The promise of genetic screens in human in vitro brain models

Julianne Beirute-Herrera; Beatriz López-Amo Calvo; Frank Edenhofer; Christopher Esk

doi:10.1515/hsz-2023-0174

Article Open Access

The promise of genetic screens in human in vitro brain models

Julianne Beirute-Herrera

Julianne Beirute-Herrera holds a master’s degree in Neuroscience from Maastricht University. She is currently a PhD student in the Edenhofer group at Innsbruck University. Her project focuses on the functional characterization of human-specific genes involved in neurodevelopment using human derived stem cell models and CRISPR technologies.
, Beatriz López-Amo Calvo

Beatriz López-Amo Calvo holds a BSc in Biotechnology from Universidad Politécnica de Madrid. She is currently studying towards a MSc in Regenerative Medicine and Technology Master studies at Utrecht University. She is performing lab work in the Esk laboratory at Innsbruck University on the genetic control of cerebral organoid growth.
, Frank Edenhofer

Frank Edenhofer holds a PhD from the Ludwig-Maximilians University of Munich, Germany. After postdoctoral training in the laboratory of Dr. Klaus Rajewsky (Institute for Genetics, University of Cologne, Germany) he became junior group leader at the Institute of Reconstructive Neurobiology at the University of Bonn, Germany. He is now full professor at the University of Innsbruck, Head of the Department of Molecular Biology and leading the research group Genomics, Stem Cell Biology & Regenerative Medicine. He devised pioneering studies in the field of cellular reprogramming, particularly the direct conversion of somatic cells into induced neural stem cells.
and Christopher Esk

Christopher Esk holds a PhD from Leibniz University Hannover following thesis work at the University of California, San Francisco. After postdoctoral work in Jürgen Knoblich’s lab at the Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria, he became assistant professor at the Institute of Molecular Biology of the University of Innsbruck, Innsbruck, Austria. He studies neurodevelopment of the human brain and its regulation.

Published/Copyright: September 12, 2023

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From the journal Biological Chemistry Volume 405 Issue 1

Abstract

Advances of in vitro culture models have allowed unprecedented insights into human neurobiology. At the same time genetic screening has matured into a robust and accessible experimental strategy allowing for the simultaneous study of many genes in parallel. The combination of both technologies is a newly emerging tool for neuroscientists, opening the door to identifying causal cell- and tissue-specific developmental and disease mechanisms. However, with complex experimental genetic screening set-ups new challenges in data interpretation and experimental scope arise that require a deep understanding of the benefits and challenges of individual approaches. In this review, we summarize the literature that applies genetic screening to in vitro brain models, compare experimental strengths and weaknesses and point towards future directions of these promising approaches.

Keywords: neurobiology; in vitro brain models; organoid; CRISPR; genetic screens

1 Introduction: experimental models to study the brain

Neuroscience has excelled studying the brain’s development, function and diseases using animal models and post-mortem human tissue. However, these experiments also have limitations as animal models may not accurately model human-specific brain aspects, while human post-mortem tissue is limited in experimental approaches and availability. The advent of human pluripotent stem cell culture opened the door to in vitro models of the human brain. Ranging from defined 2D cell culture models to complex organized 3D organoid cultures and bioengineering approaches, stem cell derived in vitro models have become important tools for the study of the human brain. Together with recent advances in genomic screening these models promise to further expand the tools available to neuroscientists (Ahmed et al. 2023). Here we summarize current technologies for genetic screening in human in vitro brain models focusing on recent developments in the field.

1.1 2D neural cell cultures

Advances in stem cell biology, particularly in the field of cellular differentiation and reprogramming has led to defined protocols for deriving numerous cells of the central nervous system in cell culture (Giacomelli et al. 2022; Mertens et al. 2016; Takahashi and Yamanaka 2006; Tao and Zhang 2016; Thier et al. 2019) (Figure 1). These approaches make human neurons and assays traditionally reserved for animal models including genome engineering, multiomics as well as electrophysiology accessible for experimental interrogation. Common among protocols deriving various cell types of the central nervous system from pluripotent stem cells is the guided differentiation of pluripotent cells towards ectodermal lineages, the main germ layer of interest for neuroscience, by inhibiting TGF-β and BMP pathways, known as dual SMAD inhibition (Chambers et al. 2009). Additional modulation of WNT, SHH and RA pathways by small molecules, guide the cells to differentiate into neural stem cells of different regions, and subsequently to mature neurons of different kinds (Giacomelli et al. 2022; Mertens et al. 2016). For example, robust protocols were developed for dorsally derived excitatory neurons, ventral-born interneurons, midbrain-derived dopaminergic neurons or astrocytes. The various protocols mimic in vivo development resulting in cells differentiating along their developmental fate acquisitions. Alternatively, many cell types can be derived by direct conversion protocols, which force direct generation of target cell types without a corresponding in vivo pathway (Chambers and Studer 2011; Erharter et al. 2019; Traxler et al. 2019). Direct conversion oftentimes relies on overexpression of one or combinations of transcription factors forcing a cell fate of choice to be attained by the cells, bypassing cellular differentiation along in vivo differentiation routes. Depending on the scientific questions posed to the cell types generated, direct conversion may hold certain advantages such as maintenance of donor age marks. For example induced Neurons (iNs) derived from fibroblasts maintain the epigenetic age signatures of each donor, which is a promising approach to study age-related diseases of the nervous system and neurodegeneration (Mertens et al. 2021). Common among differentiation and conversion protocols is that the defined monolayer format used has several advantages for genetic screening approaches. It is possible to culture large numbers of cells at high purity (above 80 %), easily tens to hundreds of millions, allowing for testing large number of genes in parallel. Additional advantages include easy and consistent transfection of cultures with lentiviruses and simple downstream processing in FACS or scRNAseq.

