in translational research of brain malignancies, helping to address the difficulties related to studying the human brain. Mouse brain can be regarded as a miniaturized model of the human brain, permitting visualization of the whole tissue to provide spatial cellular context. Imaging Mass Cytometry™ (IMC™) is a highly relevant tool capable of quantitative evaluation of the multiparametric protein composition in brain tumor microenvironment (TME) without the complications of autofluorescence, tissue degradation, and spectral overlap. The Hyperion XTi™ Imaging System (Standard BioTools™) utilizes IMC technology to simultaneously assess more than 40 individual structural and functional markers in tissue, providing insight into the organization and function of the TME. We demonstrate the whole slide imaging (WSI) application using a 40-marker panel composed of the Maxpar OnDemand™ Mouse Immuno-Oncology IMC Panel Kit and the Maxpar® Neuro Phenotyping IMC Panel Kit on mouse normal and glioblastoma (GBM) brain tissue. The panel was composed of mouse-specific antibodies, which highlight tumor and immune components of the mouse TME. We performed imaging using two new features of Hyperion™ XTi. Ultrafast preview mode (PM) was applied to rapidly screen entire brain sections for marker expression signatures associated with various immuno-oncology processes. This enabled biomarker-guided selection of areas in tumor tissue that were imaged using region of interest-based IMC and analyzed using single-cell analysis (SCA). In parallel, a high-throughput tissue mode (TM) was applied to perform a detailed whole-slide scan of normal and GBM mouse brain tissues that were quantified using pixel-based analysis (PBA) to unravel the composition of the TME. Using TM, we were able to successfully visualize the entire coronal and sagittal sections of normal mouse brain as well as mouse GBM tissue. PBA on TM data provided quantitative spatial expression patterns of structural and immune markers across the whole tissue. In normal tissue, we visualized the highly organized structure of the normal brain and detected neurons, oligodendrocytes, vascular-adjacent astrocytes, and axonal tracks. In GBM tissue, necrotic cores, areas with high immune infiltration, extracellular matrix deposits, and activated tumor cells were detected. SCA of GBM tissue demonstrated expansive vascularization, replicating Olig2+ cells, activation of Ras signaling, and high abundance of infiltrating immune cells. Overall, we demonstrate the successful application of two novel WSI modes and highlight the power of IMC technology to simultaneously explore dozens of relevant biological outputs to better understand the TME of GBM and other tumors. For research use only. Not for use in diagnostic procedures. Citation Format: Qanber Raza, Nick Zabinyakov, Thomas D. Pfister, Nikesh Parsotam, David Howell, Liang Lim, Christina Loh. Novel whole slide imaging modes for imaging mass cytometry reveal cellular and structural composition of mouse glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1450.

Abstract 3800: Next generation of spatial biology: High-throughput multiplexed Imaging Mass Cytometry™ with whole slide modes

