High-throughput Experiments Research Articles

Uncovering underlying physical principles and driving forces of cell differentiation and reprogramming from single-cell transcriptomics

Recent advances in single-cell sequencing technology have revolutionized our ability to acquire whole transcriptome data. However, uncovering the underlying transcriptional drivers and nonequilibrium driving forces of cell function directly from these data remains challenging. We address this by learning cell state vector fields from discrete single-cell RNA velocity to quantify the single-cell global nonequilibrium driving forces as landscape and flux. From single-cell data, we quantified the Waddington landscape, showing that optimal paths for differentiation and reprogramming deviate from the naively expected landscape gradient paths and may not pass through landscape saddles at finite fluctuations, challenging conventional transition state estimation of kinetic rate for cell fate decisions due to the presence of the flux. A key insight from our study is that stem/progenitor cells necessitate greater energy dissipation for rapid cell cycles and self-renewal, maintaining pluripotency. We predict optimal developmental pathways and elucidate the nucleation mechanism of cell fate decisions, with transition states as nucleation sites and pioneer genes as nucleation seeds. The concept of loop flux quantifies the contributions of each cycle flux to cell state transitions, facilitating the understanding of cell dynamics and thermodynamic cost, and providing insights into optimizing biological functions. We also infer cell-cell interactions and cell-type-specific gene regulatory networks, encompassing feedback mechanisms and interaction intensities, predicting genetic perturbation effects on cell fate decisions from single-cell omics data. Essentially, our methodology validates the landscape and flux theory, along with its associated quantifications, offering a framework for exploring the physical principles underlying cellular differentiation and reprogramming and broader biological processes through high-throughput single-cell sequencing experiments.

Synthesis of 3-Aminoazaindazoles via Cu-Catalyzed Cross Coupling of Isocyanides.

Nitrogen-containing heterocycles are commonly encountered in drug discovery, but the synthesis of such ring structures is not always efficient. Fused heterocyclic rings, in particular, can be challenging to synthesize. Herein, we report a highly convergent synthesis of 3-aminoazaindazoles via a Cu-catalyzed reaction between isocyanides and 3-halo-2-hydrazineylpyridines (and analogues). Reaction optimization through high-throughput experimentation (HTE) identified a novel set of exogenous ligand-free Cu conditions utilizing a cheap and readily available catalyst. The reaction displays high functional group tolerance and has the potential to be highly enabling for medicinal chemistry efforts. A putative mechanism is described as well as preliminary mechanistic experiments.

Machine Learning-Driven Prediction of DLD Chip Throughput

Abstract The microfluidic chip technology, capable of manipulating fluids at the micrometer-scale, is increasingly being applied in the fields of cell biology, molecular biology, chemistry, and life sciences. The densely integrated microfluidic chip devices enable high-throughput parallel experiments and integration of various operational units. However, the development of densely integrated microfluidic chips also comes with high demands on driving equipment. Due to manufacturing processes and inherent design limitations, the driving capability of the equipment is restricted. To address potential challenges faced by microfluidic chips in the development towards integrated biological microsystems and to maximize their high-throughput performance, improvements are required not only in selecting appropriate driving equipment but also in design aspects. This study focuses on the DLD chip and delves into the complexity of microfluidic chip design. By combining Bézier curves to characterize arbitrarily shaped micropillars and conducting finite element analysis to compute the pressure field of DLD chips, we explore methods utilizing random forest, XGBoost, LightGBM, and ANN machine learning algorithms to predict the impedance of DLD chips. Our objective is to guide engineers in designing chips with smaller impedance (lower pressure drop) and larger throughput more quickly and efficiently during the design phase. Ultimately, through evaluating the predictive capabilities of the four models on new data, we select the ANN algorithm model to predict the pressure drop under different designs of DLD chips. This offers possibilities for enhancing the efficiency and integration of microfluidic technology in biomedical applications.

Mapping protein conformational landscapes from crystallographic drug fragment screens.

Proteins are dynamic macromolecules. Knowledge of a protein's thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein's structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COLAV (COnformational LAndscape Visualization), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP-1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens also enable detailed mapping of proteins' conformational landscapes.

A General Three-Component Alkyl Petasis Boron-Mannich Reaction.

