Biology subjects

Zhang, M.

Publications and source records attributed to Zhang, M..

At least 379 records · Page 21Linked to original sources

bioRxiv2023

LLPS condensates of Fha initiate the inside-out assembly of the type VI secretion system

The type VI secretion system (T6SS) is one of the most powerful nanomachines employed by Gram-negative pathogens for penetrating diverse cell envelopes, including bacteria and fungi, to deliver potent effectors into target cells. While the membrane-anchored contractile tubular structure of the T6SS is well characterized, the assembly process remains poorly understood. The prevailing model suggests that the assembly of T6SS initiates from its outer-membrane component. Here, we report a distinct model that the cytoplasmic protein Fha initiates T6SS assembly in Acidovorax citrulli, an important plant pathogen. Fha dictates the formation of the inner-membrane complex and the baseplate, and directly interacts with these key components. Importantly, imaging and biochemical assays reveal that Fha undergoes liquid-liquid phase separation (LLPS), forming condensates that selectively recruit essential T6SS proteins, which are otherwise dispersed in cells. Fha also exhibited conserved functions in human pathogens Vibrio cholerae and Pseudomonas aeruginosa. These findings unveil an inside-first LLPS-driven model for T6SS assembly and suggest LLPS might be broadly involved in mediating the assembly of bacterial macromolecular complexes and facilitating interspecies interactions and pathogenesis. Significance statementThe T6SS plays a pivotal role in interspecies competition and host-microbe interactions by delivering toxins to various prokaryotes and eukaryotes. Its crucial function relies on a membrane-anchored macromolecular structure comprising at least 13 conserved components. However, the mechanisms governing the efficient assembly of its diverse cytosolic and membrane-bound components remain elusive. Here, we identify Fha, a conserved cytosolic protein, as a key initiator of T6SS assembly. Fha recruits multiple structural and effector components, forming LLPS condensates. Fha homologs of plant and human pathogens exhibit conserved functions. Our findings not only unveil an inside-first assembly model for the T6SS, initiating from inner-membrane and baseplate components, but also suggest LLPS may have a broader impact on bacterial physiology beyond intracellular activities.

microbiology↗

bioRxiv2023

A novel nested gene Aff3ir participates in vascular remodelling by enhancing endothelial cell differentiation in mice

Endothelial integrity in the vasculature is critically maintained by vascular stem/progenitor cells (SPCs) giving rise to endothelial cells (ECs). However, the genes significantly activated during differentiation remain incompletely understood. Based on mouse aorta and vein cDNA library, we unearthed a hitherto unidentified gene nested residing within intron 6 of Aff3, christened as Aff3 intron resident (Aff3ir), upregulated during laminar shear stress-induced ECs differentiation in mouse. Proteomic analysis substantiated the presence of a 45-amino acid(aa) peptide (AFF3IR-ORF1) and 109-aa or 151-aa protein (AFF3IR-ORF2) encoded from two transcript variants. During embryonic development, AFF3IR-ORF1 peaked at E14.5, while AFF3IR-ORF2 displayed a continuous increase until E19.5. In adult mice, AFF3IR-ORF1 was detected in the lung, liver, spleen, and kidney, while AFF3IR-ORF2 was most abundant in the aorta. Furthermore, Western blot and immunofluorescence analyses revealed a specific upregulation of AFF3IR-ORF2, but not AFF3IR-ORF1, three days after femoral artery injury or hindlimb ischemia in vivo. Overexpression of AFF3IR-ORF2 enhanced, while its knockdown attenuated, SPCs differentiation into ECs induced by shear stress or vascular endothelial growth factor in vitro. Notably, the upregulated AFF3IR-ORF2 hindered SPCs proliferation by sequestering minichromosome maintenance complex component 3 in the cytoplasm, thereby shifting the status of SPCs from a pro-proliferation to a pro-differentiation state. In conclusion, our discoveries unveil the novel protein-coding gene Aff3ir as a participant in ECs differentiation, providing fresh insights into the regulation of vascular endothelial integrity.

cell biology↗

bioRxiv2023

Early-stage idiopathic pulmonary fibrosis is characterized by bronchoalveolar accumulation of SPP1+ macrophages

