ABSTRACT Invasive fungal diseases (IFD) are life‐threatening infections with limited clinical management. Antifungal peptide (AFP)‐mimicking polymers are promising candidates for tackling IFD. However, due to the structural diversity of polymers, it remains a great challenge to discover new polymers with potent antifungal activity and biocompatibility. Herein, we propose a novel active learning‐guided framework PolyCAML for accelerated discovery of antifungal polymers. Inspired by AFPs, we construct a quaternary copolymer library comprising 516,114 combinations with different side chains. By integrating automated synthesis platform, Graph Transformer machine learning model, and greedy algorithm, 11 top‐performing candidates are screened via the Design‐Build‐Test‐Learn (DBTL) intelligent iterative workflow within merely 18 days. Through further data mining, key factors that influence antifungal potency and biocompatibility of polymers are identified. By more rigorous biological evaluation, four target polymers are screened, exhibiting a minimum fungicidal concentration ≤ 8 µg/mL and a 50% inhibitory concentration of L929 cells ≥ 512 µg/mL. These polymers are self‐assembled into positively charged nanoparticles for delivering fluconazole, which exhibit synergistic anti‐biofilm activity by enhanced biofilm penetration. The in vivo therapeutic efficacy is confirmed by a fungemia model and a fungal keratitis model. Overall, we establish a multi‐task intelligent prediction platform PolyCAML to discover antifungal polymers for tackling IFD.

Simultaneous characterization of proteomic and metabolomic profiles at the single-cell level is crucial for deciphering cellular heterogeneity and elucidating disease mechanisms. However, it is still a great challenge to achieve high-depth dual-omics analysis in the same single cell. Here, we propose a unified strategy called one-shot hybrid-mode single-cell proteome and metabolome analysis (hybrid-scPMA), in which the mass spectrometry (MS) detection mode of data-independent acquisition (DIA) is utilized for the analysis of peptides from protein digestion, and the data-dependent acquisition (DDA) mode is used for metabolite analysis in a single liquid chromatography-mass spectrometry (LC-MS) analysis run, enabling deep analysis of the proteome and metabolome in single-cell samples. Building upon the strategy, we established an improved single-cell multiomics analysis workflow that integrated automated single-cell capture, simplified sample pretreatment, LC injection and separation, and DIA-DDA hybrid-mode MS detection. With this approach, we achieved an average identification of 3510 protein groups and 255 metabolites from single HepG2 cells, representing a substantial increase in identification depth over previous approaches. We also performed time-resolved proteomic and metabolomic profiling of HepG2 single cells undergoing sorafenib drug intervention, resolving drug response characteristics at the single-cell level and providing multiomics insights into drug mechanisms from a proteo-metabolomic perspective.

Recent years have seen a rise of single-cell proteomics by data-independent acquisition mass spectrometry (DIA MS). While diverse data analysis strategies have been reported in literature, their impact on the outcome of single-cell proteomic experiments has been rarely investigated. Here, we present a framework for benchmarking data analysis strategies for DIA-based single-cell proteomics. This framework provides a comprehensive comparison of popular DIA data analysis software tools and searching strategies, as well as a systematic evaluation of method combinations in subsequent informatic workflow, including sparsity reduction, missing value imputation, normalization, batch effect correction, and differential expression analysis. Benchmarking on simulated single-cell samples consisting of mixed proteomes and real single-cell samples with a spike-in scheme, recommendations are provided for the data analysis for DIA-based single-cell proteomics.

