Here, we introduce a comprehensive protocol for the generation and downstream analysis of human brain organoids using single-cell and single-nucleus RNA sequencing.
Over the past decade, single-cell transcriptomics has significantly evolved and become a standard laboratory method for simultaneous analysis of gene expression profiles of individual cells, allowing the capture of cellular diversity. In order to overcome limitations posed by difficult-to-isolate cell types, an alternative approach aiming at recovering single nuclei instead of intact cells can be utilized for sequencing, making transcriptome profiling of individual cells universally applicable. These techniques have become a cornerstone in the study of brain organoids, establishing them as models of the developing human brain. Leveraging the potential of single-cell and single-nucleus transcriptomics in brain organoid research, this protocol presents a step-by-step guide encompassing key procedures such as organoid dissociation, single-cell or nuclei isolation, library preparation and sequencing. By implementing these alternative approaches, researchers can obtain high-quality datasets, enabling the identification of neuronal and non-neuronal cell types, gene expression profiles, and cell lineage trajectories. This facilitates comprehensive investigations into cellular processes and molecular mechanisms shaping brain development.
Over the last years, organoid technologies have emerged as a promising tool to culture organ-like tissues1,2,3. Especially for organs that cannot be easily accessed, such as the human brain, organoids offer the opportunity to gain insights into development and disease manifestation4. As such, brain organoids have been widely used as an experimental model to investigate various human brain disorders, including developmental, psychiatric, or even neurodegenerative diseases4,5,6.
With the advent of single-cell transcriptome profiling technologies, primary human tissues and complex in vitro models could be studied with an unprecedented level of granularity, providing mechanistic insights into gene expression changes on the level of cell subpopulations in health and disease and informing about new putative therapeutic targets7,8,9. The organoid field has progressed by utilizing single-cell transcriptome profiling to assess cellular composition, reproducibility and the fidelity of brain organoid technologies10,11,12. Single-cell RNA-sequencing (scRNA-seq) enabled cell classification and the identification of genetic dysregulation in diseased organoids13,14. Importantly, it is the complexity of organoid tissues that necessitates the implementation of techniques that enable the profiling of individual cells. Characterization of organoids using methods such as bulk transcriptome profiling (bulk RNA sequencing) leads to masked cellular heterogeneity and gene expression profiles which are averaged across all types of cells within the complex tissue, ultimately limiting our understanding of ongoing processes during organoid development in health and disease15,16,17. As scRNA-seq methods continue to advance, an increasing number of atlases are being created, exemplified by resources like the Allen Brain Atlas or the Single cell atlas of human brain organoids by Uzquiano et al.18.
Accomplishing successful scRNA-seq from brain organoids relies on effective isolation and capture of intact cells. As the dissociation of brain organoids to obtain individual cells is based on enzymatic digestion, it can influence gene expression patterns by inducing stress and cell damage19,20. Hence, the dissociation of the tissue into individual cells is the most crucial step. An alternative approach is single-nucleus RNA sequencing (snRNA-seq), which facilitates the enzyme-free extraction of nuclei from both, fresh and frozen, tissue21,22. However, the isolation of nuclei from a tissue poses other challenges such as the enrichment of cell types of interest and the low RNA content of nuclei in comparison to cells.
Transcriptome studies of brain organoids are commonly conducted using scRNA-seq10,18,23. However, the isolation of single nuclei might provide an orthogonal and supplemental method to investigate the transcriptomic profile of organoids. Here, we introduce a toolbox for scRNA- and snRNA-seq for brain organoids and discuss the critical points for obtaining the best quality sequencing data.
The described protocol is performed in a biosafety level 1 laboratory of the Max Delbrück Center for Molecular Medicine (approval number: 138/08), in accordance with the requirements and in compliance with EU and national rules on ethics in research.
1. Derivation of forebrain organoids from induced pluripotent stem cells (iPSCs)
NOTE: This protocol was tested for several different iPSC lines cultured in a variety of stem cell media from different companies (Table 1). The generation of forebrain organoids is highly dependent on high quality iPSCs and a confluence of 60%-70% prior to starting the protocol. Here we used a commercially available cell line (see Table of Materials).
2. Derivation of single cell from organoids
NOTE: Single cell dissociation is performed using the Neural Tissue Dissociation Kit (Table 2), which uses mechanical and enzymatic dissociation. Here we describe a manual mechanical dissociation. As an alternative, a dissociation machine can be used.
