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Citation: Nowakowski, T.J.; Salama,
S.R. Cerebral Organoids as an
Experimental Platform for Human
Neurogenomics. Cells 2022,11, 2803.
https://doi.org/10.3390/
cells11182803
Academic Editor: Leonora Buzanska
Received: 17 August 2022
Accepted: 7 September 2022
Published: 8 September 2022
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cells
Review
Cerebral Organoids as an Experimental Platform for
Human Neurogenomics
Tomasz J. Nowakowski 1,2,3,4,5,* and Sofie R. Salama 6, 7, *
1Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94158, USA
2Department of Anatomy, University of California San Francisco, San Francisco, CA 94158, USA
3Department of Psychiatry and Behavioral Sciences, University of California San Francisco,
San Francisco, CA 94158, USA
4Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
5
Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California San
Francisco, San Francisco, CA 94158, USA
6Department of Molecular, Cellular and Developmental Biology, University of California Santa Cruz,
Santa Cruz, CA 95060, USA
7UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
*Correspondence: tomasz.nowakowski@ucsf.edu (T.J.N.); ssalama@ucsc.edu (S.R.S.)
Abstract:
The cerebral cortex forms early in development according to a series of heritable neurode-
velopmental instructions. Despite deep evolutionary conservation of the cerebral cortex and its
foundational six-layered architecture, significant variations in cortical size and folding can be found
across mammals, including a disproportionate expansion of the prefrontal cortex in humans. Yet
our mechanistic understanding of neurodevelopmental processes is derived overwhelmingly from
rodent models, which fail to capture many human-enriched features of cortical development. With
the advent of pluripotent stem cells and technologies for differentiating three-dimensional cultures of
neural tissue
in vitro
, cerebral organoids have emerged as an experimental platform that recapitulates
several hallmarks of human brain development. In this review, we discuss the merits and limitations
of cerebral organoids as experimental models of the developing human brain. We highlight innova-
tions in technology development that seek to increase its fidelity to brain development
in vivo
and
discuss recent efforts to use cerebral organoids to study regeneration and brain evolution as well as
to develop neurological and neuropsychiatric disease models.
Keywords: organoid
1. Pluripotent Stem Cells
Human tissues are frequently inaccessible to experimentation. In particular, long-term
perturbational studies involving human cells resort to cell-based models. This is especially
challenging for organs such as the brain, where limited or no stem cell populations can
be robustly isolated and cultured
in vitro
. The isolation of mouse embryonic stem (ES)
cells [
1
,
2
], the discovery of factors that enable propagation of undifferentiated stem cells
in vitro
[
3
], and the establishment of early protocols for neuronal differentiation [
4
] laid the
scientific groundwork for utilizing stem cell-based models of nervous system development.
Subsequent isolation of ES cell lines from human blastocysts [
5
], as well as the discovery of
transcription factors that enable reprogramming of somatic cells to ‘induced’ pluripotent
stem (iPS) cells [
6
,
7
], represent transformative advances that opened up immense opportu-
nities for applying the approach to human cells (Figure 1a). Among early studies, optimized
protocols for deriving human cortical cell lineage from embryonic stem cells using small
molecules [
8
,
9
] and from induced pluripotent stem cells [
10
] have been instrumental in
widespread adoption of the technology by the wider scientific community.
Cells 2022,11, 2803. https://doi.org/10.3390/cells11182803 https://www.mdpi.com/journal/cells
Cells 2022,11, 2803 2 of 26
Non-human
primate Rodent
X+
X+
X+
X+
X+
Cortical
Retinal Fused
organoid
'Assembloid'
Connectoid
Thalamic
Co-culture of regionalized organoids
Fibroblast
WNT
SHH
FGF
Dened Morphogen Concentrations
Co-culture with morphogen
producing cells
Regionalized organoids
Functional genomic screens In vivo transplantationComparative organoid studies
Multi-lineage organoids
Stem cell derivation Cerebral organoid differentiation High-throughput phenotyping
FGF
WNT
guide RNA
library
Phenotypic
selection
(e.g. proliferation)
Pathway
analysis
Anatomical integration
gene
KRAB
dCas9
overrepresented depleted
sgRNA
Endothelial cells
Microglia
lineage-specic
transcription factor
e.g. ETV2, PU.1
Functional integration
Cross-species
Blastocyst
Neuron
IPC
Radial glia
oRG
Astrocyte
Interneuron
scRNAseq
Histology
MEA scATACseqElectrophysiology
Timelapse
microscopy
AAAAA
AAAAA
AAAAA
OPC
Off-target
cells
Transplantation
Induction
Induced pluripotent
stem cells
Embryonic
stem cells
Pluripotent
stem cell lines
Genome
engineering
Disease individuals
Human genetic
variation
Tn5
A B C
D E F
G H I
Figure 1. Derivation, refinement, and applications of cerebral organoid technologies.
(
A
) Pluripo-
tent stem cells can be derived from embryonic material or somatic cells. (
B
) Cerebral organoids
recapitulate key cell types of the developing brain. (
C
) Scalable organoid characterization tech-
nologies. (
D
) Multi-lineage organoids can be derived to recapitulate tissue cell type diversity more
completely. (
E
) Improved regional specificity can be achieved by manipulating developmental signal-
ing pathways according to the blueprint of normal developing tissue. (
F
) Interactions between brain
regions can be modeled using co-cultured organoids. (
G
) Organoids derived from different sources of
pluripotent stem cells can be used to compare developmental trajectories across individuals, species,
or disease states. (
H
) Functional genomic screens can be used to map the genetic architecture of
biological phenotypes relevant to neural development. (
I
) Combining organoids with animal models
to advance future regenerative medicine applications.
2. Characterization
In parallel with the advances in stem cell differentiation into neuroectoderm in serum-
free conditions using defined combinations of small molecules, a protocol for differen-
tiating organoids as models of cortical development was introduced [
11
]. By culturing
differentiating cells in three-dimensional aggregates, as opposed to two-dimensional cul-
tures, neuroepithelial stem cells induced from pluripotent stem cells were allowed to
self-organize and form clusters (rosettes), forming apical-like structures composed of tight
and adherens junctions as well as apical cilia. These apical zone-like structures resemble
the early developing cortical neuroepithelium with apico-basal polarity. Glutamatergic
neurons emerging from these progenitors were capable of radial migration and formed
axonal projections consistent with glutamatergic neuron identities after transplantation
into immunocompromised mice [11].
The advent of human ES and iPS cells enabled the adoption of these three-dimensional
culture protocols for human cells [
12
–
14
] (Figure 1b). In particular, the pioneering work
of Lancaster et al. used a minimally guided differentiation strategy to generate cerebral
Cells 2022,11, 2803 3 of 26
organoid cultures that recapitulated many distinct brain regions, including dorsal cerebral
cortex, ganglionic eminences (ventral cortex), midbrain, and hindbrain, within a single
organoid [
12
]. In contrast to mouse ES cell-derived organoids [
11
], human stem cell-derived
organoids developed a substantial population of outer radial glia (oRG) cells, which are
a distinguishing feature of the mouse and human developing brains (discussed in the
following section). In parallel, human ES cell differentiation using directed differentiation
protocols demonstrated efficient induction of organoids containing only the dorsal cortical
neural lineage [14].
Protocols for differentiating cortical organoids have been further refined, and cell
types that emerge within brain organoids have been further characterized. For example,
the emergence of cortical astrocytes has been demonstrated [
13
,
15
,
16
], as well as that of
oligodendrocytes [
17
]. Neurons have been shown to develop functional synapses [
13
] and
to organize into laminar patterns that resemble cortical layers [16].
More recently, by using multi-electrode arrays to measure spontaneous and evoked
activity, organoids have been shown to be able to respond to light stimulation [
18
], and
to develop complex oscillatory patterns of network behavior [
19
,
20
]. New engineering
approaches are urgently needed to advance the capacity for performing longitudinal extra-
cellular recordings from cultured organoids with minimal interference to their normal neu-
rodevelopmental processes, and exciting solutions are currently being developed
[21–23]
.
Integration of such systems with cloud-enabled infrastructure for data processing will
be necessary to provide scalability of such methods and wide adoption by the scientific
community [24].
Together, these studies have demonstrated a remarkable capacity of neural differen-
tiations to self-organize, recapitulating many complex features of the developing brain,
including cell type heterogeneity, cellular organization, and even cell-cell interactions
necessary to enable complex patterns of neural activity to emerge (Figure 1b,c).
3. Benchmarking against Primary Tissue
Transcriptomic and epigenomic datasets generated from primary human tissue serve
as an invaluable resource for benchmarking
in vitro
models. For example, brain organoids
have been profiled using single cell transcriptomics and compared against microdissected
bulk tissue transcriptomic data from developing human cerebral cortex, revealing that the
molecular programs underlying human cortical neurogenesis are broadly expressed in cor-
tical organoids [
25
]. This study also revealed that organoids contained primarily the dorsal
telencephalic neural lineage with a small proportion of non-neural cells. Thanks to the gen-
eration of multiple single cell transcriptomic datasets from primary human tissue [26–31],
it is now possible to compare cell types emerging in brain organoids to their primary coun-
terparts in even greater detail. These studies have revealed that as development proceeds,
cells from brain organoids fail to accurately recapitulate the full breadth of mature cell types
and the associated diversity of specific patterns of gene expression found in primary tissue.
Furthermore, organoid cells upregulate molecular signatures of cell stress [
32
,
33
]. Similar
findings have been reported more broadly for
in vitro
cultured cells [
31
], including cultured
human primary cells [
33
]. Recently developed bioinformatic approaches can be leveraged
to regress stress signatures [
34
]. As new protocols and approaches are being developed to
limit the effects of cell stress in culture conditions, single-cell transcriptomic studies will
serve as an invaluable resource for comparing the fidelity and robustness of these protocols
to the normal developing brain. Data sharing resources, such as the UCSC Cell Browser,
have emerged to facilitate rapid data sharing and access, particularly for single cell RNA
sequencing datasets whose processing steps have been largely standardized [35].
Similarly to transcriptomic analyses, epigenomic and epitranscriptomic (mRNA modi-
fications) profiling have been applied to brain organoid and primary human tissue samples,
which also revealed that broad neurodevelopmental trajectories and transitions are reca-
pitulated in organoids sampled at different stages of differentiation [
36
–
39
]. Single cell
epigenomic datasets from primary human tissue are only beginning to be generated [
40
,
41
].
Cells 2022,11, 2803 4 of 26
Therefore systematic comparisons of epigenetic states between primary tissue and organoid
cell types remain to be performed. Chromatin accessibility changes occurring during neu-
rodevelopmental transitions have been shown to occur in brain organoids, and organoid-
derived neural cells recapitulate a substantial fraction of the putative enhancers identified
in developing human tissue [39,41].
While transcriptomic and epigenomic data are tremendously valuable in that they
enable genome-wide comparisons, they are unable to capture the dynamic neurodevelop-
mental processes that give rise to the complex cell types of the developing brain. Mechanis-
tic studies of cortical development have been overwhelmingly conducted in mice. These
studies have underscored the role of radial glia as the neural stem cells of the brain [
42
–
44
].
Radial glia are a specialized cell type that consists of a spindle-shaped cell body with bipolar
morphology, including an apical fiber contacting the lateral ventricle and forming an apical
junction, and a basal fiber extending to and contacting the pial surface [
45
] (Figure 2a).
These cells have been shown to generate glutamatergic neurons directly [
46
,
47
] or indi-
rectly via intermediate neural progenitor cells [
48
–
50
]. In the cerebral cortex, neurogenesis
occurs within a specific time window [
51
] and in a temporally hierarchical order, with deep
layer neurons generated before upper layer neurons [
52
]. During mouse cortical develop-
ment, neurogenesis is followed by gliogenesis [
44
,
48
,
53
]. This series of neurodevelopmental
events is often referenced as a template for cortical development across mammalian species.
DeepLayer
Neurogenesis
Neuroepithelial
expansion
UpperLayer
Neurogenesis
vRG oRG
Deep layer
neuron
Upper layer
neuron
IPC
tRGNESC
B
Thalamus
Cortex
B
B
A
Early-born
neuron
Cell types of the
developing human brain
Endothelial
cell
Astrocyte Microglia
Figure 2. Development of the human brain.
(
A
) Schematic representation of the key cellular
populations during stages of peak neurogenesis and early gliogenesis in the human cerebral cor-
tex. NESC—neuroepithelial stem cells, vRG—ventricular radial glia, tRG—truncated radial glia,
oRG—outer
radial glia, IPC-intermediate progenitor cells. (
B
) Development of the long-range con-
nectivity between prospective subdivisions of the thalamus and cortical areas. The schematic shows a
horizontal section through a developing human brain. The left half highlights major projection path-
ways between the emerging thalamic nuclei and cortical areas. The right half highlights differences
in expression levels of specific genes with rostro-caudal expression gradients.
In many species outside mice, such as ferrets, macaques, and human developing
cerebral cortex, radial glia extensively diversify into outer radial glia (‘oRG’, also referred to
as ‘basal’ radial glia) in the outer subventricular zone (OSVZ) [
54
–
59
], and truncated radial
glia [
60
,
61
] in the ventricular zone (VZ). At late stages of cortical development, the OSVZ
harbors most of the proliferating cells in the developing cerebral cortex [
54
]. The discovery
of molecular programs enriched in oRG cells [
62
,
63
] has identified mechanisms by which
the development of these cell types can be promoted, and the hypothesis that LIF/STAT3
signaling can enhance oRG cell generation has been tested in organoids [
64
]. The role of
this pathway was surprising, given the central role of this pathway in gliogenesis [65].
Cells 2022,11, 2803 5 of 26
Importantly, oRG cells have been shown to give rise to oligodendrocyte progenitor
cells (OPC) [
66
], although new studies suggest that other neural stem cell populations
may generate OPCs at early developmental periods [
67
]. Astrocytes have been shown to
be a major cellular output of oRG cells [
68
,
69
], but recent studies have revealed further
complexity that exists within this cell class in both humans and mice [
70
–
73
], which
underscores the need for further studies of their developmental cell lineage. Adding to this
complexity, human glial progenitor cells have been shown to generate oligodendrocytes
and astrocytes [
74
], and overlapping molecular signatures of the oligodendrocyte and
astrocyte lineages have been identified in single cell studies [40,75,76].
Glutamatergic neurons of the cerebral cortex are generated by radial glia and IPCs
during human development [
54
–
58
]. At late stages of cortical neurogenesis, oRG cells
have been shown to generate neurons directly and indirectly [
55
]. Notably, periods of
neurogenesis and gliogenesis in humans, as well as ferrets, overlap extensively [
60
,
71
], in
contrast to mice [
44
,
48
,
53
]. Importantly, glutamatergic neurons that emerge in anatomically
distinct areas of the cerebral cortex are molecularly divergent [27,30].
GABAergic neurons of the cerebral cortex originate in the ganglionic eminences
[77–81]
.
However, a substantial fraction of progenitor cells and newly born neurons in the cerebral
cortical wall at late stages of neurogenesis express markers of GABAergic lineage [
28
,
82
–
84
].
This presumed local production of cortical GABAergic neurons has been confirmed by a
barcoded lineage tracing study of human cortical radial glia [
79
]. Notably, many studies
utilizing cortical organoids have observed GABAergic neurons emerging at late stages of
differentiation, but their origin and subtype identity remain unclear [32,85,86].
A deeper understanding of radial glia heterogeneity and differentiation trajectories
in the human cerebral cortex will provide critical resources for benchmarking cerebral
organoids as models of human neural development and may provide important insights
into the cell of origin of brain tumors [
87
–
89
]. While many bioinformatic methods have
been developed to reconstruct developmental trajectories from single cell transcriptomic
data [
90
,
91
], they are limited in several important ways that may be important for our un-
derstanding of developmental processes [
92
]. Timelapse microscopy experiments in human
and non-human primate tissue have provided critical insights into the complex patterns of
progenitor cell behavior and differentiation trajectories in the developing brain [
55
,
56
,
93
,
94
].
These studies have revealed the neural stem cell potential of oRG cells [
56
], demonstrated
evolutionary conservation of oRG cell behaviors [
93
], identified mechanisms underlying
oRG cell generation [
94
], and revealed the remarkable neurogenic capacity of oRG cells, in-
cluding their striking potential to give rise to neurons via direct neurogenesis in addition to
their capacity to generate intermediate neural progenitors (‘indirect neurogenesis’) [
55
,
62
].
Timelapse microscopy studies conductedin cerebral organoids have demonstrated that
the characteristic behaviors of radial glia subtypes are recapitulated in organoids [
95
,
96
],
as well as their capacity to differentiate via direct and indirect neurogenesis [
97
]. In
addition to timelapse microscopy, technologies that enable multiplexed tracking of cell
lineage at high-throughput using single cell transcriptomics can be applied to cerebral
organoids
[98,99] and primary tissue [79,100,101]
. Examining mechanisms underlying hu-
man brain development using brain organoids will critically depend on rigorous bench-
marking of the differentiation trajectories of neural progenitor cells.
4. Protocol and Data Standards
Cerebral organoid protocols are being widely adopted to model human neurode-
velopment, the consequences of genetic mutations, and in the context of evolutionary
comparisons. In parallel, advanced methods are being developed to apply next-generation
technologies to brain organoid culture. Publications describing detailed protocols as well
as hands-on training workshops represent important steps towards developing protocol
commons, as well as quality control standards that could be agreed upon by the scientific
community to support the generation of interpretable data. One difference in experimental
design that could lead to different experimental outcomes involves the choice of neural
Cells 2022,11, 2803 6 of 26
induction protocol. Some studies have relied on protocols that are more directed and
involve dual smad inhibition [
11
,
13
,
14
], while other studies utilize minimally guided differ-
entiations, which result in the generation of multiple lineages within the same organoid [
12
].
Depending on the nature of the assay (e.g., single cell transcriptomics or neural activity),
the results of studies utilizing these divergent methods could require different interpreta-
tive frameworks.