Figure 1:

Generation of human brain models in vitro. Biopsied cells such as skin fibroblasts or peripheral blood monocytes (PBMCs) may be reprogrammed to induced pluripotent stem cells. Pluripotent cells may be differentiated along in vivo differentiation routes (solid arrows) towards different in vitro brain models, while fibroblasts may also be directly converted (dashed arrows) using different molecule cocktails or directed transcription factor expression towards the desired cell types and culturing method. OSKM: Yamanaka reprogramming factors OCT4, SOX2, KLF4, and MYC. iPSCs, induced pluripotent stem cells; iNSCs, induced neural stem cells; iNs, induced neurons. Scaled color gradients at the bottom indicate high reproducibility and scalability in 2D cultures, while neuruloids and 3D brain organoids better recapitulate tissue features.

1.2 3D brain models

A more recent addition to the neuroscience toolbox is the development of 3D tissue-like neuronal cultures. These include neuruloids (Metzger et al. 2022; Tang et al. 2020), bioengineered models (Gupta et al. 2021) as well as brain organoids, 3D in vitro brain models based on pluripotent stem cells (Arlotta and Paşca 2019; Sidhaye and Knoblich 2021). Neuruloids resemble neurulation in vitro by using geometrically confined colonies in micro patterned chips. Neuruloids preserve the evolutionary conserved ectodermal differentiation that is orchestrated by developmental pathways including BMP4, Wnt and fibroblast growth factor (FGF) signaling, giving rise to neural progenitors, neural crest and neurons. The system has been used to study aspects of early human developmental disorders, such as Huntington’s disease and Down’s syndrome with the advantage of being highly scalable and reproducible (Haremaki et al. 2019; Metzger et al. 2022). Expanding on neuruloid technology, several bioengineered models of the human brain have been developed. This category comprises a whole range of approaches involving the seeding of one or multiple cell types in hydrogels or biomaterial scaffolds, decellularized or synthetic porous scaffolds for patterning, direct bioprinting desired cells, or the introduction of microfluidics in chips representing a tissue-like structure at mini scale (Kajtez et al. 2021; Lovett et al. 2020; Tang et al. 2020). By defining the spatial orientation of different cell types to each other specific questions regarding tissue interaction may be addressed. Furthermore, the interaction of cells derived from different germ layers may be studied. In contrast to neuruloids and bioengineered models, brain organoids rely of the intrinsic property of developing brain tissue to self-organize. Individual brain organoid regions resemble the developing human brain remarkably well in terms of morphology of defined areas, cell-type composition and neuronal maturation. Building on well-characterized protocols developed in 2D following in vivo differentiation, these models typically involve the formation of pluripotent stem cell aggregates termed embryoid bodies which are coaxed to form mature self-organized brain organoids. Their generation can be spontaneous or follow guided differentiation approaches using external patterning chemicals or biomaterials (Arlotta and Paşca 2019; Sidhaye and Knoblich 2021). By using different protocols, resulting brain organoids mimic different brain regions. Individual regionalized organoids may also be fused together into so called assembloids to study connections and cellular migration between defined brain regions (Paşca 2019). Brain organoids typically resemble brain tissue containing multiple mixed cell types, such as various neural progenitors and different types of neurons. Hence, they are useful to study cellular behavior in a tissue context but lack the purity of well-defined 2D monolayer cultures. Out of all in vitro human brain models discussed here, brain organoids achieve the highest tissue complexity and most closely resemble the actual human brain. They are relatively easy to establish and maintain with moderate costs, which allows for short and long-term experiments. Regarding genetic screens, there are several disadvantages that all 3D brain models have in common. First, the number of cells one can culture in individual 3D cultures is limited. Secondly, cells develop and expand more uneven (and more in vivo like) increasing model variability. Thus, to date examples of genetic screens in 3D brain models are scarce and limited in the numbers of genes tested in comparison to genetic screens in 2D models.

