High-throughput Infrastructure Research Articles

Millisecond cryo-trapping by the spitrobot crystal plunger simplifies time-resolved crystallography

We introduce the spitrobot, a protein crystal plunger, enabling reaction quenching via cryo-trapping with a time-resolution in the millisecond range. Protein crystals are mounted on canonical micromeshes on an electropneumatic piston, where the crystals are kept in a humidity and temperature-controlled environment, then reactions are initiated via the liquid application method (LAMA) and plunging into liquid nitrogen is initiated after an electronically set delay time to cryo-trap intermediate states. High-magnification images are automatically recorded before and after droplet deposition, prior to plunging. The SPINE-standard sample holder is directly plunged into a storage puck, enabling compatibility with high-throughput infrastructure. Here we demonstrate binding of glucose and 2,3-butanediol in microcrystals of xylose isomerase, and of avibactam and ampicillin in microcrystals of the extended spectrum beta-lactamase CTX-M-14. We also trap reaction intermediates and conformational changes in macroscopic crystals of tryptophan synthase to demonstrate that the spitrobot enables insight into catalytic events.

Assessing the quality of relaxation-time approximations with fully automated computations of phonon-limited mobilities

The mobility of carriers, as limited by their scattering with phonons, can now routinely be obtained from first-principles electron-phonon coupling calculations. However, so far, most computations have relied on some form of simplification of the linearized Boltzmann transport equation based on either the self-energy, the momentum- or constant relaxation time approximations. Here, we develop a high-throughput infrastructure and an automatic workflow and we compute 69 phonon-limited mobilities in semiconductors. We compare the results resorting to the approximations with the exact iterative solution. We conclude that the approximate values may deviate significantly from the exact ones and are thus not reliable. Given the minimal computational overhead, our work encourages to rely on this exact iterative solution and warns on the possible inaccuracy of earlier results reported using relaxation time approximations.

(Invited) A Multi-Scale Infrastructure for Automating Materials Science Computations

In this talk, I will present our group’s effort to build a high-throughput infrastructure for the automated execution of materials science computations and analysis. This work is an extension of open-source Python packages that have been developed in Materials Project [1]: (1) pymatgen [2] for structure representation and input/output files generation and handling, (2) FireWorks [3] for managing workflows over computing resources, and (3) Custodian [4] for monitoring inevitable errors during the simulations and applying on-the-fly fixes. In our work, we benefit from these three libraries to build well-tested workflows for speeding up the prediction and screening of materials properties relevant to various applications with a focus on battery systems. At the backend, the infrastructure interfaces with the Gaussian [5] software which enables electronic structure calculations of chemical systems, and LAMMPS [6] open-source code for molecular dynamics (MD) simulations. In addition to its ability to handle the generation of input/data files and parsing of output files from the mentioned computational software, the infrastructure allows management of the collected data and storing it in MongoDB [7], a NoSQL database program using JSON-like documents with flexible schema. Examples of implemented Gaussian-based workflows include the calculation of electrostatic partial charges, NMR chemical shift, binding energy, ionization potential, electron affinity, and bond dissociation energy. Each derived property is saved in its own collection with auxiliary information like molecular metadata (smiles representation, chemical formula, ...), which makes it easy to query and data-mine structure-property relationships. The user can tune the calculations by overriding default workflow parameters, for example, the functional and basis set, by-passing selected steps, or packing many jobs over multiple nodes for supercomputing resources. LAMMPS workflows allow the execution of MD simulations in different ensembles and analysis of the dumped trajectories for various dynamical and structural properties. The infrastructure enables coupling first-principles calculations with classical MD simulations to address chemical and physical phenomena occurring in a given system at different length and time scales. This approach aids in the development of databases required for training machine-learning models to accelerate the design and screening of materials with optimized properties for various materials science applications.References Jain, A., et al., Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. Apl Materials, 2013. 1(1): p. 011002.Ong, S.P., et al., Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Computational Materials Science, 2013. 68: p. 314-319.Jain, A., et al., FireWorks: a dynamic workflow system designed for high‐throughput applications. Concurrency and Computation: Practice and Experience, 2015. 27(17): p. 5037-5059.Custodian. <https://github.com/materialsproject/custodian>.Frisch, M.J., et al., Gaussian 16 Rev. C.01. 2016: Wallingford, CT.Plimpton, S., Fast parallel algorithms for short-range molecular dynamics. 1993, Sandia National Labs., Albuquerque, NM (United States).MongoDB Inc., M., 2014.