Patients affected by idiopathic pulmonary fibrosis (IPF), a progressive chronic and eventually fatal lung disease with unknown cause, suffer from delayed diagnosis and limited personalized treatment options due to the lack of predictive and staging relevant disease markers. Prior studies focused on the cellular and transcriptomic changes found in lung tissue during terminal IPF-associated lung fibrosis. In clinical routine, bronchoscopy is applied for diagnosis and staging of suspected IPF affected individuals, thus providing us with a clinically applicable window, to study the cellular and transcriptional changes within affected individuals at the time of diagnosis. Here we study a cohort consisting of 11 IPF affected individuals and 11 healthy controls. We investigate their single cell transcriptomic profile alongside the surface phenotype of alveolar-resident immune cells. Single cell transcriptional analysis reveals accumulation of SPP1+ macrophages (SPP1+M{varphi}) within the alveolar space during early stage diagnosed IPF, in the absence of a decline in lung function. SPP1+M{varphi} were characterized by high expression of lipid storage and handling genes, accumulation of intracellular cholesterol and expression of proinflammatory cytokine expression. In silico developmental trajectory analysis revealed that SPP1+M{varphi} are likely derived from classical monocytes. Finally, to confirm the clinical diagnostic value of SPP1+ M{varphi}, we developed a flow cytometry panel to rapidly identify and test for the presence of SPP1+ M{varphi} in bronchoalveolar lavage samples. The new panel has confirmed the increased frequency of SPP1+ M{varphi} in early-IPF in an independent validation cohort. Taken together, we show that SPP1+ M{varphi} are associated with early IPF pathogenesis in the absence of a decline in lung function, thus providing a clinically valuable markers for diagnosis of early IPF in clinical practice. HighlightsO_LIIPF patients can be stratified into early and advanced disease groups based on clinical features. C_LIO_LISingle cell transcriptomic and high dimensional flow cytometry enabled mapping of the myeloid compartment across the continuum of idiopathic pulmonary fibrosis associated disease states. C_LIO_LISPP1+M{varphi} accumulated during early clinical stage of IPF. C_LIO_LIDevelopment of a clinically applicable SPP1M{varphi} identification panel. C_LI

immunology↗

bioRxiv2023

Low-dimensional neural geometry underlies distributed goal representations in working memory

Successful goal-directed behavior requires the maintenance and implementation of abstract task goals on concrete stimulus information in working memory. Previous working memory research has revealed distributed neural representations of task information across cortex. However, how the distributed task representations emerge and communicate with stimulus-specific information to implement flexible goal-directed computations is still unclear. Here, leveraging EEG and fMRI along with state space analyses, we provided converging evidence in support of a low-dimensional neural geometry of goal information consistent with a designed task space, which first emerged in frontal cortex during goal maintenance and then transferred to posterior sensory cortex through frontomedial-to-posterior theta coherence for implementation on stimulus-specific representations. Importantly, the fidelity of the goal geometry predicted memory performance. Collectively, our findings suggest that abstract goals in working memory are represented in an organized, low-dimensional neural geometry for communications from frontal to posterior cortex to enable computations necessary for goal-directed behaviors.

neuroscience↗

bioRxiv2023

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of {beta} T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

immunology↗

bioRxiv2023

Predicting the dynamic interaction between intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) participate in various biological processes. Interactions involving IDPs are usually dynamic and affected by their inherent conformation fluctuations. Comprehensive characterization of these interactions based on current techniques is challenging. Here, we present GSALIDP, a GraphSAGE-LSTM Network to capture the dynamic nature of IDP-involved interactions and predict their behaviors. This framework models multiple conformations of IDP as a dynamic graph which can effectively describe the fluctuation of its flexible conformation. The dynamic interaction between IDPs is studied and the datasets of IDP conformations and their interactions are obtained through atomistic molecular dynamic (MD) simulations. Residues of IDP are encoded through a series of features, including their frustration. GSALIDP can effectively predict the interaction sites of IDP and the contact residue pairs between IDPs. Its performance in predicting IDP interaction is on par with or even better than the conventional models in predicting the interaction of structural proteins. To the best of our knowledge, this is the first model to extend the protein interaction prediction to IDP-involved interactions.