Another important issue that is rarely discussed is the fact that lines from very few
donors have thus far been utilized by most organoid studies. There is an unmet need to
establish lines from donors that represent a more complete spectrum of human genetic
diversity. Validation of their differentiation capacity using newly developed protocols that
increase the reproducibility of cerebral organoid differentiations [
85
,
102
] will be key to ad-
vancing the field of organoid research. Equally important, datasets generated from cerebral
organoids should ideally follow the principles of findability, accessibility, interoperability,
and reusability (FAIR standards) [
103
]. Such datasets, together with platforms for data
sharing, will be necessary to enable rigorous comparisons of experimental results from
studies involving organoids. It is not unexpected that protocols and outcomes will vary
depending on the research question being asked and the cell lines being used. However,
benchmarking the data generated in cerebral organoid studies to both human primary
tissue compendia and publically available cerebral organoid datasets, such as those in the
UCSC Cell Browser, will go a long way towards establishing the relevance and utility of
newly published cerebral organoid studies.
5. Reducing Stress
A pervasive cell culture-associated cell stress response has been identified as a poten-
tial confounding factor in organoid experiments [
32
,
33
]. Some of the potential consequences
include genomic instability and the acquisition of somatic mutations in iPS derived mod-
els [
104
]. To overcome these limitations, algorithmic approaches have been developed
to regress gene expression signatures related to cell stress [
34
]. While a variety of ap-
proaches are being examined, protocols that incorporate slicing of organoids or culture at
the air-liquid interface [
16
,
105
] have shown a substantial reduction in the level of hypoxia
within organoids. Moreover, incorporation of non-neuronal cells that do not emerge spon-
taneously in large numbers during neural differentiation may provide beneficial outcomes
for organoid development. For example, microglia are the tissue-resident macrophages
of the brain that arise in the yolk sac and are involved in innate immune and homeostatic
functions [
106
]. Incorporation of microglia into organoids via transplantation or induction
of the PU.1 transcription factor expression in a subset of organoid cells [
107
–
110
] (Figure 1d)
has been shown to attenuate DNA damage responses in organoids and to promote the
maturation of neuronal activity.
Similarly, most cell types that form the cerebrovasculature [
111
] do not arise from
neural stem cells but can be introduced into organoids by co-culture [
112
–
114
] or by ectopic
expression of the ETV2 transcription factor [
115
] (Figure 1d). Endothelial cells can self-
organize to form tubular structures reminiscent of early developing vascular networks and
can improve the viability of organoid cells and enhance functional maturation [
115
]. An
important future direction involves developing vascular cell differentiation protocols that
can more rapidly generate vascular cells at high purity [
116
], as well as those that more
robustly resemble cerebrovascular endothelial cells as opposed to endothelial cells found
in other organs [117].
Other cell types that are involved in cerebrovascular interactions, such as pericytes,
smooth muscle cells, fibroblasts, fibromyocytes, or perivascular macrophages [
111
], remain
to be explored. Incorporation of flow may be required to support successful vascularization
of brain organoids and enable mechanistic studies of neurovascular interactions and disease
modeling using organoids. Dysfunction of the cerebrovascular system is thought to be
central to the pathophysiology of vascular malformations and neurodegeneration [
118
,
119
].
Cells 2022,11, 2803 7 of 26
Therefore developing vascularized organoid models will be important for studies of these
conditions using human cell-based models.
6. Multi-Brain Region Organoids
During development, neurons form local and long-range interactions with cells located
in ontogenetically distinct regions (Figure 1e). While the initial differentiation protocols
focused on accomplishing high efficiency differentiations for dorsal cortex organoids, proto-
cols that enable other brain region specific differentiations have also emerged, including for
ganglionic eminence [
120
–
122
], striatum [
123
], hippocampus [
124
], hypothalamus [
86
,
125
],
pituitary gland [
125
,
126
], thalamus [
127
,
128
], midbrain [
86
,
129
–
131
], and cerebellum [
132
]
organoids. Algorithms that enable data-driven alignment of organoids to a spatially re-
solved transcriptomic atlas of the developing brain can provide rapid validation of newly
optimized differentiation protocols. Specifically, by leveraging spatially resolved gene
expression data, such as the Allen Institute RNA in situ hybridization and laser capture
microdissection microarray databases, the VoxHunt algorithm can annotate organoid single
cell RNAseq data with brain regional identity information [
133
]. One limitation of this
method is that the reference data currently available are not transcriptome-wide or single
cell resolved. With the advent of brain region-resolved single cell or spatial datasets with
single cell resolution, the accuracy of predicting regional identities of organoid cells will
likely increase. Broader comparisons to not only brain tissue but also non-brain reference
data will additionally enable fully agnostic, data-driven benchmarking of organoid cells
against the blueprint of developing human tissue.
Physical co-culture of two or more brain region-specific organoids enables interac-
tions between cells that emerge from different sets of progenitors to be studied
in vitro
.
This approach is sometimes referred to as an ‘assembloid’ assay (Figure 1f), and allows
for new neurodevelopmental processes to be assayed
in vitro
, such as the migration of
GABAergic neurons from ventral telencephalic progenitors to the cortical plate [
120
–
122
].
Early developmental events involved in the formation of long-range neuronal tracts can
also be examined, such as the cortico-striatal [
123
] or cortico-thalamic tracts [
128
]. In an-
other exciting application, organoids co-cultured with non-neural tissue, such as muscle
tissue [
134
,
135
], demonstrated the potential for organoid neurons to functionally innervate
non-neural tissues, opening an exciting new frontier for regenerative medicine research
(Figure 1g).
However, organoid fusions require parallel organoids to be differentiated into distinct
regions, and in most cases, organoid fusion is not performed until these regional identities
are fully acquired. As an alternative strategy, a growing number of studies are employing
methodologies that deliver localized sources of developmental morphogens into parts of
an organoid, such that they can mimic the effects of organizer regions that normally pattern
the developing neuroepithelium. As a result, multiple brain regions can be simultaneously
induced within the same organoid [136,137].
Another experimental approach that enables studies of neural interactions involves
the separate induction of brain organoids that are cultured separately while allowing for
neurons to form reciprocal axonal connections. These “connectoid” cultures have been
shown to develop more complex neural activity profiles [
138
] than individually cultured
organoids [20] (Figure 1f).
7. Specification of Cortical Neuron Subtypes
Cortical organoid differentiation protocols have been convincingly shown to generate
cortical lineage neurons. However, the brain contains dozens of neuronal subtypes that
ultimately give rise to its complex function. In this section, we will discuss broadly how the
diversity of neurons in the mammalian forebrain is specified and how these are modeled
using organoids.
Cortical neurons are specified along two major axes. First, their position in the six-
layered cortical plate defines their morphology and connectivity. For example, “deep
Cells 2022,11, 2803 8 of 26
layer” neurons located in layers five and six send axons to subcortical areas, including
the spinal cord and thalamus, respectively, whereas “upper layer” neurons of layers two,
three, and four project to other cortical layers, to the contralateral hemisphere, or to the
striatum [
139
]. The specification of cortical layer neurons follows a temporal, inside-out,
order [
52
] and involves the induction of transcription factors that specify the major neuronal
subtypes [
140
]. The sequential generation of deep and upper layer neurons is recapitulated
in iPS derived cultures and cerebral organoids [
8
,
10
,
141
], although the stereotypical layering
of cortical neurons has only been reported in a few studies [16].
The second axis follows the surface of the cerebral cortex, which can be divided into
dozens of anatomically and functionally distinct areas, called aerial or regional identities.
During early development, developmental morphogens secreted by organizer regions
induce the expression of patterning transcription factors in radial glial cells, which sub-
sequently give rise to glutamatergic neurons (reviewed by [
142
]). Different areas of the
cerebral cortex have transcriptionally divergent glutamatergic neurons [
27
,
30
] (Figure 2b).
As an early application of the organoid technology, Sasai et al. demonstrated that corti-
cal neuron differentiation can be further refined by harnessing lessons from developmental
biology to increase regional specificity. In their experiments, inhibiting FGF signaling
favored the specification of caudal cortical identities (NR2F2+ ve neurons), whereas acti-
vating Wnt and BMP signaling favored the specification of medial structures such as the
cortical hem and choroid plexus [
11
]. Subsequently, these findings have been extended to
form the basis for developing protocols for generating hippocampal organoids [
86
,
124
]. By
modulating the duration of FGF signaling exposure during initial patterning, Studer et al.
developed a protocol that promotes the specification of prefrontal cortical neurons [143].
Regulatory programs underlying cortical neuron specification and arealization have
been extensively studied in mice [
142
]. Targeted knock-down of such master-regulatory
transcription factors, such as GLI3, using CRISPR can be used to examine the effects of the
manipulation on neuronal subtype specification [
99
] (Figure 1h), recapitulating the findings
from mouse models [144].
Finally, epigenomic datasets generated from distinct areas of the cerebral cortex are be-
ginning to emerge [
41
,
145
,
146
], and these studies have the potential to uncover regulatory
programs underlying cortical arealization more comprehensively. For example, transcrip-
tion factor motif enrichment analysis of prefrontal cortex enriched open chromatin regions
identified retinoic acid receptor motif enrichment. Mechanistic studies of retinoic acid
signaling have revealed a role for this pathway in the specification of the prefrontal cortex in
the developing human brain and shown that modulation of retinoic acid signaling during
organoid differentiation can regulate the differentiation of prefrontal and occipital cortical
neurons in cerebral organoids [
41
,
147
]. Additional studies are needed to more comprehen-
sively predict the regulatory grammar of cortical arealization which could be leveraged to
derive next-generation protocols for generating area-specific cortical organoids.
8. Transplantation
One of the key applications of stem cell-based technologies is regenerative medicine.
Pluripotent stem cell derived neurons have been successfully transplanted into neonatal
immunocompromised mice and shown to develop projection patterns consistent with their
molecular identities [
11
,
148
]. Moreover, transplanted human cells have been shown to
integrate into mouse cortical circuits and develop evoked responses consistent with in situ
neurons [
149
] (Figure 1i). The remarkable capacity of transplanted cells to integrate into the
brain offers a promising outlook for future application of this approach in clinical settings.
However, most transplantation paradigms are performed in the early postnatal period,
when the majority of the mouse brain is still undergoing normal developmental processes.
Transplantation into the adult brain is highly inefficient and rarely performed.
Several pioneering studies have successfully transplanted intact organoids into adult
mouse brains [
150
–
153
]. These studies have shown that neurons within brain organoids
continue to undergo normal developmental transitions, integrate into cortical tissue, and
Cells 2022,11, 2803 9 of 26
form functional synapses with mouse cells. Endogenous vascular cells of the mouse have
been shown to progressively vascularize brain organoids.
In one study, organoids were transplanted into 3-year-old cynomolgus monkeys [
152
].
By 12 weeks post transplantation, axons of organoid neurons extended projections within
the cortex, to the corpus callosum, and the striatum. No projections were found extending
through the internal capsule, but this may be unsurprising given that the vast majority
of neurons in the grafted organoids expressed SATB2, a marker of intratelencephalic
neurons [
154
]. Future optimizations may be needed to develop protocols that could
enhance the survival and integration of other neuronal subtypes into the monkey brain,
including corticospinal and corticothalamic projection neurons.
While chimeric animal studies are necessary to understand the fidelity and function
of
in vitro
derived human brain tissue and are a necessary first step for regenerative
medicine approaches, they are not without controversy. Biomedical researchers, ethicists,
and the public have questioned whether and when transplanting human neural tissue into
experimental animals leads to human-like perception or cognition. Would chimeric animals
modeling neuropsychiatric diseases potentially experience human disease symptoms in
a distressing manner? The National Academies of Science, Engineering, and Medicine
published a report on neural chimeric tissues in 2021 [
155
] that called out areas of concern
with respect to this research, although it also stated that current regulation of stem cell and
animal research was adequate. This issue continues to spur a lively debate in the research
community that will only increase as brain organoids increase in their complexity and
similarity to human brain tissue [156].
9. Disease Modeling
The discovery of reprogramming factors has allowed for the generation of pluripotent
stem cells from patient cells, allowing for
in vitro
studies in a patient-specific genetic
background. As a result, one of the most common applications of iPS technology today is
disease modeling. Two early studies differentiated glutamatergic neurons from iPS lines
derived from idiopathic schizophrenia [
157
] and Timothy Syndrome [
158
] patients. These
studies heralded an era of disease modeling using human cell-based models [
159
]. The
results of these studies have been extensively discussed in a number of recent reviews [
160
],
and will not be further discussed in this review.
In addition to patient-derived cells, genome engineering technologies using CRISPR
have also enabled the generation of ‘isogenic’ iPS lines where a putative disease-causing
variant can be introduced into control iPS lines or corrected in a patient iPS line. This
approach offers tremendous potential for screening large numbers of candidate muta-
tions as well as for assessing the role of genetic background in specific disease-associated
variants. However, rigorous validation of isogenic iPS lines will be necessary because
non-specific ‘passenger’ mutations are frequently introduced during clonal selection of
isogenic lines [161].
9.1. Neurological Disorders
Cerebral organoids generated from patient-derived iPS lines have also been applied to
studies of disease mechanisms. For example, organoids derived from an iPS line derived
from a patient with compound heterozygous truncating mutations in CDK5RAP2 have
been shown to exhibit slower growth kinetics compared to control iPS lines [
12
]. This study
also identified precocious differentiation and alterations in the cell division plane of radial
glia that likely underlie the microcephaly phenotype. This study was, to the best of our
knowledge, the first application of cerebral organoids to disease modeling.
The advent of new technologies, such as single cell barcoding and CRISPR perturba-
tion screening, has enabled parallel and multiplexed perturbations of genes in cerebral
organoids. Esk et al. applied this approach to study the effects of loss of function of 173 can-
didate microcephaly genes [
98
]. This study has revealed a novel role for genes involved in
endoplasmic reticulum (ER) function as a point of vulnerability in microcephaly.
Cells 2022,11, 2803 10 of 26
Mechanisms underlying microcephaly have been successfully studied using mouse
models thanks to the conservation of many biochemical processes of cell division. By
contrast, lissencephaly is a condition inherently difficult to model in mice due to the
lissencephalic nature of the mouse cerebral cortex. LIS1 (encoded by PAFAH1B1) encodes
one of the five genes implicated in primary lissencephaly in humans [
162
]. Studies in mice
have found subtle neurodevelopmental phenotypes in transgenic mice with Lis1 loss of
function, including neuronal migration and neurite extension deficits [
163
,
164
]. By contrast,
introduction of LIS1 loss of function mutations into human ES cell lines using CRISPR,
followed by differentiation into cerebral organoids, was sufficient to detect a striking
difference in neuroepithelial stem cell and radial glia proliferation [
165
], suggesting that, in
humans, LIS1 could play an important role beyond the control of neuronal migration that
was discovered using a mouse model.
Miller-Dieker Syndrome is characterized by lissencephaly but also microcephaly, and
organoids derived from patients with this condition show a number of distinct phenotypes
impacting the survival of neuroepithelial cells, outer radial glia cell division, and neuronal
migration [
95
]. Notably, Miller-Dieker Syndrome patients carry mutations in PAFAH1B1
in addition to other genes, highlighting the need for isogenic lines for dissecting the
contribution of individual genes to distinct phenotypes.
Tuberous sclerosis is a rare condition that involves the formation of many non-
cancerous tumors throughout the body. Tuberous sclerosis patients often develop epileptic
seizures [
166
], and two high-confidence risk genes, TSC1 and TSC2, have been identi-
fied [
167
,
168
]. Both TSC1 and TSC2 act as negative regulators of the mammalian target of
rapamycin gene (mTOR) in mammalian cells [
169
]. In the brain, mTOR signaling has been
shown to be a major regulator of synaptic function, and its inhibition has been shown to
rescue synaptic phenotypes in tuberous sclerosis [
170
]. However, mTOR signaling also
plays a significant role in brain development. In humans, mTOR signaling is highly en-
riched in oRG cells [
27
] and regulates radial glia fiber maintenance [
171
]. By contrast, the
radial glia of the developing mouse brain are less dependent on mTOR signaling [
172
]
and do not develop tubers, arguing for the development of a human cell-based model of
tuberous sclerosis. Organoids derived from human ES cell lines with TCS1 and TSC2 muta-
tions have been shown to have precocious gliogenesis that can be rescued by rapamycin
inhibition [173].
Another study used cerebral organoids to investigate neurodevelopmental phenotypes
underlying periventricular heterotopia by generating mutations in DCHS1 and FAT4. Radial
glia deficient for these proteins showed severe disorganization of radial morphology and
neuronal migration defects, as well as possible defects in neuronal fate specification [174].
9.2. Psychiatric Conditions
Perhaps the most aspirational application of brain organoids is in studies of psychiatric
disorders, where iPS-derived organoids allow researchers to study human neurodevel-
opmental processes in the human genetic background. This may be especially important
in conditions such as schizophrenia or autism spectrum disorders (ASD), which involve
the contribution of many genetic variants. Polygenic risk architecture may be difficult to
capture in animal models.
Starting from syndromic disorders that include nervous system alterations, iPS cells
derived from patients with Timothy syndrome have been used to identify neural deficits
associated with the condition [
158
], including calcium signaling defects in progenitors
and neurons. More recently, assembloids derived from cortical and ganglionic eminence
organoids have been used to show that tangentially migrating interneurons exhibit abnor-
mal patterns of neuronal migration in Timothy syndrome and can be rescued by inhibiting
GABA-A receptor signaling [120,175].
Several studies utilized pluripotent stem cells derived from patients with DiGeorge
syndrome, which is caused by a microdeletion at the 22q11.2 locus. Cortical differentiations
of these lines have been shown to result in precocious gliogenesis [
176
] and a delayed
Cells 2022,11, 2803 11 of 26
switch of the GABA reversal potential [
177
]. In addition, mitochondrial defects have been
identified [
178
]. Cortical organoids derived from 22q11.2 microdeletion patients have
been shown to exhibit neuronal hyperexcitability that could be rescued by restoring the
expression of DGCR8 [179], an enzyme critical to the synthesis of microRNAs [180].