Abstract PO-012: AI and digital pathology based on nucleus morphology for diagnosis, prognosis, and morphological-gene isolation Li Fraumeni as a model

Abstract Li–Fraumeni syndrome (LFS), also known as the sarcoma, breast, leukemia, and adrenal gland (SBLA) syndrome, is a rare, autosomal dominant, hereditary disorder that predisposes carriers to cancer development. An early diagnosis and prognosis of LFS and identification of a novel target gene are critically important so that affected families can seek appropriate genetic counseling as well as screening for early detection and personalized therapy of cancer. We created a p53 knockdown collaborative-cross (CC) model to isolate the nuclear morphology features' genes. CC consists of recombinant inbred lines generated by reciprocal crosses between numerous founder lines. The CC animal model is a reference to apply genetic traits mapping with high-resolution phenotypes such as digital pathology features. The tumors developed in the LFS-CC Model were extracted, embedded in paraffin, sectioned, stained with Hematoxylin-eosin, and subjected to whole-slide scanning. We developed a high-throughput infrastructure for nuclear segmentation as a tool to create new LFS tumor biomarkers. These novel biomarkers are based on extracted features from 1) classical image segmentation and feature calculation tools (QuPath). 2) convolutional neural networks (CNN) from a deep learning nuclei segmentation classifier. 3) combining the two approaches by calculating features with QuPath for the nuclei detected by the CNN. The k-mean grouping of the features extracted from the digital pathology shows mediastinal (90-95% accuracy) vs. non-mediastinal LFS-lymphoma (30% – 50%) diagnosis. K-mean grouping of the nuclear morphological features and survival analysis (Kaplan-Meier) demonstrate the prognosis capacity of the digital morphology groups (seven significantly different survival curcles with p-values ranging between &amp;lt;0.001 –0.0162). K-mean grouping of the features creates four morphological phenotypes for QTL (Quantitative Trait Locus) analysis. These analyses identify 12 loci in the chromosomes that code for new nuclear morphological candidate genes. We also developed a knowledge-based infrastructure to select candidate genes relevant to human lymphoma and sarcoma survival. These modifier genes can serve as targets for personalized therapy. These results demonstrate that digital pathology using a unique animal model can identify features that can serve as prognostic factors for LFS and help identify new targets for therapy that code for the alteration of the nuclear structure. These infrastructures can be utilized for other tumors in human research. Citation Format: Ilan Tsarfaty, Ayman Iraqi, Or Megides, Nizan Cauzmer, Hila Shacham, Doruk Barokas, Fuad Iraqi, Dvora Kidron. AI and digital pathology based on nucleus morphology for diagnosis, prognosis, and morphological-gene isolation Li Fraumeni as a model [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-012.

A Faster Path to Solar Water Splitting

Finding new wide-bandgap light absorbers that are stable in aqueous solutions is a long-standing challenge in photoelectrochemical water splitting research. Two papers in this issue describe recent advances in high throughput experimentation that may accelerate the discovery of suitable materials.

AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA’s workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with external simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible.

Monolithic hydrogel nanowells-in-microwells enabling simultaneous single cell secretion and phenotype analysis.

Cytokine secretion is a form of cellular communication that regulates a wide range of biological processes. A common approach for measuring cytokine secretion from single cells is to confine individual cells in arrays of nanoliter wells (nanowells) fabricated using polydimethylsiloxane. However, this approach cannot be easily integrated in standard microwell plates in order to take advantage of high-throughput infrastructure for automated and multiplexed analysis. Here, we used laser micropatterning to fabricate monolithic hydrogel nanowells inside wells in a microwell plate (microwells) using polyethylene glycol diacrylate (PEGDA). This approach produces high-aspect ratio nanowells that retain cells and beads during reagent exchange, enabling simultaneous profiling of single cell secretion and phenotyping via immunostaining. To limit contamination between nanowells, we used methylcellulose as a media additive to reduce diffusion distance. Patterning nanowells monolithically in microwells also dramatically increases density, providing ∼1200 nanowells per microwell in a microwell plate. Using this approach, we profiled IL-8 secretion from single MDA-MB-231 cells, which showed significant heterogeneity. We further profiled the polarization of THP-1 cells into M1 and M2 macrophages, along with their associated IL-1β and CCL-22 secretion profiles. These results demonstrate the potential to use this approach for high-throughput secretion and phenotype analysis on single cells.