molecular biology↗

bioRxiv2023

The Effects of Different Fatigue Types on Action Anticipation and Physical Performance in High-level Volleyball Players

This study examined the effects of different fatigue types on action anticipation and physical performance in high-level volleyball players. Twenty-four participants underwent four counterbalanced conditions: 60-min of cycling at 60% peak power output, 60-min Stroop task, 60-min cycling at 60% peak power output with Stroop task and 60-min neutral documentary to induce physical fatigue (PF), mental fatigue (MF), dual fatigue (DF) and control group (CG), respectively. Action anticipation (anticipation test and visual search test) and physical performance (countermovement jump, T-test, and spike test) were conducted at baseline, immediately after (Post1), and 40-min after fatigue intervention (Post2). DF and PF led to significantly lower jump height, T-test completion time, and spiking speed at Post1 and Post2 compared with CG and MF. Compared with PF, DF led to a significant decline in jumping, agility, and spiking performance at Post1 and decreased jumping performance at Post2. MF significantly decreased reaction time in the anticipation test at Post1 compared with CG. Reaction times in the visual search test were significantly lower with DF and MF at Post2 compared with CG. PF led to a greater decline in physical performance and this was exacerbated in DF, yet anticipation performance remained lesser impact.

physiology↗

bioRxiv2023

High-fidelity, high-resolution light-field reconstruction with physics-informed neural representation learning

Light-field microscopy (LFM) captures 3D biological dynamics by single 2D snapshots but suffers from limited resolution and artifacts during 2D-to-3D inversion. Here, we introduce light-field meta neural representation (LFMNR), a novel self-supervised paradigm that utilizes physics-informed light-field implicit neural representation (LFINR) and meta learning for high-quality 3D reconstruction in Fourier-LFM. By developing a physics-based hybrid-rendering model, LFINR achieves artifact-free light-field reconstruction with enhanced spatial resolution (>1.4-fold improvement). Additionally, the integration of meta-learning and progressive sampling strategies mitigates INRs intrinsic limitations in low reconstruction speed caused by scene-specific optimization, enabling a [~]100-fold acceleration in the representation of consecutive volumes and facilitating the visualization of sustained 3D dynamics. These advancements enable LFMNR to deliver superior imaging with high spatiotemporal resolution and low phototoxicity, as demonstrated by capturing instantaneous voltage signals in C. elegans at 100 volumes per second and recording 6000 time points of organelle dynamics over 25 hours.

cell biology↗

bioRxiv2023

Millimeter-scale magnetic implants paired with a fully integrated wearable device for wireless biophysical and biochemical sensing

Implantable sensors can directly interface with various organs for precise evaluation of health status. However, extracting signals from such sensors must rely on transcutaneous wires, integrated circuit chips, or cumbersome readout equipment, which increases the risks of infection, reduces the biocompatibility, or limits the portability. Here, we develop a set of millimeter-scale, chip-less and battery-less magnetic implants that can measure biophysical and biochemical signals wirelessly. In particular, the implants form two-way communications with a fully integrated wearable device, where the wearable device can induce a large-amplitude damped vibration of the magnetic implants and capture their subsequent motions in a wireless manner. Such damped vibrations reflect not only the biophysical conditions surrounding the implants movements, but also the concentration of a specific biochemical depending on the surface modification. Experiments in rat models demonstrate the capabilities in measuring cerebrospinal fluid (CSF) viscosity, intracranial pressure (ICP), and CSF glucose levels. This miniaturized system opens possibility for continuous, wireless monitoring of a wide range of biophysical and biochemical conditions within the living organism.

bioengineering↗

bioRxiv2023

Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy

Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential--often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation in vivo without noticeable cytotoxicity. Moreover, sequential cycles of albendazole and paclitaxel--a commonly used chemotherapeutic --displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations. Statement of significanceNetwork-based approaches, as shown in a study on metastatic breast cancer, can develop effective combinatorial therapies targeting complementary subpopulations. By analyzing scRNA-seq data and using clinically relevant drugs, researchers identified and depleted chemoresistant Cancer Stem-Like Cells, enhancing the efficacy of standard chemotherapies.

