Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.06.13.659629

High-throughput single-cell CRISPRi screens stratify neurodevelopmental functions of schizophrenia-associated genes

Abstract

Schizophrenia is a complex neuropsychiatric disorder with strong genetic underpinnings, yet the molecular mechanisms linking genetic risk to disrupted brain development remain poorly understood. Transcription factors (TFs) and chromatin regulators (CRs) are increasingly implicated in neuropsychiatric disorders, where their dysregulation may disrupt neurodevelopmental programs. Despite this, systematic functional interrogation in human models has been limited. Here, we combine pooled CRISPR interference (CRISPRi) screens with high- throughput single-cell multiomic profiling in hiPSC-derived neural progenitors and neurons to functionally assess 65 schizophrenia-associated genes. Based on public datasets and literature review, we selected 55 TFs and CRs, along with ten additional risk genes whose loss-of-function has been linked to schizophrenia. Our single-cell CRISPRi readouts revealed that perturbations in TFs and CRs converge on disrupting neurodevelopmental timing. CRISPRi of several factors delayed neural differentiation, whereas others, such as the knockdown of MCRS1, drove precocious neural commitment. Validation screens combined with cell cycle and metabolic indicators confirmed the differentiation-restricting or -promoting roles of these TFs and CRs. Multimodal trajectory analysis uncovered discrete transcriptional and epigenomic states representing delayed and accelerated neurodevelopment, enriched for schizophrenia GWAS loci and disease-relevant pathways. Gene regulatory network (GRN) inference identified TCF4 and ZEB1 as critical mediators opposing the neural differentiation trajectory. Functional overexpression of these TFs followed by chromatin profiling demonstrated that TCF4 restrains, while ZEB1 promotes, neural differentiation in a stage-specific and competitive manner. Furthermore, we show that MCRS1 represses ZEB1 expression, positioning MCRS1 as a key brake on premature neurodevelopment. Together, our study establishes a scalable framework that integrates genetic perturbation, single- cell multiomics, and GRN modeling to functionally annotate disease-linked genes. We reveal convergent regulatory axes that underlie altered neurodevelopmental timing in schizophrenia, offering mechanistic insights into how chromatin misregulation contributes to disease pathogenesis.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yildiz, U., Claringbould, A., Marttinen, M., Campos-Fornes, V., Lamprousi, M., Saraswat, M., Saver, M., Bunina, D., Dorrity, M., Zaugg, J. B., Noh, K.-M.. 2025-06-16. High-throughput single-cell CRISPRi screens stratify neurodevelopmental functions of schizophrenia-associated genes. https://doi.org/10.1101/2025.06.13.659629

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

CoReg, tracking-based motion compensation for in vivo calcium imaging

In vivo calcium imaging enables the observation of neural activity with high spatial and temporal resolution. However, recordings are frequently affected by motion artifacts caused by animal movement, physiological processes, tissue drift, or instability of the imaging setup. These artifacts make signal extraction harder and reduce the consistency of downstream analyses. Current registration methods can correct rigid and non-rigid motion, but often estimate motion from individual frames or image patches and make limited use of temporal information. In addition, activity-driven fluorescence changes can make it difficult to distinguish changes in image appearance from actual motion. Here, we treat motion correction in calcium imaging as a point tracking problem and introduce CoReg, an open-source Python- based software tool with a graphical user interface. We benchmark CoReg against established registration methods using fluorescence imaging datasets from multiple species. CoReg performs competitively with or better than existing approaches across several settings. We further show that its performance is robust to the choice of tracked points, making the method straightforward to apply. CoReg includes implementations of both our method and the evaluated established methods. Our results show that tracking-based registration provides a simple and reliable approach for motion correction in vivo calcium imaging.

neuroscience↗

A shared slowing axis and phenotype-specific rhythms link clinical EEG to FDG-PET dementia phenotypes

Early and accurate identification of neurodegenerative disorders has notable implications for treatment and quality of life. Whereas neuroimaging-based biomarkers can accurately identify neurodegenerative subtypes, EEG represents a low-cost alternative that could expand access to dementia care. Here, we investigated whether six data-driven EEG latent factors, derived by nonnegative tensor decomposition of clinical EEGs, differ systematically across FDG-PET-defined neurodegenerative phenotypes. In 455 patients with EEG and FDG-PET acquired within one year of each other, factor expression was compared across 14 diagnostic classes assigned by StateViewer, a validated artificial intelligence framework for FDG-PET interpretation. To separate disease-general severity from phenotype-specific topography, each factor was also regressed on a subject-level slowing index. Finally, we related the factors to regional FDG uptake and to neuropsychological performance (n = 98) and assessed their potential utility for single-subject classification. Four factors reflecting neurophysiological oscillations (posterior alpha, Rolandic beta, frontotemporal delta-theta and posterior theta-slow alpha) varied significantly across phenotypes, with attenuation of faster and augmentation of slower rhythms most pronounced in dementia with Lewy bodies (DLB), dysexecutive Alzheimer disease (AD) and posterior cortical atrophy, whereas factors dominated by eye-movement and muscle artifact showed no robust phenotypic association. Alpha and beta effects were largely attributable to overall slowing, whereas posterior theta-slow alpha became more phenotype-specific after adjustment (eta-squared 0.082 to 0.147), dissociating the Alzheimer disease spectrum from frontotemporal syndromes. The slowing index tracked with an FDG-PET pattern highly associated with DLB across the cohort, irrespective of assigned phenotype (r = 0.51, P < 0.001). Consistent with a model in which dementia phenotypes reflect altered hippocampal-neocortical oscillatory coupling (stochastic latent oscillatory diffusion), frontotemporal delta-theta tracked a limbocortical axis of metabolic pattern expression, rising with neocortical and falling with limbic-predominant involvement, independent of overall slowing and phenotype. Each factor mapped onto a distinct metabolic topography, with frontotemporal delta-theta resembling Alzheimer-type hypometabolism and posterior theta-slow alpha the medial occipital hypometabolism of DLB. In terms of neuropsychological performance, Rolandic beta was associated with executive functions, and frontotemporal delta-theta with memory, visuospatial and global performance. Using these four factors alone, an exploratory logistic regression classifier separated cognitively unimpaired participants from DLB, posterior cortical atrophy, typical AD, and dysexecutive AD (area under the curve > 0.8) but poorly discriminated frontotemporal dementia phenotypes canonically associated with 4-repeat tau. Thus, data-driven decomposition of clinical EEG separates oscillatory activity from artifact into factors that track metabolic phenotype, regional metabolism and cognition, capturing both overall slowing and phenotype-specific patterns. This work informs the further development of scalable, biology-informed EEG-based phenotyping of neurodegenerative disease.

neuroscience↗

An application of fNIRS for the prediction of hearing thresholds in sleeping infants: a proof of principle

This study applied a two-component-response modelling approach to fNIRS recordings obtained from naturally sleeping infants presented with auditory stimuli at different sound intensities. We investigated whether the relationship between fNIRS response measures and stimulus intensity could be modelled to estimate individual auditory hearing thresholds. In the normal-hearing group, 75% of infants had threshold estimates for the sound /ba/ within the expected normal-hearing range, with an estimated mean equivalent normal loudness (ENL) of 10.72 dB HL (SD 8.64 dB HL). A case study further explored the potential application of this approach in an infant with hearing impairment, highlighting the importance of reliable responses at lower stimulus intensities and appropriate regression model selection. These findings provide preliminary evidence that cortical haemodynamic responses can be quantitatively related to sound intensity for estimating auditory sensitivity.

neuroscience↗