Black Phosphorus Quantum Dot Loaded Bioinspired Nanoplatform Synergized with aPD-L1 for Multimode Cancer Immunotherapy.

Efforts to prolong the blood circulation time and bypass immune clearance play vital roles in improving the therapeutic efficacy of nanoparticles (NPs). Herein, a multifunctional nanoplatform (BPP@RTL) that precisely targets tumor cells is fabricated by encapsulating ultrasmall phototherapeutic agent black phosphorus quantum dot (BPQD), chemotherapeutic drug paclitaxel (PTX), and immunomodulator PolyMetformin (PM) in hybrid membrane-camouflaged liposomes. Specifically, the hybrid cell membrane coating derived from the fusion of cancer cell membrane and red blood cell membrane displays excellent tumor targeting efficiency and long blood circulation property due to the innate features of both membranes. After collaboration with aPD-L1-based immune checkpoint blockade therapy, a boosted immunotherapeutic effect is obtained due to elevated dendritic cell maturation and T cell activation. Significantly, laser-irradiated BPP@RTL combined with aPD-L1 effectively eliminates primary tumors and inhibits lung metastasis in 4T1 breast tumor model, offering a promising treatment plan to develop personalized antitumor strategy.

Blood-Brain Barrier-Penetrating and Lesion-Targeting Nanoplatforms Inspired by the Pathophysiological Features for Synergistic Ischemic Stroke Therapy.

Ischemic stroke is a dreadful vascular disorder that poses enormous threats to the public health. Due to its complicated pathophysiological features, current treatment options after ischemic stroke attack remains unsatisfactory. Insufficient drug delivery to ischemic lesions impeded by the blood-brain barrier (BBB) largely limits the therapeutic efficacy of most anti-stroke agents. Herein, inspired by the rapid BBB penetrability of 4T1 tumor cells upon their brain metastasis and natural roles of platelet in targeting injured vasculatures, a bio-derived nanojacket is developed by fusing 4T1 tumor cell membrane with platelet membrane, which further clothes on the surface of paeonol and polymetformin-loaded liposome to obtain biomimetic nanoplatforms (PP@PCL) for ischemic stroke treatment. The designed PP@PCL could remarkably alleviate ischemia-reperfusion injury by efficiently targeting ischemic lesion, preventing neuroinflammation, scavenging excess reactive oxygen species (ROS), reprogramming microglia phenotypes, and promoting angiogenesis due to the synergistic therapeutic mechanisms that anchor the pathophysiological characteristics of ischemic stroke. As a result, PP@PCL exerts desirable therapeutic efficacy in injured PC12 neuronal cells and rat model of ischemic stroke, which significantly attenuates neuronal apoptosis, reduces infarct volume, and recovers neurological functions, bringing new insights into exploiting promising treatment strategies for cerebral ischemic stroke management.

Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances.

Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended on the similar chemical structures and pharmacological action with known drugs. Such approaches could not face the complicated circumstance of emerging new psychoactive substances. Herein, mass spectrometry imaging and convolutional neural networks (CNN) were used for preliminary screening and pre-classification of suspected psychoactive substances. Mass spectrometry imaging was performed simultaneously on two brain slices as one was from blank group and another one was from psychoactive substance-induced group. Then, fused neurotransmitter variation mass spectrometry images (Nv-MSIs) reflecting the difference of neurotransmitters between two slices were achieved through two homemade programs. A CNN model was developed to classify the Nv-MSIs. Compared with traditional classification methods, CNN achieved better estimation accuracy and required minimal data preprocessing. Also, the specific region on Nv-MSIs and weight of each neurotransmitter that affected the classification most could be unraveled by CNN. Finally, the method was successfully applied to assist the identification of a new psychoactive substance seized recently. This sample was identified as cannabinoids, which greatly promoted the screening process.

Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework.

The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational framework to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to interleukin (IL)-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified cytokine-specific genes associated with late pSTAT3 time frames and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems. A record of this paper's transparent peer review process is included in the supplemental information.