MECP2 encodes a CpG binding protein and is mutated in patients with Rett syn-
drome [
181
,
182
]. Rett syndrome patient-derived iPS cells differentiated into cortical glu-
tamatergic neurons have been shown to develop reduced synaptic arbors, a phenotype
that could be rescued by overexpression of MECP2 as well as the addition of IGF1 based
on findings from a mouse model [
183
,
184
]. Inhibition of ribosomal proofreading activity
using gentimicin was also suggested to elevate MeCP2 expression, but the effect was highly
sensitive to drug concentration [
184
]. A subsequent study using brain organoids further
revealed neuronal network activity phenotypes that could be attenuated using Nefiracetam
and PHA 543,613 [
185
]. Fused cortical and ganglion eminence organoids derived from
stem cell lines carrying loss of function mutations in MECP2 have been shown to exhibit
abnormal patterns of oscillatory network activity that can be partially rescued by Pifithrin-
α
, a TP53 inhibitor [
186
], consistent with studies in patient derived fibroblasts showing
increased induction of P53 and senescence [
187
]. These studies illustrate the complexity
of neurodevelopmental processes regulated by MeCP2 and suggest that MeCP2 loss of
function consequences may be mediated by many biological processes and do not converge
upon a small number of biochemical targets.
Studies of ASD pathobiology have attracted substantial interest in the scientific commu-
nity due to the complex nature of this early-onset condition. Human genetics studies have
implicated hundreds of genetic variants that might underlie ASD [
188
–
190
]. The genetic
architecture of ASD involves rare as well as common variants and following the discovery
of high-confidence ASD risk loci comes the challenge of understanding their function.
Copy number variants in the 16p11.2 locus are strongly associated with ASD diagno-
sis [
191
,
192
]. Studies investigating neurodevelopmental phenotypes using two-dimensional
cultures of patient-derived cells identified several phenotypes associated with 16p11.2 mi-
crodeletion [
193
]. Neurons derived from cells with 16p11.2 deletion showed enlarged cell
soma and deficits in synaptic morphology. In addition, a study of cortical organoids derived
from 16p11.2 microdeletion patients revealed deficits in neuronal migration, and deficits
in Wnt signaling pathway activation, and a reduced pool of neural progenitor cells [
194
].
Additionally, a recent study measuring gene expression in 16p11.2 microdeletion organoids
derived from 13 donors implicates neural progenitor cells in early developmental changes
and suggests transcriptional dysregulation through gene coexpression network analy-
sis [195].
High-confidence ASD risk genes discovered through whole exome sequencing stud-
ies coalesce into several categories, including gene expression regulation and synaptic
transmission. Understanding the points of convergence between them that might underlie
the highly stereotypical nature of ASD symptoms remains challenging. However, early
analyses have uncovered brain regions and developmental time points that are enriched
for ASD risk gene expression. Specifically, by intersecting ASD candidate risk genes with
gene expression information from the developing human brain [
196
], two studies identified
enriched expression of ASD risk genes in prenatally developing human prefrontal cor-
tex [
197
,
198
]. Intersection of these genes with single cell expression data from the human
brain [
27
,
29
] has further shown that many ASD risk genes show enriched expression in
glutamatergic and gabaergic neurons [
188
,
190
]. Classical annotations of protein function
are often based on studies in cell lines and animal models. This can inaccurately capture
the complex roles of these proteins across the wide spectrum of cells in the human brain.
Moreover, assumptions that expression level correlates with functional significance can
be misleading. For example, the vast majority of studies into the role of the ASD risk
gene SynGAP1 have been focused on the role of this protein in synaptic transmission [
199
].
Indeed, in the developing human brain, SYNGAP1 mRNA is enriched in glutamatergic
neurons, but its expression can also be detected in radial glia [
27
]. Emerging evidence from
Cells 2022,11, 2803 12 of 26
cerebral organoids suggests that SynGAP1 may also play a role in radial glia during early
neurogenesis [
200
]. This finding highlights the need to systematically interrogate gene
function across the range of cell types where risk genes are expressed.
In an effort to systematically screen high-confidence ASD risk genes for their function
in neurodevelopment, several studies have begun to interrogate neurodevelopmental phe-
notypes for multiple genes in parallel, using iPS-derived neurons and cerebral organoids.
Cederquist et al. generated isogenic pluripotent stem cell lines carrying loss of function
mutations for 27 high confidence ASD risk genes and differentiated the cells in a pooled
assay to glutamatergic neurons of the prefrontal cortex [
143
]. Loss of function mutations
in CUL3, KDM5B, ASH1L, ASXL3, ANKRD11, RELN, DEAF1, and KMT2C resulted
in decreased neurogenesis. Mutations in KMT2A, SUV420H1, DYRK1A, GRIN2B, and
CHD8 resulted in proportionately increased numbers of newborn neurons at the expense
of progenitors.
Lalli et al. investigated progenitor cell proliferation and neuronal differentiation
phenotypes across 13 high-confidence ASD risk genes using a pooled CRISPR screen
approach in neural progenitor cells [
201
]. This study found that out of the 13 ASD risk genes
investigated, knock-down of five genes (CHD2, ARID1B, ADNP, ASH1L, and DYRK1A)
resulted in reduced proliferation, while knock-down of two genes (PTEN and CHD8)
led to accelerated maturation and increased proliferation. Four genes (ASH1L, ADNP,
ARID1B, and DYRK1A) resulted in reduced neurite extension, while one gene (PTEN) led
to increased neurite extension in cortical neurons.
An organoid-based study investigated the neurodevelopmental consequences of
SUV420H1 (also known as KMT5B), ARID1B, and CHD8 loss of function using compre-
hensive single cell RNA sequencing analysis. In this study, heterochrony of neuronal
maturation and excessive production of GABAergic versus glutamatergic neurons were
observed [
202
]. Transcriptional changes between mutant and control cells were found to
be more consistent across cell types within candidate mutations (especially for ARID1B
and CHD8) than across candidate genes. A parallel study using CHD8 isogenic mutant
cerebral organoids reported alterations in the proliferative capacity of progenitor cells, with
prolonged proliferation of mutant cells [203].
Together, these studies suggest that loss of function mutations of at least a subset of
high-confidence ASD risk genes disrupt radial glia proliferation or neuronal differentiation.
This is consistent with clinical reports suggesting that patients with ASD can have either
increased or decreased head circumference relative to the general population [
204
]. How-
ever, while functional genomic information may support patient stratification, the results
of these studies do not offer a simple answer to the question of phenotype convergence
across diverse risk genes.
Complementing the studies of rare risk variants, organoids derived from idiopathic
ASD patients have identified phenotypes of increased abundance of cortical interneu-
rons [
205
,
206
] in addition to alterations of cortical progenitor cell proliferation. These
findings appear to be consistent with the recent study in organoids with SUV420H1,
ARID1B, and CHD8 mutations [
202
]. Follow-up studies are beginning to further investi-
gate this question using iPS lines derived from idiopathic ASD individuals who were either
normocephalic or macrocephalic [
207
]. Phenotypic differences in progenitor cell develop-
ment, neurogenesis, and neuronal differentiation between donors stratified according to
macrocephaly status may provide important insights into neurodevelopmental phenotypes
underlying patient phenotypes.
Additional studies are needed to investigate the consequences of ASD risk gene muta-
tions on additional neurodevelopmental phenotypes, including cell behavior, epigenetics,
neurophysiology, synaptogenesis, cell dynamics, gliogenesis, and connectivity. Progress in
addressing these questions will require advances in culture methods that promote neuronal
maturation [
208
,
209
], scalable methods combining multiplexed perturbation strategies
with increasingly complex phenotypes, such as pooled optical CRISPR screens [
210
], or
perturb-ATAC [
211
]. Moreover, it will be important to apply these strategies across a wide
Cells 2022,11, 2803 13 of 26
range of cell types, not limited to a specific brain region or even just the neural lineage.
The advent of quantitative frameworks that can take advantage of sparse datasets to learn
latent representations of biological phenotypes will be critical to interpreting the results of
such experiments [212,213].
One of the general themes that has emerged thus far from organoid-based modeling of
neurodevelopmental phenotypes associated with psychiatric variants is that such mutations
frequently lead to aberrant timing of neurodevelopmental events. Such heterochronicity
could lead to disorganized development of neural circuits, likely leading to functional
abnormalities. Given the inherent variability of cerebral organoid differentiations as well
as the current inability to precisely control many neurodevelopmental processes, such as
the positional identity of radial glia, insights gained from organoids should be carefully
validated to determine their predictive value.
However, validating early neurodevelopmental phenotypes in human tissue can be
challenging due to the limited availability of suitable research material. Still, insights
gained from brain organoids can and should lead to predicted consequences in brain
structure or function. Functional imaging studies that have been performed in patients
with psychiatric conditions can serve as a point of reference [
214
]. Similarly, studies of
postmortem tissue derived from patients with autism or schizophrenia have provided
insights into molecular and cellular changes associated with these conditions [
215
–
219
].
Even though postmortem tissue likely involves additional changes related to co-morbidities
and drug treatments, one would expect some degree of overlap between primary tissue
and organoid models, especially where the comparison is performed between samples
with the same mutation. Finally, comparisons to animal models can be extremely useful
in identifying robust phenotypes and can serve as a guiding principle for the use of such
models in preclinical studies [220,221].
9.3. Neurodegeneration
Another area of growing interest is the application of organoid technology to interro-
gate mechanisms underlying neurodegenerative phenotypes. Neurodegenerative disorders
involve many genetic and non-genetic risk factors and are thought to involve dysfunction
of homeostatic mechanisms involving not only neurons but also non-neuronal cells. Four
major disease areas are being actively studied: Parkinson’s disease, Huntington’s disease,
Alzheimer’s disease, and Down syndrome.
Multiple studies have leveraged patient derived iPS cells to recapitulate deficits in
neuronal survival as well as abnormalities in biochemical processes relevant to these
conditions [31,222–226]. For an in-depth review, see [227].
Cerebral organoids have also been applied to the study of neuronal phenotypes associ-
ated with neurodegeneration. Organoids derived from patients with familial Alzheimer’s
disease as well as Down syndrome have been shown to recapitulate toxic accumulation
of the Ab fragment of the amyloid precursor protein [
228
,
229
]. Disruption of nuclear
architecture of progenitor cells has been observed [
230
]. The advent of CRISPR interference
technology, which enables flexible targeting of genes upregulated in disease states
[231–233]
,
can be applied to target and limit abnormal activation of pathways discovered using
in vitro
models [234].
In an excellent demonstration of organoid technology for disease modeling, a re-
cent study modeled neuronal phenotypes using iPS lines carrying MAPT mutations that
underlie frontotemporal dementia [
235
]. Extensive biochemical, morphological, and tran-
scriptomic characterization identified accelerated neuronal maturation and upregulation
of synaptic genes as a key feature associated with MAPT mutations. ELAVL4, a splicing
factor involved in neuronal maturation, was identified as a core master-regulatory gene
upregulated in mutant cells, leading to transcriptome-wide changes in splicing and changes
to synaptic recycling rates. This could be reversed by inhibition of PIKFYVE, a lipid kinase
that regulates endolysosomal trafficking, consistent with findings in a mouse model of
Cells 2022,11, 2803 14 of 26
C9ORF72 hexanucleotide repeat expansion that underlies both frontotemporal dementia
and amyotrophic lateral sclerosis [236].
10. Evolutionary Insights
The mammalian neocortex is a highly evolved structure, but even across mammals
it shows substantial variation in size, cellular organization, and folding pattern [
237
].
Many hypotheses have been put forward about the developmental mechanisms that might
underlie these differences across species [
238
–
244
]. Of particular interest are those changes
that might underlie the remarkable adaptations of the human brain to higher cognition,
but for practical reasons, many studies have thus far focused on the analysis of progenitor
cell proliferation, which might underlie the disproportionate evolutionary expansion of the
human cerebral cortex.
Three prominent hypotheses have been promoted to account for the expansion of the
neocortex. One, changes to the proliferative expansion of the neuroepithelial stem cells,
which serve as the founder population of cortical radial glia [
245
]. Two, a reduction in pro-
grammed apoptotic cell death of neuroepithelial or radial glial cells [
246
]. Three, the expan-
sion of the secondary proliferative populations in the outer subventricular zone
[54,238,247]
is driven by changes in gene expression or activity-dependent processes [248].
Cerebral organoids have the potential to serve as an experimental platform for estab-
lishing causality between genetic changes between species and specific neuro-developmental
or neurophysiological phenotypes. Two main approaches have emerged so far in the field.
The first approach has been to use organoids to study the function of genetic variants
that have been identified as derived from the human lineage [
249
–
252
]. Two examples of
variants include NOTCH2NL and NOVA1. NOTCH2NL has been shown to control the
production and proliferation of radial glia cells [
253
,
254
]. The archaic allele of NOVA1
has been shown to increase progenitor cell apoptosis [
255
]. Both of these mechanisms are
consistent with the prevailing hypotheses about human brain expansion.
The second approach has been to derive brain organoids from multiple species. Thanks
to the discovery of somatic reprogramming factors, it is possible to derive brain organoids
from many species, including great apes [
256
–
259
]. By comparing organoid differentiations
from human and chimpanzee, Mora-Bermudez et al. identified differences in cell cycle
kinetics between species [
260
] and identified candidate modern human-specific causative
mutations [
261
]. Pollen et al. demonstrated that organoids derived from non-human
primates recapitulate cross-species gene expression differences observed in primary tissue
and identified changes in mTOR signaling pathway activation [
32
]. Benito-Kwiecinski
reported differences in neuroepithelial stem cell proliferation [
262
]. In addition, Kanton et al.
captured transcriptomic and epigenomic differences in developmental trajectories in human
and chimpanzee organoids, including changes that overlap with human accelerated regions,
and confirmed at least a subset of these molecular differences in primary human and non-
human primate prefrontal cortex tissue [263].
Organoids derived from tetrapoid iPS cells generated by fusing cells from different
species provide another avenue for interrogating cis- and trans-regulatory programs for
transcriptional divergence between species [
264
]. As an example, cortical organoids derived
from human-chimpanzee tetraploid iPS cells showed accelerated expression of gliogenic
programs and increased expression of human somatostatin receptor 2. Differential ex-
pression of these pathways between humans and chimpanzees represents yet another
candidate pathway that may have contributed to the evolutionary expansion of the human
cerebral cortex.
11. Conclusions
In the last two decades, pluripotent stem cell-derived brain organoids have advanced
from a novel finding to the premier research tool for studying human brain development
and disease. These models are utilized by hundreds of researchers. Many challenges remain
to improve the fidelity of organoids relative to
in vivo
tissues and to expand the features
Cells 2022,11, 2803 15 of 26
of brain development and function that can be effectively studied in these cell culture
models. However, there is tremendous enthusiasm to tackle these issues, as outlined in the
research reviewed here.
In vitro
differentiation of human pluripotent stem cells to cerebral
organoids offers a tremendous opportunity to understand human neurodevelopment,
identify the consequences of disease-relevant mutations, and begin to develop strategies to
advance regenerative medicine applications.
Author Contributions:
Writing—S.R.S. and T.J.N. All authors have read and agreed to the published
version of the manuscript.
Funding:
This research was funded by NIH awards R01MH120295, 1RM1HG011543, NSF 2134955
(to S.R.S.), R01NS123263, U01MH115747 and gift from the William K. Bowes Jr. Foundation (to T.J.N.)
and Schmidt Futures Foundation SF857 (to S.R.S and T.J.N.).
Acknowledgments: The authors wish to thank Alex Pollen for critical feedback on the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Evans, M.J.; Kaufman, M.H. Establishment in Culture of Pluripotential Cells from Mouse Embryos. Nature
1981
,292, 154–156.
[CrossRef] [PubMed]
2.
Martin, G.R. Isolation of a Pluripotent Cell Line from Early Mouse Embryos Cultured in Medium Conditioned by Teratocarcinoma
Stem Cells. Proc. Natl. Acad. Sci. USA 1981,78, 7634–7638. [CrossRef] [PubMed]
3.
Smith, A.G.; Heath, J.K.; Donaldson, D.D.; Wong, G.G.; Moreau, J.; Stahl, M.; Rogers, D. Inhibition of Pluripotential Embryonic
Stem Cell Differentiation by Purified Polypeptides. Nature 1988,336, 688–690. [CrossRef]
4.
Jones-Villeneuve, E.M.; McBurney, M.W.; Rogers, K.A.; Kalnins, V.I. Retinoic Acid Induces Embryonal Carcinoma Cells to
Differentiate into Neurons and Glial Cells. J. Cell Biol. 1982,94, 253–262. [CrossRef] [PubMed]
5.
Thomson, J.A.; Itskovitz-Eldor, J.; Shapiro, S.S.; Waknitz, M.A.; Swiergiel, J.J.; Marshall, V.S.; Jones, J.M. Embryonic Stem Cell
Lines Derived from Human Blastocysts. Science 1998,282, 1145–1147. [CrossRef]
6.
Takahashi, K.; Yamanaka, S. Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by
Defined Factors. Cell 2006,126, 663–676. [CrossRef]
7.
Park, I.-H.; Zhao, R.; West, J.A.; Yabuuchi, A.; Huo, H.; Ince, T.A.; Lerou, P.H.; Lensch, M.W.; Daley, G.Q. Reprogramming of
Human Somatic Cells to Pluripotency with Defined Factors. Nature 2008,451, 141–146. [CrossRef]
8.
Chambers, S.M.; Fasano, C.A.; Papapetrou, E.P.; Tomishima, M.; Sadelain, M.; Studer, L. Highly Efficient Neural Conversion of
Human ES and iPS Cells by Dual Inhibition of SMAD Signaling. Nat. Biotechnol. 2009,27, 275–280. [CrossRef]
9.
Watanabe, K.; Kamiya, D.; Nishiyama, A.; Katayama, T.; Nozaki, S.; Kawasaki, H.; Watanabe, Y.; Mizuseki, K.; Sasai, Y. Directed
Differentiation of Telencephalic Precursors from Embryonic Stem Cells. Nat. Neurosci. 2005,8, 288–296. [CrossRef]
10.
Shi, Y.; Kirwan, P.; Smith, J.; Robinson, H.P.C.; Livesey, F.J. Human Cerebral Cortex Development from Pluripotent Stem Cells to
Functional Excitatory Synapses. Nat. Neurosci. 2012,15, 477–486, S1. [CrossRef]
11.
