Biology subjects

Nadig, A.

Publications and source records attributed to Nadig, A..

9 recordsLinked to original sources

bioRxiv2025

Gene editing in "cell villages" enables exploring disease-relevant mutations in many genetic backgrounds

Understanding how individual genetic backgrounds shape the effects of disease-associated mutations is central to elucidating the biology of complex psychiatric disorders. We developed a scalable village editing strategy that enables simultaneous genome editing across multiple induced pluripotent stem cell (iPSC) lines, allowing systematic assessment of how polygenic context modulates the impact of specific mutations. Using pooled CRISPR editing in 15 iPSC lines spanning a range of schizophrenia (SCZ) polygenic risk scores, we generated homozygous and heterozygous knockouts in two known SCZ-associated genes: LRP1, involved in cholesterol import, and NRXN1, a presynaptic adhesion molecule. By mixing all lines prior to editing and de-multiplexing them afterward, we efficiently produced multi-donor knockout neurons at scale. Transcriptomic profiling revealed that LRP1 and NRXN1 loss produce both shared and donor-specific effects on neuronal gene expression, with variable perturbation of neurotransmitter transport and cholesterol biosynthesis pathways across genetic backgrounds. These results demonstrate that village editing enables systematic dissection of gene-background interactions in human neurons, offering a powerful framework for studying the polygenic architecture of psychiatric disease.

cell biology↗

bioRxiv2025

Meta-analysis of the brain transcriptomes of multiple genetic mouse models of schizophrenia highlights dysregulation in striatum and thalamus

Schizophrenia is a severe mental illness with high heritability, but its underlying mechanisms are poorly understood. We meta-analyzed large-scale brain transcriptomic data from mice harboring individual loss-of-function mutations in seven schizophrenia risk genes (Akap11, Dagla, Gria3, Grin2a, Sp4, Srrm2, Zmym2). While all studied brain regions were affected, the striatum and the thalamus emerged as key brain regions of convergence. Striatum showed downregulation of synapse-and oxidative phosphorylation-related gene sets in all models. In the thalamus, mutants separated into two groups based on transcriptomic phenotype: synapse-related gene sets were upregulated in mutants with only schizophrenia and bipolar association, and were downregulated in mutants that are associated with developmental delay/intellectual disability in addition to schizophrenia. Overall, our meta-analysis reveals convergence and divergence in brain transcriptomic phenotype in these schizophrenia genetic models, supports the involvement of striatal disturbance and synapse dysfunction in schizophrenia, and points to a key role of the thalamus.

neuroscience↗

bioRxiv2025

Consequences of training data composition for deep learning models in single-cell biology

Foundation models for single-cell transcriptomics have the potential to augment (or replace) purpose-built tools for a variety of common analyses, especially when data are sparse. Recent work with large language models has shown that training data composition greatly shapes performance; however, to date, single-cell foundation models have ignored this aspect, opting instead to train on the largest possible corpus. We systematically investigate the consequences of training dataset composition on the behavior of deep learning models of single-cell transcriptomics, focusing on human hematopoiesis as a tractable model system and including cells from adult and developing tissues, disease states, and perturbation atlases. We find that (1) these models generalize poorly to unseen cell types, (2) adding malignant cells to a healthy cell training corpus does not necessarily improve modeling of unseen malignant cells, and (3) including an embryonic stem cell differentiation atlas during training improves performance on out-of-distribution tasks. Our results emphasize the importance of diverse training data and suggest strategies to optimize future single-cell foundation models.

genomics↗

bioRxiv2024

Transcriptome-wide characterization of genetic perturbations

Single cell CRISPR screens such as Perturb-seq enable transcriptomic profiling of genetic perturbations at scale. However, the data produced by these screens are often noisy due to cost and technical constraints, limiting power to detect true effects with conventional differential expression analyses. Here, we introduce TRanscriptome-wide Analysis of Differential Expression (TRADE), a statistical framework which estimates the transcriptome-wide distribution of true differential expression effects from noisy gene-level measurements. Within TRADE, we derive multiple novel, interpretable statistical metrics, including the "transcriptome-wide impact", an estimator of the overall transcriptional effect of a perturbation which is stable across sampling depths. We analyze new and published large-scale Perturb-seq datasets to show that many true transcriptional effects are not statistically significant, but detectable in aggregate with TRADE. In a genome-scale Perturb-seq screen, we find that a typical gene perturbation affects an estimated 45 genes, whereas a typical essential gene perturbation affects over 500 genes. An advantage of our approach is its ability to compare the transcriptomic effects of genetic perturbations across contexts and dosages despite differences in power. We use this ability to identify perturbations with cell-type dependent effects and to find examples of perturbations where transcriptional responses are not only larger in magnitude, but also qualitatively different, as a function of dosage. Lastly, we expand our analysis to case/control comparison of gene expression for neuropsychiatric conditions, finding that transcriptomic effect correlations are greater than genetic correlations for these diagnoses. TRADE lays an analytic foundation for the systematic comparison of genetic perturbation atlases, as well as differential expression experiments more broadly.