Better resource utilisation and quality of care for ovarian cancer patients using internet-based pathology review

Background:The current literature indicates that a considerable number of patients in ovarian carcinoma clinical trials have histopathological diagnoses in conflict with inclusion criteria. It has been suggested that specialised pathology review prior to randomisation should become the standard procedure in study protocols. We hypothesised that our new, internet-based high-throughput infrastructure would be capable of providing specialised pathology review within 10 working days (w.d.).Methods:Patients scheduled for the AGO OVAR17 ovarian carcinoma chemotherapy trial were registered for expert pathologic case review using a new internet-based central pathology review platform prior to randomisation. All original slides were requested from local pathologists. Slides were scanned and uploaded to a secured internet server. A network of experienced gynaecological pathologists was connected to the server through a custom-designed software platform. If deemed necessary by the expert pathologists, immunohistochemistry was available through a collaborating pathology lab.Results:A total of 880 patients with an original diagnosis of ovarian epithelial carcinoma were registered for expert pathology review from October 2011 to July 2013. For case review, five gynaecopathologists from Austria, Switzerland and Germany were available online. Median number of w.d. required to complete the whole process from patient registration to transmission of final review diagnoses was 4 (range 2–31) (w.d.), and in 848 out of 880 (97.5%) cases, it amounted to ⩽10 w.d. In 2.5% (n=22) of cases, a major diagnostic discrepancy of potential clinical relevance was found leading to exclusion from the chemotherapy trial.Conclusions:Our results show that the use of a new internet-based infrastructure makes timely specialised case review, prior to patient randomisation feasible within ⩽10 w.d. Our new approach helped to protect against overtreatment with chemotherapy of patients with ovarian borderline tumours and inadequate treatment of patients with ovarian metastases, as a result of their inappropriate entry into a clinical trial designed for patients with primary ovarian carcinoma.

High Throughput Approach to Accelerate Electrolyte Discovery for Batteries

Increasing demand of high energy density and high capacity batteries require development beyond Li-ion technology and innovations in electrodes and electrolytes, alike. An approach to achieve energy density higher than the existing Li-ion batteries is to replace monovalent Li ion by multivalent ions such as Mg, Ca. The ability of Mg batteries to provide much higher volumetric capacity, particularly on the anode side where the Mg metal can theoretically provide 3833 mA h/cc as compared to the Li counterpart graphite (∼ 800 mA h/cc) at a lower cost makes the technology an attractive candidate for future batteries. However, development and commercialization of Mg batteries require not only improved electrode discovery and development but also novel electrolytes, which are compatible with the Mg metal as conventional electrolytes fail to penetrate the Mg metal passivation layer. Thus a fundamental understanding of molecular level properties of these electrolytes is required to improve the electrochemical stability and the charge transfer properties. An automatic High-throughput infrastructure has been constructed for the electrolyte genome project supported by the US Joint Center for Energy Storage Research (JCESR)2. In this work, we present classical molecular dynamics simulations coupled with ab initio calculations for Mg salts in various solvents. We uncover a novel effect between concentration dependent ion pair formation and anion stability at reducing potential, e.g., at the metal anode1. We elucidate systematic correlations between molecular level interactions and composite electrolyte properties, such as electrochemical stability, solvation structure, and dynamics. We find that Mg electrolytes are highly prone to ion pair formation, even at modest concentrations, for a wide range of solvents with different dielectric constants, which have implications for dynamics as well as charge transfer. Specifically, we observe that, at Mg metal potentials, the ion pair undergoes partial reduction at the Mg cation center (Mg2+ → Mg+), which competes with the charge transfer mechanism and can activate the anion to render it susceptible to decomposition. Specifically, TFSI− exhibits a significant bond weakening while paired with the transient, partially reduced Mg+. Furthermore, we observe that higher order glymes as well as DMSO improve the solubility of Mg salts, but only the longer glyme chains reduce the dynamics of the ions in solution. This information provides critical design metrics for future electrolytes as it elucidates a close connection between bulk solvation and cathodic stability as well as the dynamics of the salt.

