人神经回路的胶质母细胞瘤重塑可降低生存

　　该研究的每个参与者都是从18-85岁的成年人的前瞻性注册表中招募的，新诊断为额叶，时间和顶壁IDH-WT高级神经胶质瘤，具有详细的语言评估和基线MEG记录。包容性标准包括以下内容：英语讲话，年龄18-85岁，没有以前的精神病，神经疾病或药物或酒精滥用的史。所有人类的皮质学数据均在词汇检索语言任务中获得，从14名接受手术切除的术中大脑映射的成人清醒患者。来自八名患者的肿瘤用于RNA-SEQ实验。来自19例患者的位置定向的肿瘤活检用于免疫荧光/免疫组织化学分析，并将24例患者用于免疫细胞化学和基于细胞的功能测定。来自八名患者的肿瘤用于小鼠异种移植实验。所有参与者均提供了书面知情同意，以参与​​这项研究，该研究得到了加州大学旧金山大学（UCSF）的机构审查委员会（IRB）的批准，用于人类研究（UCSF CC-171027，CHR 17-23215），并根据赫尔辛基的宣布。 　　我们首先研究了14例主要半球胶质母细胞瘤患者的短距离电路动力学，在术中环境中使用ECOG浸润下叶的下叶语音生产区域（扩展数据图1A）。然后，我们将分子研究集中在手术治疗的IDH-WT胶质母细胞瘤患者上，进行了术语外语言评估和假想的连贯性，作为使用MEG的长期衡量功能连通性的度量（扩展数据图1A，B）。This enabled us to import functional connectivity data into the operating room in which we performed site-specific tissue biopsies of human glioma from regions with differing measures of functional connectivity for in vivo and in vitro cell biology experiments including primary patient cultures (n = 19 patients) and multimodal tissue profiling, including microscopy, sequencing, proteomics and patient-derived tumour xenografting (Extended Data Fig. 1c).这种分层的方法 - 将临床变量，认知评估，网络动力学的人类和动物模型以及细胞生物学之外 - 作为研究神经胶质瘤 - 神经元相互作用的临床意义的平台（扩展数据图1D和补充表1和2）。 　　语言优势的半球使用基线磁源成像确定。简而言之，参与者坐在275通道的全率CTF Omega 2000系统（CTF Systems）中，以1,200 Hz进行采样，同时他们执行了听觉动词生成任务。然后在使用高分辨率MRI注册后，在源空间中以自适应空间过滤器在源空间中重建所得的时间序列。最后，比较了整个半球的动词生成过程中β波段活性的变化，以产生总体侧向指数。所有参与者均为左半球的左主流，并进行了电生理记录。我们实施了以前已建立的术中测试范例。通过严格执行以下操作室中的噪音最小化：（1）所有人员都被要求停止口头交流；（2）电话和警报被静音；（3）手术吸力和所有其他非必需机械均已暂时关闭。一台15英寸的笔记本电脑（60 Hz刷新率）运行与PsychToolBox 3集成的自定义MATLAB脚本（http://psychtoolbox.org/），距离每个参与者30厘米。该脚本启动了一项图像命名任务，该任务由一个48个独特刺激的单个块组成，每个刺激都通过彩色线图描绘了一个共同的对象或动物。每个刺激都在中央固定点显示，并占据了75％的显示屏。在呈现每个刺激后，要求参与者发声一个最能描述该项目的单词。 　　使用和不存在下部硬膜下电极的术中照片被用于将每个电极接触与立体定向技术结合使用9,53。使用回旋解剖结构和血管排列的地标进行了图像，以术前T1和T2加权MRI扫描。肿瘤边界位于MRI扫描上，并在坏死性肿瘤核组织10 mm内的电极被确定为“肿瘤”接触。从对比度增强的边缘向对比延伸到Flair边缘的肿瘤的低位核心的电极被认为是肿瘤电极，并且在任何T1后的Gadolinium或Flair信号完全外部被视为非肿瘤或正常的电极外，被训练有素的共同作者出现在训练有素的共同作者身上。基于先前在文献中建立的两个标准，包括T2加权的FLAIR序列信号的质量区域，定义了胶质瘤浸润区域。通过严格检查皮层确认扩张和/或异常血管模式来证实成像。先前的研究表明，非增强疾病的区域包括与神经元和正常神经胶质细胞混合的浸润肿瘤细胞2,54。这些标签由研究首席研究者（S.L.H.-J。）审查，并与术中立体定向神经元驾驶中得出的标签进行了比较以达成共识（Brainlab）。 　　每个参与者在参与前2天接受了培训课程，以确保熟悉任务。在停止使用麻醉药后的一段时间内获得了ECOG信号（最少的药物清洗时间为20分钟），并且在广泛的出现后出现后的清醒评估后，该患者被认为是警觉和清醒的，以确保充分的唤醒55。术中任务包括命名常见对象和动物的绘画表示（图片命名）以及通过听觉描述（听觉命名）命名常见对象和动物56。重新分配术后视频，以确保收集所有数据并仅包括正确的响应以进行分析。从参与者放置5 cm的双通道麦克风以44.1 kHz采样音频，并放大电生理信号（G.TEC）。在处理初始阶段，以4,800 Hz获取记录，并将其下采样至1,200 Hz。在离线分析过程中，将音频和电生理记录手动对齐，重新采样并分割为时代（语音锁定）。这些时期将时间= 0 ms设置为语音发作，并包括±2,000毫秒，每次试验总计4,000毫秒的信号。如果（1）给出了不正确的响应（包括填充剂和插入），或者（2）在刺激呈现和响应之间延迟了2 s，以维持一致的试验动力学并确保神经信号确实反映了实验性操纵，则将试验丢弃。如果其峰度超过5.0，则在视觉上识别出噪声过多的通道。拒绝人为通道后，将数据引用了一个公共平均值，以0.1 Hz的高度过滤，以去除慢跑伪像，并使用300级FIR滤波器在70-110 Hz之间过滤，从而将分析集中在高gamma频段范围上，高-GAMMA频段范围（ 这与当地平均人口峰值率密切相关。为了提取ERSP，首先将电生理信号降采样至600 Hz，然后在0.1 Hz处进行高通滤波以去除DC偏移和低频漂移，在60 Hz处进行缺口过滤，并谐波谐波以去除线路噪声，并使用70和170 Hz（使用高γ范围）进行了固定范围。这些信号最终使用100毫秒的高斯内核进行平滑，在每个试验中均采样至100 Hz，并在每次试验中进行了Z得分。随后，电极重新以每个参与者的共同平均值来促进群体比较，并根据自动解剖标签ATLAS（https://wwwwwww.gin.cnrs.fr/en/tools/aal/）定义了目标区域。网格植入的位置仅通过临床适应症指导。使用Gyral和Sulcal解剖结构独立确认了每个参与者的最终注册的准确性，以将每个电极注册到模板表面的位置进行三角调节，然后将其与实际皮层的术中照片进行比较，并带有上面的网格57。然后使用希尔伯特转换的平方在过滤的数据上计算HGP。然后将HGP平均在整个静息状态时间序列上平均，从而为每个电极接触提供单一的神经反应性度量。然后，在任务反应期间，将HGP平均在患者之间平均，从而为每个通道提供了单一的神经元反应性量度。然后比较肿瘤和正常出现通道之间的HGP水平。线性混合效应模型用于使用R（v.3.1-161; https：//cran.r-project.org/web/web/packages/nlme/citation.html）中的NLME软件包进行重复测量的统计比较。信号的起源（即正常出现/神经胶质瘤浸润的皮质） 被建模为固定效应，并将参与者建模为随机效应。对于没有重复测量的连续变量，使用t检验。P的阈值< 0.05 was used to denote statistical significance and corrections for multiple comparisons were made using the Bonferroni method. 　　To decode between low-frequency words (for example, rooster) and high-frequency words (for example, car), signals from normal-appearing and glioma-infiltrated electrodes were extracted from the anterior temporal lobe after participant-level registration to a common MNI atlas. Responses were time-locked to speech onset and the signal envelope was extracted using a Hilbert transform after applying a bandpass filter in the high-gamma range (70–170 Hz). Subsequently, an l2-regularized logistic regression classifier was trained (cost of 1) to distinguish neural responses during vocalization of low-frequency words (for example, rooster) from high-frequency words (for example, car). Model performance was determined by taking the accuracy on a held-out participant and averaging it across all folds (that is, leave-one-participant-out cross-validation) and statistical significance was determined by testing this accuracy against a binomial distribution. This process was conducted separately for normal-appearing and glioma-infiltrated cortex using an identical preprocessing, training and testing paradigm. 　　