Eiraku, M.; Watanabe, K.; Matsuo-Takasaki, M.; Kawada, M.; Yonemura, S.; Matsumura, M.; Wataya, T.; Nishiyama, A.;
Muguruma, K.; Sasai, Y. Self-Organized Formation of Polarized Cortical Tissues from ESCs and Its Active Manipulation by
Extrinsic Signals. Cell Stem Cell 2008,3, 519–532. [CrossRef] [PubMed]
12.
Lancaster, M.A.; Renner, M.; Martin, C.-A.; Wenzel, D.; Bicknell, L.S.; Hurles, M.E.; Homfray, T.; Penninger, J.M.; Jackson, A.P.;
Knoblich, J.A. Cerebral Organoids Model Human Brain Development and Microcephaly. Nature 2013,501, 373–379. [CrossRef]
13.
Pa¸sca, A.M.; Sloan, S.A.; Clarke, L.E.; Tian, Y.; Makinson, C.D.; Huber, N.; Kim, C.H.; Park, J.-Y.; O’Rourke, N.A.;
Nguyen, K.D.; et al.
Functional Cortical Neurons and Astrocytes from Human Pluripotent Stem Cells in 3D Culture. Nat. Methods
2015,12, 671–678. [CrossRef] [PubMed]
14.
Kadoshima, T.; Sakaguchi, H.; Nakano, T.; Soen, M.; Ando, S.; Eiraku, M.; Sasai, Y. Self-Organization of Axial Polarity, inside-out
Layer Pattern, and Species-Specific Progenitor Dynamics in Human ES Cell-Derived Neocortex. Proc. Natl. Acad. Sci. USA
2013
,
110, 20284–20289. [CrossRef] [PubMed]
15.
Sloan, S.A.; Darmanis, S.; Huber, N.; Khan, T.A.; Birey, F.; Caneda, C.; Reimer, R.; Quake, S.R.; Barres, B.A.; Pa¸sca, S.P. Human
Astrocyte Maturation Captured in 3D Cerebral Cortical Spheroids Derived from Pluripotent Stem Cells. Neuron
2017
,95,
779–790.e6. [CrossRef] [PubMed]
16.
Qian, X.; Su, Y.; Adam, C.D.; Deutschmann, A.U.; Pather, S.R.; Goldberg, E.M.; Su, K.; Li, S.; Lu, L.; Jacob, F.; et al. Sliced Human
Cortical Organoids for Modeling Distinct Cortical Layer Formation. Cell Stem Cell 2020,26, 766–781.e9. [CrossRef]
17.
Marton, R.M.; Miura, Y.; Sloan, S.A.; Li, Q.; Revah, O.; Levy, R.J.; Huguenard, J.R.; Pa
s
,
ca, S.P. Differentiation and Maturation of
Oligodendrocytes in Human Three-Dimensional Neural Cultures. Nat. Neurosci. 2019,22, 484–491. [CrossRef]
18.
Quadrato, G.; Nguyen, T.; Macosko, E.Z.; Sherwood, J.L.; Min Yang, S.; Berger, D.R.; Maria, N.; Scholvin, J.; Goldman, M.;
Kinney, J.P.; et al.
Cell Diversity and Network Dynamics in Photosensitive Human Brain Organoids. Nature
2017
,545, 48–53.
[CrossRef]
Cells 2022,11, 2803 16 of 26
19.
Sharf, T.; van der Molen, T.; Glasauer, S.M.K.; Guzman, E.; Buccino, A.P.; Luna, G.; Cheng, Z.; Audouard, M.; Ranasinghe, K.G.;
Kudo, K.; et al. Functional Neuronal Circuitry and Oscillatory Dynamics in Human Brain Organoids. Nat. Commun.
2022
,
13, 4403. [CrossRef]
20.
Trujillo, C.A.; Gao, R.; Negraes, P.D.; Gu, J.; Buchanan, J.; Preissl, S.; Wang, A.; Wu, W.; Haddad, G.G.; Chaim, I.A.; et al. Complex
Oscillatory Waves Emerging from Cortical Organoids Model Early Human Brain Network Development. Cell Stem Cell
2019
,25,
558–569.e7. [CrossRef]
21.
Park, Y.; Franz, C.K.; Ryu, H.; Luan, H.; Cotton, K.Y.; Kim, J.U.; Chung, T.S.; Zhao, S.; Vazquez-Guardado, A.; Yang, D.S.; et al.
Three-Dimensional, Multifunctional Neural Interfaces for Cortical Spheroids and Engineered Assembloids. Sci. Adv.
2021
,
7, eabf9153. [CrossRef] [PubMed]
22.
Le Floch, P.; Li, Q.; Lin, Z.; Zhao, S.; Liu, R.; Tasnim, K.; Jiang, H.; Liu, J. Stretchable Mesh Nanoelectronics for 3D Single-Cell
Chronic Electrophysiology from Developing Brain Organoids. Adv. Mater. 2022,34, e2106829. [CrossRef] [PubMed]
23.
McDonald, M.; Sebinger, D.; Brauns, L.; Gonzalez-Cano, L.; Menuchin-Lasowski, Y.; Mierzejewski, M.; Psathaki, O.-E.;
Stumpf, A.;
Wickham, J.; Rauen, T.; et al. A Mesh Microelectrode Array for Non-Invasive Electrophysiology within Neural Organoids. bioRxiv
2021, bioRxiv:2020.09.02.279125.
24.
Voitiuk, K.; Geng, J.; Keefe, M.G.; Parks, D.F.; Sanso, S.E.; Hawthorne, N.; Freeman, D.B.; Currie, R.; Mostajo-Radji, M.A.;
Pollen, A.A.; et al.
Light-Weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experi-
ments. J. Neural Eng. 2021,18, 066004. [CrossRef]
25.
Camp, J.G.; Badsha, F.; Florio, M.; Kanton, S.; Gerber, T.; Wilsch-Bräuninger, M.; Lewitus, E.; Sykes, A.; Hevers, W.;
Lancaster, M.; et al
. Human Cerebral Organoids Recapitulate Gene Expression Programs of Fetal Neocortex Development. Proc.
Natl. Acad. Sci. USA 2015,112, 15672–15677. [CrossRef]
26.
Pollen, A.A.; Nowakowski, T.J.; Shuga, J.; Wang, X.; Leyrat, A.A.; Lui, J.H.; Li, N.; Szpankowski, L.; Fowler, B.; Chen, P.; et al.
Low-Coverage Single-Cell mRNA Sequencing Reveals Cellular Heterogeneity and Activated Signaling Pathways in Developing
Cerebral Cortex. Nat. Biotechnol. 2014,32, 1053–1058. [CrossRef]
27.
Nowakowski, T.J.; Bhaduri, A.; Pollen, A.A.; Alvarado, B.; Mostajo-Radji, M.A.; Di Lullo, E.; Haeussler, M.; Sandoval-
Espinosa, C.
;
Liu, S.J.; Velmeshev, D.; et al. Spatiotemporal Gene Expression Trajectories Reveal Developmental Hierarchies of the Human
Cortex. Science 2017,358, 1318–1323. [CrossRef]
28.
Zhong, S.; Zhang, S.; Fan, X.; Wu, Q.; Yan, L.; Dong, J.; Zhang, H.; Li, L.; Sun, L.; Pan, N.; et al. A Single-Cell RNA-Seq Survey of
the Developmental Landscape of the Human Prefrontal Cortex. Nature 2018,555, 524–528. [CrossRef]
29.
Polioudakis, D.; de la Torre-Ubieta, L.; Langerman, J.; Elkins, A.G.; Shi, X.; Stein, J.L.; Vuong, C.K.; Nichterwitz, S.; Gevorgian, M.;
Opland, C.K.; et al. A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-Gestation. Neuron
2019
,
103, 785–801.e8. [CrossRef]
30.
Bhaduri, A.; Sandoval-Espinosa, C.; Otero-Garcia, M.; Oh, I.; Yin, R.; Eze, U.C.; Nowakowski, T.J.; Kriegstein, A.R. An Atlas of
Cortical Arealization Identifies Dynamic Molecular Signatures. Nature 2021,598, 200–204. [CrossRef]
31.
La Manno, G.; Gyllborg, D.; Codeluppi, S.; Nishimura, K.; Salto, C.; Zeisel, A.; Borm, L.E.; Stott, S.R.W.; Toledo, E.M.;
Villaescusa, J.C.; et al.
Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells. Cell
2016
,167,
566–580.e19. [CrossRef] [PubMed]
32.
Pollen, A.A.; Bhaduri, A.; Andrews, M.G.; Nowakowski, T.J.; Meyerson, O.S.; Mostajo-Radji, M.A.; Di Lullo, E.; Alvarado, B.;
Bedolli, M.; Dougherty, M.L.; et al. Establishing Cerebral Organoids as Models of Human-Specific Brain Evolution. Cell
2019
,176,
743–756.e17. [CrossRef] [PubMed]
33.
Bhaduri, A.; Andrews, M.G.; Mancia Leon, W.; Jung, D.; Shin, D.; Allen, D.; Jung, D.; Schmunk, G.; Haeussler, M.; Salma, J.; et al.
Cell Stress in Cortical Organoids Impairs Molecular Subtype Specification. Nature 2020,578, 142–148. [CrossRef] [PubMed]
34.
Vértesy, Á.; Eichmüller, O.L.; Naas, J.; Novatchkova, M.; Esk, C.; Balmaña, M.; Ladstaetter, S.; Bock, C.; von Haeseler, A.;
Knoblich, J.A.
Gruffi: An Algorithm for Computational Removal of Stressed Cells from Brain Organoid Transcriptomic Datasets.
EMBO J. 2022, e111118. [CrossRef]
35.
Speir, M.L.; Bhaduri, A.; Markov, N.S.; Moreno, P.; Nowakowski, T.J.; Papatheodorou, I.; Pollen, A.A.; Raney, B.J.; Seninge, L.;
Kent, W.J.; et al. UCSC Cell Browser: Visualize Your Single-Cell Data. Bioinformatics 2021,37, 4578–4580. [CrossRef]
36.
Gordon, A.; Yoon, S.-J.; Tran, S.S.; Makinson, C.D.; Park, J.Y.; Andersen, J.; Valencia, A.M.; Horvath, S.; Xiao, X.;
Huguenard, J.R.; et al.
Long-Term Maturation of Human Cortical Organoids Matches Key Early Postnatal Transitions.
Nat. Neurosci. 2021,24, 331–342. [CrossRef] [PubMed]
37.
Luo, C.; Lancaster, M.A.; Castanon, R.; Nery, J.R.; Knoblich, J.A.; Ecker, J.R. Cerebral Organoids Recapitulate Epigenomic
Signatures of the Human Fetal Brain. Cell Rep. 2016,17, 3369–3384. [CrossRef]
38.
Yoon, K.-J.; Ringeling, F.R.; Vissers, C.; Jacob, F.; Pokrass, M.; Jimenez-Cyrus, D.; Su, Y.; Kim, N.-S.; Zhu, Y.; Zheng, L.; et al.
Temporal Control of Mammalian Cortical Neurogenesis by m6A Methylation. Cell 2017,171, 877–889.e17. [CrossRef]
39.
Amiri, A.; Coppola, G.; Scuderi, S.; Wu, F.; Roychowdhury, T.; Liu, F.; Pochareddy, S.; Shin, Y.; Safi, A.; Song, L.; et al.
Transcriptome and Epigenome Landscape of Human Cortical Development Modeled in Organoids. Science
2018
,362, eaat6720.
[CrossRef]
40.
Trevino, A.E.; Müller, F.; Andersen, J.; Sundaram, L.; Kathiria, A.; Shcherbina, A.; Farh, K.; Chang, H.Y.; Pa
s
,
ca, A.M.;
Kundaje, A.; et al.
Chromatin and Gene-Regulatory Dynamics of the Developing Human Cerebral Cortex at Single-Cell Resolu-
tion. Cell 2021,184, 5053–5069.e23. [CrossRef]
Cells 2022,11, 2803 17 of 26
41.
Ziffra, R.S.; Kim, C.N.; Ross, J.M.; Wilfert, A.; Turner, T.N.; Haeussler, M.; Casella, A.M.; Przytycki, P.F.; Keough, K.C.;
Shin, D.; et al.
Single-Cell Epigenomics Reveals Mechanisms of Human Cortical Development. Nature
2021
,598, 205–213.
[CrossRef] [PubMed]
42.
Malatesta, P.; Hartfuss, E.; Götz, M. Isolation of Radial Glial Cells by Fluorescent-Activated Cell Sorting Reveals a Neuronal
Lineage. Development 2000,127, 5253–5263. [CrossRef] [PubMed]
43.
Hartfuss, E.; Galli, R.; Heins, N.; Götz, M. Characterization of CNS Precursor Subtypes and Radial Glia. Dev. Biol.
2001
,229,
15–30. [CrossRef]
44. Temple, S. The Development of Neural Stem Cells. Nature 2001,414, 112–117. [CrossRef] [PubMed]
45.
Rakic, P. Mode of Cell Migration to the Superficial Layers of Fetal Monkey Neocortex. J. Comp. Neurol.
1972
,145, 61–83. [CrossRef]
[PubMed]
46.
Noctor, S.C.; Flint, A.C.; Weissman, T.A.; Dammerman, R.S.; Kriegstein, A.R. Neurons Derived from Radial Glial Cells Establish
Radial Units in Neocortex. Nature 2001,409, 714–720. [CrossRef]
47.
Miyata, T.; Kawaguchi, A.; Okano, H.; Ogawa, M. Asymmetric Inheritance of Radial Glial Fibers by Cortical Neurons. Neuron
2001,31, 727–741. [CrossRef]
48.
Haubensak, W.; Attardo, A.; Denk, W.; Huttner, W.B. Neurons Arise in the Basal Neuroepithelium of the Early Mammalian
Telencephalon: A Major Site of Neurogenesis. Proc. Natl. Acad. Sci. USA 2004,101, 3196–3201. [CrossRef]
49.
Noctor, S.C.; Martínez-Cerdeño, V.; Ivic, L.; Kriegstein, A.R. Cortical Neurons Arise in Symmetric and Asymmetric Division
Zones and Migrate through Specific Phases. Nat. Neurosci. 2004,7, 136–144. [CrossRef]
50.
Englund, C.; Fink, A.; Lau, C.; Pham, D.; Daza, R.A.M.; Bulfone, A.; Kowalczyk, T.; Hevner, R.F. Pax6, Tbr2, and Tbr1 Are
Expressed Sequentially by Radial Glia, Intermediate Progenitor Cells, and Postmitotic Neurons in Developing Neocortex. J.
Neurosci. 2005,25, 247–251. [CrossRef]
51.
Takahashi, T.; Nowakowski, R.S.; Caviness, V.S., Jr. Cell Cycle Parameters and Patterns of Nuclear Movement in the Neocortical
Proliferative Zone of the Fetal Mouse. J. Neurosci. 1993,13, 820–833. [CrossRef] [PubMed]
52.
Angevine, J.B., Jr.; Sidman, R.L. Autoradiographic Study of Cell Migration during Histogenesis of Cerebral Cortex in the Mouse.
Nature 1961,192, 766–768. [CrossRef] [PubMed]
53.
Okano, H.; Temple, S. Cell Types to Order: Temporal Specification of CNS Stem Cells. Curr. Opin. Neurobiol.
2009
,19, 112–119.
[CrossRef] [PubMed]
54.
Smart, I.H.M.; Dehay, C.; Giroud, P.; Berland, M.; Kennedy, H. Unique Morphological Features of the Proliferative Zones and
Postmitotic Compartments of the Neural Epithelium Giving Rise to Striate and Extrastriate Cortex in the Monkey. Cereb. Cortex
2002,12, 37–53. [CrossRef] [PubMed]
55.
Betizeau, M.; Cortay, V.; Patti, D.; Pfister, S.; Gautier, E.; Bellemin-Ménard, A.; Afanassieff, M.; Huissoud, C.; Douglas, R.J.;
Kennedy, H.; et al. Precursor Diversity and Complexity of Lineage Relationships in the Outer Subventricular Zone of the Primate.
Neuron 2013,80, 442–457. [CrossRef]
56.
Hansen, D.V.; Lui, J.H.; Parker, P.R.L.; Kriegstein, A.R. Neurogenic Radial Glia in the Outer Subventricular Zone of Human
Neocortex. Nature 2010,464, 554–561. [CrossRef]
57.
Kelava, I.; Reillo, I.; Murayama, A.Y.; Kalinka, A.T.; Stenzel, D.; Tomancak, P.; Matsuzaki, F.; Lebrand, C.; Sasaki, E.;
Schwamborn, J.C.; et al.
Abundant Occurrence of Basal Radial Glia in the Subventricular Zone of Embryonic Neocortex of a
Lissencephalic Primate, the Common Marmoset Callithrix Jacchus. Cereb. Cortex 2012,22, 469–481. [CrossRef]
58.
Fietz, S.A.; Kelava, I.; Vogt, J.; Wilsch-Bräuninger, M.; Stenzel, D.; Fish, J.L.; Corbeil, D.; Riehn, A.; Distler, W.; Nitsch, R.; et al.
OSVZ Progenitors of Human and Ferret Neocortex Are Epithelial-like and Expand by Integrin Signaling. Nat. Neurosci.
2010
,13,
690–699. [CrossRef]
59.
Reillo, I.; de Juan Romero, C.; García-Cabezas, M.Á.; Borrell, V. A Role for Intermediate Radial Glia in the Tangential Expansion
of the Mammalian Cerebral Cortex. Cereb. Cortex 2011,21, 1674–1694. [CrossRef]
60.
Nowakowski, T.J.; Pollen, A.A.; Sandoval-Espinosa, C.; Kriegstein, A.R. Transformation of the Radial Glia Scaffold Demarcates
Two Stages of Human Cerebral Cortex Development. Neuron 2016,91, 1219–1227. [CrossRef]
61.
Bilgic, M.; Wu, Q.; Suetsugu, T.; Tsunekawa, Y.; Sitamukai, A.; Kadota, M.; Nishimura, O.; Kuraku, S.; Matsuzaki, F. Single Cell
Transcriptomics of Ferrets Reveal a Common Temporal Pattern of Progenitors in Brain Development of Gyrencephalic Mammals.
bioRxiv 2022. bioRxiv:2022.05.05.490846.