systems biology↗

bioRxiv2023

Poisoning medical knowledge using large language models

Biomedical knowledge graphs constructed from scientific literature have been widely used to validate biological discoveries and generate new hypotheses. Recently, large language models (LLMs) have demonstrated a strong ability to generate human-like text data. While most of these text data have been useful, LLM might also be used to generate malicious content. Here, we investigate whether it is possible that a malicious actor can use LLM to generate a malicious paper that poisons scientific knowledge graphs and further affects downstream biological applications. As a proof-of-concept, we develop Scorpius, a conditional text generation model that generates a malicious paper abstract conditioned on a promoting drug and a target disease. The goal is to fool the knowledge graph constructed from a mixture of this malicious abstract and millions of real papers so that knowledge graph consumers will misidentify this promoting drug as relevant to the target disease. We evaluated Scorpius on a knowledge graph constructed from 3,818,528 papers and found that Scorpius can increase the relevance of 71.3% drug disease pairs from the top 1000 to the top 10 by only adding one malicious abstract. Moreover, the generation of Scorpius achieves better perplexity than ChatGPT, suggesting that such malicious abstracts cannot be efficiently detected by humans. Collectively, Scorpius demonstrates the possibility of poisoning scientific knowledge graphs and manipulating downstream applications using LLMs, indicating the importance of accountable and trustworthy scientific knowledge discovery in the era of LLM.

bioinformatics↗

bioRxiv2023

Bacteriophages from human skin infecting coagulase-negative Staphylococcus: diversity, novel species and host resistance

The human skin microbiome comprises diverse populations that differ temporally between body sites and individuals. The virome is a less studied component of the skin microbiome and the study of bacteriophages is required to increase knowledge of the modulation and stability of bacterial communities. Staphylococcus species are among the most abundant colonisers of skin and are associated with both health and disease yet the bacteriophages infecting the most abundant species on skin are less well studied. Here, we report the isolation and genome sequencing of 40 bacteriophages from human skin swabs that infect coagulase-negative Staphylococcus (CoNS) species, which extends our knowledge of phage diversity. Six genetic clusters of phages were identified with two clusters representing novel phage species, one of which we characterise and name Alsa phage. We identified that Alsa phages have a greater ability to infect the species S. hominis that was otherwise infected less than other CoNS species by the isolated phages, indicating an undescribed barrier to phage infection that could be in part due to numerous restriction-modification systems. The extended diversity of Staphylococcus phages here enables further research to define their contribution to skin microbiome research and the mechanisms that limit phage infection.

microbiology↗

bioRxiv2023

Lactate accumulation induced genioglossus myoblast injury under hypoxia

Obstructive sleep apnea (OSA) is an increasingly common disorder of repeated upper airway collapse during sleep, leading to chronic intermittent hypoxia and metabolic alteration such as lactate accumulation. Genioglossus (GG), the largest upper airway dilator, is of great significance in maintaining the patency of the upper airway collapse. However, the lactate mechanism in OSA/CIH models is incomplete understood. Here, extracellular and intracellular contents of lactate were detected, and found hypoxia induced lactate accumulation in GG myoblasts. The we treat GG with lactate, and found lactate inhibited myoblasts proliferation and myotubes formation under hypoxia. When the lactate efflux was blocked by monocarboxylic acid transporter 1 (MCT1, a lactate transport) inhibitor AZD3965 (AZD), the expression levels of myogenic markers MyoD, Myogenin and MyHC were reduced. In summary, lactate efflux obstruction and lactate accumulation induced GG myoblast injury under hypoxia. These findings provide a theoretical basis for regulation lactate metabolism to alleviate muscle fatigue in the treatment of OSA.

