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A mammalian brain contains numerous neurons with distinct cell types for complex neural circuits. Virus-based circuit tracing tools are powerful in tracking the interaction among the different brain regions. However, detecting brain-wide neural networks in vivo remains challenging since most viral tracing systems rely on postmortem optical imaging. We developed a novel approach that enables in vivo detection of brain-wide neural connections based on metal-free magnetic resonance imaging (MRI). The recombinant adeno-associated virus (rAAV) with retrograde ability, the rAAV2-retro, encoding the human water channel aquaporin 1 (AQP1) MRI reporter gene was generated to label neural connections. The mouse was micro-injected with the virus at the Caudate Putamen (CPU) region and subjected to detection with Diffusion-weighted MRI (DWI). The prominent structure of the CPU-connected network was clearly defined. In combination with a Cre-loxP system, rAAV2-retro expressing Cre-dependent AQP1 provides a CPU-connected network of specific type neurons. Here, we established a sensitive, metal-free MRI-based strategy for in vivo detection of cell type-specific neural connections in the whole brain, which could visualize the dynamic changes of neural networks in rodents and potentially in non-human primates.

Gene co-expression network inference from bulk tissue samples often misses cell-type-specific interactions, which can be detected through single-cell gene expression data. However, the noise and sparsity of single-cell data challenge the inference of these networks. We developed scNET, a framework for integrative cell-type-specific co-expression network inference from single-cell transcriptome data, demonstrating its utility in augmenting the human interactome for more accurate disease gene prediction. We address the limitations of network inference from single-cell expression data through dropout imputation, metacell formation, and data transformation. Employing this data preprocessing pipeline, we inferred cell-type-specific co-expression links from single-cell atlas data, covering various cell types and tissues, and integrated over 850K of these inferred links into a preexisting human interactome, HumanNet, resulting in HumanNet-plus. This integration notably enhanced the accuracy of network-based disease gene prediction. These findings suggest that with proper data preprocessing, network inference from single-cell gene expression data can be highly effective, potentially enriching the human interactome and advancing the field of network medicine.

Gene regulatory networks play pivotal roles in our understanding of biological processes/mechanisms at the molecular level. Many studies have developed sample-specific or cell-type-specific gene regulatory networks from single-cell transcriptomic data based on a large amount of cell samples. Here, we review the state-of-the-art computational algorithms and describe various applications of gene regulatory networks in biological studies.

Background Functional gene networks (FGNs) capture functional relationships among genes that vary across tissues and cell types. Construction of cell-type-specific FGNs enables the understanding of cell-type-specific functional gene relationships and insights into genetic mechanisms of human diseases in disease-relevant cell types. However, most existing FGNs were developed without consideration of specific cell types within tissues. Results In this study, we created a multimodal deep learning model (MDLCN) to predict cell-type-specific FGNs in the human brain by integrating single-nuclei gene expression data with global protein interaction networks. We systematically evaluated the prediction performance of the MDLCN and showed its superior performance compared to two baseline models (boosting tree and convolutional neural network). Based on the predicted cell-type-specific FGNs, we observed that cell-type marker genes had a higher level of hubness than non-marker genes in their corresponding cell type. Furthermore, we showed that risk genes underlying autism and Alzheimer’s disease were more strongly connected in disease-relevant cell types, supporting the cellular context of predicted cell-type-specific FGNs. Conclusions Our study proposes a powerful deep learning approach (MDLCN) to predict FGNs underlying a diverse set of cell types in human brain. The MDLCN model enhances prediction accuracy of cell-type-specific FGNs compared to single modality convolutional neural network (CNN) and boosting tree models, as shown by higher areas under both receiver operating characteristic (ROC) and precision-recall curves for different levels of independent test datasets. The predicted FGNs also show evidence for the cellular context and distinct topological features (i.e. higher hubness and topological score) of cell-type marker genes. Moreover, we observed stronger modularity among disease-associated risk genes in FGNs of disease-relevant cell types. For example, the strength of connectivity among autism risk genes was stronger in neurons, but risk genes underlying Alzheimer’s disease were more connected in microglia.

