Embryonic day E 0. All experiments using mice were performed according to the German Animal Welfare Legislation. PR was obtained as a kind gift from F. Gage Marchetto et al. Sandra A and Toba were generated in collaboration with Shinya Yamanaka following a nonviral transfection method Okita et al. Briefly, blood was collected from a chimpanzee and an orangutan, both housed at the Leipzig Zoo, and leukocytes were isolated by Ficoll gradient centrifugation, which were then used for reprogramming to iPSCs.
DNA sequencing revealed no chromosome aberrations, and RNA-seq and immunohistochemistry confirmed pluripotent gene and protein expression signatures. Primate blood samples used to generate iPSCs were obtained by certified veterinarians during annual medical examinations or other necessary medical interventions, meaning that no invasive procedures were performed on primates for the sole purpose of our research project.
Human endothelial cells. Single cell transcriptome analysis confirmed the identity of human and chimpanzee iPSCs and human endothelial cells, and showed no contamination with other cell lines. B-cell lines were generated from blood obtained from three human A, A, A and three chimpanzee Dorien, Jahaga, Ulla individuals.
Withdrawal and processing of blood samples was performed according to approved protocols, and was performed for chimpanzee during necessary veterinary interventions. Lymphocytes were isolated from blood using a Ficoll gradient centrifugation.
Immortalization was performed by adding Epstein Barr virus EBV supernatant to the lymphocytes and further cultivation of the cells until colonies of immortalized B-lymphocytes were established Tosato and Cohen, Cell lines were regularly tested for mycoplasma using a PCR-based test Minerva Biolabs and found to be negative.
Human and chimpanzee cerebral organoids were generated from the above iPSCs and cultured for the indicated times as described previously for human cerebral organoids Lancaster and Knoblich, ; Lancaster et al. To generate single-cell suspensions, cerebral organoids were either dissociated as a whole or first sliced using a vibratome to dissect cortical regions.
Additional mechanical dissociation was performed by triturating the tissue. Counting of cells was performed using a Countess automated cell counter Invitrogen and by staining with Trypan blue. These steps were performed as described Camp et al. Resulting cDNA was quantified and checked for its size distribution using a capillary gel electrophoresis system Fragment Analyzer, Advanced Analytical, 1— bps High Sensitivity.
Up to 96 single-cell libraries were pooled and cleaned up using solid phase reversible immobilization SPRI beads Thermo Scientific. Up to cells were pooled and sequenced in bp paired-end mode on one lane of an Illumina HiSeq platform rapid mode. Base-calling, adaptor trimming and demultiplexing of reads was performed using a custom pipeline based on freeIbis Renaud et al.
Demultiplexed reads were mapped using TopHat v2. Human reads were mapped to the hg38 reference genome release 77 and chimpanzee reads were mapped to panTro4 release RStudio Team, was used to run R Development Core Team, , scripts to perform principal component analysis PCA, FactoMineR package , hierarchical clustering stats package , differential expression analysis SCDE package , and to construct heatmaps, scatter and line plots, dendrograms, bar graphs, pie charts and histograms.
Generally, ggplot2 and gplots packages were used to visualize the data. The Seurat package Macosko et al. In Figure 1E we calculated for each chimpanzee organoid cortex cell the Spearman correlation of its transcriptome all genes with bulk transcriptome data from each of 4 microdissected human cortical zones VZ, iSVZ, oSVZ, CP, mean expression value of each gene across 4 replicates from 13 weeks post conception, data published in [ Fietz et al.
The scaling enables a better comparison between cells, since the maximum and minimum correlation for each cell is color-coded in the same way after scaling. We used this analysis to identify the zone with which each individual cell had a maximum correlation. Each fetal, human organoid, and chimpanzee organoid cortex cell was scored for the NSPC or neuron signature by summing the number of genes from each signature that have an expression greater than log2 FPKM of 5, and normalizing by the number of all genes expressed above log2 FPKM of 5 for each cell.