Chemoimmunotherapeutic Nanogel for Pre- and Postsurgical Treatment of Malignant Melanoma by Reprogramming Tumor-Associated Macrophages.

Surgery is the primary method to treat malignant melanoma; however, the residual microtumors that cannot be resected completely often trigger tumor recurrence, causing tumor-related mortality following melanoma resection. Herein, we developed a feasible strategy based on the combinational chemoimmunotherapy by cross-linking carboxymethyl chitosan (CMCS)-originated polymetformin (PolyMetCMCS) with cystamine to prepare stimuli-responsive nanogel (PMNG) owing to the disulfide bond in cystamine that can be cleaved by the massive glutathione (GSH) in tumor sites. Then, chemotherapeutic agent doxorubicin (DOX) was loaded in PMNG, which was followed by a hyaluronic acid coating to improve the overall biocompatibility and targeting ability of the prepared nanogel (D@HPMNG). Notably, PMNG effectively reshaped the tumor immune microenvironment by reprogramming tumor-associated macrophage phenotypes and recruiting intratumoral CD8+ T cells owing to the inherited immunomodulatory capability of metformin. Consequently, D@HPMNG treatment remarkably suppressed melanoma growth and inhibited its recurrence after surgical resection, proposing a promising solution for overcoming lethal melanoma recurrence.

Study on the detection method of biological characteristics of hepatoma cells based on terahertz time-domain spectroscopy.

Liver cancer usually has a high degree of malignancy and its early symptoms are hidden, therefore, it is of significant research value to develop early-stage detection methods of liver cancer for pathological screening. In this paper, a biometric detection method for living human hepatocytes based on terahertz time-domain spectroscopy was proposed. The difference in terahertz response between normal and cancer cells was analyzed, including five characteristic parameters in the response, namely refractive index, absorption coefficient, dielectric constant, dielectric loss and dielectric loss tangent. Based on class separability and variable correlation, absorption coefficient and dielectric loss were selected to better characterize cellular properties. Maximum information coefficient and principal component analysis were employed for feature extraction, and a cell classification model of support vector machine was constructed. The results showed that the algorithm based on parameter feature fusion can achieve an accuracy of 91.6% for human hepatoma cell lines and one normal cell line. This work provides a promising solution for the qualitative evaluation of living cells in liquid environment.

Research Progress on Machine Learning Assisted Non-Targeted Screening Strategy for Identification of Fentanyl Analogs.

In recent years, the types and quantities of fentanyl analogs have increased rapidly. It has become a hotspot in the illicit drug control field of how to quickly identify novel fentanyl analogs and to shorten the blank regulatory period. At present, the identification methods of fentanyl analogs that have been developed mostly rely on reference materials to target fentanyl analogs or their metabolites with known chemical structures, but these methods face challenges when analyzing new compounds with unknown structures. In recent years, emerging machine learning technology can quickly and automatically extract valuable features from massive data, which provides inspiration for the non-targeted screening of fentanyl analogs. For example, the wide application of instruments like Raman spectroscopy, nuclear magnetic resonance spectroscopy, high resolution mass spectrometry, and other instruments can maximize the mining of the characteristic data related to fentanyl analogs in samples. Combining this data with an appropriate machine learning model, researchers may create a variety of high-performance non-targeted fentanyl identification methods. This paper reviews the recent research on the application of machine learning assisted non-targeted screening strategy for the identification of fentanyl analogs, and looks forward to the future development trend in this field.

TCR ligand potency differentially impacts PD-1 inhibitory effects on diverse signaling pathways.

