Akanksha Jain’s research while affiliated with ETH Zurich and other places

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Publications (10)


Long-term live imaging of sparse and multi-mosaic fluorescently labelled brain organoids
a, Schematic of the mosaic fluorescent organoid protocol and light-sheet image acquisition setup. EB, Embryoid body. b, UMAP embedding of organoid time-course scRNA-seq data with cells coloured by cluster and labelled by cell population (left) or time point (right). See Methods for cell numbers (n) at each timepoint. c, Stacked barplot showing the proportion of each cell population per time point. d, Feature plots showing normalized expression of representative marker genes. e, Maximum intensity projection image at 75 h of imaging from a 188-h imaging experiment. Organoids contain five different cell lines that contain stable genetic tagging of proteins with red or green fluorescent protein (RFP and GFP, respectively), as well as unlabelled cells. Scale bar, 100 µm. n = 16 organoids, imaged together. f, Organoid cross-section (84 h) showing nuclear membrane (lamin, RFP in magenta), plasma membrane label (CAAX, RFP in magenta), actin (GFP in green), tubulin (RFP in magenta) and nuclei (histone, GFP in green). Scale bar, 50 µm. g, Images of one organoid at different time points from a timecourse imaging experiment showing the maximum intensity projection (left) and cross-section (right). Scale bar, 100 µm. h, Cross-sections of an organoid showing lumen formation and fusion over time (hours). Dashed lines outline the lumen. Scale bar, 100 µm. 16 organoids, imaged together. i, 3D-rendered organoid showing segmented lumen and organoid epithelium masks. j, Graph showing total organoid volume measured per day from day 4 to day 9. k, Graph showing total volume of all lumen over time. l, Graph showing change in total number of segmented lumen over time. The dashed vertical line indicates the peak lumen number. The grey shading indicates standard deviation and the centre line denotes the mean (j–l). The dashed vertical lines in j and k denote the minimum (first line) and maximum (second line) of the lumen volume (%).
Extrinsic ECM affects brain organoid morphogenesis
a, UMAP embedding of scRNA-seq data with cells coloured by diffusion component ranking. b, Density plots showing cell distributions arranged in a pseudotime prediction (diffusion component 1 (DC1)) for time point (left) and cell type (right) labels. c, Heatmap showing normalized gene expression over DC1 ranking. d, Top DAVID Gene Ontology analysis terms calculated for genes that change over pseudotime from day 5 to day 11. A Fisher’s exact test was used to assess the significance of enrichment. e, Feature plots showing normalized expression of example ECM-related genes that show an increase in expression over time. f, Schematic representation of the extracellular microenvironment and the corresponding brightfield image for organoids grown with Matrigel (extrinsic ECM), without any external embedding (no-matrix) and with a low-melting agarose embedding (diffusion barrier). N = 3, n = 4 organoids. Scale bars, 100 µm. g, Images show cross-sections of sparse and multi-mosaic organoids containing cells labelled with nuclear membrane (lamin, RFP in orange), actin (GFP in cyan), tubulin (RFP in orange) and unlabelled cells from a light-sheet imaging experiment where organoids were embedded in Matrigel (n = 4), no-matrix (n = 8) or 0.6% agarose diffusion barrier (n = 4). The dashed lines outline the lumen. Scale bars, 100 µm. h, 3D renderings of segmented lumen in the organoids shown in panel g, colour coded for lumen axis measurements. i, Graphs showing total lumen number (top) measured per day from day 4 to day 9 for all imaged organoids, and total volume of all lumen (bottom) over time. j, Graph showing the number of lumen fusions over time. The shading indicates standard deviation and the centre line denotes the mean (i,j).
Cell and nucleus morphology transitions using demultiplexed mosaic cellular labels
a, Image analysis pipeline used for cell segmentation, demultiplexing and downstream analysis of mosaic cell labels. b, Maximum intensity projection image of an organoid at day 6 (left) showing dual-channel data (lamin, CAAX, tubulin (RFP, magenta), and actin, histone (GFP, green). Right: Corresponding demultiplexed images labelled with nuclear membrane (lamin, red), plasma membrane (CAAX, orange), actin (blue), tubulin (magenta) and nuclei (histone, green). Scale bar, 100 µm. c, Maximum intensity projection of demultiplexed image (lamin in red; CAAX in orange; actin in blue; tubulin in magenta; and histone in green). Scale bar, 100 µm. d, 4D cell tracks and tissue flows (averaged from tracks up to the last 24 h) during lumen expansion measured for all segmented actin-labelled cells from Matrigel, no-matrix and agarose conditions. The inset shows the colour key for arrow movements in 3D: white is towards the imaging objective and black is away from the imaging objective. e, Violin plot showing cell movements from the organoid centre towards the organoid surface (+1); n = 256 for Matrigel, 191 for no-matrix and 279 for agarose. f, PAGA initialized UMAP embeddings of all demultiplexed labels based on morphometric feature extraction. g, PAGA-initialized UMAP embeddings showing change in axis length for actin, tubulin and CAAX labels and change in nuclei volume measured using histone and lamin segmentations. PAGA plots show change in average cluster age (days), node size indicates the number of cells within one cluster, and edge width reflects the strength of connection between two clusters. h, PAGA-initialized UMAP embeddings and PAGA plots showing cell morphotype clusters using cells segmented from Matrigel, no-matrix and agarose conditions. The plots are based on morphometric measurements extracted for all segmented cells (actin). i, PAGA-initialized UMAP embeddings show a change in axis ratio of cells over time overlaid with average cluster age shown using PAGA plots. PAGA plots are colour coded based on the average age of the cluster from light grey to black. j, Heatmap showing example morphometric measurements for each morphotype cluster that are used to generate PAGA-initialized UMAP in panels h,i. k, Spatial distributions of actin-labelled cells in organoids showing cells coloured by their morphotype clusters. l, Example cells (actin) belonging to each of the morphotype clusters. m, Stacked barplots showing the proportion of cells in individual actin morphotype clusters in Matrigel, no-matrix and agarose conditions. n, All cells (actin) coloured by their alignment index (absolute cosine of the angle to the nearest organoid surface normal). Scale is from 0 to 1, with 1 (red) corresponding to cells that align perpendicular to the organoid surface. o, Violin plot showing the cell alignment (actin) values across all segmented cells from day 4 to day 12 for all three conditions. The boxes of the violin plots show the interquartile range, the line at the centre is the median and the whiskers extend to the data range excluding outliers (e,o).