Aryl amines are one of the most common moieties in biologically active molecules, and approximately 37% of drug candidates contain aromatic amines. Recent advancements in medicinal chemistry, coined "escaping from flatland", have led to a greater focus on accessing highly functionalized C (sp3)-rich amines to improve the physicochemical and pharmacokinetic properties of compounds. This article presents a modular and operationally straightforward three-component alkyl Petasis boron-Mannich (APBM) reaction that utilizes ubiquitous starting materials, including amines, aldehydes, and alkyl boronates. By adaptation of this transformation to high-throughput experimentation (HTE), it offers rapid access to an array of diverse C(sp3)-rich complex amines, amenable for rapid identification of drug candidates.

GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes.

High-throughput experiments often produce ranked gene outputs, with forward genetic screening being a notable example. While there are various tools for analyzing individual datasets, those that perform comparative and meta-analytical examination of such ranked gene lists remain scarce. Here, we introduce Gene Rank Meta Analyzer (GeneRaMeN), an R Shiny tool utilizing rank statistics to facilitate the identification of consensus, unique, and correlated genes across multiple hit lists. We focused on two key topics to showcase GeneRaMeN: virus host factors and cancer dependencies. Using GeneRaMeN 'Rank Aggregation', we integrated 24 published and new flavivirus genetic screening datasets, including dengue, Japanese encephalitis, and Zika viruses. This meta-analysis yielded a consensus list of flavivirus host factors, elucidating the significant influence of cell line selection on screening outcomes. Similar analysis on 13 SARS-CoV-2 CRISPR screening datasets highlighted the pivotal role of meta-analysis in revealing redundant biological pathways exploited by the virus to enter human cells. Such redundancy was further underscored using GeneRaMeN's 'Rank Correlation', where a strong negative correlation was observed for host factors implicated in one entry pathway versus the alternate route. Utilizing GeneRaMeN's 'Rank Uniqueness', we analyzed human coronaviruses 229E, OC43, and SARS-CoV-2 datasets, identifying host factors uniquely associated with a defined subset of the screening datasets. Similar analyses were performed on over 1000 Cancer Dependency Map (DepMap) datasets spanning 19 human cancer types to reveal unique cancer vulnerabilities for each organ/tissue. GeneRaMeN, an efficient tool to integrate and maximize the usability of genetic screening datasets, is freely accessible via https://ysolab.shinyapps.io/GeneRaMeN.

From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

Actin Filament Barbed-End Depolymerization by Combined Action of Profilin, Cofilin, and Twinfilin

Cellular actin dynamics results from the collective action of hundreds of regulatory proteins, majority of which target actin filaments at their barbed ends. Three key actin binding proteins—profilin, cofilin, and twinfilin—individually depolymerize filament barbed ends. Notwithstanding recent leaps in our understanding of their individual action, how they collectively regulate filament dynamics remains an open question. In the absence of direct and simultaneous visualization of these proteins at barbed ends, gaining mechanistic insights has been challenging. We have here investigated multicomponent dynamics of profilin, cofilin, and twinfilin using a hybrid approach that combines high-throughput single filament experiments with theory. We discovered that while twinfilin competes with profilin, it promotes binding of cofilin to filament sides. Interestingly, contrary to previous expectations, we found that profilin and cofilin can simultaneously bind the same filament barbed end, resulting in its accelerated depolymerization. Our study reveals that pairwise interactions can effectively capture depolymerization dynamics in simultaneous presence of all three proteins. We thus believe that our approach of employing a theory-experiment dialog can potentially help decipher multicomponent regulation of actin dynamics. Published by the American Physical Society 2024

Desymmetrization of Cyclic Sulfonimidamides by Asymmetric Allylation

AbstractHerein we report the first transition metal‐catalyzed approach to the enantioenriched synthesis of cyclic sulfonimidamides relying on commercially available palladium catalysts and ligands. High‐throughput experimentation (HTE) was employed to identify the optimal catalyst system and solvent. The method is applied to a variety of saturated and unsaturated rings and exhibits the highest selectivity for 2‐substituted allyl electrophiles. The products are further elaborated to complex, tricyclic scaffolds. DFT experiments presented herein highlight the key ligand substrate interactions leading to the high levels of enantioselectivity.

Predictions of Chromatography Methods by Chemical Structure Similarity to Accelerate High-Throughput Medicinal Chemistry.

We introduce a new workflow that relies heavily on chemical quantitative structure-retention relationship (QSRR) models to accelerate method development for micro/mini-scale high-throughput purification (HTP). This provides faster access to new active pharmaceutical ingredients (APIs) through high-throughput experimentation (HTE). By comparing fingerprint structural similarity (e.g., Tanimoto index) with small training data sets containing a few hundred diverse small molecule antagonists of a lipid metabolizing enzyme, we can predict retention time (RT) of new compounds. Machine learning (ML) helps to identify optimal separation conditions for purification without performing the traditional crude QC step involving ultrahigh performance liquid chromatography (UHPLC) analyses of each compound. This green-chemistry approach with the use of predictive tools reduces cost and significantly shortens the design-make-test (DMT) cycle of new drugs by way of HTE.

