AbstractDissecting human neurobiology at high resolution and with mechanistic precision requires a major leap in scalability, given the need for experimental designs that include multiple individuals and, prospectively, population cohorts. To lay the foundation for this, we have developed and benchmarked complementary strategies to multiplex brain organoids by pooling cells from different pluripotent stem cell (PSC) lines either during organoid generation (mosaic models) or before single-cell RNA sequencing (scRNA-seq) library preparation (downstream multiplexing).

摘要鉴于需要包括多个个体和前瞻性人群的实验设计，以高分辨率和机械精度解剖人类神经生物学需要在可扩展性方面取得重大飞跃。为了奠定基础，我们通过在类器官生成（镶嵌模型）期间或在单细胞RNA测序（scRNA-seq）之前汇集来自不同多能干细胞（PSC）系的细胞，开发并基准化了多重脑类器官的补充策略。文库制备（下游多路复用）。

We have also developed a new computational method, SCanSNP, and a consensus call to deconvolve cell identities, overcoming current criticalities in doublets and low-quality cell identification. We validated both multiplexing methods for charting neurodevelopmental trajectories at high resolution, thus linking specific individuals’ trajectories to genetic variation.

我们还开发了一种新的计算方法SCanSNP，并一致呼吁对细胞身份进行去卷积，克服了目前双峰和低质量细胞识别的关键性。我们验证了两种多路复用方法，以高分辨率绘制神经发育轨迹，从而将特定个体的轨迹与遗传变异联系起来。

Finally, we modeled their scalability across different multiplexing combinations and showed that mosaic organoids represent an enabling method for high-throughput settings. Together, this multiplexing suite of experimental and computational methods provides a highly scalable resource for brain disease and neurodiversity modeling..

最后，我们模拟了它们在不同多路复用组合中的可扩展性，并表明马赛克类器官代表了高通量设置的一种可行方法。总之，这种多路复用的实验和计算方法套件为大脑疾病和神经多样性建模提供了高度可扩展的资源。。

MainThe polygenic underpinnings of human neurodiversity, in its physiological and pathological unfolding alike, have been eloquently referred to as terra incognita, calling for new maps to trace that unfolding in the authenticity of human genetic backgrounds and thereby render it mechanistically actionable.

人类神经多样性的多基因基础，在生理和病理学上都被雄辩地称为隐姓埋名地，呼吁绘制新的图谱来追踪人类遗传背景的真实性，从而使其在机制上可行。

Developmental stochasticity and environmental triggers add to such complexity, and the increasingly broader range of exposome that is becoming measurable promises to make gene–environment interactions finally tractable at meaningful scales1,2,3,4.Toward these overarching goals, brain organoid and single-cell multiomic technologies have afforded major strides in the mechanistic dissection of human neurodevelopment, enabling transformative insights from the study of genetic and environmental causes of neuropsychiatric disorders, a community-wide effort to which we and several others have been contributing2,5,6,7,8,9,10,11,12,13,14.

发展的随机性和环境触发因素增加了这种复杂性，并且越来越广泛的暴露体正在成为可测量的，有望使基因与环境的相互作用最终在有意义的范围内易于处理1,2,3,4。为了实现这些总体目标，脑类器官和单细胞多组学技术在人类神经发育的机制解剖方面取得了重大进展，从而能够从神经精神疾病的遗传和环境原因的研究中获得变革性的见解，这是我们和其他一些人在社区范围内做出的努力2,5,6,7,8,9,10,11,12,13,14。

Importantly, our recent benchmark of cortical brain organoids (CBOs) compared to the human fetal cortex confirmed the preservation in CBOs of transcriptional programs pinpointed as relevant for disease modeling15.Despite these advances, the characterization of brain organoids at single-cell resolution from entire cohorts and, in perspective, at population scale remains however an unmet challenge, although an obviously required one if we are to capture how individual genomes and developmental trajectories shape variability in vulnerability and resilience across the spectrum of neurodiversity16,17,18.

重要的是，与人类胎儿皮层相比，我们最近的皮质脑类器官（CBOs）基准证实了在CBOs中保留了与疾病建模相关的转录程序15。尽管取得了这些进展，但在整个队列中单细胞分辨率下的脑类器官的表征，以及从长远来看，在人口规模上仍然是一个未得到满足的挑战，尽管如果我们要捕捉个体基因组和发育轨迹如何影响整个神经多样性范围内脆弱性和恢复力的变异性，显然需要一个挑战16,17,18。

Scaling up human brain organoid modeling and molecular profiling by single-cell omics would allow us to understand how the molecular causes of neurodevelopmental disorders trigger deviations from physiological trajectories19, in line with the exp.

通过单细胞组学扩大人脑类器官建模和分子谱分析将使我们能够了解神经发育障碍的分子原因如何触发与生理轨迹的偏差19，这与实验一致。

1.

1.

As expected from recent studies51,52, the line balance in mCBOs is altered already at early stages of differentiation. This result, coupled with the lack of correlation with PSC and CBO growth rates, suggests that interindividual differences across PSC lines in response to the patterning factors used in CBO differentiation protocols (as also shown in ref.

正如最近的研究51,52所预期的那样，MCBO中的线平衡在分化的早期阶段已经发生了改变。这个结果，再加上与PSC和CBO生长率缺乏相关性，表明PSC系之间的个体差异响应于CBO分化方案中使用的模式因子（也如参考文献所示）。

68) may be a key determinant for the clonal asymmetry. However, with our data, we cannot exclude that the imbalance already originates during the first 2 d of the protocol, during which embryoid bodies are generated, a phenomenon that was also shown to be relevant in ref. 51 where the authors showed that clones were lost during the formation of embryoid bodies using a cerebral organoid protocol..

68）可能是克隆不对称的关键决定因素。然而，根据我们的数据，我们不能排除这种不平衡已经起源于方案的前2天，在此期间产生了胚状体，这一现象在参考文献51中也被证明是相关的，作者表明克隆在使用大脑类器官方案形成胚状体的过程中丢失了。。

2.

2.

Mosaic models showed reproducibility in the relative abundance among different individual lines across replicates of the same multiplexing combinations and across different combinations. This points to the genetic background as a key determinant of clonal dynamics. Naturally, this does not per se rule out that also the epigenetic state of each line at the time of mCBO generation may exert a substantial impact, in line with results from a large collection of PSC lines69.

马赛克模型显示，在相同多路复用组合的重复和不同组合的重复中，不同个体品系之间的相对丰度具有可重复性。这表明遗传背景是克隆动力学的关键决定因素。自然，这本身并不排除mCBO产生时每条线的表观遗传状态也可能产生重大影响，这与大量PSC线的结果一致69。

Indeed, the lines that were overrepresented in the experiments in Fig. 6 were not overrepresented in previous single-cell transcriptomic experiments over different years and thus from different passages of the same iPSC lines..

事实上，在图6的实验中被过度代表的品系在不同年份的先前单细胞转录组学实验中并没有被过度代表，因此来自同一iPSC品系的不同传代。。

3.

3.

The comparison between mosaic and downstream multiplexing modalities showed that non-cell-autonomous effects are not evident in the mosaic model, as expected, considering that we only used wild-type PSC lines to generate the single-cell dataset and confirming independent works that found the same result in two-dimensional38 and three-dimensional52 cultures.

马赛克和下游多路复用模式之间的比较表明，正如预期的那样，马赛克模型中的非细胞自主效应并不明显，因为我们只使用野生型PSC系来生成单细胞数据集，并确认了在二维38和三维52培养物中发现相同结果的独立作品。

However, considering the few genes that emerged from the differential expression analysis between genotypes in the two multiplexing modalities, it is possible that, by increasing the number of cells and replicates and profiling mosaic models through spatial omics, the molecular impact of cell-to-cell interactions between different lines could come into relief even within the range of genetically normotypical lines.

然而，考虑到两种多路复用模式中基因型之间差异表达分析中出现的少数基因，通过增加细胞数量和复制以及通过空间组学分析镶嵌模型，细胞间相互作用的分子影响是可能的。不同品系之间的相互作用甚至可以在遗传正常品系的范围内缓解。

Also, it will be relevant to investigate deeper non-cell-autonomous effects using PSC lines carrying disease-relevant genetic mutations, such as the ones that already showed evidence that non-cell-autonomous mechanisms are relevant for the pathogenesis70,71..