MEG recordings were performed according to an established protocol30,31. In brief, the study participants had continuous resting state MEG recorded with a 275-channel whole-head CTF Omega 2000 system (CTF Systems) using a sampling rate of 1,200 Hz. During resting-state recordings, the participants were awake with their eyes closed. Surface landmarks were co-registered to structural magnetic resonance images to generate the head shape. Within the alpha frequency band, an artifact-free 1 min epoch was selected for further analysis if the patient’s head movement did not exceed 0.5 cm. This artifact-free, 1 min epoch was then analysed using the NUTMEG software suite (v.4; UCSF Biomagnetic Imaging Laboratory) to reconstruct whole-brain oscillatory activity from MEG sensors so as to construct functional connectivity (imaginary coherence (IC)) metrics54,58,59. Spatially normalized structural magnetic resonance images were used to overlay a volume-of-interest projection (grid size = 8 mm; approximately 3,000 voxels per participant) such that each voxel contained the entire time series of activity for that location derived by all the MEG sensor recordings. The time series within each voxel was then bandpass-filtered for the alpha band (8–12 Hz) and reconstructed in source space using a minimum-variance adaptive spatial filtering technique54,60. The alpha frequency band was selected because it was the most consistently identified peak in the power spectra from this sampling window in our patient series. Functional connectivity estimates were calculated using IC, a technique known to reduce overestimation biases in MEG data generated from common references, cross-talk and volume conduction26,28. 　　Resting-state MEG was also used to measure intratumoural gamma activity. A spatial beamformer was applied to extract neural signals at the voxel level from manually defined regions of interest corresponding to FLAIR signal abnormality (that is, within the infiltrative margin of the tumour)61. These source-space signals were then downsampled to 300 Hz, notch filtered at 60 Hz to remove line noise and rereferenced to the common average. Spectral activity from 1 to 50 Hz was estimated at each voxel using Thomson’s multitaper method (pmtm in MATLAB R2021b) with 29 Slepian tapers. Next, gamma power from 30 to 50 Hz was computed at each voxel after subtracting the aperiodic component from each spectrum by fitting a Lorentzian function in semi-log space62. A point estimate of intratumoural gamma activity was subsequently computed by averaging the activity across all voxels for each participant and regressed against the corresponding number of manually counted intratumoural HFC nodes. 　　The functional connectivity of an individual voxel was derived by the mean IC between the index voxel and the rest of the brain, referenced to its contralesional pair30. It is possible that there are regions within gliomas with varying amounts of functional connectivity. Moreover, there are individual patients with more or less functional connectivity. We have addressed these differences in our experimental model. Intratumoural differences in functional connectivity were addressed by the following: in comparison to contralesional voxels, we used a two-tailed t-test to test the null hypothesis that the Z-transformed connectivity IC between the index voxel and non-tumour voxel is equal to the mean of the Z-transformed connectivity between all contralateral voxels and the same set of voxels. The resultant functional connectivity values were separated