2 Genetic screens: methodologies and readouts

Loss of function genetic screens have long been used to identify genes and mechanisms underlying biological processes in various model systems. Classically based on random mutagenesis in models such as yeast or drosophila, genetic screens were performed to generate novel phenotypes (Irion and Nüsslein-Volhard 2022). Causative genes had to be identified in isolated phenotypic mutants, oftentimes using time-consuming and labor-intensive backcrossing schemes. In subsequent approaches, the identification and utilization of RNA interference allowed to suppress gene function in a directed manner on the mRNA level. RNA interference employing gene-specific short-hairpin RNA (shRNA) reduces mRNA levels of targeted genes thereby downregulating corresponding protein levels. However, unlike random mutagenesis, RNA interference does not result in knockout alleles, leaving variable residual protein levels in cells. With the characterization of clustered regularly interspaced short palindromic repeats (CRISPR) in genomic DNA and CRISPR associated (Cas) proteins, genomic DNA sequence specific mutagenesis has become available (Jinek et al. 2012). Both RNA-interference and more recent CRISPR-based loss-of-function screens can be performed in arrays but also parallelized using viral libraries such that thousands of genes can be tested simultaneously in cell culture (Shalem et al. 2014; Wang et al. 2014). Typically, readouts of such parallel screens are based on deep sequencing of transduced viral construct shRNAs or gRNAs in total genomic DNA prepared from tested cell population. Thereby an enrichment or depletion of cells transduced with viruses carrying shRNAs or gRNAs are measured as a readout for targeted genes’ functions. Here, we will concentrate on screening approaches using CRISPR-based systems employing different Cas proteins which have become ever more popular in recent years.

2.1 Cas variants in parallel screens

CRISPR-Cas systems are now routinely used for manipulating defined genomic loci. The two-component system comprises a gRNA of choice, which defines the genomic locus to be manipulated as well as an accessory Cas protein, which is guided to the genomic locus defined by the gRNA (Table 1). The earliest described Cas protein for genome modification in mammalian cells and most-widely used today is the Cas9 protein from Streptococcus pyogenes (spCas9) (Jinek et al. 2012). This protein is a DNA endonuclease inducing double-strand breaks at its gRNA-defining target site. In cells, induced DNA double-strand breaks are oftentimes repaired by non-homologous end joining leading to the formation of base insertions and/or deletions, which in coding regions may result in functional knockout of encoded genes. Cas9, despite being guided by 20 nt gRNAs shows some off-target double-strand break activity, which may result in unwanted DNA editing in non-targeted sequences of the target cells. To minimize such off-target effects enhanced versions of Cas9 displaying higher specificity for its intended target sequence have been developed (Kleinstiver et al. 2016; Slaymaker et al. 2016). Importantly, Cas9’s two endonuclease activities (one per DNA strand) may be disrupted by point mutations, leading to either nicking Cas9 variants or a catalytically dead Cas9 variant (dCas9). dCas9 may be fused to several effector proteins serving as a platform to localize effector activity to specific genomic loci. Effector domains may include transcriptional activators or repressors of local transcription as well as epigenetic modifiers to rewrite the local epigenetic code (Chavez et al. 2015; Gilbert et al. 2014; Konermann et al. 2015; Nuñez et al. 2021) (Table 1). Notably, such induction of local epigenetic changes may also be used to test non-coding elements of the genome such as enhancers or transcription factor binding sites. Several other Cas variant proteins have been engineered, which allow for additional functionality beyond gene regulation and are covered elsewhere (Breunig et al. 2021). The different Cas variants have various features regarding target selection, target applicability and optimal gRNA design rules such that it is up to the researcher’s question, which methodology is most suited for a given biological question.

Table 1:

Overview of Cas9 variants capable of screening. Cas9 protein variants used for regulating gene expression.

Gene expression modification, CRISPR class	Cas variant	Description/fused effector	Effect on genomic DNA target/comments	Advantages	Disadvantages	Reference
DNA strand break, CRISPR-knockout (CRISPR-KO)	Cas9	Wildtype protein	Double strand breaks (DSBs) to targeted genomic loci, recognizes NGG protospacer	High activity in many genomic contexts	High off-target effects rates	Jinek et al. (2012)
	eCas9	Wildtype protein, optimized	Reduced helicase activity compared to wildtype	Higher precision than wildtype, less off-target activity	Low activity at some target sites	Slaymaker et al. (2016)
	Cas9-HF	Wildtype protein, optimized	DNA nuclease with reduced number of DNA contacts	Higher precision than wildtype, less off-target activity	Low activity at some target sites	Kleinstiver et al. (2016)
Transcriptional repression, CRISPR-inactivation (CRISPRi)	dCas9	Catalytically dead protein	Catalytically inactive Cas9 serving as platform to be localized to genomic DNA by gRNA	No DNA breaks. Mild gene regulatory effects	Some leakiness. Mild repression	Gilbert et al. (2014)
	dCas9	Catalytically dead fused to KRAB	Repressor fused protein KRAB, recruits histone deacetylases and methyltransferases to silence targeted genomic loci	No DNA breaks. Strong transciptional repression. No complete knockout	Some leakiness. Narrow target window	Gilbert et al. (2014)
	dCas9	Catalytically dead fused to DNMTL3A	Targeted methylation of CpGs islands of targeted regions. dCas9 fused to methyltransferase	Heritable DNA methylation at target site	Repression persists (depending on application)	Nuñez et al. (2021)
Transcriptional activation, CRISPR-activation (CRISPRa)	dCas9	Catalytically dead fused to VP64	Transcriptional activation of targeted genes	Transcriptional activation	Weak upregulation. Narrow target window	Gilbert et al. (2014)
	dCas9	Catalytically dead fused to VPR	Activator protein VPR composed of VP64, P65, and Rta, robustly upregulates the transcription of the target gene matching the protospacer sequence	Higher transcriptional activation than VP64	Strong gene upregulation	Chavez et al. (2015)
	dCas9	Catalytically dead fused to TETv4	Targeted demethylation of CpGs islands of targeted regions. dCas9 fused to methyltransferase activity	Heritable DNA de-methylation at target site	Derepression persists (depending on application)	Nuñez et al. (2021)