Antimicrobial peptides (AMPs)-mimicking antimicrobial polymers show great potential as therapeutic alternatives to antibiotics in the looming “post-antibiotic era”. However, the discovery of new AMP-mimicking antimicrobial polymers is challenging due to the vast chemical space of side-chain combinations. The advancement of AI-guided high-throughput screening enables more efficient, precise, and intelligent material design. Herein, we integrate combinatorial chemistry, machine learning, and automated high-throughput synthesis and characterization platforms to establish a new paradigm for the design of antimicrobial polymers with excellent biocompatibility. Starting with a library of 13,728 combinations, a seed dataset of 400 structures is generated, followed by four Design-Build-Test-Learn iterations using a new machine learning model. 7 top-performing candidates are screened with a minimum inhibitory concentration (MIC) ≤ 8 μg/mL and an inhibitory concentration causing 20 % cell death (IC20) ≥ 64 μg/mL. The highest-performing polymer (MIC 2 μg/mL, IC20 256 μg/mL) shows similar in vivo therapeutic efficacy with ceftazidime. Overall, the integration of AI-guided high-throughput screening and combinatorial chemistry accelerates the discovery of new antimicrobial polymers, which provides a scalable strategy for developing novel antimicrobial agents.

Protein phosphorylation plays an important role in cellular regulation and signal transduction. In this study, we developed a microamount phosphopeptide enrichment system (mPES) to achieve the enrichment of phosphopeptides in nanoliter-scale based on microfluidics and the solid-phase microextraction (SPME) technique. Based on mPES, we established a complete set of phosphoproteomic analysis workflow for trace cell samples, including operations of cell capture, cell lysis, protein proteolysis, phosphopeptide enrichment, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) detection, which can profile phosphoproteomics of complex samples as low as picogram amounts. Cells pretreatment and phosphopeptide enrichment were carried out in the nanoliter-scale volume range, which significantly increased the enrichment efficiency and reduced the sample loss by container adsorption. We applied the present system to the analysis of phosphorylation in 1, 3, 5, 10 HeLa cells, and 83, 117, 187, 227 phosphopeptides were identified after enrichment, respectively. The intensities of phosphopeptides increased by 2.91, 2.80, 8.81, and 9.95 times compared with the control groups. The mPES was further applied to the phosphoproteomic analysis of single mouse oocytes, with an average of 221 phosphoproteins identified in single germinal vesicle (GV) oocytes and 460 in single metaphase II (MII) oocytes, which provided a single-cell perspective for further elucidating the mechanism of oocyte maturation.

Although single-cell multi-omics technologies are undergoing rapid development, simultaneous transcriptome and proteome analysis of a single-cell individual still faces great challenges. Here, we developed a single-cell simultaneous transcriptome and proteome (scSTAP) analysis platform based on microfluidics, high-throughput sequencing, and mass spectrometry technology to achieve deep and joint quantitative analysis of transcriptome and proteome at the single-cell level, providing an important resource for understanding the relationship between transcription and translation in cells. This platform was applied to analyze single mouse oocytes at different meiotic maturation stages, reaching an average quantification depth of 19,948 genes and 2,663 protein groups in single mouse oocytes. In particular, we analyzed the correlation of individual RNA and protein pairs, as well as the meiosis regulatory network with unprecedented depth, and identified 30 transcript-protein pairs as specific oocyte maturational signatures, which could be productive for exploring transcriptional and translational regulatory features during oocyte meiosis.

The efficacy of cancer immunotherapy is significantly influenced by the heterogeneity of individual tumors and immune responses. To investigate this phenomenon, a microfluidic platform is constructed for profiling immune-cancer cell interactions at the single-cell proteomics level for the first time. Based on the platform, a comprehensive workflow is proposed for achieving accurate single-cell pairing of an immune cell and a cancer cell with low cell damage and high success rate up to 95%, cell pair co-culture, and real-time microscopic monitoring of the cell-pair interactions, cell pair retrieval, mass spectrometry-based proteomic analysis of singe cell pairs, and decoupling of the proteomic information for each cell within the cell pair with the stable-isotope labeling method. With the workflow, the interactions of single natural killer (NK) cells and single K562 tumor cells are investigated based on real-time images and single cell-pair proteomics. Notably, an identification depth of over 1000 protein groups in a single cell-pair is achieved, leading to the discovery of sub-clusters of NK cells with different functions and the identification of important biomarkers for cancer treatments. This demonstrates the unique capability of the present platform in providing substantial and comprehensive datasets for profiling immune-cancer cell interactions, discovering heterogeneous immune responses, and predicting biomarkers in the study of cancer immunotherapy.