The uploaded files are source datasets for the scMTNI algorithm. scMTNI is a multi-task learning framework that integrates the cell lineage structure, scRNA-seq and scATAC-seq measurements to enable joint inference of cell type-specific GRNs. See more details at Zhang, S., Pyne, S., Pietrzak, S. et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 14, 3064 (2023). https://doi.org/10.1038/s41467-023-38637-9 The source data scMTNI_sourcedata.tar.gz contains the following 3 parts: 1) The cluster-specific scRNA-seq matrices and the prior networks for all three datasets and scMTNI inferred consensus networks. 2) Gold standard human and mouse datasets for evaluation. 3) Source data for scMTNI figures 2-8 and supplementary figures. The key for each figure and its corresponding file path is in SourceData_Key_v2.xlsx. The source data Buenrostro_Hematopoiesis.tar.gz contains the scRNA-seq data for human hematopoietic differentiation downloaded from Data S2 of Buenrostro et al. The source data RawMotifFiles.tar.gz contains the motif instance files and promoter files for human and mouse for generating prior networks using scATAC-seq data for scMTNI. Check https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md for examples and scripts. The Buenrostro_priorNetwork_bamfiles.tar.gz contains the raw bam files of scATAC-seq data for human hematopoietic differentiation downloaded from Buenrostro et al.

Mesembryanthemum crystallinum (ice plant) exhibits extreme tolerance to salt. Epidermal bladder cells (EBCs), developing on the surface of aerial tissues and specialized in sodium sequestration and other protective functions, are critical for the plant's stress adaptation. We present the first transcriptome analysis of EBCs isolated from intact plants, to investigate cell type-specific responses during plant salt adaptation. We developed a de novo assembled, nonredundant EBC reference transcriptome. Using RNAseq, we compared the expression patterns of the EBC-specific transcriptome between control and salt-treated plants. The EBC reference transcriptome consists of 37 341 transcript-contigs, of which 7% showed significantly different expression between salt-treated and control samples. We identified significant changes in ion transport, metabolism related to energy generation and osmolyte accumulation, stress signalling, and organelle functions, as well as a number of lineage-specific genes of unknown function, in response to salt treatment. The salinity-induced EBC transcriptome includes active transcript clusters, refuting the view of EBCs as passive storage compartments in the whole-plant stress response. EBC transcriptomes, differing from those of whole plants or leaf tissue, exemplify the importance of cell type-specific resolution in understanding stress adaptive mechanisms.

•Our study first applied a novel similarity network method (MIND) to investigate the intricate interplay between brain macrostructure abnormalities and specific transcriptional expression patterns for EOS patients.•Major MIND differences of EOS patients focused on the orbitofrontal cortex, pericalcarine cortex, lingual gyrus, and multiple networks.•PLS2- genes were enriched in metabolism-related pathways, specific astrocytes, cortical layers, and posterior developmental stages.•PLS2+ genes were enriched in synapses signaling-related pathways and early developmental stages but not in special cell types or layers. Early-onset Schizophrenia (EOS) is a profoundly progressive psychiatric disorder characterized by both positive and negative symptoms, whose pathogenesis is influenced by genes, environment and brain structure development. In this study, the MIND (Morphometric Inverse Divergence) network was employed to explore the relationship between morphological similarity and specific transcriptional expression patterns in EOS patients. This study involved a cohort of 187 participants aged between 7 and 17 years, consisting of 97 EOS patients and 90 healthy controls (HC). Multiple morphological features were used to construct the MIND network for all participants. Furthermore, we explored the associations between MIND network and brain-wide gene expression in EOS patients through partial least squares (PLS) regression, shared genetic predispositions with other psychiatric disorders, functional enrichment of PLS weighted genes, as well as transcriptional signature assessment of cell types, cortical layers, and developmental stages. The MIND showed similarity differences in the orbitofrontal cortex, pericalcarine cortex, lingual gyrus, and multiple networks in EOS patients compared to HC. Moreover, our exploration revealed a significant overlap of PLS2 weighted genes linking to EOS-related MIND differences and the dysregulated genes reported in other psychiatric diseases. Interestingly, genes correlated with MIND changes (PLS2-) exhibited a significant enrichment not only in metabolism-related pathways, but also in specific astrocytes, cortical layers (specifically layer I and III), and posterior developmental stages (late infancy to young adulthood stages). However, PLS2+ genes were primarily enriched in synapses signaling-related pathways and early developmental stages (from early-mid fetal to neonatal early infancy) but not in special cell types or layers. These findings provide a novel perspective on the intricate relationship between macroscopic morphometric structural abnormalities and microscopic transcriptional patterns during the onset and progression of EOS.