Many Cas variants used to manipulate genomic loci can be used in parallel screening setups, in which multiple loci are targeted simultaneously. Such parallel screening approaches were first employed using wildtype Cas9 to induce gene knockouts in genes’ coding region using genome-wide gRNA libraries (Shalem et al. 2014; Wang et al. 2014). Subsequently, dCas9 variants were coupled to repressor (KRAB) and activator domains (VP64, SAM, VPR) to extend functional properties in screen settings. These approaches were termed CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) respectively (Chavez et al. 2015; Gilbert et al. 2014; Konermann et al. 2015). More recently, dCas9 was fused to methylation and demethylation enzymes to reprogram the epigenome at targeted genomic loci to induce heritable gene expression changes, a system named CRISPRoff and CRISPRon respectively (Nuñez et al. 2021). Also, other Cas proteins have been employed for additional functionality and targeting of different loci or molecules.

2.2 Technical considerations for parallel screens

Common among parallel screening approaches is that one needs sufficient input material to measure the effect of each gRNA in multiple independent cellular events. Typically, a coverage (independent cell transductions per gRNA) of at least 500–1000× is suggested to overcome experimental and statistical noise in a pooled readout (Doench 2017). To this end, typically, a large number of cells is transduced with a lentiviral library containing gRNAs that target the genes-of-interest. This number is usually further increased by the requirement to only express a single gRNA per target cell, requiring a low infection rate with the initial virus below 10–30 % of cells. Lentivirus infected cells are then selected by drugs or flow cytometry to generate a screenable start population of cells carrying a single gRNA per cell. This screen-ready cell population containing gRNAs and Cas variant proteins can then be tested in various experimental conditions such as drug screens, viral infections, cell fate conversions, organoid development or any other assay imagined by researchers.

For gRNA choice, there are commercially available gRNAs libraries provided by companies, as well as publicly available sequences provided by published literature (Horlbeck et al. 2016; Michlits et al. 2020; Sanjana et al. 2014; Sanson et al. 2018). One can also design gRNAs specific for a given question using available online gRNA prediction tools. These include CHOP–CHOP (Labun et al. 2019), CRISPick (Doench et al. 2016), E-CRISP (Heigwer et al. 2014), and CCTop (Stemmer et al. 2015) among others, which allow Cas protein variants to be specified and provide a predicted efficiency score to be tested empirically. First generation gRNA libraries concentrated on designing gRNAs specific for on-target efficiency and predicted low off-target efficiency based on genomic references, while later library design strategies also took gRNA sequence preference and protein domain structure into account, potentially providing gRNA predictions of higher effectiveness. However, one needs to consider that as of today, not all predicted gRNAs perform as suggested by these algorithms and in a screen setting several gRNAs should target a specific target to ensure robust interpretation of screening results for each target gene (Doench 2017). Alternatively, one can test a limited number of individual gRNAs in reporter assays and use multiple gRNAs per cell (Li et al. 2022). Dual gRNA design may also be used to test gene combinatorial effects but have so far not been implemented in neural systems (Han et al. 2017; Replogle et al. 2020).

2.3 Screen readouts

In screening methodologies based on viral transduction of a test cell population followed by an experimental paradigm, the resulting cell population is typically collected either as a whole or selected for certain features, i.e., via fluorescence-activated cell sorting. The experimental final population is analyzed for their viral content (including gRNA content indicating the targeted genomic feature targeted) using deep sequencing on the collected genomic DNA. This allows to identify genes which upon perturbation lead to a population enrichment or depletion. Thereby a collection of hit genes is curated that are involved in a given process tested in the experimental paradigm. The Next Generation Sequencing (NGS)-based sequencing requirements of such readouts are moderate in terms of read number and therefore costs but limited to gRNA/cell abundance.