此外，使用携带疾病相关基因突变的PSC系研究更深层次的非细胞自主效应将是相关的，例如那些已经证明非细胞自主机制与发病机制相关的证据70,71。。

4.

4.

The variance in mCBO clonal dynamics across PSC lines was not dependent on the specific mosaic combinations (where different lines were used) nor on the number of lines at generation, indicating high scalability of the system. Additional experiments will clarify the impact of increasing to hundreds of lines in single mCBOs on these clonal dynamics.

跨越PSC品系的mCBO克隆动态的差异不依赖于特定的镶嵌组合（使用不同的品系），也不依赖于产生时的品系数量，表明系统具有很高的可扩展性。额外的实验将阐明在单个MCBO中增加数百个品系对这些克隆动力学的影响。

However, considering the current limitations in laboratory settings for accurate cell counting, which is needed to generate balanced mCBOs (excluding flow cytometry setups for the logistics of generating mosaic models with many lines), we do not suggest going lower than about 1,000 cells per line during mCBO generation.

然而，考虑到目前实验室设置中精确细胞计数的局限性，这是生成平衡的mCBO所必需的（不包括用于生成具有多条线的马赛克模型的物流的流式细胞术设置），我们不建议在mCBO生成期间每行低于约1000个细胞。

Thus, our guidelines suggest using a maximum of about 20 lines in mosaic models (because 20,000 cells are needed to generate a mCBO with our current protocol)..

因此，我们的指南建议在镶嵌模型中最多使用约20条线（因为按照我们目前的协议需要20000个细胞才能生成mCBO）。。

5.

5.

Monte Carlo simulations incorporating the empirical growth rates from our experiments allowed us to compute the probability of recovering, from each PSC line, a sufficient number of cells for single-cell omics analysis of neurodevelopmental cell types, given the number of lines used at the generation.

结合我们实验的经验增长率的蒙特卡罗模拟使我们能够计算从每个PSC系中恢复足够数量的细胞的概率，用于神经发育细胞类型的单细胞组学分析，给定该代使用的系数。

This in turn enables a precise design of disease-modeling experiments with mosaic models. With our protocol, the number of PSC lines that can be properly analyzed in terms of single-cell transcriptomic characterization of all major neurodevelopmental cell types (considering proliferating progenitors, radial glial progenitors, neurons, migrating neurons, excitatory neurons) from a mosaic experiment is ~12 lines (empirical mean ≈ 12.89; 95% confidence interval, 12.85 to 12.93) if starting from 20 lines and sequencing ~100,000 cells.

这反过来又可以使用镶嵌模型精确设计疾病建模实验。根据我们的方案，可以根据马赛克实验中所有主要神经发育细胞类型（考虑增殖祖细胞，放射状神经胶质祖细胞，神经元，迁移神经元，兴奋性神经元）的单细胞转录组学特征正确分析的PSC系数约为12行（经验平均值≈12.89；如果从20个品系开始并测序约100000个细胞，则95%置信区间为12.85至12.93）。

This means that the ideal settings to apply mCBO designs as a transformative tool are cohort-level screenings for trait-relevant in vitro endophenotypes, drug screening and gene–environment interaction studies72. Indeed, if it is not crucial to recover all starting PSC lines, mCBOs allow studying the impact of genetic makeup, environmental chemicals11 or drugs on the gene expression of specific neurodevelopmental cell types for very large cohorts of PSC lines, even without automated setups, as the only bottleneck is the maintenance of the lines and the generation of the organoids, thereby massively increasing the feasibility of large-scale experiments.

这意味着将mCBO设计作为转化工具的理想设置是对性状相关的体外内表型，药物筛选和基因-环境相互作用研究的队列水平筛选72。事实上，如果恢复所有起始的PSC系不是至关重要的，那么MCBO可以研究遗传组成，环境化学物质11或药物对非常大的PSC系群的特定神经发育细胞类型的基因表达的影响，即使没有自动设置，因为唯一的瓶颈是维持系和类器官的产生，从而大大增加了大规模实验的可行性。

For example, by leveraging large cohorts of banked PSC lines (with already thousands of lines available in standardized repositories (Table 2 from ref. 73)) and generating batches of mCBOs with 20 lines, the experimental timeline for profiling at day 50 would be, respectively, halved for 100 lines .

例如，通过利用大量库存PSC线（标准化存储库中已有数千条线可用（参考文献73中的表2））并生成具有20条线的MCBO批次，在第50天进行分析的实验时间表将分别为100条线减半。

To conclude, our findings indicate to opt for the downstream multiplexing strategy when the biological question at hand requires strict balance among individual lines, with the mosaic model ideally suited instead, and in fact transformative, for unbiased large-scale studies, in the same way as developmental neuroscientists can leverage pooled CRISPR perturbation strategies for screening the impact of genetic variants as a complementary approach to the validation of single mutations74,75,76,77.MethodsCulture of pluripotent stem cellsPSC lines were cultured under feeder-free conditions on Matrigel-coated plates at 37 °C with 5% CO2 and 3% O2.

总之，我们的研究结果表明，当生物学问题需要在各个品系之间保持严格平衡时，选择下游多路复用策略，而镶嵌模型非常适合，事实上是变革性的，用于无偏见的大规模研究，就像发育神经科学家可以利用汇集的CRISPR扰动策略来筛选遗传变异的影响，作为验证单个突变的补充方法74,75,76,77。方法多能干细胞的培养将多能干细胞系在无饲养层条件下培养在基质胶包被的平板上，温度为37℃，CO2浓度为5%，O2浓度为3%。

To coat culture dishes, Matrigel solution was prepared by diluting Matrigel (Corning, 354277) 1:40 in ice-cold DMEM/F12 medium (Gibco, 11330057) and stored at 4 °C until use. Before plating cells, 6-cm dishes were coated with 1 ml Matrigel solution and incubated for 30 min at 37 °C. PSCs were maintained in TeSR/E8 medium (Stemcell Technologies, 05990) supplemented with 100 U ml−1 penicillin and 100 µg ml−1 streptomycin (Thermo Fisher, 15140122) with daily medium changes and passaged 1:8 to 1:10 when confluency reached around 70%.

为了涂覆培养皿，通过在冰冷的DMEM/F12培养基（Gibco，11330057）中以1:40稀释Matrigel（Corning，354277）制备Matrigel溶液，并在4℃下储存直至使用。在铺板细胞之前，将6cm培养皿用1mL Matrigel溶液包被，并在37℃下孵育30分钟。将PSC维持在补充有100μU-ml-1青霉素和100μg-ml-1链霉素（Thermo Fisher，15140122）的TeSR/E8培养基（Stemcell Technologies，05990）中，每天更换培养基，当融合率达到约70%时传代1:8至1:10。

To detach cells, plates were rinsed with 2–3 ml PBS (Gibco, 10010023) and treated with 0.5 ml ReLeSR reagent (Stemcell Technologies, 05872) for 5 min at 37 °C. When single-cell dissociation was needed, Accutase (Sigma-Aldrich, A6964) was used instead of ReLeSR, and 5 µM ROCK inhibitor Y-27632 (Tocris, 1254) was added to the medium to enhance cell survival in the first 24 h.

为了分离细胞，将板用2-3ml PBS（Gibco，10010023）冲洗，并在37℃下用0.5ml ReLeSR试剂（Stemcell Technologies，05872）处理5分钟。当需要单细胞解离时，使用Accutase（Sigma-Aldrich，A6964）代替ReLeSR，并向培养基中加入5µM ROCK抑制剂Y-27632（Tocris，1254）以增强前24小时的细胞存活率。

All participants signed an informed consent form, and the use of PSCs was approved by the ethical committee of the University of Milan. All iPSC lines were reprogrammed by at least 15 passages. All PSCs have been routinely verified to be .

所有参与者都签署了知情同意书，PSC的使用得到了米兰大学伦理委员会的批准。所有iPSC系均被重新编程至少15代。所有PSC均已常规验证。

1.

1.