(Invited) Insights into Solvation, Dynamics and Stability of Electrolytes for the Design of Novel Multivalent Systems from Coupled Molecular Dynamics and First-Principles Modeling

Within the Joint Center for Energy Storage (JCESR) we have developed a high-throughput infrastructure for the automated calculation of molecular properties with a focus on battery electrolytes. Computational screening techniques have been found to be an effective alternative to the trial and error of experimentation for discovery of new materials. Using this infrastructure, we have computed the ionization potential (IP) and electron affinities (EA) of 16,000 molecules relevant to electrolytes targeting both multivalent intercalation as well as non-aqueous redox flow systems. For some potential multi-valent electrolytes we use classical molecular dynamics simulations coupled with ab initio calculations for Mg salts in various solvents. We uncover a novel effect between concentration dependent ion pair formation and anion stability at reducing potential, e.g., at the metal anode1. We elucidate systematic correlations between molecular level interactions and composite electrolyte properties, such as electrochemical stability, solvation structure, and dynamics. Specifically, we find that multivalent electrolytes are highly prone to ion pair formation, even at modest concentrations, for a wide range of solvents with different dielectric constants, which have implications for dynamics as well as charge transfer. For example, at Mg metal potentials, the ion pair undergoes partial reduction at the Mg cation center (Mg2+→Mg+), which competes with the charge transfer mechanism and can activate the anion to render it susceptible to decomposition. Specifically, TFSI− exhibits a significant bond weakening while paired with the transient, partially reduced Mg+. This information provides critical design metrics for future electrolytes as it elucidates a close connection between bulk solvation and cathodic stability as well as the dynamics of the salt. We use our previous insights to design for new Mg electrolyte anions with the aim to stabilize the electrolyte during possible ion pair formation and charge transfer reactions. Through comprehensive calculations using both first-principles as well as well-benchmarked classical molecular dynamics, we proposed 16 new electrolyte salts. Based on successive property evaluation such as electrochemical stability window, chemical decomposition and hydrolysis vulnerability we down-select 4 potential new anions for Mg electrolytes.

The Electrolyte Genome project: A big data approach in battery materials discovery

We present a high-throughput infrastructure for the automated calculation of molecular properties with a focus on battery electrolytes. The infrastructure is largely open-source and handles both practical aspects (input file generation, output file parsing, and information management) as well as more complex problems (structure matching, salt complex generation, and failure recovery). Using this infrastructure, we have computed the ionization potential (IP) and electron affinities (EA) of 4830 molecules relevant to battery electrolytes (encompassing almost 55,000 quantum mechanics calculations) at the B3LYP/6-31+G∗ level. We describe automated workflows for computing redox potential, dissociation constant, and salt-molecule binding complex structure generation. We present routines for automatic recovery from calculation errors, which brings the failure rate from 9.2% to 0.8% for the QChem DFT code. Automated algorithms to check duplication between two arbitrary molecules and structures are described. We present benchmark data on basis sets and functionals on the G2-97 test set; one finding is that a IP/EA calculation method that combines PBE geometry optimization and B3LYP energy evaluation requires less computational cost and yields nearly identical results as compared to a full B3LYP calculation, and could be suitable for the calculation of large molecules. Our data indicates that among the 8 functionals tested, XYGJ-OS and B3LYP are the two best functionals to predict IP/EA with an RMSE of 0.12 and 0.27eV, respectively. Application of our automated workflow to a large set of quinoxaline derivative molecules shows that functional group effect and substitution position effect can be separated for IP/EA of quinoxaline derivatives, and the most sensitive position is different for IP and EA.