Abstract Gaining spatial insights into the cellular composition of tumor tissue has tremendous potential to inform clinical and translational researchers about mechanisms behind spatial predictors of immunotherapy success, disease etiology and progression. Imaging Mass Cytometry™ (IMC™) is a high-plex spatial biology imaging technique that enables deep characterization of the diversity and complexity of the tumor microenvironment (TME). IMC supports detailed assessment of cell phenotype and function using 40-plus metal-tagged antibodies simultaneously on a single slide without issues associated with fluorescence-based spectral overlap, tissue autofluorescence or implementation of multiple washing and acquisition cycles. Currently, IMC enables user-defined regions of interest (ROI) in tissues to evaluate cellular and structural composition. To enhance the IMC user experience, we developed two new whole slide imaging (WSI) modes which enable streamlined workflows using ultrafast preview mode (PM) and high-throughput tissue mode (TM). PM samples the entire tissue at predefined spacing to rapidly capture a low-resolution image of all expressed markers in the antibody panel. PM generates an image in minutes to enable informed ROI placements while leaving the stained tissue intact for higher-resolution imaging. PM and TM are designed so acquisitions can easily occur on the same slide without additional processing steps. TM rapidly acquires images of the whole tissue at lower resolution (7 μm pixel size) at a quality that can be used for quantitative analysis of the tissue spatial biology. Specifically designed for high-throughput applications, TM in combination with a newly available 40-slide loader for the Hyperion XTi™ Imaging System permits automated and continuous imaging of more than 40 large tissue samples (400 mm2 per tissue) per week. We showcase the application of WSI modes using the newly developed Maxpar® Human Immuno-Oncology IMC Panel Kit. The 31-marker panel was combined with catalog antibodies to create a 40-plus-marker panel that expands the ability to conduct comprehensive high-plex tumor and immune cell profiling. Tumor tissue microarrays (TMA) and whole tumor tissue sections were stained with the expanded panel. Single-cell analysis of selected ROIs, on whole tumor sections and TMAs, guided by PM data successfully provided quantitative analyses of spatial biology at single-cell resolution. In parallel, TM on whole tumor sections followed by pixel-based analysis provided a spatially resolved quantitative assessment of specific tumor and immune components of the TME. This work demonstrates the expanded capabilities of IMC and establishes it as a reliable high-plex spatial biology imaging platform with high-throughput imaging capabilities ideally suited for translational and clinical applications. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Qanber Raza, Thomas D. Pfister, Jyh Yun Chwee, Liang Lim, David Howell, Nikesh Parsotam, David King, Christina Loh. Next generation of spatial biology: High-throughput multiplexed Imaging Mass Cytometry™ with whole slide modes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3800.

Abstract 5501: Novel whole slide imaging modes for imaging mass cytometry unveil extensive cellular heterogeneity in human glioma

Abstract Gliomas present a complex form of cancer that is challenging to diagnose and treat, with a median survival of just over one year after diagnosis for primary glioblastoma (GBM). GBM can occur as multi-lesion, remote, or diffuse tumors, and often contains a tumor microenvironment (TME) that is devoid of peripheral immune cells. Additional hallmark features of GBM include necrosis, hemorrhage, and pseudo palisades, making it a highly heterogeneous disease requiring further investigation. Identification of the cellular and spatial level composition of the TME is vital for interpretation of GBM disease origin, progression, prediction, and treatment. A 40-plus-marker neuro-oncology Imaging Mass Cytometry™ (IMC™) antibody panel was used to determine the cellular and structural landscape of the brain TME. We applied the panel on a tissue microarray (TMA) containing dozens of human glioma cores and uncovered the spatial distribution of over 40 distinct molecular markers. We performed imaging using two new features of the Hyperion XTi™ Imaging System that provide whole slide scanning capabilities. Ultrafast preview mode was applied to rapidly screen tumor cores for expression signatures associated with tumor immuno-oncology processes. This enabled biomarker-guided selection of areas in tumor tissue that were imaged at higher resolution and analyzed using single-cell analysis. In parallel, a high-throughput tissue mode was applied to perform a detailed scan of the brain tumor TMA followed by pixel-based analysis to unravel the spatial composition of the TME. Using tissue mode imaging, we successfully mapped the spatial location of cell populations making up human gliomas, such as neurons, astrocytes, microglia, and oligodendrocytes, across the entire TMA. Various tumor cell phenotypes, resident and infiltrating cells, and resting and activated microglia were detected across all TMA cores. Subsequent single-cell analysis of select regions of interest provided a quantitative assessment of the cellular composition of the brain TME. We classified the distinct states of neurons and quantified myeloid and lymphoid immune cell infiltration across normal, astrocytoma, and GBM tissues. Striking cellular and protein heterogeneity was observed between acquired cores, indicating the complexity of each case that is impossible to capture with low-plex visualization techniques. Therefore, IMC is a highly relevant tool capable of quantitative spatial evaluation of the high-plex protein composition in the brain TME without the complications of autofluorescence, tissue degradation, and spectral overlap. Empowered by the neuro-oncology panel and new whole slide imaging modes, IMC accelerates neurological research and provides insights into the spatial complexity of gliomas and other tumors. For research purpose only. Not for use in diagnostic procedures. Citation Format: Nick Zabinyakov, Qanber Raza, Thomas D. Pfister, Nikesh Parsotam, David Howell, Liang Lim, Christina Loh. Novel whole slide imaging modes for imaging mass cytometry unveil extensive cellular heterogeneity in human glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5501.