Best ID detection per droplet: here, as for other approaches, we leveraged the accessibility of bulk RNA-seq data to generate a function that maximizes the score difference of each ID to the sequenced droplets:$${S}_{g}=\mathop{\sum }\limits_{i=1}^{n}\left(\left(\frac{{A}_{i}\times {a}_{ig}}{{t}_{i}}+\left(\frac{{R}_{i}\times {r}_{ig}}{{T}_{i}}\right)\right)\right),$$where Sg is the score for ID g in each droplet, i are the loci for which allelic information is accessible from bulk RNA-seq, A and R are, respectively, the number of reads supporting alternative and reference alleles, a and r are the number of alternative and reference alleles in g and t and T are total alternative and reference alleles in the cohort at locus i..

每个液滴的最佳ID检测：在这里，与其他方法一样，我们利用大量RNA-seq数据的可访问性来生成一个函数，该函数可以最大化每个ID与测序液滴的得分差异：$${S}_{g} =\mathop{\sum}\limits\ui=1}^{n}\left（\left（\frac{{A}_{i} \次{a}_{ig}}{{t}_{i} }+\左（\frac{{R}_{i} \次{r}_{ig}}{{T}_{i} }\ right）\ right）\ right），$$其中Sg是每个液滴中ID g的得分，i是可从大量RNA-seq访问等位基因信息的基因座，A和R分别是支持替代和参考等位基因的读数，A和R是g和t中替代和参考等位基因的数量，t是基因座i队列中的总替代和参考等位基因。。

2.

2.

Second-best ID determination: given an m-by-g contribution matrix, where m are the droplets and g are the multiplexed IDs containing the number of reads supporting genotype-specific alleles. We used this matrix to iteratively train a multinomial logistic regression model to predict which is the most likely ID after the first one, assuming ambient contamination consistent across droplets.

第二好的ID确定：给定m×g贡献矩阵，其中m是液滴，g是包含支持基因型特异性等位基因的读数的多重ID。我们使用该矩阵迭代训练多项式逻辑回归模型，以预测第一个ID之后最可能的ID，假设环境污染在液滴之间一致。

We split the contribution matrix into groups of droplets sharing the best ID according to step 1; for each group, we trained the model on counts and labels from other groups to predict the second-best ID of barcodes in the current group..

我们根据步骤1将贡献矩阵分成共享最佳ID的液滴组；对于每个组，我们根据其他组的计数和标签对模型进行训练，以预测当前组中第二个最佳条形码ID。。

3.

3.

Doublet detection: to allow doublet detection to be specific and flexible while accommodating genetic contributions ranging from balanced doublets to the presence of a cell and debris in the same drop, we implemented a method similar to the one adopted in ref. 22. Starting from the previous m-by-g contribution matrix, for every genotype g, we define as negative droplets the ones that do not contain that genotype as the best ID according to the first step and fit a negative binomial distribution via the fitdistrplus91 R function on counts supporting private g alleles.

双峰检测：为了使双峰检测具有特异性和灵活性，同时适应从平衡双峰到同一液滴中存在细胞和碎片的遗传贡献，我们实施了一种类似于参考文献22中采用的方法。从之前的m-by-g贡献矩阵开始，对于每个基因型g，我们根据第一步将不包含该基因型的那些定义为负液滴，作为最佳ID，并通过FitDistplus91 R函数拟合负二项分布。支持私人g等位基因的计数。

We therefore used the 99% quantile of the fitted distribution as the positivity threshold. Droplets positive for more than one ID are considered multiplets..

因此，我们使用拟合分布的99%分位数作为阳性阈值。多个ID为正的液滴被认为是多重的。。

4.

4.

We finally took advantage of the mixed-genotype design to structure an added layer of a low-quality droplet detection to be used during consensus call aggregation.

我们最终利用混合基因型设计来构建低质量液滴检测的附加层，以在共识呼叫聚合期间使用。

We applied a Gaussian mixture model expectation-maximization algorithm (implemented through the R mixtools92 package) to separate droplets with ‘low’ and ‘high’ signal-to-noise ratios by computing log (FC) between the first- and second-best predicted IDs. We started by preparing a new contribution matrix similar to the one in passage 2 but considering only non-ambiguous loci between each possible pair of best and second-best IDs in the dataset.

我们应用高斯混合模型期望最大化算法（通过R mixtools92软件包实现）通过计算log来分离具有“低”和“高”信噪比的液滴 (FC) between the first- and second-best predicted IDs. 我们首先准备了一个新的贡献矩阵，类似于第2段中的贡献矩阵，但只考虑了数据集中每个可能的最佳和次优ID对之间的非模糊位点。

Additionally, before log (FC) calculation, we add pseudocounts, which mimics average ambient RNA contamination coming from each ID, calculated as the average rate of reads deriving from the other genotype’s unambiguous reads when they are not labeled as first ID or second ID across all droplets (according to the contribution matrix); similar to the approach proposed in the hashedDrops function from the package MarioniLab/DropletUtils93,94, this step ensures that log (FC) is always defined for all droplets.

Additionally, before log （FC）计算，我们添加了伪计数，它模拟了来自每个ID的平均环境RNA污染，当它们在所有液滴中未标记为第一ID或第二ID时，计算为从其他基因型的明确读数得出的平均读数率（根据贡献矩阵）；类似于程序包MarioniLab/DropletUtils93,94中的hashedDrops函数中提出的方法，此步骤可确保日志 (FC) is always defined for all droplets.

Given the nature of the model, the resulting classification assumes the presence of two distinct populations that can be separated based on the proportion of the two IDs, and, given that it is computed after doublet detection, it will likely detect those droplets that embed enough ambient RNA to pass the Cell Ranger emptyDrops filter, while it should not be used if any sort of prior filtering of low-quality droplets has already been done.

鉴于模型的性质，由此产生的分类假设存在两个不同的群体，可以根据两个ID的比例进行分离，并且，鉴于它是在双重检测后计算的，它可能会检测到那些嵌入足够环境RNA以通过Cell Ranger Empthydrops过滤器的液滴，而如果已经对低质量液滴进行了任何形式的事先过滤，则不应使用它。

Benchmarking of SCanSNP and genetic demultiplexing in barcode-tagged samples are described in the Supplementary Methods.Power estimation for single-cell eQTLsWe estimated the eQTL power using our R package scPower (version 1.0.2)50 for sample sizes between 25 and 200 and for number of cells per sample between 250 and 1,500, keeping the read depth as in our experiment.

补充方法中描述了条形码标记样品中SCanSNP的基准测试和遗传解复用。单细胞eQTL的功率估计我们使用我们的R包scPower（版本1.0.2）50估计eQTL功率，样本量在25到200之间，每个样本的细胞数在250到1500之间，保持读取深度与我们的实验相同。

We fitted the required ex.

我们安装了所需的ex。

Data availability

数据可用性

The scRNA-seq data generated in this study are accessible via ArrayExpress (accession E-MTAB-14574). WGS and low-pass WGS sequencing data have been deposited at the European Genome–Phenome Archive with the study identifier EGAD50000000978. Additional resources include the reference genome Ensembl GRCh38 version 93, dbSNP version b151 GRCh38p7 (00-All.vcf) and single-cell eQTLs from Jerber et al.35 (Table 7).

本研究中产生的scRNA-seq数据可通过ArrayExpress（登录号E-MTAB-14574）访问。WGS和低通WGS测序数据已保存在欧洲基因组-表型库中，研究标识符为EGAD50000000978。其他资源包括参考基因组Ensembl GRCh38版本93，dbSNP版本b151 GRCh38p7（00 All.vcf）和Jerber等人的单细胞eQTL（表7）。

Source data are provided with this paper..

本文提供了源数据。。

Code availability

代码可用性

Full code used for the analyses can be retrieved at https://github.com/GiuseppeTestaLab/organoidMultiplexing_release. The latest release of SCanSNP and the docker image link are available at https://github.com/GiuseppeTestaLab/SCanSNP.

用于分析的完整代码可以在https://github.com/GiuseppeTestaLab/organoidMultiplexing_release.SCanSNP的最新版本和docker图像链接可在https://github.com/GiuseppeTestaLab/SCanSNP.