Checkpoint blockade revolutionized cancer therapy, but we still lack a quantitative, mechanistic understanding of how inhibitory receptors affect diverse signaling pathways. To address this issue, we developed and applied a fluorescent intracellular live multiplex signal transduction activity reporter (FILMSTAR) system to analyze PD-1-induced suppressive effects. These studies identified pathways triggered solely by TCR or requiring both TCR and CD28 inputs. Using presenting cells differing in PD-L1 and CD80 expression while displaying TCR ligands of distinct potency, we found that PD-1-mediated inhibition primarily targets TCR-linked signals in a manner highly sensitive to peptide ligand quality. These findings help resolve discrepancies in existing data about the site(s) of PD-1 inhibition in T cells while emphasizing the importance of neoantigen potency in controlling the effects of checkpoint therapy.

Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework.

The JAK-STAT pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational workflow to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to IL-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified select cytokine-induced gene sets associated with late pSTAT3 timeframes and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying dynamically regulated genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems.

Characterizing temporal variability and repeatability of dose-dependent functional genomics approach for evaluating triclosan toxification.

Dose-dependent functional genomics approach has shown great advantage in identifying the molecular initiating event (MIE) of chemical toxification and yielding point of departure (POD) at genome-wide scale. However, POD variability and repeatability derived from experimental design (settings of dose, replicate number, and exposure time) has not been fully determined. In this work, we evaluated POD profiles perturbed by triclosan (TCS) using dose-dependent functional genomics approach in Saccharomyces cerevisiae at multiple time points (9 h, 24 h and 48 h). The full dataset (total 9 concentrations with 6 replicates per treatment) at 9 h was subsampled 484 times to generate subsets of 4 dose groups (Dose A - Dose D with varied concentration range and spacing) and 5 replicate numbers (2 reps - 6 reps). Firstly, given the accuracy of POD and the experimental cost, the POD profiles from 484 subsampled datasets demonstrated that the Dose C group (space narrow at high concentrations and wide dose range) with three replicates was best choice at both gene and pathway levels. Secondly, the variability of POD was found to be relatively robustness and stability across different experimental designs, but POD was more dependent on the dose range and interval than the number of replicates. Thirdly, MIE of TCS toxification was identified to be the glycerophospholipid metabolism pathway at all-time points, supporting the ability of our approach to accurately recognize MIE of chemical toxification at both short- and long-term exposure. Finally, we identified and validated 13 key mutant strains involved in MIE of TCS toxification, which could serve as biomarkers for TCS exposure. Taken together, our work evaluated the repeatability of dose-dependent functional genomics approach and the variability of POD and MIE of TCS toxification, which will benefit the experimental design for future dose-dependent functional genomics study.

Extracellular Vesicles-Derived Hybrid Nanoplatforms for Amplified CD47 Blockade-Based Cancer Immunotherapy.

Immunomodulation of tumor-associated macrophages (TAMs) into tumor-inhibiting M1-like phenotype is a promising but challenging strategy. Cleverly, tumor cells overexpress CD47, a "don't eat me" signal that ligates with the signal regulatory protein alpha (SIRPα) on macrophages to escape phagocytosis. Thus, effective re-education of TAMs into the "eat me" type and blocking the CD47-SIRPα signaling play pivotal roles in tumor immunotherapy. Herein, it is reported that hybrid nanovesicles (hEL-RS17) derived from extracellular vesicles of M1 macrophages and decorated with RS17 peptide, an antitumor peptide that specifically binds to CD47 on tumor cells and blocks CD47-SIRPα signaling, can actively target tumor cells and remodel TAM phenotypes. Consequently, more M1-like TAMs infiltrate into tumor tissue to phagocytize more tumor cells due to CD47 blockade. By further co-encapsulating chemotherapeutic agent shikonin, photosensitizer IR820, and immunomodulator polymetformin in hEL-RS17, an enhanced antitumor effect is obtained due to the combinational treatment modality and close synergy among each component. Upon laser irradiation, the designed SPI@hEL-RS17 nanoparticles exert potent antitumor efficacy against both 4T1 breast tumor and B16F10 melanoma models, which not only suppresses primary tumor growth but also inhibits lung metastasis and prevents tumor recurrence, exhibiting great potential in boosting CD47 blockade-based antitumor immunotherapy.