62.
Pollen, A.A.; Nowakowski, T.J.; Chen, J.; Retallack, H.; Sandoval-Espinosa, C.; Nicholas, C.R.; Shuga, J.; Liu, S.J.; Oldham, M.C.;
Diaz, A.; et al. Molecular Identity of Human Outer Radial Glia during Cortical Development. Cell
2015
,163, 55–67. [CrossRef]
[PubMed]
63.
Thomsen, E.R.; Mich, J.K.; Yao, Z.; Hodge, R.D.; Doyle, A.M.; Jang, S.; Shehata, S.I.; Nelson, A.M.; Shapovalova, N.V.;
Levi, B.P.; et al.
Fixed Single-Cell Transcriptomic Characterization of Human Radial Glial Diversity. Nat. Methods
2016
,13,
87–93. [CrossRef] [PubMed]
64.
Watanabe, M.; Buth, J.E.; Vishlaghi, N.; de la Torre-Ubieta, L.; Taxidis, J.; Khakh, B.S.; Coppola, G.; Pearson, C.A.; Yamauchi, K.;
Gong, D.; et al. Self-Organized Cerebral Organoids with Human-Specific Features Predict Effective Drugs to Combat Zika Virus
Infection. Cell Rep. 2017,21, 517–532. [CrossRef]
65.
Bonni, A.; Sun, Y.; Nadal-Vicens, M.; Bhatt, A.; Frank, D.A.; Rozovsky, I.; Stahl, N.; Yancopoulos, G.D.; Greenberg, M.E. Regulation
of Gliogenesis in the Central Nervous System by the JAK-STAT Signaling Pathway. Science 1997,278, 477–483. [CrossRef]
Cells 2022,11, 2803 18 of 26
66. Huang, W.; Bhaduri, A.; Velmeshev, D.; Wang, S.; Wang, L.; Rottkamp, C.A.; Alvarez-Buylla, A.; Rowitch, D.H.; Kriegstein, A.R.
Origins and Proliferative States of Human Oligodendrocyte Precursor Cells. Cell 2020,182, 594–608.e11. [CrossRef]
67.
van Bruggen, D.; Pohl, F.; Langseth, C.M.; Kukanja, P.; Lee, H.; Kabbe, M.; Meijer, M.; Hilscher, M.M.; Nilsson, M.;
Sundström, E.; et al.
Developmental Landscape of Human Forebrain at a Single-Cell Level Unveils Early Waves of Oligodendro-
genesis. bioRxiv 2021. bioRxiv:2021.07.22.453317.
68.
deAzevedo, L.C.; Fallet, C.; Moura-Neto, V.; Daumas-Duport, C.; Hedin-Pereira, C.; Lent, R. Cortical Radial Glial Cells in Human
Fetuses: Depth-Correlated Transformation into Astrocytes. J. Neurobiol. 2003,55, 288–298. [CrossRef]
69.
Zhang, Y.; Sloan, S.A.; Clarke, L.E.; Caneda, C.; Plaza, C.A.; Blumenthal, P.D.; Vogel, H.; Steinberg, G.K.; Edwards, M.S.B.;
Li, G.; et al.
Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional
Differences with Mouse. Neuron 2016,89, 37–53. [CrossRef]
70.
Bayraktar, O.A.; Bartels, T.; Holmqvist, S.; Kleshchevnikov, V.; Martirosyan, A.; Polioudakis, D.; Ben Haim, L.; Young, A.M.H.;
Batiuk, M.Y.; Prakash, K.; et al. Astrocyte Layers in the Mammalian Cerebral Cortex Revealed by a Single-Cell in Situ Transcrip-
tomic Map. Nat. Neurosci. 2020,23, 500–509. [CrossRef]
71.
Allen, D.E.; Donohue, K.C.; Cadwell, C.R.; Shin, D.; Keefe, M.G.; Sohal, V.S.; Nowakowski, T.J. Fate Mapping of Neural Stem Cell
Niches Reveals Distinct Origins of Human Cortical Astrocytes. Science 2022,376, eabm5224. [CrossRef] [PubMed]
72.
Falcone, C.; McBride, E.L.; Hopkins, W.D.; Hof, P.R.; Manger, P.R.; Sherwood, C.C.; Noctor, S.C.; Martínez-Cerdeño, V. Redefining
Varicose Projection Astrocytes in Primates. Glia 2022,70, 145–154. [CrossRef]
73.
Falcone, C.; Penna, E.; Hong, T.; Tarantal, A.F.; Hof, P.R.; Hopkins, W.D.; Sherwood, C.C.; Noctor, S.C.; Martínez-Cerdeño, V.
Cortical Interlaminar Astrocytes Are Generated Prenatally, Mature Postnatally, and Express Unique Markers in Human and
Nonhuman Primates. Cereb. Cortex 2021,31, 379–395. [CrossRef] [PubMed]
74.
Kondo, T.; Raff, M. Oligodendrocyte Precursor Cells Reprogrammed to Become Multipotential CNS Stem Cells. Science
2000
,289,
1754–1757. [CrossRef] [PubMed]
75.
Yang, L.; Li, Z.; Liu, G.; Li, X.; Yang, Z. Developmental Origins of Human Cortical Oligodendrocytes and Astrocytes. Neurosci.
Bull. 2022,38, 47–68. [CrossRef]
76.
Fu, Y.; Yang, M.; Yu, H.; Wang, Y.; Wu, X.; Yong, J.; Mao, Y.; Cui, Y.; Fan, X.; Wen, L.; et al. Heterogeneity of Glial Progenitor Cells
during the Neurogenesis-to-Gliogenesis Switch in the Developing Human Cerebral Cortex. Cell Rep.
2021
,34, 108788. [CrossRef]
77.
Hansen, D.V.; Lui, J.H.; Flandin, P.; Yoshikawa, K.; Rubenstein, J.L.; Alvarez-Buylla, A.; Kriegstein, A.R. Non-Epithelial Stem
Cells and Cortical Interneuron Production in the Human Ganglionic Eminences. Nat. Neurosci. 2013,16, 1576–1587. [CrossRef]
78.
Ma, T.; Wang, C.; Wang, L.; Zhou, X.; Tian, M.; Zhang, Q.; Zhang, Y.; Li, J.; Liu, Z.; Cai, Y.; et al. Subcortical Origins of Human and
Monkey Neocortical Interneurons. Nat. Neurosci. 2013,16, 1588–1597. [CrossRef]
79.
Delgado, R.N.; Allen, D.E.; Keefe, M.G.; Mancia Leon, W.R.; Ziffra, R.S.; Crouch, E.E.; Alvarez-Buylla, A.; Nowakowski, T.J.
Individual Human Cortical Progenitors Can Produce Excitatory and Inhibitory Neurons. Nature 2022,601, 397–403. [CrossRef]
80.
Schmitz, M.T.; Sandoval, K.; Chen, C.P.; Mostajo-Radji, M.A.; Seeley, W.W.; Nowakowski, T.J.; Ye, C.J.; Paredes, M.F.; Pollen, A.A.
The Development and Evolution of Inhibitory Neurons in Primate Cerebrum. Nature 2022,603, 871–877. [CrossRef]
81.
Mayer, C.; Jaglin, X.H.; Cobbs, L.V.; Bandler, R.C.; Streicher, C.; Cepko, C.L.; Hippenmeyer, S.; Fishell, G. Clonally Related
Forebrain Interneurons Disperse Broadly across Both Functional Areas and Structural Boundaries. Neuron
2015
,87, 989–998.
[CrossRef] [PubMed]
82.
Pebworth, M.-P.; Ross, J.; Andrews, M.; Bhaduri, A.; Kriegstein, A.R. Human Intermediate Progenitor Diversity during Cortical
Development. Proc. Natl. Acad. Sci. USA 2021,118, e2019415118. [CrossRef] [PubMed]
83.
Letinic, K.; Zoncu, R.; Rakic, P. Origin of GABAergic Neurons in the Human Neocortex. Nature
2002
,417, 645–649. [CrossRef]
[PubMed]
84.
Al-Jaberi, N.; Lindsay, S.; Sarma, S.; Bayatti, N.; Clowry, G.J. The Early Fetal Development of Human Neocortical GABAergic
Interneurons. Cereb. Cortex 2015,25, 631–645. [CrossRef]
85.
Velasco, S.; Kedaigle, A.J.; Simmons, S.K.; Nash, A.; Rocha, M.; Quadrato, G.; Paulsen, B.; Nguyen, L.; Adiconis, X.;
Regev, A.; et al.
Individual Brain Organoids Reproducibly Form Cell Diversity of the Human Cerebral Cortex. Nature
2019
,570, 523–527.
[CrossRef]
86.
Qian, X.; Nguyen, H.N.; Song, M.M.; Hadiono, C.; Ogden, S.C.; Hammack, C.; Yao, B.; Hamersky, G.R.; Jacob, F.; Zhong, C.; et al.
Brain-Region-Specific Organoids Using Mini-Bioreactors for Modeling ZIKV Exposure. Cell 2016,165, 1238–1254. [CrossRef]
87. Wang, R.; Sharma, R.; Shen, X.; Laughney, A.M.; Funato, K.; Clark, P.J.; Shpokayte, M.; Morgenstern, P.; Navare, M.; Xu, Y.; et al.
Adult Human Glioblastomas Harbor Radial Glia-like Cells. Stem Cell Reports 2020,14, 338–350. [CrossRef]
88.
Tirosh, I.; Venteicher, A.S.; Hebert, C.; Escalante, L.E.; Patel, A.P.; Yizhak, K.; Fisher, J.M.; Rodman, C.; Mount, C.;
Filbin, M.G.; et al.
Single-Cell RNA-Seq Supports a Developmental Hierarchy in Human Oligodendroglioma. Nature
2016
,539, 309–313. [CrossRef]
89.
Bhaduri, A.; Di Lullo, E.; Jung, D.; Müller, S.; Crouch, E.E.; Espinosa, C.S.; Ozawa, T.; Alvarado, B.; Spatazza, J.;
Cadwell, C.R.; et al.
Outer Radial Glia-like Cancer Stem Cells Contribute to Heterogeneity of Glioblastoma. Cell Stem Cell
2020
,26, 48–63.e6. [CrossRef]
90.
La Manno, G.; Soldatov, R.; Zeisel, A.; Braun, E.; Hochgerner, H.; Petukhov, V.; Lidschreiber, K.; Kastriti, M.E.; Lönnerberg, P.;
Furlan, A.; et al. RNA Velocity of Single Cells. Nature 2018,560, 494–498. [CrossRef]
91.
Trapnell, C.; Cacchiarelli, D.; Grimsby, J.; Pokharel, P.; Li, S.; Morse, M.; Lennon, N.J.; Livak, K.J.; Mikkelsen, T.S.; Rinn, J.L. The
Dynamics and Regulators of Cell Fate Decisions Are Revealed by Pseudotemporal Ordering of Single Cells. Nat. Biotechnol.
2014
,
32, 381–386. [CrossRef]
Cells 2022,11, 2803 19 of 26
92.
Wagner, D.E.; Klein, A.M. Lineage Tracing Meets Single-Cell Omics: Opportunities and Challenges. Nat. Rev. Genet.
2020
,21,
410–427. [CrossRef] [PubMed]
93.
Gertz, C.C.; Lui, J.H.; LaMonica, B.E.; Wang, X.; Kriegstein, A.R. Diverse Behaviors of Outer Radial Glia in Developing Ferret and
Human Cortex. J. Neurosci. 2014,34, 2559–2570. [CrossRef] [PubMed]
94.
LaMonica, B.E.; Lui, J.H.; Hansen, D.V.; Kriegstein, A.R. Mitotic Spindle Orientation Predicts Outer Radial Glial Cell Generation
in Human Neocortex. Nat. Commun. 2013,4, 1665. [CrossRef] [PubMed]
95.
Bershteyn, M.; Nowakowski, T.J.; Pollen, A.A.; Di Lullo, E.; Nene, A.; Wynshaw-Boris, A.; Kriegstein, A.R. Human iPSC-Derived
Cerebral Organoids Model Cellular Features of Lissencephaly and Reveal Prolonged Mitosis of Outer Radial Glia. Cell Stem Cell
2017,20, 435–449.e4. [CrossRef] [PubMed]
96.
Subramanian, L.; Bershteyn, M.; Paredes, M.F.; Kriegstein, A.R. Dynamic Behaviour of Human Neuroepithelial Cells in the
Developing Forebrain. Nat. Commun. 2017,8, 14167. [CrossRef]
97.
Coquand, L.; Macé, A.-S.; Farcy, S.; Avalos, C.B.; Di Cicco, A.; Lampic, M.; Bessières, B.; Attie-Bitach, T.; Fraisier, V.;
Guimiot, F.; et al.
A Cell Fate Decision Map Reveals Abundant Direct Neurogenesis in the Human Developing Neocortex. bioRxiv
2022. bioRxiv:2022.02.01.478661.
98.
Esk, C.; Lindenhofer, D.; Haendeler, S.; Wester, R.A.; Pflug, F.; Schroeder, B.; Bagley, J.A.; Elling, U.; Zuber, J.; von
Haeseler, A.; et al.
A Human Tissue Screen Identifies a Regulator of ER Secretion as a Brain-Size Determinant. Science
2020
,370, 935–941. [CrossRef]
99.
He, Z.; Maynard, A.; Jain, A.; Gerber, T.; Petri, R.; Lin, H.-C.; Santel, M.; Ly, K.; Dupré, J.-S.; Sidow, L.; et al. Lineage Recording in
Human Cerebral Organoids. Nat. Methods 2022,19, 90–99. [CrossRef]
100.
Bandler, R.C.; Vitali, I.; Delgado, R.N.; Ho, M.C.; Dvoretskova, E.; Ibarra Molinas, J.S.; Frazel, P.W.; Mohammadkhani, M.;
Machold, R.; Maedler, S.; et al. Single-Cell Delineation of Lineage and Genetic Identity in the Mouse Brain. Nature
2021
,601,
404–409. [CrossRef]
101.
Ratz, M.; von Berlin, L.; Larsson, L.; Martin, M.; Westholm, J.O.; La Manno, G.; Lundeberg, J.; Frisén, J. Clonal Relations in the
Mouse Brain Revealed by Single-Cell and Spatial Transcriptomics. Nat. Neurosci. 2022,25, 285–294. [CrossRef] [PubMed]
102.
Yoon, S.-J.; Elahi, L.S.; Pa
s
,
ca, A.M.; Marton, R.M.; Gordon, A.; Revah, O.; Miura, Y.; Walczak, E.M.; Holdgate, G.M.;
Fan, H.C.; et al.
Reliability of Human Cortical Organoid Generation. Nat. Methods 2019,16, 75–78. [CrossRef] [PubMed]
103.
Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.-W.; da Silva
Santos, L.B.; Bourne, P.E.; et al. The FAIR Guiding Principles for Scientific Data Management and Stewardship. Sci. Data
2016
,
3, 160018. [CrossRef] [PubMed]
104.
Puigdevall, P.; Jerber, J.; Danecek, P.; Castellano, S.; Kilpinen, H. Effects of Somatic Mutations on Cellular Differentiation in iPSC
Models of Neurodevelopment. bioRxiv 2022. bioRxiv:2022.03.04.482992.
105.
Giandomenico, S.L.; Sutcliffe, M.; Lancaster, M.A. Generation and Long-Term Culture of Advanced Cerebral Organoids for
Studying Later Stages of Neural Development. Nat. Protoc. 2021,16, 579–602. [CrossRef]
106.
Schafer, D.P.; Lehrman, E.K.; Kautzman, A.G.; Koyama, R.; Mardinly, A.R.; Yamasaki, R.; Ransohoff, R.M.; Greenberg, M.E.;
Barres, B.A.; Stevens, B. Microglia Sculpt Postnatal Neural Circuits in an Activity and Complement-Dependent Manner. Neuron
2012,74, 691–705. [CrossRef]
107.
Popova, G.; Soliman, S.S.; Kim, C.N.; Keefe, M.G.; Hennick, K.M.; Jain, S.; Li, T.; Tejera, D.; Shin, D.; Chhun, B.B.; et al. Human
Microglia States Are Conserved across Experimental Models and Regulate Neural Stem Cell Responses in Chimeric Organoids.
Cell Stem Cell 2021,28, 2153–2166.e6. [CrossRef]
108.
Cakir, B.; Tanaka, Y.; Kiral, F.R.; Xiang, Y.; Dagliyan, O.; Wang, J.; Lee, M.; Greaney, A.M.; Yang, W.S.; duBoulay, C.; et al.
Expression of the Transcription Factor PU.1 Induces the Generation of Microglia-like Cells in Human Cortical Organoids. Nat.
Commun. 2022,13, 430. [CrossRef]
109.
Sabate-Soler, S.; Nickels, S.L.; Saraiva, C.; Berger, E.; Dubonyte, U.; Barmpa, K.; Lan, Y.J.; Kouno, T.; Jarazo, J.; Robertson, G.; et al.
Microglia Integration into Human Midbrain Organoids Leads to Increased Neuronal Maturation and Functionality. Glia
2022
,70,
1267–1288. [CrossRef]
110.
Fagerlund, I.; Dougalis, A.; Shakirzyanova, A.; Gómez-Budia, M.; Pelkonen, A.; Konttinen, H.; Ohtonen, S.; Fazaludeen, M.F.;
Koskuvi, M.; Kuusisto, J.; et al. Microglia-like Cells Promote Neuronal Functions in Cerebral Organoids. Cells
2021
,11, 124.
[CrossRef]
111.
Ross, J.M.; Kim, C.; Allen, D.; Crouch, E.E.; Narsinh, K.; Cooke, D.L.; Abla, A.A.; Nowakowski, T.J.; Winkler, E.A. The Expanding
Cell Diversity of the Brain Vasculature. Front. Physiol. 2020,11, 600767. [CrossRef] [PubMed]
112.
Shi, Y.; Sun, L.; Wang, M.; Liu, J.; Zhong, S.; Li, R.; Li, P.; Guo, L.; Fang, A.; Chen, R.; et al. Vascularized Human Cortical Organoids
(vOrganoids) Model Cortical Development in Vivo. PLoS Biol. 2020,18, e3000705. [CrossRef] [PubMed]
113.