Multiplexed immunohistochemistry (4i) reveals spatial region emergence in organoid development
a, Overview of the 4i data acquisition pipeline. Organoids from a timecourse were fixed and sectioned followed by mounting on a glass coverslip. b, Image showing an example organoid section with segmented compartments (extracellular, cytoplasmic and nuclear) used for downstream quantitative analysis. c, Selected images showing protein stainings on day 21 on organoid slices in Matrigel (n = 4) and no-matrix (n = 3) conditions. d, UMAP embedding based on the combined cellular (nuclear + cytoplasmic) protein expression, with each dot representing a cell, clustered and annotated as distinct cell types. e, Example organoids (day 21) from each condition (Matrigel and no-matrix) with cell clusters projected back to the image. f, UMAP embedding based on the combined protein expression in the extracellular compartment showing individual clusters. g, Example organoids (day 21) from each condition (Matrigel and no-matrix) with extracellular cell clusters projected back to the image. h, Stacked barplot showing the cluster proportion of each cell population from all days in Matrigel and no-matrix conditions. *P < 0.05, calculated using a Fisher's exact test (two-sided) between the cluster proportions of the conditions corrected for multiple testing using the Benjamini–Hochberg method. For P values, see Supplementary Table 13. i, Stacked barplot showing the ECM cluster proportion per cell population. The violin plots show the major axis of the lumen that have been assigned to each cell cluster. Dienceph, diencephalic; NC, neural crest; NCC, neural crest cell; prog., progenitor; prosenceph., prosencephalic; tel., telencephalic. j, Violin plot showing the protein expression in extracellular and cellular compartments in Matrigel and no-matrix conditions. *P < 0.05, calculated using a one-way analysis of variance (ANOVA) across the timepoints for a condition corrected for multiple testing using the Benjamini–Hochberg method. n = 5,137 for Matrigel and n = 3,958 for no-matrix for the extracellular quantifications, and n = 17,140 for Matrigel and n = 16,007 for no-matrix for the cytoplasmic quantifications. The boxes of the violin plots show the interquartile range, the line at the centre is the median and the whiskers extend to the data range excluding outliers (i,j). For P values, see Supplementary Table 13. Scale bars, 100 µm (all panels).
YAP1 mechanotransduction-mediated WLS activation
a, Images show cross-sections of organoids stained with antibodies labelling YAP1 (day 16) and WLS (day 15) from Matrigel and no-matrix conditions. Scale bars, 100 µm. b, Violin plots showing protein expression distribution of nuclear YAP1 (day 16) and cytoplasmic WLS (day 15) from Matrigel and no-matrix conditions. *P < 0.05, calculated using a Wilcoxon rank-sum test (two-sided) between conditions corrected for multiple testing using the Benjamini–Hochberg method. The boxes of the violin plots show the interquartile range, the line at the centre is the median and the whiskers extend to the data range excluding outliers. For P values, see Supplementary Table 13. c, Schematic showing the developing brain with distinct regions along the rostrocaudal axis: prosencephalon (telencephalon (Tel.) + diencephalon (Die.)), mesencephalon (Mes.) and rhombencephalon (Rh.). The dotted lines show coronal sections to illustrate lumen (brain ventricle) size differences. A schematic summarizing the morphological distinctions between Matrigel and no-matrix organoids with corresponding YAP1 and WLS expression differences is also shown (right). d, Signal tracks of bulk CUT&Tag sequencing data showing the enrichment intensity of YAP1 binding to the WLS gene, profiled with two different YAP1 antibodies. Tracks are shown for IgG and Tn5 control and the repressive and active marks profiled with H3K27me3, H3K4me3 and H3K9ac antibodies. Chr. 1, chromosome 1. e, Schematic of the light-sheet imaging and scRNA-seq experiment with control and YAP1 activator-treated organoids (top). EBs were cultured in NIM with Matrigel embedding on day 4. YAP1 activator (Py-60) or DMSO (control) was added to the imaging sub-chamber on day 5 or day 7. Imaging was terminated on day 10, and corresponding organoids from all three conditions were profiled with scRNA-seq on day 10. UMAP embeddings of scRNA-seq data from day 10 organoids in control and YAP1 treatment conditions are also shown (bottom left). A stacked barplot showing the cluster proportion of each cell population is also shown (bottom right). *P < 0.05, calculated using a Fisher’s exact test between the cluster proportions of the control and day 5-treated or day 7-treated conditions corrected for multiple testing using the Benjamini–Hochberg method. For P values, see Supplementary Table 13. The number of cells recovered after pre-processing of the scRNA-seq experiment: n = 2,085 for control, n = 763 for Py-60 on day 5 and n = 1,955 for Py-60 on day 7. f, Dotplot showing average expression and percentage of cells expressing selected regional marker genes per cell population. g, Maximum intensity projections (left) and cross-sections (right) at day 8, showing control organoids and YAP1 activator (given on day 5) treated organoids imaged with light-sheet microscopy. Sparse and multi-mosaic organoids contain cells labelled with nuclear membrane (lamin, RFP in orange), actin (GFP in cyan) and tubulin (RFP in orange) and unlabelled cells. Scale bars, 100 µm. Organoids imaged per condition, n = 4. h, Schematic of the scRNA-seq experiment with organoids generated from control and WLS-knockout (WLS-KO) iPS cell lines with five treatments. EBs were cultured in Matrigel or no-matrix conditions starting at day 4. WNT (CHIR99021) or YAP1 (Py-60) activators were added to a subset of organoids cultured with Matrigel from day 10 to day 12 and day 10 to day 16, respectively. Organoids were hashed and profiled with scRNA-seq on day 55. i, UMAP embeddings of scRNA-seq data coloured by cell population (top), genetic status (bottom left) or condition (bottom right). Mes./rhomb., mesencephalon or rhombencephalon. j, Stacked barplot showing the cluster proportion of each cell population in the different treatment conditions. k, Dotplot showing the average expression and the percentage of cells expressing selected regional marker genes per cell populations.
Morphodynamics of human early brain organoid development
  • Article
  • Full-text available