In plants, microRNAs (miRNAs) participate in complex gene regulatory networks together with the transcription factors (TFs) in response to biotic and abiotic stresses. To date, analyses of miRNAs‐induced transcriptome remodeling are at the whole plant or tissue levels. Here, Arabidopsis’s ABA‐induced single‐cell RNA‐seq (scRNA‐seq) is performed at different stages of time points–early, middle, and late. Single‐cell level primary miRNAs (pri‐miRNAs) atlas supported the rapid, dynamic, and cell‐type specific miRNA responses under ABA treatment. MiRNAs respond rapidly and prior to target gene expression dynamics, and these rapid response miRNAs are highly cell‐type specific, especially in mesophyll and vascular cells. MiRNA‐TF‐mRNA regulation modules are identified by identifying miRNA‐contained feed‐forward loops (M‐FFLs) in the regulatory network, and regulatory networks with M‐FFLs have higher co‐expression and clustering coefficient (CC) values than those without M‐FFLs, suggesting the hub role of miRNAs in regulatory networks. The cell‐type‐specific M‐FFLs are regulated by these hub miRNAs rather than TFs through sc‐RNA‐seq network analysis. MiR858a‐FBH3‐MYB module inhibited the expression of MYB63 and MYB20, which related to the formation of plant secondary wall and the production of lignin, through M‐FFL specifically in vascular. These results can provide prominent insights into miRNAs' dynamic and cell‐type‐specific roles in plant development and stress responses. This integrated network study offers insights into miRNA regulation, impacting plant development and cell differentiation. MiRNAs and pri‐miRNAs show dynamic, cell‐type‐specific ABA responses, especially in the early time‐point. The TF‐miRNA‐target network (TMTN) reveals that miRNAs enhance co‐expression with cell‐type‐specific roles. MiR858a inhibits MYB63 and MYB20 in vascular cells by MiR858a‐FBH3‐MYB loop in TMTN, affecting the secondary wall and lignin formation.

First-episode schizophrenia (FES) is a progressive psychiatric disorder influenced by genetics, environmental factors, and brain function. The functional gradient deficits of drug-naïve FES and its relationship to gene expression profiles and treatment outcomes are unknown. In this study, we engaged a cohort of 116 FES and 100 healthy controls (HC), aged 7 to 30 years, including 15 FES over an 8-week antipsychotic medication regimen. Our examination focused on primary-to-transmodal alterations in voxel-based connection gradients in FES. Then, we employed network topology, Neurosynth, postmortem gene expression, and support vector regression to evaluate integration and segregation functions, meta-analytic cognitive terms, transcriptional patterns, and treatment predictions. FES displayed diminished global connectome gradients (Cohen's = 0.32-0.57) correlated with compensatory integration and segregation functions (Cohen's = 0.31-0.36). Predominant alterations were observed in the default (67.6%) and sensorimotor (21.9%) network, related to high-order cognitive functions. Furthermore, we identified notable overlaps between partial least squares (PLS1) weighted genes and dysregulated genes in other psychiatric conditions. Genes linked with gradient alterations were enriched in synaptic signaling, neurodevelopment process, specific astrocytes, cortical layers (layer II and IV), and developmental phases from late/mid fetal to young adulthood. Additionally, the onset age influenced the severity of FES, with discernible differences in connection gradients between minor- and adult-FES. Moreover, the connectivity gradients of FES at baseline significantly predicted treatment outcomes. These results offer significant theoretical foundations for elucidating the intricate interplay between macroscopic functional connection gradient changes and microscopic transcriptional patterns during the onset and progression of FES.