Desymmetrization of Cyclic Sulfonimidamides by Asymmetric Allylation.

Herein we report the first transition metal-catalyzed approach to the enantioenriched synthesis of cyclic sulfonimidamides relying on commercially available palladium catalysts and ligands. High-throughput experimentation (HTE) was employed to identify the optimal catalyst system and solvent. The method is applied to a variety of saturated and unsaturated rings and exhibits the highest selectivity for 2-substituted allyl electrophiles. The products are further elaborated to complex, tricyclic scaffolds. DFT experiments presented herein highlight the key ligand substrate interactions leading to the high levels of enantioselectivity.

Improved Imaging Surface for Quantitative Single-Molecule Microscopy.

Preventing nonspecific binding is essential for sensitive surface-based quantitative single-molecule microscopy. Here we report a much-simplified RainX-F127 (RF-127) surface with improved passivation. This surface achieves up to 100-fold less nonspecific binding from protein aggregates compared to commonly used polyethylene glycol (PEG) surfaces. The method is compatible with common single-molecule techniques including single-molecule pull-down (SiMPull), super-resolution imaging, antibody-binding screening and single exosome visualization. This method is also able to specifically detect alpha-synuclein (α-syn) and tau aggregates from a wide range of biofluids including human serum, brain extracts, cerebrospinal fluid (CSF) and saliva. The simplicity of this method further allows the functionalization of microplates for robot-assisted high-throughput single-molecule experiments. Overall, this simple but improved surface offers a versatile platform for quantitative single-molecule microscopy without the need for specialized equipment or personnel.

A perspective on additives for flexible packaging

AbstractAdditives have been critical to the growth of flexible packaging since its inception nearly 100 years ago. Additives help tailor the performance of new materials to meet packaging needs and to efficiently run on film converting and packaging equipment. Examples include antioxidants for thermo‐oxidative stability during extrusion, slip and antiblock agents to improve film handling and frictional properties, polymer process aides (PPAs) to delay the onset of flow instabilities and increase output, pigments for coloring films and creating light barrier, antifog agents to improve wetting of film surfaces, antistat agents to reduce static charge, and nucleating agents for increasing the rate of crystallization and controlling crystalline morphology. This paper provides a brief history of the development and use of additives in flexible packaging, focusing on polyolefins. It then looks forward at new challenges and opportunities for additive development. One area is regulatory driven, most recently replacing per‐and polyfluoroalkyl substances (PFAS)‐containing PPAs with non‐PFAS technology. The industry has had to respond very rapidly to changing regulations and introduce new technology. The challenge is understanding the impact of the new technology on performance. A second area is sustainability where additives will be critical for upgrading the performance of mechanically recycled post‐consumer packaging waste, redesigning flexible packaging structures so that they can be recycled in the PE‐stream, and using bio‐based or compostable polymers. New research methods such as high throughput experimentation, artificial intelligence and computer modeling and simulation will likely accelerate additive development in the future.Highlights Additives are critical for meeting flexible packaging performance needs. Additives improve surface and bulk properties and processability. Replacing PFAS‐containing process aides is a current area of focus. Sustainability is driving new opportunities for additive development. New research tools will help accelerate additive development.

Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems

The optimization of yields in multi-reactor systems, which are advanced tools in heterogeneous catalysis research, presents a significant challenge due to hierarchical technical constraints. To this respect, this work introduces a novel approach called process-constrained batch Bayesian optimization via Thompson sampling (pc-BO-TS) and its generalized hierarchical extension (hpc-BO-TS). This method, tailored for the efficiency demands in multi-reactor systems, integrates experimental constraints and balances between exploration and exploitation in a sequential batch optimization strategy. It offers an improvement over other Bayesian optimization methods. The performance of pc-BO-TS and hpc-BO-TS is validated in synthetic cases as well as in a realistic scenario based on data obtained from high-throughput experiments done on a multi-reactor system available in the REALCAT platform. The proposed methods often outperform other sequential Bayesian optimizations and existing process-constrained batch Bayesian optimization methods. This work proposes a novel approach to optimize the yield of a reaction in a multi-reactor system, marking a significant step forward in digital catalysis and generally in optimization methods for chemical engineering.

HTE OS: A High-Throughput Experimentation Workflow Built from the Ground Up

HTE OS: A High-Throughput Experimentation Workflow Built from the Ground Up

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.

INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design

Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(|Q|,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. We expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS. Program summaryProgram Title: INSPIREDCPC Library link to program files:https://doi.org/10.17632/8g3s8f9n2p.1Developer's repository link:https://github.com/cyqjh/inspired (software), https://doi.org/10.5281/zenodo.11478889 (database, models files, and virtual machine appliance file)Licensing provisions: MITProgramming language: PythonNature of problem: How to easily and quickly assess the expected INS spectra for a given crystal structure has been a major challenge in the INS user community. It is a main bottleneck affecting almost every stage of the workflow, from experimental design and steering to data analysis and interpretation. The widely used approach involving DFT calculations is time-consuming, requires advanced computing resources, and has a steep learning curve. With the growing power of neutron sources and more high-throughput INS experiments, there is a pressing need to address this problem, preferably by taking advantage of the recent developments in machine learning and artificial intelligence.Solution method: We take a data-driven approach to tackle the problem. A symmetry-aware neural network is trained to make direct predictions from the crystal structure to either 1D spectra or latent space vectors, which can be decoded to reconstruct 2D spectra. The database used for the training contains over ten thousand crystals, which can also be used to calculate INS spectra for single crystals and powders. The recently emerging universal machine learning force fields provide another venue to accelerate the simulation significantly. All these solutions are implemented in a graphic user interface so that a user with no modeling/programming background or access to powerful computers can still easily run the workflow.

Comparing Prion Proteins Across Species: Is Zebrafish a Useful Model?

Despite the considerable body of research dedicated to the field of neurodegeneration, the gap in knowledge on the prion protein and its intricate involvement in brain diseases remains substantial. However, in the past decades, many steps forward have been taken toward a better understanding of the molecular mechanisms underlying both the physiological role of the prion protein and the misfolding event converting it into its pathological counterpart, the prion. This review aims to provide an overview of the main findings regarding this protein, highlighting the advantages of many different animal models that share a conserved amino acid sequence and/or structure with the human prion protein. A particular focus will be given to the species Danio rerio, a compelling research organism for the investigation of prion biology, thanks to its conserved orthologs, ease of genetic manipulation, and cost-effectiveness of high-throughput experimentation. We will explore its potential in filling some of the gaps on physiological and pathological aspects of the prion protein, with the aim of directing the future development of therapeutic interventions.

A novel insertional allele of the CG18135 gene is associated with severe mutant phenotypes in Drosophila melanogaster.

Drosophila melanogaster has been at the forefront of genetic studies and biochemical modeling for over a century. Yet, the functions of many genes are still unknown, mainly because no phenotypic data are available. Herein, we present the first evidence data regarding the particular molecular and other quantifiable phenotypes, such as viability and anatomical anomalies, induced by a novel P{lacW} insertional mutant allele of the CG18135 gene. So far, the CG18135 functions have only been theorized based on electronic annotation and presumptive associations inferred upon high-throughput proteomics or RNA sequencing experiments. The descendants of individuals harboring the CG18135 P{lacW}CG18135 allele were scored in order to assess mutant embryonic, larval, and pupal viability versus Canton Special (CantonS). Our results revealed that the homozygous CG18135 P{lacW}CG18135 /CG18135 P{lacW}CG18135 genotype determines significant lethality both at the inception of the larval stage and during pupal development. The very few imago escapers that either breach or fully exit the puparium exhibit specific eye depigmentation, wing abnormal unfolding, strong locomotor impairment with apparent spasmodic leg movements, and their maximum lifespan is shorter than 2days. Using the quantitative real-time PCR (qRT-PCR) method, we found that CG18135 is upregulated in male flies, but an unexpected gene upregulation was also detected in heterozygous mutants compared to wild-type flies, probably because of regulatory perturbations induced by the P{lacW} transposon. Our work provides the first phenotypic evidence for the essential role of CG18135, a scenario in accordance with the putative role of this gene in carbohydrate-binding processes.

Total Syntheses of Clausenawallines A and E.

The first total syntheses of glycoborinine, clausenawalline A, and clausenawalline E were achieved. The key step employed a vanadium-catalyzed oxidative coupling of two hydroxycarbazole monomers. High-throughput experimentation was used to identify conditions favoring selective heterocoupling of these monomers that possess similar redox potentials. A combination of a vanadium catalyst and 4-acetamido-TEMPO gives rise to greatly enhanced cross selectivity relative to the vanadium catalyst alone. Conditions to selectively form homodimer clausenawalline A or heterodimer clausenawalline E as the major product were found.