Sun, X.-Y.; Ju, X.-C.; Li, Y.; Zeng, P.-M.; Wu, J.; Zhou, Y.-Y.; Shen, L.-B.; Dong, J.; Chen, Y.-J.; Luo, Z.-G. Generation of Vascularized
Brain Organoids to Study Neurovascular Interactions. Elife 2022,11, e76707. [CrossRef]
114.
Kook, M.G.; Lee, S.-E.; Shin, N.; Kong, D.; Kim, D.-H.; Kim, M.-S.; Kang, H.K.; Choi, S.W.; Kang, K.-S. Generation of Cortical Brain
Organoid with Vascularization by Assembling with Vascular Spheroid. Int J Stem Cells 2022,15, 85–94. [CrossRef] [PubMed]
115.
Cakir, B.; Xiang, Y.; Tanaka, Y.; Kural, M.H.; Parent, M.; Kang, Y.-J.; Chapeton, K.; Patterson, B.; Yuan, Y.; He, C.-S.; et al.
Engineering of Human Brain Organoids with a Functional Vascular-like System. Nat. Methods 2019,16, 1169–1175. [CrossRef]
Cells 2022,11, 2803 20 of 26
116.
Ang, L.T.; Nguyen, A.T.; Liu, K.J.; Chen, A.; Xiong, X.; Curtis, M.; Martin, R.M.; Raftry, B.C.; Ng, C.Y.; Vogel, U.; et al. Generating
Human Artery and Vein Cells from Pluripotent Stem Cells Highlights the Arterial Tropism of Nipah and Hendra Viruses. Cell
2022,185, 2523–2541.e30. [CrossRef]
117.
Nikolova, M.T.; He, Z.; Wimmer, R.A.; Seimiya, M.; Nikoloff, J.M.; Penninger, J.M.; Gray Camp, J.; Treutlein, B. Fate and State
Transitions during Human Blood Vessel Organoid Development. bioRxiv 2022. bioRxiv:2022.03.23.485329.
118.
Winkler, E.A.; Kim, C.N.; Ross, J.M.; Garcia, J.H.; Gil, E.; Oh, I.; Chen, L.Q.; Wu, D.; Catapano, J.S.; Raygor, K.; et al. A Single-Cell
Atlas of the Normal and Malformed Human Brain Vasculature. Science 2022,375, eabi7377. [CrossRef]
119.
Yang, A.C.; Vest, R.T.; Kern, F.; Lee, D.P.; Agam, M.; Maat, C.A.; Losada, P.M.; Chen, M.B.; Schaum, N.; Khoury, N.; et al. A
Human Brain Vascular Atlas Reveals Diverse Mediators of Alzheimer’s Risk. Nature 2022,603, 885–892. [CrossRef]
120.
Birey, F.; Andersen, J.; Makinson, C.D.; Islam, S.; Wei, W.; Huber, N.; Fan, H.C.; Metzler, K.R.C.; Panagiotakos, G.; Thom, N.; et al.
Assembly of Functionally Integrated Human Forebrain Spheroids. Nature 2017,545, 54–59. [CrossRef]
121.
Xiang, Y.; Tanaka, Y.; Patterson, B.; Kang, Y.-J.; Govindaiah, G.; Roselaar, N.; Cakir, B.; Kim, K.-Y.; Lombroso, A.P.;
Hwang, S.-M.; et al.
Fusion of Regionally Specified hPSC-Derived Organoids Models Human Brain Development and
Interneuron Migration. Cell Stem Cell 2017,21, 383–398.e7. [CrossRef] [PubMed]
122.
Bagley, J.A.; Reumann, D.; Bian, S.; Lévi-Strauss, J.; Knoblich, J.A. Fused Cerebral Organoids Model Interactions between Brain
Regions. Nat. Methods 2017,14, 743–751. [CrossRef] [PubMed]
123.
Miura, Y.; Li, M.-Y.; Birey, F.; Ikeda, K.; Revah, O.; Thete, M.V.; Park, J.-Y.; Puno, A.; Lee, S.H.; Porteus, M.H.; et al. Generation
of Human Striatal Organoids and Cortico-Striatal Assembloids from Human Pluripotent Stem Cells. Nat. Biotechnol.
2020
,38,
1421–1430. [CrossRef] [PubMed]
124.
Sakaguchi, H.; Kadoshima, T.; Soen, M.; Narii, N.; Ishida, Y.; Ohgushi, M.; Takahashi, J.; Eiraku, M.; Sasai, Y. Generation of
Functional Hippocampal Neurons from Self-Organizing Human Embryonic Stem Cell-Derived Dorsomedial Telencephalic Tissue.
Nat. Commun. 2015,6, 8896. [CrossRef]
125.
Suga, H.; Kadoshima, T.; Minaguchi, M.; Ohgushi, M.; Soen, M.; Nakano, T.; Takata, N.; Wataya, T.; Muguruma, K.;
Miyoshi, H.; et al.
Self-Formation of Functional Adenohypophysis in Three-Dimensional Culture. Nature
2011
,480, 57–62.
[CrossRef]
126.
Ozone, C.; Suga, H.; Eiraku, M.; Kadoshima, T.; Yonemura, S.; Takata, N.; Oiso, Y.; Tsuji, T.; Sasai, Y. Functional Anterior Pituitary
Generated in Self-Organizing Culture of Human Embryonic Stem Cells. Nat. Commun. 2016,7, 10351. [CrossRef]
127.
Shiraishi, A.; Muguruma, K.; Sasai, Y. Generation of Thalamic Neurons from Mouse Embryonic Stem Cells. Development
2017
,
144, 1211–1220. [CrossRef]
128.
Xiang, Y.; Tanaka, Y.; Cakir, B.; Patterson, B.; Kim, K.-Y.; Sun, P.; Kang, Y.-J.; Zhong, M.; Liu, X.; Patra, P.; et al. hESC-Derived
Thalamic Organoids Form Reciprocal Projections When Fused with Cortical Organoids. Cell Stem Cell
2019
,24, 487–497.e7.
[CrossRef]
129.
Jo, J.; Xiao, Y.; Sun, A.X.; Cukuroglu, E.; Tran, H.-D.; Göke, J.; Tan, Z.Y.; Saw, T.Y.; Tan, C.-P.; Lokman, H.; et al. Midbrain-like
Organoids from Human Pluripotent Stem Cells Contain Functional Dopaminergic and Neuromelanin-Producing Neurons. Cell
Stem Cell 2016,19, 248–257. [CrossRef]
130.
Zagare, A.; Gobin, M.; Monzel, A.S.; Schwamborn, J.C. A Robust Protocol for the Generation of Human Midbrain Organoids.
STAR Protoc 2021,2, 100524. [CrossRef]
131.
Fiorenzano, A.; Sozzi, E.; Birtele, M.; Kajtez, J.; Giacomoni, J.; Nilsson, F.; Bruzelius, A.; Sharma, Y.; Zhang, Y.; Mattsson, B.; et al.
Single-Cell Transcriptomics Captures Features of Human Midbrain Development and Dopamine Neuron Diversity in Brain
Organoids. Nat. Commun. 2021,12, 7302. [CrossRef] [PubMed]
132.
Muguruma, K.; Nishiyama, A.; Kawakami, H.; Hashimoto, K.; Sasai, Y. Self-Organization of Polarized Cerebellar Tissue in 3D
Culture of Human Pluripotent Stem Cells. Cell Rep. 2015,10, 537–550. [CrossRef]
133.
Fleck, J.S.; Sanchís-Calleja, F.; He, Z.; Santel, M.; Boyle, M.J.; Camp, J.G.; Treutlein, B. Resolving Organoid Brain Region Identities
by Mapping Single-Cell Genomic Data to Reference Atlases. Cell Stem Cell 2021,28, 1148–1159.e8. [CrossRef] [PubMed]
134.
Andersen, J.; Revah, O.; Miura, Y.; Thom, N.; Amin, N.D.; Kelley, K.W.; Singh, M.; Chen, X.; Thete, M.V.; Walczak, E.M.; et al.
Generation of Functional Human 3D Cortico-Motor Assembloids. Cell 2020,183, 1913–1929.e26. [CrossRef] [PubMed]
135.
Giandomenico, S.L.; Mierau, S.B.; Gibbons, G.M.; Wenger, L.M.D.; Masullo, L.; Sit, T.; Sutcliffe, M.; Boulanger, J.; Tripodi, M.;
Derivery, E.; et al. Cerebral Organoids at the Air-Liquid Interface Generate Diverse Nerve Tracts with Functional Output. Nat.
Neurosci. 2019,22, 669–679. [CrossRef] [PubMed]
136.
Takata, N.; Sakakura, E.; Eiraku, M.; Kasukawa, T.; Sasai, Y. Self-Patterning of Rostral-Caudal Neuroectoderm Requires Dual Role
of Fgf Signaling for Localized Wnt Antagonism. Nat. Commun. 2017,8, 1339. [CrossRef]
137.
Cederquist, G.Y.; Asciolla, J.J.; Tchieu, J.; Walsh, R.M.; Cornacchia, D.; Resh, M.D.; Studer, L. Specification of Positional Identity in
Forebrain Organoids. Nat. Biotechnol. 2019,37, 436–444. [CrossRef]
138.
Osaki, T.; Ikeuchi, Y. Advanced Complexity and Plasticity of Neural Activity in Reciprocally Connected Human Cerebral
Organoids. bioRxiv 2021. bioRxiv:2021.02.16.431387.
139.
Molyneaux, B.J.; Arlotta, P.; Menezes, J.R.L.; Macklis, J.D. Neuronal Subtype Specification in the Cerebral Cortex. Nat. Rev.
Neurosci. 2007,8, 427–437. [CrossRef]
140.
Greig, L.C.; Woodworth, M.B.; Galazo, M.J.; Padmanabhan, H.; Macklis, J.D. Molecular Logic of Neocortical Projection Neuron
Specification, Development and Diversity. Nat. Rev. Neurosci. 2013,14, 755–769. [CrossRef]
Cells 2022,11, 2803 21 of 26
141.
Anderson, S.; Vanderhaeghen, P. Cortical Neurogenesis from Pluripotent Stem Cells: Complexity Emerging from Simplicity. Curr.
Opin. Neurobiol. 2014,27, 151–157. [CrossRef] [PubMed]
142.
Cadwell, C.R.; Bhaduri, A.; Mostajo-Radji, M.A.; Keefe, M.G.; Nowakowski, T.J. Development and Arealization of the Cerebral
Cortex. Neuron 2019,103, 980–1004. [CrossRef] [PubMed]
143.
Cederquist, G.Y.; Tchieu, J.; Callahan, S.J.; Ramnarine, K.; Ryan, S.; Zhang, C.; Rittenhouse, C.; Zeltner, N.; Chung, S.Y.;
Zhou, T.; et al.
A Multiplex Human Pluripotent Stem Cell Platform Defines Molecular and Functional Subclasses of Autism-
Related Genes. Cell Stem Cell 2020,27, 35–49.e6. [CrossRef]
144.
Hasenpusch-Theil, K.; Watson, J.A.; Theil, T. Direct Interactions Between Gli3, Wnt8b, and Fgfs Underlie Patterning of the Dorsal
Telencephalon. Cereb. Cortex 2017,27, 1137–1148. [PubMed]
145.
Ypsilanti, A.R.; Pattabiraman, K.; Catta-Preta, R.; Golonzhka, O.; Lindtner, S.; Tang, K.; Jones, I.R.; Abnousi, A.; Juric, I.;
Hu, M.; et al.
Transcriptional Network Orchestrating Regional Patterning of Cortical Progenitors. Proc. Natl. Acad. Sci. USA
2021
,
118, e2024795118. [CrossRef] [PubMed]
146.
Markenscoff-Papadimitriou, E.; Whalen, S.; Przytycki, P.; Thomas, R.; Binyameen, F.; Nowakowski, T.J.; Kriegstein, A.R.;
Sanders, S.J.
; State, M.W.; Pollard, K.S.; et al. A Chromatin Accessibility Atlas of the Developing Human Telencephalon. Cell
2020
,
182, 754–769.e18. [CrossRef]
147.
Shibata, M.; Pattabiraman, K.; Lorente-Galdos, B.; Andrijevic, D.; Kim, S.-K.; Kaur, N.; Muchnik, S.K.; Xing, X.; Santpere, G.;
Sousa, A.M.M.; et al. Regulation of Prefrontal Patterning and Connectivity by Retinoic Acid. Nature
2021
,598, 483–488. [CrossRef]
148.
Espuny-Camacho, I.; Michelsen, K.A.; Gall, D.; Linaro, D.; Hasche, A.; Bonnefont, J.; Bali, C.; Orduz, D.; Bilheu, A.;
Herpoel, A.; et al.
Pyramidal Neurons Derived from Human Pluripotent Stem Cells Integrate Efficiently into Mouse Brain
Circuits in Vivo. Neuron 2013,77, 440–456. [CrossRef]
149.
Linaro, D.; Vermaercke, B.; Iwata, R.; Ramaswamy, A.; Libé-Philippot, B.; Boubakar, L.; Davis, B.A.; Wierda, K.; Davie, K.;
Poovathingal, S.; et al. Xenotransplanted Human Cortical Neurons Reveal Species-Specific Development and Functional
Integration into Mouse Visual Circuits. Neuron 2019,104, 972–986.e6. [CrossRef]
150.
Mansour, A.A.; Gonçalves, J.T.; Bloyd, C.W.; Li, H.; Fernandes, S.; Quang, D.; Johnston, S.; Parylak, S.L.; Jin, X.; Gage, F.H. An in
Vivo Model of Functional and Vascularized Human Brain Organoids. Nat. Biotechnol. 2018,36, 432–441. [CrossRef]
151.
Daviaud, N.; Friedel, R.H.; Zou, H. Vascularization and Engraftment of Transplanted Human Cerebral Organoids in Mouse
Cortex. eNeuro 2018,5, ENEURO.0219-18.2018. [CrossRef] [PubMed]
152. Kitahara, T.; Sakaguchi, H.; Morizane, A.; Kikuchi, T.; Miyamoto, S.; Takahashi, J. Axonal Extensions along Corticospinal Tracts
from Transplanted Human Cerebral Organoids. Stem Cell Reports 2020,15, 467–481. [CrossRef] [PubMed]
153.
Chen, H.I.; Song, H.; Ming, G.-L. Applications of Human Brain Organoids to Clinical Problems. Dev. Dyn.
2019
,248, 53–64.
[CrossRef] [PubMed]
154.
Britanova, O.; de Juan Romero, C.; Cheung, A.; Kwan, K.Y.; Schwark, M.; Gyorgy, A.; Vogel, T.; Akopov, S.; Mitkovski, M.;
Agoston, D.; et al. Satb2 Is a Postmitotic Determinant for Upper-Layer Neuron Specification in the Neocortex. Neuron
2008
,57,
378–392. [CrossRef] [PubMed]
155.
National Academies of Sciences, Engineering, and Medicine; Policy and Global Affairs; Committee on Science, Technology, and
Law; Committee on Ethical, Legal, and Regulatory Issues Associated with Neural Chimeras and Organoids. The Emerging Field of
Human Neural Organoids, Transplants, and Chimeras: Science, Ethics, and Governance; National Academies Press (US): Washington,
DC, USA, 2021; ISBN 9780309303361.
156. Powell, K. Hybrid Brains: The Ethics of Transplanting Human Neurons into Animals. Nature 2022,608, 22–25. [CrossRef]
157.
Brennand, K.J.; Simone, A.; Jou, J.; Gelboin-Burkhart, C.; Tran, N.; Sangar, S.; Li, Y.; Mu, Y.; Chen, G.; Yu, D.; et al. Modelling
Schizophrenia Using Human Induced Pluripotent Stem Cells. Nature 2011,473, 221–225. [CrossRef]
158.
Pa¸sca, S.P.; Portmann, T.; Voineagu, I.; Yazawa, M.; Shcheglovitov, A.; Pa¸sca, A.M.; Cord, B.; Palmer, T.D.; Chikahisa, S.;
Nishino, S.; et al.
Using iPSC-Derived Neurons to Uncover Cellular Phenotypes Associated with Timothy Syndrome. Nat. Med.
2011,17, 1657–1662. [CrossRef]
159.
Dolmetsch, R.; Geschwind, D.H. The Human Brain in a Dish: The Promise of iPSC-Derived Neurons. Cell
2011
,145, 831–834.
[CrossRef]
160.
Rowe, R.G.; Daley, G.Q. Induced Pluripotent Stem Cells in Disease Modelling and Drug Discovery. Nat. Rev. Genet.
2019
,20,
377–388. [CrossRef]
161.
Panda, A.; Suvakov, M.; Mariani, J.; Drucker, K.L.; Park, Y.; Jang, Y.; Kollmeyer, T.M.; Sarkar, G.; Bae, T.; Kim, J.J.; et al. Clonally
Selected Lines after CRISPR/Cas Editing Are Not Isogenic. bioRxiv 2022. bioRxiv:2022.05.17.492193.
162.
Kato, M.; Dobyns, W.B. Lissencephaly and the Molecular Basis of Neuronal Migration. Hum. Mol. Genet.
2003
,12, R89–R96.
[CrossRef] [PubMed]
163.
Cahana, A.; Escamez, T.; Nowakowski, R.S.; Hayes, N.L.; Giacobini, M.; von Holst, A.; Shmueli, O.; Sapir, T.; McConnell, S.K.;
Wurst, W.; et al. Targeted Mutagenesis of Lis1 Disrupts Cortical Development and LIS1 Homodimerization. Proc. Natl. Acad. Sci.
USA 2001,98, 6429–6434. [CrossRef] [PubMed]
164.
Youn, Y.H.; Pramparo, T.; Hirotsune, S.; Wynshaw-Boris, A. Distinct Dose-Dependent Cortical Neuronal Migration and Neurite
Extension Defects in Lis1 and Ndel1 Mutant Mice. J. Neurosci. 2009,29, 15520–15530. [CrossRef] [PubMed]
165.