June 2025

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39 Reads

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2 Citations

Nature

Akanksha Jain

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Gilles Gut

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[...]

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Barbara Treutlein

Brain organoids enable the mechanistic study of human brain development and provide opportunities to explore self-organization in unconstrained developmental systems1, 2–3. Here we establish long-term, live light-sheet microscopy on unguided brain organoids generated from fluorescently labelled human induced pluripotent stem cells, which enables tracking of tissue morphology, cell behaviours and subcellular features over weeks of organoid development⁴. We provide a novel dual-channel, multi-mosaic and multi-protein labelling strategy combined with a computational demultiplexing approach to enable simultaneous quantification of distinct subcellular features during organoid development. We track actin, tubulin, plasma membrane, nucleus and nuclear envelope dynamics, and quantify cell morphometric and alignment changes during tissue-state transitions including neuroepithelial induction, maturation, lumenization and brain regionalization. On the basis of imaging and single-cell transcriptome modalities, we find that lumenal expansion and cell morphotype composition within the developing neuroepithelium are associated with modulation of gene expression programs involving extracellular matrix pathway regulators and mechanosensing. We show that an extrinsically provided matrix enhances lumen expansion as well as telencephalon formation, and unguided organoids grown in the absence of an extrinsic matrix have altered morphologies with increased neural crest and caudalized tissue identity. Matrix-induced regional guidance and lumen morphogenesis are linked to the WNT and Hippo (YAP1) signalling pathways, including spatially restricted induction of the WNT ligand secretion mediator (WLS) that marks the earliest emergence of non-telencephalic brain regions. Together, our work provides an inroad into studying human brain morphodynamics and supports a view that matrix-linked mechanosensing dynamics have a central role during brain regionalization.