into tertiles: upper tertile (HFC) and lower tertile (LFC). Functional connectivity maps were created by projecting connectivity data onto each individual patient’s preoperative structural magnetic resonance images and imported into the operating room neuronavigation console. Stereotactic site-directed biopsies from HFC (upper tertile) and LFC (lower tertile) intratumoural regions were taken and x, y, z coordinates determined using Brainlab neuro-navigation. Thus, only the extremes of intratumoural connectivity (high and low connectivity, HFC and LFC, respectively) were analysed for these experiments. Rather than raw values, each functional connectivity measure represents a Z-transformed value and it therefore remains likely that the HFC distinction for one patient does not perfectly coincide with the HFC distinction in another patient’s tumour (intertumoural heterogeneity). 　　Pre-operative and post-operative tumour volumes were quantified using BrainLab Smartbrush (v.2.6; Brainlab). Pre-operative MRI scans were obtained within 24 h before resection, and post-operative scans were all obtained within 72 h after resection. Total contrast-enhancing tumour volumes were measured at both pre-operative and post-operative timepoints. The total contrast-enhancing tumour volume was measured on T1-weighted post-contrast images, and the non- enhancing tumour volume was measured on T2 or FLAIR sequences. Manual segmentation was performed with region-of-interest analysis ‘painting’ inclusion regions based on fluid-attenuated inversion-recovery (FLAIR) sequences from pre- and post-operative MRI scans to quantify tumour volume. The extent of resection was calculated as follows: (pre-operative tumour volume − post-operative tumour volume)/pre-operative tumour volume × 100%. Manual segmentations were performed for which the tumour volumetric measurements were verified for accuracy after an initial training period. Volumetric measurements were performed blinded to patients’ clinical outcomes. All of the patients in the cohort had available preoperative and postoperative MRI scans for analysis. To ensure that post-operative FLAIR signal was not surgically induced oedema or ischaemia, FLAIR pre- and post-operative MRIs were carefully compared alongside DWI sequences before including each region in the volume segmentation42. HFC voxels with T1 post gadolinium contrast enhancing tumour were considered to be HFC-positive for survival analysis. 　　One to two days before tumour resection, patients underwent baseline language evaluation, which consisted of naming pictorial representations of common objects and animals (picture naming) and naming common objects and animals through auditory descriptions (auditory naming). Visual picture naming and auditory stimulus naming testing were used given their known significance and clinical correlation with outcomes in clinical patient population63,64. The correct answers for these tasks (delivered on a laptop with a 15 inch monitor (60 Hz refresh rate) positioned two feet away from the seated patient in a quiet clinical setting) were matched on word frequency (that is, commonality within the English language) using SUBTLEXWF scores provided by the Elixcon project and content category. Task stimuli were randomized and presented using PsychToolbox. The task order was randomly selected by the psychometrist for each participant. Slides were manually advanced by the psychometrist either immediately after the participant provided a response or after 6 s if no response was given. The tasks were scored on a scale from 0 to 4 by a trained clinical research coordinator who was initially blinded to all clinical data (including imaging studies). No participants had uncorrectable visual or hearing loss. Details of the administration and scoring of auditory and picture naming language tasks can be found in previous studies27,55,65. 　　