Delving into the molecular initiating event of cadmium toxification via the dose-dependent functional genomics approach in Saccharomyces cerevisiae.

Determining dose-response relationship is essential for comprehensively revealing chemical-caused effects on organisms. However, uncertainty and complexity of gene/protein interactions cause the inability of traditional toxicogenomic methods (e.g., transcriptomics, proteomics and metabolomics) to effectively establish the direct relationship between chemical exposure and genes. In this work, we built an effective dose-dependent yeast functional genomics approach, which can clearly identify the direct gene-chemical link in the process of cadmium (Cd) toxification from a genome-wide scale with wide range concentrations (0.83, 2.49, 7.48, 22.45, 67.34, 202.03 and 606.1 µM). Firstly, we identified 220 responsive strains, and found that 142, 110, 91, 34, 8, 0 and 0 responsive strains can be respectively modulated by seven different Cd exposure concentrations ranging from high to low. Secondly, our results demonstrated that these genes induced by the high Cd exposure were mainly enriched in the process of cell autophagy, but ones caused by the low Cd exposure were primarily involved in oxidative stress. Thirdly, we found that the top-ranked GO biological processes with the lowest point of departure (POD) were transmembrane transporter complex and mitochondrial respiratory chain complex III, suggesting that mitochondrion might be the toxicity target of Cd. Similarly, nucleotide excision repair was ranked first in KEGG pathway with the least POD, indicating that this dose-dependent functional genomics approach can effectively detect the molecular initiating event (MIE) of cadmium toxification. Fourthly, we identified four key mutant strains (RIP1, QCR8, CYT1 and QCR2) as biomarkers for Cd exposure. Finally, the dose-dependent functional genomics approach also performed well in identifying MIE for additional genotoxicity chemical 4-nitroquinoline-1-oxide (4-NQO) data. Overall, our study developed a dose-dependent functional genomics approach, which is powerful to delve into the MIE of chemical toxification and is beneficial for guiding further chemical risk assessment.

An online derivatization strategy targeting carbon-carbon double bonds by laser-ablation carbon fiber ionization mass spectrometry imaging: Unraveling the spatial characteristic in mountain-cultivated ginseng and garden-cultivated ginseng with different ages.

Serving as a world-renowned tonic, ginseng contains various types of bioactive metabolites. The comprehensive profiling of these metabolites may help explore the nutritional value of ginseng. Due to high variety in chemical structures, simultaneous monitoring of these metabolites remains a challenge. Herein, a high-throughput and high-selectivity online derivatization mass spectrometry imaging strategy targeting CC was developed. As a widely existed chemical group, CC acts like a bridge connecting different kinds of metabolites. [d0]/[d10]-Bis(pyridine) iodine tetrafluoroboride reagent was chosen for the derivatization of CC, the detection sensitivity of which increased about 3 magnitudes after derivatization. Assisted by laser ablation carbon fiber ionization mass spectrometry, the spatial distribution of bioactive metabolites in mountain-cultivated and garden-cultivated ginseng were visualized. The correlation heatmap results revealed that metabolites in mountain-cultivated ginseng hold higher correlation than those in garden-cultivated ginseng. The proposed method showed potential in providing comprehensive information on the nutrient content of foods.

Harnessing nanobiotechnology for cerebral ischemic stroke management.

Cerebral ischemic stroke remains one of the most serious neurological disorders that pose threats to human health, causing a large amount of long-term disability or even death throughout the world. Based on its physiologic and pathological features, there are limited available therapeutic options for effective ischemic stroke management. Encouragingly, a rapid advancement of nanobiotechnology is bringing new insights into exploring more alternative strategies against cerebral ischemic stroke, which can cleverly overcome the limitations related to conventional treatment methods. Therefore, this review focuses on the recent achievements of nanobiotechnology for ischemic stroke management, which emphasizes diverse targeted delivery strategies using various nanoplatforms including liposomes, micelles, polymeric nanoparticles, nanogels, inorganic nanomaterials, and cell-derived nano-vectors based on the pathophysiological features of ischemic stroke. Moreover, different therapeutic approaches against ischemic stroke such as neuroprotection, anti-inflammation, thrombolysis, increased blood-brain barrier penetration and reactive oxygen species scavenging are highlighted. Meanwhile, this review discusses how these versatile nanoplatforms were designed to assist in the treatment of ischemic stroke. Based on this, challenges, opportunities, and future perspectives using nanobiotechnology through rational design for effective ischemic stroke management are revealed.