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Decoding morphogen patterning of human neural organoids with a multiplexed single-cell transcriptomic screen

February 2024

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153 Reads

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1 Citation

Morphogens, secreted signaling molecules that direct cell fate and tissue development, are used to instruct neuroepithelium to differentiate towards discrete brain region identities. Neural tissues derived from pluripotent stem cells in vitro (neural organoids) provide new models for studying brain region development, however, we lack a comprehensive survey of how the developing human neuroepithelium responds to morphogen cues. Here, we produce a detailed map of morphogen-induced effects on the axial and regional specification of human neural organoids using a multiplexed single-cell transcriptomics screen. We find that the timing, concentration, and combination of morphogens strongly influence organoid cell type and regional composition, and that cell line and neural induction method strongly impact the response to a given morphogen condition. We apply concentration gradients in microfluidic chips or a range of static concentrations in multi-well plates to explore how human neuroepithelium interprets morphogen concentrations and observe similar dose-dependent induction of patterned domains in both scenarios. Altogether, we provide a detailed resource that supports the development of new regionalized neural organoid protocols and enhances our understanding of human central nervous system patterning.



Morphodynamics of human early brain organoid development

August 2023

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116 Reads

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8 Citations

Brain organoids enable the mechanistic study of human brain development, and provide opportunities to explore self-organization in unconstrained developmental systems. Here, we establish long-term, live light sheet microscopy on unguided brain organoids generated from fluorescently labeled human induced pluripotent stem cells, which enables tracking of tissue morphology, cell behaviors, and subcellular features over weeks of organoid development. We provide a novel dual-channel, multi-mosaic and multi-protein labeling strategy combined with a computational demultiplexing approach to enable simultaneous quantification of distinct subcellular features during organoid development. We track Actin, Tubulin, plasma membrane, nucleus, and nuclear envelope dynamics, and quantify cell morphometric and alignment changes during tissue state transitions including neuroepithelial induction, maturation, lumenization, and brain regionalization. Based on imaging and single-cell transcriptome modalities, we find that lumenal expansion and cell morphotype composition within the developing neuroepithelium are associated with modulation of gene expression programs involving extracellular matrix (ECM) pathway regulators and mechanosensing. We show that an extrinsically provided matrix enhances lumen expansion as well as telencephalon formation, and unguided organoids grown in the absence of an extrinsic matrix have altered morphologies with increased neural crest and caudalized tissue identity. Matrix-induced regional guidance and lumen morphogenesis are linked to the WNT and Hippo (YAP1) signaling pathways, including spatially restricted induction of the Wnt Ligand Secretion Mediator (WLS) that marks the earliest emergence of non-telencephalic brain regions. Altogether, our work provides a new inroad into studying human brain morphodynamics, and supports a view that matrix-linked mechanosensing dynamics play a central role during brain regionalization.