Adaptable SEC-SAXS data collection for higher quality structure analysis in solution.

The two major challenges in synchrotron size-exclusion chromatography coupled in-line with small-angle x-ray scattering (SEC-SAXS) experiments are the overlapping peaks in the elution profile and the fouling of radiation-damaged materials on the walls of the sample cell. In recent years, many post-experimental analyses techniques have been developed and applied to extract scattering profiles from these problematic SEC-SAXS data. Here, we present three modes of data collection at the BioSAXS Beamline 4-2 of the Stanford Synchrotron Radiation Lightsource (SSRL BL4-2). The first mode, the High-Resolution mode, enables SEC-SAXS data collection with excellent sample separation and virtually no additional peak broadening from the UHPLC UV detector to the x-ray position by taking advantage of the low system dispersion of the UHPLC. The small bed volume of the analytical SEC column minimizes sample dilution in the column and facilitates data collection at higher sample concentrations with excellent sample economy equal to or even less than that of the conventional equilibrium SAXS method. Radiation damage problems during SEC-SAXS data collection are evaded by additional cleaning of the sample cell after buffer data collection and avoidance of unnecessary exposures through the use of the x-ray shutter control options, allowing sample data collection with a clean sample cell. Therefore, accurate background subtraction can be performed at a level equivalent to the conventional equilibrium SAXS method without requiring baseline correction, thereby leading to more reliable downstream structural analysis and quicker access to new science. The two other data collection modes, the High-Throughput mode and the Co-Flow mode, add agility to the planning and execution of experiments to efficiently achieve the user's scientific objectives at the SSRL BL4-2.

Target mediated bioreaction to engineer surface vacancy effect on Bi2O2S nanosheets for photoelectrochemical detection of FEN1

Photoelectrochemistry represents a promising technique for bioanalysis, though its application for the detection of Flap endonuclease 1 (FEN1) has not been tapped. Herein, this work reports the exploration of creating oxygen vacancies (Ov) in situ onto the surface of Bi2O2S nanosheets via the attachment of dopamine (DA), which underlies a new anodic PEC sensing strategy for FEN1 detection in label-free, immobilization-free and high-throughput modes. In connection to the target-mediated rolling circle amplification (RCA) reaction for modulating the release of the DA aptamer to capture DA, the detection system showed good performance toward FEN1 analysis with a linear detection range of 0.001–10 U/mL and a detection limit of 1.4 × 10−4 U/mL (S/N = 3). This work features the bioreaction engineered surface vacancy effect of Bi2O2S nanosheets as a PEC sensing strategy, which allows a simple, easy to perform, sensitive and selective method for the detection of FEN1. This sensing strategy might have wide applications in versatile bioasssays, considering the diversity of a variety of biological reactions may produce the DA aptamer.

Potential Regulatory Gene Network Associated with the Ameliorative Effect of Oat Antibacterial Peptides on Rat Colitis.

Oat protein is unstable in intestinal fluid digestion, and it is easily degraded by trypsin and chymotrypsin, producing low molecular weight peptides. Endopeptidase hydrolysis can improve the bioavailability of active peptides and avoid further digestion in the gastrointestinal tract. Antimicrobial peptides (AMPs) can effectively improve host immunity, but most related studies focus on physiology and ecology, and there are few reports on their molecular level. Therefore, in this article, oat peptides were prepared via the simulated digestion method in vitro, and the main metabolites and action factors affecting colitis were screened by using the multi-omics methods in a high-throughput mode to analyze the effect and mechanism of colitis. Firstly, oat antimicrobial peptides were prepared from cationic resin combined with HPLC, and the anti-inflammatory effects of antimicrobial peptides were analyzed in vitro through the use of human colon epithelial (HCoEpiC) anti-inflammatory cells. In vivo experiments using rats have verified that AMPs can effectively prevent colitis caused by dextran sodium sulfate (DSS), reduce intestinal inflammatory cell infiltration and glandular disappearance in the colon, and reduce the apoptosis rate of colon cells. Secondly, metabolomics and transcriptomics were combined to analyze the mechanism of preventing enteritis, and it was found that oat antimicrobial peptides can promote DAG diglycerol production and inhibit the activation of T helper cells (TH), resulting in the down-regulation of key factors in the main downstream pathways of TH1, TH2 and TH17, and inhibit the production of inflammatory cells. At the same time, AMP can activate the wnt pathway, improve the expression of key genes of wnt and frizzled, promote the generation of intestinal stem cells, facilitate the differentiation and repair of intestinal epithelial cells, and prevent the generation of enteritis. Finally, the underlying genetic regulatory network of the important pathway was constructed from the effect of AMP on rat colitis.