3. Isolation of single nuclei from organoids
4. Library preparation and sequencing
5. Analysis
To investigate cell type composition of brain organoids using scRNA-seq and snRNA-seq, brain organoids were harvested after 30 days of culture as organoids at this stage already exhibit neuroepithelial loops consisting of progenitors surrounded by intermediate progenitors and early stage neurons4,18. Monitoring the quality of the organoids throughout growth and culturing is essential for obtaining reliable single-cell and single-nucleus data.
Organoids are formed through the aggregation of iPSCs into embryoid bodies (Figure 1B,C). Upon assembly, these embryoid bodies are expected to exhibit clear edges with minimal cellular debris (Figure 1C). Following the induction of the neuroectoderm, the embryoid bodies manifest an observable brightening around the surface with a comparatively darker inner region, indicating successful neural induction (Figure 1D). In the following weeks, the organoids develop so-called loops, neuronal rosette-like structures, primarily composed of neuroepithelial cells (Figure 1E,F). Although these organoids can be maintained in culture over several months, it should be noted that with their increasing size, there is a corresponding increase of cell death in the inner part of the organoids due to the lack of nutrients and oxygen availability, eventually resulting in the development of a necrotic core.
In order to explore the cellular diversity within cortical organoids through sequencing analysis, we isolated both single cells and nuclei from organoids. To balance out the inherent heterogeneity within a single batch of brain organoids, we conducted sequencing of four pooled organoids from one batch. Additionally, for comparative purposes between the isolation processes and to remove batch effects between the snRNA-seq and the scRNA-seq library, these organoids were divided into halves (eight halves in total; Figure 2). Following the enzymatic isolation of individual cells, it is crucial to ensure that cell viability remains above 80%, while observing that the cells maintain a round morphology (Figure 3A,B). Similarly, the mechanical isolation of individual nuclei should yield intact nuclei of varying sizes with minimal to no detectable debris (Figure 3C,D).
Sequencing analysis revealed that more cells than nuclei were captured and sequenced. Both datasets showed a good quality assessed by the high gene and UMI count per cell and nucleus, and the low percentage of mitochondrial reads (Figure 4A-C). As expected, mitochondrial as well as ribosomal reads comprise a higher fraction of total reads recovered in the scRNA-seq dataset as compared to the snRNA-seq dataset. In the scRNA-seq dataset, most cells contained less than 30% ribosomal reads, whereas in the snRNA-seq dataset, the majority of nuclei contained less than 5% ribosomal reads. Additionally, the majority of cells captured within the scRNA-seq dataset contain less than 5% mitochondrial reads. After filtering out mitochondrial reads, around 10,000 cells and 3,000 nuclei passed the quality threshold.
Integrative analysis of both datasets revealed that all clusters are represented in both datasets, indicating both methods recover the same cell types (Figure 5A,C). The annotation of the dataset shows that the major cell populations comprise of radial glia, intermediate progenitors, subcortical progenitors and neurons, as well as newborn deep layer projection neurons and less represented cell types such as Cajal-Retzius, the cortical hem and choroid plexus cells (Figure 5B). Overall, scRNA-seq and snRNA-seq could recover all cell types expected to be present in a 30-day-old brain organoid20.
Figure 1: Generation of brain organoids from iPSCs. Time course of brain organoid generation (A). Exemplary images of a successful cortical differentiation from iPSCs (B), which form embryoid bodies (72 h after seeding) (C) and show a successful neural induction after 7 days of culture (D). Organoid depicts distinct loops at day 15 (E) and day 30 (F). Scale bar: B-D 200 µm, E 400 µm, F 800 µm. Please click here to view a larger version of this figure.
Figure 2: Workflow to obtain single cells and single nuclei from brain organoids. Dissociation of organoids into single cells is conducted by mincing organoids with a scalpel, followed by an enzymatic digestion (top). Isolation of nuclei is conducted by mechanical dissociation, followed by purification via a Percoll gradient (bottom). The single cell and nuclei suspension are filtered and loaded into a microfluidics system for library generation and sequencing. Please click here to view a larger version of this figure.
Figure 3: Representative results of isolated cells and nuclei from 30-day-old organoids. (A) Exemplary picture of trypan blue-stained cells taken using a cell counter and (B) corresponding close-up. (C) Overlay of brightfield and fluorescent picture of DAPI stained nuclei and (D) corresponding close-up. Scale bar: 100 µM Please click here to view a larger version of this figure.