To construct the chimpanzee cellular network, we computed a pairwise correlation matrix for all chimpanzee cerebral cortex cells and using genes discovered in PCA of fetal neocortex single cell transcriptomes Camp et al.
These same genes had been used to infer lineage relationships in the fetal neocortex. We then generated a weighted adjacency network graph using the graph. The fruchterman reingold layout was used to plot the network graph. The combined species network was constructed in a similar way using the same genes and a correlation threshold of 0. Monocle Trapnell et al.
We re-aligned reads from each cell to a human-chimpanzee consensus genome to account for mapping bias originating from the different genome qualities of the human and chimpanzee genome. The consensus genome was generated as previously described He et al. In brief, the consensus genome was constructed based on the chained and netted pairwise alignment of human hg38 and chimpanzee panTro4 obtained from UCSC. Discordant sites and indels including 6 bp upstream and downsteam of the indel position were masked replacing the base with N.
STAR v2. For quantification, HTSeq Anders et al. Resulting count files were combined into one master table containing all cells and genes.
To identify differentially expressed genes between human and chimpanzee, cells were first annotated as AP, BP or neuron based on the fetal cortex cell type with which each cell maximally correlated. A more stringent threshold of twice the standard deviation of the z-score was used to define differential expression between human and chimpanzee Z.
For the differential gene expression analysis during mitotic phases, we aimed to identify relatively homogeneous clusters of human organoid APs, chimpanzee organoid APs, endothelial cells ECs , or iPSCs in G2M or G1 phases.
We hierarchically clustered cells Pearson correlation using expression of genes that correlated with PC1 from PCA on human fetal cortex progenitor cells Camp et al. For the organoid APs, this assignment was consistent with an independent assignment using the method published by Scialdone et al. The secondary antibodies, used in combination with DAPI staining, were all donkey-derived and conjugated with Alexa , or Life Technologies. An average of cells per sample were counted.
Statistical significance was calculated using the Mann—Whitney U-test. Cell cycle parameters were determined using linear regression based on a model described previously Nowakowski et al. Live tissue imaging was performed as described previously Mora-Bermudez et al. Potential phototoxicity was stringently controlled as previously described Mora-Bermudez and Ellenberg, Brightness and contrast of images were recorded and adjusted linearly.
Spindle orientation analysis was performed as described Mora-Bermudez et al. In short, the degree values given in Figure 4 are deviations from a perfect orthogonality with the local apical surface plane, as seen from a coronal perspective Figure 4A—F. For Figure 4G , the maximal range of orientations per every mitotic AP was calculated from the formation of a metaphase plate to anaphase onset. To measure the duration of mitotic phases, the start of each different phase was defined as follows, based on morphology, dynamics and condensation of chromosomes as reported by vital DNA staining Figures 5 and 6.
The total duration of mitosis was the sum of these phases. We note that our measurements of mitotic phases are limited by the use of chromosomes as markers. Statistical tests: for two groups of observations, the Mann—Whitney U-test was used.
In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.
Thank you for submitting your article "Differences and similarities between human and chimpanzee neural progenitors during cerebral cortex development" for consideration by eLife.
Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and K VijayRaghavan as the Senior Editor.
The following individuals involved in review of your submission have agreed to reveal their identity: Victor Borrell Reviewer 3.
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission. To address this technologically very challenging question, the authors use state of the art approaches including brain organoid cultures and single cell RNAseq. In a very remarkable methodological tour-de-force, the authors develop brain organoids from human, chimpanzee and orangutan iPSCs, and go on to perform high resolution live imaging of individual progenitor cells.
The authors find that same classes of neural stem progenitor cells in humans and chimps are nearly identical at the transcriptome level, and also in their behavior in the cell cycle. A consistent difference identified by the authors is a lengthening of 5 min of metaphase during cell division. The study is timely and addresses a very important question. The reviewers, however, identified a number of technical and conceptual issues that will require the authors' attention.