A workflow for single-cell proteomic analysis was developed, named in situ simplified single-cell proteomics (IS-SCP), based on a comprehensive evaluation of the reagents, reaction conditions, and reproducibility for single-cell proteomic analysis.

Investigating the heterogeneous responses of individual cancer cells to chemotherapeutic drugs is crucial for deciphering the mechanisms of cancer drug resistance. In recent years, single-cell proteomics has demonstrated its significant capability in exploring drug response in cancer cells. Meanwhile, there are increasing reports suggesting that the cellular morphology is potentially associated with drug resistance. However, integrating the single-cell proteomic results with morphological information remains challenging. Here, we present a morphology-aware single-cell proteomic analysis (Morp-SCP) platform to precisely capture cells of interest with real-time and high-resolution imaging, and to conduct deep proteomic analysis at the single-cell level, providing multidimensional information on the target single cells. The Morp-SCP platform was applied for exploring the time-dependent proteomic alterations of human nonsmall cell lung cancer cells (A549) upon cisplatin exposure. Subpopulations of drug-resistant A549 cells were identified, which exhibited distinct proteomic and morphological patterns when resisting cell death induced by cisplatin exposure. By revealing the proteomics-morphology relationship, the Morp-SCP platform offers an effective strategy to provide insights into the heterogeneity of drug resistance at the single-cell level.

Rapid screening of personalized drugs based on patients' primary cell samples can provide precise and timely treatment guidance for clinical oncology patients. However, this goal faces great challenges due to the scarce clinical samples and large sample consumption, and long experimental time required by current drug screening methods. Here, a rapid, high-throughput microfluidic drug sensitivity testing system capable of accomplishing single and combination drug screening of multiple antitumor drugs in 5 days is established with minimal amounts of clinical primary tumor samples, avoiding the need for cell pre-expansion and preserving the tumor heterogeneity. An airflow-impacting approach is developed to fabricate nanoliter-scale microcavity arrays with ultra-smooth microcavity surfaces, with which rapid formation and 3D culture of tumor cell spheroids from small numbers of cell samples, as well as the subsequent high-throughput drug sensitivity testing can be achieved within 5 days. This applies the system in rapid drug sensitivity testing on primary samples from 21 clinical breast cancer patients to quantify the responses of patient-derived cells to chemotherapy and endocrine drugs under both the mono-drug and combinational-drug treatment modes.

Multiomics analysis at the single-cell level is essential for both fundamental research and clinical applications, with proteomics and metabolomics being particularly crucial for providing insights into cellular states and functions. The state of the art flow cytometry has shown great potential in identifying cellular proteins, while emerging metabolite mass spectrometry cytometry techniques address metabolite detection. Herein, we propose a tandem platform that integrates fluorescence flow cytometry with electrospray ionization mass spectrometry for one-step single-cell analysis of protein and metabolites. An algorithm was established to correlate multidimensional information in individual cells, with additional data processing modules designed to ensure accuracy and facilitate further analysis. The tandem cytometry platform demonstrated efficacy in profiling breast cancer cells, particularly under hypoxic conditions, revealing metabolic shifts with decreased glutathione and increased l-glutamine levels, indicative of hypoxia-inducible factor activity. This platform introduces a powerful analytical capability that promises to elevate the precision of cell-based diagnostics and therapeutic strategies.