Multi-omic atlas of brain organoid development reveals developmental hierarchies and critical stages of fate decision
a, Schematic of the experimental design and UMAP embedding of integrated multi-omic metacells. Organoids from three iPS cell lines and one ES cell line were dissociated for paired scRNA-seq and scATAC-seq at time points spanning 4 days to 2 months of development. The two modalities were integrated to form metacells with RNA and ATAC components. EB, embryoid body; IPs, intermediate progenitors; N.ect., neuroectoderm; N.epi., neuroepithelium; PSCs, pluripotent stem cells. b, Examples of loci with differential accessibility during organoid development from pluripotency. c, Schematic of the branch-inference strategy. High-resolution clusters were assigned to branches on the basis of terminal fate transition probabilities calculated based on RNA velocity. d, Branch visualization in a force-directed layout. The circles represent high-resolution clusters of metacells coloured by assignment (neuroepithelium (grey); non-telencephalon progenitors (teal); telencephalon progenitors (plum); dorsal telencephalon (orange); ventral telencephalon (purple)). e, Graph representation of regional branches coloured by mean expression (log[transcript counts per 10,000 + 1]) (top) and gene activity (log[transcript counts per 10,000 + 1]) (bottom) of marker genes. The range of values is indicated for each plot. Norm., normalized. f, Stage- and branch-specific gene expression and motif enrichment z-score (Methods). Values are minimum–maximum (min–max) scaled across rows. N.t., non-telencephalon; t., telencephalon.
Pando leverages multimodal measurements to infer a multiphasic GRN underlying human brain organoid development
a, Schematic of the Pando GRN-inference framework. Candidate regions are identified through intersection of accessible peaks with CREs or conserved elements. Predicted TFs are selected for each candidate region through binding-motif matching. The relationship between TF–binding-site pairs and the expression of target genes is then fitted with a regression model. E, expression; A, accessibility; g1, target gene 1; tf1,2, transcription factors; p1,4, peaks; GLM, generalized linear model; reg., regularized. b, Signal tracks showing normalized accessibility at the transcription start site of EMX1 in the different branches and inferred regulatory regions for various transcription factors. The line colour represents the sign of the interaction and the box colour (greyscale) represents the false-discovery rate (FDR) of the most significant interaction for this region. c, UMAP embedding of the inferred TF network based on co-expression and inferred interaction strength between TFs. Colour and size represent the expression-weighted pseudotime and PageRank centrality of each TF, respectively. d, UMAP embedding shaded by module features. e, Target specificity for branch-specific TFs. f, UMAP embedding of branch-specific TF networks highlighting TFs with branch-specific targets and interactions with branch-specific accessibility. g, Groups of TFs with differential activity between the dorsal (red) and ventral (purple) telencephalon branch. TF activity is indicated by a coloured dot for each branch, connected by a line, and was calculated by multiplying the mean regulatory coefficient (coef.) with the average expression (expr.) in the branch. The sign of the activity indicates whether the regulation is mainly activating (+) or repressing (−).
TF perturbations in mosaic organoids reveal critical regulators of neurodevelopmental fate decisions
a, Schematic of the single-cell TF perturbation experiment using the CRISPR droplet sequencing (CROP-seq) method. b, The minimum–maximum-scaled average expression (log[transcript counts per 10,000 + 1]) of targeted genes in NPCs, IPs and neurons of the primary and organoid cortex. c, The proportion of cells with each perturbation for each experiment. d, UMAP embedding with cells coloured by detected gRNA (left) and branch assignment (right). e, Regional enrichment of gRNAs. The sidebar shows the number of gRNAs that were consistent and the circles represent consistent effects between experiments and statistically significant (FDR < 0.01) effects on composition. The arrows indicate the predominant observed effect. f, UMAP embedding coloured by consistent gRNAs for selected genes that had a strong effect on fate regulation. g, The Spearman correlation of HES1-target (top, n = 18 genes) and GLI3-target (bottom, n = 42 genes) genes to transition probabilities into the dorsal branch. The GRN was subsetted to retain connections that are accessible at the branchpoint (>5% detection rate). The centre line represents the median, the box limits show the 25–75% interquartile range and the whiskers indicate 1.5× the interquartile range. h, Schematic of the GLI3 loss-of-function experiment using an inducible CRISPR–Cas9 nickase system. i, UMAP embedding of scRNA-seq data from 6-week-old WT and GLI3-KO brain organoids showing the trajectories from NPCs to neurons coloured by different clusters assigned to regional branches. The inset is coloured by genetic condition. j, Stacked bar plots showing the distribution of cluster (colour) assignment per organoid for each condition. k, Differential expression (DE) in ventral telencephalic neurons for GLI3-KO data and CROP-seq data containing a GLI3 gRNA. The x and y axes indicate the coefficients of the linear model. Colours indicate significance (FDR < 10⁻⁴) in CROP-seq, the KO cell line or both.
Single-cell multiome view of GLI3 loss of function reveals distinct regulomes and effectors of dorsoventral telencephalon specification
a, Schematic of the experiment measuring the transcriptome and chromatin accessibility in the same cell at 3 weeks of brain organoid development. b, UMAP embedding coloured by cluster and labelled by projected cell fate. Inset: UMAP coloured by genetic state. c, The number of DEGs of control (WT) versus GLI3-KO cells in the different clusters. d, Differential expression in telencephalic progenitors (clusters 0 and 2) after GLI3 KO. e, DEGs after GLI3 KO for early telencephalic progenitors (week 3), ventral telencephalic progenitors (week 6) and neurons (week 6), and differential accessibility after GLI3 KO in early telencephalic progenitors (week 3). Genes are coloured according to the associated signalling pathway (if applicable) and molecular function. f,g, GRN subgraph for early telencephalic (f) and ventral telencephalon (g) progenitors, showing first- and second-order GLI3 targets. The circles represent genes for which all TFs are labelled. The edges are coloured on the basis of TF regulatory interaction. h, The GLI3-binding score (the sum of CUT&Tag signal intensity for the gene body + 2 kb) in WT organoids versus log-transformed fold change in differential expression in early telencephalic progenitors (week 3). Genes with differentially accessible (DA) CREs are coloured black. Signal tracks of GLI3 binding matched with differential accessibility peaks of HES4 and HES5 in early telencephalic progenitors. i, The z-scored mean correlation between module gene expression and branch probabilities (branch activation score) for differentially expressed TFs. j, The log-transformed fold change of genes after treatment with SHH versus GLI3 KO. GO terms are shown for common DEGs, SHH-treatment-specific and GLI3-specific DEGs. k, Schematic summarizing the results from the GLI3 and SHH perturbations.
Inferring and perturbing cell fate regulomes in human brain organoids