Karzbrun, E.; Kshirsagar, A.; Cohen, S.R.; Hanna, J.H.; Reiner, O. Human Brain Organoids on a Chip Reveal the Physics of
Folding. Nat. Phys. 2018,14, 515–522. [CrossRef] [PubMed]
Cells 2022,11, 2803 22 of 26
166.
Thiele, E.A. Managing and Understanding Epilepsy in Tuberous Sclerosis Complex. Epilepsia
2010
,51 (Suppl. S1), 90–91.
[CrossRef]
167.
European Chromosome 16 Tuberous Sclerosis Consortium Identification and Characterization of the Tuberous Sclerosis Gene on
Chromosome 16. Cell 1993,75, 1305–1315. [CrossRef]
168.
van Slegtenhorst, M.; de Hoogt, R.; Hermans, C.; Nellist, M.; Janssen, B.; Verhoef, S.; Lindhout, D.; van den Ouweland, A.;
Halley, D.; Young, J.; et al. Identification of the Tuberous Sclerosis Gene TSC1 on Chromosome 9q34. Science
1997
,277, 805–808.
[CrossRef]
169.
Tee, A.R.; Fingar, D.C.; Manning, B.D.; Kwiatkowski, D.J.; Cantley, L.C.; Blenis, J. Tuberous Sclerosis Complex-1 and -2 Gene
Products Function Together to Inhibit Mammalian Target of Rapamycin (mTOR)-Mediated Downstream Signaling. Proc. Natl.
Acad. Sci. USA 2002,99, 13571–13576. [CrossRef]
170.
Costa, V.; Aigner, S.; Vukcevic, M.; Sauter, E.; Behr, K.; Ebeling, M.; Dunkley, T.; Friedlein, A.; Zoffmann, S.; Meyer, C.A.; et al.
mTORC1 Inhibition Corrects Neurodevelopmental and Synaptic Alterations in a Human Stem Cell Model of Tuberous Sclerosis.
Cell Rep. 2016,15, 86–95. [CrossRef]
171.
Andrews, M.G.; Subramanian, L.; Kriegstein, A.R. mTOR Signaling Regulates the Morphology and Migration of Outer Radial
Glia in Developing Human Cortex. Elife 2020,9, e58737. [CrossRef]
172.
Onda, H.; Crino, P.B.; Zhang, H.; Murphey, R.D.; Rastelli, L.; Gould Rothberg, B.E.; Kwiatkowski, D.J. Tsc2 Null Murine
Neuroepithelial Cells Are a Model for Human Tuber Giant Cells, and Show Activation of an mTOR Pathway. Mol. Cell. Neurosci.
2002,21, 561–574. [CrossRef] [PubMed]
173.
Blair, J.D.; Hockemeyer, D.; Bateup, H.S. Genetically Engineered Human Cortical Spheroid Models of Tuberous Sclerosis. Nat.
Med. 2018,24, 1568–1578. [CrossRef]
174.
Klaus, J.; Kanton, S.; Kyrousi, C.; Ayo-Martin, A.C.; Di Giaimo, R.; Riesenberg, S.; O’Neill, A.C.; Camp, J.G.; Tocco, C.;
Santel, M.; et al.
Altered Neuronal Migratory Trajectories in Human Cerebral Organoids Derived from Individuals with Neuronal
Heterotopia. Nat. Med. 2019,25, 561–568. [CrossRef] [PubMed]
175.
Birey, F.; Li, M.-Y.; Gordon, A.; Thete, M.V.; Valencia, A.M.; Revah, O.; Pa¸sca, A.M.; Geschwind, D.H.; Pa¸sca, S.P. Dissecting the
Molecular Basis of Human Interneuron Migration in Forebrain Assembloids from Timothy Syndrome. Cell Stem Cell
2022
,29,
248–264.e7. [CrossRef] [PubMed]
176.
Toyoshima, M.; Akamatsu, W.; Okada, Y.; Ohnishi, T.; Balan, S.; Hisano, Y.; Iwayama, Y.; Toyota, T.; Matsumoto, T.;
Itasaka, N.; et al.
Analysis of Induced Pluripotent Stem Cells Carrying 22q11.2 Deletion. Transl. Psychiatry
2016
,6, e934.
[CrossRef] [PubMed]
177.
Amin, H.; Marinaro, F.; De Pietri Tonelli, D.; Berdondini, L. Developmental Excitatory-to-Inhibitory GABA-Polarity Switch Is
Disrupted in 22q11.2 Deletion Syndrome: A Potential Target for Clinical Therapeutics. Sci. Rep. 2017,7, 15752. [CrossRef]
178.
Li, J.; Ryan, S.K.; Deboer, E.; Cook, K.; Fitzgerald, S.; Lachman, H.M.; Wallace, D.C.; Goldberg, E.M.; Anderson, S.A. Mitochondrial
Deficits in Human iPSC-Derived Neurons from Patients with 22q11.2 Deletion Syndrome and Schizophrenia. Transl. Psychiatry
2019,9, 302. [CrossRef]
179.
Khan, T.A.; Revah, O.; Gordon, A.; Yoon, S.-J.; Krawisz, A.K.; Goold, C.; Sun, Y.; Kim, C.H.; Tian, Y.; Li, M.-Y.; et al. Neuronal
Defects in a Human Cellular Model of 22q11.2 Deletion Syndrome. Nat. Med. 2020,26, 1888–1898. [CrossRef]
180.
Landthaler, M.; Yalcin, A.; Tuschl, T. The Human DiGeorge Syndrome Critical Region Gene 8 and Its D. Melanogaster Homolog
Are Required for miRNA Biogenesis. Curr. Biol. 2004,14, 2162–2167. [CrossRef]
181.
Amir, R.E.; Van den Veyver, I.B.; Wan, M.; Tran, C.Q.; Francke, U.; Zoghbi, H.Y. Rett Syndrome Is Caused by Mutations in
X-Linked MECP2, Encoding Methyl-CpG-Binding Protein 2. Nat. Genet. 1999,23, 185–188. [CrossRef]
182.
Lewis, J.D.; Meehan, R.R.; Henzel, W.J.; Maurer-Fogy, I.; Jeppesen, P.; Klein, F.; Bird, A. Purification, Sequence, and Cellular
Localization of a Novel Chromosomal Protein That Binds to Methylated DNA. Cell 1992,69, 905–914. [CrossRef]
183.
Tropea, D.; Giacometti, E.; Wilson, N.R.; Beard, C.; McCurry, C.; Fu, D.D.; Flannery, R.; Jaenisch, R.; Sur, M. Partial Reversal of
Rett Syndrome-like Symptoms in MeCP2 Mutant Mice. Proc. Natl. Acad. Sci. USA 2009,106, 2029–2034. [CrossRef] [PubMed]
184.
Marchetto, M.C.N.; Carromeu, C.; Acab, A.; Yu, D.; Yeo, G.W.; Mu, Y.; Chen, G.; Gage, F.H.; Muotri, A.R. A Model for Neural
Development and Treatment of Rett Syndrome Using Human Induced Pluripotent Stem Cells. Cell
2010
,143, 527–539. [CrossRef]
[PubMed]
185.
Trujillo, C.A.; Adams, J.W.; Negraes, P.D.; Carromeu, C.; Tejwani, L.; Acab, A.; Tsuda, B.; Thomas, C.A.; Sodhi, N.;
Fichter, K.M.; et al.
Pharmacological Reversal of Synaptic and Network Pathology in Human MECP2-KO Neurons and Cortical
Organoids. EMBO Mol. Med. 2021,13, e12523. [CrossRef]
186.
Samarasinghe, R.A.; Miranda, O.A.; Buth, J.E.; Mitchell, S.; Ferando, I.; Watanabe, M.; Allison, T.F.; Kurdian, A.; Fotion, N.N.;
Gandal, M.J.; et al. Identification of Neural Oscillations and Epileptiform Changes in Human Brain Organoids. Nat. Neurosci.
2021,24, 1488–1500. [CrossRef]
187.
Ohashi, M.; Korsakova, E.; Allen, D.; Lee, P.; Fu, K.; Vargas, B.S.; Cinkornpumin, J.; Salas, C.; Park, J.C.; Germanguz, I.; et al. Loss
of MECP2 Leads to Activation of P53 and Neuronal Senescence. Stem Cell Reports 2018,10, 1453–1463. [CrossRef]
188.
Satterstrom, F.K.; Kosmicki, J.A.; Wang, J.; Breen, M.S.; De Rubeis, S.; An, J.-Y.; Peng, M.; Collins, R.; Grove, J.; Klei, L.; et al.
Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism.
Cell 2020,180, 568–584.e23. [CrossRef]
Cells 2022,11, 2803 23 of 26
189.
Sanders, S.J.; He, X.; Willsey, A.J.; Ercan-Sencicek, A.G.; Samocha, K.E.; Cicek, A.E.; Murtha, M.T.; Bal, V.H.; Bishop, S.L.;
Dong, S.; et al.
Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron
2015
,87,
1215–1233. [CrossRef]
190.
Wang, T.; Kim, C.; Bakken, T.E.; Gillentine, M.A.; Henning, B.; Mao, Y.; Gilissen, C.; The SPARK Consortium;
Nowakowski, T.J.
;
Eichler, E.E. Integrated Gene Analyses of de Novo Mutations from 46,612 Trios with Autism and Developmental Disorders.
bioRxiv 2021. bioRxiv:2021.09.15.460398.
191.
Willsey, H.R.; Willsey, A.J.; Wang, B.; State, M.W. Genomics, Convergent Neuroscience and Progress in Understanding Autism
Spectrum Disorder. Nat. Rev. Neurosci. 2022,23, 323–341. [CrossRef]
192.
Weiss, L.A.; Shen, Y.; Korn, J.M.; Arking, D.E.; Miller, D.T.; Fossdal, R.; Saemundsen, E.; Stefansson, H.; Ferreira, M.A.R.;
Green, T.; et al.
Association between Microdeletion and Microduplication at 16p11.2 and Autism. N. Engl. J. Med.
2008
,358,
667–675. [CrossRef] [PubMed]
193.
Deshpande, A.; Yadav, S.; Dao, D.Q.; Wu, Z.-Y.; Hokanson, K.C.; Cahill, M.K.; Wiita, A.P.; Jan, Y.-N.; Ullian, E.M.; Weiss, L.A.
Cellular Phenotypes in Human iPSC-Derived Neurons from a Genetic Model of Autism Spectrum Disorder. Cell Rep.
2017
,21,
2678–2687. [CrossRef] [PubMed]
194.
Urresti, J.; Zhang, P.; Moran-Losada, P.; Yu, N.-K.; Negraes, P.D.; Trujillo, C.A.; Antaki, D.; Amar, M.; Chau, K.; Pramod, A.B.; et al.
Cortical Organoids Model Early Brain Development Disrupted by 16p11.2 Copy Number Variants in Autism. Mol. Psychiatry
2021,26, 7560–7580. [CrossRef] [PubMed]
195.
Lim, E.T.; Chan, Y.; Dawes, P.; Guo, X.; Erdin, S.; Tai, D.J.C.; Liu, S.; Reichert, J.M.; Burns, M.J.; Chan, Y.K.; et al. Orgo-Seq
Integrates Single-Cell and Bulk Transcriptomic Data to Identify Cell Type Specific-Driver Genes Associated with Autism Spectrum
Disorder. Nat. Commun. 2022,13, 3243. [CrossRef]
196.
Kang, H.J.; Kawasawa, Y.I.; Cheng, F.; Zhu, Y.; Xu, X.; Li, M.; Sousa, A.M.M.; Pletikos, M.; Meyer, K.A.; Sedmak, G.; et al.
Spatio-Temporal Transcriptome of the Human Brain. Nature 2011,478, 483–489. [CrossRef]
197.
Willsey, A.J.; Sanders, S.J.; Li, M.; Dong, S.; Tebbenkamp, A.T.; Muhle, R.A.; Reilly, S.K.; Lin, L.; Fertuzinhos, S.; Miller, J.A.; et al.
Coexpression Networks Implicate Human Midfetal Deep Cortical Projection Neurons in the Pathogenesis of Autism. Cell
2013
,
155, 997–1007. [CrossRef]
198.
Parikshak, N.N.; Luo, R.; Zhang, A.; Won, H.; Lowe, J.K.; Chandran, V.; Horvath, S.; Geschwind, D.H. Integrative Functional
Genomic Analyses Implicate Specific Molecular Pathways and Circuits in Autism. Cell 2013,155, 1008–1021. [CrossRef]
199.
Gamache, T.R.; Araki, Y.; Huganir, R.L. Twenty Years of SynGAP Research: From Synapses to Cognition. J. Neurosci.
2020
,40,
1596–1605. [CrossRef]
200.
Birtele, M.; Del Dosso, A.; Xu, T.; Nguyen, T.; Wilkinson, B.; Urenda, J.-P.; Knight, G.; Moore, R.; Sharma, R.; Pirrotte, P.; et al. The
Autism-Associated Gene SYNGAP1 Regulates Human Cortical Neurogenesis. bioRxiv 2022. bioRxiv:2022.05.10.491244.
201.
Lalli, M.A.; Avey, D.; Dougherty, J.D.; Milbrandt, J.; Mitra, R.D. High-Throughput Single-Cell Functional Elucidation of
Neurodevelopmental Disease-Associated Genes Reveals Convergent Mechanisms Altering Neuronal Differentiation. Genome Res.
2020,30, 1317–1331. [CrossRef]
202.
Paulsen, B.; Velasco, S.; Kedaigle, A.J.; Pigoni, M.; Quadrato, G.; Deo, A.J.; Adiconis, X.; Uzquiano, A.; Sartore, R.;
Yang, S.M.; et al.
Autism Genes Converge on Asynchronous Development of Shared Neuron Classes. Nature
2022
,602, 268–273. [CrossRef]
[PubMed]
203.
Villa, C.E.; Cheroni, C.; Dotter, C.P.; López-Tóbon, A.; Oliveira, B.; Sacco, R.; Yahya, A.Ç.; Morandell, J.; Gabriele, M.;
Tavakoli, M.R.; et al.
CHD8 Haploinsufficiency Links Autism to Transient Alterations in Excitatory and Inhibitory Trajecto-
ries. Cell Rep. 2022,39, 110615. [CrossRef]
204.
Crucitti, J.; Hyde, C.; Enticott, P.G.; Stokes, M.A. Head Circumference Trends in Autism between 0 and 100 Months. Autism
2020
,
24, 1726–1739. [CrossRef] [PubMed]
205.
Mariani, J.; Coppola, G.; Zhang, P.; Abyzov, A.; Provini, L.; Tomasini, L.; Amenduni, M.; Szekely, A.; Palejev, D.; Wilson, M.; et al.
FOXG1-Dependent Dysregulation of GABA/Glutamate Neuron Differentiation in Autism Spectrum Disorders. Cell
2015
,162,
375–390. [CrossRef] [PubMed]
206.
Adhya, D.; Swarup, V.; Nagy, R.; Dutan, L.; Shum, C.; Valencia-Alarcón, E.P.; Jozwik, K.M.; Mendez, M.A.; Horder, J.;
Loth, E.; et al.
Atypical Neurogenesis in Induced Pluripotent Stem Cells from Autistic Individuals. Biol. Psychiatry
2021
,89, 486–496. [CrossRef]
[PubMed]
207.
Jourdon, A.; Wu, F.; Mariani, J.; Capauto, D.; Norton, S.; Tomasini, L.; Amiri, A.; Suvakov, M.; Schreiner, J.D.; Jang, Y.; et al. ASD
Modelling in Organoids Reveals Imbalance of Excitatory Cortical Neuron Subtypes during Early Neurogenesis. bioRxiv
2022
.
bioRxiv:2022.03.19.484988.
208.
Hergenreder, E.; Zorina, Y.; Zhao, Z.; Munguba, H.; Calder, E.L.; Baggiolini, A.; Minotti, A.P.; Walsh, R.M.; Liston, C.;
Levitz, J.; et al.
Combined Small Molecule Treatment Accelerates Timing of Maturation in Human Pluripotent Stem Cell-Derived
Neurons. bioRxiv 2022. bioRxiv:2022.06.02.494616.
209.
Ciceri, G.; Cho, H.; Kshirsagar, M.; Baggiolini, A.; Aromolaran, K.A.; Walsh, R.M.; Goldstein, P.A.; Koche, R.P.; Leslie, C.S.;
Studer, L. An Epigenetic Barrier Sets the Timing of Human Neuronal Maturation. bioRxiv 2022. bioRxiv:2022.06.02.490114.
210.
Feldman, D.; Singh, A.; Schmid-Burgk, J.L.; Carlson, R.J.; Mezger, A.; Garrity, A.J.; Zhang, F.; Blainey, P.C. Optical Pooled Screens
in Human Cells. Cell 2019,179, 787–799.e17. [CrossRef]
Cells 2022,11, 2803 24 of 26
211.
Rubin, A.J.; Parker, K.R.; Satpathy, A.T.; Qi, Y.; Wu, B.; Ong, A.J.; Mumbach, M.R.; Ji, A.L.; Kim, D.S.; Cho, S.W.; et al. Coupled
Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks. Cell
2019
,176, 361–376.e17.
[CrossRef]
212.
Cleary, B.; Regev, A. The Necessity and Power of Random, under-Sampled Experiments in Biology. arXiv
2020
, arXiv:2012.12961.
213.
Wu, Z.; Chhun, B.B.; Popova, G.; Guo, S.-M.; Kim, C.N.; Yeh, L.-H.; Nowakowski, T.; Zou, J.; Mehta, S.B. DynaMorph: Self-
Supervised Learning of Morphodynamic States of Live Cells. Mol. Biol. Cell 2022,33, ar59. [CrossRef]
214.
Moreau, C.A.; Kumar, K.; Harvey, A.; Huguet, G.; Urchs, S.; Douard, E.A.; Schultz, L.M.; Sharmarke, H.; Jizi, K.;
Martin, C.-O.; et al.
Atlas of Functional Connectivity Relationships across Rare and Common Genetic Variants, Traits,
and Psychiatric Conditions. bioRxiv 2021. [CrossRef]
215.