Figure 4: Quality control of scRNA- and snRNA-seq. Violin plots illustrate the absolute UMI count (A), gene count (B), mitochondrial (C), and ribosomal reads (D) of cells (blue) and nuclei (pink-purple). Please click here to view a larger version of this figure.
Figure 5: scRNA-seq and snRNA-seq retrieve similar cell populations in 30-day old organoids. Integrated embedded UMAP of 30-day old organoids, showing scRNA-seq (blue) and snRNA-seq results (pink-purple) (A), integrated and annotated and individual profiles of scRNA-seq and snRNA-seq (B,C). Please click here to view a larger version of this figure.
Table 1: Cell culture media and coating components Please click here to download this Table.
Table 2: Dissociation buffers for scRNA and snRNA-seq. Please click here to download this Table.
Supplementary Coding File 1: Coding file used to analyze the scRNA-seq and snRNA-seq data. Please click here to download this File.
Transcriptomic analysis of single cells and single nuclei has emerged as a pivotal tool for understanding gene regulatory mechanisms within complex tissues. Both methods enable transcriptome studies of brain organoids. To ensure an overall successful experiment, the quality of the starting material is of high relevance. Therefore, it is necessary to cut the organoids regularly to prevent the formation of a necrotic core26. It is also possible to eliminate this issue with an Air-Liquid Interface culture27. To reduce batch effects due to organoid heterogeneity, we suggest using at least four organoids per extraction. Alternatively, it is possible to conduct multiple sequencing runs of individual organoids, which additionally allows examination of the variability within the same batch17. When using both methods, scRNA-seq and snRNA-seq, in parallel, cutting organoids into two and dividing them for each method can further reduce differences caused by organoid heterogeneity.
To obtain a good quality transcriptomic profile of an individual cell or nucleus, it is crucial that the cellular or nuclear membrane remains intact during the whole procedure. Therefore, it is crucial to optimize both the enzyme concentration and the timing of the enzymatic digestion, which need to be adjusted to the age and size of the organoid. Additionally, it is essential to use the correct temperatures and centrifugation speed and to avoid harsh pipetting. In case of low viability, a dead cell removal kit can be used after the dissociation of the organoids into single cells. Complementary, for the isolation of nuclei it is vital to carefully wash the organoid with PBS and adapt the number of strokes with the douncer to organoid size. It is also important to carefully layer the Percoll gradient in order to avoid disturbances in the layering. If damaged nuclei remain after isolation, it is advised to implement a sorting step via Fluorescence-activated cell sorting22. Overall, the use of RNAse inhibitors is vital for all steps from sample dissociation to library generation to preserve the high quality RNA of each cell or nucleus.
In this study, scRNA-seq yielded more cells, allowing for a broader overview of cell populations. Additionally, cells contain more RNA and are easier to enrich for specific cell types through surface markers as compared to nuclei. However, single cell dissociation relies on an enzymatic process which can induce the transcription of stress-related RNAs19,28. Especially in diseases that are associated with an increased stress response, this can result in dissociation-induced artifacts20. Moreover, it was shown that single cell isolation resulted in the loss of sensitive cell populations3,29. While this may not be evident in this study, it is a possible outcome when using older brain organoids which are characterized by a higher cellular diversity.
As both datasets retrieve the same cell populations, the choice of the isolation method strongly depends on research questions, tissue acquisition and time management. While after dissociation the fixation and storage of single cells from brain organoids in methanol is well established30, the isolation of single nuclei from frozen brain organoids still requires optimization.
Overall, our protocols provide a versatile and adaptable platform to investigate the transcriptomic profile of individual cells and nuclei in brain organoids, paving the way to in-depth investigations of developmental- and disease-related questions in the in vitro human brain models.
The authors have nothing to disclose.
We thank Valeria Fernandez-Vallone for the original instructions for the Miltenyi Neural Dissociation kit. We also thank the Genomics Technology Platform of the Max Delbrueck Centrum for providing the recipe for the NP40 lysis buffer and valuable advice setting up this protocol. We also thank Margareta Herzog and Alexandra Tschernycheff for the lab organizational support.