ReferencesSilbereis, J. C., Pochareddy, S., Zhu, Y., Li, M. & Sestan, N. The cellular and molecular landscapes of the developing human central nervous system. Neuron 89, 248–268 (2016).Article

参考文献Silbereis，J.C.，Pochareddy，S.，Zhu，Y.，Li，M。＆Sestan，N。发育中的人类中枢神经系统的细胞和分子景观。神经元89248-268（2016）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Cheroni, C., Caporale, N. & Testa, G. Autism spectrum disorder at the crossroad between genes and environment: contributions, convergences, and interactions in ASD developmental pathophysiology. Mol. Autism 11, 69 (2020).Article

Cheroni，C.，Caporale，N。＆Testa，G。处于基因和环境之间十字路口的自闭症谱系障碍：ASD发育病理生理学中的贡献，融合和相互作用。摩尔自闭症11,69（2020）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Tărlungeanu, D. C. & Novarino, G. Genomics in neurodevelopmental disorders: an avenue to personalized medicine. Exp. Mol. Med. 50, 1–7 (2018).Article

Tărlungeanu，D.C。和Novarino，G。神经发育障碍中的基因组学：个性化医学的途径。实验分子医学50,1-7（2018）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Hyman, S. E. The daunting polygenicity of mental illness: making a new map. Philos. Trans. R. Soc. Lond. B Biol. Sci. 373, 20170031 (2018).López-Tobón, A. et al. Human cortical organoids expose a differential function of GSK3 on cortical neurogenesis. Stem Cell Reports 13, 847–861 (2019).Article .

Hyman，S.E.《精神疾病令人畏惧的多基因性：绘制新地图》。菲洛斯。事务处理。R、 社会责任。生物科学学士。37320170031（2018）。López-Tobón，A。等人。人类皮质类器官暴露了GSK3在皮质神经发生中的不同功能。干细胞报告13847-861（2019）。文章。

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

López-Tobón, A. et al. GTF2I dosage regulates neuronal differentiation and social behavior in 7q11.23 neurodevelopmental disorders. Sci. Adv. 9, eadh2726 (2023).Article

López-Tobón，A。等人，GTF2I剂量调节7q11.23神经发育障碍中的神经元分化和社会行为。科学。Adv.9，eadh2726（2023）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Mihailovich, M. et al. 7q11.23 CNV alters protein synthesis and REST-mediated neuronal intrinsic excitability. Preprint at bioRxiv https://doi.org/10.1101/2022.10.10.511483 (2022).Marangon, D. et al. Novel in vitro experimental approaches to study myelination and remyelination in the central nervous system.

Mihailovich，M。等人7q11.23 CNV改变蛋白质合成和REST介导的神经元内在兴奋性。bioRxiv预印本https://doi.org/10.1101/2022.10.10.511483（2022年）。Marangon，D.等人。研究中枢神经系统髓鞘形成和髓鞘再生的新型体外实验方法。

Front. Cell. Neurosci. 15, 748849 (2021).Article .

正面。细胞。神经科学。15748849（2021）。文章。

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Drakulic, D. et al. Copy number variants (CNVs): a powerful tool for iPSC-based modelling of ASD. Mol. Autism 11, 42 (2020).Article

Drakulic，D。等人。拷贝数变异（CNV）：基于iPSC的ASD建模的强大工具。摩尔自闭症11,42（2020）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Villa, C. E. et al. CHD8 haploinsufficiency links autism to transient alterations in excitatory and inhibitory trajectories. Cell Rep. 39, 110615 (2022).Article

Villa，C.E.等人，CHD8单倍剂量不足将自闭症与兴奋性和抑制性轨迹的短暂改变联系起来。细胞代表39110615（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Caporale, N. et al. From cohorts to molecules: adverse impacts of endocrine disrupting mixtures. Science 375, eabe8244 (2022).Article

Caporale，N.等人，《从队列到分子：内分泌干扰混合物的不利影响》。科学375，eabe8244（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Corsini, N. S. & Knoblich, J. A. Human organoids: new strategies and methods for analyzing human development and disease. Cell 185, 2756–2769 (2022).Article

Corsini，N.S。＆Knoblich，J.A。人类类器官：分析人类发育和疾病的新策略和方法。细胞1852756-2769（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Kelley, K. W. & Pașca, S. P. Human brain organogenesis: toward a cellular understanding of development and disease. Cell 185, 42–61 (2022).Article

Kelley，K.W。＆Pașca，S.P。人脑器官发生：对发育和疾病的细胞理解。细胞185,42-61（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Eichmüller, O. L. & Knoblich, J. A. Human cerebral organoids — a new tool for clinical neurology research. Nat. Rev. Neurol. 18, 661–680 (2022).Article

Eichmüller，O.L。和Knoblich，J.A。人脑类器官-临床神经病学研究的新工具。神经病学杂志。18661-680（2022）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Cheroni, C. et al. Benchmarking brain organoid recapitulation of fetal corticogenesis. Transl. Psychiatry 12, 520 (2022).Article

Cheroni，C.等人。胎儿皮质发生的脑类器官重现基准。翻译。精神病学12520（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Koi, P. Genetics on the neurodiversity spectrum: genetic, phenotypic and endophenotypic continua in autism and ADHD. Stud. Hist. Philos. Sci. 89, 52–62 (2021).Article

Koi，P。神经多样性谱的遗传学：自闭症和多动症的遗传，表型和内表型连续性。螺柱历史。菲洛斯。科学。89,52-62（2021）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Baron-Cohen, S. Editorial Perspective: Neurodiversity — a revolutionary concept for autism and psychiatry. J. Child Psychol. Psychiatry 58, 744–747 (2017).Article

Baron Cohen，S。编辑观点：神经多样性-自闭症和精神病学的革命性概念。J、 儿童心理学。精神病学58744-747（2017）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Pretzsch, C. M. et al. Neurobiological correlates of change in adaptive behavior in autism. Am. J. Psychiatry 179, 336–349 (2022).Article

Pretzsch，C.M.等人，《自闭症适应行为变化的神经生物学相关性》。《美国精神病学杂志》179336-349（2022）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Rajewsky, N. et al. LifeTime and improving European healthcare through cell-based interceptive medicine. Nature 587, 377–386 (2020).Article

Rajewsky，N.等人，《通过基于细胞的截留医学提高欧洲医疗保健的寿命》。《自然》587377-386（2020）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G. & Powell, J. E. Single-cell genomics meets human genetics. Nat. Rev. Genet. 24, 535–549 (2023).Article

Cuomo，A.S.E.，Nathan，A.，Raychaudhuri，S.，MacArthur，D.G。＆Powell，J.E。单细胞基因组学与人类遗传学相结合。Genet自然Rev。24535-549（2023）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

De Donno, C. et al. Population-level integration of single-cell datasets enables multi-scale analysis across samples. Nat. Methods 20, 1683–1692 (2023).Article

De Donno，C。等人。单细胞数据集的群体水平整合可以实现跨样本的多尺度分析。自然方法201683-1692（2023）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Stoeckius, M. et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).Article

Stoeckius，M。等人。使用条形码抗体进行细胞散列可以实现单细胞基因组学的多路复用和双重检测。基因组生物学。19224（2018）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).Article

Stoeckius，M.等人。单细胞中表位和转录组的同时测量。自然方法14865-868（2017）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Gehring, J., Hwee Park, J., Chen, S., Thomson, M. & Pachter, L. Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. Nat. Biotechnol. 38, 35–38 (2020).Article

Gehring，J.，Hwee Park，J.，Chen，S.，Thomson，M。＆Pachter，L。通过细胞蛋白的DNA寡核苷酸标记高度多重化的单细胞RNA-seq。美国国家生物技术公司。38，35-38（2020）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Hwang, B. et al. SCITO-seq: single-cell combinatorial indexed cytometry sequencing. Nat. Methods 18, 903–911 (2021).Article

Hwang，B。等人。SCITO-seq：单细胞组合索引细胞计数测序。自然方法18903-911（2021）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Datlinger, P. et al. Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing. Nat. Methods 18, 635–642 (2021).Article

Datlinger，P。等人。超高通量单细胞RNA测序和组合流体索引扰动筛选。自然方法18635-642（2021）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).Article

Kang，H.M.等人。使用自然遗传变异的多重液滴单细胞RNA测序。美国国家生物技术公司。36,89-94（2018）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Heaton, H. et al. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. Nat. Methods 17, 615–620 (2020).Article

Heaton，H。et al。Souporcell：按基因型对单细胞RNA-seq数据进行稳健聚类，无需参考基因型。自然方法17615-620（2020）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Huang, Y., McCarthy, D. J. & Stegle, O. Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference. Genome Biol. 20, 273 (2019).Weber, L. M. et al. Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design.