Tumour tissues with high (HFC) and low (LFC) functional connectivity sampled during surgery based on preoperative MEG were processed for quality control by a certified neuropathologist and were subsequently used to generate primary patient-derived cultures. Patient-matched samples were acquired from site-directed HFC and LFC intratumoural regions from the same patient. Intratumoural HFC and LFC tissues were dissociated both mechanically and enzymatically and then passed through a 40 µm filter to remove debris. The filtered cell suspension was then treated with ACK lysis buffer (Invitrogen) to remove red blood cells and subsequently cultured as free-floating neurospheres in a defined, serum-free medium designated tumour sphere culture medium, consisting of Dulbecco’s modified Eagle’s medium (DMEM-F12; Invitrogen), B27 (Invitrogen), N2 (Invitrogen), human-EGF (20 ng ml−1; Peprotech), human-FGF (20 ng ml−1; Peprotech). Normocin (InvivoGen) was also added to the cell culture medium in combination with penicillin–streptomycin (Invitrogen) to prevent mycoplasma, bacterial and fungal contaminations. Cell cultures were routinely tested for mycoplasma (PCR Mycoplasma Test Kit I/C, PromoCell) and no positive results were obtained (Extended Data Fig. 7d). 　　RNA was isolated from HFC (n = 3) and LFC (n = 4) tumour samples using the RNeasy Plus Universal Mini Kit (QIAGEN) and RNA quality was confirmed using the Advanced Analytical Fragment Analyzer. RNA-seq libraries were generated using the TruSeq Stranded RNA Library Prep Kit v2 (RS-122- 2001, Illumina) and 100 bp paired-end reads were sequenced on the Illumina HiSeq 2500 system to at least 26 million reads per sample at the Functional Genomics Core Facility at UCSF. Quality control of FASTQ files was performed using FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were trimmed with Trimmomatic (v.0.32)66 to remove leading and trailing bases with quality scores of less than 20 as well as any bases that did not have an average quality score of 20 within a sliding window of 4 bases. Any reads shorter than 72 bases after trimming were removed. Reads were subsequently mapped to the human reference genome GRCh38 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.39/)67 using HISAT268 (v.2.1.0) with the default parameters. For differential expression analysis, we extracted exon-level count data from the mapped HISAT2 output using featureCounts69. Differentially expression analysis was performed using DESeq270 using the apeglm parameter71 to accurately calculate log-transformed fold changes and setting a false-discovery rate of 0.05. Differentially expressed genes were identified as those with log-transformed fold changes of greater than 1 and an adjusted P value of less than 0.05. Unsupervised gene expression principal component analysis and volcano plots of IDH-WT glioblastoma (Extended Data Fig. 5b,c) revealed 144 differentially expressed genes between HFC and LFC tumour regions, including 40 genes involved in nervous system development (Supplementary Table 3). 　　Fresh tumour samples were acquired from the operating room and transported to the laboratory space in PBS and on ice. Tumour tissue was minced with #10 scalpels (Integra LifeSciences) and then digested in papain (Worthington Biochemical, LK003178) for 45 min at 37 °C. Digested tumour tissue was then incubated in red blood cell lysis buffer (eBioscience, 00-4300-54) for 10 min at room temperature. Finally, the samples were sequentially filtered through 70 μm and 40 μm filters to generate a single-cell suspension. 　　Single-cell suspensions of three patient-matched HFC and LFC tumour tissues were generated as described above and processed for single-cell RNA-seq using the Chromium Next GEM Single Cell 3′ GEM, Library & Gel Bead Kit v3.1 on the 10x Chromium controller (10x Genomics) using the manufacturer’s recommended default protocol and settings, at a target cell recovery of 5,000 cells per sample. Although single-cell sequencing does not capture all cell types within the central nervous system microenvironment, the sequencing pipeline used in this study has been demonstrated to identify neurons and was therefore chosen for use in physiologically annotated