October 2022

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774 Reads

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188 Citations

Nature

Self-organizing neural organoids grown from pluripotent stem cells1–3 combined with single-cell genomic technologies provide opportunities to examine gene regulatory networks underlying human brain development. Here we acquire single-cell transcriptome and accessible chromatin data over a dense time course in human organoids covering neuroepithelial formation, patterning, brain regionalization and neurogenesis, and identify temporally dynamic and brain-region-specific regulatory regions. We developed Pando—a flexible framework that incorporates multi-omic data and predictions of transcription-factor-binding sites to infer a global gene regulatory network describing organoid development. We use pooled genetic perturbation with single-cell transcriptome readout to assess transcription factor requirement for cell fate and state regulation in organoids. We find that certain factors regulate the abundance of cell fates, whereas other factors affect neuronal cell states after differentiation. We show that the transcription factor GLI3 is required for cortical fate establishment in humans, recapitulating previous research performed in mammalian model systems. We measure transcriptome and chromatin accessibility in normal or GLI3-perturbed cells and identify two distinct GLI3 regulomes that are central to telencephalic fate decisions: one regulating dorsoventral patterning with HES4/5 as direct GLI3 targets, and one controlling ganglionic eminence diversification later in development. Together, we provide a framework for how human model systems and single-cell technologies can be leveraged to reconstruct human developmental biology. A multi-omic atlas of brain organoid development facilitates the inference of an underlying gene regulatory network using the newly developed Pando framework and shows—in conjunction with perturbation experiments—that GLI3 controls forebrain fate establishment through interaction with HES4/5 regulomes.


Figure 3. Single-nucleus RNA-sequencing uncovers nuclei diversification within the syncytia. (A) Nuclei extraction using a douncer with subsequent nuclei enrichment through FACS was performed prior to singlenucleus experiments. (B) Uniform Manifold Approximation and Projection (UMAP) embedding visualizes nuclei heterogeneity of a freshly grown plasmodium. Cluster-specific marker genes are visualized as features on the embedding. (C) Pseudotemporal ordering of nuclei extracted from clusters with high TOP2A expression in (B). Extracted nuclei were re-embedded by UMAP with pseudotime ranks shown on the embedding. (D) Gene expression changes of cluster marker (see Figure 2F) genes across pseudotime are shown for snRNA-seq (left) and spatially resolved grid RNA-seq data (right). (E) UMAP embedding reveals nuclei heterogeneity of a 1-week-old slime mold plasmodium with nuclei originating from different parts of the plasmodium. Pie charts reveal different nuclei proportions of two exemplary clusters. The UMAP inset encodes the origin of each nucleus by color. (F) Cluster marker genes for (E) are presented as feature plots on the embedding. (G) From left to right: bar chart representing proportions of nuclei origin per cluster shown in (E). Heatmap representation of scaled correlation values between the pseudobulk gene expression per cluster of the plasmodium in (E) and the pseudobulk gene expression per cluster for SM4 (Figure 2G). Arrows indicate for which clusters correlations against individual grids of SM4 were estimated with the result being presented as a feature on the grid embedding. The online version of this article includes the following figure supplement(s) for figure 3: Figure supplement 1. Nuclei heterogeneity revealed by single-nuclei RNA-seq.
Figure 4. Single-nucleus amoebae are heterogeneous and differentially regulate cytokinetic programs compared with syncytial nuclei. (A) Brightfield image of amoebae (single frame from Video 3). Scale bar: 10 µm. (B) Schematic illustrating mechanistic differences during mitosis in amoebae (left) and plasmodia (right). (C) Histograms reveal the distribution of the raw transcript counts per gene (left) and the number of different genes per cell detected (right) for amoeba scRNA-seq data. (D) Venn diagram shows the number of genes detected per experimental setup and their overlap. A 10% quantile cutoff was applied to remove sparsely expressed genes for the comparison. (E) Uniform Manifold Approximation and Projection (UMAP) embedding of single haploid amoeba cells (center). Cell cycle stages are indicated and gene expression intensities are visualized as features on the embedding (left/right). (F) UMAP embedding of single amoeba cells in G2M phase and samples of a cycling plasmodium (SM1). RNA-seq samples were integrated using Seurat's built-in CCA 'anchoring' (Stuart et al., 2019) to allow a comparison between single-cell (10x Genomics) and bulk RNA-seq (SmartSeq2) data. Feature plots of PATS1 and CDKA1 reveal cell/nuclei cycle directionality for samples. Drawn arrows reveal directionality. (G) Cell cycle-related marker gene expression identified through cluster-specific gene expression analysis between amoeba cells and plasmodium grids in (F) is visualized as a boxplot for plasmodium and amoeba samples, respectively. TBAD and TBB1 are shown as reference for non-DE genes. (H) Amoeba-specific genes are visualized as feature plots on the UMAP embedding in (F). The online version of this article includes the following figure supplement(s) for figure 4: Figure supplement 1. Analysis of the impact of unannotated transcripts on amoeba cell heterogeneity.
Spatial transcriptomic and single-nucleus analysis reveals heterogeneity in a gigantic single-celled syncytium

