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Summary The human neocortex exhibits characteristic regional patterning (arealization) critical for higher-order cognitive function. Disrupted arealization is strongly implicated in neurodevelopmental disorders (NDDs), but current neocortical organoid models largely fail to recapitulate this patterning, limiting mechanistic understanding. Here, we establish a straightforward method for generating arealized organoids through short-term early exposure to anterior (FGF8) or posterior (BMP4/CHIR-99021) morphogens. These treatments created distinct anterior and posterior signaling centers, supporting long-lasting polarization, which we validated with single-cell RNA sequencing that revealed area-specific molecular signatures matching prenatal human cortex. To demonstrate the utility of this platform, we modeled Fragile X Syndrome (FXS) in organoids with distinct anterior and posterior regional identities. FXS organoids showed highly disrupted SOX4/SOX11 expression gradients along the anterior-posterior axis, consistent with alterations found in autism spectrum disorder (ASD) and demonstrate how regional patterning defects may contribute to NDD pathology. Together, our study provides a robust platform for generating neocortical organoids with anterior-posterior molecular signatures and highlights the importance of modeling NDDs using experimental platforms with neuroanatomic specificity. 

Abstract Cell-cell communication (CCC) occurs across different biological scales, ranging from interactions between large groups of cells to interactions between individual cells, forming a hierarchical structure. Globally, CCC may exist between clusters or only subgroups of a cluster with varying size, while locally, a group of cells as sender or receiver may exhibit distinct signaling properties. Current existing methods infer CCC from single-cell RNA-seq or Spatial Transcriptomics only between predefined cell groups, neglecting the existing hierarchical structure within CCC that are determined by signaling molecules, in particular, ligands and receptors. Here, we develop CrossChat, a novel computational framework designed to infer and analyze the hierarchical cell-cell communication structures using two complementary approaches: a global hierarchical structure using a multi-resolution clustering method, and multiple local hierarchical structures using a tree detection method. This framework provides a comprehensive approach to understand the hierarchical relationships within CCC that govern complex tissue functions. By applying our method to two nonspatial scRNA-seq datasets sampled from COVID-19 patients and mouse embryonic skin, and two spatial transcriptomics datasets generated from Stereo-seq of mouse embryo and 10x Visium of mouse wounded skin, we showcase CrossChat’s functionalities for analyzing both global and local hierarchical structures within cell-cell communication. 

Cell–cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either conditions alone. There has been a recent and rapid growth of methods to infer CCI from single-cell RNA-sequencing (scRNA-seq), revealing complex CCI networks at a previously inaccessible scale. However, the majority of current CCI analyses from scRNA-seq data focus on direct comparisons between individual CCI networks of individual samples from patients, rather than “group-level” comparisons between sample groups of patients comprising different conditions. To illustrate new biological features among different disease statuses, we investigated the diversity of key network features on groups of CCI networks, as defined by different disease statuses. We considered three levels of network features: node level, as defined by cell type; node-to-node level; and network level. By applying these analysis to a large-scale single-cell RNA-sequencing dataset of coronavirus disease 2019 (COVID-19), we observe biologically meaningful patterns aligned with the progression and subsequent convalescence of COVID-19.