fresh glioblastoma samples, compared with single-nucleus RNA-seq, which is commonly applied for frozen archived tissues72,73. One hundred base pair paired-end reads were sequenced on the Illumina NovaSeq 6000 system at the Center for Advanced Technology at the University of California San Francisco, and the resulting FASTQ files were processed using the CellRanger analysis suite (v.3.0.2; https://github.com/10XGenomics/cellranger) for alignment to the hg38 reference genome, identification of empty droplets, and determination of the count threshold for further analysis. A cell quality filter of greater than 500 features but fewer than 10,000 features per cell, and less than 20% of read counts attributed to mitochondrial genes, was used. Single-cell UMI count data were preprocessed in Seurat (v.3.0.1)74,75 using the sctransform workflow76, with scaling based on the regression of UMI count and the percentage of reads attributed to mitochondrial genes per cell. Dimensionality reduction was performed using principal component analysis and then principal component loadings were corrected for batch effects using Harmony77. Uniform manifold approximation and projection was performed on the reduced data with a minimum distance metric of 0.4 and Louvain clustering was performed using a resolution of 0.2. Marker selection was performed in Seurat using a minimum difference in the fraction of detection of 0.5 and a minimum log-transformed fold change of 0.5. We assessed the single-cell transcriptome from 6,666 HFC-region cells and 7,065 LFC-region cells (Supplementary Table 4). 　　After rehydration, 5.0 μm paraffin-embedded sections were processed for antigen retrieval followed by blocking and primary antibody incubation overnight at 4 °C. The following primary antibodies were used: rabbit anti-synapsin 1 (1:1,000, EMD Millipore), mouse anti-PSD95 (1:100, UC Davis), mouse anti-nestin (1:500, Abcam), mouse anti-neurofilament (M+H; 1:1,000, Novus Biologicals), mouse anti-TSP-1 (1:20, Invitrogen), rabbit anti-TSP-1 (1:50, Abcam), rabbit anti-MET (1:100, Abcam) and rabbit anti-Ki-67 (1:100, Abcam). We used species-specific secondary antibodies: Alexa 488 goat anti-chicken IgG, Alexa 488 goat anti-rabbit IgG, Alexa 568 goat anti-rabbit IgG, Alexa 568 goat anti-mouse IgG, Alexa 647 goat anti-rabbit IgG, all used at 1:500 (Invitrogen). After DAPI nuclear counter staining (Vector Laboratories, 1:1,000), coverslips were mounted with Fluoromount-G mounting medium (SouthernBiotech) for immunofluorescence analysis. The number of synapsin-1 and PSD95 puncta was quantified using spots (with automatic intensity maximum spot detection thresholds and a spot diameter of 1.0 µm) detection function of Imaris. The ratio of pre- and postsynaptic puncta was calculated by dividing the total number of synapsin-1 or PSD95 puncta on neurofilament-positive neurons to the total number of cells stained with DAPI in 135 μm × 135 μm field areas for quantification. Alternatively, the sections were incubated in DAB horseradish peroxidase (Vector Laboratories) for chemical colorimetric detection after incubation in ImmPress anti-rabbit IgG (Novus Biologicals) and counterstained with Harris haematoxylin for immunohistochemistry analysis. 　　Glioma cells were plated on poly--lysine and laminin-coated coverslips (Neuvitro) at a density of 10,000 cells per well in 24-well plates. Approximately 24 h later, 40,000 embryonic mouse hippocampal neurons (Gibco) were seeded on top of the glioma cells and maintained with serum-free Neurobasal medium supplemented with B27, gentamicin and GlutaMAX (Gibco). After 2 weeks of co-culture, cells were fixed with 4% paraformaldehyde (PFA) for 30 min at 4 °C and incubated in blocking solution (5% normal donkey and goat serum, 0.25% Triton X-100 in PBS) at room temperature for 1 h. Next, they were treated with primary antibodies diluted in the blocking solutions overnight at 4 °C. The following antibodies were used: rabbit anti-homer-1 (1:250, Pierce), mouse anti-synapsin-1 (1:200) and chicken anti-MAP2 (1:500, Abcam). The coverslips were then rinsed three times in PBS and incubated in secondary antibody solution (Alexa 488 goat anti-chicken IgG; Alexa 568 goat anti-mouse IgG, and Alexa 647 goat anti-rabbit IgG, all used at 1:500 (Invitrogen) in antibody diluent solution for 1 h at room temperature. The coverslips were rinsed three times in PBS and then mounted with VECTA antifade mounting medium with DAPI (Vector Laboratories). 　　