As an alternative parallel screen readout, scRNAseq has been introduced in approaches termed Perturb-seq and CROP-seq (Adamson et al. 2016; Datlinger et al. 2017; Dixit et al. 2016; Replogle et al. 2020). In this approach cells in target populations are tested not just for their viral gRNA status but for their transcriptome. This allows for the identification of target cells’ transcriptomic profile and therefore cell type dependent gRNA action. Thereby screenable experimental paradigms are vastly expanded because phenotypes that do not change cell numbers but cell type composition in an experiment become detectable. The technical requirements for screens based on scRNAseq readout are more complicated and require significantly higher sequencing depth than standard cell abundance measurements. A typical cell abundance screen is measured using 1–100 sequencing reads per cell collected, while scRNAseq typically requires 5000–20,000 sequencing reads (plus scRNAseq reagents) per cell, resulting in significantly higher costs. Also, the computational and biological interpretation of screen results are more complicated than cell abundance readouts.

Computational packages for CRISPR screen analyses are available with the most widely used being MAGeCK (Li et al. 2014). In-depth information on the bioinformatic analyses of genomic screens are beyond the scope of this review and have been covered elsewhere (Hwang et al. 2018; Wang et al. 2022; Zhao et al. 2022). However, when planning genetic screens, one needs to consider that sequencing data is data intensive, particularly when screen readouts rely on scRNAseq. Thus, researchers should carefully plan the bioinformatic resources in terms of computer access and bioinformatic skills at their disposal. Generally, screens for gRNA abundance may be easier to analyze than screens with a transcriptomic and/or lineage readout.

3 Genetic screens in brain models

Several examples of successful screens in human brain models exist, exemplifying different approaches regarding CRISPR systems of choice, brain models and readout strategies (Table 2). Here we will highlight some of these examples and point out biological findings uncovered. One of the first papers to describe a CRISPR-based loss-of-function experiment in human neurons was published by the Kampmann lab in 2019 (Tian et al. 2019). This work described a CRISPRi platform in iPSCs which were differentiated in 2D to neurons and a pooled survival screen was performed. Importantly, the expression of the dCas9-KRAB did not induce toxicity nor did it affect neuronal differentiation. The analysis revealed genes associated with sterol metabolism to be enriched as survival-related genes in neurons. The screen tested 2325 genes (using overall 13,025 sgRNAs) in 4 × 10⁶–2 × 10⁷ cells depending on condition. The scalability of 2D models was further expanded by the same team when genome-wide screens were performed both for gene activation and inhibition (Tian et al. 2021). In this work, the effect of oxidative stress on neurons was explored. Essential neuronal genes which hampered differentiation when under- or overexpressed were identified with subsequent analysis of GPX4 as a factor in neuronal survival and handling of ROS and lipid peroxidation levels.

Table 2:

Screens in human neural models. Details of screens published in human neural models.

Model	System	Cas9 variant	Genes tested	gRNA coverage	gRNAs/gene	Readout	Sequencing method	Sequence depth	Reference
2D NPCs	CRISPR-KO	Cas9	736	800× coverage	4	Genomic DNA	Illumina NextSeq	ca. 400 M reads	Guo et al. (2023)
2D NPCs	CRISPR-KO	Cas9	Genome-wide and 1377	250–500× coverage	3–4	Genomic DNA	Illumina HiSeq	ca. 250 M reads	O’Connor et al. (2021)
2D NPCs	CRISPR-KO	Cas9	18,663	Over 1000× coverage	Ca. 10	Genomic DNA	Illumina HiSeq	ca. 250 M reads	Li et al. (2019)
Human and chimpanzee PSCs	CRISPRi	dCas9-KRAB	18,915	Over 1000× coverage	5	Genomic DNA	Illumina HiSeq	ca. 400 M reads	She et al. (2023)
2D glutamatergic neurons	CRISPRi	dCas9-KRAB	2325	1200× coverage	5	Genomic DNA	Illumina HiSeq	ca. 400 M reads	Tian et al. (2019)
2D astrocytes	CRISPRi	dCas9-KRAB	2320	Ca. 1000× coverage	5	Genomic DNA	Illumina HiSeq	∼5000 reads per cell	Leng et al. (2022)
2D microglia	CRISPRi/a	dCas9-KRAB/VPH	2325 (CRISPR-i)/2320 (CRISPR-a)	Ca. 1000× coverage	5	Genomic DNA	Illumina HiSeq	ca. 400 M reads	Dräger et al. (2022)
2D glutamatergic neurons	CRISPRi/a	dCas9-KRAB/VPH	Genome-wide	Ca. 1000× coverage	5	Genomic DNA	Illumina HiSeq	ca. 400 M reads	Tian et al. (2021)
2D NPCs	CRISPRa	dCas9-VP64	1496	550× coverage	5	Genomic DNA	Illumina MiSeq	ca. 25 M reads	Black et al. (2020)
2D NPCs	CRISPRa	dCas9-VP64/SunTag	2428	Ca. 600× coverage	Ca. 20	Genomic DNA	Illumina HiSeq	ca. 400 M reads	Liu et al. (2018)
3D cortical organoids	CRISPR-KO	eCas9	173	Ca. 1900× coverage at experiment start	4	Genomic DNA, lineage barcode	Illumina HiSeq	ca. 1B reads	Esk et al. (2020)
3D assembloids	CRISPR-KO	Cas9	438	Ca. 1600× coverage at experiment start	5	Genomic DNA	Illumina NextSeq	ca. 400 M reads	Meng et al. (2022)
2D neurons	CROP-seq	dCas9-KRAB	14	High coverage	3	scRNA-seq	Illumina HiSeq	50 K mean reads per cell, 14 K cells	Lalli et al. (2020)
2D glutamatergic neurons	CROP-seq	dCas9-KRAB	58	High coverage	2	scRNA-seq	Illumina NovaSeq	84–91 K mean reads per cell, 4.6–5 K cells	Tian et al. (2019)
2D glutamatergic neurons	CROP-seq	dCas9-KRAB/VPH	184 (CRISPRi)/100 (CRISPRa)	High coverage	2	scRNA-seq	Illumina NovaSeq	36 K–48 K mean reads per cell, 38, 58 K cells	Tian et al. (2021)
2D astrocytes	CROP-seq	dCas9-KRAB	30	High coverage	2	scRNA-seq	Illumina NovaSeq	29 K mean reads per cell, ca. 88 K cells	Leng et al. (2022)
2D microglia	CROP-seq	dCas9-KRAB	39	High coverage	2	scRNA-seq	Illumina NovaSeq	41 K mean reads per cell, 58 K cells	Dräger et al. (2022)
3D cortical organoids	CROP-seq	Cas9 and Cas9n	20	1500× coverage	3	scRNA-seq	Illumina NovaSeq	500–30 K UMIs per cell, 22.4 K cells	Fleck et al. (2022)
3D cortical organoids	CROP-seq based CHOOSE	eCas9	36	2770× coverage	2	scRNA-seq	Illumina NovaSeq	1–8 K genes per cell, 80 K cells	Li et al. (2022)