cancer biology↗

bioRxiv2023

A foundation model for bioactivity prediction using pairwise meta-learning

Compound bioactivity plays an important role in different stages of drug development and discovery. Existing machine learning approaches have poor generalization ability in compound bioactivity prediction due to the small number of compounds in each assay and incompatible measurements among assays. Here, we propose ActFound, a foundation model for bioactivity prediction trained on 2.3 million experimentally-measured bioactivity compounds and 50, 869 assays from ChEMBL and BindingDB. The key idea of ActFound is to employ pairwise learning to learn the relative value differences between two compounds within the same assay to circumvent the incompatibility among assays. ActFound further exploits meta-learning to jointly optimize the model from all assays. On six real-world bioactivity datasets, ActFound demonstrates accurate in-domain prediction and strong generalization across datasets, assay types, and molecular scaffolds. We also demonstrated that ActFound can be used as an accurate alternative to the leading computational chemistry software FEP+(OPLS4) by achieving comparable performance when only using a few data points for fine-tuning. The promising results of ActFound indicate that ActFound can be an effective foundation model for a wide range of tasks in compound bioactivity prediction, paving the path for machine learning-based drug development and discovery.

bioinformatics↗

bioRxiv2023

Three-dimensional visualization of blood vessels in human gliomas based on tissue clearing and deep learning

Gliomas, with their intricate and aggressive nature, call for a detailed visualization of their vasculature. While many studies lean towards 2D imaging of thin sections, this method often overlooks the full spatial heterogeneity inherent to tumors. To overcome this limitation, our study melded state-of-the-art techniques, encompassing tissue clearing technology, 3D confocal microscopy imaging, and deep learning-aided vessel extraction, resulting in a comprehensive 3D visualization of glioma vasculature in intact human tissue. Specifically, we treated formalin-fixed thick human glioma tissue sections (500 m) with OPTIClear for transparency and subsequently performed immunofluorescent labeling using CD31. Using confocal microscopy, we obtained 3D images of the glioma vasculature. For vessel extraction, we employed a specialized 3D U-Net, enriched with image preprocessing and post-processing methods, and benchmarked its performance against the Imaris software. Our findings indicated that OPTIClear-enabled tissue clearing yielded a holistic 3D representation of immunolabeled vessels in clinical human glioma samples. Impressively, our deep learning technique outshined the traditional Imaris approach in terms of accuracy and efficiency in vessel extraction. Further, discernible variations in vascular metrics, such as mean diameter, branching point count, and volume ratio, were observed between low-grade and high-grade gliomas. In essence, our innovative blend of tissue clearing and deep learning not only enables enhanced 3D visualization of human glioma vasculature but also underscores morphological disparities across glioma grades, potentially influencing pathological grading, therapeutic strategies, and prognostic evaluations.

neuroscience↗

bioRxiv2023

Platelet-specific TGFβ1 deficiency aggravates atherosclerosis, vascular inflammation, and hypercholesterolemia in mice

Atherosclerosis involves inflammatory and thrombotic mechanisms, to which both platelets and transforming growth factor {beta} (TGF{beta}) contribute. The effect of platelet-derived TGF{beta} on atherosclerosis is, however, unknown and therefore investigated. Murine platelet-selective TGF{beta}-deficiency (plt-TGF{beta}-/-) was created by a Pf4-Cre approach, and an atherosclerotic mouse model was established by functional abrogation of Ldlr and 10-15 weeks of a high-fat diet in plt-TGF{beta}-/- mice and their non-plt-TGF{beta}-/- littermates. En face Oil Red O staining of the aorta showed more atherosclerotic lesion formation in plt-TGF{beta}-/- mice, with significant increases in both lesion size and lesion coverage of the total aortic area. Cryosections of the aortic root confirmed the aggravation of atherogenesis. Platelet-derived TGF{beta} deficiency increased circulating platelets and plasma levels of total cholesterol, LDL-cholesterol, and triglycerides after a 10 or 15 week high-fat diet period. RNA sequencing and proteomic analyses of the aorta showed signs of CD4+ T effector cell and macrophage activation in plt-TGF{beta}-/- mice. In conclusion, platelet-specific TGF{beta} deficiency aggravates atherosclerosis, via increasing arterial inflammation and plasma levels of cholesterol. Our findings demonstrate that platelet-derived TGF{beta} is prominently athero-protective. Key pointsO_LIPlatelet-specific transforming growth factor {beta} (TGF{beta}) deficiency markedly enhances atherosclerosis in a high-fat diet-fed murine model. C_LIO_LIPlatelet TGF{beta} deficiency aggravates hyperlipidemia, with further elevations of total cholesterol, LDL-cholesterol, and triglycerides. C_LI