Effect of facemask oxygenation with and without positive pressure ventilation on gastric volume during anesthesia induction in patients undergoing laparoscopic cholecystectomy or partial hepatectomy: a randomized controlled trial.

BACKGROUND: Studies focusing on the relationship between gastric volume and facemask oxygenation without ventilation during apnea in anesthesia induction are scarce. This study compared the change in gastric volume during apnea in anesthesia induction using facemask ventilation and facemask oxygenation without ventilation in adults undergoing laparoscopic surgery. METHODS: In this prospective, randomized, double-blinded trial, 70 adults undergoing laparoscopic surgery under general anesthesia were divided into two groups to receive facemask oxygenation with and without ventilation for 60 seconds after loss of consciousness. Before anesthesia induction and after endotracheal intubation, the gastric antral cross-sectional area was measured with ultrasound imaging. Arterial blood gases were tested at baseline (T1), after preoxygenation (T2), after loss of consciousness (T3), and before and after endotracheal intubation (T4 and T5, respectively). RESULTS: Sixty patients were included (ventilation n = 30; non ventilation n = 30, 10 patients were excluded). The median [IQR] change of gastric antral cross-sectional area in ventilation group was significantly higher than in non ventilation group (0.83 [0.20 to 1.54] vs. 0.10 [- 0.11 to 0.56] cm2, P = 0.001). At T4 and T5, the PaO2 in ventilation group was significantly higher than in non ventilation group (T4: 391.83 ± 61.53 vs. 336.23 ± 74.99 mmHg, P < 0.01; T5: 364.00 ± 58.65 vs. 297.13 ± 86.95 mmHg, P < 0.01), while the PaCO2 in non ventilation group was significantly higher (T4: 46.57 ± 5.78 vs. 37.27 ± 6.10 mmHg, P < 0.01; T5: 48.77 ± 6.59 vs. 42.63 ± 6.03 mmHg, P < 0.01) and the pH value in non ventilation group was significantly lower (T4: 7.35 ± 0.029 vs 7.42 ± 0.047, P < 0.01; T5: 7.34 ± 0.033 vs 7.39 ± 0.044, P < 0.01). At T4, the HCO3- in non ventilation group was significantly higher (25.79 ± 2.36 vs. 23.98 ± 2.18 mmol l- 1, P < 0.01). CONCLUSIONS: During apnoea, the increase in gastric volume was milder in patients undergoing facemask oxygenation without ventilation than with positive pressure ventilation. TRIAL REGISTRATION: ChiCTR2100054193, 10/12/2021, Title: "Effect of positive pressure and non-positive pressure ventilation on gastric volume during induction of general anesthesia in laparoscopic surgery: a randomized controlled trial". Website: https://www.chictr.ogr.cn .

Single-cell RNA sequencing for traumatic spinal cord injury.