Characterizing the profiles of proteome and metabolome at the single-cell level is of great significance in single-cell multiomic studies. Herein, we proposed a novel strategy called one-shot single-cell proteome and metabolome analysis (scPMA) to acquire the proteome and metabolome information in a single-cell individual in one injection of LC-MS/MS analysis. Based on the scPMA strategy, a total workflow was developed to achieve the single-cell capture, nanoliter-scale sample pretreatment, one-shot LC injection and separation of the enzyme-digested peptides and metabolites, and dual-zone MS/MS detection for proteome and metabolome profiling. Benefiting from the scPMA strategy, we realized dual-omic analysis of single tumor cells, including A549, HeLa, and HepG2 cells with 816, 578, and 293 protein groups and 72, 91, and 148 metabolites quantified on average. A single-cell perspective experiment for investigating the doxorubicin-induced antitumor effects in both the proteome and metabolome aspects was also performed.

The current throughput of conventional organic chemical synthesis is usually a few experiments for each operator per day. We develop a robotic system for ultra-high-throughput chemical synthesis, online characterization, and large-scale condition screening of photocatalytic reactions, based on the liquid-core waveguide, microfluidic liquid-handling, and artificial intelligence techniques. The system is capable of performing automated reactant mixture preparation, changing, introduction, ultra-fast photocatalytic reactions in seconds, online spectroscopic detection of the reaction product, and screening of different reaction conditions. We apply the system in large-scale screening of 12,000 reaction conditions of a photocatalytic [2 + 2] cycloaddition reaction including multiple continuous and discrete variables, reaching an ultra-high throughput up to 10,000 reaction conditions per day. Based on the data, AI-assisted cross-substrate/photocatalyst prediction is conducted. The current throughput of conventional organic chemical synthesis is usually a few experiments for each operator per day. Here the authors develop a robotic system for ultra-high-throughput chemical synthesis, online characterization and large-scale condition screening of photocatalytic reactions.

The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF’s broader applicability and versability was validated on various synthesis tasks of three distinct reactions (SNAr reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction). The rise of large language model (LLM) technology offers new opportunities for advancing chemical synthesis. Here, the authors developed an LLM-based reaction development framework (LLM-RDF) to copilot the design and experimental tasks throughout the end-to-end chemical synthesis development.

With the increasing demand for trace sample analysis, injecting trace samples into liquid chromatography-mass spectrometry (LC-MS) systems with minimal loss has become a major challenge. Herein, we describe an in situ LC-MS analytical probe, the Falcon probe, which integrates multiple functions of high-pressure sample injection without sample loss, high-efficiency LC separation, and electrospray. The main body of the Falcon probe is made of stainless steel and fabricated by the computer numerical control (CNC) technique, which has ultrahigh mechanical strength. By coupling a nanoliter-scale droplet reactor made of polyether ether ketone (PEEK) material, the Falcon probe-based LC-MS system was capable of operating at mobile-phase pressures up to 800 bar, which is comparable to those of conventional ultraperformance liquid chromatography (UPLC) systems. Using the probe pressing microamount in situ (PPMI) injection approach, the Falcon probe-based LC-MS system showed high separation efficiency and good repeatability with relative standard deviations (RSDs) of retention time and peak area of 1.8% and 9.9%, respectively, in peptide mixture analysis (n = 6). We applied this system to the analysis of a trace amount of 200 pg of HeLa protein digest and successfully identified an average of 766 protein groups (n = 5). By combining in situ sample pretreatment at the nanoliter range, we further applied the present system in single-cell proteomic analysis, and 241 protein groups were identified in single 293 cells, which preliminarily demonstrated its potential in the analysis of trace amounts of samples with complex compositions.

The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of ~1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a mammalian cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 2449–3500, 2278–3257 and 1621–2904 protein groups are quantified in single A549 cells ( n = 37), HeLa cells ( n = 44) and U2OS cells ( n = 27) under the DIA (MBR) mode, respectively. Benefiting from the flexible cell picking-up ability, we study HeLa cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.