genomics↗

bioRxiv2020

Resting-state functional connectivity and psychopathology in Klinefelter syndrome (47, XXY)

Klinefelter syndrome (47, XXY; Henceforth: XXY syndrome) is a high impact but poorly understood genetic risk factor for neuropsychiatric impairment. Here, we provide the first neuroimaging study to map resting-state functional connectivity (rsFC) changes in XXY syndrome and ask how these might relate to brain anatomy and psychopathology. We collected resting state functional magnetic resonance imaging data from 75 individuals with XXY and 84 healthy XY males. We implemented a brain-wide screen to identify regions with altered global rsFC in XXY vs. XY males, and then used seed-based analysis to decompose these alterations. We further compared rsFC changes with regional changes in brain volume from voxel-based morphometry and tested for correlations between rsFC and symptom variation within XXY syndrome. We found that XXY syndrome was characterized by increased global rsFC in the left dorsolateral prefrontal cortex (DLPFC), associated with overconnectivity with diverse rsFC networks. Regional rsFC changes were partly coupled to regional volumetric changes in XXY syndrome. Within the precuneus, variation in DLPFC rsFC within XXY syndrome was correlated with the severity of psychopathology in XXY individuals. Our findings provide the first view of altered functional brain connectivity in XXY syndrome and delineate links between these alterations and those relating to both brain anatomy and psychopathology. Taken together, these insights advance biological understanding of XXY syndrome as a disorder in its own right, and as a model of genetic risk for psychopathology more broadly.

neuroscience↗

bioRxiv2019

Sex-Biased Trajectories of Amygdalo-Hippocampal Morphology Change Over Human Development

The amygdala and hippocampus are two adjacent allocortical structures implicated in sex-biased and developmentally-emergent psychopathology. However, the spatiotemporal dynamics of amygdalo-hippocampal development remain poorly understood in healthy humans. The current study defined trajectories of volume and shape change for the amygdala and hippocampus by applying a multi-atlas segmentation pipeline (MAGeT-Brain) and semi-parametric mixed-effects spline modeling to 1,529 longitudinally-acquired structural MRI brain scans from a large, single-center cohort of 792 youth (403 males, 389 females) between the ages of 5 and 25 years old. We found that amygdala and hippocampus volumes both follow curvilinear and sexually dimorphic growth trajectories. These sex-biases were particularly striking in the amygdala: males showed a significantly later and slower adolescent deceleration in volume expansion (at age 20 years) than females (age 13 years). Shape analysis localized significant hot-spots of sex-biased anatomical development in sub-regional territories overlying rostral and caudal extremes of the CA1/2 in the hippocampus, and the centromedial nuclear group of the amygdala. In both sexes, principal components analysis revealed close integration of amygdala and hippocampus shape change along two main topographically-organized axes - low vs. high areal expansion, and early vs. late growth deceleration. These results bring greater resolution to our spatiotemporal understanding of amygdalo-hippocampal development in healthy males and females and discover focal sex-differences in the structural maturation of the brain components that may contribute to differences in behavior and psychopathology that emerge during adolescence.\n\nSIGNIFICANCE STATEMENTThe amygdala and hippocampus are implicated in several developmentally-dynamic and sex-biased psychiatric disorders, but the spatiotemporal organization and sex-biased patterning of amygdalo-hippocampal maturation remains unclear in humans. Here, by integrating new methods for analysis of longitudinal neuroimaging data, we resolve the developmental milestones and spatial gradients that organize human amygdalo-hippocampal maturation. Each structures volume follows a tri-phasic, curvilinear growth trajectory which - for the amygdala - shows rapid male-female size divergence in mid-adolescence through delayed growth deceleration in males. Spatially fine-grained shape analyses localize these sex differences, and further reveal highly orchestrated shape changes across the amygdala and hippocampus that are organized by two topographical gradients. These data provide a new framework for understanding amygdalo-hippocampal organization in human development.