DESIGN AND DEVELOPMENT OF A CUSTOM PLATFORM TO GROW AND CHARACTERIZE OSTEOSARCOMA SPHEROIDS

Osteosarcoma is a highly malignant primary tumor of bone tissue. The 5-year survival rate of patients with metastasis is below 20% and this scenario is unchanged in the last two decades, despite great efforts in pre-clinical and clinical research. Traditional preclinical models of osteosarcoma do not consider the whole complexity of its microenvironment, leading to poor correlation between in vitro/in vivo results and clinical outcomes. Spheroids are a promising in vitro model to mimic osteosarcoma and perform drug-screening tests, as they (i) reproduce the microarchitecture of the tumor, (ii) are characterized by hypoxic regions and necrotic core as the in vivo tumor, (iii) and recapitulate the chemo-resistance phenomena. However, to date, the spheroid model is scarcely used in osteosarcoma research.Our aim is to develop a customized culture dish to grow and characterize spheroids and to perform advanced drug-screening tests. The resulting platform must be adapted to automated image acquisition systems, to overcome the drawbacks of commercial spheroids platforms.To this purpose, we designed and developed a micro-patterned culture dish by casting agarose on a 3D printed mold from a CAD design. We successfully obtained viable and reproducible homotypic osteosarcoma spheroids, with two different cells lines from osteosarcoma (i.e., 143b and MG-63). Using the platform, we performed viability assays and live fluorescent stainings (e.g., Calcein AM) with low reagent consumption. Moreover, the culture dish was validated as drug screening platform, administrating Doxorubicin at different doses, and evaluating its effect on OS spheroids, in terms of morphology and viability. This platform can be considered an attractive alternative to the highly expensive commercial spheroid platforms to obtain homogeneous and reproducible spheroids in a high-throughput and cost effective mode.

TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods

TurboGenius is an open-source Python package designed to fully control ab initio quantum Monte Carlo (QMC) jobs using a Python script, which allows one to perform high-throughput calculations combined with TurboRVB [Nakano et al. J. Phys. Chem. 152, 204121 (2020)]. This paper provides an overview of the TurboGenius package and showcases several results obtained in a high-throughput mode. For the purpose of performing high-throughput calculations with TurboGenius, we implemented another open-source Python package, TurboWorkflows, that enables one to construct simple workflows using TurboGenius. We demonstrate its effectiveness by performing (1) validations of density functional theory (DFT) and QMC drivers as implemented in the TurboRVB package and (2) benchmarks of Diffusion Monte Carlo (DMC) calculations for several datasets. For (1), we checked inter-package consistencies between TurboRVB and other established quantum chemistry packages. By doing so, we confirmed that DFT energies obtained by PySCF are consistent with those obtained by TurboRVB within the local density approximation (LDA) and that Hartree–Fock (HF) energies obtained by PySCF and Quantum Package are consistent with variational Monte Carlo energies obtained by TurboRVB with the HF wavefunctions. These validation tests constitute a further reliability check of the TurboRVB package. For (2), we benchmarked the atomization energies of the Gaussian-2 set, the binding energies of the S22, A24, and SCAI sets, and the equilibrium lattice parameters of 12 cubic crystals using DMC calculations. We found that, for all compounds analyzed here, the DMC calculations with the LDA nodal surface give satisfactory results, i.e., consistent either with high-level computational or with experimental reference values.