Traumatic spinal cord injury (tSCI) is a severe injury of the central nervous system (CNS) with complicated pathological microenvironment that results in hemorrhage, inflammation, and scar formation. The microenvironment of the injured spinal cord comprises heterogeneous neurons, glial cells, inflammatory cells, and stroma-related cells. Increasing evidence has indicated that the altered cellular and molecular microenvironment following tSCI is a key factor impeding functional recovery. Single-cell RNA sequencing (scRNA-seq) has provided deep insights into the dynamic cellular and molecular changes in the microenvironment by comprehensively characterizing the diversity of spinal cord cell types. Specifically, scRNA-seq enables the exploration of the molecular mechanisms underlying tSCI by elucidating intercellular communication in spinal cord samples between normal and injury conditions at a single-cell resolution. Here, we first described the pathological and physiological processes after tSCI and gave a brief introduction of the scRNA-seq technology. We then focused on the recent scRNA-seq researches in tSCI, which characterized diverse cell-type populations and specific cell-cell interactions in tSCI. In addition, we also highlighted some potential directions for the research of scRNA-seq in tSCI in the future.

Alfalfa modified the effects of degraded black soil cultivated land on the soil microbial community.

Legume alfalfa (Medicago sativa L.) is extensively planted to reduce chemical fertilizer input to the soil and remedy damaged fields. The soil mechanism of these effects is potentially related to the variations in alfalfa-mediated interactions of the soil microbial community. To understand the impact of planting alfalfa on the soil microbial community in degraded black soil cultivated land, a 4-year experiment was conducted in degraded black soil cultivated land. We assessed soil parameters and characterized the functional and compositional diversity of the microbial community by amplicon sequencing that targeted the 16S rDNA gene of bacteria and ITS of fungi in four systems under corn cultivation at the Harbin corn demonstration base (Heilongjiang, China): multiyear corn planting (more than 30 years, MC1); 2 years of alfalfa-corn rotation (OC); 3 years of alfalfa planting (TA); and 4 years of alfalfa planting (FA). It was found out that alfalfa led to changes in the alpha diversity of soil bacteria rather than in fungi in the degraded arable land. The abundance of the bacterial groups Gemmatimonadetes, Actinobacteria, Planctomycetes, and Chloroflexi was increased in OC, while Proteobacteria and Acidobacteria and the fungal group Glomeromycota were increased in TA and FA. OC, TA, and FA significantly increased the pH level but reduced soil electrical conductivity, but they had no impact on soil available nitrogen and soil available potassium at the 0-15 cm soil depth. However, with the years of alfalfa planting, soil available nitrogen and soil available potassium were reduced at the 15-30 cm soil depth. OC, TA, and FA significantly reduced the soil available phosphorus and soil total phosphorus at the 15-30 cm soil depth. There was no significant impact made on soil total nitrogen. FA significantly reduced the soil organic matter at the 15-30 cm soil depth. Planting alfalfa in degraded black soil cultivated land can reduce the salt content of the soil, and the nutrient content of soil planted with alfalfa without fertilization was equivalent to that of degraded corn cultivated land with annual fertilization. Besides, alfalfa recruited and increased contained taxa with the capacity to improve soil nutrient utilization and inhibit the harmful influences of pathogens for subsequent crops. Meanwhile, the planting of alfalfa can modify soil conditions by promoting the proliferation of specific beneficial microbiota groups.

Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method.

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms.

Single-cell sequencing reveals microglia induced angiogenesis by specific subsets of endothelial cells following spinal cord injury.

Spinal cord injury (SCI) results in dynamic alterations of the microenvironment at the lesion site, which inevitably leads to neuronal degeneration and functional impairment. The destruction of the spinal vascular system leads to a significant deterioration of the milieu, which exacerbates inflammatory response and deprives cells of nutrient support in the lesion. Limited endogenous angiogenesis occurs after SCI, but the cellular events at the lesion site during this process are unclear so far. Here, we performed single-cell RNA sequencing (scRNA-seq) on spinal cord tissues of rats at different time points after SCI. After clustering and cell-type identification, we focused on vascular endothelial cells (ECs), which play a pivotal role in angiogenesis, and drew the cellular and molecular atlas for angiogenesis after SCI. We found that microglia and macrophages promote endogenous angiogenesis by regulating EC subsets through SPP1 and IGF signaling pathways. Our results indicate that immune cells promote angiogenesis by regulating specific subsets of vascular ECs, which provides new clues for exploring SCI intervention.