1,4-DITHIO-DL-THREIT-LSG., F. D. MOL.-BIOL., ~1 M IN H2O (DTT) | Sigma | 43816-10ML | |
1.5 ml DNA low binding tubes | VWR | 525-0130 | microcentrifuge tube |
10x Cellranger pipeline | analysis pipline | ||
15 ml Falcon | Falcon | Centrifuge tube | |
2-Mercaptoethanol (BME) | Life Technologies | 21985023 | |
50 ml Falcon | Falcon | Centrifuge tube | |
A83-01 | Bio Technologies | 379762 | |
Antibiotic/Antimycotic Solution (100X) | Life Technologies | 15240062 | |
B-27 Plus Supplement | Life Technologies | 17504044 | |
B-27 Supplement without vitamin A | Life Technologies | 12587010 | |
Bovine serum albumin, fatty acid free (BSA) | Sigma Aldrich | A8806-5G | |
cAMP | Biogems | 6099240 | |
cAMP | Biogems | 6099240 | |
C-CHIP NEUBAUER IMPROVED | VWR | DHC-N01 | |
Cell strainer 40 µm | Neolab | 352340 | |
Cell strainer 70 µm (white) Nylon | Sigma | CLS431751-50EA | |
Chromium Controller & Next GEM Accessory Kit | 10X Genomics | 1000204 | |
Chromium Next GEM Chip G Single Cell Kit, 16 rxns | 10X Genomics | 1000127 | |
Chromium Next GEM Single Cell 3' Kit v3.1 | 10X Genomics | 1000268 | |
Complete, EDTA-free Protease Inhibitor Cocktaill | Roche | 11873580001 | |
DAPI | MERCK Chemicals | 0000001722 | |
DMEM/F12 | Life Technologies | 11320074 | |
Dounce tissue grinder set 2 mL complete | Sigma Aldrich | 10536355 | |
Essential E8 Flex Medium | Life Technologies | A2858501 | |
EVE Cell Counting Slides | VWR | EVS-050 ( 734-2676) | |
Foetal bovine serum tetracycline free (FBS) | PAN Biotech | P30-3602 | |
Geltrex LDEV-Free (coating) | Life Technologies | A1413302 | |
gentleMACS | Miltenyi Biotec | dissociation maschine | |
GlutaMAX supplements | Life Technologies | 35050038 | |
Heparin sodium cell culture tested | Sigma | H3149-10KU | |
human recombinant BDNF | StemCell Technologies | 78005.3 | |
human recombinant GDNF | StemCell Technologies | 78058.3 | |
Insulin Solution Human | Sigma Aldrich | I2643-25MG | |
Knockout serum replacement | Life Technologies | 10828028 | |
LDN193189 Hydrochloride 98% | Sigma Aldrich | 130-106-540 | |
MEM non-essential amino acid (100x) | Sigma Aldrich | M7145-100ml | |
MgCl2 Magnesium Chloride (1M) RNAse free | Thermo Scientific | AM9530G | |
mTeSR Plus | StemCell Technologies | 100-0276 | stem cell medium |
mTeSR1 | StemCell Technologies | 85850 | stem cell medium |
N2 Supplement | StemCell Technologies | 17502048 | |
Neural Tissue Dissociation Kit | Miltenyi Biotec B.V. & Co. KG | 130-092-628 | |
Neurobasal Plus | Life Technologies | A3582901 | |
NextSeq500 system | Illumina | Sequencer | |
NP-40 Surfact-Amps Detergent Solution | Life Technologies | 28324 | |
PBS Dulbecco’s | Invitrogen | 14190169 | |
PenStrep (Penicillin – Streptomycin) | Life Technologies | 15140122 | |
Percoll | Th. Geyer | 10668276 | |
Pluronic (R) F-127 | Sigma Aldrich | P2443-1KG | |
RiboLock RNase Inhibitor | Life Technologies | EO0382 | |
Rock Inhibitor (Y-27632 dihydrochloride) SB | Biomol | Cay10005583-10 | |
SB 431542 | Biogems | 3014193 | |
Sodium chloride NaCl (5M), RNase-free-100 mL | Invitrogen | AM9760G | |
StemFlex Medium | Thermo Scientific | A3349401 | stem cell medium |
StemMACS iPS-Brew XF | Miltenyi Biotec | 130-104-368 | stem cell medium |
TC-Platte 96 Well, round bottom | Sarstedt | 83.3925.500 | |
TISSUi006-A | TissUse GmbH | https://hpscreg.eu/cell-line/TISSUi006-A | |
Trypan Blue | T8154-20ml | Sigma | |
TrypLE Express Enzyme, no phenol red | Life Technologies | 12604013 | Trypsin-based reagent |
UltraPure 1M Tris-HCl Buffer, pH 7.5 | Life Technologies | 15567027 | |
XAV939 | Enzo Life sciences | BML-WN100-0005 |