Huang，Y.，McCarthy，D。J。＆Stegle，O。Vireo：在没有基因型参考的情况下对合并的单细胞RNA-seq数据进行贝叶斯解复用。基因组生物学。20273（2019）。Weber，L.M.等人。癌症中合并的单细胞RNA测序样品的遗传解复用有助于有效的实验设计。

Gigascience 10, giab062 (2021).Yazar, S. et al. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science 376, eabf3041 (2022).Article .

Gigascience 10，giab062（2021）。Yazar，S。等人。单细胞eQTL定位鉴定自身免疫性疾病的细胞类型特异性遗传控制。科学376，eabf3041（2022）。文章。

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Perez, R. K. et al. Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus. Science 376, eabf1970 (2022).Article

Perez，R.K.等人。单细胞RNA-seq揭示了与狼疮相关的细胞类型特异性分子和遗传关联。科学376，eabf1970（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Srivatsan, S. R. et al. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367, 45–51 (2020).Article

Srivatsan，S.R.等人以单细胞分辨率大规模多重化学转录组学。科学367,45-51（2020）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Cuomo, A. S. E. et al. Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. Nat. Commun. 11, 810 (2020).Article

Cuomo，A.S.E.等人。分化iPS细胞的单细胞RNA测序揭示了基因表达的动态遗传效应。国家公社。11810（2020）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Jerber, J. et al. Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. Nat. Genet. 53, 304–312 (2021).Article

Jerber，J.等人。多巴胺能神经元分化的群体规模单细胞RNA-seq分析。纳特·吉内特。53304–312（2021）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Mitchell, J. M. et al. Mapping genetic effects on cellular phenotypes with ‘cell villages’. Preprint at bioRxiv https://doi.org/10.1101/2020.06.29.174383 (2020).Wells, M. F. et al. Natural variation in gene expression and viral susceptibility revealed by neural progenitor cell villages.

Mitchell，J.M.等人。用“细胞村”绘制细胞表型的遗传效应。bioRxiv预印本https://doi.org/10.1101/2020.06.29.174383（2020年）。Wells，M.F.等人，《神经祖细胞村揭示的基因表达和病毒易感性的自然变异》。

Cell Stem Cell 30, 312–332 (2023).Article .

细胞干细胞30312-332（2023）。文章。

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Neavin, D. R. et al. A village in a dish model system for population-scale hiPSC studies. Nat. Commun. 14, 3240 (2023).Article

Neavin，D.R.等人。一个用于人口规模hiPSC研究的碟模型系统中的村庄。国家公社。143240（2023）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Farbehi, N. et al. Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases. Nat. Genet. 56, 758–766 (2024).Article

Farbehi，N.等人。整合群体遗传学，干细胞生物学和细胞基因组学来研究复杂的人类疾病。纳特·吉内特。56758-766（2024）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Pașca, S. P. et al. A nomenclature consensus for nervous system organoids and assembloids. Nature 609, 907–910 (2022).Article

Pașca，S.P.等人。神经系统类器官和组合体的命名共识。自然609907-910（2022）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Birey, F. et al. Assembly of functionally integrated human forebrain spheroids. Nature 545, 54–59 (2017).Article

Birey，F.等人。功能整合的人类前脑球体的组装。自然545,54-59（2017）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Bizzotto, S. et al. Landmarks of human embryonic development inscribed in somatic mutations. Science 371, 1249–1253 (2021).Article

Bizzotto，S.等人。体细胞突变中人类胚胎发育的里程碑。科学3711249-1253（2021）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Germain, P.-L., Lun, A., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Res. 10, 979 (2021).Article

Germain，P.-L.，Lun，A.，Macnair，W。＆Robinson，M.D。使用scDblFinder在单细胞测序数据中进行双重鉴定。F1000Res。10979（2021）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Heiser, C. N., Wang, V. M., Chen, B., Hughey, J. J. & Lau, K. S. Automated quality control and cell identification of droplet-based single-cell data using dropkick. Genome Res. 31, 1742–1752 (2021).Article

Heiser，C.N.，Wang，V.M.，Chen，B.，Hughey，J.J。＆Lau，K.S。使用dropkick对基于液滴的单细胞数据进行自动质量控制和细胞识别。基因组研究311742-1752（2021）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40, 245–253 (2022).Article

Dann，E.，Henderson，N.C.，Teichmann，S.A.，Morgan，M.D。和Marioni，J.C。使用k-最近邻图对单细胞数据进行差异丰度测试。美国国家生物技术公司。40245-253（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Cleary, S. & Seoighe, C. Perspectives on allele-specific expression. Annu. Rev. Biomed. Data Sci. 4, 101–122 (2021).Article

Cleary，S。＆Seoighe，C。等位基因特异性表达的观点。年。生物医学评论。数据科学。4101-122（2021）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Zhao, D., Lin, M., Pedrosa, E., Lachman, H. M. & Zheng, D. Characteristics of allelic gene expression in human brain cells from single-cell RNA-seq data analysis. BMC Genomics 18, 860 (2017).Article

Zhao，D.，Lin，M.，Pedrosa，E.，Lachman，H.M。＆Zheng，D。来自单细胞RNA-seq数据分析的人脑细胞中等位基因表达的特征。BMC Genomics 18860（2017）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Sollis, E. et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res. 51, D977–D985 (2023).Article

Sollis，E.等人，《NHGRI-EBI GWAS目录：知识库和沉积资源》。核酸研究51，D977–D985（2023）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Cuomo, A. S. E. et al. CellRegMap: a statistical framework for mapping context-specific regulatory variants using scRNA-seq. Mol. Syst. Biol. 18, e10663 (2022).Article

Cuomo，A.S.E.等人，《CellRegMap：使用scRNA-seq绘制上下文特定调控变体的统计框架》。分子系统。生物学18，e10663（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Schmid, K. T. et al. scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies. Nat. Commun. 12, 6625 (2021).Article

scPower加速并优化了多样本单细胞转录组学研究的设计。国家公社。126625（2021年）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Lindenhofer, D. et al. Cerebral organoids display dynamic clonal growth and tunable tissue replenishment. Nat. Cell Biol. 26, 710–718 (2024).Article

Lindenhofer，D。等人。脑类器官显示动态克隆生长和可调节的组织补充。自然细胞生物学。26710-718（2024）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Antón-Bolaños, N. et al. Brain chimeroids reveal individual susceptibility to neurotoxic triggers. Nature 631, 142–149 (2024).Article

Antón-Bolaños，n。等人。脑嵌合体揭示了个体对神经毒性触发的易感性。自然631142-149（2024）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Uozumi, T. et al. Generation of a growth curve for iPS cells in a feeder-free culture by non-invasive image analysis. Nikon Instruments www.microscope.healthcare.nikon.com/resources/application-notes/generation-of-a-growth-curve-for-i-ps-cells-in-a-feeder-free-culture-by-non-invasive-image-analysis (2011).Uzquiano, A.

Uozumi，T。等人。通过非侵入性图像分析在无饲养层培养物中产生iPS细胞的生长曲线。尼康仪器www.microscope.healthcare.Nikon.com/resources/application-notes/generation-of-a-growth-curve-for-i-ps-cells-in-a-feeder-free-culture-by-non-invasive-image-analysis（2011）。乌兹基亚诺，A。

et al. Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex. Cell 185, 3770–3788 (2022).Article .

正确获取类器官中的细胞类别身份可以定义人类大脑皮层的命运规范程序。细胞1853770-3788（2022）。文章。

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Elorriaga, V., Pierani, A. & Causeret, F. Cajal–Retzius cells: recent advances in identity and function. Curr. Opin. Neurobiol. 79, 102686 (2023).Article

Elorriaga，V.，Pierani，A。＆Causeret，F。Cajal-Retzius细胞：身份和功能的最新进展。货币。奥平。神经生物学。79102686（2023）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Pebworth, M.-P., Ross, J., Andrews, M., Bhaduri, A. & Kriegstein, A. R. Human intermediate progenitor diversity during cortical development. Proc. Natl. Acad. Sci. USA 118, e2019415118 (2021).Braun, E. et al. Comprehensive cell atlas of the first-trimester developing human brain. Science 382, eadf1226 (2023).Article .