Energy Landscape and Phase Competition of CsV3Sb5, CsV6Sb6 and TbMn6Sn6-Type Kagome Materials

Finding viable Kagome lattices is vital for materializing novel phenomena in quantum materials. In this study, we performed element substitutions on CsV3Sb5 with space group P6/mmm, TbMn6Sn6 with space group P6/mmm, and CsV6Sb6 with space group , as the parent compounds. Totally 4158 materials were obtained through element substitutions, and these materials were then calculated via density functional theory in high-throughput mode. Afterwards, 48 materials were identified with high thermodynamic stability (E hull < 5 meV/atom). Furthermore, we compared the thermodynamic stability of three different phases with the same elemental composition and predicted some competing phases that may arise during material synthesis. Finally, by calculating the electronic structures of these materials, we attempted to identify patterns in the electronic structure variations as the elements change. This study provides guidance for discovering promising AM3X5/AM6X6 Kagome materials from a vast phase space.

Blood molecular profile to predict genotoxicity from exposure to antineoplastic drugs.

Genotoxicity is an important information that should be included in human biomonitoring programmes. However, the usually applied cytogenetic assays are laborious and time-consuming, reason why it is critical to develop rapid and economic new methods. The aim of this study was to evaluate if the molecular profile of frozen whole blood, acquired by Fourier Transform Infrared (FTIR) spectroscopy, allows to assess genotoxicity in occupational exposure to antineoplastic drugs, as obtained by the cytokinesis-block micronucleus assay. For that purpose, 92 samples of peripheral blood were studied: 46 samples from hospital professionals occupationally exposed to antineoplastic drugs and 46 samples from workers in academia without exposure (controls). It was first evaluated the metabolome from frozen whole blood by methanol precipitation of macromolecules as haemoglobin, followed by centrifugation. The metabolome molecular profile resulted in 3 ratios of spectral bands, significantly different between the exposed and non-exposed group (p<0.01) and a spectral principal component-linear discriminant analysis (PCA-LDA) model enabling to predict genotoxicity from exposure with 73 % accuracy. After optimization of the dilution degree and solution used, it was possible to obtain a higher number of significant ratios of spectral bands, i.e., 10 ratios significantly different (p<0.001), highlighting the high sensitivity and specificity of the method. Indeed, the PCA-LDA model, based on the molecular profile of whole blood, enabled to predict genotoxicity from the exposure with an accuracy, sensitivity, and specificity of 92 %, 93 % and 91 %, respectively. All these parameters were achieved based on 1μL of frozen whole blood, in a high-throughput mode, i.e., based on the simultaneous analysis of 92 samples, in a simple and economic mode. In summary, it can be conclude that this method presents a very promising potential for high-dimension screening of exposure to genotoxic substances.

Comparison of two supporting matrices for patient-derived cancer cells in 3D drug sensitivity and resistance testing assay (3D-DSRT).

Central to the success of functional precision medicine of solid tumors is to perform drug testing of patient-derived cancer cells (PDCs) in tumor-mimicking ex vivo conditions. While high throughput (HT) drug screening methods have been well-established for cells cultured in two-dimensional (2D) format, this approach may have limited value in predicting clinical responses. Here, we describe the results of the optimization of drug sensitivity and resistance testing (DSRT) in three-dimensional (3D) growth supporting matrices in a HT mode (3D-DSRT) using the hepatocyte cell line (HepG2) as an example. Supporting matrices included widely used animal-derived Matrigel and cellulose-based hydrogel, GrowDex, which has earlier been shown to support 3D growth of cell lines and stem cells. Further, the sensitivity of ovarian cancer PDCs, from two patients included in the functional precision medicine study, was tested for 52 drugs in 5 different concentrations using 3D-DSRT. Shortly, in the optimized protocol, the PDCs are embedded with matrices and seeded to 384-well plates to allow the formation of the spheroids prior to the addition of drugs in nanoliter volumes with acoustic dispenser. The sensitivity of spheroids to drug treatments is measured with cell viability readout (here, 72 h after addition of drugs). The quality control and data analysis are performed with openly available Breeze software. We show the usability of both matrices in established 3D-DSRT, and report 2D vs 3D growth condition dependent differences in sensitivities of ovarian cancer PDCs to MEK-inhibitors and cytotoxic drugs. This study provides a proof-of-concept for robust and fast screening of drug sensitivities of PDCs in 3D-DSRT, which is important not only for drug discovery but also for personalized ex vivo drug testing in functional precision medicine studies. These findings suggest that comparing results of 2D- and 3D-DSRT is essential for understanding drug mechanisms and for selecting the most effective treatment for the patient.