February 2022

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342 Reads

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12 Citations

eLife

In multicellular organisms, the specification, coordination, and compartmentalization of cell types enable the formation of complex body plans. However, some eukaryotic protists such as slime molds generate diverse and complex structures while remaining in a multinucleate syncytial state. It is unknown if different regions of these giant syncytial cells have distinct transcriptional responses to environmental encounters and if nuclei within the cell diversify into heterogeneous states. Here, we performed spatial transcriptome analysis of the slime mold Physarum polycephalum in the plasmodium state under different environmental conditions and used single-nucleus RNA-sequencing to dissect gene expression heterogeneity among nuclei. Our data identifies transcriptome regionality in the organism that associates with proliferation, syncytial substructures, and localized environmental conditions. Further, we find that nuclei are heterogenous in their transcriptional profile and may process local signals within the plasmodium to coordinate cell growth, metabolism, and reproduction. To understand how nuclei variation within the syncytium compares to heterogeneity in single-nucleus cells, we analyzed states in single Physarum amoebal cells. We observed amoebal cell states at different stages of mitosis and meiosis, and identified cytokinetic features that are specific to nuclei divisions within the syncytium. Notably, we do not find evidence for predefined transcriptomic states in the amoebae that are observed in the syncytium. Our data shows that a single-celled slime mold can control its gene expression in a region-specific manner while lacking cellular compartmentalization and suggests that nuclei are mobile processors facilitating local specialized functions. More broadly, slime molds offer the extraordinary opportunity to explore how organisms can evolve regulatory mechanisms to divide labor, specialize, balance competition with cooperation, and perform other foundational principles that govern the logic of life.


Lineage recording in human cerebral organoids

January 2022

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563 Reads

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150 Citations

Nature Methods

Induced pluripotent stem cell (iPSC)-derived organoids provide models to study human organ development. Single-cell transcriptomics enable highly resolved descriptions of cell states within these systems; however, approaches are needed to directly measure lineage relationships. Here we establish iTracer, a lineage recorder that combines reporter barcodes with inducible CRISPR–Cas9 scarring and is compatible with single-cell and spatial transcriptomics. We apply iTracer to explore clonality and lineage dynamics during cerebral organoid development and identify a time window of fate restriction as well as variation in neurogenic dynamics between progenitor neuron families. We also establish long-term four-dimensional light-sheet microscopy for spatial lineage recording in cerebral organoids and confirm regional clonality in the developing neuroepithelium. We incorporate gene perturbation (iTracer-perturb) and assess the effect of mosaic TSC2 mutations on cerebral organoid development. Our data shed light on how lineages and fates are established during cerebral organoid formation. More broadly, our techniques can be adapted in any iPSC-derived culture system to dissect lineage alterations during normal or perturbed development.


Nuclei are mobile processors enabling specialization in a gigantic single-celled syncytium

April 2021

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1,506 Reads

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3 Citations

In multicellular organisms, the specification, coordination, and compartmentalization of cell types enable the formation of complex body plans. However, some eukaryotic protists such as slime molds generate diverse and complex structures while remaining in a multinucleated syncytial state. It is unknown if different regions of these giant syncytial cells have distinct transcriptional responses to environmental encounters, and if nuclei within the cell diversify into heterogeneous states. Here we performed spatial transcriptome analysis of the slime mold Physarum polycephalum in the plasmodium state under different environmental conditions, and used single-nucleus RNA-sequencing to dissect gene expression heterogeneity among nuclei. Our data identifies transcriptome regionality in the organism that associates with proliferation, syncytial substructures, and localized environmental conditions. Further, we find that nuclei are heterogenous in their transcriptional profile, and may process local signals within the plasmodium to coordinate cell growth, metabolism, and reproduction. To understand how nuclei variation within the syncytium compares to heterogeneity in single-nucleated cells, we analyzed states in single Physarum amoebal cells. We observed amoebal cell states at different stages of mitosis and meiosis, and identified cytokinetic features that are specific to nuclei divisions within the syncytium. Notably, we do not find evidence for predefined transcriptomic states in the amoebae that are observed in the syncytium. Our data shows that a single-celled slime mold can control its gene expression in a region-specific manner while lacking cellular compartmentalization, and suggests that nuclei are mobile processors facilitating local specialized functions. More broadly, slime molds offer the extraordinary opportunity to explore how organisms can evolve regulatory mechanisms to divide labor, specialize, balance competition with cooperation, and perform other foundational principles that govern the logic of life.