Pebworth，M.-P.，Ross，J.，Andrews，M.，Bhaduri，A。＆Kriegstein，A.R。皮质发育过程中的人类中间祖细胞多样性。程序。纳特尔。阿卡德。科学。美国118，e2019415118（2021）。Braun，E.等人，《妊娠早期发育人脑的综合细胞图谱》。科学382，eadf1226（2023）。文章。

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Aguet, F. et al. Molecular quantitative trait loci. Nat. Rev. Methods Primers 3, 4 (2023).Article

Aguet，F。等人。分子数量性状基因座。Nat.Rev.Methods引物3,4（2023）。文章

CAS

中科院

Google Scholar

谷歌学者

van der Wijst, M. et al. The single-cell eQTLGen consortium. eLife 9, e52155 (2020).GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).Article

van der Wijst，M.等人，单细胞eQTLGen联盟。eLife 9，e52155（2020）。GTEx Consortium等人。对人体组织中基因表达的遗传影响。《自然》550204-213（2017）。文章

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Coquand, L. et al. A cell fate decision map reveals abundant direct neurogenesis bypassing intermediate progenitors in the human developing neocortex. Nat. Cell Biol. 26, 698–709 (2024).Article

Coquand，L。等人。细胞命运决定图揭示了人类发育中的新皮层中绕过中间祖细胞的大量直接神经发生。自然细胞生物学。26698-709（2024）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Sloan, S. A., Andersen, J., Pașca, A. M., Birey, F. & Pașca, S. P. Generation and assembly of human brain region-specific three-dimensional cultures. Nat. Protoc. 13, 2062–2085 (2018).Article

Sloan，S.A.，Andersen，J.，Pașca，A.M.，Birey，F。＆Pașca，S.P。人脑区域特异性三维培养物的产生和组装。自然协议。132062-2085（2018）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Delgado, R. N. et al. Individual human cortical progenitors can produce excitatory and inhibitory neurons. Nature 601, 397–403 (2022).Article

Delgado，R.N。等人。单个人类皮质祖细胞可以产生兴奋性和抑制性神经元。自然601397-403（2022）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Andrews, M. G. et al. LIF signaling regulates outer radial glial to interneuron fate during human cortical development. Cell Stem Cell 30, 1382–1391 (2023).Article

Andrews，M.G。等人。LIF信号调节人类皮层发育过程中外放射状胶质细胞到中间神经元的命运。细胞干细胞301382-1391（2023）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Breuss, M. W. et al. Somatic mosaicism reveals clonal distributions of neocortical development. Nature 604, 689–696 (2022).Article

Breuss，M.W.等人。体细胞镶嵌揭示了新皮质发育的克隆分布。自然604689-696（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Huang, A. Y. et al. Parallel RNA and DNA analysis after deep sequencing (PRDD-seq) reveals cell type-specific lineage patterns in human brain. Proc. Natl Acad. Sci. USA 117, 13886–13895 (2020).Article

Huang，A.Y.等人。深度测序后的平行RNA和DNA分析（PRDD-seq）揭示了人脑中细胞类型特异性谱系模式。程序。国家科学院。科学。美国1171386–13895（2020）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Pauklin, S. & Vallier, L. The cell-cycle state of stem cells determines cell fate propensity. Cell 155, 135–147 (2013).Article

Pauklin，S。＆Vallier，L。干细胞的细胞周期状态决定了细胞的命运倾向。细胞155135-147（2013）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Bertucci, T. et al. Improved protocol for reproducible human cortical organoids reveals early alterations in metabolism with MAPT mutations. Preprint at bioRxiv https://doi.org/10.1101/2023.07.11.548571 (2023).Kim, S.-K. et al. Individual variation in the emergence of anterior-to-posterior neural fates from human pluripotent stem cells.

Bertucci，T。等人。可重复的人类皮质类器官的改进方案揭示了MAPT突变对代谢的早期改变。bioRxiv预印本https://doi.org/10.1101/2023.07.11.548571（2023年）。Kim，S.-K.等人。人类多能干细胞前后神经命运出现的个体差异。

Stem Cell Reports 19, 1336–1350 (2024).Article .

干细胞报告191336-1350（2024）。文章。

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Baek, S. T. et al. An AKT3–FOXG1–reelin network underlies defective migration in human focal malformations of cortical development. Nat. Med. 21, 1445–1454 (2015).Article

Baek，S.T.等人，AKT3–FOXG1–reelin网络是人类皮质发育局灶性畸形迁移缺陷的基础。《自然医学》211445-1454（2015）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Nakagawa, N. et al. Memo1-mediated tiling of radial glial cells facilitates cerebral cortical development. Neuron 103, 836–852 (2019).Article

Nakagawa，N。等人。Memo1介导的放射状神经胶质细胞平铺促进大脑皮层发育。神经元103836-852（2019）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Seah, C. et al. Modeling gene × environment interactions in PTSD using human neurons reveals diagnosis-specific glucocorticoid-induced gene expression. Nat. Neurosci. 25, 1434–1445 (2022).Article

Seah，C。等人。使用人类神经元对PTSD中的基因-环境相互作用进行建模，揭示了诊断特异性糖皮质激素诱导的基因表达。自然神经科学。251434-1445（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Huang, C.-Y. et al. Human iPSC banking: barriers and opportunities. J. Biomed. Sci. 26, 87 (2019).Article

Huang，C.-Y.等人，《人类iPSC银行：障碍与机遇》。J、 生物医学。科学。26，87（2019）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Fleck, J. S. et al. Inferring and perturbing cell fate regulomes in human brain organoids. Nature 621, 365–372 (2022).Esk, C. et al. A human tissue screen identifies a regulator of ER secretion as a brain-size determinant. Science 370, 935–941 (2020).Article

Fleck，J.S.等人。推断和扰乱人脑类器官中的细胞命运调节。自然621365-372（2022）。人体组织筛查确定ER分泌的调节剂是大脑大小的决定因素。科学370935-941（2020）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Meng, X. et al. Assembloid CRISPR screens reveal impact of disease genes in human neurodevelopment. Nature 622, 359–366 (2023).Article

Meng，X。等人。Assembloid CRISPR筛选揭示了疾病基因在人类神经发育中的影响。自然622359-366（2023）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Li, C. et al. Single-cell brain organoid screening identifies developmental defects in autism. Nature 621, 373–380 (2023).Article

Li，C.等人。单细胞脑类器官筛查可识别自闭症的发育缺陷。自然621373-380（2023）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Yoon, S.-J. et al. Reliability of human cortical organoid generation. Nat. Methods 16, 75–78 (2019).Article

Yoon，S.-J.等人。人类皮质类器官生成的可靠性。自然方法16,75-78（2019）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).Article

Zheng，G.X.Y.等人。单细胞的大规模并行数字转录谱分析。国家公社。814049（2017）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).Article

Dobin，A。等人STAR：超快通用RNA-seq比对仪。生物信息学29,15-21（2013）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2017).Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).Article .

Poplin，R.等人将准确的遗传变异发现扩展到数万个样本。bioRxiv预印本https://doi.org/10.1101/201178（2017年）。Wolf，F.A.，Angerer，P。＆Theis，F.J。SCANPY：大规模单细胞基因表达数据分析。基因组生物学。19,15（2018）。文章。

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).Article

Korsunsky，I。等人。单细胞数据与和谐的快速，灵敏和准确整合。自然方法161289-1296（2019）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).Article

Wolf，F.A.等人（PAGA）：图形抽象通过单个单元的拓扑保持图将聚类与轨迹推断相协调。基因组生物学。20,59（2019）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and function using NetworkX. In Proc. Python in Science Conference (SciPy) (eds Varoquaux, G. et al.) https://doi.org/10.25080/TCWV9851 (SciPy Proceedings, 2008).Chen, Y., Lun, A. T. L. & Smyth, G. K.

Hagberg，A.A.，Schult，D.A。＆Swart，P.J。使用NetworkX探索网络结构，动力学和功能。在过程中。Python in Science Conference（SciPy）（编辑Varoquaux，G.等人）https://doi.org/10.25080/TCWV9851（SciPy Proceedings，2008）。Chen，Y.，Lun，A.T.L。和Smyth，G.K。

From reads to genes to pathways: differential expression analysis of RNA-seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res. 5, 1438 (2016).PubMed .