Screens may also be performed on disease backgrounds to better understand the underlying genetic regulators with several examples published. In the context of glioblastoma, gRNA-mediated screens in 2D neural progenitor cells and neurons identified Hippo/YAP and p53 pathway members in accelerating neural progenitor cell cycle progression (O’Connor et al. 2021). Another study probed 14 Autism risk genes in parallel to reveal modules of gene expression control (Lalli et al. 2020). In a model for frontotemporal dementia (FTD) and amyotropic lateral sclerosis (ALS) caused by C9orf72 mutation a screen for kinases modulating C9orf72 effects in neurons identified NEK6 as mediator of DNA damage and axonopathy (Guo et al. 2023). Viral infection pathways have also been revealed such as ZIKA entry intro neural progenitor cells (Li et al. 2019).

Loss-of-function screens based on iPSCs as a starting population are very versatile and may be used to study cell types other than neurons as well. For example, in astrocytes derived from CRISPRi-ready cells the cytokine response was tested with the purpose of exploring inflammatory astrocyte reactivity. Two different inflammatory signatures driven or inhibited by STAT3 were identified (Leng et al. 2022). Another population derived from screen-ready iPSCs and probed for functionality were microglia. In this context their ability to perform efficient phagocytosis was probed (Dräger et al. 2022).

CRISPRi-based loss of function screens have furthermore been used to elucidate species differences by screening genome-wide in iPSCs of different species origin, such as human and chimpanzee (She et al. 2023). Following genome-wide and targeted screens in multiple cell lines from these species, alterations identified were associated with cell-cycle progression and lysosomal signaling. This study illustrates the power of parallel screens across multiple backgrounds combined with careful cross-screen analysis to elucidate unique species dependencies.

CRISPRa, the targeted upregulation of genes to probe their function in a specified experimental paradigm has also been explored in 2D iPSC-derived cultures (e.g., neurons and astrocytes) (Dräger et al. 2022; Tian et al. 2021). Another interesting application is the use of CRISPRa to identify genes that allow for cell type conversions. CRISPRa identified proneural genes driving cell differentiation and fate determination from iPSCs well (Liu et al. 2018). A genetic interaction network related to Wnt/β-catenin as a key regulator of neuronal-fate determination was found, including a level dependency on Ngn1 for successful conversion. Similarly Black and co-workers (Black et al. 2020), used a CRISPRa screen to identify neurogenic transcription factor candidates before individually validating their findings. This approach allowed the characterization of 17 proneural transcription factors for their contribution to neuronal fate determination, maturation as well as interconnected cofactors. These types of gain of function studies allow direct applications to the reprogramming field by facilitating a catalog of factors to improve and develop cell differentiation and conversion protocols (Black et al. 2020).

The examples mentioned probed a large number of genes relying on measuring cell populations knocked out for targeted genes. A deeper phenotypic readout including cell type and state is achieved by applying scRNAseq transcriptome analyses of target populations. scRNAseq-based screens have been used to verify hits from previous large scale screens in several studies (Dräger et al. 2022; Leng et al. 2022; Tian et al. 2021, 2019) and will likely become more popular as costs involved are further reduced.