Lineage recording reveals dynamics of cerebral organoid regionalization

June 2020

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356 Reads

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11 Citations

Diverse regions develop within cerebral organoids generated from human induced pluripotent stem cells (iPSCs), however it has been a challenge to understand the lineage dynamics associated with brain regionalization. Here we establish an inducible lineage recording system that couples reporter barcodes, inducible CRISPR/Cas9 scarring, and single-cell transcriptomics to analyze lineage relationships during cerebral organoid development. We infer fate-mapped whole organoid phylogenies over a scaring time course, and reconstruct progenitor-neuron lineage trees within microdissected cerebral organoid regions. We observe increased fate restriction over time, and find that iPSC clones used to initiate organoids tend to accumulate in distinct brain regions. We use lineage-coupled spatial transcriptomics to resolve lineage locations as well as confirm clonal enrichment in distinctly patterned brain regions. Using long term 4-D light sheet microscopy to temporally track nuclei in developing cerebral organoids, we link brain region clone enrichment to positions in the neuroectoderm, followed by local proliferation with limited migration during neuroepithelial formation. Our data sheds light on how lineages are established during brain organoid regionalization, and our techniques can be adapted in any iPSC-derived cell culture system to dissect lineage alterations during perturbation or in patient-specific models of disease.

Citations (7)


... Concretely, fibronectin is considered a key molecule for cell adhesion and migration [52]. Therefore, it is worth mentioning that this molecule is often used to functionalize biomaterials and matrices [53][54][55], and that in the present BCs generated presented a complex, well-structured matrix containing fibronectin and other molecules correctly distributed in the network of collagen fibers. ...

Reference:

Novel genipin-crosslinked acellular biogenic conduits for tissue engineering applications
Engineering fibronectin-templated multi-component fibrillar extracellular matrices to modulate tissue-specific cell response
  • Citing Article
  • April 2024

Biomaterials

... Most studies recommend limiting their use to the first 24 to 48 hours after seeding or passaging, followed by withdrawal to permit natural morpho-genic processes to resume. Additionally, combining ROCK inhibition with biomimetic ECM environments or temporal gene control systems may mitigate these limitations and improve organoid reproducibility [50]. ...

Morphodynamics of human early brain organoid development

... Integration of network-based TF prioritization identifies Tcf21 as a top candidate to alter SMC to FMC transition Using our co-embedded scRNA and scATAC dataset, we leveraged complementary network inference methods, Pando 45 and CellOracle 46 , to create a custom workflow to infer transcription factor-target interaction networks (gene regulatory networks, GRNs) that direct phenotypic transition and simulate cell identity changes with in silico TF perturbations (Fig. 3C, S-Tables 6,7, Methods). The dataset was divided by PseudoEarly and PseudoLate bins to infer GRNs for these analyses. ...

Inferring and perturbing cell fate regulomes in human brain organoids

Nature

... Based on current syncytium research, the main origin mechanism is the cell fusion, which is based on numerical highlights from [1] to [5] in "A", "B," and "C". "A" highlights an initial process of contact between cells [1] and [2], mainly because of chemoattraction, and, thus, there are contacts between transmembrane fusogenic proteins that allow cell surface contact and union between uninuclear cells. In "B," the union of uninuclear cells product makes two different paths: (i) nuclear fusion, resulting in the ability to generate hybrids with reciprocal genetic exchange (horizontal transfer of genes) responsive to the microenvironment; (ii) no nuclear fusion results in syncytia generation, that is, an enlarged multinucleated cell with a predominance of internuclear communication due to gap junctions and chemical compounds in the medium, mainly. ...

Spatial transcriptomic and single-nucleus analysis reveals heterogeneity in a gigantic single-celled syncytium

eLife

... [146][147][148] Recently, development of spatial transcriptomics has shed light on the understanding of the spatial organization of cell types and their lineage relationships. 149 In the last few years, technologies utilizing organoids have developed rapidly. As human stem-cell-derived organoids could mimic the organ or tissue development of humans, cell lineage tracing on human organoids may provide a basis for studying lineages of human cell types. ...

Lineage recording in human cerebral organoids

Nature Methods

... An interesting study by Gerber et al. (2021) examined transcriptome evidence of differentiated encoding of proteins by nuclei at different locations in the amoeboid, plasmodial (syncytial) stage of the slime mold Physarum polycephalum (Amoebozoa) in laboratory culture. The plasmodia were grown under different environmental conditions, and single-nucleus RNAsequencing was used to dissect gene expression heterogeneity among nuclei. ...

Nuclei are mobile processors enabling specialization in a gigantic single-celled syncytium

... Starting from human iPSCs, such cultures partially mimic certain aspects of the human brain that 2D cultures cannot efficiently model due to the lack of other cellular subtypes. Naturally, the possibility of dissecting specific neural lineages in a more complex 3D organ model is an attractive proposition for researchers investigating neurodevelopment with a high degree of granularity (71)(72)(73)(74)(75). Currently, single-cell RNA sequencing offers a snapshot of different cell types in organoids (71,75), but such studies cannot be done without isolating cells by dissociating an organoid, a procedure that cannot be used for in-toto studies in growing organoid cultures. ...

Lineage recording reveals dynamics of cerebral organoid regionalization