The Impact of the Serum Extraction Protocol on Metabolomic Profiling Using UPLC-MS/MS and FTIR Spectroscopy.

Biofluid metabolomics is a very appealing tool to increase the knowledge associated with pathophysiological mechanisms leading to better and new therapies and biomarkers for disease diagnosis and prognosis. However, due to the complex process of metabolome analysis, including the metabolome isolation method and the platform used to analyze it, there are diverse factors that affect metabolomics output. In the present work, the impact of two protocols to extract the serum metabolome, one using methanol and another using a mixture of methanol, acetonitrile, and water, was evaluated. The metabolome was analyzed by ultraperformance liquid chromatography associated with tandem mass spectrometry (UPLC-MS/MS), based on reverse-phase and hydrophobic chromatographic separations, and Fourier transform infrared (FTIR) spectroscopy. The two extraction protocols of the metabolome were compared over the analytical platforms (UPLC-MS/MS and FTIR spectroscopy) concerning the number of features, the type of features, common features, and the reproducibility of extraction replicas and analytical replicas. The ability of the extraction protocols to predict the survivability of critically ill patients hospitalized at an intensive care unit was also evaluated. The FTIR spectroscopy platform was compared to the UPLC-MS/MS platform and, despite not identifying metabolites and consequently not contributing as much as UPLC-MS/MS in terms of information concerning metabolic information, it enabled the comparison of the two extraction protocols as well as the development of very good predictive models of patient's survivability, such as the UPLC-MS/MS platform. Furthermore, FTIR spectroscopy is based on much simpler procedures and is rapid, economic, and applicable in the high-throughput mode, i.e., enabling the simultaneous analysis of hundreds of samples in the microliter range in a couple of hours. Therefore, FTIR spectroscopy represents a very interesting complementary technique not only to optimize processes as the metabolome isolation but also for obtaining biomarkers such as those for disease prognosis.

SophosQM: Accurate Binding Affinity Prediction in Compound Optimization.

The optimization of compounds' binding affinity for a biological target is a crucial aspect of the drug development process. Being able to accurately predict binding energies in advance of synthesizing compounds would have a massive impact on the speed of the drug discovery process. The ideal binding affinity prediction method should combine accuracy, reliability, and speed. In this paper, we present SophosQM, a quantum mechanics (QM)-based approach, which can accurately predict the binding affinities of compounds to proteins. The binding affinity predictive models generated by SophosQM are based on the fragment molecular orbital (FMO) method to estimate the enthalpic component of the binding free energy, and a macroscopic descriptor, clog P, is used as an approximation of the entropic component. The affinity prediction is performed using multilinear regression, fitting the experimental values against the FMO-computed enthalpic term and clog P. The quality of the prediction can be assessed in terms of the correlation coefficient between experimental and predicted values. In this work, the method's reliability and accuracy are exemplified by applying SophosQM to 70 compounds binding to six different targets of pharmaceutical relevance. Overall, the results show a very satisfactory performance with a global correlation coefficient in the order of 0.9. Our predictions also show a satisfactory performance compared to data based on free energy perturbation. Finally, SophosQM can also be applied in high-throughput mode by using semiempirical QM methods to evaluate large portions of chemical space, while retaining a good level of accuracy, but decreasing the computing time to just a few seconds per compound.