从读数到基因再到途径：使用Rsubread和edgeR准似然管道对RNA-seq实验进行差异表达分析。F1000Res。51438（2016）。PubMed。

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).Article

Haghverdi，L.，Buettner，F。＆Theis，F。J。扩散图，用于分化数据的高维单细胞分析。生物信息学312989-2998（2015）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).Article

Haghverdi，L.，Büttner，M.，Wolf，F.A.，Buettner，F。＆Theis，F.J。扩散伪时间稳健地重建谱系分支。自然方法13845-848（2016）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Van den Berge, K. et al. Trajectory-based differential expression analysis for single-cell sequencing data. Nat. Commun. 11, 1201 (2020).Article

Van den Berge，K。等人。单细胞测序数据的基于轨迹的差异表达分析。国家公社。11201（2020）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).Article

Virtanen，P.等人，《SciPy 1.0：Python科学计算的基本算法》。自然方法17261-272（2020）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Delignette-Muller, M. L. & Dutang, C. fitdistrplus: an R package for fitting distributions. J. Stat. Softw. 64, 1–34 (2015).Article

Delignette Muller，M.L。和Dutang，C.fitdistrplus：用于拟合分布的R包。J、 统计软件。64，1-34（2015）。文章

Google Scholar

谷歌学者

Benaglia, T., Chauveau, D., Hunter, D. R. & Young, D. S. mixtools: an R package for analyzing mixture models. J. Stat. Softw. 32, 1–29 (2010).

Benaglia，T.，Chauveau，D.，Hunter，D.R。＆Young，D.S。mixtools：用于分析混合模型的R包。J、 统计软件。32，1-29（2010）。

Google Scholar

谷歌学者

Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).Article

Lun，A.T.L.等人。EmptyDrops：在基于液滴的单细胞RNA测序数据中区分细胞和空液滴。基因组生物学。20,63（2019）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L. & Marioni, J. C. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat. Commun. 9, 2667 (2018).Article

Griffiths，J.A.，Richard，A.C.，Bach，K.，Lun，A.T.L。和Marioni，J.C。检测和删除单细胞RNA-seq数据中的条形码交换。国家公社。92667（2018）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Download referencesAcknowledgementsWe thank present and former members of G.T.’s laboratory for collaborating to various extents on this study with both technical help and conceptual discussion. We also thank the Flow Cytometry and Imaging facilities of Human Technopole and the European Institute of Oncology (IEO) and colleagues in the Genomics Unit of the IEO and the Center for Genomic Science of the Istituto Italiano di Tecnologia for helpful discussion at the onset of the project.

下载参考文献致谢我们感谢G.T.实验室的现任和前任成员在技术帮助和概念讨论的基础上在这项研究上进行了不同程度的合作。我们还感谢人类Technopole和欧洲肿瘤研究所（IEO）的流式细胞术和成像设备以及IEO基因组学部门和意大利技术研究所基因组科学中心的同事在项目开始时进行了有益的讨论。

We thank the European School of Molecular Medicine (SEMM) at which D.C., M.T.R., A. Valenti, S.S., M.P., S.T. and M.L. are/were enrolled as students for their PhD degree program in Systems Medicine. Some components of the schematics were adapted from BioRender. The project, carried out in G.T.’s laboratory at the IEO and at Human Technopole, has been funded by European Union Horizon 2020 research and innovation program grants EDC-MixRisk (634880), ENDpoiNTs (825759), NEUROCOV (101057775), RE-MEND (101057604), R2D2-MH (101057385) and PNRR (Centro Nazionale CN3: RNA, ‘National Center for Gene Therapy and Drugs based on RNA Technology’).Author informationAuthor notesThese authors contributed equally: Nicolò Caporale, Davide Castaldi, Marco Tullio Rigoli.These authors jointly supervised this work: Nicolò Caporale, Carlo Emanuele Villa, Giuseppe Testa.Authors and AffiliationsDepartment of Oncology and Hemato-Oncology, University of Milan, Milan, ItalyNicolò Caporale, Davide Castaldi, Marco Tullio Rigoli, Alessia Valenti, Sarah Stucchi, Manuel Lessi, Martina Pezzali & Giuseppe TestaHuman Technopole, Milan, ItalyNicolò Caporale, Davide Castaldi, Marco Tullio Rigoli, Cristina Cheroni, Alessia Valenti, Sarah Stucchi, Manuel Lessi, Davide Bulgheresi, Sebastiano Trattaro, Martina Pezza.

我们感谢欧洲分子医学院（SEMM），D.C.，M.T.R.，A.Valenti，S.S.，M.P.，S.T.和M.L.被录取为系统医学博士学位课程的学生。示意图的一些组件改编自BioRender。该项目在IEO和Human Technopole的G.T.实验室进行，由欧盟地平线2020研究与创新计划资助EDC MixRisk（634880），ENDpoiNTs（825759），NEUROCOV（101057775），RE-MEND（101057604），R2D2-MH（101057385）和PNRR（Centro Nazionale CN3:RNA，“基于RNA技术的国家基因治疗和药物中心”）。作者信息作者注意到这些作者做出了同样的贡献：NicolòCaporale，Davide Castaldi，Marco Tullio Rigoli。这些作者共同监督了这项工作：NicolCaporale，CarloEmanueleVilla，GiuseppeTesta。作者和附属机构米兰大学肿瘤学和血液肿瘤学系，米兰，ItalyNicolòCaporale，Davide Castaldi，Marco Tullio Rigoli，Alessia Valenti，Sarah Stucchi，Manuel Lessi，Martina Pezzali＆Giuseppe TestaHuman Technopole，米兰，ItalyNicolòCaporale，Davide Castaldi，Marco Tullio Rigoli，Cristina Cheroni，Alessia Valenti，Sarah Stucchi，Manuel Lessi，Davide Bulgheresi，Sebastiano Trattaro，Martina Pezza。

PubMed Google ScholarDavide CastaldiView author publicationsYou can also search for this author in

PubMed Google ScholarDavide CastaldiView作者出版物您也可以在

PubMed Google ScholarMarco Tullio RigoliView author publicationsYou can also search for this author in

PubMed Google ScholarMarco Tullio RigoliView作者出版物您也可以在

PubMed Google ScholarCristina CheroniView author publicationsYou can also search for this author in

PubMed谷歌学术评论CheroniView作者出版物您也可以在

PubMed Google ScholarAlessia ValentiView author publicationsYou can also search for this author in

PubMed Google ScholarAlessia ValentiView作者出版物您也可以在

PubMed Google ScholarSarah StucchiView author publicationsYou can also search for this author in

PubMed Google ScholarSarah StucchiView作者出版物您也可以在

PubMed Google ScholarManuel LessiView author publicationsYou can also search for this author in

PubMed Google ScholarManuel LessiView作者出版物您也可以在

PubMed Google ScholarDavide BulgheresiView author publicationsYou can also search for this author in

PubMed Google ScholarDavide BulgheresiView作者出版物您也可以在

PubMed Google ScholarSebastiano TrattaroView author publicationsYou can also search for this author in

PubMed Google ScholarSebastiano TrattaroView作者出版物您也可以在

PubMed Google ScholarMartina PezzaliView author publicationsYou can also search for this author in

PubMed Google ScholarMartina PezzaliView作者出版物您也可以在

PubMed Google ScholarAlessandro VitrioloView author publicationsYou can also search for this author in

PubMed Google ScholarAlessandro镜检阅作者出版物您也可以在

PubMed Google ScholarAlejandro Lopez-TobonView author publicationsYou can also search for this author in

PubMed Google ScholarAlejandro Lopez TobonView作者出版物您也可以在

PubMed Google ScholarMatteo BonfantiView author publicationsYou can also search for this author in

PubMed Google ScholarMatteo BonfantiView作者出版物您也可以在

PubMed Google ScholarDario RiccaView author publicationsYou can also search for this author in

PubMed Google ScholarDario RiccaView作者出版物您也可以在

PubMed Google ScholarKatharina T. SchmidView author publicationsYou can also search for this author in

PubMed Google ScholarKatharina T.SchmidView作者出版物您也可以在

PubMed Google ScholarMatthias HeinigView author publicationsYou can also search for this author in

PubMed Google ScholarMatthias HeinigView作者出版物您也可以在

PubMed Google ScholarFabian J. TheisView author publicationsYou can also search for this author in

PubMed谷歌学者Fabian J.TheisView作者出版物您也可以在

PubMed Google ScholarCarlo Emanuele VillaView author publicationsYou can also search for this author in

PubMed Google ScholarCarlo Emanuele VillaView作者出版物您也可以在

PubMed Google ScholarGiuseppe TestaView author publicationsYou can also search for this author in

PubMed Google ScholarGiuseppe TestaView作者出版物您也可以在

PubMed Google ScholarContributionsN.C., D.C. and M.T.R. contributed equally, are listed in alphabetical order and have the right to list their name first in their CV. D.C., M.T.R., A. Valenti, S.S., M.L., S.T. and M.P. are/were PhD students at the European School of Molecular Medicine (SEMM).