3D models of the developing human brain are more complex than 2D cultures and pose unique challenges to applying Cas9 screens. 3D models are typically limited by the number of cells contributing to a single neuruloid or a single embryoid bodies, the starting point for many brain organoid protocols. This complicates parallel screens because sufficient gRNA coverage is difficult to achieve with limited initial cells. In further contrast to 2D models, organoids contain multiple cell types, building a developing tissue. While the tissue-like properties of organoids allow for the study of cellular behavior within their surroundings, the inherent variability and stochasticity of organoid growth pose a challenge for parallel screening approaches. Nonetheless, there are several studies that have successfully used parallel screening approaches in developing brain tissue.

In 2020 the first brain organoid screen based on CRISPR-Cas9 was published targeting 173 microcephaly candidate genes (Esk et al. 2020). Lineage tracing of individual gRNA-carrying cells was used to overcome the limitation of cells numbers and increase screening sensitivity in an approach termed CRISPR-Lineage tracing at cellular resolution in heterogenous tissues (CRISPR-LICHT). 25 new microcephaly genes were validated in brain organoids, in addition to previously described causative genes (Lancaster et al. 2013). Furthermore, a new pathway vulnerable in microcephaly was identified in IER3IP1-regulated secretion of ECM material and its deposition in tissue. The use of gRNA-mediated screens was recently extended to assembloids to probe neuronal migration. A screen of 425 genes revealed LNPK as a required gene for endoplasmic reticulum (ER) displacement that precedes nuclear translocation and interneuron migration (Meng et al. 2022). Importantly, the discovered functions of IER3IP1-mediated ECM secretion and LNPK-controlled interneuron migration only occur in a tissue context, highlighting an advantage of 3D brain tissue models.

The two previous examples of CRISPR screens in brain organoid models rely on cells being depleted or expanded upon gene perturbation. However, a key advantage of organoid over defined 2D neuronal models is the generation of complex tissue containing multiple cell types in an in vivo like environment. To probe cell-type composition of brain organoids and changes driven by perturbing regulatory genes, CRISPR screens have been coupled to scRNAseq. The first example of such a screen was published by the Treutlein lab targeting 20 transcription factors important for cell fate acquisition of different cell types in the developing brain (Fleck et al. 2022). With this approach they were able to verify the gene-regulatory network underlying fate acquisition inferred from single-cell transcriptomes and chromatin accessibility. GLI3 was further characterized as a HES genes regulator in NPCs through SHH signaling, which lead to an early telencephalic regionalization (Fleck et al. 2022). A conceptually similar screening approach was taken and refined in a study perturbing 36 genes involved in autism-spectrum disorder (Li et al. 2022). Imbalances in cell type composition between neuronal progenitors and differential generation of excitatory and inhibitory neurons were described for several genes in this study. Importantly, the phenotype found for one gene, ARID1B, was followed up in detail and matched patient phenotypes as seen in MRI imaging. This highlights the predictive power of such screens in brain organoids for patient phenotypes (Li et al. 2022).

Numerous examples confirm the power of parallel gene perturbation screens in human neuronal models, and it will be interesting to also compare such results to accessible in vivo models such as the mouse (Jin et al. 2020) to elucidate human specific aspects of brain function.

4 Screen considerations and limitations

Many different human brain cell models have been developed and used for parallel gene perturbation studies. It is important to choose an appropriate model system for the biological question at hand (Table 2). If a genetic screen is considered in any model system, the right choice of methodology and scale is crucial. 2D cell culture models are well established, with defined conditions boosting reproducibility and scalability. However, 2D models of defined cell types may fall short regarding cellular organization and tissue architecture of the brain. 2D brain models oftentimes display altered cell morphology, cell-to-cell and cell-matrix interactions and genetic profiles (Mertens et al. 2016). In the context of genetic screening, 2D models’ greatest benefits are the reliable generation of large numbers of defined cells. This allows for large scale screens, including genome-wide scale. The large number of cells that may be used for screens in 2D also increases the reliability of screens as gRNA coverage per individual gRNA may be increased. 2D models have also been shown to be amendable to screening using multiple Cas9-based modifiers, a feat not yet achieved in brain organoids, proving 2D culture models’ flexibility (Ahmed et al. 2023; Black et al. 2020; Dräger et al. 2022; Leng et al. 2022; She et al. 2023; Tian et al. 2021, 2019).

Brain tissue organoid models improve studies questioning cell type composition in the developing brain and in tissue specific biology. They mimic brain tissue architecture on a small scale. However, common limitations to date are the scalability and inter organoid reproducibility. To overcome these limitations, methods have been devised to increase screen sensibility by combining genetic perturbations with lineage tracing to gain additional information on genes’ effects in case of cell depletion or enrichment phenotypes (Esk et al. 2020). Alternatively, higher dimensional phenotyping by using cells’ transcriptomes as readout for genetic perturbation has been developed. scRNAseq-profiling of genetic screens in brain organoids has so far been limited to below 50 genes in published studies, not least because of considerable costs and computational demands (Fleck et al. 2022; Li et al. 2022). Regardless, these very recent studies provide an interesting avenue forward.