A Quick Pre-Deployment Fibre Optic Link Design Methodology based on Q-factor in a Digital Eye

optic cables have become the main driving force behind high data throughput infrastructure. In modern wideband communication systems, they are deployed as backbone cables in many long haul links as well as links to clients (Fibre to home). Several Gigabits per second data rates have become common. Field engineers need to have a thorough knowledge of physics behind any physical component used to accomplish fibre optic cable deployment tasks. To investigate different phenomena in such fibre optic links design, a 10 Km repeaterless backbone link, arbitrarily between an onshore control station and an offshore configuration terminal, is designed. Design process is done based on specifications of products already on market. Power and bandwidth constraints are taken into account as major causes of errors upon signal reception. The link budget is done by combining different fibre optic link design approaches from a number of sources. Precise and summarised tables for quick power and bandwidth budgeting process are depicted. Design simulation is done by Optisystem 8.0 simulation software, a product of optiwave Inc. The relationship between Quality factor (Q-factor) and BER (Bit Error Rates), their implications on received signal and how both are affected by different physical phenomena in optical communication systems is illustrated. Finally, quick design tables are drawn and graphs for design safety margin that conforms to ITU recommendations are plotted. The same tables can be extended and or modified to be customised to designers target.

Creating an infrastructure for high-throughput high-resolution cryogenic electron microscopy

New instrumentation for three-dimensional electron microscopy is facilitating an increase in the throughput of data collection and reconstruction. The increase in throughput creates bottlenecks in the workflow for storing and processing the image data. Here we describe the creation and quantify the throughput of a high-throughput infrastructure supporting collection of three-dimensional data collection.

A high-throughput infrastructure for density functional theory calculations

The use of high-throughput density functional theory (DFT) calculations to screen for new materials and conduct fundamental research presents an exciting opportunity for materials science and materials innovation. High-throughput DFT typically involves computations on hundreds, thousands, or tens of thousands of compounds, and such a change of scale requires new calculation and data management methodologies. In this article, we describe aspects of the necessary data infrastructure for such projects to handle data generation and data analysis in a scalable way. We discuss the problem of accurately computing properties of compounds across diverse chemical spaces with a single exchange correlation functional, and demonstrate that errors in the generalized gradient approximation are highly dependent on chemical environment.

Expanding the Available Assays: Adapting and Validating In-Cell Westerns in Microfluidic Devices for Cell-Based Assays

Microfluidic methods for cellular studies can significantly reduce costs due to reduced reagent and biological specimen requirements compared with many traditional culture techniques. However, current types of readouts are limited and this lack of suitable readouts for microfluidic cultures has significantly hindered the application of microfluidics for cell-based assays. The In-Cell Western (ICW) technique uses quantitative immunocytochemistry and a laser scanner to provide an in situ measure of protein quantities in cells grown in microfluidic channels of arbitrary geometries. The use of ICWs in microfluidic channels was validated by a detailed comparison with current macroscale methods and shown to have excellent correlation. Transforming growth factor-β-induced epithelial-to-mesenchymal transition of an epithelial cell line was used as an example for further validation of the technique as a readout for soluble-factor-based assays performed in high-throughput microfluidic channels. The use of passive pumping for sample delivery and laser scanning for analysis opens the door to high-throughput quantitative microfluidic cell-based assays that integrate seamlessly with existing high-throughput infrastructure.

MASPECTRAS 2: An integration and analysis platform for proteomic data

MASPECTRAS 2 is a freely available platform for integrating MS protein identifications with information from the major bioinformatics databases (ontologies, domains, literature, etc.). It assists researchers in understanding their data and publishing through sample comparisons, targeted queries, summaries, and exports in multiple formats such as PRIDE XML (Jones et al., Nucleic Acids Res. 2008, 36, D878-D883). MASPECTRAS 2 also comprises mechanisms to facilitate its integration in a high-throughput infrastructure. We illustrate application of MASPECTRAS 2 with unpublished tyrosine kinase inhibitor drug proteomics profiles in cancerous cells.

The molecular biology of breast cancer: accelerating clinical applications.

Recent advances in basic science have led to a better understanding of the molecular events important in the pathogenesis of breast cancer. Very little of this new knowledge, however, has had a significant impact on improving the diagnosis and therapy of breast cancer. We review many of the molecular events important in the pathogenesis of breast cancer, including inherited abnormalities in BRCA-1 and BRCA-2, p53, ATM, and PTEN and sporadic alterations in growth factors and their receptors, signal transduction, cell cycle control, DNA repair, cell death, angiogenesis, and invasion and metastasis. We suggest ways to speed up clinical applications of the new molecular knowledge base through the use of preclinical disease models, development of high throughput sample analysis and infrastructure programs to facilitate translational research, implementation of practice guidelines, and development of regional oncology networks. Only through the implementation of such a deliberate, multifaceted strategy will the gap between the research laboratory and the clinic be closed.