PubMed谷歌学术贡献。C、 ，D.C.和M.T.R.的贡献相同，按字母顺序排列，有权在简历中首先列出他们的名字。D.C.，M.T.R.，A.Valenti，S.S.，M.L.，S.T.和M.P.是欧洲分子医学院（SEMM）的博士生。

N.C., C.E.V. and G.T. conceived the project and, with M.T.R. and D.C., implemented the experimental and analytical design; M.T.R. has driven the experimental activities with the help of S.S., M.L., D.B. and S.T.; D.C. has driven the computational work with the help of C.C., A. Valenti, M.B. and K.T.S.; M.P., A.

N、 C.，C.E.V.和G.T.构思了该项目，并与M.T.R.和D.C.一起实施了实验和分析设计；M、 T.R.在S.S.，M.L.，D.B.和S.T.的帮助下推动了实验活动。；D、 C.在C.C.，A.Valenti，M.B.和K.T.S.的帮助下推动了计算工作。；M、 宾夕法尼亚州。

Vitriolo, A.L.T., D.R., M.H. and F.J.T. contributed to the study design and critical discussions and interpretation of the results; N.C., C.E.V. and G.T. supervised wet and computational activities; N.C., D.C., M.T.R., C.E.V. and G.T. wrote the paper with input from all other authors. All authors read and approved the final paper.Corresponding authorCorrespondence to.

Vitrolo，A.L.T.，D.R.，M.H.和F.J.T.为研究设计以及对结果的批判性讨论和解释做出了贡献；N、 C.，C.E.V.和G.T.监督wet和计算活动；N、 C.，D.C.，M.T.R.，C.E.V.和G.T.在所有其他作者的意见下撰写了这篇论文。所有作者都阅读并批准了最终论文。对应作者对应。

Giuseppe Testa.Ethics declarations

朱塞佩·特斯塔。道德宣言

Competing interests

相互竞争的利益

F.J.T. consults for Immunai, Singularity Bio, CytoReason and Omniscope and has ownership interest in Dermagnostix and Cellarity. The other authors declare no competing interests.

F、 J.T.为Immunai、Singularity Bio、CytoReason和Omniscope提供咨询，并拥有Dermagnostix和Cellarity的所有权。其他作者声明没有利益冲突。

Peer review

同行评审

Peer review information

同行评审信息

Nature Methods thanks Carlo Colantuoni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Madhura Mukhopadhyay, in collaboration with the Nature Methods team. Peer reviewer reports are available.

Nature Methods感谢Carlo Colantuoni和另一位匿名审稿人对这项工作的同行评审做出的贡献。主要处理编辑：Madhura Mukhopadhyay，与Nature Methods团队合作。同行评审报告可用。

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 mCBO immunofluorescence characterization.Immunofluorescence-based benchmarking of different mosaic CBOs combinations.

Additional informationPublisher的注释Springer Nature在已发布地图和机构隶属关系中的管辖权主张方面保持中立。扩展数据扩展数据图1 mCBO免疫荧光表征。基于免疫荧光的不同镶嵌CBOs组合的基准测试。

At differentiation day 50 (a, b), mosaic CBOs mixes 1, 6, and 7 show consistent expression of the neuronal lineage-specific tubulin TUBB3 as well as the presence of ventricular-like structures positive for the neural stem cell marker SOX2 (a). Similarly to the in vivo counterpart, these structures display high rates of proliferation as shown by the focal enrichment in mKI67 positive cells (b).

在分化第50天（a，b），镶嵌CBOs混合物1,6和7显示神经元谱系特异性微管蛋白TUBB3的一致表达以及神经干细胞标记物SOX2阳性的心室样结构的存在（a）。与体内对应物类似，这些结构显示出高增殖率，如mKI67阳性细胞中的局灶性富集所示（b）。

Outside ventricular-like structures, the presence of neurons can be appreciated by the broad presence of NeuN positive nuclei as well as by the uniform presence of MAP2 positive cellular processes (b). At differentiation day 135 (c), mosaic CBOs mix1 display more mature ventricular-like structures characterised by reduced luminal area and a reduced and scattered expression of both SOX2 and mKI67 positive cells, whereas both NeuN positive nuclei and the sharpness of TUBB3 and MAP2 signal appears increased with longer cellular processes being clearly detected by anti-MAP2 staining.Extended Data Fig.

在心室样结构之外，神经元的存在可以通过NeuN阳性细胞核的广泛存在以及MAP2阳性细胞过程的均匀存在来理解（b）。在分化第135天（c），马赛克CBOs mix1显示出更成熟的心室样结构，其特征在于管腔面积减少，SOX2和mKI67阳性细胞的表达减少和分散，而NeuN阳性细胞核和TUBB3的锐度和MAP2信号似乎随着抗MAP2染色清楚地检测到更长的细胞过程而增加。扩展数据图。

2 Dataset composition by genotype.Barplot representing number of cells by genotype according to the consensus call prior to filtering. WVS01H, WVS02A, WVS03B, WVS04A, CTL09A were not included in downstream analysis since there were no replicates across multiplexing modalities.Extended Data Fig. 3 Demultiplexing performance assessment.a) Doublet rate by dataset and algorithm.

2按基因型划分的数据集组成。根据过滤前的共识调用，条形图按基因型表示细胞数量。WVS01H，WVS02A，WVS03B，WVS04A，CTL09A不包括在下游分析中，因为在多路复用模式中没有重复。扩展数据图3解复用性能评估。a）数据集和算法的倍增率。

Lines are coloured by demultiplexing algorithm, datasets (x axis) are ordered by number of retrieved cells. b) .

线通过解复用算法着色，数据集（x轴）按检索到的单元数排序。b） 。

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.

开放获取本文是根据知识共享署名非商业性NoDerivatives 4.0国际许可证授权的，该许可证允许以任何媒介或格式进行任何非商业性使用，共享，分发和复制，只要您对原始作者和来源给予适当的信任，提供知识共享许可证的链接，并指出您是否修改了许可材料。

You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

根据本许可证，您无权共享源自本文或其部分的改编材料。本文中的图像或其他第三方材料包含在文章的知识共享许可证中，除非该材料的信用额度中另有说明。如果材料未包含在文章的知识共享许可中，并且您的预期用途不受法律法规的许可或超出许可用途，则您需要直接获得版权所有者的许可。

To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/..

要查看此许可证的副本，请访问http://creativecommons.org/licenses/by-nc-nd/4.0/..

Reprints and permissionsAbout this articleCite this articleCaporale, N., Castaldi, D., Rigoli, M.T. et al. Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single-cell resolution.

转载和许可本文引用本文Caporale，N.，Castaldi，D.，Rigoli，M.T.等人，将皮质脑类器官多路复用，以单细胞分辨率纵向解剖发育特征。

Nat Methods (2024). https://doi.org/10.1038/s41592-024-02555-5Download citationReceived: 18 August 2023Accepted: 31 October 2024Published: 09 December 2024DOI: https://doi.org/10.1038/s41592-024-02555-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Nat方法（2024）。https://doi.org/10.1038/s41592-024-02555-5Download引文接收日期：2023年8月18日接收日期：2024年10月31日发布日期：2024年12月9日OI：https://doi.org/10.1038/s41592-024-02555-5Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

Provided by the Springer Nature SharedIt content-sharing initiative

由Springer Nature SharedIt内容共享计划提供