Reviewer 1's main criticism is conceptual. The reviewer argues that an important part of the regulation of cell cycle progression is post-translational.
As the cell cycle differences were detected in the VZ in AP which is not the major proliferative region in the human and in chimpanzee cortex this finding may not necessarily provide sufficient explanation to the difference between the human and chimpanzee brains.
Alternatively, it may be advisable to analyze the changes at the level of protein expression in the AP during metaphase. In case such a study is beyond the scope of the current paper, the manuscript may be modified emphasizing the validity of the organoid system to study the conservation and diversification of cortical development in different species, the technical advances conducted and how they can assist in approaching such a complex and difficult study.
C The developmental time period for human and chimpanzee is different in vivo. How could the authors account for this age difference in vitro in organoids? Should the time in culture be normalized for gestation length? D What is the evidence that the organoid developmental clock parallels the in vivo clock and how reliably does that clock operate from culture to culture? E The error bars in Figure 2B should not be drawn in a manner that obscures the lower SD boundary-error extends in both directions.
F Although the authors build their story based on Figure 2B there is not much difference in the number of Pax6 Tbr2- cells considered as APs at Day between chimpanzee and human.
How many cells are counted per sample? The difference in the number of Pax6 Tbr2 cells between chimpanzee and human is not evident in the immunostaining images in Figure 2A. The author should provide a better view of the image marking some cells with co-localization of Tbr2 and Pax6. Also, they should comment on what kind of cells are Pax6 Tbr2 cells or comment that the cell type cannot be identified due to the unclear lamination of their organoids.
Almost all cells from 45d are clustered as mesenchyme cells and all cells from 62d are clustered as hindbrain cells. Are those Pax6 Tbr2- cells? In Figures 4 — 6 , no markers have been used to assign those cells as AP. Markers such as Pax6 and Tbr2 with reporter plasmids could be used to trace the cells. This kind of experiment would support the data presented in Figure 2B. In Figure 7 , the authors have tried to address this question but they have used developing mouse neocortex to distinguish proliferative and neurogenic APs.
The dynamics of prometaphase-metaphase is very different in mouse as compared to human or chimpanzee, therefore, it is difficult to relate that the lengthening of prometaphase-metaphase characterizes proliferating NPSCs in humans.
However, in Figure 5——figure supplement 1 , no significant difference is shown. Moreover, the bar value for prophase and anaphase does not correlate with the values shown in the live images in Figure 5. Please clarify this difference in figures. Furthermore, they found lengthening of the metaphase in human apical progenitors compared to chimp and no difference was observed in other phases during cell mitosis, but they didn't show whether the length of cell cycle for this kind of progenitors is different, which is also important for cell fate determination.
Moreover, the authors didn't include cells in S phase of cell cycle. If the authors used the same set of genes as described in Camp, Badsha et al.
Please explain the method and also explain why S phase is not included in this analysis. What is the difference in the list of genes highly expressed in AP cells in humans obtained in Figure 3E and in Figure 8D? According to the data, it would mean that the prometaphase-metaphase duration could be prolonged by expressing any of these genes in chimpanzee organoid.
Silver has reported that prolonged mitosis of progenitors could lead to apoptosis or differentiation , Neuron.
Based on this finding one would draw the opposite conclusion. Reviewer 3's criticism is also mainly conceptual. Nat Comm and others? Are human organoids larger more cells than chimpanzee after a similar period in culture? This simple piece of information may help understand if these organoids are indeed helpful to unravel differences between chimp and human cortex development, and if so where to look.
Do human aRGs undergo more rounds of cell division than chimp? Reviewer 3 also asks: Are the differences observed in length of cell cycle phases and those not observed due to human-chimp differences in brain development?
Or rather they may be due to the transition between iPSC and organoid? The authors nicely compare metaphase in blood B cells from chimp and human and don't find differences. But then, will they find similar differences between human and chimp when comparing organoids from other tissues i.