Velmeshev, D.; Schirmer, L.; Jung, D.; Haeussler, M.; Perez, Y.; Mayer, S.; Bhaduri, A.; Goyal, N.; Rowitch, D.H.; Kriegstein, A.R.
Single-Cell Genomics Identifies Cell Type-Specific Molecular Changes in Autism. Science
2019
,364, 685–689. [CrossRef] [PubMed]
216.
Ramaswami, G.; Won, H.; Gandal, M.J.; Haney, J.; Wang, J.C.; Wong, C.C.Y.; Sun, W.; Prabhakar, S.; Mill, J.; Geschwind, D.H.
Integrative Genomics Identifies a Convergent Molecular Subtype That Links Epigenomic with Transcriptomic Differences in
Autism. Nat. Commun. 2020,11, 4873. [CrossRef]
217.
de la Torre-Ubieta, L.; Won, H.; Stein, J.L.; Geschwind, D.H. Advancing the Understanding of Autism Disease Mechanisms
through Genetics. Nat. Med. 2016,22, 345–361. [CrossRef]
218.
Chen, J.A.; Peñagarikano, O.; Belgard, T.G.; Swarup, V.; Geschwind, D.H. The Emerging Picture of Autism Spectrum Disorder:
Genetics and Pathology. Annu. Rev. Pathol. 2015,10, 111–144. [CrossRef]
219.
Ruzicka, W.B.; Mohammadi, S.; Davila-Velderrain, J.; Subburaju, S.; Tso, D.R.; Hourihan, M.; Kellis, M. Single-Cell Dissection of
Schizophrenia Reveals Neurodevelopmental-Synaptic Axis and Transcriptional Resilience. bioRxiv 2020. [CrossRef]
220.
Bhaduri, A.; Andrews, M.G.; Kriegstein, A.R.; Nowakowski, T.J. Are Organoids Ready for Prime Time? Cell Stem Cell
2020
,27,
361–365. [CrossRef]
221.
Willsey, H.R.; Exner, C.R.T.; Xu, Y.; Everitt, A.; Sun, N.; Wang, B.; Dea, J.; Schmunk, G.; Zaltsman, Y.; Teerikorpi, N.; et al. Parallel
in Vivo Analysis of Large-Effect Autism Genes Implicates Cortical Neurogenesis and Estrogen in Risk and Resilience. Neuron
2021,109, 788–804.e8. [CrossRef]
222.
Ortiz-Virumbrales, M.; Moreno, C.L.; Kruglikov, I.; Marazuela, P.; Sproul, A.; Jacob, S.; Zimmer, M.; Paull, D.;
Zhang, B.
;
Schadt, E.E.; et al.
CRISPR/Cas9-Correctable Mutation-Related Molecular and Physiological Phenotypes in iPSC-Derived
Alzheimer’s PSEN2 N141I Neurons. Acta Neuropathol. Commun. 2017,5, 77. [CrossRef] [PubMed]
223.
Cooper, O.; Seo, H.; Andrabi, S.; Guardia-Laguarta, C.; Graziotto, J.; Sundberg, M.; McLean, J.R.; Carrillo-Reid, L.; Xie, Z.;
Osborn, T.; et al.
Pharmacological Rescue of Mitochondrial Deficits in iPSC-Derived Neural Cells from Patients with Familial
Parkinson’s Disease. Sci. Transl. Med. 2012,4, 141ra90. [CrossRef] [PubMed]
224.
Woodard, C.M.; Campos, B.A.; Kuo, S.-H.; Nirenberg, M.J.; Nestor, M.W.; Zimmer, M.; Mosharov, E.V.; Sulzer, D.; Zhou, H.;
Paull, D.; et al.
iPSC-Derived Dopamine Neurons Reveal Differences between Monozygotic Twins Discordant for Parkinson’s
Disease. Cell Rep. 2014,9, 1173–1182. [CrossRef] [PubMed]
225.
Ochalek, A.; Mihalik, B.; Avci, H.X.; Chandrasekaran, A.; Téglási, A.; Bock, I.; Giudice, M.L.; Táncos, Z.; Molnár, K.;
László, L.; et al.
Neurons Derived from Sporadic Alzheimer’s Disease iPSCs Reveal Elevated TAU Hyperphosphorylation, Increased Amyloid
Levels, and GSK3B Activation. Alzheimers. Res. Ther. 2017,9, 90. [CrossRef] [PubMed]
226.
Choi, S.H.; Kim, Y.H.; Hebisch, M.; Sliwinski, C.; Lee, S.; D’Avanzo, C.; Chen, H.; Hooli, B.; Asselin, C.; Muffat, J.; et al. A
Three-Dimensional Human Neural Cell Culture Model of Alzheimer’s Disease. Nature 2014,515, 274–278. [CrossRef]
227.
Penney, J.; Ralvenius, W.T.; Tsai, L.-H. Modeling Alzheimer’s Disease with iPSC-Derived Brain Cells. Mol. Psychiatry
2020
,25,
148–167. [CrossRef]
228.
Ali´c, I.; Goh, P.A.; Murray, A.; Portelius, E.; Gkanatsiou, E.; Gough, G.; Mok, K.Y.; Koschut, D.; Brunmeir, R.; Yeap, Y.J.; et al.
Correction: Patient-Specific Alzheimer-like Pathology in Trisomy 21 Cerebral Organoids Reveals BACE2 as a Gene Dose-Sensitive
AD Suppressor in Human Brain. Mol. Psychiatry 2021,26, 5789. [CrossRef]
229.
Gonzalez, C.; Armijo, E.; Bravo-Alegria, J.; Becerra-Calixto, A.; Mays, C.E.; Soto, C. Modeling Amyloid Beta and Tau Pathology in
Human Cerebral Organoids. Mol. Psychiatry 2018,23, 2363–2374. [CrossRef]
230.
Meharena, H.S.; Marco, A.; Dileep, V.; Lockshin, E.R.; Akatsu, G.Y.; Mullahoo, J.; Watson, L.A.; Ko, T.; Guerin, L.N.;
Abdurrob, F.; et al.
Down-Syndrome-Induced Senescence Disrupts the Nuclear Architecture of Neural Progenitors. Cell Stem Cell
2022,29, 116–130.e7. [CrossRef]
231.
Tian, R.; Abarientos, A.; Hong, J.; Hashemi, S.H.; Yan, R.; Dräger, N.; Leng, K.; Nalls, M.A.; Singleton, A.B.; Xu, K.; et al.
Genome-Wide CRISPRi/a Screens in Human Neurons Link Lysosomal Failure to Ferroptosis. Nat. Neurosci.
2021
,24, 1020–1034.
[CrossRef]
232.
Tian, R.; Gachechiladze, M.A.; Ludwig, C.H.; Laurie, M.T.; Hong, J.Y.; Nathaniel, D.; Prabhu, A.V.; Fernandopulle, M.S.;
Patel, R.
;
Abshari, M.; et al. CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons. Neuron
2019,104, 239–255.e12. [CrossRef] [PubMed]
233.
Dräger, N.M.; Sattler, S.M.; Huang, C.T.-L.; Teter, O.M.; Leng, K.; Hashemi, S.H.; Hong, J.; Aviles, G.; Clelland, C.D.; Zhan, L.; et al.
A CRISPRi/a Platform in iPSC-Derived Microglia Uncovers Regulators of Disease States. bioRxiv
2022
. bioRxiv:2021.06.16.448639.
[CrossRef] [PubMed]
Cells 2022,11, 2803 25 of 26
234.
Tang, X.-Y.; Xu, L.; Wang, J.; Hong, Y.; Wang, Y.; Zhu, Q.; Wang, D.; Zhang, X.-Y.; Liu, C.-Y.; Fang, K.-H.; et al. DSCAM/PAK1
Pathway Suppression Reverses Neurogenesis Deficits in iPSC-Derived Cerebral Organoids from Patients with Down Syndrome.
J. Clin. Investig. 2021,131, e135763. [CrossRef] [PubMed]
235.
Bowles, K.R.; Silva, M.C.; Whitney, K.; Bertucci, T.; Berlind, J.E.; Lai, J.D.; Garza, J.C.; Boles, N.C.; Mahali, S.; Strang, K.H.; et al.
ELAVL4, Splicing, and Glutamatergic Dysfunction Precede Neuron Loss in MAPT Mutation Cerebral Organoids. Cell
2021
,184,
4547–4563.e17. [CrossRef]
236.
Staats, K.A.; Seah, C.; Sahimi, A.; Wang, Y.; Koutsodendris, N.; Lin, S.; Kim, D.; Chang, W.-H.; Gray, K.A.; Shi, Y.; et al. Small
Molecule Inhibition of PIKFYVE Kinase Rescues Gain- and Loss-of-Function C9ORF72 ALS/FTD Disease Processes in Vivo.
bioRxiv 2019. bioRxiv:685800.
237.
Elston, G.N. Cortex, Cognition and the Cell: New Insights into the Pyramidal Neuron and Prefrontal Function. Cereb. Cortex
2003
,
13, 1124–1138. [CrossRef]
238.
Kriegstein, A.; Noctor, S.; Martínez-Cerdeño, V. Patterns of Neural Stem and Progenitor Cell Division May Underlie Evolutionary
Cortical Expansion. Nat. Rev. Neurosci. 2006,7, 883–890. [CrossRef]
239.
Borrell, V.; Reillo, I. Emerging Roles of Neural Stem Cells in Cerebral Cortex Development and Evolution. Dev. Neurobiol.
2012
,
72, 955–971. [CrossRef]
240.
Borrell, V.; Calegari, F. Mechanisms of Brain Evolution: Regulation of Neural Progenitor Cell Diversity and Cell Cycle Length.
Neurosci. Res. 2014,86, 14–24. [CrossRef]
241.
Molnár, Z.; Métin, C.; Stoykova, A.; Tarabykin, V.; Price, D.J.; Francis, F.; Meyer, G.; Dehay, C.; Kennedy, H. Comparative Aspects
of Cerebral Cortical Development. Eur. J. Neurosci. 2006,23, 921–934. [CrossRef]
242.
Rakic, P. Evolution of the Neocortex: A Perspective from Developmental Biology. Nat. Rev. Neurosci.
2009
,10, 724–735. [CrossRef]
[PubMed]
243.
Rakic, P. Developmental and Evolutionary Adaptations of Cortical Radial Glia. Cereb. Cortex
2003
,13, 541–549. [CrossRef]
[PubMed]
244.
Monuki, E.S.; Walsh, C.A. Mechanisms of Cerebral Cortical Patterning in Mice and Humans. Nat. Neurosci.
2001
,4, 1199–1206.
[CrossRef]
245.
Chenn, A.; Walsh, C.A. Regulation of Cerebral Cortical Size by Control of Cell Cycle Exit in Neural Precursors. Science
2002
,297,
365–369. [CrossRef] [PubMed]
246.
Kuida, K.; Haydar, T.F.; Kuan, C.Y.; Gu, Y.; Taya, C.; Karasuyama, H.; Su, M.S.; Rakic, P.; Flavell, R.A. Reduced Apoptosis and
Cytochrome c-Mediated Caspase Activation in Mice Lacking Caspase 9. Cell 1998,94, 325–337. [CrossRef]
247.
Lui, J.H.; Hansen, D.V.; Kriegstein, A.R. Development and Evolution of the Human Neocortex. Cell
2011
,146, 18–36. [CrossRef]
248.
Dehay, C.; Giroud, P.; Berland, M.; Killackey, H.P.; Kennedy, H. Phenotypic Characterisation of Respecified Visual Cortex
Subsequent to Prenatal Enucleation in the Monkey: Development of Acetylcholinesterase and Cytochrome Oxidase Patterns. J.
Comp. Neurol. 1996,376, 386–402. [CrossRef]
249.
Sulovari, A.; Li, R.; Audano, P.A.; Porubsky, D.; Vollger, M.R.; Logsdon, G.A.; Human Genome Structural Variation Consortium;
Warren, W.C.; Pollen, A.A.; Chaisson, M.J.P.; et al. Human-Specific Tandem Repeat Expansion and Differential Gene Expression
during Primate Evolution. Proc. Natl. Acad. Sci. USA 2019,116, 23243–23253. [CrossRef]
250.
Dougherty, M.L.; Underwood, J.G.; Nelson, B.J.; Tseng, E.; Munson, K.M.; Penn, O.; Nowakowski, T.J.; Pollen, A.A.; Eichler, E.E.
Transcriptional Fates of Human-Specific Segmental Duplications in Brain. Genome Res. 2018,28, 1566–1576. [CrossRef]
251.
Kronenberg, Z.N.; Fiddes, I.T.; Gordon, D.; Murali, S.; Cantsilieris, S.; Meyerson, O.S.; Underwood, J.G.; Nelson, B.J.;
Chaisson, M.J.P.
; Dougherty, M.L.; et al. High-Resolution Comparative Analysis of Great Ape Genomes. Science
2018
,
360, eaar6343. [CrossRef]
252.
Pollard, K.S.; Salama, S.R.; Lambert, N.; Lambot, M.-A.; Coppens, S.; Pedersen, J.S.; Katzman, S.; King, B.; Onodera, C.;
Siepel, A.; et al.
An RNA Gene Expressed during Cortical Development Evolved Rapidly in Humans. Nature
2006
,443, 167–172.
[CrossRef] [PubMed]
253.
Fiddes, I.T.; Lodewijk, G.A.; Mooring, M.; Bosworth, C.M.; Ewing, A.D.; Mantalas, G.L.; Novak, A.M.; van den Bout, A.;
Bishara, A.
; Rosenkrantz, J.L.; et al. Human-Specific NOTCH2NL Genes Affect Notch Signaling and Cortical Neurogenesis. Cell
2018,173, 1356–1369.e22. [CrossRef] [PubMed]
254.
Suzuki, I.K.; Gacquer, D.; Van Heurck, R.; Kumar, D.; Wojno, M.; Bilheu, A.; Herpoel, A.; Lambert, N.; Cheron, J.;
Polleux, F.; et al.
Human-Specific NOTCH2NL Genes Expand Cortical Neurogenesis through Delta/Notch Regulation. Cell
2018
,173,
1370–1384.e16. [CrossRef] [PubMed]
255.
Trujillo, C.A.; Rice, E.S.; Schaefer, N.K.; Chaim, I.A.; Wheeler, E.C.; Madrigal, A.A.; Buchanan, J.; Preissl, S.; Wang, A.;
Negraes, P.D.; et al.
Reintroduction of the Archaic Variant of NOVA1 in Cortical Organoids Alters Neurodevelopment. Sci-
ence 2021,371, eaax2537. [CrossRef]
256.
Gallego Romero, I.; Pavlovic, B.J.; Hernando-Herraez, I.; Zhou, X.; Ward, M.C.; Banovich, N.E.; Kagan, C.L.; Burnett, J.E.;
Huang, C.H.
; Mitrano, A.; et al. A Panel of Induced Pluripotent Stem Cells from Chimpanzees: A Resource for Comparative
Functional Genomics. Elife 2015,4, e07103. [CrossRef]
257.
Otani, T.; Marchetto, M.C.; Gage, F.H.; Simons, B.D.; Livesey, F.J. 2D and 3D Stem Cell Models of Primate Cortical Development
Identify Species-Specific Differences in Progenitor Behavior Contributing to Brain Size. Cell Stem Cell
2016
,18, 467–480. [CrossRef]
Cells 2022,11, 2803 26 of 26
258.
Marchetto, M.C.; Hrvoj-Mihic, B.; Kerman, B.E.; Yu, D.X.; Vadodaria, K.C.; Linker, S.B.; Narvaiza, I.; Santos, R.; Denli, A.M.;
Mendes, A.P.; et al. Species-Specific Maturation Profiles of Human, Chimpanzee and Bonobo Neural Cells. Elife
2019
,8, e37527.
[CrossRef]
259.
Field, A.R.; Jacobs, F.M.J.; Fiddes, I.T.; Phillips, A.P.R.; Reyes-Ortiz, A.M.; LaMontagne, E.; Whitehead, L.; Meng, V.;
Rosenkrantz, J.L.
; Olsen, M.; et al. Structurally Conserved Primate LncRNAs Are Transiently Expressed during Human Cortical
Differentiation and Influence Cell-Type-Specific Genes. Stem Cell Reports 2019,12, 245–257. [CrossRef]
260.
Mora-Bermúdez, F.; Badsha, F.; Kanton, S.; Camp, J.G.; Vernot, B.; Köhler, K.; Voigt, B.; Okita, K.; Maricic, T.; He, Z.; et al.
Differences and Similarities between Human and Chimpanzee Neural Progenitors during Cerebral Cortex Development. Elife
2016,5, e18683. [CrossRef]
261.
Mora-Bermúdez, F.; Kanis, P.; Macak, D.; Peters, J.; Naumann, R.; Xing, L.; Sarov, M.; Winkler, S.; Oegema, C.E.; Haffner, C.; et al.
Longer Metaphase and Fewer Chromosome Segregation Errors in Modern Human than Neanderthal Brain Development. Sci.
Adv. 2022,8, eabn7702. [CrossRef]
262.
Benito-Kwiecinski, S.; Giandomenico, S.L.; Sutcliffe, M.; Riis, E.S.; Freire-Pritchett, P.; Kelava, I.; Wunderlich, S.; Martin, U.;
Wray, G.A.
; McDole, K.; et al. An Early Cell Shape Transition Drives Evolutionary Expansion of the Human Forebrain. Cell
2021
,
184, 2084–2102.e19. [CrossRef] [PubMed]
263.
Kanton, S.; Boyle, M.J.; He, Z.; Santel, M.; Weigert, A.; Sanchís-Calleja, F.; Guijarro, P.; Sidow, L.; Fleck, J.S.; Han, D.; et al. Organoid
Single-Cell Genomic Atlas Uncovers Human-Specific Features of Brain Development. Nature
2019
,574, 418–422. [CrossRef]
[PubMed]
264.
Agoglia, R.M.; Sun, D.; Birey, F.; Yoon, S.-J.; Miura, Y.; Sabatini, K.; Pa
s
,
ca, S.P.; Fraser, H.B. Primate Cell Fusion Disentangles Gene
Regulatory Divergence in Neurodevelopment. Nature 2021,592, 421–427. [CrossRef] [PubMed]
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