neuroscience↗

bioRxiv2019

Transcriptomic and Cellular Decoding of Regional Brain Vulnerability to Neurodevelopmental Disorders

Neurodevelopmental disorders are highly heritable and associated with spatially-selective disruptions of brain anatomy. The logic that translates genetic risks into spatially patterned brain vulnerabilities remains unclear but is a fundamental question in disease pathogenesis. Here, we approach this question by integrating (i) in vivo neuroimaging data from patient subgroups with known causal genomic copy number variations (CNVs), and (ii) bulk and single-cell gene expression data from healthy cortex. First, for each of six different CNV disorders, we show that spatial patterns of cortical anatomy change in youth are correlated with spatial patterns of expression for CNV region genes in bulk cortical tissue from typically-developing adults. Next, by transforming normative bulk-tissue cortical expression data into cell-type expression maps, we further link each disorders anatomical change map to specific cell classes and specific CNV-region genes that these cells express. Finally, we establish convergent validity of this "transcriptional vulnerability model" by inter-relating patient neuroimaging data with measures of altered gene expression in both brain and blood-derived patient tissue. Our work clarifies general biological principles that govern the mapping of genetic risks onto regional brain disruption in neurodevelopmental disorders. We present new methods that can harness these principles to screen for potential cellular and molecular determinants of disease from readily available patient neuroimaging data.

neuroscience↗

bioRxiv2018

Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology

Childhood socioeconomic status (SES) impacts cognitive development and mental health, but its association with structural brain development is not yet well-characterized. Here, we analyzed 1243 longitudinally-acquired structural MRI scans from 623 youth to investigate the relation between SES and cortical and subcortical morphology between ages 5 and 25 years. We found positive associations between SES and total volumes of the brain, cortical sheet, and four separate subcortical structures. These associations were developmentally fixed rather than age-dependent. Surface-based shape analysis revealed that higher SES is associated with areal expansion of (i) lateral prefrontal, anterior cingulate, lateral temporal, and superior parietal cortices and (ii) ventrolateral thalamic, and medial amygdalo-hippocampal sub-regions. Meta-analyses of functional imaging data indicate that cortical correlates of SES are centered on brain systems subserving sensorimotor functions, language, memory, and emotional processing. We further show that anatomical variation within a subset of these cortical regions partially mediates the positive association between SES and IQ. Finally, we identify neuroanatomical correlates of SES that exist above and beyond accompanying variation in IQ. Our findings clarify the spatiotemporal patterning of SES-related neuroanatomical variation and inform ongoing efforts to dissect the causal pathways underpinning observed associations between childhood SES and regional brain anatomy.

neuroscience↗

bioRxiv2018

Carriage of Supernumerary Sex Chromosomes Decreases the Volume and Alters the Shape of Limbic Structures.

Sex chromosome aneuploidy (SCA) enhances risk for several psychiatric disorders associated with the limbic system, including mood and autism spectrum disorders. These patients provide a powerful genetics-first model for understanding the biological basis of psychopathology. Additionally, these disorders are frequently sex-biased in prevalence, further suggesting an etiological role for sex chromosomes. To clarify how limbic anatomy varies across sex and sex chromosome complement, we characterized amygdala and hippocampus structure in a uniquely large sample of patients carrying supernumerary sex chromosomes (n = 132) and typically developing controls (n=166). After correction for sex-differences in brain size, karyotypically normal males (XY) and females (XX) did not differ in volume or shape of either structure. In contrast, all SCAs were associated with lowered amygdala volume relative to gonadally-matched controls. This effect was robust to three different methods for total brain volume correction, including an allometric analysis that derived normative scaling rules for these structures in a separate, typically developing population (n = 79). Hippocampal volume was insensitive to SCA after correction for total brain volume. However, surface-based analysis revealed that SCA, regardless of specific karyotype, was consistently associated with a spatially specific pattern of shape change in both amygdala and hippocampus. In particular, SCA was accompanied by contraction around the basomedial nucleus of the amygdala and an area within the hippocampal surface that cuts across hippocampal subfields. These results demonstrate the power of SCA as a model to understand how copy number variation can precipitate changes in brain systems relevant to psychiatric disease.

neuroscience↗