Images were captured at 1,024 × 1,024 resolution using a ×10 objective on the Nikon C2 confocal microscope. The confocal microscope settings for the homer-1 Alexa488 and synapsin-1 Alexa647 channels were held constant across all of the samples that were used for the experiment. Collected images were then imported into Imaris software (Imaris v.9.2.1, Bitplane) and the threshold value for each channel was manually adjusted and the colocalized voxels of the synapsin-1 puncta with the homer-1 marker was detected by creating a colocalization channel using the built-in the colocalization module of the Imaris software. Furthermore, the colocalization events were quantified by running the built-in spot detection algorithm of Imaris in conjunction with the colocalization channel. Next, dendrites labelled by MAP2 was visualized in TRITC channel and reconstructed using the Filament tool of Imaris software; the number of colocalized puncta representing synapses were counted and presented as the number of synapsin-1- and homer-1-positive puncta per 10 μm of dendrite length. Areas of homer-1 immunolabelled synaptic puncta were reconstructed using Imaris software Surface tool on maximal-intensity projections. Surfaces were built using a surface area detail level of 0.1 µm, thresholding by absolute intensity and taking all voxel >1.0考虑到。分析了单个抗Homer1免疫抑制点的面积大小，并计算了平均值。 　　如先前所述38,78所述，由人NGN2转基因诱导整合的WTC11 IPS细胞克隆产生诱导的神经元器官。简而言之，通过NGN2诱导通过NGN2诱导通过添加2μgml-1强力霉素在1：1的神经性神经性和脑膜神经培养基中添加2μgml-1强力霉素，从NT-3 10天诱导神经元分化。接下来，通过用大约8个月大的器官条件培养基衍生出的类带器官来触发神经元的成熟。Astrocytes were differentiated from the human iPS cell WTC11 line and cultured in a medium consisting of DMEM/F12 containing GlutaMAX, sodium bicarbonate, sodium pyruvate, N-2 supplement, B-27 supplement (Gibco), 2 μg ml−1 heparin, 10  ng ml−1 EGF and 10 ng ml−1 FGF2.神经元的器官被描述为有丝分裂后的MAP2和βIII-微管蛋白染色，以验证神经元诱导效率。神经元分化14天后，将标记为RFP标记的HFC/LFC神经胶质瘤细胞以1：3的比例添加到神经元器官培养物中。在非诱导之前，用GFP慢病毒转导转基因人IPS细胞系WTC11。Zeiss细胞观察者旋转盘共聚焦显微镜（Carl Zeiss）配备了温度和二氧化碳控制的腔室，以记录神经胶质瘤细胞与神经元类器官的实时相互作用。使用0.4 Na的10×物镜从共培养起始开始时，每10分钟开始每10分钟，在6小时内成像6小时。为了评估外源TSP-1对神经胶质瘤细胞和神经元之间功能整合的影响，以5 µg mL-1的剂量将人重组TSP-1（R＆D系统）应用于LFC-Neuron Organoid共培养。使用ImageJ进行了活细胞图像分析。简而言 在每个GFP阳性神经元器官周围绘制了一个感兴趣的区域，并在每个指定的时间点的概述区域中测量了RFP阳性胶质母细胞瘤细胞的荧光强度（综合密度）。在两周结束时，将来自HFC和LFC共培养的类器官嵌入OCT中，并以10μm厚度的截面以进行Homer-1免疫荧光染色。通过分析神经元-HFC和LFC共培养的Homer-1点密度来确定HOMER-1表达。 　　我们通过与聚赖氨酸（Thermo Fisher Scientific），层粘连蛋白（Fisher Scientific）和Fibronectin（Corning）涂层，在添加细胞之前，在添加细胞之前，准备了24孔Cytoview多电极板（轴突生物系统）。简而言之，在建立培养物之前的1天，将0.1 mg ml-1的聚赖氨酸的溶液以每孔100μl的体积添加到MEA板上，并在室温下孵育2小时。2小时后，将聚赖氨酸吸入，并用无菌水洗涤板，并在生物安全柜中晾干，并在4°C下晾干。第二天，将板与100μl的5 µg ml -1层粘连蛋白和1μgml -1纤连蛋白涂在一起，并在细胞播种前在37°C下在37°C下孵育2小时。 　　由E18 CD1小鼠（Charles River Laboratories）建立了原发性皮质培养物。根据UCSF机构动物护理和使用委员会（IACUC），定时怀孕的CD1大坝被二氧化碳安乐死杀死。在解剖显微镜（Zeiss）下，在冰冷的HBSS（Gibco）中对E18胚胎的完全皮质进行解剖。将解剖的皮质切成1 mm2片，并在5 ml的0.25％胰蛋白酶中从钙和无镁的无镁汉克的平衡盐溶液（Worthington Biiochemical Corporation）中从2.5％胰蛋白酶（康宁）重构30分钟，在37°C的30分钟内从2.5％的胰蛋白酶（康宁）中重生。然后，在解离的最后5分钟内添加了0.5 ml的10 mg ml-1的DNase（Sigma-Aldrich）。然后，使用火力抛光的玻璃巴斯德移液管来进行机械解离，直到将组织均匀悬浮，而没有可见的切片/聚集体，然后通过40μM细胞过滤器（Thermo Fisher Scientific）过滤。通过在500g下离心5分钟收集细胞，并将所得的细胞沉淀恢复在新鲜的完整脑细胞培养基中（1×Brainphys培养基培养基（Stemcell Technologies），并补充了B27（Invitrogen），N2（Invitrogen）（Invitrogen）（Invitrogen）和Penicillin -Streteptlycin -Streteptlycin and -Streptheptycin抗生素抗生素（Inviterics），然后使用1 celliperics（Invitrogen）。蓝色的细胞浓度是使用稀释仪进行的。在组织培养孵化器中与半周半卷中等变化成熟。 　　使用Maestro Edge系统与轴支24孔细胞层MEA平板（每个井相位井相处是4×4 16通道电极相距350 µm）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIES）（VIS）（VIES）（VIES）（VIES）（VIES）（VIES）（VIES）（VIS），使用了轴24孔细胞层（轴）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（VIS）（V）生物系统）。简而言之，为了记录自发的神经元活性，使用了神经实时模块。