Finally, the reviewer argues: The authors state that "each chimpanzee cell represents a cell state on a continuum from NPSCs to neurons based on gene expression signatures". Whereas one agrees on the concept of transcriptomic continuity across cell types in development, how does this concept fit with the canonical criterion of classifying cell types in the cerebral cortex in discrete groups, as is also done in this study?
Although this is clearly not the main focus of the study, this type of classification analysis is quite used throughout the manuscript, and so the authors would do well in discussing this point. The 2D cortical area of an arbor was determined by the total areas of unique anchors occupied by its nodes. For neurons with tufted apical dendrite, we vertically shifted the arbors, so the top of apical dendrites reached L1.
We manually confirmed that all tufted apical dendrites reached L1 in the original image. We performed PCA to reduce the effect of noise. We developed the Neuron-beta metric by borrowing the concept of the beta value from the finance field Projection strength is defined as ln axon length in mm. Strength values for regions with axon length below 1 mm were set as 0.
Only non-cortical areas were included. Hierarchical clustering was performed using UMAP embeddings. Minimum branch length for clusters was manually determined.
Data normalization: morphological features were normalized by the mean and standard variation in a feature-wise manner. Projection pattern features were defined as ln axon length in mm.
For regions with axon length below 1 mm, projection pattern feature values are set as 0. Soma locations were flipped to the same hemisphere. Similarity metrics: for each feature set, we first calculated the Euclidean distance matrix. Then a ranked k -nearest neighbour KNN matrix was created. We then applied the shared nearest neighbor SNN approach to measure the similarity between each pair of samples x i and x j. The SNN metric was defined as the maximum average rank among their common neighbours:.
Co-clustering analysis: the co-clustering matrix for each feature set was calculated by iterative random sampling. We then applied the Fast-greedy community detection algorithm using the Python package python-igraph for clustering assignment. For each pair of samples, the co-clustering score was defined as the times of co-clustering normalized by the iterations of co-occurring. Resampling was performed 1, times to reach saturation. The overall co-clustering matrix is a weighted average of the four feature sets.
Agglomerative clustering was performed on the co-clustering matrix to get clusters. Outlier removal: outliers were detected by comparing the Euclidean distance between a sample and the other samples with the same cluster identity. We used overall within-cluster distance as the background distribution. Samples with significantly higher one-sided Mann—Whitney test within-cluster distance were filtered out as outliers.
Agglomerative clustering was performed for the remaining co-clustering matrix. This process iterated until no new outlier could be detected. Characterization of cell types: for each feature set, we performed two-sided Mann-Whitney tests: claustrum versus cortical neurons; each cluster versus other clusters.
P -values were adjusted by Bonferroni correction. Stereotaxic injection procedures were performed as previously described Mice survived 3 weeks or 4 weeks for the tamoxifen-induced mice after injection, and brains were perfused and collected for TissueCyte imaging. Stereotaxic injection procedures were performed as described Mice were injected at P40 or older, and survived for 16—31 days after injection.
Cells from transgenic mice or transgenic mice injected with retrograde tracers were collected by microdissection of different cortical regions. Single-cell suspensions were generated and cells were collected using fluorescence activated cell sorting FACS. After sequencing, raw data was quantified using STAR v2. Only uniquely aligned reads were used for gene quantification. Clustering was performed using in-house developed R package scrattch.
All the cells from CLA were mapped to the Car3 subclass. The cortical and CLA Retro-seq cells previously mapped to the Car3 subclass were then re-mapped to the new clusters, using marker genes that discriminate these 8 clusters. Further information on research design is available in the Nature Research Reporting Summary linked to this paper.
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J Cell Biol. Download references. All authors read and approved the final manuscript. The authors are grateful to members of the David L. Bennett, Linda Greensmith, and Giampietro Schiavo laboratories for helpful discussions, and to Ione Meyer and Bernadett Kalmar for critical reading of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. You can also search for this author in PubMed Google Scholar.
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