cell biology↗

bioRxiv2023

Thiolation-Based Protein-Protein Hydrogels for Improved Wound Healing

The limitations of protein-based hydrogels, including their insufficient mechanical properties and restricted biological functions, arise from the highly specific functions of proteins as natural building blocks. A potential solution to overcome these shortcomings is the development of protein-protein hydrogels, which integrate structural and functional proteins. In this study, we introduce a protein-protein hydrogel formed by crosslinking bovine serum albumin (BSA) and a genetically engineered intrinsically disordered collagen-like protein (CLP) through Ag-S bonding. Our approach involves thiolating lysine residues of BSA and crosslinking CLP with Ag+ ions, utilizing thiolation of BSA and the free-cysteines of CLP. The resulting protein-protein hydrogels exhibit exceptional properties, including notable plasticity, inherent self-healing capabilities, and gel-sol transition in response to redox conditions. Furthermore, in comparison to standalone BSA hydrogels, these protein-protein hydrogels demonstrate remarkable antibacterial properties, enhanced cellular viability, and improved cellular migration. In vivo experiments provide conclusive evidence of accelerated wound healing, observed not only in murine models with streptozotocin (Step)-induced diabetes but also in zebrafish models subjected to UV-burn injuries. Detailed mechanistic insights, combined with assessments of pro-inflammatory cytokines and the expression of epidermal differentiation-related proteins, robustly validate the protein-protein hydrogels effectiveness in promoting wound repair. This pioneering approach advances the development of protein-protein hydrogels and serves as a reference for the creation of multifunctional protein-based hydrogels.

bioengineering↗

bioRxiv2023

High-throughput single-cell sorting by stimulated Raman-activated cell ejection

Single-cell sorting is essential to explore cellular heterogeneity in biology and medicine. Recently developed Raman-activated cell sorting (RACS) circumvents the limitations of fluorescence-activated cell sorting, such as the cytotoxicity of labels. However, the sorting throughputs of all forms of RACS are limited by the intrinsically small cross-section of spontaneous Raman scattering. Here, we report a stimulated Raman-activated cell ejection (S-RACE) platform that enables high-throughput single-cell sorting based on high-resolution multi-channel stimulated Raman chemical imaging, in situ image decomposition, and laser-induced cell ejection. The performance of this platform was illustrated by sorting a mixture of 1 m polymer beads, where 95% yield, 98% purity, and 14 events per second throughput were achieved. Notably, our platform allows live cell ejection, allowing for the growth of single colonies of bacteria and fungi after sorting. To further illustrate the chemical selectivity, lipid-rich Rhodotorula glutinis cells were successfully sorted from a mixture with Saccharomyces cerevisiae, confirmed by downstream quantitative PCR. Furthermore, by integrating a closed-loop feedback control circuit into the system, we realized real-time single-cell imaging and sorting, and applied this method to precisely eject regions of interest from a rat brain tissue section. The reported S-RACE platform opens exciting opportunities for a wide range of single-cell applications in biology and medicine. Significance statementImage-guided single-cell sorting is a potent tool in diverse biological applications. Current microfluidic cell sorting methods encounter challenges in handling smaller cells and are not applicable to tissue sections. To address these challenges, we have developed a stimulated Raman-activated cell ejection (S-RACE) platform, which is the first demonstration of single-cell ejection coupled with coherent Raman scattering. S-RACE allows label-free chemical imaging guided cell sorting through multispectral stimulated Raman scattering (SRS) imaging, on-the-fly image analysis, and laser-induced cell ejection. Versatile applications of S-RACE to a wide range of samples, such as polymer particles, single-live bacteria, single-live fungus, and tissue sections, are demonstrated.

bioengineering↗