通过应用自适应阈值交叉方法来定义神经元的发射事件/动作电位（此处称为尖峰），该方法将每个通道/电极的尖峰检测阈值设置为5 s.d。噪声级别79;超过此阈值的活动被计为尖峰。除非另有说明，否则所有分析仅考虑活跃的通道，该通道定义为每分钟显示≥5个尖峰的通道。通过使用带通滤波器（200-3,000 Hz）同时以1,000×的增益为1,000倍的增益和每通道12.5 kHz的采样频率，从而获得了原始数据文件。为了检测单电极爆发活动，使用了串联间隔阈值，将最小尖峰数定为5，并在100 ms处将最大尖峰间隔设置为最大尖峰间隔。通过神经度量工具（V.1.2.3; Axion Biosystems）分析了网络爆发活动（在多个MEA电极处同时突发）。为此，使用以下设置选择了自适应算法：尖峰的最小数量= 50，最小电极= 35％。如先前所述80,81,82,83，通过轴软件计算归一化跨率图（AUNCC）下的面积来计算网络同步的量化。AUNCC表示归一化到自相关的互相关相关的区域，其值较高，表明网络的同步性更大。有关其他神经数据分析，包括每个电极的平均发射速率 （每秒尖峰总数与记录的总持续时间（1,800 s））和加权平均点火速率（定义为相关井中的活性电极数量的尖峰速率），使用AXIS中的统计数据编译器在离线函数上处理了原始数据文件。在Microsoft Excel（Microsoft）和自定义的Python脚本中处理统计数据编译器输出文件，以组织和提取每个MEA板的每个孔的单个参数数据以及数据归一化。使用神经指标工具（Axion Biosystems）生成了说明尖峰直方图和网络爆发的栅格图。 　　在体外1（DIV1），DIV7和DIV15的天数中记录了在MEA板上生长的皮质培养物的自发神经元活性。在上述每个时间点上捕获了明亮场图像，以评估神经元细胞密度和电极覆盖率。原发性皮质神经元显示出从DIV7到DIV15的恒定成熟趋势，当神经元在DIV15处显示同步活性模式网络时，开始共培养实验。因此，在存在或不存在加巴喷丁的情况下，在添加神经胶质瘤细胞之前，在DIV15上记录了基线数据。对于胶质瘤细胞共培养，制备了原发性患者衍生的HFC和LFC的培养神经球的单细胞悬浮液，并将每5μl的活细胞浓度稀释至20,000个细胞的活细胞浓度。然后将5μL的液滴铺在MEA板中分化神经元的顶部。电镀后，将神经胶质瘤细胞粘附约1小时，然后将HFC培养物暴露于接下来的24-48 h，以在完全脑培养基中稀释的50 µM Gabapentin84,85的工作浓度，或作为对照的载有等效量的媒介物（无菌水）。每个条件都在两个井（实验重复）上运行。来自两个不同胚胎的神经元用作生物学三份（n = 2）。来自MEA记录的数据反映了来自活动电极的整个平均值，每个条件的井数由N值表示。 　　所有体内实验均根据UCSF机构动物护理和使用委员会（IACUC）批准的协议进行，并根据机构指南进行。在温度和湿度控制的外壳中，在无病原体条件下保持动物，并在12 h – 12 h的光周期循环下自由获取食物和水。对于脑肿瘤异种移植实验，IACUC并未对最大肿瘤体积设定限制，而是对发病率的迹象。这些限制在任何实验中均不超过，因为小鼠表现出神经系统发病的迹象或减少15％或更多的体重，将其安乐死。 　　对于所有异种移植研究，都使用了NSG小鼠（NOD-SCID-SCID-IL2R伽马链缺陷型，杰克逊实验室）。雄性和雌性小鼠平均使用。对于免疫电子显微镜实验，在异种移植手术之前，在无菌DMEM中在无菌DMEM中制备了来自HFC和LFC（n = 2的）培养的神经球（n = 2）的单细胞悬浮液。在产后第28-30天，用1-4％异氟烷麻醉小鼠（每位患者每条患者的生物学重复），并将其放入立体定位设备中。在无菌条件下通过中线切口暴露了颅骨。在2 µL无菌PBS中，大约50,000个细胞通过一个31号骨孔立体定位地植入海马的CA1区域，使用数字泵以0.4 µl min-1和31号汉密尔顿注射器的输注速率使用数字泵。使用的立体定向坐标如下：1.5 mm横向到中线，1.8毫米，前核中后1.8毫米，深到颅面深到颅面。输注完成后，允许注射器针保持在适当的位置2分钟，然后以0.875 mm min -1的速度手动撤回，以最大程度地减少注入的细胞悬浮液的回流。对于生存研究，使用的发病率标准是：减轻重量的初始体重15％，或者弯曲的姿势，嗜睡或持续的弯曲等临床体征。使用对数秩检验进行了Kaplan-Meier生存分析，以确定统计显着性。 　　通过盲目的研究者使用Zeiss LSM800扫描共聚焦显微镜和Zen 2011成像软件（Carl Zeiss）以10–20倍的放大倍率进行细胞定量。选择定量面积的1英寸6系列40μm冠状切片（彼此之间的240 µm）。对HFC和LFC异种移植物的脑切片进行免疫组织化学，以染色人类核抗原（HNA）阳性肿瘤细胞。如先前报道的86，使用盲级分析评估了肿瘤负担。对于每只小鼠，选择了最大肿瘤负担的截面，根据HNA阳性细胞的数量定义。从本节中，创建了截面中心的瓷砖×10图像（缝合在一起的3×3瓷砖），因此为每只小鼠生成一个表示最大肿瘤负担量的单个图像。接下来，比较了从每只鼠标产生的图像，并按顺序排名，从图像最少到最大的单元格数。随后，分别分配得分和范围为1到24，分别代表HNA阳性细胞数量最低和最高数量的图像。评分后，实验者没有盲目，每个图像的得分都分配给每个相应的HFC或LFC实验条件。使用两尾未配对的Mann-Whitney测试评估了HFC和LFC分数之间的统计差异。 　　Xenografting十二周后，通过Karnovsky的固定剂通过心心灌注对小鼠安乐死：2％戊二醛（EMS，16000）和4％PFA（EMS，15700）在0.1 M钠cacodylate中（EMS，12300），pH 7.4。在海马的CA1区域内的肿瘤质量中进行透射电子显微镜（TEM），以进行所有异种移植分析。然后将样品在1％四氧化os（EMS，19100）中固定在4°C下1小时，用超过滤水洗涤3次，然后在4°C下染色过夜。在4°C下，将样品在分级乙醇（50％，75％和95％）中脱水15分钟；然后将样品平衡至室温，并在100％乙醇中冲洗两次，然后用乙腈持续15分钟。将样品用嵌入-812树脂（EMS，14120）与乙腈混合2 h混合2 h，然后渗透2：1嵌入812：乙腈过夜。然后将样品放入嵌入-812中2小时，然后将其放入装满新鲜树脂的Taab胶囊中，然后将其放入65°C的烤箱中过夜。在Leica Ultracut S（Leica）上取出40 nm至60 nm之间的切片，并安装在100英寸NI网格（EMS FCF100-NI）上。对于免疫组织化学，用10％周期性酸进行微蚀刻，并在室温下在室温下用10％Metaperiodate用10％Metaperapoiodate洗脱osmium。将网格用水冲洗三遍，然后用0.5 m的甘氨酸淬灭，然后在室温下在阻断溶液（0.5％BSA，0.5％ovalbumin）中孵育20分钟。将初级山羊抗RFP（1：300，ABIN6254205）稀释在同一阻断溶液中，并在4°C下孵育过夜。第二天，将网格冲洗液在PBS中冲洗3次，并在辅助抗体（1:10 10 nm偶联的IgG，Ted Pella，15796）中孵育1小时，并在室温下用PBST冲洗，然后用水。对于每个染色组， 同时染色不包含任何表达RFP的细胞的样品以控制任何非特异性结合。在3.5％乙酸铀酰中的50％丙酮中，将网格染色为30 s，然后在0.2％的柠檬酸铅中染色90 s。使用JEOL JEM-1400 TEM在120 kV中成像样品，并使用Gatan Orius数码相机收集图像。 　　使用TEM成像如上图像上面成像，从异种移植的海马中的切片成像。在这里分析了4只小鼠跨4只小鼠的HFC异种移植的101个切片，并分析了104个跨3只小鼠的LFC异种移植。电子显微镜图像以×6,000的视野为15.75μm2拍摄。在明确鉴定具有15个或更多颗粒的免疫原颗粒标记的簇后，对神经胶质瘤细胞进行计数和分析。突触的总数，包括神经元到神经元和神经元到神经瘤突触（通过：（1）存在突触囊泡簇的存在；（2）（2）观察表观明显的突触裂；（3）（3）（3）鉴定了gliomoma细胞中明显的突触后密度）。 　　根据制造商的说明，使用EDU分析试剂盒（Invitrogen）进行EDU-CORPORATION分析。将患者来源的HFC/LFC神经胶质瘤细胞在每24孔板的10,000个细胞中以10,000个细胞的含量 - 涂在聚赖氨酸和层粘连蛋白涂层的盖玻片上。播种24小时后，将胚胎小鼠海马神经元添加到胶质瘤细胞中，每孔40,000个细胞中的神经胶质瘤 - 神经元共培养组。72小时后，将单独的神经胶质瘤细胞或在与神经元共培养中用20μMEDU处理在37°C下过夜。随后，将细胞用4％PFA固定，并使用Click-It EDU套件协议染色。然后，通过使用共聚焦显微镜以×10放大倍率来量化Edu标记的细胞/DAPI标记细胞的分数来确定增殖指数。 　　根据制造商的方案，使用Cultrex 3D球体入侵测定试剂盒（Trevigen）进行入侵测定法评估了神经胶质瘤细胞侵袭。简而言之，将3,000个细胞重悬于50μl的球体形成矩阵溶液中（在培养基中制备），在圆底96孔板中。在添加入侵矩阵之前，允许球体形成72小时，并以×10放大倍率拍摄图像（0 h）。在冰上工作，然后将50μl的浸润矩阵添加到每个孔中，并在37°C下孵育板。凝胶形成1小时后，对于胶质瘤细胞+小鼠条件培养基（MCM）组，将100μl小鼠海马神经元上清液添加到井中，以评估神经元分泌因子对胶质瘤细胞侵入性的影响。