MicroRNAs (miRNAs) have emerged as essential biomarkers for disease diagnosis, and several techniques are available to determine type 2 diabetes (T2D) relevant miRNAs. However, detecting circulating miRNAs can be challenging due to their small size, low abundance, and high sequence similarity, often requiring sensitive detection approaches combined with additional amplification processes. Laser-induced fluorescence (LIF) is a classic analytical method suitable for sensitively detecting trace amounts of nucleotide acid. Duplex-specific nuclease (DSN)-mediated amplification recently gained attention due to its catalytic activity based on target recycling, demonstrating a promising approach for miRNA amplification. This work developed a novel N-annulated perylene fluorescent dye to create a biosensor to analyze the miRNA (miR-223) relevant to T2D. The amine-reactive fluorescent dye assists the amidation reaction for nucleotide labeling, giving the oligonucleotide probe a high fluorescence quantum yield and sufficient water solubility. By combining the locked nucleic acid (LNA) modified oligonucleotide fluorescent probe to enhance the stability of LNA/RNA hybrids, thereby improving the DSN-mediated target miR-223 recycling for signal amplification, the proposed biosensor can highly selectively determine miR-223 with a limit of detection (LOD, S/N = 3) of 9.5 pM. When applied to real-world samples, the biosensor demonstrated its potential to distinguish between T2D patients and healthy controls.

The rapid emergence of large language model (LLM) technology presents significant opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build a multi-agent system to handle fundamental tasks involved throughout the chemical synthesis development process. The multi-agent system comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application was built with the multi-agent system as the backend to allow chemist users to interact with experimental platforms and analyze results via natural language, thus, requiring zero-coding skills to allow easy access for all chemists. We demonstrated this multi-agent system on the development of a recently developed copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, and this LLM multi-agent copiloted end-to-end reaction development process includes: literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. This work showcases the trilogy among chemist users, LLM-based agents, and automated experimental platforms to reform the traditional expert-centric and labor-intensive reaction development workflow.

Automation and high-throughput techniques provide a solid technical foundation for realizing the deep fusion of artificial intelligence and chemistry as well as the full utilization of their advantages. In recent years, with the unique advantages of low consumption, low risk, high efficiency, high reproducibility, high flexibility and good versatility, intelligent automated platforms for high-throughput chemical synthesis aroused widespread concerns of synthetic chemists. In this review, the automated high-throughput chemical synthesis, automated high-throughput sample treatment and characterization technique, as well as the application of artificial intelligence technique in chemical synthesis are introduced. The characteristics of the systems and platforms based on these techniques, including the iChemFoundry platform developed in the ZJU-Hangzhou Global Scientific and Technological Innovation Center, are introduced. The intelligent automated platforms for high-throughput chemical synthesis will reshape the thinking mode of traditional disciplines, promote the innovation of disruptive techniques, redefine the rate of chemical synthesis, and innovate the way of material manufacturing.

Organoids are three-dimensional cell complexes formed by stem cells or tumor cells cultured and self-organized in vitro,which have higher simulation in morphology and function than the traditional two-dimensional cell drug screening model.However,the conventional organoid culture technology based on matrigel dome in culture dish or well plate has the problems of low throughput and time-consuming,which limits the application of high-throughput organoid drug screening.Therefore,a series of microfluidic high-throughput organoid culture and drug screening technologies have been developed.From the perspective of microfluidic technology,this paper reviews the current application of high-throughput organoid drug screening in three aspects:organoid culture,drug delivery and result detection.According to the different forms of organoid culture units,organoid culture techniques are divided into three categories:microfluidic droplets,microwells,and micropillars.According to the structure and working mode of drug delivery system,drug delivery system is divided into microchannel network and drug droplet array.According to the different forms of information obtained,high-throughput results detection methods can be divided into four categories:microplate reader,microscope,high-content imaging analyzer and chip sensor.Based on the above technologies,the application of typical organoid drug screening is introduced.Finally,the summary and prospect of organoid drug screening are presented.