Another important experimental choice concerns the kind of screening methodology. While methods based on CRISPR systems have become overwhelmingly popular due to the ease of use and effectiveness, different Cas9 variants offer different functionalities. CRISPR KO screens results in full gene function loss in the best case but may also lead to variable outcomes on a population level due to variable gene-editing results including heterozygous KO, reading frameshifts resulting in truncated protein expression and amino acid changes (Jinek et al. 2012; Shalem et al. 2014). These variable outcomes on a population level can complicate experimental interpretation. CRISPR-interference and related approaches (CRISPRi, CRISPRoff etc.) attenuates gene function but does not fully perturb gene function, such that variability in readouts may be due to differential levels of gene downregulation (Gilbert et al. 2013; Tian et al. 2021). Similarly, CRISPRa may lead to variable upregulation of individual genes (Gilbert et al. 2013). Newer approaches such as base editing or prime editing offer potential solutions in that gene-editing outcomes are more predictable but have not been implemented in neural models yet (Rees and Liu 2018).

For all CRISPR-based screening applications, it should be noted that outcomes on individual gene functions depend on gRNAs used, genomic context of the targeted site and cell type. Thus, all screens result in some variable false positive screen hits. Therefore, before studying individual genes in more detail it is advisable to verify individual screen hits in independent assays such as pharmacological intervention or knockout cell lines to faithfully interpret genes’ biological functions.

5 Future directions

The promise of genetic screening in human in vitro brain models looks bright as we are only seeing the beginning of the combination of ever evolving culture techniques and refinement in screening methodology. On the in vitro culture side, protocols for generating large numbers of reproducible cell types in 2D as well as 3D brain organoids become more reliable. This will allow for screening larger gene collections. Advances in CRISPR technologies will extend the testing of the genome beyond coding regions with more precise and expanding Cas variants. Also, screen readouts will become more accessible with new scRNAseq technologies cutting costs and computational pipelines capable of interpretation of large datasets being developed. We expect these developments to further advance our understanding of human brain function as well as disease mechanism of neuropathological conditions.

Corresponding author: Christopher Esk, Institute of Molecular Biology, University Innsbruck, Technikerstr. 25, A-6020 Innsbruck, Austria; Center for Molecular Biosciences, University Innsbruck, Technikerstr. 25, A-6020 Innsbruck, Austria; and Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna BioCenter (VBC), Dr. Bohr Gasse 3, 1030 Vienna, Austria, E-mail: christopher.esk@uibk.ac.at

About the authors

Julianne Beirute-Herrera

Julianne Beirute-Herrera holds a master’s degree in Neuroscience from Maastricht University. She is currently a PhD student in the Edenhofer group at Innsbruck University. Her project focuses on the functional characterization of human-specific genes involved in neurodevelopment using human derived stem cell models and CRISPR technologies.

Beatriz López-Amo Calvo

Beatriz López-Amo Calvo holds a BSc in Biotechnology from Universidad Politécnica de Madrid. She is currently studying towards a MSc in Regenerative Medicine and Technology Master studies at Utrecht University. She is performing lab work in the Esk laboratory at Innsbruck University on the genetic control of cerebral organoid growth.

Frank Edenhofer

Frank Edenhofer holds a PhD from the Ludwig-Maximilians University of Munich, Germany. After postdoctoral training in the laboratory of Dr. Klaus Rajewsky (Institute for Genetics, University of Cologne, Germany) he became junior group leader at the Institute of Reconstructive Neurobiology at the University of Bonn, Germany. He is now full professor at the University of Innsbruck, Head of the Department of Molecular Biology and leading the research group Genomics, Stem Cell Biology & Regenerative Medicine. He devised pioneering studies in the field of cellular reprogramming, particularly the direct conversion of somatic cells into induced neural stem cells.

Christopher Esk

Christopher Esk holds a PhD from Leibniz University Hannover following thesis work at the University of California, San Francisco. After postdoctoral work in Jürgen Knoblich’s lab at the Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria, he became assistant professor at the Institute of Molecular Biology of the University of Innsbruck, Innsbruck, Austria. He studies neurodevelopment of the human brain and its regulation.

Acknowledgments

We thank all members of the Edenhofer and Esk labs for valuable input and discussion.

Research ethics: Not applicable.
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The authors state no conflict of interest.
Research funding: F.E. acknowledges funding from the Austrian Science Fund (FWF, I 4791-B, I 5184, TAI 801, and SFB F7810), and the European Union (E-RARE CureMILS and Horizon 2020 Marie Sklodowska-Curie research grant No. 847681 ARDRE).
Data availability: Not applicable.

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Received: 2023-04-04

Accepted: 2023-08-21

Published Online: 2023-09-12

Published in Print: 2024-01-29

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/hsz-2023-0174

Keywords for this article

neurobiology; in vitro brain models; organoid; CRISPR; genetic screens

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BY 4.0