在37°C下孵育24小时后，在显微镜下观察到入侵，并以×10拍摄图像。使用ImageJ分析了在0小时（染色前）和24小时测得的微管长度以及每个球体的面积，并使用ImageJ分析了差异来计算细胞浸润的总面积。 　　如先前所述进行SEM进行，以更详细地研究肿瘤微管。将原发性患者衍生的HFC/LFC神经胶质瘤细胞播种在每孔10,000个24孔板上的10,000个细胞的聚赖氨酸和层粘连蛋白涂层的盖玻片上。对于神经胶质瘤细胞+MCM和胶质瘤细胞+MCM+GBP组，在不存在或存在Gabapentin（32 µM）的情况下，将小鼠神经元的条件培养基添加到孔中，以评估神经元分泌因子和Gabapentin调节对肿瘤微管形成的胶质瘤细胞的作用。培养后1周后，用4％PFA在4°C固定样品，用PBS和超过滤水洗涤。串行逐步乙醇脱水后，使用导电胶带（12毫米OD Pelco Tabs，TED Pella）将盖玻片安装在SEM存根（TED PELLA）上。然后将样品用2 nm层的金钯溅射。在场发射SEM（Sigma 500； Carl Zeiss显微镜）下观察到肿瘤微管，并以1.0 kV的加速电压记录显微照片。 　　将原发性患者衍生的HFC胶质瘤细胞以每个孔的2×105细胞密度在6孔板中播种。隔夜孵育后，用齿病毒颗粒感染了细胞，表达靶向THBS1的shRNA或根据制造商的方案，使用多紧透转染试剂的方案表达绿色荧光蛋白（GFP），然后使用多紧纤维转染试剂，然后将其保存在37°C的5％CO2孵育器中24小时。24小时后，将培养基替换为新鲜的完整培养基。转导GFP表达后72小时检查细胞，以评估转导的效率。针对THBS1（5'-Agacatcttccaagcatataa-3'）和对照扰流的shRNA（5'-CCTAAGGTTAAGTCGCCCTCG-3'）的慢病毒shRNA构建体的设计和构建。 　　使用Live/Dead活性/细胞毒性试剂盒（Molecular Probes）评估了原代患者衍生的HFC细胞的细胞活力（分子探针）。转导后一周，将表达争夺shRNA或靶向thbs1的hfc细胞以每24孔板的10,000个细胞为10,000个细胞，将靶向THBS1的靶向Thbs1播种。培养1周后，将DPB中的500μl细胞染色溶液在DPB中的最终浓度为2 µM钙铁棒钙钙蛋白酶AM和4 µM溴化乙锭（ETHD-1），并将板在黑暗中在室温下在室温下孵育45分钟。作为细胞生存力的指标，钙软蛋白AM通过细胞内酯酶活性代谢转换，从而导致绿色的荧光产物钙调蛋白。ETHD-1被排除在活细胞之外，但很容易被死细胞吸收，并染色DNA发出的红色荧光。活细胞和死细胞是从每个孔的四个随机场成像，并在荧光显微镜下可视化。活细胞的百分比被计算为活细胞的数量（绿色）除以每个图像场的总细胞数（绿色+红色）。 　　转导1周后，将表达争吵的SHRNA或SHRNA针对THBS1的原发性衍生的高连通性神经胶质瘤细胞在每24孔板的每孔10,000个细胞上播种在聚赖氨酸和粘胺涂层的盖玻片（Neuvitro）上。大约24小时后，将40,000个胚胎小鼠海马神经元（Gibco）播种在神经胶质瘤细胞的顶部，并用补充B27，庆大霉素和谷氨酸（Gibco）的无血清神经质培养基维持。共培养1周后，在4°C下用4％PFA固定30分钟的细胞，并在阻塞溶液中孵育（5％正常驴和山羊血清，PBS中的0.25％Triton X-100）在室温下孵育1小时。接下来，用在4°C的封闭溶液中稀释的原代抗体处理细胞。使用了以下抗体：兔抗KI-67（1：500，ABCAM）和人核抗原（HNA；小鼠抗人类核，235-1; 1：100，Millipore）。然后将盖玻片在PBS中冲洗3次，并在辅助抗体溶液（Alexa 488山羊抗兔IgG和Alexa 647山羊抗小鼠IgG）中孵育，均在1：500（Invitrogen）在抗体稀释溶液中使用。将盖玻片在PBS中冲洗了三次，然后用DAPI（矢量实验室）的Vecta防利率安装培养基安装。为了计算增殖指数，将与KI-67共同标记的HNA阳性细胞的总数除以使用共聚焦显微镜以×40放大倍率可视化的人核标记细胞的总数。 　　如上所述，通过KI-67免疫荧光染色评估了加巴喷丁抑制TSP-1对神经胶质瘤细胞增殖的影响。简而言之，将原发性患者衍生的高连通性神经胶质瘤细胞播种在每孔24孔板的10,000个细胞的聚赖氨酸和层粘连蛋白涂层的盖玻片（Neuvitro）上。大约24小时后，将40,000个胚胎小鼠海马神经元（Gibco）播种在神经胶质瘤细胞的顶部，并用补充B27，庆大霉素和谷氨酸（Gibco）的无血清神经质培养基维持。第二天，用媒介物（无菌水）或32 µm加巴喷丁处理培养物，然后每天每天半半个新鲜的32 µm加巴喷丁开关，直到共培养1周。随后，如上所述，将细胞固定并进行KI-67和HNA标记进行免疫染色，以进行增殖评估。 　　通过与Tryple（Thero Fisher Scientific）解离，在异种移植手术前，在无菌HBSS中制备了原代患者衍生的HFC和LFC培养的神经球的单细胞悬浮液。在产后第28-30天，用1-4％的异氟烷​​麻醉小鼠（每位患者每条患者的生物学重复4-6个生物学重复），并将其放入立体定位仪中。在无菌条件下通过中线切口暴露了颅骨。在3 µL无菌HBS中，大约150,000个细胞通过26号burr孔立体定位地植入前皮层（M2），使用数字泵以1.0μlmin-1的输注速率使用数字泵。使用的立体定向坐标如下：1.0 mm横向到中线，前核前1.0毫米，深到皮质表面的深度为-1.0 mm。异种移植后四周，通过腹膜内注射连续28天，用Gabapentin（200 mg kg-1; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; Sigma-Aldrich; sigma-Aldrich; sigma-Aldrich;用相同体积的相关车辆处理对照。异种移植后8周，将小鼠安乐死，并在40 µM处获得冠状脑切片以进行免疫组织化学。 　　通过盲目的研究者以×10–20的放大倍率使用Zeiss LSM800扫描共聚焦显微镜和ZEN成像软件（Carl Zeiss）进行细胞定量。选择定量面积的1英寸6系列40μm冠状切片（彼此之间的240 µm）。在最大肿瘤负担的3个部分中，对所有HNA阳性（小鼠抗人核，235-1; 1：100，Millipore）细胞进行了量化，以确定定量区域内的肿瘤负担。然后，用KI-67评估HNA阳性肿瘤细胞的双标签。为了计算增殖指数（每只动物的增殖肿瘤细胞的增殖百分比），在所有量化的区域中与KI-67共同标记的HNA阳性细胞的总数除以所有量化区域的人类核阳性细胞的总数。使用未配对的单尾学生的t检验计算增殖指数的差异。 　　收集来自新诊断的胶质母细胞瘤患者（n = 56）的外周血样本，并在室温下凝结30分钟，然后以1,000克离心15分钟。血清储存在-80°C直至分析。根据制造商的说明（R＆D Systems），使用Quantikine Immunosorbent测定套件确定TSP-1水平。为了确认THBS1的功能性蛋白质水平敲低，还在使用picokine Elisa kit（Boster生物学技术）的（感染后一周收集了一周后收集的HFC细胞）（感染后的一周收集了一周后收集的HFC细胞），还测量了TSP-1水平的TSP-1水平。 　　除非另有说明，否则使用Prism（GraphPad）软件进行统计测试。使用学生的t检验和单向方差分析确定了不同组的显着性测试，并通过Tukey的事后测试确定。P≤0.05被认为具有统计学意义。使用与上述相同的显着性表示，使用两尾未配对的Mann-Whitney检验来分析HFC和LFC海马异种移植物的肿瘤细胞负担。使用两尾对数秩分析来分析人类患者的Kaplan-卑鄙生存曲线的统计意义。所显示的所有显微照片代表了三个独立进行的实验，并获得了相似的结果。RNA-seq数据的统计分析在各个部分中描述了。通过手动分割，根据Flair序列，通过手动分割进行手动分割，通过手动分割来计算胶质母细胞瘤患者的肿瘤量。使体积测量对患者的临床结果视而不见。手动分割由合着者A.E.，A.A。进行。和A.L.具有肿瘤体积的验证，在初始训练期后的精度已验证。学生的t检验和χ2检验分别用于比较患者队列之间的连续变量和分类变量。患者的总生存期（OS）定义为从第一次手术或原始活检（如果发生在手术之前）到死亡或最后一次接触日期的时间。活着的患者在失去随访或最后一次随访日期进行审查。使用反向Kaplan – Meier方法估算中位随访。 　　为了在多元环境中确定临床相关的生存风险组，我们使用PARTDSA算法（V.0.9.14）42,43,44使用了递归分区分析生存数据的分析。生存树利用递归分区将患者分为不同的风险群体。布里尔分数是分裂和修剪的选择损失功能。这种方法是非参数的，因此不需要比例危害假设。树木中包括所有已知的预后变量，包括诊断，性别，MGMT启动子甲基化状态，肿瘤位置，化学疗法，放疗，肿瘤内的功能连通性，术前和术后肿瘤体积和切除程度。MGMT甲基化状态除了多变量递归分区的生存分析外，两种比例危害建模都包括在两个单变量COX中。选择了五倍的交叉验证误差以及在一个总体最小误差的一个标准误差中最小化最小化的树的树。所得树木的叶子定义了最终风险群，从中产生了相应的kaplan – Meier曲线。使用Kaplan -Meier方法和COX比例危害模型产生了中位OS时间和危害比率，并在风险组之间进行了比较。验证了比例危害假设。 　　有关研究设计的更多信息可在与本文有关的自然投资组合报告摘要中获得。